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    • 专利摘要:
    The present disclosure provides a guarding system against attackfromnentallydisturbedpatients,comprisingaacomputer, aVNearable device, a first Bluetooth beaconJ a second Bluetooth beacon and an administrator mobile terminal, wherein the computer is connected with the wearable device, the first Bluetooth beacon, the second Bluetooth beacon and the administrator mobile terminal; and when the computer judges that the final risk state coefficient of a patient is greater thaneapreset value and the physical distanceretween.the first Bluetooth beacon and the second Bluetooth beacon is less than a preset value, an alarm is sent to the administrator mobile terminal. The system has the beneficial effects that when the mentally disturbed patient is in an unstable state of a set danger level, the risk state coefficient is large and is close to a high—risk group, the computer sends an alarm to an administratorofijuahigh—riskgroupijitime,tflmaadministrator takes emergency measures quickly, and the danger that the high—risk group is attacked by the mentally disturbed patient can be reduced.
    公开号:NL2027940A
    申请号:NL2027940
    申请日:2021-04-08
    公开日:2021-10-25
    发明作者:Xiang Jing
    申请人:Southwest Univ Of Political Science & Law；
    IPC主号:
    专利说明:

    NO. P139781NLOOGUARDING SYSTEM AGAINST ATTACK FROM MENTALLY DISTURBEDPATIENTS
    TECHNICAL FIELD The present disclosure relates to the technical field of information, and in particular to a guarding system against attack from mentally disturbed patients.
    BACKGROUND ART The risk assessment standard published in the Code for the Management and Treatment of Severe Mental Disorders (2018 release) is simple, which only considers behavior and ignores multi-factor risks. It is seriously separated from pathological mental symptoms. It can only be used as a simple classification to facilitate follow-up management, and it is impossible to accurately assess whether the disease occurs and whether the disease is serious. Most grass-roots management and control people have limited manpower and have multiple jobs, and rely on telephone follow-up so as not to play an effective role in supervision.
    SUMMARY In order to solve the problem of evaluating and prewarning the mental state of mentally disturbed patients through physiological signal data and action image data of mentally disturbed patients, the present disclosure provides a guarding system against attack from mentally disturbed patients, which comprises a computer, wearable devices, a first Bluetooth beacon, a second Bluetooth beacon and an administrator mobile terminal; 40 wherein the computer is connected with the wearable device, the first Bluetooth beacon, the second Bluetooth beacon and the administrator mobile terminal; the first Bluetooth beacon is worn by mentally disturbed patients, and the second Bluetooth beacon is arranged in the activity places of high-risk groups; the compute calculates the final risk state coefficient of the mentally disturbed patients; when the computer judges that the final risk state coefficient of a patient is greater than a preset value and the physical distance between the first Bluetooth beacon and the second Bluetooth beacon is less than a preset value, an alarm is sent to the administrator mobile terminal.
    Further, the computer executes the following steps: S1, acquiring heartbeat data, somatosensory posture data, voice data and monitoring video data of a patient through a wearable device worn by the patient and a security monitoring device in the action range of the patient; 52, analyzing patient behavior according to the heartbeat data, the voice data, the somatosensory posture data and the monitoring video data, automatically generating patient behavior data, and acquiring patient risk level data from the patient behavior data; S3,; acquiring the final risk state coefficient of the patient according to the user risk level data.
    Further, the risk level comprises three levels: a low risk level, a medium risk level and a high risk level.
    Further, the step S3 comprises the following steps: 531: determining the level score according to the following rules: the score of each item with a low level is 1, the score of each item with a medium level is 10, the score of each item with a high level is 100, classifying the target column, Y corresponds to the matrix of 17X1, corresponding to features 1 17, if there is a corresponding feature, it is marked as 1; otherwise, it is marked as 0, Hi Fal if the final score is Yscorep Viore = 3; +10) 3 £100) 3, jz j= j=is if Yeeore = 0, the patient is normal and does not need to be calculated;
    if Yscore €(0,10], the corresponding level of the patient is low; if Yscore E{10,100), the corresponding level of the patient is medium; if Yssore 2100, the corresponding level of the patient is high; S321: according to the setting in step S31, determining the contrast matrices established by the low, medium and high levels as follows: Xow = ES x, cc %, | X13 = 1 when 1 = 1, 2, 3, 4, 5 and 6, and x4; = 0 in other cases (1 takes an integer from 1 to 17) X medion = [x X99 a xX, 17 ] X2i = 1, where i = 7, 8, 9, 10, 11, and Xz; = 0 in other cases (1 takes an integer from 1 to 17) Xa =| A3 oo | X33 = 1 when 1 = 12, 13, 14, 15, 16 and 17, and x3; = 0 in other cases (i takes an integer from 1 to 17) 5322: let: lve 00 0 ‘ 10 ox, OD St | 0 { + | ij L000 x 7 ”, Xaii = 1 when 1 = 1, 2, … 16, and x41: = 0 in other cases (1 takes an integer from 1 to 17)
    EA UE u § x 00 A oy 5 ij or . . . > { {} OÙ i 0 0 xa. ies Xaoi = 1 when 1 = 7, 8, … 11, and X42: = 0 in other cases (1 takes an integer from 1 to 17)
    ts U OD 0 Oe a, 09 X af w= i o oo . i
    LD VD LO U od Baap | f Xa3i = 1 when i = 12, 13, … 17, and Xa3i = 0 in other cases (i takes an integer from 1 to 17) x) 1 Xx — 21 X 1 . Xs 1 a t 171 wherein when the patient level is low, assuming that mee i wwe ol Ig | io | Eve Eg Xd : 3 Hd Mola. FF 3 S A EA Iq Ky = Kgs Kg i oT Ng Alis) 77 Ld Rt Am Ky | calculate the grey correlation: when the patient level is medium, let Xi; =X’ mediums Krek U Koka TY Î Î Î NE “9 8, substituting Xs =Xa1 and Xs=Xo2 into X (1) = [Xo Xi] to calculate the grey correlation, and finally obtaining two correlation coefficient ry and rye respectively; when the patient level is high, Xi =X/ high; Kor mm Xe eV Vorst ae ¥ KEE | let AUT Jial i hoe LN: and 3 == , and let X, =Xo1, Xos=Xos and Xo=X9:3 respectively; substituting them into X{1) = [Xg Xi] to calculate the grey correlation, and finally obtaining three correlation coefficients rg, To: and rg; respectively; S323: the step of calculating the grey correlation comprises S3231: making:
    Xe Ay or Xow Xp | Alyson TH x, aN os perform normalization row by row, the formula is as follows: A ‘ - : Bg moped = Lee 7 f= 00 Maxx} fel 5 5 the new matrix X{2}) consisted of each new x;; after normalization is calculated one by one by the above formula: Xa Ay Al)= Arg Fim 53232: finding the difference sequences A, Amax and Amin for matrix X (2) let: A=[Aii 1, 1=1,..,17 Ai; =l|Xi0 —-Xsil, 1=1,..,17, 3=0,1 then: 24 a 5 rR { 5 i rin JA | . Nie >» fs 4 3
    TREES 53233 uses the following formula to find the matrix § of the grey correlation coefficient E=[&,].i=117 £, — A + BA ven Ji =1,---,17 A, + B: A ax where B is the correlation coefficient; 53234: assuming that the grey correlation between the comparison sequence X and the target sequence Y is R, and calculating r by the following formula r=— 17 26 5324, calculating the state coefficient R, comprising, assuming that W = [wy wo ws] is the weight matrix, where Wi corresponds to the low weight coefficient of patients, w; corresponds to the medium weight coefficient of patients, ws corresponds to the high weight coefficient of patients, indicating the proportion of low-, medium- and high-level components in mental diseases of patients, respectively, wherein the coefficients are preset by users, and wi Wz Ws satisfies wy + ws: + wa = 1; the calculating formula of the state coefficient R is as follows: Ww, R= Rent ° W = [7 Ya fyi] Wy | SW Fy TW Fy + WF Ww, when the patient level is low, R=w rx, when the patient level is medium, R=w 4 +w, 4, when the patient level is high, R=W 4 +W, Fp, +W, Fy, The present disclosure has the beneficial effects that by monitoring physiological information, activity videos and voices of patients, symptom and risk state evaluation data are automatically acquired and sent to the server for graded early warning. When the mentally disturbed patient is in an unstable state of a set danger level, the risk state coefficient is large and is close to a high-risk group, the computer sends an alarm to an administrator of the high-risk group in time, the administrator takes emergency measures quickly, and the danger that the high-risk group is attacked by the mentally disturbed patient can be reduced.
    BRIEFT DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart of the present disclosure.
    FIG. 2 is an architecture diagram of the present disclosure,
    DETAILED DESCRIPTION OF THE EMBODIMENTS As shown in FIG. 1, the present disclosure provides a guarding system against attack frommentally disturbed patients, which comprises a computer, wearable devices, a first Bluetooth beacon, a second Bluetooth beacon and an administrator mobile terminal; wherein the computer is connected with the wearable device, the first Bluetooth beacon, the second Bluetooth beacon and the administrator mobile terminal; the first Bluetooth beacon is worn by mentally disturbed patients, and the second Bluetooth beacon is arranged in the activity places of high-risk groups; the compute calculates the final risk state coefficient of the mentally disturbed patients; when the computer judges that the final risk state coefficient of a patient is greater than a preset value and the physical distance between the first Bluetooth beacon and the second Bluetooth beacon is less than a preset value, an alarm is sent to the administrator mobile terminal.
    The Bluetooth beacon can be Apple ibeacon Bluetooth beacon, the high-risk group can be a kindergarten group, the second Bluetooth beacon is arranged in a kindergarten, and the administrator mobile terminal can be a smart phone.
    When the mentally disturbed patient is in an unstable state, the risk state coefficient is large and the mentally disturbed patient is close to the kindergarten, the computer sends an alarm to the kindergarten administrator in time, the administrator takes emergency measures quickly, and the danger that the high-risk group is attacked by the mentally disturbed patient can be reduced.
    Further, the computer executes the following steps: S1, acquiring heartbeat data, somatosensory posture data, voice data and monitoring video data of a patient through a wearable device worn by the patient and a security monitoring device in the action range of the patient; S2, analyzing patient behavior according to the heartbeat data, the voice data, the somatosensory posture data and the monitoring video data, automatically generating patient 40 behavior data, and acquiring patient risk level data from the patient behavior data; S3, acquiring the final risk state coefficient of the patient according to the user risk level data.
    In the implementation process of the present disclosure,
    the risk level comprises three levels: a low risk level, a medium risk level and a high risk level according to the following table.
    Low level the abnormal daily schedule (such as getting up many times in the middle of the night and losing sleep for several days), which can be obtained by analyzing the heartbeat of the patient and the somatosensory posture data with a computer
    2. not admitting to be sick or suddenly refusing to takemedicine or treatment, which can be obtained by calculating and analyzing the behavior video data and voice data of the patient
    3. the abnormal phenomenon of emotional indifference or emotional anger and excitement for several days, which can be obtained by calculating and analyzing the behavior video data and voice data of the patient
    4. many strange actions, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    5. Talking to yourself for a long time or ignoring inquiries from other people, which can be obtained by calculating and analyzing the behavior video data and voice data of the patient
    6. Self-reporting illusory sounds, images, smells, etc., which can be obtained by calculating and analyzing the behavior video data and voice data of the patient Medium 7. scolding at the air, which can be obtained by level calculating and analyzing the behavior video data and voice data of the patient
    8. describing false contents that endanger the safety of oneself or others, which can be obtained by calculating and analyzing the behavior video data and voice data of the patient
    9. There are strong delusions of murder and exaggerated delusions (the content endangers the safety of oneself or others), which can be obtained by calculating and analyzing the behavior video data and voice data of the patient
    10. Insulting others, the speech content involving by calculating and analyzing the behavior video
    11. Often having verbal disputes with others without reason, which can be obtained by calculating and analyzing the behavior video data and voice data of the patient High level | 12. Setting fire, waving or holding dangerous tools without reason, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    13. Attacking others without reason, and an attack that endangers the safety of others occurring, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    14. Special abnormal behaviors that are resistant and aggressive to a specific person, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    15. throwing, smashing and other acts of vandalism, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    16. attempting to commit suicide or harm yourself to a serious degree, which can be obtained by calculating and analyzing the behavior video data and somatosensory posture data of the patient
    17. Attacking and guarding against the illusory "enemy" or giving an alarm, which can be obtained by calculating and analyzing the behavior video data, somatosensory posture data and voice data of the patient The above behaviors and symptoms are automatically obtained by analyzing the patient heartbeat data, the voice data, the somatosensory posture data and the monitoring video data by a computer.
    In the implementation process of the present disclosure, the wearable device can be a smart watch with a communication function, a heartbeat sensor and a somatosensory posture sensor. The smart watch detects heartbeat data and somatosensory posture data of a user, intelligently analyzes sleep conditions (deep sleep time, light sleep time and times of getting up) and sends them to a computer. The smart watch has a microphone, which can acquire the voice data of the patient and send the voice data to the computer. In an embodiment of the present disclosure, an apple smart watch is used, and in another embodiment of the present disclosure, a Huawei watchGT2 integrated with a microphone is used as the smart watch.
    The patient video data can be acquired by the monitoring device in the environment where the patient is located. In one embodiment of the present disclosure, the computer is connected with the security monitoring device, and the security monitoring device in the residential district of the patient acquires the patient video dara.
    The somatosensory data can be acquired by the accelerometer and the gyroscope integrated in the smart watch.
    The above technical scheme that the computer analyzes the patient behavior through video data and voice data is a well-known technology.
    The process of acquiring the final risk state coefficient of patients according to the user risk level data is described in detail hereinafter.
    A first step 1: determining the classification Stepl: determining the level score low 1 i Step2: classifying the target column Y corresponds to the matrix of 17X1, corresponding to features 1 ~17, if there is a corresponding feature, it is marked as 1; otherwise, it is marked as 0, 5 Va *2 If the final score is Yscores Yioore = 3 ¥; +10 3; +160 7; If Yscore = 0, the patient is normal and does not need to be calculated; if Yscore €(0,10], the corresponding level of the patient is low; if Yscore €{(10,100), the corresponding level of the patient is medium; if Yscore 2 100, the corresponding level of the patient is high.
    A second step: determining the severity scores in different levels Stepl: determining the contrast matrices X,, =| x, Xp oo Xi | xX1i = 1 when 1 = 1, 2, 3, 4, 5 and 6, and x3; = 0 in other cases (1 takes an integer from 1 to 17) X tim = [x Xp oe X17 ] X23; = 1, where i = 7, 8, 9, 10, 11, and X:; = 0 in other cases (1 takes an integer from 1 to 17) X= 6 Xa Xp | X33 = 1 when 1 = 12, 13, 14, 15, 16 and 17, and x3; = 0 in other cases (i takes an integer from 1 to 17) Step2: determining the input matrix let: (ve 0 0 0 v U ox, OO) Tete DO] L000 Aas) ë - 7 Xa1i = 1 when 1 = 1, 2, … 16, and Xa; = 0 in other cases {i takes an integer from 1 to 17) lx, 0 0 0 ff x, 0 {} * { Do 6 0 0 x. 7 oo”, Xa2i = 1 when 1 = 7, 8, … 11, and Xa2; = 0 in other cases {i takes an integer from 1 to 17)
    OE x, Og A os :
    PH U OÙ 0 0 0 x # Xaai = 1 when 1 = 12, 13, … 17, and Xa3i = 0 in other cases {i takes an integer from 1 to 17) x) i x _ 21 X 1 . Xs 1 e t 171 wherein when the patient level is low, let Ha | TP ma | Ide EN x | Xow 7 A col A | ee ye TRE 23 Ny Kors ee | : | Al at sj vk £4) . . i Foy | ik Xs | When the patient level is medium, let Xi =X medium; Vond oe Vat ne ¥ because | wast * Tian vd Xo ww 4 2 Vo Xoo Kan *v ¥Y Î Î Î ¢ 3 He == ‚ substituting X; =X91; and Xg=X%9: into X(1) = [Xs Xi] to calculate the grey correlation, and finally obtaining two correlation coefficient ry and rye respectively.
    When the patient level is high, Xi =X/ high; because nl a 32 Vor mm * ¥ Yann ae ¥ Mpa Nan * Y ‚ let fd: Aal * Tos== as è and © ‚ and let Xo =X%o1; Xs=X92 and Xs=X03 respectively; substituting them into X (1) = [Xs Xi] to calculate the grey correlation, and finally obtaining three correlation coefficients rp, Tos and rg; respectively.
    From the above formula, rs3 = 0 when the patient level is medium; ros = rg: = 0 when the patient level is low.
    For a mentally disturbed patient, there must be three coefficients ry; rg; Fos, and the characteristic matrix Rpatisnt of the patient, so that Rpatient = [For Taz ros].
    Step3: calculating the grey correlation Step3.1 Normalization making: rity TRY 3 A= 7 OD] perform normalization row by row, the formula is as follows: Ag zg X, Spt fu | FERRIS F= OT Maxx, } it ¥ the new matrix X (2) consisted of each new xi; after normalization is calculated one by one by the above formula: Mo Ig) Alia | . Lm (Ld Step3.2: finding the difference sequences A, Amaz and Amin for matrix X(2) let: A=[Ay 1, i=1,..,17 Ais =| ig —-Xi1 ly i=1,..,17, j=0,1 then: & vous FA AX ce NEST § A 3 Step3.3 finds the matrix & of the grey correlation coefficient £=[&]i=1-17 &, — A in + BA ax Ji — 1,17 A, + A A a where B is the correlation coefficient, which is generally
    0.5 unless there is any special requirement.
    Step3.4: calculating the grey correlation r assuming that the grey correlation between the comparison sequence X and the target sequence Y is R: 1 17 r=—>»£& 17 2 = Stepd: calculating the state coefficient R The grey correlation r obtained in Step3.4 reflects the correlation between the patient characteristics and the most serious current level.
    In order to obtain a feature quantity that can reflect the patient state more completely, it is necessary to further process the grey correlation r.
    Assuming that W = [wi we ws] is the weight matrix, where Wi W2 W3 correspond to the low, medium and high weight coefficient of patients, indicating the proportion of low-, medium- and high-level components in mental diseases of patients, respectively, wherein the coefficients are generally judged by experts, and wi wy ws satisfies wi + we + wa = 1. According to step2, the characteristic matrix Rgparient = [ro1 Fez Fos] of the patient can be known, wherein ro Fos ros correspond to the three correlation coefficients Tm, ro: and rg: obtained in step2, respectively.
    The calculating formula of the state coefficient R is as follows: Ww, R= R ien ey = [7 Bor Tos ] OW, | SW Fy TW) Fy TWF, wy X'iow represents transposition of matrix Xiow, X'medium represents transposition of matrix Xmediums X'nigh represents transposition of matrix Xig5, and W' represents transposition of matrix W.
    The final coefficient R is the severity score in different levels, and Ris a value between 0 and 1. When R is larger, it means that the patient is more serious in the current level.
    Furthermore, when the patient level is low, R=w ry when the patient level is medium, R=W hy WK, when the patient level is high, R=w hj Wi +W, I,
    According to the present disclosure, three weight coefficients are preset and three correlation coefficients ra, ro; and re; are calculated to realize more accurate numerical quantification of mental state of mentally disturbed patients.
    The present disclosure has the beneficial effects that by monitoring physiological information, activity videos and voices of patients, symptom and risk state evaluation data are automatically acquired and sent to the server for graded early warning.
    When the mentally disturbed patient is in an unstable state of a set danger level, the risk state coefficient is large and is close to a high-risk group, the computer sends an alarm to an administrator of the high-risk group in time, the administrator takes emergency measures quickly, and the danger that the high-risk group is attacked by the mentally disturbed patient can be reduced.
    权利要求:
    Claims (4)
    [1]
    A monitoring system against an attack of mentally confused patients, comprising a computer, a portable device, a first Bluetooth beacon, a second Bluetooth beacon and a mobile terminal for a manager, the computer being connected to the portable device, the first Bluetooth beacon, the second Bluetooth beacon and the mobile terminal for an administrator; wherein the first Bluetooth beacon is carried by mentally confused patients, and wherein the second Bluetooth beacon is placed in the sites of activity of high-risk groups; wherein the computer calculates the final risk state coefficient of the mentally confused patients; when the computer judges that a patient's final risk state coefficient is greater than a preset value and the physical distance between the first Bluetooth beacon and the second Bluetooth beacon is less than a preset value, then a alarm sent to the mobile terminal for an administrator.
    [2]
    The attack monitoring system of mentally confused patients according to claim 1, wherein the computer performs the following steps: S1, obtaining heart rate data, somatosensory posture data, voice data and monitoring video data of a patient by means of a portable device worn by the patient and a safety monitoring device in the patient's area of activity; 52, analyzing patient behavior in accordance with the heart rate data, the somatosensory posture data and the monitoring video data, for automatically generating patient behavioral data, and obtaining risk level data of a patient from the behavioral data from a patient; S3, obtaining the patient's final risk state coefficient in accordance with the user's risk level data.
    [3]
    The attack surveillance system of mentally confused patients according to claim 1, wherein the risk level comprises three levels: a low-risk level,
    a medium-risk level and a high-risk level.
    [4]
    The attack surveillance system of mentally confused patients according to claim 2, wherein the step S3 comprises the steps of: 331, determining a level score according to the following rules: the score of each low level item is 1, the score of each intermediate level item is 10, the score of each high level item is 100, classifying the target column, Y corresponds to the matrix of 17 x 1, corresponding to the attributes 1 + 17, if there is a corresponding feature, then it is marked as 1; otherwise it is marked as 0, and Pol if the final score is Yscore, then ä 13 & Ww — 47 aro 18 OR an LN * ar, Ypoorm — So Fi By ¥s + 180 > ¥; if Yseore = 0, then the patient is normal and does not need to be calculated; if Yscore €(0.10], then the patient's corresponding level is low; if Yscore is €(10,100), then the patient's corresponding level is medium; if Yscore is 2 100, then the patient's corresponding level is high S321: According to the setting in step $31, determine the contrast matrices obtained by the low, medium and high levels as follows: Xi, =x, Xa x | X13 = 1 when 1 = 1, 2, 3, 4 , 5 and 6, and X1 = 0 in other cases (i takes an integer from 1 - 17) X media and 132 and $i | Xz; = 1, where i = 7, 8, 9, 10, 11, and xz; = 0 in other cases (i takes an integer from 1 - 17) Xp =|Xyp Xo | X3i = 1 when 1 = 12, 13, 14, 15, 16 and 17, and xi; = 0 in other cases (i takes an integer from 1 - 17) 3 35322: suppose: te UD | AX at = a po LO DB) | 0 0 i Tes | u ” ‚ Xa1i = 1 when 1 = 1, 2, … 16, and %,1; = 0 otherwise {i takes an integer from 1 - 17) x, DO 0 00 DD x. = Se, Xazi = 1 when 1 = 7, 8, … 11, and Xaz:i = 0 otherwise {i takes an integer from 1 - 17) | ig Tw 4 {t A= PO ie 0D | U U Kam Xa3i = 1 when 1 = 12, 13, … 17, and x43; = 0 in other cases {i takes an integer from 1 - 17) suppose x X, | Xa Xt where when the patient's level is low, assuming
    £2 aod A ava = . o xl. en Eee Ea) 2 ~ ie ot re in - Ex ee aa i EN 3 i | Sens ep, | calculate the Gray correlation; if the patient's level is average, set X; = Valy Vaes YW Km pitch Tides Xan eV Î Î SX! mediums oes Re , substitute Xo =Xo9i and X¢=Xp into X{1) = [Xs Xi] to calculate the Gray correlation, and finally obtain two correlation coefficients Ta and rg: respectively; when the patient's level is high, Xi = X/nign Noor yo ®% Yeoon¥X ae VV Aas Naz sy set ADI Aal i AAE Half i and Ad LES ‚ and set Xo =Xo1, Xo=Xos and Xo=X93 respectively; substitute these into X(1) = [X; Xi] to calculate the Gray correlation, and finally obtain three correlation coefficients roi, Tez and Tos respectively; S325: the step of calculating the Gray correlation includes S3231: fabricate: Fig RS i : eo: Ea Ee] SIE ig vay | Ala 7 perform a row by row normalization, where the formula is as follows: . + : 1% ; At, = re ef = 1, Ye i Ei 5 = (3, i Wears} A= The new matrix X{2}) consisting of each new x;y after normalization, is calculated one by one by the above formula: Ky A - Ey Ry Ali) © Lg Fim) S3232: finding difference sequences A, Amsz and Amin FOR matrix X{2) Suppose: A= [Aq ] 7 i=1,.., 17 Ai =|xXi9 Xl, 1= 1,..,17, j=0.1 then: A = PY | A 1 Ap - HY ay § oe 53233 uses the following formula to find the matrix & of the Gray correlation coefficient &=[&,|i=1:17 & — A in + BA nx i=]. 17 il 3 2 2 A, + BA axis where B is the correlation coefficient; $3234: assuming the Gray correlation between the comparison sequence X and the target sequence Y, is r, and calculating r by the following formula IL, F=—>£& 72 $324, calculating the state coefficient R , including, assuming that W = [wy WwW » ws] is the weight matrix, where wi corresponds to the low weight coefficient of patients, wy corresponds to the average weight coefficient of patients, ws corresponds to the high weight coefficients of patients, representing the proportion of low-weight , indicates medium- and high-level components in patients' mental illness, respectively, where the coefficients are preset by users, and wi wz: ws satisfy wi + wz + wy = 1; where the formula for calculating the coefficient of state R is as follows: Ww, R= R pions ey = [ro Fr Fn ] OW, | SW Fy TW, Fy FW W, 3 when patient level is low, R=w 1, when patient level is medium, R=w 4, +w, Fr, when patient level is high, R= w ny WT Wig
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US8112153B2|2004-12-17|2012-02-07|Medtronic, Inc.|System and method for monitoring or treating nervous system disorders|
    CN204050311U|2014-08-29|2014-12-31|徐扬|The special wrist strap of a kind of Mental Disorder Caused by Alcohol|
    CN104796485B|2015-04-30|2019-09-10|深圳市全球锁安防系统工程有限公司|A kind of the elderly Yunan County health service platform and big data processing method|
    CN205866740U|2016-05-09|2017-01-11|鲍崇智|Acquire collection system of multidimension heartbeat information in step|
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    CN202010275106.8A|CN111369763A|2020-04-09|2020-04-09|Attack prevention system for mental disorder patient|
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