Data acquisition optimization method and system for charging pile error analysis algorithm

Abstract

The invention relates to a data acquisition optimization method and system for a charging pile error analysis algorithm. The method comprises the following steps: setting an initial acquisition period and an initial time window size of charging pile operation data; creating a time window according to the size of the initial time window, and sliding the time window to collect operation data of all charging piles according to an initial collection period; calculating the error of each charging pile according to the collected data of the time window of each sliding, and calculating the stability index of the charging pile according to the error of the charging pile in the time window of each sliding; fixing the size of the current time window, adjusting the acquisition period, recalculating the stability index of each charging pile according to the above steps, and calculating the optimal acquisition period; and fixing the optimal acquisition period, adjusting the size of the current time window, recalculating the stability index of each charging pile according to the steps, and calculating the size of the optimal time window.

Description

Technical Field

[0001] The invention relates to a data acquisition optimization method and system for a charging pile error analysis algorithm, belonging to the technical field of charging piles. Background Art

[0002] The invention patent with the publication number "CN115455689A" discloses a high-precision analysis method for the electric energy metering error of a charging pile. Based on the law of conservation of electric quantity and the circuit topology of the charging station, a calculation model for the electric energy metering error of the charging station is established; taking the charging station as a unit, multiple linear regression and other methods are used to analyze and obtain the electric energy metering error of each charging pile in the charging station, thereby realizing real-time error detection of the charging piles in the charging station during the entire operation cycle. The electric energy metering value of the total meter of the charging station, the electric energy metering value of each charging pile sub-meter, the line loss, and the cumulative value of the fixed loss are obtained in the same metering cycle; the influence of the conversion efficiency of each DC charging pile in the station is fully considered, and the conversion efficiency of each DC charging pile is obtained in combination with the conversion efficiency analysis model, and brought into the fitting model, thereby realizing a high-precision evaluation of the errors of each charging pile in the charging station;

[0003] However, the collection cycle and time window proposed in the above patent for collecting charging pile data will significantly affect the accuracy and stability of the charging pile error analysis algorithm;

[0004] The data collection cycle refers to the time interval for the data collection system to obtain data, such as obtaining a data point every 3 seconds or every 15 minutes. Too frequent data collection may increase data noise and processing burden, causing the algorithm to overfit the data, reducing the algorithm's generalization ability and accuracy. Too long a data collection cycle may cause the algorithm to be unable to capture rapid changes in data, reducing the algorithm's real-time performance and accuracy.

[0005] The time window is used to limit the data range processed by the algorithm. However, the size of the time window will have a significant impact on the efficiency of the algorithm. Specifically, a time window that is too large may cause the algorithm to process too much data, increase the computational complexity, and introduce too much historical data will cover up the existing characteristics of the current charging pile, thereby reducing the accuracy and operating efficiency of the algorithm. On the other hand, a time window that is too small may cause the algorithm to be unable to fully capture the characteristics of the data, affecting the accuracy and reliability of the algorithm. Therefore, the time window of the charging pile data is crucial to the accuracy of the algorithm. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention proposes a data acquisition optimization method and system for a charging pile error analysis algorithm.

[0007] The technical solution of the present invention is as follows:

[0008] In one aspect, the present invention provides a data acquisition optimization method for a charging pile error analysis algorithm, comprising the following steps:

[0009] Set the initial collection period and initial time window size of charging pile operation data;

[0010] Create a time window according to the size of the initial time window, slide the time window, and collect the operating data of all charging piles according to the initial collection cycle within each sliding time window;

[0011] The error of each charging pile in each sliding time window is calculated by a preset algorithm according to the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold, and the preset threshold of the number of sliding times of the time window is greater than or equal to 12, the sliding is ended, and the stability index of the charging pile is calculated according to the error of the charging pile in each sliding time window;

[0012] Fix the current time window size, adjust the collection cycle, collect charging pile data according to the sliding time window of each adjusted collection cycle, and recalculate the stability index of each charging pile according to the above steps, compare the calculated value of the stability index after each adjustment, and when the stability index of the charging pile is the smallest, it is the optimal collection cycle;

[0013] The optimal collection period is fixed, and the current time window size is adjusted. According to the optimal collection period and the time window after each adjustment, the sliding time window is used to collect the charging pile data. The stability index of each charging pile is recalculated according to the above steps. The calculated value of the stability index after each adjustment is compared. When the stability index of the charging pile is the smallest, it is the optimal time window size.

[0014] As a preferred embodiment of the present invention, the calculation formula of the stability index of the charging pile is:

[0015]

[0016] Among them: msa n represents the stability index of the nth charging pile; m represents the total number of sliding times in the time window; ε i,n represents the error of the nth charging pile in the i-th sliding time window; Represents the mean error of the nth charging pile in all sliding time windows.

[0017] As a preferred embodiment of the present invention, the calculation formula for the mean error of the charging pile in all sliding time windows is:

[0018] On the other hand, the present invention also provides a data acquisition optimization system for a charging pile error analysis algorithm, including an initial data acquisition parameter configuration module, a data acquisition module, a stability index calculation module, and a data acquisition parameter optimization module;

[0019] The initial data acquisition parameter configuration module is used to set the initial acquisition period and initial time window size of the charging pile operation data;

[0020] The data acquisition module is used to create a time window according to the size of the initial time window, slide the time window, and collect the operating data of all charging piles according to the initial acquisition cycle within each sliding time window;

[0021] The stability index calculation module is used to calculate the error of each charging pile in each sliding time window through a preset algorithm according to the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold, the sliding is ended, and the stability index of the charging pile is calculated according to the error of the charging pile in each sliding time window;

[0022] The data acquisition parameter optimization module is used to fix the current time window size, adjust the acquisition cycle, collect charging pile data through the data acquisition module according to each adjusted acquisition cycle, and recalculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal acquisition cycle; fix the optimal acquisition cycle, adjust the current time window size, recollect the charging pile data through the data acquisition module according to the optimal acquisition cycle and the time window after each adjustment, and calculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal time window size.

[0023] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0024] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0025] The present invention has the following beneficial effects:

[0026] 1. The present invention adjusts the collection period and time window of input data through repeatability evaluation, thereby improving the accuracy and reliability of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

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

[0029] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0030] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0031] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0032] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0033] Embodiment 1:

[0034] See also Figure 1 , a data acquisition optimization method for a charging pile error analysis algorithm, comprising the following steps:

[0035] Set the initial collection period and initial time window size of charging pile operation data;

[0036] Create a time window according to the initial time window size, slide the time window (if the time window length is 15 days, and the last time window is 12.1-12.15, then the time window after sliding once is 12.16-12.30), and collect the operation data of all charging piles according to the initial collection cycle in each sliding time window;

[0037] The error of each charging pile in each sliding time window is calculated by a preset algorithm based on the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold m (m≥12), the sliding is ended, and the stability index of the charging pile is calculated based on the error of the charging pile in each sliding time window;

[0038] The current time window size is fixed, and the collection period is adjusted. The collection period can be adjusted according to the performance indicators of the collection device, such as 15 minutes, 30 minutes, and 45 minutes. The charging pile data is collected by sliding the time window according to the collection period after each adjustment, and the stability index of each charging pile is recalculated according to the above steps. The calculated value of the stability index after each adjustment is compared. When the stability index of the charging pile is the smallest, this is the optimal collection period; the optimal collection period is fixed, and the current time window size is adjusted. The time window length can be adjusted according to the capacity of the server storing data, such as half a month, 1 month, and 45 days. According to the optimal collection period and the time window after each adjustment, the charging pile data is collected by sliding the time window, and the stability index of each charging pile is recalculated according to the above steps. The calculated value of the stability index calculated value after each adjustment is compared. When the stability index of the charging pile is the smallest, this is the optimal time window size;

[0039] In this embodiment, the stability index msa under different data collection cycles and different time window lengths can be plotted. n (n=1,2,...,n) scatter plot, and determine the optimal acquisition period and optimal time window size based on the scatter plot;

[0040] As a preferred implementation of this embodiment, the calculation formula of the stability index of the charging pile is:

[0041]

[0042] Among them: msa n represents the stability index of the nth charging pile; m represents the total number of sliding times in the time window; ε i,n represents the error of the nth charging pile in the i-th sliding time window; Represents the mean error of the nth charging pile in all sliding time windows.

[0043] As a preferred implementation of this embodiment, taking charging pile n as an example, the error set of charging pile n is ε n ={ε 1,n ,ε 2,n ,...,ε m,n}, the calculation formula for the mean error of the charging pile in all sliding time windows is:

[0044] Embodiment 2:

[0045] A data acquisition optimization system for a charging pile error analysis algorithm, comprising an initial data acquisition parameter configuration module, a data acquisition module, a stability index calculation module, and a data acquisition parameter optimization module;

[0046] The initial data acquisition parameter configuration module is used to set the initial acquisition period and initial time window size of the charging pile operation data;

[0047] The data acquisition module is used to create a time window according to the size of the initial time window, slide the time window, and collect the operating data of all charging piles according to the initial acquisition cycle within each sliding time window;

[0048] The stability index calculation module is used to calculate the error of each charging pile in each sliding time window through a preset algorithm according to the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold, the sliding is ended, and the stability index of the charging pile is calculated according to the error of the charging pile in each sliding time window;

[0049] The data acquisition parameter optimization module is used to fix the current time window size, adjust the acquisition cycle, collect charging pile data through the data acquisition module according to each adjusted acquisition cycle, and recalculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal acquisition cycle; fix the optimal acquisition cycle, adjust the current time window size, recollect the charging pile data through the data acquisition module according to the optimal acquisition cycle and the time window after each adjustment, and calculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal time window size.

[0050] As a preferred implementation of this embodiment, the calculation formula of the stability index of the charging pile is:

[0051]

[0052] Among them: msa n represents the stability index of the nth charging pile; m represents the total number of sliding times in the time window; ε i,n represents the error of the nth charging pile in the i-th sliding time window; Represents the mean error of the nth charging pile in all sliding time windows.

[0053] As a preferred implementation of this embodiment, the calculation formula for the mean error of the charging pile in all sliding time windows is:

[0054] Embodiment three:

[0055] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0056] Embodiment 4:

[0057] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0058] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0059] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0060] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0061] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0062] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A data acquisition optimization method for a charging pile error analysis algorithm, characterized in that: The following steps are involved: Set the initial collection period and initial time window size of charging pile operation data; Create a time window according to the size of the initial time window, slide the time window, and collect the operating data of all charging piles according to the initial collection cycle within each sliding time window; The error of each charging pile in each sliding time window is calculated by a preset algorithm according to the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold, and the preset threshold of the number of sliding times of the time window is greater than or equal to 12, the sliding is ended, and the stability index of the charging pile is calculated according to the error of the charging pile in each sliding time window; Fix the current time window size, adjust the collection cycle, collect charging pile data according to the sliding time window of each adjusted collection cycle, and recalculate the stability index of each charging pile according to the above steps, compare the calculated value of the stability index after each adjustment, and when the stability index of the charging pile is the smallest, it is the optimal collection cycle; The optimal collection period is fixed, and the current time window size is adjusted. According to the optimal collection period and the time window after each adjustment, the sliding time window is used to collect the charging pile data. The stability index of each charging pile is recalculated according to the above steps. The calculated value of the stability index after each adjustment is compared. When the stability index of the charging pile is the smallest, it is the optimal time window size.

2. The data acquisition optimization method of a charging pile error analysis algorithm according to claim 1 is characterized in that: The calculation formula of the stability index of the charging pile is: Among them: msa n represents the stability index of the nth charging pile; m represents the total number of sliding times in the time window; ε i,n represents the error of the nth charging pile in the i-th sliding time window; Represents the mean error of the nth charging pile in all sliding time windows.

3. The data acquisition optimization method of a charging pile error analysis algorithm according to claim 2 is characterized in that: The calculation formula for the mean error of the charging pile in all sliding time windows is:

4. A data acquisition optimization system for a charging pile error analysis algorithm, characterized in that: It includes initial data acquisition parameter configuration module, data acquisition module, stability index calculation module and data acquisition parameter optimization module; The initial data acquisition parameter configuration module is used to set the initial acquisition period and initial time window size of the charging pile operation data; The data acquisition module is used to create a time window according to the size of the initial time window, slide the time window, and collect the operating data of all charging piles according to the initial acquisition cycle within each sliding time window; The stability index calculation module is used to calculate the error of each charging pile in each sliding time window through a preset algorithm according to the charging pile operation data collected in each sliding time window. When the number of sliding times of the time window reaches a preset threshold, the sliding is ended, and the stability index of the charging pile is calculated according to the error of the charging pile in each sliding time window; The data acquisition parameter optimization module is used to fix the current time window size, adjust the acquisition cycle, collect charging pile data through the data acquisition module according to each adjusted acquisition cycle, and recalculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal acquisition cycle; fix the optimal acquisition cycle, adjust the current time window size, recollect the charging pile data through the data acquisition module according to the optimal acquisition cycle and the time window after each adjustment, and calculate the stability index of each charging pile through the stability index calculation module, compare the calculated value of the stability index adjusted each time, and when the stability index of the charging pile is the smallest, this is the optimal time window size.

5. The data acquisition optimization system of the charging pile error analysis algorithm according to claim 4 is characterized in that: The calculation formula of the stability index of the charging pile is: Among them: msa n represents the stability index of the nth charging pile; m represents the total number of sliding times in the time window; ε i,n represents the error of the nth charging pile in the i-th sliding time window; Represents the mean error of the nth charging pile in all sliding time windows.

6. The data acquisition optimization system of the charging pile error analysis algorithm according to claim 5 is characterized in that: The calculation formula for the mean error of the charging pile in all sliding time windows is:

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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