Route loss abnormal state identification method and device, equipment and medium

Abstract

The embodiment of the invention provides a route loss abnormal state identification method and device, equipment and a medium. The method comprises the following steps: obtaining a line loss vector list set corresponding to an initial user ID list according to a first line loss rate list set, a second line loss rate list set, a third line loss rate list set and a fourth line loss rate list set corresponding to the initial user ID list obtained based on a root-mean-square current method, an average current method, a maximum current method and an equivalent resistance method; and on the basis of the line loss vector list set, identifying whether the line loss of the target medium-voltage electric wire is abnormal or not. According to the method, the line loss vector list set is obtained by combining four algorithms including the root-mean-square current method, the average current method, the maximum current method and the equivalent resistance method, and whether the line loss of the target medium-voltage electric wire is abnormal or not is identified based on the line loss vector list set; and the accuracy of acquiring the line loss vector and the accuracy of a result of identifying whether the route loss of the target medium-voltage electric wire is abnormal or not can be improved.

Description

Technical Field

[0001] The present invention relates to the field of route loss technology, and in particular to a method, device, equipment and medium for identifying an abnormal state of route loss. Background Art

[0002] In the power system, route loss will lead to energy waste and increase operating costs. Therefore, identifying whether there is an abnormality in the route loss of the wire is crucial to ensure the efficient, safe and reliable operation of electricity. Most of the methods for identifying whether there is an abnormality in the route loss of the wire in the prior art are to obtain the corresponding line loss rate of the wire based on the root mean square current method, the average current method, the maximum current method or the equivalent resistance method, and compare the obtained line loss rate with the historical line loss rate or the preset line loss rate to determine whether there is an abnormality in the route loss of the wire.

[0003] However, the above method also has the following technical problems:

[0004] In the above method, the line loss rate corresponding to the electric wire is obtained according to one of the algorithms including the root mean square current method, the average current method, the maximum current method or the equivalent resistance method. A single algorithm may not be able to fully consider all factors affecting the line loss, resulting in inaccurate calculation results. Moreover, each method has its optimal conditions for application. A single method may not be suitable for all types of application scenarios. Therefore, the accuracy of the line loss rate obtained by the above method is limited and the scope of application is limited. Furthermore, the accuracy of the result of whether there is an abnormality in the route loss of the electric wire determined by the above method is low. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is:

[0006] According to a first aspect of the present invention, a method for identifying an abnormal state of route loss is provided, the method being used to identify whether there is an abnormality in the route loss of a target medium voltage power line, the method comprising the following steps:

[0007] S1. Obtain the initial user ID list A = {A1, A2, ..., A i , ..., A m}, A i is the i-th initial user ID, the value range of i is 1 to m, m is the number of initial user IDs, and the initial user is the user who uses the target medium-voltage power line.

[0008] S2. Obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m}, F i ={F i1 , F i2 , F i3 , Fi4}, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding fourth line loss vector includes the following sub-steps S21-S25:

[0009] S21, based on the root mean square current method, obtain the first line loss rate list set B corresponding to A = {B1, B2, ..., B i , ..., B m}, B i = {B i1 , B i2 , ..., B ij , ..., B in}, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j, has a value range of 1 to n, where n is the number of preset time slices in the preset time period, and the preset time slice is a time slice in the preset time period.

[0010] S22, based on the average current method, obtain a second line loss rate list set C corresponding to A = {C1, C2, ..., C i , ..., C m}, C i ={C i1 , C i2 , ..., C ij , ..., C in}, C i A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate.

[0011] S23, based on the maximum current method, obtain the third line loss rate list set D corresponding to A = {D1, D2, ..., D i , ..., D m}, D i ={D i1 , D i2 , ..., D ij , ..., D in}, Di A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate.

[0012] S24, based on the equivalent resistance method, obtain the fourth line loss rate list set E corresponding to A = {E1, E2, ..., E i ,……,E m}, E i ={E i1 , E i2 ,……,E ij ,……,E in}, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate.

[0013] S25. According to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively:

[0014] F i1 =∑ n j＝1 B ij / n;F i2 =∑ n j＝1 C ij / n;F i3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n.

[0015] S3. Based on F, identify whether there is an abnormality in the route loss of the target medium-voltage power line.

[0016] According to a second aspect of the present invention, there is provided a device for identifying an abnormal state of route loss, the device being used to identify whether there is an abnormality in the route loss of a target medium voltage electric wire, the device comprising:

[0017] Initial user ID list acquisition module, used to acquire the initial user ID list A = {A1, A2, ..., Ai , ..., A m}, A i is the i-th initial user ID, the value range of i is 1 to m, m is the number of initial user IDs, and the initial user is the user who uses the target medium-voltage power line.

[0018] The line loss vector list set acquisition module is used to obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m}, F i ={F i1 , F i2 , F i3 , F i4}, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding fourth line loss vector; the line loss vector list set acquisition module includes the following units:

[0019] The first line loss rate list set acquisition unit is used to acquire the first line loss rate list set B corresponding to A based on the root mean square current method = {B1, B2, ..., B i , ..., B m}, B i = {B i1 , B i2 , ..., B ij , ..., B in}, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j, has a value range of 1 to n, where n is the number of preset time slices in the preset time period, and the preset time slice is a time slice in the preset time period.

[0020] The second line loss rate list set acquisition unit is used to acquire the second line loss rate list set C corresponding to A based on the average current method = {C1, C2, ..., C i , ..., C m}, C i ={C i1 , C i2 , ..., C ij , ..., Cin}, C i A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate.

[0021] The third line loss rate list set acquisition unit is used to acquire the third line loss rate list set D corresponding to A based on the maximum current method = {D1, D2, ..., D i , ..., D m}, D i ={D i1 , D i2 , ..., D ij , ..., D in}, D i A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate.

[0022] A fourth line loss rate list set acquisition unit is used to acquire a fourth line loss rate list set E corresponding to A based on the equivalent resistance method = {E1, E2, ..., E i ,……,E m}, E i ={E i1 , E i2 ,……,E ij ,……,E in}, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate.

[0023] The line loss vector acquisition unit is used to obtain the line loss vector according to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively:

[0024] F i1 =∑ n j＝1 B ij / n;F i2 =∑ n j＝1 C ij / n;Fi3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n.

[0025] The abnormality identification module is used to identify whether there is an abnormality in the route loss of the target medium-voltage power line based on F.

[0026] According to a third aspect of the present invention, there is provided a non-transitory computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the aforementioned method.

[0027] According to a fourth aspect of the present invention, there is provided an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned method when executing the computer program.

[0028] The present invention has at least the following beneficial effects:

[0029] The embodiments of the present invention provide a method, device, equipment and medium for identifying abnormal route loss status. The method can obtain a first line loss rate list set corresponding to an initial user ID list based on a root mean square current method, obtain a second line loss rate list set corresponding to an initial user ID list based on an average current method, obtain a third line loss rate list set corresponding to an initial user ID list based on a maximum current method, obtain a fourth line loss rate list set corresponding to an initial user ID list based on an equivalent resistance method, obtain an initial user ID list according to the first line loss rate list set, the second line loss rate list set, the third line loss rate list set and the fourth line loss rate list set. A set of line loss vector lists corresponding to the household ID list is used to identify whether there is any abnormality in the route loss of the target medium-voltage power line based on the line loss vector list set. It can be seen that the present invention combines four algorithms, namely the root mean square current method, the average current method, the maximum current method and the equivalent resistance method, to obtain a line loss vector list set and identifies whether there is any abnormality in the route loss of the target medium-voltage power line based on the line loss vector list set. This can obtain more comprehensive data and reduce the error caused by using a single algorithm to obtain the line loss rate. Therefore, it is beneficial to improve the accuracy of obtaining the line loss vector, and further to improve the accuracy of the result of identifying whether there is any abnormality in the route loss of the target medium-voltage power line. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A flowchart of a method for identifying an abnormal route loss state provided in Embodiment 1 of the present invention;

[0032] Figure 2 A schematic diagram of the structure of a device for identifying an abnormal state of route loss provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar tasks, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0035] Embodiment 1

[0036] Embodiment 1 of the present invention provides a method for identifying an abnormal state of route loss, the method being used to identify whether there is an abnormality in the route loss of a target medium-voltage power line, the method comprising the following steps: Figure 1 As shown:

[0037] S1. Obtain the initial user ID list A = {A1, A2, ..., A i , ..., A m}, A iis the i-th initial user ID, the value range of i is 1 to m, m is the number of initial user IDs, the initial user ID is the unique identity of the initial user, and the initial user is the user who uses the target medium-voltage power line.

[0038] S2. Obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m}, F i ={F i1 , F i2 , F i3 , F i4}, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding 4th line loss vector.

[0039] Specifically, the line loss vector is a one-dimensional vector.

[0040] Specifically, step S2 includes the following sub-steps S21-S25:

[0041] S21, based on the root mean square current method, obtain the first line loss rate list set B corresponding to A = {B1, B2, ..., B i , ..., B m}, B i = {B i1 , B i2 , ..., B ij , ..., B in}, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j ranges from 1 to n, n is the number of preset time slices in the preset time period, B ij Meet the following conditions:

[0042] B ij =3×I j 2 ×R×T×10 -3 / G ij ×100%,I jis the RMS current of the target medium-voltage power line in the jth preset time slice, R is the line resistance of the target medium-voltage power line, T is the length of the preset time slice, G ij The target medium voltage power line A in the jth preset time slice i The total amount of electricity provided by the corresponding initial user, I j Meet the following conditions:

[0043] T 0 is the current cycle of the target medium voltage power line, h_j(t) is the function of the current of the target medium voltage power line changing with time in the jth preset time slice, and the current cycle is the time required for the alternating current to complete a complete change.

[0044] Specifically, the preset time slice is a time slice in a preset time period, and the length of the preset time slice is 24 hours. Those skilled in the art know that the preset time period is a time period pre-set by those skilled in the art according to actual needs, and will not be repeated here.

[0045] S22, based on the average current method, obtain a second line loss rate list set C corresponding to A = {C1, C2, ..., C i , ..., C m}, C i ={C i1 , C i2 , ..., C ij , ..., C in}, C i A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate, C ij Meet the following conditions:

[0046] C ij =3×AVI j 2 ×R×T×K j 2 ×10 -3 / G ij ×100%,AVI j is the average current of the target medium voltage power line in the jth preset time slice, K j is the load curve shape coefficient corresponding to the target medium voltage power line in the jth preset time slice.

[0047] Specifically, the load curve shape factor is one of the indicators used to describe the shape of the load curve.

[0048] S23, based on the maximum current method, obtain the third line loss rate list set D corresponding to A = {D1, D2, ..., D i, ..., D m}, D i ={D i1 , D i2 , ..., D ij , ..., D in}, D i A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate, D ij Meet the following conditions:

[0049] D ij =3×MAXI j 2 ×R×T×S j ×10 -3 / G ij ×100%,MAXI j is the maximum current of the target medium voltage power line in the jth preset time slice, S j is the loss factor corresponding to the target medium voltage power line in the jth preset time slice.

[0050] Specifically, the loss factor corresponding to the target medium voltage power line is a value for evaluating the degree of energy loss of the target medium voltage power line at the maximum current.

[0051] Specifically, the larger the loss factor corresponding to the target medium voltage electric wire is, the smaller the energy loss degree of the target medium voltage electric wire is at the maximum current.

[0052] S24, based on the equivalent resistance method, obtain the fourth line loss rate list set E corresponding to A = {E1, E2, ..., E i ,……,E m}, E i ={E i1 , E i2 ,……,E ij ,……,E in}, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate, E ij Meet the following conditions:

[0053] E ij =3×SI j 2 ×((SP j 2 +WP j 2 ) / U j 2)×T×10 -3 / G ij ×100%,SI j is the line head end current of the target medium voltage power line in the jth preset time slice, SP j is the active power at the head end of the target medium voltage power line in the jth preset time slice, WP j is the reactive power at the head end of the target medium voltage power line in the jth preset time slice, U j It is the line head end voltage of the target medium voltage power line in the j-th preset time slice.

[0054] S25. According to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively:

[0055] F i1 =∑ n j＝1 B ij / n;F i2 =∑ n j＝1 C ij / n;F i3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n.

[0056] Through the above steps, a first line loss rate list set corresponding to the initial user ID list is obtained based on the root mean square current method, a second line loss rate list set corresponding to the initial user ID list is obtained based on the average current method, a third line loss rate list set corresponding to the initial user ID list is obtained based on the maximum current method, a fourth line loss rate list set corresponding to the initial user ID list is obtained based on the equivalent resistance method, and a line loss vector list set corresponding to the initial user ID list is obtained according to the first line loss rate list set, the second line loss rate list set, the third line loss rate list set and the fourth line loss rate list set. The line loss vector list set is obtained by combining the four algorithms of the root mean square current method, the average current method, the maximum current method and the equivalent resistance method, so that more comprehensive data can be obtained, and the error caused by using a single algorithm to obtain the line loss rate can be reduced, which is conducive to improving the accuracy of obtaining the line loss vector. Furthermore, whether there is an abnormality in the route loss of the target medium-voltage power line is identified based on the line loss vector list set, which is conducive to improving the accuracy of the result of identifying whether there is an abnormality in the route loss of the target medium-voltage power line.

[0057] S3. Based on F, identify whether there is an abnormality in the route loss of the target medium-voltage power line.

[0058] Specifically, step S3 also includes the following sub-steps S31-S34:

[0059] S31, for F1, F2, ..., F i , ..., F m Perform clustering to obtain the total set of cluster vector list sets H = {H1, H2, ..., H e , ..., H f}, H e ={H e1 , H e2 , ..., H er , ..., H es(e)}, H e is the e-th clustering vector list set, the value of e ranges from 1 to f, f is the number of clustering vector list sets, H er H e The rth cluster vector list in H, where r ranges from 1 to s(e), and s(e) is e The number of clustering vector lists in , those skilled in the art know that any clustering method in the prior art belongs to the protection scope of the present invention and will not be described in detail here.

[0060] S32. Get the GeoHash string list set J corresponding to H = {J1, J2, ..., J e , ..., J f}, J e ={J e1 , J e2 , ..., J er, ..., J es(e)}, J e H e The corresponding GeoHash string list, J er H er The corresponding GeoHash string, H er The corresponding GeoHash string is the GeoHash algorithm for H er The character string obtained by processing the electricity usage address corresponding to the corresponding initial user.

[0061] Specifically, the electricity usage address corresponding to the initial user can be understood as the address where electricity is required and filled in by the initial user when applying to use the target medium-voltage line.

[0062] S33, if J e If the distance between the geographical locations corresponding to any two GeoHash strings in the table is less than the preset distance, then H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is a non-abnormal user. If J e If the distance between the geographical locations corresponding to the two GeoHash strings in the , is not less than the preset distance, then determine H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is the suspicious user, the preset distance is the distance pre-set by technical personnel in this field according to actual needs, non-abnormal users can be understood as users who do not have abnormal electricity usage behavior, and suspicious users can be understood as users who need further judgment on whether they have abnormal electricity usage behavior. Abnormal electricity usage behavior can be understood as electricity theft.

[0063] S34, when H1, H2, ..., H e , ..., H f When all the initial users corresponding to the cluster vector lists in are non-abnormal users, it is determined that there is no abnormality in the route loss of the target medium-voltage power line; otherwise, it is determined that there is an abnormality in the route loss of the target medium-voltage power line.

[0064] Through the above steps, all line loss vector lists in the line loss vector list set corresponding to the initial user ID list are clustered to obtain the total set of cluster vector list sets, and the GeoHash string list set corresponding to the total set of cluster vector list sets is obtained. According to the geographical location corresponding to the GeoHash string in the GeoHash string list, it is determined whether the initial user is a non-abnormal user or a suspicious user. When the initial users corresponding to all cluster vector lists in all cluster vector list sets in the total set of cluster vector list sets are non-abnormal users, it is determined that there is no abnormality in the route loss of the target medium-voltage power line. Otherwise, it is determined that there is an abnormality in the route loss of the target medium-voltage power line, which is conducive to improving the accuracy of the result of identifying whether there is an abnormality in the route loss of the target medium-voltage power line.

[0065] Specifically, in a specific embodiment, after step S34, the following steps S10-S30 are further included to determine abnormal users from suspicious users, where abnormal users can be understood as users with abnormal electricity usage behavior:

[0066] S10, when A i When the corresponding initial user is a questionable user and 1<j<n, obtain A in the jth preset time slice i The corresponding initial abnormal behavior judgment identification list L ij ={L 1 ij , L 2 ij , L 3 ij , L 4 ij}, L 1 ij For L ij The first initial abnormal behavior judgment mark, L 2 ij For L ij The second initial abnormal behavior judgment mark, L 3 ij For L ij The third initial abnormal behavior judgment mark, L 4 ij For L ij The fourth initial abnormal behavior judgment mark in 1 ij , L 2 ij , L 3 ij , L 4 ij Meet the following conditions respectively:

[0067] When B ij -B i(j-1)>△YZ and B i(j+1) -B ij >△YZ, determine L 1 ij For identification 1, when B ij -B i(j-1) ≤△YZ or B i(j+1) -B ij ≤△YZ, determine L 1 ij is the identifier 0, B i(j-1) A in the j-1th preset time slice i The corresponding first line loss rate, B i(j+1) A in the j+1th preset time slice i The corresponding first line loss rate, wherein △YZ is a preset line loss rate difference, which is known to those skilled in the art, and those skilled in the art can set the preset line loss rate difference according to actual needs, which will not be described in detail here;

[0068] When C ij -C i(j-1) >△YZ and C i(j+1) -C ij >△YZ, determine L 2 ij For identification 1, when C ij -C i(j-1) ≤△YZ or C i(j+1) -C ij ≤△YZ, determine L 2 ij For the logo 0, C i(j-1) A in the j-1th preset time slice i The corresponding second line loss rate, C i(j+1) A in the j+1th preset time slice i The corresponding second line loss rate;

[0069] When D ij -D i(j-1) >△YZ and D i(j+1) -D ij >△YZ, determine L 3 ij For identification 1, D ij -D i(j-1) ≤△YZ or D i(j+1) -D ij ≤△YZ, determine L 3 ij is the identifier 0, D i(j-1) A in the j-1th preset time slice i The corresponding third line loss rate, D i(j+1) A in the j+1th preset time slice i The corresponding third line loss rate;

[0070] When E ij -E i(j-1) >△YZ and E i(j+1) -E ij >△YZ, determine L 4 ij For identification 1, E ij -E i(j-1) ≤△YZ or E i(j+1) -E ij ≤△YZ, determine L 4 ij For identification 0, E i(j-1) A in the j-1th preset time slice i The corresponding fourth line loss rate, E i(j+1) A in the j+1th preset time slice i The corresponding fourth line loss rate.

[0071] Specifically, the initial abnormal behavior judgment mark includes mark 1 and mark 0, mark 1 represents that the line loss rate changes greatly before and after, and mark 0 represents that the line loss rate changes little before and after.

[0072] S20, when L 1 ij , L 2 ij , L 3 ij , L 4 ij When there are 3 initial abnormal behavior judgment marks with mark 1 or 4 initial abnormal behavior judgment marks with mark 1 in the A, the jth preset time slice is used as A i The corresponding abnormal time slice and L ij As A i The corresponding first abnormal behavior judgment identifier list, when L 1 ij , L 2 ij , L 3 ij , L 4 ij When there are two initial abnormal behavior judgment marks with mark 1 in the A, the jth preset time slice is used as A i The corresponding questionable time slice and L ij As A i The corresponding second abnormal behavior judgment identifier list.

[0073] S30, when A i When the number of corresponding abnormal time slices is not 0, determine A i The corresponding initial user is an abnormal user; when A iThe number of corresponding abnormal time slices and A i When the number of corresponding questionable time slices is 0, determine A i The corresponding initial user is a non-abnormal user; when A i The number of corresponding abnormal time slices is 0 and A i When the number of corresponding questionable time slices is not 0, determine A i The corresponding initial user is the secondary suspicious user.

[0074] Through the above steps, when the initial user corresponding to the initial user ID is a suspicious user, the initial abnormal behavior judgment identifier list corresponding to the initial user ID in the preset time slice is obtained, and the abnormal time slices and suspicious time slices corresponding to the initial user ID are further obtained. The abnormal user is determined according to the number of abnormal time slices and the number of suspicious time slices corresponding to the initial user ID, which is conducive to improving the accuracy of obtaining abnormal users.

[0075] Specifically, in a specific embodiment, after step S30, the following steps S301-S306 are further included to determine abnormal users again from the secondary suspicious users:

[0076] S301: Obtain the total set N of the first abnormal behavior judgment identification list set corresponding to the abnormal user = {N1, N2, ..., N y , ..., N q},N y = {N y1 , N y2 , ..., N yg , ..., N yh(y)},N yg = {N 1 yg , N 2 yg , N 3 yg ,……,N 4 yg},N y is the first abnormal behavior judgment identifier list set corresponding to the yth abnormal user, the value of y ranges from 1 to q, q is the number of abnormal users, N yg is the g-th first abnormal behavior judgment identification list corresponding to the y-th abnormal user, the value of g ranges from 1 to h(y), h(y) is the number of the first abnormal behavior judgment identification lists corresponding to the y-th abnormal user, N 1 yg N yg The first abnormal behavior judgment mark in 2 yg N yg The second first abnormal behavior judgment mark, N 3yg N yg The third first abnormal behavior judgment mark, N 4 yg N yg The fourth first abnormal behavior judgment mark.

[0077] S302, obtain the total set of second abnormal behavior judgment identification list sets corresponding to the second suspicious users M = {M1, M2, ..., M x , ..., M p},M x ={M x1 , M x2 , ..., M xa , ..., M xc(x)},M xa ={M 1 xa , M 2 xa , M 3 xa , M 4 xa},M x is the second abnormal behavior judgment identifier list set corresponding to the xth secondary suspicious user, x ranges from 1 to p, p is the number of secondary suspicious users, M xa is the ath second abnormal behavior judgment identification list corresponding to the xth second suspicious user, the value range of a is 1 to c(x), c(x) is the number of the second abnormal behavior judgment identification lists corresponding to the xth second suspicious user, M 1 xa M xa The first second abnormal behavior judgment mark in M 2 xa M xa The second abnormal behavior judgment mark in M 3 xa M xa The third second abnormal behavior judgment mark in M 4 xa M xa The fourth second abnormal behavior judgment mark in .

[0078] S303, when x=1, obtain M xa The corresponding key score list P xa = {P 1 xa , P 2 xa , P 3 xa , P 4 xa}, P1 xa M 1 xa The corresponding key score, P 2 xa M 2 xa The corresponding key score, P 3 xa M 3 xa The corresponding key score, P 4 xa M 4 xa The corresponding key score, P 1 xa , P 2 xa , P 3 xa , P 4 xa Meet the following conditions respectively:

[0079] P 1 xa =∑ q y＝1 ∑ h(y) g＝1 N 01 yg , N 01 yg N 1 yg The corresponding initial score, when N 1 yg When the mark is 1, N 01 yg =1, when N 1 yg When the mark is 0, N 01 yg =0;

[0080] P 2 xa =∑ q y＝1 ∑ h(y) g＝1 N 02 yg , N 02 yg N 2 yg The corresponding initial score, when N 2 yg When the mark is 1, N 02 yg =1, when N 2 yg When the mark is 0, N02 yg =0;

[0081] P 3 xa =∑ q y＝1 ∑ h(y) g＝1 N 03 yg , N 03 yg N 3 yg The corresponding initial score, when N 3 yg When the mark is 1, N 03 yg =1, when N 3 yg When the mark is 0, N 03 yg =0;

[0082] P 4 xa =∑ q y＝1 ∑ h(y) g＝1 N 04 yg , N 04 yg N 4 yg The corresponding initial score, when N 4 yg When the mark is 1, N 04 yg =1, when N 4 yg When the mark is 0, N 04 yg =0.

[0083] S304, when max(P 1 xa , P 2 xa , P 3 xa , P 4 xa ) are all the first abnormal behavior judgment marks 1, determine M xa is the third abnormal behavior judgment identification list, when max(P 1 xa , P 2 xa , P 3 xa , P 4 xa) are all 0, determine M xa is a list of non-abnormal behavior judgment identifiers, otherwise, determine M xa is a list of suspected abnormal behavior judgment identifiers, where max() is a maximum value acquisition function, for example: when P 1 xa , P 2 xa , P 3 xa , P 4 xa When 10, 5, 4, and 10 are respectively, max(P 1 xa , P 2 xa , P 3 xa , P 4 xa ) is 10, max(P 1 xa , P 2 xa , P 3 xa , P 4 xa ) The first abnormal behavior judgment mark corresponding to 1 xa The corresponding N 1 yg and P 4 xa The corresponding N 4 yg , when N 1 yg and N 4 yg When all are marked as 1, determine M xa is the third abnormal behavior judgment identifier list, when N 1 yg and N 4 yg When both are marked as 0, determine M xa is a list of non-abnormal behavior judgment identifiers. 1 yg is identified by 1 and N 4 yg Identifies 0 or N 1 yg For the identifier 0 and N 4 yg When the mark is 1, determine M xa A list of suspected abnormal behavior identifications.

[0084] S305, if M x1 , M x2, ..., M xa , ..., M xc(x) If all of them are non-abnormal behavior judgment identification lists, then determine M x The corresponding secondary suspicious user is a non-abnormal user and M x Delete from M so that M is updated and the process proceeds to step S303;

[0085] If M x1 , M x2 , ..., M xa , ..., M xc(x) If all of them are suspicious abnormal behavior judgment identification lists, then M x Delete from M and then add M x Insert to the end of M so as to update M and proceed to step S303;

[0086] If M x1 , M x2 , ..., M xa , ..., M xc(x) If any one of the second abnormal behavior judgment identification lists is the third abnormal behavior judgment identification list, then determine M x The corresponding second suspicious user is an abnormal user and the process goes to step S306;

[0087] S306, M x1 , M x2 , ..., M xa , ..., M xc(x) The second abnormal behavior judgment identification list is a non-abnormal behavior judgment identification list or a suspicious abnormal behavior judgment identification list from M x Delete it so that M x Update and update the M x Delete from M so that M is updated and the updated M x Insert into N to update N and proceed to step S303.

[0088] Specifically, if M is NULL or the number of second abnormal behavior judgment identification list sets in M ​​after the most recent GX updates to M has not changed, then the process ends, where GX is the sum of the number of second abnormal behavior judgment identification list sets in M ​​after the most recent update to M and 1. For example: if the number of second abnormal behavior judgment identification list sets in M ​​after the most recent update to M is 5 and M was updated 6 times before, and the number of second abnormal behavior judgment identification list sets in M ​​after each update was 5, then the process ends.

[0089] Through the above steps, the total set of the first abnormal behavior judgment identifier list set corresponding to the abnormal users and the total set of the second abnormal behavior judgment identifier list set corresponding to the secondary suspicious users are obtained and updated. According to the first abnormal behavior judgment identifier in the total set of the first abnormal behavior judgment identifier list set corresponding to the abnormal users, the key score list corresponding to the first second abnormal behavior judgment identifier list corresponding to the secondary suspicious user in the total set of the second abnormal behavior judgment identifier list set corresponding to the secondary suspicious users is obtained. The abnormal users are determined according to the key score list, which is conducive to improving the accuracy of obtaining abnormal users.

[0090] Embodiment 2

[0091] A second embodiment of the present invention provides a device for identifying abnormal route loss status, the device being used to identify whether the route loss of a target medium voltage power line is abnormal, such as Figure 2 As shown, the device comprises:

[0092] Initial user ID list acquisition module 1, used to acquire the initial user ID list A = {A1, A2, ..., A i , ..., A m}, A i is the i-th initial user ID, the value range of i is 1 to m, m is the number of initial user IDs, the initial user ID is the unique identity of the initial user, and the initial user is the user who uses the target medium-voltage power line.

[0093] Line loss vector list set acquisition module 2, used to obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m}, F i ={F i1 , F i2 , F i3 , F i4}, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding 4th line loss vector.

[0094] Specifically, the line loss vector is a one-dimensional vector.

[0095] Specifically, the line loss vector list set acquisition module 2 includes the following units:

[0096] The first line loss rate list set acquisition unit is used to acquire the first line loss rate list set B corresponding to A based on the root mean square current method = {B1, B2, ..., B i , ..., B m}, B i = {B i1 , B i2 , ..., B ij , ..., B in}, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j ranges from 1 to n, n is the number of preset time slices in the preset time period, B ij Meet the following conditions:

[0097] B ij =3×I j 2 ×R×T×10 -3 / G ij ×100%,I j is the RMS current of the target medium-voltage power line in the jth preset time slice, R is the line resistance of the target medium-voltage power line, T is the length of the preset time slice, G ij The target medium voltage power line A in the jth preset time slice i The total amount of electricity provided by the corresponding initial user, I j Meet the following conditions:

[0098] T 0 is the current cycle of the target medium voltage power line, h_j(t) is the function of the current of the target medium voltage power line changing with time in the jth preset time slice, and the current cycle is the time required for the alternating current to complete a complete change.

[0099] Specifically, the preset time slice is a time slice in a preset time period, and the length of the preset time slice is 24 hours. Those skilled in the art know that the preset time period is a time period pre-set by those skilled in the art according to actual needs, and will not be repeated here.

[0100] The second line loss rate list set acquisition unit is used to acquire the second line loss rate list set C corresponding to A based on the average current method = {C1, C2, ..., C i , ..., C m}, C i ={C i1 , C i2 , ..., C ij , ..., C in}, Ci A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate, C ij Meet the following conditions:

[0101] C ij =3×AVI j 2 ×R×T×K j 2 ×10 -3 / G ij ×100%,AVI j is the average current of the target medium voltage power line in the jth preset time slice, K j is the load curve shape coefficient corresponding to the target medium voltage power line in the jth preset time slice.

[0102] Specifically, the load curve shape factor is one of the indicators used to describe the shape of the load curve.

[0103] The third line loss rate list set acquisition unit is used to acquire the third line loss rate list set D corresponding to A based on the maximum current method = {D1, D2, ..., D i , ..., D m}, D i ={D i1 , D i2 , ..., D ij , ..., D in}, D i A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate, D ij Meet the following conditions:

[0104] D ij =3×MAXI j 2 ×R×T×S j ×10 -3 / G ij ×100%,MAXI j is the maximum current of the target medium voltage power line in the jth preset time slice, S j is the loss factor corresponding to the target medium voltage power line in the jth preset time slice.

[0105] Specifically, the loss factor corresponding to the target medium voltage power line is a value for evaluating the degree of energy loss of the target medium voltage power line at the maximum current.

[0106] Specifically, the larger the loss factor corresponding to the target medium voltage electric wire is, the smaller the energy loss degree of the target medium voltage electric wire is at the maximum current.

[0107] The fourth line loss rate list set acquisition unit acquires the fourth line loss rate list set E corresponding to A based on the equivalent resistance method = {E1, E2, ..., E i ,……,E m}, E i ={E i1 , E i2 ,……,E ij ,……,E in}, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate, E ij Meet the following conditions:

[0108] E ij =3×SI j 2 ×((SP j 2 +WP j 2 ) / U j 2 )×T×10 -3 / G ij ×100%,SI j is the line head end current of the target medium voltage power line in the jth preset time slice, SP j is the active power at the head end of the target medium voltage power line in the jth preset time slice, WP j is the reactive power at the head end of the target medium voltage power line in the jth preset time slice, U j It is the line head end voltage of the target medium voltage power line in the j-th preset time slice.

[0109] Line loss vector acquisition unit, according to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively:

[0110] F i1 =∑ n j＝1 B ij / n;F i2 =∑ nj＝1 C ij / n;F i3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n.

[0111] The abnormality identification module 3 is used to identify whether there is an abnormality in the route loss of the target medium-voltage power line based on F.

[0112] Specifically, the abnormality identification module 3 includes the following units:

[0113] The total set acquisition unit of the clustering vector list set is used to obtain F1, F2, ..., F i , ..., F m Perform clustering to obtain the total set of cluster vector list sets H = {H1, H2, ..., H e , ..., H f}, H e ={H e1 , H e2 , ..., H er , ..., H es(e)}, H e is the e-th clustering vector list set, the value of e ranges from 1 to f, f is the number of clustering vector list sets, H er H e The rth cluster vector list in H, where r ranges from 1 to s(e), and s(e) is e The number of clustering vector lists in , those skilled in the art know that any clustering method in the prior art belongs to the protection scope of the present invention and will not be described in detail here.

[0114] The GeoHash string list set acquisition unit is used to obtain the GeoHash string list set J corresponding to H = {J1, J2, ..., J e , ..., J f}, J e ={J e1 , J e2 , ..., J er , ..., J es(e)}, J e H e The corresponding GeoHash string list, J er H er The corresponding GeoHash string, H er The corresponding GeoHash string is the GeoHash algorithm for H erThe character string obtained by processing the electricity usage address corresponding to the corresponding initial user.

[0115] Specifically, the electricity usage address corresponding to the initial user can be understood as the address where electricity is required and filled in by the initial user when applying to use the target medium-voltage line.

[0116] Initial user determination unit, used if J e If the distance between the geographical locations corresponding to any two GeoHash strings in the table is less than the preset distance, then H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is a non-abnormal user. If J e If the distance between the geographical locations corresponding to the two GeoHash strings in the , is not less than the preset distance, then determine H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is the suspicious user, the preset distance is the distance pre-set by technical personnel in this field according to actual needs, non-abnormal users can be understood as users who do not have abnormal electricity usage behavior, and suspicious users can be understood as users who need further judgment on whether they have abnormal electricity usage behavior. Abnormal electricity usage behavior can be understood as electricity theft.

[0117] Abnormal identification unit, used when H1, H2, ..., H e , ..., H f When all the initial users corresponding to the cluster vector lists in are non-abnormal users, it is determined that there is no abnormality in the route loss of the target medium-voltage power line; otherwise, it is determined that there is an abnormality in the route loss of the target medium-voltage power line.

[0118] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store a computer program related to a method in a method embodiment, and the computer program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0119] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the above embodiment when executing the computer program.

[0120] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0121] The embodiments of the present invention provide a method, device, equipment and medium for identifying abnormal route loss status. The method can obtain a first line loss rate list set corresponding to an initial user ID list based on a root mean square current method, obtain a second line loss rate list set corresponding to an initial user ID list based on an average current method, obtain a third line loss rate list set corresponding to an initial user ID list based on a maximum current method, obtain a fourth line loss rate list set corresponding to an initial user ID list based on an equivalent resistance method, obtain an initial user ID list according to the first line loss rate list set, the second line loss rate list set, the third line loss rate list set and the fourth line loss rate list set. A set of line loss vector lists corresponding to the household ID list is used to identify whether there is any abnormality in the route loss of the target medium-voltage power line based on the line loss vector list set. It can be seen that the present invention combines four algorithms, namely the root mean square current method, the average current method, the maximum current method and the equivalent resistance method, to obtain a line loss vector list set and identifies whether there is any abnormality in the route loss of the target medium-voltage power line based on the line loss vector list set. This can obtain more comprehensive data and reduce the error caused by using a single algorithm to obtain the line loss rate. Therefore, it is beneficial to improve the accuracy of obtaining the line loss vector, and further to improve the accuracy of the result of identifying whether there is any abnormality in the route loss of the target medium-voltage power line.

[0122] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for identifying abnormal line loss status, the method is used to identify whether there is abnormal line loss of a target medium voltage power line, characterized in that: The method comprises the following steps: S1. Obtain the initial user ID list A = {A1, A2, ..., A i , ..., A m }, A i is the i-th initial user ID, i ranges from 1 to m, m is the number of initial user IDs, and the initial user is the user who uses the target medium-voltage power line; S2. Obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m }, F i ={F i1 , F i2 , F i3 , F i4 }, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding fourth line loss vector includes the following sub-steps S21-S25: S21, based on the root mean square current method, obtain the first line loss rate list set B corresponding to A = {B1, B2, ..., B i , ..., B m }, B i = {B i1 , B i2 , ..., B ij , ..., B in }, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j has a value range of 1 to n, n is the number of preset time slices in the preset time period, and the preset time slice is the time slice in the preset time period; S22, based on the average current method, obtain a second line loss rate list set C corresponding to A = {C1, C2, ..., C i , ..., C m }, C i ={C i1 , C i2 , ..., C ij , ..., C in }, C i A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate; S23, based on the maximum current method, obtain the third line loss rate list set D corresponding to A = {D1, D2, ..., D i , ..., D m }, D i ={D i1 , D i2 , ..., D ij , ..., D in }, D i A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate; S24, based on the equivalent resistance method, obtain the fourth line loss rate list set E corresponding to A = {E1, E2, ..., E i ,……,E m }, E i ={E i1 , E i2 ,……,E ij ,……,E in }, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate; S25. According to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively: F i1 =∑ n j＝1 B ij / n;F i2 =∑ n j＝1 C ij / n;F i3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n; S3. Based on F, identify whether there is an abnormality in the route loss of the target medium-voltage power line.

2. The method for identifying abnormal route loss state according to claim 1, characterized in that: B ij Meet the following conditions: B ij =3×I j 2 ×R×T×10 -3 / G ij ×100%,I j is the RMS current of the target medium-voltage power line in the jth preset time slice, R is the line resistance of the target medium-voltage power line, T is the length of the preset time slice, G ij The target medium voltage power line A in the jth preset time slice i The total amount of electricity provided by the corresponding initial user, I j Meet the following conditions: T 0 is the current cycle of the target medium voltage power line, and h_j(t) is the function of the current of the target medium voltage power line changing with time in the jth preset time slice.

3. The method for identifying abnormal route loss state according to claim 2, characterized in that: C ij Meet the following conditions: C ij =3×AVI j 2 ×R×T×K j 2 ×10 -3 / G ij ×100%,AVI j is the average current of the target medium voltage power line in the jth preset time slice, K j is the load curve shape coefficient corresponding to the target medium voltage power line in the jth preset time slice.

4. The method for identifying abnormal route loss state according to claim 2, characterized in that: D ij Meet the following conditions: D ij =3×MAXI j 2 ×R×T×S j ×10 -3 / G ij ×100%,MAXI j is the maximum current of the target medium voltage power line in the jth preset time slice, S j is the loss factor corresponding to the target medium voltage power line in the jth preset time slice.

5. The method for identifying abnormal route loss state according to claim 2, characterized in that: E ij Meet the following conditions: E ij =3×SI j 2 ×((SP j 2 +WP j 2 ) / U j 2 )×T×10 -3 / G ij ×100%,SI j is the line head end current of the target medium voltage power line in the jth preset time slice, SP j is the active power at the head end of the target medium voltage power line in the jth preset time slice, WP j is the reactive power at the head end of the target medium voltage power line in the jth preset time slice, U j It is the line head end voltage of the target medium voltage power line in the j-th preset time slice.

6. The method for identifying abnormal route loss state according to claim 1, characterized in that: Step S3 also includes the following sub-steps S31-S34: S31, for F1, F2, ..., F i , ..., F m Perform clustering to obtain the total set of cluster vector list sets H = {H1, H2, ..., H e , ..., H f }, H e ={H e1 , H e2 , ..., H er , ..., H es(e) }, H e is the e-th clustering vector list set, the value of e ranges from 1 to f, f is the number of clustering vector list sets, H er H e The rth cluster vector list in H, where r ranges from 1 to s(e), and s(e) is e The number of cluster vector lists in ; S32. Get the GeoHash string list set J corresponding to H = {J1, J2, ..., J e , ..., J f }, J e ={J e1 , J e2 , ..., J er , ..., J es(e) }, J e H e The corresponding GeoHash string list, J er H er The corresponding GeoHash string, H er The corresponding GeoHash string is the GeoHash algorithm for H er A character string obtained by processing the electricity usage address corresponding to the corresponding initial user; S33, if J e If the distance between the geographical locations corresponding to any two GeoHash strings in the table is less than the preset distance, then H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is a non-abnormal user. If J e If the distance between the geographical locations corresponding to the two GeoHash strings in the , is not less than the preset distance, then determine H e H e1 , H e2 , ..., H er , ..., H es(e) The corresponding initial user is the suspicious user; S34, when H1, H2, ..., H e , ..., H f When all the initial users corresponding to the cluster vector lists in are non-abnormal users, it is determined that there is no abnormality in the route loss of the target medium-voltage power line; otherwise, it is determined that there is an abnormality in the route loss of the target medium-voltage power line.

7. The method for identifying abnormal route loss state according to claim 2, characterized in that: The default time slice length is 24 hours.

8. A device for identifying abnormal line loss status, the device being used to identify whether there is abnormal line loss in a target medium voltage electric wire, characterized in that: The device comprises: Initial user ID list acquisition module, used to acquire the initial user ID list A = {A1, A2, ..., A i , ..., A m }, A i is the i-th initial user ID, i ranges from 1 to m, m is the number of initial user IDs, and the initial user is the user who uses the target medium-voltage power line; The line loss vector list set acquisition module is used to obtain the line loss vector list set F corresponding to A = {F1, F2, ..., F i , ..., F m }, F i ={F i1 , F i2 , F i3 , F i4 }, F i A i The corresponding line loss vector list, F i1 A i The corresponding first line loss vector, F i2 A i The corresponding second line loss vector, F i3 A i The corresponding third line loss vector, F i4 A i The corresponding fourth line loss vector; the line loss vector list set acquisition module includes the following units: The first line loss rate list set acquisition unit is used to acquire the first line loss rate list set B corresponding to A based on the root mean square current method = {B1, B2, ..., B i , ..., B m }, B i = {B i1 , B i2 , ..., B ij , ..., B in }, B i A i The corresponding first line loss rate list, B ij A in the jth preset time slice i The corresponding first line loss rate, j has a value range of 1 to n, n is the number of preset time slices in the preset time period, and the preset time slice is the time slice in the preset time period; The second line loss rate list set acquisition unit is used to acquire the second line loss rate list set C corresponding to A based on the average current method = {C1, C2, ..., C i , ..., C m }, C i ={C i1 , C i2 , ..., C ij , ..., C in }, C i A i The corresponding second line loss rate list, C ij A in the jth preset time slice i The corresponding second line loss rate; The third line loss rate list set acquisition unit is used to acquire the third line loss rate list set D corresponding to A based on the maximum current method = {D1, D2, ..., D i , ..., D m }, D i ={D i1 , D i2 , ..., D ij , ..., D in }, D i A i The corresponding third line loss rate list, D ij A in the jth preset time slice i The corresponding third line loss rate; A fourth line loss rate list set acquisition unit is used to acquire a fourth line loss rate list set E corresponding to A based on the equivalent resistance method = {E1, E2, ..., E i ,……,E m }, E i ={E i1 , E i2 ,……,E ij ,……,E in }, E i A i The corresponding fourth line loss rate list, E ij A in the jth preset time slice i The corresponding fourth line loss rate; The line loss vector acquisition unit is used to obtain the line loss vector according to B i , C i , D i and E i Get F i , where F i1 、F i2 、F i3 、F i4 Meet the following conditions respectively: F i1 =∑ n j＝1 B ij / n;F i2 =∑ n j＝1 C ij / n;F i3 =∑ n j＝1 D ij / n;F i4 =∑ n j＝1 E ij / n; The abnormality identification module is used to identify whether there is an abnormality in the route loss of the target medium-voltage power line based on F.

9. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer program, which is loaded and executed by a processor to implement the method for identifying an abnormal route loss state as described in any one of claims 1 to 7.

10. An electronic device comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying an abnormal route loss state as described in any one of claims 1 to 7 is implemented.

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