Abnormal power grid data alarm method, device and equipment and computer readable medium

Abstract

The embodiment of the invention discloses an abnormal power grid data alarm method, device and equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a power data set sequence; performing data preprocessing on each electric power data in the electric power data set sequence to generate a preprocessed electric power data set sequence; extracting power attribute information corresponding to each piece of preprocessed power data to obtain a power attribute information set sequence; for each power attribute information set, executing a first generation step: executing clustering processing to generate a power attribute information cluster set; inputting each piece of power attribute information into a power use classification model to generate initial power use information; generating predicted electric power use information corresponding to each piece of electric power attribute information; performing electric power purpose checking on the target electric power area to generate electric power purpose checking information; and executing data alarm processing. According to the embodiment, checking and data warning can be accurately and efficiently carried out on the power utilization purpose in the target power area.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to an abnormal power grid data alarm method, device, equipment, and computer-readable medium. Background Art

[0002] At present, in the field of electricity, it is of great significance to determine the use of electricity (for example, commercial electricity and residential electricity) to achieve the rational distribution of electricity. The verification of the use of electricity in a region is usually carried out by having relevant experts manually verify the electricity data in the region to determine whether the use of electricity in the region is abnormal.

[0003] However, when using the above method to verify electricity usage, the following technical problems often occur: Manual verification of electricity usage is labor-intensive and abnormality determination is cumbersome, resulting in low verification efficiency. There may be a large number of power equipment being used abnormally, which poses a risk to electricity usage.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose abnormal power grid data alarm methods, devices, equipment and media to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide an abnormal power grid data alarm method, comprising: obtaining a power data set sequence corresponding to a target power area, wherein the power data sets in the power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the target power area includes: each power sub-area; performing data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence; extracting power attribute information corresponding to each preprocessed power data in the preprocessed power data set sequence to obtain a power attribute information set sequence; for For each power attribute information set, the following first generation step is performed: clustering processing is performed on the above power attribute information set to generate a power attribute information cluster set; each power attribute information in the above power attribute information set is input into the power usage classification model to generate initial power usage information; based on the obtained initial power usage information set and the above power attribute information cluster set, predicted power usage information corresponding to each power attribute information in the above power attribute information set is generated; based on the obtained predicted power usage information set sequence, power usage verification is performed on the above target power area to generate power usage verification information; data alarm processing is performed on the above power usage verification information.

[0008] In a second aspect, some embodiments of the present disclosure provide an abnormal power grid data alarm device, including: an acquisition unit, configured to acquire a power data set sequence corresponding to a target power area, wherein the power data sets in the above power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the above target power area includes: each power sub-area; a preprocessing unit, configured to perform data preprocessing on each power data in the above power data set sequence to generate a preprocessed power data set sequence; an extraction unit, configured to extract power attribute information corresponding to each preprocessed power data in the above preprocessed power data set sequence to obtain a power attribute information set sequence; an execution unit, configured to execute The row unit is configured to perform the following first generation step for each power attribute information set: perform clustering processing on the above power attribute information set to generate a power attribute information cluster set; input each power attribute information in the above power attribute information set into the power usage classification model to generate initial power usage information; generate predicted power usage information corresponding to each power attribute information in the above power attribute information set based on the obtained initial power usage information set and the above power attribute information cluster set; the verification unit is configured to perform power usage verification on the above target power area based on the obtained predicted power usage information set sequence to generate power usage verification information; the alarm unit is configured to perform data alarm processing on the above power usage verification information.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0011] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the abnormal power grid data alarm method of some embodiments of the present disclosure, the use of electricity in the target power area can be accurately and efficiently checked. Specifically, the reason why the verification of the relevant use of electricity is not efficient is that the workload of manually checking the use of electricity is large and the abnormal determination is cumbersome, resulting in low verification efficiency. Based on this, the abnormal power grid data alarm method of some embodiments of the present disclosure first obtains the power data set sequence corresponding to the target power area. Among them, the power data set in the above-mentioned power data set sequence has a corresponding power acquisition time, and the power data is the power detection data of each power device in the power sub-area at the corresponding power acquisition time. The above-mentioned target power area includes: each power sub-area. Here, by obtaining the power data at each power acquisition time in the target power area, sufficient data support can be provided for the verification of the use of electricity in the subsequent target power area. Then, the data preprocessing is performed on each power data in the above-mentioned power data set sequence to generate a preprocessed power data set sequence to remove the data noise existing in each power data and ensure the data accuracy of the subsequent power use. Next, extract the power attribute information corresponding to each pre-processed power data in the above-mentioned pre-processed power data set sequence to obtain a power attribute information set sequence, so as to extract the characteristic information under each power attribute feature, so as to facilitate the subsequent accurate determination of the power usage information from the attribute feature. Then, for each power attribute information set, perform the following first generation step: the first step is to perform clustering processing on the above-mentioned power attribute information set to generate a power attribute information cluster. Here, through clustering processing, the power attribute information belonging to the same type of power usage can be accurately classified into one category, so as to facilitate the subsequent determination of the corresponding power usage. The second step is to input each power attribute information in the above-mentioned power attribute information set into the power usage classification model to accurately and efficiently generate the initial power usage information. The third step is to accurately generate the predicted power usage information corresponding to each power attribute information in the above-mentioned power attribute information set according to the obtained initial power usage information set and the above-mentioned power attribute information cluster. Here, the accuracy of the predicted power usage information can be guaranteed by mutual verification of the power attribute information cluster after clustering processing and the initial power usage information output by the power usage classification model. Finally, according to the obtained predicted power usage information set sequence, the power usage of the above-mentioned target power area can be quickly checked to generate power usage verification information for data alarm processing. In summary, through clustering processing and power usage classification model, the power usage in the target power area can be accurately and efficiently checked under mutual verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of the abnormal power grid data alarm method according to the present disclosure; Figure 2 is a structural schematic diagram of some embodiments of the abnormal power grid data alarm device according to the present disclosure; Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0015] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0016] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0019] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] refer to Figure 1 , shows a process 100 of some embodiments of the abnormal power grid data alarm method according to the present disclosure. The abnormal power grid data alarm method comprises the following steps: Step 101: Acquire a power data set sequence corresponding to a target power area.

[0021] In some embodiments, the execution subject (for example, an electronic device) of the above abnormal power grid data alarm method can obtain a power data set sequence corresponding to the target power area through a wired connection method or a wireless connection method. Among them, the target power area can be an area where the power use purpose is to be reviewed. For example, the target power area can be Tianjin. The power use purpose review can be the purpose of reviewing the use of electricity. For example, the purpose of power use can be one of the following: commercial use of electricity, civil use of electricity. There is a corresponding power acquisition time for the power data set in the above power data set sequence. There is a one-to-one correspondence between the power data set in the power data set sequence and the power acquisition time in the power acquisition time sequence. The power acquisition times in the power acquisition time sequence are sorted from early to late. In practice, the duration corresponding to the power acquisition time can be one day. For example, the power data set sequence includes: power data set A, power data set B, and power data set C. The power acquisition time corresponding to the power data set A can be January 1. The power acquisition time corresponding to the power data set B can be January 2. The power acquisition time corresponding to the power data set C can be January 3. The power data is the power detection data of each power device in the power sub-area at the corresponding power acquisition time. The power detection data may include a power data attribute information set corresponding to a power data attribute feature set. In practice, the power data attribute feature set may include: voltage features, power usage features, transformer-related features, and line-related features. The above-mentioned target power area includes: various power sub-areas. For example, for the target power area of ​​Tianjin, the corresponding power sub-areas may include: Heping District, Hedong District, Hexi District, Nankai District, Hebei District, Hongqiao District, Dongli District, Xiqing District, Jinnan District, Beichen District, Wuqing District, Baodi District, Binhai New Area, Ninghe District, Jinghai District and Jizhou District.

[0022] Step 102 : performing data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence.

[0023] In some embodiments, the execution subject may perform data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence. The data preprocessing may include but is not limited to at least one of the following: wrong data deletion, data completion, data format unification, and data fine-tuning.

[0024] Step 103: extract the power attribute information corresponding to each pre-processed power data set in the pre-processed power data set sequence to obtain a power attribute information set sequence.

[0025] In some embodiments, the execution subject may extract power attribute information corresponding to each preprocessed power data in the preprocessed power data set sequence to obtain a power attribute information set sequence, wherein the power attribute information may be a power data attribute feature information set corresponding to a power data attribute feature set.

[0026] As an example, the execution subject may extract a power data attribute feature information set corresponding to a power data attribute feature set from each preprocessed power data in the preprocessed power data set sequence as power attribute information to obtain a power attribute information set sequence.

[0027] Step 104: for each power attribute information set, perform the following first generation step: Step 1041 , performing clustering processing on the above power attribute information set to generate a power attribute information cluster set.

[0028] In some embodiments, the execution subject may perform clustering processing on the power attribute information set to generate a power attribute information cluster set, wherein the power data attribute feature information sets corresponding to the power attribute information in the power attribute information cluster have a high degree of similarity.

[0029] As an example, the execution entity may perform clustering processing on the power attribute information set by using a K-means algorithm to generate a power attribute information cluster set.

[0030] In some optional implementations of some embodiments, the above-mentioned performing clustering processing on the above-mentioned power attribute information set to generate a power attribute information cluster set may include the following steps: The first step is to obtain the power data attribute feature set.

[0031] The second step is to determine the feature importance of each power data attribute feature in the above power data attribute feature set relative to the power usage, so as to generate feature importance information and obtain a feature importance information set.

[0032] The third step is to determine the feature set importance corresponding to the predetermined power data attribute feature subset in the above-mentioned power data attribute feature set to generate feature set importance information and obtain at least one feature set importance information. Among them, the predetermined power data attribute feature can be a part of the power data attribute feature set by relevant experience (for example, a key power data attribute feature). The power data attribute feature set includes: at least one predetermined power data attribute feature subset. The feature set importance can characterize the importance of the feature combination corresponding to the predetermined power data attribute feature subset. The feature set importance can be a numerical value between 0 and 1, and the higher the numerical value, the higher the importance of the corresponding predetermined power data attribute feature subset. There is a one-to-one correspondence between the feature importance information in at least one feature set importance information and the predetermined power data attribute feature subset in at least one predetermined power data attribute feature subset. At least one feature set importance information can be pre-set.

[0033] The fourth step is to perform clustering processing on the power attribute information set according to the feature importance information set and the at least one feature set importance information, so as to generate the power attribute information cluster set.

[0034] Wherein, in the process of determining the information distance between each two power attribute information, the feature distance set corresponding to the power data attribute feature set is determined. The feature distance set is weighted and summed with the feature importance information set to generate a first weighted sum value. For each feature set importance information in at least one feature set importance information, first, a predetermined power data attribute feature subset corresponding to the feature set importance information is determined. Then, a feature distance subset in the above feature distance set corresponding to the predetermined power data attribute feature subset is determined. Next, the feature distance subset and the feature set importance information are weighted and summed to generate a second weighted sum value. The obtained second weighted sum value set and the first weighted sum value are added to generate the information distance between each two power attribute information. Based on this information distance determination method, the above power attribute information cluster is generated by clustering.

[0035] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the technical problem: how to accurately perform clustering processing on the above power attribute information set. Based on this, the present disclosure can accurately determine the information distance between two power attribute information in the clustering process by setting at least one feature set importance information and determining the feature importance information set, so as to achieve accurate clustering processing.

[0036] Step 1042: input each power attribute information in the above power attribute information set into the power usage classification model to generate initial power usage information.

[0037] In some embodiments, the execution entity may input each power attribute information in the power attribute information set into a power usage classification model to generate initial power usage information. The power usage classification model may be a neural network model for processing classification tasks that generates power usage information. In practice, the power usage classification model may be a multi-layer fully connected layer in series. The initial power usage information may be information that characterizes the power attribute information for the purpose of electricity use. In practice, the initial power usage information may be one of the following: information that characterizes the power attribute information for the purpose of electricity use as commercial electricity, and information that characterizes the power attribute information for the purpose of electricity use as residential electricity.

[0038] Step 1043: Generate predicted power usage information corresponding to each power attribute information in the power attribute information set according to the obtained initial power usage information set and the power attribute information cluster set.

[0039] In some embodiments, the execution subject may generate predicted power usage information corresponding to each power attribute information in the power attribute information set based on the obtained initial power usage information set and the power attribute information cluster set. The predicted power usage information corresponding to the power attribute information may be the power usage predicted under the power attribute information.

[0040] In some optional implementations of some embodiments, the generating of predicted power usage information corresponding to each power attribute information in the power attribute information set according to the obtained initial power usage information set and the power attribute information cluster set may include the following steps: For each power attribute information cluster in the power attribute information cluster set, the following fourth generation step is performed: Sub-step 1: determine the cluster center information corresponding to the above power attribute information cluster as the target cluster center information.

[0041] Sub-step 2: determining the power attribute information in the power attribute information cluster whose vector distance with the target cluster center information is less than the first vector distance as the first power attribute information, and obtaining at least one first power attribute information. The first vector distance may be a preset value. The first vector distance may be set based on experience.

[0042] Sub-step 3, determining the power attribute information in the power attribute information cluster whose vector distance with the target cluster center information is less than the second vector distance and greater than the first vector distance as the second power attribute information, and obtaining at least one second power attribute information. The second vector distance can be set based on experience. The second vector distance is greater than the first vector distance.

[0043] Sub-step 4: determine the power attribute information in the power attribute information cluster whose vector distance with the target cluster center information is greater than the second vector distance as the third power attribute information, and obtain at least one third power attribute information.

[0044] Sub-step 5: determining at least one first initial power usage information corresponding to the at least one first power attribute information. There is a one-to-one correspondence between the first power attribute information in the at least one first power attribute information and the first initial power usage information in the at least one first initial power usage information. The first initial power usage information may be preliminarily determined power usage information.

[0045] Sub-step 6: determining at least one second initial power usage information corresponding to the at least one second power attribute information, wherein the second power attribute information in the at least one second power attribute information and the second initial power usage information in the at least one second initial power usage information have a one-to-one correspondence.

[0046] Sub-step 7: determining at least one third initial power usage information corresponding to the at least one third power attribute information, wherein the third power attribute information in the at least one third power attribute information and the third initial power usage information in the at least one third initial power usage information have a one-to-one correspondence.

[0047] Sub-step 8, generates predicted power usage information corresponding to each power attribute information in the above-mentioned power attribute information cluster based on the above-mentioned at least one first initial power usage information, the corresponding at least one first vector distance, the above-mentioned at least one second initial power usage information, the corresponding at least one second vector distance, the above-mentioned at least one third initial power usage information and the corresponding at least one third vector distance.

[0048] In some optional implementations of some embodiments, the first initial power usage information includes: power usage type and power usage type probability, the second initial power usage information includes: power usage type and power usage type probability, and the third initial power usage information includes: power usage type and power usage type probability. The power usage type may be a power usage type. In practice, the power usage type may be one of the following: commercial power, residential power. The power usage type probability may characterize the accuracy of the power usage type determination. The higher the power usage type probability, the more accurate the power usage type determined by the corresponding characterization.

[0049] Optionally, the generating of predicted power usage information corresponding to each power attribute information in the power attribute information cluster according to the at least one first initial power usage information, the corresponding at least one first vector distance, the at least one second initial power usage information, the corresponding at least one second vector distance, the at least one third initial power usage information and the corresponding at least one third vector distance may include the following steps: The first step is to determine at least one power usage type probability corresponding to the at least one first initial power usage information as at least one first power usage type probability. There is a one-to-one correspondence between the first initial power usage information in the at least one first initial power usage information and the first power usage type probability in the at least one first power usage type probability.

[0050] The second step is to determine at least one power usage type probability corresponding to the at least one second initial power usage information as at least one second power usage type probability. There is a one-to-one correspondence between the second initial power usage information in the at least one second initial power usage information and the second power usage type probability in the at least one second power usage type probability.

[0051] The third step is to determine at least one power usage type probability corresponding to the at least one third initial power usage information as at least one third power usage type probability. There is a one-to-one correspondence between the third initial power usage information in the at least one third initial power usage information and the third power usage type probability in the at least one third power usage type probability.

[0052] The fourth step is to determine whether each of the at least one first power usage type probability and each of the at least one first vector distance corresponding values ​​are inversely proportional. The inverse relationship may be that the larger the power usage type probability, the smaller the corresponding first vector distance.

[0053] The fifth step is to determine whether each second power usage type probability in the at least one second power usage type probability is inversely proportional to the corresponding numerical values ​​of each second vector distance in the at least one second vector distance in response to determining that the corresponding numerical values ​​of each first vector distance in the at least one first vector distance are inversely proportional to each other.

[0054] In step 6, in response to determining that the corresponding values ​​of each second vector in the at least one second vector are in an inversely proportional relationship, determine whether the probability average corresponding to the at least one first power usage type probability is greater than the probability average corresponding to the at least one second power usage type probability. The probability average corresponding to the at least one first power usage type probability may be a value obtained by averaging the probabilities of the first power usage type. The probability average corresponding to the at least one second power usage type probability may be a value obtained by averaging the probabilities of the second power usage type.

[0055] In a seventh step, in response to determining that is greater than, determining whether each third power usage type probability in the at least one third power usage type probability is inversely proportional to the corresponding value of each third vector in the at least one third vector.

[0056] In step 8, in response to determining that the values ​​corresponding to the respective third vectors in the at least one third vector are in inverse proportion, determine whether the probability average corresponding to the at least one second power usage type probability is greater than the probability average corresponding to the at least one third power usage type probability. The probability average corresponding to the at least one third power usage type probability may be a value obtained by averaging the respective third power usage type probabilities.

[0057] In a ninth step, in response to determining that it is greater than, the initial power usage information corresponding to each power attribute information in the power attribute information cluster is determined as the corresponding predicted power usage information.

[0058] Step 105: Based on the obtained predicted power usage information set sequence, verify the power usage of the target power area to generate power usage verification information.

[0059] In some embodiments, the execution subject may verify the power usage of the target power area according to the obtained predicted power usage information set sequence to generate power usage verification information, wherein the power usage verification information may be information indicating whether there is abnormal power usage in the target power area.

[0060] In some optional implementations of some embodiments, the above-mentioned verification of power usage for the target power area according to the obtained predicted power usage information set sequence to generate power usage verification information may include the following steps: In the first step, for each of the above power sub-areas, the following second generation step is performed: Sub-step 1: Determine the power equipment usage registration information corresponding to the power sub-area at the current time. The power equipment usage registration information may represent the registration status of the power usage type of each power equipment in the power sub-area.

[0061] Sub-step 2: determining the predicted power usage information sequence corresponding to the above power sub-region as the target predicted power usage information sequence.

[0062] Sub-step 3, determining the power usage verification sub-information corresponding to the power sub-area based on the target predicted power usage information sequence and the power equipment usage registration information.

[0063] As an example, the above-mentioned execution entity may perform a difference comparison between the target predicted power usage information sequence and the above-mentioned power equipment usage registration information to determine the power usage verification sub-information corresponding to the above-mentioned power sub-area.

[0064] The second step is to generate the above-mentioned power usage verification information based on the obtained power usage verification sub-information.

[0065] As an example, the above-mentioned execution entity may directly determine each power usage verification sub-information as power usage verification information.

[0066] Optionally, the determining of the power usage verification sub-information corresponding to the power sub-area according to the target predicted power usage information sequence and the power equipment usage registration information may include the following steps: The first step is to determine whether each target predicted power usage information in the above target predicted power usage information sequence is the same as the above power equipment usage registration information.

[0067] The second step is to generate power usage verification sub-information indicating that there is no abnormal usage in the power sub-area in response to the determination that the power usage is the same.

[0068] The third step is, in response to determining that there is a difference, determining the target predicted power usage information in the above-mentioned target predicted power usage information sequence that is different from the above-mentioned power equipment usage registration information, and obtaining at least one target predicted power usage information.

[0069] The fourth step is to determine at least one power acquisition time corresponding to the at least one target predicted power usage information.

[0070] The fifth step is to determine, for each power acquisition time of the at least one power acquisition time, the power equipment usage registration information corresponding to the power sub-area at the power acquisition time as the target power equipment usage registration information.

[0071] The sixth step is to determine whether the at least one target predicted power usage information is the same as the at least one target power equipment usage registration information obtained.

[0072] In the seventh step, in response to determining the same, generating power usage verification sub-information indicating that there is no abnormal usage in the above power sub-area.

[0073] In the eighth step, in response to determining that there is a discrepancy, generating power usage verification sub-information indicating that there is abnormal usage in the above power sub-area.

[0074] In some optional implementations of some embodiments, after step 105, the steps further include: For each of the above-mentioned power sub-areas, the following third generation step is performed: Sub-step 1: determine the power attribute information sequence corresponding to the above power sub-area.

[0075] Sub-step 2: Determine the power-related data set of the power sub-area in the future time period according to the power attribute information sequence, wherein the power-related data set includes: power usage data, power generation data, and power usage distribution.

[0076] As an example, the execution entity may input the power attribute information sequence into a power-related data generation model (eg, a neural network model based on a recurrent neural network) to generate a power-related data set for the power sub-area in a future time period.

[0077] Step 106: Execute data alarm processing for the power usage verification information.

[0078] In some embodiments, the above-mentioned execution entity can perform data alarm processing for the electricity usage verification information.

[0079] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the abnormal power grid data alarm method of some embodiments of the present disclosure, the use of electricity in the target power area can be accurately and efficiently checked. Specifically, the reason why the verification of the relevant use of electricity is not efficient is that the workload of manually checking the use of electricity is large and the abnormal determination is cumbersome, resulting in low verification efficiency. Based on this, the abnormal power grid data alarm method of some embodiments of the present disclosure first obtains the power data set sequence corresponding to the target power area. Among them, the power data set in the above-mentioned power data set sequence has a corresponding power acquisition time, and the power data is the power detection data of each power device in the power sub-area at the corresponding power acquisition time. The above-mentioned target power area includes: each power sub-area. Here, by obtaining the power data at each power acquisition time in the target power area, sufficient data support can be provided for the verification of the use of electricity in the subsequent target power area. Then, the data preprocessing is performed on each power data in the above-mentioned power data set sequence to generate a preprocessed power data set sequence to remove the data noise existing in each power data and ensure the data accuracy of the subsequent power use. Next, extract the power attribute information corresponding to each pre-processed power data in the above-mentioned pre-processed power data set sequence to obtain a power attribute information set sequence, so as to extract the characteristic information under each power attribute feature, so as to facilitate the subsequent accurate determination of the power usage information from the attribute feature. Then, for each power attribute information set, perform the following first generation step: the first step is to perform clustering processing on the above-mentioned power attribute information set to generate a power attribute information cluster. Here, through clustering processing, the power attribute information belonging to the same type of power usage can be accurately classified into one category, so as to facilitate the subsequent determination of the corresponding power usage. The second step is to input each power attribute information in the above-mentioned power attribute information set into the power usage classification model to accurately and efficiently generate the initial power usage information. The third step is to accurately generate the predicted power usage information corresponding to each power attribute information in the above-mentioned power attribute information set according to the obtained initial power usage information set and the above-mentioned power attribute information cluster. Here, the accuracy of the predicted power usage information can be guaranteed by mutual verification of the power attribute information cluster after clustering processing and the initial power usage information output by the power usage classification model. Finally, according to the obtained predicted power usage information set sequence, the power usage of the above-mentioned target power area can be quickly checked to generate power usage verification information for data alarm processing. In summary, through clustering processing and power usage classification model, the power usage in the target power area can be accurately and efficiently checked under mutual verification.

[0080] Further references Figure 2As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an abnormal power grid data alarm device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the abnormal power grid data alarm device can be specifically applied to various electronic devices.

[0081] like Figure 2 As shown, an abnormal power grid data alarm device 200 includes: an acquisition unit 201, a preprocessing unit 202, an extraction unit 203, an execution unit 204, a verification unit 205 and an alarm unit 206. The acquisition unit 201 is configured to acquire a power data set sequence corresponding to a target power area, wherein the power data sets in the power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the target power area includes: each power sub-area; the preprocessing unit 202 is configured to perform data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence; the extraction unit 203 is configured to extract the power attribute information corresponding to each preprocessed power data in the preprocessed power data set sequence to obtain a power attribute information set sequence. ; The execution unit 204 is configured to perform the following first generation step for each power attribute information set: perform clustering processing on the above power attribute information set to generate a power attribute information cluster set; input each power attribute information in the above power attribute information set into the power usage classification model to generate initial power usage information; generate predicted power usage information corresponding to each power attribute information in the above power attribute information set based on the obtained initial power usage information set and the above power attribute information cluster set; The verification unit 205 is configured to perform power usage verification on the above target power area based on the obtained predicted power usage information set sequence to generate power usage verification information. The alarm unit 206 is configured to perform data alarm processing on the above power usage verification information.

[0082] It can be understood that the units recorded in the abnormal power grid data alarm device 200 are similar to the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the abnormal power grid data alarm device 200 and the units included therein, and will not be described in detail here.

[0083] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0084] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0085] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0086] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0087] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0088] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0089] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains a power data set sequence corresponding to the target power area, wherein the power data sets in the power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the target power area includes: each power sub-area; performs data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence; extracts power attribute information corresponding to each preprocessed power data in the preprocessed power data set sequence to obtain a power attribute information set sequence; for each power attribute information set, performs the following first generation step: performs clustering processing on the power attribute information set to generate a power attribute information cluster; inputs each power attribute information in the power attribute information set into a power usage classification model to generate initial power usage information; generates predicted power usage information corresponding to each power attribute information in the power attribute information set based on the obtained initial power usage information set and the power attribute information cluster; performs power usage verification on the target power area based on the obtained predicted power usage information set sequence to generate power usage verification information.

[0090] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0091] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0092] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor including an acquisition unit, a preprocessing unit, an extraction unit, an execution unit, a verification unit, and an alarm unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the acquisition unit may also be described as a "unit for acquiring a sequence of power data sets corresponding to a target power area".

[0093] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0094] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An abnormal power grid data alarm method, comprising: Acquire a power data set sequence corresponding to a target power area, wherein the power data sets in the power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the target power area includes: each power sub-area; Performing data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence; Extracting power attribute information corresponding to each preprocessed power data set in the preprocessed power data set sequence to obtain a power attribute information set sequence; For each power attribute information set, the following first generation step is performed: Performing clustering processing on the power attribute information set to generate a power attribute information cluster set; Inputting each power attribute information in the power attribute information set into a power usage classification model to generate initial power usage information; Generate predicted power usage information corresponding to each power attribute information in the power attribute information set according to the obtained initial power usage information set and the power attribute information cluster set; According to the obtained predicted power usage information set sequence, verifying the power usage of the target power area to generate power usage verification information; A data alarm process is performed on the power usage verification information.

2. The method according to claim 1, wherein: The step of checking the power usage of the target power area according to the obtained predicted power usage information set sequence to generate power usage verification information includes: For each of the power sub-areas, the following second generating step is performed: Determine the registration information of the use of electric equipment corresponding to the electric power sub-area at the current time; Determining a predicted power usage information sequence corresponding to the power sub-region as a target predicted power usage information sequence; Determining the power usage verification sub-information corresponding to the power sub-area according to the target predicted power usage information sequence and the power equipment usage registration information; The power usage verification information is generated according to each of the obtained power usage verification sub-information.

3. The method according to claim 1, wherein: The method further comprises: For each of the power sub-areas, the following third generating step is performed: Determining a power attribute information sequence corresponding to the power sub-area; According to the power attribute information sequence, a power-related data set of the power sub-area in a future time period is determined, wherein the power-related data set includes: power usage data, power generation data, and power usage distribution.

4. The method according to claim 1, wherein: The generating, based on the obtained initial power usage information set and the power attribute information cluster set, predicted power usage information corresponding to each power attribute information in the power attribute information set includes: For each power attribute information cluster in the power attribute information cluster set, the following fourth generation step is performed: Determine cluster center information corresponding to the power attribute information cluster as target cluster center information; Determine, in the power attribute information cluster, power attribute information whose vector distance with the target cluster center information is less than the first vector distance, as first power attribute information, and obtain at least one first power attribute information; Determine, as second power attribute information, power attribute information whose vector distance from the target cluster center information is less than the second vector distance and greater than the first vector distance in the power attribute information cluster, and obtain at least one second power attribute information; Determine, in the power attribute information cluster, power attribute information whose vector distance with the target cluster center information is greater than the second vector distance, as third power attribute information, and obtain at least one third power attribute information; Determine at least one first initial power usage information corresponding to the at least one first power attribute information; Determine at least one second initial power usage information corresponding to the at least one second power attribute information; Determine at least one third initial power usage information corresponding to the at least one third power attribute information; Based on the at least one first initial power usage information, the corresponding at least one first vector distance, the at least one second initial power usage information, the corresponding at least one second vector distance, the at least one third initial power usage information and the corresponding at least one third vector distance, the predicted power usage information corresponding to each power attribute information in the power attribute information cluster is generated.

5. The method according to claim 4, wherein: The first initial power usage information includes: power usage type and power usage type probability, the second initial power usage information includes: power usage type and power usage type probability, and the third initial power usage information includes: power usage type and power usage type probability; and The generating, according to the at least one first initial power usage information, the corresponding at least one first vector distance, the at least one second initial power usage information, the corresponding at least one second vector distance, the at least one third initial power usage information and the corresponding at least one third vector distance, the generating of predicted power usage information corresponding to each power attribute information in the power attribute information cluster comprises: determining at least one power usage type probability corresponding to the at least one first initial power usage information as at least one first power usage type probability; determining at least one power usage type probability corresponding to the at least one second initial power usage information as at least one second power usage type probability; determining at least one power usage type probability corresponding to the at least one third initial power usage information as at least one third power usage type probability; Determining whether each first power usage type probability in the at least one first power usage type probability is inversely proportional to a corresponding value of each first vector distance in the at least one first vector distance; In response to determining that the respective values ​​corresponding to the respective first vector distances in the at least one first vector distance are in an inversely proportional relationship, determining whether the respective second power usage type probabilities in the at least one second power usage type probability are in an inversely proportional relationship with the respective values ​​corresponding to the respective second vector distances in the at least one second vector distance; In response to determining that the values ​​corresponding to the respective second vector distances of the at least one second vector distance are in an inversely proportional relationship, determining whether the probability average corresponding to the at least one first power usage type probability is greater than the probability average corresponding to the at least one second power usage type probability; In response to determining that it is greater than, determining whether each of the at least one third power usage type probability and each of the at least one third vector distance corresponding values ​​are inversely proportional to each other; In response to determining that the values ​​corresponding to the respective third vector distances of the at least one third vector distance are in an inversely proportional relationship, determining whether the probability average corresponding to the at least one second power usage type probability is greater than the probability average corresponding to the at least one third power usage type probability; In response to determining that it is greater than, determining the initial power usage information corresponding to each power attribute information in the power attribute information cluster as the corresponding predicted power usage information.

6. The method according to claim 2, wherein: The determining, according to the target predicted power usage information sequence and the power equipment usage registration information, the power usage verification sub-information corresponding to the power sub-area includes: Determining whether each target predicted power usage information in the target predicted power usage information sequence is the same as the power equipment usage registration information; In response to determining that they are the same, generating power usage verification sub-information indicating that there is no abnormal usage in the power sub-area; In response to determining that there is a difference, determining target predicted power usage information in the target predicted power usage information sequence that is different from the power equipment usage registration information, and obtaining at least one target predicted power usage information; determining at least one power acquisition time corresponding to the at least one target predicted power usage information; For each power acquisition time of the at least one power acquisition time, determining the power equipment usage registration information at the power acquisition time corresponding to the power sub-area as the target power equipment usage registration information; determining whether the at least one target predicted power usage information is the same as the obtained at least one target power equipment usage registration information; In response to determining that they are the same, generating power usage verification sub-information indicating that there is no abnormal usage in the power sub-area; In response to determining that there is a difference, power usage verification sub-information indicating that there is abnormal usage in the power sub-area is generated.

7. An abnormal power grid data alarm device, comprising: The acquisition unit is configured to acquire a power data set sequence corresponding to a target power area, wherein the power data sets in the power data set sequence have corresponding power acquisition times, and the power data are power detection data of each power device in the power sub-area at the corresponding power acquisition time, and the target power area includes: each power sub-area; A preprocessing unit, configured to perform data preprocessing on each power data in the power data set sequence to generate a preprocessed power data set sequence; an extraction unit, configured to extract power attribute information corresponding to each pre-processed power data set in the pre-processed power data set sequence to obtain a power attribute information set sequence; The execution unit is configured to perform the following first generation step for each power attribute information set: perform clustering processing on the power attribute information set to generate a power attribute information cluster set; input each power attribute information in the power attribute information set into a power usage classification model to generate initial power usage information; generate predicted power usage information corresponding to each power attribute information in the power attribute information set according to the obtained initial power usage information set and the power attribute information cluster set; A verification unit configured to perform a power usage verification on the target power area according to the obtained predicted power usage information set sequence to generate power usage verification information; The alarm unit is configured to execute data alarm processing for the power usage verification information.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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