Error domain upgrading method and device for cooperative diagnosis of electric energy meter and concentrator

Abstract

The invention discloses an error domain upgrading method and device for cooperative diagnosis of an electric energy meter and a concentrator, and the method comprises the steps: obtaining a real-time uploading data list of a plurality of to-be-detected intelligent electric meters; acquiring upload data lists of different types of intelligent electric meters; determining old equipment in the plurality of intelligent ammeters to be detected according to the real-time uploading data list and the uploadable data list; acquiring distribution information of old equipment in the plurality of intelligent ammeters to be detected; when the distribution information of the old equipment meets a preset condition, determining abnormal equipment in the old equipment based on reading error parameters of the old equipment; and upgrading the abnormal equipment in the old equipment. The upgrading efficiency of the intelligent electric meter can be improved.

Description

Technical Field

[0001] This application relates to the technical field of smart meters, and specifically to an error domain division and upgrade method and device for collaborative diagnosis between an electricity meter and a concentrator. Background Art

[0002] A smart meter is an intelligent terminal of a smart grid. In addition to having the basic power consumption measurement function of a traditional electricity meter, in order to adapt to the smart grid and the use of new energy, it also has intelligent functions such as two-way multiple rate measurement functions, user-side control functions, two-way data communication functions with multiple data transmission modes, and anti-stealing electricity functions. Smart meters represent the development direction of the intelligent terminal of the end user of the future energy-saving smart grid. However, there are errors in the readings of smart meters. Checking the reading errors one by one for equipment upgrade has a low upgrade efficiency, resulting in a poor user experience. Summary of the Invention

[0003] An embodiment of this application provides an error domain division and upgrade method and device for collaborative diagnosis between an electricity meter and a concentrator, which can improve the upgrade efficiency of smart meters.

[0004] In a first aspect, the error domain division and upgrade method for collaborative diagnosis between an electricity meter and a concentrator provided by this application includes:

[0005] Obtain the real-time upload data list of multiple smart meters to be detected;

[0006] Obtain the uploadable data list of smart meters of different models;

[0007] Determine the old equipment among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list;

[0008] Obtain the distribution information of the old equipment among the multiple smart meters to be detected;

[0009] When the distribution information of the old equipment meets the preset conditions, determine the abnormal equipment among the old equipment based on the reading error parameters of the old equipment;

[0010] Upgrade the abnormal equipment among the old equipment.

[0011] In an optional embodiment, the determining the abnormal equipment among the old equipment based on the reading error parameters of the old equipment includes:

[0012] Determine the target error upper limit value of the old equipment based on the equipment model of the old equipment, and different equipment models correspond to different target error upper limit values;

[0013] Determine the old equipment with a reading error parameter higher than the target error upper limit value as the abnormal equipment.

[0014] In an alternative embodiment, the method includes:

[0015] Compare the real-time uploaded data list with the uploadable data lists of smart meters of different models to obtain a data comparison result;

[0016] Determine the device model of the smart meter to be detected according to the data comparison result.

[0017] In an alternative embodiment, the determining of old equipment among multiple smart meters to be detected according to the real-time uploaded data list and the uploadable data list includes:

[0018] Compare the device model of the smart meter to be detected with the device models of preset old equipment to determine whether the smart meter to be detected is old equipment.

[0019] In an alternative embodiment, before obtaining the uploadable data lists of smart meters of different models, the method includes:

[0020] Collect the uploadable data of multiple smart meters of different models;

[0021] Generate the uploadable data lists of smart meters of different models according to the multiple uploadable data.

[0022] In an alternative embodiment, the distribution information is the distribution ratio of old equipment. When the distribution information of old equipment meets a preset condition, determining abnormal equipment among old equipment based on the reading error parameters of old equipment includes:

[0023] Compare the distribution ratio of old equipment with a preset old equipment ratio threshold to obtain a distribution ratio comparison result;

[0024] When the distribution ratio comparison result is that the distribution ratio of old equipment exceeds the preset old equipment ratio threshold, determine that the distribution information of old equipment meets the preset condition, and determine abnormal equipment among old equipment based on the reading error parameters of old equipment.

[0025] In a second aspect, the error domain upgrade device for collaborative diagnosis of electric energy meters and concentrators provided in this application includes:

[0026] A first acquisition module, configured to acquire the real-time uploaded data lists of multiple smart meters to be detected;

[0027] A second acquisition module, configured to acquire the uploadable data lists of smart meters of different models;

[0028] A first determination module, configured to determine old equipment among multiple smart meters to be detected according to the real-time uploaded data list and the uploadable data list;

[0029] A third acquisition module, configured to acquire the distribution information of old devices among multiple smart meters to be detected;

[0030] A second determination module, configured to, when the distribution information of the old devices meets a preset condition, determine abnormal devices among the old devices based on the reading error parameters of the old devices;

[0031] An upgrade module, configured to upgrade the abnormal devices among the old devices.

[0032] In an optional embodiment, the determining the abnormal devices among the old devices based on the reading error parameters of the old devices includes:

[0033] Determining a target error upper limit value of the old devices based on the device models of the old devices, where different device models correspond to different target error upper limit values;

[0034] Determining the old devices with reading error parameters higher than the target error upper limit value as abnormal devices.

[0035] In an optional embodiment, comparing the real-time upload data list with the uploadable data lists of smart meters of different models to obtain a data comparison result;

[0036] Determining the device models of the smart meters to be detected according to the data comparison result.

[0037] In an optional embodiment, the determining the old devices among multiple smart meters to be detected according to the real-time upload data list and the uploadable data list includes:

[0038] Comparing the device models of the smart meters to be detected with the device models of the preset old devices to determine whether the smart meters to be detected are old devices.

[0039] In an optional embodiment, before acquiring the uploadable data lists of smart meters of different models, it includes:

[0040] Collecting the uploadable data of multiple smart meters of different models;

[0041] Generating the uploadable data lists of smart meters of different models according to the multiple uploadable data.

[0042] In an optional embodiment, the distribution information is the distribution ratio of the old devices, and the determining the abnormal devices among the old devices based on the reading error parameters of the old devices when the distribution information of the old devices meets a preset condition includes:

[0043] Comparing the distribution ratio of the old devices with a preset old device ratio threshold to obtain a distribution ratio comparison result;

[0044] When the comparison result of the distribution ratio indicates that the distribution ratio of the old equipment exceeds a preset threshold of the distribution ratio of old equipment, it is determined that the distribution information of the old equipment meets the preset conditions, and abnormal equipment among the old equipment is determined based on the reading error parameters of the old equipment.

[0045] In a third aspect, the electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program in the memory to implement the steps in the error domain division and upgrade method for collaborative diagnosis of an electric energy meter and a concentrator provided in this application.

[0046] In a fourth aspect, the computer-readable storage medium provided in this application stores multiple instructions, and these instructions are suitable for being loaded by a processor to implement the steps in the error domain division and upgrade method for collaborative diagnosis of an electric energy meter and a concentrator provided in this application.

[0047] In a fifth aspect, the computer program product provided in this application includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the steps in the error domain division and upgrade method for collaborative diagnosis of an electric energy meter and a concentrator provided in this application are implemented.

[0048] In this application, compared with the related art, a real-time upload data list of multiple smart electric meters to be detected is obtained; an uploadable data list of smart electric meters of different models is obtained; old equipment among the multiple smart electric meters to be detected is determined according to the real-time upload data list and the uploadable data list; distribution information of the old equipment among the multiple smart electric meters to be detected is obtained; when the distribution information of the old equipment meets the preset conditions, abnormal equipment among the old equipment is determined based on the reading error parameters of the old equipment; and the abnormal equipment among the old equipment is upgraded. This application can improve the upgrade efficiency of smart electric meters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of this application, and those skilled in the art can obtain other drawings without creative efforts based on these drawings.

[0050] Figure 1 is a schematic diagram of the scenario of the error domain division and upgrade system for collaborative diagnosis of an electric energy meter and a concentrator provided by an embodiment of this application;

[0051] Figure 2 is a schematic flowchart of an embodiment of the error domain division and upgrade method for collaborative diagnosis of an electric energy meter and a concentrator provided by an embodiment of this application;

[0052] Figure 3It is a schematic structural diagram of an embodiment of an error domain division and upgrade device for collaborative diagnosis between an electric energy meter and a concentrator provided by an embodiment of the present application;

[0053] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Specific embodiments

[0054] It should be noted that the principle of the present application is illustrated by being implemented in a suitable computing environment. The following description is based on the specific embodiments of the present application illustrated, and it should not be regarded as limiting other specific embodiments of the present application not detailed herein.

[0055] In the following description of the present application, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0056] In the following description of the present application, the terms "first / second / third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0058] In order to improve the effect of error domain division and upgrade for collaborative diagnosis between an electric energy meter and a concentrator, an embodiment of the present application provides an error domain division and upgrade method for collaborative diagnosis between an electric energy meter and a concentrator, an error domain division and upgrade device for collaborative diagnosis between an electric energy meter and a concentrator, an electronic device, a computer-readable storage medium, and a computer program product. Among them, the error domain division and upgrade method for collaborative diagnosis between an electric energy meter and a concentrator can be executed by the error domain division and upgrade device for collaborative diagnosis between an electric energy meter and a concentrator, or by an electronic device integrated with the error domain division and upgrade device for collaborative diagnosis between an electric energy meter and a concentrator.

[0059] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0060] Please refer toFigure 1 , this application also provides an error domain division and upgrade system for collaborative diagnosis of an electricity meter and a concentrator. As Figure 1 shown, the error domain division and upgrade system for collaborative diagnosis of the electricity meter and the concentrator includes an electronic device. The error domain division and upgrade device for collaborative diagnosis of the electricity meter and the concentrator provided by this application is integrated in the electronic device.

[0061] Among them, the electronic device can be any device configured with a processor and having processing capabilities, such as mobile electronic devices with a processor like smartphones, tablets, handheld computers, laptops, smart speakers, etc., or fixed electronic devices with a processor like desktop computers, TVs, servers, industrial devices, etc.

[0062] In addition, as Figure 1 shown, the error domain division and upgrade system for collaborative diagnosis of the electricity meter and the concentrator may further include a memory for storing the original data, intermediate data, and result data during audio processing.

[0063] In the embodiments of this application, the memory can 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 a storage system) refers to a storage system that combines a large number of various types of storage devices (storage devices are also called storage nodes) in the network through functions such as cluster applications, grid technology, and distributed file systems, and collaborates through application software or application interfaces to jointly provide data storage and service access functions to the outside world.

[0064] Currently, the storage method of the storage system is as follows: create a logical volume. When creating a logical volume, physical storage space is allocated for each logical volume, and this physical storage space may be composed of a certain storage device or the disks of several storage devices. The client stores data on a certain logical volume, that is, stores the data on the file system. The file system divides the data into many parts, and each part is an object. The object not only contains data but also additional information such as a data identifier (ID entity, ID). The file system writes each object into the physical storage space of the logical volume respectively, and the file system will record the storage location information of each object. Thus, when the client requests to access the data, the file system can enable the client to access the data according to the storage location information of each object.

[0065] The process of the storage system allocating physical storage space to a logical volume is specifically as follows: According to the capacity estimation of the objects stored in the logical volume (this estimation often has a large margin relative to the capacity of the objects to be actually stored) and the group of the Redundant Array of Independent Disk (RAID), the physical storage space is pre-divided into stripes, and a logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0066] It should be noted that Figure 1 The schematic diagram of the error domain division and upgrade system for collaborative diagnosis between the electric energy meter and the concentrator shown is only an example. The error domain division and upgrade system and scenario for collaborative diagnosis between the electric energy meter and the concentrator described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the error domain division and upgrade system for collaborative diagnosis between the electric energy meter and the concentrator and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0067] The following will be described in detail respectively. It should be noted that the serial numbers of the following embodiments do not limit the preferred order of the embodiments.

[0068] Please refer to Figure 2 , Figure 2 is a schematic flowchart of an embodiment of the error domain division and upgrade method for collaborative diagnosis between the electric energy meter and the concentrator provided by the embodiments of the present application. As Figure 2 shown, the process of the error domain division and upgrade method for collaborative diagnosis between the electric energy meter and the concentrator provided by the present application is as follows:

[0069] 201. Obtain the real-time upload data list of multiple smart electric meters to be detected.

[0070] Among them, the smart electric meters to be detected are smart electric meters of the smart electric meter models that need to be detected.

[0071] When it is necessary to conduct inspection of new and old devices for the smart electric meters in the target area, obtain the real-time upload data list of the smart electric meters of the smart electric meter models that need to be detected. The real-time upload data list includes communication protocol, voltage form, current operating range, billing method, power factor (PF), reactive power (kVar), and harmonic content. Each item of data together constitutes the upload data list. For example, the communication protocol is 4G, NB-IoT, DL / T645, or Modbus protocol. The billing method supports prepayment or does not support prepayment. The voltage form is 220V single-phase voltage or 380V three-phase voltage. The current operating range is 5A to 60A or above 100A.

[0072] 202. Obtain the list of uploadable data for smart meters of different models.

[0073] The list of uploadable data for smart meters of different models includes the device model tags of the corresponding smart meters. The list of uploadable data for smart meters of different models includes different parameters. The parameters of the list of uploadable data for smart meters include communication mode, voltage form, current operating range, billing method, power factor (PF), reactive power (kVar), and harmonic content. The list of uploadable data for smart meters of different signals is different.

[0074] Before conducting the new and old equipment inspection on the smart meter to be detected, collect the list of uploadable data for smart meters of different models. It can be collected when initially deploying all smart meters in the Internet of Things area, or it can be collected after the smart meters have been running for a period of time.

[0075] In the embodiment of the present application, before obtaining the list of uploadable data for smart meters of different models, the method includes:

[0076] (1) Collect the uploadable data of multiple smart meters of different models.

[0077] Before conducting the new and old equipment inspection on the smart meter to be detected, pre-collect the uploadable data of each smart meter of different models through a data collection device. It is possible to obtain the uploadable data of all smart meters in the same target area, or it is possible to obtain the uploadable data of all smart meters in different target areas.

[0078] When multiple smart meters to be detected are in the same target area, it is only necessary to collect the uploadable data of multiple smart meters of different models in the preset same target area. Subsequently, compare the real-time upload data of the smart meter to be detected with the uploadable data of multiple smart meters of different models in the same target area, and the device model of the smart meter to be detected can be analyzed, improving the error domain upgrade speed of the collaborative diagnosis of the electric energy meter and the concentrator.

[0079] In another embodiment of the present application, collecting the uploadable data of multiple smart meters of different models may include:

[0080] Among them, the data acquisition device is a data acquisition tool that runs inside the intermediate device. The data acquisition device is responsible for the data source of the intermediate device and the communication between the intermediate device and the Internet of Things platform. That is, the data acquisition device is used to collect the uploadable data of multiple smart meters connected to the intermediate device and transmit the uploadable data of the multiple smart meters to the Internet of Things platform. The communication protocol adopted by the data acquisition device can be standard protocols such as Modbus, OPC, BACnet, etc., as well as non-standard protocols such as SDK.

[0081] (2) Generate an uploadable data list for smart meters of different models based on the multiple uploadable data.

[0082] After receiving the uploadable data of multiple smart meters, integrate and sort the uploadable data. The sorting method can be sorting according to the order of the first letters, or sorting the multiple received uploadable data according to specific needs. After sorting, an uploadable data list for smart meters of different models is formed.

[0083] 203. Determine the old equipment among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list.

[0084] In the embodiment of the present application, determining the old equipment among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list includes:

[0085] (1) Compare the real-time upload data list with the uploadable data lists of smart meters of different models to obtain a data comparison result.

[0086] After collecting the uploadable data lists of all smart meters of different models, compare the real-time upload data list of the smart meters to be detected obtained with the uploadable data lists of smart meters of different models, that is, match the smart meters to be detected with smart meters of different models to obtain a data comparison result.

[0087] (2) Determine the device model of the smart meter to be detected according to the data comparison result.

[0088] After obtaining the data comparison result, identify and analyze the data comparison result to determine the device model of the smart meter to be detected. Exemplarily, when the data comparison result is that the real-time upload data of the smart meter to be detected is the same as the uploadable data of one of the multiple smart meters of different models, then determine the device model of the smart meter to be detected as the device model of one of the above smart meters.

[0089] This application pre-obtains the list of uploadable data of multiple different models of smart meters. Subsequently, when it is necessary to analyze the new and old situations of smart meters, by obtaining the real-time upload data list of the smart meter to be detected and comparing the real-time upload data list of the smart meter with the uploadable data list, the device model of the current smart meter to be detected can be obtained, and thus the new and old situations of the smart meter to be detected can be analyzed, solving the problem in the prior art that the relevant recorded data of the terminal device is easily lost and it is difficult to analyze the new and old situations of the smart meter.

[0090] (3) Compare the real-time upload data list with the uploadable data lists of smart meters of different models to obtain a data comparison result.

[0091] After collecting the uploadable data of smart meters of different models, when forming the uploadable data list, mark the service life and new and old situation labels corresponding to smart meters of different models.

[0092] Compare the device model of the smart meter to be detected with the device models of preset old equipment to obtain an old equipment comparison result, including:

[0093] Match the device model of the smart meter to be detected with the uploadable data list to obtain a matching result, and use the matching result as the old equipment comparison result.

[0094] The old equipment comparison result includes the data corresponding to the smart meter to be detected in the uploadable data list, and the data includes device model, device parameter data, environmental data, and service life.

[0095] (4) Determine that the smart meter to be detected is old equipment according to the old equipment comparison result.

[0096] After obtaining the old equipment comparison result, analyze the old equipment comparison result to determine whether the smart meter to be detected is old equipment.

[0097] In a specific embodiment, when the obtained old equipment comparison result shows that the service life has exceeded the date of the detection day, it is determined that the smart meter to be detected is old equipment; otherwise, it is determined that the smart meter to be detected is not old equipment.

[0098] In another specific embodiment, collect the first vibration signal sequence of the protection switch through the vibration sensor on the protection switch. The first vibration signal sequence includes the amplitudes at multiple moments. Obtain multiple power-off moments of the protection switch. Among them, the power-off moment is the moment when the circuit protected by the protection switch is powered off. Determine the decay oscillation curve closest to the power-off moment in the first vibration signal sequence as the second closing vibration signal corresponding to the power-off moment, and obtain multiple second closing vibration signals corresponding to multiple power-off moments. Among them, the decay oscillation curve is a sine function curve with the amplitude gradually decreasing to zero.

[0099] In the embodiment of the present application, the amplitude maximum moment when the amplitude of the damped oscillation curve is the largest is obtained, and the damped oscillation curve corresponding to the amplitude maximum moment closest to the power-off moment is determined as the damped oscillation curve closest to the power-off moment.

[0100] It is judged whether an automatic protection event occurs in the protection circuit within a preset time period before the power-off moment, where the automatic protection event includes a leakage time, an overload event, and a short-circuit time. If an automatic protection event occurs in the protection circuit within the preset time period before the power-off moment, the second closing vibration signal corresponding to the power-off moment is determined as the third closing vibration signal, and a plurality of third closing vibration signals are obtained. A plurality of first closing vibration signals are screened out from the plurality of third closing vibration signals. The plurality of third closing vibration signals are clustered to obtain a plurality of signal clustering clusters; the plurality of third closing vibration signals in the signal clustering cluster with the largest number of vibration signals among the plurality of signal clustering clusters are determined as the plurality of first closing vibration signals.

[0101] In the embodiment of the present application, the first closing vibration signal includes a vibration frequency and a vibration duration. The first frequency average value of the vibration frequencies in the first closing vibration signals during multiple closings and the duration average value of the vibration durations in the first closing vibration signals during multiple closings are obtained. A first aging parameter is determined based on the first frequency average value and the duration average value. Among them, the smaller the first frequency average value, the larger the first aging parameter, and the higher the duration average value, the larger the first aging parameter. A target aging parameter of the protection switch is determined based on the first aging parameter, where the larger the first aging parameter, the larger the target aging parameter.

[0102] Further, the closing moment when the closing signal is received closest to the power-off moment before the power-off moment of the protection switch is obtained. The time difference between the closing moment and the power-off moment is determined as the target turn-off delay time corresponding to the power-off moment. A second aging parameter is determined based on the target turn-off delay time, where the larger the target turn-off delay time, the larger the second aging parameter. The first aging parameter and the second aging parameter are weighted and summed to obtain the target aging parameter of the protection switch.

[0103] When the obtained old equipment comparison result is that the service life has exceeded the date of the detection day, and the target aging parameter of the protection switch is greater than the preset value, it is determined that the smart meter to be detected is an old equipment, otherwise it is determined that the smart meter to be detected is not an old equipment.

[0104] Further, obtain the closing time when the closing signal is received closest to the power-off time before the power-off time of the protection switch. Determine the time difference between the closing time and the power-off time as the target turn-off delay time corresponding to the power-off time. Determine the second aging parameter based on the target turn-off delay time, where the greater the target turn-off delay time, the greater the second aging parameter. Determine the target aging parameter of the protection switch based on the second aging parameter. Sum the weights of the first aging parameter and the second aging parameter to obtain the target aging parameter of the protection switch.

[0105] Further, collect the second vibration signal sequence of the smart meter through the vibration sensor on the smart meter. The second vibration signal sequence includes the amplitudes at multiple moments. Obtain the power reading sequence of the smart meter, obtain multiple time intervals on the power reading sequence where the power variance is less than the preset variance, obtain the interval reading sequence located in the multiple time intervals on the power reading sequence, determine the power average value of the interval reading sequence corresponding to the time interval, determine the second frequency average value of the vibration frequency of the third vibration signal sequence of the second vibration signal sequence located in the time interval, and obtain multiple power average values and corresponding second frequency average values corresponding to the multiple time intervals. Obtain the preset power-frequency mapping relationship between power and frequency, where the power-frequency mapping relationship can be obtained by measuring a newly manufactured standard smart meter in advance. Determine multiple frequency mapping values corresponding to the multiple power average values according to the power-frequency mapping relationship, calculate the frequency difference between the frequency mapping value and the corresponding second frequency average value, determine the ratio of the frequency difference to the frequency mapping value as the deviation ratio, obtain multiple deviation ratios corresponding to the multiple power average values, determine the time interval corresponding to the power average value with a deviation ratio greater than the preset ratio as the abnormal interval, and determine the third aging parameter based on the proportion of the number of abnormal intervals. The greater the proportion of the number of abnormal intervals, the greater the third aging parameter. Sum the weights of the first aging parameter, the second aging parameter, and the third aging parameter to obtain the target aging parameter of the protection switch. When the average power value is higher than the preset power value and the power variance is lower than the preset variance, it indicates that the power is high and stable within the time interval, belonging to the high-load time. At this time, if the meter is aging, the vibration characteristics are more obvious.

[0106] When the obtained comparison result of the old equipment is that the service life has exceeded the date of the detection day, and the target aging parameter of the protection switch is greater than the preset value, it is determined that the smart meter to be detected is an old equipment; otherwise, it is determined that the smart meter to be detected is not an old equipment.

[0107] 204. Obtain the distribution information of old equipment among multiple smart meters to be detected.

[0108] In the embodiment of the present application, the distribution information is the distribution ratio of old equipment.

[0109] 205. When the distribution information of the old equipment meets the preset conditions, determine the abnormal equipment in the old equipment based on the reading error parameter of the old equipment.

[0110] In the embodiment of the present application, the distribution information is the distribution ratio of the old equipment. When the distribution information of the old equipment meets the preset conditions, determining the abnormal equipment in the old equipment based on the reading error parameter of the old equipment includes: comparing the distribution ratio of the old equipment with the preset old equipment ratio threshold to obtain a distribution ratio comparison result; when the distribution ratio comparison result is that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold, it is determined that the distribution information of the old equipment meets the preset conditions. When the distribution ratio comparison result is that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold, it is determined that the distribution information of the old equipment meets the preset conditions, and the abnormal equipment in the old equipment is determined based on the reading error parameter of the old equipment. Exemplarily, when the distribution status of the smart meters of all equipment models is that there are 30 power equipment in the target area, and the detected distribution status of the smart meters to be detected is that there are 10 old equipment in the target area, then the obtained distribution ratio of the old equipment in the preset target area is 10 / 30 = 1 / 3.

[0111] Obtain the preset old equipment ratio threshold in the Internet of Things platform. The old equipment ratio threshold can be set manually or adaptively adjusted by the Internet of Things platform according to the distribution status of all physical network equipment in the target area. The distribution ratio comparison result includes that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold and the distribution ratio of the old equipment does not exceed the preset old equipment ratio threshold. Exemplarily, when the obtained distribution ratio of the old equipment in the preset target area is 1 / 3, and the preset old equipment ratio threshold in the Internet of Things platform is set to 1 / 4, then the corresponding distribution ratio comparison result is that the distribution ratio comparison result includes that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold.

[0112] In the embodiment of the present application, determining the abnormal equipment in the old equipment based on the reading error parameter of the old equipment includes:

[0113] (1) Determine the target error upper limit value of the old equipment based on the equipment model of the old equipment. Different equipment models correspond to different target error upper limit values.

[0114] In a specific embodiment, the reading error parameter of the old equipment is obtained through manual measurement.

[0115] In another specific embodiment, the electricity consumption reading sequences of the main meter device in the power consumption area where the old equipment is located and the electricity consumption reading sequences 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 old equipment is obtained. Among them, during the independent power consumption period, the ratio of the electricity consumption reading of the old equipment to the electricity consumption reading of the main meter device is greater than the preset electricity ratio, where the preset electricity ratio is 95% or 99%, which can be set according to specific circumstances. When the independent power consumption period of the old equipment is obtained, the total electricity consumption reading of the other smart meters in the power consumption area except the old equipment during the independent power consumption period is obtained. The difference between the electricity consumption reading of the main meter device and the total electricity consumption reading is determined as the true electricity consumption of the old equipment, the difference between the electricity consumption reading of the old equipment and the true electricity consumption of the old equipment is determined as the error difference, and the ratio of the error difference to the electricity consumption reading of the old equipment is determined as the reading error parameter of the old equipment. For example, during the independent power consumption period, the electricity consumption reading of the main meter device is 1 degree. The main meter device is a meter calibrated by the power department, and the error can be ignored. Therefore, the electricity consumption reading of the main meter device is the true electricity consumption. The electricity consumption reading of the old equipment is 0.95 degrees. Due to errors, the true electricity consumption is 0.94 degrees. The total electricity consumption reading of the other smart meters is 0.06 degrees. Since it is relatively small compared to 0.95 degrees, the error can be ignored. Therefore, the total true electricity consumption of the other smart meters is 0.06 degrees. The difference between the electricity consumption reading of the main meter device and the total electricity consumption reading can be determined as the true electricity consumption of the old equipment, that is, 0.94 degrees.

[0116] Further, it is possible that there is no independent power consumption period for old equipment. In another specific embodiment, the power consumption reading sequences of the main meter device in the power consumption area where the old equipment is located and the power consumption reading sequences of each smart meter in the power consumption area are obtained. The power consumption area can be a building, and the power consumption area includes multiple smart meters. When the independent power consumption period of the old equipment is not obtained, the other smart meters except the old equipment are respectively determined as target devices, and the total power consumption reading of the other smart meters except the target devices in the power consumption area during the independent power consumption period is obtained. The difference between the power consumption reading of the main meter device and the total power consumption reading is determined as the true power consumption of the target device, the difference between the power consumption reading of the target device and the true 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, the power consumption reading of the main meter device during the target period is obtained, the power consumption readings of each target device during the target period are obtained, the true power consumption of each target device during the target period is determined according to the reading error parameters of each target device, and the sum of the true power consumptions is obtained. The difference between the power consumption reading of the main meter device during the target period and the sum of the true power consumptions is determined as the true power consumption of the old equipment, the difference between the power consumption reading of the old equipment and the true power consumption of the old equipment is determined as the error difference, and the ratio of the error difference to the power consumption reading of the old equipment is determined as the reading error parameter of the old equipment.

[0117] In the embodiments of the present application, the mapping relationship between the preset device model and the target error upper limit value is obtained, the rated error upper limit value of the old equipment is determined based on the mapping relationship, and the target error upper limit value of the old equipment is determined based on the rated error upper limit value of the old equipment. For example, the rated error upper limit value of device model A is 0.1%, and the rated error upper limit value of device model B is 0.15%.

[0118] In a specific embodiment, the rated error upper limit value of the old equipment is determined as the target error upper limit value of the old equipment.

[0119] In another specific embodiment, the average environmental temperature, average environmental humidity, and average current operation of the old equipment within a preset historical period are obtained. Based on the average environmental temperature, average environmental humidity, average current operation of the old equipment, and the standard working temperature, standard working humidity, and standard current value of the old equipment, the temperature difference, humidity difference, and current difference are determined. Based on the temperature difference, humidity difference, and current difference of the old equipment, the error weighting coefficient of the old equipment is determined. The rated error upper limit value is weighted based on the error weighting coefficient to obtain the target error upper limit value of the old equipment. 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 environmental temperature, environmental humidity, and working current deviate too much from the standard, it will cause an increase in error. This is a natural phenomenon, and it is necessary to increase the error upper limit value to prevent normal smart meters from being determined as abnormal equipment.

[0120] Specifically, denote the weighting coefficient as d. Normalize the temperature difference to obtain the temperature normalization value m, normalize the humidity difference to obtain the humidity normalization value n, and normalize the current difference to obtain the current normalization value k. The temperature normalization value m, humidity normalization value n, and current normalization value k are weighted and summed to obtain the weighting coefficient d.

[0121] In a specific embodiment, the calculation formula for the weighting coefficient d is as follows:

[0122] d = a*m + b*n + c*k,

[0123] Among them, d is the weighting coefficient, m is the temperature normalization value, n is the humidity normalization value n, k is the current normalization value, and a, b, and c are the weight coefficients of the temperature normalization value m, humidity normalization value n, and current normalization value k respectively. Among them, a, b, and c can be obtained by least squares fitting.

[0124] (2) Determine the old equipment with a reading error parameter higher than the target error upper limit value as an abnormal equipment.

[0125] 206. Upgrade the abnormal equipment in the old equipment.

[0126] Furthermore, obtain the preset equipment upgrade strategy. According to the preset equipment upgrade strategy, locate the old equipment that needs to be upgraded within the preset target area.

[0127] The equipment upgrade strategy can be to replace some of the severely old smart meters in the target area, repair the smart meters with a low degree of oldness in the target area, and locate the corresponding smart meters that need to be replaced and corrected. It can also be other equipment upgrade strategies according to the actual situation, which are not limited here.

[0128] Compared with the related art, obtain the real-time upload data list of multiple smart meters to be detected; obtain the uploadable data list of smart meters of different models; determine the old devices among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list; obtain the distribution information of the old devices among the multiple smart meters to be detected; when the distribution information of the old devices meets the preset conditions, determine the abnormal devices among the old devices based on the reading error parameters of the old devices; upgrade the abnormal devices among the old devices. This application can improve the upgrade efficiency of smart meters.

[0129] To facilitate better implementation of the error domain division and upgrade method for collaborative diagnosis of electric energy meters and concentrators provided in the embodiments of the present application, the embodiments of the present application also provide an error domain division and upgrade device for collaborative diagnosis of electric energy meters and concentrators based on the above error domain division and upgrade method for collaborative diagnosis of electric energy meters and concentrators. The meanings of the nouns are the same as those in the above error domain division and upgrade method for collaborative diagnosis of electric energy meters and concentrators, and the specific implementation details can be referred to the descriptions in the above method embodiments.

[0130] Please refer to Figure 3 , Figure 3 FIG. is a schematic structural diagram of an embodiment of an error domain division and upgrade device for collaborative diagnosis of an electric energy meter and a concentrator provided by an embodiment of the present application. The error domain division and upgrade device for collaborative diagnosis of an electric energy meter and a concentrator may include a first acquisition module 701, a second acquisition module 702, a first determination module 703, a third acquisition module 704, a second determination module 705, and an upgrade module 706. Among them,

[0131] The first acquisition module 701 is configured to acquire a real-time upload data list of multiple smart meters to be detected;

[0132] The second acquisition module 702 is configured to acquire an uploadable data list of smart meters of different models;

[0133] The first determination module 703 is configured to determine old devices among multiple smart meters to be detected according to the real-time upload data list and the uploadable data list;

[0134] The third acquisition module 704 is configured to acquire distribution information of old devices among multiple smart meters to be detected;

[0135] The second determination module 705 is configured to determine abnormal devices among the old devices based on the reading error parameters of the old devices when the distribution information of the old devices meets the preset conditions;

[0136] The upgrade module 706 is configured to upgrade the abnormal devices among the old devices.

[0137] In an optional embodiment, determining the abnormal devices among the old devices based on the reading error parameters of the old devices includes:

[0138] Determine the upper limit of the target error of the old equipment based on the equipment model of the old equipment, and different equipment models correspond to different upper limits of the target error;

[0139] Determine the old equipment with a reading error parameter higher than the upper limit of the target error as abnormal equipment.

[0140] In an optional embodiment, compare the real-time uploaded data list with the uploadable data lists of smart meters of different models to obtain a data comparison result;

[0141] Determine the equipment model of the smart meter to be detected according to the data comparison result.

[0142] In an optional embodiment, the determining the old equipment among multiple smart meters to be detected according to the real-time uploaded data list and the uploadable data list includes:

[0143] Compare the equipment model of the smart meter to be detected with the equipment models of the preset old equipment to determine whether the smart meter to be detected is old equipment.

[0144] In an optional embodiment, before obtaining the uploadable data lists of smart meters of different models, it includes:

[0145] Collect the uploadable data of multiple smart meters of different models;

[0146] Generate the uploadable data lists of smart meters of different models according to the multiple uploadable data.

[0147] In an optional embodiment, the distribution information is the distribution ratio of the old equipment, and when the distribution information of the old equipment meets the preset conditions, determining the abnormal equipment among the old equipment based on the reading error parameters of the old equipment includes:

[0148] Compare the distribution ratio of the old equipment with the preset old equipment ratio threshold to obtain a distribution ratio comparison result;

[0149] When the distribution ratio comparison result is that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold, determine that the distribution information of the old equipment meets the preset conditions, and determine the abnormal equipment among the old equipment based on the reading error parameters of the old equipment.

[0150] For the specific implementation of each of the above modules, reference can be made to the previous embodiments and will not be elaborated here.

[0151] Compared with the related art, obtain the real-time upload data list of multiple smart meters to be detected; obtain the uploadable data list of smart meters of different models; determine the old devices among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list; obtain the distribution information of the old devices among the multiple smart meters to be detected; when the distribution information of the old devices meets the preset conditions, determine the abnormal devices among the old devices based on the reading error parameters of the old devices; upgrade the abnormal devices among the old devices. This application can improve the upgrade efficiency of smart meters.

[0152] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0153] 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 can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Among them:

[0154] The processor 101 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 102, and by calling the data stored in the memory 102, it executes various functions of the electronic device and processes data. Optionally, the processor 101 may include one or more processing cores; optionally, the processor 101 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 101 either.

[0155] 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 mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the electronic device. In addition, the memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 102 may also include a memory controller to provide the processor 101 with access to the memory 102.

[0156] The electronic device further 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 realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 103 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0157] The electronic device may further include an input unit 104, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0158] Although not shown, the electronic device may further include a display unit, an image acquisition element, etc., which will not be elaborated 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 in the error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator provided in this application, such as:

[0159] Obtain the real-time upload data list of multiple smart meters to be detected; obtain the uploadable data list of smart meters of different models; determine the old devices among the multiple smart meters to be detected according to the real-time upload data list and the uploadable data list; obtain the distribution information of the old devices among the multiple smart meters to be detected; when the distribution information of the old devices meets the preset conditions, determine the abnormal devices among the old devices based on the reading error parameters of the old devices; upgrade the abnormal devices among the old devices.

[0160] It should be noted that the electronic device provided in the embodiment of this application and the error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator in the above embodiment belong to the same concept. For the specific implementation process, please refer to the above relevant embodiments, which will not be elaborated here.

[0161] This application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program stored thereon is executed on the processor of the electronic device provided in the embodiment of this application, it enables the processor of the electronic device to execute the steps in the error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator provided in this application. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc.

[0162] The present application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are 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 alternative implementations of the error domain division upgrade method for collaborative diagnosis between the electric energy meter and the concentrator described above.

[0163] The above has introduced in detail an error domain division upgrade method and device for collaborative diagnosis between an electric energy meter and a concentrator provided by the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

[0164] It should be noted that when the above embodiments of the present application are applied to specific products or technologies, relevant data of users are involved, and user permission or consent needs to be obtained. Moreover, the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards of relevant countries and regions.

Claims

1. An error domain division and upgrade method for collaborative diagnosis of an electric energy meter and a concentrator, characterized in that, Including: Obtain the real-time uploaded data list of multiple smart meters to be detected; Obtain the list of uploadable data of smart meters of different models; Determine the old equipment among the multiple smart meters to be detected according to the real-time uploaded data list and the list of uploadable data; Obtain the distribution information of the old equipment among the multiple smart meters to be detected; When the distribution information of the old equipment meets the preset conditions, determine the abnormal equipment among the old equipment based on the reading error parameters of the old equipment; Upgrade the abnormal equipment among the old equipment.

2. The method according to claim 1, wherein, The determining the abnormal equipment among the old equipment based on the reading error parameters of the old equipment includes: Determine the target error upper limit value of the old equipment based on the equipment model of the old equipment, and different equipment models correspond to different target error upper limit values; Determine the old equipment with a reading error parameter higher than the target error upper limit value as the abnormal equipment.

3. The method according to claim 2, wherein The method includes: Compare the real-time uploaded data list with the list of uploadable data of smart meters of different models to obtain a data comparison result; Determine the equipment model of the smart meter to be detected according to the data comparison result.

4. The method according to claim 3, characterized in that, The determining the old equipment among the multiple smart meters to be detected according to the real-time uploaded data list and the list of uploadable data includes: Compare the equipment model of the smart meter to be detected with the equipment model of the preset old equipment to determine whether the smart meter to be detected is old equipment.

5. The method according to claim 4, wherein Before obtaining the list of uploadable data of smart meters of different models, the method includes: Collect the uploadable data of multiple smart meters of different models; Generate the list of uploadable data of smart meters of different models according to the multiple uploadable data.

6. The method according to claim 5, wherein The distribution information is the distribution ratio of the old equipment. When the distribution information of the old equipment meets the preset conditions, determining the abnormal equipment among the old equipment based on the reading error parameters of the old equipment includes: Compare the distribution ratio of the old equipment with the preset old equipment ratio threshold to obtain a distribution ratio comparison result; When the distribution ratio comparison result is that the distribution ratio of the old equipment exceeds the preset old equipment ratio threshold, determine that the distribution information of the old equipment meets the preset conditions, and determine the abnormal equipment among the old equipment based on the reading error parameters of the old equipment.

7. An error sub-region upgrade device for collaborative diagnosis between an electric energy meter and a concentrator, characterized in that The error domain upgrade device for collaborative diagnosis of the electric energy meter and the concentrator includes: The first acquisition module is used to obtain the real-time uploaded data list of multiple smart meters to be detected; The second acquisition module is used to obtain the list of uploadable data of smart meters of different models; The first determination module is used to determine the old equipment among the multiple smart meters to be detected according to the real-time uploaded data list and the list of uploadable data; The third acquisition module is used to obtain the distribution information of the old equipment among the multiple smart meters to be detected; The second determination module is used to determine the abnormal equipment among the old equipment based on the reading error parameters of the old equipment when the distribution information of the old equipment meets the preset conditions; The upgrade module is used to upgrade the abnormal equipment among the old equipment.

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 error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores multiple instructions, and the instructions are suitable for being loaded by the processor to execute the steps in the error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator according to 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 the instructions are executed by the processor, the steps in the error domain division and upgrade method for collaborative diagnosis of the electric energy meter and the concentrator according to any one of claims 1 to 6 are implemented.

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