Risk monitoring method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a risk monitoring method and device, computer equipment, a storage medium and a program product. The method comprises the steps that multi-source data are acquired, and the multi-source data comprise vehicle data, charging pile data and power grid data; a risk assessment model is constructed, and the risk assessment model comprises a data layer and a decision layer; preprocessing and fusing the multi-source data based on the data layer to obtain fused data; analyzing the fusion data based on a decision-making layer to obtain a risk assessment result; and generating first early warning information based on the risk assessment result, and sending the first early warning information, the multi-source data and the risk assessment result to an interaction terminal. By adopting the method, the risk during interaction between the electric vehicle and the power grid can be monitored, and early warning can be given out in time.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a risk monitoring method, device, computer equipment, storage medium and program product. Background Art

[0002] The interaction technology between electric vehicles and the grid, namely Vehicle-to-Grid (V2G) technology, is an innovative energy management method that has emerged in recent years with the popularization of electric vehicles and the development of smart grids. This technology allows electric vehicles to exchange energy with the grid in both directions. With the global emphasis on environmental protection and sustainable development, the popularity of electric vehicles has increased year by year. In the process of interaction between electric vehicles and the grid, due to the existence of a two-way charging and discharging process, there are many risk elements on both the vehicle side and the grid side, and the existing technology lacks a safety risk assessment part for the interaction between electric vehicles and the grid. Summary of the invention

[0003] Based on this, it is necessary to provide a risk monitoring method, device, computer equipment, computer-readable storage medium and computer program product that can monitor the risks generated during the interaction between electric vehicles and power grids in response to the above technical problems.

[0004] In a first aspect, the present application provides a risk monitoring method, comprising:

[0005] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0006] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0007] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0008] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0009] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0010] In one embodiment, the vehicle data includes first battery safety data; and a process of acquiring the first battery safety data includes:

[0011] Determine whether the vehicle battery of the current vehicle is faulty;

[0012] If yes, then obtain the fault type and failure mechanism of the fault;

[0013] Based on the fault type and the failure mechanism, the first battery safety data is generated.

[0014] In one embodiment, the vehicle data further includes second battery safety data; and a process of acquiring the second battery safety data includes:

[0015] Obtaining historical charge and discharge data and battery aging mechanism of the vehicle battery;

[0016] Predicting the battery health of the vehicle battery based on the historical charge and discharge data and the battery aging mechanism;

[0017] The second battery safety data is generated based on the battery health.

[0018] In one embodiment, the method further comprises:

[0019] generating charging safety warning information based on the first battery safety data and the second battery safety data;

[0020] The charging safety warning information is sent to the interactive terminal.

[0021] In one embodiment, the process of acquiring the charging pile data includes:

[0022] Collect the main circuit parameters and auxiliary circuit parameters of the charging pile;

[0023] Obtaining the safety configuration of the charging pile;

[0024] The charging pile data is acquired based on the main circuit parameters, the auxiliary circuit parameters and the safety configuration.

[0025] In one embodiment, the method further comprises:

[0026] Obtain the communication protocol between the power grid and the vehicle;

[0027] Obtaining the charging and discharging effects of the vehicle on the power grid and the security vulnerabilities of the communication protocol;

[0028] Based on the grid parameters, the charging and discharging impacts and the security vulnerabilities, the security situation of the grid is sensed to obtain a sensed result;

[0029] A second warning information is obtained based on the perception result, and the perception result and the second warning information are sent to the interactive terminal.

[0030] In a second aspect, the present application also provides a risk monitoring device, comprising:

[0031] An acquisition module, used to acquire multi-source data, wherein the multi-source data includes vehicle data, charging pile data and power grid data;

[0032] A construction module, used to construct a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0033] A fusion module, used for preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0034] An analysis module, used to analyze the fused data based on the decision layer to obtain a risk assessment result;

[0035] A generation module is used to generate first warning information based on the risk assessment result, and send the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0038] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0039] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0040] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0041] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0043] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0044] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0045] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0046] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0047] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0048] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0049] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0050] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0051] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0052] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0053] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0054] The above-mentioned risk monitoring method, device, computer equipment, computer-readable storage medium and computer program product can collect multi-source data from vehicles, charging piles and power grids in the interactive system of electric vehicles and power grids, and fuse the multi-source data, monitor the risks of the system based on the fused data, and issue early warnings in time, thereby reducing safety hazards in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of a process of a risk monitoring method in an embodiment;

[0057] Figure 2 Another schematic diagram of a risk monitoring method in one embodiment;

[0058] Figure 3 A schematic diagram of a module of a risk monitoring system in a specific embodiment;

[0059] Figure 4 A schematic diagram of a process of a risk monitoring system in a specific embodiment;

[0060] Figure 5 is a structural block diagram of a risk monitoring device in one embodiment;

[0061] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] In one embodiment, Figure 1 As shown, a risk monitoring method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0064] Step 101, acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data and power grid data;

[0065] Step 102, constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0066] Step 103, preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0067] Step 104, analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0068] Step 105: Generate first warning information based on the risk assessment result, and send the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0069] The interaction technology between electric vehicles and the grid, namely Vehicle-to-Grid (V2G) technology, is an innovative energy management method that has emerged in recent years with the popularization of electric vehicles and the development of smart grids. This technology allows electric vehicles to exchange energy with the grid in both directions. With the global emphasis on environmental protection and sustainable development, the popularity of electric vehicles has increased year by year. The increase in the number of electric vehicles has made their energy storage system a potential huge energy resource. Smart grids are highly information-based, automated and interactive, and can achieve precise control and optimized scheduling of the grid. The development of smart grids provides a technical basis for the interaction between electric vehicles and the grid. Electric vehicles generally use high-energy-density energy storage technologies such as lithium-ion batteries, which have high energy storage density and energy storage capacity. This enables the energy storage system of electric vehicles to provide power support for the grid.

[0070] In traditional technology, since the interaction between electric vehicles and power grids involves multiple data sources (such as vehicle OBD data, charging pile data, power grid parameters, etc.), these data are often scattered and difficult to effectively integrate. Therefore, it is necessary to solve the problem of insufficient multi-source data integration and real-time monitoring. This application combines data acquisition with data fusion to obtain vehicle data and charging pile data such as charging and discharging related information from the vehicle OBD interface, and uses data fusion technology to effectively integrate data from different levels. This fusion method solves the problem that multi-source data cannot be processed uniformly, and improves the efficiency and accuracy of data processing.

[0071] Specifically, we first study the data quality verification scheme for charging piles to solve the problems of loss, error and duplication in data collection, and ensure the accuracy and completeness of multi-source data; optimize data quality by cleaning the data, including processing of erroneous values, duplicate values ​​and missing values; at the same time, implement multi-source data fusion at the data layer and decision layer; data layer fusion ensures data consistency and reliability by checking and correcting OBD and charging pile data; decision layer fusion uses the intermediate results (fused data) of the risk assessment model, and uses intelligent algorithms such as Kalman filtering or Bayesian estimation to finally output a comprehensive risk assessment result. Specifically, the risk assessment results can be output in the form of vehicle safety risk levels, and the first warning information can be generated corresponding to different vehicle safety risk levels.

[0072] The above risk monitoring method can collect multi-source data from vehicles, charging piles and power grids in the interactive system of electric vehicles and power grids, and fuse the multi-source data. Based on the fused data, the system is monitored for risks and early warnings are issued in time, reducing the potential safety hazards in the power grid. It can significantly improve the safety and efficiency of the interaction between electric vehicles and power grids, and has broad application prospects and technological progress in industrial applications.

[0073] In an exemplary embodiment, the vehicle data includes first battery safety data; a process for obtaining the first battery safety data includes: determining whether a vehicle battery of the current vehicle has a fault; if so, obtaining the fault type and failure mechanism of the fault; and generating the first battery safety data based on the fault type and the failure mechanism.

[0074] Based on the fault types and failure mechanisms of vehicle power batteries obtained from historical research, a mapping relationship between anomalies and data representations is established, and a battery risk level scoring model is developed; the scoring model integrates various faults to comprehensively score the risk level of the battery, and intuitively presents the safety risks of the charging and discharging process through quantitative scoring to obtain the first battery safety data; further, in order to ensure the applicability of different vehicle models, the adaptive adaptation method of safety warning thresholds can also be studied to simplify the adaptation process and accelerate model application.

[0075] Specifically, we first need to conduct in-depth research on various types of faults and their failure mechanisms that may occur in vehicle power batteries, understand the root causes and sensitivity of the faults, and form historical data. Map the abnormal data corresponding to different fault types, establish the correlation between abnormal data and fault types, and provide data support for subsequent risk assessment. Based on the relationship between abnormalities and faults studied, develop a risk level scoring model to evaluate and classify different fault types and their risk levels. Considering the impact of various fault types, the overall risk level of the battery is comprehensively scored, and the safety risk of the charging and discharging process is intuitively presented in a quantitative manner (i.e., the first battery safety data). To ensure the applicability of different models, study how to design a threshold adaptive adaptation method for safety warnings based on the characteristics and needs of different models, simplify the adaptation process and improve the efficiency of model application. Through the above steps, a complete power battery fault analysis and risk assessment system can be established to provide effective support and guidance for battery safety management.

[0076] In an exemplary embodiment, the vehicle data also includes second battery safety data; the process of obtaining the second battery safety data includes: obtaining historical charge and discharge data and battery aging mechanism of the vehicle battery; predicting the battery health of the vehicle battery based on the historical charge and discharge data and the battery aging mechanism; and generating the second battery safety data based on the battery health.

[0077] Develop a power battery health prediction method based on data-driven and model fusion: Through the analysis of historical charging and discharging data and real-time data of electric vehicles, study the battery performance degradation law and identify key characteristic data; comprehensively consider the battery aging mechanism, including lithium ion activity loss, electrode material changes, electrolyte decomposition and consumption, etc., use machine learning algorithms, combined with actual charging and discharging data, to build an aging prediction model; research covers battery aging tests under different environmental conditions, and the impact of parameter inconsistency between battery cells on system performance, in order to optimize and adjust the health prediction model. Predict the battery health of the vehicle battery based on the health prediction model.

[0078] This embodiment improves the accuracy of battery health prediction. Traditional technologies for evaluating the health of power batteries are mostly based on a single data source or a simple model, resulting in low prediction accuracy. This embodiment combines data-driven and model fusion technology to more accurately predict the health of power batteries. In addition, this embodiment can be dynamically adjusted in combination with real-time data, which significantly improves the reliability and accuracy of battery life prediction, helps to extend battery life and reduce replacement costs.

[0079] In an exemplary embodiment, the method further comprises:

[0080] Based on the first battery safety data and the second battery safety data, charging safety warning information is generated; and the charging safety warning information is sent to the interactive terminal.

[0081] In an exemplary embodiment, the process of acquiring charging pile data includes:

[0082] Collect the main circuit parameters and auxiliary circuit parameters of the charging pile;

[0083] Obtaining the safety configuration of the charging pile;

[0084] The charging pile data is acquired based on the main circuit parameters, the auxiliary circuit parameters and the safety configuration.

[0085] Research on charging pile safety monitoring and control technology deeply analyzes the safety influencing factors in charging scenarios and constructs a safety early warning method for charging scenarios; focuses on the safety monitoring of DC fast charging piles, involving the main circuit and auxiliary circuit of the charging pile, as well as insulation detection and output voltage discharge safety measures during the charging process;

[0086] Through multi-level influencing factor analysis of batteries, charging piles, and power grids, a charging safety influencing factor system is constructed, and data modeling and data-driven methods are used to construct a safety early warning model.

[0087] This embodiment optimizes the monitoring and response speed of charging piles. In the prior art, the safety monitoring of charging piles often has a slow response speed, making it difficult to deal with abnormal charging and discharging in a timely manner. This embodiment can monitor the operating status of the charging pile in real time and take emergency measures when abnormal conditions are detected, such as stopping charging or cutting off the power supply, thereby effectively preventing accidents and ensuring safe interaction between electric vehicles and the power grid.

[0088] In an exemplary embodiment, see Figure 2 , the method further comprises:

[0089] Step 201, obtaining the communication protocol between the power grid and the vehicle;

[0090] Step 202, obtaining the charging and discharging impact of the vehicle on the power grid and the security loopholes of the communication protocol;

[0091] Step 203, based on the grid parameters, the charging and discharging impacts and the security loopholes, the security situation of the grid is sensed to obtain a sensed result;

[0092] Step 203: obtain second warning information based on the perception result, and send the perception result and the second warning information to the interactive terminal.

[0093] Obtain the two-way communication protocol between the power grid and the V2G system, and build a power grid security risk assessment model to comprehensively evaluate the impact of electric vehicle charging and discharging on the power grid and the security vulnerabilities of the communication protocol; establish a full-link security situation awareness technology system, covering real-time monitoring of new energy vehicles, charging pile safety control and power grid security situation awareness, obtain perception results through real-time data collection and analysis, timely identify potential faults and generate early warning information; develop a real-time monitoring system and cloud database to conduct high-frequency data collection, real-time processing and analysis, provide data visualization and risk warning functions, and ensure real-time monitoring and rapid response of the power grid security situation.

[0094] This embodiment enhances the safety warning and situational awareness capabilities. The existing technology often lacks an effective safety warning mechanism for the charging and discharging process, which easily leads to safety hazards in the interaction between electric vehicles and power grids. The present invention realizes high-frequency data collection and real-time processing through safety situation awareness, and combines artificial intelligence algorithms to perform intelligent analysis and warning of potential risks. The safety situation awareness module uses data visualization functions to intuitively present complex safety information, and realizes real-time monitoring and rapid response capabilities for the safety situation of the power grid, greatly improving the safety during the interaction process.

[0095] To explain the risk monitoring method in detail, the following is a most detailed example:

[0096] See also Figure 3 and Figure 4 A risk monitoring method provided in an embodiment of the present application is applied to a safety risk assessment and protection system for interaction between electric vehicles and power grids. Figure 3 A schematic diagram of a module of the system is shown. Figure 4 The present invention shows a development process of a real-time monitoring system and a cloud database.

[0097] The system comprises:

[0098] Data acquisition module 301 , data fusion module 302 , central control module 303 , early warning module 304 , battery health prediction module 305 , monitoring module 306 , safety situation awareness module 307 , and display module 308 .

[0099] The data acquisition module 301 is connected to the data fusion module 302 and is used to connect to the vehicle through the vehicle OBD interface and read relevant data;

[0100] The data fusion module 302 is connected to the data acquisition module 301 and the central control module 3 and is used to fuse multi-source data at the data layer and the decision layer;

[0101] The central control module 303 is connected with the data fusion module 302, the early warning module 304, the battery health prediction module 305, the monitoring module 306, the safety situation awareness module 307, and the display module 308, and is used to control the normal operation of each module;

[0102] The early warning module 304 is connected to the central control module 303 and is used for early warning of the safety of the charging and discharging process of the electric vehicle;

[0103] The battery health prediction module 305 is connected to the central control module 303 and is used to predict the health of the power battery based on data-driven and model fusion technology;

[0104] The monitoring module 306 is connected to the central control module 303 and is used to monitor the safety of the charging pile;

[0105] The security situation awareness module 307 is connected to the central control module 303 and is used for high-frequency data collection, real-time processing and analysis, providing data visualization and risk warning functions to ensure real-time monitoring and rapid response of the power grid security situation;

[0106] The display module 308 is connected to the central control module 303 and is used to display the interaction data information between the electric vehicle and the power grid through the system interface.

[0107] The risk monitoring method provided in the embodiment of the present application includes:

[0108] Acquire multi-source data, wherein the multi-source data includes vehicle data, charging pile data and power grid data; construct a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer; pre-process and fuse the multi-source data based on the data layer to obtain fused data; analyze the fused data based on the decision layer to obtain a risk assessment result; generate a first warning information based on the risk assessment result, and send the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0109] Based on the data acquisition module 301 and the data fusion module 302, the vehicle charging pile data quality verification program is implemented to solve the problems of loss, error and duplication in data acquisition and ensure the accuracy and completeness of the data; the data quality is optimized by cleaning the data, including the processing of erroneous values, duplicate values ​​and missing values; at the same time, multi-source data fusion is implemented at the data layer and the decision layer; the data layer fusion ensures the consistency and reliability of the data by checking and correcting the OBD and charging pile data; the decision layer fusion uses the intermediate results of the weighted fusion model, and uses intelligent algorithms such as Kalman filtering or Bayesian estimation to finally output a comprehensive risk assessment result.

[0110] Based on the early warning module 304, the following is achieved:

[0111] In-depth research on the fault types and failure mechanisms of power batteries, establishing a mapping relationship between anomalies and data representations, and developing a risk level scoring model; the scoring model integrates various faults to comprehensively score the risk level of the battery, and intuitively presents the safety risks of the charging and discharging process through quantitative scoring; to ensure the applicability of different vehicle models, the adaptive adaptation method of safety warning thresholds is studied to simplify the adaptation process and accelerate model application.

[0112] Based on the battery health prediction module 305, the following is achieved:

[0113] Develop a power battery health prediction method based on data-driven and model fusion, study the battery performance degradation law and identify key characteristic data through in-depth analysis of the electric vehicle charging and discharging process; comprehensively consider the battery aging mechanism, including lithium ion activity loss, electrode material changes, electrolyte decomposition and consumption, etc., use machine learning algorithms, combined with actual charging and discharging data, to build an aging prediction model;

[0114] The research covers battery aging tests under different environmental conditions and the impact of parameter inconsistency between battery cells on system performance, in order to optimize and adjust the health prediction model.

[0115] Based on the monitoring module 306, the following is achieved:

[0116] Research on charging pile safety monitoring and control technology deeply analyzes the safety influencing factors in charging scenarios and constructs a safety early warning method for charging scenarios; focuses on the safety monitoring of DC fast charging piles, involving the main circuit and auxiliary circuit of the charging pile, as well as insulation detection and output voltage discharge safety measures during the charging process;

[0117] Through multi-level influencing factor analysis of batteries, charging piles, and power grids, a charging safety influencing factor system is constructed, and data modeling and data-driven methods are used to construct a safety early warning model.

[0118] Based on the security situation awareness module 307, the following is achieved:

[0119] Design a two-way communication protocol between the power grid and the V2G system, and build a security risk assessment model to comprehensively evaluate the impact of electric vehicle charging and discharging on the power grid and the security vulnerabilities of the communication protocol; establish a full-link security situation awareness technology system, covering real-time monitoring of new energy vehicles, charging pile safety control, and power grid security situation awareness, and through real-time data collection and analysis, timely identify potential faults and generate early warning information; develop a real-time monitoring system and cloud database to conduct high-frequency data collection, real-time processing and analysis, provide data visualization and risk warning functions, and ensure real-time monitoring and rapid response of the power grid security situation.

[0120] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0121] Based on the same inventive concept, the embodiment of the present application also provides a risk monitoring device for implementing the risk monitoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more risk monitoring device embodiments provided below can refer to the limitations on the risk monitoring method above, and will not be repeated here.

[0122] In an exemplary embodiment, Figure 5As shown, a risk monitoring device 400 is provided, comprising: an acquisition module 401, a construction module 402, a fusion module 403, an analysis module 404 and a generation module 405, wherein:

[0123] An acquisition module 401 is used to acquire multi-source data, wherein the multi-source data includes vehicle data, charging pile data and power grid data;

[0124] A construction module 402 is used to construct a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0125] A fusion module 403 is used to pre-process and fuse the multi-source data based on the data layer to obtain fused data;

[0126] An analysis module 404 is used to analyze the fusion data based on the decision layer to obtain a risk assessment result;

[0127] The generating module 405 is used to generate first warning information based on the risk assessment result, and send the first warning information, the multi-source data and the risk assessment result to the interactive terminal.

[0128] The acquisition module 401 is also used to: determine whether there is a fault in the vehicle battery of the current vehicle; if so, obtain the fault type and failure mechanism of the fault; and generate the first battery safety data based on the fault type and the failure mechanism.

[0129] The acquisition module 401 is also used to: acquire the historical charge and discharge data and the battery aging mechanism of the vehicle battery; predict the battery health of the vehicle battery based on the historical charge and discharge data and the battery aging mechanism; and generate the second battery safety data based on the battery health.

[0130] The acquisition module 401 is further used for:

[0131] Based on the first battery safety data and the second battery safety data, charging safety warning information is generated; and the charging safety warning information is sent to the interactive terminal.

[0132] The acquisition module 401 is further used for:

[0133] Collecting main circuit parameters and auxiliary circuit parameters of the charging pile; obtaining the safety configuration of the charging pile;

[0134] The charging pile data is acquired based on the main circuit parameters, the auxiliary circuit parameters and the safety configuration.

[0135] In an exemplary embodiment, the risk monitoring device 400 further includes:

[0136] The perception module is used to: obtain the communication protocol between the power grid and the vehicle; obtain the charging and discharging impact of the vehicle on the power grid and the security loopholes of the communication protocol; perceive the security situation of the power grid based on the power grid parameters, the charging and discharging impact and the security loopholes to obtain a perception result; obtain a second warning information based on the perception result, and send the perception result and the second warning information to the interactive terminal.

[0137] Each module in the above risk monitoring device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0138] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a risk monitoring method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0139] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0141] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0142] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0143] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0144] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0145] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0146] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0147] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0148] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0149] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0150] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0151] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0152] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0153] Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data;

[0154] Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer;

[0155] Preprocessing and fusing the multi-source data based on the data layer to obtain fused data;

[0156] Analyzing the fused data based on the decision layer to obtain a risk assessment result;

[0157] First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0159] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0160] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0161] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A risk monitoring method, characterized in that: The method comprises: Acquiring multi-source data, wherein the multi-source data includes vehicle data, charging pile data, and power grid data; Constructing a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer; Preprocessing and fusing the multi-source data based on the data layer to obtain fused data; Analyzing the fused data based on the decision layer to obtain a risk assessment result; First warning information is generated based on the risk assessment result, and the first warning information, the multi-source data and the risk assessment result are sent to an interactive terminal.

2. The method according to claim 1, characterized in that The vehicle data includes first battery safety data; The process of acquiring the first battery safety data includes: Determine whether the vehicle battery of the current vehicle is faulty; If yes, then obtain the fault type and failure mechanism of the fault; Based on the fault type and the failure mechanism, the first battery safety data is generated.

3. The method according to claim 2, characterized in that The vehicle data also includes second battery safety data; The process of acquiring the second battery safety data includes: Obtaining historical charge and discharge data and battery aging mechanism of the vehicle battery; Predicting the battery health of the vehicle battery based on the historical charge and discharge data and the battery aging mechanism; The second battery safety data is generated based on the battery health.

4. The method according to claim 3, characterized in that The method further comprises: generating charging safety warning information based on the first battery safety data and the second battery safety data; The charging safety warning information is sent to the interactive terminal.

5. The method according to claim 1, characterized in that The process of acquiring the charging pile data includes: Collect the main circuit parameters and auxiliary circuit parameters of the charging pile; Obtaining the safety configuration of the charging pile; The charging pile data is acquired based on the main circuit parameters, the auxiliary circuit parameters and the safety configuration.

6. The method according to claim 1, characterized in that The method further comprises: Obtain the communication protocol between the power grid and the vehicle; Obtaining the charging and discharging effects of the vehicle on the power grid and the security vulnerabilities of the communication protocol; Based on the grid parameters, the charging and discharging impacts and the security vulnerabilities, the security situation of the grid is sensed to obtain a sensed result; A second warning information is obtained based on the perception result, and the perception result and the second warning information are sent to the interactive terminal.

7. A risk monitoring device, characterized in that: The device comprises: An acquisition module, used to acquire multi-source data, wherein the multi-source data includes vehicle data, charging pile data and power grid data; A construction module, used to construct a risk assessment model, wherein the risk assessment model includes a data layer and a decision layer; A fusion module, used for preprocessing and fusing the multi-source data based on the data layer to obtain fused data; An analysis module, used to analyze the fused data based on the decision layer to obtain a risk assessment result; A generation module is used to generate first warning information based on the risk assessment result, and send the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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