A method for analyzing power consumption behavior in a distribution network graph

Abstract

The present invention discloses a method for analyzing power consumption behavior of a distribution network in a graph. The present invention relates to the technical field of distribution network, and solves the problem that the original abnormal identification method has incomplete identification results and cannot accurately know whether the area is abnormal or whether such users frequently have abnormalities. The present invention performs relevant analysis on the abnormal power consumption behavior of abnormal users, preferentially identifies relevant line segments exceeding such characteristic intervals based on determined characteristic intervals, confirms abnormal users based on the identification results, and then evaluates whether such users often have relevant high power consumption in this time period based on the data display of such abnormal users in past cycles or time periods. Then, according to the relevant identification processing results, relevant managers can know the specific abnormal behavior of such abnormal users and the number of times the abnormal behavior exists, and execute relevant management measures.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method for analyzing power consumption behavior of a distribution network graph. Background Art

[0002] A distribution network refers to an electric power grid that receives electric energy from a transmission network or a regional power plant and distributes it locally or step by step according to voltage to various types of users through distribution facilities. It is a network composed of overhead lines, cables, towers, distribution transformers, disconnectors, reactive power compensators and some ancillary facilities, and plays an important role in distributing electric energy in the power grid.

[0003] The application with publication number CN114742153A discloses a method, device, electronic device, medium and product for analyzing electricity consumption behavior based on a distribution network map. The method includes: obtaining electricity consumption data of multiple users in multiple months through a distribution network map of a county; clustering and analyzing the electricity consumption data through a preset clustering algorithm to obtain multiple different categories of electricity consumption curves, where one electricity consumption curve is composed of one category of electricity consumption data; determining the corresponding target prediction model for each category of electricity consumption curve; predicting the electricity consumption data of the category to which the target user belongs according to the target prediction model, and obtaining the monthly electricity consumption data prediction result of the target user, so as to obtain the electricity consumption behavior of the target user according to the prediction result. Using this method, the monthly electricity consumption behavior of the user can be analyzed and predicted.

[0004] When analyzing abnormal user behaviors, the distribution network generally calibrates abnormal users based on the power consumption of the corresponding users, and then identifies the abnormal degree of the abnormal users based on their specific power consumption. However, the results of this abnormal identification method are not comprehensive. It is impossible to accurately know whether the area is abnormal or whether such users have frequent abnormalities. In terms of subsequent power consumption control, the management measures are difficult to implement. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for analyzing electricity consumption behavior in a distribution network map, which solves the problem that the original abnormality identification method has incomplete identification results and cannot accurately know whether the area is abnormal or whether such users frequently experience abnormalities.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for analyzing power consumption behavior of a distribution network diagram, comprising the following steps:

[0007] S1) Based on the relevant area monitored by a distribution network map, determine the relevant monitoring period, monitor the power consumption of the power users associated with the corresponding relevant area within the monitoring period, and confirm the power consumption change curve of different power users in the relevant area based on the monitoring data, specifically in the following manner:

[0008] S11, determining a set of monitoring periods T, where T is a preset value, and confirming the power consumption of different power users monitored at different times in this monitoring period T;

[0009] S12, confirming a set of two-dimensional coordinate systems, wherein the horizontal coordinate system is the timeline and the vertical coordinate system is the power consumption, and constructing a power consumption change curve for each power user based on the power consumption of different power users at different times, and the power consumption change curves of different power users are all located in the same two-dimensional coordinate system;

[0010] S2) Based on the confirmed power consumption change curve, perform interval feature analysis to identify the power consumption feature interval of the relevant area, and then based on the relevant comparison results of the power consumption feature interval and the standard interval, identify whether there is abnormal power consumption in the relevant area. The specific method is as follows:

[0011] S21, determining the maximum power consumption Dmax generated by a plurality of power consumption change curves, and then determining a set of change intervals based on a preset value X1 as an interval range, wherein X1 is a preset value, and the change range of the change interval gradually changes from [0, X1] to [Dmax-X1, Dmax];

[0012] S22, when the change range is gradually changing, the relevant line segments belonging to the inner part of the change range are marked as internal line segments, and the line segment proportion Zb of the internal line segments located in the original power consumption change curve is determined, where Zb = the length of the internal line segment ÷ the length of the power consumption change curve, and then several groups of line segment proportions Zb are averaged to determine the range mean reference Jc, and according to the different change processes corresponding to different change ranges, the range mean reference Jc corresponding to different change ranges is confirmed i , where i represents different ranges of change;

[0013] S23, from several groups of ranges, all reference Jc i The maximum value is selected, and the change range corresponding to such maximum value is marked as the power consumption characteristic interval of this area. If there are multiple maximum values, the change ranges corresponding to the multiple maximum values ​​are all marked as the power consumption characteristic interval of this area;

[0014] S24, compare the determined power consumption characteristic interval with the preset standard interval, wherein the endpoint values ​​of the standard interval are all preset values, confirm the overlap range of the two groups of intervals, identify the proportion value Z1 of this overlap range in this power consumption characteristic interval, and Z1=overlap range÷power consumption characteristic interval, compare the proportion value Z1 with the preset value Y1, wherein Y1 is the preset value, when Z1>Y1, generate and display the regional power consumption abnormal signal, when Z1≤Y1, do not generate any abnormal signal;

[0015] When there are multiple groups of power consumption feature intervals, as long as there is a group of power consumption feature intervals whose overlapping range ratio Z1 is greater than Y1, the regional power consumption abnormal signal is directly generated and displayed, otherwise, no abnormal signal is generated;

[0016] S3), based on the confirmed power consumption characteristic interval and the related power consumption change curve, identify the high-frequency abnormal users with abnormal power consumption, and then confirm the super-abnormal time period of the corresponding high-frequency abnormal users in this monitoring period; the specific method is:

[0017] S31, based on the power consumption characteristic interval and the internal line segment located within the interval, record the length XC of the internal line segment k , where k represents different power consumption change curves, and the line segment of this curve belonging to the upper end of this interval is marked as the upper end line segment;

[0018] S32, record the line length XX of the upper end line segment k , using XX k ÷XC k =PD k Determine the evaluation value PD for the application power change curve k , the evaluation value PD of the relevant power consumption change curve k Compare with the preset value Y2, when PD k > Y2, the electricity user associated with this electricity consumption change curve is marked as a high-frequency abnormal user. k When ≤Y2, no calibration is performed;

[0019] S33, based on the different upper end segments confirmed by the high-frequency abnormal users, confirm the duration S of the corresponding upper end segments k-t , based on the maximum power consumption and minimum power consumption of the corresponding upper segment, determine the power consumption value difference Zc k-t , and then determine the average power consumption Jz based on the different power consumption corresponding to the upper segment at different times k-t , where t represents different upper line segments;

[0020] S34, using: CL k-t =S k-t ×C1+Zc k-t ×C2+Jzk-t ×C3 confirms the processing value CL corresponding to the upper end line segment k-t , where C1, C2 and C3 are all preset fixed coefficient factors, from the different processing values ​​CL corresponding to different upper end segments of this high-frequency abnormal user k-t In the above example, the corresponding processing value satisfies: CL k-t The upper end line segment of >Y3 is marked as an ultra-abnormal segment, the time line corresponding to this ultra-abnormal segment is marked as an ultra-abnormal period, and high-frequency abnormal users and corresponding ultra-abnormal periods are displayed accordingly, where Y3 is a preset value.

[0021] Preferably, it also includes:

[0022] S4), based on the determined high-frequency abnormal users and the determined super-abnormal time period, identify the relevant electricity consumption data in the same period in the past, and based on the data identification results, determine the abnormal electricity consumption behavior of the relevant high-frequency abnormal users, and make relevant displays; the specific method is:

[0023] S41, based on the super abnormal period determined by the corresponding high-frequency abnormal user, record the total power consumption generated in this super abnormal period and mark it as ZY q , where q represents different high-frequency abnormal users;

[0024] S42, taking the abnormal period as the period to be searched, searching the period forward in sequence from the period to be searched, marking the searched period as the secondary period, determining at least ten groups of relevant power consumption data of the secondary period and marking them as ZL q-u , where u represents different sub-periods;

[0025] S43, the determined groups of related power consumption data ZL q-u Total power consumption ZY q Perform variance processing to determine the relevant variance parameter Fc, compare the variance parameter Fc with the preset value Y4, and when Fc>Y4, it means that the past power consumption data is significantly different from the relevant power consumption data of the current super-abnormal period, and a single super-abnormal signal is generated, and relevant displays are made for external personnel to view;

[0026] When Fc≤Y4, it means that the past electricity consumption data is slightly different from the relevant electricity consumption data of this super-abnormal period, and multiple super-abnormal signals are generated and displayed for external personnel to view.

[0027] The present invention provides a method for analyzing power consumption behavior in a distribution network diagram. Compared with the prior art, it has the following beneficial effects:

[0028] The present invention identifies the relevant power consumption of the corresponding area through the regional coverage of a distribution network map, and then performs relevant analysis based on the relevant power consumption curve of each power user, determines a characteristic area with the most concentrated power consumption in the corresponding area, determines the characteristic interval related to the characteristic area, and compares this characteristic interval with the preset interval to identify the performance of the interval, and then displays the relevant regional abnormal situation based on the specific identification processing result, so as to facilitate external personnel to know such situation in time, take corresponding management measures in time, give priority to regional analysis, and then confirm the abnormal power consumption behavior of the corresponding abnormal user when conducting power consumption behavior analysis;

[0029] When conducting relevant analysis on abnormal electricity consumption behaviors of abnormal users, priority is given to identifying relevant line segments that exceed such characteristic intervals based on the determined characteristic intervals, and confirming abnormal users based on the identification results. Then, based on the data display of such abnormal users in past cycles or time periods, it is assessed whether such users often have relevant high electricity consumption in this time period. Then, based on the relevant identification and processing results, relevant managers can know the specific abnormal behaviors of such abnormal users and the number of times the abnormal behaviors occur, and implement relevant management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0031] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] See also Figure 1 The present application provides a method for analyzing power consumption behavior of a distribution network graph, comprising the following steps:

[0034] S1), based on the relevant area monitored by a distribution network map, determine the relevant monitoring period, monitor the power consumption of the power users associated with the corresponding relevant area within the monitoring period, and confirm the power consumption change curve of different power users in the relevant area based on the monitoring data, wherein the specific method of confirmation is:

[0035] S11, determining a set of monitoring periods T, where T is a preset value, the specific value of which is determined by the operator based on experience, and confirming the power consumption of different power users monitored at different times in this monitoring period T;

[0036] S12, confirming a set of two-dimensional coordinate systems, wherein the horizontal coordinate system is the timeline and the vertical coordinate system is the power consumption, and constructing a power consumption change curve for each power user based on the power consumption of different power users at different times, and the power consumption change curves of different power users are all located in the same two-dimensional coordinate system;

[0037] Specifically, each different moment corresponds to different power consumption, and the relevant power users have different power consumption at different moments in the corresponding monitoring period T. In order to analyze whether the power consumption of the relevant power users is abnormal, it is necessary to combine the comprehensive power consumption situation in the area and conduct power consumption analysis to analyze whether the power consumption of the relevant users is abnormal. The reason for comprehensive analysis is that there will be different power consumption characteristics in different seasons;

[0038] S2) Based on the confirmed power consumption change curve, perform interval feature analysis to identify the power consumption feature interval of the relevant area, and then identify whether there is abnormal power consumption in the area based on the relevant comparison results of the power consumption feature interval and the standard interval. Specifically, when performing power consumption analysis on the corresponding area, if the power consumption of such area far exceeds the normal power consumption range, it means that there is a related abnormality, and it is necessary to confirm the abnormality in time. The specific method of identification is:

[0039] S21, determining the maximum power consumption Dmax generated by a plurality of power consumption change curves, and then determining a set of change intervals based on a preset value X1 as an interval range, wherein X1 is a preset value, and its specific value is determined by the operator based on experience, and the change range of the change interval gradually changes from [0, X1] to [Dmax-X1, Dmax];

[0040] S22, when the change range is gradually changing, the relevant line segments belonging to the inner part of the change range are marked as internal line segments, and the line segment proportion Zb of the internal line segments located in the original power consumption change curve is determined, where Zb = the length of the internal line segment ÷ the length of the power consumption change curve, and then several groups of line segment proportions Zb are averaged to determine the range mean reference Jc, and according to the different change processes corresponding to different change ranges, the range mean reference Jc corresponding to different change ranges is confirmed i , where i represents different change ranges. Specifically, each group of power consumption change curves corresponds to different internal line segments. If the internal line segments have relevant line lengths, it can be confirmed that the internal line segments are located at the relevant proportions of the corresponding change curves. From the confirmed groups of relevant proportions, the corresponding range average parameters can be identified;

[0041] S23, from several groups of ranges, all reference Jc iThe maximum value is selected, and the change range corresponding to such maximum value is marked as the power consumption characteristic interval of this area. If there are multiple maximum values, the change ranges corresponding to the multiple maximum values ​​are all marked as the power consumption characteristic interval of this area;

[0042] S24, compare the determined power consumption characteristic interval with the preset standard interval, wherein the endpoint values ​​of the standard interval are all preset values, and the specific values ​​are determined by the operator based on experience, confirm the overlap range of the two groups of intervals, identify the proportion value Z1 of this overlap range in this power consumption characteristic interval, and Z1 = overlap range ÷ power consumption characteristic interval, compare the proportion value Z1 with the preset value Y1, wherein Y1 is a preset value, and the specific value is determined by the operator based on experience, when Z1>Y1, generate and display the regional power consumption abnormal signal, when Z1≤Y1, do not generate any abnormal signal;

[0043] When there are multiple groups of power consumption feature intervals, as long as there is a group of power consumption feature intervals whose overlapping range ratio Z1 is greater than Y1, the regional power consumption abnormal signal is directly generated and displayed, otherwise, no abnormal signal is generated;

[0044] Specifically, during the electricity consumption process in the region, the maximum range of its electricity consumption is the electricity consumption characteristic interval of the region. According to the corresponding electricity consumption characteristic interval, it can be confirmed whether there is any abnormality in the electricity consumption value generated in the relevant region during this period, and the corresponding abnormal signal can be displayed, so that external personnel can be informed of such situations in a timely manner and take corresponding management measures in a timely manner. Prioritize regional analysis, and then conduct electricity consumption behavior analysis to confirm the abnormal electricity consumption behavior of the corresponding abnormal user;

[0045] S3), based on the confirmed power consumption characteristic interval and the related power consumption change curve, identify the high-frequency abnormal users with abnormal power consumption, and then confirm the super-abnormal time period of the corresponding high-frequency abnormal users in this monitoring period, wherein the specific method of confirmation is:

[0046] S31, based on the power consumption characteristic interval and the internal line segment located within the interval, record the length XC of the internal line segment k , where k represents different power consumption change curves, and the line segment of this curve belonging to the upper end of this interval is marked as the upper end line segment;

[0047] S32, record the line length XX of the upper end line segment k , using XX k ÷XC k =PD k Determine the evaluation value PD for the application power change curve k , the evaluation value PD of the relevant power consumption change curve k Compare with the preset value Y2, when PD k> Y2, the electricity user associated with this electricity consumption change curve is marked as a high-frequency abnormal user. k When ≤Y2, no calibration is performed;

[0048] Specifically, when the corresponding user performs power consumption analysis, the corresponding power consumption in the relevant characteristic interval belongs to the normal power consumption range, and the line segments beyond this power consumption range belong to the high-frequency power consumption range. When such high-frequency power consumption range accounts for a large proportion, it means that such user has abnormal power consumption;

[0049] S33, based on the different upper end segments confirmed by the high-frequency abnormal users, confirm the duration S of the corresponding upper end segments k-t (The time period between the initial moment and the end moment of the upper segment is the corresponding duration). Based on the maximum power consumption and the minimum power consumption of the corresponding upper segment, the power consumption value difference Zc is determined. k-t (The difference in power consumption is the difference between the maximum power consumption and the minimum power consumption), and then based on the different power consumption corresponding to the upper end line segment at different times, determine the average power consumption Jz k-t (The sum of its different power consumption divided by the relevant duration can determine the mean power consumption), where t represents the different upper end segments;

[0050] S34, using: CL k-t =S k-t ×C1+Zc k-t ×C2+Jz k-t ×C3 confirms the processing value CL corresponding to the upper end line segment k-t , where C1, C2 and C3 are all preset fixed coefficient factors, and their specific values ​​are determined by the operator based on experience, from the different processing values ​​CL corresponding to different upper end segments of this high-frequency abnormal user k-t In the above example, the corresponding processing value satisfies: CL k-t >The upper line segment corresponding to Y3 is marked as the super abnormal segment, the time line corresponding to this super abnormal segment is marked as the super abnormal time period, and the high-frequency abnormal users and the corresponding super abnormal time periods are displayed accordingly, where Y3 is a preset value, and its specific value is determined by the operator based on experience.

[0051] Specifically, after the corresponding abnormal users complete the relevant determination, the corresponding upper line segments correspond to different relevant parameters. When some relevant parameters are relatively small, the abnormal time period is not overly severe. Based on the relevant feature items of the corresponding line segments, it can be determined whether the corresponding line segments are overly abnormal, and timely confirmation can be made, and relevant judgments on super-abnormal time periods can be made simultaneously. Subsequently, for such abnormal time periods, relevant time period tracing analysis can be conducted on such abnormal users to identify the specific abnormal situations of such abnormal users.

[0052] Example 2

[0053] S4), based on the determined high-frequency abnormal users and the determined super-abnormal time period, identify the relevant electricity consumption data in the same period in the past, and based on the data identification results, determine the abnormal electricity consumption behavior of the relevant high-frequency abnormal users, and make relevant displays, wherein the specific sub-steps of determining include:

[0054] S41, based on the super abnormal period determined by the corresponding high-frequency abnormal user, record the total power consumption generated in this super abnormal period and mark it as ZY q , where q represents different high-frequency abnormal users;

[0055] S42, taking the abnormal period as the period to be searched, searching the period forward in sequence from the period to be searched, marking the searched period as the secondary period, determining at least ten groups of relevant power consumption data of the secondary period and marking them as ZL q-u , where u represents different sub-time periods. For example, assuming that the time period to be found is 9-10, then the relevant power consumption data from 9-10 of the previous day and 9-10 of the day before that will be confirmed as the relevant evaluation criteria for the total power consumption;

[0056] S43, the determined groups of related power consumption data ZL q-u Total power consumption ZY q Perform variance processing to determine the relevant variance parameter Fc, and compare the variance parameter Fc with the preset value Y4, where the specific value of Y4 is determined by the operator based on experience. The variance parameter processing method is: q-u With ZY q Perform mean processing to determine the relevant mean JJ, and u = 1, 2, ..., n, n represents the total number of sub-time periods, using Determine the variance parameter Fc. When Fc>Y4, it means that the past electricity consumption data is greatly different from the relevant electricity consumption data of the current super abnormal period, and there is no comparability. Generate a single super abnormal signal and display it for external personnel to view and understand the degree of abnormal behavior of this high-frequency abnormal user. When Fc≤Y4, it means that the past electricity consumption data is slightly different from the relevant electricity consumption data of the current super abnormal period, and there is comparability. Generate multiple super abnormal signals and display them for external personnel to view and understand the degree of abnormal behavior of this high-frequency abnormal user.

[0057] Specifically, during the relevant analysis and processing, when the corresponding high-frequency abnormal users are abnormal in the current monitoring period, in order to make the identification processing results more comprehensive, numerical analysis is performed from other periods to assess whether such users often have relevant high power consumption in this period. Then, according to the relevant identification processing results, the relevant management personnel can know the specific abnormal behaviors of such abnormal users and the number of times the abnormal behaviors exist, and implement relevant management measures;

[0058] This application not only conducts relevant analysis on the electricity consumption behavior of the region, but also conducts correlation analysis on the electricity consumption behavior of related users, and confirms abnormal users based on the specific analysis results. In order to identify the relevant abnormal situations of such abnormal users, combined with past electricity consumption data, the specific number of abnormal times corresponding to the abnormal users is confirmed, which is convenient for relevant management personnel to manage.

[0059] Example 3

[0060] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0061] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for analyzing power consumption behavior of a distribution network graph, characterized in that: The following steps are involved: S1) Based on the relevant area monitored by a distribution network map, determine the relevant monitoring period, monitor the power consumption of the power users associated with the corresponding relevant area within the monitoring period, and confirm the power consumption change curve of different power users in the relevant area based on the monitoring data; S2) Based on the confirmed power consumption change curve, perform interval feature analysis to identify the power consumption feature interval of the relevant area, and then based on the relevant comparison results of the power consumption feature interval and the standard interval, identify whether there is abnormal power consumption in the relevant area. The specific method is as follows: S21, determining the maximum power consumption Dmax generated by a plurality of power consumption change curves, and then determining a set of change intervals based on a preset value X1 as an interval range, wherein X1 is a preset value, and the change range of the change interval gradually changes from [0, X1] to [Dmax-X1, Dmax]; S22. When the change range is gradually changing, the relevant line segments within the change range are marked as internal line segments, and the line segment proportion Zb of the internal line segments located in the original power consumption change curve is determined, where Zb = the length of the internal line segment ÷ the length of the power consumption change curve. Then, several groups of line segment proportions Zb are averaged to determine the range mean reference Jc, and according to the different change processes corresponding to different change ranges, the range mean reference Jc corresponding to different change ranges is confirmed. i , where i represents different ranges of change; S23, from several groups of ranges, all reference Jc i The maximum value is selected, and the change range corresponding to such maximum value is marked as the power consumption characteristic interval of this area. If there are multiple maximum values, the change ranges corresponding to the multiple maximum values ​​are all marked as the power consumption characteristic interval of this area; S24, comparing the determined power consumption characteristic interval with the preset standard interval, wherein the endpoint values ​​of the standard interval are all preset values, confirming the overlap range of the two groups of intervals, identifying the proportion value Z1 of this overlap range in this power consumption characteristic interval, and Z1=overlap range÷power consumption characteristic interval, comparing the proportion value Z1 with the preset value Y1, wherein Y1 is the preset value, when Z1>Y1, generating and displaying the regional power consumption abnormal signal, and when Z1≤Y1, no abnormal signal is generated; When there are multiple groups of power consumption feature intervals, as long as there is a group of power consumption feature intervals whose overlapping range ratio Z1 is greater than Y1, the regional power consumption abnormal signal is directly generated and displayed, otherwise, no abnormal signal is generated; S3) Based on the confirmed power consumption characteristic interval and the related power consumption change curve, identify the high-frequency abnormal users with abnormal power consumption, and then confirm the super-abnormal time period of the corresponding high-frequency abnormal users in this monitoring period. The specific method is as follows: S31, based on the power consumption characteristic interval and the internal line segment located within the interval, record the length XC of the internal line segment k , where k represents different power consumption change curves, and the line segment of this curve belonging to the upper end of this interval is marked as the upper end line segment; S32, record the line length XX of the upper end line segment k , using XX k ÷XC k =PD k Determine the evaluation value PD for the application power change curve k , the evaluation value PD of the relevant power consumption change curve k Compare with the preset value Y2, when PD k > Y2, the electricity user associated with this electricity consumption change curve is marked as a high-frequency abnormal user. k When ≤Y2, no calibration is performed; S33, based on the different upper end segments confirmed by the high-frequency abnormal users, confirm the duration S of the corresponding upper end segments k-t , based on the maximum power consumption and minimum power consumption of the corresponding upper segment, determine the power consumption value difference Zc k-t , and then determine the average power consumption Jz based on the different power consumption corresponding to the upper segment at different times k-t , where t represents different upper line segments; S34, using: CL k-t =S k-t ×C1+Zc k-t ×C2+Jz k-t ×C3 confirms the processing value CL corresponding to the upper end line segment k-t , where C1, C2 and C3 are all preset fixed coefficient factors, from the different processing values ​​CL corresponding to different upper end segments of this high-frequency abnormal user k-t In the above example, the corresponding processing value satisfies: CL k-t The upper line segment of >Y3 is marked as an ultra-abnormal segment, the time line corresponding to this ultra-abnormal segment is marked as an ultra-abnormal period, and high-frequency abnormal users and corresponding ultra-abnormal periods are displayed accordingly, where Y3 is a preset value.

2. The method for analyzing power consumption behavior of a distribution network diagram according to claim 1, characterized in that: In step S1, the specific method of confirming the power consumption change curves of different power users is: S11, determining a set of monitoring periods T, where T is a preset value, and confirming the power consumption of different power users monitored at different times in this monitoring period T; S12. Confirm a set of two-dimensional coordinate systems, where the horizontal coordinate system is the timeline and the vertical coordinate system is the power consumption. Based on the power consumption of different power users at different times, construct a power consumption change curve for each power user, and the power consumption change curves of different power users are all located in the same two-dimensional coordinate system.

3. The method for analyzing power consumption behavior of a distribution network diagram according to claim 1, characterized in that: Also includes: S4) Based on the determined high-frequency abnormal users and the determined super-abnormal time periods, identify the relevant electricity consumption data in the same period in the past, and based on the data identification results, determine the abnormal electricity consumption behavior of the relevant high-frequency abnormal users and make relevant displays.

4. The method for analyzing power consumption behavior of a distribution network diagram according to claim 3, characterized in that: In step S4, the specific method of determining that the power consumption behavior of the relevant high-frequency abnormal user is abnormal is: S41, based on the super abnormal period determined by the corresponding high-frequency abnormal user, record the total power consumption generated in this super abnormal period and mark it as ZY q , where q represents different high-frequency abnormal users; S42, taking the abnormal period as the period to be searched, searching the period forward in sequence from the period to be searched, marking the searched period as the secondary period, determining at least ten groups of relevant power consumption data of the secondary period and marking them as ZL q-u , where u represents different sub-periods; S43, the determined groups of related power consumption data ZL q-u Total power consumption ZY q Perform variance processing to determine the relevant variance parameter Fc, and compare the variance parameter Fc with the preset value Y4. When Fc>Y4, it means that the past electricity consumption data is significantly different from the relevant electricity consumption data of this super-abnormal period, and a single super-abnormal signal is generated, and relevant display is made for external personnel to view.

5. The method for analyzing power consumption behavior of a distribution network diagram according to claim 4, characterized in that: In step S43: When Fc≤Y4, it means that the past electricity consumption data is slightly different from the relevant electricity consumption data of this super-abnormal period, and multiple super-abnormal signals are generated and displayed for external personnel to view.

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