• 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:
    A method for correcting estimated fishery resources in a swept area and an application thereof. The method includes that a surveyed sea area is equally divided 5 into n survey districts, and m survey districts are selected therefrom. A trawling survey is performed in the m survey districts according to a swept area method. Using a standard trawl net within a specified time, fish catches of the m survey districts and marine environmental factors of the n survey districts are respectively obtained. A resource adaptability index SSI of respective m survey districts is 10 calculated. For all the marine environmental factors, models that reflect relationships between respective marine environmental factors and the resource adaptability index SSI are built in sequence. Statistically significant models are selected from the obtained models and used to build a TSI model. Fishery resources of the n survey districts are respectively estimated by combining the TSI 15 index and summed. In the method, the influence of the environment on the resource distribution is considered when fishery resources are calculated, and a correction coefficient based on the resource adaptability index is provided, which greatly improves the measurement accuracy, thereby showing good adaptability and having potential applications. 20
    公开号:NL2027467A
    申请号:NL2027467
    申请日:2021-02-01
    公开日:2021-10-25
    发明作者:Chen Xinjun；Zhang Chang；Hu Guanyu；Yu Wei；Wei Guangen
    申请人:Univ Shanghai Ocean；
    IPC主号:
    专利说明:

    A METHOD FOR CORRECTING ESTIMATED FISHERY RESOURCES IN ASWEPT AREA AND APPLICATIONS THEREOF
    TECHNICAL FIELD The present application relates to marine fishery resource forecasting, and more particularly to a method for correcting estimated fishery resources in a swept area and applications thereof.
    BACKGROUND Scientific estimation of fishery resources plays a key role in the sustainable development and scientific management of resources. In order to ascertain types and quantities of fishery resources, marine surveys are usually performed using fishing gear and hydroacoustic equipment to carry out trial fishing or detection in designated waters, and the obtained survey data is analyzed and processed using data approaches and statistical methods to estimate the fishery resources, especially resources of important aguatic animals in an economic aspect, in surveyed seas. There are two main methods for fishery resource investigation and evaluation: the trial fishing investigation and hydroacoustic survey. The trial fishing investigation mainly uses a fishing gear to implement systematic trial fishing. The fishing gear includes a trawl net, barrier net, floating gill net, fishing tackle and fixed fishing tackle. Among them, the trawl net is the most commonly used. The method of using the trawl net to investigate and estimate resources is called the swept area method. The method of using acoustic equipment to investigate and estimate the amount of resources is called the acoustic investigation and evaluation method. In the early fishery resource investigation and evaluation, the trawl net investigation and the swept area method together are used to estimate the absolute quantity or relative abundance index of fishery resources in a certain sea area. At present, this combined method is still widely used in countries around the world. The swept area method uses the trawl net to perform investigation and trial fishing, so as to estimate the amount of resources in the entire sea area through a fish catch per unit area. This method has strict assumptions, i.e., the average fish catch per unit area is an index of resource distribution, that is, the average fish catch per unit area is assumed to be proportional to resource abundance. The average fish catch of all investigation sites is set as C, and a ratio of the weight of fish caught by the trawl net on the sweeping channel to the total weight of all fish in the sweeping channel is set as X1. An area of the surveyed sea area is set as A, and a catchable coefficient is set as a, so the total resources of the entire surveyed sea area are calculated according to B=CA/aX1. However, because of the influence of the marine environment on fish distribution, the above assumptions are usually invalid. Therefore, the traditional method of estimating fishery resources of the swept area generally has a disadvantage in that the measurement and calculation are inaccurate.
    Therefore, it is of great practical significance to develop a method of correcting estimated fishery resources in a swept area.
    SUMMARY In order to overcome the shortcoming that the measurement and calculation are inaccurate in the prior art, the present application provides a method of correcting estimated fishery resources in a swept area and an electronic device applying the method.
    The technical solutions of the present application are described as follows.
    A method for correcting estimated fishery resources in a swept area, the method being applicable to electronic equipment, and the method being configured to correct results of the estimated fishery resource of the swept area, the method comprising:
    A) equally dividing a surveyed sea area into n survey districts; selecting m survey districts from the n survey districts (due to the cost of the survey, sampling surveys are usually performed on the m survey districts according to a certain proportion.
    Of course, surveys on all the survey districts can be carried out by those skilled in the art); performing a trawling survey in the m survey districts according to a swept area method using a standard trawl net within a specified time; respectively obtaining fish catches of the m survey districts; and measuring marine environmental factors of the n survey districts, wherein n21, msn; B) calculating a resource adaptability index SSI of respective m survey districts according to SSl=Catch/MAX (Catch); wherein SSI is the resource adaptability index within a value of 0-1; Catch; is the fish catch of a survey district i, and i is an integer between 1 and m; and MAX (Catch) is the largest fish catch among fish catches of all the survey districts; the fish catches and resource adaptability are positively correlated, that is, the survey district with the highest fish catch has a resource adaptability index of 1; where a resource adaptability composite index TSI directly reflects the degree of adaptability of fishery resources to various environmental factors.
    Therefore, this index can be used as a correction coefficient for calculating the amount of resources in the surveyed sea area; C) building models that reflect relationships between respective marine environmental factors and the resource adaptability index SS! in sequence; D) selecting statistically significant models from the models obtained in step C); building a resource adaptability composite index model using the selected statistically significant models according to TSI=(SSH1+SSla+......+88H)/j; wherein TS! is the resource adaptability composite index; SSl, SSI.
    SSI; are the statistically significant models which respectively correspond to marine environmental variables 1, 2, ... j; and j is the number of the marine environmental factors; in other words, the TSI model is built based on the data of the m survey districts in the surveyed sea area, and the data includes the fish catches and the marine environmental factors; E) respectively calculating TSI values of the n survey districts according to marine environmental factor data of the n survey districts and through the resource adaptability composite index (TS!) model obtained in step Dj; F) estimating fishery resources of a survey district with the highest fish catch according to Buax=C/a; wherein Buax is fishery resources of the survey district with the highest fish catch; C is the highest fish catch in the survey district; and a is the catchable coefficient with a value of 0-1; G) calculating fishery resources of the n survey districts respectively according to Bik=Bumax*TSlk; wherein Bx is fishery resources of a survey district k; and TSlk is the resource adaptability composite index of the survey district k; and H) summing fishery resources of the n survey districts to complete the estimation of total fishery resources in the surveyed sea area.
    The method of the present application is specifically applied to the investigation and evaluation of fishery resources and the resources of other aguatic organisms. The method overcomes the insufficiency in estimating fishery resources by the traditional swept area method. When fishery resources are calculated, the influence of the environment on the resource distribution is considered and a correction coefficient based on the resource adaptability index (i.e., the resource adaptability composite index) is provided, and a specific method is given to correct the estimated fishery resources of the swept area based on the resource adaptability composite index, which greatly improves the measurement accuracy and provide good guidance for fishery production, thereby improving fishing efficiency, reducing fishing costs and having great application prospects.
    At the same time, the resource 5 adaptability composite index model obtained in the present application is changeable according to the latest data obtained in real time, showing good adaptability and good application prospects.
    In an embodiment, the marine environmental factors comprise a surface temperature (SST), salinity (SSS), chlorophyll-a (Chl-a) and a sea surface height (SHL), which are important factors that affect the distribution of fishing resources.
    The marine environmental factors of the present invention include the above factors, but are not limited thereto, and other appropriate marine environmental factors can be selected by those skilled in the art according to actual needs.
    In an embodiment, the survey districts have the same size, and their latitude and longitude are respectively 0.5°*0.5°, but the scope of the present application is not limited thereto.
    The size of the survey districts can be set by those skilled in the art according to actual needs.
    Of course, the size of respective survey districts should be the same, otherwise, the survey districts are not comparable with each other.
    In an embodiment, in step C), a model that reflects a relationship between the surface temperature and the resource adaptability index SSI is: SShi=exp(-1.3728 (Xss7-28.70)2); wherein Xssr is the surface temperature of a survey district, and the unit thereof is °C; and where P< 0.01. The P value is a parameter used in statistics to determine results of a hypothesis test.
    The P value is a probability of sample observation results or more extreme results when the original hypothesis is true.
    If the P value is small, it means that the probability that the original hypothesis occurs is very small, but if the original hypothesis occurs, there are reasons to reject the original hypothesis according to the principle of small probability.
    The smaller the P value, the more sufficient the reasons for rejecting the original hypothesis.
    In short, the smaller the P value, the more significant results, where P<0.01 means that SS1 is statistically significant, which applies to the same situation below. in an embodiment, in step C), a model reflecting a relationship between the salinity and the resource adaptability index SS! is: SSlo=exp(-5.5154(Xss5-33.81)2); wherein Xsss is the salinity of a survey district, and the unit thereof is psu; and where P< 0.01. In an embodiment, in step C), a model reflecting a relationship between the chlorophyll-a and the resource adaptability index SSI is: SSls=exp{(-0.5517 (In(Xcn-a)+1.378)2); wherein, Xenia is the chlorophyll-a concentration of a survey district, and the unit thereof is mg/m3; and where P< 0.01. In an embodiment, in step C), a model reflecting a relationship between the sea surface height and the resource adaptability index SS! is: SSl4=exp(-1.3260 (XsuL-0.4712)2); wherein Xs is the sea surface height of a survey district, and the unit thereof is m; and where P< 0.01. Usually, the adaptability index and the environmental factors present a normal distribution or a partial normal distribution.
    The scope of the present application is not limited to the models that reflect the relationship between respective four single marine environmental factors and the resource adaptability index SSI which are specifically provided herein.
    The four models given by the present application are reasonable models based on certain data.
    These models can be optimized by those skilled in the art according to actual needs in actual operations.
    In an embodiment, in step H), the total fishery resources B in the n survey districts is calculated according to: B=B1+B2+...+Bn.
    The present application further provides an electronic device applying the method, comprising at least one processor, at least one memory, and at least one program and a data input device; characterized in that the data input device is configured to input the fish catches of the m survey districts and the marine environmental factors of the n survey districts; the at least one program is stored in the at least one memory; and when the at least one program is executed by the at least one processor, the electronic device executes the method.
    Compared to the prior art, the present application has the following beneficial effects. (1) The method of the present application overcomes the insufficiency in estimating fishery resources by the traditional swept area method.
    In the method of the present application, the influence of the environment on the resource distribution is considered when fishery resources are calculated, and a correction coefficient based on the resource adaptability index (i.e., the resource adaptability composite index) is provided, and a specific method is given to correct the estimated fishery resources of the swept area based on the resource adaptability composite index, which greatly improves the measurement accuracy and provide good guidance for fishery production, thereby improving fishing efficiency and reducing fishing costs. (2) In the method of the present application, the resource adaptability composite index model is changeable according to the latest data obtained in real time, showing good adaptability. (3) The device which applies the method of the present application involves the simple structure and low cost, and can quickly realize accurate estimation of fishery resources based on the swept area method, having great application prospects.
    BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a flow chart of a method of correcting estimated fishery resources in a swept area according to the present application. Fig. 2 is a schematic diagram showing division of a surveyed sea area. Fig. 3 is a schematic diagram of a model reflecting a relationship between a surface temperature and a resource adaptability index SSI. Fig. 4 is a schematic diagram of a model reflecting a relationship between salinity and a resource adaptability index SSI. Fig. 5 is a schematic diagram of a model reflecting a relationship between chlorophyll-a and a resource adaptability index SSI. Fig. 6 is a schematic diagram of a model reflecting a relationship between a sea surface height and a resource adaptability index SSI. Fig. 7 is a schematic diagram of a route for calculating a resource adaptability composite index according to the present application. Fig. 8 is a schematic diagram showing an electronic device according to the present application.
    DETAILED DESCRIPTION OF EMBODIMENTS The embodiments of the present application will be further described below with reference to the accompanying drawings. Embodiment 1 Provided herein is a method for correcting estimated fishery resources in a swept area, which is applicable to electronic equipment and configured to correct results of estimated fishery resource of the swept area. The steps of the method are shown in Fig. 1 and described as follows. A) As shown in Fig. 2, a surveyed sea area is equally divided into 42 survey districts that have the same size with the latitude and longitude of 0.5°*0.5°,
    respectively.
    In 2/3 of the 42 survey districts (that is, 28 survey districts), trawling survey is performed according to a swept area method using a standard trawl net within a specified time.
    Fish catches of the 28 survey districts are respectively obtained, and marine environmental factors including a surface temperature, salinity, chlorophyll-a and a sea surface height of the 42 survey districts are measured.
    B) A resource adaptability index SSI of respective 28 survey districts is calculated according to SSl=Catch/MAX (Catch); where SSI is the resource adaptability index within a value of 0-1; Catch; is the fish catch of a survey district i; and MAX (Catch) is the largest fish catch among the fish catches of all the survey districts;
    C) (for the 28 survey districts with data of both the fish catches and the marine environmental factors) models that reflect relationships between respective marine environmental factors and the resource adaptability index SSI are built in sequence,
    specifically: a model that reflects a relationship between the surface temperature and the resource adaptability index SSI is shown as Fig. 3 and is calculated according to SSh=exp(-1.3728 (Xss1-28.70)2), where Xsst is the surface temperature of a survey district, and the unit thereof is °C; and where P< 0.01; a model that reflects a relationship between the salinity and the resource adaptability index SSI is shown as Fig. 4 and is calculated according to SSls=exp{(-5.5154(Xsss-33.81)%}, where Xsss is the salinity of a survey district, and the unit thereof is psu; where P<0.01;
    a model that reflects a relationship between the chlorophyll-a and the resource adaptability index SS! is shown as Fig. 5 and calculated according to SSls=exp{(-0.5517 (In(Xcnra)+1.378)2),
    where, Xcnia is the chlorophyll-a concentration of a survey district, and the unit thereof is mg/m3; and where P< 0.01; and a model that reflects a relationship between the sea surface height and the resource adaptability index SS! is shown as Fig. 6 and calculated according to SSl4=exp(-1.3260 (XsH-0.4712)2), where Xs is the sea surface height of a survey district, and the unit thereof is m; and where P< 0.01. D) Statistically significant models are selected from the models obtained in step C) to build a resource adaptability composite index (TSI) model, and resource adaptability index (TSI) thereof is calculated according to a route shown in Fig. 7 and according to the following formula: TSI=(SSl+SSl2+ SSl3+SS14)/4; where TS is the resource adaptability composite index.
    E) TS! values of the 42 survey districts are respectively calculated according to the data of the marine environmental factors of the 42 survey districts and through the resource adaptability composite index (TSI) model obtained in step D). F) Fishery resources of a survey district with the highest fish catch is estimated according to the following formula: Buax=C/a; where Buax is fishery resources of the survey district with the highest fish catch; C is the highest fish catch in the survey district; and a is the catchable coefficient with a value of 0-1. G) Fishery resources of the 42 survey districts are respectively calculated according to Bx=Buax“T Slk; where Bs is fishery resources of a survey district k; and TSlk is the resource adaptability composite index of the survey district k.
    H) Fishery resources of all the survey districts are summed to complete the estimation of total fishery resources B in the surveyed sea area.
    B is calculated according to B=B1+B2+...+Ba2. Comparative Example 1 Provided herein is a method for estimating fishery resources by a swept area method, which uses a traditional swept area method for estimating fishery resources, and specifically uses an average method.
    The fishery resources of respective survey districts are calculated respectively using the traditional method and the method of the present application, and results are shown in the following table.
    Survey Resources Resource Corrected districts obtained by | adaptability resources traditional composite index TS! | (Tons) methods (Tons) To wm ww ww eww ww em z
    E x a 2 z 2
    Tw] wal ow] ae Tw we ow aw a a a a a a a os Cw mr Tw es eae Cw a a & it can be seen that total resources obtained by the traditional method is 1,832.64 tons, while total resources obtained by the method of the present application is 1,685.99 tons, where there is a difference of 146.65 tons and an error of 8.7%, which indicates that the method of the present application can overcome the shortcomings of the traditional method and improve the measurement and calculation accuracy to a certain extent. it has been verified that the method of the present application aims to overcome the insufficiency in estimating fishery resources by the traditional swept area method. In the method of the present application, the influence of the environment on the resource distribution is considered when fishery resources are calculated, and a correction coefficient based on the resource adaptability index (i.e., the resource adaptability composite index) is provided, and a specific method is given to correct the estimated fishery resources of the swept area based on the resource adaptability composite index, which greatly improves the measurement accuracy and provide good guidance for fishery production, thereby improving fishing efficiency and reducing fishing costs. The resource adaptability composite index model is changeable according to the latest data obtained in real time, displaying good adaptability and having application prospects. Embodiment 2 Provided herein is an electronic device, as shown in Fig. 8, including at least one processor, at least one memory, and at least one program and a data input device.
    The data input device is configured to input fish catches of 28 survey districts and the marine environmental factors of 42 survey districts. The at least one program is stored in the at least one memory. When the at least one program is executed by the at least one processor, the electronic device executes the method as described in Embodiment 1.
    It has been verified that the device which applies the method of the present application involves the simple structure and low cost, and can quickly realize accurate estimation of fishery resources based on the swept area method, having great application prospects.
    lt can be understood that the embodiments described above are merely ilustrative of the present invention. Any variations or modifications can be made by those skilled in the art without departing from the principle and spirit of the present application.
    权利要求:
    Claims (9)
    [1]
    A method of correcting estimated fish stocks in a swept area, wherein the method is applicable to electronic equipment and the method is configured to correct the results of the estimated fish stocks of the swept area, comprising: A) dividing equally a surveyed marine area in n research districts; selecting m research districts from the n research districts; conducting a trawling survey in the m survey districts according to a swept area method using a standard trawl net within a specified time; respectively obtaining catches from the m research districts; and measuring marine environmental factors of the n research districts where n>1,m<n; B) calculating a resource matching index SSI of respective m research districts according to SSI = Catch/MAX(Catch); where SSI is the resource matching index within a value of 0-1; catch; the catch of a survey area is i, and i is an integer between 1 and m; and MAX(Catch) is the largest catch of the fish catches in the m research districts C) for all marine environmental factors, building models reflecting relationships between the respective marine environmental factors and the resource adaptation index SS! in order; D) selecting statistically significant models from the models obtained in step C); building a composite resource matching index model using the selected statistically significant models according to TSI=(SS1+SS12+......+SS1)/j;
    where TS! is the composite resource matching index; SSh, SSl....SSl; are the statistically significant models corresponding respectively to the marine environment variables 1, 2, … |; and | is the number of marine environmental factors; E) respectively calculating TS1 values of the n research districts according to the marine environmental factors data of the n research districts an by means of the composite resource matching index model from step D); F) estimating the fish stocks of a research district with the highest fish catch according to Buax =C/a; where Buax is the fish stocks of the research district with the highest fish catch; C is the highest catch in the research district; and a is the catchable coefficient between 0-1; G) calculating the fish stocks of the n research districts according to respectively; B= Buax*TSlk; where Bx is the fish stocks of a research district k; TSk the composite resource matching index of the search district k; and H) summing the stocks of the n survey districts to complete the estimation of the total stocks in the surveyed marine area.
    [2]
    Method according to claim 1, characterized in that the marine environmental factors comprise a surface temperature, salinity, chlorophyll-a and a sea surface height.
    [3]
    A method according to any one of the preceding claims, characterized in that the research districts have substantially the same size, and wherein the latitude and longitude are 0.5°*0.5° respectively.
    [4]
    Method according to claim 2, characterized in that in step C), a model representing a relationship between the surface temperature and the source matching index SS! is; SSli=exp(-1.3728(Xss7-28.70)2); where Xsst is the surface temperature of a research district, and its unit is °C; and where P< 0.01.
    [5]
    Method according to claim 2, characterized in that in step C), a model representing a relationship between the salinity and the source matching index is SSI; SSlz=exp(-5.5154(Xsss-33.81)2); where Xsss is the salinity of the research district, and its unit is psu; and where P<0.01.
    [6]
    A method according to claim 2, characterized in that in step C), a model representing a relationship between the chlorophyll-a and the source matching index is SSI; SSla=exp(-0.5517 (In{Xcnra)+1.378)2); where Xcn-a is the chlorophyll-a concentration of the research district, and its unit is mg/m is ; and where P<0.01.
    [7]
    Method according to claim 2, characterized in that in step C), a model representing a relationship between the height of the sea surface and the source matching index is SSI;
    SS1=exp(-1.3260(XsuL-0.4712)2); where Xs is the height of the sea surface, and its unit is m; and where P<0.01.
    [8]
    Method according to one of the preceding claims, characterized in that in step H), the total fish stocks B in the n research districts are calculated according to; B=B1+B2+...+Bn.
    [9]
    An electronic device applying the method according to any one of claims 1-8, comprising at least one processor, at least one memory, and at least one program and a data input device; characterized in that the data input device is configured to input fish catches of the m research districts and the marine environmental factors of the n research districts; the at least one program is stored in the at least one memory; and when the at least one program is executed by the at least one processor, the electronic device executes the method according to any one of claims 1-8.
    类似技术:
    公开号 | 公开日 | 专利标题
    Miller et al.2019|Distance sampling in R
    Thorson et al.2015|Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes
    Thorson et al.2015|The importance of spatial models for estimating the strength of density dependence
    Cusens et al.2012|What is the form of the productivity–animal‐species‐richness relationship? A critical review and meta‐analysis
    Pledger et al.2009|Stopover duration analysis with departure probability dependent on unknown time since arrival
    Farmer et al.2008|Fundamental limits to the accuracy of deuterium isotopes for identifying the spatial origin of migratory animals
    Liu et al.2016|Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting
    Stein et al.2015|Metrics for assessing earthquake‐hazard map performance
    JP6679086B2|2020-04-15|Learning device, prediction device, learning method, prediction method, and program
    Pilz et al.2019|Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model?
    Gupte et al.2021|A guide to pre‐processing high‐throughput animal tracking data
    Johnson et al.2011|Bayesian multimodel inference for geostatistical regression models
    NL2027467B1|2022-02-22|A method for correcting estimated fishery resources in a swept area and applications thereof
    Peel et al.2014|A simulation study of acoustic-assisted tracking of whales for mark-recapture surveys
    Cummings et al.2011|The impact of different performance measures on model selection for Fraser River sockeye salmon
    Ndjaula et al.2013|Establishing a baseline for evaluating changes in body condition and population dynamics of sardine | in the southern Benguela ecosystem
    Lindström et al.2011|Bayesian analysis of animal movements related to factors at herd and between herd levels: Implications for disease spread modeling
    Crewe et al.2015|Modeling systematic change in stopover duration does not improve bias in trends estimated from migration counts
    Woehler et al.2014|Climate and competition in abundance trends in native and invasive Tasmanian gulls
    Lee et al.2020|Life history changes and fisheries assessment performance: a case study for small yellow croaker
    Muradian et al.2019|A framework for assessing which sampling programmes provide the best trade-off between accuracy and cost of data in stock assessments
    CN109165765A|2019-01-08|Section object information forecasting method and system, Building Checks Price prediction technique and system
    Olsson et al.2016|Testing reproductive allometry in fish
    Breidt et al.2018|Model-assisted survey estimation with imperfectly matched auxiliary data
    JP7022456B2|2022-02-18|Correction method for the amount of fishery resources estimated based on the swept area and its application
    同族专利:
    公开号 | 公开日
    CN111626467A|2020-09-04|
    NL2027467B1|2022-02-22|
    JP2021166507A|2021-10-21|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    JP2006158239A|2004-12-03|2006-06-22|Kiyoshi Ito|Fishery forecasting device and oceanic condition forecasting device|
    WO2017002533A1|2015-06-29|2017-01-05|国立研究開発法人海洋研究開発機構|Fishing-ground prediction device, fishing-ground prediction system, marine-environmental-data sharing device, and marine-environmental-data sharing system|
    CN107578125A|2017-08-29|2018-01-12|上海海洋大学|Morocco's siphonopods fishing ground forecasting procedure based on the different weight marine environment factors|
    CN109767040A|2019-01-15|2019-05-17|上海海洋大学|Saury cental fishing ground prediction technique based on habitat suitability index|
    US10546260B2|2014-12-24|2020-01-28|General Electric Company|System and method for rule-based analytics of temporal-spatial constraints on noisy data for commercial airlineflight operations|
    CN105678399A|2015-12-29|2016-06-15|中国地质科学院矿产资源研究所|Regional mineral resource quantity estimation and analysis method and system|
    CN106204313B|2016-07-22|2021-08-10|上海海洋大学|Method for predicting space distribution of brachyurus hubner resources|
    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    CN202010270161.8A|CN111626467A|2020-04-08|2020-04-08|Correction method for estimating fishery resource amount by sea sweeping area and application thereof|
    [返回顶部]