Power plant operation risk management method, system, device and medium

Abstract

The embodiment of the application provides a power plant operation risk management method, system, device and medium, and belongs to the field of thermal power plant safety. The method comprises the following steps: API automatically extracts the work ticket operation site, risk level and guardian, generates a risk coordinate tuple; a heat map is rendered; no camera is matched or offline blocking permission is implemented, so that no monitoring and no work hard constraint are realized; clustering, continuous duration threshold, automatic upgrade and push. Through the conversion of isolated work ticket information into spatialized risk coordinate tuples, and taking the same as the core to drive the closed-loop process of risk heat map generation, video monitoring automatic matching, operation permission hard interlocking and risk intelligent judgment, the real-time monitoring capability for high-risk operation, the automatic enforcement efficiency of safety rules and the active prediction capability for risk evolution are improved, and the overall safety management level of the power plant is improved.

Description

Technical Field

[0001] This invention relates to the field of safety technology for thermal power plants, and more specifically to a method, system, equipment, and medium for controlling operational risks in power plants. Background Technology

[0002] In traditional thermal power plant safety operation management, risk control mainly relies on paper or electronic work permit systems and personnel's experience. Information such as work location, risk level, and supervisor is usually recorded in text form on the work permit, which is disconnected from the power plant's physical space, video surveillance system, and personnel positioning system. Safety management personnel find it difficult to intuitively and in real time grasp core safety issues such as "where are the risks," "how great are the risks," "is there surveillance coverage," and "are supervisors in place" across the entire plant.

[0003] The existing approach has significant drawbacks: First, the locations of risks are not visible, making it impossible to present the spatial distribution and clustering of risks in real time on digital maps; second, monitoring resources are mismatched, failing to automatically and accurately correlate video cameras with dynamically changing operational risk points, resulting in monitoring blind spots; and third, the enforcement of safety procedures is based on soft constraints, with regulations such as "no work without monitoring" relying on personnel self-discipline and spot checks, lacking a system-level hard-line blocking mechanism. This fragmented and manual management model makes it difficult to achieve full-process, visualized, real-time, and precise control of high-risk operations, becoming a prominent pain point in the safe operation of power plants. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, equipment, and medium for controlling operational risks in power plants. By transforming isolated work order information into spatialized risk coordinate tuples, and using this as the core to drive a closed-loop process of risk heat map generation, automatic video surveillance matching, hard interlocking of work permits, and intelligent risk assessment, the invention improves the real-time monitoring capability for high-risk operations, the effectiveness of automatic enforcement of safety rules, and the ability to proactively predict risk evolution, thereby enhancing the overall safety management level of power plants.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for managing operational risks in power plants, comprising: Obtain work permit information for power plant operations and extract structured data from it, including at least the work location, risk level, and supervisor information; The work site is mapped to the risk location coordinates in the power plant spatial coordinate system, and combined with the risk level and the guardian information, a risk coordinate tuple containing location, level and status information is generated. Based on the location and level information in the risk coordinate tuple, a dynamic risk heat map is generated on the power plant digital map, serving as a visual monitoring interface for power plant operational risks; and spatial density analysis is performed on the location coordinates in the risk coordinate tuple within the same time period to identify densely distributed risk cluster areas and generate clustering results. Based on the position coordinates in the risk coordinate tuple, spatial matching calculation is performed with the camera field of view model of the power plant video monitoring system to associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, the work permit process of its corresponding work ticket is blocked. During the operation, the duration of risk based on the risk coordinate tuple and the clustering results are monitored. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, the risk level enhancement mechanism is triggered, the display status of the risk heat map is updated, and an early warning message is pushed to the responsible person.

[0006] Optionally, the work site is mapped to risk location coordinates in the power plant's spatial coordinate system, and combined with the risk level and supervisor information, a risk coordinate tuple containing location, level, and status information is generated, including: The system automatically extracts the work location, risk level, and guardian fields from the work ticket system through a preset API interface. Call the coordinate transformation service of the power plant's geographic information system to convert the text description of the work site into planar or three-dimensional coordinates; Risk levels are mapped to preset visual codes and numerical labels; Real-time query of the personnel location system to obtain the current location status of the guardian; The coordinates, level identifiers, and guardian status are encapsulated into a structured risk coordinate tuple.

[0007] Optionally, the risk heatmap serves as a visual monitoring interface, implementing the following functions: Using different colors and color depths, the spatial distribution and concentration of different levels of risk are visually rendered on digital maps; Receive video association information, provide an interactive interface, and allow users to retrieve associated real-time monitoring videos by clicking on risk points on the risk heatmap; The system receives and triggers a risk level escalation mechanism, dynamically updates the color, size, or flashing status of the corresponding risk points in the risk heatmap, and provides real-time graphical feedback on risk evolution.

[0008] Optionally, spatial density analysis is performed on the location coordinates in the risk coordinate tuples within the same time period to identify densely distributed risk clusters and generate clustering results, including: Extract all active risk coordinate tuples and their location coordinates from the real-time risk database; Based on the density clustering algorithm, other risk points near each coordinate point are searched with a preset scanning radius; if the number of neighboring points of a certain point reaches the minimum density threshold, the neighboring points are marked as the same risk cluster. Calculate the geometric center, the total number of risk points contained in each risk cluster, and the average risk level, and generate clustering result data containing cluster boundary and level information; The clustering results data are used as the trigger for the risk level enhancement mechanism, and the cluster boundary information is overlaid and displayed on the risk heat map.

[0009] Optionally, when the duration of the risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, a risk level enhancement mechanism is triggered, including two parallel judgment logics: Start a timer for each risk coordinate tuple. When its duration exceeds the preset duration threshold corresponding to its current risk level, raise its risk level by one level. The clustering results are received in real time. When the point density or overall risk value of a certain risk cluster exceeds a preset threshold, the risk level of all points in that risk cluster is increased in a coordinated manner. Regardless of which of the above logic triggers the upgrade, the system will perform the following operations: update the level information in the risk coordinate tuple, send a graphic update command to the risk heatmap, and push an alarm to the relevant responsible persons through the messaging platform.

[0010] Optionally, based on the position coordinates in the risk coordinate tuple, spatial matching calculations are performed with the camera field-of-view model of the power plant video surveillance system to associate each risk point with a video surveillance source. If any risk point is not associated with a video surveillance source, the work permit process for its corresponding work order is blocked, including: Obtain the spatial parameters of all cameras in the power plant, including their position, orientation angle, and field of view, and construct a spatial model of the field of view for each camera; The generated risk location coordinates are then compared with the field-of-view spatial model of each camera to calculate spatial inclusion. For risk points that can be covered by multiple cameras, calculate the spatial distance between the point and the center of the field of view of each camera, and select at least one camera that is closest to the point as the first matching source; The successfully matched camera IDs are associated with the risk coordinate tuples. This association is used to determine whether the work permit process for the work ticket corresponding to the risk point is blocked, and serves as the data basis for interactively retrieving video from the risk heatmap.

[0011] Optionally, during the work permit approval process, the system can retrieve the matched video association status. If a risk coordinate tuple is not associated with any camera, or the associated camera is offline, a forced blocking instruction for missing video monitoring will be returned in the permission process to prevent the work ticket from entering the permission execution state until a valid monitoring is matched. Blocking events are recorded and associated with the corresponding risk coordinate tuple as a security audit log.

[0012] Secondly, the present invention also provides a power plant operation risk management system, comprising: The data acquisition module is used to acquire work order information for power plant operations and parse out structured data from it, including at least the work location, risk level, and supervisor information. The coordinate generation module is used to map the work site to risk location coordinates in the power plant spatial coordinate system, and combine the risk level and the guardian information to generate a risk coordinate tuple containing location, level and status information. The risk clustering module is used to render and generate a dynamic risk heat map on the power plant digital map based on the location and level information in the risk coordinate tuple, serving as a visual monitoring interface for power plant operation risks; and to perform spatial density analysis on the location coordinates in the risk coordinate tuple within the same time period to identify spatially dense risk cluster areas and generate clustering results. The monitoring association module is used to perform spatial matching calculations with the camera field of view model of the power plant video monitoring system based on the position coordinates in the risk coordinate tuple, and associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, the work permit process of its corresponding work ticket is blocked. The risk management module is used to monitor the duration of risk based on the risk coordinate tuple and the clustering results during the operation. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, the risk level improvement mechanism is triggered, the display status of the risk heat map is updated, and an early warning information is pushed to the responsible person.

[0013] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for controlling power plant operational risks.

[0014] Fourthly, the present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for controlling power plant operational risks.

[0015] The above technical solution transforms isolated work order information into spatialized risk coordinate tuples, and uses these as the core to drive a closed-loop process of risk heat map generation, automatic video surveillance matching, hard interlocking of work permits, and intelligent risk assessment. This enhances the real-time monitoring capability of high-risk operations, the effectiveness of automatic enforcement of safety rules, and the proactive prediction capability of risk evolution, thereby improving the overall safety management level of the power plant.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for controlling operational risks in power plants, provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a power plant operation risk management system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0019] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0020] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] See Figure 1 The diagram shows a flowchart of a method for controlling operational risks in power plants, including the following steps: Step 100: Obtain the power plant work order information and parse out the structured data, which includes at least the work location, risk level, and supervisor information.

[0023] Step 101: Map the work site to risk location coordinates in the power plant spatial coordinate system, and combine the risk level and the guardian information to generate a risk coordinate tuple containing location, level and status information.

[0024] Specifically, when executing step 101, the following steps can be performed: S1010: Automatically extracts the work location, risk level, and guardian fields from the work ticket system through a preset API interface.

[0025] S1011: Call the coordinate transformation service of the power plant's geographic information system to convert the text description of the work site into planar or three-dimensional coordinates.

[0026] For example, this can be achieved through the spatial analysis engine in the power plant's built-in Geographic Information System (GIS) or Building Information Modeling (BIM). The system first intelligently analyzes the textual description of the work site, identifying key elements such as "unit," "floor," "equipment," and "location." Then, based on the standard coordinate reference system in the power plant's digital model, these elements are matched and associated with predefined equipment coordinates, area boundaries, or structural surfaces within the model. Finally, by calculating the spatial geometric relationship between the relative position in the description and reference objects, the ambiguous textual description is converted into unique, precise planar or three-dimensional coordinates within the digital model, thus providing a precise spatial positioning basis for subsequent risk map annotation and video linkage.

[0027] S1012: Map risk levels to preset visual codes and numerical identifiers.

[0028] S1013: Real-time query personnel location system to obtain the current location status of the guardian.

[0029] S1014: Encapsulate the coordinates, level identifier, and guardian status into a structured risk coordinate tuple.

[0030] For example, encapsulating coordinates, risk level identifiers, and guardian status into structured risk coordinate tuples is a systematic data integration and standardization process. Specifically, the system first uses three types of heterogeneous data as basic inputs: spatial coordinates (X, Y, Z) obtained from GIS services, level codes and visualization parameters obtained from risk standardization mapping tables, and guardian on-duty status and location information obtained from real-time queries from personnel positioning systems. Subsequently, according to a predefined metadata architecture, these data are placed into key fields such as "location," "risk," and "supervisor," and a globally unique identifier and current timestamp are generated for each tuple. Finally, this encapsulated complete data object containing spatial, attribute, and status information—the risk coordinate tuple—is published to the system's real-time data bus or written to the risk database as an indivisible logical unit.

[0031] Step 102: Based on the location and level information in the risk coordinate tuple, a dynamic risk heat map is generated on the power plant digital map as a visual monitoring interface for power plant operation risks; and spatial density analysis is performed on the location coordinates in the risk coordinate tuple within the same time period to identify densely distributed risk cluster areas and generate clustering results.

[0032] Specifically, the risk heatmap serves as a visual monitoring interface, enabling the following functions: visually rendering the spatial distribution and clustering of risks at different levels on a digital map using different colors and color depths; receiving video-related information and providing an interactive interface, allowing users to retrieve associated real-time monitoring videos by clicking on risk points on the risk heatmap; and receiving triggered risk level escalation mechanisms to dynamically update the color, size, or flashing status of corresponding risk points on the risk heatmap, achieving real-time graphical feedback on risk evolution.

[0033] In one specific implementation, when performing step 102, the following steps may be specifically performed: S1020: Extract all active risk coordinate tuples and their location coordinates from the real-time risk database.

[0034] S1021: Based on the density clustering algorithm, search for other risk points near each coordinate point with a preset scanning radius; if the number of neighboring points of a certain point reaches the minimum density threshold, then the neighboring points are marked as the same risk cluster.

[0035] For example, two key parameters are first set: the neighborhood scan radius (R) and the minimum density threshold (MinPts). During processing, the system traverses all active risk coordinate points, draws a circular neighborhood with each point as the center and radius R, and counts the number of other risk points falling within this circular area. If a center point contains at least MinPts of other points within its neighborhood, that point is marked as a "core point". Subsequently, the system recursively absorbs all other risk points (including new core points or ordinary points) within its neighborhood and groups them into the same cluster, starting from this core point, until no new points can be added. This process is repeated until all points have been visited. Finally, the system outputs all identified risk clusters, each containing a set of spatially densely related risk points, and calculates the geographic center, coverage boundary, and comprehensive risk level of each cluster, thus providing a direct basis for subsequent risk heatmap area rendering and dynamic risk escalation judgment.

[0036] S1022: Calculate the geometric center, the total number of risk points contained in each risk cluster, and the average risk level, and generate clustering result data containing cluster boundary and level information.

[0037] For example, suppose a risk cluster contains n risk points, each risk point P i The plane coordinates are Then the coordinates C of the geometric center (centroid) of the cluster are: ; Let the risk level weight of each risk point Pi be . The average risk level for: ; Define the point set of a cluster Its boundary is calculated by the convex hull of the point set S. Obtain the convex hull. It is the smallest convex polygon that satisfies the following conditions: ; In actual computation, the output is an ordered sequence of vertices that constitute the convex polygon. ,in And all of these vertices belong to S.

[0038] Ultimately, the system generates a structured result data object for each risk cluster. The object contains: ; In the formula, C is the unique identifier for the cluster, C is the coordinate of the geometric center, and N is the total number of risk points. Let V be the average risk level value, and V be the sequence of vertices of the convex hull defining the boundary. This is a set of identifiers for all risk point coordinate tuples within the cluster, used for data association.

[0039] S1023: The clustering results data are used as the trigger for the risk level enhancement mechanism, and the cluster boundary information is overlaid on the risk heat map.

[0040] Step 103: Based on the position coordinates in the risk coordinate tuple, perform spatial matching calculation with the camera field of view model of the power plant video monitoring system, and associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, block the work permit process of its corresponding work order.

[0041] Specifically, when executing step 103, the following steps can be performed: S1030: Obtain spatial parameters of all cameras in the power plant, including position, orientation angle and field of view, and construct a field of view spatial model for each camera.

[0042] Specifically, firstly, the system obtains the precise spatial parameters of each camera from the power plant's video surveillance management platform or digital twin model. This includes the three-dimensional coordinates (X, Y, C, D) of the installation location. c , Y c Z c ), horizontal orientation angle α, vertical pitch angle β, and horizontal field of view θ h and vertical field of view θ v Based on the above parameters, a geometric model of a square pyramid or frustum is constructed for each camera in a unified power plant spatial coordinate system to represent its effective field of view. The construction process follows standard computer graphics and spatial geometry principles: based on the horizontal orientation angle α and the vertical pitch angle β, the unit vector V in the direction the camera is facing is calculated through spherical coordinate transformation. view Define a point perpendicular to V at a distance d meters from the camera along the line of sight vector. view A rectangular projection plane. Using the center of the projection plane as a reference, based on the horizontal field of view θ... h and vertical field of view θ v Calculate the half-width W and half-height H of the rectangular field of view on this plane. The specific relationship is as follows: Subsequently, the coordinates of the four corner points of the rectangle were inversely transformed and calculated back into the power plant's unified coordinate system to obtain the three-dimensional coordinates P1, P2, P3, and P4 of the four corner points. The camera's installation coordinates (X...) were then... c , Y c Z cConnecting the camera to the four corner points P1, P2, P3, and P4 forms a quadrangular pyramid with the camera as the vertex and the projected rectangle as the base. If the closest focusing distance is considered, the model is a quadrangular frustum with two parallel rectangles (near and far ends) as its base.

[0043] Finally, a field-of-view spatial model object is generated for each camera, which can be directly invoked by mathematical computing software. This object is essentially a structured geometric description containing data such as vertex coordinates, view axis vectors, and boundary plane equations. This model is the sole and accurate geometric basis for subsequent spatial inclusion calculations (determining whether a risk point is within the field of view) and intersection area calculations (selecting the best camera for risk points).

[0044] S1031: The generated risk location coordinates are used to perform spatial inclusion calculations with the field-of-view spatial models of each camera.

[0045] S1032: For a risk point that can be covered by multiple cameras, calculate the spatial distance between the point and the center of the field of view of each camera, and select at least one camera that is closest to it as the first matching source.

[0046] For example, the spatial distance between this point and the center of each camera's field of view is calculated using the Euclidean distance formula.

[0047] S1033: Associate the successfully matched camera ID with the risk coordinate tuple. This association is used to determine whether the work permit process for the work ticket corresponding to the risk point is blocked, and serves as the data basis for interactively retrieving video from the risk heatmap.

[0048] Step 104: During the operation, monitor the duration of risk based on the risk coordinate tuple and the clustering results. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, trigger the risk level upgrade mechanism, update the display status of the risk heat map, and push early warning information to the responsible person.

[0049] Specifically, in step 104, when the duration of the risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, a risk level upgrade mechanism is triggered, including two parallel judgment logics: a timer is started for each risk coordinate tuple, and when its duration exceeds the preset duration threshold corresponding to its current risk level, its risk level is upgraded by one level; clustering results are received in real time, and when the point density or overall risk value of a certain risk cluster is identified to exceed a preset threshold, the risk level of all points in the risk cluster is upgraded collaboratively; regardless of whether the upgrade is triggered by either of the above logics, the system performs the following operations: updates the level information in the risk coordinate tuple, sends a graphic update command to the risk heatmap, and pushes an alarm to the relevant responsible persons through the message platform.

[0050] In one specific implementation, during the work permit approval process, the system calls the matched video association status; if a certain risk coordinate tuple is not associated with any camera, or the associated camera is offline, a forced blocking instruction for missing video monitoring is returned in the permit process to prevent the work ticket from entering the permit execution state until a valid monitoring is matched; the blocking event is recorded and associated back with the corresponding risk coordinate tuple as a safety audit log.

[0051] In this embodiment, by transforming isolated work order information into spatialized risk coordinate tuples, and using these as the core to drive a closed-loop process of risk heat map generation, automatic video surveillance matching, hard interlocking of work permits, and intelligent risk assessment, the real-time monitoring capability for high-risk operations, the effectiveness of automatic enforcement of safety rules, and the proactive prediction capability for risk evolution are improved, thereby enhancing the overall safety management and control level of the power plant.

[0052] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] like Figure 2 As shown, the following are embodiments of the power plant operation risk management system provided in this disclosure. The power plant operation risk management method belongs to the same inventive concept as the power plant operation risk management method in the above embodiments. For details not described in detail in the embodiments of the power plant operation risk management system, please refer to the embodiments of the power plant operation risk management method described above.

[0054] The power plant operation risk management system includes: The data acquisition module is used to acquire work order information for power plant operations and parse out structured data from it, including at least the work location, risk level, and supervisor information. The coordinate generation module is used to map the work site to risk location coordinates in the power plant spatial coordinate system, and combine the risk level and the guardian information to generate a risk coordinate tuple containing location, level and status information. The risk clustering module is used to render and generate a dynamic risk heat map on the power plant digital map based on the location and level information in the risk coordinate tuple, serving as a visual monitoring interface for power plant operation risks; and to perform spatial density analysis on the location coordinates in the risk coordinate tuple within the same time period to identify spatially dense risk cluster areas and generate clustering results. The monitoring association module is used to perform spatial matching calculations with the camera field of view model of the power plant video monitoring system based on the position coordinates in the risk coordinate tuple, and associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, the work permit process of its corresponding work ticket is blocked. The risk management module is used to monitor the duration of risk based on the risk coordinate tuple and the clustering results during the operation. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, the risk level improvement mechanism is triggered, the display status of the risk heat map is updated, and an early warning information is pushed to the responsible person.

[0055] Figure 3 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0056] The power plant operation risk management method provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0057] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0058] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0059] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0060] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0061] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0062] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0063] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0064] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0065] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0066] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0067] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0068] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0069] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0070] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0071] The storage medium provided in this application stores a program product that enables the management and control of power plant operational risks.

[0072] The method for controlling power plant operation risks includes: acquiring power plant operation work order information and parsing it to extract structured data including at least the work location, risk level, and supervisor information; mapping the work location to risk location coordinates in the power plant's spatial coordinate system, and combining the risk level and supervisor information to generate risk coordinate tuples containing location, level, and status information; based on the location and level information in the risk coordinate tuples, rendering a dynamic risk heat map on the power plant's digital map as a visual monitoring interface for power plant operation risks; and performing spatial density analysis on the location coordinates in the risk coordinate tuples within the same time period to identify empty... Clustering results are generated for densely distributed risk clusters. Based on the location coordinates in the risk coordinate tuple, spatial matching calculations are performed with the camera field-of-view model of the power plant video surveillance system to associate each risk point with a video surveillance source. If any risk point is not associated with a video surveillance source, the work permit process for its corresponding work order is blocked. During the operation, the duration of the risk based on the risk coordinate tuple and the clustering results are monitored. When the duration of the risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, a risk level escalation mechanism is triggered, the display status of the risk heat map is updated, and a warning message is pushed to the responsible person.

[0073] In some possible implementations, the subject matter of this disclosure, namely, the method and system for controlling operational risks in power plants, can be implemented as a program product comprising program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure.

[0074] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0075] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling operational risks in power plants, characterized in that, include: Obtain work permit information for power plant operations and extract structured data from it, including at least the work location, risk level, and supervisor information; The work site is mapped to the risk location coordinates in the power plant spatial coordinate system, and combined with the risk level and the guardian information, a risk coordinate tuple containing location, level and status information is generated. Based on the location and level information in the risk coordinate tuple, a dynamic risk heat map is generated on the power plant digital map, serving as a visual monitoring interface for power plant operational risks; and spatial density analysis is performed on the location coordinates in the risk coordinate tuple within the same time period to identify densely distributed risk cluster areas and generate clustering results. Based on the position coordinates in the risk coordinate tuple, spatial matching calculation is performed with the camera field of view model of the power plant video monitoring system to associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, the work permit process of its corresponding work ticket is blocked. During the operation, the duration of risk based on the risk coordinate tuple and the clustering results are monitored. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, the risk level enhancement mechanism is triggered, the display status of the risk heat map is updated, and an early warning message is pushed to the responsible person.

2. The method for controlling operational risks in power plants according to claim 1, characterized in that, The work site is mapped to risk location coordinates in the power plant's spatial coordinate system, and combined with the risk level and supervisor information, a risk coordinate tuple containing location, level, and status information is generated, including: The system automatically extracts the work location, risk level, and guardian fields from the work ticket system through a preset API interface. Call the coordinate transformation service of the power plant's geographic information system to convert the text description of the work site into planar or three-dimensional coordinates; Risk levels are mapped to preset visual codes and numerical labels; Real-time query of the personnel location system to obtain the current location status of the guardian; The coordinates, level identifiers, and guardian status are encapsulated into a structured risk coordinate tuple.

3. The method for controlling operational risks in power plants according to claim 1, characterized in that, The risk heatmap serves as a visual monitoring interface, enabling the following functions: Using different colors and color depths, the spatial distribution and concentration of different levels of risk are visually rendered on digital maps; Receive video association information, provide an interactive interface, and allow users to retrieve associated real-time monitoring videos by clicking on risk points on the risk heatmap; The system receives and triggers a risk level escalation mechanism, dynamically updates the color, size, or flashing status of the corresponding risk points in the risk heatmap, and provides real-time graphical feedback on risk evolution.

4. The method for controlling operational risks in power plants according to claim 1, characterized in that, Spatial density analysis is performed on the location coordinates in the risk coordinate tuples within the same time period to identify densely distributed risk clusters and generate clustering results, including: Extract all active risk coordinate tuples and their location coordinates from the real-time risk database; Based on the density clustering algorithm, other risk points near each coordinate point are searched with a preset scanning radius; if the number of neighboring points of a certain point reaches the minimum density threshold, the neighboring points are marked as the same risk cluster. Calculate the geometric center, the total number of risk points contained in each risk cluster, and the average risk level, and generate clustering result data containing cluster boundary and level information; The clustering results data are used as the trigger for the risk level enhancement mechanism, and the cluster boundary information is overlaid and displayed on the risk heat map.

5. The method for controlling operational risks in power plants according to claim 4, characterized in that, When the duration of the risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, a risk level escalation mechanism is triggered, which includes two parallel judgment logics: Start a timer for each risk coordinate tuple. When its duration exceeds the preset duration threshold corresponding to its current risk level, raise its risk level by one level. The clustering results are received in real time. When the point density or overall risk value of a certain risk cluster exceeds a preset threshold, the risk level of all points in that risk cluster is increased in a coordinated manner. Regardless of which of the above judgment logics triggers the upgrade, the system will perform the following operations: update the level information in the risk coordinate tuple, send a graphic update command to the risk heatmap, and push an alarm to the relevant responsible persons through the messaging platform.

6. The method for controlling operational risks in power plants according to claim 1, characterized in that, Based on the position coordinates in the risk coordinate tuple, spatial matching calculations are performed with the camera field-of-view model of the power plant video surveillance system. Each risk point is associated with a video surveillance source. If any risk point is not associated with a video surveillance source, the work permit process for its corresponding work order is blocked, including: Obtain the spatial parameters of all cameras in the power plant, including their position, orientation angle, and field of view, and construct a spatial model of the field of view for each camera; The generated risk location coordinates are then compared with the field-of-view spatial model of each camera to calculate spatial inclusion. For risk points that can be covered by multiple cameras, calculate the spatial distance between the point and the center of the field of view of each camera, and select at least one camera that is closest to the point as the first matching source; The successfully matched camera IDs are associated with the risk coordinate tuples. This association is used to determine whether the work permit process for the work ticket corresponding to the risk point is blocked, and serves as the data basis for interactively retrieving video from the risk heatmap.

7. The method for controlling operational risks in power plants according to claim 6, characterized in that, In the work permit approval process, the system retrieves the associated status of the matched video. If a risk coordinate tuple is not associated with any camera, or the associated camera is offline, a forced blocking instruction for missing video monitoring will be returned in the permission process to prevent the work ticket from entering the permission execution state until a valid monitoring is matched. Blocking events are recorded and associated with the corresponding risk coordinate tuple as a security audit log.

8. A power plant operation risk management system, characterized in that, include: The data acquisition module is used to acquire work order information for power plant operations and parse out structured data from it, including at least the work location, risk level, and supervisor information. The coordinate generation module is used to map the work site to risk location coordinates in the power plant spatial coordinate system, and combine the risk level and the guardian information to generate a risk coordinate tuple containing location, level and status information. The risk clustering module is used to render and generate a dynamic risk heat map on the power plant digital map based on the location and level information in the risk coordinate tuple, serving as a visual monitoring interface for power plant operation risks; and to perform spatial density analysis on the location coordinates in the risk coordinate tuple within the same time period to identify spatially dense risk cluster areas and generate clustering results. The monitoring association module is used to perform spatial matching calculations with the camera field of view model of the power plant video monitoring system based on the position coordinates in the risk coordinate tuple, and associate each risk point with a video monitoring source. If any risk point is not associated with a video monitoring source, the work permit process of its corresponding work ticket is blocked. The risk management module is used to monitor the duration of risk based on the risk coordinate tuple and the clustering results during the operation. When the duration of risk exceeds a preset threshold or the clustering results show that the risk density exceeds the standard, the risk level improvement mechanism is triggered, the display status of the risk heat map is updated, and an early warning information is pushed to the responsible person.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the power plant operation risk control method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power plant operation risk control method as described in any one of claims 1 to 7.

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