Super-capacitor data filtering method and system based on particle filtering

Abstract

The invention relates to the technical field of data processing, in particular to a super-capacitor data filtering method and system based on particle filtering, and the method comprises the steps: obtaining an outlier factor of each data point in each time period according to the distribution of all data points in each time period; obtaining the difference degree of each data point of each time period according to the difference between each piece of data in each time period and the data of the corresponding position of the adjacent fitting curve of each time period; obtaining a mutation factor of each data point in each time period according to the difference degree and the outlier factor of each data point in each time period; obtaining a new weight of each data point in each time period according to the mutation factor of each data point in each time period; and data filtering is carried out. According to the invention, the weight of resampling is corrected, so that the quality of data filtering is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a supercapacitor data filtering method and system based on particle filtering. Background Art

[0002] A supercapacitor is an electrical energy storage device with a high capacitance value, capable of providing rapid charge and discharge, having characteristics such as high power density and long life, and is widely used in fields such as electric vehicle energy recovery, instantaneous electrical energy supplementation, and backup power supplies. In order to monitor the supercapacitor, various data need to be collected through sensors. Due to the instability of charge transfer during the electrochemical reaction process, the interaction between the battery and the capacitor, electromagnetic interference from the external environment, and noise in the internal circuit of the system, the collected data has poor credibility. Therefore, it is necessary to use various data in the supercapacitor system for denoising processing to improve the measurement accuracy of the sensor, ensure the accuracy of signals such as the voltage and current of the capacitor; optimize the control system to maintain the stability and efficiency of the charge and discharge process; improve the reliability of the system and reduce the risk of failures caused by noise; reduce false alarms and ensure the accuracy of fault diagnosis; and improve the user experience to make the monitoring data more stable and credible. Therefore, denoising and filtering of supercapacitor data play a very important role.

[0003] During the particle filtering process, the weight distribution of particles is usually uneven, and there may be a situation where the weights of some particles are too large and the weights of other particles are almost zero. This will cause duplicate particles to be generated during the resampling process, losing the diversity of the particle set. At the mutation point of the supercapacitor, the data of the supercapacitor changes greatly. Then, in a particle set, the mutated particles have relatively small weights. Therefore, important state information with smaller weights will be ignored and lost during the resampling process. Summary of the Invention

[0004] The present invention provides a supercapacitor data filtering method and system based on particle filtering to solve the existing problems.

[0005] The object of the present invention can be achieved by the following technical solutions: The first aspect of the present invention is to provide a supercapacitor data filtering method based on particle filtering, including: Obtaining a plurality of time periods of each signal data in the supercapacitor, and a plurality of data points for each time period; According to the distribution of all data points in each time period, obtain the outlier factor of each data point in each time period; perform curve fitting on all data points in each time period and the adjacent time periods on the left and right, and denote it as the adjacent fitting curve of each time period; according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period, obtain the degree of difference of each data point in each time period. According to the degree of difference and the outlier factor of each data point in each time period, obtain the mutation factor of each data point in each time period; according to the mutation factor of each data point in each time period, obtain the new weight of each data point in each time period; perform data filtering through the new weight of each data point in each time period.

[0006] Further, the obtaining of several time periods of each signal data in the super capacitor and several data points in each time period includes: Obtain various signal data in the super capacitor through various sensors; wherein, with a preset time duration to divide each type of signal data to obtain several time periods; and then obtain the data points at all moments in each time period through a preset time interval to obtain several data points in each time period.

[0007] Further, the obtaining of the outlier factor of each data point in each time period according to the distribution of all data points in each time period includes: Perform outlier detection on all data points in each time period through the LOF algorithm to obtain the outlier factor of each data point in each time period and determine the outlier points in each time period, and denote all data points outside the outlier points in each time period as the non-outlier points in each time period.

[0008] Further, the performing of curve fitting on all data points in each time period and the adjacent time periods on the left and right, and denoting it as the adjacent fitting curve of each time period includes: Perform curve fitting on all data points in each time period and the adjacent time periods on the left and right through the least squares method, and denote it as the adjacent fitting curve of each time period.

[0009] Further, the obtaining of the degree of difference of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period includes:

[0010] In the formula, represents the th data value in each time period, represents the The data values corresponding to each data on the adjacent fitting curves in each time period, is the absolute value symbol, indicating the degree of difference of the th data point in each time period.

[0011] Furthermore, obtaining the mutation factor of each data point in each time period according to the degree of difference and the outlier factor of each data point in each time period includes:

[0012] In the formula, represents the degree of difference of the th data in each time period, represents the outlier factor of the th data in each time period, represents the mutation factor of the th data point in each time period, represents the exponential function with the natural constant as the base.

[0013] Furthermore, obtaining the new weight of each data point in each time period according to the mutation factor of each data point in each time period; performing data filtering through the new weight of each data point in each time period includes: Obtain the initial weight of each data point;

[0014] In the formula, represents the initial weight of the th outlier in each time period, represents the corrected weight of the th outlier in each time period, represents the mutation factor of the th outlier in each time period; Take the initial weight of each non-outlier in each time period as the corrected weight of each non-outlier;

[0015] In the formula, represents the corrected weight of the th data point in each time period, represents the corrected weight of the th data point in each time period, represents the total number of all data points in each time period, represents the new weight of the th data point in each time period; Update the particle set in each time period with the new weights of each data point in each time period to obtain a new particle set for each time period, perform resampling with the new particle set for each time period, and perform filtering on subsequent data with the resampled particle set.

[0016] The second aspect of the present invention is to provide a supercapacitor data filtering system based on particle filtering, including: Data acquisition module: used to obtain several time periods of each signal data in the supercapacitor and several data points in each time period; Data analysis module: used to obtain the outlier factor of each data point in each time period according to the distribution of all data points in each time period; perform curve fitting on all data points in each time period and the adjacent time periods on the left and right, denoted as the adjacent fitting curves of each time period; obtain the degree of difference of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period; Data filtering module: used to obtain the mutation factor of each data point in each time period according to the degree of difference and outlier factor of each data point in each time period; obtain the new weight of each data point in each time period according to the mutation factor of each data point in each time period; perform data filtering with the new weights of each data point in each time period.

[0017] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the supercapacitor data filtering method based on particle filtering.

[0018] The fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the supercapacitor data filtering method based on particle filtering.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: according to the distribution of all data points in each time period, the outlier factor of each data point in each time period is obtained, improving the accuracy of data outlier anomaly analysis; according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve in each time period, the degree of difference of each data point in each time period is obtained, improving the accuracy of the analysis of noise points and mutation points; according to the degree of difference and the outlier factor of each data point in each time period, the mutation factor of each data point in each time period is obtained; according to the mutation factor of each data point in each time period, the new weight of each data point in each time period is obtained, improving the accuracy of the resampling weight; data filtering is performed through the new weight of each data point in each time period, and by correcting the weight of resampling, the quality of data filtering is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of the steps of a method for filtering supercapacitor data based on particle filtering provided by the present invention; Figure 2 It is a schematic module flow chart of a system for filtering supercapacitor data based on particle filtering provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] In view of the problems existing in the background technology, a supercapacitor data filtering method and system based on particle filtering are designed through research, which has important practical significance.

[0025] As Figure 1 shown, the first aspect of the present invention is to provide a supercapacitor data filtering method based on particle filtering, including the following steps: Step S001: Collect each signal data in the supercapacitor.

[0026] It should be noted that in order to realize the health assessment of the supercapacitor, the prediction of the service life, the optimization of the control accuracy of the supercapacitor, and the fault diagnosis of the supercapacitor, it is necessary to collect various signal data of the supercapacitor through various sensors, and analyze various signal data of the supercapacitor, so as to realize the health assessment, life prediction, control optimization and fault diagnosis of the supercapacitor, so as to ensure its efficient and stable operation in various application scenarios.

[0027] Specifically, various signal data in the supercapacitor are obtained through various sensors; among them, various signal data in the supercapacitor include: current signal data, voltage signal data, temperature signal data, internal resistance signal data, and power signal data. Among them, each signal data is divided with a preset time length to obtain several time periods; and then all data points at each moment in each time period are obtained through a preset time interval to obtain several data points in each time period. Among them, in this embodiment, the preset time length seconds, where in this embodiment, the preset time length is not specifically limited, and the implementer can determine it according to the specific situation. Among them, in this embodiment, the preset time interval seconds, where in this embodiment, the preset time interval is not specifically limited, and the implementer can determine it according to the specific situation.

[0028] Among them, the current signal data is collected by a current sensor, the voltage signal data is collected by a voltage sensor, the temperature signal data is collected by a temperature sensor, the internal resistance signal data is simply calculated and obtained by an AC impedance spectrometer, and the power signal data is collected by a power meter.

[0029] So far, several time periods of each signal data in the supercapacitor, and several data points in each time period are obtained.

[0030] Step S002: According to the distribution of all data points in each time period, obtain the outlier factor of each data point in each time period; perform curve fitting on all data points in each time period and its adjacent time periods on the left and right, and record it as the adjacent fitting curve of each time period; according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period, obtain the difference degree of each data point in each time period.

[0031] It should be noted that during the operation of the supercapacitor, since the supercapacitor undergoes short-time transient charge and discharge, the temperature will rise, resulting in an increase in internal resistance, which will cause mutations in some data; when mutations occur, particle degradation problems will occur when using particle filtering to filter various data of the supercapacitor. For the degradation problem, the prior art uses sampling with replacement, that is, resampling. In resampling, it is not clear whether a small number of data points are noise points or real data points. Therefore, the distribution of data points in multiple time periods is analyzed.

[0032] Furthermore, it should be noted that when mutations occur at the end of each time period, there may be a large difference between many data and the mutant data in a time period. If resampling is directly performed according to the proportion of data points, information loss may occur. However, for the mutant data points in each time period, the characteristic information of a small number of data points in each time period can be analyzed through the distribution of data in adjacent time periods.

[0033] Specifically, perform outlier detection on all data points in each time period through the LOF algorithm, obtain the outlier factor of each data point in each time period, and determine the outlier points in each time period. All data points outside the outlier points in each time period are recorded as the non-outlier points in each time period; among them, the LOF algorithm is a well-known technology and will not be specifically described here.

[0034] Perform curve fitting on all data points in each time period and its adjacent time periods on the left and right through the least squares method, and record it as the adjacent fitting curve of each time period; among them, the least squares method is a well-known technology and will not be specifically described here.

[0035] Obtain the degree of difference of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period; the degree of difference of each data point in each time period is specifically expressed by the formula:

[0036] In the formula, represents the th data value in each time period, represents the data value of the th data corresponding to each time period on the adjacent fitting curve of each time period, is the absolute value symbol, represents the degree of difference of the th data point in each time period.

[0037] Among them, because the above-mentioned fitting curve is fitted through all data points in adjacent time periods, so even if it is a mutation point, in the next time period, the mutation point is not an abnormally discrete point. Therefore, analyze through the curve fitted by the data in adjacent time periods; so when the degree of difference of each data point in each time period is greater, it means that the possibility that this data point is noise is greater, and vice versa, it means that the possibility that this data point is noise is smaller.

[0038] Thus, the degree of difference of each data point in each time period is obtained.

[0039] Step S003: Obtain the mutation factor of each data point in each time period according to the degree of difference of each data point in each time period and the outlier factor; obtain the new weight of each data point in each time period according to the mutation factor of each data point in each time period; perform data filtering through the new weight of each data point in each time period.

[0040] It should be noted that when the outlier factor of each data point in each time period is greater and the degree of difference of each data point in each time period is greater, it means that the possibility that this point is a noise point is greater, and vice versa, it means that the possibility that this point is a noise point is smaller and it is real data, that is, the greater the possibility that it is a data point with a mutation caused by the increase in internal resistance due to the increase in temperature.

[0041] Specifically, obtain the mutation factor of each data point in each time period according to the degree of difference of each data point in each time period and the outlier factor of each data point in each time period; the mutation factor of each data point in each time period is specifically expressed by the formula:

[0042] In the formula, Indicates the degree of difference of the th data for each time period, Indicates the outlier factor of the th data for each time period, Indicates the mutation factor of the th data point for each time period, Indicates the exponential function with the natural constant as the base.

[0043] Among them, when the outlier factor of each data point in each time period is larger, and the degree of difference of each data point in each time period is larger, it indicates that the greater the possibility that this point is a noise point, and the smaller the possibility that it is a mutation point, that is, the smaller the mutation factor of each data point; on the contrary, the greater the possibility that it is a mutation point, so the corresponding mutation factor of each data point is larger.

[0044] Thus, the mutation factor of each data point in each time period is obtained.

[0045] It should be noted that when the mutation factor of the data points in each time period is larger, in order not to lose important information during the resampling process, it is necessary to increase the weight of the data points with a larger mutation factor in order to retain important information.

[0046] Specifically, according to the mutation factor of each outlier point in each time period, the initial weight of each outlier point in each time period is adjusted to obtain the corrected weight of each outlier point in each time period, where the outlier point is a small number of data points in each time period; the corrected weight of each outlier point in each time period is specifically expressed by the formula:

[0047] In the formula, Indicates the initial weight of the th outlier point in each time period, Indicates the corrected weight of the th outlier point in each time period, Indicates the mutation factor of the th outlier point in each time period. Among them, the initial weight of each data point in each time period is obtained by the existing method, that is, the proportion of the data point type.

[0048] Then, the weights of all data points in each time period are adjusted by the corrected weights of the outlier points in each time period so that the sum of the weights of all data points is 1. Among them, the initial weight of each non-outlier point in each time period is used as the corrected weight of each non-outlier point; thus, the corrected weights of all data points in each time period are obtained.

[0049] The new weight of each data point in each time period is obtained according to the corrected weight of all data points in each time period; specifically, it is expressed by the formula:

[0050] In the formula, represents the corrected weight of the th data point in each time period, represents the corrected weight of the th data point in each time period, represents the total number of all data points in each time period, represents the new weight of the th data point in each time period.

[0051] The particle set in each time period is updated by the new weight of each data point in each time period to obtain a new particle set for each time period. Resampling is performed through the new particle set of each time period, and subsequent data filtering is performed through the resampled particle set.

[0052] As Figure 2 shown, the second aspect of the present invention is to provide a supercapacitor data filtering system based on particle filtering, including the following modules: Data acquisition module 101: used to obtain several time periods of each signal data in the supercapacitor and several data points in each time period; Data analysis module 102: used to obtain the outlier factor of each data point in each time period according to the distribution of all data points in each time period; perform curve fitting on all data points in each time period and the adjacent time periods on the left and right, denoted as the adjacent fitting curve of each time period; obtain the degree of difference of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period; Data filtering module 103: used to obtain the mutation factor of each data point in each time period according to the degree of difference and outlier factor of each data point in each time period; obtain the new weight of each data point in each time period according to the mutation factor of each data point in each time period; perform data filtering through the new weight of each data point in each time period.

[0053] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a supercapacitor data filtering method based on particle filtering is implemented.

[0054] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor implements a supercapacitor data filtering method based on particle filtering.

[0055] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0057] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A supercapacitor data filtering method based on particle filtering, characterized in that, Including: Obtaining a plurality of time periods of each signal data in the super capacitor, and a plurality of data points in each time period; According to the distribution of all data points in each time period, obtaining the outlier factor of each data point in each time period; performing curve fitting on all data points in each time period and the adjacent time periods on the left and right, denoted as the adjacent fitting curves of each time period; according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period, obtaining the difference degree of each data point in each time period; According to the difference degree and outlier factor of each data point in each time period, obtaining the mutation factor of each data point in each time period; according to the mutation factor of each data point in each time period, obtaining the new weight of each data point in each time period; Performing data filtering through the new weight of each data point in each time period.

2. The supercapacitor data filtering method based on particle filtering according to claim 1, characterized in that The obtaining a plurality of time periods of each signal data in the super capacitor, and a plurality of data points in each time period, includes: Obtain various signal data in the supercapacitor through various sensors; among them, with a preset time duration to divide each type of signal data to obtain several time periods; and then through a preset time interval obtain the data points at all moments in each time period. Thus, several data points in each time period are obtained.

3. A supercapacitor data filtering method based on particle filtering according to claim 1, characterized in that The obtaining the outlier factor of each data point in each time period according to the distribution of all data points in each time period, includes: Performing outlier detection on all data points in each time period through the LOF algorithm, obtaining the outlier factor of each data point in each time period, and determining the outlier points in each time period, and denoting all data points outside the outlier points in each time period as the non-outlier points in each time period.

4. A supercapacitor data filtering method based on particle filtering according to claim 1, characterized in that, The performing curve fitting on all data points in each time period and the adjacent time periods on the left and right, denoted as the adjacent fitting curves of each time period, includes: Performing curve fitting on all data points in each time period and the adjacent time periods on the left and right through the least squares method, denoted as the adjacent fitting curves of each time period.

5. A supercapacitor data filtering method based on particle filtering according to claim 1, characterized in that The obtaining the difference degree of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period, includes: In the formula, represents the th data value in each time period, represents the data value corresponding to the th data on the adjacent fitting curves in each time period, is the absolute value symbol, represents the degree of difference of the th data point in each time period.

6. A supercapacitor data filtering method based on particle filtering according to claim 1, characterized in that The obtaining the mutation factor of each data point in each time period according to the difference degree and outlier factor of each data point in each time period, includes: wherein, represents the degree of difference of the th data for each time period, represents the outlier factor of the th data for each time period, represents the mutation factor of the th data point for each time period, represents the exponential function with the natural constant as the base.

7. A supercapacitor data filtering method based on particle filter according to claim 3, characterized in that, The obtaining the new weight of each data point in each time period according to the mutation factor of each data point in each time period; The performing data filtering through the new weight of each data point in each time period, includes: Obtaining the initial weight of each data point; wherein, represents the initial weight of the -th outlier in each time period, represents the corrected weight of the -th outlier in each time period, represents the mutation factor of the -th outlier in each time period; Taking the initial weight of each non-outlier point in each time period as the corrected weight of each non-outlier point; wherein, represents the correction weight of the -th data point in each time period, represents the correction weight of the -th data point in each time period, represents the total number of all data points in each time period, represents the new weight of the -th data point in each time period; Updating the particle set in each time period through the new weight of each data point in each time period, obtaining the new particle set of each time period, performing resampling through the new particle set of each time period, and performing filtering on subsequent data through the resampled particle set.

8. A supercapacitor data filtering system based on particle filtering, characterized in that Including: Data acquisition module: used for obtaining a plurality of time periods of each signal data in the super capacitor, and a plurality of data points in each time period; Data analysis module: used to obtain the outlier factor of each data point in each time period according to the distribution of all data points in each time period; perform curve fitting on all data points in each time period and the adjacent time periods on the left and right, denoted as the adjacent fitting curves of each time period; obtain the degree of difference of each data point in each time period according to the difference between each data in each time period and the data at the corresponding position of the adjacent fitting curve of each time period. Data filtering module: used to obtain the mutation factor of each data point in each time period according to the degree of difference and the outlier factor of each data point in each time period; obtain the new weight of each data point in each time period according to the mutation factor of each data point in each time period. Perform data filtering through the new weight of each data point in each time period.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for filtering supercapacitor data based on particle filtering according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method for filtering supercapacitor data based on particle filtering according to any one of claims 1-7.

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