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

Abstract

The application relates to a risk monitoring method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring multi-source data, wherein the multi-source data comprises vehicle data, charging pile data and power grid data; constructing a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer; pre-processing 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; 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 interactive terminal. The method can monitor the risk when an electric vehicle and a power grid interact, and timely early warning can be given.

Description

TECHNICAL FIELD

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

[0002] Vehicle-to-Grid (V2G) technology, which is the interaction technology between electric vehicles and power grids, 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 power grids in both directions. With the increasing emphasis on environmental protection and sustainable development worldwide, the popularity of electric vehicles is increasing year by year. In the interaction process between electric vehicles and power grids, there are many risk elements on both the vehicle side and the grid side due to the bidirectional charging and discharging process, and the existing technology lacks a safety risk assessment part for the interaction between electric vehicles and power grids. SUMMARY

[0003] Therefore, it is necessary to provide a risk monitoring method, device, computer equipment, computer readable storage medium and computer program product for monitoring the risks generated in the interaction process between electric vehicles and power grids.

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

[0005] obtaining 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] generating first warning information based on the risk assessment result, and sending the first warning information, the multi-source data and the risk assessment result to an interaction terminal.

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

[0011] determining whether the vehicle battery of the current vehicle has a fault;

[0012] if so, obtaining the fault type and failure mechanism of the fault;

[0013] generate the first battery safety data based on the fault type and the failure mechanism.

[0014] In one of the embodiments, the vehicle data further comprises second battery safety data; and the process of obtaining the second battery safety data comprises:

[0015] obtaining historical charging and discharging data and battery aging mechanism of the vehicle battery;

[0016] predicting the battery health of the vehicle battery based on the historical charging and discharging data and the battery aging mechanism;

[0017] generating the second battery safety data based on the battery health.

[0018] In one of the embodiments, the method further comprises:

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

[0020] sending the charging safety warning information to the interactive terminal.

[0021] In one of the embodiments, the process of obtaining the charging pile data comprises:

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

[0023] obtaining safety configuration of the charging pile;

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

[0025] In one of the embodiments, the method further comprises:

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

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

[0028] sensing a security situation of the power grid based on the power grid parameters, the charging and discharging influence and the security vulnerabilities, to obtain a sensing result;

[0029] obtaining second warning information based on the sensing result, and sending the sensing result and the second warning information to the interactive terminal.

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

[0031] acquire multi-source data, wherein the multi-source data comprises vehicle data, charging pile data and power grid data;

[0032] construct a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer;

[0033] fuse the multi-source data based on the data layer to obtain fused data;

[0034] analyze the fused data based on the decision layer to obtain a risk assessment result;

[0035] generate first early warning information based on the risk assessment result, and send the first early 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 comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

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

[0038] construct a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer;

[0039] fuse the multi-source data based on the data layer to obtain fused data;

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

[0041] generate first early warning information based on the risk assessment result, and send the first early warning information, the multi-source data and the risk assessment result 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 the computer program is executed by a processor to implement the following steps:

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

[0044] construct a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer;

[0045] fuse the multi-source data based on the data layer to obtain fused data;

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

[0047] 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 interactive terminal.

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

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

[0050] constructing a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer;

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

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

[0053] 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 interactive terminal.

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

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can be obtained without creative labor on the basis of these drawings.

[0056] Figure 1 a flowchart of a risk monitoring method in an embodiment;

[0057] Figure 2 another flowchart of a risk monitoring method in an embodiment;

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

[0059] Figure 4 a flowchart of a risk monitoring system in a specific embodiment;

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

[0061] Figure 6 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0062] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0063] In an embodiment, as shown in Figure 1 a risk monitoring method is provided, and the embodiment is exemplified by applying the method to a terminal. It should 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 realized through the interaction of the terminal and the server. In the 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, pre-processing 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, generating first warning information based on the risk assessment result, and sending the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0069] Vehicle-to-Grid (V2G) technology, which allows electric vehicles to exchange energy with the grid, has emerged as an innovative energy management approach with the popularization of electric vehicles and the development of smart grids in recent years. As the world pays more attention to environmental protection and sustainable development, the popularity of electric vehicles is increasing year by year. The increase in the number of electric vehicles makes their energy storage systems a potential huge energy resource. Smart grids are highly information-based, automated, and interactive, enabling precise control and optimized scheduling of the grid. The development of smart grids provides a technical foundation for the interaction between electric vehicles and the grid. Electric vehicles commonly use high-energy-density energy storage technologies such as lithium-ion batteries, which have high energy storage density and capacity. This enables electric vehicles' energy storage systems to provide power support for the grid.

[0070] In traditional technology, multiple data sources (such as vehicle OBD data, charging pile data, and grid parameters) are involved in the interaction between electric vehicles and the grid. These data are often scattered and difficult to effectively integrate. Therefore, the problem of insufficient multi-source data integration and real-time monitoring needs to be addressed. This application can obtain vehicle data such as charging and discharging related information and charging pile data from the vehicle OBD interface through data collection and data fusion, and effectively integrate data from different levels using data fusion technology. This integration method solves the problem of not being able to uniformly process multi-source data, improving the efficiency and accuracy of data processing.

[0071] Specifically, first, the vehicle and pile data quality verification scheme is studied to solve the problems of loss, error, and repetition in data collection, ensuring the accuracy and integrity of multi-source data; through data cleaning, including processing of error values, duplicate values, and missing values, data quality is optimized; at the same time, multi-source data fusion is implemented at the data layer and the decision layer; data layer fusion ensures data consistency and reliability through OBD and charging pile data verification and correction; decision layer fusion uses intelligent algorithms such as Kalman filtering or Bayesian estimation to output comprehensive risk assessment results based on the intermediate results (fusion data) of the risk assessment model. Specifically, the risk assessment results can be output in the form of vehicle safety risk levels, and first warning information is generated corresponding to different vehicle safety risk levels.

[0072] In the above risk monitoring method, multi-source data from vehicles, charging piles, and the grid in the interaction system of electric vehicles and the grid can be collected and fused, and the system can be monitored for risks based on the fused data, and timely warnings can be issued to reduce potential safety hazards in the grid. This can significantly improve the safety and efficiency of the interaction between electric vehicles and the grid, and has broad application prospects and technological progress in industrial applications.

[0073] In an exemplary embodiment, the vehicle data includes first battery safety data; the acquisition process of the first battery safety data includes: judging whether the vehicle battery of the current vehicle has a fault; if yes, acquiring a fault type and a 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 the vehicle power battery obtained through historical research, a mapping relationship between the abnormality and the data representation 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 directly presents the safety risk of the charging and discharging process through quantitative scoring, to obtain the first battery safety data; further, to ensure the applicability of different vehicle models, a safety warning threshold self-adaptive adaptation method can also be researched to simplify the adaptation process and accelerate the model application.

[0075] Specifically, first, various fault types and failure mechanisms that may occur in the vehicle power battery need to be researched in depth to understand the root cause and sensitivity of the fault, and form historical data. The abnormal data corresponding to different fault types are mapped to establish the correlation between the abnormal data and the fault types, to provide data support for subsequent risk assessment. Based on the abnormality and fault relationship of the research, a risk level scoring model is developed to evaluate and classify different fault types and their risk levels. The overall risk level of the battery is comprehensively scored by considering the influence of various fault types, to directly present the safety risk of the charging and discharging process in a quantitative manner (i.e., the first battery safety data). To ensure the applicability of different vehicle models, how to design a safety warning threshold self-adaptive adaptation method according to the characteristics and needs of different vehicle models is researched to 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 further includes second battery safety data; the acquisition process of the second battery safety data includes: acquiring historical charging and discharging data and a battery aging mechanism of the vehicle battery; predicting the battery health degree of the vehicle battery based on the historical charging and discharging data and the battery aging mechanism; and generating the second battery safety data based on the battery health degree.

[0077] Develop a data-driven and model-fusion-based power battery health prediction method: Through the analysis of historical charging and discharging data and real-time data of electric vehicles, the performance degradation law of the battery is studied, and the key feature data is identified; considering the battery aging mechanism, including lithium ion activity loss, electrode material change, electrolyte decomposition and consumption, etc., a machine learning algorithm is used, combined with the actual charging and discharging data, to build an aging prediction model; research covering battery aging test under different environmental conditions, and the influence of parameter inconsistency between battery monomers on system performance, to optimize and adjust the health prediction model. Based on the health prediction model, the battery health of the vehicle battery is predicted.

[0078] The 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 simple models, resulting in low prediction accuracy. The embodiment combines data-driven and model fusion techniques to more accurately predict the health of power batteries. In addition, the embodiment can dynamically adjust based on real-time data, significantly improving the reliability and accuracy of battery life prediction, helping to extend the service life of the battery 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, generate charging safety warning information; send the charging safety warning information to the interactive terminal.

[0081] In an exemplary embodiment, the process of obtaining the charging pile data comprises:

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

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

[0084] Based on the main circuit parameters, the auxiliary circuit parameters and the safety configuration, obtain the charging pile data.

[0085] Charging pile safety monitoring and control technology deeply analyzes the safety influencing factors in the charging scene, and constructs a charging scene safety warning method; focusing on the safety monitoring of direct current fast charging piles, involving the main circuit and auxiliary circuit of the charging pile, as well as the insulation detection and output voltage discharge safety measures during the charging process;

[0086] Through the analysis of battery, charging pile, and power grid multi-level influencing factors, a charging safety influencing factor system is constructed, and data modeling and data-driven methods are applied to build a safety warning model.

[0087] The embodiment optimizes the charging pile monitoring and response speed. In the prior art, the safety monitoring of the charging pile is often slow in response, and it is difficult to handle in time when the charging and discharging is abnormal. The embodiment can monitor the running state of the charging pile in real time, and take emergency measures such as stopping charging or cutting off power supply when detecting abnormal conditions, thereby effectively preventing accidents and ensuring the safe interaction of electric vehicles and power grids.

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

[0089] Step 201, obtaining a communication protocol of a power grid and a vehicle;

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

[0091] Step 203, based on the power grid parameters, the charging and discharging influence and the security vulnerability, perceiving a security situation of the power grid to obtain a perception result;

[0092] Step 203, obtaining a second warning information based on the perception result, and sending the perception result and the second warning information to the interaction terminal.

[0093] Obtain the bidirectional communication protocol of the power grid and the V2G system, and construct a power grid security risk assessment model to comprehensively evaluate the influence of electric vehicle charging and discharging on the power grid and the security vulnerability of the communication protocol; establish a full-link security situation perception technology system, covering real-time monitoring of new energy vehicles, safety control of charging piles and security situation perception of power grids, obtain a perception result through real-time data collection and analysis, identify potential faults in time and generate warning information; develop a real-time monitoring system and a cloud database for 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 security situation of the power grid.

[0094] The embodiment enhances the security warning and situation awareness capability. The prior art often lacks an effective charging and discharging process security warning mechanism, which easily leads to safety hazards in the interaction process of electric vehicles and power grids. The present application realizes high-frequency data collection and real-time processing through security situation perception, and intelligently analyzes and warns potential risks by combining artificial intelligence algorithms. The security situation perception module intuitively presents complex security information through the data visualization function, and realizes real-time monitoring and rapid response capability of the security situation of the power grid, greatly improving the safety in the interaction process.

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

[0096] Referring to Figure 3 andFigure 4 The risk monitoring method provided by the embodiment of the application is applied to a safety risk assessment and protection system for interaction between an electric vehicle and a power grid, Figure 3 A schematic diagram of a module of the system is shown, Figure 4 A development process of the real-time monitoring system and the cloud database provided by the application is shown.

[0097] The system comprises:

[0098] The system comprises a data acquisition module 301, a data fusion module 302, a central control module 303, a pre-warning module 304, a battery health prediction module 305, a monitoring module 306, a security situation awareness module 307, and a display module 308.

[0099] The data acquisition module 301 is connected with the data fusion module 302, and is configured to connect a vehicle through a vehicle OBD interface and read relevant data.

[0100] The data fusion module 302 is connected with the data acquisition module 301 and the central control module 303, and is configured to fuse multi-source data of a data layer and a decision layer.

[0101] The central control module 303 is connected with the data fusion module 302, the pre-warning module 304, the battery health prediction module 305, the monitoring module 306, the security situation awareness module 307, and the display module 308, and is configured to control normal work of the modules.

[0102] The pre-warning module 304 is connected with the central control module 303, and is configured to pre-warn a safety of a charging and discharging process of the electric vehicle.

[0103] The battery health prediction module 305 is connected with the central control module 303, and is configured to predict a health degree of a power battery based on data driving and model fusion technology.

[0104] The monitoring module 306 is connected with the central control module 303, and is configured to monitor a safety of a charging pile.

[0105] The security situation awareness module 307 is connected with the central control module 303, and is configured to perform high-frequency data acquisition, real-time processing and analysis, provide data visualization and risk pre-warning functions, and ensure real-time monitoring and rapid response of a safety situation of the power grid.

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

[0107] The risk monitoring method provided by the embodiment of the application comprises:

[0108] Obtain 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; preprocess 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 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, a vehicle pile data quality checking scheme is realized to solve the problems of loss, error and repetition in data acquisition, and to ensure the accuracy and integrity of the data. By cleaning the data, including processing of error values, repeated values and missing values, the data quality is optimized. At the same time, multi-source data fusion of the data layer and the decision layer is implemented. The data layer fusion ensures the consistency and reliability of the data through the verification and correction of OBD and charging pile data. The decision layer fusion finally outputs the comprehensive risk assessment result by using intelligent algorithms such as Kalman filtering or Bayesian estimation based on the intermediate results of the weighted fusion model.

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

[0111] The failure types and failure mechanisms of power batteries are deeply researched, the mapping relationship between anomalies and data representation is established, and a risk level scoring model is developed. The scoring model integrates the risk levels of various faults on the battery to comprehensively score the safety risks in the charging and discharging process through quantitative scoring. To ensure the applicability of different vehicle models, a safety warning threshold self-adaptive adaptation method is researched to simplify the adaptation process and accelerate the application of the model.

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

[0113] A power battery health degree prediction method based on data driving and model fusion is developed. Through in-depth analysis of the charging and discharging process of electric vehicles, the battery performance degradation law is researched, and key feature data is identified. Considering the battery aging mechanism, including lithium ion activity loss, electrode material change, electrolyte decomposition and consumption, etc., a machine learning algorithm is used to construct an aging prediction model combined with actual charging and discharging data.

[0114] The aging test of the battery under different environmental conditions and the influence of parameter inconsistency between battery monomers on system performance are researched to optimize and adjust the health degree prediction model.

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

[0116] The safety monitoring and control technology of charging piles deeply analyzes the safety influencing factors in the charging scene, and constructs a safety early warning method for the charging scene. The safety monitoring of the direct current fast charging pile is emphasized, and the main circuit and auxiliary circuit of the charging pile, as well as the insulation detection and output voltage discharge safety measures in the charging process are involved.

[0117] Through the multi-level influencing factor analysis of the battery, the charging pile and the power grid, a charging safety influencing factor system is constructed, and a safety early warning model is constructed by using data modeling and data driven methods.

[0118] Based on the safety situation awareness module 307, the following is realized:

[0119] A bidirectional communication protocol for the power grid and the V2G system is designed, and a safety risk assessment model is constructed to comprehensively evaluate the influence of electric vehicle charging and discharging on the power grid and the safety vulnerabilities of the communication protocol. A full-link safety situation awareness technology system is established, covering real-time monitoring of new energy vehicles, safety control of charging piles and safety situation awareness of the power grid. Through real-time data collection and analysis, potential faults are identified in time and early warning information is generated. A real-time monitoring system and a cloud database are developed for high-frequency data collection, real-time processing and analysis, providing data visualization and risk early warning functions to ensure real-time monitoring and rapid response of the power grid safety situation.

[0120] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

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

[0122] In one exemplary embodiment, as 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] The acquisition module 401 is configured to acquire multi-source data, wherein the multi-source data comprises vehicle data, charging pile data and power grid data.

[0124] The construction module 402 is configured to construct a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer.

[0125] The fusion module 403 is configured to pre-process and fuse the multi-source data based on the data layer to obtain fused data.

[0126] The analysis module 404 is configured to analyze the fused data based on the decision layer to obtain a risk assessment result.

[0127] The generation module 405 is configured 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.

[0128] The acquisition module 401 is further configured to determine whether a vehicle battery of a current vehicle has a fault, and if so, acquire a fault type and a 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 further configured to acquire historical charging and discharging data and a battery aging mechanism of the vehicle battery, predict the battery health degree of the vehicle battery based on the historical charging and discharging data and the battery aging mechanism, and generate the second battery safety data based on the battery health degree.

[0130] The acquisition module 401 is further configured to:

[0131] Generate charging safety warning information based on the first battery safety data and the second battery safety data, and send the charging safety warning information to the interactive terminal.

[0132] The acquisition module 401 is further configured to:

[0133] Acquire main circuit parameters and auxiliary circuit parameters of a charging pile, and acquire safety configurations of the charging pile.

[0134] Acquire the charging pile data based on the main circuit parameters, the auxiliary circuit parameters and the safety configurations.

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

[0136] The perception module is configured to: acquire a communication protocol of a power grid and a vehicle; acquire a charge-discharge influence of the vehicle on the power grid and a security vulnerability of the communication protocol; perceive a security situation of the power grid based on the power grid parameter, the charge-discharge influence and the security vulnerability, to obtain a perception result; obtain second early warning information based on the perception result, and send the perception result and the second early warning information to the interaction terminal.

[0137] The various modules in the risk monitoring device can be implemented wholly or partially by software, hardware, or a combination thereof. The various modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the various modules.

[0138] In an exemplary embodiment, a computer device, which can be a terminal, has an internal structure as shown in Figure 6 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 configured 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 running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, near field communication (NFC), or other technologies. The computer program is executed by the processor to implement a risk monitoring method. The display unit of the computer device is configured 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. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad arranged on the shell of the computer device. The input device can also be an external keyboard, touchpad, or mouse, etc.

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

[0140] In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

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

[0142] Constructing a risk assessment model, wherein the risk assessment model comprises 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] Generating first warning information based on the risk assessment result, and sending the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0146] In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

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

[0148] Constructing a risk assessment model, wherein the risk assessment model comprises 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] Generating first warning information based on the risk assessment result, and sending the first warning information, the multi-source data and the risk assessment result to an interactive terminal.

[0152] In one embodiment, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the following steps:

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

[0154] Constructing a risk assessment model, wherein the risk assessment model comprises 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] 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 interactive terminal.

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

[0159] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to 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. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0160] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0161] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A risk monitoring method, characterized by, The method comprises: acquiring multi-source data, wherein the multi-source data comprises vehicle data, charging pile data and power grid data; constructing a risk assessment model, wherein 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 fused data based on the decision layer to obtain a risk assessment result; generating first warning information based on the risk assessment result, and sending the first warning information, the multi-source data and the risk assessment result to an interactive terminal; acquiring a communication protocol between the power grid and the vehicle; acquiring the charging and discharging influence of the vehicle on the power grid and security vulnerabilities of the communication protocol; based on the power grid parameters, the charging and discharging influence and the security vulnerabilities, perceiving the security situation of the power grid to obtain a perception result; based on the perception result, obtaining second warning information, and sending the perception result and the second warning information to the interactive terminal; wherein the preprocessing and fusion of the multi-source data based on the data layer to obtain fused data comprises data cleaning of the multi-source data, and verification and correction of vehicle data and charging pile data; the analysis of the fused data based on the decision layer to obtain a risk assessment result comprises obtaining a risk assessment result according to an intelligent algorithm and an intermediate result of the risk assessment model; the intermediate result is the fused data, and the intelligent algorithm comprises Kalman filtering or Bayesian estimation.

2. The method of claim 1, wherein, The vehicle data comprises first battery safety data; the acquisition process of the first battery safety data comprises: determining whether the vehicle battery of the current vehicle has a fault; if yes, obtaining the fault type and failure mechanism of the fault; based on the fault type and the failure mechanism, generating the first battery safety data.

3. The method of claim 2, wherein, The vehicle data further comprises second battery safety data; the acquisition process of the second battery safety data comprises: acquiring historical charging and discharging data and battery aging mechanism of the vehicle battery; based on the historical charging and discharging data and the battery aging mechanism, predicting the battery health degree of the vehicle battery; based on the battery health degree, generating the second battery safety data; wherein the prediction of the battery health degree of the vehicle battery based on the historical charging and discharging data and the battery aging mechanism comprises: constructing a health degree prediction model according to a machine learning algorithm, the battery aging mechanism of the vehicle battery and actual charging and discharging data; optimizing and adjusting the health degree prediction model according to the aging test of the battery under different environmental conditions and the parameter inconsistency between battery monomers; predicting the battery health degree of the vehicle battery based on the health degree prediction model.

4. The method of claim 3, wherein, The method further comprises: based on the first battery safety data and the second battery safety data, generating charging safety warning information; sending the charging safety warning information to the interactive terminal.

5. The method of claim 1, wherein, The acquisition process of the charging pile data comprises: collecting main circuit parameters and auxiliary circuit parameters of the charging pile; acquiring safety configurations of the charging pile; The charging pile data is obtained based on the main circuit parameter, the auxiliary circuit parameter and the safety configuration.

6. A risk monitoring apparatus characterized by comprising: The device comprises: An acquisition module is configured to acquire multi-source data, wherein the multi-source data comprises vehicle data, charging pile data and power grid data; A construction module is configured to construct a risk assessment model, wherein the risk assessment model comprises a data layer and a decision layer; A fusion module is configured to pre-process and fuse the multi-source data based on the data layer to obtain fused data; An analysis module is configured to analyze the fused data based on the decision layer to obtain a risk assessment result; A generation module is configured 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; A perception module is configured to acquire a communication protocol of a power grid and a vehicle, acquire a charging and discharging influence of the vehicle on the power grid and a security vulnerability of the communication protocol, perceive a security situation of the power grid based on the power grid parameter, the charging and discharging influence and the security vulnerability to obtain a perception result, and obtain second warning information based on the perception result, and send the perception result and the second warning information to the interactive terminal; The fusion module is further configured to clean the multi-source data, and check and correct the vehicle data and the charging pile data. The analysis module is further configured to acquire a risk assessment result according to an intelligent algorithm and an intermediate result of the risk assessment model, wherein the intermediate result is the fused data, and the intelligent algorithm comprises Kalman filtering or Bayesian estimation. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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