Dynamic automatic mapping display method for graphic tree of energy storage monitoring system

Abstract

The invention belongs to the technical field of energy storage monitoring systems, and particularly relates to an energy storage monitoring system graph tree dynamic automatic mapping display method which comprises the following specific steps: firstly, acquiring operation data of each device in an energy storage system in real time through various sensors and communication interfaces; decoding, format conversion and data verification are carried out on the collected data, and then semantic analysis is carried out on the processed energy storage system data and related log information. According to the invention, through real-time dynamic graphic tree display, monitoring personnel can rapidly obtain the operation state information of the energy storage system and timely discover equipment faults and abnormal conditions, so that the monitoring efficiency and the response speed are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage monitoring system technology, specifically to a method for dynamically and automatically generating and displaying a graphical tree for an energy storage monitoring system. Background Technology

[0002] Energy storage systems are complex energy management systems whose core function is to store energy through media or devices and release it when needed to meet energy demands in different scenarios. With the widespread application of energy storage technology in the energy sector, the scale and complexity of energy storage systems are constantly increasing.

[0003] Traditional energy storage monitoring systems suffer from numerous problems in graphical display, making it difficult to meet the current demands for efficient management and monitoring of energy storage systems:

[0004] 1. Limitations of Static Graphics: Existing systems mostly use static graphics for display, which cannot reflect dynamic information such as equipment status and data changes in the energy storage system in real time. When the scale of the energy storage system expands and the number of devices increases, the static graphics are not updated in a timely manner, making it difficult for monitoring personnel to quickly obtain accurate system operating status.

[0005] 2. Low efficiency of manual mapping: For complex energy storage system topologies, manually drawing diagrams is not only time-consuming and labor-intensive, but also prone to errors. When system equipment changes, such as adding new equipment or altering connection relationships, the workload of manually modifying the diagrams is enormous, making it difficult to ensure consistency between the diagrams and the actual system.

[0006] 3. Lack of Interrelationship Display: The system fails to effectively demonstrate the relationships between various devices and data within the energy storage system. Energy storage systems comprise multiple devices such as battery packs, PCS (Power Conversion System), and BMS (Battery Management System). The data from these devices interact with each other, and traditional graphical displays cannot intuitively present these complex relationships, hindering comprehensive analysis and fault diagnosis by monitoring personnel.

[0007] Based on the above, a method for dynamically and automatically generating and displaying a graphical tree for an energy storage monitoring system is invented. Summary of the Invention

[0008] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0009] A method for dynamically and automatically generating and displaying a graphical tree in an energy storage monitoring system includes the following specific steps:

[0010] S1, Data Acquisition and Analysis: First, the operating data of each device in the energy storage system is collected in real time through various sensors and communication interfaces. Then, the collected data is decoded, converted in format, and verified. The operating data includes battery voltage, current, temperature, PCS power and frequency, and BMS status information.

[0011] S2, Semantic Analysis: Perform semantic analysis on the processed energy storage system data and related log information;

[0012] S3, Graph Tree Generation Algorithm: First, design a graph tree generation algorithm based on the topology and device connection relationship of the energy storage system. Then, traverse the device connection relationship of the energy storage system according to the depth-first search or breadth-first search algorithm to construct the topology of the graph tree.

[0013] S4, Graphic Layout Optimization: Automatically adjusts node positions based on the connection relationships and hierarchical structure between nodes, reducing line intersections and making the graphic layout more regular and clear;

[0014] S5, Personalized Graphic Customization: Through the user interface, monitoring personnel can customize the display elements, color themes, and node styles of the graphic tree;

[0015] S6, Dynamic Update Mechanism: Establish a mapping relationship between energy storage system data and graphical tree nodes, so that when the collected real-time data changes, the corresponding graphical tree node can be found through the mapping relationship, and the color, shape, and display value attributes of the node can be updated according to the data change, so as to intuitively display the dynamic changes of equipment status and data.

[0016] S7: Relationship Display Technology: Utilizing graphical visualization technology, different line styles, colors, or transparency are used to represent the physical connection relationships between devices and the logical relationships between data.

[0017] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of step S2 are as follows:

[0018] S21, Annotated Corpus Construction: Semantically annotate the data, label each text fragment or data record with corresponding semantic tags to build an annotated corpus containing rich semantic information as the basic data for model training;

[0019] S22, Model Selection and Training: First, select the corresponding natural language processing model based on the characteristics of the energy storage system data and the semantic analysis requirements. Then, use an annotated corpus to train the selected model, adjust the model parameters, and optimize the model performance so that it can accurately identify and extract semantic information in the energy storage data.

[0020] S23, Model Evaluation and Optimization: Evaluate the trained model using the test dataset. If the model performance does not meet expectations, analyze the reasons and optimize it until the model achieves satisfactory semantic analysis results.

[0021] S24, Text Feature Extraction: The cleaned unstructured text data is input into the trained model so that the model can convert the words in the text into vector representations through word embedding technology and extract the semantic features of the text.

[0022] S25, Semantic Recognition and Classification: Based on the extracted text features, the model performs semantic recognition and classification on the text to determine the semantic category expressed by the text;

[0023] S26, Semantic Relationship Analysis: After identifying the semantic categories, analyze the relationships between different semantic information to construct a semantic relationship graph, showing the correlation between the causes and impact range of equipment failures, changes in operating status and related parameters.

[0024] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S3 are as follows:

[0025] S31, Create node data structure: Define the corresponding node data structure according to the equipment type of the energy storage system. Each node includes equipment name, equipment type, unique identifier, physical connection information, and data attributes.

[0026] S32, Construct a topology list: Based on the design drawings or actual connection relationships of the energy storage system, compile a list of topology connections between devices, recording the connection status of each device with other devices in the list;

[0027] S33, Select the root node: Based on the logical architecture of the energy storage system, select the corresponding device as the root node of the graph tree;

[0028] S34, Traversal: Traverse the list of topological relationships according to the depth-first search or breadth-first search algorithm. During the traversal, create node objects and add them to the graph tree structure according to the connection relationship of the devices to build a complete topological structure.

[0029] S35, Determine the hierarchical relationship: During the traversal, record the hierarchical information of each node so that the layout of the graphic tree can be planned in layers through the hierarchical relationship, making the graphic display more hierarchical.

[0030] S36, Define node style templates: Design different node style templates according to the device type;

[0031] S37, Assign Node Style: Traverse each node in the graph tree and select the corresponding style from the style template according to the device type of the node for assignment. At the same time, the visual attributes of the node will be dynamically adjusted according to the real-time data attributes of the node, so that monitoring personnel can intuitively understand the operating status of the device through the node style.

[0032] S38, Determine the layout algorithm: The hierarchical layout algorithm arranges the nodes in layers according to their hierarchical relationship, making the graphic structure clear and easy to understand;

[0033] S39, Calculate node positions: First, calculate the position coordinates of each node in the graphic display area using a layered layout algorithm. Then, draw connecting lines between nodes based on the topological connection relationship between nodes to represent the physical connection or data transmission relationship between devices.

[0034] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S4 are as follows:

[0035] S41, Collect layout data: Obtain the node position coordinates, connecting line information, and hierarchical relationship data between nodes after the current graphic tree is generated. At the same time, record the size and resolution parameters of the graphic display area to provide basic data for subsequent analysis.

[0036] S42, Identify layout problems: Through visual inspection and algorithm analysis, identify problems in the graphic layout. At the same time, evaluate the display effect of the layout on different device screen sizes and discover possible display anomalies.

[0037] S43, Analyze algorithm characteristics: Based on the identified layout problems, analyze the corresponding scenarios and characteristics of different optimization algorithms;

[0038] S44, Determine the optimization algorithm: Based on the characteristics and requirements of the graph tree of the energy storage monitoring system, select one or more optimization algorithms;

[0039] S45, Initialization parameter settings: Set the corresponding initial parameters for the selected optimization algorithm;

[0040] S46, Perform optimization operation: Input the layout data of the graphic tree into the selected optimization algorithm, and perform calculation and iteration according to the algorithm flow. During the iteration process, the algorithm will continuously adjust the position and layout relationship of the nodes according to the set objective function.

[0041] S47, Monitoring the optimization process: During the optimization process, monitor the algorithm's running status and layout changes in real time;

[0042] S48, Result Evaluation: After optimization, the new layout is evaluated, and it is also demonstrated on different devices and screen sizes to test the adaptability and readability of the layout.

[0043] S49, Adjustment and Optimization: If the optimization results do not achieve the expected effect, analyze the reasons and make adjustments until a satisfactory graphic layout effect is obtained.

[0044] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S5 are as follows:

[0045] S51, Develop a customized entry point: Design a dedicated, personalized graphical entry point in the user interface of the energy storage monitoring system;

[0046] S52, Construct a customized parameter panel: Within the customized interface, construct a rich parameter setting panel, which includes functional modules for selecting graphic display elements, customizing color themes, adjusting node styles, and selecting layout modes, to meet the diverse customization needs of users;

[0047] S53, Obtain Customized Parameters: When the user operates on the customized parameter panel, the system will obtain the customized parameter information entered by the user in real time, and perform preliminary format conversion and verification to ensure the accuracy and validity of the parameter data.

[0048] S54, integrating customized parameters with system data: combining the acquired customized parameters with the real-time data of the energy storage system and the equipment topology data, so that the graph tree generation algorithm can generate rules based on the customized parameters during the operation of the graph tree generation algorithm;

[0049] S55, Output Custom Graphics: Based on the fused parameters and data, generate a graphic tree that meets the user's personalized needs and display it in the user interface.

[0050] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S7 are as follows:

[0051] S71, Related Data Integration and Analysis: First, sort out the relationships, then integrate and process the data, and finally quantify the strength of the relationships;

[0052] S72, Visual Style Design: First, design the associated graphic elements, then color-encode the associated attributes, and finally optimize and adapt the layout.

[0053] S73, interactive function implementation: First, basic interactive design is carried out, then dynamic interactive display is carried out, and then multi-dimensional correlation analysis is carried out.

[0054] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S71 are as follows:

[0055] S711, Identify Relationships: Delve into the energy storage system architecture and, in conjunction with the physical connections of equipment and data interaction logic, identify various relationships to form a list of relationships;

[0056] S712, Data Integration Processing: Integrates data describing relationships with equipment operation data and graphical tree node data;

[0057] S713, Quantifying the strength of association: For the logical relationship between data, statistical methods and machine learning algorithms are used to quantify the strength of the association.

[0058] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S72 are as follows:

[0059] S721, Design Related Graphic Elements: Design unique visual elements for different types of relationships;

[0060] S722, Color-coded Association Attributes: Using color coding to display different attributes of association relationships;

[0061] S723, Layout Optimization and Adaptation: Based on the graphic tree layout, the layout of nodes and related lines is adjusted to avoid line intersections and overlaps affecting the display effect.

[0062] As a preferred embodiment of the dynamic automatic graphing and display method for an energy storage monitoring system according to the present invention, the specific steps of S73 are as follows:

[0063] S731, Basic Interaction Design: Implement basic interactive functions such as mouse hover and click;

[0064] S732, Dynamic Interactive Display: Supports users to dynamically adjust the display of related relationships through interactive operations;

[0065] S733, Multi-dimensional Correlation Analysis: Develops advanced interactive functions to achieve multi-dimensional correlation analysis. When the user enters query conditions, the system automatically displays the correlation paths that meet the conditions. When switching through the time axis, users can view the evolution of correlations over different time periods and compare the differences in correlations between multiple devices, assisting users in performing complex system operation analysis and fault diagnosis.

[0066] Compared with existing technologies:

[0067] 1. Improve monitoring efficiency: Through real-time dynamic graphical tree display, monitoring personnel can quickly obtain the operating status information of the energy storage system, promptly detect equipment failures and abnormalities, and greatly improve monitoring efficiency and response speed.

[0068] 2. Reduced operation and maintenance costs: The automatic mapping function reduces the workload of manually drawing and updating graphics, reduces the probability of human error, and facilitates the maintenance and management of energy storage systems, thereby reducing operation and maintenance costs.

[0069] 3. Enhance decision support capabilities: Clear and intuitive display of relationships helps monitoring personnel gain a deeper understanding of the internal structure and operation mechanism of the energy storage system, providing strong support for comprehensive analysis and fault diagnosis, and improving the operating efficiency and reliability of the energy storage system. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the process of the present invention;

[0071] Figure 2 This is a schematic diagram of the graphical tree topology of the present invention;

[0072] Figure 3 This is a schematic diagram illustrating the state of the related technologies of this invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0074] This invention provides a method for dynamically and automatically generating and displaying a graphical tree in an energy storage monitoring system. Please refer to [the relevant documentation]. Figures 1-3 .

[0075] The specific steps are as follows:

[0076] S1, Data Acquisition and Analysis: First, the operating data of each device in the energy storage system is collected in real time through various sensors and communication interfaces. Then, the collected data is decoded, converted in format, and verified. The operating data includes battery voltage, current, temperature, PCS power and frequency, and BMS status information.

[0077] S2, Semantic Analysis: Perform semantic analysis on the processed energy storage system data and related log information;

[0078] The specific steps of S2 are as follows:

[0079] S21, Annotated Corpus Construction: Semantically annotate the data, label each text fragment or data record with corresponding semantic tags to build an annotated corpus containing rich semantic information as the basic data for model training;

[0080] S22, Model Selection and Training: First, select the corresponding natural language processing model based on the characteristics of the energy storage system data and the semantic analysis requirements. Then, use an annotated corpus to train the selected model, adjust the model parameters, and optimize the model performance so that it can accurately identify and extract semantic information in the energy storage data.

[0081] S23, Model Evaluation and Optimization: Evaluate the trained model using the test dataset. If the model performance does not meet expectations, analyze the reasons and optimize it until the model achieves satisfactory semantic analysis results.

[0082] S24, Text Feature Extraction: The cleaned unstructured text data is input into the trained model so that the model can convert the words in the text into vector representations through word embedding technology and extract the semantic features of the text.

[0083] S25, Semantic Recognition and Classification: Based on the extracted text features, the model performs semantic recognition and classification on the text to determine the semantic category expressed by the text;

[0084] S26, Semantic Relationship Analysis: After identifying the semantic categories, analyze the relationships between different semantic information to construct a semantic relationship graph, showing the correlation between the causes and impact range of equipment failures, changes in operating status and related parameters;

[0085] S3, Graph Tree Generation Algorithm: First, design a graph tree generation algorithm based on the topology and device connection relationship of the energy storage system. Then, traverse the device connection relationship of the energy storage system according to the depth-first search or breadth-first search algorithm to construct the topology of the graph tree.

[0086] The specific steps of S3 are as follows:

[0087] S31, Create node data structure: Define the corresponding node data structure according to the equipment type of the energy storage system. Each node includes equipment name, equipment type, unique identifier, physical connection information, and data attributes.

[0088] S32, Construct a topology list: Based on the design drawings or actual connection relationships of the energy storage system, compile a list of topology connections between devices, recording the connection status of each device with other devices in the list;

[0089] S33, Select the root node: Based on the logical architecture of the energy storage system, select the corresponding device as the root node of the graph tree;

[0090] S34, Traversal: Traverse the list of topological relationships according to the depth-first search or breadth-first search algorithm. During the traversal, create node objects and add them to the graph tree structure according to the connection relationship of the devices to build a complete topological structure.

[0091] S35, Determine the hierarchical relationship: During the traversal, record the hierarchical information of each node so that the layout of the graphic tree can be planned in layers through the hierarchical relationship, making the graphic display more hierarchical.

[0092] S36, Define node style templates: Design different node style templates according to the device type;

[0093] S37, Assign Node Style: Traverse each node in the graph tree and select the corresponding style from the style template according to the device type of the node for assignment. At the same time, the visual attributes of the node will be dynamically adjusted according to the real-time data attributes of the node, so that monitoring personnel can intuitively understand the operating status of the device through the node style.

[0094] S38, Determine the layout algorithm: The hierarchical layout algorithm arranges the nodes in layers according to their hierarchical relationship, making the graphic structure clear and easy to understand;

[0095] S39, Calculate node position: First, calculate the position coordinates of each node in the graphic display area through a layered layout algorithm. Then, draw connecting lines between nodes according to the topological connection relationship between nodes to represent the physical connection or data transmission relationship between devices.

[0096] S4, Graphic Layout Optimization: Automatically adjusts node positions based on the connection relationships and hierarchical structure between nodes, reducing line intersections and making the graphic layout more regular and clear;

[0097] The specific steps of S4 are as follows:

[0098] S41, Collect layout data: Obtain the node position coordinates, connecting line information, and hierarchical relationship data between nodes after the current graphic tree is generated. At the same time, record the size and resolution parameters of the graphic display area to provide basic data for subsequent analysis.

[0099] S42, Identify layout problems: Through visual inspection and algorithm analysis, identify problems in the graphic layout. At the same time, evaluate the display effect of the layout on different device screen sizes and discover possible display anomalies.

[0100] S43, Analyze algorithm characteristics: Based on the identified layout problems, analyze the corresponding scenarios and characteristics of different optimization algorithms;

[0101] S44, Determine the optimization algorithm: Based on the characteristics and requirements of the graph tree of the energy storage monitoring system, select one or more optimization algorithms;

[0102] S45, Initialization parameter settings: Set the corresponding initial parameters for the selected optimization algorithm;

[0103] S46, Perform optimization operation: Input the layout data of the graphic tree into the selected optimization algorithm, and perform calculation and iteration according to the algorithm flow. During the iteration process, the algorithm will continuously adjust the position and layout relationship of the nodes according to the set objective function.

[0104] S47, Monitoring the optimization process: During the optimization process, monitor the algorithm's running status and layout changes in real time;

[0105] S48, Result Evaluation: After optimization, the new layout is evaluated, and it is also demonstrated on different devices and screen sizes to test the adaptability and readability of the layout.

[0106] S49, Adjustment and Optimization: If the optimization results do not achieve the expected effect, analyze the reasons and make adjustments until a satisfactory graphic layout effect is obtained;

[0107] S5, Personalized Graphic Customization: Through the user interface, monitoring personnel can customize the display elements, color themes, and node styles of the graphic tree;

[0108] The specific steps of S5 are as follows:

[0109] S51, Develop a customized entry point: Design a dedicated, personalized graphical entry point in the user interface of the energy storage monitoring system;

[0110] S52, Construct a customized parameter panel: Within the customized interface, construct a rich parameter setting panel, which includes functional modules for selecting graphic display elements, customizing color themes, adjusting node styles, and selecting layout modes, to meet the diverse customization needs of users;

[0111] S53, Obtain Customized Parameters: When the user operates on the customized parameter panel, the system will obtain the customized parameter information entered by the user in real time, and perform preliminary format conversion and verification to ensure the accuracy and validity of the parameter data.

[0112] S54, integrating customized parameters with system data: combining the acquired customized parameters with the real-time data of the energy storage system and the equipment topology data, so that the graph tree generation algorithm can generate rules based on the customized parameters during the operation of the graph tree generation algorithm;

[0113] S55, Output Custom Graphics: Based on the fused parameters and data, generate a graphic tree that meets the user's personalized needs and display it in the user interface;

[0114] S6, Dynamic Update Mechanism: Establish a mapping relationship between energy storage system data and graphical tree nodes, so that when the collected real-time data changes, the corresponding graphical tree node can be found through the mapping relationship, and the color, shape, and display value attributes of the node can be updated according to the data change, so as to intuitively display the dynamic changes of equipment status and data.

[0115] S7: Relationship Display Technology: Utilizing graphic visualization technology, different line styles, colors, or transparency are used to represent the physical connection relationships between devices and the logical relationship relationships between data;

[0116] The specific steps of S7 are as follows:

[0117] S71, Related Data Integration and Analysis: First, sort out the relationships, then integrate and process the data, and finally quantify the strength of the relationships;

[0118] The specific steps of S71 are as follows:

[0119] S711, Identify Relationships: Delve into the energy storage system architecture and, in conjunction with the physical connections of equipment and data interaction logic, identify various relationships to form a list of relationships;

[0120] S712, Data Integration Processing: Integrates data describing relationships with equipment operation data and graphical tree node data;

[0121] S713, Quantifying the strength of association: For the logical relationship between data, statistical methods and machine learning algorithms are used to quantify the strength of the association.

[0122] S72, Visual Style Design: First, design the associated graphic elements, then color-encode the associated attributes, and finally optimize and adapt the layout.

[0123] The specific steps of S72 are as follows:

[0124] S721, Design Related Graphic Elements: Design unique visual elements for different types of relationships;

[0125] S722, Color-coded Association Attributes: Using color coding to display different attributes of association relationships;

[0126] S723, Layout Optimization and Adaptation: Based on the graphic tree layout, the layout of nodes and related lines is adjusted to avoid lines crossing or overlapping and affecting the display effect;

[0127] S73, Implementation of interactive functions: First, perform basic interactive design, then perform dynamic interactive display, and then perform multi-dimensional correlation analysis;

[0128] The specific steps of S73 are as follows:

[0129] S731, Basic Interaction Design: Implement basic interactive functions such as mouse hover and click;

[0130] S732, Dynamic Interactive Display: Supports users to dynamically adjust the display of related relationships through interactive operations;

[0131] S733, Multi-dimensional Correlation Analysis: Develops advanced interactive functions to achieve multi-dimensional correlation analysis. When the user enters query conditions, the system automatically displays the correlation paths that meet the conditions. When switching through the time axis, users can view the evolution of correlations over different time periods and compare the differences in correlations between multiple devices, assisting users in performing complex system operation analysis and fault diagnosis.

[0132] Among them, such as Figure 2 As shown, the overall topology adopts a horizontally radial hierarchical layout, with the "energy storage system" as the root node, and subsystems, devices, modules, and other levels unfolding sequentially to the right. For example:

[0133] Root node (energy storage system) → Level 1 subsystem (battery system, PCS, BMS) → Level 2 equipment (battery cluster, PCS equipment) → Level 3 module (battery module, sensor group).

[0134] The levels are distinguished by horizontal spacing, and nodes at the same level are arranged horizontally, which conforms to the traversal logic of breadth-first search (BFS) and makes it easy to quickly identify the system architecture from left to right.

[0135] In addition, all nodes are connected by solid arrows to visually demonstrate the physical connection relationship (such as battery modules connected in series to form clusters, and the electrical connection between the PCS and the power grid); and the direction of the connection lines is from left to right to reflect the data flow (such as the root node summarizing subsystem data, and the subsystem collecting data from lower-level devices).

[0136] like Figure 3 As shown in the diagram, the BMS feeds back battery pack status information, such as voltage, temperature, and state of charge (SOC), to the EMS. Based on this information and optimized scheduling decisions, the EMS issues control commands to the BMS, such as battery balancing control commands, and also issues charge / discharge control commands to the PCS. Furthermore, the BMS shares battery status information with the PCS so that the PCS can make corresponding adjustments based on the battery status. The PCS, in turn, feeds back its own operating status information, such as DC-side voltage, AC-side three-phase voltage, and IGBT module temperature, to the EMS, allowing the EMS to have a comprehensive understanding of the energy storage system's operation.

[0137] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for dynamically and automatically generating and displaying a graphical tree in an energy storage monitoring system, characterized in that, The specific steps are as follows: S1, Data Acquisition and Analysis: First, the operating data of each device in the energy storage system is collected in real time through various sensors and communication interfaces. Then, the collected data is decoded, converted in format, and verified. The operating data includes battery voltage, current, temperature, PCS power and frequency, and BMS status information. S2, Semantic Analysis: Perform semantic analysis on the processed energy storage system data and related log information; S3, Graph Tree Generation Algorithm: First, design a graph tree generation algorithm based on the topology and device connection relationship of the energy storage system. Then, traverse the device connection relationship of the energy storage system according to the depth-first search or breadth-first search algorithm to construct the topology of the graph tree. S4, Graphic Layout Optimization: Automatically adjusts node positions based on the connection relationships and hierarchical structure between nodes, reducing line intersections and making the graphic layout more regular and clear; S5, Personalized Graphic Customization: Through the user interface, monitoring personnel can customize the display elements, color themes, and node styles of the graphic tree; S6, Dynamic Update Mechanism: Establish a mapping relationship between energy storage system data and graphical tree nodes, so that when the collected real-time data changes, the corresponding graphical tree node can be found through the mapping relationship, and the color, shape, and display value attributes of the node can be updated according to the data change, so as to intuitively display the dynamic changes of equipment status and data. S7: Relationship Display Technology: Utilizing graphical visualization technology, different line styles, colors, or transparency are used to represent the physical connection relationships between devices and the logical relationships between data.

2. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 1, characterized in that, The specific steps of S2 are as follows: S21, Annotated Corpus Construction: Semantically annotate the data, label each text fragment or data record with corresponding semantic tags to build an annotated corpus containing rich semantic information as the basic data for model training; S22, Model Selection and Training: First, select the corresponding natural language processing model based on the characteristics of the energy storage system data and the semantic analysis requirements. Then, use an annotated corpus to train the selected model, adjust the model parameters, and optimize the model performance so that it can accurately identify and extract semantic information in the energy storage data. S23, Model Evaluation and Optimization: Evaluate the trained model using the test dataset. If the model performance does not meet expectations, analyze the reasons and optimize it until the model achieves satisfactory semantic analysis results. S24, Text Feature Extraction: The cleaned unstructured text data is input into the trained model so that the model can convert the words in the text into vector representations through word embedding technology and extract the semantic features of the text. S25, Semantic Recognition and Classification: Based on the extracted text features, the model performs semantic recognition and classification on the text to determine the semantic category expressed by the text; S26, Semantic Relationship Analysis: After identifying the semantic categories, analyze the relationships between different semantic information to construct a semantic relationship graph, showing the correlation between the causes and impact range of equipment failures, changes in operating status and related parameters.

3. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 1, characterized in that, The specific steps of S3 are as follows: S31, Create node data structure: Define the corresponding node data structure according to the equipment type of the energy storage system. Each node includes equipment name, equipment type, unique identifier, physical connection information, and data attributes. S32, Construct a topology list: Based on the design drawings or actual connection relationships of the energy storage system, compile a list of topology connections between devices, recording the connection status of each device with other devices in the list; S33, Select the root node: Based on the logical architecture of the energy storage system, select the corresponding device as the root node of the graph tree; S34, Traversal: Traverse the list of topological relationships according to the depth-first search or breadth-first search algorithm. During the traversal, create node objects and add them to the graph tree structure according to the connection relationship of the devices to build a complete topological structure. S35, Determine the hierarchical relationship: During the traversal, record the hierarchical information of each node so that the layout of the graphic tree can be planned in layers through the hierarchical relationship, making the graphic display more hierarchical. S36, Define node style templates: Design different node style templates according to the device type; S37, Assign Node Style: Traverse each node in the graph tree and select the corresponding style from the style template according to the device type of the node for assignment. At the same time, the visual attributes of the node will be dynamically adjusted according to the real-time data attributes of the node, so that monitoring personnel can intuitively understand the operating status of the device through the node style. S38, Determine the layout algorithm: The hierarchical layout algorithm arranges the nodes in layers according to their hierarchical relationship, making the graphic structure clear and easy to understand; S39, Calculate node positions: First, calculate the position coordinates of each node in the graphic display area using a layered layout algorithm. Then, draw connecting lines between nodes based on the topological connection relationship between nodes to represent the physical connection or data transmission relationship between devices.

4. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 1, characterized in that, The specific steps of S4 are as follows: S41, Collect layout data: Obtain the node position coordinates, connecting line information, and hierarchical relationship data between nodes after the current graphic tree is generated. At the same time, record the size and resolution parameters of the graphic display area to provide basic data for subsequent analysis. S42, Identify layout problems: Through visual inspection and algorithm analysis, identify problems in the graphic layout. At the same time, evaluate the display effect of the layout on different device screen sizes and discover possible display anomalies. S43, Analyze algorithm characteristics: Based on the identified layout problems, analyze the corresponding scenarios and characteristics of different optimization algorithms; S44, Determine the optimization algorithm: Based on the characteristics and requirements of the graph tree of the energy storage monitoring system, select one or more optimization algorithms; S45, Initialization parameter settings: Set the corresponding initial parameters for the selected optimization algorithm; S46, Perform optimization operation: Input the layout data of the graphic tree into the selected optimization algorithm, and perform calculation and iteration according to the algorithm flow. During the iteration process, the algorithm will continuously adjust the position and layout relationship of the nodes according to the set objective function. S47, Monitoring the optimization process: During the optimization process, monitor the algorithm's running status and layout changes in real time; S48, Result Evaluation: After optimization, the new layout is evaluated, and it is also demonstrated on different devices and screen sizes to test the adaptability and readability of the layout. S49, Adjustment and Optimization: If the optimization results do not achieve the expected effect, analyze the reasons and make adjustments until a satisfactory graphic layout effect is obtained.

5. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 1, characterized in that, The specific steps of S5 are as follows: S51, Develop a customized entry point: Design a dedicated, personalized graphical entry point in the user interface of the energy storage monitoring system; S52, Construct a customized parameter panel: Within the customized interface, construct a rich parameter setting panel, which includes functional modules for selecting graphic display elements, customizing color themes, adjusting node styles, and selecting layout modes, to meet the diverse customization needs of users; S53, Obtain Customized Parameters: When the user operates on the customized parameter panel, the system will obtain the customized parameter information entered by the user in real time, and perform preliminary format conversion and verification to ensure the accuracy and validity of the parameter data. S54, integrating customized parameters with system data: combining the acquired customized parameters with the real-time data of the energy storage system and the equipment topology data, so that the graph tree generation algorithm can generate rules based on the customized parameters during the operation of the graph tree generation algorithm; S55, Output Custom Graphics: Based on the fused parameters and data, generate a graphic tree that meets the user's personalized needs and display it in the user interface.

6. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 1, characterized in that, The specific steps of S7 are as follows: S71, Related Data Integration and Analysis: First, sort out the relationships, then integrate and process the data, and finally quantify the strength of the relationships; S72, Visual Style Design: First, design the associated graphic elements, then color-encode the associated attributes, and finally optimize and adapt the layout. S73, interactive function implementation: First, basic interactive design is carried out, then dynamic interactive display is carried out, and then multi-dimensional correlation analysis is carried out.

7. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 6, characterized in that, The specific steps of S71 are as follows: S711, Identify Relationships: Delve into the energy storage system architecture and, in conjunction with the physical connections of equipment and data interaction logic, identify various relationships to form a list of relationships; S712, Data Integration Processing: Integrates data describing relationships with equipment operation data and graphical tree node data; S713, Quantifying the strength of association: For the logical relationship between data, statistical methods and machine learning algorithms are used to quantify the strength of the association.

8. The method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 6, characterized in that, The specific steps of S72 are as follows: S721, Design Related Graphic Elements: Design unique visual elements for different types of relationships; S722, Color-coded Association Attributes: Using color coding to display different attributes of association relationships; S723, Layout Optimization and Adaptation: Based on the graphic tree layout, the layout of nodes and related lines is adjusted to avoid line intersections and overlaps affecting the display effect.

9. A method for dynamic automatic graphing and display of a graph tree in an energy storage monitoring system according to claim 6, characterized in that, The specific steps of S73 are as follows: S731, Basic Interaction Design: Implement basic interactive functions such as mouse hover and click; S732, Dynamic Interactive Display: Supports users to dynamically adjust the display of related relationships through interactive operations; S733, Multi-dimensional Correlation Analysis: Develops advanced interactive functions to achieve multi-dimensional correlation analysis. When the user enters query conditions, the system automatically displays the correlation paths that meet the conditions. When switching through the time axis, users can view the evolution of correlations over different time periods and compare the differences in correlations between multiple devices, assisting users in performing complex system operation analysis and fault diagnosis.

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