A 5G Internet of Things data acquisition optimization method and system

Abstract

The present invention relates to the technical field of data acquisition for battery packs, and particularly to a method and system for optimizing 5G Internet of Things data acquisition. The present invention obtains the abnormal area of the abnormal battery pack through the analysis of the battery operating condition data; then, through the analysis of the operating condition data of the single battery in the abnormal area, the failure probability of the single battery is obtained; in addition, the influence coefficient between battery packs of the same scale is analyzed; finally, the sampling frequency is adjusted by combining the failure probability and the influence coefficient. On the one hand, the method and system of the present invention can reduce the amount of data collected and the upload of invalid data, thereby effectively reducing the pressure on the network bandwidth and enhancing the transmission pressure; on the other hand, it can ensure the effectiveness and reliability of the data in the abnormal area, enabling the timely and accurate identification and feedback of the data in the abnormal area.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition of battery packs, and specifically relates to a method and system for optimizing 5G Internet of Things data acquisition. Background Art

[0002] With the continuous development of 5G technology, the Internet of Things (IoT) has entered a new stage, which can provide users with higher network speeds, lower latency, and larger-capacity communication services. Among them, the larger-capacity communication services can enable the IoT to connect more network devices, further improving the convenience of users in work and life.

[0003] A storage battery is a device that can store electrical energy and release it when needed. Multiple storage batteries form a battery pack. For a battery pack, due to long-term charging and discharging, and the performance of the battery is greatly affected by the environment, there are usually some differences in the health status of each storage battery. In order to ensure the safety and reliability of the battery pack during use, it is necessary to collect battery data for each storage battery in each battery pack in a certain factory or system and upload it to the central server through the Internet of Things, so as to determine the health status of each storage battery.

[0004] The existing data acquisition method (edge computing) can perform preliminary processing on the collected battery data at a position close to the data source (battery), such as data screening, compression, aggregation, etc., so as to reduce the data acquisition and transmission volume while sharing part of the data processing pressure of the IoT central server and realizing more efficient data acquisition. However, in the actual application process of this data acquisition method, in order to upload richer data to the central server, data acquisition is usually carried out on a large scale, and a large amount of redundant information or invalid information exists in the uploaded data, which will consume a large amount of computing resources of the central server to identify the data, resulting in a decrease in the efficiency of data processing. Furthermore, when a battery fails, it cannot be quickly discovered and located, resulting in a slow reaction speed when an accident occurs. At the same time, data damage, data loss, etc. may occur when compressing and uploading a large amount of data. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for optimizing 5G Internet of Things data acquisition.

[0006] According to the first aspect of the embodiments of the present invention, a method for optimizing 5G Internet of Things data acquisition is provided, and the technical solution adopted is specifically as follows:

[0007] Set an initial sampling frequency, and collect multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current;

[0008] Analyze the first - state anomaly index of the battery pack according to the working voltage and the working current to obtain the abnormal battery pack;

[0009] Analyze the voltage change trend and current change trend of the abnormal battery pack to obtain the local anomaly judgment coefficient and determine the possible abnormal area;

[0010] Analyze the second - state anomaly index of the possible abnormal area to determine the abnormal area;

[0011] For the single battery in the abnormal area, analyze the relative deviation of the working data in each dimension, and combine with the local anomaly judgment coefficient to obtain the failure probability of the single battery;

[0012] Cluster the battery pack to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering cluster, and combine with the failure probability to obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack;

[0013] Adjust the initial sampling frequency according to the influence coefficient and the failure probability to obtain the corrected sampling frequency.

[0014] In some embodiments of the present invention, analyzing the first - state anomaly index of the battery pack according to the working voltage and the working current to obtain the abnormal battery pack includes:

[0015] According to the working voltage and the working current of the battery pack at the current moment, combine with the theoretical voltage and theoretical current of the battery pack to obtain the first - state anomaly index of the battery pack at the current moment;

[0016] Preset the first anomaly index threshold;

[0017] Judge whether the first - state anomaly index is greater than the first anomaly index threshold;

[0018] If so, the battery pack corresponding to the first - state anomaly index is the abnormal battery pack.

[0019] In some embodiments of the present invention, analyzing the voltage change trend and current change trend of the abnormal battery pack to obtain the local anomaly judgment coefficient and determine the possible abnormal area includes:

[0020] Based on the working voltage and the working current of the battery pack at the current moment and the previous moments, analyze the voltage fitting slope and current fitting slope at the current moment to obtain the voltage change trend and current change trend of the abnormal battery pack;

[0021] According to the voltage change trend, combined with the working current and theoretical current of the battery pack at the current moment, an in-series anomaly coefficient is obtained;

[0022] According to the voltage change trend and the current change trend, a parallel anomaly coefficient is obtained;

[0023] According to the in-series anomaly coefficient and the parallel anomaly coefficient, a local anomaly judgment coefficient is obtained;

[0024] According to the local anomaly judgment coefficient, a possible anomaly area is determined.

[0025] In some embodiments of the present invention, analyzing the second-state anomaly index of the possible anomaly area to determine the anomaly area includes:

[0026] According to the working voltage and working current of the possible anomaly area at the current moment, combined with the theoretical voltage and theoretical current of the possible anomaly area, the second-state anomaly index of the possible anomaly area at the current moment is obtained;

[0027] A second anomaly index threshold is preset;

[0028] It is judged whether the second-state anomaly index is greater than the second anomaly index threshold;

[0029] If so, the possible anomaly area corresponding to the second-state anomaly index is the anomaly area.

[0030] In some embodiments of the present invention, for the single battery in the anomaly area, analyzing the relative deviation of the working data in each dimension further includes:

[0031] According to the second-state anomaly index corresponding to the anomaly area, combined with the initial sampling frequency, the temporary sampling frequency of the anomaly area is obtained;

[0032] According to the temporary sampling frequency, the latest multi-dimensional working data of the anomaly area is re-obtained.

[0033] In some embodiments of the present invention, for the single battery in the anomaly area, analyzing the relative deviation of the working data in each dimension, combined with the local anomaly judgment coefficient, to obtain the failure probability of the single battery includes:

[0034] For the single battery in the anomaly area, calculate the mean value of the working data in each dimension, analyze the difference in the working data in each dimension between the current single battery and other single batteries in the anomaly area, and obtain the relative deviation of the working data in each dimension;

[0035] According to the relative deviation of the working data in each dimension, the total deviation of the single battery is obtained;

[0036] Based on the total deviation and in combination with the local anomaly determination coefficient of the local area where the single battery is located, the failure probability of the single battery is obtained.

[0037] In some embodiments of the present invention, the battery packs are clustered to obtain clustering clusters, the dispersion degree among all the battery packs within the clustering clusters is analyzed, and in combination with the failure probability, the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack is obtained, including:

[0038] According to the scale of the battery pack, clustering analysis is performed on the battery pack to obtain clustering clusters;

[0039] Calculate the mean value of the working data of each battery pack within the clustering cluster where the current abnormal battery pack is located in each dimension;

[0040] Calculate the variance of the mean values of the working data of each dimension among all the battery packs within the clustering cluster where the current abnormal battery pack is located to obtain the dimension data dispersion degree of the working data of each dimension among all the battery packs within the clustering cluster, and further obtain the dispersion degree among all the battery packs within the clustering cluster;

[0041] Based on the dispersion degree and in combination with the failure probability, the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack is obtained.

[0042] In some embodiments of the present invention, based on the dispersion degree and in combination with the failure probability, the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack is obtained, including:

[0043] According to the failure probability of the single battery, calculate the failure probability of the current abnormal battery pack and calculate the average failure probability of all the battery packs within the clustering cluster where the current abnormal battery pack is located to obtain the degree of abnormality of the failure probability of the current abnormal battery pack;

[0044] Based on the dispersion degree and in combination with the degree of abnormality of the failure probability, the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack is obtained.

[0045] According to the second aspect of the embodiments of the present invention, a 5G Internet of Things data acquisition optimization system is provided, including: a memory and a processor, wherein:

[0046] The memory is used for storing program codes;

[0047] The processor is used for reading the program codes stored in the memory and executing the method described in the first aspect of the embodiments of the present invention.

[0048] In some embodiments of the present invention, the processor includes:

[0049] A working data acquisition module, configured to set an initial sampling frequency and acquire multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current;

[0050] An abnormal area determination module, configured to analyze a first state abnormal index of the battery pack according to the working voltage and the working current to obtain an abnormal battery pack;

[0051] A fault probability analysis module, configured to analyze the voltage change trend and current change trend of the abnormal battery pack to obtain a local abnormal judgment coefficient and determine a possible abnormal area; and analyze a second state abnormal index of the possible abnormal area to determine the abnormal area; finally, for each single battery in the abnormal area, analyze the relative deviation of the working data in each dimension, and combine with the local abnormal judgment coefficient to obtain the fault probability of the single battery;

[0052] An influence coefficient analysis module, configured to cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering clusters, and combine with the fault probability to obtain the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack;

[0053] A sampling frequency adjustment module, configured to adjust the initial sampling frequency according to the influence coefficient and the fault probability to obtain a corrected sampling frequency.

[0054] Compared with the prior art, a 5G Internet of Things data acquisition optimization method and system provided by the present invention has the following beneficial effects:

[0055] 1. The present invention analyzes the abnormal battery pack by analyzing the abnormal indexes of the working voltage and the working current, and determines the possible abnormal area by analyzing the voltage change trend and the current change trend, and further determines the abnormal area, so as to accurately locate the abnormal area (certain series areas or parallel areas) of the battery pack; only increasing the data sampling frequency of the sensor for the abnormal area can, on the one hand, reduce the amount of data acquisition and reduce the upload of invalid data, thereby effectively reducing the pressure on the network bandwidth and improving the transmission pressure; on the other hand, it can ensure the effectiveness and reliability of the data in the abnormal area, so that the data in the abnormal area can be identified and fed back in a timely and accurate manner.

[0056] 2. The present invention analyzes the fault probability of each single battery in the abnormal area and analyzes the influence relationship among battery packs of the same scale, and then obtains the adjustment coefficient of the data sampling frequency of the sensor to ensure the effectiveness and reliability of the collected data samples.

[0057] 3. The edge computing node adjusts the data acquisition parameters of the sensors according to the real-time working conditions of the battery pack, and uploads the obtained battery data as appropriate; for the battery pack with a relatively low failure probability, only the overall data is uploaded, and the data of individual batteries is stored under the edge computing node; while for the battery pack with a relatively high failure probability, the data of each individual battery is uploaded, which can reduce the upload of invalid data and ensure the timely and accurate identification and feedback of abnormal battery packs or abnormal batteries. Description of the Drawings

[0058] To more clearly illustrate the technical solutions and advantages 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.

[0059] Figure 1 It is a schematic diagram of the basic process of a 5G Internet of Things data acquisition optimization method provided by an embodiment of the present invention;

[0060] Figure 2 It is a schematic diagram of the basic composition of a 5G Internet of Things data acquisition optimization system provided by an embodiment of the present invention. Detailed Embodiments

[0061] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features and effects of a 5G Internet of Things data acquisition optimization method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. Such terms as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the element.

[0063] The specific scenario targeted by the present invention is: a storage battery, which is a device capable of storing electrical energy and releasing it when needed. It has very wide applications in fields such as industry, transportation, and power grids, and usually plays a key role in ensuring energy supply, improving energy efficiency, and supporting the utilization of renewable energy. In some places with large usage requirements, multiple storage batteries are connected in series to obtain a storage battery pack, thereby increasing the electrical energy storage capacity of the storage battery.

[0064] Due to the long-term charge and discharge of the storage battery or storage battery pack, and the performance of the battery being greatly affected by the environment, there are usually some differences in the health status among the storage batteries. In order to ensure the safety and reliability of the storage battery during use, it is necessary to collect storage battery data for each storage battery in each storage battery pack in a certain factory or system, and upload it to the central server through the Internet of Things, so as to determine the health status of each storage battery.

[0065] There are many data dimensions for the health status of storage batteries, such as voltage, current, temperature, battery capacity, etc. Therefore, multi-dimensional data and a large number of storage batteries will result in a large amount of data needing to be uploaded under each node. When these data are aggregated to the central server, it will take a long time to process. When a storage battery fails, it cannot be quickly discovered and located, resulting in a slow reaction speed when an accident occurs. At the same time, when compressing and uploading a large amount of data, data damage, data loss, etc. may occur.

[0066] The purpose of the present invention is to realize the health status monitoring and fault prediction of the storage battery pack by analyzing the working conditions of the storage batteries at the edge computing nodes. At the same time, adjust the amount of data uploaded by the nodes to the central server, reduce the pressure of data processing and uploading, and improve the overall data collection and processing efficiency.

[0067] The following specifically describes the specific solution of a 5G Internet of Things data collection optimization method provided by the present invention in conjunction with the accompanying drawings.

[0068] Please refer to Figure 1 , which shows the basic process of a 5G Internet of Things data collection optimization method provided by an embodiment of the present invention.

[0069] As Figure 1 shown, a 5G Internet of Things data collection optimization method provided by an embodiment of the present invention specifically includes:

[0070] S100: Set an initial sampling frequency, and collect multi-dimensional working data of the storage battery pack. The multi-dimensional working data includes working voltage and working current.

[0071] In the actual application process, battery packs are usually more common than single batteries, especially in high-power and large-capacity scenarios. Single batteries are usually used as backup power sources for certain functions within the system to ensure that when the battery pack fails, the single battery can guarantee that some functions can still be used for a short period of time. Therefore, in the embodiments of the present invention, the battery pack is taken as the object for analysis.

[0072] Set to collect the battery data of the battery or battery pack once every 1 minute, that is, the data collection frequency times / hour. Select sensors to collect the required multi-dimensional working data of the battery or battery pack under each edge computing node through the sensors. The multi-dimensional working data includes voltage data and current data, and also includes temperature data and internal resistance data. Among them:

[0073] 1) Voltage sensor: Installed at both ends (positive and negative poles) of each battery to collect voltage data of the entire battery pack and each battery within the battery pack respectively, obtaining the group voltage and single battery voltage;

[0074] 2) Current sensor: Installed at the charging end and the discharging end to collect the inflowing and outflowing current data of the entire battery pack and each single battery within the battery pack respectively, obtaining the group current and single battery current;

[0075] 3) Infrared temperature sensor: Installed at a position facing the battery or directly above the battery pack to obtain the surface temperature data of the entire battery pack and each single battery within the battery pack respectively, obtaining the group temperature and single battery temperature;

[0076] 4) Internal resistance sensor: Installed at both ends (positive and negative poles) of the battery monomer to obtain the internal resistance data of the entire battery pack and each single battery within the battery pack respectively, obtaining the group internal resistance and single battery internal resistance.

[0077] According to the obtained voltage data, current data, temperature data, and internal resistance data, estimate the capacity of the entire battery pack and each single battery within the battery pack according to the existing methods (such as the integration method based on the charging / discharging process, etc.).

[0078] Then store all the collected multi-dimensional working data of the single battery or battery pack in the specified storage area of the edge computing node according to the collection order.

[0079] In addition, simultaneously obtain the scale of the battery pack corresponding to each edge computing node, that is, the number of single batteries included in the battery pack.

[0080] Thus, the collection of the multi-dimensional working data of the battery pack is completed.

[0081] The battery pack is composed of multiple single batteries connected in series and parallel. When an abnormality occurs in one single battery in the battery pack, it will cause the working data of the single batteries connected in series or parallel with it to be abnormal at the same time. By analyzing the abnormal values and the connection methods between two or more single batteries, the true abnormal point (single battery) in the battery pack is located. The specific steps include S200 to S400.

[0082] S200: Analyze the first state abnormality index of the battery pack based on the working voltage and working current to obtain the abnormal battery pack.

[0083] Analyze the first state abnormality index of the battery pack based on the working voltage and working current to obtain the abnormal battery pack. The specific implementation method is as follows:

[0084] First, based on the working voltage and working current of the battery pack at the current moment, that is, the group voltage and group current of the battery pack at the current moment, combined with the theoretical voltage and theoretical current of the battery pack, the first state abnormality index of the battery pack at the current moment is obtained. Among them, the calculation formula for constructing the first state abnormality index of the battery pack at the current moment is:

[0085]

[0086] In the formula, represents the first state abnormality index of the th battery pack at the current moment; of the first state abnormality index; represents the group current of the th battery pack at the current moment; of the group current; represents the group voltage of the th battery pack at the current moment; of the group voltage; represents the theoretical current of the th battery pack; represents the theoretical voltage of the th battery pack; represents the linear normalization function.

[0087] Then, a first abnormality index threshold is preset. The value of the first abnormality index threshold can be 0.4; and, it is determined whether the first state abnormality index is greater than the first abnormality index threshold; if so, that is , it indicates that the working state of the battery pack corresponding to the first state abnormality index is abnormal, then the current th battery pack is the abnormal battery pack.

[0088] S300: Analyze the voltage change trend and current change trend of the abnormal battery pack to obtain the local abnormality judgment coefficient and determine the possible abnormal area.

[0089] The series-parallel combination design realizes the required voltage by connecting multiple single batteries in series, and then realizes the required capacity and current by connecting multiple series-connected battery packs in parallel. When the group voltage drops and the group current is low, it indicates that there is an abnormality in the series part of the battery pack; when the group voltage is relatively stable and the change range of the group current is large, it indicates that there is an abnormality in the parallel part of the battery pack.

[0090] Therefore, by analyzing the voltage change trend and current change trend of the abnormal battery pack, the local abnormality judgment coefficient is obtained to determine the possible abnormal area. The specific implementation method is as follows:

[0091] First, based on the working voltage and working current of the battery pack at the current moment and the previous moments, analyze the voltage fitting slope and current fitting slope at the current moment, that is, Construct a time-voltage curve and a time-current curve respectively with the group voltage and group current obtained at the current moment and the previous moments. Obtain the voltage fitting slope and current fitting slope at the current moment on the time-voltage curve and the time-current curve respectively. Among them, the voltage fitting slope is the voltage change trend of the abnormal battery pack, and the current fitting slope is the current change trend of the abnormal battery pack.

[0092] Then, according to the voltage change trend, combined with the working current and theoretical current of the battery pack at the current moment, obtain the series abnormality coefficient, and construct the current series abnormality coefficient calculation formula for the nth abnormal battery pack at the current moment:

[0093]

[0094] In the formula, represents the series abnormality coefficient of the nth abnormal battery pack at the current moment; represents the voltage fitting slope of the nth abnormal battery pack at the current moment; represents the theoretical current of the nth abnormal battery pack; represents the group current of the

[0095] nth abnormal battery pack at the current moment. At the same time, according to the voltage change trend and current change trend, obtain the parallel abnormality coefficient, and construct the parallel abnormality coefficient calculation formula for the

[0096]

[0097] In the formula, represents the parallel anomaly coefficient of the th abnormal battery pack at the current moment ; represents the voltage fitting slope of the th abnormal battery pack at the current moment ; represents the current fitting slope of the th abnormal battery pack at the current moment ; represents the first correction parameter, which is used to prevent the denominator from being 0. In this embodiment, is set.

[0098] For the series anomaly coefficient , when the value is negative and the smaller the data, the greater the series anomaly probability; for the parallel anomaly coefficient , when the value is larger, the greater the parallel anomaly probability.

[0099] Then, based on the series anomaly coefficient and the parallel anomaly coefficient, a local anomaly judgment coefficient is obtained, and the calculation formula for the local anomaly judgment coefficient of the th abnormal battery pack at the current moment is as follows:

[0100]

[0101] In the formula, represents the local anomaly judgment coefficient of the th abnormal battery pack at the current moment ; represents the series anomaly coefficient of the th abnormal battery pack at the current moment ; represents the parallel anomaly coefficient of the th abnormal battery pack at the current moment ; is a normalization function, and the normalization value range is [-1, 1].

[0102] Finally, based on the local anomaly judgment coefficient, the possible abnormal area is determined, that is, the magnitude relationship between the local anomaly judgment coefficient and 0 is judged to determine whether it is a series area anomaly or a parallel area anomaly. Specifically, when the local anomaly judgment coefficient of the abnormal battery pack, the abnormal battery pack shows a series anomaly; when the local anomaly judgment coefficient of the abnormal battery pack, the abnormal battery pack shows a parallel anomaly; when the local anomaly judgment coefficient When it is normal, the abnormal battery pack appears normal.

[0103] S400: Analyze the second state anomaly index of the possible abnormal area and determine the abnormal area.

[0104] According to the local anomaly judgment coefficient of the abnormal battery pack The possible abnormal area of the abnormal battery pack can be determined, that is, the parallel area is abnormal or the series area is abnormal. However, for the abnormal battery pack, there may be multiple parallel areas or multiple series areas, and the above cannot determine which specific parallel area or which specific series area, and further analysis is required.

[0105] Therefore, by analyzing the second state anomaly index of the possible abnormal area, the abnormal area is determined. The specific implementation method is as follows:

[0106] First, according to the working voltage and working current of the possible abnormal area at the current moment, that is, the local voltage and local current of the possible abnormal area at the current moment, combined with the theoretical voltage and theoretical current of the possible abnormal area, the second state anomaly index of the possible abnormal area at the current moment is obtained. Among them, the calculation formula for constructing the second state anomaly index of the possible abnormal area at the current moment is:

[0107]

[0108] In the formula, represents the second state anomaly index of the th possible abnormal area at the current moment ; represents the group current of the th possible abnormal area at the current moment ; represents the group voltage of the th possible abnormal area at the current moment ; represents the theoretical current of the th possible abnormal area; represents the theoretical voltage of the th possible abnormal area; represents the linear normalization function.

[0109] Then, a second anomaly index threshold is preset. The value of the second anomaly index threshold can be 0.4; and, determine whether the second state anomaly index is greater than the second anomaly index threshold; if so, that is , it indicates that the working state of the possible abnormal area corresponding to the second state anomaly index is abnormal, and the current th possible abnormal area is the abnormal area.

[0110] S500: Analyze the relative deviation of the working data of each dimension for the single battery in the abnormal area, and combine it with the local abnormal judgment coefficient to obtain the failure probability of the single battery.

[0111] Through the above steps, the abnormal battery pack is determined, and the specific abnormal area in the abnormal battery pack is determined. However, this abnormal area is a certain parallel area or a certain series area, that is, this abnormal area includes multiple single batteries, and it may be that one or several single batteries are abnormal, resulting in the entire parallel area or series area showing abnormalities. Therefore, it is necessary to further analyze the failure probability of each single battery in the abnormal area.

[0112] In the embodiment of the present invention, for the single battery in the abnormal area, analyze the relative deviation of the working data of each dimension, and combine it with the local abnormal judgment coefficient to obtain the failure probability of the single battery.

[0113] For the single battery in the abnormal area, since the initial sampling frequency is 60 times per hour and the sampling frequency is small, it is possible that the single battery did not show abnormalities in the previous minute and just showed abnormalities within the current minute range. Then, if the existing multi-dimensional working data is directly used for analysis, there may be a large deviation. Therefore, in some embodiments of the present invention, for the single battery in the abnormal area, analyzing the relative deviation of the working data of each dimension further includes: according to the second state abnormal index corresponding to the abnormal area, combined with the initial sampling frequency, obtaining the temporary sampling frequency of the abnormal area, and constructing the The calculation formula for the temporary sampling frequency of the nth abnormal area is:

[0114]

[0115] In the formula, represents the temporary sampling frequency of the nth abnormal area; represents the second state abnormal index at the current moment t of the nth abnormal area; represents the initial sampling frequency. That is, the larger the second state abnormal index is, the larger the corresponding temporary sampling frequency. is. represents the initial sampling frequency. That is, the larger the second state abnormal index is, the larger the corresponding temporary sampling frequency.

[0116] According to the temporary sampling frequency, re-acquire the latest multi-dimensional working data of the abnormal area and each single battery in this abnormal area. Among them, the time for data collection according to the temporary sampling frequency can be reasonably set as needed, such as it can be set to 10 minutes.

[0117] After obtaining the latest multi-dimensional working data, for the individual storage batteries in the abnormal area, analyze the relative deviation of the working data in each dimension, and combine the local anomaly judgment coefficient to obtain the failure probability of the individual storage battery. The specific implementation method is as follows:

[0118] First, for the individual storage batteries in the abnormal area, calculate the mean value of the latest working data in each dimension, analyze the differences in the working data in each dimension between the current individual storage battery and other individual storage batteries in the abnormal area, and obtain the relative deviation of the working data in each dimension. Construct the relative deviation calculation formula for the working data of the th individual storage battery in the th abnormal area at the current moment of the th dimension as follows:

[0119]

[0120] In the formula, represents the relative deviation of the working data of the th individual storage battery in the th abnormal area at the current moment of the th dimension; represents the number of individual storage batteries in the th abnormal area; represents the mean value of the latest working data of the th individual storage battery in the th abnormal area of the th dimension; represents the mean value of the latest working data of the th individual storage battery in the th abnormal area other than the th individual storage battery of the th dimension.

[0121] By analyzing the differences in the latest working data in each dimension between the current individual storage battery and other individual storage batteries in the abnormal area, obtain the relative deviation of the working data in each dimension of the current individual storage battery at the current moment .

[0122] It should be noted that when an abnormality occurs in a certain dimension data (such as voltage, current, temperature, etc.) of an individual storage battery, the data in other dimensions does not necessarily show abnormalities, that is, the data in different dimensions of an individual storage battery are usually independent. Therefore, each dimension data is analyzed as independent dimension data.

[0123] According to the relative deviation of the working data in each dimension, obtain the total deviation of the individual storage battery, that is, the mean value of the relative deviations of the working data in all dimensions of the individual storage battery.

[0124] Then, based on the total deviation and combined with the local anomaly judgment coefficient of the local area where the single battery is located, the failure probability of the single battery is obtained, and the failure probability calculation formula for the th single battery in the th abnormal area at the current moment is as follows:

[0125]

[0126] In the formula, represents the failure probability of the th single battery in the th abnormal area at the current moment ; represents the total deviation of the th single battery in the th abnormal area at the current moment ; represents the local anomaly judgment coefficient of the abnormal battery group where the th single battery in the th abnormal area is located at the current moment ; represents the linear normalization function.

[0127] S600: Cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering clusters, and combine with the failure probability to obtain the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack.

[0128] Among multiple edge computing nodes, if the scale numbers of their battery packs are quite different, then the influence between them is relatively small and can be ignored. However, if the scale numbers of the battery packs are similar, then the influence between them is relatively large; and if the similarity of the working conditions among the battery packs corresponding to each edge computing node is relatively large, then the influence between them will also be relatively large. When the influence among the battery packs is relatively large, it is necessary to increase the sampling frequency to obtain more working data samples to improve the accuracy of data analysis.

[0129] Based on the above analysis, in the embodiments of the present invention, cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering clusters, and combine with the failure probability to obtain the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack. The specific implementation method is as follows:

[0130] First, according to the scale of the battery pack, perform clustering analysis on the battery pack to obtain clustering clusters. Then, calculate the mean of the working data of each battery pack in each dimension within the clustering cluster where the current abnormal battery pack is located; and calculate the variance of the mean of the working data of each dimension between all battery packs within the clustering cluster where the current abnormal battery pack is located, to obtain the dimensional data dispersion degree of the working data of each dimension between all battery packs within the clustering cluster, and further obtain the dispersion degree between all battery packs within the clustering cluster, that is, the mean of the dimensional data dispersion degrees of all working data. Finally, according to the dispersion degree, combined with the failure probability, obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack; specifically, according to the failure probability of a single battery cell, calculate the failure probability of the current abnormal battery pack, that is, the mean of the failure probabilities of all single battery cells in the current abnormal battery pack. It should be noted that only the failure probabilities of single battery cells in the abnormal area are calculated above. For the failure probabilities of single battery cells in the non-abnormal area, 0 is used for calculation here, and calculate the average failure probability of all battery packs within the clustering cluster where the current abnormal battery pack is located to obtain the failure probability abnormality degree of the current abnormal battery pack; according to the dispersion degree, combined with the failure probability abnormality degree, obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack. Construct the influence coefficient of other battery packs within the clustering cluster where the th abnormal battery pack is located on the th abnormal battery pack, and the calculation formula is:

[0131]

[0132] In the formula, represents the influence coefficient of other battery packs within the clustering cluster where the th abnormal battery pack is located on the th abnormal battery pack; represents the mean of the failure probabilities of all battery packs within the clustering cluster where the th abnormal battery pack is located; represents the failure probability of the th abnormal battery pack; represents the dispersion degree between all battery packs within the clustering cluster where the th abnormal battery pack is located; and respectively represent the second correction parameter and the third correction parameter, both of which are used to prevent the denominator from being 0. In this embodiment, , .

[0133] It represents the degree of abnormality of the failure probability of the current abnormal battery pack. The smaller the value, the smaller the difference in the failure probability between the current abnormal battery pack and other battery packs within its clustering cluster, indicating a greater similarity in the operating conditions among the battery packs and a greater influence among the battery packs. It represents the dispersion degree among all battery packs within the clustering cluster where the current abnormal battery pack is located. The smaller the value, the smaller the difference in the operating condition data among all battery packs within the clustering cluster where the current abnormal battery pack is located, indicating a greater similarity in the operating conditions among the battery packs and a greater influence among the battery packs.

[0134] S700: Adjust the initial sampling frequency according to the influence coefficient and the failure probability to obtain the corrected sampling frequency.

[0135] Adjust the initial sampling frequency according to the influence coefficient and the failure probability to obtain the corrected sampling frequency, and construct the calculation formula for the corrected sampling frequency of the th abnormal battery pack as follows:

[0136]

[0137] In the formula, represents the corrected sampling frequency of the current th abnormal battery pack; represents the failure probability of the current th abnormal battery pack; The th abnormal battery pack is within the clustering cluster, and the influence coefficient of other battery packs on the current th abnormal battery pack; represents the initial sampling frequency.

[0138] The failure probability The greater it is, the more abnormal the current abnormal battery pack is, and the more necessary it is to increase the sampling frequency of the current abnormal battery pack; at the same time, the influence coefficient The greater it is, the greater the interference of other battery packs on the current abnormal battery pack. Therefore, the more necessary it is to increase the sampling frequency of the current abnormal battery pack.

[0139] The edge computing node adjusts the data acquisition parameters of the sensor according to the real-time operating conditions of the battery pack. Subsequently, the obtained battery operating data is uploaded according to the situation. For battery packs with a low failure probability, only the overall data is uploaded, and the data of individual batteries is stored under the edge computing node. For battery packs with a high failure probability, the data of each individual battery also needs to be uploaded. When the required data is uploaded to the central server, the health status of the battery packs under each edge computing node is evaluated respectively, and the evaluated data, etc., are displayed through Internet of Things terminals (such as mobile phones, computers, etc.).

[0140] Based on the same inventive concept as the above method, this embodiment also provides a 5G Internet of Things data acquisition optimization system.

[0141] Please refer to Figure 2 , which shows the basic composition of a 5G Internet of Things data acquisition optimization system provided by an embodiment of the present invention.

[0142] As Figure 2 shown, a 5G Internet of Things data acquisition optimization system includes: a memory 10 and a processor 20, where:

[0143] The memory 10 is used to store program codes;

[0144] The processor 20 is used to read the program codes stored in the memory 10, and execute setting an initial sampling frequency, acquiring multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current; analyzing a first state anomaly index of the battery pack according to the working voltage and working current to obtain an abnormal battery pack; analyzing the voltage change trend and current change trend of the abnormal battery pack to obtain a local anomaly judgment coefficient and determine a possible abnormal area; analyzing a second state anomaly index of the possible abnormal area to determine the abnormal area; for each single battery in the abnormal area, analyzing the relative deviation of the working data in each dimension, and combining with the local anomaly judgment coefficient to obtain the failure probability of the single battery; clustering the battery packs to obtain clustering clusters, analyzing the dispersion degree between all battery packs in the clustering cluster, and combining with the failure probability to obtain the influence coefficient of other battery packs in the clustering cluster on the current abnormal battery pack; adjusting the initial sampling frequency according to the influence coefficient and the failure probability to obtain a corrected sampling frequency.

[0145] Further, the processor includes: a working data acquisition module 21, an abnormal area determination module 22, a failure probability analysis module 23, an influence coefficient analysis module 24, and a sampling frequency adjustment module 25. Wherein:

[0146] The working data acquisition module 21 is used to set the initial sampling frequency and acquire multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current;

[0147] The abnormal area determination module 22 is used to analyze a first state anomaly index of the battery pack according to the working voltage and working current to obtain an abnormal battery pack;

[0148] The fault probability analysis module 23 is used to analyze the voltage change trend and current change trend of the abnormal battery pack, obtain the local abnormality judgment coefficient, determine the possible abnormal area; and analyze the second state abnormality index of the possible abnormal area to determine the abnormal area; finally, for each single battery in the abnormal area, analyze the relative deviation of the working data in each dimension, and combine it with the local abnormality judgment coefficient to obtain the fault probability of the single battery.

[0149] The influence coefficient analysis module 24 is used to cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs in the clustering clusters, and combine it with the fault probability to obtain the influence coefficient of other battery packs in the clustering cluster on the current abnormal battery pack.

[0150] The sampling frequency adjustment module 25 is used to adjust the initial sampling frequency according to the influence coefficient and the fault probability to obtain the corrected sampling frequency.

[0151] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0152] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. An optimization method for 5G Internet of Things data collection, characterized in that, The method includes: Setting an initial sampling frequency and collecting multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current; Analyzing a first state anomaly index of the battery pack based on the working voltage and the working current to obtain an abnormal battery pack; Analyzing the voltage change trend and current change trend of the abnormal battery pack to obtain a local anomaly judgment coefficient and determine a possible abnormal area; Analyzing a second state anomaly index of the possible abnormal area to determine the abnormal area; For the single batteries in the abnormal area, analyzing the relative deviation of the working data in each dimension and combining with the local anomaly judgment coefficient to obtain the failure probability of the single battery; Clustering the battery pack to obtain clustering clusters, analyzing the dispersion degree among all battery packs within the clustering cluster, and combining with the failure probability to obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack; Adjusting the initial sampling frequency according to the influence coefficient and the failure probability to obtain a corrected sampling frequency; For the single batteries in the abnormal area, analyzing the relative deviation of the working data in each dimension, and previously also includes: According to the second state anomaly index corresponding to the abnormal area and combining with the initial sampling frequency, obtaining a temporary sampling frequency of the abnormal area; According to the temporary sampling frequency, re-obtaining the latest multi-dimensional working data of the abnormal area; For the single batteries in the abnormal area, analyzing the relative deviation of the working data in each dimension and combining with the local anomaly judgment coefficient to obtain the failure probability of the single battery, including: For the single batteries in the abnormal area, calculating the mean value of the working data in each dimension, analyzing the difference in the working data in each dimension between the current single battery and other single batteries in the abnormal area, and obtaining the relative deviation of the working data in each dimension; Obtaining the total deviation of the single battery according to the relative deviation of the working data in each dimension; According to the total deviation and combining with the local anomaly judgment coefficient of the local area where the single battery is located, obtaining the failure probability of the single battery.

2. The 5G Internet of Things data acquisition optimization method according to claim 1, wherein Analyzing a first state anomaly index of the battery pack based on the working voltage and the working current to obtain an abnormal battery pack, including: According to the working voltage and the working current of the battery pack at the current moment and combining with the theoretical voltage and theoretical current of the battery pack, obtaining the first state anomaly index of the battery pack at the current moment; Presetting a first anomaly index threshold; Judging whether the first state anomaly index is greater than the first anomaly index threshold; If so, the battery pack corresponding to the first state anomaly index is the abnormal battery pack.

3. The 5G Internet of Things data collection optimization method according to claim 1, wherein Analyzing the voltage change trend and current change trend of the abnormal battery pack to obtain a local anomaly judgment coefficient and determine a possible abnormal area, including: Based on the working voltage and the working current of the battery pack at the current moment and the previous moments, analyzing the voltage fitting slope and current fitting slope at the current moment to obtain the voltage change trend and current change trend of the abnormal battery pack; According to the voltage change trend, combining with the working current and the theoretical current of the battery pack at the current moment, obtain the series anomaly coefficient; According to the voltage change trend and the current change trend, obtain the parallel anomaly coefficient; According to the series anomaly coefficient and the parallel anomaly coefficient, obtain the local anomaly judgment coefficient; According to the local anomaly judgment coefficient, determine the possible abnormal area; 4. The 5G Internet of Things data collection optimization method according to claim 1, characterized in that, Analyze the second state anomaly index of the possible abnormal area to determine the abnormal area, including: According to the working voltage and the working current of the possible abnormal area at the current moment, combining with the theoretical voltage and the theoretical current of the possible abnormal area, obtain the second state anomaly index of the possible abnormal area at the current moment; Preset the second anomaly index threshold; Judge whether the second state anomaly index is greater than the second anomaly index threshold; If so, the possible abnormal area corresponding to the second state anomaly index is the abnormal area; 5. The 5G Internet of Things data collection optimization method according to claim 1, characterized in that Cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering clusters, and combine with the failure probability to obtain the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack, including: According to the scale of the battery pack, perform clustering analysis on the battery pack to obtain clustering clusters; Calculate the mean value of the working data of each battery pack within the clustering cluster where the current abnormal battery pack is located in each dimension; Calculate the variance of the mean values of the working data of each dimension among all battery packs within the clustering cluster where the current abnormal battery pack is located to obtain the dimension data dispersion degree of the working data of each dimension among all battery packs within the clustering cluster, and further obtain the dispersion degree among all battery packs within the clustering cluster; According to the dispersion degree, combining with the failure probability, obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack; 6. The 5G Internet of Things data acquisition optimization method according to claim 5, characterized in that, According to the dispersion degree, combining with the failure probability, obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack, including: According to the failure probability of the single battery, calculate the failure probability of the current abnormal battery pack and the average failure probability of all battery packs within the clustering cluster where the current abnormal battery pack is located to obtain the failure probability abnormality degree of the current abnormal battery pack; According to the dispersion degree, combining with the failure probability abnormality degree, obtain the influence coefficient of other battery packs within the clustering cluster on the current abnormal battery pack; 7. A 5G Internet of Things data acquisition optimization system, characterized in that, The system includes: a memory and a processor, where: The memory is used to store program codes; The processor is used to read the program codes stored in the memory and execute the method according to any one of claims 1 to 6; 8. The 5G Internet of Things data acquisition optimization system according to claim 7, wherein The processor includes: A working data acquisition module, used to set an initial sampling frequency and acquire multi-dimensional working data of the battery pack, where the multi-dimensional working data includes working voltage and working current; An abnormal area determination module, used to analyze the first state anomaly index of the battery pack according to the working voltage and the working current to obtain an abnormal battery pack; The fault probability analysis module is used to analyze the voltage change trend and current change trend of the abnormal battery pack, obtain the local abnormality judgment coefficient, determine the possible abnormal area; and analyze the second state abnormality index of the possible abnormal area to determine the abnormal area; finally, for each single battery in the abnormal area, analyze the relative deviation of the working data in each dimension, and combine the local abnormality judgment coefficient to obtain the fault probability of the single battery; The influence coefficient analysis module is used to cluster the battery packs to obtain clustering clusters, analyze the dispersion degree among all battery packs within the clustering clusters, and combine the fault probability to obtain the influence coefficient of other battery packs within the clustering clusters on the current abnormal battery pack; The sampling frequency adjustment module is used to adjust the initial sampling frequency according to the influence coefficient and the fault probability to obtain the corrected sampling frequency; For each single battery in the abnormal area, analyzing the relative deviation of the working data in each dimension, previously also included: According to the second state abnormality index corresponding to the abnormal area, combined with the initial sampling frequency, obtain the temporary sampling frequency of the abnormal area; According to the temporary sampling frequency, re-obtain the latest multi-dimensional working data of the abnormal area; For each single battery in the abnormal area, analyzing the relative deviation of the working data in each dimension, and combining the local abnormality judgment coefficient to obtain the fault probability of the single battery, including: For each single battery in the abnormal area, calculate the mean value of the working data in each dimension, analyze the difference in the working data in each dimension between the current single battery and other single batteries in the abnormal area, and obtain the relative deviation of the working data in each dimension; According to the relative deviation of the working data in each dimension, obtain the total deviation of the single battery; According to the total deviation, combined with the local abnormality judgment coefficient of the part where the single battery is located, obtain the fault probability of the single battery.

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