Power grid disaster monitoring and early warning method and system based on Hadoop big data platform

Abstract

The invention discloses a power grid disaster monitoring and early warning method and system based on a Hadoop big data platform, and relates to the technical field of big data, and the method comprises the steps: building a distributed data architecture, integrating multi-source heterogeneous power disaster prevention big data, carrying out the storage and retrieval of data, and carrying out the discrimination and positioning of a fault range through dispatching operation. The method comprises the following steps: establishing a power grid, evaluating the reliability and high-value links of the power grid, calculating the load loss after failure equipment is shut down, implementing emergency resource optimization scheduling based on global factor analysis, regularly checking the reserve condition, performing recovery control, adjusting the maintenance plan and the operation mode, paying attention to the risk index in real time, and performing risk early warning. According to the method, optimal recovery strategy formulation and maintenance plan adjustment are carried out on the local power grid power failure accident, it is ensured that the power grid can recover operation rapidly and safely after the accident, the power failure time is shortened, the recovery efficiency of the power grid is improved, and the adaptability and flexibility of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and in particular to a power grid disaster monitoring and early warning method and system based on a Hadoop big data platform. Background Art

[0002] As a public utility, electricity plays an important role in daily production and life. The safety of the power system will have a great impact on social security and stability and the safety of people's lives and property. The ever-increasing demand for electricity has led to the continuous expansion of the power grid, accompanied by the increasing complexity of the power grid and the increasing safety hazards. Affected by various frequent natural disasters and human factors, the safety of the power grid has been greatly challenged, and the frequent power emergencies have put forward higher requirements for the power emergency management capabilities.

[0003] Big data technology can provide strong technical support for power grid disaster monitoring and emergency response. The differences between the various existing information systems in the power system are large, and the traditional relational database has a low level of integration and sharing of disaster information, which can hardly meet the management needs of massive heterogeneous disaster prevention big data. The multi-department information sharing and collaborative work process especially requires a disaster prevention big data management platform with high real-time and security to provide support for the analysis of massive information data and intelligent decision-making functions. Summary of the invention

[0004] In view of the above existing problems, the present invention provides a power grid disaster monitoring and early warning method and system based on the Hadoop big data platform to solve the problem of insufficient power grid disaster prevention work in the prior art.

[0005] In order to solve the above technical problems, a power grid disaster monitoring and early warning method based on Hadoop big data platform is proposed, including:

[0006] Establish a distributed data architecture, integrate multi-source heterogeneous power disaster prevention big data, and store and retrieve data; identify and locate the scope of the fault through scheduling operations, evaluate the reliability of the power grid and high-value links, and calculate the load loss after the faulty equipment is shut down; implement emergency resource optimization scheduling based on global factor analysis, conduct regular inspections of reserves, perform recovery control, adjust maintenance plans and operating methods, pay real-time attention to risk indicators and issue risk warnings.

[0007] As a preferred solution of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in the present invention, the establishment of a distributed data architecture includes establishing an independent data retention mechanism for the server, and realizing parallel processing by sending control instructions to each system, and integrating multi-source heterogeneous disaster prevention big data for data storage and retrieval.

[0008] The multi-source heterogeneous disaster prevention big data includes meteorological and geological disasters, equipment status, relay protection actions, plant loads and alarms, and emergency resource data.

[0009] As a preferred solution of the power grid disaster monitoring and early warning method based on Hadoop big data platform described in the present invention, the scheduling operation includes scheduling operation support considering disaster information and providing valuable online decision support for the emergency command center.

[0010] The identification and location of the fault range includes intelligent correlation and analysis of emergency repair feedback information, accurate location of the power grid accident point, and providing an assessment result of the accident impact range.

[0011] As a preferred solution of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in the present invention, the evaluation of power grid reliability and high-value links includes identifying key components of the system, calculating the load loss after the faulty equipment is shut down, and identifying the high-value links of the power grid operation.

[0012] The identifying system key components includes determining a component as a key component when a topological weight coefficient of the component exceeds 0.7.

[0013] As a preferred solution of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in the present invention, the load loss calculation includes calculating the load loss after the faulty equipment is shut down, identifying the high-value links of the power grid operation, and conducting risk assessment and early warning issuance.

[0014] The load loss calculation also includes, when the equipment is shut down, calculating the direct load loss, i.e., the product of the equipment rated power and the load rate; when the equipment is shut down and causes equipment overload, calculating the indirect load loss, i.e., the product of the affected equipment rated power and the overload loss rate.

[0015] As a preferred solution of the power grid disaster monitoring and early warning method based on Hadoop big data platform described in the present invention, the emergency resource optimization scheduling includes determining the pre-positioning locations of emergency materials and manpower based on global factor analysis and conducting regular inspections on the reserve situation.

[0016] The emergency resource optimization scheduling also includes, on the basis of comprehensive consideration of global factors, completing the determination of corresponding locations and the pre-positioning of emergency materials and manpower by mining and analyzing the data obtained by the system, and conducting regular inspections on the reserves of emergency materials, optimizing the emergency resource scheduling in real time, and making timely system responses; the global factors include expected accident levels and weak points in the power grid.

[0017] As a preferred solution of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in the present invention, the recovery control includes making an optimal recovery strategy for the local power grid power outage accident, and determining the importance level and distribution of the corresponding loads and the optimal recovery path based on the reasonable division and recovery of the recovery subsystem.

[0018] The adjustment of the maintenance plan and the operation mode includes adjusting the predetermined maintenance plan and the operation mode, and performing safety verification and adjustment in combination with the equipment time-varying outage model and the situation and prediction of natural disasters.

[0019] The risk warning includes disaster prevention data modeling of unstructured data through an unstructured data galaxy model, realizing data sharing, and analyzing the disaster prevention capabilities of the power grid through data mining and association, providing time-varying, rich, and vivid information under disaster conditions to the disaster prevention emergency command center in groups, evaluating the risk indicators of important components and high-value links of the power grid, and issuing warning information when the risk assessment results reach the upper limit of the risk indicators for warning.

[0020] Another object of the present invention is to provide a power grid disaster monitoring and early warning system based on the Hadoop big data platform. The present invention ensures the data independence and integrity between different system servers, while realizing centralized management and efficient use of data; the system of the present invention improves the reliability of the power grid, optimizes resource allocation, reduces disaster risks, and enhances emergency response capabilities by establishing a distributed data architecture, identifying key components in the power grid, calculating load losses, and evaluating the impact of faults on the operation of the power grid. It also provides strong technical support for the stable operation of the power grid and disaster management through efficient data processing and visualization technology.

[0021] As a preferred solution of the power grid disaster monitoring and early warning system based on the Hadoop big data platform described in the present invention, it is characterized by including a distributed data architecture module, a fault range identification and positioning module, a power grid high-value link evaluation module, an emergency resource optimization and scheduling module and a risk warning module.

[0022] The distributed data architecture module is used to establish a server-independent data retention mechanism and integrate multi-source heterogeneous disaster prevention big data from different sources and structures, and use the efficient storage and retrieval capabilities of the Hadoop platform to quickly access and process large-scale data, thereby improving the system's data processing capabilities.

[0023] The fault range identification and positioning module is used to utilize disaster information to perform power grid dispatching operations, provide real-time decision support, intelligently process emergency repair feedback information, accurately locate the accident site, and evaluate the impact of the accident on the power grid, providing a basis for emergency response.

[0024] The high-value link assessment module of the power grid is used to identify components that are critical to the operation of the power grid through topological weight coefficient indicators, calculate the direct and indirect load losses after the faulty equipment is shut down, provide data for risk assessment, and conduct risk assessment based on the load loss calculation results, and issue an early warning when the risk reaches a certain threshold.

[0025] The emergency resource optimization and scheduling module is used to perform global factor analysis, optimize the preset locations of emergency resources, regularly check the reserve status, and dynamically adjust the allocation of emergency resources according to the disaster situation and the power grid status.

[0026] The risk warning module is used to formulate the optimal recovery strategy for local power grid blackout accidents, and adjust the maintenance plan and operation mode of the power grid in combination with the equipment outage model and disaster prediction, perform disaster prevention data modeling through the unstructured data galaxy model, realize data sharing, analyze the power grid disaster prevention capabilities, and provide risk assessment results and warning information to the disaster prevention emergency command center.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the method for monitoring and warning of power grid disasters based on the Hadoop big data platform are implemented.

[0028] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in the method for monitoring and early warning of power grid disasters based on the Hadoop big data platform are implemented.

[0029] Beneficial effects of the present invention: The present invention realizes the integration and management of multi-source heterogeneous power disaster prevention big data by establishing a distributed data architecture, ensures the independence and integrity of the data, and provides a comprehensive and reliable data basis for power grid disaster monitoring and early warning, thereby improving data processing efficiency, reducing the risk of single point failure, and enhancing the robustness of the system; the fault range is identified and located through scheduling operations, providing accurate data support for the evaluation of power grid reliability and high-value links, helping operators to quickly identify power grid faults, shortening the fault processing time, reducing power losses, and improving the stability and reliability of the power grid; by identifying key system components and calculating the load loss after the faulty equipment is shut down, the high-value links of the power grid operation are evaluated, the power grid operation and maintenance decisions are guided, resource allocation is optimized, the economy of the power grid operation is improved, the stable operation of the key links is ensured, and the overall operation risk is reduced; the emergency resource optimization scheduling is based on global factor analysis, which ensures the timeliness and effectiveness of the disaster response, improves the emergency response speed, reduces the impact of disasters, and ensures the safe operation of the power grid; risk warning uses an unstructured data galaxy model to model disaster prevention data, discover potential risks in advance, reduce the possibility of disasters, reduce losses, and improve the risk management level of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0031] Figure 1 An overall flow chart of a power grid disaster monitoring and early warning method based on a Hadoop big data platform provided by an embodiment of the present invention.

[0032] Figure 2 A schematic diagram of an application framework for high-value link assessment and fault location functions of a power grid disaster monitoring and early warning method based on a Hadoop big data platform provided by an embodiment of the present invention.

[0033] Figure 3 A schematic diagram of data flow and logical decision-making of a power grid disaster monitoring and early warning method based on the Hadoop big data platform provided by an embodiment of the present invention.

[0034] Figure 4 A system solution flow chart of a power grid disaster monitoring and early warning system based on the Hadoop big data platform provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0038] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0039] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0040] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Example 1, reference Figure 1-Figure 3 , which is the first embodiment of the present invention, and provides a power grid disaster monitoring and early warning method based on the Hadoop big data platform, comprising:

[0042] S1: Establish a distributed data architecture, integrate multi-source heterogeneous power disaster prevention big data, and perform data storage and retrieval.

[0043] The establishment of a distributed data architecture includes establishing a server-independent data retention mechanism, achieving parallel processing by sending control instructions to each system, and integrating multi-source heterogeneous disaster prevention big data to store and retrieve data.

[0044] The multi-source heterogeneous disaster prevention big data includes meteorological and geological disasters, equipment status, relay protection actions, plant loads and alarms, and emergency resource data.

[0045] It should be noted that if Figure 2 ,The integrated multi-source heterogeneous power disaster prevention big data includes, the first step, integrated management technology, ,on the basis of each system server retaining its own data, ,forming distributed data, sending different control instructions to each system, and returning the ,integrated results after the execution of the instructions, which is a parallel processing ,process.

[0046] The second step is data mining technology, including cluster analysis, association rules and feature extraction, which uses artificial intelligence technology to mine valuable data information.

[0047] The third step is to provide data processing technology for large-scale distributed computing solutions, complete the real-time processing and analysis of disaster big data through stream processing technology, and effectively meet the online real-time computing needs of the monitoring system through the use of memory computing technology with efficient data reading capabilities.

[0048] The fourth step is data visualization technology, which uses graphical means to realize the intuitive and clear display of data information. Disaster monitoring and early warning mainly use three data visualization technologies: spatial information, on-site images and real-time data, to realize the intuitive display of information on the status of different areas of the power grid, on-site conditions, and emergency resource distribution.

[0049] S2: Identify and locate the fault scope through dispatching operations, evaluate the reliability of the power grid and high-value links, and calculate the load loss after the faulty equipment is shut down.

[0050] Furthermore, the dispatching operation includes dispatching operation support considering disaster information and providing valuable online decision support for the emergency command center.

[0051] The identification and location of the fault range includes intelligent correlation and analysis of emergency repair feedback information, accurate location of the power grid accident point, and providing an assessment result of the accident impact range.

[0052] Furthermore, the evaluation of the reliability and high-value links of the power grid includes identifying key components of the system, calculating the load loss after the faulty equipment is shut down, and identifying the high-value links of the power grid operation.

[0053] The identifying system key components includes determining a component as a key component when a topological weight coefficient of the component exceeds 0.7.

[0054] The load loss calculation includes calculating the load loss after the faulty equipment is shut down, identifying the high-value links in the operation of the power grid, and conducting risk assessment and issuing early warnings.

[0055] The load loss calculation also includes, when the equipment is shut down, calculating the direct load loss, i.e., the product of the equipment rated power and the load rate; when the equipment is shut down and causes equipment overload, calculating the indirect load loss, i.e., the product of the affected equipment rated power and the overload loss rate.

[0056] S3: Implement optimal scheduling of emergency resources based on global factor analysis, conduct regular inspections of reserves, perform recovery control, adjust maintenance plans and operating methods, pay real-time attention to risk indicators and issue risk warnings.

[0057] Furthermore, the optimization and dispatching of emergency resources includes determining the pre-positioning locations of emergency materials and manpower based on global factor analysis and conducting regular inspections of the reserve situation;

[0058] The emergency resource optimization scheduling also includes, on the basis of comprehensive consideration of global factors, completing the determination of corresponding locations and the pre-positioning of emergency materials and manpower by mining and analyzing the data obtained by the system, and conducting regular inspections on the reserves of emergency materials, optimizing the emergency resource scheduling in real time, and making timely system responses; the global factors include expected accident levels and weak points in the power grid.

[0059] like Figure 3 , the recovery control includes making an optimal recovery strategy for the local power grid blackout accident, and determining the importance level and distribution of the corresponding loads and the optimal recovery path based on the reasonable division and recovery of the recovery subsystem;

[0060] The adjustment of the maintenance plan and operation mode includes adjusting the predetermined maintenance plan and operation mode, and performing safety verification and adjustment in combination with the equipment time-varying outage model and the situation and prediction of natural disasters;

[0061] The risk warning includes disaster prevention data modeling of unstructured data through an unstructured data galaxy model, realizing data sharing, and analyzing the disaster prevention capabilities of the power grid through data mining and association, providing time-varying, rich, and vivid information under disaster conditions to the disaster prevention emergency command center in groups, evaluating the risk indicators of important components and high-value links of the power grid, and issuing warning information when the risk assessment results reach the upper limit of the risk indicators for warning.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0063] Example 2, reference Figure 4 , which is the second embodiment of the present invention, provides a power grid disaster monitoring and early warning system based on the Hadoop big data platform, including a distributed data architecture module 100, a fault range identification and positioning module 200, a power grid high-value link assessment module 300, an emergency resource optimization and scheduling module 400 and a risk early warning module 500.

[0064] The distributed data architecture module 100 is used to establish a server-independent data retention mechanism and integrate multi-source heterogeneous disaster prevention big data from different sources and structures, and use the efficient storage and retrieval capabilities of the Hadoop platform to quickly access and process large-scale data, thereby improving the system's data processing capabilities.

[0065] The fault range identification and positioning module 200 is used to utilize disaster information to perform grid dispatching operations, provide real-time decision support, intelligently process emergency repair feedback information, accurately locate the accident site, and evaluate the impact of the accident on the grid, providing a basis for emergency response.

[0066] The power grid high-value link assessment module 300 is used to identify components that are critical to the operation of the power grid through topological weight coefficient indicators, calculate direct and indirect load losses after the faulty equipment is shut down, provide data for risk assessment, and conduct risk assessment based on the load loss calculation results, and issue an early warning when the risk reaches a certain threshold.

[0067] The emergency resource optimization and scheduling module 400 is used to perform global factor analysis, optimize the preset locations of emergency resources, regularly check the reserve status, and dynamically adjust the emergency resource allocation according to the disaster situation and the power grid status.

[0068] The risk warning module 500 is used to formulate the optimal recovery strategy for local power grid power outage accidents, and adjust the maintenance plan and operation mode of the power grid in combination with the equipment outage model and disaster prediction, perform disaster prevention data modeling through the unstructured data galaxy model, realize data sharing, analyze the disaster prevention capabilities of the power grid, and provide risk assessment results and warning information to the disaster prevention emergency command center.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0070] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0071] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A power grid disaster monitoring and early warning method based on the Hadoop big data platform, characterized by: include, Establish a distributed data architecture, integrate multi-source heterogeneous power disaster prevention big data, and perform data storage and retrieval; Identify and locate the fault scope through dispatching operations, evaluate the reliability of the power grid and high-value links, and calculate the load loss after the faulty equipment is shut down; Based on the analysis of global factors, we implement optimal scheduling of emergency resources, conduct regular inspections of reserves, carry out recovery control, adjust maintenance plans and operating methods, pay real-time attention to risk indicators and issue risk warnings.

2. The power grid disaster monitoring and early warning method based on the Hadoop big data platform as claimed in claim 1, characterized in that: The establishment of a distributed data architecture includes establishing a server-independent data retention mechanism, and implementing parallel processing by sending control instructions to each system, and integrating multi-source heterogeneous disaster prevention big data to store and retrieve data; The multi-source heterogeneous disaster prevention big data includes meteorological and geological disasters, equipment status, relay protection actions, plant loads and alarms, and emergency resource data.

3. The power grid disaster monitoring and early warning method based on the Hadoop big data platform as claimed in claim 2, characterized in that: The dispatching operation includes dispatching operation support considering disaster information and providing valuable online decision support for the emergency command center; The identification and location of the fault range includes intelligent correlation and analysis of emergency repair feedback information, accurate location of the power grid accident point, and providing an assessment result of the accident impact range.

4. The power grid disaster monitoring and early warning method based on Hadoop big data platform as claimed in claim 3, characterized in that: The evaluation of the reliability and high-value links of the power grid includes identifying key components of the system, calculating the load loss after the failure equipment is shut down, and identifying the high-value links of the power grid operation; The identifying system key components includes determining a component as a key component when a topological weight coefficient of the component exceeds 0.

7.

5. The power grid disaster monitoring and early warning method based on Hadoop big data platform as claimed in claim 4, characterized in that: The load loss calculation includes calculating the load loss after the faulty equipment stops operating, identifying the high-value links of the power grid operation, and conducting risk assessment and issuing early warnings; The load loss calculation also includes, when the equipment is shut down, calculating the direct load loss, i.e., the product of the equipment rated power and the load rate; when the equipment is shut down and causes equipment overload, calculating the indirect load loss, i.e., the product of the affected equipment rated power and the overload loss rate.

6. The power grid disaster monitoring and early warning method based on Hadoop big data platform as claimed in claim 5, characterized in that: The said optimization and dispatch of emergency resources includes determining the pre-positioning locations of emergency materials and manpower based on global factor analysis and conducting regular inspections of the reserve situation; The emergency resource optimization scheduling also includes, on the basis of comprehensive consideration of global factors, completing the determination of corresponding locations and the pre-positioning of emergency materials and manpower by mining and analyzing the data obtained by the system, and conducting regular inspections on the reserves of emergency materials, optimizing the emergency resource scheduling in real time, and making timely system responses; the global factors include expected accident levels and weak points in the power grid.

7. The power grid disaster monitoring and early warning method based on Hadoop big data platform as claimed in claim 6, characterized in that: The recovery control includes making an optimal recovery strategy for a local power grid blackout accident, determining the importance level and distribution of the corresponding loads and the optimal recovery path based on a reasonable division and recovery of the recovery subsystems; The adjustment of the maintenance plan and operation mode includes adjusting the predetermined maintenance plan and operation mode, and performing safety verification and adjustment in combination with the equipment time-varying outage model and the situation and prediction of natural disasters; The risk warning includes disaster prevention data modeling of unstructured data through an unstructured data galaxy model, realizing data sharing, and analyzing the disaster prevention capabilities of the power grid through data mining and association, providing time-varying, rich, and vivid information under disaster conditions to the disaster prevention emergency command center in groups, evaluating the risk indicators of important components and high-value links of the power grid, and issuing warning information when the risk assessment results reach the upper limit of the risk indicators for warning.

8. A system using the power grid disaster monitoring and early warning method based on the Hadoop big data platform as claimed in any one of claims 1 to 7, characterized in that: It includes distributed data architecture module, fault range identification and location module, power grid high-value link assessment module, emergency resource optimization and dispatch module and risk warning module; The distributed data architecture module is used to establish a server-independent data retention mechanism and integrate multi-source heterogeneous disaster prevention big data from different sources and structures, and use the efficient storage and retrieval capabilities of the Hadoop platform to quickly access and process large-scale data, thereby improving the system's data processing capabilities; The fault range identification and positioning module is used to use disaster information to perform grid dispatch operations, provide real-time decision support, intelligently process emergency repair feedback information, accurately locate the accident site, and evaluate the impact of the accident on the grid, providing a basis for emergency response; The high-value link assessment module of the power grid is used to identify components that are critical to the operation of the power grid through topological weight coefficient indicators, calculate the direct and indirect load losses after the faulty equipment is shut down, provide data for risk assessment, and conduct risk assessment based on the load loss calculation results, and issue an early warning when the risk reaches a certain threshold; The emergency resource optimization and scheduling module is used to analyze global factors, optimize the pre-placement location of emergency resources, regularly check the reserve status, and dynamically adjust the allocation of emergency resources according to the disaster situation and power grid status; The risk warning module is used to formulate the optimal recovery strategy for local power grid blackout accidents, and adjust the maintenance plan and operation mode of the power grid in combination with the equipment outage model and disaster prediction, perform disaster prevention data modeling through the unstructured data galaxy model, realize data sharing, analyze the power grid disaster prevention capabilities, and provide risk assessment results and warning information to the disaster prevention emergency command center.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid disaster monitoring and early warning method based on the Hadoop big data platform described in any one of claims 1 to 7 are implemented.

Images