• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    the present disclosure provides an ability to use an array of data inputs to enter a network and thereby provides an upgradeable database in real time. the present invention is innovative in its ability to maximize the consumer interface with a pest control system, thereby allowing maximum effectiveness for current and future projects, as well as a high level of compatibility with auxiliary functions of the planning type, financial, regulatory.
    公开号:BR112019021298A2
    申请号:R112019021298
    申请日:2018-04-12
    公开日:2020-05-19
    发明作者:Reid Byron；Pienaar Chris；Fages Gaelle；Zimmermann Michael；Jardine Peter；Gutsmann Volker
    申请人:Bayer Ag；
    IPC主号:
    专利说明:

    VALUE ADDED PEST CONTROL SYSTEM WITH INTELLIGENT LEARNING
    CROSS REFERENCE TO RELATED ORDERS
    [0001] This application claims the benefit of Application No. US 15 / 485,838, filed on April 12, 2017, the content of which is incorporated into this document for reference in its entirety.
    BACKGROUND
    1. Field
    [0002] The present invention is a pest control management system based on logic that includes machine learning so that, in real time, the optimal performance of one or more pest control platforms can be achieved. More preferably, the present invention generates and maintains a database that has machine learning capability in order to achieve optimal placement and type of pest control platforms for a location during use or even as a control system design. pest control before implantation. The output of this management system can optimize the design of the pest control system, but also display recommendations learned by machine and history on a platform that is useful for establishment owners, pest control operators, auditors and / or consumers.
    . Description of the Related Art
    [0003] Documents US4682145, US6792395, US6937156,
    US7395161, US7656300, US8830071 refer to pest monitoring systems and / or devices with consideration
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    2/26 specific with the use of hardware and / or software integrations so that the presence of pests, especially rodents, can be made more readily known to the end user.
    [0004] US4682145 mentions a pest monitoring device that is digitally linked to observe the presence of pests, especially rodents, from a central location. The '145 patent uses a connected detector system to transmit a unique signal to a receiver to determine whether and on which detector a pest is located. US6792395 uses remote detection and monitoring of a hardware and software interface to send data that is detected (eg, pressure, camera, humidity) via electrical signal to a computer server system; it also includes the software interface for displaying the results of said signal. US6937156 is a detector and / or collector that includes a capacitance detection circuit to create a signal to be transmitted as an output to notify a likely scenario that a pest has entered a monitored area. US7395161 makes use of polymodal sensors to collect data on a server network for analysis. The '161 patent considers the use of a biological pest specialist to examine the data with the intention of repositioning the sensors and / or collectors to improve the system. US7656300 refers to the digitization of all aspects to monitor pests with the intention of automating the system as well as possible. The '300 patent aims to monitor pests, specifically rodents, by transmitting a signal from the detectors to
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    3/26 local or remote communication servers to emit an alarm and / or logarithm record that uses infrared motion and / or temperature sensors, mechanical entrapment sensors, or optical sensors to detect and report the presence of a rodent. US8830071 refers to the wireless transmission of information from each pest detector. The '071 patent uses a wireless communication circuit and a radio frequency receiver, which provides the data collector as a form of communication port to allow user interface of the compiled data.
    [0005] Some published US patent applications include a description of smart data for agricultural use. For example, US 2016/0150744 published on June 2, 2016 discloses a system for applying a pesticide to a crop, which comprises a collector and counter device that generates information on an insect quantity, and sends the quantity information insect through a communication network. The data collection platform gathers environmental parameter information and insect quantity information through the communication network. A data analysis platform that analyzes historical monitoring data is also provided. Environmental parameter information and insect quantity information are also used to generate a control criterion; and a pesticide application control device is used to control the amount of the pesticide to be applied to the crop based on the control criteria. Order '744 thus uses information related to which pests are present in order to determine which
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    4/26 pesticide must be applied in real time.
    [0006] In another published application, document US 2015/0208636 published on July 30, 2015, a method of detecting a condition of biological activity is described, which comprises: positioning one or more polymodal sensors that detect data related to at least two conditions in at least one zone; accumulate data in at least one data collector to generate a local knowledge base. The '63 6 order focuses on a slightly complex polymodal system that requires detection of at least two parameters to determine the presence of a biological activity which then, in turn, causes the system to react accordingly to treat the pest.
    BRIEF SUMMARY OF THE INVENTION
    [0007] In each of the prior art, the main objective is to detect the presence of a pest and notify a management system in an attempt to quickly observe where the pests are. The present invention, on the other hand, relates to the use of digital sensor signals that are communicated wirelessly over a network to a database for observation along with the ability to use that data for a wide variety of purposes; namely, the use of this data for analysis of current systems for improved pest control projects and future types and designs of pest control systems provides environmental and / or structural restrictions. The technology immediately described allows constant and autonomous improvement of current and future pest control systems. There is a need to collect and analyze data in a control application
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    5/26 pests so that, from a central location, the type and number or density of pests are known, but also use this historical data to make more educated decisions regarding the placement and types of pest collectors , types of pest control or chemical agent and dosage rates, and general system status. In addition, this machine learning infrastructure would trigger a future pest control system design by analyzing other system designs in combination with external factors (ie environmental conditions). The present invention uses a specific type of sensor to collect and transmit data relevant to a signal processor. This centralized data collection can then be effectively analyzed so that the system situation is known and conclusions for improvement are readily understood with the use of integrated software. The system, which uses machine learning, also optionally has the ability to optimize operation of the pest control collector and location placement using this system situation history data, as well as external data. The aim of the present invention is not only to detect and collect pests; the same search provides insight into how types and locations of pests refer to the design of system installation and external conditions. This will ultimately increase efficiency and reduce costs and risks.
    [0008] As an example, the present invention preferably uses a wireless alert sensor, namely, vibrating switches, to detect the presence of a pest. The switch incorporates two metallic elements that
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    6/26 are electrically charged using a small battery, creating an analog signal with a small payload (ie 6 bytes). When a pest enters the collector, the charged metal elements connect and complete the circuit, which transmits the signal to an off-site station. In one embodiment, the collector sensor comprises one or more of the devices described in US Patent Application No. 62,443,384, filed on January 6, 2017, the contents of which are incorporated into this document for reference in its entirety. . When using simple sensors, which is all that is necessary for the present invention to work, a high degree of accuracy and reliability is provided. This does not mean that more advanced sensors could optionally be used, if desired, for any reason. Non-limiting examples of other sensor options include: bluetooth, RFID, camera, infrared, capacitance, piezoelectric, bioimpedance, movement and / or any possible mechanism known or contemplated hereinafter capable of verifying pest detection and transmitting the signal to an outside station the location.
    [0009] When a pest is detected by the sensor, this signal is transmitted to a centralized computer, preferably with cloud-based network capability. This enables continuous connectivity between the sensor status and a central location for easier access in pest control management. The network is responsible for transmitting customized signal data to a database for analysis. Custom signal data refers to selecting only the data that
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    7/26 are relevant for analysis. This can be the situation of sensor, location, environmental conditions inside and outside a facility, time of day, types of pest control agents and respective dosages, and any other situation that is considered useful for the understanding and optimization of the control system. of pests and / or cost and risk reduction.
    [0010] When the signal is transmitted over the network to a remote center for analysis, the relevant data must be displayed in a way that is intuitive for the viewer to quickly understand the current situation of the pest control system. Specifically, as an example, an interior map of the facility can identify (using appropriate software), the system situation can be displayed by converting the sensor signals into quantitative or qualitative descriptions of how effective the pest control system is. is designed. In this way, the viewer can make recommendations to manually improve the system by moving collectors, changing agents and / or dosage rates, or by making temporary or permanent structural changes to the installation itself or its next project. This software also has the ability to generate reports that are useful for understanding the infestation situation that is useful for pest control operators, facility managers and auditors. Finally, this software is machine-learning and fully capable of determining trend analysis and recommendations on it. Machine learning means that the highly advantageous system includes artificial intelligence (AI) that provides the system with the ability to
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    8/26 learn without being explicitly programmed. Machine learning focuses on analyzing trends and the development of computer programs that can change when exposed to new data. The machine learning process is similar to that of data extraction, also trend analysis. That is, according to the present invention, most, if not all, of the relevant data can be statistically analyzed to influence future installation models, geographical locations, types of collectors for use, types of agent and dosage rates for use, and other elements that can be designed to reduce pest infestation. The purpose of the software is to continuously optimize the pest control system and make changes to existing systems or influence the design of future systems.
    [0011] While collecting, analyzing, displaying and improving, the pest control system has an intuitive benefit to reduce infestation of a current or future installation, there are greater benefits for reducing cost and risk. Optimizing the number, type and location of collectors reduces the cost of buying unnecessary collectors or placing collectors in ineffective locations. Understanding what type of mechanism to use to better control the potential for pests, as well as possible dosages, type of barrier or containment, or the like is fully capable with a system of the present invention. This ability reduces cost by ensuring the minimum amount of the current agent (or agents) that is used throughout an installation and / or the appropriate barrier or remedy that is used to maximize results at any given time based on the ability to
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    9/26 machine and method learning. This is applicable to improve systems already in place and knowing this data also minimizes the trial and error approach required when designing a new facility and its pest control system, which also reduces time, energy and money for the control operation. of pests.
    [0012] There are health and environmental benefits to adopting the present invention. Operators are better informed about the types of agents that are used and are aware that the collectors need maintenance and reduce exposure to some of the chemicals. For that same reason, the pest control system can be designed to be more environmentally friendly, which means minimizing specific chemicals released into the environment. The software also has the ability to learn and recommend changes to products used in pest control to combat physiological changes or behavioral resistance to the current system. This learning system will allow comparative analysis of the performance for a given product in relation to a given pest. Over time, including an element of self-learning, the system can learn to propose products that are better suited to a pest in specific locations.
    [0013] The machine learning database of the present invention creates a system that has the capacity to be optimized in many specific accounts. The design of a facility and factors, such as where pest resources (ie food, water, shelter) are located, allow a written algorithm to determine the ideal number and which sensors
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    10/26 type of pests effectively monitor the installation. Ο object by placing sensors more strategically reduces costs of unnecessary collectors and minimizes audit failures for inadequate monitoring of pest infestation. And, since the system is self-learning, statistical or predictive analysis can achieve more reliable security that pest management is optimized, depending on conditions, especially to adhere to regulatory standards, thereby reducing risk sensitivity business
    [0014] A system of the present invention is capable of tracking pesticide use at the level of individual applicators. This is beneficial in determining the type of agent and dosage that is effective in controlling pests; it can also adjust chemical compositions or concentrations of automatic or manual sprinklers by sending a signal to the applicators. A non-limiting example of a possible arrangement of a digital sprinkler suitable for use in combination with the present invention is disclosed in document EP 16178766.8 filed on July 11, 2016, serial number US 62/360548 filed on July 11, 2016 EP 16178764.3 filed on July 11, 2016, serial number US 62 / 360,555 filed on July 11, 2016, and PCT document 2016/0255826 published on September 8, 2016, the content of which is incorporated into this document to reference title in its entirety.
    [0015] In some modalities, the software could signal to applicators, both automatic and manual, to digitally control the pesticide used and
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    11/26 can ensure that the required amount of pesticide is used, thereby reducing costs and time. This can be accomplished either by generating a report for employees or even smart applicators for the sprinkler to be electronic or manually aware of how both the type and pesticide to install. In terms of human performance, the software has the ability to track how effectively individuals are performing pest control installation and maintenance. Statistical conclusions can be drawn for time and cost for applicators to carry out work related to the maintenance of the pest control system, which is useful for managers to index or have as a benchmark in an effort to reduce costs and risk. Managers could make changes to employees, implement additional training, or manually improve the process in an effort to reduce the time to maintain the system. The data derived from the software is especially useful for statistical and qualitative analysis so that the current process can be improved and so that the implementation of new processes can be implemented with best practices in place.
    [0016] The software integrates the pest sensor data with the external data to draw additional conclusions about the installation situation. Ambient monitors, construction maps, installation sensors, and other sensors can be transmitted on the same network to the same remote computing location to be analyzed. Environmental monitors, such as time, temperature, humidity, etc. can be correlated with the presence of pests, especially compare these data with data from pest sensors. At
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    12/26 documented operations, such as distributions with construction maps, or structural arrangements for specific environments, presence of resources such as food, etc. can be compared with data from other sensors to optimize the best design of a future installation and / or the design of the pest control system. And installation sensors, such as outdoors, air flow, indoor temperatures, etc. can provide insight into how these conditions affect the presence of pests. All of these sensors work in harmony to understand the entire condition of the building associated with the presence of pests, which is captured by the pest sensors. This also includes the integration of external climate and meteorological data that also provide a correlation with prediction of pest invasion based on an event, series of events or conditions. The ability to use all harmonic data in a meaningful way is especially powerful for reporting. Due to a current and future design of a facility, pest sensors, environmental, installation and times and types of operations (ie, resource distribution), the pest control method in place can be reported. Types, quantities, dates, times can be used to more precisely understand the situation of an installation. This is useful for billing, inventory management, improving processes and government standard compliance (USDA, EPA, FDA, etc.). The system can automatically schedule a supply order, request employee maintenance for a specific location, alert for inconsistencies or emergency conditions, and reduce the time required to audit a facility as non-limiting examples of what
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    13/26 is provided by the present invention.
    BRIEF DESCRIPTION OF THE DRAWINGS
    The patent or order file contains at least one drawing executed with color. Copies of that patent or patent application publication with drawing (or drawings) in color will be provided by the Office upon request and payment of the necessary fee.
    Figures 1 to 8 represent embodiments as described in the present document.
    DETAILED DESCRIPTION OF A PREFERENTIAL MODE
    [0017] Figure 1 defines a suitable flowchart that represents an embodiment of the present invention.
    [0018] This management platform is composed of: [0019] Entries, which can be sensors, monitors, integrated devices, or other collected pest control data that can be transferred through a communication network.
    [0020] Network, specifically connectivity continues between device inputs in usable data. This is especially useful for this IoT application. This continuous connectivity allows constant and autonomous detection and monitoring of all pest control and detection inputs at any location.
    [0021] Database, which is maintained and optimized to display the data in an approvable and usable form. This is especially useful in the machine learning process mentioned earlier in order to optimize placement of devices and / or pest control systems. This is relevant both for validating pest control configuration and for predictive projects that provide
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    14/26 environments, similar arrangements, etc.
    [0022] Software, which allows a user interface to display the usable data to the consumer in order to allow the consumer to quickly make adjustments to the system, creating a feedback loop that tries to maximize efficiency; this, in turn, will try to reduce costs and risks for the consumer by creating a more intelligent and learnable system for pest control.
    [0023] One objective of the platform is to present consumers with improved pest control devices and detection that enable an innovative increase in data collection and analysis. These digitized data, which are highlighted in Figure 2, are valuable for the consumer so that more knowledge is known about pest detection. The platforms serve as a product that enables an increased strategy in pesticide types, locations, dosage rates / times and types and locations of pest control devices. In this way, the improved ability to understand and analyze how this digital data influences the frequency and type of pest allows for a more thoughtful pest control project and a reduction in cost and risk for the consumer.
    [0024] The data collection value is expanded by an analytical feedback system, which has an even higher value added for the consumer. The ability to continuously monitor and change pest control systems improves the effectiveness of the systems, thereby allowing long-term pest control costs to be reduced in a particular facility.
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    15/26
    In addition, the machine learning process can be adopted while designing a pest control system for a new facility - considering that environmental, structural and preliminary pest assumptions would also reduce the associated costs and risks while installing a pest control system .
    [0025] As an example, consider a construction with insect and rodent infestation. The present invention takes into account detection and / or collector monitoring for both insects and rodents within the structural constraints of the building. Once the system is in real time, continuous data related to the type and location of pests is sent to a usable interface so that the collectors can be adjusted (that is, critical pest points according to the design of a room). construction). The present invention also takes into account a compilation of these data to strategically launch a pest control system in a new construction - compare historical data and understanding of environmental conditions, structural arrangements in Figure 2 to more effectively initialize a pest control system pests with the aim of reducing costs and risks.
    [0026] This continuous, data-driven improvement system has intuitive benefits for pest control and food processor consumers; it also has benefits related to the audit of commercial buildings for pests. The US Agency for Environmental Protection and Drug Administration and Food, for example, improved land trust
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    16/26 work system with the emergence of the platform, which can generate reports that would indicate the location, type and number of pests, location, type and number of collectors, as well as the construction project effect, the environmental impact (see Figure 2) on the type, number and location of pests. This machine learning process creates a system that will minimize pests that become undetected and / or trapped; it also allows for quick analysis and display of the pest control system within a building.
    [0027] An additional example of an existing installation is shown, for example, in Figure 3. A representative output from the software includes a project for a particular installation that documents the location of several sensors. The program would represent environmental sensors 3 that continuously capture data with date and time on temperature, humidity, air flow, etc. so that the condition of different environments is understood from a central location. Installation sensors 2 represent actions within the installation that could influence the presence of pests (ie, door openings). Several collectors are oriented within the installation project and sensors 1 are shown in the project so that the situation of these sensors is readily available for a central location for analysis.
    [0028] Continuing with the example, consider an alert on a sensor in a particular environment of an installation. In Figure 3, it is understood that there is an alert at the processing facility. The operator is also able to investigate this environment and, in Figure 4, the situation of the
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    17/26 environment is quickly understood. Historical and current data are represented in a readable format and the operator can quickly see where the alert is derived from. In this example, pest sensor 1.3 indicates an alert and the operator can investigate to see what triggered the alert and actions to solve the problem.
    [0029] Figure 5 indicates specific sensor data related to the collector that is installed. Continuing with this example, it is evident that this collector is used as a trap and to exterminate cockroaches due to the conditions indicated in the software. The chemical agents, manufacturer, technician are displayed and a description of the alert is displayed for analysis. Based on the continuous learning system, automated or recommended actions are submitted to an appropriate part and the software will indicate how to improve the system to the required degree of specificity.
    [0030] In connection with the present invention, an important aspect is found in the ability to predict with high clarity areas of high and low risk for different levels, depending on the type of environment in which the system would be used. Examples of high-risk applications include operations where there is any potential for 3 - person audits, dealer audits, FDA inspections, pharmaceutical facilities and the like. Possible less risky operations include, animal feed, sensitive electronic devices, hospital / health care, storage, transport and other environments, where pests are intended to be eradicated, but the priority is somewhat less than in environments with adverse consequences.
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    18/26
    [0031] In accordance with the present invention, the algorithm will independently search for the nature of the business operation and / or, optionally, be in the client's description if the business has a high or low risk. Then, the algorithm derives the number of collectors or treatments required and locations based on installation size, and independent factors, such as the specific business, what is the risk, if there were failures in the past and the nature of such failures (deficiencies, fines , withdrawals / product recall).
    [0032] Alternatively or, in addition, the algorithm can search for dependent factors such as the environment (climate / humidity / population) and current pest biology / behavior. For example, the algorithm can be configured so that pest activity (ie, a mouse) will be monitored and cataloged over time, and then the number or collectors and / or treatment locations can be defined by situational analysis . In an area where no mice have been located in a given period of time (ie, 12 months), there is a standard arrangement. In other areas where there was perhaps 1 mouse / 12 months, then there is a +1 to the standard at that location. In still other areas where more than 1 mouse was observed in 12 months, then +2 is added to the standard.
    [0033] In addition, when constructing a situational analysis for placing collectors, the algorithm will perform a site assessment to determine a typical characterization of the location. For example, there will be an analysis of usual features, such as whether external ports exist and, if so, how many, structural integrity fails in any
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    19/26 location, spilled food, external waste, open water source, canteen / kitchen environments / breaks, raw material handling, raw material storage, finished goods storage, etc. Micro-habitats include proximity to water, food, shelter and heat. Each of these factors will be assigned to a characterization and numerical reference of importance for the algorithm to calculate and further differentiate how the placement should be performed. For each usual resource, the algorithm will designate a number of treatment areas and / or collectors based on the prioritization of machine learning as data is collected over time for multiple locations.
    [0034] The risk profile module will then be advantageously based on choices and data already developed that recommend a placement for high risk business operations, low risk business operations based on how many business failures have been observed or were expected to be observed based on their criteria (less than 1, equal to 1, or greater than 1). For a high-risk area where there is already 1 fault or 1 fault is predicted from machine learning, the algorithm can suggest 1.5 collectors per 30 meters (100 linear feet), while for the same business, but with no recorded or expected failures, the algorithm would suggest 1.25 collectors per 30 meters (100 linear feet). If the business is of low risk as illustrated above, the algorithm would suggest 1.25 collectors per 100 linear feet for areas of 1 failure or 1 predicted failure and 1.0 collectors per 30 meters (100 linear feet) for areas with no failure or no failure
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    Expected 20/26. These recommendations are merely illustrative, but provide a clear sense of how the inventive algorithm
    would work to predict a project in placing and modifications the same over the time for an determined operation. [0035] On Figure 6, is shown one eyesight general of one
    system according to the present invention. According to the figure, in an advantageous embodiment, the pest control platform 10 is optionally equipped with a monitor 12 to transmit a signal to a conduit communication port 14, optionally via LoRaWAN 16, a wide area network low power. This telecommunication mode, as previously described, can be wired or wireless; the conduit communication port 14 exists to manage the radio frequency modules 20 and / or communications 18 that are connected to the pest control platform 10. The data is then sent over the Internet to a Digital Backend Pest Management System 22, which incorporates services customizable according to the present invention. The Simple Notification System (SNS) 24 allows automatic notification (ie, email, text message, push notification of integrated mobile order) of an irregularity or even a specific system situation. A User Management 26 capability is optionally included and serves to add / remove / edit operators, as well as viewing performances. Also as shown in Figure 6, a Tip Service 28 is advantageously included to allow control of the telecommunication device (or devices) to also include a Load Compensator
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    21/26 of Application (ALB) 30 to automatically direct telecommunications traffic based on network availability. The backend system 22 also includes a virtualization component 32 intended to optimize the computing power of the system 10 as associated with the software that is using this information. The virtual representation of the data storage includes the ability to configure as the system uses the data on an automatic or manual basis.
    [0036] The other components of the Digital Backend Pest Management System 22 enable customization of the installation 34, consumer 36, or frontend display 38. The data can also be used to create distributed chronological records 40, which could be streaming data through of any desired mechanism, such as Amazon Kinesis Web Service (AWS) 42. This chronological record of data 40, or the same streaming data, could be used for auditing, continuous improvement, consumer reporting, or any other means for understanding full of
    historical or same current system. [0037] The end users, preferably, must to be presented to the system situation with based on data in one easy readable format. The Interface in User (UI) 44 or
    Representational State Transfer (REST) 46 create an advantageous interface that allows interoperability between all systems over the Internet. The display of the pest management system 10 can be effectively sent to a mobile app for backend display 48 or even to a rear web portal 50 for reporting and
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    22/26 administrative supervision can be quickly and readily understood by managers and / or consumers and / or contractors.
    EXAMPLES
    [0038] Example 1 represented in Figure 7 presents a generalized scheme that illustrates how the system is used to optimize monitoring for pest incidence.
    [0039] Each account that is monitored is marked with descriptive resources for an installation, such as the nature of the business, its location, the size of the installation, the type (or types) of pests considered or the type (or types) of monitors employed. Two site-specific tests are then completed. One observes abiotic factors that allow pest incidence in a facility, such as the number of external openings (for example, doors, loading docks, windows) through which the pests could move. Another observes biotic factors that support the incidence of pests within a facility, such as open food or water sources. Results of these tests are performed using an algorithm (scheme) to determine the number and placement of pest monitors; for example, a monitor is placed either beside doors that serve as points of entry for pests or a monitor is located within a prescribed distance from food and water sources where pests frequently move. As a base number of monitor locations determined in this way, recent history of pest activity at a facility is cross-referenced to increase the number of monitors at locations with a record of pest activity history. Finally, the
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    The user can apply one of two risk ratios to further modify the number of monitors employed when considering the record of previous pest incidents reflected in audits or historical inspections, and a more subjective ratio related to the risk tolerance for the business. particular.
    [0040] The previous process will define the recommended placement scheme for monitors within a facility to detect the presence of pests. Once the system is operated, and the accumulation of individual pest reports, the system can learn and further optimize the placement of pest monitors in response to continued pest activity within the facility itself, or by extracting a monitoring at other facilities with similar businesses, installation size, location, etc. In this way, the number and placement of monitors can be optimized (increased or reduced, or repositioned) to minimize the cost of pest monitoring hardware while keeping the incidence of pests within acceptable limits defined for the installation.
    [0041] Example 2 shown in Figure 8 presents a generalized scheme that illustrates how the system is used to optimize pest management, in particular, the application of pesticide formulations to mitigate the incidence of pests in a facility.
    [0042] Each account that is served is marked with descriptive resources for an installation, such as the nature of the business, its location, the size of the installation, or the type (or types) of pests found. In each service, the
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    24/26 characteristics of the pesticide application event are recorded, such as on what date the pesticide was applied, at what concentration and in what volume. This pesticide application treatment event record is recorded in the database for analysis. Analyzes performed on accumulated treatment events are diverse, but any number of examples can see some of these ideas. In one example, the database can access external databases for temperature and precipitation events at the location and they can be correlated with the frequency of treatment events to learn how time affects the persistence / effectiveness of a particular used pesticide. The system can also be configured to query various libraries that summarize government or private restrictions on when or where a particular pesticide can be used, and can send alerts to a technician (via connected application devices) to prevent unintended violations from adversely affecting the pest management firm's compliance record. A pest management firm can establish limits, a priori, or, a posteriori, conducted analysis of treatment events, to signal outliers at treatment events where corrective action is required. For example, when tracking applications by all of its technicians, the firm can set limits on application volumes or even length of service, as indicated by the arithmetic mean x plus or minus a standard deviation Std (x). Hereby, the firm can identify employees who are applying too much pesticide or who are not spending enough time to
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    25/26 perform proper maintenance of the installation. With this insight, the firm can direct employees to training resources to ensure compliance with the company's service standards or industry standard practice based on treatment analysis records for similar facilities from a wide variety of companies whose data is in the database. main.
    [0043] In response to an interface with the optimized pest monitoring described in Example 1, the user can establish limits for the incidence of pests that signal the need for treatment in a facility by an appropriate arithmetic formula. Whenever the pest detection system (or systems) reports pest incidence above such limits, the optimized pest management system can generate an alert that notifies the applicator of the need for additional service at a facility. The analyzes are positioned to modify this need for service alert as follows. A particular product can be recommended if, for example, the time since the last service is less than expected.
    [0044] Based on records from a single firm or a wide variety of firms, the expected duration of pest suppression after treatment can be established, as determined by the arithmetic mean x plus or minus a standard deviation Std (X). The deviation from this threshold could be suggestive of insufficient performance, or of the applicator, or of the chemical, or both. There may be a certain tolerance of 67% or, perhaps, 90% of the ideal before the shot is taken to alert an end user.
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    26/26
    [0045] For each rate of chemical R, there is a pest suppression period x. Then, Std (x) and average x are determined.
    [0046] Over time, pest suppression is measured, and if it falls below tolerance, the alert is determined to be appropriate.
    [0047] Additionally, to prevent the development of physiological resistance in a pest population for a particular pesticide, the user can establish a scheduled rotation between or among different pesticides and the analyzes will keep a record of sequential use of a pesticide within a facility and recommend the next product to be used in the resistance of a management scheme. Insufficient performance outside a normative result also serves to alert the user to a risk of developing resistance or other conditions that reduce the effectiveness of the system as a whole.
    [0048] These examples illustrate how the system can be used to regulate pesticide application events, in terms of frequency or quality, by perceptions of distribution data. In addition, through interactions with an integration for pest alerts derived from a pest monitoring system, analyzes can optionally be changed as desired to optimize pest management in a facility while minimizing the cost and risk associated with pesticide treatments while maintaining the incidence of pests within acceptable limits defined for the installation.
    权利要求:
    Claims (8)
    [1]
    1. Integrated digitalized pest control management system characterized by comprising:
    - a computer and / or cloud-based network that is adapted to receive multiple data sources and process data in it and, in this way, create a database;
    - at least one location-specific pest control platform adapted to send and receive data, where said specific location criteria are based, at least in part, on a risk profile of said location, on which said platform comprises one or more pest control mechanisms at said location, wherein said one or more mechanisms optionally comprise (i) a pest control agent distribution protocol, and / or (ii) a remote monitoring device with at least one sensor that generates and receives said location-specific data;
    - one or more external sensors that collect external data for said pest control platform, in which said external data optionally comprises one or more of atmospheric conditions, pest populations, human interaction with said location or third party data correlated to predict the likelihood of pests at that location,
    - in which said database creates a recommended protocol for the provision of said one or more mechanisms for pest management based on a risk profile
    Petition 870190101790, of 10/10/2019, p. 42/57
    [2]
    2. System according to claim 1, characterized by the fact that said location-specific pest control platform comprises one or more pesticide applicator devices that have an ability to be connected to a network.
    2/8 inserted from said location and machine learning developed by said database that uses real time data received by said computer, as well as historical data from said location and, optionally, from one or more other locations with a profile of similar risk as said location specifies, and also, in that said database continues to collect and integrate new data so that, at any given moment, a recommended protocol for the disposition of said one or more mechanisms is updated in a real time basis.
    [3]
    3/8 reversibly connect the cartridge to the device and a memory unit,
    - optionally, a mobile computer system, and
    - an external computer system that can configure a communication link to the control unit of the applicator device and / or the memory unit of the cartridge and / or the mobile computer system in order to transmit information about a sprinkling process that has occurred to the external computer system.
    3. System, according to claim 2, characterized by the fact that the said pest control agent distribution protocol comprises
    - a portable device that additionally comprises the following components:
    - a container to hold a thinner,
    - a distribution port,
    - means for feeding the thinner towards an applicator,
    - means for reversibly connecting a replaceable cartridge containing a concentrate to the applicator device,
    - means for feeding the concentrate diluent, and
    - control unit,
    - a replaceable cartridge comprising means for
    Petition 870190101790, of 10/10/2019, p. 43/57
    [4]
    4/8 closed, so that the signal unit transmits a signal to an external receiver.
    4. System according to claim 1, characterized by the fact that said pest control platform comprises a remote monitoring device with at least one sensor that includes a collector.
    [5]
    5/8 said location, wherein said one or more mechanisms optionally comprise (i) a pest control agent distribution protocol, and / or (ii) a remote monitoring device with at least one sensor that generates and receives said location-specific data;
    - insert data from one or more external sensors that collect data external to said pest control platform, in which said external data optionally comprises one or more of atmospheric conditions, pest populations, human interaction with said location or third party data correlated to predict the likelihood of pests in that location,
    - in which said database creates a recommended protocol for the provision of said one or more mechanisms for pest management based on a risk profile inserted in said location and machine learning developed by said database that uses data in real time received by said computer, as well as historical data from said location and, optionally, from one or more other locations with a similar risk profile as said specific location, and also, in which said database continues to collect and integrate new data so that, at any given moment, a recommended protocol for the provision of said one or more mechanisms is updated on a real-time basis,
    - optionally, validate and implement said recommended protocol, in which said recommended protocol can be controlled in an active or passive way;
    - optionally, generate a report by said
    Petition 870190101790, of 10/10/2019, p. 46/57
    5. System according to claim 4, characterized by the fact that said remote monitoring device with at least one sensor comprises:
    a base comprising a distal end and a proximal end, a two-way switch, optionally, a spring switch comprising a first metallic element and a second metallic element, and a signal unit, wherein the first metallic element and the second metallic element are electrically separated at the distal end of the base and electrically connected to the signal unit at the proximal end of the base, thus forming an open circuit, in which, when the first metallic element comes into contact with the second metallic element, thus forming a circuit
    Petition 870190101790, of 10/10/2019, p. 44/57
    [6]
    6/8 computer and / or cloud network using said database identifying one or more of a recommended change in treatment protocol, one or more pest species (or pests) identified and / or treatment success over a chosen time period for
    said location. the claim 9, 10. Method, according with characterized by fact of what the said platform in pest control specific location comprises one pesticide applicator. 11. Method, according with the claim 10, characterized by fact of what said applicator in
    pesticide comprises:
    - a portable device that additionally comprises the following components:
    - a container to hold a thinner,
    - a distribution port,
    - means for feeding the thinner towards an applicator,
    - means for reversibly connecting a replaceable cartridge containing a concentrate to the applicator device,
    - means for feeding the concentrate diluent, and
    - a control unit,
    - a replaceable cartridge comprising means for reversibly connecting the cartridge to the device and a memory unit,
    - optionally, a mobile computer system, and
    - an external computer system that can configure a communication link for the control unit of the
    Petition 870190101790, of 10/10/2019, p. 47/57
    6. System, according to claim 1, characterized by the fact that said database uses said historical data and real-time data from other locations in addition to said location to prepare the recommended protocol for disposal for said one or more mechanisms for pest management.
    [7]
    7/8 applicator device and / or the cartridge memory unit and / or the mobile computer system in order to transmit information about a spray process that has occurred to the external computer system.
    12. Method according to claim 9, characterized by the fact that said location-specific pest control platform comprises a remote monitoring device with a two-mode sensor.
    13. Method according to claim 12, characterized by the fact that the two-way sensor comprises:
    a base comprising a distal end and a proximal end, a two-way switch optionally comprising a first metallic element and a second metallic element, and a signal unit, wherein the first metallic element and the second metallic element are separated electrically at the distal end of the base and electrically connected to the signal unit at the proximal end of the base, thus forming an open circuit, in which, when the first metallic element contacts the second metallic element, thus forming a closed circuit, so that the signal unit transmits a signal to an external receiver.
    14. Method for preparing a proposed pest treatment plan for a location that has a risk profile, where the method is characterized by comprising:
    Petition 870190101790, of 10/10/2019, p. 48/57
    7. System, according to claim 1, characterized by the fact that the said location-specific pest control platform monitors the presence of a pest and its identity and data related to its presence and identity are processed by said database to create instructions for said platform to achieve optimal performance by minimizing the impact of said pest.
    8. System according to claim 1, characterized by the fact that said location-specific data includes identification of the type and / or number of a pest that invades said location.
    9. Method for simultaneously monitoring and treating a pest location characterized by comprising:
    - provide a computer and / or cloud-based network that is adapted to receive multiple data sources and process data on it and, thus, create a database;
    - insert specific location criteria that include a risk profile associated with said location to create a specific location platform that comprises one or more mechanisms for pest control in the
    Petition 870190101790, of 10/10/2019, p. 45/57
    [8]
    8/8
    insert one risk profile for said location with base in use gives said location and degree of tolerance in risk; Insert a said project location in one
    computer and / or cloud-based network, where said project will identify critical points that comprise one or more of food preparation areas, food storage areas, storage areas for potential pest invasion that optionally comprise shelter areas, storage of linen, damp and / or wet areas, low relief water, and other known areas, where pests can invade or live;
    determine physical environment and climate criteria of said location by user input of parameters at a data entry point of said computer;
    compare said critical points, physical environment and climate criteria with historical data stored in a database and generate a proposed treatment scheme and plan for said location.
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    同族专利:
    公开号 | 公开日
    EP3609321A1|2020-02-19|
    US10152035B2|2018-12-11|
    US20180299842A1|2018-10-18|
    CN110519984A|2019-11-29|
    CA3059523A1|2018-10-18|
    WO2018189293A1|2018-10-18|
    US20190121302A1|2019-04-25|
    JP2020519238A|2020-07-02|
    US11073801B2|2021-07-27|
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    法律状态:
    2021-10-19| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    优先权:
    申请号 | 申请日 | 专利标题
    US15/485,838|US10152035B2|2017-04-12|2017-04-12|Value added pest control system with smart learning|
    PCT/EP2018/059393|WO2018189293A1|2017-04-12|2018-04-12|Value added pest control system with smart learning|
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