Multi-source data operation state diagnosis and early warning method and device for power terminal

Abstract

The invention discloses a multi-source data operation state diagnosis and early warning method and device for a power terminal, and the method comprises the steps: obtaining the historical power utilization information, collected by a plurality of intelligent ammeters, of a plurality of users; determining an electricity utilization cost performance index corresponding to the electricity utilization behavior information of each user based on the historical electricity utilization information of the plurality of users; screening out a plurality of pieces of target power utilization behavior information from the power utilization behavior information of the plurality of users based on each power utilization cost performance index; determining pushed power consumption behavior information of each user based on the multiple pieces of target power consumption behavior information; and pushing the pushed power consumption behavior information of each user to each user. According to the invention, the accuracy of diagnosis and early warning of the running state of the multi-source data oriented to the power terminal can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart meters, and in particular to a multi-source data operation status diagnosis and early warning method and device for power terminals. Background Art

[0002] Smart meters are smart terminals of smart grids. In addition to the basic electricity consumption metering function of traditional energy meters, in order to adapt to the use of smart grids and new energy, they also have two-way multi-rate metering function, user-side control function, two-way data communication function with multiple data transmission modes, anti-electricity theft function and other intelligent functions. Smart meters represent the development direction of intelligent terminals for end users of energy-saving smart grids in the future. The existing technology does not utilize the multi-source data collected by power terminals, and the accuracy of multi-source data operation status diagnosis and early warning for power terminals is low.

[0003] That is, the existing technology has low accuracy in multi-source data operation status diagnosis and early warning for power terminals. Summary of the invention

[0004] The embodiments of the present application provide a method and device for diagnosing and warning the operating status of a multi-source data terminal, which can utilize the historical electricity consumption information and electricity cost performance indicators of multiple users collected by the power terminal to accurately push electricity consumption behavior information with better electricity cost performance indicators to users, thereby improving the accuracy of the diagnosis and warning of the operating status of the multi-source data terminal.

[0005] In the first aspect, the multi-source data operation status diagnosis and early warning method for power terminals provided by the present application includes: Obtain historical electricity consumption information of multiple users collected by multiple smart meters; Determine the electricity cost performance index corresponding to the electricity consumption behavior information of each user based on the historical electricity consumption information of multiple users; Filtering out multiple target electricity usage behavior information from the electricity usage behavior information of multiple users based on various electricity cost performance indicators; Determine the push electricity usage behavior information of each user based on multiple target electricity usage behavior information; Push each user's electricity usage behavior information to each user.

[0006] In an optional embodiment, the historical electricity usage information includes the user's total electricity cost, total electricity consumption and the usage time periods of various electrical appliances, the electricity usage behavior information includes the user's regular usage time periods of various electrical appliances, and the electricity cost-effectiveness index is the ratio of total electricity cost to total electricity consumption.

[0007] In an optional embodiment, the push electricity usage behavior information of each user is determined based on multiple target electricity usage behavior information, including: Clustering multiple users based on their electricity usage behavior information to obtain multiple user clusters; Determine a preset number of electricity usage behavior information in the user clustering clusters that are ranked top in terms of electricity cost performance from large to small as a preset number of target electricity usage behavior information, and obtain multiple target electricity usage behavior information corresponding to the user clustering clusters; The method of determining the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information includes: A target power usage behavior information is selected from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user.

[0008] In an optional embodiment, the step of selecting a target power usage behavior information from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user includes: Allocating target electricity usage behavior information to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, wherein the electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different. Determine the regional power average value of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Calculate the power capacity ratio of the regional power average value of the power consumption area and the transformer capacity of the power consumption area to obtain the ratio variance of the power capacity ratios of multiple power consumption areas; Determining a distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance, wherein the larger the ratio variance, the smaller the distribution evaluation parameter; Determine the power consumption behavior allocation information with the largest allocation evaluation parameter as the target allocation information; The target electricity usage behavior information corresponding to each user in the target allocation information is used as the user's pushed electricity usage behavior information.

[0009] In an optional embodiment, the step of determining the distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance includes: Determine the regional power variation coefficient of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Based on the number of users in the multiple power consumption areas, a weighted average of the regional power variation coefficients of the multiple power consumption areas is performed to obtain a weighted average value of the variation coefficients; The distribution evaluation parameter of the electricity consumption behavior distribution information is determined based on the ratio variance and the weighted average of the coefficient of variation, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

[0010] In an optional embodiment, the obtaining of electricity usage behavior information of multiple users collected by multiple smart meters includes: The usage time of each type of electrical appliance is collected through the built-in electricity meter of each type of electrical appliance or the smart socket of each type of electrical appliance.

[0011] In the second aspect, the multi-source data operation status diagnosis and early warning device for power terminals provided by the present application includes: A first acquisition module is used to acquire historical electricity consumption information of multiple users collected by multiple smart meters; A first determination module is used to determine the electricity cost performance index corresponding to the electricity consumption behavior information of each user based on the historical electricity consumption information of multiple users; A screening module, used to screen out multiple target electricity usage behavior information from the electricity usage behavior information of multiple users based on various electricity cost performance indicators; A second determination module is used to determine the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information; The push module is used to push the power usage behavior information of each user to each user.

[0012] In an optional embodiment, the historical electricity usage information includes the user's total electricity cost, total electricity consumption and the usage time periods of various electrical appliances, the electricity usage behavior information includes the user's regular usage time periods of various electrical appliances, and the electricity cost-effectiveness index is the ratio of total electricity cost to total electricity consumption.

[0013] In an optional embodiment, the push electricity usage behavior information of each user is determined based on multiple target electricity usage behavior information, including: Clustering multiple users based on their electricity usage behavior information to obtain multiple user clusters; Determine a preset number of electricity usage behavior information in the user clustering clusters that are ranked top in terms of electricity cost performance from large to small as a preset number of target electricity usage behavior information, and obtain multiple target electricity usage behavior information corresponding to the user clustering clusters; The method of determining the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information includes: A target power usage behavior information is selected from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user.

[0014] In an optional embodiment, the step of selecting a target power usage behavior information from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user includes: Allocating target electricity usage behavior information to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, wherein the electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different. Determine the regional power average value of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Calculate the power capacity ratio of the regional power average value of the power consumption area and the transformer capacity of the power consumption area to obtain the ratio variance of the power capacity ratios of multiple power consumption areas; Determining a distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance, wherein the larger the ratio variance, the smaller the distribution evaluation parameter; Determine the power consumption behavior allocation information with the largest allocation evaluation parameter as the target allocation information; The target electricity usage behavior information corresponding to each user in the target allocation information is used as the user's pushed electricity usage behavior information.

[0015] In an optional embodiment, the step of determining the distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance includes: Determine the regional power variation coefficient of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Based on the number of users in the multiple power consumption areas, a weighted average of the regional power variation coefficients of the multiple power consumption areas is performed to obtain a weighted average value of the variation coefficients; The distribution evaluation parameter of the electricity consumption behavior distribution information is determined based on the ratio variance and the weighted average of the coefficient of variation, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

[0016] In an optional embodiment, the obtaining of electricity usage behavior information of multiple users collected by multiple smart meters includes: The usage time of each type of electrical appliance is collected through the built-in electricity meter of each type of electrical appliance or the smart socket of each type of electrical appliance.

[0017] On the third aspect, the electronic device provided in the present application includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to implement the steps in the multi-source data operation status diagnosis and early warning method for power terminals provided in the present application.

[0018] In a fourth aspect, the computer-readable storage medium provided in the present application stores a plurality of instructions, which are suitable for loading by a processor to implement the steps in the multi-source data operation status diagnosis and early warning method for power terminals provided in the present application.

[0019] In a fifth aspect, the computer program product provided in the present application includes a computer program or instructions, which, when executed by a processor, implement the steps in the multi-source data operation status diagnosis and early warning method for power terminals provided in the present application.

[0020] In this application, compared with the related art, the historical electricity consumption information of multiple users collected by multiple smart meters is obtained; the electricity cost performance index corresponding to the electricity consumption behavior information of each user is determined based on the historical electricity consumption information of multiple users; multiple target electricity consumption behavior information is screened out from the electricity consumption behavior information of multiple users based on each electricity cost performance index; the pushed electricity consumption behavior information of each user is determined based on the multiple target electricity consumption behavior information; and the pushed electricity consumption behavior information of each user is pushed to each user. This application can use the historical electricity consumption information and electricity cost performance index of multiple users collected by the power terminal to accurately push electricity consumption behavior information with better electricity cost performance index to users, thereby improving the accuracy of multi-source data operation status diagnosis and early warning for power terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a schematic diagram of a scenario of a multi-source data operation status diagnosis and early warning system for power terminals provided in an embodiment of the present application; Figure 2 This is a flow chart of an embodiment of a multi-source data operation status diagnosis and early warning method for a power terminal provided in an embodiment of the present application; Figure 3 It is a structural diagram of an embodiment of a multi-source data operation status diagnosis and early warning device for a power terminal provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] It should be noted that the principles of the present application are illustrated by implementing them in an appropriate computing environment. The following description is based on the illustrated specific embodiments of the present application and should not be considered as limiting other specific embodiments of the present application that are not described in detail herein.

[0024] In the following description of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0025] In the following description of the present application, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0027] In order to improve the effect of multi-source data operation status diagnosis and early warning for power terminals, the embodiments of the present application provide a multi-source data operation status diagnosis and early warning method for power terminals, a multi-source data operation status diagnosis and early warning device for power terminals, an electronic device, a computer-readable storage medium, and a computer program product. Among them, the multi-source data operation status diagnosis and early warning method for power terminals can be executed by the multi-source data operation status diagnosis and early warning device for power terminals, or by an electronic device integrated with the multi-source data operation status diagnosis and early warning device for power terminals.

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0029] Please refer to Figure 1 , the present application also provides a multi-source data operation status diagnosis and early warning system for power terminals, such as Figure 1 As shown, the multi-source data operation status diagnosis and early warning system for power terminals includes an electronic device. The electronic device is integrated with the multi-source data operation status diagnosis and early warning device for power terminals provided by the present application.

[0030] Among them, electronic devices can be any devices that are equipped with a processor and have processing capabilities, such as smart phones, tablet computers, PDAs, laptops, smart speakers and other mobile electronic devices with processors, or desktop computers, televisions, servers, industrial equipment and other fixed electronic devices with processors.

[0031] In addition, if Figure 1 As shown, the multi-source data operation status diagnosis and early warning system for power terminals may also include a memory for storing original data, intermediate data and result data in the audio processing process.

[0032] In the embodiment of the present application, the memory may be a cloud memory. Cloud storage is a new concept extended and developed from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as the storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of storage devices of various types (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.

[0033] At present, the storage method of the storage system is: create a logical volume, and when creating the logical volume, allocate physical storage space for each logical volume, and the physical storage space may be composed of disks of a storage device or several storage devices. The client stores data on a logical volume, that is, stores the data on the file system. The file system divides the data into many parts, each of which is an object. The object contains not only data but also additional information such as data identification (ID entity, ID). The file system writes each object into the physical storage space of the logical volume, and the file system records the storage location information of each object, so that when the client requests to access the data, the file system can allow the client to access the data according to the storage location information of each object.

[0034] The process of the storage system allocating physical storage space to a logical volume is as follows: based on the estimated capacity of the objects stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the grouping of the Redundant Array of Independent Disks (RAID), the physical storage space is pre-divided into stripes. A logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0035] It should be noted that Figure 1The scenario diagram of the multi-source data operation status diagnosis and early warning system for power terminals shown is merely an example. The multi-source data operation status diagnosis and early warning system for power terminals and the scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the multi-source data operation status diagnosis and early warning system for power terminals and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0036] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0037] Please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart of an embodiment of a multi-source data operation status diagnosis and early warning method for a power terminal provided in an embodiment of the present application. Figure 2 As shown, the process of the multi-source data operation status diagnosis and early warning method for power terminals provided in this application is as follows: 201. Obtain historical electricity usage information of multiple users collected by multiple smart meters.

[0038] Smart meters are smart terminals of smart grids. In addition to the basic electricity consumption metering function of traditional electricity meters, in order to adapt to the use of smart grids and new energy, they also have two-way multi-rate metering function, user-side control function, two-way data communication function with multiple data transmission modes, anti-electricity theft function and other intelligent functions. Smart meters represent the development direction of intelligent terminals for end users of future energy-saving smart grids.

[0039] In the embodiment of the present application, historical electricity consumption information of multiple users collected by multiple smart meters within a target time period is obtained. The target time period may be half a month or a month. Each user corresponds to a smart meter.

[0040] In the embodiment of the present application, the multiple users may be users in one city.

[0041] In the embodiment of the present application, the historical electricity usage information includes the user's total electricity cost, total electricity usage, total electricity usage time, and the usage time periods of various electrical appliances.

[0042] Specifically, the usage time periods of various electrical appliances are collected through built-in electric meters of various electrical appliances or smart sockets of various electrical appliances. For example, user A uses electrical appliance B from 9:00 to 10:00.

[0043] 202. Determine an electricity cost performance index corresponding to the electricity usage behavior information of each user based on historical electricity usage information of multiple users.

[0044] In the embodiment of the present application, the electricity usage behavior information includes the time periods when the user uses various electrical appliances. The electricity cost performance index is the ratio of the total electricity cost to the total electricity consumption.

[0045] Specifically, the electricity usage behavior information includes the time periods during which users frequently use various electrical appliances.

[0046] Specifically, the time periods when the user used various types of electrical appliances every day in history are obtained from the user's historical electricity usage information. Based on the duration of each time period and the central time of the time period, multiple time periods when the user used the target type of electrical appliances on multiple days are clustered to obtain at least one time period clustering cluster of the user, and the clustering center time period of the time period clustering cluster is determined as the frequently used time period when the user used the target type of electrical appliances, thereby obtaining the frequently used time periods when the user used various types of electrical appliances.

[0047] 203. Filter out a plurality of target electricity usage behavior information from the electricity usage behavior information of a plurality of users based on each electricity usage cost performance index.

[0048] In a specific embodiment, a preset number of electricity usage behavior information that are ranked top in order of electricity cost performance are determined as a plurality of target electricity usage behavior information.

[0049] In another specific embodiment, a plurality of target electricity usage behavior information is screened out from the electricity usage behavior information of a plurality of users based on each electricity cost performance index, including: (1) Cluster multiple users based on their electricity usage behavior information to obtain multiple user clusters.

[0050] In an embodiment of the present application, the electricity usage behavior information of multiple users is randomly divided into Q electricity usage information sets. The Q electricity usage information sets are clustered respectively, and each electricity usage information set is clustered into N second information clusters. Specifically, the Q electricity usage information sets are clustered respectively by the K-means algorithm, and each electricity usage information set is clustered into N second information. Each second information cluster is determined as a target information cluster, and the information cluster similarity between the target information cluster and the second information clusters in the Q electricity usage information sets is calculated respectively. The second information clusters with the greatest information cluster similarity with the target information cluster in each electricity usage information set are merged to obtain the first information cluster corresponding to the target information cluster, to obtain the first information cluster corresponding to each second information cluster, and to obtain N first information clusters corresponding to N second information clusters. The users corresponding to each electricity usage behavior information of the first information cluster are determined as a user clustering cluster, and N user clustering clusters corresponding to N first information clusters are obtained.

[0051] (2) A preset number of electricity usage behavior information with the highest electricity cost performance ratio in the user clusters is determined as a preset number of target electricity usage behavior information, thereby obtaining a plurality of target electricity usage behavior information corresponding to the user clusters.

[0052] The preset number can be set according to specific circumstances. Specifically, a preset number of target electricity usage behavior information is determined for each user cluster, and multiple target electricity usage behavior information corresponding to the user cluster is obtained.

[0053] In another specific embodiment, it is determined whether the user's smart meter is an old meter, and a preset number of electricity usage behavior information of users whose smart meters are not old meters in the user clustering cluster are ranked in descending order of electricity cost performance as multiple target electricity usage behavior information.

[0054] In another specific embodiment, it is determined whether the user's smart meter is an old meter, and a preset number of electricity usage behavior information of users whose smart meters are not old meters and whose electricity cost performance is ranked in descending order are determined as multiple target electricity usage behavior information.

[0055] In the embodiment of the present application, determining whether the user's smart meter is an old meter includes: (1) The target upper limit of the error of the smart meter is determined based on the device model of the smart meter. Different device models correspond to different target upper limits of the error.

[0056] In the embodiment of the present application, a mapping relationship between a preset device model and a target error upper limit is obtained, a rated error upper limit of the smart meter is determined based on the mapping relationship, and a target error upper limit of the smart meter is determined based on the rated error upper limit of the smart meter. For example, the rated error upper limit of device model A is 0.1%, and the rated error upper limit of device model B is 0.15%.

[0057] In a specific embodiment, the rated upper error limit value of the smart meter is determined as the target upper error limit value of the smart meter.

[0058] In another specific embodiment, the mean value of the ambient temperature, the mean value of the ambient humidity, and the mean value of the current operation of the smart meter in a preset historical period are obtained, and the temperature difference, the humidity difference, and the current difference are determined based on the mean value of the ambient temperature, the mean value of the ambient humidity, the mean value of the current operation, and the standard operating temperature, the standard operating humidity, and the standard current value of the smart meter. The error weighting coefficient of the smart meter is determined based on the temperature difference, the humidity difference, and the current difference of the smart meter. The rated error upper limit value is weighted based on the error weighting coefficient to obtain the target error upper limit value of the smart meter. Among them, the larger the temperature difference, the larger the weighting coefficient; the larger the humidity difference, the larger the weighting coefficient; the larger the current difference, the larger the weighting coefficient. When the ambient temperature, ambient humidity, and operating current deviate too much from the standard, the error will increase. This is a natural phenomenon. It is necessary to increase the error upper limit value to prevent the normal smart meter from being judged as an old device.

[0059] (2) Smart meters whose reading error parameters are higher than the target error upper limit are identified as old devices.

[0060] In a specific embodiment, the reading error parameter of the smart meter is obtained by manual measurement.

[0061] In another specific embodiment, the sequence of power consumption readings of the total meter device in the power consumption area where the smart meter is located and the sequence of power consumption readings of each smart meter in the power consumption area are obtained. The power consumption area may be a building, and the power consumption area includes multiple smart meters. The independent power consumption period of the smart meter is obtained. In the independent power consumption period, the ratio of the power consumption reading of the smart meter to the power consumption reading of the total meter device is greater than the preset power ratio, wherein the preset power ratio is 95% or 99%, which can be set according to the specific situation. When the independent power consumption period of the smart meter is obtained, the sum of the power consumption readings of the other smart meters in the power consumption area except the smart meter in the independent power consumption period is obtained. The difference between the power consumption reading of the total meter device and the sum of the power consumption readings is determined as the actual power consumption of the smart meter, the difference between the power consumption reading of the smart meter and the actual power consumption of the smart meter is determined as the error difference, and the ratio of the error difference to the power consumption reading of the smart meter is determined as the reading error parameter of the smart meter. For example, during the independent electricity consumption period, the electricity consumption reading of the total meter device is 1 kWh. The total meter device is a calibrated meter of the power department, and the error can be ignored. Therefore, the electricity consumption reading of the total meter device is the actual electricity consumption. The electricity consumption reading of the smart meter is 0.95 kWh. Due to the error, the actual electricity consumption is 0.94 kWh. The total electricity consumption readings of other smart meters are 0.06 kWh. Since it is very small relative to 0.95 kWh, the error can be ignored. Therefore, the total actual electricity consumption of other smart meters is 0.06 kWh. The difference between the electricity consumption reading of the total meter device and the total electricity consumption reading can be determined as the actual electricity consumption of the smart meter, that is, 0.94 kWh.

[0062] Furthermore, there may be a situation where the smart meter does not have an independent power consumption period. In another specific embodiment, the power consumption reading sequence of the total meter device in the power consumption area where the smart meter is located and the power consumption reading sequence of each smart meter in the power consumption area are obtained. The power consumption area may be a building, and the power consumption area includes multiple smart meters. When the independent power consumption period of the smart meter is not obtained, the other smart meters except the smart meter are respectively determined as target devices, and the sum of the power consumption readings of the other smart meters in the power consumption area except the target device during the independent power consumption period is obtained. The difference between the power consumption reading of the total meter device and the sum of the power consumption readings is determined as the actual power consumption of the target device, the difference between the power consumption reading of the target device and the actual power consumption of the target device is determined as the error difference, and the ratio of the error difference to the power consumption reading of the target device is determined as the reading error parameter of the target device. After obtaining the reading error parameters of each target device, obtain the power consumption reading of the total meter device within the target period, obtain the power consumption reading of each target device within the target period, determine the actual power consumption of each target device within the target period according to the reading error parameters of each target device, add them up to get the total actual power consumption, and determine the difference between the power consumption reading of the total meter device within the target period and the total actual power consumption as the actual power consumption of the smart meter, determine the difference between the power consumption reading of the smart meter and the actual power consumption of the smart meter as the error difference, and determine the ratio of the error difference to the power consumption reading of the smart meter as the reading error parameter of the smart meter.

[0063] Smart meters with reading error parameters higher than the target error upper limit are identified as obsolete devices.

[0064] 204. Determine the pushed electricity usage behavior information of each user based on the multiple target electricity usage behavior information.

[0065] In the embodiment of the present application, a target power usage behavior information is selected from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user.

[0066] In the embodiment of the present application, selecting a target power usage behavior information from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the user's pushed power usage behavior information includes: (1) Allocating target electricity usage behavior information to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, wherein the electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different.

[0067] In a specific embodiment, multiple exhaustive power usage behavior allocation information are obtained by traversing and enumerating, and the multiple exhaustive power usage behavior allocation information are determined as multiple power usage behavior allocation information after multiple allocations.

[0068] In another specific embodiment, target electricity usage behavior information is allocated to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, including: Step 1-1, obtain multiple exhaustive electricity usage behavior allocation information by traversing exhaustively.

[0069] The exhaustive electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different.

[0070] Step 1-2, calculating the information similarity between one target power usage behavior information and other target power usage behavior information in the exhaustive power usage allocation information, and obtaining multiple information similarities corresponding to the target power usage behavior information.

[0071] In a specific embodiment, the target electricity usage behavior information is converted into a vector, and the cosine similarity of the vectors of two target electricity usage behavior information is determined as the information similarity.

[0072] Step 1-3, sorting multiple information similarities corresponding to the target electricity usage behavior information from large to small and calculating the similarity difference between any two adjacent information similarities after sorting, to obtain multiple similarity differences corresponding to the target electricity usage behavior information.

[0073] Step 1-4: determine the coefficient of variation of multiple similarity differences corresponding to the target power usage behavior information as the uniform quantized value of the target power usage behavior information, and obtain the quantitative average of the uniform quantized values ​​of multiple target power usage behavior information.

[0074] Coefficient of Variation: When you need to compare the degree of dispersion of two sets of data, if the measurement scales of the two sets of data are too different, or the data dimensions are different, it is not appropriate to use the standard deviation to compare directly. At this time, the influence of the measurement scale and dimension should be eliminated, and the coefficient of variation can do this. It is the ratio of the standard deviation of the original data to the average of the original data. CV has no dimension, so it can be compared objectively. In fact, it can be considered that the coefficient of variation, like the range, standard deviation and variance, is an absolute value that reflects the degree of dispersion of the data. Its data size is not only affected by the degree of dispersion of the variable value, but also by the average level of the variable value.

[0075] Step 1-5, determining the information feature uniformity of the exhaustive power consumption allocation information by taking the quantized average value of the uniformity quantized values ​​of multiple target power consumption behavior information in the exhaustive power consumption allocation information, and obtaining the information feature uniformity of multiple exhaustive power consumption allocation information, wherein the higher the quantized average value, the smaller the information allocation uniformity.

[0076] The smaller the coefficient of variation, the more uniform the similarity difference between the target electricity consumption behavior information, that is, the characteristics of each target electricity consumption behavior information change evenly. At this time, the lower the uniformity quantization value, the lower the quantization average value, and the higher the uniformity of information distribution. That is, the higher the uniformity of information distribution, the more uniform the characteristics of each target electricity consumption behavior information change, which can effectively cover various electricity consumption situations, thereby improving the accuracy of distribution.

[0077] For example, there are 4 target electricity usage behavior information, and the multiple information similarities corresponding to one target electricity usage behavior information are 3 information similarities, which are 0.5, 0.6 and 0.7 respectively, and the multiple similarity differences are 0.1 and 0.1. The coefficient of variation of multiple similarity differences is 0. The smaller the coefficient of variation, the lower the uniformity quantization value, the lower the quantization average value, and the higher the uniformity of information distribution. This shows that the similarity difference of the information similarity between the target electricity usage behavior information is relatively uniform, and the characteristics between each target electricity usage behavior information change uniformly.

[0078] Step 1-5: determine multiple exhaustive electricity usage allocation information with information feature uniformity higher than a preset uniformity value as multiple electricity usage behavior allocation information after multiple allocations.

[0079] The preset uniformity value may be set according to specific circumstances. For example, the preset uniformity value is the median of uniformities of multiple information features of multiple exhaustive electricity consumption distribution information.

[0080] (2) Determine a regional power average value of power consumption in a power consumption area in a plurality of preset periods based on the power consumption behavior distribution information and the power consumption area to which each user belongs.

[0081] The power consumption area to which the user belongs may be a cell where the user is located. The preset period may be 1s or 10s, etc., which may be set according to specific circumstances. The power consumption of the power consumption area in the preset period is the total power consumption of all users in the power consumption area within the preset period.

[0082] (3) Calculate the power capacity ratio of the regional power average value of the power consumption area and the transformer capacity of the power consumption area, and obtain the ratio variance of the power capacity ratios of multiple power consumption areas.

[0083] Transformer capacity refers to the maximum apparent power that the transformer can transmit under rated operating conditions, usually in kilovolt-amperes (kVA) or megavolt-amperes (MVA). The selection of transformer capacity is a comprehensive and integrated technical issue that requires consideration of factors such as load type and characteristics, load rate, demand rate, power factor, transformer active and reactive losses, electricity prices, infrastructure investment (including transformer prices), service life, transformer depreciation, maintenance costs, and future plans.

[0084] Since different power consumption areas have different transformer capacities, it is necessary to analyze the characteristics of different power consumption areas. Therefore, the variance of the power capacity ratio of multiple power consumption areas indicates that the power capacity ratio of each power consumption area is the same, indicating that the distribution of power consumption areas meets the characteristics of power consumption areas.

[0085] (4) Determine an allocation evaluation parameter of the electricity consumption behavior allocation information based on the ratio variance, wherein the larger the ratio variance, the smaller the allocation evaluation parameter.

[0086] In a specific embodiment, the ratio variance is determined as the allocation evaluation parameter of the electricity consumption behavior allocation information.

[0087] (5) The electricity consumption behavior allocation information with the largest allocation evaluation parameter is determined as the target allocation information.

[0088] (6) The target electricity usage behavior information corresponding to each user in the target allocation information is used as the user's pushed electricity usage behavior information.

[0089] Since the target allocation information includes the target electricity usage behavior information of each user, the target electricity usage behavior information corresponding to each user is used as the pushed electricity usage behavior information of the user.

[0090] In the embodiment of the present application, the allocation evaluation parameter of the electricity consumption behavior allocation information determined based on the ratio variance includes: (1) Determine the regional power variation coefficient of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior distribution information and the power consumption area to which each user belongs.

[0091] (2) Based on the number of users in multiple power consumption areas, the regional power variation coefficients of multiple power consumption areas are weighted averaged to obtain a weighted average value of the variation coefficients.

[0092] Specifically, the regional weights of the multiple power consumption areas are determined based on the number of users in the multiple power consumption areas, wherein the greater the number of users in the power consumption area, the greater the regional weight of the power consumption area.

[0093] The regional power variation coefficients of the multiple power consumption areas are weighted averaged based on the regional weights of the multiple power consumption areas to obtain a weighted average value of the variation coefficients.

[0094] (3) Determine the allocation evaluation parameter of the electricity consumption behavior allocation information based on the ratio variance and the weighted average value of the coefficient of variation, wherein the larger the ratio variance, the smaller the allocation evaluation parameter, and the larger the weighted average value of the coefficient of variation, the smaller the allocation evaluation parameter.

[0095] In an embodiment of the present application, a distribution evaluation parameter of the electricity consumption behavior distribution information is obtained by weighted averaging the ratio variance and the weighted average of the coefficient of variation based on a preset weight coefficient, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

[0096] Among them, the weight coefficients of the ratio variance and the weighted average of the coefficient of variation can be set according to specific circumstances.

[0097] Furthermore, the historical electricity usage information includes voltage fluctuation information, current change information, meter working time, frequency change information, and harmonic content. The predicted life of the first meter of the smart meter is determined based on the historical electricity usage information, and the predicted life of the target meter is determined based on the predicted life of the first meter. Specifically, the historical electricity usage information is input into a support vector machine to obtain the predicted life of the first meter.

[0098] Further, the thermodynamic temperature measured by the smart meter under working conditions is obtained, the predicted life of the second meter is determined based on the thermodynamic temperature under normal operating conditions and the thermodynamic temperature under working conditions, and the predicted life of the target meter is determined based on the predicted life of the first meter and the predicted life of the second meter. Specifically, the predicted life of the target meter is determined by taking the average of the predicted life of the first meter and the predicted life of the second meter. The thermodynamic temperature measured by the smart meter under working conditions is the highest temperature of the smart meter under working conditions.

[0099] Among them, the calculation formula of the target meter predicted life is as follows: , in, represents the target meter prediction life of the smart meter, j represents the Boltzmann parameter, Indicates the energy required to activate the meter. It represents the thermodynamic temperature of the smart meter under normal operating conditions. Represents the thermodynamic temperature under operating conditions.

[0100] The distribution evaluation parameters of the electricity consumption behavior distribution information are determined based on the ratio variance, the weighted average of the coefficient of variation and the predicted life of the target meter. The larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

[0101] In an embodiment of the present application, based on the preset weight coefficient ratio variance, the weighted average of the coefficient of variation, and the weighted average of the predicted life of the target meter, the distribution evaluation parameter of the electricity consumption behavior distribution information is obtained, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter, the longer the predicted life of the meter, and the larger the distribution evaluation parameter.

[0102] 205. Push the power usage behavior information of each user to each user.

[0103] Compared with the related technology, the historical electricity consumption information of multiple users collected by multiple smart meters is obtained; based on the historical electricity consumption information of multiple users, the electricity cost performance index corresponding to the electricity consumption behavior information of each user is determined; based on each electricity cost performance index, multiple target electricity consumption behavior information is screened out from the electricity consumption behavior information of multiple users; based on the multiple target electricity consumption behavior information, the pushed electricity consumption behavior information of each user is determined; and the pushed electricity consumption behavior information of each user is pushed to each user. This application can improve the accuracy of multi-source data operation status diagnosis and early warning for power terminals.

[0104] Furthermore, the similarity between the user's own electricity usage behavior information and the user's pushed electricity usage behavior information is calculated as the push similarity. When the push similarity is less than the first preset push value, a first warning signal is issued, and the user's pushed electricity usage behavior information is pushed to the user. When the push similarity is greater than the first preset push value and less than the second preset push value, the user's pushed electricity usage behavior information is not pushed to the user, and a second warning signal is issued. When the push similarity is greater than the second preset push value, the user's pushed electricity usage behavior information is not pushed to the user, and no warning signal is issued.

[0105] In order to facilitate better implementation of the multi-source data operation status diagnosis and early warning method for power terminals provided in the embodiment of the present application, the embodiment of the present application also provides a multi-source data operation status diagnosis and early warning device for power terminals based on the multi-source data operation status diagnosis and early warning method for power terminals. The meanings of the terms are the same as those in the multi-source data operation status diagnosis and early warning method for power terminals. For specific implementation details, please refer to the description in the above method embodiment.

[0106] Please refer to Figure 3 , Figure 3 70 is a schematic diagram of a structure of an embodiment of a multi-source data operation status diagnosis and early warning device for a power terminal provided in an embodiment of the present application. The multi-source data operation status diagnosis and early warning device for a power terminal may include a first acquisition module 701, a first determination module 702, a screening module 703, a second determination module 704, and a push module 705, wherein: The first acquisition module 701 is used to acquire historical electricity usage information of multiple users collected by multiple smart meters; A first determination module 702 is used to determine the electricity cost performance index corresponding to the electricity consumption behavior information of each user based on the historical electricity consumption information of multiple users; A screening module 703 is used to screen out a plurality of target electricity usage behavior information from the electricity usage behavior information of a plurality of users based on each electricity usage cost performance index; A second determination module 704 is used to determine the pushed power usage behavior information of each user based on multiple target power usage behavior information; The push module 705 is used to push the power usage behavior information of each user to each user.

[0107] In an optional embodiment, the historical electricity usage information includes the user's total electricity cost, total electricity consumption and the usage time periods of various electrical appliances, the electricity usage behavior information includes the user's regular usage time periods of various electrical appliances, and the electricity cost-effectiveness index is the ratio of total electricity cost to total electricity consumption.

[0108] In an optional embodiment, the push electricity usage behavior information of each user is determined based on multiple target electricity usage behavior information, including: Clustering multiple users based on their electricity usage behavior information to obtain multiple user clusters; Determine a preset number of electricity usage behavior information in the user clustering clusters that are ranked top in terms of electricity cost performance from large to small as a preset number of target electricity usage behavior information, and obtain multiple target electricity usage behavior information corresponding to the user clustering clusters; The method of determining the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information includes: A target power usage behavior information is selected from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user.

[0109] In an optional embodiment, the step of selecting a target power usage behavior information from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user includes: Allocating target electricity usage behavior information to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, wherein the electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different. Determine the regional power average value of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Calculate the power capacity ratio of the regional power average value of the power consumption area and the transformer capacity of the power consumption area to obtain the ratio variance of the power capacity ratios of multiple power consumption areas; Determining a distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance, wherein the larger the ratio variance, the smaller the distribution evaluation parameter; Determine the power consumption behavior allocation information with the largest allocation evaluation parameter as the target allocation information; The target electricity usage behavior information corresponding to each user in the target allocation information is used as the user's pushed electricity usage behavior information.

[0110] In an optional embodiment, the step of determining the distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance includes: Determine the regional power variation coefficient of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Based on the number of users in the multiple power consumption areas, a weighted average of the regional power variation coefficients of the multiple power consumption areas is performed to obtain a weighted average value of the variation coefficients; The distribution evaluation parameter of the electricity consumption behavior distribution information is determined based on the ratio variance and the weighted average of the coefficient of variation, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

[0111] In an optional embodiment, the obtaining of electricity usage behavior information of multiple users collected by multiple smart meters includes: The usage time of each type of electrical appliance is collected through the built-in electricity meter of each type of electrical appliance or the smart socket of each type of electrical appliance.

[0112] The specific implementation of each of the above modules can be found in the previous embodiments and will not be described in detail here.

[0113] Compared with the related technology, the historical electricity consumption information of multiple users collected by multiple smart meters is obtained; based on the historical electricity consumption information of multiple users, the electricity cost performance index corresponding to the electricity consumption behavior information of each user is determined; based on each electricity cost performance index, multiple target electricity consumption behavior information is screened out from the electricity consumption behavior information of multiple users; based on the multiple target electricity consumption behavior information, the pushed electricity consumption behavior information of each user is determined; and the pushed electricity consumption behavior information of each user is pushed to each user. This application can improve the accuracy of multi-source data operation status diagnosis and early warning for power terminals.

[0114] Please refer to Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0115] The electronic device may include components such as a processor 101 with one or more processing cores, a memory 102 with one or more computer-readable storage media, a power supply 103, and an input unit 104. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them: The processor 101 is the control center of the electronic device, which uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 102, and calling data stored in the memory 102. Optionally, the processor 101 may include one or more processing cores; optionally, the processor 101 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 101.

[0116] The memory 102 can be used to store software programs and modules. The processor 101 executes various functional applications and data processing by running the software programs and modules stored in the memory 102. The memory 102 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 102 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 102 may also include a memory controller to provide the processor 101 with access to the memory 102.

[0117] The electronic device also includes a power supply 103 for supplying power to each component. Optionally, the power supply 103 can be logically connected to the processor 101 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 103 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0118] The electronic device may further include an input unit 104, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0119] Although not shown, the electronic device may also include a display unit, an image acquisition element, etc., which will not be described in detail here. Specifically in this embodiment, the processor 101 in the electronic device will load the executable code corresponding to one or more computer programs into the memory 102 according to the following instructions, and the processor 101 will execute the steps of the multi-source data operation status diagnosis and early warning method for power terminals provided in this application, such as: Obtain historical electricity usage information of multiple users collected by multiple smart meters; determine electricity cost performance index corresponding to the electricity usage behavior information of each user based on the historical electricity usage information of multiple users; filter out multiple target electricity usage behavior information from the electricity usage behavior information of multiple users based on the multiple electricity cost performance indexes; determine the pushed electricity usage behavior information of each user based on the multiple target electricity usage behavior information; and push the pushed electricity usage behavior information of each user to each user.

[0120] It should be noted that the electronic device provided in the embodiment of the present application belongs to the same concept as the multi-source data operation status diagnosis and early warning method for power terminals in the above embodiments. The specific implementation process is detailed in the above related embodiments and will not be repeated here.

[0121] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program stored therein is executed on the processor of the electronic device provided in the embodiment of the present application, the processor of the electronic device executes the steps in the multi-source data operation status diagnosis and early warning method for the power terminal provided in the present application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0122] The present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes various optional implementations of the multi-source data operation status diagnosis and early warning method for power terminals.

[0123] The above is a detailed introduction to a multi-source data operation status diagnosis and early warning method and device for power terminals provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0124] It should be noted that when the above embodiments of the present application are applied to specific products or technologies, the relevant data of the user is involved, and the user's permission or consent is required, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. A multi-source data operation status diagnosis and early warning method for power terminals, characterized in that: include: Obtain historical electricity consumption information of multiple users collected by multiple smart meters; Determine the electricity cost performance index corresponding to the electricity consumption behavior information of each user based on the historical electricity consumption information of multiple users; Filtering out multiple target electricity usage behavior information from the electricity usage behavior information of multiple users based on various electricity cost performance indicators; Determine the push electricity usage behavior information of each user based on multiple target electricity usage behavior information; Push each user's electricity usage behavior information to each user.

2. The method according to claim 1, characterized in that Historical electricity usage information includes the user's total electricity costs, total electricity consumption, and the usage time periods of various electrical appliances. Electricity usage behavior information includes the user's regular usage time periods for various electrical appliances. The electricity cost-effectiveness index is the ratio of total electricity costs to total electricity consumption.

3. The method according to claim 2, characterized in that Determine the push electricity usage behavior information of each user based on multiple target electricity usage behavior information, including: Clustering multiple users based on their electricity usage behavior information to obtain multiple user clusters; Determine a preset number of electricity usage behavior information in the user clustering clusters that are ranked top in terms of electricity cost performance from large to small as a preset number of target electricity usage behavior information, and obtain multiple target electricity usage behavior information corresponding to the user clustering clusters; The method of determining the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information includes: A target power usage behavior information is selected from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the pushed power usage behavior information of the user.

4. The method according to claim 3, characterized in that The step of selecting a target power usage behavior information from a plurality of target power usage behavior information corresponding to the user cluster to which the user belongs as the user's pushed power usage behavior information includes: Allocating target electricity usage behavior information to each user multiple times to obtain multiple electricity usage behavior allocation information after multiple allocations, wherein the electricity usage behavior allocation information includes a target electricity usage behavior information allocated to each user in a single allocation, and each electricity usage behavior allocation information is different. Determine the regional power average value of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Calculate the power capacity ratio of the regional power average value of the power consumption area and the transformer capacity of the power consumption area to obtain the ratio variance of the power capacity ratios of multiple power consumption areas; Determining a distribution evaluation parameter of the electricity consumption behavior distribution information based on the ratio variance, wherein the larger the ratio variance, the smaller the distribution evaluation parameter; Determine the power consumption behavior allocation information with the largest allocation evaluation parameter as the target allocation information; The target electricity usage behavior information corresponding to each user in the target allocation information is used as the user's pushed electricity usage behavior information.

5. The method according to claim 4, characterized in that The method of determining the distribution evaluation parameters of the electricity consumption behavior distribution information based on the ratio variance includes: Determine the regional power variation coefficient of the power consumption of the power consumption area in multiple preset periods based on the power consumption behavior allocation information and the power consumption area to which each user belongs; Based on the number of users in the multiple power consumption areas, a weighted average of the regional power variation coefficients of the multiple power consumption areas is performed to obtain a weighted average value of the variation coefficients; The distribution evaluation parameter of the electricity consumption behavior distribution information is determined based on the ratio variance and the weighted average of the coefficient of variation, wherein the larger the ratio variance, the smaller the distribution evaluation parameter, and the larger the weighted average of the coefficient of variation, the smaller the distribution evaluation parameter.

6. The method according to claim 1, characterized in that The obtaining of the electricity usage behavior information of multiple users collected by multiple smart meters includes: The usage time of each type of electrical appliance is collected through the built-in electricity meter of each type of electrical appliance or the smart socket of each type of electrical appliance.

7. A multi-source data operation status diagnosis and early warning device for power terminals, characterized in that: The multi-source data operation status diagnosis and early warning device for power terminals includes: A first acquisition module is used to acquire historical electricity consumption information of multiple users collected by multiple smart meters; A first determination module is used to determine the electricity cost performance index corresponding to the electricity consumption behavior information of each user based on the historical electricity consumption information of multiple users; A screening module, used to screen out multiple target electricity usage behavior information from the electricity usage behavior information of multiple users based on various electricity cost performance indicators; A second determination module is used to determine the pushed electricity usage behavior information of each user based on multiple target electricity usage behavior information; The push module is used to push the power usage behavior information of each user to each user.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the multi-source data operation status diagnosis and early warning method for power terminals as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, which are suitable for a processor to load to execute the steps in the multi-source data operating status diagnosis and early warning method for power terminals as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the multi-source data operation status diagnosis and early warning method for power terminals as described in any one of claims 1 to 6 are implemented.

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