A method for efficient data processing of meter reading at power collection terminal

Abstract

The present invention relates to the field of data processing technology, and in particular to a method for efficient data processing for meter reading of an electric power acquisition terminal, the method comprising: obtaining target feature values ​​corresponding to each user in an initial category, reclassifying all users in the initial category according to the target feature values ​​corresponding to the users, and obtaining first target categories and second target categories corresponding to each initial category; obtaining target sequences corresponding to each user in the first target category and target sequences corresponding to each user in the second target category corresponding to each initial category according to the trend line, the data points on the power consumption line graph corresponding to the users in the first target category, and the slopes of each straight line on the power consumption line graph corresponding to the users in the second target category; compressing the target sequences corresponding to all users monitored by the electric power acquisition terminal to obtain compressed data. The present invention can improve the compression effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for efficiently processing data for meter reading at an electric power collection terminal. Background Art

[0002] Since the power collection terminal is a device for collecting data in the power system, and the current power collection terminal usually cooperates with the remote meter reading system to realize the function of automated remote meter reading; and since the current power company usually formulates power dispatch and allocation plans based on the user power consumption data collected by the power collection terminal to ensure that the power company can carry out effective power management, the collected user power consumption data will be transmitted and stored at present. However, with the continuous growth of time and the continuous increase of power users, more and more data will be required for transmission and storage. In order to ensure storage space, the collected user power consumption data is usually compressed and stored, and currently run-length encoding (RLE) is usually used to directly compress the power consumption data to be compressed. However, there are differences in the power consumption behavior of different users. This method of directly compressing the collected power consumption data of each user using run-length encoding will result in poor compression effect, such as low compression rate. When the compression effect is not good, not only will the storage space not be effectively reduced, but it will also affect the reliability of the subsequent power dispatch and allocation plan formulated by the power company. Therefore, when compressing the collected user power consumption data, how to improve the compression effect is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a method for efficiently processing data for meter reading at an electric power collection terminal, and the technical solution adopted is as follows:

[0004] An embodiment of the present invention provides a method for efficiently processing data for meter reading by a power collection terminal, comprising the following steps:

[0005] Obtaining the power consumption data sequence of all users monitored by the power collection terminal;

[0006] According to the first and last power consumption data in the power consumption data sequence, all users monitored by the power collection terminal are initially classified to obtain initial categories and unclassified users, and the power consumption data sequence of the unclassified users is used as the target sequence corresponding to the corresponding users;

[0007] Obtain a power consumption line graph corresponding to each user in the initial category, and obtain a trend line corresponding to the initial category based on the power consumption line graph corresponding to the user; obtain a target feature value corresponding to each user in the initial category based on the trend line and all data points on the power consumption line graph corresponding to all users in the initial category; and reclassify all users in the initial category based on the target feature values ​​corresponding to the users to obtain a first target category and a second target category corresponding to each initial category;

[0008] According to the trend line, the data points on the power consumption line graph corresponding to the users in the first target category, and the slopes of the straight lines on the power consumption line graph corresponding to the users in the second target category, the target sequences corresponding to the users in the first target category and the target sequences corresponding to the users in the second target category corresponding to the initial categories are obtained respectively;

[0009] The target sequences corresponding to all users monitored by the power collection terminal are compressed to obtain compressed data.

[0010] Beneficial effects: The present invention first obtains the power consumption data sequence of all users monitored by the power collection terminal; then, according to the first and last power consumption data in the power consumption data sequence, all users monitored by the power collection terminal are initially classified to obtain the initial category and unclassified users, and the power consumption data sequence of the unclassified users is used as the target sequence corresponding to the corresponding user; then, the power consumption line graph corresponding to each user in the initial category is obtained, and according to the power consumption line graph corresponding to the user, the trend line corresponding to the initial category is obtained, and according to the trend line and all data points on the power consumption line graph corresponding to all users in the initial category, the target characteristic value corresponding to each user in the initial category is obtained, and according to the target characteristic value corresponding to the user, all users in the initial category are reclassified again to obtain the first target category and the second target category corresponding to each initial category; then, according to the trend line, the data points on the power consumption line graph corresponding to the users in the first target category and the slopes of each straight line on the power consumption line graph corresponding to the users in the second target category, the target sequence corresponding to each user in the first target category and the target sequence corresponding to each user in the second target category corresponding to each initial category are obtained respectively; finally, the target sequence corresponding to all users monitored by the power collection terminal is compressed to obtain compressed data. Furthermore, the present invention does not directly compress the user's electricity consumption data sequence, but compresses the target sequence corresponding to the user, and the obtained compression effect is better, that is, the present invention improves the compression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 The present invention is a flow chart of a method for efficiently processing data for meter reading at an electric power collection terminal. DETAILED DESCRIPTION

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0014] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0015] This embodiment provides a method for efficiently processing data for meter reading at an electric power collection terminal, which is described in detail as follows:

[0016] like Figure 1 As shown, the method for efficiently processing data for meter reading at a power collection terminal comprises the following steps:

[0017] Step S001, obtaining the power consumption data sequence of all users monitored by the power collection terminal.

[0018] The main purpose of this embodiment is to improve the compression effect. In order to achieve the purpose of improving the compression effect, this embodiment will first pre-process the data to be compressed, and then use run-length coding to compress the sequence obtained after the pre-processing, that is, this embodiment first pre-processes the collected user's power consumption data sequence to obtain the target sequence corresponding to the user, and then uses run-length coding to compress the target sequence corresponding to the user. In addition, for the convenience of analysis, this embodiment will be described later by taking any power collection terminal as an example. Therefore, the power collection terminal that appears later in this embodiment is the same power collection terminal, and the users that appear are all users monitored by the power collection terminal, that is, one power collection terminal can monitor multiple users at the same time and can collect the power consumption of multiple users at the same time.

[0019] This embodiment first collects and obtains the electricity consumption data of all users monitored by the power collection terminal at each meter reading time in the current monitoring time period, and records the time series constructed by all the electricity consumption data belonging to the same user collected in the current monitoring time period as the electricity consumption data sequence of the corresponding user, that is, in the current monitoring time period, one user corresponds to one sequence.

[0020] In addition, in specific applications, the implementer needs to set the time interval between adjacent meter reading times and the current monitoring time period according to actual conditions. For example, in this embodiment, the time length of the current monitoring time period can be consistent with the transmission time interval, that is, if the transmission time interval is 5 minutes, then the time length of the current monitoring time period is also set to 5 minutes, and the current moment in the current monitoring time period is the transmission moment. In this embodiment, the time interval between adjacent meter reading times can be set to an empirical value, such as 5 seconds.

[0021] Therefore, this embodiment can obtain the power consumption data sequence of all users monitored by the power collection terminal through the above process.

[0022] Step S002, based on the first and last power consumption data in the power consumption data sequence, all users monitored by the power collection terminal are initially classified to obtain initial categories and unclassified users, and the power consumption data sequence of the unclassified users is used as the target sequence corresponding to the corresponding users.

[0023] Since the acquired power consumption data sequence can reflect the user's power consumption behavior, power consumption habits, etc., in order to achieve the purpose of improving the compression effect, this embodiment will classify the users based on the power consumption behavior, and then set different compression schemes based on the classification results, that is, based on the classification results to obtain the target sequence corresponding to the user, and this embodiment firstly classifies all users monitored by the power collection terminal according to the first and last power consumption data in the power consumption data sequence of each user, and obtains the initial category and unclassified users, that is, the specific process of obtaining the initial category and unclassified users in this embodiment is:

[0024] First, the set constructed by all users monitored by the power collection terminal is recorded as the first set, and the second characterization value between any two users in the first set is obtained. It should be noted that the larger the second characterization value between the users, the more similar the power consumption or power consumption behavior of the two users are, and the possibility of being classified into one category is greater. Otherwise, it indicates that the power consumption or power consumption behavior of the two users are relatively dissimilar, and the possibility of being classified into one category is smaller. The method for obtaining the second characterization value between any two users in the first set is as follows: in the first set, any two users are selected and recorded as user A1 and user A2 respectively, and the value after subtracting the first power consumption data from the last power consumption data in the power consumption data sequence of user A1 is recorded as the first characteristic value corresponding to user A1, and the power consumption data of user A2 is recorded as the first characteristic value corresponding to user A1. The value obtained by subtracting the first power consumption data from the last power consumption data in the sequence is recorded as the first characteristic value corresponding to user A2, the absolute value of the difference between the first characteristic value corresponding to user A1 and the first characteristic value corresponding to user A2 is obtained, and recorded as the first characterization value between user A1 and user A2, and then the function exp() is used to perform negative correlation mapping on the first characterization value between user A1 and user A2, and the mapping result is recorded as the second characterization value between user A1 and user A2, that is, the second characterization value between user A1 and user A2 is exp(-A3), A3 is the first characterization value between user A1 and user A2, and the exp() is an exponential function with a constant e as the base; in addition, the smaller the first characterization value between user A1 and user A2, the larger the second characterization value between user A1 and user A2.

[0025] After obtaining the second characterization value between any two users in the first set, the first classification is performed according to the size of the second characterization value. The specific first classification process is as follows:

[0026] First, obtain the first quantity characterization value corresponding to each user in the first set, and select the user corresponding to the maximum first quantity characterization value as the first reference user; then record the set constructed by all users in the first set except the first reference user as the first subset, and record all users in the first subset as first users to be classified; then record the second characteristic values ​​between the first reference user and each first user to be classified in the first subset as the classification index value corresponding to the first user to be classified; then determine whether there is a first user to be classified in the first subset whose classification index value is greater than a preset classification threshold; if so, record the category formed by all first users to be classified in the first subset and the first reference user with a classification index value greater than the preset classification threshold as the first initial category; otherwise, record the first reference user as unclassified user a1.

[0027] After obtaining the first initial category or unclassified user a1, this embodiment obtains a set of all users except the first initial category or the unclassified user a1 in the first set, and records it as the first set to be judged; then, it is determined whether the first set to be judged is an empty set. If so, the initial classification is stopped immediately; otherwise, it is continued to be determined whether the number of users in the first set to be judged is equal to the preset number threshold. If so, all users in the first set to be judged are recorded as unclassified users, and the initial classification is stopped immediately; otherwise, the first set to be judged is recorded as the second set.

[0028] Then, continue to obtain the second quantity characterization value corresponding to each user in the second set, and select the user corresponding to the maximum second quantity characterization value as the second reference user; then record the set constructed by all users in the second set except the second reference user as the second subset, and record all users in the second subset as second users to be classified; then record the second characteristic values ​​between the second reference user and each second user to be classified in the second subset as the classification index value corresponding to the corresponding second user to be classified; then determine whether there is a second user to be classified in the second subset whose classification index value is greater than the preset classification threshold; if so, record the category formed by all second users to be classified in the second subset whose classification index value is greater than the preset classification threshold and the second reference user as the second initial category; otherwise, record the second reference user as unclassified user a2.

[0029] After obtaining the second initial category or unclassified user a2, this embodiment obtains a set of all users except the second initial category or the unclassified user a2 in the second set, and records it as the second set to be judged; then it is determined whether the second set to be judged is an empty set. If so, the initial classification is stopped immediately; otherwise, it is continued to be determined whether the number of users in the second set to be judged is equal to the preset number threshold. If so, all users in the second set to be judged are recorded as unclassified users, and the initial classification is stopped immediately; otherwise, the second set to be judged is recorded as the third set, and according to the above process of obtaining the first initial category or unclassified user a1 according to the first set and obtaining the second initial category or unclassified user a2 according to the second set, a new initial category or a new unclassified user is continued to be obtained according to the third set. Since the above process has described the process of obtaining the first initial category or unclassified user a1 according to the first set and obtaining the second initial category or unclassified user a2 according to the second set, the subsequent repeated process will not be described in detail.

[0030] In addition, in this embodiment, the process of obtaining the first quantity characterization value corresponding to each user in the first set and the second quantity characterization value corresponding to each user in the second set is as follows:

[0031] For any user B1 in the first set, the set constructed by all users in the first set except user B1 is recorded as the first feature set, and then the second feature value subset corresponding to user B1 is obtained, and the number of second feature values ​​greater than the preset classification threshold in the second feature value subset corresponding to user B1 is counted and recorded as the first quantity characterization value corresponding to user B1, and the kth second feature value in the second feature value subset corresponding to user B1 is the second feature value between user B1 and the kth user in the first feature set. The method for obtaining the second quantity characterization value corresponding to each user in the second set is the same as the method for obtaining the first quantity characterization value corresponding to each user in the first set, so this embodiment will not be described in detail.

[0032] In addition, in specific applications, the implementer needs to set the preset classification threshold and the preset quantity threshold according to actual conditions. For example, in this embodiment, the preset classification threshold can be set to 0.8, and the preset quantity threshold can be set to 1.

[0033] Therefore, this embodiment can obtain each initial category and each unclassified user through the above process, and this embodiment uses the power consumption data sequence of the unclassified user as the target sequence corresponding to the corresponding user, that is, the target sequence corresponding to the unclassified user is its corresponding power consumption data sequence.

[0034] Step S003, obtaining the electricity consumption line graph corresponding to each user in the initial category, and obtaining the trend line corresponding to the initial category based on the electricity consumption line graph corresponding to the user; obtaining the target feature value corresponding to each user in the initial category based on the trend line and all data points on the electricity consumption line graph corresponding to all users in the initial category; and reclassifying all users in the initial category based on the target feature values ​​corresponding to the users to obtain the first target category and the second target category corresponding to each initial category.

[0035] Based on the above steps, it can be known that the initial category can be obtained, but the classification result obtained based only on the above classification method cannot reliably determine that the electricity consumption behavior or electricity consumption of all users in the same category is similar. Therefore, this embodiment needs to be classified on the basis of the above classification, that is, this embodiment needs to obtain the first target category and the second target category corresponding to each initial category next, but before obtaining the first target category and the second target category corresponding to each initial category, it is necessary to first obtain the target feature value corresponding to each user in the initial category, and the target feature value corresponding to the user is determined by the trend line corresponding to the initial category and all data points on the electricity consumption line graph corresponding to all users in the initial category, and the trend line corresponding to the initial category is determined based on the electricity consumption line graph corresponding to each user in the initial category. It can be seen that this embodiment needs to first obtain the electricity consumption line graph corresponding to each user in the initial category, then the specific process of the electricity consumption line graph corresponding to each user in the initial category is:

[0036] First, the marking value of each power consumption data in each power consumption data sequence is obtained, and then a two-dimensional coordinate system corresponding to each initial category is constructed, and the marking value of the c-th power consumption data in each power consumption data sequence is c, and the horizontal axis value of each two-dimensional coordinate system is the marking value, and the vertical axis value is the power consumption.

[0037] After obtaining the tag value of each power consumption data in each power consumption data sequence and the two-dimensional coordinate system corresponding to the initial category, the power consumption line graph corresponding to each user in each initial category is obtained; and for ease of understanding, this embodiment will be described below by taking the acquisition process of the power consumption line graph corresponding to any user d in any initial category D as an example, that is, the acquisition process of the power consumption line graph corresponding to user d in the initial category D is:

[0038] First, each electricity consumption data and the mark value of each electricity consumption data in the electricity consumption data sequence of user d are mapped to the two-dimensional coordinate system corresponding to the initial category D to obtain all data points corresponding to the user d, that is, each electricity consumption data in the electricity consumption data sequence of user d corresponds to a data point, and then a line graph is drawn according to all the data points corresponding to user d, and recorded as the electricity consumption line graph corresponding to user d. The horizontal coordinate of the data point is the mark value, and the vertical coordinate is the electricity consumption data. When the position of the data point is known, the method of drawing the line graph is a well-known technology, so it will not be described in detail.

[0039] After obtaining the power consumption line graph corresponding to each user in the initial category, the trend line corresponding to the initial category is obtained according to the power consumption line graph corresponding to each user in each initial category; in addition, for ease of understanding, this embodiment will take the process of obtaining the trend line corresponding to the initial category D as an example for description, that is, the process of obtaining the trend line corresponding to the initial category D is: first, the set formed by the first data point on the power consumption line graph corresponding to all users in the initial category D is recorded as the first data point set, and the set formed by the last data point on the power consumption line graph corresponding to all users in the initial category D is recorded as the second data point set; then, the average data point of the first data point set is obtained and used as the first feature point, and the average data point of the second data point set is obtained and used as the second feature point; then, the straight line obtained by connecting the first feature point and the second feature point is recorded as the trend line corresponding to the initial category D; and the horizontal coordinate value of the average data point of the data point set is the mean of the horizontal coordinates of all data points in the corresponding data point set, and the vertical coordinate value of the average data point of the data point set is the mean of the vertical coordinates of all data points in the corresponding data point set.

[0040] Therefore, the trend line corresponding to each initial category can be obtained in the above manner, and the trend line corresponding to the initial category can reflect the average trend of electricity consumption of users in the corresponding category; after obtaining the trend line corresponding to the initial category, this embodiment obtains the target feature value corresponding to each user in the initial category according to the trend line corresponding to the initial category and all data points on the power consumption line graph corresponding to all users in the initial category, and the target feature value is the basis for further classification; in addition, for ease of understanding, this embodiment will take the acquisition process of the target feature value corresponding to user d in the initial category D as an example to describe, that is, the acquisition process of the target feature value corresponding to user d in the initial category D is:

[0041] First, the trend points corresponding to the data points on the electricity consumption line graph corresponding to user d are obtained, and the horizontal coordinates of the trend points corresponding to the data points on the electricity consumption line graph corresponding to user d are the same as the horizontal coordinates of the corresponding data points, and the trend points corresponding to the data points on the electricity consumption line graph corresponding to user d are located on the trend line corresponding to the initial category D; and the method for obtaining the trend points corresponding to the data points on the electricity consumption line graph corresponding to other users in other initial categories is the same as the method for obtaining the trend points corresponding to the data points on the electricity consumption line graph corresponding to user d in the initial category D.

[0042] Then, the Euclidean distance between each data point on the electricity consumption line graph corresponding to user d and its corresponding trend point is obtained and recorded as the first difference value of the corresponding data point; then, the sequence formed by the first difference values ​​of all data points on the electricity consumption line graph corresponding to user d is recorded as the first difference value sequence corresponding to user d, and the f-th first difference value in the first difference value sequence is the Euclidean distance between the f-th data point on the electricity consumption line graph corresponding to user d and the trend point corresponding to the f-th data point.

[0043] Next, the number of intersections between the electricity consumption line graph corresponding to user d and the trend line corresponding to the initial category D is counted, and the obtained number of intersections is positively mapped, and the mapping result is recorded as the first mapping value, and the first mapping value is exp(N), N is the number of intersections between the electricity consumption line graph corresponding to user d and the trend line corresponding to the initial category D; then the product of the mean of the first difference value sequence and the first mapping value is obtained, and recorded as the first product value; then, negative correlation mapping is performed on the first product value, and the mapping result is recorded as the first indicator value corresponding to user d, and the first indicator value is exp(-M1), M1 is the first product value corresponding to user d.

[0044] Then, the ordinate value of each data point on the power consumption line graph corresponding to user d is added to the slope of the trend line corresponding to the initial category D, and the addition result is recorded as the first addition value of the corresponding data point; then, according to the first addition value of each data point on the power consumption line graph corresponding to user d, the second difference value sequence corresponding to user d is obtained, and the cumulative sum of all data in the second difference value sequence is recorded as the first cumulative value; then, the first cumulative value is positively correlated mapped, and the mapping result is recorded as the second index value corresponding to user d, and the second index value corresponding to user d is exp(N1), N1 is the first cumulative value; in addition, the gth second difference value in the second difference value sequence is the absolute value of the difference between the ordinate value of the g+1th data point on the power consumption line graph corresponding to user d and the first addition value of the gth data point, and the g+1th second difference value in the second difference value sequence is the absolute value of the difference between the ordinate value of the g+2th data point on the power consumption line graph corresponding to user d and the first addition value of the g+1th data point.

[0045] Finally, a weighted sum is performed on the first index value and the second index value corresponding to user d, and the result of the weighted sum is recorded as the target feature value corresponding to user d. In this embodiment, the specific expression for calculating the target feature value corresponding to user d is:

[0046]

[0047] in, is the target feature value corresponding to user d, is the first weight value, is the second weight value; and when The smaller and The larger the The larger the value of The larger the value of is, the more it indicates that the power consumption line graph corresponding to user d not only changes relatively smoothly, but also is closer to the trend line corresponding to the initial category D, that is, The larger the value of is, the more it indicates that the power consumption line graph corresponding to user d is not only closer to the trend line corresponding to the initial category D, but also more similar.

[0048] In addition, in a specific application, the implementer needs to set the first weight value and the second weight value according to the actual situation. For example, in this embodiment, the first weight value and the second weight value are set to 0.7 and 0.3 respectively.

[0049] Therefore, this embodiment can obtain the target feature value corresponding to each user in each initial category according to the above process; and after obtaining the target feature value corresponding to the user, all users in the initial category are reclassified according to the target feature value corresponding to each user in each initial category to obtain the first target category and the second target category corresponding to each initial category, and the specific process is:

[0050] For the initial category D, all users in the initial category D whose target feature values ​​are greater than the preset feature threshold and all users in the initial category D whose target feature values ​​are not greater than the preset feature threshold are obtained, and the category formed by all users in the initial category D whose target feature values ​​are greater than the preset feature threshold is recorded as the first target category corresponding to the initial category D, and the category formed by all users in the initial category D whose target feature values ​​are not greater than the preset feature threshold is recorded as the second target category corresponding to the initial category D.

[0051] In a specific application, the implementer needs to set the preset feature threshold according to the actual situation. For example, in this embodiment, the preset feature threshold can be set to 0.75.

[0052] Therefore, according to the above method for obtaining the first target category and the second target category corresponding to the initial category D, this embodiment can obtain the first target category and the second target category corresponding to each initial category.

[0053] Step S004, based on the trend line, the data points on the electricity consumption line graph corresponding to the users in the first target category, and the slopes of each straight line on the electricity consumption line graph corresponding to the users in the second target category, obtain the target sequence corresponding to each user in the first target category corresponding to each initial category and the target sequence corresponding to each user in the second target category respectively.

[0054] Based on step S003, this embodiment obtains the first target category and the second target category corresponding to each initial category. Next, this embodiment will set different methods for obtaining the target sequence corresponding to the user based on the different categories. That is, this embodiment will obtain the target sequence corresponding to each user in the first target category corresponding to each initial category and the target sequence corresponding to each user in the second target category according to the trend line corresponding to the initial category, the data points on the power consumption line graph corresponding to the users in the first target category corresponding to the initial category, and the slopes of each straight line on the power consumption line graph corresponding to the users in the second target category; and this embodiment first obtains the target sequence corresponding to each user in the first target category corresponding to each initial category according to the trend line corresponding to the initial category and the data points on the power consumption line graph corresponding to the users in the first target category corresponding to the initial category. That is, the specific process of obtaining the target sequence corresponding to each user in the first target category corresponding to each initial category is:

[0055] For any user f1 in the first target category corresponding to the initial category D: first, obtain the target difference corresponding to each data point on the electricity consumption line graph corresponding to user f1, and sort the target difference corresponding to all data points on the electricity consumption line graph corresponding to user f1 in ascending order of the horizontal axis value, and record the sorted sequence as the target sequence corresponding to user f1, that is, the vth data in the target sequence corresponding to user f1 is the target difference corresponding to the vth data point on the electricity consumption line graph corresponding to user f1; in addition, the target difference corresponding to the vth data point on the electricity consumption line graph corresponding to user f1 refers to the value obtained by subtracting the vertical axis value of the trend point corresponding to the vth data point from the vertical axis value of the vth data point.

[0056] In addition, for users in the first target category, their corresponding electricity consumption line graph is closer and more similar to the trend line corresponding to the initial category. Therefore, the repetition rate of the data in the target sequence obtained based on the difference between the electricity consumption line graph corresponding to the users in the first target category and the trend line corresponding to the initial category is higher, and therefore the compression effect is better.

[0057] In this embodiment, according to the above method of obtaining the target sequence corresponding to user f1 in the first target category corresponding to the initial category D, the target sequences corresponding to all users in the first target category corresponding to other initial categories can be obtained, so this embodiment will not be described in detail.

[0058] Next, according to the slopes of the straight lines on the power consumption line graph corresponding to the users in the second target category corresponding to the initial category, the target sequence corresponding to each user in the second target category corresponding to each initial category is obtained. That is, the process of obtaining the target sequence corresponding to each user in the second target category corresponding to each initial category is as follows:

[0059] For any user f2 in the second target category corresponding to the initial category D, first obtain the linear trend characteristic coefficient corresponding to user f2, and then determine whether the linear trend characteristic coefficient corresponding to user f2 is greater than the preset trend threshold. If so, obtain the first-order difference sequence of the electricity consumption data sequence of user f2, and use the first-order difference sequence of the electricity consumption data sequence of user f2 as the target sequence corresponding to user f2; otherwise, use the electricity consumption data sequence of user f2 as the target sequence corresponding to user f2.

[0060] In addition, in this embodiment, the implementer needs to set the preset trend threshold according to the actual situation. For example, in this embodiment, the preset trend threshold can be set to 0.8.

[0061] In this embodiment, the method for obtaining the linear trend characteristic coefficient corresponding to user f2 is: first obtain the slope sequence corresponding to user f2, then obtain the mean of the slope sequence corresponding to user f2, and record it as the first slope mean, then obtain the slope of the straight line formed by the first data point and the last data point on the electricity consumption line graph corresponding to user f2, and record it as the comprehensive slope; finally, obtain the ratio of the first slope mean to the comprehensive slope, and record it as the linear trend characteristic coefficient corresponding to user f2; and the hth slope in the slope sequence corresponding to user f2 is the slope of the hth straight line on the electricity consumption line graph corresponding to user f2, and the slope of the hth straight line on the electricity consumption line graph corresponding to user f2 is the slope of the straight line formed by the hth data point and the h+1th data point on the electricity consumption line graph corresponding to user f2.

[0062] In addition, it should be noted that the larger the linear trend characteristic coefficient corresponding to user f2 is, the stronger the power consumption data sequence of user f2 has. For the power consumption data sequence of users with stronger trends, the data repetitiveness in the differential sequence of the corresponding sequence is higher, so the compression effect of encoding the differential sequence is better.

[0063] Therefore, in this embodiment, according to the above method of obtaining the target sequence corresponding to user f2 in the second target category corresponding to the initial category D, the target sequences corresponding to all users in the second target category corresponding to other initial categories can be obtained, so this embodiment will not be described in detail.

[0064] Step S005: compress the target sequences corresponding to all users monitored by the power collection terminal to obtain compressed data.

[0065] Based on the above steps, it can be seen that the present embodiment obtains the target sequence corresponding to all users monitored by the power collection terminal. Then, the present embodiment performs run-length encoding on the target sequence corresponding to all users monitored by the power collection terminal to obtain encoded compressed data, which can be subsequently transmitted and stored. In addition, the process of encoding the data to be compressed using run-length encoding is a well-known technology, so this embodiment will not be described in detail.

[0066] In summary, the present embodiment first obtains the power consumption data sequence of all users monitored by the power collection terminal; then, according to the first and last power consumption data in the power consumption data sequence, all users monitored by the power collection terminal are initially classified to obtain the initial category and unclassified users, and the power consumption data sequence of the unclassified users is used as the target sequence corresponding to the corresponding user; then, the power consumption line graph corresponding to each user in the initial category is obtained, and according to the power consumption line graph corresponding to the user, the trend line corresponding to the initial category is obtained, and according to the trend line and all data points on the power consumption line graph corresponding to all users in the initial category, the target feature value corresponding to each user in the initial category is obtained, and according to the target feature value corresponding to the user, all users in the initial category are reclassified again to obtain the first target category and the second target category corresponding to each initial category; then, according to the trend line, the data points on the power consumption line graph corresponding to the users in the first target category, and the slopes of each straight line on the power consumption line graph corresponding to the users in the second target category, the target sequence corresponding to each user in the first target category and the target sequence corresponding to each user in the second target category corresponding to each initial category are obtained respectively; finally, the target sequence corresponding to all users monitored by the power collection terminal is compressed to obtain compressed data. Furthermore, this embodiment does not directly compress the user's electricity consumption data sequence, but compresses the target sequence corresponding to the user, and the obtained compression effect is better, that is, this embodiment improves the compression effect.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for efficient data processing of meter reading at a power collection terminal, characterized in that: The method comprises the following steps: Obtain the power consumption data sequence of all users monitored by the power collection terminal; the power consumption data sequence of any user is composed of the power consumption data of the user at each meter reading time in the current monitoring time period; According to the first and last power consumption data in the power consumption data sequence, all users monitored by the power collection terminal are initially classified to obtain initial categories and unclassified users, and the power consumption data sequence of the unclassified users is used as the target sequence corresponding to the corresponding users; Obtain a power consumption line graph corresponding to each user in the initial category, and obtain a trend line corresponding to the initial category based on the power consumption line graph corresponding to the user; obtain a target feature value corresponding to each user in the initial category based on the trend line and all data points on the power consumption line graph corresponding to all users in the initial category; and reclassify all users in the initial category based on the target feature values ​​corresponding to the users to obtain a first target category and a second target category corresponding to each initial category; According to the trend line, the data points on the power consumption line graph corresponding to the users in the first target category, and the slopes of the straight lines on the power consumption line graph corresponding to the users in the second target category, the target sequences corresponding to the users in the first target category and the target sequences corresponding to the users in the second target category corresponding to the initial categories are obtained respectively; Compressing target sequences corresponding to all users monitored by the power collection terminal to obtain compressed data; The method for obtaining the target feature value corresponding to each user in the initial category includes: For any user d in any initial category D: Obtain trend points corresponding to the data points on the power consumption line graph corresponding to the user d, wherein the abscissa of the trend points corresponding to the data points on the power consumption line graph corresponding to the user d is the same as the abscissa of the corresponding data point, and the trend points corresponding to the data points are located on the trend line corresponding to the initial category D; Obtain a first difference value sequence corresponding to the user d, wherein the fth first difference value in the first difference value sequence is the Euclidean distance between the fth data point on the power consumption line graph corresponding to the user d and the trend point corresponding to the fth data point; count the number of intersections between the power consumption line graph corresponding to the user d and the trend line corresponding to the initial category D, and record the result of positive correlation mapping of the number of intersections as a first mapping value; record the product of the mean value of the first difference value sequence and the first mapping value as a first product value; perform negative correlation mapping on the first product value, and record the mapping result as the first indicator value corresponding to the user d; Add the ordinate value of each data point on the power consumption line graph corresponding to the user d and the slope of the trend line corresponding to the initial category D, and record the addition result as the first addition value of the corresponding data point; obtain the second difference value sequence corresponding to the user d according to the first addition value of each data point on the power consumption line graph corresponding to the user d, and record the cumulative sum of all data in the second difference value sequence as the first cumulative value; record the result of positive correlation mapping of the first cumulative value as the second indicator value corresponding to the user d; the g-th second difference value in the second difference value sequence is the absolute value of the difference between the ordinate value of the g+1-th data point on the power consumption line graph corresponding to the user d and the first addition value of the g-th data point; Performing a weighted sum of the first index value and the second index value corresponding to the user d, and recording the result of the weighted sum as the target feature value corresponding to the user d; The method for acquiring the first target category and the second target category corresponding to each initial category includes: For any initial category D, the category formed by all users whose target feature values ​​in the initial category D are greater than the preset feature threshold is recorded as the first target category corresponding to the initial category D, and the category formed by all users whose target feature values ​​in the initial category D are not greater than the preset feature threshold is recorded as the second target category corresponding to the initial category D; The method for acquiring the target sequence corresponding to each user in the first target category includes: For any user f1 in the first target category corresponding to the initial category D, obtain the target difference corresponding to each data point on the power consumption line graph corresponding to the user f1, sort the target difference corresponding to all the data points on the power consumption line graph corresponding to the user f1 in ascending order of the horizontal coordinate value, and record the sorted sequence as the target sequence corresponding to the user f1. The target difference corresponding to the vth data point on the power consumption line graph corresponding to the user f1 refers to the value obtained by subtracting the vertical coordinate value of the trend point corresponding to the vth data point from the vertical coordinate value of the vth data point; The method for obtaining a target sequence corresponding to each user in the second target category includes: For any user f2 in the second target category corresponding to any initial category D, obtain the linear trend characteristic coefficient corresponding to the user f2, and determine whether the linear trend characteristic coefficient corresponding to the user f2 is greater than the preset trend threshold. If so, take the differential sequence of the power consumption data sequence of the user f2 as the target sequence corresponding to the user f2; otherwise, take the power consumption data sequence of the user f2 as the target sequence corresponding to the user f2.

2. A method for efficient data processing for meter reading at a power collection terminal according to claim 1, characterized in that: The method for obtaining the initial categories and unclassified users includes: The set constructed by all users monitored by the power collection terminal is recorded as the first set, and the second characterization value between any two users in the first set is obtained, the second characterization value between any two users refers to the value after negative correlation mapping of the first characterization value between the corresponding users, the first characterization value between any two users refers to the absolute value of the difference between the first characteristic values ​​corresponding to the two users, and the first characteristic value corresponding to the user is the difference between the last power consumption data and the first power consumption data in the power consumption data sequence of the corresponding user; Obtain a first quantity characterization value corresponding to each user in the first set, and select a user corresponding to the maximum first quantity characterization value as a first reference user; record a set constructed by all users in the first set except the first reference user as a first subset, and record all users in the first subset as first users to be classified, and record the second characteristic values ​​between the first reference user and each first user to be classified in the first subset as classification index values ​​corresponding to the first users to be classified; determine whether there is a first user to be classified in the first subset whose classification index value is greater than a preset classification threshold, and if so, record the first user in the first subset whose classification index value is greater than the preset classification threshold. The category formed by all the first users to be classified and the first reference user of the class threshold is recorded as the first initial category, otherwise, the first reference user is recorded as an unclassified user; in the first set, a set formed by all users except the first initial category or except the unclassified user is obtained, and recorded as the first set to be judged; it is judged whether the first set to be judged is an empty set, if so, the initial classification is stopped; otherwise, it is continued to be judged whether the number of users in the first set to be judged is equal to the preset number threshold, if so, all users in the first set to be judged are recorded as unclassified users, and the initial classification is stopped; otherwise, the first set to be judged is recorded as the second set; Continue to obtain the second quantity characterization value corresponding to each user in the second set, and select the user corresponding to the maximum second quantity characterization value as the second reference user; record the set constructed by all users in the second set except the second reference user as the second subset, and record all users in the second subset as second users to be classified, and record the second characteristic values ​​between the second reference user and each second user to be classified in the second subset as the classification index value corresponding to the second user to be classified; determine whether there is a second user to be classified in the second subset with a classification index value greater than a preset classification threshold, and if so, select the second user in the second subset. The category formed by all the second users to be classified and the second reference users whose classification index values ​​are greater than the preset classification threshold is recorded as the second initial category, otherwise, the second reference users are recorded as unclassified users; continue to obtain a set formed by all users except the second initial category or except the unclassified users in the second set, and record it as the second set to be judged; continue to judge whether the second set to be judged is an empty set, if so, stop the initial classification, otherwise, continue to judge whether the number of users in the second set to be judged is equal to the preset number threshold, if so, all users in the second set to be judged are recorded as unclassified users, and stop the initial classification.

3. A method for efficient data processing for meter reading of power collection terminal according to claim 2, characterized in that: The method for obtaining the first quantity characterization value and the second quantity characterization value includes: For any user B1 in the first set, record the set constructed by all users in the first set except the user B1 as the first feature set, obtain the second feature value subset corresponding to the user B1, count the number of second feature values ​​greater than a preset classification threshold in the second feature value subset corresponding to the user B1, and record it as the first quantity representation value corresponding to the user B1, and the kth second feature value in the second feature value subset corresponding to the user B1 is the second feature value between the user B1 and the kth user in the first feature set; The method for acquiring the second quantity characterization value corresponding to each user in the second set is the same as the method for acquiring the first quantity characterization value corresponding to each user in the first set.

4. A method for efficient data processing for meter reading of power collection terminal according to claim 1, characterized in that: The method for obtaining the power consumption line graph corresponding to each user in the initial category includes: Acquire the mark value of each power consumption data in the power consumption data sequence and the two-dimensional coordinate system corresponding to each initial category, the mark value of the cth power consumption data in the power consumption data sequence being c; For any user d in any initial category D, each power consumption data and the mark value of each power consumption data in the power consumption data sequence of the user d are mapped to the two-dimensional coordinate system corresponding to the initial category D to obtain all data points corresponding to the user d. A line graph is drawn based on all the data points corresponding to the user d, and the drawn line graph is recorded as the power consumption line graph corresponding to the user d, where the horizontal axis of the data point is the mark value and the vertical axis is the power consumption data.

5. A method for efficient data processing for electric power acquisition terminal meter reading as claimed in claim 1, characterized in that: The method for obtaining the trend line corresponding to the initial category includes: For any initial category D: the set formed by the first data point on the electricity consumption line graph corresponding to all users in the initial category D is recorded as the first data point set, and the set formed by the last data point on the electricity consumption line graph corresponding to all users in the initial category D is recorded as the second data point set; the average data point of the first data point set is taken as the first feature point, the average data point of the second data point set is taken as the second feature point, and the straight line formed by connecting the first feature point and the second feature point is recorded as the trend line corresponding to the initial category D, the horizontal coordinate value of the average data point of the data point set is the mean of the horizontal coordinates of all data points in the corresponding data point set, and the vertical coordinate value of the average data point of the data point set is the mean of the vertical coordinates of all data points in the corresponding data point set.

6. A method for efficient data processing for electric power acquisition terminal meter reading as claimed in claim 1, characterized in that: The method for obtaining the linear trend characteristic coefficient corresponding to the user f2 includes: Obtain a slope sequence corresponding to the user f2, wherein the hth slope in the slope sequence is the slope of the hth straight line on the electricity consumption line graph corresponding to the user f2, and the slope of the hth straight line on the electricity consumption line graph corresponding to the user f2 is the slope of the straight line formed by the hth data point and the h+1th data point on the electricity consumption line graph corresponding to the user f2; record the mean of the slope sequence as the first slope mean, and record the slope of the straight line formed by the first data point and the last data point on the electricity consumption line graph corresponding to the user f2 as the comprehensive slope; record the ratio of the first slope mean to the comprehensive slope as the linear trend characteristic coefficient corresponding to the user f2.

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