A dynamic safety assessment method and system for a flexible DC energy router

Abstract

The present invention relates to the field of security assessment technology, and in particular to a dynamic security assessment method and system for a flexible DC energy router. The method comprises the following steps: deploying corresponding sensors at key points of the flexible DC energy router and connecting to establish a DC router operation sensor monitoring network; performing real-time monitoring of the equipment operation and equipment-level risk factor assessment and analysis on the flexible DC energy router to obtain DC equipment-level risk factors, and performing system-level and network-level risk factor assessment and analysis to obtain DC system-level risk factors and DC network-level risk factors; performing safety risk warning response adjustment on DC equipment-level risk factors, DC system-level risk factors, and DC network-level risk factors, generating a DC router safety risk warning adjustment strategy, and executing corresponding DC router operation risk warning adjustment measures. The present invention can improve the safety of DC energy routers in complex and dynamic power environments.

Description

Technical Field

[0001] The present invention relates to the technical field of safety assessment, and in particular to a dynamic safety assessment method and system for a flexible direct current energy router. Background Art

[0002] In modern power systems, Flexible DC Energy Router (FDER), as an important power transmission and distribution equipment, is being widely used in the design of efficient and reliable power systems. It realizes flexible energy distribution between different power sources and loads through intelligent control and optimized scheduling. Existing dynamic safety assessment methods establish a dynamic model of FDER to accurately describe its behavioral characteristics under different operating conditions, and collect key data during operation, including current, voltage, power and system status information, through a real-time monitoring system. At the same time, data analysis technology and intelligent algorithms (such as machine learning and artificial intelligence) are used to dynamically analyze real-time data to evaluate the safety of the router. According to the evaluation results, the control strategy is adjusted in time or necessary preventive measures are taken to ensure the stable operation of the router. However, traditional safety assessment methods mainly rely on static analysis and experimental testing. These methods usually cannot fully cover the various dynamic behaviors of flexible DC energy routers in actual operation, and often have problems such as response lag and insufficient safety assessment accuracy. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a dynamic safety assessment method and system for a flexible DC energy router to solve at least one of the above technical problems.

[0004] To achieve the above object, a dynamic safety assessment method for a flexible DC energy router comprises the following steps:

[0005] Step S1: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connecting them to establish a DC router operation sensor monitoring network; performing real-time monitoring of the operation of the flexible DC energy router through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, real-time data of DC operation voltage, real-time data of DC operation temperature and real-time data of DC operation power;

[0006] Step S2: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network; using the central data processing unit to perform device-level risk factor evaluation and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to obtain DC device-level risk factors, wherein the DC device-level risk factors include current and voltage overload fluctuations, device temperature overheating and device operating power load;

[0007] Step S3: obtaining the DC router system configuration status data and the DC router circuit network status data, and performing system-level and network-level risk factor evaluation and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities;

[0008] Step S4: Use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC device-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; adjust the safety risk warning response of the flexible DC energy router based on the DC router safety risk assessment index, generate a DC router safety risk warning adjustment strategy, and execute corresponding DC router operation risk warning adjustment measures.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: acquiring historical heat distribution data of the DC router and historical current distribution data of the DC router, and performing key point mining and analysis on the flexible DC energy router based on the historical heat distribution data of the DC router and the historical current distribution data of the DC router to obtain key points of the DC router;

[0011] Step S12: performing a sensor deployment planning analysis on the flexible DC energy router according to the key points of the DC router to generate a sensor deployment plan for the key points of the DC router;

[0012] Step S13: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan and connecting them to establish a DC router operation sensor monitoring network;

[0013] Step S14: Real-time monitoring of the operation of the flexible DC energy router is performed through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power.

[0014] Furthermore, step S11 includes the following steps:

[0015] Obtain historical thermal distribution data of the DC router and historical current distribution data of the DC router;

[0016] Based on the historical thermal distribution data of the DC router, the thermal effect analysis of the flexible DC energy router is carried out to obtain the thermal effect characteristic data of the DC router structure.

[0017] Based on the DC router structure thermal effect characteristic data, the electromagnetic field strength evaluation and analysis is performed on the DC router historical current distribution data to obtain the DC router electromagnetic field strength distribution data;

[0018] According to the electromagnetic field intensity distribution data of the DC router, the flexible DC energy router is divided into electrical reliability areas to obtain the DC router electrical reliability areas; the DC router electrical reliability areas are subdivided by dynamic load response test to obtain the DC router dynamic load response partitions;

[0019] Based on the dynamic load response partitioning of the DC router, the key points of the flexible DC energy router are mined and analyzed to obtain the key points of the DC router.

[0020] Furthermore, the subdivision of the dynamic load response test of the DC router electrical reliability area includes the following steps:

[0021] Perform load benchmark measurement on each regional point within the electrical reliability area of ​​the DC router to obtain benchmark load data at each regional point within the electrical reliability area;

[0022] Perform load response spectrum analysis on the benchmark load data at each regional point in the electrical reliability area to obtain the load response signal spectrum characteristic data at each regional point;

[0023] Perform load response fluctuation frequency analysis on the load response signal spectrum characteristic data at each regional point to obtain the load response signal fluctuation frequency value at each regional point; draw a frequency over-threshold hotspot map for each regional point in the electrical reliability area of ​​the DC router based on the load response signal fluctuation frequency value at each regional point to generate a regional load response fluctuation frequency over-threshold hotspot distribution map;

[0024] The point distance between each fluctuation frequency exceeding threshold hot spot in the regional load response fluctuation frequency exceeding threshold hot spot distribution map is measured to obtain the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map;

[0025] According to the preset point distance threshold, the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map is compared and judged. When the point distance value is less than or equal to the preset point distance threshold, the corresponding fluctuation frequency exceeding threshold hot spots are aggregated into the same hot spot set to obtain the regional load response fluctuation frequency exceeding threshold hot spot set; when the point distance value is greater than the preset point distance threshold, no processing is performed;

[0026] Each fluctuation frequency exceeding threshold hotspot in the regional load response fluctuation frequency exceeding threshold hotspot set is connected by point area to obtain the dynamic load response partition of the DC router.

[0027] Further, step S13 includes the following steps:

[0028] Step S131: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan to obtain DC router operation sensor monitoring nodes;

[0029] Step S132: performing monitoring range evaluation and analysis on sensors in each DC router operation sensor monitoring node to obtain the sensor monitoring range size of each DC operation sensor monitoring node;

[0030] Step S133: optimizing the deployment position of each DC router operation sensor monitoring node based on the sensor monitoring range of each DC operation sensor monitoring node, and obtaining each DC router operation monitoring interval optimization node;

[0031] Step S134: Use the network transmission protocol to establish a network connection between the DC router operation monitoring interval optimization nodes to establish a DC router operation sensor monitoring network.

[0032] Further, step S2 includes the following steps:

[0033] Step S21: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network;

[0034] Step S22: using the central data processing unit to preprocess and standardize the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power, to obtain standard data of DC operating current, standard data of DC operating voltage, standard data of DC operating temperature, and standard data of DC operating power;

[0035] Step S23: evaluating and analyzing the current and voltage overload risk factors of the DC operating current standard data and the DC operating voltage standard data to obtain current and voltage overload fluctuations;

[0036] Step S24: performing overheat risk factor evaluation and analysis on the DC operating temperature standard data according to a preset DC device temperature overheat calibration threshold value to obtain device temperature overheat;

[0037] Step S25: Perform statistical analysis on the power peak and fluctuation range of the DC operating power standard data to obtain the DC operating power peak and the DC operating power fluctuation range; perform long-term change trend analysis on the DC operating power standard data based on the DC operating power peak and the DC operating power fluctuation range to obtain the DC operating power long-term change trend data; perform power load risk factor assessment and analysis on the DC operating power standard data based on the DC operating power long-term change trend data to obtain the equipment operating power load.

[0038] Further, step S23 includes the following steps:

[0039] Step S231: performing time series synchronization processing on the DC operating current standard data and the DC operating voltage standard data to obtain the DC operating current data and the DC operating voltage data in the same time series dimension;

[0040] Step S232: Draw an operation change hyperbola for the DC operation current data and the DC operation voltage data in the same time series dimension to generate a DC operation current-voltage change hyperbola;

[0041] Step S233: performing a statistical analysis of the fluctuation amplitude at each time point in the DC operating current-voltage variation hyperbola to obtain the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point; performing arithmetic average calculation on the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point to obtain the average DC operating current fluctuation amplitude and the average DC operating voltage fluctuation amplitude;

[0042] Step S234: performing current-voltage calibration processing on the DC operation current-voltage variation hyperbola according to the average amplitude of the DC operation current fluctuation and the average amplitude of the DC operation voltage fluctuation to obtain a DC operation current fluctuation calibration line and a DC operation voltage fluctuation calibration line;

[0043] Step S235: Based on the DC operating current fluctuation calibration line and the DC operating voltage fluctuation calibration line, the DC operating current-voltage change hyperbola is evaluated for current and voltage overload risk. When it is determined that the current in the DC operating current-voltage change hyperbola exceeds the DC operating current fluctuation calibration line or the voltage exceeds the DC operating voltage fluctuation calibration line, the corresponding DC equipment risk factor is evaluated and determined as current and voltage overload fluctuation, otherwise no processing is performed.

[0044] Further, step S3 includes the following steps:

[0045] Step S31: obtaining DC router system configuration status data and DC router circuit network status data;

[0046] Step S32: performing system-level vulnerability risk factor assessment and analysis on the DC router system configuration status data to obtain DC system-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities;

[0047] Step S33: performing circuit network topology feature extraction processing on the DC router circuit network status data to obtain DC router circuit network topology feature data;

[0048] Step S34: performing circuit network transmission attack topology analysis on the DC router circuit network status data based on the DC router circuit network topology feature data to generate a DC router circuit network transmission attack topology surface;

[0049] Step S35: Perform network-level risk factor assessment and analysis on the DC router circuit network transmission attack topology surface to obtain DC network-level risk factors, wherein the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities.

[0050] Further, step S4 includes the following steps:

[0051] Step S41: using a dynamic Bayesian network to perform dynamic dependency structure modeling on DC device-level risk factors and DC system-level risk factors to obtain a DC device-system-level risk factor dependency structure;

[0052] Step S42: Based on the DC device-system-level risk factor dependency structure, network-level dependency extension modeling is performed on the DC network-level risk factors to obtain a multi-level extension structure of DC device-system-network-level risk factor dependency;

[0053] Step S43: performing statistical analysis on the time series change probability and impact degree of each risk factor with dependency relationship in the multi-level extended structure of DC equipment-system-network level risk factors, and obtaining the time series risk change probability and risk impact degree of each risk factor with dependency relationship;

[0054] Step S44: performing a multi-level security risk assessment calculation on each risk factor with a dependency relationship in the multi-level extension structure of the DC device-system-network level risk factor dependency based on the probability of a time series risk change and the risk impact degree of each risk factor with a dependency relationship, so as to obtain a DC router security risk assessment index;

[0055] Step S45: Based on the DC router safety risk assessment index, the safety risk warning response of the flexible DC energy router is adjusted to generate a DC router safety risk warning adjustment strategy to execute corresponding DC router operation risk warning adjustment measures.

[0056] Furthermore, the present invention also provides a dynamic safety assessment system for a flexible DC energy router, which is used to execute the dynamic safety assessment method for a flexible DC energy router as described above. The dynamic safety assessment system for a flexible DC energy router includes:

[0057] The DC equipment operation real-time monitoring module is used to deploy current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connect them to establish a DC router operation sensor monitoring network; the flexible DC energy router is monitored in real time through the DC router operation sensor monitoring network, so as to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power;

[0058] A DC equipment-level risk factor assessment module is used to transmit the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to a central data processing unit through a preset data communication network; the central data processing unit is used to perform equipment-level risk factor assessment and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to obtain DC equipment-level risk factors, wherein the DC equipment-level risk factors include current and voltage overload fluctuations, equipment temperature overheating, and equipment operating power load;

[0059] A DC system-level and network-level risk factor assessment module is used to obtain DC router system configuration status data and DC router circuit network status data, and to perform system-level and network-level risk factor assessment and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities;

[0060] The DC router safety risk warning adjustment module is used to use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC equipment-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; based on the DC router safety risk assessment index, the module adjusts the safety risk warning response of the flexible DC energy router, generates a DC router safety risk warning adjustment strategy, and executes corresponding DC router operation risk warning adjustment measures.

[0061] The beneficial effects of the present invention are:

[0062] First, the present invention deploys current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connects them to establish a DC router operation sensor monitoring network. The deployment of these sensors is to achieve comprehensive monitoring of the equipment operation status, wherein the current sensor is used to monitor the magnitude and change of the current in real time, the voltage sensor is used to detect the stability and fluctuation of the voltage, the temperature sensor is responsible for monitoring the thermal state of the equipment to prevent overheating, and the power sensor provides analysis of power usage. These sensors are interconnected by establishing a DC router operation sensor monitoring network to form a complete data acquisition and transmission network. Through this network, various types of data can be collected and transmitted to the monitoring center in real time, providing real-time basis for the analysis of the equipment operation status. The establishment of this network can ensure the timely discovery of potential problems, optimize the operation performance, and provide detailed data support for maintenance and overhaul, so as to comprehensively monitor the various dynamic behaviors of the flexible DC energy router in actual operation. At the same time, the flexible DC energy router is monitored in real time by using the DC router operation sensor monitoring network. This step can fully understand the operating status of the equipment by collecting real-time data on current, voltage, temperature and power. These real-time data are crucial for timely detection of abnormal conditions in equipment operation, such as excessive current and excessive temperature. These are early warning signals of equipment failure. Through real-time monitoring and analysis of data, measures can be taken quickly to prevent potential equipment failures or performance degradation, thereby improving the reliability and stability of the router.

[0063] Secondly, by transmitting the real-time data of DC operating current, DC operating voltage, DC operating temperature and DC operating power to the central data processing unit through the preset data communication network, the efficiency and reliability of the monitoring and management process can be significantly improved. Real-time data transmission ensures the instant grasp of the router status, so that the data can be transmitted to the central processing unit for analysis in the shortest time. This real-time performance is crucial because it enables operators to respond quickly to any abnormal situation in the router, thereby reducing potential risks and damage. By using the central data processing unit to conduct device-level risk factor assessment and analysis on the real-time data of DC operating current, DC operating voltage, DC operating temperature and DC operating power, risk factors such as current and voltage overload fluctuations, equipment temperature overheating and equipment operating power load can be effectively assessed and analyzed. In this way, it is possible to better monitor whether there is a current and voltage overload problem, determine whether there is a risk of overheating of the equipment, whether the power load is too high and other router equipment failures, optimize the load management of the router, and thus improve the operating efficiency and reliability of the DC router equipment.

[0064] Then, by obtaining the DC router system configuration status data and the DC router circuit network status data, it can provide basic data support for subsequent risk assessment and analysis. By obtaining the DC router system configuration status data, we can understand the key information of the system such as hardware configuration, software version, system settings, user permissions, etc. in detail. These data are essential for identifying potential configuration vulnerabilities, unauthorized access and fault recovery problems of the DC router system. At the same time, obtaining the circuit network status data can provide information about network topology, connection devices, link quality, etc. These data help to understand the working mode of the circuit network in the router and its possible vulnerabilities, thus laying the foundation for the circuit network transmission attack topology analysis. This step ensures that the subsequent analysis has high-quality and accurate data support, thereby improving the effectiveness of the overall risk assessment. By conducting a system-level vulnerability risk factor assessment and analysis on the DC router system configuration status data, a detailed risk analysis can be provided for the router system security. This step can identify vulnerabilities in the operating system, potential risks of unauthorized access, and problems in the fault recovery process through the analysis of the system configuration status data. Among them, operating system vulnerabilities can cause the system to be attacked or function abnormally, unauthorized access vulnerabilities allow illegal users to obtain sensitive information or control the system, and fault recovery vulnerabilities affect the system's recovery capabilities. By conducting a network-level risk factor assessment and analysis on the DC router circuit network status data, the security of the circuit network can be comprehensively assessed and the existing network-level risks can be revealed. By analyzing the circuit network transmission attacks, risk factors such as circuit network attacks, circuit network transmission security issues, and vulnerabilities of network connection devices can be identified. This assessment helps understand the vulnerability of the network in the face of various attacks, including data leakage, service interruption, or network performance degradation caused by attacks. By identifying these risk factors, corresponding defense strategies and improvement measures can be formulated to strengthen the overall security of the network and improve the ability to resist attacks. This step ensures the scientificity and effectiveness of network risk management and helps maintain the stability and security of the network.

[0065] Finally, by using the dynamic Bayesian network to perform multi-level security risk assessment calculations on DC device-level risk factors, DC system-level risk factors, and DC network-level risk factors, a detailed security risk assessment index can be provided for the DC router. This step comprehensively considers the risk factors at the equipment, system, and network levels. Through multi-level assessment, a panoramic view of security risks is provided. The risk assessment calculation helps identify the most critical risk points and potential threats in the router, thereby providing a basis for formulating targeted risk management strategies. Through this multi-level assessment, the ability to predict and respond to risks in complex DC network systems can be improved, resource allocation can be optimized, the system's security protection level can be enhanced, and ultimately the safety and reliability of DC routers can be improved. In addition, the security risk warning response adjustment of the flexible DC energy router is performed based on the DC router security risk assessment index, and the corresponding warning adjustment strategy is generated. The core of this step is to convert the assessment results into actual operational measures. Through the early warning system and adjustment strategy, potential security risks can be monitored and responded to in real time. By dynamically adjusting the operation strategy, preventive measures can be taken before risks occur to reduce router downtime and losses. This step ensures that the DC router can be adjusted quickly and effectively when facing various security threats, thereby enhancing the router's emergency response capabilities and continuous operation capabilities. This proactive risk management measure improves the safety of the DC router, reduces the probability of potential safety accidents, and ensures the stable and efficient operation of the DC energy system, thereby improving the accuracy of the DC router's safety response and evaluation analysis.

[0066] The dynamic safety assessment system of the flexible DC energy router proposed in the present invention is generally composed of a DC equipment operation real-time monitoring module, a DC equipment-level risk factor assessment module, a DC system-level and network-level risk factor assessment module, and a DC router safety risk early warning adjustment module. It can implement the dynamic safety assessment method of any flexible DC energy router described in the present invention, and is used to combine the operations between computer programs running on each module to implement the dynamic safety assessment method of the flexible DC energy router. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient dynamic safety assessment process of the flexible DC energy router, thereby simplifying the operation process of the dynamic safety assessment system of the flexible DC energy router. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0068] Figure 1 It is a schematic diagram of the steps of the dynamic safety assessment method of the flexible DC energy router of the present invention;

[0069] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0070] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0071] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0073] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figures 1 to 3 The present invention provides a dynamic safety assessment method for a flexible DC energy router, the method comprising the following steps:

[0075] Step S1: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connecting them to establish a DC router operation sensor monitoring network; performing real-time monitoring of the operation of the flexible DC energy router through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, real-time data of DC operation voltage, real-time data of DC operation temperature and real-time data of DC operation power;

[0076] Step S2: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network; using the central data processing unit to perform device-level risk factor evaluation and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to obtain DC device-level risk factors, wherein the DC device-level risk factors include current and voltage overload fluctuations, device temperature overheating and device operating power load;

[0077] Step S3: obtaining the DC router system configuration status data and the DC router circuit network status data, and performing system-level and network-level risk factor evaluation and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities;

[0078] Step S4: Use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC device-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; adjust the safety risk warning response of the flexible DC energy router based on the DC router safety risk assessment index, generate a DC router safety risk warning adjustment strategy, and execute corresponding DC router operation risk warning adjustment measures.

[0079] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic flow chart of the steps of the dynamic safety assessment method of the flexible DC energy router of the present invention. In this example, the dynamic safety assessment method of the flexible DC energy router includes the following steps:

[0080] Step S1: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connecting them to establish a DC router operation sensor monitoring network; performing real-time monitoring of the operation of the flexible DC energy router through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, real-time data of DC operation voltage, real-time data of DC operation temperature and real-time data of DC operation power;

[0081] In an embodiment of the present invention, historical heat distribution data and current distribution data of the DC router are obtained from the historical record database. These data can be collected by temperature sensors and current sensors during the operation of the device to ensure that the data is representative. Subsequently, these historical data are analyzed in detail using data analysis tools (such as data analysis libraries in MATLAB or Python), and data mining algorithms (such as cluster analysis or principal component analysis) are applied to identify key points of the flexible DC energy router. These key points refer to the areas where the heat and current distribution in the flexible DC energy router are most concentrated or change most significantly. The flexible DC energy router is planned and analyzed for sensor deployment by combining the key points determined by previous mining, so as to determine the optimal arrangement of sensors based on the previously identified key points using computer-aided design (CAD) software for simulation. Deployment location, deployment planning includes selecting appropriate sensor types, such as current sensors, voltage sensors, temperature sensors, and power sensors, and considering their installation requirements at key points (such as sensor size, fixing method, etc.) to generate a detailed sensor deployment plan, which includes the specific location, installation angle, and connection line of each sensor. At the same time, by using the previously generated sensor deployment plan, the actual sensor installation is performed at the key points of the DC router. During the installation process, first prepare and fix the current sensor, voltage sensor, temperature sensor, and power sensor at the specified location to ensure that they are compatible and firm with the interface of the device. According to the connection requirements in the deployment plan, the sensors are connected to the central processing unit through a dedicated wiring system to ensure that all sensors can stably transmit data to the monitoring network, thereby connecting and establishing the DC router operation sensor monitoring network. Then, the flexible DC energy router is monitored in real time by using the DC router operation sensor monitoring network established by the previous connection, so as to obtain the real-time monitoring data of the current sensor, voltage sensor, temperature sensor and power sensor. These data are transmitted to the data analysis platform through the network. On this platform, the specific values ​​of DC operating current, voltage, temperature and power can be displayed in real time, and data analysis and visualization can be performed to track the equipment operation status in real time and warn of faults, so as to ensure the stable and safe operation of the equipment, and finally obtain the real-time data of DC operating current, DC operating voltage, DC operating temperature and DC operating power.

[0082] Step S2: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network; using the central data processing unit to perform device-level risk factor evaluation and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to obtain DC device-level risk factors, wherein the DC device-level risk factors include current and voltage overload fluctuations, device temperature overheating and device operating power load;

[0083] In an embodiment of the present invention, the real-time data of the DC operating current, DC operating voltage, DC operating temperature and DC operating power previously monitored in real time are sent to a central data processing unit by using a preset data communication network. Specifically, industrial Ethernet or wireless communication technology (such as Wi-Fi or Zigbee) is used for data transmission, and the format and structure of the data packet are set to ensure the integrity and real-time performance of the data during the transmission process. In this process, a data acquisition terminal can be used to capture the real-time data at a high sampling frequency, and the data is converted into a transmittable data format, which is then sent to the central processing unit through the network for subsequent processing. At the same time, the previously received real-time data of DC operating current, real-time data of DC operating voltage, real-time data of DC operating temperature and real-time data of DC operating power are preprocessed and standardized by using a central data processing unit, so as to remove noise and outliers in the data by using a data preprocessing algorithm, and normalize the data by means of statistical methods such as mean and variance. In this process, tools such as Python or Matlab are used to achieve effective cleaning and standardization of the data, and the overload risk is evaluated by using the DC operating current real-time data and DC operating voltage real-time data obtained after standardization. In specific operations, by setting the calibration thresholds of current and voltage, and by using statistical analysis tools (such as the analysis toolkit in R language or Excel), the fluctuation of standard data is calculated, so as to evaluate and analyze the risk factor indicators of current and voltage overload fluctuations. By setting a reasonable equipment temperature overheating calibration threshold and using judgment logic in the central data processing unit to compare the standardized temperature data with the preset threshold, the overheating risk assessment is completed. If the temperature exceeds the threshold, it is marked as an overheating risk, and the data recording and alarm system will be activated to ensure timely processing. At the same time, the temperature curve chart can be used to show the impact of historical temperature fluctuations on overheating risks for further analysis and prevention, thereby evaluating and analyzing the risk factor indicators of equipment temperature overheating.Then, by using data statistics tools (such as the Pandas library in Python), the power peak and fluctuation range of the DC operating power standard data obtained after standardization are statistically analyzed to statistically analyze the corresponding operating power peak and fluctuation range, and the size of the fluctuation range is identified by collecting peak data and calculating the standard deviation. In addition, by combining the operating power peak and operating power fluctuation range obtained in the previous analysis, a longer time period is selected to analyze the corresponding DC operating power standard data, so as to use the time series analysis method to organize and model the DC operating power standard data, thereby realizing the stationarity test of the time series and ensuring that the data can be effectively analyzed. The autoregressive integrated sliding average is also used. The ARIMA model is used to model the DC operating power standard data, analyze its long-term change trend, and extract potential cyclical and seasonal fluctuations. Finally, the corresponding DC operating power is evaluated for load risk by combining the long-term cyclical and seasonal change trend data obtained in the previous analysis. It is necessary to conduct in-depth analysis on the correlation and causal relationship of power data, use the load forecasting model, combine the operating power data with environmental variables (such as temperature, humidity, etc.), conduct multivariate regression analysis, and identify the key factors affecting the power load, thereby forming a comprehensive power load risk assessment report to help decision makers formulate targeted response strategies to ensure that the equipment operates under a safe load, and finally obtain the risk factor assessment results of the equipment operating power load.

[0084] Step S3: obtaining the DC router system configuration status data and the DC router circuit network status data, and performing system-level and network-level risk factor evaluation and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities;

[0085] In an embodiment of the present invention, a specific network monitoring tool is used to obtain the system configuration status of the DC router, including key information such as the system hardware configuration, software version, system settings, and user permissions, thereby obtaining the DC router system configuration status data. At the same time, the circuit network status of the DC router is collected by using a current monitoring instrument to provide information about the network topology, connection equipment, link quality, etc., thereby obtaining the DC router circuit network status data. Subsequently, the collected DC router system configuration status data is evaluated for system-level vulnerability risk factors by using a corresponding security assessment tool. This process uses vulnerability scanning software to perform a comprehensive scan of the operating system and its related applications to identify potential operating system vulnerabilities. At the same time, the user access rights are analyzed through the access control audit tool to identify the risk of unauthorized access. In addition, the backup and recovery process is simulated and tested by using a fault recovery analysis tool to ensure that the fault recovery vulnerability is evaluated, thereby obtaining the DC system-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities. In addition, the network traffic analyzer is used to extract the network topology features of the previously acquired DC router circuit network status data to extract the corresponding network topology connection feature data, including the connection relationship between devices, data transmission path and bandwidth usage, so as to show the real-time status of the DC router circuit network, and the data packet generation tool is used to simulate various attack scenarios. By covering and simulating the network topology, possible attack paths and key nodes are marked. Then, the DC router circuit network transmission attack topology generated by the previous connection is evaluated and analyzed at the network level with the help of a network vulnerability scanning tool to analyze and identify the attack risks existing in the circuit network. Subsequently, the attack vector is cross-compared with the actual status of the circuit network to identify the potential threats of circuit network attacks, and the network security monitoring platform is used to evaluate the indicators of circuit network transmission security, such as encryption protocols, data integrity, etc., so as to finally obtain the corresponding DC network-level risk factors, including circuit network attacks, the security status of circuit network transmission and potential vulnerabilities of network connection devices.

[0086] Step S4: Use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC device-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; adjust the safety risk warning response of the flexible DC energy router based on the DC router safety risk assessment index, generate a DC router safety risk warning adjustment strategy, and execute corresponding DC router operation risk warning adjustment measures.

[0087] In an embodiment of the present invention, a dynamic Bayesian network modeling tool (such as a PGM toolkit or similar software) is used to model and analyze the dependencies of various DC device-level risk factors and DC system-level risk factors obtained from previous evaluation and analysis, so as to identify and model the dependencies between various risk factors in DC devices and systems. For example, the failure of a certain device may cause the shutdown of other systems, etc., and an algorithm (such as a Markov chain Monte Carlo method) can be used for inference to obtain the probability distribution of the dynamic dependencies between various risk factors, and the DC network-level risk factor dependencies can be expanded from the established DC device-system-level risk factors. Through a comprehensive analysis of the DC network-level risk factors, the core risk factors that affect the entire network are identified and incorporated into the network-level risk modeling. Graph theory is used to establish a network model to connect various devices and their risks. Factors, set network nodes and edges, represent the mutual dependence relationship between various device-level, system-level and network-level risk factors, further reveal the interactive relationship between DC equipment and the corresponding risk factors at the system and network levels, and form a multi-level risk factor dependency architecture. At the same time, by using the time series analysis method, the time series changes of each risk factor with dependency relationship in the multi-level risk factor dependency architecture previously constructed by connection are statistically analyzed to achieve statistical calculation of the frequency of occurrence of each risk factor in different time periods. The analysis tool can use the pandas and statsmodels libraries in Python to accurately calculate the time series probability of each risk factor, and use regression analysis to determine the degree of influence of each risk factor on the overall safety of the flexible DC energy router, so as to obtain the time series risk change probability and risk impact degree of each risk factor with dependency relationship. Then, by combining the time series risk change probability and risk impact degree obtained by the previous statistical analysis, a weighted algorithm is used to perform a comprehensive assessment and calculation of the safety risk of each risk factor with dependency relationship in the multi-level risk factor dependency architecture, so as to combine the time series change probability and impact degree of different risk factors, and use the formula Calculate the safety risk assessment index of the DC router, where For the dependency The impact of each risk factor, For the dependency The probability of time series change of each risk factor is calculated, so as to quantitatively calculate the DC router safety risk assessment index. In addition, by using a rule engine or a threshold-based trigger mechanism, it is analyzed whether the DC router safety risk assessment index obtained by the previous quantitative calculation exceeds the preset safety risk threshold limit, and the safety risk warning response adjustment strategy of the flexible DC energy router is formulated with early warning response. Based on the evaluation and judgment results, once the early warning condition is triggered (that is, exceeding the preset safety risk threshold limit), a list of response strategies for safety risk warnings is automatically generated, involving real-time adjustment of energy scheduling strategies, increase in the frequency of equipment status monitoring, and corresponding alarm prompts. In addition, the risk warning response plan is tested and drilled regularly to ensure that the corresponding adjustment measures can be accurately and efficiently implemented in actual operations, thereby further improving the safety of the flexible DC energy router, ensuring its normal operation and reducing the impact of potential risks on the router.

[0088] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0089] Step S11: acquiring historical heat distribution data of the DC router and historical current distribution data of the DC router, and performing key point mining and analysis on the flexible DC energy router based on the historical heat distribution data of the DC router and the historical current distribution data of the DC router to obtain key points of the DC router;

[0090] In an embodiment of the present invention, historical heat distribution data and current distribution data of the DC router are obtained from a historical record database. These data can be collected by temperature sensors and current sensors during the operation of the device to ensure that the data is representative. Subsequently, these historical data are analyzed in detail using data analysis tools (such as data analysis libraries in MATLAB or Python), and data mining algorithms (such as cluster analysis or principal component analysis) are applied to identify key points of the flexible DC energy router. These key points refer to the areas in the flexible DC energy router where the heat and current distribution are most concentrated or the changes are most significant. By comparing the data in different areas, the specific locations and distribution characteristics of these points can be determined, and finally the key points of the DC router are obtained.

[0091] Step S12: performing a sensor deployment planning analysis on the flexible DC energy router according to the key points of the DC router to generate a sensor deployment plan for the key points of the DC router;

[0092] In an embodiment of the present invention, a planning analysis of sensor deployment is performed on a flexible DC energy router by combining the key points of the DC router previously mined and determined, so as to use computer-aided design (CAD) software to simulate the layout according to the previously identified key points to determine the optimal deployment position of the sensor. The deployment planning includes selecting appropriate sensor types, such as current sensors, voltage sensors, temperature sensors, and power sensors, and considering their installation requirements at key points (such as sensor size, fixing method, etc.), and also combining actual application needs to finally generate a detailed DC router key point sensor deployment plan, which includes the specific location, installation angle and connection line of each sensor, to ensure that the monitoring network can fully cover all key points.

[0093] Step S13: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan and connecting them to establish a DC router operation sensor monitoring network;

[0094] In an embodiment of the present invention, by using the previously generated sensor deployment plan, actual sensor installation is performed at key points of the DC router. During the installation process, current sensors, voltage sensors, temperature sensors and power sensors are first prepared and fixed at designated locations to ensure that they are compatible and secure with the device interface. Then, in accordance with the connection requirements in the deployment plan, the sensors are connected to the central processing unit through a dedicated wiring system to ensure that all sensors can stably transmit data to the monitoring network. After the installation is completed, a system test is performed to verify the working status of the sensors and the accuracy of data collection to ensure that the sensor monitoring network functions normally. Finally, the connection is established to obtain the DC router operation sensor monitoring network.

[0095] Step S14: Real-time monitoring of the operation of the flexible DC energy router is performed through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power.

[0096] In an embodiment of the present invention, the flexible DC energy router is monitored in real time by using the DC router operation sensor monitoring network established by the previous connection, so as to obtain data monitored in real time by the current sensor, voltage sensor, temperature sensor and power sensor. These data are transmitted to the data analysis platform through the network, and the specific values ​​of the DC operating current, voltage, temperature and power can be displayed in real time on this platform, and data analysis and visualization can be performed to track the equipment operation status in real time and warn of faults, so as to ensure the stable and safe operation of the equipment, and finally obtain the real-time data of DC operating current, DC operating voltage, DC operating temperature and DC operating power.

[0097] Furthermore, step S11 includes the following steps:

[0098] Obtain historical thermal distribution data of the DC router and historical current distribution data of the DC router;

[0099] In an embodiment of the present invention, by extracting and recording the historical thermal distribution and the historical current operation distribution of the DC router from the historical record database, the distribution of temperature changes and current changes of the DC router under various historical working conditions is obtained, and finally the historical thermal distribution data of the DC router and the historical current distribution data of the DC router are obtained.

[0100] Preferably, the thermal effect analysis of the flexible DC energy router is performed based on the historical thermal distribution data of the DC router to obtain the thermal effect characteristic data of the DC router structure;

[0101] In an embodiment of the present invention, the thermal effect analysis of the device structure of the corresponding flexible DC energy router is performed by using the previously acquired historical thermal distribution data of the DC router, so as to import the historical thermal distribution data into thermal analysis software, such as ANSYS or COMSOL Multiphysics. These software can perform heat conduction and thermal stress analysis. By inputting the structural model of the router and the thermal distribution data, the software will simulate the thermal effect of the device under different load conditions. The process includes a detailed analysis of the thermal stress, thermal expansion and temperature gradient of each part of the device, and finally obtains the thermal effect characteristic data of the DC router structure. These data will include thermal stress distribution, thermal expansion coefficient and existing thermal failure areas.

[0102] Preferably, based on the DC router structure thermal effect characteristic data, the DC router historical current distribution data is evaluated and analyzed for electromagnetic field strength to obtain the DC router electromagnetic field strength distribution data;

[0103] In an embodiment of the present invention, the obtained DC router structural thermal effect characteristic data is input into an electromagnetic field analysis software, such as HFSS or CST Studio. The software uses these data to analyze the strength of the DC router current magnetic field generated by simulating the historical current distribution data. During the analysis process, the input includes the structural thermal effect characteristics and the historical current distribution data. The electromagnetic field simulation function of the software is used to calculate the influence of temperature changes on the electromagnetic field, and finally the electromagnetic field strength distribution data of the DC router is obtained, which includes the electromagnetic field strength in different areas, the field distribution law, and the existing electromagnetic interference areas.

[0104] Preferably, the flexible DC energy router is divided into electrical reliability regions according to the DC router electromagnetic field intensity distribution data to obtain the DC router electrical reliability regions; the DC router electrical reliability regions are subdivided by dynamic load response test to obtain the DC router dynamic load response partitions;

[0105] In an embodiment of the present invention, the electrical reliability area of ​​the corresponding flexible DC energy router is divided by combining the electromagnetic field intensity distribution data of the DC router obtained by the previous analysis, so as to import the electromagnetic field intensity distribution data into the electrical reliability analysis tool, such as Simulink of MATLAB or other special electrical reliability analysis software. Through the analysis software, the area with strong electrical performance reliability can be identified according to the electromagnetic field intensity data, that is, the area with more electromagnetic field intensity distribution, so as to divide the electrical reliability area of ​​the DC router. At the same time, by performing a dynamic load response test based on the divided electrical reliability area, loading the test equipment to simulate different load conditions, monitoring and recording the performance of the DC router under the dynamic load, and screening out the area with strong load response whose performance score exceeds the threshold, so as to subdivide the dynamic load response area. These test data are used to further determine the actual performance of the electrical reliability area, ensure that the load response characteristics of each area are fully understood, and finally obtain the dynamic load response partition of the DC router.

[0106] Preferably, key points of the flexible DC energy router are mined and analyzed based on the dynamic load response partitioning of the DC router to obtain the key points of the DC router.

[0107] In an embodiment of the present invention, the corresponding flexible DC energy router is matched with the dynamic load response partition of the DC router obtained by combining the previous subdivision, and the most critical points of the corresponding load response partition in the flexible DC energy router under load conditions are identified through statistical analysis and data mining methods, such as cluster analysis or principal component analysis. These key points usually refer to those areas with significant effects under dynamic loads, such as the parts with the most drastic temperature rise or the largest changes in electromagnetic field intensity, and finally the key points of the DC router are mined.

[0108] Furthermore, the subdivision of the dynamic load response test of the DC router electrical reliability area includes the following steps:

[0109] Perform load benchmark measurement on each regional point within the electrical reliability area of ​​the DC router to obtain benchmark load data at each regional point within the electrical reliability area;

[0110] In an embodiment of the present invention, a load benchmark is measured and processed for each regional point in the electrical reliability area of ​​the DC router obtained by the previous analysis and division. In the specific implementation process, a high-precision load test instrument (for example, a digital multimeter or a load tester) is connected to each regional point in the DC router. The instrument should be set to measure DC current and voltage to obtain electrical load data of each point. At the same time, after each measurement, the current value and voltage value are recorded, and the load power is calculated, and finally the benchmark load data at each regional point in the electrical reliability area is obtained. These benchmark data will serve as the basis for subsequent analysis to ensure that the measurement is accurate and covers all key points.

[0111] Preferably, a load response spectrum analysis is performed on the reference load data at each regional point in the electrical reliability area to obtain the load response signal spectrum characteristic data at each regional point;

[0112] In an embodiment of the present invention, a spectrum conversion process of load response is performed on the benchmark load data at each regional point in the electrical reliability area previously measured by using a spectrum analysis method, so that the time domain signal of the corresponding load data is converted into a frequency domain signal by methods such as Fourier transform, thereby displaying the spectrum characteristic data of the load response signal at each point, including the amplitude and phase information of each frequency component. These spectrum characteristic data can reveal the frequency characteristics and their intensity in the load signal, help identify the load characteristics of different points, and finally obtain the spectrum characteristic data of the load response signal at each regional point.

[0113] Preferably, a load response fluctuation frequency analysis is performed on the load response signal spectrum characteristic data at each regional point to obtain the load response signal fluctuation frequency value at each regional point; based on the load response signal fluctuation frequency value at each regional point, a frequency over-threshold hotspot map is drawn for each regional point in the electrical reliability area of ​​the DC router to generate a regional load response fluctuation frequency over-threshold hotspot distribution map;

[0114] In an embodiment of the present invention, a fluctuation analysis tool (such as fast Fourier transform (FFT) or other frequency analysis methods) is used to perform a statistical analysis of the fluctuation frequency of the load response signal spectrum characteristic data at each regional point obtained by the previous analysis, so as to statistically analyze the corresponding signal fluctuation frequency value, and record the frequency fluctuation of each point, so as to obtain the load response signal fluctuation frequency value at each regional point. At the same time, the corresponding load response signal fluctuation frequency value is compared and judged by using a pre-set load response fluctuation frequency threshold, and the regional points corresponding to the fluctuation frequency threshold are marked as hot spots in the figure according to the set frequency threshold, so as to draw the corresponding frequency over-threshold hot spot map, which can clearly show the hot spots of the load response, and finally draw a regional load response fluctuation frequency over-threshold hot spot distribution map.

[0115] Preferably, the point distance between each fluctuation frequency exceeding threshold hot spot in the regional load response fluctuation frequency exceeding threshold hot spot distribution map is measured to obtain the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map;

[0116] In an embodiment of the present invention, a laser rangefinder or a high-precision electronic rangefinder is used to measure the point distance between each fluctuation frequency super-threshold hotspot in the previously drawn regional load response fluctuation frequency super-threshold hotspot distribution map, so as to accurately measure the actual distance between each hotspot, and record the distance value between each pair of hotspots. These distance values ​​can be used to determine the relative position relationship between the hotspots, and finally measure the point distance value between each fluctuation frequency super-threshold hotspot in the hotspot distribution map.

[0117] Preferably, the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map is compared and judged according to the preset point distance threshold. When the point distance value is less than or equal to the preset point distance threshold, the corresponding fluctuation frequency exceeding threshold hot spots are aggregated into the same hot spot set to obtain the regional load response fluctuation frequency exceeding threshold hot spot set; when the point distance value is greater than the preset point distance threshold, no processing is performed;

[0118] In an embodiment of the present invention, a pre-set point distance threshold is used to compare and judge the point distance values ​​between each fluctuation frequency exceeding threshold hotspot in the hotspot distribution map obtained by the previous quantitative calculation, so as to set a specific distance threshold (for example, 5 meters) and compare the distance value of each pair of hotspots. If the distance value between a pair of hotspots is less than or equal to the preset threshold, the two hotspots are summarized in the same hotspot set. This process can be implemented by programming, using an automated algorithm to process distance data and generate a hotspot set. For hotspots with distance values ​​greater than the threshold, no aggregation processing is performed. In this way, the obtained hotspot set can reflect the concentrated area and distribution of load response in the region, and finally obtain the regional load response fluctuation frequency exceeding threshold hotspot set.

[0119] Preferably, each fluctuation frequency exceeding threshold hotspot in the regional load response fluctuation frequency exceeding threshold hotspot set is connected by point area to obtain the dynamic load response partition of the DC router.

[0120] In an embodiment of the present invention, point areas are connected between each fluctuation frequency exceeding threshold hotspot in the previously summarized regional load response fluctuation frequency exceeding threshold hotspot set to determine the connection relationship between the hotspots, and the aggregated hotspot set is connected and drawn into a regional partition map, and the final dynamic load response partition map is generated through a visualization tool. The map can display the boundaries and ranges of each load response area, and finally the dynamic load response partition of the DC router is obtained.

[0121] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0122] Step S131: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan to obtain DC router operation sensor monitoring nodes;

[0123] In an embodiment of the present invention, the key points of the DC router are determined according to the key point sensor deployment plan of the DC router generated according to the previous design. These key points are usually the electrical interface of the equipment, the vicinity of the heat sink or the load connection point, and for each key point, according to the sensing requirements of current, voltage, temperature and power, current sensors, voltage sensors, temperature sensors and power sensors are installed at these positions respectively, wherein the current sensor is used to monitor the change of current in real time; the voltage sensor is used to detect the voltage level; the temperature sensor is used to monitor the temperature of the equipment; the power sensor is used to calculate the system power load, and the layout of all sensors should follow the equipment structure design to ensure that the data of each key point can be accurately obtained, and finally the DC router operation sensor monitoring node is obtained.

[0124] Step S132: performing monitoring range evaluation and analysis on sensors in each DC router operation sensor monitoring node to obtain the sensor monitoring range size of each DC operation sensor monitoring node;

[0125] In an embodiment of the present invention, the monitoring range of the sensors installed in each DC router operation sensor monitoring node is evaluated and analyzed, and each sensor is tested using professional calibration equipment and data acquisition tools to determine its effective measurement range. For example, the current sensor is tested using a standard current source, and the difference between its output data and the actual value is recorded; a similar test is performed on the voltage sensor to ensure that it can accurately capture the required voltage range. The evaluation results will be used to determine the monitoring range size of each sensor, that is, the area or range that the sensor can effectively cover, and finally the sensor monitoring range size of each DC operation sensor monitoring node is obtained.

[0126] Step S133: optimizing the deployment position of each DC router operation sensor monitoring node based on the sensor monitoring range of each DC operation sensor monitoring node, and obtaining each DC router operation monitoring interval optimization node;

[0127] In an embodiment of the present invention, the deployment position of each DC router operation sensor monitoring node is optimized by combining the sensor monitoring range size of each DC operation sensor monitoring node obtained by previous monitoring, so as to calculate the best sensor optimization position by using an optimization algorithm or a calculation model and combining the coverage range of each sensor to avoid overlapping or blind spots in the monitoring range. For example, different sensor layout schemes are simulated by simulation software, and their monitoring effects are analyzed to select the location configuration with the widest coverage and the least data redundancy, and optimize the layout of each monitoring node to ensure that all key points can be effectively monitored, and finally optimize the arrangement to obtain the operation monitoring gap optimization nodes of each DC router.

[0128] Step S134: Use the network transmission protocol to establish a network connection between the DC router operation monitoring interval optimization nodes to establish a DC router operation sensor monitoring network.

[0129] In an embodiment of the present invention, by selecting a suitable network transmission protocol, the optimized DC router operation monitoring gap nodes are connected to the network, so as to ensure that each sensor node can be connected to each other through the network by configuring network communication equipment such as switches and routers, and by using network protocols such as TCP / IP or Modbus protocol to transmit the real-time monitoring data in each node, to ensure the stable and accurate transmission of sensor data, configure the data transmission path and transmission frequency, to ensure that the monitoring data can be transmitted to the central processing system in real time for analysis, and finally, by using network monitoring tools to verify the connection status and data transmission effect, a stable and efficient DC router operation sensor monitoring network is established.

[0130] Further, step S2 includes the following steps:

[0131] Step S21: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network;

[0132] In an embodiment of the present invention, the real-time data of the DC operating current, DC operating voltage, DC operating temperature and DC operating power previously monitored in real time are sent to a central data processing unit by using a preset data communication network. Specifically, industrial Ethernet or wireless communication technology (such as Wi-Fi or Zigbee) is used for data transmission, and the format and structure of the data packet are set to ensure the integrity and real-time performance of the data during the transmission process. In this process, a data acquisition terminal can be used to capture the real-time data at a high sampling frequency, and the data is converted into a transmittable data format, which is then sent to the central processing unit through the network for subsequent processing.

[0133] Step S22: using the central data processing unit to preprocess and standardize the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power, to obtain standard data of DC operating current, standard data of DC operating voltage, standard data of DC operating temperature, and standard data of DC operating power;

[0134] In an embodiment of the present invention, a central data processing unit is used to preprocess and standardize previously received real-time data of DC operating current, real-time data of DC operating voltage, real-time data of DC operating temperature, and real-time data of DC operating power, so as to remove noise and outliers in the data by using a data preprocessing algorithm, and normalize the data by statistical methods such as mean and variance. In this process, tools such as Python or Matlab are used to achieve effective cleaning and standardization of data, and finally obtain standard data of DC operating current, standard data of DC operating voltage, standard data of DC operating temperature, and standard data of DC operating power.

[0135] Step S23: evaluating and analyzing the current and voltage overload risk factors of the DC operating current standard data and the DC operating voltage standard data to obtain current and voltage overload fluctuations;

[0136] In an embodiment of the present invention, an overload risk assessment is performed on the DC operating current standard data and the DC operating voltage standard data obtained after standardization. In a specific operation, the calibration thresholds of the current and voltage are set, and the fluctuation of the standard data is calculated by using a statistical analysis tool (such as an analysis toolkit in R language or Excel), and finally the overload fluctuation index of the current and voltage is evaluated and analyzed.

[0137] Step S24: performing overheat risk factor evaluation and analysis on the DC operating temperature standard data according to a preset DC device temperature overheat calibration threshold value to obtain device temperature overheat;

[0138] In the embodiment of the present invention, a reasonable equipment temperature overheating calibration threshold is set, and the judgment logic is used in the central data processing unit to compare the standardized temperature data with the preset threshold to complete the overheating risk assessment. If the temperature exceeds the threshold, it is marked as an overheating risk, and the data recording and alarm system will be activated to ensure timely processing. At the same time, the temperature curve chart can be used to display the impact of historical temperature fluctuations on the overheating risk for further analysis and prevention. Finally, the risk factor indicators of equipment temperature overheating are evaluated and analyzed.

[0139] Step S25: Perform statistical analysis on the power peak and fluctuation range of the DC operating power standard data to obtain the DC operating power peak and the DC operating power fluctuation range; perform long-term change trend analysis on the DC operating power standard data based on the DC operating power peak and the DC operating power fluctuation range to obtain the DC operating power long-term change trend data; perform power load risk factor assessment and analysis on the DC operating power standard data based on the DC operating power long-term change trend data to obtain the equipment operating power load.

[0140] In an embodiment of the present invention, a statistical analysis of the power peak and fluctuation range of the DC operating power standard data obtained after standardization is performed by using a data statistics tool (such as the Pandas library in Python), so as to statistically analyze the corresponding operating power peak and fluctuation range, and to identify the size of the fluctuation range by collecting peak data and calculating the standard deviation, thereby obtaining the DC operating power peak and the size of the DC operating power fluctuation range. At the same time, by combining the operating power peak and the operating power fluctuation range obtained by the previous analysis, a longer time period is selected to analyze the corresponding DC operating power standard data, so as to use the time series analysis method to organize and model the DC operating power standard data, thereby realizing the stationarity test of the time series, ensuring that the data can be effectively analyzed, and also using the autoregressive integrated moving average model (ARIMA) to model the DC operating power standard data, analyze its long-term change trend, extract potential periodic and seasonal fluctuations, and thereby analyze and obtain the DC operating power long-term change trend data, which is convenient for observing and judging the future change direction of the equipment power. Then, by combining the long-term change trend data obtained from the previous analysis, the corresponding DC operating power is evaluated for load risk. It is necessary to conduct in-depth analysis of the correlation and causal relationship of the power data, use the load forecasting model, combine the operating power data with environmental variables (such as temperature, humidity, etc.), conduct multivariate regression analysis, and identify the key factors affecting the power load, thereby forming a comprehensive power load risk assessment report to help decision makers formulate targeted response strategies to ensure that the equipment operates under a safe load, and finally obtain the risk factor assessment results of the equipment operating power load.

[0141] Further, step S23 includes the following steps:

[0142] Step S231: performing time series synchronization processing on the DC operating current standard data and the DC operating voltage standard data to obtain the DC operating current data and the DC operating voltage data in the same time series dimension;

[0143] In an embodiment of the present invention, a timing synchronization processing method is used to perform timing synchronization processing on the DC operating current standard data and the DC operating voltage standard data previously obtained after standardization, so as to use the data acquisition system to align the timestamps of the two sets of data to ensure that they are in the same time dimension, and by using a data synchronization algorithm, such as a complementary filter or a Kalman filter, the data is processed so that the timing relationship between the DC operating current and voltage data accurately reflects the actual situation, ensuring that the DC operating current and voltage data obtained after the synchronization processing can be normally applied in subsequent analysis, and finally obtaining the DC operating current data and DC operating voltage data in the same timing dimension.

[0144] Step S232: Draw an operation change hyperbola for the DC operation current data and the DC operation voltage data in the same time series dimension to generate a DC operation current-voltage change hyperbola;

[0145] In an embodiment of the present invention, professional data visualization software (such as Matplotlib or MATLAB) is used to draw the operation change hyperbola of the DC operating current and voltage data that have been processed by time-series synchronization. In this process, a fitting algorithm such as polynomial fitting is used to more accurately depict the relationship between current and voltage, ensuring that the hyperbola can truly reflect the operating conditions and characteristics of the actual equipment, and finally draw and generate a DC operating current-voltage change hyperbola.

[0146] Step S233: performing a statistical analysis of the fluctuation amplitude at each time point in the DC operating current-voltage variation hyperbola to obtain the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point; performing arithmetic average calculation on the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point to obtain the average DC operating current fluctuation amplitude and the average DC operating voltage fluctuation amplitude;

[0147] In an embodiment of the present invention, by performing statistical calculations on the fluctuation amplitude of each time point in the previously drawn DC operating current-voltage change hyperbola, the generated hyperbola data points are processed by writing a script to obtain the changes of current and voltage at each moment, thereby statistically calculating the corresponding fluctuation amplitude, thereby obtaining the fluctuation amplitude of the DC operating current and the fluctuation amplitude of the DC operating voltage at each time point. At the same time, by summarizing the fluctuation amplitude of the DC operating current and the fluctuation amplitude of the DC operating voltage at each time point obtained by the previous statistical analysis, and calculating the arithmetic mean of the DC operating current and voltage, to obtain the overall fluctuation average amplitude, finally obtaining the average fluctuation amplitude of the DC operating current and the average fluctuation amplitude of the DC operating voltage.

[0148] Step S234: performing current-voltage calibration processing on the DC operation current-voltage variation hyperbola according to the average amplitude of the DC operation current fluctuation and the average amplitude of the DC operation voltage fluctuation to obtain a DC operation current fluctuation calibration line and a DC operation voltage fluctuation calibration line;

[0149] In an embodiment of the present invention, the corresponding DC operating current-voltage change hyperbola is calibrated by using the average amplitude of the DC operating current fluctuation and the average amplitude of the DC operating voltage fluctuation obtained after arithmetic averaging, so as to compare the standard current and voltage data with the average fluctuation amplitude and establish a corresponding calibration line. The data can be fitted by a linear regression method to ensure that the calibration line reflects the current and voltage values ​​within the normal working range of the equipment. If the corresponding calibration line is exceeded, it is considered that there is a current or voltage overload, and finally a DC operating current fluctuation calibration line and a DC operating voltage fluctuation calibration line are obtained.

[0150] Step S235: Based on the DC operating current fluctuation calibration line and the DC operating voltage fluctuation calibration line, the DC operating current-voltage change hyperbola is evaluated for current and voltage overload risk. When it is determined that the current in the DC operating current-voltage change hyperbola exceeds the DC operating current fluctuation calibration line or the voltage exceeds the DC operating voltage fluctuation calibration line, the corresponding DC equipment risk factor is evaluated and determined as current and voltage overload fluctuation, otherwise no processing is performed.

[0151] In an embodiment of the present invention, by combining the DC operating current fluctuation calibration line and the DC operating voltage fluctuation calibration line obtained by previous calibration, the current and voltage overload risk situation is evaluated and judged within the DC operating current-voltage change hyperbola, so as to monitor the current and voltage values ​​of the DC equipment in real time, and by using the fluctuation calibration line to judge whether its change curve exceeds the calibration line, if it is found that the current value exceeds the fluctuation calibration line or the voltage value exceeds the corresponding calibration line, the corresponding DC equipment risk factor is evaluated and determined as the current and voltage overload fluctuation situation through the alarm system or the automatic adjustment device; if it does not exceed the calibration line, the flexible DC energy router will continue to operate safely without additional processing, thereby ensuring the safety and reliability of the flexible DC energy router.

[0152] Further, step S3 includes the following steps:

[0153] Step S31: obtaining DC router system configuration status data and DC router circuit network status data;

[0154] In the embodiment of the present invention, the system configuration status of the DC router is obtained by using a specific network monitoring tool, including key information such as the system hardware configuration, software version, system settings, user permissions, etc., so as to obtain the DC router system configuration status data. At the same time, the circuit network status of the DC router is collected by using a current monitoring instrument to provide information about the network topology, connection equipment, link quality, etc., and finally the DC router circuit network status data is obtained.

[0155] Step S32: performing system-level vulnerability risk factor assessment and analysis on the DC router system configuration status data to obtain DC system-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities;

[0156] In an embodiment of the present invention, a system-level vulnerability risk factor assessment is performed on the collected DC router system configuration status data by using a corresponding security assessment tool. This process uses vulnerability scanning software to perform a comprehensive scan on the operating system and its related applications to identify potential operating system vulnerabilities. At the same time, the user access rights are analyzed through the access control audit tool to identify the risk of unauthorized access. In addition, the backup and recovery process is simulated and tested by using a fault recovery analysis tool to ensure that the fault recovery vulnerability is evaluated. Finally, all risk factors are integrated to obtain DC system-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities.

[0157] Step S33: performing circuit network topology feature extraction processing on the DC router circuit network status data to obtain DC router circuit network topology feature data;

[0158] In an embodiment of the present invention, a network traffic analyzer is used to extract network topology features from previously acquired DC router circuit network status data to extract corresponding network topology connection feature data, including the connection relationship between devices, data transmission path, and bandwidth usage, to display the real-time status of the DC router circuit network, and finally obtain the DC router circuit network topology feature data.

[0159] Step S34: performing circuit network transmission attack topology analysis on the DC router circuit network status data based on the DC router circuit network topology feature data to generate a DC router circuit network transmission attack topology surface;

[0160] In an embodiment of the present invention, a network topology connection analysis of transmission attacks is performed on the circuit network corresponding to the DC router circuit network status data by combining the previously extracted DC router circuit network topology feature data, so as to use a data packet generation tool to simulate various attack scenarios, and through the coverage and simulation of the network topology, possible attack paths and key nodes are marked out, and finally a DC router circuit network transmission attack topology surface is generated.

[0161] Step S35: Perform network-level risk factor assessment and analysis on the DC router circuit network transmission attack topology surface to obtain DC network-level risk factors, wherein the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities.

[0162] In an embodiment of the present invention, a network vulnerability scanning tool is used to evaluate and analyze the risk factors at the network level on the DC router circuit network transmission attack topology surface previously connected and generated, so as to analyze and identify the attack risks existing in the circuit network, and then the attack vector is cross-compared with the actual state of the circuit network to identify the potential threats of circuit network attacks, and the network security monitoring platform is used to evaluate the indicators of circuit network transmission security, such as encryption protocol, data integrity, etc., so as to finally obtain the corresponding DC network-level risk factors, including circuit network attacks, the security state of circuit network transmission and potential vulnerabilities of network connection devices.

[0163] Further, step S4 includes the following steps:

[0164] Step S41: using a dynamic Bayesian network to perform dynamic dependency structure modeling on DC device-level risk factors and DC system-level risk factors to obtain a DC device-system-level risk factor dependency structure;

[0165] In an embodiment of the present invention, a dynamic Bayesian network modeling tool (such as the PGM toolkit or similar software) is used to perform dependency modeling analysis on various DC device-level risk factors and DC system-level risk factors obtained from previous evaluation and analysis, so as to identify and model the dependencies between various risk factors in DC devices and systems. For example, the failure of a certain device may cause the shutdown of other systems, etc., and inference can be performed by using an algorithm (such as the Markov chain Monte Carlo method) to obtain the probability distribution of the dynamic dependencies between the risk factors, and finally obtain the DC device-system-level risk factor dependency structure.

[0166] Step S42: Based on the DC device-system-level risk factor dependency structure, network-level dependency extension modeling is performed on the DC network-level risk factors to obtain a multi-level extension structure of DC device-system-network-level risk factor dependency;

[0167] In an embodiment of the present invention, the risk factor dependency of the DC network level is expanded from the established DC device-system-level risk factor dependency structure, so as to identify the core risk factors affecting the entire network through a comprehensive analysis of the DC network-level risk factors, and incorporate them into the network-level risk modeling. A network model is established using graph theory to connect various devices and their risk factors, and network nodes and edges are set to represent the dependencies between various device-level, system-level and network-level risk factors that influence each other. At the same time, a network analysis tool (such as Gephi or NetworkX) is used to dynamically visualize the network dependency structure to further reveal the interactive relationship between the DC devices and the corresponding risk factors at the system and network levels, forming a multi-level risk factor dependency architecture, and finally obtaining a multi-level extended structure of DC device-system-network-level risk factor dependency.

[0168] Step S43: performing statistical analysis on the time series change probability and impact degree of each risk factor with dependency relationship in the multi-level extended structure of DC equipment-system-network level risk factors, and obtaining the time series risk change probability and risk impact degree of each risk factor with dependency relationship;

[0169] In an embodiment of the present invention, a time series analysis method is used to perform statistical analysis on the time series changes of each risk factor with a dependency relationship in the multi-level extended structure of the DC device-system-network-level risk factors previously connected and constructed, so as to achieve statistical calculation of the frequency of occurrence of each risk factor in different time periods. The analysis tool can use the pandas and statsmodels libraries in Python to accurately calculate the time series probability of each risk factor, and use regression analysis to determine the degree of influence of each risk factor on the safety of the overall flexible DC energy router, and finally obtain the time series risk change probability and risk impact degree of each risk factor with a dependency relationship.

[0170] Step S44: performing a multi-level security risk assessment calculation on each risk factor with a dependency relationship in the multi-level extension structure of the DC device-system-network level risk factor dependency based on the probability of a time series risk change and the risk impact degree of each risk factor with a dependency relationship, so as to obtain a DC router security risk assessment index;

[0171] In the embodiment of the present invention, by combining the time series risk change probability and risk impact degree of each risk factor with dependency relationship obtained by previous statistical analysis, a weighted algorithm is used to perform a comprehensive assessment and calculation of the security risk of each risk factor with dependency relationship in the multi-level extended structure of DC equipment-system-network level risk factor dependency, so as to combine the time series change probability and impact degree of different risk factors, and calculate the comprehensive assessment and calculation of the security risk through the formula Calculate the safety risk assessment index of the DC router, where For the dependency The impact of each risk factor, For the dependency The temporal change probability of each risk factor is calculated and finally the DC router security risk assessment index is obtained by quantitative calculation.

[0172] Step S45: Based on the DC router safety risk assessment index, the safety risk warning response of the flexible DC energy router is adjusted to generate a DC router safety risk warning adjustment strategy to execute corresponding DC router operation risk warning adjustment measures.

[0173] In an embodiment of the present invention, by using a rule engine or a threshold-based trigger mechanism, it is analyzed whether the DC router safety risk assessment index obtained by previous quantitative calculation exceeds the preset safety risk threshold limit, and a safety risk warning response adjustment strategy for the flexible DC energy router is formulated in response to the warning. Based on the evaluation and judgment results, once the warning condition is triggered (that is, exceeding the preset safety risk threshold limit), a list of response strategies for the safety risk warning is automatically generated, involving real-time adjustment of the energy scheduling strategy, increase in the frequency of equipment status monitoring, and corresponding alarm prompts. In addition, the risk warning response plan is tested and drilled regularly to ensure that the corresponding adjustment measures can be accurately and efficiently executed in actual operation, thereby further improving the safety of the flexible DC energy router, ensuring its normal operation and reducing the impact of potential risks on the router.

[0174] Furthermore, the present invention also provides a dynamic safety assessment system for a flexible DC energy router, which is used to execute the dynamic safety assessment method for a flexible DC energy router as described above. The dynamic safety assessment system for a flexible DC energy router includes:

[0175] The DC equipment operation real-time monitoring module is used to deploy current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connect them to establish a DC router operation sensor monitoring network; the flexible DC energy router is monitored in real time through the DC router operation sensor monitoring network, so as to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power;

[0176] A DC equipment-level risk factor assessment module is used to transmit the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to a central data processing unit through a preset data communication network; the central data processing unit is used to perform equipment-level risk factor assessment and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to obtain DC equipment-level risk factors, wherein the DC equipment-level risk factors include current and voltage overload fluctuations, equipment temperature overheating, and equipment operating power load;

[0177] A DC system-level and network-level risk factor assessment module is used to obtain DC router system configuration status data and DC router circuit network status data, and to perform system-level and network-level risk factor assessment and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities;

[0178] The DC router safety risk warning adjustment module is used to use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC equipment-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; based on the DC router safety risk assessment index, the module adjusts the safety risk warning response of the flexible DC energy router, generates a DC router safety risk warning adjustment strategy, and executes corresponding DC router operation risk warning adjustment measures.

[0179] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A dynamic safety assessment method for a flexible DC energy router, characterized in that: The following steps are involved: Step S1: deploy current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connect them to establish a DC router operation sensor monitoring network; perform real-time monitoring of the flexible DC energy router through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, real-time data of DC operation voltage, real-time data of DC operation temperature and real-time data of DC operation power; wherein, step S1 includes the following steps: Step S11: acquiring historical thermal distribution data of the DC router and historical current distribution data of the DC router, and performing key point mining and analysis on the flexible DC energy router based on the historical thermal distribution data of the DC router and the historical current distribution data of the DC router to obtain key points of the DC router; wherein step S11 includes the following steps: Obtain historical thermal distribution data of the DC router and historical current distribution data of the DC router; Based on the historical thermal distribution data of the DC router, the thermal effect analysis of the flexible DC energy router is carried out to obtain the thermal effect characteristic data of the DC router structure. Based on the DC router structure thermal effect characteristic data, the electromagnetic field strength evaluation and analysis is performed on the DC router historical current distribution data to obtain the DC router electromagnetic field strength distribution data; According to the electromagnetic field intensity distribution data of the DC router, the flexible DC energy router is divided into electrical reliability areas to obtain the DC router electrical reliability areas; the DC router electrical reliability areas are subdivided by dynamic load response test to obtain the DC router dynamic load response partitions; wherein the dynamic load response test subdivision includes the following steps: Perform load benchmark measurement on each regional point within the electrical reliability area of ​​the DC router to obtain benchmark load data at each regional point within the electrical reliability area; Perform load response spectrum analysis on the benchmark load data at each regional point in the electrical reliability area to obtain the load response signal spectrum characteristic data at each regional point; Perform load response fluctuation frequency analysis on the load response signal spectrum characteristic data at each regional point to obtain the load response signal fluctuation frequency value at each regional point; draw a frequency over-threshold hotspot map for each regional point in the electrical reliability area of ​​the DC router based on the load response signal fluctuation frequency value at each regional point to generate a regional load response fluctuation frequency over-threshold hotspot distribution map; The point distance between each fluctuation frequency exceeding threshold hot spot in the regional load response fluctuation frequency exceeding threshold hot spot distribution map is measured to obtain the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map; According to the preset point distance threshold, the point distance value between each fluctuation frequency exceeding threshold hot spot in the hot spot distribution map is compared and judged. When the point distance value is less than or equal to the preset point distance threshold, the corresponding fluctuation frequency exceeding threshold hot spots are aggregated into the same hot spot set to obtain the regional load response fluctuation frequency exceeding threshold hot spot set; when the point distance value is greater than the preset point distance threshold, no processing is performed; Each fluctuation frequency exceeding threshold hotspot in the regional load response fluctuation frequency exceeding threshold hotspot set is connected by point area to obtain the dynamic load response partition of the DC router; Based on the dynamic load response partition of the DC router, the key points of the flexible DC energy router are mined and analyzed to obtain the key points of the DC router; Step S12: performing a sensor deployment planning analysis on the flexible DC energy router according to the key points of the DC router to generate a sensor deployment plan for the key points of the DC router; Step S13: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan and connecting them to establish a DC router operation sensor monitoring network; Step S14: performing real-time monitoring of the operation of the flexible DC energy router through the DC router operation sensor monitoring network to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power; Step S2: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network; using the central data processing unit to perform equipment-level risk factor evaluation and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to obtain DC equipment-level risk factors, wherein the DC equipment-level risk factors include current and voltage overload fluctuations, equipment temperature overheating and equipment operating power load; wherein step S2 includes the following steps: Step S21: transmitting the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature and the real-time data of DC operating power to the central data processing unit through a preset data communication network; Step S22: using the central data processing unit to preprocess and standardize the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power, to obtain standard data of DC operating current, standard data of DC operating voltage, standard data of DC operating temperature, and standard data of DC operating power; Step S23: evaluating and analyzing the current and voltage overload risk factors of the DC operating current standard data and the DC operating voltage standard data to obtain current and voltage overload fluctuations; wherein step S23 includes the following steps: Step S231: performing time series synchronization processing on the DC operating current standard data and the DC operating voltage standard data to obtain the DC operating current data and the DC operating voltage data in the same time series dimension; Step S232: Draw an operation change hyperbola for the DC operation current data and the DC operation voltage data in the same time series dimension to generate a DC operation current-voltage change hyperbola; Step S233: performing a statistical analysis of the fluctuation amplitude at each time point in the DC operating current-voltage variation hyperbola to obtain the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point; performing arithmetic average calculation on the DC operating current fluctuation amplitude and the DC operating voltage fluctuation amplitude at each time point to obtain the average DC operating current fluctuation amplitude and the average DC operating voltage fluctuation amplitude; Step S234: performing current-voltage calibration processing on the DC operation current-voltage variation hyperbola according to the average amplitude of the DC operation current fluctuation and the average amplitude of the DC operation voltage fluctuation to obtain a DC operation current fluctuation calibration line and a DC operation voltage fluctuation calibration line; Step S235: Based on the DC operation current fluctuation calibration line and the DC operation voltage fluctuation calibration line, the DC operation current-voltage change hyperbola is evaluated for current and voltage overload risk. When it is determined that the current in the DC operation current-voltage change hyperbola exceeds the DC operation current fluctuation calibration line or the voltage exceeds the DC operation voltage fluctuation calibration line, the corresponding DC equipment risk factor is evaluated and determined as current and voltage overload fluctuation, otherwise no processing is performed; Step S24: performing overheat risk factor evaluation and analysis on the DC operating temperature standard data according to a preset DC device temperature overheat calibration threshold to obtain device temperature overheat; Step S25: Perform statistical analysis on the power peak and fluctuation range of the DC operating power standard data to obtain the DC operating power peak and the DC operating power fluctuation range; perform long-term change trend analysis on the DC operating power standard data based on the DC operating power peak and the DC operating power fluctuation range to obtain the DC operating power long-term change trend data; perform power load risk factor assessment and analysis on the DC operating power standard data based on the DC operating power long-term change trend data to obtain the equipment operating power load; Step S3: obtaining the DC router system configuration status data and the DC router circuit network status data, and performing system-level and network-level risk factor evaluation and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities; Step S4: Use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC device-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; adjust the safety risk warning response of the flexible DC energy router based on the DC router safety risk assessment index, generate a DC router safety risk warning adjustment strategy, and execute corresponding DC router operation risk warning adjustment measures.

2. The dynamic safety assessment method of the flexible DC energy router according to claim 1 is characterized in that: Step S13 includes the following steps: Step S131: deploying current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router according to the DC router key point sensor deployment plan to obtain DC router operation sensor monitoring nodes; Step S132: performing monitoring range evaluation and analysis on sensors in each DC router operation sensor monitoring node to obtain the sensor monitoring range size of each DC operation sensor monitoring node; Step S133: optimizing the deployment position of each DC router operation sensor monitoring node based on the sensor monitoring range of each DC operation sensor monitoring node, and obtaining each DC router operation monitoring interval optimization node; Step S134: Use the network transmission protocol to establish a network connection between the DC router operation monitoring interval optimization nodes to establish a DC router operation sensor monitoring network.

3. The dynamic safety assessment method of the flexible DC energy router according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: obtaining DC router system configuration status data and DC router circuit network status data; Step S32: performing system-level vulnerability risk factor assessment and analysis on the DC router system configuration status data to obtain DC system-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities; Step S33: performing circuit network topology feature extraction processing on the DC router circuit network status data to obtain DC router circuit network topology feature data; Step S34: performing circuit network transmission attack topology analysis on the DC router circuit network status data based on the DC router circuit network topology feature data to generate a DC router circuit network transmission attack topology surface; Step S35: Perform network-level risk factor assessment and analysis on the DC router circuit network transmission attack topology surface to obtain DC network-level risk factors, wherein the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities.

4. The dynamic safety assessment method of the flexible DC energy router according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: using a dynamic Bayesian network to perform dynamic dependency structure modeling on DC device-level risk factors and DC system-level risk factors to obtain a DC device-system-level risk factor dependency structure; Step S42: Based on the DC device-system-level risk factor dependency structure, network-level dependency extension modeling is performed on the DC network-level risk factors to obtain a multi-level extension structure of DC device-system-network-level risk factor dependency; Step S43: performing statistical analysis on the time series change probability and impact degree of each risk factor with dependency relationship in the multi-level extended structure of DC equipment-system-network level risk factors, and obtaining the time series risk change probability and risk impact degree of each risk factor with dependency relationship; Step S44: performing a multi-level security risk assessment calculation on each risk factor with a dependency relationship in the multi-level extension structure of the DC device-system-network level risk factor dependency based on the probability of a time series risk change and the risk impact degree of each risk factor with a dependency relationship, so as to obtain a DC router security risk assessment index; Step S45: Based on the DC router safety risk assessment index, the safety risk warning response of the flexible DC energy router is adjusted to generate a DC router safety risk warning adjustment strategy to execute corresponding DC router operation risk warning adjustment measures.

5. A dynamic safety assessment system for a flexible DC energy router, characterized in that: The method for dynamic safety assessment of a flexible DC energy router according to claim 1 is used to implement the method, wherein the dynamic safety assessment system of the flexible DC energy router comprises: The DC equipment operation real-time monitoring module is used to deploy current sensors, voltage sensors, temperature sensors and power sensors at key points of the flexible DC energy router and connect them to establish a DC router operation sensor monitoring network; the flexible DC energy router is monitored in real time through the DC router operation sensor monitoring network, so as to obtain real-time data of DC operation current, DC operation voltage, DC operation temperature and DC operation power; A DC equipment-level risk factor assessment module is used to transmit the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to a central data processing unit through a preset data communication network; the central data processing unit is used to perform equipment-level risk factor assessment and analysis on the real-time data of DC operating current, the real-time data of DC operating voltage, the real-time data of DC operating temperature, and the real-time data of DC operating power to obtain DC equipment-level risk factors, wherein the DC equipment-level risk factors include current and voltage overload fluctuations, equipment temperature overheating, and equipment operating power load; A DC system-level and network-level risk factor assessment module is used to obtain DC router system configuration status data and DC router circuit network status data, and to perform system-level and network-level risk factor assessment and analysis on the DC router system configuration status data and the DC router circuit network status data to obtain DC system-level risk factors and DC network-level risk factors, wherein the DC system-level risk factors include operating system vulnerabilities, unauthorized access vulnerabilities, and fault recovery vulnerabilities, and the DC network-level risk factors include circuit network attacks, circuit network transmission security, and network connection device vulnerabilities; The DC router safety risk warning adjustment module is used to use a dynamic Bayesian network to perform multi-level safety risk assessment calculations on DC equipment-level risk factors, DC system-level risk factors, and DC network-level risk factors to obtain a DC router safety risk assessment index; based on the DC router safety risk assessment index, the module adjusts the safety risk warning response of the flexible DC energy router, generates a DC router safety risk warning adjustment strategy, and executes corresponding DC router operation risk warning adjustment measures.

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