Power grid operation risk assessment method, device, electronic device and readable storage medium

Abstract

The present invention discloses a power grid operation risk assessment method, device, electronic device and readable storage medium. The method includes: determining the internal device risk level through the device status data of power grid devices; determining the external environment risk level through the operation environment data of power grid devices; determining a risk correction coefficient; and determining the power grid operation risk level according to the risk correction coefficient, the internal device risk level and the external environment risk level, so as to perform power grid operation risk assessment. The power grid operation risk assessment method, device, electronic device and computer readable storage medium involved in the present disclosure can make the power grid monitoring more intelligent and rigorous, and the power grid operation safer and more reliable.

Description

Technical Field

[0001] The present disclosure relates to a method, apparatus, and computer-readable storage medium for evaluating grid operation risks. Background Art

[0002] In the 1980s, grid monitoring mainly relied on manual monitoring in substations, which was the 1.0 era of grid monitoring. In the mid- to late 1990s, power transmission and transformation equipment could be monitored in the background of substations relying on an automated system, and grid monitoring entered the 2.0 era. After the implementation of the "Three Concentrations and Five Major Reforms", a large amount of grid equipment operation data was aggregated in the power dispatching center to achieve integrated dispatching and control, which is the 3.0 era of grid monitoring. However, currently, although the grid system has a large amount of grid information data, there is a lack of effective means for mining, and the value of the data has not been fully utilized.

[0003] Therefore, a new method, apparatus, electronic device, and computer-readable storage medium for evaluating grid operation risks are needed.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] In view of this, the present disclosure provides a method, apparatus, electronic device, and computer-readable storage medium for evaluating grid operation risks, which can make grid monitoring more intelligent and rigorous, and grid operation safer and more reliable.

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or will be partially learned through the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for evaluating grid operation risks is provided. The method includes: determining the internal equipment risk level through the equipment status data of grid equipment; determining the external environment risk level through the operation environment data of grid equipment; determining a risk correction coefficient according to historical internal risk level data, historical external environment risk level data, and historical grid operation-related data; and determining the grid operation risk level according to the risk correction coefficient, internal equipment risk level, and external environment risk level for grid operation risk assessment.

[0008] In an exemplary embodiment of the present disclosure, determining the internal equipment risk level through the equipment status data of grid equipment includes: obtaining the usage log data of the grid equipment; obtaining the maintenance log data of the grid equipment; obtaining the fault log data of the grid equipment; and performing data cleaning processing on the usage log data, the maintenance log data, and the fault log data to obtain the equipment status data.

[0009] In an exemplary embodiment of the present disclosure, determining the internal device risk degree through the device status data of the power grid device further includes: inputting the processed usage log data into a device life model to determine the life risk degree; inputting the processed maintenance log data into a maintenance work model to determine the maintenance risk degree; inputting the processed fault log data into a device fault model to determine the fault risk degree; and determining the internal device risk degree according to the life risk degree, the maintenance risk degree, and the fault risk degree.

[0010] In an exemplary embodiment of the present disclosure, determining the external environment risk degree through the operating environment data of the power grid device includes: obtaining the temperature and humidity record data of the power grid device; obtaining the power grid load data of the power grid device; and performing data cleaning on the temperature and humidity record data and the power grid load data to obtain the operating environment data.

[0011] In an exemplary embodiment of the present disclosure, determining the internal device risk degree through the device status data of the power grid device further includes: inputting the processed temperature and humidity record data into a temperature and humidity model to determine the temperature and humidity risk degree; inputting the processed power grid load data into a maximum load model to determine the load risk degree; and determining the internal device risk degree according to the temperature and humidity risk degree and the load risk degree.

[0012] In an exemplary embodiment of the present disclosure, determining the power grid operation risk degree according to the risk correction coefficient, the internal device risk degree, and the external environment risk degree for power grid operation risk assessment includes:

[0013]

[0014] where K is the power grid operation risk degree, δ and τ are the risk correction coefficients, I i is the internal device risk degree of the i-th device, and O i is the external environment risk degree of the i-th device.

[0015] According to one aspect of the present disclosure, a power grid operation risk assessment device is provided, which includes: an internal risk module for determining the internal device risk degree through the device status data of the power grid device; an external risk module for determining the external environment risk degree through the operating environment data of the power grid device; a risk coefficient module for determining the risk correction coefficient; and a risk assessment module for determining the power grid operation risk degree according to the risk correction coefficient, the internal device risk degree, and the external environment risk degree for power grid operation risk assessment.

[0016] According to one aspect of the present disclosure, an electronic device is provided, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0017] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method as described above is implemented.

[0018] According to the power grid operation risk assessment method, device, electronic device and computer-readable storage medium of the present disclosure, the power grid monitoring can be made more intelligent and rigorous, and the power grid operation can be made safer and more reliable.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other objects, features and advantages of the present disclosure will become more apparent. The following described drawings are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0021] Figure 1 is a system block diagram of a power grid operation risk assessment method and device shown according to an exemplary embodiment.

[0022] Figure 2 is a flowchart of a power grid operation risk assessment method shown according to an exemplary embodiment.

[0023] Figure 3 is a schematic diagram of a power grid operation risk assessment method shown according to an exemplary embodiment.

[0024] Figure 4 is a schematic diagram of a power grid operation risk assessment method shown according to another exemplary embodiment.

[0025] Figure 5 is a block diagram of a power grid operation risk assessment device shown according to an exemplary embodiment.

[0026] Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment.

[0027] Figure 7 is a schematic diagram of a computer-readable storage medium shown according to an exemplary embodiment. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar parts, and thus their repetitive description will be omitted.

[0029] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be used. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0030] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the drawings are merely illustrative and do not necessarily include all the content and operations / steps, nor do they necessarily have to be executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0032] It should be understood that although terms such as first, second, and third may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below can be referred to as the second component without departing from the teachings of the concept of the present disclosure. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0033] Those skilled in the art can understand that the drawings are only schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily essential for implementing the present disclosure, and thus cannot be used to limit the protection scope of the present disclosure.

[0034] Figure 1 is a system block diagram of a power grid operation risk assessment method, device, electronic device, and computer-readable storage medium shown according to an exemplary embodiment.

[0035] As Figure 1As shown, the system architecture may include terminal devices 101, 102, 103, network 104, and server 105. Network 104 is used to provide a medium for communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0036] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0037] Terminal devices 101, 102, 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop computers, and desktop computers, etc.

[0038] Server 105 can be a server that provides various services, such as analyzing and processing device status data obtained by using terminal devices 101, 102, 103. Server 105 can analyze and process the received data and feedback the processing results to the terminal device.

[0039] Server 105 can determine the internal device risk level through, for example, the device status data of grid devices; Server 105 can determine the external environment risk level through, for example, the operating environment data of grid devices; Server 105 can determine, for example, a risk correction coefficient; Server 105 can determine the grid operation risk level based on the risk correction coefficient, internal device risk level, and external environment risk level for grid operation risk assessment.

[0040] Server 105 can be a physical server or, for example, composed of multiple servers. It should be noted that the grid operation risk assessment method provided in the embodiments of the present disclosure can be executed by server 105. Correspondingly, the grid operation risk assessment device can be set in server 105. And the data request end for providing data to users is generally located in terminal devices 101, 102, 103.

[0041] Figure 2 It is a flowchart of a grid operation risk assessment method shown according to an exemplary embodiment. The grid operation risk assessment method at least includes steps S202 to S208.

[0042] As Figure 2 shown, in S202, the internal device risk level is determined through the device status data of grid devices.

[0043] In one embodiment, determining the internal device risk level through the device status data of power grid devices includes: obtaining the usage log data of the power grid devices; obtaining the maintenance log data of the power grid devices; obtaining the fault log data of the power grid devices; and performing data cleaning on the usage log data, the maintenance log data, and the fault log data to obtain the device status data.

[0044] In one embodiment, determining the internal device risk level through the device status data of power grid devices further includes: inputting the usage log data after cleaning into a device life model to determine the life risk level; inputting the maintenance log data after cleaning into a maintenance work model to determine the maintenance risk level; inputting the fault log data after cleaning into a device fault model to determine the fault risk level; and determining the internal device risk level according to the life risk level, the maintenance risk level, and the fault risk level.

[0045] The amount of data of the device status data of power grid devices is extremely large. There are nearly 150 status information items in one switch interval of a substation; there are 230 status information items for one transformer; there are approximately 2,500 status information items in one substation, and nearly 80,000 pieces of data are sent every day. Taking the Hangzhou power grid as an example, the Hangzhou power grid has a total of 361 substations, and more than 28 million pieces of data are sent every day. In 2016, the dispatching center of the Hangzhou company received more than 10 billion pieces of data. If these data are printed into books and stacked up, the height can rival Mount Everest.

[0046] In the face of processing such a large amount of information data, first, encrypt and isolate the transmission of all data to meet the requirements of power grid information security.

[0047] In S204, determine the external environment risk level through the operation environment data of power grid devices.

[0048] In one embodiment, determining the external environment risk level through the operation environment data of power grid devices includes: obtaining the temperature and humidity record data of the power grid devices; obtaining the power grid load data of the power grid devices; and performing data cleaning on the temperature and humidity record data and the power grid load data to obtain the operation environment data.

[0049] In one embodiment, determining the internal device risk level through the device status data of power grid devices further includes: inputting the temperature and humidity record data after cleaning into a temperature and humidity model to determine the temperature and humidity risk level; inputting the power grid load data after cleaning into a maximum load model to determine the load risk level; and determining the internal device risk level according to the temperature and humidity risk level and the load risk level.

[0050] In S206, determine the risk correction coefficient.

[0051] In one embodiment, determining the risk correction coefficient includes: determining the risk correction coefficient according to historical internal risk degree data, historical external environment risk degree, and historical power grid operation related data.

[0052] In S208, according to the risk correction coefficient, internal equipment risk degree, and external environment risk degree, determine the power grid operation risk degree to perform power grid operation risk assessment.

[0053] In one embodiment, determining the power grid operation risk degree according to the risk correction coefficient, internal equipment risk degree, and external environment risk degree to perform power grid operation risk assessment includes:

[0054]

[0055] Wherein, K is the power grid operation risk degree, δ and τ are the risk correction coefficients, I i is the internal equipment risk degree of the i-th device, and O i is the external environment risk degree of the i-th device.

[0056] According to the power grid operation risk assessment method of the present disclosure, by cleaning and processing the data, effective information is optimized and integrated; finally, using decision tree and artificial neural network algorithms, based on distributed computing technology, a model is built from multiple dimensions of space, time, type, and equipment, and deeply mined to realize functions such as pre-judging equipment hidden dangers in advance, assisting in abnormal handling during the event, and evaluating power grid risks after the event.

[0057] For example, the system analyzes data such as uploaded information, defect records in the same period of previous years, and environmental meteorology, and uses the box-and-whisker plot in statistics to mine the monitoring data. On June 2, 2016, the system prompted that there might be hidden dangers in the 110kV Shidai transformer, and pre-judged that the probability of hidden dangers in its secondary circuit was the largest. After on-site inspection by the maintenance unit, it was found that due to the high humidity during the plum rain season, the insulation of the secondary terminal of the voltage transformer in the station decreased. If measures are not taken in time, it will evolve into a defect in the secondary circuit of the voltage transformer. Through the analysis of uploaded information and historical records, pre-warning of equipment hidden dangers is realized.

[0058] The factors of power grid operation risks are divided into two categories. Internally, a model is built based on data such as equipment life, maintenance work, and defect records. Externally, the impacts of temperature, humidity, and power grid load are analyzed. Through the model correction of re - relevance and accuracy, the concept of power grid risk degree is innovatively proposed to achieve the ex - post assessment of power grid operation risks. For example, during the preparation for a certain summit, by evaluating the risk degree of XX power grid in the past 24 hours every day, it is found that the risk degree index of XX power grid generally shows an upward trend from March to June. According to the evaluation results, measures such as equipment defect elimination, centralized maintenance, and mode adjustment are carried out targeted. Through two months of rectification, the risk degree index of XX power grid has decreased significantly and reached the minimum value before the summit, making a significant contribution to the power supply guarantee task for the summit.

[0059] In addition, for the situations of bus voltage and reactive power over - limit in each sub - station at different time periods, the system collects the historical operation information of reactive voltage control, analyzes the regulation effects of each device in the power grid, and provides different AVC control strategies, providing an optimal real - time reactive voltage control scheme for some sub - stations. The number of reactive voltage regulation times is reduced by 32.5%, providing auxiliary decision - making for the on - site disposal by monitors under abnormal power grid conditions.

[0060] It should be clearly understood that this disclosure describes how to form and use specific examples, but the principles of this disclosure are not limited to any details of these examples. On the contrary, based on the teachings of the content disclosed in this disclosure, these principles can be applied to many other embodiments.

[0061] Figure 3 It is a schematic diagram of a power grid operation risk assessment method shown according to an exemplary embodiment. Figure 3 The shown power grid operation risk assessment method is a further detailed description of Figure 2 the content described above.

[0062] Determining the power grid operation risk degree based on the risk correction coefficient, internal equipment risk degree, and external environment risk degree for power grid operation risk assessment includes:

[0063]

[0064] where K is the power grid operation risk degree, δ and τ are the risk correction coefficients, I i is the internal equipment risk degree of the i - th device, and O i is the external environment risk degree of the i - th device.

[0065] First, by inputting the processed usage log data into the equipment life model, the life risk degree is determined; by inputting the processed maintenance log data into the maintenance work model, the maintenance risk degree is determined; by inputting the processed fault log data into the equipment fault model, the fault risk degree is determined; and the internal equipment risk degree is determined according to the life risk degree, the maintenance risk degree, and the fault risk degree. Through the results calculated by the above models, the internal production risk degree is comprehensively obtained.

[0066] Then, by inputting the processed temperature and humidity record data into the temperature and humidity model, the temperature and humidity risk degree is determined; by inputting the processed power grid load data into the maximum load model, the load risk degree is determined; and the internal equipment risk degree is determined according to the temperature and humidity risk degree and the load risk degree. Through the results calculated by the above models, the external risk degree is comprehensively obtained through calculation.

[0067] The characteristics of different risk degrees in different seasons and environments can be comprehensively regulated through risk correction factors. For example, in the rainy season, the temperature and humidity coefficient in the risk correction factor will be set relatively large. Another example is that in the severe winter season, due to the sharp increase in electricity consumption, the coefficient of the maximum load correlation model in the risk correction factor will be set relatively large. The above are only exemplary descriptions, and the present application is not limited thereto.

[0068] Figure 4 It is a schematic diagram of a power grid operation risk assessment method shown according to another exemplary embodiment. Figure 4 The dynamic risk assessment results are described exemplarily. The risk assessment results are executed regularly through manual setting. When the risk is relatively high, risk warning information can also be generated for the staff to handle.

[0069] Another example is to establish standardized hidden danger response measures and templatized hidden danger warning contact forms, construct a closed-loop management process for equipment hidden dangers from discovery, warning to handling, earnestly do a good job in risk pre-control, closed-loop treatment of hidden dangers, and various organizational measures, technical measures, and safety measures to ensure safety, and truly achieve seamless connection of equipment hidden danger warning work among various departments.

[0070] For example, a review system for potential hazard warning work can also be established. At the end of each quarter, the control agencies of power companies in each city of the province report the potential hazard cases found during this period. The provincial dispatching center organizes a centralized review of the reported cases by experts in the province's monitoring profession to determine the nature of the cases. In addition, to promote the in-depth development of potential hazard investigation work for centralized monitoring operation and enhance the breadth and depth of potential hazard investigation, the provincial dispatching center centrally organizes a potential hazard case exchange meeting, selects typical safety potential hazard cases from the potential hazards identified through centralized review by experts from each city's power company, publicizes and announces them after optimization and improvement within the province, and arranges for each city's power company to check whether there are similar potential hazards, laying a solid foundation for the continued stable operation of the province's power grid system's safety production.

[0071] Those skilled in the art can understand that all or part of the steps for implementing the above embodiments are realized as a computer program executed by a CPU. When the computer program is executed by the CPU, the above functions defined by the above method provided by the present disclosure are executed. The program can be stored in a computer-readable storage medium, and the storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0072] In addition, it should be noted that the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0073] The following is an embodiment of the device of the present disclosure, which can be used to execute the embodiment of the method of the present disclosure. For details not disclosed in the embodiment of the device of the present disclosure, please refer to the embodiment of the method of the present disclosure.

[0074] Figure 5 It is a block diagram of a power grid operation risk assessment device shown according to an exemplary embodiment. Among them, the risk assessment device includes: an internal risk module 502, an external risk module 504, a risk coefficient module 506, and a risk assessment module 508.

[0075] The internal risk module 502 is used to determine the internal equipment risk degree through the equipment status data of power grid equipment; it includes: obtaining the usage log data of the power grid equipment; obtaining the maintenance log data of the power grid equipment; obtaining the fault log data of the power grid equipment; and performing data cleaning processing on the usage log data, the maintenance log data, and the fault log data to obtain the equipment status data.

[0076] The external risk module 504 is used to determine the external environmental risk level through the operation environment data of grid equipment, including: obtaining the temperature and humidity record data of the grid equipment; obtaining the grid load data of the grid equipment; and performing data cleaning on the temperature and humidity record data and the grid load data to obtain the operation environment data.

[0077] The risk coefficient module 506 is used to determine the risk correction coefficient, including: determining the risk correction coefficient according to historical internal risk level data, historical external environmental risk levels, and historical grid operation-related data.

[0078] The risk assessment module 508 is used to determine the grid operation risk level based on the risk correction coefficient, internal equipment risk level, and external environmental risk level for grid operation risk assessment, including:

[0079]

[0080] where K is the grid operation risk level, δ and τ are the risk correction coefficients, I i is the internal equipment risk level of the i-th device, and O i is the external environmental risk level of the i-th device.

[0081] According to the grid operation risk assessment device of the present disclosure, by performing data cleaning on the data, effective information is optimized and integrated; finally, using decision tree and artificial neural network algorithms, based on distributed computing technology, a model is built from multiple dimensions of space, time, type, and equipment for in-depth mining, realizing functions such as predicting equipment hidden dangers in advance, assisting in abnormal handling during the event, and evaluating grid risks after the event.

[0082] Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment.

[0083] Next, refer to Figure 6 to describe the electronic device 200 according to this embodiment of the present disclosure. Figure 6 The electronic device 200 shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0084] As Figure 6 shown, the electronic device 200 is presented in the form of a general computing device. The components of the electronic device 200 may include but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including the storage unit 220 and the processing unit 210), a display unit 240, etc.

[0085] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 210, so that the processing unit 210 executes the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription transfer processing method part of this specification. For example, the processing unit 210 can execute as Figure 2 the steps shown therein.

[0086] The storage unit 220 may include a readable storage medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 2201 and / or a cache storage unit 2202, and may further include a read-only storage unit (ROM) 2203.

[0087] The storage unit 220 may also include a program / utilities 2204 having a set (at least one) of program modules 2205. Such program modules 2205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0088] The bus 230 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0089] The electronic device 200 may also communicate with one or more external devices 300 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 200, and / or communicate with any device that enables the electronic device 200 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 250. Moreover, the electronic device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 through the bus 230. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0090] Based on the descriptions of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a portable hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute the above-mentioned method according to the embodiments of the present disclosure.

[0091] Figure 7 Schematically shows a schematic diagram of a readable storage medium in an exemplary embodiment of the present disclosure.

[0092] Refer to Figure 7 As shown, a program product 400 for implementing the above method according to an embodiment of the present disclosure is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0093] The program product can adopt any combination of one or more readable storage media. A readable storage medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0094] The readable storage medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable storage medium other than the readable storage medium, and this readable storage medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0095] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or, it can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0096] The above-readable storage medium carries one or more programs, when the above one or more programs are executed by a device, enabling the computer-readable storage medium to implement the following functions: determining the internal device risk degree through the device status data of the power grid device; determining the external environment risk degree through the operating environment data of the power grid device; determining the risk correction coefficient; and determining the power grid operation risk degree according to the risk correction coefficient, the internal device risk degree, and the external environment risk degree for power grid operation risk assessment.

[0097] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiments, or can be correspondingly changed and distributed in one or more devices that are uniquely different from this embodiment. The modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0098] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described here can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0099] The above specifically illustrates and describes the exemplary embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the detailed structures, settings, or implementation methods described here; on the contrary, the present disclosure intends to cover various modifications and equivalent settings included within the spirit and scope of the appended claims.

[0100] In addition, the structures, ratios, sizes, etc. shown in the accompanying drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in the art to understand and read, and are not used to limit the conditions for the implementation of the present disclosure. Therefore, they do not have technical substantial significance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the technical effects that the present disclosure can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present disclosure. At the same time, the terms such as "upper", "first", "second" and "one" cited in this specification are only for the convenience of clear description and are not used to limit the scope of implementation of the present disclosure. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope that the present disclosure can implement.

Claims

1. A method for evaluating the operation risk of a power grid, characterized in that, it includes: Determining the internal equipment risk degree through the equipment status data of power grid equipment: obtaining the usage log data, maintenance log data, and fault log data of the power grid equipment; Performing data cleaning on the usage log data, the maintenance log data, and the fault log data to obtain the equipment status data; inputting the cleaned usage log data into the equipment life model to determine the life risk degree; inputting the cleaned maintenance log data into the maintenance work model to determine the maintenance risk degree; Inputting the cleaned fault log data into the equipment fault model to determine the fault risk degree; And determining the internal equipment risk degree according to the life risk degree, the maintenance risk degree, and the fault risk degree; Determining the external environment risk degree through the operation environment data of power grid equipment; Determining the risk correction coefficient according to the historical internal risk degree data, the historical external environment risk degree, and the historical power grid operation related data; And determining the power grid operation risk degree according to the risk correction coefficient, the internal equipment risk degree, and the external environment risk degree for power grid operation risk assessment, including: Among them, K is the grid operation risk degree, δ and τ are the risk correction coefficients, and I i is the internal device risk degree of the i-th device, and O i is the external environment risk degree of the i-th device.

2. The method according to claim 1, characterized in that, Determining the external environment risk degree through the operation environment data of power grid equipment includes: Obtaining the temperature and humidity record data of the power grid equipment; Obtaining the power grid load data of the power grid equipment; and Performing data cleaning on the temperature and humidity record data and the power grid load data to obtain the operation environment data.

3. The method according to claim 2, characterized in that, Determining the internal equipment risk degree through the equipment status data of power grid equipment further includes: Inputting the cleaned temperature and humidity record data into the temperature and humidity model to determine the temperature and humidity risk degree; cleaning Processing the power grid load data and inputting it into the maximum load model to determine the load risk degree; and Determining the internal equipment risk degree according to the temperature and humidity risk degree and the load risk degree.

4. An electronic device, characterized in that, it includes: One or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-3.

5. A readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by a processor, it implements the method according to any one of claims 1-3.

Images