Charging pile metering performance online monitoring method, device, electronic equipment and medium

Abstract

The embodiments of the present disclosure disclose a method, device, electronic device and medium for online monitoring of the metering performance of charging piles. A specific implementation of the method includes: for a pre-processed charging data information group, performing the following processing steps: determining the type of charging pile corresponding to the pre-processed charging data information group; performing error identification on the pre-processed charging data information group based on the charging pile metering error model corresponding to the charging pile type and the corresponding charging pile type operation information; determining the charging pile confidence interval corresponding to each pre-processed charging data information in the pre-processed charging data information group; determining abnormal performance charging piles and abnormal electric energy meters based on the confidence intervals of each charging pile and the charging pile error information. This implementation creates comprehensive metering error models for DC charging piles, AC charging piles, and AC / DC hybrid charging piles respectively, and performs online monitoring of the metering performance of charging piles, so as to timely and accurately locate charging piles with metering deviations or failure risks.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of charging pile monitoring, and more particularly to a method, device, electronic device, and medium for online monitoring of charging pile metering performance. Background Art

[0002] With the rapid development of the new energy vehicle industry, charging infrastructure construction has expanded rapidly. AC and DC charging stations are built by numerous manufacturers over a long period of time. Charging pile efficiency losses and internal losses are complex, and the testing process is energy-consuming and time-consuming, with a low degree of automation. Many regions have implemented mandatory testing requirements for electric vehicle charging piles. However, annual or even longer-term mandatory testing cannot guarantee that charging piles are in good operating condition during the testing period. Efficient and accurate testing methods are needed to meet daily monitoring needs.

[0003] At present, the commonly used charging pile metering solution is: install a monitoring metering module on the basis of the original internal structure of existing charging piles and new charging piles, and upload the data to the platform in real time through the data module interface, and compare the billing electricity meter data with the electricity data of the monitoring metering module in real time, and then screen out charging piles that may have abnormalities.

[0004] However, current charging pile metering solutions often have the following technical problems: installing a monitoring metering module takes a long time, has low detection accuracy, and has low metering efficiency.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0006] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure provide a method, device, electronic device, and computer-readable medium for online monitoring of charging pile metering performance to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for online monitoring of charging pile metering performance, the method comprising: obtaining a charging data information set and a charging pile type operation information group of a target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; performing data preprocessing on each charging data information in the above-mentioned charging data information set to generate preprocessed charging data information and obtain a preprocessed charging data information set; performing clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set, wherein one preprocessed charging data information group corresponds to one charging pile type; for the above-mentioned preprocessed charging data information set, For each pre-processed charging data information group in the charging data information group set, the following processing steps are executed: determining the charging pile type corresponding to the above-mentioned pre-processed charging data information group; performing error identification on the above-mentioned pre-processed charging data information group according to the charging pile metering error model corresponding to the above-mentioned charging pile type and the corresponding charging pile type operation information, and obtaining the charging pile error information corresponding to the above-mentioned charging pile type; determining the charging pile confidence interval corresponding to each pre-processed charging data information in the above-mentioned pre-processed charging data information group; determining the abnormal performance charging pile and the abnormal electric energy meter according to the confidence interval of each charging pile and the above-mentioned charging pile error information; and replacing the determined abnormal performance charging pile and abnormal electric energy meter.

[0009] In a second aspect, some embodiments of the present disclosure provide an online monitoring device for metering performance of a charging pile, the device comprising: an acquisition unit configured to acquire a charging data information set and a charging pile type operation information group of a target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; a preprocessing unit configured to perform data preprocessing on each charging data information in the above-mentioned charging data information set to generate preprocessed charging data information and obtain a preprocessed charging data information set; a clustering unit configured to perform clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set, wherein one preprocessed charging data information group corresponds to one charging pile type; and determine The unit is configured to perform the following processing steps for each pre-processed charging data information group in the above-mentioned pre-processed charging data information group set: determine the charging pile type corresponding to the above-mentioned pre-processed charging data information group; perform error identification on the above-mentioned pre-processed charging data information group according to the charging pile metering error model corresponding to the above-mentioned charging pile type and the corresponding charging pile type operation information, and obtain the charging pile error information corresponding to the above-mentioned charging pile type; determine the charging pile confidence interval corresponding to each pre-processed charging data information in the above-mentioned pre-processed charging data information group; determine the abnormal performance charging pile and the abnormal electric energy meter according to the confidence interval of each charging pile and the above-mentioned charging pile error information; the replacement unit is configured to replace each abnormal performance charging pile and abnormal electric energy meter determined.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the online monitoring method of the metering performance of charging piles of some embodiments of the present disclosure, by creating comprehensive metering error models for DC charging piles, AC charging piles, and AC / DC hybrid charging piles respectively, the metering performance of the charging piles is monitored online, and charging piles with metering errors or failure risks are located in a timely and accurate manner, and the replacement, inspection, and operation and maintenance of the charging pile metering modules are guided in a targeted manner. The metering error modeling and metering evaluation of various types of charging piles, including DC charging piles, AC charging piles, and AC / DC hybrid charging piles, are supported. The metering error model has high accuracy and wider applicability. Through multi-process processing before, during, and after calculation, the accuracy of the metering error model is improved. Combined with simulation evaluation, it can be applied to substations of different sizes, different line loss rates, and different power consumption characteristics, and the adaptation parameters of the model for various charging stations are continuously iterated and optimized. Accurately locate the abnormal points that cause energy efficiency loss, and realize timely inspection, maintenance, and disposal of abnormal charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of the method for online monitoring of charging pile metering performance according to the present disclosure;

[0015] Figure 2 Schematic diagram of the structure of some embodiments of the online monitoring device for charging pile metering performance according to the present disclosure;

[0016] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Figure 1 1 is a flow chart of some embodiments of the method for online monitoring of charging pile metering performance according to the present disclosure. It shows a process 100 of some embodiments of the method for online monitoring of charging pile metering performance according to the present disclosure. The method for online monitoring of charging pile metering performance includes the following steps:

[0024] Step 101: Obtain a charging data information set and a charging pile type operation information group of a target charging station within a preset time period.

[0025] In some embodiments, the execution subject (for example, a computing device) of the charging pile metering performance online monitoring method can obtain a charging data information set and a charging pile type operation information group of the target charging station within a preset time period. Among them, each charging data information corresponds to a charging pile in the target charging station. The target charging station may be a charging station including various types of charging piles. Charging pile type operation information may be the operation information of a certain type of charging station within a preset time period, and may include: fixed loss and line loss. Fixed loss may refer to the fixed loss of a certain type of charging pile. Line loss may refer to the loss of each line involved in a certain type of charging pile. Charging data information may refer to the charging data of a certain charging pile within a preset time period, and may include: the metered value of the charging pile electric energy meter. For example, the metered value of the charging pile electric energy meter may refer to the practical amount of electricity measured by the electric energy meter of the charging pile.

[0026] Step 102 : performing data preprocessing on each piece of charging data information in the charging data information set to generate preprocessed charging data information, thereby obtaining a preprocessed charging data information set.

[0027] In some embodiments, the execution entity may perform data preprocessing on each charging data information in the charging data information set to generate preprocessed charging data information, thereby obtaining a preprocessed charging data information set. Data preprocessing may include a series of data cleaning and preprocessing steps, such as missing data detection, abnormal data detection, available data detection, and consistency detection.

[0028] Step 103 : performing clustering processing on each piece of pre-processed charging data information in the pre-processed charging data information set to obtain a pre-processed charging data information group set.

[0029] In some embodiments, the execution entity may cluster the individual pieces of pre-processed charging data information in the pre-processed charging data information set to obtain a set of pre-processed charging data information groups. Each pre-processed charging data information group corresponds to a charging pile type. For example, charging pile types may include, but are not limited to, AC charging piles, DC charging piles, and AC / DC hybrid charging piles. In other words, individual pieces of pre-processed charging data information for the same charging pile type may be clustered into one group.

[0030] Step 104: For each pre-processed charging data information group in the pre-processed charging data information group set, perform the following processing steps:

[0031] Step 1041 : Determine the charging pile type corresponding to the pre-processed charging data information group.

[0032] In some embodiments, the execution entity may determine the type of charging pile corresponding to the pre-processed charging data information group.

[0033] Step 1042 : performing error identification on the pre-processed charging data information group based on the charging pile metering error model corresponding to the charging pile type and the corresponding charging pile type operation information to obtain charging pile error information corresponding to the charging pile type.

[0034] In some embodiments, the above-mentioned execution entity can perform error identification on the above-mentioned pre-processed charging data information group based on the charging pile metering error model corresponding to the above-mentioned charging pile type and the corresponding charging pile type operation information to obtain the charging pile error information corresponding to the above-mentioned charging pile type.

[0035] In practice, the execution entity may perform error identification on the pre-processed charging data information group through the following steps:

[0036] In the first step, in response to determining that the charging pile type represents an AC station charging pile, the corresponding charging pile metering error model is determined to be an AC station charging pile metering error model.

[0037] In the second step, the above-mentioned pre-processed charging data information group and the above-mentioned charging pile type operation information are input into the above-mentioned AC station charging pile metering error model to obtain the AC station charging pile metering error information. The above-mentioned charging pile type operation information includes: the AC total electric energy meter measurement value, the AC total electric energy meter measurement error, the AC station charging pile line loss, and the AC station charging pile fixed loss; the pre-processed charging data information includes: the AC station charging pile sub-electricity meter measurement value. The AC total electric energy meter measurement value may refer to the measurement value (total electricity usage) of the total electric energy meter of each charging pile corresponding to the same charging pile type. The AC total electric energy meter measurement error may refer to the error ratio of the total electric energy meter. For example, the AC total electric energy meter measurement error may be 0.03. The AC station charging pile sub-electricity meter measurement value may be the electricity measured by the sub-electricity meter of each AC charging pile.

[0038] For example, the total energy meter measurement value, the AC total energy meter measurement error, the AC station charging pile line loss, the AC station charging pile fixed loss, and the measurement value of each AC station charging pile sub-energy meter included in the pre-processed charging data information group are input into the following AC station charging pile measurement error model to obtain the AC station charging pile measurement error information:

[0039]

[0040] Among them, ε y represents the measurement error of the total AC electric energy meter, y represents the measurement value of the total AC electric energy meter, ε i represents the measurement error of the charging pile energy meter of the i-th AC station, φ i represents the metered value of the charging pile of the i-th AC station, N represents the number of pre-processed charging data information included in the above pre-processed charging data information group, E LLrepresents the line loss of the AC station charging pile, ε0 represents the fixed loss of the AC station charging pile, and the above-mentioned AC station charging pile metering error information includes the metering error of each AC station charging pile sub-electricity meter.

[0041] For example, when calculating the metering error of the sub-energy meter of each AC station charging pile, the historical average metering error of the sub-energy meters of other AC station charging piles can be determined first; then, the above-mentioned total energy meter measurement value, the AC total energy meter measurement error, the AC station charging pile line loss, the AC station charging pile fixed loss, the metering value of each AC station charging pile sub-energy meter included in the above-mentioned pre-processed charging data information group, and the historical average metering error of the sub-energy meters of other AC station charging piles can be substituted into the AC station charging pile metering error model to obtain the metering error of the sub-energy meter of the AC station charging pile.

[0042] In a third step, in response to determining that the charging pile type represents a DC station charging pile, the corresponding charging pile measurement error model is determined to be a DC station charging pile measurement error model.

[0043] In the fourth step, the pre-processed charging data information group and the charging pile type operation information are input into the DC station charging pile metering error model to obtain the DC station charging pile metering error information. The charging pile type operation information includes: the DC total energy meter value, the DC total energy meter measurement error, the DC station charging pile line loss, and the DC station charging pile fixed loss; the pre-processed charging data information includes: the DC station charging pile sub-energy meter value and the AC-DC conversion efficiency. The DC total energy meter value may refer to the total energy meter value (total electricity usage) of each charging pile corresponding to the same charging pile type (DC charging pile type). The DC total energy meter measurement error may refer to the error ratio of the total energy meter. For example, the DC total energy meter measurement error may be 0.03. The DC station charging pile sub-energy meter value may refer to the electricity measured by the sub-energy meter of each DC charging pile. The DC station charging pile fixed loss may refer to the fixed loss (power loss) of the DC station charging pile. The DC station charging pile line loss may refer to the loss of each line involved in the DC charging pile type. AC-DC conversion efficiency may refer to AC-DC (AC input to DC output) conversion efficiency.

[0044] For example, the total DC energy meter measurement value, the total DC energy meter measurement error, the DC station charging pile line loss, the DC station charging pile fixed loss, the measurement value of each DC station charging pile sub-energy meter included in the pre-processed charging data information group, and the AC-DC conversion efficiency are input into the following DC station charging pile measurement error model to obtain the DC station charging pile measurement error information:

[0045]

[0046] Among them, A xrepresents the measurement error of the DC total electric energy meter, x represents the measurement value of the DC total electric energy meter, θ t A represents the measurement error of the energy meter of the t-th DC station charging pile, t represents the energy meter value of the t-th DC station charging pile, β t represents the t-th AC-DC conversion efficiency, T represents the number of pre-processed charging data information included in the above-mentioned pre-processed charging data information group, ε represents the DC station charging pile line loss, μ represents the DC station charging pile fixed loss, and the above-mentioned DC station charging pile measurement error information includes the measurement error of each DC station charging pile sub-electricity meter.

[0047] For example, when calculating the metering error of each DC station charging pile's energy meter, the historical average metering error of the energy meter at other DC station charging piles can be determined first. Then, the aforementioned DC total energy meter value, DC total energy meter error, DC station charging pile line loss, DC station charging pile fixed loss, the energy meter value of each DC station charging pile included in the pre-processed charging data information group, the AC-DC conversion efficiency, and the historical average metering error of the energy meter at other DC station charging piles can be substituted into the DC station charging pile metering error model to obtain the energy meter error of the DC station charging pile. Thus, the energy meter error of each DC station charging pile is obtained as the DC station charging pile metering error information.

[0048] In a fifth step, in response to determining that the charging pile type represents an AC / DC hybrid station charging pile, determining that the corresponding charging pile metering error model is an AC / DC hybrid station charging pile metering error model.

[0049] In the sixth step, the pre-processed charging data information group and the charging pile type operation information are input into the AC / DC hybrid station charging pile metering error model to obtain the AC / DC hybrid station charging pile metering error information. The pre-processed charging data information group corresponding to the AC / DC hybrid station charging pile includes: pre-processed charging data information for the DC station charging pile and pre-processed charging data information for the AC station charging pile. Specifically, the pre-processed charging data information for the DC station charging pile includes: the DC station charging pile's energy meter reading and the AC / DC conversion efficiency; the pre-processed charging data information for the AC station charging pile includes: the AC station charging pile's energy meter reading.

[0050] For example, the pre-processed charging data information group and the charging pile type operation information are input into the following AC / DC hybrid station charging pile metering error model to obtain the AC / DC hybrid station charging pile metering error information:

[0051]

[0052] Among them, A crepresents the measurement error of the DC total electric energy meter included in the above charging pile type operation information, c represents the measurement value of the DC total electric energy meter included in the above charging pile type operation information, θ j A represents the measurement error of the jth DC station charging pile electric energy meter, j represents the energy meter value of the t-th DC station charging pile, β j represents the jth AC-DC conversion efficiency, ε l It represents the measurement error of the energy meter of the charging pile of the lth AC station, represents the metering value of the lth AC station charging pile sub-electricity meter, L represents the number of pre-processed charging data information representing the AC station charging piles included in the above-mentioned pre-processed charging data information group, J represents the number of pre-processed charging data information representing the DC station charging piles included in the above-mentioned pre-processed charging data information group, R represents the line loss of the AC / DC hybrid station charging pile, K represents the fixed loss of the AC / DC hybrid station charging pile, and the above-mentioned DC station charging pile metering error information includes the metering errors of the sub-electricity meters of each DC station charging pile.

[0053] For example, the metering errors of the DC station charging pile sub-meters and the AC station charging pile sub-meters in the pre-processed charging data information group can be calculated separately. For example, when calculating the metering error of each DC station charging pile sub-meter, the historical average metering error of the other DC station charging pile sub-meters and the historical average metering error of the other AC station charging pile sub-meters can be determined first; then, the above-mentioned DC total energy meter measurement value, DC total energy meter measurement error, AC / DC hybrid station charging pile line loss, AC / DC hybrid station charging pile fixed loss, the metering values of each DC station charging pile sub-meter included in the pre-processed charging data information group, AC / DC conversion efficiency, AC station charging pile sub-meter measurement value, other DC station charging pile sub-meters, and the historical average metering error of other AC station charging pile sub-meters can be substituted into the DC station charging pile metering error model to obtain the metering error of the DC station charging pile sub-meter.

[0054] Therefore, the charging pile metering performance online monitoring system provides functions such as charging pile anomaly analysis, charging pile conversion efficiency analysis, and energy efficiency analysis. It analyzes and diagnoses the energy consumption loss within the charging station based on the line loss within the charging station, AC / DC conversion efficiency, and abnormal power consumption analysis within the station, determines the main causes of energy consumption loss within the station, and makes quantitative estimates.

[0055] Step 1043 : Determine the charging pile confidence interval corresponding to each pre-processed charging data information in the pre-processed charging data information group.

[0056] In some embodiments, the execution entity may determine a confidence interval for each charging pile corresponding to each piece of pre-processed charging data information in the pre-processed charging data information group. Specifically, the confidence interval for the metering error of each sub-meter corresponding to each piece of pre-processed charging data information may be a confidence interval pre-calculated based on historical metering data (historical metering error values) for each charging pile.

[0057] Therefore, a confidence interval evaluation is set for the charging pile metering error, and the confidence interval is used to screen abnormal charging piles to improve the accuracy of the model calculation results.

[0058] Step 1044 : Determine the abnormal performance charging pile and the abnormal electric energy meter based on the confidence interval of each charging pile and the above charging pile error information.

[0059] In some embodiments, the execution entity may determine an abnormal performance charging pile and an abnormal electric energy meter based on the confidence interval of each charging pile and the error information of the charging pile.

[0060] In practice, the above-mentioned execution entities can identify abnormal performance charging piles and abnormal energy meters through the following steps:

[0061] The first step is to determine whether any of the aforementioned charging pile confidence intervals meet an abnormal condition. The abnormal condition can be: the interval length of the charging pile confidence interval is greater than or equal to a preset length. For example, the charging pile confidence interval can be 0.95-0.98, with an interval length of 0.03; the preset length can be 0.02.

[0062] In the second step, in response to determining that there is a charging pile confidence interval that meets the abnormal condition among the above charging pile confidence intervals, the charging pile corresponding to the charging pile confidence interval that meets the abnormal condition is determined as an abnormal performance charging pile.

[0063] The third step is to remove the pre-processed charging data information corresponding to the confidence interval of the charging pile that meets the abnormal condition in the pre-processed charging data information group, so as to update the pre-processed charging data information group and obtain an updated pre-processed charging data information group.

[0064] In the fourth step, based on the charging pile metering error model and the corresponding charging pile type operating information, error identification is performed on the updated pre-processed charging data information group to obtain updated charging pile error information corresponding to the charging pile type. For example, the error identification method here can refer to the implementation method of step 1042 and is not further described here.

[0065] Step 5: Scale the energy meter operating data corresponding to the target updated pre-processed charging data information to obtain simulated energy meter operating data. For example, the energy meter operating data can be scaled down by a certain ratio. The energy meter operating data can include: measured values of sub-meters.

[0066] In the sixth step, the updated pre-processed charging data information corresponding to the target updated pre-processed charging data information in the updated pre-processed charging data information group is replaced with the simulated electric energy meter operation data to obtain a simulated pre-processed charging data information group.

[0067] In the seventh step, based on the charging pile metering error model and the corresponding charging pile type operating information, error identification is performed on the simulated pre-processed charging data information group to obtain simulated charging pile error information corresponding to the charging pile type. For example, the error identification method here can refer to the implementation method of step 1042 and is not further described here.

[0068] In step 8, in response to determining that the error information between the simulated charging pile error information and the updated charging pile error information is greater than or equal to a preset error information, the electric energy meter corresponding to the target updated pre-processed charging data information is determined to be an abnormal electric energy meter. For example, first, the metering error corresponding to the target updated pre-processed charging data information in the simulated charging pile error information can be determined, and the metering error corresponding to the target updated pre-processed charging data information in the updated charging pile error information can be determined; then, it is determined whether the error value between the two metering errors is greater than or equal to the preset error information. The preset error information can represent a preset error value.

[0069] Therefore, by proportionally adjusting the operating data of a particular electric energy meter and substituting the simulation data into the error calculation model, we can determine the meter's measurement error relative to the simulation data and the model's detection sensitivity to changes in the meter's operating error. Through simulation evaluations of substations of varying sizes, line loss rates, and power usage characteristics, we continuously iteratively optimize the model's adaptation parameters for various charging stations.

[0070] Step 105: Replace each of the abnormal performance charging piles and abnormal energy meters determined.

[0071] In some embodiments, the execution entity may replace each abnormal performance charging pile and abnormal energy meter. For example, each abnormal performance charging pile and abnormal energy meter may be replaced with a new charging pile and energy meter.

[0072] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device for online monitoring of the metering performance of a charging pile. These embodiments of the device for online monitoring of the metering performance of a charging pile are similar to Figure 1Corresponding to the method embodiments shown, the charging pile metering performance online monitoring device can be specifically applied to various electronic devices.

[0073] like Figure 2 As shown, some embodiments of the charging pile metering performance online monitoring device 200 include: an acquisition unit 201, a preprocessing unit 202, a clustering unit 203, a determination unit 204 and a replacement unit 205. The acquisition unit 201 is configured to acquire a charging data information set and a charging pile type operation information group of a target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; the preprocessing unit 202 is configured to perform data preprocessing on each charging data information in the above-mentioned charging data information set to generate preprocessed charging data information and obtain a preprocessed charging data information set; the clustering unit 203 is configured to perform clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set, wherein one preprocessed charging data information group corresponds to one charging pile type; the determination unit 204 is configured to perform clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set, wherein one preprocessed charging data information group corresponds to one charging pile type; the determination unit 204 is configured to perform clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set. For each pre-processed charging data information group in the charging data information group set, the following processing steps are performed: determining the charging pile type corresponding to the above pre-processed charging data information group; performing error identification on the above pre-processed charging data information group according to the charging pile metering error model corresponding to the above charging pile type and the corresponding charging pile type operation information, to obtain the charging pile error information corresponding to the above charging pile type; determining the charging pile confidence interval corresponding to each pre-processed charging data information in the above pre-processed charging data information group; determining the abnormal performance charging pile and the abnormal electric energy meter according to the confidence interval of each charging pile and the above charging pile error information; the replacement unit 205 is configured to perform a replacement operation on each determined abnormal performance charging pile and abnormal electric energy meter.

[0074] It is understandable that the units recorded in the charging pile metering performance online monitoring device 200 are the same as those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the charging pile metering performance online monitoring device 200 and the units included therein, and will not be repeated here.

[0075] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., as well as fixed terminals such as digital TVs and desktop computers. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0076] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0077] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0078] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0079] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0080] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0081] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the charging data information set and the charging pile type operation information group of the target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; performs data preprocessing on each charging data information in the above-mentioned charging data information set to generate preprocessed charging data information and obtain a preprocessed charging data information set; performs clustering processing on each preprocessed charging data information in the above-mentioned preprocessed charging data information set to obtain a preprocessed charging data information group set, wherein one preprocessed charging data information group corresponds to one charging pile type; For each pre-processed charging data information group in the above-mentioned pre-processed charging data information group set, the following processing steps are performed: determining the charging pile type corresponding to the above-mentioned pre-processed charging data information group; performing error identification on the above-mentioned pre-processed charging data information group according to the charging pile metering error model corresponding to the above-mentioned charging pile type and the corresponding charging pile type operation information, and obtaining the charging pile error information corresponding to the above-mentioned charging pile type; determining the charging pile confidence interval corresponding to each pre-processed charging data information in the above-mentioned pre-processed charging data information group; determining the abnormal performance charging pile and the abnormal electric energy meter according to the confidence interval of each charging pile and the above-mentioned charging pile error information; and replacing the determined abnormal performance charging pile and abnormal electric energy meter.

[0082] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as: a processor comprising: an acquisition unit, a preprocessing unit, a clustering unit, a determination unit, and a replacement unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the analysis unit may also be described as "a unit for performing spectral analysis on the above-mentioned music audio to extract music feature information."

[0085] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0086] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for online monitoring of charging pile metering performance, comprising: Obtaining a charging data information set and a charging pile type operation information group of a target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; performing data preprocessing on each charging data information in the charging data information set to generate preprocessed charging data information, thereby obtaining a preprocessed charging data information set; performing clustering processing on each pre-processed charging data information in the pre-processed charging data information set to obtain a pre-processed charging data information group set, wherein one pre-processed charging data information group corresponds to one charging pile type; For each pre-processed charging data information group in the pre-processed charging data information group set, the following processing steps are performed: Determining the charging pile type corresponding to the pre-processed charging data information group; performing error identification on the pre-processed charging data information group according to a charging pile metering error model corresponding to the charging pile type and corresponding charging pile type operation information to obtain charging pile error information corresponding to the charging pile type; Determining a charging pile confidence interval corresponding to each pre-processed charging data information in the pre-processed charging data information group; Determining abnormal performance charging piles and abnormal electric energy meters based on the confidence intervals of each charging pile and the error information of the charging piles; Replace the charging piles and electric energy meters with abnormal performance that have been identified.

2. The method according to claim 1, wherein The performing error identification on the pre-processed charging data information group according to the charging pile metering error model corresponding to the charging pile type and the corresponding charging pile type operation information to obtain the charging pile error information corresponding to the charging pile type includes: In response to determining that the charging pile type indicates an AC station charging pile, determining that the corresponding charging pile metering error model is an AC station charging pile metering error model; The pre-processed charging data information group and the charging pile type operation information are input into the AC station charging pile metering error model to obtain the AC station charging pile metering error information.

3. The method according to claim 1, wherein The performing error identification on the pre-processed charging data information group according to the charging pile metering error model corresponding to the charging pile type and the corresponding charging pile type operation information to obtain the charging pile error information corresponding to the charging pile type includes: In response to determining that the charging pile type indicates a DC station charging pile, determining that the corresponding charging pile metering error model is a DC station charging pile metering error model; The pre-processed charging data information group and the charging pile type operation information are input into the DC station charging pile metering error model to obtain DC station charging pile metering error information.

4. The method according to claim 1, wherein The performing error identification on the pre-processed charging data information group according to the charging pile metering error model corresponding to the charging pile type and the corresponding charging pile type operation information to obtain the charging pile error information corresponding to the charging pile type includes: In response to determining that the charging pile type indicates an AC / DC hybrid station charging pile, determining that the corresponding charging pile metering error model is an AC / DC hybrid station charging pile metering error model; The pre-processed charging data information group and the charging pile type operation information are input into the AC / DC hybrid station charging pile metering error model to obtain AC / DC hybrid station charging pile metering error information.

5. An online monitoring device for charging pile metering performance, comprising: an acquiring unit configured to acquire a charging data information set and a charging pile type operation information group of a target charging station within a preset time period, wherein each charging data information corresponds to a charging pile in the target charging station; a preprocessing unit configured to perform data preprocessing on each charging data information in the charging data information set to generate preprocessed charging data information, thereby obtaining a preprocessed charging data information set; a clustering unit configured to perform clustering processing on each pre-processed charging data information in the pre-processed charging data information set to obtain a pre-processed charging data information group set, wherein one pre-processed charging data information group corresponds to one charging pile type; The determination unit is configured to perform the following processing steps for each pre-processed charging data information group in the pre-processed charging data information group set: determining the type of charging pile corresponding to the pre-processed charging data information group; performing error identification on the pre-processed charging data information group based on a charging pile metering error model corresponding to the charging pile type and corresponding charging pile type operation information to obtain charging pile error information corresponding to the charging pile type; determining a charging pile confidence interval corresponding to each pre-processed charging data information in the pre-processed charging data information group; and determining an abnormal performance charging pile and an abnormal electric energy meter based on each charging pile confidence interval and the charging pile error information. The replacement unit is configured to replace each abnormal performance charging pile and abnormal electric energy meter determined.

6. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer-readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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