A data acquisition method and system for an electrical energy storage tank

Abstract

The application relates to the field of data processing, in particular to a data acquisition method and system for an electric energy storage box, the method comprising: binning energy storage box data of each dimension to obtain a data range, and constructing an item set according to the data range; calculating a frequent coefficient according to the item set; calculating a weight coefficient of the energy storage data according to the frequent coefficient, and obtaining a filtered value after filtering the energy storage data according to the weight coefficient. The application improves the limitation of the weighted filtering on the processing of burst noise, and has the effect of improving the accuracy of the energy storage box data filtering.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a data acquisition method and system for an electric energy storage box. BACKGROUND

[0002] The electric energy storage box is a key component to ensure the normal operation of new energy vehicles. The purpose of setting it is to timely find its potential faults and optimize the charging and discharging strategy. It is crucial to collect data of the electric energy storage box. For example, the patent application file with publication number CN115689841A discloses a new energy vehicle charging, replacing, storing and transporting integrated platform, which discloses setting an energy storage box and collecting data. This not only helps to realize fault warning and predictive protection, but also effectively avoids the adverse effects on the performance of the whole vehicle caused by sudden faults.

[0003] However, due to environmental interference and other factors, the collected data of the energy storage box often contains noise, which affects the accuracy of real-time monitoring and safety management. Therefore, reducing the influence of environmental interference on data acquisition and ensuring the accuracy and reliability of data have become key technical problems to be solved at present. The collected data of the energy storage box can be denoised by using a weighted moving filtering algorithm. The weighted moving filtering algorithm is a filtering method based on a sliding window. By assigning different weights to the data points in the window, a weighted average is obtained to smooth the data.

[0004] However, in a complex vehicle operating environment, there are certain burst data in the data collected by the energy storage box. The processing capability of the weighted moving filtering algorithm for burst noise (such as sensor transient fault and electromagnetic interference) is limited. If the weight distribution of the data is not appropriate, the filtered data may still contain obvious abnormal values, which affects the monitoring of the state of the electric energy storage box. SUMMARY

[0005] In order to solve the technical problem of how to improve the filtering effect of the data of the energy storage box, the present application provides a data acquisition method and system for an electric energy storage box.

[0006] In the first aspect, the present application provides a data acquisition method for an electric energy storage box, which adopts the following technical scheme:

[0007] A data acquisition method for an electric energy storage box, comprising the steps of: binning the energy storage box data of each dimension to obtain a data range, and constructing an item set according to the data range;

[0008] According to the item set, a frequency coefficient is calculated; according to the frequency coefficient, a weight coefficient of the energy storage data is calculated, and the energy storage data is filtered according to the weight coefficient to obtain a filtered value; the calculation formula of the filtered value is: ; in the formula, is the filtered value of the real-time collected energy storage box data, The first time before the real-time collection of energy storage box data Real-time energy storage tank data, for The frequency coefficient of the corresponding itemset, This represents the size of the filtering window.

[0009] The beneficial effects are as follows: The frequency coefficient of each itemset is calculated using data within all itemsets. The frequency coefficient of the corresponding itemset for each energy storage tank data is used to calculate the filtering weight for each data point. This filtering weight is then used to filter the collected energy storage tank data. By using the frequency coefficient of the corresponding itemsets, data points or abnormal patterns affected by noise can be effectively identified, allowing for dynamic adjustment of the weights of the energy storage tank data during the filtering process. This system can adaptively respond to sudden noise and abnormal data in the operating environment, improving data accuracy and system robustness. It overcomes the limitations of weighted filtering in handling sudden noise, reduces noise in energy storage tank data acquisition, and makes energy storage tank data acquisition more accurate, thus enabling better monitoring of energy storage tank status.

[0010] Optionally, the energy storage box data for each dimension can be binned, including the following steps: For any dimension, obtain a frequency line graph of the energy storage box data, where the horizontal axis of the frequency line graph represents the energy storage box data and the vertical axis represents the frequency; smooth the frequency line graph to obtain a smooth curve, calculate the absolute value of the slope of each energy storage box data on the smooth curve, sort the energy storage box data in descending order according to the absolute value of the slope to form a sequence, and use the energy storage box data corresponding to the maximum value in the sequence as the data binning point, and bin the data for this dimension through the data binning point.

[0011] The beneficial effects are as follows: By dividing continuous data into discrete intervals, the dimensionality and complexity of the data are reduced, facilitating subsequent analysis and processing. Using the absolute value of the slope as the basis for binning allows for better capture of data trends and characteristic points, thus more accurately reflecting the inherent patterns of energy storage tank data. The reduced data volume after binning also reduces the computational resources required to calculate frequent coefficients and weighting coefficients, thereby improving the speed of the entire data processing process.

[0012] Optionally, a spline regression algorithm can be used to smooth the frequency line plot to obtain a smooth curve.

[0013] The beneficial effects are: the spline regression algorithm can effectively smooth out noise and fluctuations in the frequency line graph, making the smooth curve smoother and better reflecting the overall trend of the data.

[0014] Optionally, the expression for an itemset is: In the formula, Let A be an itemset consisting of the energy storage tank data at any given time. a data range corresponding to the energy storage box voltage data at the moment, a data range corresponding to the energy storage box current data at the moment, a data range corresponding to the energy storage box temperature data at the moment, a data range corresponding to the energy storage box acceleration data at the moment, the acceleration data being axis, axis, vector sum and module of the acceleration in three directions.

[0015] The beneficial effect is that the item set contains the data range of multiple dimensions such as voltage, current, temperature and acceleration of the energy storage box, which can comprehensively reflect the running state of the energy storage box under different working conditions and provide more abundant information for subsequent analysis and processing. The joint expression of multi-dimensional data is realized, the internal correlation between each data can be captured, and after the continuous data is processed by binning, the original continuous data is converted into discrete categories (data range), which not only reduces the influence of random fluctuations of data, but also facilitates the calculation of subsequent frequent coefficients by using the Apriori association rule mining algorithm, so that the subsequent analysis is more efficient and accurate.

[0016] Optionally, the method for calculating the frequent coefficient according to the item set is that: a subset of the item set is constructed, the subset containing one element is the first subset, the subset containing two elements is the second subset, and similarly, all subsets are divided; the support degree of each subset of the item set and the support degree of each item set are obtained through the Apriori association rule mining algorithm, and the frequent coefficient of each item set is calculated through the support degree of the subset, the weight of the subset and the support degree of the item set.

[0017] The beneficial effect is that the weights of different subsets and item sets are introduced, the weights of different subsets in the calculation of the frequent coefficient can be flexibly adjusted according to actual needs, so that the frequent coefficient can better reflect the actual importance of the data. The support degrees of different subsets and the overall support degree of the item set are comprehensively considered, which can comprehensively evaluate the frequency of the item set and avoid inaccurate calculation of the frequent coefficient due to the interference of a single factor.

[0018] Optionally, when the subsets of the item set include the first subset, the second subset and the third subset, the expression of the frequent coefficient is:

[0019] ;

[0020] ; ; ;

[0021] ; wherein, the frequent coefficient of any item set, support of the first subset of the item set, denotes the total number of the first subset, support of the second subset of the item set, denotes the total number of the second subset, support of the third subset of the item set, denotes the total number of the third subset, support of the third subset of the item set, denotes the total number of the third subset, support of the item set, is the weight value of the first subset, is the weight value of the second subset, is the weight value of the third subset, is the weight value of the third subset, is the weight of the item set; is the information entropy of the first subset of all item sets, is the information entropy of the second subset of all item sets, is the information entropy of the third subset of all item sets, is the information entropy of all item sets.

[0022] Optionally, when the subsets of the item set include the first subset and the second subset, the expression of the frequent coefficient is:

[0023]

[0024] ; wherein, is the frequent coefficient of any item set, support of the first subset of the item set, denotes the total number of the first subset, support of the second subset of the item set, denotes the total number of the second subset, support of the third subset of the item set, denotes the total number of the third subset, support of the item set, is the weight value of the first subset, is the weight value of the second subset, is the weight of the item set.

[0025] In a second aspect, the application provides a data acquisition system of an electric energy storage box, which adopts the following technical scheme:

[0026] A data acquisition system of an electric energy storage box, comprising a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, realizing the data acquisition method for the electric energy storage box according to the above. ​​​

[0027] The beneficial effect is that the data acquisition method of the electric energy storage box is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that the system is convenient to use according to the memory and the processor.

[0028] The present application has the following technical effects:

[0029] 1. The item set is constructed by the energy storage box data, vibration data and temperature data at the energy storage box, the joint expression of multidimensional data is realized, the internal correlation between each data can be captured, and after the continuous data is processed by binning, the original continuous data is converted into discrete categories (data range), which not only reduces the influence of random fluctuations of the data, but also facilitates the subsequent calculation of the frequent coefficient by using the Apriori association rule mining algorithm, so that the subsequent analysis is more efficient and accurate.

[0030] 2. The frequent coefficient of each item set is calculated by the data in all item sets, the data points or abnormal patterns disturbed by noise can be effectively identified by the frequent coefficient of the data corresponding to the item set, and then the weight of the energy storage box data in the filtering process is dynamically adjusted. The frequent coefficient of the energy storage box data corresponding to the item set calculates the filtering weight of each data, and the collected energy storage box data is filtered through the filtering weight, which can adaptively cope with the sudden noise and abnormal data in the running environment, and improves the accuracy of the data and the robustness of the system. BRIEF DESCRIPTION OF DRAWINGS

[0031] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description read in conjunction with the accompanying drawings. In the drawings, several embodiments of the present application are shown by way of example, and the same or corresponding reference numbers indicate the same or corresponding parts.

[0032] Figure 1 is a method flowchart of a data acquisition method for an electric energy storage box according to an embodiment of the present application.

[0033] Figure 2 is a method flowchart of a data acquisition method for an electric energy storage box according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] It should be understood that when the terms "first," "second," etc., are used in the claims, description, and drawings of this application, they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the description and claims of this application indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0036] This application discloses a data acquisition method for an energy storage box, referring to... Figure 1 The process includes steps S1-S3, as detailed below:

[0037] S1: Divide the energy storage box data of each dimension into bins to obtain the data range, and construct itemsets based on the data range.

[0038] When a car is running, the energy storage box moves synchronously with the car. When the car bumps along, the energy storage box also bumps synchronously, and the temperature of the energy storage box changes with the car's movement. Both the bumps and temperature changes affect the data collection from the energy storage box. Since the data related to the energy storage box is continuous, it creates inconvenience for the subsequent application of the Apriori association rule mining algorithm. Therefore, this application bins the data for each dimension and constructs itemsets based on the binned data. Specifically, refer to... Figure 2 The process of binning the energy storage box data across various dimensions includes steps S10-S11:

[0039] S10: For any dimension, obtain a frequency line graph of the energy storage box data. The horizontal axis of the frequency line graph represents the energy storage box data, and the vertical axis represents the frequency.

[0040] S11: Smooth the frequency line graph to obtain a smooth curve, calculate the absolute value of the slope of each energy storage box data on the smooth curve, sort the energy storage box data in descending order according to the absolute value of the slope to form a sequence, take the energy storage box data corresponding to the maximum value in the sequence as the data binning point, and bin the data of this dimension through the data binning point.

[0041] Obtain a frequency line graph of data in any dimension, and smooth the frequency line graph using a spline regression algorithm. Alternatively, other existing algorithms applicable to the application scenario of this application can be used to smooth the frequency line graph, which will not be elaborated here.

[0042] Calculate the absolute value of the slope at each point on the smooth curve, and construct a sequence of absolute slope values ​​in order of the size of the energy storage bin data corresponding to the absolute values ​​of the slopes. Use the data value corresponding to the maximum value in the sequence of absolute slope values ​​as the data binning point, and bin the data in this dimension through the data binning point.

[0043] For example, the original slope is: {1, 2, 3, 2, 2, 2, 1, 0, -1, -2, -1}; the energy storage box data corresponding to the slope is: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; the absolute value sequence of the slope is: {1, 2, 3, 2, 2, 2, 1, 0, 1, 2, 1}; the absolute value maximum of the slope is 3 and the last 2 in the absolute value sequence, and the binning result obtained is {1, 2, 3}, {4, 5, 6, 7, 8, 9, 10}, {11}.

[0044] In the process of driving the automobile, or in the process of testing the energy storage box out of the factory, the energy storage box data (current data and voltage data) in the working state of the energy storage box is collected, the temperature data of the energy storage box is collected through a temperature sensor, and the vibration data (acceleration data) of the energy storage box is collected through an accelerometer. The vector sum and the module of the acceleration of the energy storage box in three directions (x-axis, y-axis, and z-axis) at each moment are calculated, and the module of the vector sum of the acceleration data is taken as the acceleration data of the energy storage box. The current data, the voltage data, and the acceleration data and the temperature data of the energy storage box in the working state are binned. x-axis, y-axis, z-axis

[0045] In one embodiment, the expression of the item set is: ; in the formula, is an item set composed of the energy storage box data at any moment, is the data range corresponding to the voltage data of the energy storage box at the moment, is the data range corresponding to the current data of the energy storage box at the moment, is the data range corresponding to the temperature data of the energy storage box at the moment, is the data range corresponding to the acceleration data of the energy storage box at the moment, and the acceleration data is x-axis, y-axis, z-axis

[0046] For example, if the data binning points are 10, 20, 32, and 50, the data range from 0 to 10 is 1, the data range from 11 to 20 is 2, the data range from 21 to 32 is 3, the data range from 33 to 50 is 4, and the data range greater than 50 is 5. Data 8 and 9 both correspond to the data range 1 of 0 to 10, and data 11 and 20 correspond to the data range 2. The current data, the voltage data, the temperature data, and the acceleration data are binned by this method. The set of the energy storage box data item set is constructed by the data range to which the energy storage box data and the vibration data and the temperature data of the energy storage box belong at each moment, that is, the data range corresponding to each dimension data at each moment constitutes an item set.

[0047] and respectively represent the external environmental state indicators during the use of the energy storage box. When the external environment where the energy storage box is located is in different states, the data of the energy storage box (that is, voltage and current ) collection will also be interfered to varying degrees, that is , and , have a certain correlation. When the correlation changes, the data of the energy storage box may be noise data. Therefore, is used as the item set for the subsequent steps.

[0048] In other embodiments, in order to simplify the calculation, the item set can also be set to or , which will not be elaborated here.

[0049] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ Support for the third subset This represents the total number of the third subset. The support of this itemset, The weight of the first subset. The weight of the second subset. The weight of the third subset. The weight of the itemset.

[0055] For example, each itemset is constructed with subsets: items containing one element are the first subset, items containing two elements are the second subset, and items containing three elements are the third subset. Thus, each itemset contains 4 first subsets, 6 second subsets, and 4 third subsets. The support of each subset and the support of the entire itemset are obtained using the Apriori association rule mining algorithm. The frequency coefficient of each itemset is then calculated using the support of its subsets and the support of the entire itemset. The calculation method for the frequency coefficient is as follows:

[0056] ; ; ;

[0057] ;

[0058] ;in, Let be the frequency coefficient of any item set. For the first item in this collection Support for the first subset For the first item in this collection Support of the second subset For the first item in this collection Support for the third subset The support of this itemset, The weight of the first subset. The weight of the second subset. The weight of the third subset. The weight of the itemset. The information entropy of the first subset of all itemsets. The information entropy of the second subset of all itemsets. The information entropy of the third subset of all itemsets. The information entropy for all itemsets.

[0059] The mean value of the support degree of the first subset of the item set, the greater the value, the more frequent the first subset of the item set appears in the set of item sets, and the greater the frequent coefficient of the item set; the smaller the value, the less frequent the first subset of the item set appears in the set of item sets, and the smaller the frequent coefficient of the item set. , and Similarly, details are not repeated here.

[0060] The stability degree of all first subsets of all item sets in the set of item sets, usually when the energy storage tank is normally running, the data of the energy storage tank is relatively stable. The greater, the higher consistency and certainty they show in normal data, thus better reflecting whether the item set appears frequently, and vice versa, The smaller, the lower consistency and certainty they show in normal data, thus less reflecting whether the item set appears frequently. Therefore, when is taken as the weight of , The greater, the more important role it plays in the calculation of the frequent coefficient of the item set; The smaller, the smaller role it plays in the calculation of the frequent coefficient of the item set. , and the principle of w is similar, details are not repeated here.

[0061] In other embodiments, when the subset of the item set includes two types of first subset and second subset, the expression of the frequent coefficient is: ; wherein, is the frequent coefficient of any item set, is the support degree of the first subset in the item set, represents the total number of first subsets, is the support degree of the second subset in the item set, represents the total number of second subsets, is the support degree of the item set, is the weight value of the first subset, is the weight value of the second subset, is the weight of the item set. This embodiment is similar to the analysis of the aforementioned embodiment in which the subset of the item set is of three types, and details are not repeated here. S3: Calculate the weight coefficient of the energy storage data according to the frequent coefficient, and obtain the filtered value after filtering the energy storage data according to the weight coefficient.

[0062] S3: Calculate the weight coefficient of the energy storage data according to the frequent coefficient, and obtain the filtered value after filtering the energy storage data according to the weight coefficient.

[0063] ​Since the noise in the energy storage box data is sporadic, the sporadic nature of the noise data can correspond to the frequency coefficient of the corresponding itemset of the energy storage box data. The energy storage box data can be weighted by the frequency coefficient of the corresponding itemset of the energy storage box data to better remove sudden noise. Therefore, this application calculates the filtering weight of each data based on the frequency coefficient of the corresponding itemset of the energy storage box data, and filters the collected energy storage box data through the filtering weight.

[0064] During the operation of the energy storage tank, the aforementioned relevant data are collected in real time. The real-time data is then used to construct itemsets using the method described above, and the frequency coefficient of the real-time data itemsets is calculated. The frequency coefficient of the itemsets is then used to calculate the filtering weight of the energy storage tank data (current or voltage), and N is used as the size of the filtering window based on the filtering weight (in this example). Users can choose according to the actual situation. The data from the energy storage tank is filtered using the above method. Continuously filtering the energy storage tank data during data acquisition can reduce sudden noise in the data and minimize its impact on the energy storage tank status monitoring.

[0065] In one embodiment, the formula for calculating the filter value is: In the formula, This is the filtered value of the real-time collected energy storage tank data. The first time before the real-time collection of energy storage box data Real-time energy storage tank data, for The frequency coefficient of the corresponding itemset, This represents the size of the filtering window.

[0066] Frequency coefficient of corresponding itemsets The larger, the more it means The more common and stable the combination of elements in the corresponding itemset, the better. The more credible it is; The smaller, the better. The less common the combination of elements in the corresponding itemset, the more... The more likely it is to be a sudden noise, the better. The less credible it is, the more likely it is to be discredited. As The weight, The larger, The lower the probability of it being noise, the better. The greater the weight; The smaller, The greater the likelihood of it being noise, the better. The smaller the weight.

[0067] The embodiment of the present application further discloses a data acquisition system for the electric energy storage box, comprising a processor and a memory, and the memory stores computer program instructions, which realize the data acquisition method for the electric energy storage box according to the present application when executed by the processor.

[0068] The above system further comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0069] In the present application, the aforementioned memory can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or apparatus. For example, the computer readable storage medium can be any appropriate magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module or both. Any such computer storage medium can be part of a device or accessible or connectable to the device.

[0070] Although the present specification has shown and described multiple embodiments of the present application, it will be apparent to those skilled in the art that many changes, modifications and alterations to the embodiments described herein can be made without departing from the spirit and scope of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments described herein can be employed.

[0071] The above are the preferred embodiments of the present application, which do not limit the protection scope of the present application, therefore: any equivalent changes made on the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A data acquisition method for an electric energy storage box, characterized in that, Including the following steps: The data of the energy storage box in each dimension is divided into bins to obtain the data range, and itemsets are constructed based on the data range; Calculate the frequency coefficient based on the itemset; Calculate the weighting coefficient of the energy storage data based on the frequency coefficient, and then filter the energy storage data based on the weighting coefficient to obtain the filtered value. The formula for calculating the filter value is: In the formula, This is the filtered value of the real-time collected energy storage tank data. The first time before the real-time collection of energy storage box data Real-time energy storage tank data, for The frequency coefficient of the corresponding itemset, This refers to the size of the filtering window; The method for calculating the frequency coefficient based on itemsets is as follows: construct subsets of itemsets, with the subset containing one element as the first subset, the subset containing two elements as the second subset, and so on, dividing all subsets; The support of each subset of an itemset and the support of each itemset are obtained through the Apriori association rule mining algorithm. The frequency coefficient of each itemset is calculated using the support of the subset, the weight of the subset, and the support of the itemset. When the itemset subsets include three categories: first subset, second subset, and third subset, the expression for the frequency coefficient is: ； ； ； ； ； in, Let be the frequency coefficient of any item set. For the first item in this collection Support for the first subset This represents the total number of elements in the first subset. For the first item in this collection Support of the second subset This represents the total number of elements in the second subset. For the first item in this collection Support for the third subset This represents the total number of the third subset. The support of this itemset, The weight of the first subset. The weight of the second subset. The weight of the third subset. The weight of the itemset; The information entropy of the first subset of all itemsets. The information entropy of the second subset of all itemsets. The information entropy of the third subset of all itemsets. The information entropy of all itemsets; When the itemset subsets include two classes, the first subset and the second subset, the expression for the frequency coefficient is: ； ； ； 。 2. The data acquisition method for an energy storage box according to claim 1, characterized in that, The energy storage box data is binned according to various dimensions, including the following steps: For any dimension, obtain a frequency line graph of the energy storage box data. The horizontal axis of the frequency line graph represents the energy storage box data, and the vertical axis represents the frequency. The frequency line graph is smoothed to obtain a smooth curve. The absolute value of the slope of each energy storage box data on the smooth curve is calculated. The energy storage box data corresponding to the absolute value of the slope are sorted from largest to smallest to form a sequence. The energy storage box data corresponding to the maximum value in the sequence is used as the data binning point. The data in this dimension is binned through the data binning point.

3. The data acquisition method for an energy storage box according to claim 2, characterized in that, The frequency piecewise linear graph was smoothed using a spline regression algorithm to obtain a smooth curve.

4. The data acquisition method for an energy storage box according to claim 1, characterized in that, The expression for an itemset is: In the formula, Let A be an itemset consisting of the energy storage tank data at any given time. This represents the data range corresponding to the energy storage tank voltage data at that moment. This represents the data range corresponding to the energy storage tank current data at that moment. This represents the data range corresponding to the energy storage tank temperature at that moment. This represents the data range corresponding to the acceleration data of the energy storage tank at that moment. The acceleration data is... axis, axis, The vector and magnitude of acceleration in the three directions of the axis.

5. A data acquisition system for an electrical energy storage box, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the data acquisition method for an energy storage box according to any one of claims 1-4.

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