Work error determination method, apparatus, and non-transitory storage medium

Abstract

The application discloses a kind of working error determination method, device and nonvolatile storage medium.Therein, the method comprises: the total amount of electric energy of charging station and the electric energy measurement value corresponding to each of multiple charging piles are respectively obtained in multiple time intervals;The difference between the total amount of electric energy in multiple time intervals and the sum of the electric energy measurement value corresponding to each of multiple charging piles is respectively calculated as the electric energy measurement difference value corresponding to each of multiple time intervals;Based on the functional relationship between electric energy measurement difference value and the electric energy measurement value corresponding to each of multiple charging piles, determine the equation including to-be-estimated parameter corresponding to each of multiple time intervals;Based on the equation corresponding to each of multiple time intervals, determine the estimated value of the rate of change corresponding to each of multiple charging piles;Based on the estimated value of the rate of change corresponding to each of multiple charging piles, determine the working error corresponding to each of multiple charging piles.The application solves the technical problem of low efficiency of current on-site verification of charging pile working error.

Description

Technical Field

[0001] This invention relates to the field of metrology, and more specifically, to a method, apparatus, and non-volatile storage medium for determining operating errors. Background Technology

[0002] As a crucial infrastructure supporting the development of the electric vehicle industry, charging piles serve as the basis for electricity trading between power grid companies and electric vehicle users. Currently, with the rapid development of the domestic electric vehicle industry, the volume of electricity trading behind charging piles continues to rise. The fairness of electricity trading has gradually become one of the key concerns for power grid companies and electric vehicle users, and the accuracy of charging pile electricity metering is the cornerstone of ensuring fair electricity trading in the electric vehicle industry.

[0003] Due to the complex operating environment of charging piles, the energy metering module may develop operational errors after prolonged operation, leading to inaccurate energy metering values ​​and unnecessary disputes. Therefore, it is necessary to verify the metering performance of charging piles. Currently, the verification method for charging pile energy metering performance is mainly on-site verification, which can take up to 1 hour per test. As of the end of 2023, there were approximately 1.6 million public AC charging piles nationwide, making it difficult to complete the verification of energy metering for all public AC charging piles through on-site verification. Addressing the challenges of comprehensive verification of AC charging piles, there is an urgent need to explore an efficient method for monitoring the energy metering performance of charging piles, completing the pre-verification of energy metering performance, and thus reducing the workload of on-site verification.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and non-volatile storage medium for determining working errors, in order to at least solve the technical problem of low efficiency in current on-site verification of charging pile working errors.

[0006] According to one aspect of the present invention, a method for determining operating error is provided, comprising: acquiring the total electrical energy of a charging station and the electrical energy metering values ​​corresponding to each of a plurality of charging piles in a plurality of time intervals, wherein the plurality of charging piles are located in the charging station, and the operating state of the plurality of charging piles in any one of the plurality of time intervals is the same as in the other time intervals, the operating state including charging state and idle state; calculating the difference between the total electrical energy in the plurality of time intervals and the sum of the electrical energy metering values ​​corresponding to each of the plurality of charging piles, as the electrical energy metering difference corresponding to each of the plurality of time intervals; determining an equation for each of the plurality of time intervals, including a parameter to be estimated, based on the functional relationship between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each of the plurality of charging piles, wherein the parameter to be estimated includes the rate of change corresponding to each of the plurality of charging piles; determining an estimated value of the rate of change corresponding to each of the plurality of charging piles based on the equation for each of the plurality of time intervals; and determining the operating error corresponding to each of the plurality of charging piles based on the estimated value of the rate of change corresponding to each of the plurality of charging piles and the functional relationship between the rate of change corresponding to each of the plurality of charging piles and their respective operating errors.

[0007] Optionally, the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles is calculated as the electrical energy metering difference for each of the multiple time intervals. This includes: obtaining the power transmission network structure within the charging station; determining the line power loss value within the charging station based on the power transmission network structure; calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles as the initial electrical energy metering difference for each of the multiple time intervals; and calculating the difference between the initial electrical energy metering difference for each of the multiple time intervals and the line power loss value within the charging station as the electrical energy metering difference for each of the multiple time intervals.

[0008] Optionally, the functional relationship between the electricity metering difference and the electricity metering values ​​corresponding to each of the multiple charging piles is expressed as follows:

[0009] ΔE=a1E1+a2E2+…a n E n +b

[0010] Where ΔE is the difference in electricity metering, from E1 to E n These are the respective electricity metering values ​​for multiple charging piles, a1 to a n denoted as the rate of change for each of the multiple charging piles, and b represents other energy loss values ​​in the charging station.

[0011] Optionally, the expression corresponding to the estimated rate of change of any one of the multiple charging piles and the functional relationship between it and the operating error is as follows:

[0012]

[0013] Where, γ i Let represent the operating error of the i-th charging pile. Let be the estimated rate of change corresponding to the i-th charging pile.

[0014] Optionally, multiple sets of total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles are obtained over multiple time intervals. Based on the total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles over multiple time intervals, multiple operating errors corresponding to each of the multiple charging piles are determined. Based on the confidence level calculation formula, the confidence level corresponding to each of the multiple operating errors is calculated. The operating error with the highest confidence level among the multiple operating errors is selected as the target operating error of the corresponding charging pile.

[0015] Optionally, the confidence level can be calculated using the following formula:

[0016]

[0017] Among them, C i Let R be the confidence level of the operating error corresponding to the i-th charging pile, and let R be the correlation index corresponding to the fitting process for determining the operating error. Let be the correlation coefficient between the energy metering value and the energy metering difference corresponding to the i-th charging pile.

[0018] Optionally, the number of multiple time intervals is not less than 2(n+1), where n is the number of charging piles in the charging state among the multiple charging piles.

[0019] According to another aspect of the present invention, a working error determination device is also provided, comprising: an acquisition module, configured to acquire the total electrical energy of a charging station and the electrical energy metering value corresponding to each of the multiple charging piles in multiple time intervals, wherein the multiple charging piles are located in the charging station, and the working state of the multiple charging piles in any one of the multiple time intervals is the same as in the other time intervals, the working state including charging state and idle state; a calculation module, configured to calculate the difference between the total electrical energy in the multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the electrical energy metering difference corresponding to each of the multiple time intervals; a first determination module, configured to determine an equation corresponding to each of the multiple time intervals, including a parameter to be estimated, based on the functional relationship between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each of the multiple charging piles, wherein the parameter to be estimated includes the rate of change corresponding to each of the multiple charging piles; a second determination module, configured to determine an estimated value of the rate of change corresponding to each of the multiple charging piles based on the equation corresponding to each of the multiple time intervals; and a third determination module, configured to determine the working error corresponding to each of the multiple charging piles based on the estimated value of the rate of change corresponding to each of the multiple charging piles and the functional relationship with the corresponding working error.

[0020] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described working error determination methods.

[0021] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described methods for determining working errors during runtime.

[0022] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for determining working errors.

[0023] In this embodiment of the invention, a working error determination method is employed. This involves acquiring the total electrical energy of the charging station and the electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals. The multiple charging piles are located within the charging station, and their operating states within any one time interval are the same as in all other time intervals, including charging and idle states. The difference between the total electrical energy within each time interval and the sum of the electrical energy metering values ​​corresponding to each charging pile is calculated as the electrical energy metering difference for each time interval. A functional relationship is then established between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each charging pile. This method involves determining equations for multiple time intervals, each containing parameters to be estimated, including the rate of change for each charging pile. Based on these equations, estimates of the rate of change for each charging pile are determined. Finally, based on these estimates and their functional relationship with the corresponding operating errors, the operating errors for each charging pile are determined. This method achieves the goal of efficiently detecting the operating errors of charging piles, thereby reducing the cost and workload of charging pile calibration and improving efficiency. It also solves the current technical problem of low efficiency in on-site calibration of charging pile operating errors. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 A hardware block diagram of a computer terminal for implementing a working error determination method is shown.

[0026] Figure 2 This is a flowchart illustrating the working error determination method provided according to an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of a charging pile power metering performance evaluation process provided by an optional embodiment of the present invention;

[0028] Figure 4 This is a wiring diagram of a charging pile and a main meter according to an optional embodiment of the present invention;

[0029] Figure 5 This is a structural block diagram of a working error determination device provided according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to an embodiment of the present invention, an embodiment of a method for determining working error is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method for determining operational errors is shown. Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the working error determination method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the working error determination method of the application described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0037] Figure 2 This is a flowchart illustrating the working error determination method provided by an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0038] Step S201: Obtain the total electrical energy of the charging station and the electrical energy metering value of each of the multiple charging piles in multiple time intervals. The multiple charging piles are located in the charging station. The working state of the multiple charging piles in any one time interval is the same as in the other time intervals. The working state includes charging state and idle state.

[0039] In this step, a high-precision electricity meter can be installed at the incoming end of the AC charging station to obtain the total electricity consumption of the entire charging station. This electricity meter is called the master meter. For the time interval for uploading electricity metering data from multiple charging piles, it is first necessary to ensure that the electricity consumed by a single charging pile within each time interval is greater than the electricity value corresponding to the smallest division value of the electricity meter. Additionally, to increase data diversity and improve the accuracy of linear fitting, the size of each time interval can be different. For example, the time interval can start from 5 minutes and gradually increase to 115 minutes, forming an arithmetic sequence to ensure data diversity and improve the accuracy of linear fitting. Then, the working status of each charging pile within the set time interval is monitored and recorded, including charging and idle states. From all the recorded data, time intervals with the same working status for all charging piles are selected to form a dataset for subsequent calculation of working errors. For example, in a dataset, within each time interval, charging piles 2, 3, and 4 are in a charging state, while charging piles 1 and 5 are in an idle state.

[0040] Step S202: Calculate the difference between the total amount of electricity in multiple time intervals and the sum of the electricity metering values ​​corresponding to each of the multiple charging piles, and use it as the electricity metering difference for each of the multiple time intervals.

[0041] In this step, for each time interval in the acquired data set, the difference between the total energy consumption of the master meter and the sum of the energy consumption values ​​of all charging piles in a charging state is calculated. This difference is the energy metering difference, reflecting the deviation between the master meter and the charging pile energy metering module. Ideally, it should equal the line loss within the charging station and the energy consumption of other non-charging piles. However, in practical applications, the charging pile energy metering module has operating errors. Therefore, this difference can be used to estimate the operating error.

[0042] Step S203: Based on the functional relationship between the difference in electricity metering and the electricity metering values ​​corresponding to each of the multiple charging piles, determine the equations corresponding to each of the multiple time intervals, including the parameters to be estimated, wherein the parameters to be estimated include the rate of change corresponding to each of the multiple charging piles.

[0043] In this step, according to the principle of energy conservation, the total electrical energy of the charging station should equal the sum of the electrical energy consumed by all charging piles plus other electrical energy losses within the charging station (such as line losses). However, when there are metering errors in the electrical energy of the charging piles, a deviation will occur between the actual observed total electrical energy and the sum of the metered electrical energy values ​​of the charging piles. Therefore, a functional relationship can be established to describe this deviation. Substituting the data corresponding to multiple time intervals into this functional relationship, we can obtain an equation corresponding to each of the multiple time intervals. The equation contains parameters to be estimated, including the rate of change corresponding to each of the multiple charging piles. The operating error corresponding to each charging pile can be calculated from the rate of change.

[0044] Step S204: Based on the equations corresponding to the multiple time intervals, determine the estimated values ​​of the change rates corresponding to the multiple charging piles.

[0045] In this step, based on the equations corresponding to multiple time intervals, the parameters in the equations, i.e., the change rates corresponding to each of the multiple charging piles, can be estimated using the least squares method. The basic idea of ​​the least squares method is to find a set of parameters that minimizes the sum of the squared errors between the energy metering differences and the model predictions over all time intervals. By fitting the equations, the optimal set of parameters to be estimated is obtained, including the estimated change rates corresponding to each of the multiple charging piles and the estimated values ​​of other energy losses in the charging station.

[0046] Step S205: Based on the estimated values ​​of the change rates corresponding to each of the multiple charging piles and the functional relationship between them and their respective operating errors, determine the operating errors corresponding to each of the multiple charging piles.

[0047] In this step, based on the law of conservation of energy, the relationship between the rate of change and the operating error of each charging pile can be obtained. The rate of change reflects the relationship between the energy metering value of the charging pile and the energy metering value of the main meter. The degree to which its value deviates from 1 represents the operating error of the charging pile's energy metering module; the greater the difference from 1, the greater the operating error. For example, for three different charging piles, the parameter estimation results are a2 = -0.00031, a3 = -0.013, and a4 = -0.046, respectively. The corresponding operating errors are calculated as follows: γ2 = 0.0003, γ3 = 0.0135, and γ4 = 0.0486. The rate of change of charging pile number 3 deviates from 1 the smallest, therefore its corresponding operating error is also the smallest.

[0048] Through the above steps, the goal of efficiently detecting the working error of charging piles is achieved, thereby reducing the cost and workload of charging pile calibration and improving efficiency, thus solving the current technical problem of low efficiency in on-site calibration of charging pile working errors.

[0049] As an optional embodiment, the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles is calculated as the electrical energy metering difference for each of the multiple time intervals. This includes: obtaining the power transmission network structure within the charging station; determining the line power loss value within the charging station based on the power transmission network structure; calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles as the initial electrical energy metering difference for each of the multiple time intervals; and calculating the difference between the initial electrical energy metering difference for each of the multiple time intervals and the line power loss value within the charging station as the electrical energy metering difference for each of the multiple time intervals.

[0050] Optionally, if the transmission network topology within the charging station is known, the line energy loss in the transmission network can be calculated based on the node voltage and load current values ​​measured at the charging piles. This loss value can then be subtracted from the initial energy metering difference, thus avoiding the impact of line loss fluctuations on the evaluation results. The transmission network structure within the charging station includes the connections between charging piles, the main meter, and the lines, as well as parameters such as line resistance and reactance. This information is crucial for accurately calculating line energy loss values, as different network structures lead to different line losses.

[0051] As an optional embodiment, the functional relationship between the electricity metering difference and the electricity metering values ​​corresponding to each of the multiple charging piles is expressed as follows:

[0052] ΔE=a1E1+a2E2+…a n E n +b

[0053] Where ΔE is the difference in electricity metering, from E1 to E n These are the respective electricity metering values ​​for multiple charging piles, a1 to a n denoted as the rate of change for each of the multiple charging piles, and b represents other energy loss values ​​in the charging station.

[0054] Optionally, the expression corresponding to the functional relationship between the energy metering difference and the energy metering values ​​of each of the multiple charging piles can be determined based on the law of conservation of energy. According to the principle of conservation of energy, over a period of time, the energy metering value of the charging pile and the total energy of the main meter should satisfy the following relationship:

[0055]

[0056] Among them, E sum E represents the total electrical energy consumed by the main meter. i Let E be the energy metering value of the i-th charging pile. additionOther energy loss values ​​include line losses within the charging station. Therefore, based on the above relationship, a functional relationship can be obtained between the energy metering difference and the energy metering values ​​corresponding to each of the multiple charging piles. That is, ΔE. That is, the rate of change a i E addition This refers to other energy loss values, b. Furthermore, if other loads are connected to the main meter, these other loads within the charging station must remain constant when acquiring energy data to ensure the constancy of other energy loss values ​​and avoid affecting parameter estimation. This functional relationship allows for more accurate identification and evaluation of charging pile operating errors, ensuring the accuracy and fairness of energy trading. The least squares method can find the optimal parameter estimate, thus more accurately reflecting the metering performance of the charging pile. By calculating the rate of change parameter for each charging pile individually, the actual operating error of the charging pile can be more accurately reflected, rather than just the overall or average deviation.

[0057] As an optional embodiment, the expression corresponding to the estimated rate of change of any one of the multiple charging piles and the functional relationship between the operating error is as follows:

[0058]

[0059] Where, γ i Let represent the operating error of the i-th charging pile. Let be the estimated rate of change corresponding to the i-th charging pile.

[0060] Alternatively, based on the principle of energy conservation, the relationship between operating error and rate of change can be obtained as follows:

[0061]

[0062] Among them, a i Let γ be the rate of change of the i-th charging pile. i Let be the operating error corresponding to the i-th charging pile. Therefore, given the estimated value of the rate of change, we can obtain an expression for the operating error based on the estimated value of the rate of change, and calculate the operating error corresponding to the charging pile.

[0063] As an optional embodiment, the total electrical energy of the charging station and the electrical energy metering values ​​of each charging pile are obtained in multiple time intervals. Based on the total electrical energy of the charging station and the electrical energy metering values ​​of each charging pile in multiple time intervals, multiple operating errors corresponding to each charging pile are determined. Based on the confidence level calculation formula, the confidence level corresponding to each of the multiple operating errors is calculated. The operating error with the highest confidence level among the multiple operating errors is selected as the target operating error of the corresponding charging pile.

[0064] Optionally, multiple sets of data can be acquired, provided that the operating status of all charging piles is identical across multiple time intervals within each set of data. This ensures that the operating error can be calculated for each set of data. After performing the aforementioned calculation steps to determine the operating error for each set of data, multiple possible operating errors for each charging pile can be obtained. The value that best reflects reality must be selected as the final result. Based on the correlation index in the least squares fitting process and the correlation coefficient between the charging pile's energy metering value and the energy metering difference, a specific calculation formula can be used to evaluate the confidence level of each operating error. The confidence level index is an important quantitative standard for assessing the reliability of the results and helps determine which assessment results are more credible. After obtaining the operating errors and their confidence levels for multiple charging piles, the operating error with the highest confidence level is selected as the target operating error for the charging pile.

[0065] As an optional implementation, the confidence level is calculated using the following formula:

[0066]

[0067] Among them, C i Let R be the confidence level of the operating error corresponding to the i-th charging pile, and let R be the correlation index corresponding to the fitting process for determining the operating error. Let be the correlation coefficient between the energy metering value and the energy metering difference corresponding to the i-th charging pile.

[0068] Optionally, confidence level is an important indicator for measuring the reliability of electricity metering performance evaluation results. Based on mathematical statistics principles, it provides a quantitative standard for judging the credibility of the evaluation results by analyzing the correlation index and the correlation between data during the fitting process. In the confidence level calculation formula of this invention, R is the correlation index corresponding to the fitting process that determines the working error, representing the degree of matching between the fitted line and the actual data points. Its value range is typically between -1 and 1. R close to 1 indicates that the data points are closely distributed along the fitted line, the model has a good fit, and the evaluation results are highly reliable; conversely, R close to 0 or further indicates that the data points are scattered, the fit is poor, and the evaluation results are unreliable. The formula for calculating R is as follows:

[0069]

[0070] Wherein, the subscript k represents the electricity metering data in the k-th time interval. This is the average value of ΔE.

[0071] In the confidence calculation formula of this invention Let be the correlation coefficient between the energy metering value and the energy metering difference for the i-th charging pile, describing the linear correlation between the metering value and the energy metering difference for the i-th charging pile. Its calculation is based on the covariance and variance of the two sets of data. If... If the value is close to 1 or -1, it indicates a strong linear relationship between the two sets of data, meaning that the working status of the charging pile has a significant impact on the change in the electricity metering difference, and the confidence level of the assessment result is high; while if A value close to 0 indicates a weak correlation between the two sets of data, an unclear relationship between the working status and the change in the difference in electricity metering, and a low confidence level in the assessment results. The calculation formula is as follows:

[0072]

[0073] Among them, Cov(E) i ,ΔE) is E i The covariance of ΔE, and Var ΔE E respectively i The variance of ΔE.

[0074] As an optional embodiment, the number of multiple time intervals is not less than 2(n+1), where n is the number of charging piles in the charging state among the multiple charging piles.

[0075] Optionally, during the fitting process, the number of data points acquired across multiple time intervals should be at least 2(n+1), where n represents the number of charging piles in a charging state. For example, if three charging piles are charging within a certain time interval, then other datasets used together with these time intervals to calculate the charging pile operating error should also ensure that the same three charging piles are charging, and the number of these time intervals should be at least eight (i.e., 2(3+1) = 8). This requirement is based on the principle of least squares in statistics, ensuring sufficient data points for model fitting, thereby improving the accuracy of least squares estimation of operating error parameters. More data points can better reflect the energy metering characteristics of charging piles under different conditions, reducing the bias in model fitting and the impact of random errors. If the number of collected data time intervals is less than 2(n+1), then the least squares model fitting based on these data may be affected, leading to a decrease in the accuracy of operating error assessment. In this case, the assessment results may be affected by random errors or uneven data distribution, resulting in low reliability.

[0076] As an optional embodiment, a process for evaluating the energy metering performance of charging piles is also provided. Figure 3 This is a flowchart of a charging pile energy metering performance evaluation process according to an optional embodiment of the present invention. Figure 3As shown, by analyzing the relationship between the total meter reading and the power meter reading of the charging pile, the working error of the charging pile was estimated using the least squares method. The working error with the highest confidence level was taken as the final performance evaluation result of the corresponding charging pile.

[0077] Optionally, a specific embodiment may also be provided. For example, the charging station in this embodiment may include 5 charging piles. Figure 4 This is a wiring diagram of a charging pile and a main meter according to an optional embodiment of the present invention. The working errors measured on-site by the charging pile are 0.01, 0.02, -0.01, 0.05, and -0.04, respectively. The original power metering data of the charging pile and the main meter are shown in Tables 1 to 6, respectively, containing a total of 120 time intervals. Since the power of the charging pile remains basically constant during the charging process, in order to increase the diversity of data and improve the accuracy of linear fitting, the time interval in this embodiment starts from 5 minutes and increases to 115 minutes in an arithmetic sequence, with a common difference of 5 minutes.

[0078]

[0079]

[0080] Table 1. Original Electricity Metering Data for Charging Pile No. 1

[0081]

[0082]

[0083] Table 2. Original Electricity Metering Data for Charging Pile No. 2

[0084]

[0085]

[0086] Table 3. Original Electricity Metering Data for Charging Pile No. 3

[0087]

[0088]

[0089] Table 4. Original Electricity Metering Data for Charging Pile No. 4

[0090]

[0091]

[0092] Table 5. Original Electricity Metering Data for Charging Pile No. 5

[0093]

[0094]

[0095] Table 6. Summary of Original Electricity Metering Data

[0096] Furthermore, based on the original electricity metering data, a dataset for evaluating the electricity metering performance of charging piles is selected. For example, in this embodiment, in time intervals numbered 3, 4, 5, 6, 7, 20, 84, 95, and 96, all charging piles have the same operating status, with piles 2, 3, and 4 in a charging state and piles 1 and 5 in an idle state. Since the time interval number is 9 and the number of charging piles in a charging state is 3, this dataset meets the requirements for a dataset used for evaluating the electricity metering of charging piles.

[0097] Furthermore, the difference between the total electricity meter reading and the sum of the electricity meter readings of the charging piles (referred to as the electricity metering difference) is calculated, and the relationship between the charging pile electricity meter reading and the electricity metering difference is fitted based on the least squares method. For example, in the above dataset, the electricity metering differences at different intervals are shown in Table 7.

[0098]

[0099]

[0100] Table 7. Data table for evaluating the energy metering performance of a charging pile.

[0101] Within a single time interval, the difference in electricity metering and the electricity metering value of the charging pile should satisfy the following relationship:

[0102] ΔE=a2E2+a3E3+a4E4+b

[0103] Using the data in Table 7, the parameters in the formula are estimated based on the principle of least squares. The estimation results are: a2 = -0.00031, a3 = -0.013, a4 = -0.046, b = 0.018. Then, based on the working error calculation formula, the working error evaluation values ​​of the corresponding charging piles are calculated as follows: γ2 = 0.0003, γ3 = 0.0135, γ4 = 0.0486.

[0104] Furthermore, based on the correlation index of the least squares fitting and the correlation coefficient between the electricity metering values, the confidence level of the evaluation results is given. The correlation index of the least squares fitting for the above dataset is 0.99. The correlation coefficients between the electricity metering data and the electricity metering differences for charging piles 2, 3, and 4 are -0.79, -0.88, and -0.98, respectively. Therefore, the confidence levels of the evaluation results of the working error of charging piles 2, 3, and 4 for this dataset are 0.78, 0.87, and 0.97, respectively.

[0105] Furthermore, other datasets were used to evaluate the operating error of the charging piles. When the operating error of the same charging pile was evaluated multiple times, the value with the highest confidence level was used as the final evaluation result. Ultimately, the evaluation results in this embodiment were: γ1 = 0.013, γ2 = 0.019, γ3 = -0.009, γ4 = 0.048, γ5 = -0.041, with corresponding confidence levels of 0.32, 0.88, 0.99, 0.97, and 0.55, respectively. As can be seen from the above results, the evaluation results in this example are very close to the actual operating error of the charging piles during on-site verification.

[0106] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the working error determination method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0108] According to an embodiment of the present invention, an apparatus for implementing the above-described method for determining working errors is also provided. Figure 5 This is a structural block diagram of the working error determination device provided according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 51, a calculation module 52, a first determination module 53, a second determination module 54, and a third determination module 55. The device will be described below.

[0109] The acquisition module 51 is used to acquire the total electrical energy of the charging station and the electrical energy metering value of each of the multiple charging piles in multiple time intervals. The multiple charging piles are located in the charging station. The working state of the multiple charging piles in any one time interval is the same as in the other time intervals. The working state includes charging state and idle state.

[0110] The calculation module 52, connected to the acquisition module 51, is used to calculate the difference between the total amount of electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, and use these differences as the electrical energy metering differences corresponding to each of the multiple time intervals.

[0111] The first determining module 53, connected to the calculation module 52, is used to determine the equations corresponding to multiple time intervals, including the parameters to be estimated, based on the functional relationship between the energy metering difference and the energy metering values ​​corresponding to the multiple charging piles. The parameters to be estimated include the rate of change corresponding to the multiple charging piles.

[0112] The second determining module 54, connected to the first determining module 53, is used to determine the estimated value of the rate of change of each of the multiple charging piles based on the equations corresponding to the multiple time intervals.

[0113] The third determining module 55, connected to the second determining module 54, is used to determine the operating error of each of the multiple charging piles based on the estimated value of the rate of change of each of the multiple charging piles and the functional relationship between the rate of change and the corresponding operating error.

[0114] It should be noted that the acquisition module 51, calculation module 52, first determination module 53, second determination module 54, and third determination module 55 mentioned above correspond to steps S201 to S205 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0115] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0116] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the working error determination method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned working error determination method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] The processor can invoke information and application programs stored in the memory via a transmission device to execute the following steps: acquiring the total electrical energy of the charging station and the electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals, wherein the multiple charging piles are located in the charging station, and the operating state of the multiple charging piles in any one of the multiple time intervals is the same as in all other time intervals, including charging state and idle state; calculating the difference between the total electrical energy and the sum of the electrical energy metering values ​​corresponding to each of the multiple time intervals, as the electrical energy metering difference for each of the multiple time intervals; determining the equations for each of the multiple time intervals, including the parameters to be estimated, based on the functional relationship between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each of the multiple charging piles, wherein the parameters to be estimated include the rate of change corresponding to each of the multiple charging piles; determining the estimated value of the rate of change corresponding to each of the multiple time intervals based on the equations for each of the multiple time intervals; and determining the operating error corresponding to each of the multiple charging piles based on the estimated value of the rate of change corresponding to each of the multiple charging piles and the functional relationship between the rate of change corresponding to each of the multiple charging piles and their corresponding operating errors.

[0118] Optionally, the processor may also execute program code for the following steps: calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the electrical energy metering difference for each of the multiple time intervals, including: obtaining the power transmission network structure within the charging station; determining the line power loss value within the charging station based on the power transmission network structure; calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the initial electrical energy metering difference for each of the multiple time intervals; and calculating the difference between the initial electrical energy metering difference for each of the multiple time intervals and the line power loss value within the charging station, as the electrical energy metering difference for each of the multiple time intervals.

[0119] Optionally, the processor may also execute program code for the following steps: The expression corresponding to the functional relationship between the energy metering difference and the energy metering values ​​corresponding to each of the multiple charging piles is as follows:

[0120] ΔE=a1E1+a2E2+…a n E n +b

[0121] Where ΔE is the difference in electricity metering, from E1 to E n These are the respective electricity metering values ​​for multiple charging piles, a1 to a n denoted as the rate of change for each of the multiple charging piles, and b represents other energy loss values ​​in the charging station.

[0122] Optionally, the processor may also execute program code that performs the following steps: The expression corresponding to the functional relationship between the estimated rate of change of any one of the multiple charging piles and the operating error is as follows:

[0123]

[0124] Where, γ i Let represent the operating error of the i-th charging pile. Let be the estimated rate of change corresponding to the i-th charging pile.

[0125] Optionally, the processor may also execute program code that performs the following steps: continue to acquire multiple sets of total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals; determine multiple operating errors corresponding to each of the multiple charging piles based on the multiple sets of total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals; calculate the confidence level corresponding to each of the multiple operating errors based on the confidence level calculation formula; and select the operating error with the highest confidence level among the multiple operating errors as the target operating error of the corresponding charging pile.

[0126] Optionally, the processor described above can also execute program code with the following steps: The confidence level is calculated using the following formula:

[0127]

[0128] Among them, C i Let R be the confidence level of the operating error corresponding to the i-th charging pile, and let R be the correlation index corresponding to the fitting process for determining the operating error. Let be the correlation coefficient between the energy metering value and the energy metering difference corresponding to the i-th charging pile.

[0129] Optionally, the processor may also execute program code that performs the following steps: the number of multiple time intervals is not less than 2(n+1), where n is the number of charging piles in the charging state among the multiple charging piles.

[0130] The present invention provides a method for determining working error. By acquiring the total electrical energy of the charging station and the electrical energy metering values ​​of each charging pile within multiple time intervals, where the charging piles are located within the charging station and their operating states are the same in any given time interval (including charging and idle states), the differences between the total electrical energy and the sum of the electrical energy metering values ​​of each charging pile within each time interval are calculated as the electrical energy metering differences for each time interval. Based on the functional relationship between the electrical energy metering differences and the electrical energy metering values ​​of each charging pile, equations are determined for each time interval, including the estimated parameters, such as the rate of change for each charging pile. Estimates of the rate of change for each charging pile are then determined based on these equations. Finally, based on the estimated rates of change and their functional relationship with the corresponding operating errors, the operating errors for each charging pile are determined. This method achieves high-efficiency detection of charging pile operating errors, thereby reducing the cost and workload of charging pile calibration and improving efficiency. This solves the current problem of low efficiency in on-site calibration of charging pile operating errors.

[0131] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0132] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the working error determination method provided in the above embodiments.

[0133] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0134] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the total electrical energy of the charging station and the electrical energy metering value corresponding to each of the multiple charging piles in multiple time intervals, wherein the multiple charging piles are located in the charging station, and the working state of the multiple charging piles in any one of the multiple time intervals is the same as in the other time intervals, the working state including charging state and idle state; calculating the difference between the total electrical energy in the multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the electrical energy metering difference corresponding to each of the multiple time intervals; determining the equations including the parameters to be estimated for each of the multiple time intervals based on the functional relationship between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each of the multiple charging piles, wherein the parameters to be estimated include the rate of change corresponding to each of the multiple charging piles; determining the estimated value of the rate of change corresponding to each of the multiple charging piles based on the equations corresponding to each of the multiple time intervals; and determining the working error corresponding to each of the multiple charging piles based on the estimated value of the rate of change corresponding to each of the multiple charging piles and the functional relationship with the corresponding working error.

[0135] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the electrical energy metering difference for each of the multiple time intervals, including: obtaining the power transmission network structure within the charging station; determining the line power loss value within the charging station based on the power transmission network structure; calculating the difference between the total electrical energy in multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the initial electrical energy metering difference for each of the multiple time intervals; and calculating the difference between the initial electrical energy metering difference for each of the multiple time intervals and the line power loss value within the charging station, as the electrical energy metering difference for each of the multiple time intervals.

[0136] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the expression corresponding to the functional relationship between the energy metering difference and the energy metering values ​​corresponding to each of the multiple charging piles is as follows:

[0137] ΔE=a1E1+a2E2+…a n E n +b

[0138] Where ΔE is the difference in electricity metering, from E1 to E n These are the respective electricity metering values ​​for multiple charging piles, a1 to a n denoted as the rate of change for each of the multiple charging piles, and b represents other energy loss values ​​in the charging station.

[0139] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the expression corresponding to the estimated rate of change of any one of the multiple charging piles and the functional relationship between the operating error is as follows:

[0140]

[0141] Where, γ i Let represent the operating error of the i-th charging pile. Let be the estimated rate of change corresponding to the i-th charging pile.

[0142] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: continuing to acquire multiple sets of total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals; determining multiple operating errors corresponding to each of the multiple charging piles based on the multiple sets of total electrical energy of the charging station and electrical energy metering values ​​corresponding to each of the multiple charging piles within multiple time intervals; calculating the confidence level corresponding to each of the multiple operating errors based on the confidence level calculation formula; and selecting the operating error with the highest confidence level among the multiple operating errors as the target operating error of the corresponding charging pile.

[0143] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The confidence level is calculated using the following formula:

[0144]

[0145] Among them, C i Let R be the confidence level of the operating error corresponding to the i-th charging pile, and let R be the correlation index corresponding to the fitting process for determining the operating error. Let be the correlation coefficient between the energy metering value and the energy metering difference corresponding to the i-th charging pile.

[0146] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the number of multiple time intervals is not less than 2(n+1), where n is the number of charging piles in the charging state among the multiple charging piles.

[0147] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire the total electrical energy of a charging station and the electrical energy metering value corresponding to each of the multiple charging piles in multiple time intervals, wherein the multiple charging piles are located in the charging station, and the working state of the multiple charging piles in any one of the multiple time intervals is the same as in the other time intervals, the working state including charging state and idle state; calculate the difference between the total electrical energy in the multiple time intervals and the sum of the electrical energy metering values ​​corresponding to each of the multiple charging piles, as the electrical energy metering difference corresponding to each of the multiple time intervals; determine the equations corresponding to each of the multiple time intervals, including the parameters to be estimated, based on the functional relationship between the electrical energy metering difference and the electrical energy metering values ​​corresponding to each of the multiple charging piles, wherein the parameters to be estimated include the rate of change corresponding to each of the multiple charging piles; determine the estimated value of the rate of change corresponding to each of the multiple charging piles based on the equations corresponding to each of the multiple time intervals; and determine the working error corresponding to each of the multiple charging piles based on the estimated value of the rate of change corresponding to each of the multiple charging piles and the functional relationship between the rate of change corresponding to each of the multiple charging piles and their corresponding working errors.

[0148] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0149] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0153] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0154] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of determining a working error, characterized by, The method comprises the following steps: obtaining the total amount of electric energy of the charging station and the electric energy measurement value corresponding to each of the plurality of charging piles in a plurality of time intervals, wherein the working state of the plurality of charging piles in any one of the plurality of time intervals is the same as that in each of the remaining time intervals, and the working state comprises a charging state and an idle state; calculating the difference between the total amount of electric energy and the sum of the electric energy measurement values corresponding to each of the plurality of charging piles in each of the plurality of time intervals as the electric energy measurement difference corresponding to each of the plurality of time intervals; determining the equation including the to-be-estimated parameters corresponding to each of the plurality of time intervals based on the functional relationship between the electric energy measurement difference and the electric energy measurement value corresponding to each of the plurality of charging piles, wherein the to-be-estimated parameters include the change rate corresponding to each of the plurality of charging piles; determining the estimated value of the change rate corresponding to each of the plurality of charging piles based on the equation corresponding to each of the plurality of time intervals; determining the working error corresponding to each of the plurality of charging piles based on the functional relationship between the estimated value of the change rate corresponding to each of the plurality of charging piles and the working error corresponding to each of the plurality of charging piles.

2. The method of claim 1, wherein, The step of calculating the difference between the total amount of electric energy and the sum of the electric energy measurement values corresponding to each of the plurality of charging piles in each of the plurality of time intervals as the electric energy measurement difference corresponding to each of the plurality of time intervals comprises the following steps: obtaining the power transmission network structure in the charging station; determining the line electric energy loss value in the charging station based on the power transmission network structure; calculating the difference between the total amount of electric energy and the sum of the electric energy measurement values corresponding to each of the plurality of charging piles in each of the plurality of time intervals as the initial electric energy measurement difference corresponding to each of the plurality of time intervals; calculating the difference between the initial electric energy measurement difference corresponding to each of the plurality of time intervals and the line electric energy loss value in the charging station as the electric energy measurement difference corresponding to each of the plurality of time intervals.

3. The method of claim 1, wherein, The expression corresponding to the functional relationship between the electric energy measurement difference and the electric energy measurement value corresponding to each of the plurality of charging piles is as follows: ΔE = a1E1 + a2E2 +... anEn n E n +b Wherein, ΔE is the electric energy measurement difference, E1 to E n are respectively the electric energy measurement values corresponding to each of the plurality of charging piles, a1 to a n are respectively the change rates corresponding to each of the plurality of charging piles, and b is the other electric energy loss value in the charging station.

4. The method of claim 1, wherein, The expression corresponding to the functional relationship between the estimated value of the change rate corresponding to each of the plurality of charging piles and the working error is as follows: wherein γ i is the working error of the i-th charging pile, is the estimated value of the rate of change corresponding to the i-th charging pile.

5. The method of claim 1, wherein, The method further comprises the following steps: continuously obtaining a plurality of sets of the total amount of electric energy of the charging station and the electric energy measurement value corresponding to each of the plurality of charging piles in a plurality of time intervals; determining a plurality of working errors corresponding to each of the plurality of charging piles based on the plurality of sets of the total amount of electric energy of the charging station and the electric energy measurement value corresponding to each of the plurality of charging piles in a plurality of time intervals; calculating the confidence degree corresponding to each of the plurality of working errors based on the confidence degree calculation formula; selecting the working error with the largest confidence degree from the plurality of working errors as the target working error of the corresponding charging pile.

6. The method of claim 5, wherein, The confidence degree calculation formula is as follows: wherein C i is the confidence of the working error corresponding to the i-th charging pile, R is the correlation index corresponding to the fitting process for determining the working error, is the correlation coefficient of the electric energy measurement value and the electric energy measurement difference value corresponding to the i-th charging pile.

7. The method of claim 1, wherein, The number of the plurality of time intervals is not less than 2(n+1), wherein n is the number of charging piles in the charging state in the plurality of charging piles.

8. An operation error determination device characterized by comprising: The method comprises the following steps: An acquisition module is configured to acquire total electric energy of a charging station and electric energy measurement values of a plurality of charging piles respectively in a plurality of time intervals, wherein the plurality of charging piles are located in the charging station, working states of the plurality of charging piles in any one of the plurality of time intervals are same as those in each of the remaining time intervals, and the working states include charging states and idle states; A calculation module is configured to calculate differences between the total electric energy and the sum of the electric energy measurement values of the plurality of charging piles respectively in the plurality of time intervals as electric energy measurement difference values corresponding to the plurality of time intervals respectively; A first determination module is configured to determine equations including to-be-estimated parameters corresponding to the plurality of time intervals respectively based on a functional relationship between the electric energy measurement difference values and the electric energy measurement values of the plurality of charging piles respectively, wherein the to-be-estimated parameters include change rates of the plurality of charging piles respectively; A second determination module is configured to determine estimated values of the change rates of the plurality of charging piles respectively based on the equations corresponding to the plurality of time intervals respectively; A third determination module is configured to determine working error values of the plurality of charging piles respectively based on a functional relationship between the estimated values of the change rates of the plurality of charging piles respectively and the working error values respectively.

9. A non-volatile storage medium, comprising: The non-volatile storage medium includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to perform the working error determination method in any one of claims 1 to 7 when the program is running.

10. A computer device, comprising: Comprise: A memory and a processor, The memory stores a computer program; The processor is configured to execute the computer program stored in the memory, and the computer program makes the processor execute the working error determination method in any one of claims 1 to 7 when running.

11. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the working error determination method in any one of claims 1 to 7.

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