Energy storage power station cell data monitoring method and device, electronic equipment, storage medium and product

Abstract

The invention discloses an energy storage power station cell data monitoring method and device, electronic equipment, a storage medium and a product, and relates to the technical field of electrical energy, the disclosed energy storage power station cell data monitoring method is applied to an energy storage power station cell data monitoring system, and the system comprises a safety communication management device and a cloud platform. The method comprises the following steps: acquiring battery cell data of a monitored energy storage power station through a safety communication management device, and encrypting and transmitting the battery cell data to a cloud platform by adopting a message queue telemetry transmission protocol; and battery safety monitoring and evaluation are carried out through the cloud platform based on a preset battery cell fuzzy evaluation model, and input data of the battery cell fuzzy evaluation model are battery cell data. According to the invention, the effectiveness of large-scale energy storage power station cell data monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to methods, devices, electronic equipment, storage media and products for monitoring data of battery cells in energy storage power stations. Background Art

[0002] In the rapidly developing electric energy sector, large-scale energy storage power stations, as key facilities in the power system, are becoming increasingly important.

[0003] In the operation of large-scale energy storage power stations, the performance and health of battery cells, as the basic unit of energy storage, directly affect the safety and reliability of the entire energy storage system. In order to ensure the stable operation of energy storage power stations, it is necessary to monitor the battery cell data in real time and accurately. However, traditional monitoring methods often have problems such as data transmission security and insufficient universality of data evaluation, making it difficult to improve the effectiveness of monitoring battery cell data in large-scale energy storage power stations. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, electronic device, storage medium and product for monitoring battery cell data in an energy storage power station, aiming to improve the effectiveness of monitoring battery cell data in large-scale energy storage power stations.

[0005] To achieve the above purpose, the present application proposes a method for monitoring data of battery cells in an energy storage power station, which is applied to a battery cell data monitoring system in an energy storage power station. The system includes a secure communication management device and a cloud platform. The method for monitoring data of battery cells in an energy storage power station includes:

[0006] Acquire the battery cell data of the monitoring energy storage power station through the secure communication management device, and encrypt and transmit the battery cell data to the cloud platform using a message queue telemetry transmission protocol;

[0007] Battery safety monitoring and evaluation is performed through the cloud platform based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the cell data.

[0008] In one embodiment, the cloud platform includes a data security verification and forwarding module, a message queue telemetry transmission server, a time series database and a battery cell safety assessment module, and the step of performing battery safety monitoring and assessment based on a preset battery cell fuzzy assessment model through the cloud platform includes:

[0009] Performing security verification on the cell data through the data security verification and forwarding module, and after determining that the security verification has passed, forwarding the cell data to the message queue telemetry transmission server;

[0010] Performing data conversion processing on the battery cell data through the message queue telemetry transmission server to obtain battery cell timing data, and transmitting the battery cell timing data to the timing database;

[0011] Performing data cleaning processing on the battery cell timing data through the timing database, and transmitting the processed battery cell timing data to the battery cell safety assessment module;

[0012] The battery cell safety assessment module performs battery safety monitoring and assessment based on a preset battery cell fuzzy assessment model.

[0013] In one embodiment, the step of performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform includes:

[0014] A battery cell fuzzy evaluation model is established through the cloud platform, wherein the evaluation object of the battery cell fuzzy evaluation model is the battery of the monitored energy storage power station, and the influencing factors of the evaluation object include the battery cell data, and the battery cell data includes current value, ambient temperature and humidity, and voltage value;

[0015] Performing fuzzy evaluation on each influencing factor in the influencing factor set based on a preset evaluation level set to obtain a fuzzy relationship matrix, wherein the fuzzy relationship matrix represents the membership degree of each influencing factor with respect to each evaluation level;

[0016] Multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor;

[0017] According to the maximum membership principle, the battery safety monitoring assessment level is determined based on the comprehensive evaluation results.

[0018] In one embodiment, before the step of multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor, the step further includes:

[0019] Calculate the entropy weight of each of the influencing factors according to the fuzzy relationship matrix;

[0020] A factor weight index matrix is ​​constructed according to the entropy weight of each influencing factor.

[0021] In one embodiment, the step of acquiring the cell data of the monitoring energy storage power station through the secure communication management device, and encrypting and transmitting the cell data to the cloud platform using a message queue telemetry transmission protocol includes:

[0022] Acquiring the battery cell data of the monitoring energy storage power station through the secure communication management device;

[0023] Combine the preset user password, true random number and device serial number into data to be signed;

[0024] The SM2 algorithm (an encryption algorithm) is used to sign the hash value of the data to be signed to obtain a signature result;

[0025] The signature result and the battery cell data are transmitted to the cloud platform using a message queue transmission protocol, so that the cloud platform uses an SM3 algorithm (an encryption algorithm) to perform hash processing on the received battery cell data, and uses the signature and public key provided by the secure communication management device to perform data security verification.

[0026] In one embodiment, after the step of performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform, the method further includes:

[0027] After receiving the evaluation result request instruction, the evaluation result of the battery safety monitoring evaluation is fed back to the user end, wherein the evaluation result includes the battery safety monitoring evaluation level.

[0028] In addition, to achieve the above-mentioned purpose, the present application also proposes a battery cell data monitoring system for an energy storage power station, the battery cell data monitoring system for an energy storage power station comprising: a secure communication management device and a cloud platform;

[0029] The secure communication management device is used to obtain the battery cell data of the monitoring energy storage power station, and encrypt and transmit the battery cell data to the cloud platform using a message queue telemetry transmission protocol;

[0030] The cloud platform is used to perform battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the cell data.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the energy storage power station cell data monitoring method as described above.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the energy storage power station cell data monitoring method as described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the energy storage power station cell data monitoring method as described above are implemented.

[0034] The present application proposes a method for monitoring battery cell data in an energy storage power station, which is applied to a battery cell data monitoring system in an energy storage power station, the system comprising a secure communication management device and a cloud platform; the operating data of each battery cell, i.e., the battery cell data, is acquired in real time and accurately through the secure communication management device, and the acquired battery cell data is encrypted and transmitted to the cloud platform using a message queue telemetry transmission protocol (Message QueuingTelemetry Transport, MQTT), so as to ensure the integrity and security of the data during the transmission process and effectively prevent data leakage or tampering; then, a battery cell fuzzy evaluation model is used on the cloud platform to parse and evaluate the received battery cell data, the model being based on fuzzy logic theory and being able to comprehensively and accurately evaluate the health status, safety risks, etc. of the battery cell by comprehensively considering a variety of influencing factors.

[0035] In summary, the present application adopts the MQTT protocol combined with data encryption technology to transmit battery cell data, which ensures the security and integrity of battery cell data during transmission and reduces the risk of illegal access or use of battery cell data; at the same time, by introducing the battery cell fuzzy evaluation model on the cloud platform, the evaluation process can better handle the uncertainty and ambiguity in the battery cell data, improve the universality and accuracy of the evaluation results, and can flexibly respond to the monitoring needs of battery cells of different brands and models, meet the needs of large-scale energy storage power stations for battery cell data monitoring, and provide support for the safety management, performance optimization and fault warning of energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flow chart of the first embodiment of the method for monitoring data of battery cells in an energy storage power station of the present application;

[0039] Figure 2 A schematic diagram of cloud-edge collaboration provided for Embodiment 2 of the method for monitoring battery cell data in an energy storage power station of the present application;

[0040] Figure 3 A schematic diagram of a system for monitoring battery cell data in an energy storage power station provided in Embodiment 2 of the method for monitoring battery cell data in an energy storage power station of the present application;

[0041] Figure 4 This is a schematic diagram of the module structure of the energy storage power station cell data monitoring system according to an embodiment of the present application;

[0042] Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the method for monitoring battery cell data in an energy storage power station in an embodiment of the present application.

[0043] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0046] Since the performance and health of battery cells, as the basic unit of energy storage, directly affect the safety and reliability of the entire energy storage system during the operation of large-scale energy storage power stations, in order to ensure the stable operation of energy storage power stations, it is necessary to monitor the battery cell data in real time and accurately. However, traditional monitoring methods often have problems such as insufficient data transmission security and data evaluation accuracy, making it difficult to improve the effectiveness of monitoring battery cell data in large-scale energy storage power stations.

[0047] It is worth mentioning that although the real-time cloud-based battery data technology has been proposed, the real-time collection, transmission and processing of battery data has been realized through the powerful processing capabilities of cloud computing and the extensive connectivity of the Internet of Things. However, the current real-time cloud-based monitoring and evaluation of energy storage power stations still has the following problems:

[0048] First, current strategies for migrating energy storage data to the cloud often adopt a cooperative or self-built approach without fully considering data security factors, resulting in potential data leakage risks.

[0049] Secondly, in early energy storage power stations, there was a lack of suitable data transmission conditions, so data collection transformation was quite difficult and took a long time.

[0050] Third, current energy storage safety monitoring and evaluation indicators are usually designed only for specific energy storage power stations and lack comprehensive coverage.

[0051] An embodiment of the present application provides a solution, which is applied to a battery cell data monitoring system of an energy storage power station, wherein the system includes a secure communication management device and a cloud platform; the battery cell data of the monitored energy storage power station is obtained through the secure communication management device, and the battery cell data is encrypted and transmitted to the cloud platform using a message queue telemetry transmission protocol; the cloud platform performs battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the battery cell data.

[0052] In summary, the embodiment of the present application adopts the MQTT protocol combined with data encryption technology to transmit battery cell data, thereby ensuring the security and integrity of battery cell data during transmission and reducing the risk of illegal access or use of battery cell data; at the same time, by introducing a battery cell fuzzy evaluation model on the cloud platform, the evaluation process can better handle the uncertainty and ambiguity in the battery cell data, improve the universality and accuracy of the evaluation results, and can flexibly respond to the monitoring needs of battery cells of different brands and models, meet the needs of large-scale energy storage power stations for battery cell data monitoring, and provide support for safety management, performance optimization and fault warning of energy storage power stations.

[0053] It should be noted that the execution subject of this embodiment can be an electronic device with data processing, network communication and program running functions, such as a computer, a server, etc., or an electronic device capable of realizing the above functions, etc. The following takes the energy storage power station battery cell data monitoring system as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the present application embodiment provides a method for monitoring battery cell data in an energy storage power station, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for monitoring battery cell data in an energy storage power station of the present application.

[0055] In this embodiment, the energy storage power station battery cell data monitoring method is applied to the energy storage power station battery cell data monitoring system, the system includes a secure communication management device and a cloud platform; the method includes steps S10 to S20:

[0056] Step S10, obtaining the cell data of the monitored energy storage power station through the secure communication management device, and encrypting and transmitting the cell data to the cloud platform using a message queue telemetry transmission protocol;

[0057] It should be noted that, in this embodiment, the energy storage power station battery cell data monitoring method is applied to the energy storage power station battery cell data monitoring system, which includes a secure communication management device and a cloud platform, wherein the secure communication management device is a hardware device embedded with a secure encryption algorithm, which is specially designed to establish a secure and reliable communication link between the data collection point for monitoring the energy storage power station and the cloud platform, and can realize encrypted transmission of data to ensure the security of the data transmission process; the cloud platform is responsible for receiving and processing the battery cell data from the secure communication management device, and is a remote computing platform and service model based on Internet technology, providing functions such as data storage, processing, analysis, and application hosting.

[0058] Through the secure communication management device deployed on the edge side, the operating data of each battery cell in the energy storage power station is collected and aggregated in real time, namely the battery cell data. The battery cell data refers to the various parameter information generated by each battery cell in the energy storage power station during operation, including but not limited to voltage, current, temperature, capacity, internal resistance, etc. In order to ensure the security and integrity of the data transmission process, the message queue telemetry transmission protocol is used to encrypt and transmit the battery cell data to the cloud platform.

[0059] Step S20: performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform, wherein the input data of the battery cell fuzzy evaluation model is the battery cell data.

[0060] It should be noted that the battery cell fuzzy evaluation model is a mathematical model built based on fuzzy logic theory. It is used to obtain the evaluation results of the battery safety status based on the cell data of the input model through a series of fuzzy reasoning rules.

[0061] After receiving the battery cell data, the cloud platform uses a preset battery cell fuzzy evaluation model to analyze the data. This model is based on fuzzy logic theory and can comprehensively consider multiple performance indicators and operating environment parameters of the battery cell to accurately and comprehensively evaluate the safety status of the battery cell, thereby promptly discovering potential safety hazards or signs of performance degradation.

[0062] In summary, compared with traditional detection methods, the embodiments of the present application adopt the MQTT protocol combined with data encryption technology to transmit battery cell data, thereby ensuring the security and integrity of battery cell data during transmission and reducing the risk of illegal access or use of battery cell data; at the same time, by introducing a battery cell fuzzy evaluation model on the cloud platform, the evaluation process can better handle the uncertainty and ambiguity in the battery cell data, improve the universality and accuracy of the evaluation results, and can flexibly respond to the monitoring needs of battery cells of different brands and models, meet the needs of large-scale energy storage power stations for battery cell data monitoring, and provide support for safety management, performance optimization and fault warning of energy storage power stations.

[0063] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, the cloud platform includes a data security verification and forwarding module, a message queue telemetry transmission server, a time series database and a battery cell safety assessment module, and step S20 may include steps S201 to S204:

[0064] Step S201, performing security verification on the battery cell data through the data security verification and forwarding module, and after determining that the security verification passes, forwarding the battery cell data to the message queue telemetry transmission server;

[0065] It should be noted that, in the present embodiment, the cloud platform includes a data security verification and forwarding module, a message queue telemetry transmission server, a time series database and a battery cell safety assessment module, wherein the data security verification and forwarding module is used to receive battery cell data and perform strict security verification and legality checks. After ensuring the security and legality of the data, the module is responsible for forwarding the data to the message queue telemetry transmission server; the message queue telemetry transmission server receives the battery cell data from the data security verification and forwarding module, performs data conversion processing, generates battery cell time series data, and transmits it to the time series database for storage; the time series database is used to process large amounts of high-frequency time series data and supports rapid query, analysis and visualization of data; the battery cell safety assessment module performs in-depth analysis of the processed battery cell time series data based on a preset battery cell fuzzy evaluation model, evaluates the safety status of the battery, and outputs the evaluation results.

[0066] The cell data from the security communication management device is received through the data security verification and forwarding module of the cloud platform. The received cell data is strictly verified through this module, including but not limited to data integrity verification, source verification, encryption and decryption. After ensuring the security and legality of the data, the cell data is forwarded to the message queue telemetry transmission server for subsequent processing.

[0067] Step S202, performing data conversion processing on the battery cell data through the message queue telemetry transmission server to obtain battery cell timing data, and transmitting the battery cell timing data to the timing database;

[0068] After receiving the cell data, the message queue telemetry transmission server performs data conversion processing to convert the original cell data into a format suitable for timing analysis, namely, cell timing data, and transmits the cell timing data to the timing database for storage.

[0069] Step S203, performing data cleaning processing on the battery cell timing data through the timing database, and transmitting the processed battery cell timing data to the battery cell safety assessment module;

[0070] In the timing database, the battery cell timing data is comprehensively cleaned to eliminate noise, outliers and redundant information in the data to ensure the accuracy and consistency of the data. The cleaned battery cell timing data is then transmitted to the battery cell safety assessment module.

[0071] Step S204: Performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the battery cell safety evaluation module.

[0072] After receiving the processed battery cell timing data, the battery cell safety assessment module conducts in-depth battery safety monitoring and assessment based on the preset battery cell fuzzy assessment model. The model uses fuzzy logic theory, comprehensively considers various performance indicators and operating environment parameters of the battery cell, accurately assesses the safety status of the battery, and outputs the assessment results.

[0073] In a feasible embodiment, step S20 may further include steps S205 to S208:

[0074] Step S205, establishing a battery cell fuzzy evaluation model through the cloud platform, wherein the evaluation object of the battery cell fuzzy evaluation model is the battery of the monitored energy storage power station, and the influencing factors of the evaluation object include the battery cell data, and the battery cell data includes current value, ambient temperature and humidity, and voltage value;

[0075] The cloud platform has built a battery cell fuzzy evaluation model for monitoring energy storage power station batteries. The evaluation object of this model is the battery in the monitoring energy storage power station. The influencing factors of the evaluation object include battery cell data, which includes but is not limited to current value, ambient temperature and humidity, voltage value and other parameters. The influencing factor set can be expressed as U = {u1,u2,...,u n}.

[0076] Step S206, performing fuzzy evaluation on each influencing factor in the influencing factor set based on a preset evaluation level set to obtain a fuzzy relationship matrix, wherein the fuzzy relationship matrix represents the degree of membership of each influencing factor with respect to each evaluation level;

[0077] Based on the preset evaluation level set V = {v1, v2, ..., v n}, usually five levels, such as safe, relatively safe, general, relatively dangerous, dangerous, etc., fuzzy judgment is performed on each factor in the influencing factor set. This process uses fuzzy mathematics to convert the specific value of each factor into its membership in each evaluation level, and then forms a fuzzy relationship matrix R. This matrix intuitively shows the fuzzy relationship between each influencing factor and the evaluation level. The fuzzy relationship matrix R can be expressed as:

[0078] Among them, r ij Indicates u i About v j The membership degree of (U, V, R) constitutes the battery cell fuzzy evaluation model.

[0079] Step S207, multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor;

[0080] In order to obtain a comprehensive evaluation of battery safety performance, the generated fuzzy relationship matrix is ​​multiplied with the preset factor weight indicator matrix. The factor weight indicator matrix reflects the importance of different factors in the evaluation. Through this operation, all influencing factors can be comprehensively considered to obtain a comprehensive evaluation result of each influencing factor.

[0081] Step S208, according to the maximum membership principle, determine the battery safety monitoring assessment level according to the comprehensive evaluation result.

[0082] According to the principle of maximum membership, the evaluation level with the highest membership is selected from the comprehensive evaluation results as the final battery safety monitoring and assessment level, which ensures the objectivity and accuracy of the assessment results and provides a reliable reference for monitoring the safe operation and maintenance of energy storage power stations.

[0083] It is worth mentioning that the performance parameters of battery cells are complex, and there are mutual influences and dependencies, and they are also affected by multiple factors such as charge and discharge temperature and current. Traditional evaluation methods are often based on precise mathematical models, but it is difficult to establish precise mathematical models for the performance parameters of battery cells, so a fuzzy evaluation method is needed. In this embodiment, a single factor is evaluated first, and then all factors are comprehensively considered for fuzzy evaluation to prevent the omission of any statistical information and the loss of information midway, thereby improving the reliability and accuracy of battery safety performance evaluation.

[0084] In a feasible embodiment, before step S207, steps S2071 to S2072 may also be included:

[0085] Step S2071, calculating the entropy weight of each of the influencing factors according to the fuzzy relationship matrix;

[0086] It is worth mentioning that in the fuzzy comprehensive evaluation method, the weight index is a very important link. Generally, there are three methods for calculating weights: subjective assignment method, objective assignment method and combined weighting method. This embodiment adopts a combined weighting method, which combines the subjective method and the objective method, and combines the weights of the two methods to finally obtain a weight index. This approach can take into account both subjective and objective factors and improve the accuracy and reliability of the evaluation results.

[0087] The entropy weight of each influencing factor is calculated by the entropy weight method using the information contained in the fuzzy relationship matrix. The entropy weight method determines the weight according to the discrete degree of the data itself. The greater the discrete degree of the factor, the greater its weight, which means that the factor has a more significant impact on the evaluation result.

[0088] In the specific calculation process, the entropy value of each influencing factor is first calculated, and then the entropy weight of each factor is calculated based on the entropy value. These entropy weights reflect the importance of different factors in the battery safety performance evaluation.

[0089] Step S2072: construct a factor weight index matrix according to the entropy weight of each influencing factor.

[0090] After obtaining the entropy weight of each influencing factor, a factor weight indicator matrix is ​​constructed based on the entropy weight. The matrix is ​​a two-dimensional array, whose rows represent different influencing factors and columns represent weight values. The entropy weight of each influencing factor is used as the value of its corresponding column in the matrix, thereby completing the construction of the factor weight indicator matrix.

[0091] For example, let the fuzzy relationship matrix composed of m evaluation schemes (i.e., multiple schemes with combined weights) and n indicators (i.e., various influencing factors) be R=(r ij ) m×n , i=1,2,...,m,j=1,2,...,n.

[0092] The entropy of each indicator is E j :

[0093] When R ij = 0, let R ij LqCy ij =0.

[0094] Entropy weight W of each indicator j for: in: Entropy weight W j The larger the value, the greater the impact of the indicator on comprehensive decision-making.

[0095] First, determine the r in the matrix R 1m 、r 2m 、r 3m If any of the values ​​is greater than 0, the battery consistency performance is determined to be "poor". 1m 、r 2m 、r 3mIf all items are 0, the judgment matrix B is calculated based on the weight vector and the fuzzy relationship matrix, B = (b1, b2, b3) = w·R, and the energy storage battery consistency performance level is determined according to the maximum membership principle. For example, if b1 is the largest in matrix B, the battery consistency performance is good.

[0096] In this way, by introducing a more scientific and reasonable weight calculation method, the accuracy and reliability of the evaluation are further improved.

[0097] In a feasible embodiment, step S10 may include steps S101 to S104:

[0098] Step S101, obtaining the battery cell data of the monitoring energy storage power station through the security communication management device;

[0099] Step S102, combining a preset user password, a true random number and a device serial number into data to be signed;

[0100] It should be noted that the user password is a means of identity authentication provided by the cloud platform. Users need to use this password to access the system or perform specific operations to ensure that only authorized users can obtain or operate data; true random numbers are randomly generated values ​​​​by the security communication management device. The generation process is based on the randomness of physical processes (such as quantum phenomena, noise, etc.), rather than pseudo-random numbers forged by algorithms, and are therefore more difficult to predict and copy; the device serial number is a unique identifier assigned to each security communication management device, which is used to track the device and ensure the legitimacy and traceability of the data source.

[0101] The battery cell data of the monitored energy storage power station is automatically acquired through the secure communication management device. Then, in order to enhance the security of data transmission, the user password, true random number and device serial number of the secure communication management device are combined to form the data to be signed.

[0102] Step S103, using the SM2 algorithm to perform signature protection on the hash value of the data to be signed to obtain a signature result;

[0103] The SM2 algorithm is used to sign the hash value of the signature data. The SM2 algorithm is a public key cryptography algorithm based on elliptic curve cryptography. It verifies the integrity of the data and the authenticity of the source by generating an unforgeable digital signature.

[0104] Step S104, using a message queue transmission protocol to transmit the signature result and the battery cell data to the cloud platform, so that the cloud platform uses an SM3 algorithm to perform hash processing on the received battery cell data, and uses the signature and public key provided by the secure communication management device to perform data security verification.

[0105] The signature result and the cell data are transmitted to the cloud platform together through the message queue transmission protocol. After receiving the data, the cloud platform first uses the SM3 algorithm to hash the cell data, and then uses the signature and public key provided by the secure communication management device to verify it, ensuring that the data has not been tampered with during transmission and that it is indeed from a legitimate secure communication management device. The SM3 algorithm is a cryptographic hash algorithm that is used to generate a fixed-length summary (hash value) of data, and has the characteristics of strong anti-collision and fast calculation speed.

[0106] Exemplarily, in a feasible implementation, the secure communication management device refers to a device embedded with a secure encryption algorithm, which effectively combines user passwords, true random numbers and device serial numbers. The device uses the SM2 algorithm to sign and protect data. In addition, the device supports multiple communication protocols, including Modbus, OPC UA, IEC101, IEC104 and IEC61850, etc., and can adapt to different communication media, such as Ethernet, RS232 / RS422 / RS485, etc. It uses the message queue telemetry transmission (MQTT) protocol, which is responsible for data parsing, preprocessing and forwarding. Through the high capacity, high speed and low latency characteristics of the 5G (5th Generation Mobile Communication Technology) network, the device realizes the high concurrency and high frequency of real-time data of energy storage power station cells on site and securely uploads them to the cloud platform. This real-time data collection and upload process realizes cloud-edge collaboration, such as Figure 2 As shown. The data source is the BMS (Battery Management System) that monitors the energy storage power station. The transmitted data is the data of each battery cell. The 5G secure transmission of the battery cell data is realized based on the communication management device. The data transmission process can be wired transmission or wireless transmission. The cloud platform (i.e., the cloud energy storage in the figure) builds a cloud-based battery cell model (i.e., the battery cell fuzzy evaluation model) based on the received battery cell data and performs diagnostic evaluation of the battery cell.

[0107] In this way, the system realizes the safe and reliable transmission of battery cell data from the energy storage power station to the cloud platform.

[0108] In a feasible embodiment, step S20 may further include step S30:

[0109] Step S30, after receiving the evaluation result request instruction, the evaluation result of the battery safety monitoring evaluation is fed back to the user end, wherein the evaluation result includes the battery safety monitoring evaluation level.

[0110] When the cloud platform receives an evaluation result request instruction from the user end or triggered by the system, the system will respond quickly and execute the instruction. At this stage, the battery safety monitoring and evaluation results are first retrieved and sorted. These results include but are not limited to the battery safety monitoring and evaluation level, which directly reflects the current safety status and potential risks of the battery. Then, the evaluation results are transmitted to the user end using the established data transmission protocol (such as HTTPS, WebSocket, etc.). After receiving the evaluation results, the user end can view the battery safety monitoring and evaluation level intuitively and conveniently through a variety of methods such as a graphical interface, SMS notification, and email push, so as to timely grasp the operation status of the energy storage power station.

[0111] In this way, the evaluation of the safety performance of the batteries in the energy storage power station was completed, efficient feedback and sharing of the evaluation results were achieved, and a closed-loop battery monitoring and evaluation system was built, providing a solid guarantee for the safe and stable operation of the energy storage power station.

[0112] For example, in a feasible embodiment, the energy storage power station battery cell data monitoring system is as follows: Figure 3 As shown, the system starts collecting cell data from the on-site equipment (device side), and the cell data is obtained by the 5G secure communication management device deployed on the edge side. Then, the cell data is transmitted to the cloud platform through the 5G network. The cloud platform analyzes and evaluates the cell data and finally presents the results to the user.

[0113] Specifically, the equipment side includes energy storage power station equipment, energy storage power station monitoring platform, group-level platform, etc. The energy storage power station equipment is connected to the 5G secure communication management device through its own equipment interface, and the energy storage power station monitoring platform is connected to the 5G secure communication management device through the background service interface. The group-level platform sends the battery cell data in its own spring database to the 5G secure communication management device. The 5G secure communication management device supports M2 / 3 / 4 national encryption signature verification, and is responsible for collecting battery cell data and uploading it to the cloud platform after encrypting the data.

[0114] The cloud platform includes a data security verification and forwarding module, an MQTT server, a time series database, and a cell safety assessment module. The data security verification and forwarding service relies on a standardized national secret algorithm to perform security verification on the data processing subject, the data body, and the data transmission behavior. Once the security verification is successful, the system forwards the data to the MQTT server to ensure the integrity of the data and the non-repudiation of the behavior throughout its life cycle. The MQTT server is configured on the cloud, and EMQX Broker (an MQTT message server) is selected as the server platform. The server has real-time, low latency, high concurrent millions of carrying capacity and message distribution services to achieve real-time processing and distribution of large-scale energy storage power station cell data. A time series database is set up on the cloud platform to receive large-scale time series data of energy storage cells distributed by the MQTT service. The time series database adopts a specific data storage method to perform fast processing and efficient storage in a stable and continuous manner with high concurrency, thereby greatly enhancing the processing capability of time-related data and efficiently meeting the cross-time section query requirements. The cell safety monitoring and evaluation presents data such as battery operation data, basic cell information, and battery alarm information. Through fuzzy theory, the historical operating data of the three indicators of battery cell real-time temperature, extreme difference and standard deviation over a period of time are integrated and analyzed to realize the evaluation of the consistency of energy storage batteries within this period of time.

[0115] In this way, this embodiment analyzes the evolution trend of the safety risk of energy storage power stations. The evaluation and analysis results obtained help power station owners, equipment manufacturers, industry associations, scientific research institutions and other relevant user entities to timely understand the safety status of power stations, and help build and expand energy storage power stations, providing a basis for their quality risks and orderly access. In addition, it provides assistance to project developers in project selection and optimizing project development timing.

[0116] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the energy storage power station cell data monitoring method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0117] The present application also provides a battery cell data monitoring system for an energy storage power station. Figure 4 , the energy storage power station battery cell data monitoring system includes: a security communication management device 10 and a cloud platform 20;

[0118] The secure communication management device 10 is used to obtain the battery cell data of the monitoring energy storage power station, and encrypt and transmit the battery cell data to the cloud platform using a message queue telemetry transmission protocol;

[0119] The cloud platform 20 is used to perform battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the cell data.

[0120] Optionally, the cloud platform 20 includes a data security verification and forwarding module 201, a message queue telemetry transmission server 202, a time series database 203 and a battery cell safety assessment module 204;

[0121] The data security verification and forwarding module 201 is used to perform security verification on the battery cell data, and after determining that the security verification has passed, forward the battery cell data to the message queue telemetry transmission server 202;

[0122] The message queue telemetry transmission server 202 is used to perform data conversion processing on the battery cell data to obtain battery cell timing data, and transmit the battery cell timing data to the timing database 203;

[0123] The time series database 203 is used to perform data cleaning processing on the battery cell time series data and transmit the processed battery cell time series data to the battery cell safety assessment module 204;

[0124] The battery cell safety assessment module 204 is used to perform battery safety monitoring and assessment based on a preset battery cell fuzzy assessment model.

[0125] Optionally, the cloud platform 20 is further used for:

[0126] Establishing a battery cell fuzzy evaluation model, wherein the evaluation object of the battery cell fuzzy evaluation model is the battery of the monitored energy storage power station, the influencing factors of the evaluation object include the battery cell data, and the battery cell data includes current value, ambient temperature and humidity, and voltage value;

[0127] Performing fuzzy evaluation on each influencing factor in the influencing factor set based on a preset evaluation level set to obtain a fuzzy relationship matrix, wherein the fuzzy relationship matrix represents the membership degree of each influencing factor with respect to each evaluation level;

[0128] Multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor;

[0129] According to the maximum membership principle, the battery safety monitoring assessment level is determined based on the comprehensive evaluation results.

[0130] Optionally, the cloud platform 20 is further used for:

[0131] Calculate the entropy weight of each of the influencing factors according to the fuzzy relationship matrix;

[0132] A factor weight index matrix is ​​constructed according to the entropy weight of each influencing factor.

[0133] Optionally, the secure communication management device 10 is further used for:

[0134] Obtain and monitor the battery cell data of the energy storage power station;

[0135] Combine the preset user password, true random number and device serial number into data to be signed;

[0136] The SM2 algorithm is used to sign the hash value of the data to be signed to obtain a signature result;

[0137] The signature result and the battery cell data are transmitted to the cloud platform 20 using a message queue transmission protocol, so that the cloud platform 20 uses an SM3 algorithm to perform hash processing on the received battery cell data, and uses the signature and public key provided by the secure communication management device 10 to perform data security verification.

[0138] Optionally, the cloud platform 20 is further used for:

[0139] After receiving the evaluation result request instruction, the evaluation result of the battery safety monitoring evaluation is fed back to the user end, wherein the evaluation result includes the battery safety monitoring evaluation level.

[0140] The energy storage power station cell data monitoring system provided in the embodiment of the present application adopts the energy storage power station cell data monitoring method in the above embodiment, which can improve the effectiveness of large-scale energy storage power station cell data monitoring. Compared with the prior art, the beneficial effects of the energy storage power station cell data monitoring system provided in the embodiment of the present application are the same as the beneficial effects of the energy storage power station cell data monitoring method provided in the above embodiment, and other technical features in the energy storage power station cell data monitoring system are the same as the features disclosed in the energy storage power station cell data monitoring method in the above embodiment, which will not be repeated here.

[0141] An embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy storage power station cell data monitoring method in the above-mentioned embodiment 1.

[0142] Reference below Figure 5, which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include but is not limited to mobile terminals such as laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0143] like Figure 5 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0144] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0145] The electronic device provided in the embodiment of the present application adopts the energy storage power station battery cell data monitoring method in the above embodiment, which can improve the effectiveness of large-scale energy storage power station battery cell data monitoring. Compared with the prior art, the beneficial effects of the electronic device provided in the embodiment of the present application are the same as the beneficial effects of the energy storage power station battery cell data monitoring method provided in the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the energy storage power station battery cell data monitoring method in the previous embodiment, which will not be repeated here.

[0146] It should be understood that the various parts disclosed in the embodiments of the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0147] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0148] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the energy storage power station cell data monitoring method in the above-mentioned embodiment.

[0149] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0150] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0151] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: obtains the battery cell data of the monitored energy storage power station through the secure communication management device, and encrypts and transmits the battery cell data to the cloud platform using a message queue telemetry transmission protocol; performs battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform, wherein the input data of the battery cell fuzzy evaluation model is the battery cell data.

[0152] The computer program code for performing the operation of the embodiment of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0153] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0154] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0155] The readable storage medium provided in the embodiment of the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned energy storage power station battery cell data monitoring method, which can improve the effectiveness of large-scale energy storage power station battery cell data monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the embodiment of the present application are the same as the beneficial effects of the energy storage power station battery cell data monitoring method provided in the above-mentioned embodiment, and will not be repeated here.

[0156] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned energy storage power station battery cell data monitoring method when executed by a processor.

[0157] The computer program product provided in this application can solve the technical problem of monitoring the battery cell data of energy storage power stations. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the battery cell data monitoring method of energy storage power stations provided in the above embodiments, which will not be repeated here.

[0158] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring battery cell data of an energy storage power station, characterized in that: Applicable to the battery cell data monitoring system of energy storage power station, the system includes a secure communication management device and a cloud platform; The method comprises: Acquire the battery cell data of the monitoring energy storage power station through the secure communication management device, and encrypt and transmit the battery cell data to the cloud platform using a message queue telemetry transmission protocol; Battery safety monitoring and evaluation is performed through the cloud platform based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the cell data.

2. The method for monitoring battery cell data of an energy storage power station according to claim 1, characterized in that: The cloud platform includes a data security verification and forwarding module, a message queue telemetry transmission server, a time series database and a battery cell safety assessment module. The step of performing battery safety monitoring and assessment based on a preset battery cell fuzzy assessment model through the cloud platform includes: Performing security verification on the cell data through the data security verification and forwarding module, and after determining that the security verification has passed, forwarding the cell data to the message queue telemetry transmission server; Performing data conversion processing on the battery cell data through the message queue telemetry transmission server to obtain battery cell timing data, and transmitting the battery cell timing data to the timing database; Performing data cleaning processing on the battery cell timing data through the timing database, and transmitting the processed battery cell timing data to the battery cell safety assessment module; The battery cell safety assessment module performs battery safety monitoring and assessment based on a preset battery cell fuzzy assessment model.

3. The method for monitoring battery cell data of an energy storage power station according to claim 1, characterized in that: The step of performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform includes: A battery cell fuzzy evaluation model is established through the cloud platform, wherein the evaluation object of the battery cell fuzzy evaluation model is the battery of the monitored energy storage power station, and the influencing factors of the evaluation object include the battery cell data, and the battery cell data includes current value, ambient temperature and humidity, and voltage value; Performing fuzzy evaluation on each influencing factor in the influencing factor set based on a preset evaluation level set to obtain a fuzzy relationship matrix, wherein the fuzzy relationship matrix represents the membership degree of each influencing factor with respect to each evaluation level; Multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor; According to the maximum membership principle, the battery safety monitoring assessment level is determined based on the comprehensive evaluation results.

4. The method for monitoring battery cell data of an energy storage power station according to claim 3, characterized in that: Before the step of multiplying the fuzzy relationship matrix with a preset factor weight index matrix to obtain a comprehensive evaluation result of each influencing factor, the method further includes: Calculate the entropy weight of each of the influencing factors according to the fuzzy relationship matrix; A factor weight index matrix is ​​constructed according to the entropy weight of each influencing factor.

5. The method for monitoring battery cell data of an energy storage power station according to claim 1, characterized in that: The step of acquiring the cell data of the monitoring energy storage power station through the secure communication management device, and encrypting and transmitting the cell data to the cloud platform using a message queue telemetry transmission protocol includes: Acquiring the battery cell data of the monitoring energy storage power station through the secure communication management device; Combine the preset user password, true random number and device serial number into data to be signed; The SM2 algorithm is used to sign the hash value of the data to be signed to obtain a signature result; The signature result and the battery cell data are transmitted to the cloud platform using a message queue transmission protocol, so that the cloud platform uses an SM3 algorithm to perform hash processing on the received battery cell data, and uses the signature and public key provided by the secure communication management device to perform data security verification.

6. The method for monitoring battery cell data of an energy storage power station according to any one of claims 1 to 5, characterized in that: After the step of performing battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model through the cloud platform, the method further includes: After receiving the evaluation result request instruction, the evaluation result of the battery safety monitoring evaluation is fed back to the user end, wherein the evaluation result includes the battery safety monitoring evaluation level.

7. A battery cell data monitoring system for an energy storage power station, characterized in that: The system includes: a secure communication management device and a cloud platform; The secure communication management device is used to obtain the battery cell data of the monitoring energy storage power station, and encrypt and transmit the battery cell data to the cloud platform using a message queue telemetry transmission protocol; The cloud platform is used to perform battery safety monitoring and evaluation based on a preset battery cell fuzzy evaluation model, wherein the input data of the battery cell fuzzy evaluation model is the cell data.

8. An electronic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for monitoring battery cell data in an energy storage power station as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for monitoring battery cell data in an energy storage power station according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for monitoring battery cell data of an energy storage power station according to any one of claims 1 to 6 are implemented.

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