Power plant whole scene fire operation intelligent management and control and risk early warning system and method

Abstract

The present application relates to the technical field of intelligent management and control, in particular to a power plant full-scene hot work intelligent management and control and risk early warning system and method, the system comprising a full-scene hot work account and permission management unit, a multi-source heterogeneous sensing data acquisition unit, a hot work risk dynamic quantitative calculation unit, a multi-modal graded early warning and linkage management and control unit, and a full-process closed-loop management and control execution unit; the method is realized based on the system, the risk weight is determined by a combination weighting algorithm combining the analytic hierarchy process and the improved entropy weight method, a quantitative model considering multi-dimensional risk coupling effect is used to calculate the real-time comprehensive risk degree, and a multi-source data space-time alignment, graded early warning linkage management and control, and full-process closed-loop management mechanism are matched. The present application realizes the risk precise quantification, early warning and full life cycle closed-loop management and control of all types of hot work in power plants, and significantly improves the safety management and control capability and intelligent level of hot work in power plants.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management and control, and particularly to an intelligent management and control and risk early warning system and method for full-scenario hot work operations in a power plant. Background Art

[0002] As a typical industrial site with high risks of inflammable and explosive substances, hot work operations in a power plant are a high-frequency operation type during equipment maintenance, operation and maintenance transformation processes, and also a high-risk operation link that can trigger major production safety accidents such as fires and explosions. The risk control effect of hot work operations directly determines the safety production level of the power plant. Therefore, realizing the intelligent management and control of the full process of hot work operations and the accurate early warning of risks is one of the core research directions in the field of safety production in the power industry.

[0003] In the existing related technologies, the comparative document CN117456447A discloses an intelligent management system and method for hot work operations applied to a power plant. The system mainly consists of a video monitoring unit, an algorithm server, a central management and control platform, and a display terminal. The core technical solution is as follows: by accessing and analyzing various types of monitoring videos in the power plant through the video monitoring unit, the algorithm server provides a similarity measurement algorithm and a decision tree algorithm. Based on the similarity calculation of the horizontal and vertical variance vectors of the images, abnormal events such as unsafe hot work operation behaviors of personnel and the lack of on-site protection measures in the monitoring videos are identified and determined. The abnormal events are real-time alarmed through the alarm module, and then the central management and control platform completes the recording, processing, and statistical analysis of the abnormal event data. Finally, functions such as data query and report generation are realized through the display terminal, so as to realize the intelligent management of the anti-violation work of hot work operations in the power plant, reduce the labor costs of manual inspections and monitoring on duty, and improve the identification and disposal efficiency of violation behaviors.

[0004] However, the technical solutions in the aforementioned comparative documents still have significant technical shortcomings in actual power plant hot work operation control applications: First, the risk identification dimension is singular. This solution relies solely on video image recognition to identify and alert on personnel violations, failing to incorporate multi-dimensional key risk factors such as the inherent risks of the hot work scenario, the operating status of surrounding production equipment, on-site environmental parameters such as combustible gases / dust, and the operating status of the hot work equipment. It cannot achieve a comprehensive assessment of all-dimensional risks in hot work operations and cannot effectively identify non-visual potential risks, easily leading to missed risk assessments in high-risk scenarios. Second, the risk assessment capability has inherent defects. This solution only achieves post-event judgment of abnormal events through image similarity comparison and decision tree classification, failing to construct a dynamic risk quantification model for hot work operations and neglecting the coupling and amplification effects between multiple risk factors, thus failing to achieve comprehensive risk assessment. The current method for accurately quantifying the comprehensive risks of hot work operations lacks the ability to provide early warnings of risks before and during the operation. Thirdly, the control loop is insufficient. This solution only identifies, alerts, and records data on violations, failing to cover the entire lifecycle management of hot work operations from application, approval, on-site verification, operation process control to completion and acceptance. It lacks a linkage mechanism between early warning levels and on-site control actions, making it impossible to achieve tiered and coordinated control based on risk levels. The implementation and timeliness of control measures are severely inadequate. Fourthly, the scenario adaptability is poor. This solution does not differentiate itself for the risk characteristics of different hot work scenarios such as power plant oil fields, hydrogen stations, boiler bodies, and cable interlayers. It cannot dynamically adjust risk assessment strategies and control rules according to the inherent risk levels of each scenario, making it difficult to cover the control needs of all types and levels of hot work operations in power plants, thus limiting its versatility and engineering practicality. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management and risk warning system and method for hot work operations in power plants across all scenarios, in order to solve the problems mentioned in the background art, such as the single dimension of risk identification in the management and control of hot work operations in power plants, the failure to consider the coupling effect of multiple factors in risk quantification, the insufficient closed-loop management and control, and the poor adaptability to different scenarios.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The intelligent control and risk warning system for hot work operations in power plants includes a hot work operation ledger and access management unit, a multi-source heterogeneous sensing data acquisition unit, a hot work risk dynamic quantitative calculation unit, a multi-modal hierarchical early warning and linkage control unit, and a full-process closed-loop control execution unit. The full-scenario hot work operation ledger and permission management unit is adapted to all types of hot work operation scenarios, including power plant boilers, steam turbines, oil fields, hydrogen stations, and cable mezzanines. It stores the full lifecycle data of hot work operation tickets, qualification data of operation and monitoring personnel, and scenario-specific risk classification data, and configures hot work operation classification approval and operation permissions. The multi-source heterogeneous sensing data acquisition unit simultaneously collects environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and production equipment operation data around the work site, and completes the spatiotemporal alignment and standardized preprocessing of the data. The dynamic quantification calculation unit for hot work risk incorporates a combined weighting algorithm and a risk coupling quantification model. Based on preprocessed real-time sensing data and ledger data, it calculates the real-time comprehensive risk level of hot work operations. The core calculation formula of the risk coupling quantification model is: ; In the formula, R represents the real-time comprehensive risk level. , , , The combined weights for each risk dimension, The inherent risk value of the scenario, This represents the dynamic risk value of the operation. This represents the real-time environmental risk value. For personnel compliance risk value, Risk coupling coefficient adapted to the scenario; The multimodal hierarchical early warning and linkage control unit triggers early warnings of the corresponding level based on the real-time comprehensive risk level and performs corresponding control actions in linkage. The full-process closed-loop control and execution unit realizes the full-process closed-loop management of hot work operations, from application, approval, on-site verification, operation process control to completion and acceptance.

[0007] Preferably, the multi-source heterogeneous sensing data acquisition unit incorporates a multi-source data synchronization and alignment module based on spatiotemporal dual dimensions and an adaptive Kalman filter fusion module. The spatiotemporal dual-dimensional synchronization and alignment module uses the three-dimensional spatial coordinates of the hot work point as a reference to match all sensing nodes within a preset protection radius of the work point. Using a millisecond-level unified timestamp as a reference, it employs linear interpolation to align the time axes of data at different sampling frequencies. The alignment calculation formula is as follows: ; In the formula, x(t) represents the interpolated aligned data at time t. , For adjacent sampling times, , The original data collected at the corresponding sampling time is used; the adaptive Kalman filter fusion module performs noise filtering and feature fusion on the aligned multi-source data and outputs standardized preprocessed data.

[0008] Preferably, the combined weighting algorithm of the dynamic quantification calculation unit for hot work risk adopts a dynamic weighting method combining the analytic hierarchy process (AHP) and the improved entropy weighting method. The combined weight calculation formula is as follows: ; ; ; In the formula, Let the combined weight of the j-th risk dimension be , The subjective weighting preference coefficients are dynamically updated according to the hot work operation stage. Here is the subjective weight of the j-th risk dimension calculated using the analytic hierarchy process. To improve the objective weight of the j-th risk dimension calculated by the entropy weight method, Let m be the information entropy of the j-th risk indicator, m be the total number of risk dimensions, and n be the sample data size. Let j be the standardized value of the i-th sample and j-th index. The indicator proportion; the unit also has a built-in risk rolling update module, which performs frame-by-frame rolling calculation and update of the real-time comprehensive risk degree R according to a preset second-level step size.

[0009] Preferably, the multimodal hierarchical early warning and linkage control unit presets four early warning levels and corresponding threshold ranges, and has a built-in false alarm suppression module and cross-system linkage interface; the four early warning levels include low-risk warning, general-risk warning, relatively high-risk warning, and major-risk warning, each corresponding to a preset R-value threshold range, and the early warning level is positively correlated with the intensity of control actions; the false alarm suppression module adopts a multi-source data cross-validation algorithm, and the false alarm suppression judgment formula is: ; In the formula, S represents the confidence level of the effective early warning. The number of valid verification data dimensions to trigger an alert. The total number of risk dimensions; the corresponding early warning and linkage control action is triggered only when S ≥ the preset confidence threshold; the cross-system linkage interface connects to the power plant's DCS system, SIS system, fire protection system, and access control system, and the linkage control action includes at least one of the following: on-site audible and visual alarm, work permit permission freezing, power outage of hot work equipment, isolation and shutdown of surrounding equipment, pre-start of fire protection system, and access control locking of work area.

[0010] On the other hand, the present invention also provides a method for intelligent management and risk warning of hot work operations in power plants across all scenarios, which is based on the above-mentioned intelligent management and risk warning system for hot work operations in power plants across all scenarios, and includes the following steps: S1 Full-Scenario Basic Data Configuration and Access Management: Adapts to all types of hot work scenarios such as power plant boilers, steam turbines, oil fields, hydrogen stations, and cable mezzanines. It records and stores the full lifecycle data of hot work permits, qualification data of operators and supervisors, and scenario-specific risk classification data, and configures hot work classification approval and operation permissions. S2 Multi-source heterogeneous sensing data acquisition and preprocessing: Simultaneously acquire environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and production equipment operation data around the work site, and complete the spatiotemporal alignment and standardized preprocessing of the data; S3 Real-time dynamic quantitative calculation of hot work risk: Based on pre-processed real-time sensing data and ledger data, the combined weight of each risk dimension is determined by a combined weighting algorithm, and the real-time comprehensive risk of hot work is calculated by a risk coupling quantitative model. S4 Multimodal graded early warning and linkage control: Match the corresponding early warning level according to the real-time comprehensive risk level, trigger the early warning signal of the corresponding mode, and link to execute the corresponding intensity of on-site control actions; S5 Hot Work Operation Closed-Loop Management: Completes closed-loop management of the entire hot work operation process, from application, approval, on-site verification, operation process control to completion acceptance, and synchronously stores the entire process data for traceability.

[0011] Preferably, in step S2, the specific steps of the spatiotemporal alignment and standardization preprocessing are as follows: using the three-dimensional spatial coordinates of the hot work point as a reference, delineate all sensing nodes within the preset protection radius of the work point, and acquire the original data collected by each node; using a unified timestamp at the millisecond level as a reference, use linear interpolation to complete the time axis alignment of data with different sampling frequencies, and the alignment calculation formula is: ; In the formula, x(t) represents the interpolated aligned data at time t. , For adjacent sampling times, , The original data collected at the corresponding sampling time is used; the aligned multi-source data is subjected to noise filtering and feature fusion using adaptive Kalman filtering, and the data is normalized by min-max normalization.

[0012] Preferably, in step S3, the specific steps of the combined weighting algorithm are as follows: S31, based on power plant hot work safety regulations and industry standards, constructs a risk dimension judgment matrix using the analytic hierarchy process (AHP). After passing consistency verification, the subjective weights of each risk dimension are calculated. ; S32 standardizes the preprocessed real-time sensing sample data, calculates the information entropy of each risk dimension using an improved entropy weight method, and then obtains the objective weights of each risk dimension. The calculation formula is: ; In the formula, Let m be the information entropy of the j-th risk indicator, m be the total number of risk dimensions, and n be the sample data size. Let j be the standardized value of the i-th sample and j-th index. For the percentage of indicators; S33 Based on the current stage of the hot work operation—application, preparation, hot work, cooling, or completion—the subjective weight preference coefficient α is dynamically updated, and the combined weights of each risk dimension are obtained through weighted fusion. The calculation formula is: ; Preferably, in step S3, the specific quantitative calculation method for the scenario-inherent risk value Rs, operational dynamic risk value Rd, real-time environmental risk value Re, and personnel compliance risk value Rp in the risk coupling quantification model is as follows: The inherent risk value Rs of the scenario is classified and quantified based on the flammability and explosiveness of the medium in the scenario to which the work point belongs, the operating status of the equipment, and the straight-line distance from the hazard source, with a value range of 0-1; The dynamic risk value Rd for the operation is quantified based on the hot work level, the operating status of the hot work equipment, the duration of the operation, and the implementation of safety measures. The calculation formula is as follows: ; In the formula, , , , These are the preset weighting coefficients for the corresponding indicators. This is a quantitative value for the hot work level. This is a quantitative value for the status of hot work equipment. Quantify the risk of operation duration. A quantitative value for the implementation of safety measures; The real-time environmental risk value Re is classified and quantified based on real-time data of combustible gas concentration, dust concentration, ambient temperature, humidity, and wind speed at the work site. The personnel compliance risk value Rp is quantified based on the personnel qualification matching degree, the type of violation, and the duration of the violation. The calculation formula is as follows: ; In the formula, n is the total number of preset violation types. The weight coefficient corresponding to the i-th type of violation is... Let T be the duration of the i-th type of violation within the statistical period, and T be the total duration of the statistical period. C represents the penalty coefficient for personnel who do not meet the qualification requirements, and C is the qualification non-compliance judgment value, which can be 0 or 1.

[0013] Preferably, in step S4, the specific steps of the multimodal hierarchical early warning and linkage control are as follows: S41 presets four warning levels and corresponding real-time comprehensive risk level R threshold ranges, namely low risk warning, general risk warning, relatively high risk warning, and major risk warning. The threshold range of each level is dynamically adapted to the inherent risk level of the hot work scenario. S42 uses a multi-source data cross-validation algorithm to calculate the effective early warning confidence level S, and the calculation formula is: ; In the formula, The number of valid verification data dimensions to trigger an alert. This represents the total number of risk dimensions. S43 When S is greater than the preset confidence threshold, match the corresponding warning level and trigger the corresponding mode of on-site sound and light, platform pop-up, SMS and telephone warnings, and simultaneously push the warning information to the corresponding level of safety management personnel, operation supervisors and on-site monitoring personnel; Based on the warning level, S44 will implement corresponding intensity of linkage control actions by connecting with the power plant's DCS system, SIS system, fire protection system, and access control system. The intensity of the control actions is positively correlated with the warning level.

[0014] Preferably, in step S5, the specific steps of the closed-loop management of the entire process are as follows: During the S51 work application stage, the system automatically verifies the validity period of the qualifications of the workers and supervisors, the risk level of the work site, and the compliance of the work time, and automatically matches the corresponding approval process. After S52 approval, during the on-site verification stage before operation, facial recognition is used to verify the consistency between the personnel present and the personnel registered on the work order, and machine vision image recognition is used to verify the implementation of safety protection measures. Only after all verifications are passed can the fire start operation permission be granted. During the S53 operation, the real-time comprehensive risk level is calculated frame by frame according to the preset second-level step size, and the corresponding early warning and linkage control are executed. The entire process of perception data, operation data and approval data are recorded synchronously and encrypted. After the S54 operation is completed, the hot work completion and site cleanup acceptance are completed through on-site image acquisition and environmental data verification. The entire life cycle data of the work ticket is automatically archived, and the entire process data is stored on the chain using blockchain notarization technology to achieve tamper-proof full-process traceability and auditing.

[0015] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention uses a dynamic combination of the analytic hierarchy process and the improved entropy weight method to assign weights, which not only conforms to the safety regulations for hot work in power plants and industry practice experience, but also achieves dynamic optimization of weights based on real-time perception data. This solves the technical problems of excessive subjective weight deviation and objective weights not being able to adapt to industry standards in traditional risk assessment. At the same time, it introduces a risk coupling coefficient that adapts to the scenario, which quantifies the coupling amplification effect between the inherent risk of the scenario and environmental risk, and the dynamic risk of the operation and the personnel compliance risk. This breaks through the technical bias of the existing technology that only performs linear superposition calculation on each risk dimension, and realizes the accurate and dynamic quantification of the real-time comprehensive risk of hot work in power plants. This effectively improves the accuracy of risk identification and avoids the problem of risk omission in high-risk scenarios.

[0016] (2) This invention solves the problem of time axis synchronization and spatial matching of data collected by different sampling frequencies and different types of sensing nodes through a linear interpolation alignment algorithm based on spatiotemporal dual dimensions. It realizes the synchronous fusion and standardized processing of multi-dimensional data of work site environment, personnel behavior, hot work equipment and surrounding production systems. Through the early warning false alarm suppression algorithm based on multi-source data cross-validation, it effectively reduces the problem of early warning false alarm caused by single data anomaly. At the same time, through the cross-system linkage interface to connect with the existing DCS, SIS, fire protection and access control systems of the power plant, it realizes the matching linkage between early warning level and control action intensity. Combined with the closed-loop control mechanism of the whole process from work application to completion acceptance and blockchain evidence storage technology, it realizes the immutable traceability of the whole life cycle data of hot work. It solves the technical problems of the disconnect between approval and on-site execution, the difficulty in implementing control measures and the difficulty in tracing responsibility in traditional control. It forms a complete closed loop of perception-computation-early warning-control-traceability.

[0017] (3) This invention adapts to all types of hot work scenarios in power plants, such as boilers, steam turbines, oil fields, hydrogen stations, and cable interlayers, covering all levels of hot work, including special-grade, first-grade, second-grade, and third-grade hot work. It dynamically adjusts the risk coupling coefficient and early warning threshold range based on the inherent risk level of the scenario, and dynamically updates the subjective weight preference coefficient according to the different stages of the hot work. This enables the control model and strategy to adapt to different scenarios and different work stages without the need for large-scale system reconstruction and secondary development for specific scenarios, which greatly reduces the deployment cost and adaptation difficulty of the system in power plants. At the same time, through standardized permission management, approval process, risk quantification standards and control process, it realizes standardized and regulated control of hot work in all scenarios of power plants, filling the technical gap of integrated intelligent control of hot work in all scenarios of power plants in the existing technology, and comprehensively improving the safety control capability and intelligence level of hot work in power plants. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0019] Figure 1 This is a block diagram of the intelligent control and risk warning system for hot work operations in power plants across all scenarios, as described in this invention. Figure 2 This is a flowchart of the intelligent control and risk warning method for hot work operations in power plants across all scenarios, as described in this invention. Figure 3 This is a block diagram of the multi-source heterogeneous sensing data acquisition unit of the present invention; Figure 4 This is a block diagram of the dynamic quantification calculation unit for hot work risk in this invention. Figure 5 This is a block diagram of the multimodal hierarchical early warning and linkage control unit of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1 , Figures 3-5 As shown, the intelligent control and risk early warning system for hot work operations in this power plant includes a hot work operation ledger and access management unit, a multi-source heterogeneous sensing data acquisition unit, a hot work risk dynamic quantification calculation unit, a multi-modal hierarchical early warning and linkage control unit, and a full-process closed-loop control execution unit.

[0022] This comprehensive hot work operation log and access control unit is compatible with all types of hot work scenarios in power plants, including boilers, turbines, oil fields, hydrogen stations, and cable trays. It stores the entire lifecycle data of hot work permits, qualification data of operators and supervisors, and scenario-specific risk classification data. It also configures hierarchical approval and operation permissions for hot work operations. The system covers all levels of hot work operations in power plants, from Class I to Class II and III. Log data includes comprehensive information on work location, time, content, personnel, supervisors, approvers, and safety measures. Personnel qualification data includes the validity period of special operation certificates, safety training and assessment records, and hot work authorization levels. Scenario-specific risk classification data is pre-configured based on power plant hazard source classification standards, corresponding to the inherent risk values ​​for different scenarios. .

[0023] The multi-source heterogeneous sensing data acquisition unit simultaneously collects environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and operating data of production equipment around the work site, completing spatiotemporal alignment and standardized preprocessing of the data. This unit incorporates a spatiotemporal dual-dimensional multi-source data synchronization alignment module and an adaptive Kalman filter fusion module. The spatiotemporal dual-dimensional synchronization alignment module uses the three-dimensional spatial coordinates of the hot work site as a reference, matches all sensing nodes within the preset protection radius of the work site, and uses a millisecond-level unified timestamp as a reference, employing linear interpolation to align the time axis of data with different sampling frequencies. The alignment calculation formula is as follows: ; In the formula, for Interpolation-aligned data at time points, , For adjacent sampling times, , This section presents the original data collected at the corresponding sampling time. The adaptive Kalman filter fusion module performs noise filtering and feature fusion on the aligned multi-source data, outputting standardized preprocessed data. The data sources collected by this unit include: environmental monitoring data (combustible gas concentration, dust concentration, ambient temperature, humidity, and wind speed data) collected by fixed and portable sensors deployed at the work site; personnel positioning and video behavior recognition data collected by UWB positioning base stations and high-definition intelligent cameras to achieve real-time personnel location tracking and identification of behaviors such as not wearing protective equipment, unauthorized absence from duty, and unauthorized hot work; hot work equipment status data (operating status, voltage, and current data of welding machines and gas cutting equipment); and operating data of production equipment around the work site, obtained through integration with the power plant's DCS and SIS systems, including equipment operating parameters, medium pressure, and temperature data.

[0024] The hot work risk dynamic quantification calculation unit incorporates a combined weighting algorithm and a risk-coupled quantification model. Based on preprocessed real-time sensing data and ledger data, it calculates the real-time comprehensive risk level of hot work operations. The core calculation formula of the risk-coupled quantification model is as follows: ; In the formula, To assess the overall risk level in real time, , , , The combined weights for each risk dimension, The inherent risk value of the scenario, This represents the dynamic risk value of the operation. This represents the real-time environmental risk value. For personnel compliance risk value, Risk coupling coefficient adapted to the scenario. Dynamically assign values ​​based on the inherent risk level of the hot work scenario; for high-risk scenarios... The value is higher than that of the low-risk scenario, and the value range is 0.1-0.3.

[0025] The combined weighting algorithm in this unit employs a dynamic weighting method that combines the analytic hierarchy process (AHP) with an improved entropy weighting method. The formula for calculating the combined weights is as follows: ; ; ; In the formula, For the first The combined weights of each risk dimension The subjective weighting preference coefficients are dynamically updated according to the hot work operation stage. The first step calculated by the analytic hierarchy process Subjective weighting of each risk dimension To improve the calculation of the entropy weight method Objective weighting of each risk dimension For the first Information entropy of each risk indicator The total number of risk dimensions. For sample data size, For the first The first sample The standardized value of each indicator, This is the percentage of the indicator. This unit also includes a built-in risk rolling update module, which updates the real-time comprehensive risk level in preset second-level steps. Perform frame-by-frame scrolling calculations and updates, with a preset step size of 1s-5s.

[0026] The calculation rules for the values ​​of each risk dimension in this unit are as follows: Dynamic risk value of operation The calculation formula is ; In the formula, , , , These are the preset weighting coefficients for the corresponding indicators. This is a quantitative value for the hot work level. This is a quantitative value for the status of hot work equipment. Quantify the risk of operation duration. A quantitative value for the implementation of safety measures.

[0027] Personnel compliance risk value The calculation formula is ; In the formula, This represents the preset total number of violation types. For the first The weighting coefficients corresponding to different types of violations For the first time in the statistical period The duration of such violations, The total duration of the statistical period. The penalty coefficient for personnel who do not meet the required qualifications. This is the value for determining non-compliance with qualifications, and can be either 0 or 1.

[0028] The multimodal hierarchical early warning and linkage control unit triggers corresponding level early warnings based on real-time comprehensive risk levels and coordinates the execution of corresponding control actions. This unit has four preset early warning levels and corresponding threshold ranges, and includes a built-in false alarm suppression module and cross-system linkage interface. The four early warning levels are low-risk, moderate-risk, significant-risk, and major-risk, each corresponding to a preset threshold range. Within the threshold range, the warning level is positively correlated with the intensity of control actions. The false alarm suppression module employs a multi-source data cross-validation algorithm, and the false alarm suppression judgment formula is as follows: ; In the formula, To ensure effective early warning confidence, The number of valid verification data dimensions to trigger an alert. This represents the total number of risk dimensions. Only when... When a pre-set confidence threshold is set, corresponding early warning and linkage control actions are triggered. The pre-set confidence threshold is 0.6. The cross-system linkage interface connects to the power plant's DCS system, SIS system, fire protection system, and access control system. The linkage control actions include at least one of the following: on-site audible and visual alarms, freezing of work permit permissions, power off of hot work equipment, isolation and shutdown of surrounding equipment, pre-start of the fire protection system, and locking of access control in the work area.

[0029] The end-to-end closed-loop management and control unit enables closed-loop management of hot work operations from application, approval, on-site verification, work process control to completion and acceptance. The specific execution process of this unit includes: automatic qualification verification and approval matching during the work application stage; personnel and safety measure verification before work; real-time risk monitoring and control during work; and acceptance and data archiving after work completion. Blockchain technology is used to store all process data on the blockchain, achieving tamper-proof, end-to-end traceability and auditing.

[0030] like Figure 2 As shown, the intelligent control and risk warning method for hot work operations across all scenarios in this power plant is implemented based on the above system and includes the following steps: S1 provides comprehensive basic data configuration and access control for all scenarios. It adapts to all types of hot work scenarios, including power plant boilers, steam turbines, oil fields, hydrogen stations, and cable trays. It records and stores the entire lifecycle data of hot work permits, qualification data of operators and supervisors, and scenario-specific risk classification data. It also configures hierarchical approval and operation permissions for hot work. Specifically, based on power plant safety production management regulations, it pre-configures scenario-specific risk classification data and corresponding risk quantification standards, records the qualification information of operators and supervisors and sets validity period reminders, and configures hierarchical approval permissions for special, first, second, and third-level hot work operations, with different levels of approval personnel corresponding to different levels of hot work operations.

[0031] S2, Multi-source heterogeneous sensing data acquisition and preprocessing. This involves simultaneously acquiring environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and operating data of production equipment surrounding the work site at the hot work location. The data undergoes spatiotemporal alignment and standardization preprocessing. Specifically, using the three-dimensional spatial coordinates of the hot work location as a reference, all sensing nodes within the preset protection radius of the work location are delineated, and the raw data collected from each node is acquired. Using a unified millisecond-level timestamp as a reference, linear interpolation is used to align the time axis of data from different sampling frequencies. The alignment calculation formula is as follows: ; In the formula, for Interpolation-aligned data at time points, , For adjacent sampling times, , The original data collected at the corresponding sampling time is used; the aligned multi-source data is subjected to noise filtering and feature fusion using adaptive Kalman filtering, and the data is normalized by min-max normalization to map all data to the 0-1 interval.

[0032] S3, Real-time dynamic quantitative calculation of hot work operation risks. Based on preprocessed real-time sensing data and ledger data, a combined weighting algorithm is used to determine the combined weights of each risk dimension, and a risk coupling quantitative model is used to calculate the real-time comprehensive risk level of hot work operations.

[0033] The specific steps of the combined weighting algorithm are as follows: S31, based on the safety regulations and industry standards for hot work operations in power plants, constructs a risk dimension judgment matrix using the analytic hierarchy process (AHP). After passing consistency verification, the subjective weights of each risk dimension are calculated. ; S32, the preprocessed real-time sensing sample data is standardized, and the information entropy of each risk dimension is calculated by improving the entropy weight method, thereby obtaining the objective weight of each risk dimension. The calculation formula is ; In the formula, For the first Information entropy of each risk indicator The total number of risk dimensions. For sample data size, For the first The first sample The standardized value of each indicator, For the percentage of indicators; S33, dynamically update the subjective weighting preference coefficient based on the current stage of the hot work operation: application, preparation, hot work, cooling, and completion. The combined weights of each risk dimension are obtained through weighted fusion. The calculation formula is ; In the risk coupling quantitative model, the specific quantitative calculation method for the values ​​of each risk dimension is as follows: Inherent risk value of the scenario Based on the flammability and explosiveness of the medium in the scene where the work site is located, the operating status of the equipment, and the straight-line distance from the hazard source, the values ​​are classified and quantified, with a range of 0-1. Dynamic risk value of operation Based on the hot work level, the operating status of the hot work equipment, the duration of the work, and the implementation of safety measures, the calculation formula is as follows: ; In the formula, , , , These are the preset weighting coefficients for the corresponding indicators. This is a quantitative value for the hot work level. This is a quantitative value for the status of hot work equipment. Quantify the risk of operation duration. A quantitative value for the implementation of safety measures; Real-time environmental risk values The data is classified and quantified based on real-time data of combustible gas concentration, dust concentration, ambient temperature, humidity, and wind speed at the work site, with a value range of 0-1. Personnel compliance risk value Based on the matching degree of personnel qualifications, the type and duration of violations are quantified, and the calculation formula is as follows: ; In the formula, This represents the preset total number of violation types. For the first The weighting coefficients corresponding to different types of violations For the first time in the statistical period The duration of such violations, The total duration of the statistical period. The penalty coefficient for personnel who do not meet the required qualifications. This is the value for determining non-compliance with qualifications, and can be either 0 or 1.

[0034] S4, Multimodal Hierarchical Early Warning and Linked Control. Based on the real-time comprehensive risk level, a corresponding early warning level is matched, triggering the corresponding early warning signal and linking it to execute on-site control actions of appropriate intensity. The specific steps are as follows: S41, presets four warning levels and corresponding real-time comprehensive risk levels. The threshold ranges are low-risk warning, general-risk warning, relatively high-risk warning, and major-risk warning, and the threshold ranges of each level are dynamically adapted to the inherent risk level of the hot work scenario. S42, uses a multi-source data cross-validation algorithm to calculate the effective early warning confidence level. The calculation formula is ; In the formula, The number of valid verification data dimensions to trigger an alert. This represents the total number of risk dimensions. S43, when When the confidence threshold is preset, the corresponding warning level is matched, and the corresponding mode of on-site sound and light, platform pop-up, SMS, and telephone warnings are triggered. The warning information is simultaneously pushed to the corresponding level of safety management personnel, operation supervisors and on-site monitoring personnel. S44, based on the warning level, executes corresponding intensity of linkage control actions by connecting with the power plant's DCS system, SIS system, fire protection system, and access control system. The intensity of the control actions is positively correlated with the warning level.

[0035] S5 provides closed-loop management of the entire hot work process. It achieves closed-loop management of the entire hot work process, from application, approval, on-site verification, work process control to completion and acceptance, and synchronously stores all process data for traceability. The specific steps are as follows: S51, during the work application stage, automatically verify the validity period of the qualifications of the workers and supervisors, the risk level of the work site, and the compliance of the work time, and automatically match the approval process of the corresponding level. S52. After approval, during the on-site verification stage before operation, facial recognition is used to verify the consistency between the personnel present and the personnel registered on the work order, and machine vision image recognition is used to verify the implementation of safety protection measures. Only after all verifications are passed can the fire start operation permission be granted. S53: During operation, the real-time comprehensive risk level is calculated frame by frame according to the preset second-level step size, and corresponding early warning and linkage control are executed. The entire process perception data, operation data and approval data are recorded synchronously and encrypted. S54 After the work is completed, the hot work completion and site cleanup acceptance are completed through on-site image acquisition and environmental data verification. The entire life cycle data of the work ticket is automatically archived, and the entire process data is stored on the chain using blockchain evidence storage technology to achieve tamper-proof full-process traceability and auditing.

[0036] Example 1 This embodiment addresses the high-level hot work operation scenario in the oil storage area of ​​a power plant, employing the system and method of this invention to achieve intelligent control and risk warning for hot work operations.

[0037] First, perform basic data configuration and access management for the entire scenario. The inherent risk level of the oil storage area scenario is set to the highest level, and the inherent risk value of the scenario is pre-configured. Risk coupling coefficient Special-level hot work operations correspond to three levels of approval authority. The special operation qualification information of both the operator and the monitoring personnel must be entered, and all qualifications must be valid. Failure to meet the qualification criteria will result in a judgment threshold. .

[0038] Multi-source heterogeneous sensing data acquisition and preprocessing: Sensing nodes within a 10m protection radius of the work site are delineated, and combustible gas concentration, ambient temperature, wind speed environmental monitoring data, personnel positioning and video behavior recognition data, welding machine equipment status data, and oil storage tank operation data are collected simultaneously. Based on a unified 100ms timestamp, linear interpolation is used to align the time axis of data with different sampling frequencies. Adaptive Kalman filtering is used to filter out noise in the collected data, and min-max normalization processing is completed.

[0039] The combined weighting algorithm calculates the combined weights of each risk dimension, and the subjective weights are obtained through the analytic hierarchy process. , , , Objective weights are obtained by improving the entropy weight method. , , , The hot work is in the hot work stage, and the subjective weighting preference coefficient is... The combined weights are calculated as follows: ; ; ; ; Numerical calculations for each risk dimension, and inherent risk values ​​for the scenario. ; Calculation of dynamic risk value for operations, with preset weighting coefficients. , , , Special Grade Hot Work Operation Level Quantification Value Normal quantification value of hot work equipment Risk quantification value for 2-hour operation All safety measures have been implemented to a quantitative standard. ,but ; Real-time environmental monitoring data shows that the concentration of combustible gas is 0, the temperature is 25℃, the wind speed is 3m / s, and the real-time environmental risk value is [missing information]. Personnel compliance risk value calculation, with no violations during the statistical period. ,but .

[0040] Real-time comprehensive risk calculation, substituted into the risk coupling quantification model: ; Calculation of effective early warning confidence level, total number of risk dimensions Risk dimensions without triggering warnings , If the value is below the preset confidence threshold of 0.6, no warning will be triggered.

[0041] During the operation, the real-time comprehensive risk level is calculated in 2-second increments. When the concentration of combustible gas rises to 20% of the lower explosive limit, the real-time environmental risk value is calculated. Meanwhile, workers were found to have violated regulations by leaving their posts, raising the personnel compliance risk value. Recalculate the real-time comprehensive risk level: ; Calculation of effective early warning confidence, number of effective verification data dimensions that trigger early warning. , This triggers a major risk warning, prompting the coordinated execution of on-site audible and visual alarms, freezing of work permit permissions, power outage of hot work equipment, and pre-activation of the fire protection system. Simultaneously, warning information is pushed to the power plant's safety management department head, the work supervisor, and monitoring personnel, achieving real-time risk warning and control. After the work is completed, on-site cleanup and acceptance are performed, and all data is uploaded to the blockchain for archiving and documentation.

[0042] Example 2 This embodiment addresses the primary hot work operation scenario of a power plant boiler body, employing the system and method of this invention to achieve intelligent control and risk warning for hot work operations.

[0043] First, perform full-scenario basic data configuration and access management. Under the boiler's operating state, the inherent risk level of hot work is medium to high, and pre-configure the inherent risk values ​​for the scenario. Risk coupling coefficient Level 1 hot work operations correspond to Level 2 approval authority. Special operation qualification information for both the operator and the monitoring personnel must be entered, and all qualifications must be valid. A qualification discrepancy will be considered a violation. .

[0044] Multi-source heterogeneous sensing data acquisition and preprocessing: Sensing nodes within an 8m protection radius of the work site are delineated, and dust concentration, ambient temperature, wind speed, environmental monitoring data, personnel positioning and video behavior recognition data, gas cutting equipment status data, and boiler body operation data are collected simultaneously. Based on a unified 100ms timestamp, linear interpolation is used to align the time axis of data with different sampling frequencies. Adaptive Kalman filtering is used to filter out noise in the collected data, and min-max normalization is completed.

[0045] The combined weighting algorithm calculates the combined weights of each risk dimension, and the subjective weights are obtained through the analytic hierarchy process. , , , Objective weights are obtained by improving the entropy weight method. , , , The hot work is in the hot work stage, and the subjective weighting preference coefficient is... The combined weights are calculated as follows: ; ; ; ; Numerical calculations for each risk dimension, and inherent risk values ​​for the scenario. ; Calculation of dynamic risk value for operations, with preset weighting coefficients. , , , Level 1 hot work operation quantification value Normal quantification value of hot work equipment Risk quantification value for 3-hour operation All safety measures have been implemented to a quantitative standard. ,but ; Real-time environmental monitoring data shows that the dust concentration meets the standard, the temperature is 30℃, the wind speed is 2m / s, and the real-time environmental risk value is [not specified]. Personnel compliance risk value calculation: One instance of failure to wear a protective face mask occurred within the statistical period, lasting 5 seconds, with a total statistical period duration of 300 seconds. The weighting coefficient for this violation is... No other violations were found. ,but ; Real-time comprehensive risk assessment, substituted into the risk coupling quantification model: ; Calculation of effective early warning confidence level, total number of risk dimensions Risk dimensions without triggering warnings , If the value is below the preset confidence threshold of 0.6, no warning will be triggered.

[0046] During the operation, the real-time comprehensive risk level is calculated in 3-second increments. If the monitoring personnel are absent from their posts for 60 seconds, the personnel compliance risk value is calculated. Meanwhile, dust concentration exceeded the standard, and the real-time environmental risk value... Recalculate the real-time comprehensive risk level: ; Calculation of effective early warning confidence, and the number of effective verification data dimensions that trigger the early warning. , If a significant risk warning is triggered, on-site audible and visual alarms will be activated, a platform pop-up alert will be displayed, and access control to the work area will be locked. The warning information will be simultaneously pushed to power plant safety management personnel, the work supervisor, and monitoring personnel. Work access will be restored once the violation is rectified and the risk level drops to a safe range. After the work is completed, on-site cleanup and acceptance will be conducted, and all data will be uploaded to the blockchain for archiving and verification.

[0047] Example 3 This embodiment addresses a secondary hot work operation scenario in the cable interlayer of a power plant, employing the system and method of this invention to achieve intelligent control and risk warning for hot work operations.

[0048] First, perform basic data configuration and access management for the entire scenario. The inherent risk level of hot work in cable mezzanine is medium, and the inherent risk value of the scenario is pre-configured. Risk coupling coefficient Level 2 hot work operations correspond to Level 1 approval authority. Special operation qualification information for both the operator and the monitoring personnel must be entered, and all qualifications must be valid. Qualification discrepancies will be judged based on the following criteria: .

[0049] Multi-source heterogeneous sensing data acquisition and preprocessing: Sensing nodes within a 5m protection radius of the work site are delineated, and environmental monitoring data such as ambient temperature, combustible gas concentration, and smoke concentration are collected simultaneously, along with personnel positioning and video behavior recognition data, welding machine equipment status data, and cable operation data. Using a unified 100ms timestamp as a benchmark, linear interpolation is used to align the time axis of data with different sampling frequencies. Adaptive Kalman filtering is used to remove noise from the collected data, and min-max normalization processing is completed.

[0050] The combined weighting algorithm calculates the combined weights of each risk dimension, and the subjective weights are obtained through the analytic hierarchy process. , , , Objective weights are obtained by improving the entropy weight method. , , , The hot work is in the hot work stage, and the subjective weighting preference coefficient is... The combined weights are calculated as follows: ; ; ; ; Numerical calculations for each risk dimension, and inherent risk values ​​for the scenario. ; Calculation of dynamic risk value for operations, with preset weighting coefficients. , , , Level II hot work operation quantification value Normal quantification value of hot work equipment Risk quantification value for 1 hour of operation All safety measures have been implemented to a quantitative standard. ,but ; Real-time environmental monitoring data shows a temperature of 28℃, no flammable gases, no smoke, and a real-time environmental risk value. Personnel compliance risk value calculation, with no violations during the statistical period. ,but .

[0051] Real-time comprehensive risk assessment, substituted into the risk coupling quantification model:

[0052] Calculation of effective early warning confidence level, total number of risk dimensions Risk dimensions without triggering warnings , If the value is below the preset confidence threshold of 0.6, no warning will be triggered.

[0053] During the operation, the real-time comprehensive risk level is calculated in 5-second increments. When smoke appears at the work site, the real-time environmental risk value is updated. Recalculate the real-time comprehensive risk level: ; Calculation of effective early warning confidence, and the number of effective verification data dimensions that trigger the early warning. , If the smoke level falls below the preset confidence threshold of 0.6, no alarm is triggered; only a notification is sent to the on-site monitoring personnel to remind them to conduct an on-site inspection. Once the smoke confirms there is no fire risk, the environmental risk value returns to normal, and the operation process is continuously monitored. After the operation is completed, the site is cleared and inspected, and all data from the entire process is uploaded to the blockchain for archiving and documentation.

[0054] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart control and risk early warning system for hot work operations across all scenarios in power plants, characterized in that: It includes a full-scenario hot work operation log and access management unit, a multi-source heterogeneous sensing data acquisition unit, a hot work risk dynamic quantitative calculation unit, a multi-modal hierarchical early warning and linkage control unit, and a full-process closed-loop control execution unit; The full-scenario hot work operation ledger and permission management unit is adapted to all types of hot work operation scenarios, including power plant boilers, steam turbines, oil fields, hydrogen stations, and cable mezzanines. It stores the full lifecycle data of hot work operation tickets, qualification data of operation and monitoring personnel, and scenario-specific risk classification data, and configures hot work operation classification approval and operation permissions. The multi-source heterogeneous sensing data acquisition unit simultaneously collects environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and production equipment operation data around the work site, and completes the spatiotemporal alignment and standardized preprocessing of the data. The dynamic quantification calculation unit for hot work risk incorporates a combined weighting algorithm and a risk coupling quantification model. Based on preprocessed real-time sensing data and ledger data, it calculates the real-time comprehensive risk level of hot work operations. The core calculation formula of the risk coupling quantification model is: ； In the formula, R represents the real-time comprehensive risk level. , , , The combined weights for each risk dimension, The inherent risk value of the scenario, This represents the dynamic risk value of the operation. This represents the real-time environmental risk value. For personnel compliance risk value, Risk coupling coefficient adapted to the scenario; The multimodal hierarchical early warning and linkage control unit triggers early warnings of the corresponding level based on the real-time comprehensive risk level and performs corresponding control actions in linkage. The full-process closed-loop control and execution unit realizes the full-process closed-loop management of hot work operations, from application, approval, on-site verification, operation process control to completion and acceptance.

2. The intelligent control and risk early warning system for hot work operations in power plants across all scenarios as described in claim 1, characterized in that, The multi-source heterogeneous sensing data acquisition unit has a built-in multi-source data synchronization and alignment module based on spatiotemporal dual dimensions and an adaptive Kalman filter fusion module. The spatiotemporal dual-dimensional synchronization alignment module uses the three-dimensional spatial coordinates of the hot work site as a reference, matches all sensing nodes within the preset protection radius of the work site, and uses a linear interpolation method to complete the time axis alignment of data with different sampling frequencies, based on a millisecond-level unified timestamp. The alignment calculation formula is as follows: ； In the formula, x(t) represents the interpolated aligned data at time t. , For adjacent sampling times, , This refers to the original data collected at the corresponding sampling time. The adaptive Kalman filter fusion module performs noise filtering and feature fusion on the aligned multi-source data and outputs standardized preprocessed data.

3. The intelligent control and risk early warning system for hot work operations in power plants across all scenarios as described in claim 1, characterized in that, The combined weighting algorithm of the dynamic quantification calculation unit for hot work risk adopts a dynamic weighting method combining the analytic hierarchy process (AHP) and the improved entropy weighting method. The combined weight calculation formula is as follows: ； ； ； In the formula, Let the combined weight of the j-th risk dimension be , The subjective weighting preference coefficients are dynamically updated according to the hot work operation stage. Here is the subjective weight of the j-th risk dimension calculated using the analytic hierarchy process. To improve the objective weight of the j-th risk dimension calculated by the entropy weight method, Let m be the information entropy of the j-th risk indicator, m be the total number of risk dimensions, and n be the sample data size. Let j be the standardized value of the i-th sample and j-th index. The indicator proportion; the unit also has a built-in risk rolling update module, which performs frame-by-frame rolling calculation and update of the real-time comprehensive risk degree R according to a preset second-level step size.

4. The intelligent control and risk early warning system for hot work operations in power plants across all scenarios as described in claim 1, characterized in that, The multimodal hierarchical early warning and linkage control unit has four preset early warning levels and corresponding threshold ranges, and includes a built-in false alarm suppression module and cross-system linkage interface. The four early warning levels include low-risk, general-risk, relatively high-risk, and major-risk warnings, each corresponding to a preset R-value threshold range. The early warning level is positively correlated with the intensity of the control action. The false alarm suppression module uses a multi-source data cross-validation algorithm, and the false alarm suppression judgment formula is: ； In the formula, S represents the confidence level of the effective early warning. The number of valid verification data dimensions to trigger an alert. The total number of risk dimensions; the corresponding early warning and linkage control action is triggered only when S ≥ the preset confidence threshold; the cross-system linkage interface connects to the power plant's DCS system, SIS system, fire protection system, and access control system, and the linkage control action includes at least one of the following: on-site audible and visual alarm, work permit permission freezing, power outage of hot work equipment, isolation and shutdown of surrounding equipment, pre-start of fire protection system, and access control locking of work area.

5. A method for intelligent control and risk early warning of hot work operations across all scenarios in power plants, characterized in that: The intelligent control and risk warning system for hot work operations in power plants across all scenarios, as described in claim 1, includes the following steps: S1 Full-Scenario Basic Data Configuration and Access Management: Adapts to all types of hot work scenarios such as power plant boilers, steam turbines, oil fields, hydrogen stations, and cable mezzanines. It records and stores the full lifecycle data of hot work permits, qualification data of operators and supervisors, and scenario-specific risk classification data, and configures hot work classification approval and operation permissions. S2 Multi-source heterogeneous sensing data acquisition and preprocessing: Simultaneously acquire environmental monitoring data, personnel positioning and video behavior recognition data, hot work equipment status data, and production equipment operation data around the work site, and complete the spatiotemporal alignment and standardized preprocessing of the data; S3 Real-time dynamic quantitative calculation of hot work risk: Based on pre-processed real-time sensing data and ledger data, the combined weight of each risk dimension is determined by a combined weighting algorithm, and the real-time comprehensive risk of hot work is calculated by a risk coupling quantitative model. S4 Multimodal graded early warning and linkage control: Match the corresponding early warning level according to the real-time comprehensive risk level, trigger the early warning signal of the corresponding mode, and link to execute the corresponding intensity of on-site control actions; S5 Hot Work Operation Closed-Loop Management: Completes closed-loop management of the entire hot work operation process, from application, approval, on-site verification, operation process control to completion acceptance, and synchronously stores the entire process data for traceability.

6. The intelligent control and risk early warning method for hot work operations in power plants across all scenarios as described in claim 5, characterized in that, In step S2, the specific steps of the spatiotemporal alignment and standardization preprocessing are as follows: using the three-dimensional spatial coordinates of the hot work point as a reference, delineate all sensing nodes within the preset protection radius of the work point, and acquire the original data collected by each node; using a millisecond-level unified timestamp as a reference, use linear interpolation to complete the time axis alignment of data with different sampling frequencies, and the alignment calculation formula is: ； In the formula, x(t) represents the interpolated aligned data at time t. , For adjacent sampling times, , This refers to the original data collected at the corresponding sampling time. Adaptive Kalman filtering is used to remove noise and fuse features from the aligned multi-source data, and the data is then normalized by min-max normalization.

7. The intelligent control and risk early warning method for hot work operations in power plants across all scenarios as described in claim 5, characterized in that, In step S3, the specific steps of the combined weighting algorithm are as follows: S31, based on power plant hot work safety regulations and industry standards, constructs a risk dimension judgment matrix using the analytic hierarchy process (AHP). After passing consistency verification, the subjective weights of each risk dimension are calculated. ; S32 standardizes the preprocessed real-time sensing sample data, calculates the information entropy of each risk dimension using an improved entropy weight method, and then obtains the objective weights of each risk dimension. The calculation formula is: ； In the formula, Let m be the information entropy of the j-th risk indicator, m be the total number of risk dimensions, and n be the sample data size. Let j be the standardized value of the i-th sample and j-th index. For the percentage of indicators; S33 Based on the current stage of the hot work operation—application, preparation, hot work, cooling, or completion—the subjective weight preference coefficient α is dynamically updated, and the combined weights of each risk dimension are obtained through weighted fusion. The calculation formula is: 。 8. The intelligent control and risk early warning method for hot work operations in power plants across all scenarios as described in claim 5, characterized in that, In step S3, the specific quantitative calculation methods for the scenario-inherent risk value Rs, operational dynamic risk value Rd, environmental real-time risk value Re, and personnel compliance risk value Rp in the risk coupling quantification model are as follows: The inherent risk value Rs of the scenario is classified and quantified based on the flammability and explosiveness of the medium in the scenario to which the work point belongs, the operating status of the equipment, and the straight-line distance from the hazard source, with a value range of 0-1; The dynamic risk value Rd for the operation is quantified based on the hot work level, the operating status of the hot work equipment, the duration of the operation, and the implementation of safety measures. The calculation formula is as follows: ； In the formula, , , , These are the preset weighting coefficients for the corresponding indicators. This is a quantitative value for the hot work level. This is a quantitative value for the status of equipment used in hot work. Quantify the risk of operation duration. A quantitative value for the implementation of safety measures; The real-time environmental risk value Re is classified and quantified based on real-time data of combustible gas concentration, dust concentration, ambient temperature, humidity, and wind speed at the work site. The personnel compliance risk value Rp is quantified based on the personnel qualification matching degree, the type of violation, and the duration of the violation. The calculation formula is as follows: ； In the formula, n is the total number of preset violation types. Let be the weight coefficient corresponding to the i-th type of violation. Let T be the duration of the i-th type of violation within the statistical period, and T be the total duration of the statistical period. C represents the penalty coefficient for personnel who do not meet the qualification requirements, and C is the qualification non-compliance judgment value, which can be 0 or 1.

9. The intelligent control and risk early warning method for hot work operations in power plants across all scenarios according to claim 5, characterized in that, In step S4, the specific steps of the multimodal hierarchical early warning and linkage control are as follows: S41 presets four warning levels and corresponding real-time comprehensive risk level R threshold ranges, namely low risk warning, general risk warning, relatively high risk warning, and major risk warning. The threshold range of each level is dynamically adapted to the inherent risk level of the hot work scenario. S42 uses a multi-source data cross-validation algorithm to calculate the effective early warning confidence level S, and the calculation formula is: ； In the formula, The number of valid verification data dimensions to trigger an alert. This represents the total number of risk dimensions. S43 When S is greater than the preset confidence threshold, match the corresponding warning level and trigger the corresponding mode of on-site sound and light, platform pop-up, SMS and telephone warnings, and simultaneously push the warning information to the corresponding level of safety management personnel, operation supervisors and on-site monitoring personnel; Based on the warning level, S44 will implement corresponding intensity of linkage control actions by connecting with the power plant's DCS system, SIS system, fire protection system, and access control system. The intensity of the control actions is positively correlated with the warning level.

10. The intelligent control and risk early warning method for hot work operations in power plants across all scenarios according to claim 5, characterized in that, In step S5, the specific steps of the closed-loop management of the entire process are as follows: During the S51 work application stage, the system automatically verifies the validity period of the qualifications of the workers and supervisors, the risk level of the work site, and the compliance of the work time, and automatically matches the corresponding approval process. After S52 approval, during the on-site verification stage before operation, facial recognition is used to verify the consistency between the personnel present and the personnel registered on the work order, and machine vision image recognition is used to verify the implementation of safety protection measures. Only after all verifications are passed can the fire start operation permission be granted. During the S53 operation, the real-time comprehensive risk level is calculated frame by frame according to the preset second-level step size, and the corresponding early warning and linkage control are executed. The entire process of perception data, operation data and approval data are recorded synchronously and encrypted. After the S54 operation is completed, the hot work completion and site cleanup acceptance are completed through on-site image acquisition and environmental data verification. The entire life cycle data of the work ticket is automatically archived, and the entire process data is stored on the chain using blockchain notarization technology to achieve tamper-proof full-process traceability and auditing.

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