Energy storage battery cabin screen mirroring method and system based on Internet of Things

Abstract

The present invention discloses a screen mirroring method and system for an energy storage battery compartment based on the Internet of Things, which relates to visualization-related fields. The method includes: obtaining basic data of the energy storage battery compartment, extracting topological structures and attribute parameters, and establishing a virtual visualization model; establishing functional partitions, performing adaptive aggregation, and establishing adaptive aggregation results; configuring monitoring sensors; reading real-time tasks, matching adaptive aggregation results, calling monitoring sensors, generating real-time data streams according to real-time monitoring results, and performing visualization; performing abnormal analysis; adding abnormal identification to the virtual visualization model, and performing screen mirroring display with the added virtual visualization model. The method solves the technical problems of insufficient data visualization, poor monitoring accuracy, and untimely abnormality detection in the existing monitoring and management of energy storage battery compartments, and achieves the technical effect of realizing comprehensive visualization of the energy storage battery compartment, accurate monitoring, and timely detection and handling of abnormal situations.

Description

Technical Field

[0001] The present application relates to visualization-related fields, and in particular to a method and system for screen mirroring of an energy storage battery compartment based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, energy storage battery compartments, as key facilities for energy storage and supply, have become a hot topic in terms of intelligent management and monitoring. Internet of Things technology enables the various components and devices in the energy storage battery compartment to be effectively connected, realizing real-time data collection and transmission. However, the existing energy storage battery compartment monitoring method usually uses a fixed sensor network for data collection. Due to the unreasonable layout of sensors, this method has poor monitoring accuracy and cannot timely detect and handle abnormal conditions in the energy storage battery compartment. The existing method displays data in the form of charts or simple animations, lacks an in-depth understanding and utilization of the internal structure and partitions of the energy storage battery compartment, resulting in insufficient data visualization and difficulty in intuitively reflecting the real-time status and relationships of the components inside the energy storage battery compartment.

[0003] Among the current related technologies, the monitoring and management of energy storage battery compartments have technical problems such as insufficient data visualization, poor monitoring accuracy, and untimely anomaly detection. Summary of the invention

[0004] The present application provides a screen mirroring method and system for an energy storage battery compartment based on the Internet of Things, and adopts technical means such as extracting the topological structure and attribute parameters of the energy storage battery compartment, establishing a virtual visualization model, establishing functional partitions, performing adaptive aggregation, configuring monitoring sensors, and performing abnormal analysis, thereby achieving the technical effect of realizing comprehensive visualization of the energy storage battery compartment, accurate monitoring, timely detection and handling of abnormal situations, and thus improving management efficiency.

[0005] This application provides a method for screen mirroring of an energy storage battery compartment based on the Internet of Things, including:

[0006] Acquire basic data of the energy storage battery compartment, extract topological structure and attribute parameters based on the basic data, and establish a virtual visualization model according to the topological structure and attribute parameters;

[0007] Establishing functional partitions of the energy storage battery compartment, wherein the functional partitions are functional partitions of minimum granularity units, and adaptively aggregating the functional partitions to establish adaptive aggregation results, wherein the adaptive aggregation results carry an identification of aggregation probability;

[0008] configuring monitoring sensors based on the functional partitions and the aggregation probability, and establishing network communication between the monitoring sensors and the virtual visualization model;

[0009] Read the real-time tasks of the energy storage battery compartment, match the adaptive aggregation results through the real-time tasks, call the corresponding monitoring sensor according to the matching results, generate a real-time data stream of the virtual visualization model according to the real-time monitoring results of the monitoring sensor, and visualize the virtual visualization model;

[0010] Input the real-time monitoring results and the real-time tasks into a multi-dimensional anomaly analysis channel, perform anomaly analysis, and establish an anomaly identification;

[0011] The abnormal identification is added to the virtual visualization model, so as to add the completed virtual visualization model to perform screen mirroring display.

[0012] In a possible implementation, the function partitions are adaptively aggregated to establish an adaptive aggregation result, the adaptive aggregation result carries an identification of the aggregation probability, and the following processing is performed:

[0013] Reading usage data of the energy storage battery compartment, performing execution frequency calls of functions based on the usage data, and establishing an aggregated frequency trust coefficient;

[0014] Obtaining a function importance identifier of the function, and establishing an aggregated important trust coefficient based on the function importance identifier;

[0015] A normalization coefficient is configured, and the aggregation probability of each adaptive aggregation result is calculated by using the normalization coefficient, the frequency trust coefficient, and the important trust coefficient, and the corresponding aggregation probability is identified to the adaptive aggregation result.

[0016] In a possible implementation, the monitoring sensor is configured based on the functional partition and the aggregation probability, and the following processing is performed:

[0017] Establishing a sensor set, wherein the sensor set is a callable sensor set;

[0018] Performing functional adaptation matching of functional partitions with the sensor set to establish a first adaptation matching result;

[0019] Performing important adaptation matching of the sensor set based on the aggregation probability, and establishing a second degree of fitness matching result;

[0020] An optimization analysis of the overall highest fitness is performed based on the first fitness matching result and the second fitness matching result, and a mapping relationship between the sensor set and the functional partition is established based on the optimization analysis result to complete the monitoring sensor configuration.

[0021] In a possible implementation, the following processing is performed:

[0022] Establish control mapping between virtual visualization model and energy storage battery compartment;

[0023] When the virtual visualization model performs 3D visualization display, the functional unit and display state of the 3D visualization are configured according to the called user characteristics;

[0024] When receiving a control instruction from the user in the virtual visualization model, a matching verification is performed according to the matching degree between the user authority and the control instruction, and a control response of the energy storage battery compartment is performed based on the matching verification result and the control mapping.

[0025] In a possible implementation, the real-time monitoring result and the real-time task are input into a multi-dimensional anomaly analysis channel, an anomaly analysis is performed, an anomaly identification is established, and the following processing is performed:

[0026] Input the real-time monitoring results and the real-time tasks into the basic analysis subchannel, perform anomaly analysis, and generate basic anomaly results;

[0027] Calling an additional analysis subchannel, wherein the additional analysis subchannel stores time-series tasks and historical response data;

[0028] Performing a pre-timing impact analysis of timing tasks and historical response data through the additional analysis subchannel, compensating the basic abnormal result through the pre-timing impact analysis, and generating an updated abnormal result;

[0029] Performing a post-time sequence execution prediction of the time sequence task according to the updated abnormal result through the additional analysis sub-channel to generate a predicted abnormal result;

[0030] An abnormal identification is established for the updated abnormal result and the predicted abnormal result.

[0031] In a possible implementation, the following processing is performed:

[0032] establishing similar substitution associations of functions according to the adaptive aggregation results;

[0033] When the continuous operation duration of any adaptive aggregation result meets the preset threshold, similar replacement determination is performed through the similar replacement association;

[0034] Replacement management of corresponding adaptive aggregation results is performed based on similar replacement judgment results.

[0035] In a possible implementation, the replacement management of the corresponding adaptive aggregation result is performed according to the similar replacement determination result, and the following processing is performed:

[0036] If the similar replacement judgment result is a failed judgment result, the operation abnormality analysis is performed according to the corresponding adaptive aggregation result and the continuous operation duration, and an abnormality warning is established;

[0037] The abnormal warning is visually reported through a virtual visualization model.

[0038] This application also provides an energy storage battery cabin screen mirroring system based on the Internet of Things, including:

[0039] A virtual visualization model building module, which is used to obtain basic data of the energy storage battery compartment, extract the topological structure and attribute parameters based on the basic data, and build a virtual visualization model according to the topological structure and attribute parameters;

[0040] A functional partition establishment module, wherein the functional partition establishment module is used to establish the functional partition of the energy storage battery compartment, wherein the functional partition is the functional partition of the minimum granularity unit, and adaptively aggregates the functional partition to establish an adaptive aggregation result, wherein the adaptive aggregation result carries an identification of the aggregation probability;

[0041] A monitoring sensor configuration module, the monitoring sensor configuration module is used to configure the monitoring sensor based on the functional partition and the aggregation probability, and establish network communication between the monitoring sensor and the virtual visualization model;

[0042] A visualization display module, the visualization display module is used to read the real-time tasks of the energy storage battery compartment, match the adaptive aggregation results through the real-time tasks, call the corresponding monitoring sensor according to the matching results, generate a real-time data stream of the virtual visualization model according to the real-time monitoring results of the monitoring sensor, and visualize the virtual visualization model;

[0043] An abnormality analysis module, the abnormality analysis module is used to input the real-time monitoring results and the real-time tasks into a multi-dimensional abnormality analysis channel, perform abnormality analysis, and establish an abnormality mark;

[0044] A screen mirroring display module is used to add the abnormal identification to the virtual visualization model, so as to perform screen mirroring display by adding the completed virtual visualization model.

[0045] The energy storage battery compartment screen mirroring method and system based on the Internet of Things proposed in this application first obtain the basic data of the energy storage battery compartment, extract the topological structure and attribute parameters based on the basic data, establish a virtual visualization model according to the topological structure and attribute parameters, and then establish the functional partition of the energy storage battery compartment, wherein the functional partition is the functional partition of the smallest granularity unit, and adaptively aggregate the functional partition to establish an adaptive aggregation result, the adaptive aggregation result carries an identification of the aggregation probability, and then configure the monitoring sensor based on the functional partition and the aggregation probability, and establish network communication between the monitoring sensor and the virtual visualization model, then read the real-time task of the energy storage battery compartment, match the adaptive aggregation result through the real-time task, call the corresponding monitoring sensor according to the matching result, generate the real-time data stream of the virtual visualization model according to the real-time monitoring result of the monitoring sensor, and visualize the virtual visualization model, then input the real-time monitoring result and the real-time task into the multi-dimensional abnormal analysis channel, perform abnormal analysis, establish abnormal identification, and finally add the abnormal identification to the virtual visualization model, and perform screen mirroring display with the added virtual visualization model, so as to achieve the technical effect of realizing comprehensive visualization, accurate monitoring, timely discovery and handling of abnormal situations in the energy storage battery compartment, thereby improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0047] Figure 1 A schematic diagram of a flow chart of a method for screen mirroring in an energy storage battery compartment based on the Internet of Things provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the structure of the energy storage battery compartment screen mirroring system based on the Internet of Things provided in an embodiment of the present application.

[0049] Explanation of the accompanying drawings: virtual visualization model establishment module 10, functional partition establishment module 20, monitoring sensor configuration module 30, visualization display module 40, abnormality analysis module 50, screen mirroring display module 60. DETAILED DESCRIPTION

[0050] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0051] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0052] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0053] The present application embodiment provides a method for screen mirroring of an energy storage battery compartment based on the Internet of Things, such as Figure 1 As shown, the method includes:

[0054] Step S100, obtaining basic data of the energy storage battery compartment, extracting the topological structure and attribute parameters based on the basic data, and establishing a virtual visualization model according to the topological structure and attribute parameters. Specifically, the basic data of the energy storage battery compartment is collected, and the basic data refers to the data describing the basic characteristics and status of the energy storage battery compartment, including physical structure, equipment layout, electrical parameters, etc. According to the basic data, the physical connection relationship of the energy storage battery compartment is analyzed, and its topological structure is extracted. The topological structure refers to the mutual connection and layout relationship between the elements in the system, such as the connection relationship between the battery module, the control system, the cooling system, etc. The attribute parameters of each component or device are extracted from the basic data. The attribute parameters are specific numerical values ​​or state information describing the characteristics of the system components, such as battery capacity, voltage range, operating temperature, etc. Using the topological structure and attribute parameters, a virtual visualization model of the energy storage battery compartment is established in a virtual environment using computer graphics technology. The virtual visualization model is a virtual model that reflects the layout, structure and function of the actual energy storage battery compartment, and is used to monitor and display the status and parameters of the energy storage battery compartment in real time.

[0055] Step S200, establish the functional partition of the energy storage battery compartment, wherein the functional partition is the functional partition of the minimum granularity unit, and adaptively aggregate the functional partition to establish an adaptive aggregation result, and the adaptive aggregation result carries the identification of the aggregation probability. Specifically, analyze the functions of each component or device in the energy storage battery compartment to determine the functional partition of the minimum granularity unit. The functional partition is different areas divided according to the functional characteristics and spatial layout of the energy storage battery compartment, and each functional partition has specific functions and roles. According to the similarity and correlation of the functional partitions, based on factors such as functional complementarity and spatial location, adaptive aggregation (the process of automatically aggregating similar or related elements together according to certain rules) is performed to form an adaptive aggregation result. According to the various factors in the aggregation process, an aggregation probability is calculated for each aggregation result to reflect its stability and reliability.

[0056] In a possible implementation, the function partition is adaptively aggregated to establish an adaptive aggregation result, and the adaptive aggregation result carries an identification of the aggregation probability. Step S200 further includes step S210, reading the usage data of the energy storage battery compartment, calling the execution frequency of the function based on the usage data, and establishing an aggregated frequency trust coefficient. Specifically, the usage data of the energy storage battery compartment is collected, including historical operation records, energy input and output records, temperature monitoring records, etc., and the usage frequency of each function partition is analyzed, that is, the number of times or frequency each function is called. Based on the usage frequency, a frequency trust coefficient is calculated for each function partition. The frequency trust coefficient reflects the frequency of use of the function partition. The more frequently it is used, the higher the frequency trust coefficient. Step S220, obtain the function importance identification of the function, and establish an aggregated important trust coefficient based on the function importance identification. Specifically, according to the functional characteristics, criticality and other factors of the function partition, the importance of each function partition is evaluated, and based on the function importance evaluation result, an important trust coefficient is calculated for each function partition. The important trust coefficient reflects the criticality and importance of the function partition in the overall system. Step S230, configure a normalization coefficient, calculate the aggregation probability of each adaptive aggregation result through the normalization coefficient, the frequency trust coefficient and the important trust coefficient, and identify the corresponding aggregation probability to the adaptive aggregation result. Specifically, configure a normalization coefficient to unify the frequency trust coefficient and the important trust coefficient to the same dimension in order to calculate the aggregation probability. According to the normalized frequency trust coefficient and the important trust coefficient, the aggregation probability of each adaptive aggregation result is calculated, wherein the aggregation probability can be a weighted and / or other form of combination of the two coefficients, and the calculated aggregation probability is identified to the corresponding adaptive aggregation result as an indicator of the importance of the aggregation result. This implementation method calculates the aggregation probability based on the frequency trust coefficient and the important trust coefficient, comprehensively considering the actual usage and importance of the functional partition, and achieves the technical effect of making the aggregation result more accurate and reasonable.

[0057] In a possible implementation, step S200 further includes step S240, establishing a similar substitution association of functions according to the adaptive aggregation result. Specifically, the adaptive aggregation result is analyzed to determine the functional characteristics represented by each aggregation result. Based on the functional characteristics, it is identified which adaptive aggregation results have similarities, that is, they can realize similar or identical functions, the identified similar functions are associated, and a similar substitution association table or database is established to record which adaptive aggregation results can replace each other. Step S250, when the continuous operation time of any adaptive aggregation result meets the preset threshold, similar substitution determination is performed through the similar substitution association. Specifically, the continuous operation time of each adaptive aggregation result is monitored in real time, that is, the time when the function is continuously used, and the monitored continuous operation time is compared with the preset threshold to determine whether it reaches or exceeds the preset threshold. When the continuous operation time meets the preset threshold, the similar substitution determination mechanism is triggered, and based on the similar substitution association established in step S240, it is determined whether there is an alternative adaptive aggregation result. Step S260, replacement management of the corresponding adaptive aggregation result is performed according to the similar substitution determination result. Specifically, according to the similar replacement determination result in step S250, the adaptive aggregation result to be replaced and the candidate object that can be replaced are determined, and according to the determined replacement scheme, the currently used adaptive aggregation result is replaced with the candidate object that can be replaced, and the detailed information of the replacement operation is recorded, including the replacement time, the adaptive aggregation results before and after the replacement, etc. This implementation method uses similar replacement determination and replacement management, and when the continuous operation time of a certain adaptive aggregation result is too long, it can be replaced with the adaptive aggregation results of other similar functions, thereby avoiding the performance degradation or resource waste caused by the long-term use of a single function, and achieving the technical effect of improving the operating efficiency of the system, enhancing the reliability of the system, and optimizing resource utilization.

[0058] In a possible implementation, step S260 further includes step S261, if the similar replacement determination result is a failure result, then the operation abnormality analysis is performed according to the corresponding adaptive aggregation result and the continuous operation duration, and an abnormality warning is established. Specifically, when the similar replacement determination result is failure, it indicates that the current adaptive aggregation result cannot be replaced by the aggregation results of other similar functions. At this time, detailed information about the current adaptive aggregation result and the continuous operation duration is collected, including the operation load, resource usage, system log, etc. Based on the collected information, the preset algorithm and model are used to analyze why the similar replacement determination failed, whether there are problems such as resource exhaustion, system bottlenecks, and functional failures. According to the analysis results, an abnormality warning is established, including abnormality type, severity, impact range, etc. Information. Step S262, the abnormal warning is visually reported through a virtual visualization model. Specifically, the detailed information of the abnormal warning is integrated into the virtual visualization model, including the abnormal location, impact range, recommended measures, etc., and the integrated virtual visualization model is visually reported and displayed to relevant personnel so that corresponding processing measures can be taken. This implementation method analyzes job anomalies and establishes abnormal warnings, processes and visualizes abnormal situations when similar replacements fail, and achieves the technical effect of further improving the reliability and operational efficiency of the system.

[0059] Step S300, configure monitoring sensors based on the functional partitions and the aggregation probability, and establish network communication between the monitoring sensors and the virtual visualization model. Specifically, analyze the boundaries, functional characteristics and relationships of each functional partition, and determine the degree of monitoring needs of each functional partition based on the aggregation probability of the functional partitions. Functional partitions with high aggregation probabilities require more intensive monitoring points. According to the monitoring needs, determine the type of sensor, including temperature sensors, humidity sensors, pressure sensors, current and voltage sensors, etc. Set sampling frequency, range, accuracy and other parameters for each sensor to ensure that the sensor can accurately capture the state changes of the target functional partition, and deploy sensors in appropriate locations based on the spatial layout of the functional partition and monitoring needs. According to the type and characteristics of the monitoring sensor, determine the communication protocol, such as Modbus, MQTT, HTTP, etc., configure the network communication parameters such as the IP address and port number of the monitoring sensor, create a data interface corresponding to the monitoring sensor data in the virtual visualization model, and establish a connection between the monitoring sensor and the virtual visualization model.

[0060] In a possible implementation, the monitoring sensor is configured based on the functional partition and the aggregation probability, and step S300 further includes step S310, establishing a sensor set, wherein the sensor set is a callable sensor set. Specifically, collect available sensor device information, including sensor type, range, accuracy, sampling frequency, etc., integrate all collected sensor information into a database to form a callable sensor set. Step S320, perform functional adaptation matching of the functional partition with the sensor set, and establish a first fitness matching result. Among them, the functional adaptation matching is a process of matching the functional characteristics of the sensor with the requirements of the functional partition, aiming to find the most suitable sensor to monitor a specific functional partition. Specifically, analyze the specific functional requirements of each functional partition, including the physical quantity to be monitored, monitoring accuracy, etc., select sensors that can meet the requirements of the functional partition from the sensor set, calculate the fitness of the selected sensor and the functional partition according to factors such as the range, accuracy, sampling frequency, etc. of the sensor, and establish a preliminary matching result of the sensor and the functional partition according to the fitness calculation result. Step S330, based on the aggregation probability, important adaptation matching of the sensor set is performed to establish a second fitness matching result. The important adaptation matching is a process of assigning weights to sensors and functional partitions based on the aggregation probability and performing fitness calculation, aiming to select sensors according to the importance of functional partitions. Specifically, according to the aggregation probability, a weight is assigned to each sensor in the sensor set, and the sensor corresponding to the functional partition with a high aggregation probability obtains a higher weight. The important fitness between the sensor and the functional partition is calculated by combining the weight and the sensor characteristics, and further matching results between the sensor and the functional partition are established according to the calculation results of the important fitness. Step S340, an optimization analysis of the overall highest fitness is performed through the first fitness matching result and the second fitness matching result, and a mapping relationship between the sensor set and the functional partition is established according to the optimization analysis result, and the monitoring sensor configuration is completed. Specifically, the first fitness matching result and the second fitness matching result are comprehensively analyzed, and a comprehensive evaluation of the overall fitness is performed. An optimization analysis is performed through an algorithm (such as a greedy algorithm, a genetic algorithm, etc.) to find the sensor configuration scheme with the highest overall fitness. According to the optimization analysis results, the mapping relationship between the sensor set and the functional partition is established, that is, the type and number of sensors that should be configured for each functional partition are determined, and the sensors are deployed according to the mapping relationship to complete the configuration of the monitoring sensors. This implementation method determines the optimal monitoring sensor configuration scheme by comprehensively analyzing the functional characteristics of the sensors and the importance of the functional partitions, achieving the technical effect of realizing the optimal configuration of the monitoring sensors.

[0061] Step S400, read the real-time task of the energy storage battery compartment, match the adaptive aggregation result through the real-time task, call the corresponding monitoring sensor according to the matching result, generate the real-time data stream of the virtual visualization model according to the real-time monitoring result of the monitoring sensor, and visualize the virtual visualization model. Specifically, establish a communication connection with the management system of the energy storage battery compartment, and read the real-time task (specific task that the energy storage battery compartment needs to perform during operation) data from the management system regularly or according to the event trigger mechanism, including task type, task requirements, execution time, etc. According to the requirements of the real-time task, analyze the functional partitions involved in the task, find the mapping relationship between the functional partitions and the monitoring sensors established in step S300 according to the functional partitions, and determine the set of monitoring sensors that need to be called based on the mapping relationship. Send a start instruction to the selected monitoring sensor to activate it for real-time monitoring. Receive the real-time data sent by the monitoring sensor, including temperature, humidity, current, voltage, etc., pre-process the received data, such as data cleaning, format conversion, etc., encapsulate the processed data according to the data interface format of the virtual visualization model, and generate a real-time data stream. The real-time data stream is transmitted to the data processing module of the virtual visualization model. The data processing module updates the state of the virtual visualization model according to the received data, and visually renders the updated virtual visualization model and displays it on the user interface.

[0062] In a possible implementation, step S400 further includes step S410, establishing a control mapping between the virtual visualization model and the energy storage battery compartment. The control mapping refers to the correspondence between the control interface in the virtual visualization model and the actual control instructions of the energy storage battery compartment. Specifically, determine which parts or functions in the energy storage battery compartment can be controlled by the virtual visualization model, such as switches, temperature adjustment, etc., create corresponding control interfaces for each controllable part in the virtual visualization model, and these control interfaces can be buttons, switches or other interactive elements. Establish a mapping relationship between the control interface in the virtual visualization model and the actual control instructions of the energy storage battery compartment, so as to send the correct control instructions to the energy storage battery compartment when the control interface is triggered. Step S420, when the virtual visualization model is displayed in 3D visualization, configure the functional units and display states of the 3D visualization according to the called user characteristics. Specifically, user characteristics such as personal information, usage habits, and authority level of the currently logged-in user are collected. The user characteristics describe the attributes and behavioral characteristics of the user, and the different parts or functions in the virtual visualization model are divided into different units, such as a temperature display unit, a power display unit, etc. According to the user characteristics, different display states are configured for each functional unit, such as color, transparency, size, etc. Step S430, when receiving the control instruction of the user in the virtual visualization model, a matching verification is performed according to the matching degree of the user authority and the control instruction, and the control response of the energy storage battery compartment is performed based on the matching verification result and the control mapping. Among them, the matching verification is to verify the user authority and the control instruction to ensure the legitimacy and accuracy of the instruction. Specifically, when the user issues a control instruction on the virtual visualization model, these instructions are received, and it is checked whether the user who issues the control instruction has the authority to execute the instruction, and the matching degree between the control instruction and the actual executable operation of the energy storage battery compartment is analyzed. If the user authority and the control instruction matching degree meet the requirements, the corresponding control instruction is sent to the energy storage battery compartment according to the control mapping relationship, and the display state of the virtual visualization model is updated. This implementation method achieves the technical effect of improving the control accuracy, user experience and operational safety of the energy storage battery compartment by establishing control mapping, personalized configuration and matching verification.

[0063] Step S500, input the real-time monitoring result and the real-time task into a multidimensional abnormal analysis channel, perform abnormal analysis, and establish an abnormal identification. Specifically, collect real-time monitoring data from monitoring sensors, obtain real-time task data of the current energy storage battery compartment, integrate the real-time monitoring data and real-time task data into a multidimensional data set, input the integrated multidimensional data set into a pre-established multidimensional abnormal analysis channel, and perform abnormal analysis, wherein the multidimensional abnormal analysis channel is an abnormal detection mechanism specifically used to process and analyze multidimensional data, and can comprehensively analyze data of multiple dimensions for abnormal judgment. The multidimensional abnormal analysis channel uses a preset abnormal detection algorithm to traverse and analyze the multidimensional data set, and by comparing the real-time monitoring data with historical data, benchmark data or preset thresholds, it is determined whether there is a deviation from the normal range, find potential abnormal data, and combine the real-time task data to analyze whether the abnormal data is related to the current task state or execution process. Based on the above analysis, determine the type, degree and possible cause of the abnormality. According to the results of the abnormal analysis, a unique abnormal identification is generated for each abnormal data point. The abnormal identification is a label or code used to mark abnormal data, including abnormal type, location, time, severity and other information.

[0064] In a possible implementation, step S500 further includes step S510, inputting the real-time monitoring results and the real-time tasks into the basic analysis subchannel, performing abnormal analysis, and generating basic abnormal results. Specifically, the real-time monitoring results and the real-time tasks are preprocessed such as cleaning and normalization to improve data quality, and key features are extracted from the preprocessed data, such as temperature and voltage fluctuations, task execution status, etc. The basic analysis subchannel uses a preset machine learning model or algorithm to perform abnormal detection on the extracted features and generate basic abnormal results. The basic abnormal results are the abnormal conditions initially identified. Step S520, calling an additional analysis subchannel, wherein the additional analysis subchannel stores timing tasks and historical response data. Specifically, loading an additional analysis subchannel, the additional analysis subchannel is a channel specifically used to perform complex timing analysis and historical data comparison, used to improve the accuracy of abnormal analysis, and the additional analysis subchannel contains timing tasks and historical response data, wherein the timing tasks are tasks executed in a certain time sequence; the historical response data are response data in the past task execution process, which are used for comparison and reference. Step S530, perform a pre-sequence impact analysis of the sequence task and the historical response data through the additional analysis sub-channel, compensate the basic abnormal result through the pre-sequence impact analysis, and generate an updated abnormal result. Specifically, align the real-time monitoring results with the historical response data to ensure time consistency, analyze the impact of the pre-sequence task on the current abnormality by comparing the historical response data and the real-time monitoring results, and correct the basic abnormal result based on the pre-sequence impact analysis to generate an updated abnormal result, which more accurately reflects the current abnormal state. Step S540, perform a post-sequence execution prediction of the sequence task based on the updated abnormal result through the additional analysis sub-channel to generate a predicted abnormal result. Among them, the post-sequence execution prediction is to predict the execution of future sequence tasks and possible abnormalities based on the current abnormal state. Specifically, load a machine learning model for sequence prediction, predict the execution of subsequent tasks and possible abnormalities based on the updated abnormal result and sequence task, and output the predicted abnormal result, including the possible abnormal type, time and impact range. Step S550, establish an abnormal identification for the updated abnormal result and the predicted abnormal result. Specifically, according to the update abnormal result and the prediction abnormal result, a corresponding abnormal identification is generated. This implementation method more accurately identifies and analyzes abnormal situations by introducing additional analysis sub-channels and time series analysis methods, achieving the technical effect of improving the accuracy and comprehensiveness of abnormal detection.

[0065] Step S600, add the abnormal identification to the virtual visualization model, and perform screen mirroring display by adding the completed virtual visualization model. Specifically, according to the specific location where the abnormality occurs (such as battery module, circuit board, etc.) and the type of abnormality (such as excessive temperature, abnormal voltage, etc.), determine where the abnormal identification should be added in the virtual visualization model, and what color, shape or text to use to display the abnormal identification. Add the abnormal identification as a new data element to the data structure of the virtual visualization model, use the graphics rendering engine, and re-render according to the updated virtual visualization model data to generate a virtual visualization model image containing the abnormal identification. Connect a display device (such as a large-screen display, a projector, etc.) to the computer or server that executes the steps, set the resolution, refresh rate, display mode and other parameters of the screen mirroring as needed, transmit the updated virtual visualization model image to the screen mirroring device, and display it on the large screen. The embodiment of the present application adopts technical means such as extracting the topological structure and attribute parameters of the energy storage battery compartment, establishing a virtual visualization model, establishing functional partitions, performing adaptive aggregation, configuring monitoring sensors, and performing abnormal analysis, thereby achieving the technical effect of realizing comprehensive visualization of the energy storage battery compartment, accurate monitoring, timely detection and processing of abnormal situations, and thus improving management efficiency.

[0066] In the above, refer to Figure 1 The energy storage battery compartment screen mirroring method based on the Internet of Things according to an embodiment of the present invention is described in detail. Figure 2 An energy storage battery cabin screen mirroring system based on the Internet of Things according to an embodiment of the present invention is described.

[0067] The energy storage battery compartment screen mirroring system based on the Internet of Things according to the embodiment of the present invention is used to solve the technical problems of insufficient data visualization, poor monitoring accuracy, and untimely abnormality detection in the existing energy storage battery compartment monitoring and management, so as to achieve the technical effect of realizing comprehensive visualization, accurate monitoring, timely discovery and handling of abnormal situations in the energy storage battery compartment, thereby improving management efficiency. The energy storage battery compartment screen mirroring system based on the Internet of Things includes: a virtual visualization model establishment module 10, a functional partition establishment module 20, a monitoring sensor configuration module 30, a visualization display module 40, an abnormality analysis module 50, and a screen mirroring display module 60.

[0068] The virtual visualization model building module 10 is used to obtain basic data of the energy storage battery compartment, extract the topological structure and attribute parameters based on the basic data, and build a virtual visualization model according to the topological structure and attribute parameters;

[0069] The functional partition establishment module 20 is used to establish the functional partition of the energy storage battery compartment, wherein the functional partition is the functional partition of the minimum granularity unit, and adaptively aggregates the functional partition to establish an adaptive aggregation result, wherein the adaptive aggregation result carries an identification of the aggregation probability;

[0070] The monitoring sensor configuration module 30 is used to configure the monitoring sensor based on the functional partition and the aggregation probability, and establish network communication between the monitoring sensor and the virtual visualization model;

[0071] The visualization display module 40 is used to read the real-time tasks of the energy storage battery compartment, match the adaptive aggregation results through the real-time tasks, call the corresponding monitoring sensor according to the matching results, generate the real-time data stream of the virtual visualization model according to the real-time monitoring results of the monitoring sensor, and visualize the virtual visualization model;

[0072] The abnormality analysis module 50 is used to input the real-time monitoring results and the real-time tasks into the multi-dimensional abnormality analysis channel, perform abnormality analysis, and establish abnormality identification;

[0073] The screen mirroring display module 60 is used to add the abnormal identification to the virtual visualization model, so as to perform screen mirroring display by adding the completed virtual visualization model.

[0074] The specific configuration of the functional partition establishment module 20 will be described in detail below. As described above, the functional partition is adaptively aggregated to establish an adaptive aggregation result, and the adaptive aggregation result carries an aggregation probability mark. The functional partition establishment module 20 may further include: a frequency trust coefficient establishment unit for reading the usage data of the energy storage battery compartment, calling the execution frequency of the function based on the usage data, and establishing an aggregated frequency trust coefficient; an important trust coefficient establishment unit for obtaining the function importance mark of the function, and establishing an aggregated important trust coefficient based on the function importance mark; an aggregation probability calculation unit for configuring a normalization coefficient, and calculating the aggregation probability of each adaptive aggregation result through the normalization coefficient, the frequency trust coefficient and the important trust coefficient, and marking the corresponding aggregation probability to the adaptive aggregation result.

[0075] The specific configuration of the monitoring sensor configuration module 30 will be described in detail below. As described above, based on the functional partition and the aggregation probability, the monitoring sensor configuration module 30 may further include: a sensor set establishment unit for establishing a sensor set, wherein the sensor set is a callable sensor set; a fitness matching result establishment unit for performing functional fitness matching of the functional partition with the sensor set, establishing a first fitness matching result, performing important fitness matching of the sensor set based on the aggregation probability, and establishing a second fitness matching result; a mapping relationship establishment unit for performing an optimization analysis of the overall highest fitness through the first fitness matching result and the second fitness matching result, establishing a mapping relationship between the sensor set and the functional partition according to the optimization analysis result, and completing the monitoring sensor configuration.

[0076] The specific configuration of the visualization display module 40 will be described in detail below. As described above, the visualization display module 40 may further include: a control mapping establishment unit for establishing a control mapping between a virtual visualization model and an energy storage battery compartment; a visualization configuration unit for configuring the functional units and display states of the 3D visualization according to the called user characteristics when the virtual visualization model is displayed in 3D visualization; and a control response unit for performing matching verification according to the matching degree of the user authority and the control instruction when receiving the control instruction of the user in the virtual visualization model, and performing control response of the energy storage battery compartment based on the matching verification result and the control mapping.

[0077] The specific configuration of the abnormality analysis module 50 will be described in detail below. As described above, the real-time monitoring results and the real-time tasks are input into the multi-dimensional abnormality analysis channel, abnormality analysis is performed, and abnormality identification is established. The abnormality analysis module 50 may further include: a basic abnormality analysis unit is used to input the real-time monitoring results and the real-time tasks into the basic analysis sub-channel, perform abnormality analysis, and generate basic abnormality results; an additional analysis sub-channel calling unit is used to call an additional analysis sub-channel, wherein the additional analysis sub-channel stores timing tasks and historical response data; a compensation unit is used to perform pre-timing impact analysis of timing tasks and historical response data through the additional analysis sub-channel, compensate the basic abnormal results through the pre-timing impact analysis, and generate updated abnormal results; a prediction unit is used to perform post-timing execution prediction of timing tasks according to the updated abnormal results through the additional analysis sub-channel, and generate predicted abnormal results; an abnormality identification establishment unit is used to establish abnormal identifications for the updated abnormal results and the predicted abnormal results.

[0078] The specific configuration of the functional partition establishment module 20 will be described in detail below. As described above, the functional partition establishment module 20 may further include: a similar substitution association establishment unit for establishing a similar substitution association of functions according to the adaptive aggregation result; a similar substitution determination unit for performing a similar substitution determination through the similar substitution association when the continuous operation duration of any adaptive aggregation result meets a preset threshold; and a replacement management unit for performing replacement management of the corresponding adaptive aggregation result according to the similar substitution determination result.

[0079] Among them, the replacement management of the corresponding adaptive aggregation result is performed according to the similar replacement judgment result, and the replacement management unit may further include: an abnormal warning establishment subunit is used to perform job abnormality analysis according to the corresponding adaptive aggregation result and continuous operation time, and establish an abnormal warning if the similar replacement judgment result is a failed result; a visualization reporting subunit is used to visualize the abnormal warning through a virtual visualization model.

[0080] The energy storage battery compartment screen mirroring system based on the Internet of Things provided in the embodiment of the present invention can execute the energy storage battery compartment screen mirroring method based on the Internet of Things provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0081] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0082] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A screen mirroring method for an energy storage battery compartment based on the Internet of Things, characterized in that: The method comprises: Acquire basic data of the energy storage battery compartment, extract topological structure and attribute parameters based on the basic data, and establish a virtual visualization model according to the topological structure and attribute parameters; Establishing functional partitions of the energy storage battery compartment, wherein the functional partitions are functional partitions of minimum granularity units, and adaptively aggregating the functional partitions to establish adaptive aggregation results, wherein the adaptive aggregation results carry an identification of aggregation probability; configuring monitoring sensors based on the functional partitions and the aggregation probability, and establishing network communication between the monitoring sensors and the virtual visualization model; Read the real-time tasks of the energy storage battery compartment, match the adaptive aggregation results through the real-time tasks, call the corresponding monitoring sensor according to the matching results, generate a real-time data stream of the virtual visualization model according to the real-time monitoring results of the monitoring sensor, and visualize the virtual visualization model; Input the real-time monitoring results and the real-time tasks into a multi-dimensional anomaly analysis channel, perform anomaly analysis, and establish an anomaly identification; Adding the abnormal identification to the virtual visualization model, so as to perform screen mirroring display of the added virtual visualization model; The adaptive aggregation of the functional partitions to establish an adaptive aggregation result, wherein the adaptive aggregation result carries an identification of the aggregation probability, includes: Reading usage data of the energy storage battery compartment, performing execution frequency calls of functions based on the usage data, and establishing an aggregated frequency trust coefficient; Obtaining a function importance identifier of the function, and establishing an aggregated important trust coefficient based on the function importance identifier; A normalization coefficient is configured, and the aggregation probability of each adaptive aggregation result is calculated by using the normalization coefficient, the frequency trust coefficient, and the important trust coefficient, and the corresponding aggregation probability is identified to the adaptive aggregation result.

2. The method for screen mirroring of an energy storage battery compartment based on the Internet of Things as claimed in claim 1, characterized in that: The configuration of monitoring sensors based on the functional partitions and the aggregation probability further includes: Establishing a sensor set, wherein the sensor set is a callable sensor set; Performing functional adaptation matching of functional partitions with the sensor set to establish a first adaptation matching result; Performing important adaptation matching of the sensor set based on the aggregation probability, and establishing a second degree of fitness matching result; An optimization analysis of the overall highest fitness is performed based on the first fitness matching result and the second fitness matching result, and a mapping relationship between the sensor set and the functional partition is established based on the optimization analysis result to complete the monitoring sensor configuration.

3. The energy storage battery compartment screen mirroring method based on the Internet of Things as claimed in claim 1, characterized in that: The method further comprises: Establish control mapping between virtual visualization model and energy storage battery compartment; When the virtual visualization model performs 3D visualization display, the functional unit and display state of the 3D visualization are configured according to the called user characteristics; When receiving a control instruction from the user in the virtual visualization model, a matching verification is performed according to the matching degree between the user authority and the control instruction, and a control response of the energy storage battery compartment is performed based on the matching verification result and the control mapping.

4. The method for screen mirroring of an energy storage battery compartment based on the Internet of Things as claimed in claim 1, characterized in that: The step of inputting the real-time monitoring result and the real-time task into a multi-dimensional anomaly analysis channel, performing anomaly analysis, and establishing an anomaly identification includes: Input the real-time monitoring results and the real-time tasks into the basic analysis subchannel, perform anomaly analysis, and generate basic anomaly results; Calling an additional analysis subchannel, wherein the additional analysis subchannel stores time-series tasks and historical response data; Performing a pre-timing impact analysis of timing tasks and historical response data through the additional analysis subchannel, compensating the basic abnormal result through the pre-timing impact analysis, and generating an updated abnormal result; Performing a post-time sequence execution prediction of the time sequence task according to the updated abnormal result through the additional analysis sub-channel to generate a predicted abnormal result; An abnormal identification is established for the updated abnormal result and the predicted abnormal result.

5. The method for screen mirroring of an energy storage battery compartment based on the Internet of Things as claimed in claim 1, characterized in that: The method further comprises: establishing similar substitution associations of functions according to the adaptive aggregation results; When the continuous operation duration of any adaptive aggregation result meets the preset threshold, similar replacement determination is performed through the similar replacement association; Replacement management of corresponding adaptive aggregation results is performed based on similar replacement judgment results.

6. The method for screen mirroring of an energy storage battery compartment based on the Internet of Things as claimed in claim 5, characterized in that: The performing replacement management of the corresponding adaptive aggregation result according to the similar replacement determination result includes: If the similar replacement judgment result is a failed judgment result, the operation abnormality analysis is performed according to the corresponding adaptive aggregation result and the continuous operation duration, and an abnormality warning is established; The abnormal warning is visually reported through a virtual visualization model.

7. The energy storage battery cabin screen mirroring system based on the Internet of Things is characterized by: The system is used to implement the energy storage battery compartment screen mirroring method based on the Internet of Things according to any one of claims 1 to 6, and the system includes: A virtual visualization model building module, which is used to obtain basic data of the energy storage battery compartment, extract topological structure and attribute parameters based on the basic data, and build a virtual visualization model according to the topological structure and attribute parameters; A functional partition establishment module, wherein the functional partition establishment module is used to establish the functional partition of the energy storage battery compartment, wherein the functional partition is the functional partition of the minimum granularity unit, and adaptively aggregates the functional partition to establish an adaptive aggregation result, wherein the adaptive aggregation result carries an identification of the aggregation probability; A monitoring sensor configuration module, the monitoring sensor configuration module is used to configure the monitoring sensor based on the functional partition and the aggregation probability, and establish network communication between the monitoring sensor and the virtual visualization model; A visualization display module, the visualization display module is used to read the real-time tasks of the energy storage battery compartment, match the adaptive aggregation results through the real-time tasks, call the corresponding monitoring sensor according to the matching results, generate a real-time data stream of the virtual visualization model according to the real-time monitoring results of the monitoring sensor, and visualize the virtual visualization model; An abnormality analysis module, the abnormality analysis module is used to input the real-time monitoring results and the real-time tasks into a multi-dimensional abnormality analysis channel, perform abnormality analysis, and establish an abnormality mark; A screen mirroring display module is used to add the abnormal identification to the virtual visualization model, so as to perform screen mirroring display by adding the completed virtual visualization model.

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