Electric power safety monitoring system

Abstract

The invention discloses an electric power safety monitoring system, and relates to the technical field of safety monitoring, and the system comprises a multi-dimensional data collection module which is used for collecting various types of operation data in an electric power system, obtaining equipment identification information, and constructing an electric power operation basic network based on the operation data and the equipment identification information; and the intelligent analysis and modeling module is connected with the multi-dimensional data acquisition module, and is used for analyzing and processing the acquired operation data, constructing a virtual analysis model of the power system, establishing and dynamically adjusting a mapping relation between the virtual analysis model and the power operation basic network, and performing trend prediction and fault identification at the same time. According to the system and the method, various types of intelligent sensors are deployed in the multi-dimensional data acquisition module, the operation data of each node of the power system are comprehensively acquired and are bound with the equipment identification information, the problems of single traditional data acquisition means and incomplete data are solved, and a rich and accurate data basis is provided for subsequent in-depth analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring, and particularly relates to a power safety monitoring system. Background Art

[0002] With the rapid development of modern society, the power system, as a key infrastructure for national economic development and social stable operation, has been continuously expanding in scale and becoming increasingly complex in structure, posing higher requirements for the safe and stable operation of the power system.

[0003] When a fault occurs in the power system, the traditional emergency management method lacks the ability of adaptive adjustment, and cannot quickly formulate a scientific and reasonable emergency strategy according to the real-time fault situation. There are also deficiencies in the evaluation and selection of emergency points, the allocation and coordination of resources, etc., resulting in low fault handling efficiency and possibly causing greater economic losses and social impacts. In addition, during the instruction transmission process, the traditional method is difficult to ensure the accuracy and security of instructions, and cannot adjust the system model in a timely manner according to the execution feedback, affecting the closed-loop management and continuous optimization of the entire power safety monitoring and processing process. Summary of the Invention

[0004] The purpose of the present invention is to provide a power safety monitoring system to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A power safety monitoring system, comprising:

[0006] A multi-dimensional data acquisition module, used for collecting various operation data in the power system, obtaining device identification information, and constructing a basic power operation network based on the operation data and device identification information;

[0007] An intelligent analysis and modeling module, connected to the multi-dimensional data acquisition module, used for analyzing and processing the collected operation data, constructing a virtual analysis model of the power system, establishing and dynamically adjusting the mapping relationship between the virtual analysis model and the basic power operation network, and simultaneously performing trend prediction and fault identification;

[0008] A hierarchical early warning module, connected to the intelligent analysis and modeling module, used for generating information to be warned, configuring risk identification to obtain a warning data packet, selecting a communication channel to send and tracking the transmission status;

[0009] An intelligent determination and classification module, connected to the hierarchical early warning module, used for extracting risk factors to calculate a risk index, and determining and classifying the warning data packet according to the index;

[0010] An adaptive emergency management module, connected to the intelligent determination and classification module, used for formulating an emergency strategy, analyzing the fault impact, evaluating and selecting an emergency point, and allocating and coordinating resources;

[0011] The precise instruction transmission module, connected to the adaptive emergency management module, is used to establish a secure channel, refine, generate, and transmit instructions, and adjust the virtual analysis model according to the feedback.

[0012] In a preferred embodiment, the multi-dimensional data acquisition module includes:

[0013] The data acquisition sub-unit deploys various types of intelligent sensors to comprehensively collect the operation data of each node in the power system, assigns a unique number to each sensor, and binds it to the device identification information of the corresponding power equipment;

[0014] The network topology construction sub-unit uses graph theory algorithms to construct a hierarchical power operation basic network topology structure based on the electrical connection relationship, energy transmission direction, and geographical layout between devices.

[0015] In a preferred embodiment, the intelligent analysis and modeling module includes:

[0016] The dynamic model construction sub-unit uses virtual reality and augmented reality technologies, combines the physical model and operation principle of the power system, and constructs a highly realistic virtual analysis model of the power system. The appearance, operation parameters, and interaction relationships of the devices in the model are synchronized with the actual power system in real time;

[0017] The mapping relationship dynamic adjustment sub-unit, based on deep learning algorithms, monitors and analyzes the operation data of the power system in real time. When the device operation state, network topology structure, or external environmental factors change, it automatically adjusts the mapping points between the virtual analysis model and the power operation basic network to ensure their consistency and accuracy;

[0018] The data analysis and prediction sub-unit uses time series analysis and neural network algorithms to mine historical operation data, predict the future operation trends and potential faults of the power system; by comparing the prediction results with real-time data, it promptly discovers abnormal situations and provides decision-making support.

[0019] In a preferred embodiment, the mapping relationship dynamic adjustment sub-unit includes:

[0020] The feature extraction and weight calculation sub-unit extracts the key feature parameters of power equipment and calculates the weight of each feature using the Analytic Hierarchy Process (AHP) according to the influence degree of the key feature parameters on the operation of the power system;

[0021] The mapping rule formulation sub-unit formulates mapping point adjustment rules according to the feature weights and device operation states;

[0022] The real-time adjustment execution subunit monitors the operation data of power equipment in real time. When it detects that the characteristic parameters change, it timely adjusts the mapping points between the virtual analysis model and the basic power operation network according to the mapping rules to ensure the accuracy and real-time nature of the mapping relationship.

[0023] In a preferred embodiment, the hierarchical early warning module includes:

[0024] An early warning information generation and identification configuration subunit comprehensively evaluates the analysis results of the intelligent analysis and modeling module, generates the to-be-early-warned information according to factors such as the fault type, the influence scope, and the urgency degree, and configures corresponding-level risk identifications for it; the risk identifications include early warning types, risk levels, and expected influence time information;

[0025] A communication channel optimization and sending subunit automatically selects the optimal communication channel for sending according to the urgency degree of the early warning data packet and the location of the target monitoring terminal; for high-risk early warnings, it preferentially selects high-speed and reliable optical fiber communication channels; for low-risk early warnings, it can select relatively low-cost wireless communication channels; at the same time, it encrypts the early warning data packet to ensure the security of information transmission;

[0026] A status tracking and feedback subunit real-time tracks the transmission status of the early warning data packet in the communication channel, including the location of the data packet, the transmission speed, and whether information is lost, and feeds back the above information to the virtual analysis model; if transmission anomalies are found, it timely takes measures such as retransmission or switching communication channels.

[0027] In a preferred embodiment, the intelligent determination and classification module includes:

[0028] A risk index calculation subunit constructs a risk assessment model using the fuzzy mathematics theory based on various risk factors in the risk identification, and calculates the risk index of the early warning data packet; the risk assessment model considers factors such as the possibility of the fault occurring, the severity of the fault consequences, and the detectability of the fault;

[0029] A determination and classification execution subunit compares the calculated risk index with multiple preset thresholds, and classifies the early warning data packet into severe danger early warning, moderate danger early warning, and general danger early warning according to the comparison results; at the same time, it records the determination process and results of each early warning data packet for subsequent query and analysis.

[0030] In a preferred embodiment, the adaptive emergency management module includes:

[0031] The emergency strategy formulation and update subunit formulates comprehensive emergency handling information by combining the historical fault data, equipment maintenance records, and expert experience of the power system; the emergency handling information includes the emergency handling procedures for different types of faults, communication channel backup plans, equipment repair plans, and personnel deployment plans, and updates the emergency strategy in a timely manner according to the operation changes of the power system and actual emergency handling experience;

[0032] The emergency point assessment and selection subunit analyzes the operating status, redundancy, and emergency operation difficulty factors of the affected power equipment and its surrounding equipment according to the location where the danger warning is issued in the communication channel, evaluates the applicability of each potential emergency point using a multi-objective optimization algorithm, and selects the optimal emergency point as the key node for performing emergency operations;

[0033] The resource allocation and coordination subunit coordinates and allocates human and material resources according to the requirements of the emergency point and the emergency handling information.

[0034] In a preferred embodiment, the precise instruction transmission module includes:

[0035] The secure channel establishment subunit uses advanced encryption algorithms, such as quantum encryption technology, to establish a secure encrypted instruction transmission channel between the virtual analysis model and the corresponding emergency point in the basic power operation network; ensure that the instructions are not stolen or tampered with during transmission, and guarantee the security and reliability of instruction transmission;

[0036] The instruction refinement and transmission subunit further refines the emergency instructions generated by the virtual analysis model into specific equipment operation instructions, including start-stop control and parameter adjustment of the equipment; sends the above precise instructions to the corresponding power equipment through the instruction transmission channel, and monitors the transmission and execution of the instructions in real time;

[0037] The feedback and adjustment subunit receives the feedback information after the power equipment executes the emergency instructions, including whether the operation is successful and whether the equipment status has returned to normal; according to the feedback information, makes corresponding adjustments to the virtual analysis model, such as updating the equipment operation status and correcting the fault prediction results, to provide more accurate support for subsequent emergency handling.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] By deploying various types of intelligent sensors in the multi-dimensional data acquisition module, the present invention comprehensively collects the operation data of each node in the power system and binds it with the device identification information, solving the problems of single traditional data acquisition means and incomplete data, providing a rich and accurate data basis for subsequent in-depth analysis. At the same time, using graph theory algorithms to construct a hierarchical power operation basic network topology structure clearly presents the electrical connections, energy transmission directions and geographical layout relationships between devices, helping to more intuitively and accurately understand the power system architecture and providing a clear framework for subsequent analysis and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0043] Embodiment 1. Please refer to Figure 1 As shown, a power safety monitoring system in this embodiment includes:

[0044] A multi-dimensional data acquisition module, which is used to collect various types of operation data in the power system, obtain device identification information, and construct a basic power operation network based on the operation data and device identification information;

[0045] An intelligent analysis and modeling module, which is connected to the multi-dimensional data acquisition module, is used to analyze and process the collected operation data, construct a virtual analysis model of the power system, establish and dynamically adjust the mapping relationship between the virtual analysis model and the basic power operation network, and at the same time perform trend prediction and fault identification;

[0046] A hierarchical early warning module, which is connected to the intelligent analysis and modeling module, is used to generate information to be warned, configure risk identification to obtain a warning data packet, select a communication channel to send and track the transmission status;

[0047] The intelligent determination and classification module, connected to the hierarchical early warning module, is used to extract risk factors, calculate the risk index, and determine and classify the early warning data packets according to the index;

[0048] The adaptive emergency management module, connected to the intelligent determination and classification module, is used to formulate emergency strategies, analyze the impact of faults, evaluate and select emergency points, and allocate and coordinate resources;

[0049] The precise instruction transmission module, connected to the adaptive emergency management module, is used to establish a secure channel, refine, generate, and transmit instructions, and adjust the virtual analysis model according to the feedback.

[0050] Furthermore, when a fault occurs in the power system, the traditional emergency management method lacks the ability of adaptive adjustment, and cannot quickly formulate scientific and reasonable emergency strategies according to the real-time fault situation. There are also deficiencies in the evaluation and selection of emergency points, the allocation and coordination of resources, etc., resulting in low fault handling efficiency and possibly causing greater economic losses and social impacts. In addition, during the instruction transmission process, the traditional method is difficult to ensure the accuracy and security of instructions, and cannot adjust the system model in time according to the execution feedback, affecting the closed-loop management and continuous optimization of the entire power safety monitoring and processing process. In this application, the present invention solves the problems of single traditional data collection means and incomplete data by deploying various types of intelligent sensors in the multi-dimensional data collection module to comprehensively collect the operation data of each node in the power system and bind it with the device identification information; at the same time, using graph theory algorithms to construct a hierarchical power operation basic network topology structure, clearly presenting the electrical connection, energy transmission direction, and geographical layout relationship between devices, which helps to more intuitively and accurately understand the power system architecture and provides a clear framework for subsequent analysis and management.

[0051] In one embodiment, the multi-dimensional data collection module includes:

[0052] The data collection sub-unit deploys various types of intelligent sensors to comprehensively collect the operation data of each node in the power system, assigns a unique number to each sensor, and binds it with the device identification information of the corresponding power equipment;

[0053] The network topology construction sub-unit uses graph theory algorithms to construct a hierarchical power operation basic network topology structure based on the electrical connection relationship, energy transmission direction, and geographical layout between devices.

[0054] Furthermore, the intelligent sensors deployed in the data acquisition sub-unit are of diverse types, covering current sensors, voltage sensors, temperature sensors, humidity sensors, etc., and can collect operation data of each node in the power system, such as current values, voltage values, equipment temperature, environmental humidity, etc., from all aspects and angles. A unique number is assigned to each sensor. This number, like the "ID card" of the sensor, is unique and has a specific pointing property, facilitating accurate identification and management by the system. At the same time, this number is bound to the device identification information of the corresponding power equipment, enabling the collected data to be accurately associated with specific power equipment, providing a clear and definite data source for subsequent data analysis and fault diagnosis; The graph theory algorithm adopted by the network topology construction sub-unit is based on rigorous mathematical theories and models. By precisely analyzing the electrical connection relationships between devices, it determines the connection methods and paths of devices in the network; According to the energy transmission direction, it clarifies the flow direction and distribution law of electricity in the system; Considering the actual geographical location information of the devices in combination with the geographical layout, the constructed hierarchical power operation basic network topology structure can not only accurately reflect the electrical characteristics of the power system but also take into account the actual geographical factors, providing an intuitive and effective network model basis for the operation monitoring, fault troubleshooting, and optimal dispatching of the power system.

[0055] In one embodiment, the intelligent analysis and modeling module includes:

[0056] The dynamic model construction sub-unit uses virtual reality and augmented reality technologies, combines the physical model and operation principle of the power system, and constructs a highly realistic virtual analysis model of the power system. The appearance, operation parameters, and interaction relationships of the devices in the model are synchronized with the actual power system in real time;

[0057] The mapping relationship dynamic adjustment sub-unit, based on deep learning algorithms, monitors and analyzes the operation data of the power system in real time. When the operation state of the device, the network topology structure, or external environmental factors change, it automatically adjusts the mapping points between the virtual analysis model and the basic power operation network to ensure their consistency and accuracy;

[0058] The data analysis and prediction sub-unit uses time series analysis and neural network algorithms to mine historical operation data, predict the future operation trends and potential faults of the power system; By comparing the prediction results with real-time data, it timely discovers abnormal situations and provides decision-making support.

[0059] It is further explained that the dynamic model construction subunit uses the unique advantages of virtual reality (VR) and augmented reality (AR) technology, takes the physical model of the power system as the basic framework, and deeply combines its operating principles to carefully create a highly realistic virtual analysis model of the power system. In this model, the appearance of each device is reproduced with high precision based on the actual power equipment. Whether it is the shape, color or material texture of the equipment, it is exactly the same as the real equipment, providing an immersive observation experience for operators. At the same time, the operating parameters in the model, such as current, voltage, power, etc., can reflect the operating status of the actual power system in real time and accurately, and are updated synchronously with the changes of the actual system. The interaction relationship between devices, such as power transmission and energy conversion, is also highly consistent with the actual situation, truly simulating the complex and orderly collaborative operation process between the various parts of the power system; the mapping relationship dynamic adjustment subunit uses the powerful data analysis capabilities of the deep learning algorithm to conduct real-time, comprehensive monitoring and in-depth analysis of the massive operating data of the power system. When the operating status of equipment in the power system fluctuates, such as equipment aging, performance degradation, etc.; or the network topology changes, such as new equipment access, old equipment removal, line switching, etc.; or external environmental factors such as temperature, humidity, weather conditions, etc. affect the power system, the subunit can quickly capture these changes and automatically and accurately adjust the mapping points between the virtual analysis model and the power operation basic network based on the intelligent model built by deep learning. This dynamic adjustment mechanism ensures that the virtual analysis model can always accurately reflect the actual status of the power operation basic network, providing a reliable basis for the analysis and decision-making of the power system; the data analysis and prediction subunit uses time series analysis and neural network algorithms to conduct in-depth mining of the historical operation data accumulated over a long period of time in the power system. Time series analysis can extract periodicity, trend and other characteristics from the time dimension of historical data, providing basic support for predicting the future operation trend of the power system. The neural network algorithm, with its powerful nonlinear mapping ability, can learn the complex internal relationship in the data and effectively predict the potential faults of the power system. By carefully comparing the prediction results with the real-time data, once a significant deviation is found between the two, it is determined to be an abnormal situation. At the same time, based on the data analysis results, the subunit will provide detailed and targeted decision-making support to the operation and maintenance personnel of the power system, such as prompting the location of equipment that may fail, the type of failure, and recommended response measures, etc., to help the operation and maintenance personnel take timely action to ensure the stable operation of the power system.

[0060] In one embodiment, the mapping relationship dynamic adjustment subunit includes:

[0061] Feature extraction and weight calculation subunit, which extracts key feature parameters of power equipment and calculates the weight of each feature using the Analytic Hierarchy Process (AHP) according to the degree of influence of the key feature parameters on the operation of the power system;

[0062] Mapping rule formulation subunit, which formulates mapping point adjustment rules according to the feature weights and equipment operating status;

[0063] Real-time adjustment execution subunit, which monitors the operating data of power equipment in real time. When a change in feature parameters is detected, it adjusts the mapping points between the virtual analysis model and the basic power operation network in a timely manner according to the mapping rules to ensure the accuracy and real-time nature of the mapping relationship.

[0064] Further explanation: The feature extraction and weight calculation subunit accurately extracts key feature parameters from numerous parameters of power equipment through a series of advanced algorithms and technical means. These key feature parameters play a crucial role in the normal operation of the power system and may include, but are not limited to, the power factor, load rate, insulation resistance, heat dissipation efficiency, etc. of the equipment. The Analytic Hierarchy Process (AHP) plays an important role in this subunit. It decomposes complex multi-objective decision-making problems into multiple levels, quantifies and evaluates the degree of influence of key feature parameters on the operation of the power system through pairwise comparisons between elements at different levels, and then calculates the weight of each feature. For example, when evaluating the influence of the power factor and load rate on the power system, through the Analytic Hierarchy Process, their respective importance weights can be determined under different operating scenarios to provide a reliable basis for subsequent mapping adjustments.

[0065] In one embodiment, the hierarchical warning module includes:

[0066] Warning information generation and identification configuration subunit, which comprehensively evaluates the analysis results of the intelligent analysis and modeling module, generates warning information to be sent according to factors such as fault type, influence range, and urgency, and configures corresponding risk identifications for it; the risk identifications include warning type, risk level, and estimated impact time information;

[0067] Communication channel optimization and sending subunit, which automatically selects the optimal communication channel for sending according to the urgency of the warning data packet and the location of the target monitoring terminal; for high-risk warnings, it preferentially selects high-speed and reliable optical fiber communication channels; for low-risk warnings, it can select wireless communication channels with lower costs; at the same time, it encrypts the warning data packet to ensure the security of information transmission;

[0068] The status tracking and feedback subunit tracks in real time the transmission status of the early warning data packet in the communication channel, including the position of the data packet, the transmission speed, and whether information is lost, and feeds back the above information to the virtual analysis model; if transmission anomalies are found, retransmission or communication channel switching measures are taken in a timely manner.

[0069] Further explanation, when the early warning information generation and identification configuration subunit comprehensively evaluates the analysis results of the intelligent analysis and modeling module, a set of rigorous and scientific evaluation systems is used. For different types of faults, such as short - circuit faults, overload faults, insulation faults, etc., it will combine the scope of influence on the power system, for example, whether it is a local line affected, or the power supply of the entire substation or even a larger area is affected, and the urgency of the fault, considering factors such as whether it will immediately threaten the safe and stable operation of the power system, or whether it will trigger a chain reaction leading to more serious consequences, etc., to generate comprehensive and accurate information to be early - warned.

[0070] In one embodiment, the intelligent determination and classification module includes:

[0071] The risk index calculation subunit constructs a risk assessment model using fuzzy mathematics theory based on various risk factors in the risk identification, and calculates the risk index of the early warning data packet; the risk assessment model takes into account the possibility of fault occurrence, the severity of fault consequences, and the detectability of the fault;

[0072] The determination and classification execution subunit compares the calculated risk index with multiple preset thresholds, and classifies the early warning data packet into severe - danger early warning, medium - danger early warning, and general - danger early warning according to the comparison results; at the same time, records the determination process and results of each early warning data packet for subsequent query and analysis.

[0073] Further explanation, the risk assessment model constructed by the risk index calculation subunit using fuzzy mathematics theory has its unique advantages and scientific nature. When constructing this model, all risk factors included in the risk identification are fully considered as the basis for evaluation. Among them, the possibility of fault occurrence covers the probabilities of various types of faults occurring in the power system, which is determined by comprehensively considering historical data analysis, the current state of equipment, and environmental factors, etc. For example, for aging equipment, the possibility of failure is relatively high, while for newly installed and well - maintained equipment, this possibility is relatively low.

[0074] In one embodiment, the adaptive emergency management module includes:

[0075] The emergency strategy formulation and update subunit formulates comprehensive emergency handling information by combining the historical fault data, equipment maintenance records, and expert experience of the power system; the emergency handling information includes the emergency handling procedures for different types of faults, communication channel backup plans, equipment repair plans, and personnel deployment plans, and updates the emergency strategy in a timely manner according to the operation changes of the power system and actual emergency handling experience;

[0076] The emergency point assessment and selection subunit analyzes the operating status, redundancy, and emergency operation difficulty factors of the affected power equipment and its surrounding equipment according to the location where the danger warning is issued in the communication channel, evaluates the applicability of each potential emergency point using a multi-objective optimization algorithm, and selects the optimal emergency point as the key node for performing emergency operations;

[0077] The resource allocation and coordination subunit coordinates and allocates human and material resources according to the requirements of the emergency point and the emergency handling information.

[0078] Furthermore, when formulating comprehensive emergency handling information, the emergency strategy formulation and update subunit adopts a comprehensive method. For the historical fault data of the power system, in-depth mining and analysis will be carried out, not only paying attention to the frequency, type, and scope of influence of faults, but also studying factors such as the time and season of fault occurrence to explore possible patterns. For example, in some areas during the peak summer electricity consumption period, equipment overheating faults are likely to occur due to excessive load, and the historical data can be used as an important basis for formulating strategies to deal with such faults.

[0079] In one embodiment, the precise instruction transmission module includes:

[0080] The secure channel establishment subunit uses advanced encryption algorithms, such as quantum encryption technology, to establish a secure encrypted instruction transmission channel between the virtual analysis model and the corresponding emergency point in the power operation basic network; ensure that the instructions are not stolen or tampered with during transmission, and guarantee the security and reliability of instruction transmission;

[0081] The instruction refinement and transmission subunit further refines the emergency instructions generated by the virtual analysis model into specific equipment operation instructions, including detailed information on the start-stop control and parameter adjustment of the equipment; sends the above precise instructions to the corresponding power equipment through the instruction transmission channel, and monitors the transmission and execution of the instructions in real time;

[0082] The feedback and adjustment subunit receives the feedback information after the power equipment executes the emergency instructions, including whether the operation is successful and whether the equipment status has returned to normal; according to the feedback information, makes corresponding adjustments to the virtual analysis model, such as updating the equipment operation status and correcting the fault prediction results, to provide more accurate support for subsequent emergency handling.

[0083] Furthermore, the security channel establishment subunit utilizes advanced encryption algorithms, especially quantum encryption technology, to provide strong security guarantees for the information transmission between the virtual analysis model and the corresponding emergency points in the power operation basic network. Quantum encryption technology is based on the principles of quantum mechanics, such as quantum entanglement and the quantum no-cloning theorem, and has unique advantages. When establishing a secure encryption instruction transmission channel, it can generate random quantum keys to ensure the security of the keys. Even if the information is eavesdropped during transmission, it can be immediately detected because any eavesdropping behavior will change the quantum state, thus ensuring the high security and reliability of the instructions during transmission.

[0084] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the said claims.

Claims

1. A power safety monitoring system, characterized in that: include: Multi-dimensional data acquisition module, used to collect various operating data in the power system, obtain equipment identification information, and build a power operation basic network based on the operating data and equipment identification information; The intelligent analysis and modeling module is connected to the multi-dimensional data acquisition module to analyze and process the collected operation data, build a virtual analysis model of the power system, establish and dynamically adjust the mapping relationship between the virtual analysis model and the power operation basic network, and perform trend prediction and fault identification; The hierarchical warning module is connected to the intelligent analysis and modeling module to generate information to be warned, configure risk identification to obtain warning data packets, select communication channels to send and track transmission status; The intelligent judgment and classification module is connected to the graded warning module to extract risk factors and calculate the risk index, and judge and classify the warning data packets according to the index; The adaptive emergency management module is connected to the intelligent judgment and classification module to formulate emergency strategies, analyze the impact of failures, evaluate and select emergency points, and deploy and coordinate resources; The precise command transmission module is connected to the adaptive emergency management module to establish a safe channel, refine the generation and transmission of commands, and adjust the virtual analysis model based on feedback.

2. The power safety monitoring system according to claim 1, characterized in that: The multi-dimensional data acquisition module comprises: The data acquisition subunit deploys various types of intelligent sensors to comprehensively collect the operating data of each node in the power system, assigns a unique number to each sensor, and binds it with the equipment identification information of the corresponding power equipment; The network topology construction sub-unit uses graph theory algorithms to build a hierarchical and graded basic network topology structure for power operation based on the electrical connection relationship between devices, energy transmission direction, and geographical layout.

3. The power safety monitoring system according to claim 1, characterized in that: The intelligent analysis and modeling module includes: The dynamic model building subunit uses virtual reality and augmented reality technologies, combined with the physical model and operating principle of the power system, to build a highly realistic virtual analysis model of the power system. The equipment appearance, operating parameters and interaction relationships in the model are synchronized with the actual power system in real time; The mapping relationship dynamic adjustment subunit monitors and analyzes the power system operation data in real time based on the deep learning algorithm. When the equipment operation status, network topology or external environmental factors change, the mapping points between the virtual analysis model and the power operation basic network are automatically adjusted to ensure the consistency and accuracy of the two. The data analysis and prediction subunit uses time series analysis and neural network algorithms to mine historical operating data and predict future operating trends and potential failures of the power system; by comparing the prediction results with real-time data, it can promptly detect abnormal situations and provide decision support.

4. The power safety monitoring system according to claim 3, characterized in that: The mapping relationship dynamic adjustment subunit includes: The feature extraction and weight calculation subunit extracts the key feature parameters of the power equipment and calculates the weight of each feature using the analytic hierarchy process (AHP) according to the degree of influence of the key feature parameters on the operation of the power system; The mapping rule formulation subunit formulates mapping point adjustment rules according to feature weights and equipment operation status; Adjust the execution sub-unit in real time and monitor the operation data of the power equipment in real time. When changes in characteristic parameters are detected, adjust the mapping points between the virtual analysis model and the power operation basic network in time according to the mapping rules to ensure the accuracy and real-time performance of the mapping relationship.

5. The power safety monitoring system according to claim 1, characterized in that: The hierarchical warning module includes: The warning information generation and identification configuration subunit comprehensively evaluates the analysis results of the intelligent analysis and modeling module, generates warning information based on the fault type, impact scope and urgency factors, and configures risk identification of corresponding levels for it; the risk identification includes warning type, risk level and expected impact time information; The communication channel optimization and sending subunit automatically selects the best communication channel for sending according to the urgency of the warning data packet and the location of the target monitoring terminal. For high-risk warnings, high-speed and reliable optical fiber communication channels are preferred; for low-risk warnings, low-cost wireless communication channels can be selected. At the same time, the warning data packet is encrypted to ensure the security of information transmission. The status tracking and feedback subunit tracks the transmission status of the warning data packet in the communication channel in real time, including the location of the data packet, the transmission speed, and whether information is lost, and feeds the above information back to the virtual analysis model; if any transmission abnormality is found, timely measures such as retransmission or switching of the communication channel are taken.

6. The power safety monitoring system according to claim 1, characterized in that: The intelligent determination and classification module includes: The risk index calculation subunit uses fuzzy mathematics theory to construct a risk assessment model based on various risk factors in the risk identification and calculates the risk index of the warning data packet; the risk assessment model takes into account the possibility of failure, the severity of the consequences of the failure, and the detectability of the failure; The determination and classification execution subunit compares the calculated risk index with multiple preset thresholds, and classifies the warning data packets into severe danger warnings, moderate danger warnings, and general danger warnings based on the comparison results; at the same time, it records the determination process and results of each warning data packet for subsequent query and analysis.

7. The power safety monitoring system according to claim 1, characterized in that: The adaptive emergency management module includes: The emergency strategy formulation and update subunit formulates comprehensive emergency handling information based on the historical fault data of the power system, equipment maintenance records and expert experience; the emergency handling information includes emergency handling procedures for different types of faults, communication channel backup plans, equipment repair plans and personnel deployment plans, and timely updates the emergency strategy based on the operation changes of the power system and actual emergency handling experience; The emergency point assessment and selection subunit analyzes the operating status, redundancy and emergency operation difficulty factors of the affected power equipment and its peripheral equipment according to the location of the danger warning in the communication channel, and uses a multi-objective optimization algorithm to evaluate the applicability of each potential emergency point and select the optimal emergency point as the key node for executing emergency operations; The resource allocation and coordination subunit coordinates and allocates human and material resources according to the needs of the emergency point and emergency handling information.

8. The power safety monitoring system according to claim 1, characterized in that: The precise instruction transmission module comprises: The secure channel establishment subunit uses advanced encryption algorithms, such as quantum encryption technology, to establish a secure encrypted instruction transmission channel between the virtual analysis model and the corresponding emergency point in the power operation basic network; ensuring that the instructions are not stolen or tampered with during the transmission process, and ensuring the security and reliability of instruction transmission; The instruction refinement and transmission subunit further refines the emergency instructions generated by the virtual analysis model into specific equipment operation instructions, including equipment start and stop control and parameter adjustment; sends the above precise instructions to the corresponding power equipment through the instruction transmission channel, and monitors the transmission and execution of the instructions in real time; The feedback and adjustment subunit receives feedback information from the power equipment after it executes the emergency command, including whether the operation is successful and whether the equipment status has returned to normal. Based on the feedback information, the virtual analysis model is adjusted accordingly, such as updating the equipment operating status and correcting the fault prediction results, to provide more accurate support for subsequent emergency processing.

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