A household and transformer association relationship abnormality identification method

Abstract

The application provides a kind of house transformer association relation abnormality identification method, belong to power grid technical field, specifically include: using transformer identification processing sequence carries out the determination of the load deviation of the historical load data of distribution transformer and the power load of the preset power user, and based on the load deviation determination, when there is a suspected abnormal transformer in the distribution substation area, the suspected abnormal transformer is taken as the investigation target to investigate and identify the abnormal relationship between the house and the transformer, which realizes the accurate identification of the abnormal relationship between the house and the transformer of the distribution transformer.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grids, and particularly relates to a household-transformer association relationship abnormality identification method. BACKGROUND

[0002] In order to realize identification of electricity stealing or network loss abnormality, a power company must establish a unified association relationship between transformers and power users, and due to urbanization progress and increasing power consumption of power users, the complexity of power supply topology is increasing, which also leads to increasing difficulty in accurately realizing abnormality identification of the association relationship between users and transformers.

[0003] To solve the above technical problems, in the invention patent CN202011053029.8 "household-transformer relationship identification method and device", the first similarity coefficient of the low-voltage meter voltage characteristic curve and the standard voltage characteristic curve is calculated to realize accurate identification of the household-transformer relationship from the perspective of voltage characteristics, but through analysis, it is not difficult to find that the following technical problems exist:

[0004] Compared with the load of distribution transformers, the load of small power users is often small, so the fluctuation of the load of small power users often does not affect the voltage waveform of the distribution transformer, so the similarity of the voltage waveform feature cannot accurately realize identification of the household-transformer relationship in the identification process of small power users.

[0005] In view of the above technical problems, the application provides a household-transformer association relationship abnormality identification method. SUMMARY

[0006] To achieve the purpose of the application, the application adopts the following technical solutions:

[0007] According to one aspect of the application, a household-transformer association relationship abnormality identification method is provided.

[0008] A household-transformer association relationship abnormality identification method, characterized in that it specifically comprises:

[0009] S1 divides the power distribution area into different power distribution sub-areas based on a preset area, obtains the number of distribution transformers of different power distribution sub-areas and the deviation condition of historical loads of different distribution transformers, and determines the identification processing sequence of household-transformer abnormality of different power distribution sub-areas in combination with the distribution transformers of adjacent power distribution sub-areas of different power distribution sub-areas;

[0010] S2 carries out the identification processing of the power supply sub-area by identifying the processing sequence, and determines the abnormal probability of the house transformer correlation of the power supply transformer in the power supply sub-area and the transformer identification processing sequence according to the number of adjacent transformers of the power supply transformer in the power supply sub-area, the distance of different adjacent transformers from the power supply transformer, and the interval building data.

[0011] S3 determines the load deviation of the historical load data of the power supply transformer and the power consumption load of the preset power user by using the transformer identification processing sequence, and when it is determined that there is a suspected abnormal transformer in the power supply sub-area based on the load deviation, proceeds to the next step.

[0012] S4 carries out the identification processing of the house transformer abnormal relationship by taking the suspected abnormal transformer as the investigation target.

[0013] The beneficial effects of the present application are:

[0014] 1. The determination of the identification processing sequence of the house transformer abnormality of different power supply sub-areas is carried out by the power supply transformers of different power supply sub-areas and the adjacent power supply transformers of different power supply sub-areas, which not only considers the identification difficulty of the house transformer abnormal relationship of the power supply sub-area caused by the number of power supply transformers of the power supply sub-area and the historical load deviation between different power supply transformers, but also fully considers the influence of the adjacent area power supply transformer on the house transformer abnormal relationship of the power supply sub-area by comprehensively considering the number of adjacent area power supply transformers, and lays a foundation for further accurate and efficient identification of the power supply sub-area with abnormal house transformer relationship.

[0015] 2. The determination of the identification processing sequence of the house transformer abnormality of different power supply sub-areas is carried out by the power supply transformers of different power supply sub-areas and the adjacent power supply transformers of different power supply sub-areas, which not only considers the identification difficulty of the house transformer abnormal relationship of the power supply sub-area caused by the number of power supply transformers of the power supply sub-area and the historical load deviation between different power supply transformers, but also fully considers the influence of the adjacent area power supply transformer on the house transformer abnormal relationship of the power supply sub-area by comprehensively considering the number of adjacent area power supply transformers, and lays a foundation for further accurate and efficient identification of the power supply sub-area with abnormal house transformer relationship.

[0016] Further, the preset area is determined according to the distribution density of the power supply transformers in the power supply area, wherein the higher the distribution density of the power supply transformers in the power supply area, the smaller the preset area.

[0017] Further, the historical load deviation of the power supply transformer includes the historical load deviation amount of the power supply transformer in different division periods.

[0018] Further, the first level is greater than the second level, and the second level is greater than the third level.

[0019] Further technical solutions are that the method for determining the abnormal probability of the house-transformer association relationship of the distribution transformer is

[0020] The abnormal influence factors of the house-transformer association relationship of different adjacent transformers are determined based on distances of different adjacent transformers from the distribution transformer and numbers of interval buildings between different adjacent transformers and the distribution transformer.

[0021] The abnormal probability of the house-transformer association relationship of the distribution transformer is determined through the abnormal influence factors of the house-transformer association relationship of different adjacent transformers.

[0022] Further technical solutions are that the transformer identification processing sequence is determined according to the abnormal probability of the house-transformer association relationship of the distribution transformer, and the higher the transformer identification processing sequence is, the greater the abnormal probability of the house-transformer association relationship of the distribution transformer is.

[0023] In another aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, characterized in that the processor executes the computer program to perform the above-mentioned house-transformer association relationship abnormality identification method.

[0024] Other features and advantages will be set forth in the following description of the application, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

[0025] In order to make the above objectives, features and advantages of the present application more apparent, the following will specifically describe preferred embodiments, and the accompanying drawings will be described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0026] The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0027] Figure 1 is a flowchart of a house-transformer association relationship abnormality identification method;

[0028] Figure 2 is a flowchart of a method for determining a house-transformer abnormality identification processing sequence of a power supply area;

[0029] Figure 3 is a framework diagram of a computer system. DETAILED DESCRIPTION

[0030] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the specification, not all. Based on the embodiments of the specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the specification.

[0031] For the sake of understanding, the following will be illustrated by example 1 and example 2.

[0032] Example 1

[0033] To solve the above problems, according to one aspect of the present application, as shown in the specification, a household transformer association relationship abnormality identification method is provided according to one aspect of the present application, which is characterized in that, specifically comprising: Figure 1

[0034] S1, based on the preset area, the power distribution area is divided into different power distribution sub-areas, the number of power distribution transformers in different power distribution sub-areas and the deviation of historical load of different power distribution transformers are obtained, and the determination of the identification processing order of the household transformer abnormality in different power distribution sub-areas is combined with the power distribution transformers of the adjacent power distribution sub-areas of different power distribution sub-areas;

[0035] S2, the identification processing of the power distribution sub-area is carried out through the identification processing order, and the determination of the abnormal probability of the household transformer association relationship of the power distribution transformer in the power distribution sub-area and the identification processing order of the transformer is carried out according to the number of adjacent transformers of the power distribution transformer in the power distribution sub-area, the distance between different adjacent transformers and the power distribution transformer and the interval building data;

[0036] S3, the determination of the load deviation of the historical load data of the power distribution transformer and the power load of the preset power user is carried out by using the transformer identification processing order, and when it is determined that there is a suspected abnormal transformer in the power distribution sub-area based on the load deviation, the next step is entered;

[0037] S4, the suspected abnormal transformer is taken as the investigation target to carry out the investigation and identification processing of the household transformer abnormality relationship.

[0038] Further, the preset area is determined according to the distribution density of the power distribution transformer of the power distribution area, wherein the higher the distribution density of the power distribution transformer of the power distribution area, the smaller the preset area.

[0039] Specifically, the deviation of the historical load of the power distribution transformer includes the deviation amount of the historical load of the power distribution transformer in different division periods. ​

[0040] Specifically, as shown in Figure 2 the method for determining the identification processing order of the household abnormality of the power consumption area is:

[0041] determining the load deviation amount between different power distribution transformers based on the deviation of the historical load of the different power distribution transformers of the power consumption area, and determining the identification processing difficulty of the power consumption area according to the load deviation amount between the different power distribution transformers and the number of power distribution transformers;

[0042] obtaining the number of power distribution transformers of different adjacent power consumption areas of the power consumption area, and determining the adjacent area interference amount of the power consumption area in combination with the load deviation amount between the different power distribution transformers of the different adjacent power consumption areas and the different power distribution transformers of the power consumption area;

[0043] determining the identification difficulty evaluation amount of the household abnormality of the power consumption area through the identification processing difficulty and the adjacent area interference amount of the power consumption area, and determining the identification processing order of the power consumption area according to the identification difficulty evaluation amount.

[0044] Further, determining the identification processing order of the power consumption area according to the identification difficulty evaluation amount, specifically comprising:

[0045] determining the identification processing order of the power consumption area from small to large according to the identification difficulty evaluation amount.

[0046] In another embodiment, the method for determining the identification processing order of the household abnormality of the power consumption area is:

[0047] determining the load deviation amount between different power distribution transformers based on the deviation of the historical load of the different power distribution transformers of the power consumption area, determining the load similarity between the different power distribution transformers through the load deviation amount, and dividing the power distribution transformers into different transformer groups based on the load similarity;

[0048] determining whether there is a transformer group whose number of power distribution transformers does not meet the requirement, if not, determining the identification processing priority of the household abnormality of the power consumption area as the first level, and determining the identification processing order of the power consumption area through the number of power distribution transformers whose load similarity is greater than a preset similarity, if yes, proceeding to the next step;

[0049] The similarity evaluation quantity of different transformer groups is determined according to the number of distribution transformers of different transformer groups and the load similarity between different distribution transformers, whether there is a transformer group whose similarity evaluation quantity does not meet the requirement is judged, if not, the identification priority of the abnormal household of the distribution subarea is set to the second level, and the determination of the identification processing order of the distribution subarea is performed through the number of transformer groups whose similarity evaluation quantity does not meet the requirement, if yes, the next step is entered;

[0050] The identification difficulty of the distribution subarea is determined according to the load deviation quantity between different distribution transformers and the number of distribution transformers, the number of distribution transformers of different adjacent distribution subareas of the distribution subarea is obtained, and the adjacent area interference quantity of the distribution subarea is determined in combination with the load deviation quantity between different distribution transformers of different adjacent distribution subareas and different distribution transformers of the distribution subarea;

[0051] The identification difficulty evaluation quantity of the abnormal household of the distribution subarea is determined through the identification difficulty and the adjacent area interference quantity of the distribution subarea, and the identification processing order of the distribution subarea is determined according to the identification difficulty evaluation quantity.

[0052] In another embodiment, the method for determining the identification processing order of the abnormal household of the distribution subarea is:

[0053] The load deviation quantity between different distribution transformers is determined based on the deviation of the historical load of different distribution transformers of the distribution subarea, the load similarity between different distribution transformers is determined through the load deviation quantity, the distribution transformers with a load similarity greater than a preset similarity are regarded as similar transformers, whether the number of similar transformers is greater than a preset transformer number is judged, if yes, the identification priority of the abnormal household of the distribution subarea is set to the third level, and the determination of the identification processing order of the distribution subarea is performed through the number of similar transformers of the distribution subarea, if not, the next step is entered;

[0054] The distribution transformers are divided into different transformer groups based on the load similarity, the similarity evaluation quantity of different transformer groups is determined according to the number of distribution transformers of different transformer groups and the load similarity between different distribution transformers, and the identification difficulty of the distribution subarea is determined in combination with the load deviation quantity between the reference transformers of different transformer groups;

[0055] determining whether the identification difficulty of the power consumption area meets the requirement, if not, entering the next step, if yes, determining that the identification priority of the abnormal transformer of the power consumption area is the first level, and determining the identification order of the power consumption area through the identification difficulty of the power consumption area;

[0056] obtaining the number of different adjacent power consumption areas of the power consumption area, and determining the adjacent area interference amount of the power consumption area in combination with the load deviation amount between the different adjacent power consumption areas of the different adjacent power consumption areas and the different power consumption transformers of the power consumption area;

[0057] setting the identification priority of the abnormal transformer of the power consumption area as the second level, determining the identification difficulty evaluation amount of the abnormal transformer of the power consumption area through the identification difficulty of the power consumption area and the adjacent area interference amount, and determining the identification order of the power consumption area according to the identification difficulty evaluation amount.

[0058] Further, the first level is greater than the second level, and the second level is greater than the third level.

[0059] Specifically, the method for determining the abnormal probability of the transformer correlation of the power consumption transformer is:

[0060] determining the abnormal influence factor of the transformer correlation of the different adjacent transformers based on the distance between the different adjacent transformers and the power consumption transformer and the number of interval buildings between the different adjacent transformers and the power consumption transformer;

[0061] determining the abnormal probability of the transformer correlation of the power consumption transformer through the abnormal influence factor of the transformer correlation of the different adjacent transformers.

[0062] Further, the transformer identification processing order is determined according to the abnormal probability of the transformer correlation of the power consumption transformer, wherein the greater the abnormal probability of the transformer correlation of the power consumption transformer, the higher the transformer identification processing order.

[0063] In another embodiment, the method for determining the abnormal probability of the transformer correlation of the power consumption transformer is:

[0064] S21 determines other power consumption transformers within a preset distance range from the power consumption transformer as adjacent transformers, determines whether the number of the adjacent transformers is less than a preset transformer number, if yes, enters the next step, if not, determines the abnormal probability of the transformer correlation of the power consumption transformer based on the number of the adjacent transformers;

[0065] S22, determining, based on distances between different adjacent transformers and the distribution transformer, interference transformers whose distances are within a preset distance interval, whether the number of the interference transformers is greater than a preset number of interference transformers, if yes, determining an abnormal probability of a house-transformer correlation of the distribution transformer based on the number of the interference transformers, and if no, proceeding to the next step;

[0066] S23, determining, based on distances between different adjacent transformers and the distribution transformer and numbers of interval buildings between different adjacent transformers and the distribution transformer, abnormal influence factors of the house-transformer correlation of different adjacent transformers, determining whether there is an adjacent transformer whose abnormal influence factor does not meet a requirement, if yes, proceeding to the next step, and if no, proceeding to step S24;

[0067] S24, taking the adjacent transformer whose abnormal influence factor does not meet the requirement as an abnormal influence transformer, determining a comprehensive abnormal influence factor of the abnormal influence transformer according to the number of the abnormal influence transformers and the abnormal influence factors of different abnormal influence transformers, determining whether the comprehensive abnormal influence factor meets the requirement, if yes, proceeding to the next step, and if no, determining the abnormal probability of the house-transformer correlation of the distribution transformer through the comprehensive abnormal influence factor;

[0068] S25, determining the abnormal probability of the house-transformer correlation of the distribution transformer through the abnormal influence factors of the house-transformer correlation of different adjacent transformers.

[0069] Further, determining, based on the load deviation condition, that there is a suspected abnormal transformer in the power distribution area, specifically comprising:

[0070] Filtering a deviation period through a deviation condition of a power load of the preset power user and historical load data of the distribution transformer;

[0071] Dividing the deviation period into different load deviation intervals according to the power load of the preset power user in different deviation periods, determining a load deviation abnormal value of different load deviation intervals through the number of deviation periods in different load deviation intervals, the power load of the preset power user in different deviation periods, and the deviation condition of the power load of the deviation period and the historical load data of the distribution transformer;

[0072] Determining a load comprehensive deviation amount of the distribution transformer through the load deviation abnormal value of different load deviation intervals, and determining whether the distribution transformer is a suspected abnormal transformer based on the load comprehensive deviation amount.

[0073] It should be noted that when the load comprehensive deviation amount of the distribution transformer does not meet the requirement, the distribution transformer is determined to be a suspected abnormal transformer.

[0074] Further, determining that there is a suspected abnormal transformer in the power distribution area based on the load deviation condition, specifically comprising:

[0075] Filtering the deviation period through the deviation condition of the power consumption load of the preset power user and the historical load data of the power distribution transformer, determining whether there is a deviation period whose load deviation is not within the preset deviation range, if yes, determining that the power distribution transformer is a suspected abnormal transformer, if no, entering the next step;

[0076] Determining whether the number of the deviation periods meets the requirement, if yes, entering the next step, if no, determining that the power distribution transformer belongs to a suspected abnormal transformer;

[0077] Dividing the deviation periods into different load deviation intervals according to the power consumption load of the preset power user in different deviation periods, determining the load deviation abnormal value of different load deviation intervals through the number of deviation periods in different load deviation intervals, the power consumption load of the preset power user in different deviation periods, and the deviation condition of the power consumption load of the deviation period and the historical load data of the power distribution transformer, determining whether there is a load deviation interval whose load deviation abnormal value does not meet the requirement, if yes, determining that the power distribution transformer is a suspected abnormal transformer, if no, entering the next step;

[0078] Determining the load comprehensive deviation amount of the power distribution transformer through the load deviation abnormal value of different load deviation intervals, and determining whether the power distribution transformer is a suspected abnormal transformer based on the load comprehensive deviation amount.

[0079] In another embodiment, determining that there is a suspected abnormal transformer in the power distribution area based on the load deviation condition, specifically comprising:

[0080] Filtering the deviation period through the deviation condition of the power consumption load of the preset power user and the historical load data of the power distribution transformer, determining the load deviation date through the number of deviation periods in different dates, determining whether the number of the load deviation dates meets the requirement, if yes, entering the next step, if no, determining that the power distribution transformer belongs to a suspected abnormal transformer;

[0081] Determining the date load deviation amount of different load deviation dates according to the number of deviation periods in different load deviation dates and the deviation condition of the power consumption load of the preset power user in different deviation periods and the historical load data of the power distribution transformer, determining whether the number of load deviation dates whose date load deviation amount does not meet the requirement meets the requirement, if yes, entering the next step, if no, determining that the power distribution transformer belongs to a suspected abnormal transformer;

[0082] The preset power user's power consumption load of different deviation time periods is divided into different load deviation intervals, and the load deviation abnormal value of different load deviation intervals is determined by the number of deviation time periods of different load deviation intervals, the preset power user's power consumption load of different deviation time periods, and the deviation of the power consumption load of the deviation time period from the historical load data of the distribution transformer.

[0083] The load comprehensive deviation amount of the distribution transformer is determined by the load deviation abnormal value of different load deviation intervals, and whether the distribution transformer is a suspected abnormal transformer is determined based on the load comprehensive deviation amount.

[0084] Embodiment 2

[0085] On the other hand, as Figure 3 shown, the application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it performs the above-mentioned household transformer association relationship abnormality identification method.

[0086] The application has the following advantages:

[0087] 1. The determination of the household transformer abnormality identification processing sequence of different power consumption areas is performed by the distribution transformers of the power consumption area and the distribution transformers of the adjacent power consumption area of the different power consumption area, which not only considers the difficulty of identifying the household transformer relationship abnormality caused by the number of distribution transformers of the power consumption area and the deviation of the historical load between different distribution transformers, but also fully considers the influence of the distribution transformers of the adjacent area on the household transformer abnormality relationship by comprehensively considering the number of distribution transformers of the adjacent area, and lays a foundation for further accurate and efficient identification of the household transformer relationship abnormality of the power consumption area.

[0088] 2. The existence of a suspected abnormal transformer in the power consumption area is determined by the load deviation, so as to realize the identification of the distribution transformer with household transformer relationship abnormality from the deviation of the historical load data of the power user and the power consumption area, and lay a foundation for further accurate identification and processing of the household transformer abnormality relationship.

[0089] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, for the device, equipment and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0090] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of this application, other aspects of the application will become apparent from consideration of the drawings and following detailed description, it being understood that such changes in the state of the art can be made without departing from the spirit and scope of the application.

[0091] The above description is merely of the embodiments of this application and is not intended to limit the scope of this application. Various modifications made within the spirit and principle of the embodiments of the application can be made by those skilled in the art and such modifications are to be included within the scope of claims of this application.

Claims

1. A method for identifying anomalies in household-transformer relationship, characterized in that, Specifically, it includes: The power distribution area is divided into different power distribution zones based on a preset area. The number of power distribution transformers in each power distribution zone and the historical load deviation of each power distribution transformer are obtained. The order of identification and processing of abnormal transformers in each power distribution zone is determined by combining the power distribution transformers in adjacent power distribution zones. The identification process of the power distribution area is carried out by identifying the processing order, and the abnormal probability of the household transformer association relationship of the power distribution transformer and the transformer identification processing order are determined based on the number of adjacent transformers in the power distribution area, the distance between different adjacent transformers and the power distribution transformer, and the data of the spaced buildings. The load deviation between the historical load data of the distribution transformer and the power load of the preset power users is determined using the transformer identification processing sequence. If the load deviation indicates that there is a suspected abnormal transformer in the distribution transformer area, the process proceeds to the next step. The suspected abnormal transformers were used as the target for investigation to identify and process the abnormal relationship between the transformers and the households. The method for determining the order of identification and processing of abnormal transformers in the power distribution area is as follows: Based on the historical load deviation of different distribution transformers in the distribution area, the load deviation between different distribution transformers is determined, and the difficulty of identification processing of the distribution area is determined according to the load deviation between different distribution transformers and the number of distribution transformers. The number of distribution transformers in different adjacent distribution areas of the distribution area is obtained, and the interference amount in the adjacent areas of the distribution area is determined by combining the load deviation between the different distribution transformers in different adjacent distribution areas and the different distribution transformers in the distribution area. The identification difficulty assessment value of the household transformer anomaly in the power distribution area is determined by the identification and processing difficulty of the power distribution area and the interference amount of the adjacent area, and the identification and processing order of the power distribution area is determined according to the identification difficulty assessment value. The identification processing order of the electronic distribution area is determined based on the identification difficulty assessment, specifically including: The identification processing order of the electronic distribution area is determined based on the identification difficulty assessment value from smallest to largest.

2. The method for identifying abnormal household-transformer relationship as described in claim 1, characterized in that, The preset area is determined based on the distribution density of the distribution transformers in the power distribution area. The higher the distribution density of the distribution transformers in the power distribution area, the smaller the preset area.

3. The method for identifying abnormal household-transformer relationship as described in claim 1, characterized in that, The deviation of the historical load of the distribution transformer includes the deviation of the historical load of the distribution transformer in different time periods.

4. The method for identifying abnormal household-transformer relationship as described in claim 1, characterized in that, The method for determining the anomaly probability of the household transformer correlation relationship of the aforementioned distribution transformer is as follows: The abnormal influence factors of the household transformer association relationship of different adjacent transformers are determined based on the different distances between adjacent transformers and the distribution transformer and the different number of buildings between adjacent transformers and the distribution transformer. The probability of anomalies in the relationship between household transformers of the distribution transformer is determined by the abnormal influence factors of the relationship between household transformers of different adjacent transformers.

5. The method for identifying abnormal household-transformer relationship as described in claim 1, characterized in that, The order of transformer identification and processing is determined based on the probability of anomalies in the household-transformer relationship of the distribution transformer. The higher the probability of anomalies in the household-transformer relationship of the distribution transformer, the higher the order of transformer identification and processing.

6. The method for identifying abnormal household-transformer relationship as described in claim 1, characterized in that, If the overall load deviation of the distribution transformer does not meet the requirements, the distribution transformer is determined to be a suspected abnormal transformer.

7. A computer system, comprising: A memory and a processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a method for identifying abnormal household-transformation relationships as described in any one of claims 1-6.

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