Special equipment life cycle supervision method and system based on characteristic parameter monitoring

Abstract

The invention discloses a special equipment life cycle supervision method and system based on characteristic parameter monitoring, and the method comprises the steps: collecting a multi-dimensional characteristic parameter data flow containing a real-time operation parameter, an accumulated damage parameter and a performance degradation parameter, and carrying out the trend analysis through employing a time sequence prediction model, and generating a trend deterioration early warning signal; an association rule mining algorithm is adopted to carry out association analysis to generate an associated fault early warning signal, then the two early warning signals are fused, inherent attribute data and historical operation and maintenance data are combined, and a real-time dynamic risk score is calculated through a dynamic risk portrait model; and finally mapping to a preset discrete supervision level and automatically executing a corresponding differential supervision instruction set. According to the method, the problems of risk identification lagging and strategy static solidification in traditional supervision are effectively solved, the transformation from passive response to active early warning and from average supervision to accurate strategy implementation is realized, and the foreseeability, pertinence and resource configuration efficiency of special equipment safety supervision are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of special equipment safety supervision technology, specifically to a method and system for lifecycle supervision of special equipment based on characteristic parameter monitoring. Background Technology

[0002] Safety supervision of special equipment is a crucial link in ensuring industrial production and public safety. Currently, the industry generally adopts a management approach that combines periodic inspections with threshold-based alarm mechanisms. This method has developed certain standards through long-term practice and can respond to explicit equipment failures or parameter exceedances. However, with the surge and increasing complexity of equipment operating data, the limitations of the existing regulatory model are becoming increasingly apparent. On the one hand, its early warning mechanisms rely heavily on independent judgments of single parameters, making it difficult to capture the implicit correlations between multiple parameters and the long-term trends in parameter evolution, resulting in a lag in the identification of potential risks. On the other hand, the formulation of regulatory strategies is often disconnected from the real-time operating status of the equipment, lacking an effective means to dynamically assess risks and automatically adjust regulatory intensity accordingly, making it difficult to optimize the allocation of regulatory resources. How to proactively identify the deteriorating trends of equipment health status and the risk of complex failures from massive, multi-source operating data, and establish a dynamically adaptive regulatory decision-making mechanism that is linked to these factors, has become a key challenge facing the technological development of this field. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method and system for lifecycle supervision of special equipment based on feature parameter monitoring. By integrating trend prediction and correlation analysis early warning signals and constructing a dynamic risk profile, it solves the problems of lagging risk identification and static and rigid regulatory strategies in the existing regulatory model.

[0004] To achieve the above objectives, in a first aspect, this application provides a method for lifecycle supervision of special equipment based on characteristic parameter monitoring, comprising: Collect multi-dimensional feature parameter data streams generated by the target special equipment during its life cycle operation. The multi-dimensional feature parameter data streams include real-time operation parameters, cumulative damage parameters, and performance degradation parameters. Based on multi-dimensional feature parameter data streams, trend analysis is performed through a preset time series prediction model to generate trend deterioration warning signals for specific fault modes. Meanwhile, the association rule mining algorithm is used to perform association analysis on the multi-dimensional feature parameter data stream to generate associated fault early warning signals that indicate potential composite faults. By integrating the trend deterioration warning signal and the associated fault warning signal, and combining the inherent attribute data and historical operation and maintenance data of the target special equipment, the real-time dynamic risk score of the target special equipment is calculated through the dynamic risk profile model; The real-time dynamic risk score is mapped to a preset discrete regulatory level to obtain the target regulatory level; Based on the mapped target regulatory level, a differentiated regulatory instruction set corresponding to the current target regulatory level is automatically triggered and executed. The differentiated regulatory instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the frequency of data monitoring.

[0005] In some embodiments, based on a multi-dimensional feature parameter data stream, trend analysis is performed using a preset time-series prediction model to generate a trend deterioration warning signal for a specific failure mode, including: Extract key feature parameter sequences associated with the target failure mode from a multi-dimensional feature parameter data stream. The key feature parameter sequences contain multiple historical observations arranged in chronological order. The key feature parameter sequences are subjected to stationarity tests and outlier removal to generate preprocessed standardized feature parameter sequences; The standardized feature parameter sequence is input into a pre-trained long short-term memory network model, which learns the degradation pattern of the feature parameters based on historical data. By using a long short-term memory network model to perform multi-step forward prediction on the standardized feature parameter sequence, a sequence of predicted feature parameter values ​​for a future preset time period is generated. Calculate the minimum relative distance between the predicted value sequence of feature parameters and the preset safety threshold, and compare the minimum relative distance with the preset distance threshold; If the minimum relative distance is less than or equal to the preset distance threshold, it is determined that the key feature parameter has a risk of trend deterioration, and a trend deterioration warning signal containing the predicted time point of exceeding the standard is generated. Furthermore, the warning signal of deteriorating trend is bound to the corresponding key feature parameter identifier and target failure mode type.

[0006] In some embodiments, a long short-term memory network model is used to perform multi-step forward prediction on a standardized feature parameter sequence to generate a sequence of predicted feature parameter values ​​for a predetermined future time period, including: The standardized feature parameter sequence is divided into a training time window and a prediction time window. The training time window contains multiple consecutive historical time step observations. The historical time step observations within the training time window are used as model inputs to construct the input feature tensor; Temporal features are extracted from the input feature tensor through the hidden layer of the Long Short-Term Memory network model to obtain the hidden state vector at the final time step. The hidden state vector of the final time step is input into the fully connected regression layer to calculate the predicted value of the feature parameters of the first prediction time step. A recursive prediction strategy is adopted to feed the current predicted value of the feature parameters back to the input of the long short-term memory network model, update the input feature tensor, and repeat the temporal feature extraction and regression calculation process to generate the predicted value of the feature parameters for each subsequent prediction time step in turn. The predicted values ​​of the feature parameters at all prediction time steps are combined in chronological order to form a complete sequence of predicted feature parameter values.

[0007] In some embodiments, a correlation analysis is performed on a multi-dimensional feature parameter data stream using an association rule mining algorithm to generate an associated fault early warning signal indicating a potential composite fault, including: Extract the observation values ​​of multiple feature parameters at the same time from the multi-dimensional feature parameter data stream to construct a multivariate synchronous observation sample set; The multivariate synchronous observation sample set is discretized, and continuous feature parameter values ​​are mapped to discrete symbols representing state levels to generate symbolic state sequences. The improved FP-Growth algorithm is used to mine frequent patterns in symbolic state sequences and identify frequent parameter combination patterns with support higher than the preset minimum support threshold. Calculate the confidence and lift indices of each frequent parameter combination pattern, and select strong association rules with confidence higher than the preset confidence threshold and lift greater than 1 to form a candidate fault association rule library; The real-time acquired multi-dimensional feature parameter data stream is converted into a symbolic state vector and matched with a candidate fault association rule base. If the symbolic state vector satisfies the antecedent condition of a strong association rule, but the consequent fault state corresponding to the strong association rule has not yet reached the alarm threshold in the real-time data, then it is determined that a precursor pattern of a potential composite fault has been detected. Based on the matched strong correlation rules, a related fault warning signal is generated. The related fault warning signal includes the parameter combination involved, the associated fault type, and the confidence level.

[0008] In some embodiments, the deterioration trend warning signal and the associated fault warning signal are integrated, and combined with the inherent attribute data and historical operation and maintenance data of the target special equipment, a real-time dynamic risk score of the target special equipment is calculated through a dynamic risk profiling model, including: The warning signal of deteriorating trend is analyzed, the predicted time point of exceeding the standard and the key feature parameter identifier are extracted, and the urgency index of deteriorating trend is calculated. The associated fault warning signals are analyzed to extract the associated fault types and confidence levels, and the composite fault severity index is calculated. An inherent risk baseline value calculation model is constructed based on the analytic hierarchy process. Inherent attribute data, including equipment type, service life and historical accident records, are input into the inherent risk baseline value calculation model to calculate the static baseline value that characterizes the inherent risk of the equipment. A real-time health status assessment model is constructed, which weights and fuses the trend deterioration urgency index and the composite fault severity index to generate a real-time health status score. The weighting coefficient of the operating load intensity is determined based on the entropy weight method, and the operating load intensity coefficient is calculated by combining the average daily running time and average load rate in historical operation and maintenance data. The static base value, real-time health status score and operating load intensity coefficient are input into a preset multi-dimensional risk fusion function, which adopts a non-linear weighted summation function. The risk score is calculated by using a multi-dimensional risk fusion function, and outputs a real-time dynamic risk score between 0 and 1. The higher the score, the higher the risk level of the equipment.

[0009] In some embodiments, a weighted fusion of the trend deterioration urgency index and the composite fault severity index is performed to generate a real-time health status score, including: Establish a weighting matrix for the trend deterioration urgency index and the composite fault severity index. The weighting matrix is ​​dynamically adjusted according to the equipment type and operation stage. The first normalized value of the trend deterioration urgency index is calculated. The first normalized value is inversely proportional to the time difference between the predicted time of exceeding the standard and the current time. The second normalized value of the composite fault severity index is calculated. The second normalized value is based on a comprehensive assessment of the historical average repair time and confidence level of the associated fault types. The first normalized value and the second normalized value are input into the preset health status scoring function, which uses an exponential weighted moving average algorithm for dynamic fusion. A preliminary health status score is calculated using a health status scoring function. The historical health status baseline of the equipment is introduced as a reference benchmark to perform relative correction on the preliminary health status score; The sigmoid function is applied to smooth the corrected preliminary health status score, transforming it into a standard score range of 0 to 100, and generating the final real-time health status score for risk fusion.

[0010] In some embodiments, the operating load intensity weighting coefficient is determined based on the entropy weighting method, and the operating load intensity coefficient is calculated by combining the average daily runtime and average load rate in historical operation and maintenance data, including: Collect data on the daily average runtime and average load rate of the target special equipment within a preset historical period; Calculate the first mean, first standard deviation, and first coefficient of variation of the daily average runtime dataset to obtain the first degree of dispersion; and calculate the second mean, second standard deviation, and second coefficient of variation of the average load rate dataset to obtain the second degree of dispersion. Based on the principle of entropy weight method, the information entropy value is calculated according to the first degree of dispersion and the second degree of dispersion; The weighting coefficients of the daily average runtime dataset and the average load rate dataset are calculated based on the information entropy value, and the indicators with greater variability are given higher weights. The calculated weighting coefficients are applied to the current device’s average daily runtime and average load rate data. A calculation model for operational load intensity is constructed, and the weighted average daily operating time is multiplied by the average load rate to obtain the original load intensity value. The original load intensity value is normalized by range and mapped to a numerical range of 0 to 1 to generate the final operating load intensity coefficient.

[0011] In some embodiments, mapping real-time dynamic risk scores to preset discrete regulatory levels to obtain a target regulatory level includes: Multiple risk score range thresholds are set to divide the risk score range into risk score ranges corresponding to different regulatory levels, including low risk level, general risk level, relatively high risk level and high risk level; Read the currently calculated real-time dynamic risk score and compare it step by step with multiple risk score interval thresholds; If the real-time dynamic risk score is less than the first risk threshold, it will be mapped to a low-risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the first risk threshold and less than the second risk threshold, it is mapped to a general risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the second risk threshold and less than the third risk threshold, it will be mapped to a higher risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the third risk threshold, it will be mapped to a high-risk level, which is the target regulatory level. Additionally, when a real-time dynamic risk score is detected to have crossed a threshold from one risk score range into another, a regulatory level change event is generated, and the timestamp of the level change and the real-time dynamic risk scores before and after the change are recorded. The final determined target regulatory level is bound to the corresponding real-time dynamic risk score and equipment identifier to generate a regulatory level determination result.

[0012] In some embodiments, based on the mapped target regulatory level, a differentiated regulatory instruction set corresponding to the current target regulatory level is automatically triggered and executed, including: Establish a mapping database between regulatory levels and regulatory actions. The database stores the basic regulatory cycle, enhanced monitoring parameter list, and set of mandatory regulatory actions corresponding to different regulatory levels. Query the mapping relationship database according to the target regulatory level to obtain the corresponding basic regulatory cycle setting value; Based on real-time dynamic risk scores and preset risk-cycle adjustment coefficients, the basic regulatory cycle setting is dynamically corrected to generate the actual inspection cycle. Extract the feature parameter identifiers that need to be increased in monitoring frequency from the enhanced monitoring parameter list, and send the parameter acquisition frequency adjustment command to the data acquisition module; When the target regulatory level is a higher risk level or a high risk level, an on-site inspection task sheet is automatically generated. The on-site inspection task sheet includes equipment location information, key parameters to be monitored, and a suggested inspection time window. When the target regulatory level is high-risk, an operation restriction command is sent to the equipment control unit. The restriction command includes at least one of the following: reduced load operation, limited operating speed, or entering maintenance mode. All triggered regulatory instructions are sorted according to their execution priority to generate a regulatory instruction execution sequence; Each regulatory instruction is executed sequentially according to the regulatory instruction execution sequence, and the execution status and results are recorded in the regulatory log database.

[0013] In a second aspect, the present invention also provides a special equipment lifecycle monitoring system based on feature parameter monitoring, applicable to the method described in the first aspect. The system includes: a multi-source data acquisition module, an intelligent early warning analysis module, a dynamic risk assessment module, a regulatory level mapping module, a differentiated regulatory execution module, and a system configuration and management module. The multi-source data acquisition module is used to collect multi-dimensional feature parameter data streams generated by the target special equipment during its lifecycle operation. These multi-dimensional feature parameter data streams include real-time operating parameters, cumulative damage parameters, and performance degradation parameters. The intelligent early warning analysis module is communicatively connected to the multi-source data acquisition module and is used to perform trend analysis based on the multi-dimensional feature parameter data streams using a preset time-series prediction model to generate a trend deterioration early warning signal. Simultaneously, it performs association analysis using an association rule mining algorithm to generate an associated fault early warning signal. Dynamic risk... The assessment module communicates with the intelligent early warning analysis module to integrate trend deterioration early warning signals and associated fault early warning signals, and calculates real-time dynamic risk scores through a dynamic risk profile model by combining the inherent attribute data and historical operation and maintenance data of the target special equipment. The regulatory level mapping module communicates with the dynamic risk assessment module to map the real-time dynamic risk scores to preset discrete regulatory levels to obtain the target regulatory level. The differentiated regulatory execution module communicates with the regulatory level mapping module to automatically trigger and execute the corresponding differentiated regulatory instruction set according to the target regulatory level. The differentiated regulatory instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the data monitoring frequency. The system configuration and management module communicates with each module to configure system parameters, manage user permissions, store historical data, and display regulatory results.

[0014] Unlike existing technologies, the above-mentioned technical solution collects multi-dimensional feature parameter data streams containing real-time operating parameters, cumulative damage parameters, and performance degradation parameters. It then uses a time-series prediction model to perform trend analysis and generate trend deterioration warning signals, and employs an association rule mining algorithm to perform association analysis and generate associated fault warning signals. These two warning signals are then fused together, combined with inherent attribute data and historical operation and maintenance data, to calculate real-time dynamic risk scores using a dynamic risk profiling model. Finally, these scores are mapped to preset discrete regulatory levels and the corresponding differentiated regulatory instruction sets are automatically executed. This invention effectively overcomes the problems of delayed risk identification and static, fixed strategies in traditional supervision, achieving a shift from passive response to proactive early warning, and from average supervision to precise policy implementation. It significantly improves the predictability, targeting, and resource allocation efficiency of special equipment safety supervision.

[0015] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0016] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on this application.

[0017] In the accompanying drawings of the instruction manual: Figure 1 This is a schematic diagram illustrating steps S101 to S105 of the regulatory method described in a specific implementation. Figure 2 A schematic diagram illustrating steps S201 to S206 of the monitoring method described in the specific implementation embodiment; Figure 3 This is a schematic diagram of the structure of the monitoring system described in a specific implementation.

[0018] The reference numerals used in the above figures are explained as follows: 1. Regulatory system; 11. Multi-source data acquisition module; 12. Intelligent early warning and analysis module; 13. Dynamic Risk Assessment Module; 14. Regulatory Level Mapping Module; 15. Differentiated regulatory enforcement module; 16. System Configuration and Management Module. Detailed Implementation

[0019] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0020] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0021] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0022] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0023] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0024] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0025] As understood in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0026] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0027] The processor described in the embodiments of this application can be implemented by hardware, firmware, software, or a combination thereof. It can be a circuit, one or more of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, or a microprocessor. It also includes other physical, biological, or chemical structures that can implement the same or equivalent functions as the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of this application, or any combination of the steps mentioned therein.

[0028] The computer program involved in the embodiments can be stored in a computer device readable storage medium, which includes, but is not limited to, disks, magnetic tapes, magnetic cards, floppy disks, flash memory, optical disks, optical cards, read-only memory (ROM), random access memory (RAM), erasable programmable ROM (EPROM), and electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical, or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, proteins, and other units with information storage capabilities. In specific embodiments, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiments can be centrally stored in a single medium, or distributed and stored in multiple media. The memory containing the computer device readable storage medium can be non-volatile memory or random access memory. These computer device readable storage media can be built into the device, or can be connected to the device involved in the embodiments as an external device or part of an external device. In some embodiments, the memory having a computer device readable storage medium is deployed locally; in other embodiments, the memory may be deployed remotely from the processor, for example, as a network-attached memory accessed via RF circuitry or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), or a suitable combination thereof, as long as computer device access to the memory is enabled. Furthermore, the computer program involved in the embodiments may be stored in plaintext / ciphertext form, or it may be designed as training data, integrated and recombined through model training and implicitly stored in the parameter states of a deep neural network or other machine learning model.

[0029] Please see Figure 1 In a first aspect, this embodiment provides a method for lifecycle supervision of special equipment based on feature parameter monitoring, including: S101. Collect multi-dimensional feature parameter data streams generated by the target special equipment during its life cycle operation. The multi-dimensional feature parameter data streams include real-time operation parameters, cumulative damage parameters, and performance degradation parameters. S102. Based on multi-dimensional feature parameter data stream, trend analysis is performed through a preset time series prediction model to generate a trend deterioration warning signal for a specific fault mode. Meanwhile, the association rule mining algorithm is used to perform association analysis on the multi-dimensional feature parameter data stream to generate associated fault early warning signals that indicate potential composite faults. S103. By integrating the trend deterioration warning signal and the associated fault warning signal, and combining the inherent attribute data and historical operation and maintenance data of the target special equipment, the real-time dynamic risk score of the target special equipment is calculated through the dynamic risk profile model. S104. Map the real-time dynamic risk score to a preset discrete regulatory level to obtain the target regulatory level; S105. Based on the mapped target regulatory level, automatically trigger and execute a differentiated regulatory instruction set corresponding to the current target regulatory level. The differentiated regulatory instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the data monitoring frequency.

[0030] In step S101, the multi-dimensional feature parameter data stream refers to the continuous monitoring data set obtained through acquisition devices such as IoT sensors and equipment monitoring systems. Real-time operating parameters characterize the current operating status of the equipment, including instantaneous measurements such as vibration frequency, temperature, and pressure; cumulative damage parameters reflect performance degradation caused by long-term use, such as metal fatigue and wear; performance degradation parameters are used to assess the degree of functional decline of the equipment, including parameters such as efficiency indicators and accuracy deviations that change over time. These parameters are standardized by the data acquisition module to form a unified data stream, providing a complete data foundation for subsequent analysis. This step constructs a multi-source data acquisition system covering the entire lifecycle of the equipment, ensuring the comprehensiveness and timeliness of the monitoring data.

[0031] In step S102, the time-series prediction model employs a time-series analysis algorithm trained on historical data. By performing pattern recognition and trend extrapolation on multi-dimensional feature parameter data streams, it generates a trend deterioration warning signal for specific fault modes. Simultaneously, the association rule mining algorithm analyzes the statistical correlations between multiple parameters to identify association patterns indicating potential complex faults, generating associated fault warning signals. Preferably, the time-series prediction model can utilize deep learning algorithms such as Long Short-Term Memory networks, and the association rule mining can employ an improved FP-Growth algorithm. This step establishes a dual-track warning mechanism, focusing not only on the evolution trend of individual parameters but also capturing the collaborative change patterns among multiple parameters, thus upgrading from single-point warning to systemic warning.

[0032] In step S103, the dynamic risk profiling model refers to a computational model that integrates multiple risk factors. It calculates a real-time dynamic risk score by fusing warning information from deteriorating trend warning signals and associated fault warning signals, combined with inherent attribute data and historical maintenance data. Inherent attribute data includes static features such as equipment type and service life, while historical maintenance data covers historical information such as maintenance records and inspection reports. Preferably, the dynamic risk profiling model employs a multi-dimensional weighted fusion algorithm, assigning corresponding weights based on the importance of different risk factors. This step constructs a dynamic risk assessment system, transforming discrete warning signals into quantifiable risk indicators, providing an objective basis for tiered supervision.

[0033] In step S104, the preset discrete regulatory level refers to a predefined risk level classification system, typically including multiple levels such as low risk, moderate risk, relatively high risk, and high risk. The real-time dynamic risk score is compared with the risk threshold of each regulatory level through preset mapping rules to determine the corresponding target regulatory level. The risk threshold can be set based on historical data analysis, expert experience, or industry standards. This step establishes a standardized mapping mechanism from risk score to regulatory level, realizing the transformation of risk assessment results into regulatory decisions.

[0034] In step S105, the differentiated regulatory instruction set refers to a combination of pre-set regulatory measures for different regulatory levels, including measures such as automatically adjusting inspection cycles, generating and dispatching specific maintenance work orders, and increasing data monitoring frequency. The execution of regulatory instructions is automatically triggered by the system, ensuring the timeliness and consistency of regulatory measures. The adjustment of inspection cycles is based on dynamic calculations of risk levels, the generation of maintenance work orders considers the specific risk characteristics of the equipment, and the increased monitoring frequency focuses on monitoring high-risk parameters. This step achieves precise allocation of regulatory resources, dynamically adjusts regulatory intensity according to risk levels, and forms a closed-loop regulatory execution mechanism.

[0035] This embodiment constructs a complete technology chain from data collection, intelligent early warning, risk assessment to tiered execution, realizing the transformation of special equipment supervision from post-event handling to pre-event early warning, and from static cyclical supervision to dynamic and precise policy implementation. This method effectively solves the problems of lagging risk identification and rigid regulatory strategies in traditional regulatory models, improving the predictability of supervision and the efficiency of resource allocation. Simultaneously, automated execution of regulatory instructions ensures the timely implementation of regulatory measures.

[0036] Please see Figure 2 In some embodiments, based on multi-dimensional feature parameter data streams, trend analysis is performed using a preset time-series prediction model to generate a trend deterioration warning signal for a specific fault mode, including: S201. Extract the key feature parameter sequence associated with the target fault mode from the multi-dimensional feature parameter data stream. The key feature parameter sequence contains multiple historical observations arranged in chronological order. S202. Perform stationarity testing and outlier removal on the key feature parameter sequences to generate preprocessed standardized feature parameter sequences; S203. Input the standardized feature parameter sequence into the pre-trained long short-term memory network model. The long short-term memory network model learns the degradation law of the feature parameters based on historical data. S204. Using a long short-term memory network model, the standardized feature parameter sequence is predicted in multiple steps to generate a sequence of predicted feature parameter values ​​for a future preset time period. S205. Calculate the minimum relative distance between the predicted value sequence of feature parameters and the preset safety threshold, and compare the minimum relative distance with the preset distance threshold; S206. If the minimum relative distance is less than or equal to the preset distance threshold, it is determined that the key feature parameter has a risk of trend deterioration, and a trend deterioration warning signal containing the predicted time point of exceeding the standard is generated. Furthermore, the warning signal of deteriorating trend is bound to the corresponding key feature parameter identifier and target failure mode type.

[0037] In step S201, the key feature parameter sequence refers to the time series of parameters strongly correlated with a specific fault mode, selected from the multi-dimensional feature parameter data stream. The target fault mode is determined through an equipment fault knowledge base. The selection of key feature parameters is based on the correlation analysis between the fault mode and the feature parameters, and the degree of correlation can be quantified using methods such as Pearson correlation coefficient or mutual information. The historical observations of the key feature parameter sequence, arranged in chronological order, form the input basis for model training. This step focuses on key risk indicators through feature selection, improving the pertinence and accuracy of early warning.

[0038] In step S202, the stationarity test uses the unit root test to determine whether the statistical characteristics of the sequence change over time. Outlier removal is based on the three-standard-deviation principle or box plot method to identify and process abnormal data points. Standardization transforms feature parameters with different dimensions to a unified numerical range, preferably using min-maximum standardization or Z-score standardization. The preprocessed standardized feature parameter sequence has better model adaptability and predictive stability. This step improves data quality and ensures that the input data of the time series prediction model meets the modeling requirements.

[0039] In step S203, the pre-trained Long Short-Term Memory (LSTM) network model refers to a deep learning model pre-trained based on a large amount of historical equipment operation data. Through a gating mechanism, it learns the long-term dependencies of feature parameters, effectively capturing the temporal patterns of equipment performance degradation. Preferably, the backpropagation algorithm is used to optimize network parameters during model training, and the loss function is typically chosen as mean squared error or mean absolute error. This step leverages the powerful temporal modeling capabilities of deep learning models to accurately learn the patterns of equipment performance degradation.

[0040] In step S204, the multi-step forward prediction employs a recursive prediction strategy, using the current predicted value as input for the next prediction, sequentially generating predicted values ​​for multiple future time steps. The length of the prediction period is determined based on the equipment type and maintenance cycle, typically covering a complete maintenance cycle. The sequence of predicted characteristic parameters reflects the expected change trajectory of key parameters within the future time period. This step, through multi-step prediction, anticipates the development trend of equipment status in advance, providing a time window for preventative maintenance.

[0041] In step S205, the minimum relative distance is calculated using the minimum relative distance between each point in the predicted value sequence of feature parameters and a preset safety threshold. The relative distance can be calculated by dividing the difference between the predicted value and the safety threshold by the safety threshold. The preset distance threshold is determined based on the equipment safety margin and historical data analysis, reflecting the system's sensitivity to trend deterioration. This step provides a quantitative basis for judging trend deterioration by quantifying the proximity of the predicted results to the safety boundary.

[0042] In step S206, the generation of the trend deterioration warning signal is based on the comparison between the minimum relative distance and a preset distance threshold. The predicted exceedance time point is determined by linear interpolation or curve fitting methods to determine the time point when the predicted value sequence of characteristic parameters first exceeds the safety threshold. The binding of the warning signal with key characteristic parameter identifiers and target failure mode types is achieved through data association technology to ensure the integrity and traceability of the warning information. This step transforms the quantitative analysis results into specific warning information, providing clear guidance for subsequent risk decision-making.

[0043] This embodiment establishes a complete trend deterioration early warning generation process, realizing a systematic approach from data preprocessing and model prediction to early warning generation. This method can identify equipment performance degradation trends in advance, providing a sufficient time window for preventative maintenance. Simultaneously, through precise binding of early warning information, it ensures that subsequent regulatory measures are targeted at specific failure modes and risk characteristics, effectively improving the foresight and accuracy of equipment safety supervision.

[0044] In some embodiments, a long short-term memory network model is used to perform multi-step forward prediction on a standardized feature parameter sequence to generate a sequence of predicted feature parameter values ​​for a predetermined future time period, including: The standardized feature parameter sequence is divided into a training time window and a prediction time window. The training time window contains multiple consecutive historical time step observations. The historical time step observations within the training time window are used as model inputs to construct the input feature tensor; Temporal features are extracted from the input feature tensor through the hidden layer of the Long Short-Term Memory network model to obtain the hidden state vector at the final time step. The hidden state vector of the final time step is input into the fully connected regression layer to calculate the predicted value of the feature parameters of the first prediction time step. A recursive prediction strategy is adopted to feed the current predicted value of the feature parameters back to the input of the long short-term memory network model, update the input feature tensor, and repeat the temporal feature extraction and regression calculation process to generate the predicted value of the feature parameters for each subsequent prediction time step in turn. The predicted values ​​of the feature parameters at all prediction time steps are combined in chronological order to form a complete sequence of predicted feature parameter values.

[0045] In this embodiment, the division of the training and prediction time windows is based on the sliding window principle. The length of the training time window is determined according to the equipment's operating cycle and the frequency of parameter changes, typically including enough historical observations to cover typical operating modes. The length of the prediction time window is set according to actual maintenance needs and prediction accuracy requirements, ensuring that the prediction results have practical guiding significance. The continuity of the time series is maintained during window division to avoid data breaks affecting model performance. This division method provides the model with sufficient historical information as a learning foundation while limiting a reasonable prediction range.

[0046] The input feature tensor is a multidimensional data structure built by arranging standardized observations within the training time window in chronological order. The tensor's dimensions include the number of time steps and the number of feature parameters, with the specific organization depending on the network architecture design. During construction, it is crucial to ensure the consistency of the temporal dimension to avoid losing temporal information. This step transforms the temporal data into a standardized format suitable for deep learning models, laying the foundation for feature extraction.

[0047] The hidden layers of the Long Short-Term Memory (LSTM) network model extract temporal features through a gating mechanism. The input gate, forget gate, and output gate work together to selectively retain important historical information and filter out irrelevant noise. The hidden state vector, as the network's internal memory unit, carries all key temporal features from the beginning of the sequence to the final time step. This process leverages the memory characteristics of recurrent neural networks to effectively capture the long-term dependencies of device parameter changes.

[0048] The fully connected regression layer maps the hidden state vector at the final time step to specific feature parameter predictions. Through a combination of linear transformations and nonlinear activation functions, it achieves the conversion from a high-dimensional feature space to predicted values. The weight parameters are optimized using the backpropagation algorithm during the model pre-training phase. This step transforms the extracted temporal features into predicted values ​​with practical physical meaning.

[0049] The recursive prediction strategy achieves multi-step forward prediction by feeding the current predicted value back to the model input. After each prediction, the input sequence is updated through a sliding window, retaining the latest predicted value as the input for the next time step. This iterative process continues until all predicted values ​​for a preset time period are generated. This strategy utilizes the autocorrelation of the sequence to deduce the state at subsequent time points from the generated predicted values.

[0050] The sequence of predicted feature parameters is constructed by arranging the output results of each prediction time step in chronological order. Each element in the sequence corresponds to the predicted state of the parameter at a specific future time point, and the sequence as a whole reflects the expected trajectory of the equipment performance parameters. The length of the predicted feature parameter sequence is consistent with the size of the prediction time window to ensure the completeness of the prediction results. This step organizes discrete prediction points into a continuous time-series trajectory, visually displaying the trend of parameter changes.

[0051] This embodiment utilizes a multi-step prediction mechanism based on Long Short-Term Memory (LSTM) networks. Through standardized window partitioning, feature extraction, and recursive prediction processes, it ensures the accuracy and reliability of the prediction results. This method effectively captures the temporal patterns of equipment parameter changes, providing precise quantitative evidence for trend early warning. Furthermore, by generating complete prediction sequences, it offers comprehensive data support for equipment condition assessment and maintenance decisions.

[0052] In some embodiments, a correlation analysis is performed on a multi-dimensional feature parameter data stream using an association rule mining algorithm to generate an associated fault early warning signal indicating a potential composite fault, including: Extract the observation values ​​of multiple feature parameters at the same time from the multi-dimensional feature parameter data stream to construct a multivariate synchronous observation sample set; The multivariate synchronous observation sample set is discretized, and continuous feature parameter values ​​are mapped to discrete symbols representing state levels to generate symbolic state sequences. The improved FP-Growth algorithm is used to mine frequent patterns in symbolic state sequences and identify frequent parameter combination patterns with support higher than the preset minimum support threshold. Calculate the confidence and lift indices of each frequent parameter combination pattern, and select strong association rules with confidence higher than the preset confidence threshold and lift greater than 1 to form a candidate fault association rule library; The real-time acquired multi-dimensional feature parameter data stream is converted into a symbolic state vector and matched with a candidate fault association rule base. If the symbolic state vector satisfies the antecedent condition of a strong association rule, but the consequent fault state corresponding to the strong association rule has not yet reached the alarm threshold in the real-time data, then it is determined that a precursor pattern of a potential composite fault has been detected. Based on the matched strong correlation rules, a related fault warning signal is generated. The related fault warning signal includes the parameter combination involved, the associated fault type, and the confidence level.

[0053] In this embodiment, the multivariate synchronous observation sample set is constructed by extracting observation values ​​of multiple feature parameters at the same timestamp, ensuring the temporal alignment between parameters. This sample set is organized as a two-dimensional data table, with rows representing time points and columns corresponding to different feature parameters. This synchronous acquisition method preserves the instantaneous correlation between parameters, providing an accurate data foundation for subsequent correlation analysis. This step captures the collaborative change characteristics between multiple parameters during equipment operation.

[0054] Discretization employs equal-frequency binning or domain-knowledge-based segmentation to convert continuous feature parameter values ​​into a finite number of discrete state levels. Each state level corresponds to a specific parameter range, such as normal, attentive, and abnormal. Symbolic state sequences are generated by mapping numerical values ​​to symbolic labels with clear physical meaning, facilitating subsequent pattern mining algorithms. This step reduces data complexity while preserving key information about parameter state changes.

[0055] The improved FP-Growth algorithm adds a time window constraint to the traditional algorithm, mining only frequent patterns within a specific time range. The support threshold is determined based on the equipment's operating characteristics and historical data distribution, used to filter parameter combinations with sufficiently high frequency. Frequent parameter combination patterns reflect stable correlations between parameter states during equipment operation. This mining process discovers statistically significant co-occurrence patterns among parameter states.

[0056] The confidence metric measures the conditional probability of the consequent occurring given the occurrence of the antecedent, while the lift metric assesses the strength of the association between the antecedent and consequent. A pre-set confidence threshold is based on the false positive rate tolerance, and a lift greater than 1 ensures the rule has practical predictive value. The selection of strongly associated rules employs a multi-metric joint judgment to ensure that each rule in the rule base has high reliability and practicality. This selection process statistically verifies the effectiveness of association rules.

[0057] The matching of symbolic state vectors with the candidate fault association rule base is implemented using a pattern matching algorithm. During the matching process, the symbol consistency between the real-time state vector and the rule antecedent is compared, and rule evaluation is triggered when a complete match is achieved. This matching mechanism can quickly identify the similarity between the current device state and known fault modes, and rapidly compare real-time monitoring data with historical fault modes.

[0058] The early warning pattern determination for potential complex faults is based on a state where the antecedent of a strong correlation rule is met, but the consequent has not yet reached the alarm threshold. This state indicates that the equipment has exhibited initial fault characteristics but has not yet developed into a significant fault stage. The generation of warning signals comprehensively considers parameter combinations, fault type, and confidence information to ensure the completeness and operability of the warning content. This determination process can identify early signs of fault development in advance.

[0059] This embodiment achieves early identification of potential complex faults by establishing a complete association rule mining and application process. This method can discover implicit correlations between multiple parameters and identify complex fault risks even when a single parameter has not reached an alarm threshold, effectively improving the timeliness and accuracy of fault warnings and providing multi-dimensional judgment criteria for equipment status assessment.

[0060] In some embodiments, the deterioration trend warning signal and the associated fault warning signal are integrated, and combined with the inherent attribute data and historical operation and maintenance data of the target special equipment, a real-time dynamic risk score of the target special equipment is calculated through a dynamic risk profiling model, including: The warning signal of deteriorating trend is analyzed, the predicted time point of exceeding the standard and the key feature parameter identifier are extracted, and the urgency index of deteriorating trend is calculated. The associated fault warning signals are analyzed to extract the associated fault types and confidence levels, and the composite fault severity index is calculated. An inherent risk baseline value calculation model is constructed based on the analytic hierarchy process. Inherent attribute data, including equipment type, service life and historical accident records, are input into the inherent risk baseline value calculation model to calculate the static baseline value that characterizes the inherent risk of the equipment. A real-time health status assessment model is constructed, which weights and fuses the trend deterioration urgency index and the composite fault severity index to generate a real-time health status score. The weighting coefficient of the operating load intensity is determined based on the entropy weight method, and the operating load intensity coefficient is calculated by combining the average daily running time and average load rate in historical operation and maintenance data. The static base value, real-time health status score and operating load intensity coefficient are input into a preset multi-dimensional risk fusion function, which adopts a non-linear weighted summation function. The risk score is calculated by using a multi-dimensional risk fusion function, and outputs a real-time dynamic risk score between 0 and 1. The higher the score, the higher the risk level of the equipment.

[0061] In this embodiment, the trend deterioration urgency index is calculated by the difference between the predicted time of exceeding the standard and the current time; the smaller the time difference, the higher the index value. Key feature parameter identifiers are used to determine specific monitoring parameters, and different parameters can be assigned different urgency weights according to their importance. The calculation of this index quantifies the time urgency of the trend deterioration warning, providing a time dimension basis for risk assessment.

[0062] The composite fault severity index is calculated based on a combination of the historical impact and current confidence level of related fault types. Fault types are graded according to their impact on the safe operation of equipment, and the confidence level reflects the reliability of the current warning. The index is calculated using a weighted product method, taking into account both fault severity and probability of occurrence. This calculation can assess the severity of the potential consequences of composite faults.

[0063] The inherent risk baseline calculation model uses the analytic hierarchy process (AHP) to determine the weight ratio of each inherent attribute. Equipment type, service life, and historical accident records correspond to different risk levels. A judgment matrix is ​​constructed through expert scoring, and normalized weight coefficients are calculated. This step quantifies the inherent and relatively stable risk characteristics of the equipment.

[0064] The real-time health status assessment model employs a dynamic weighting strategy, adjusting the fusion weights of the trend deterioration urgency index and the composite fault severity index based on the equipment's operational stage. In the early stages of operation, greater emphasis is placed on trend deterioration, while in later stages, greater attention is paid to the risk of composite faults. This model achieves adaptive fusion of early warning signals, accurately reflecting the real-time health status of the equipment.

[0065] The calculation of the operating load intensity coefficient uses the entropy weight method to determine the objective weights of the average daily operating time and average load rate. First, the information entropy of each indicator is calculated; the smaller the entropy value, the greater the role of the indicator in distinguishing equipment load status, and thus a higher weight is assigned. The weighted indicators are then mapped to the zero-to-one interval through range standardization, objectively quantifying the operating load intensity of the equipment.

[0066] The multi-dimensional risk fusion function employs a non-linear weighted summation, incorporating exponential or logarithmic functions to process each input component. This non-linear characteristic better reflects the synergistic effect among risk factors; when multiple risk factors occur simultaneously, the risk score increases faster than with linear superposition. This function accurately characterizes the comprehensive risk level under the combined influence of multiple risk factors.

[0067] This embodiment achieves a comprehensive quantitative assessment of equipment risk by constructing a complete dynamic risk profile calculation process. This method comprehensively considers multiple dimensions such as trend warnings, correlation warnings, inherent attributes, and operating load. Through a scientific calculation model, it integrates various risk information into a unified dynamic risk score, providing an accurate and reliable decision-making basis for subsequent tiered supervision and effectively improving the comprehensiveness and accuracy of risk assessment.

[0068] In some embodiments, a weighted fusion of the trend deterioration urgency index and the composite fault severity index is performed to generate a real-time health status score, including: Establish a weighting matrix for the trend deterioration urgency index and the composite fault severity index. The weighting matrix is ​​dynamically adjusted according to the equipment type and operation stage. The first normalized value of the trend deterioration urgency index is calculated. The first normalized value is inversely proportional to the time difference between the predicted time of exceeding the standard and the current time. The second normalized value of the composite fault severity index is calculated. The second normalized value is based on a comprehensive assessment of the historical average repair time and confidence level of the associated fault types. The first normalized value and the second normalized value are input into the preset health status scoring function, which uses an exponential weighted moving average algorithm for dynamic fusion. A preliminary health status score is calculated using a health status scoring function. The historical health status baseline of the equipment is introduced as a reference benchmark to perform relative correction on the preliminary health status score; The sigmoid function is applied to smooth the corrected preliminary health status score, transforming it into a standard score range of 0 to 100, and generating the final real-time health status score for risk fusion.

[0069] In this embodiment, the weight allocation matrix determines different weight ratios based on a combination of equipment type and operational phase. Equipment types include different categories such as elevators and cranes, and operational phases are divided into initial, stable, and declining phases. The weight values ​​in the matrix are derived from historical data analysis, with a greater emphasis on early warning of deteriorating trends during the initial stage of equipment operation and a greater focus on the risk of complex failures during the declining phase. The establishment of this matrix achieves dynamic adaptability in weight allocation, ensuring that the health status assessment conforms to the actual operating characteristics of the equipment.

[0070] The first normalized value is calculated using an inverse proportional conversion method, transforming the time difference between the predicted time of exceeding the standard and the current time into a standardized value. The smaller the time difference, the higher the normalized value, indicating a greater urgency of trend deterioration. A minimum time difference threshold is set during the conversion process to avoid extreme values ​​having an excessive impact on the calculation results. This calculation quantifies time-dimensional information into comparable standardized indicators.

[0071] The second normalized value is calculated by multiplying the historical average maintenance time of the associated fault type by the confidence level. The historical average maintenance time reflects the complexity of fault handling, while the confidence level characterizes the reliability of the current warning. Combining the two allows for a comprehensive assessment of the potential impact of complex faults. This calculation comprehensively considers the resource input for fault handling and the reliability of the warning.

[0072] The health status scoring function employs an exponentially weighted moving average algorithm, assigning higher weight to recent data by setting a decay factor. The first and second normalized values ​​are used as input sequences, and a dynamically fused result is obtained through iterative calculation. This algorithm achieves a smooth assessment of health status, avoiding drastic fluctuations in the scoring results.

[0073] Relative correction is achieved by comparing the initial health status score with the equipment's historical health status baseline. The baseline value is derived from historical score data of the equipment under normal conditions, and the correction process takes into account individual equipment differences and operating environment factors. This step eliminates the influence of inherent equipment characteristics on the scoring results, improving the fairness of the assessment.

[0074] The S-shaped function uses a standard logistic function to map the corrected scores to a standard range of 0 to 100. The function parameters are adjusted according to the score distribution characteristics to ensure that the mapped scores have reasonable discriminative power. This mapping process standardizes the score output, facilitating subsequent risk fusion calculations.

[0075] This embodiment establishes a complete real-time health status scoring calculation process, achieving effective integration of trend deterioration and complex fault early warning information. This method comprehensively considers dynamic factors such as equipment type and operational stage, and transforms multi-source early warning information into a unified health status score through a scientific calculation model. This provides accurate and reliable input for dynamic risk profiling, effectively improving the accuracy and practicality of health status assessment.

[0076] In some embodiments, the operating load intensity weighting coefficient is determined based on the entropy weighting method, and the operating load intensity coefficient is calculated by combining the average daily runtime and average load rate in historical operation and maintenance data, including: Collect data on the daily average runtime and average load rate of the target special equipment within a preset historical period; Calculate the first mean, first standard deviation, and first coefficient of variation of the daily average runtime dataset to obtain the first degree of dispersion; and calculate the second mean, second standard deviation, and second coefficient of variation of the average load rate dataset to obtain the second degree of dispersion. Based on the principle of entropy weight method, the information entropy value is calculated according to the first degree of dispersion and the second degree of dispersion; The weighting coefficients of the daily average runtime dataset and the average load rate dataset are calculated based on the information entropy value, and the indicators with greater variability are given higher weights. The calculated weighting coefficients are applied to the current device’s average daily runtime and average load rate data. A calculation model for operational load intensity is constructed, and the weighted average daily operating time is multiplied by the average load rate to obtain the original load intensity value. The original load intensity value is normalized by range and mapped to a numerical range of 0 to 1 to generate the final operating load intensity coefficient.

[0077] In this embodiment, the preset historical period is determined based on the equipment type and usage characteristics; preferably, the most recent complete operating cycle or a specific time period is selected. The daily average runtime dataset records the actual daily operating time of the equipment, and the average load rate dataset reflects the average level of the load carried by the equipment. These two datasets are obtained through equipment operation logs and load monitoring systems, providing basic data support for load intensity assessment.

[0078] The first and second degrees of dispersion are obtained by calculating the coefficient of variation, which is the ratio of the standard deviation to the mean. The coefficient of variation eliminates the influence of dimensions and accurately reflects the fluctuation characteristics of the dataset. The dispersion calculation considers both the mean and the standard deviation to ensure a comprehensive understanding of the data distribution characteristics. This calculation is used to quantify the stability and fluctuation range of various load indicators.

[0079] The calculation of information entropy is based on the principle of entropy weighting, transforming dispersion into a measure of information entropy. During the entropy calculation process, the greater the dispersion of an indicator, the smaller its corresponding information entropy, indicating that the indicator provides more information in distinguishing equipment load states. The calculation of information entropy allows for the evaluation of the importance of each indicator from an information theory perspective.

[0080] The determination of weighting coefficients follows the principle of entropy weighting, that is, the weight of each indicator is derived from the information entropy value. Indicators with lower information entropy values ​​are assigned higher weights because they contain more discriminative information. This weighting allocation method can objectively reflect the importance of each load indicator in actual assessment. This determination process achieves objectivity and data-driven approach in weight allocation.

[0081] The weighted processing applies the calculated weighting coefficients to the current equipment's average daily uptime and average load rate data. These weighting coefficients reflect the relative importance of each indicator in the load assessment, and the weighted data more accurately reflects the actual load condition of the equipment. This process highlights the impact of key load indicators.

[0082] The load intensity calculation model comprehensively evaluates the weighted runtime and load rate through a product operation. The product form captures the synergistic effect between the two indicators; when both runtime and load rate are high, the load intensity value increases significantly. The model's construction fully considers the combined impact of equipment usage intensity and usage time.

[0083] Range standardization linearly maps the original load intensity values ​​to a range of zero to one, with the minimum value mapped to zero and the maximum value to one. This eliminates dimensional differences and makes the load intensity coefficients of different equipment comparable. This process achieves standardized output of load intensity assessment results.

[0084] This embodiment establishes an objective weight determination method based on entropy weighting, enabling the scientific calculation of the operating load intensity coefficient. This method can automatically identify key load indicators, avoiding biases from subjective weight settings. Simultaneously, standardization ensures the comparability of assessment results, providing accurate load intensity input parameters for dynamic risk profiling.

[0085] In some embodiments, mapping real-time dynamic risk scores to preset discrete regulatory levels to obtain a target regulatory level includes: Multiple risk score range thresholds are set to divide the risk score range into risk score ranges corresponding to different regulatory levels, including low risk level, general risk level, relatively high risk level and high risk level; Read the currently calculated real-time dynamic risk score and compare it step by step with multiple risk score interval thresholds; If the real-time dynamic risk score is less than the first risk threshold, it will be mapped to a low-risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the first risk threshold and less than the second risk threshold, it is mapped to a general risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the second risk threshold and less than the third risk threshold, it will be mapped to a higher risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the third risk threshold, it will be mapped to a high-risk level, which is the target regulatory level. Additionally, when a real-time dynamic risk score is detected to have crossed a threshold from one risk score range into another, a regulatory level change event is generated, and the timestamp of the level change and the real-time dynamic risk scores before and after the change are recorded. The final determined target regulatory level is bound to the corresponding real-time dynamic risk score and equipment identifier to generate a regulatory level determination result.

[0086] In this embodiment, the risk score interval thresholds are determined by analyzing the distribution of historical risk data and equipment safety requirements. Each threshold divides the risk score range from zero to one into four consecutive intervals, each corresponding to a different regulatory level. The threshold settings take into account differences in equipment type and safety standards, ensuring that the level classification both meets regulatory requirements and is practically feasible.

[0087] The hierarchical comparison employs a sequential judgment logic, checking each real-time dynamic risk score sequentially from the lowest risk level to ensure it falls within the corresponding range. Strict boundary conditions are set during the comparison process to guarantee that each score can only map to a unique regulatory level. This sequential judgment method enables rapid and accurate level mapping, achieving efficient conversion from risk score to regulatory level.

[0088] The regulatory level mapping is performed based on preset interval boundary conditions, with each level corresponding to a specific risk score range. The mapping result directly determines the intensity level of subsequent regulatory measures; low-risk levels correspond to routine regulation, while high-risk levels trigger enhanced regulation.

[0089] Regulatory level change events are automatically generated when a risk score crosses a threshold range, recording detailed information including the change time, the previous and subsequent levels, and the risk score. This event generation mechanism promptly captures significant changes in equipment risk status, tracks the dynamic evolution of risk status, and provides a complete process record for regulatory decision-making.

[0090] The regulatory level determination result is generated by associating and binding the target regulatory level with real-time dynamic risk scores and equipment identifiers. Equipment identifiers ensure the correspondence between the determination result and specific equipment, while risk scores provide a quantitative basis for the level determination. This binding process is used to create a complete and traceable regulatory level record.

[0091] This embodiment establishes a complete regulatory level mapping mechanism, achieving accurate conversion from continuous risk scores to discrete regulatory levels. This method can promptly identify changes in equipment risk status, providing a clear basis for differentiated regulation. Simultaneously, complete change records ensure the traceability of the regulatory process, effectively supporting the implementation of precise regulatory strategies.

[0092] In some embodiments, based on the mapped target regulatory level, a differentiated regulatory instruction set corresponding to the current target regulatory level is automatically triggered and executed, including: Establish a mapping database between regulatory levels and regulatory actions. The database stores the basic regulatory cycle, enhanced monitoring parameter list, and set of mandatory regulatory actions corresponding to different regulatory levels. Query the mapping relationship database according to the target regulatory level to obtain the corresponding basic regulatory cycle setting value; Based on real-time dynamic risk scores and preset risk-cycle adjustment coefficients, the basic regulatory cycle setting is dynamically corrected to generate the actual inspection cycle. Extract the feature parameter identifiers that need to be increased in monitoring frequency from the enhanced monitoring parameter list, and send the parameter acquisition frequency adjustment command to the data acquisition module; When the target regulatory level is a higher risk level or a high risk level, an on-site inspection task sheet is automatically generated. The on-site inspection task sheet includes equipment location information, key parameters to be monitored, and a suggested inspection time window. When the target regulatory level is high-risk, an operation restriction command is sent to the equipment control unit. The restriction command includes at least one of the following: reduced load operation, limited operating speed, or entering maintenance mode. All triggered regulatory instructions are sorted according to their execution priority to generate a regulatory instruction execution sequence; Each regulatory instruction is executed sequentially according to the regulatory instruction execution sequence, and the execution status and results are recorded in the regulatory log database.

[0093] In this embodiment, the mapping library between regulatory levels and regulatory actions is constructed using predefined rules, storing the regulatory strategy configurations corresponding to each regulatory level. The basic regulatory cycle setting is determined based on equipment type and statutory inspection requirements. The enhanced monitoring parameter list includes key equipment operating parameters, and the regulatory action set defines the specific regulatory measures that must be implemented. The establishment of this mapping library achieves a standardized conversion from regulatory levels to specific regulatory actions.

[0094] The baseline regulatory cycle setting is obtained by querying a mapping database. The system retrieves the corresponding preset cycle value based on the current target regulatory level. This preset cycle value serves as a benchmark reference, providing a calculation basis for subsequent dynamic adjustments. The risk-cycle adjustment coefficient is calculated based on real-time dynamic risk scores. The higher the risk score, the smaller the adjustment coefficient, and the shorter the corresponding inspection cycle. The specific calculation process is as follows: The base adjustment factor is set to 1.0, which corresponds to the ideal state where the risk score is 0. As the real-time dynamic risk score increases, a piecewise function approach is used to adjust the coefficients. When the real-time dynamic risk score is in the range of 0 to 0.3, the adjustment coefficient decreases linearly from 1.0 to 0.8. When the risk score is in the range of 0.3 to 0.7, the adjustment coefficient decreases from 0.8 to 0.4 according to a quadratic function relationship; When the risk score exceeds 0.7, the adjustment coefficient decreases from 0.4 to 0.1 according to the exponential decay relationship.

[0095] This segmented design ensures a relatively stable inspection cycle when the risk is low, a moderately shortened inspection cycle when the risk is moderate, and a significantly increased regulatory frequency when the risk is high. The calculated risk-cycle adjustment coefficient is multiplied by the basic regulatory cycle setting to obtain the actual inspection cycle, thereby achieving a precise adaptive match between the inspection cycle and the equipment risk level.

[0096] The parameter acquisition frequency adjustment command is generated based on the enhanced monitoring parameter list and sends a request to the data acquisition module to increase the monitoring frequency of specific parameters. The command includes specific characteristic parameter identifiers and new acquisition frequency values ​​to ensure that high-risk parameters are monitored more intensively. On-site verification task sheets are automatically generated when a high-risk level is detected. These sheets include precise equipment location, a list of parameters requiring focused inspection, and a suggested inspection timeframe, and are automatically dispatched to relevant regulatory personnel through the workflow system.

[0097] Operational restriction commands are issued to high-risk equipment, enabling remote control of its operational status via the equipment control interface. Restrictive measures are selected based on equipment type and risk characteristics, including reducing operating load, limiting operating speed, or switching to maintenance mode, thereby controlling the potential hazards of high-risk equipment through operational restrictions.

[0098] The execution sequence of regulatory instructions is prioritized according to their urgency and execution dependencies. Safety-related restrictions are executed first, followed by data collection and adjustments, with on-site verification and other tasks arranged subsequently to ensure that critical regulatory measures are implemented in a timely manner.

[0099] This embodiment achieves precise implementation of regulatory strategies by establishing a complete differentiated regulatory instruction execution mechanism. This method can automatically trigger corresponding regulatory measures based on the equipment's risk level. Through dynamically adjusting inspection cycles, increasing monitoring frequency, arranging on-site inspections, and imposing operational restrictions, a multi-layered and differentiated regulatory system is formed, effectively improving the targeting and effectiveness of regulation.

[0100] Please see Figure 3In a second aspect, this embodiment also provides a special equipment lifecycle monitoring system 1 based on feature parameter monitoring, applicable to the method described in the first aspect. The system includes: a multi-source data acquisition module 11, an intelligent early warning analysis module 12, a dynamic risk assessment module 13, a regulatory level mapping module 14, a differentiated regulatory execution module 15, and a system configuration and management module 16. The multi-source data acquisition module 11 is used to collect multi-dimensional feature parameter data streams generated by the target special equipment during its lifecycle operation. The multi-dimensional feature parameter data streams include real-time operating parameters, cumulative damage parameters, and performance degradation parameters. The intelligent early warning analysis module 12 is communicatively connected to the multi-source data acquisition module 11 and is used to perform trend analysis based on the multi-dimensional feature parameter data streams through a preset time series prediction model to generate a trend deterioration early warning signal, and simultaneously perform association analysis through an association rule mining algorithm to generate an associated fault early warning signal. The dynamic risk assessment module 13 is communicatively connected to the intelligent early warning analysis module 12. It is used to integrate the trend deterioration early warning signal and the associated fault early warning signal, and combine the inherent attribute data and historical operation and maintenance data of the target special equipment to calculate the real-time dynamic risk score through the dynamic risk profile model. The regulatory level mapping module 14 is communicatively connected to the dynamic risk assessment module 13. It is used to map the real-time dynamic risk score to the preset discrete regulatory level to obtain the target regulatory level. The differentiated regulatory execution module 15 is communicatively connected to the regulatory level mapping module 14. It is used to automatically trigger and execute the corresponding differentiated regulatory instruction set according to the target regulatory level. The differentiated regulatory instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the data monitoring frequency. The system configuration and management module 16 is communicatively connected to each module. It is used to configure system parameters, manage user permissions, store historical data, and display regulatory results.

[0101] In this embodiment, the multi-source data acquisition module 11 realizes real-time acquisition and aggregation of equipment operation data through a distributed sensor network; the intelligent early warning analysis module 12 adopts a dual-engine architecture to handle time series prediction and association rule mining tasks respectively, ensuring the comprehensiveness of early warning signals; the dynamic risk assessment module 13 has a built-in multi-source data fusion algorithm to deeply integrate early warning signals with basic equipment information; the regulatory level mapping module 14 realizes rapid classification of risk scores through preset threshold ranges; the differentiated regulatory execution module 15 supports automatic matching and execution of regulatory strategies to achieve precise regulation; and the system configuration and management module 16 provides a unified operation and maintenance management interface to ensure stable system operation.

[0102] This system achieves full-process automation of special equipment supervision through modular design. The multi-source data acquisition module 11 acquires real-time equipment status, the intelligent early warning analysis module 12 identifies potential risks, the dynamic risk assessment module 13 quantifies risk levels, the supervision level mapping module 14 determines supervision intensity, and the differentiated supervision execution module 15 implements specific measures. Finally, with the support of the system configuration and management module 16, a closed-loop supervision system is formed. This system effectively solves the problems of delayed early warning and resource misallocation in traditional supervision models, realizing a shift from passive response to proactive early warning, and from static supervision to dynamic policy implementation.

[0103] By adopting the above technical solutions, this invention differs from existing technologies and possesses the following beneficial effects: By constructing a full-process regulatory system based on multi-dimensional feature parameter data flow, it effectively overcomes the problems of lagging risk identification and rigid regulatory strategies in traditional special equipment supervision. This method uses a time-series prediction model to conduct forward-looking analysis of equipment operation trends, combined with association rule mining algorithms to identify potential compound faults, achieving an upgrade from single-parameter threshold alarms to systemic risk warnings. The dynamic risk profiling model integrates trend deterioration warning signals, associated fault warning signals, and inherent equipment attributes and historical operation and maintenance data to generate quantified real-time dynamic risk scores, providing objective basis for regulatory decisions. By mapping risk scores to preset discrete regulatory levels and automatically triggering corresponding differentiated regulatory instruction sets, it achieves dynamic adjustment of inspection cycles, accurate dispatch of maintenance work orders, and intelligent optimization of monitoring frequencies, forming a complete closed loop from risk perception to regulatory execution. This invention significantly improves the predictability and accuracy of special equipment safety supervision, optimizes the allocation of regulatory resources, changes the traditional regulatory model relying on periodic inspections and passive maintenance, and provides scientific and effective technical support for special equipment safety management.

[0104] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for lifecycle supervision of special equipment based on characteristic parameter monitoring, characterized in that, include: Collect multi-dimensional feature parameter data streams generated by the target special equipment during its life cycle operation. The multi-dimensional feature parameter data streams include real-time operation parameters, cumulative damage parameters, and performance degradation parameters. Based on the multi-dimensional feature parameter data stream, trend analysis is performed through a preset time series prediction model to generate a trend deterioration warning signal for a specific fault mode. Meanwhile, the multi-dimensional feature parameter data stream is analyzed by association rule mining algorithm to generate association fault early warning signals that indicate potential compound faults; By integrating the trend deterioration warning signal and the associated fault warning signal, and combining the inherent attribute data and historical operation and maintenance data of the target special equipment, the real-time dynamic risk score of the target special equipment is calculated through a dynamic risk profile model; The real-time dynamic risk score is mapped to a preset discrete regulatory level to obtain the target regulatory level; Based on the mapped target regulatory level, a differentiated regulatory instruction set corresponding to the current target regulatory level is automatically triggered and executed. The differentiated regulatory instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the data monitoring frequency.

2. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 1, characterized in that, Based on the multi-dimensional feature parameter data stream, trend analysis is performed using a preset time-series prediction model to generate a trend deterioration warning signal for a specific fault mode, including: Extract key feature parameter sequences associated with the target fault mode from the multi-dimensional feature parameter data stream, wherein the key feature parameter sequences contain multiple historical observations arranged in chronological order; The key feature parameter sequence is subjected to stationarity testing and outlier removal to generate a preprocessed standardized feature parameter sequence. The standardized feature parameter sequence is input into a pre-trained long short-term memory network model, which learns the degradation pattern of the feature parameters based on historical data. The standardized feature parameter sequence is predicted in multiple steps using the long short-term memory network model to generate a sequence of predicted feature parameter values ​​for a future preset time period. Calculate the minimum relative distance between the predicted value sequence of the feature parameters and a preset safety threshold, and compare the minimum relative distance with the preset distance threshold; If the minimum relative distance is less than or equal to the preset distance threshold, it is determined that the key feature parameter has a risk of trend deterioration, and a trend deterioration warning signal containing the predicted time point of exceeding the standard is generated; Furthermore, the trend deterioration warning signal is bound to the corresponding key feature parameter identifier and target fault mode type.

3. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 2, characterized in that, The standardized feature parameter sequence is predicted in multiple steps using the Long Short-Term Memory (LSTM) network model to generate a sequence of predicted feature parameter values ​​for a future preset time period, including: The standardized feature parameter sequence is divided into a training time window and a prediction time window, wherein the training time window contains multiple consecutive historical time step observations; Using the historical time step observations within the training time window as model input, an input feature tensor is constructed; Temporal features are extracted from the input feature tensor through the hidden layer of the Long Short-Term Memory network model to obtain the hidden state vector at the final time step. The hidden state vector of the final time step is input into the fully connected regression layer to calculate the predicted value of the feature parameters of the first prediction time step. A recursive prediction strategy is adopted to feed the current predicted value of the feature parameters back to the input of the long short-term memory network model, update the input feature tensor, and repeat the temporal feature extraction and regression calculation process to generate the predicted value of the feature parameters for each subsequent prediction time step in turn. The predicted values ​​of the feature parameters at all prediction time steps are combined in chronological order to form a complete sequence of predicted feature parameter values.

4. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 1, characterized in that, The multi-dimensional feature parameter data stream is analyzed using an association rule mining algorithm to generate associated fault early warning signals indicating potential complex faults, including: The observation values ​​of multiple feature parameters at the same time are extracted from the multi-dimensional feature parameter data stream to construct a multivariate synchronous observation sample set; The multivariate synchronous observation sample set is discretized, and continuous feature parameter values ​​are mapped to discrete symbols representing state levels to generate symbolic state sequences. The improved FP-Growth algorithm is used to perform frequent pattern mining on the symbolic state sequence to identify frequent parameter combination patterns with support higher than the preset minimum support threshold. Calculate the confidence and lift indices of each frequent parameter combination pattern, and select strong association rules with confidence higher than the preset confidence threshold and lift greater than 1 to form a candidate fault association rule library; The real-time acquired multi-dimensional feature parameter data stream is converted into a symbolic state vector and matched with the candidate fault association rule base; If the symbolic state vector simultaneously satisfies the antecedent condition of a strong association rule, but the consequent fault state corresponding to the strong association rule has not yet reached the alarm threshold in the real-time data, then it is determined that a precursor mode of a potential composite fault has been detected. A fault warning signal is generated based on the matched strong association rules. The fault warning signal includes the parameter combination involved, the associated fault type and confidence level.

5. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 1, characterized in that, By integrating the trend deterioration warning signal and the associated fault warning signal, and combining the inherent attribute data and historical operation and maintenance data of the target special equipment, a real-time dynamic risk score of the target special equipment is calculated through a dynamic risk profiling model, including: The trend deterioration warning signal is analyzed to extract the predicted time point of exceeding the standard and the key feature parameter identifier, and the trend deterioration urgency index is calculated. The associated fault warning signals are analyzed to extract the associated fault types and confidence levels, and the composite fault severity index is calculated. An inherent risk baseline value calculation model is constructed based on the analytic hierarchy process (AHP). The inherent attribute data, including equipment type, service life, and historical accident records, are input into the inherent risk baseline value calculation model to calculate the static baseline value characterizing the inherent risk of the equipment. A real-time health status assessment model is constructed, and the trend deterioration urgency index and the composite fault severity index are weighted and fused to generate a real-time health status score. The weighting coefficient of the operating load intensity is determined based on the entropy weight method, and the operating load intensity coefficient is calculated by combining the average daily running time and average load rate in the historical operation and maintenance data. The static base value, the real-time health status score and the operating load intensity coefficient are input into a preset multi-dimensional risk fusion function, which adopts a non-linear weighted summation function. The multi-dimensional risk fusion function is used for comprehensive calculation, and the output is a real-time dynamic risk score ranging from 0 to 1. The higher the score, the higher the risk level of the equipment.

6. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 5, characterized in that, The urgency index of the deteriorating trend is weighted and fused with the composite fault severity index to generate a real-time health status score, including: Establish a weighting matrix for the trend deterioration urgency index and the composite fault severity index, wherein the weighting matrix dynamically adjusts the weight ratio according to the equipment type and operation stage; The first normalized value of the trend deterioration urgency index is calculated, and the first normalized value is inversely proportional to the time difference between the predicted time of exceeding the standard and the current time. The second normalized value of the composite fault severity index is calculated. The second normalized value is based on a comprehensive assessment of the historical average repair time and confidence level of the associated fault types. The first normalized value and the second normalized value are input into a preset health status scoring function, which uses an exponentially weighted moving average algorithm for dynamic fusion. A preliminary health status score is calculated using the aforementioned health status scoring function; The historical health status baseline value of the equipment is introduced as a reference benchmark, and the preliminary health status score value is relativized and corrected. The sigmoid function is applied to smooth the corrected preliminary health status score, transforming it into a standard score range of 0 to 100, and generating the final real-time health status score for risk fusion.

7. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 5, characterized in that, The operating load intensity weighting coefficient is determined based on the entropy weight method. Combining the average daily runtime and average load rate from the historical operation and maintenance data, the operating load intensity coefficient is calculated, including: Collect data on the daily average runtime and average load rate of the target special equipment within a preset historical period; Calculate the first mean, first standard deviation, and first coefficient of variation of the daily average runtime dataset to obtain the first degree of dispersion; and calculate the second mean, second standard deviation, and second coefficient of variation of the average load rate dataset to obtain the second degree of dispersion. Based on the principle of entropy weight method, the information entropy value is calculated according to the first degree of dispersion and the second degree of dispersion; The weighting coefficients of the daily average runtime dataset and the average load rate dataset are calculated based on the information entropy value, and the indicators with greater variability are given higher weights. The calculated weighting coefficients are applied to the current device’s average daily runtime and average load rate data. A calculation model for operational load intensity is constructed, and the weighted average daily operating time is multiplied by the average load rate to obtain the original load intensity value. The original load intensity value is normalized by range and mapped to a numerical range of 0 to 1 to generate the final operating load intensity coefficient.

8. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 1, characterized in that, Mapping the real-time dynamic risk score to a preset discrete regulatory level yields the target regulatory level, including: Multiple risk score range thresholds are set to divide the risk score range into risk score ranges corresponding to different regulatory levels, including low risk level, general risk level, relatively high risk level and high risk level; Read the currently calculated real-time dynamic risk score and compare it step by step with the threshold values ​​of the multiple risk score intervals; If the real-time dynamic risk score is less than the first risk threshold, it is mapped to a low-risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the first risk threshold and less than the second risk threshold, it is mapped to a general risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the second risk threshold and less than the third risk threshold, it is mapped to a higher risk level, which is the target regulatory level. If the real-time dynamic risk score is greater than or equal to the third risk threshold, it is mapped to a high-risk level, which is the target regulatory level. In addition, when it is detected that the real-time dynamic risk score crosses a threshold from one risk score range into another risk score range, a regulatory level change event is generated, and the level change timestamp and the real-time dynamic risk scores before and after the change are recorded. The final determined target regulatory level is bound to the corresponding real-time dynamic risk score and equipment identifier to generate a regulatory level determination result.

9. The method for lifecycle supervision of special equipment based on feature parameter monitoring according to claim 1, characterized in that, Based on the mapped target regulatory level, automatically trigger and execute a differentiated set of regulatory instructions corresponding to the current target regulatory level, including: Establish a mapping relationship library between regulatory levels and regulatory actions. The mapping relationship library stores the basic regulatory cycle, enhanced monitoring parameter list and set of regulatory actions that must be performed for different regulatory levels. Query the mapping relationship database according to the target regulatory level to obtain the corresponding basic regulatory cycle setting value; Based on the real-time dynamic risk score and the preset risk-cycle adjustment coefficient, the basic regulatory cycle setting value is dynamically corrected to generate the actual inspection cycle. Extract the feature parameter identifiers that need to be increased in monitoring frequency from the enhanced monitoring parameter list, and send the parameter acquisition frequency adjustment command to the data acquisition module; When the target regulatory level is a higher risk level or a high risk level, an on-site inspection task sheet is automatically generated. The on-site inspection task sheet includes equipment location information, key parameters to be monitored, and a suggested inspection time window. When the target regulatory level is high-risk, an operation restriction command is sent to the equipment control unit. The restriction command includes at least one of the following: reduced load operation, limiting operating speed, or entering maintenance mode. All triggered regulatory instructions are sorted according to their execution priority to generate a regulatory instruction execution sequence; Each regulatory instruction is executed sequentially according to the described regulatory instruction execution sequence, and the execution status and results are recorded in the regulatory log database.

10. A special equipment lifecycle monitoring system based on characteristic parameter monitoring, characterized in that, The system applicable to the method of any one of claims 1 to 9 comprises: The multi-source data acquisition module is used to acquire multi-dimensional feature parameter data streams generated by the target special equipment during its life cycle operation. The multi-dimensional feature parameter data streams include real-time operation parameters, cumulative damage parameters, and performance degradation parameters. The intelligent early warning analysis module is communicatively connected to the multi-source data acquisition module. It is used to perform trend analysis based on the multi-dimensional feature parameter data stream through a preset time series prediction model to generate a trend deterioration early warning signal, and at the same time to perform correlation analysis through an association rule mining algorithm to generate a correlation fault early warning signal. The dynamic risk assessment module is communicatively connected to the intelligent early warning analysis module. It is used to integrate the trend deterioration early warning signal and the associated fault early warning signal, and combine the inherent attribute data and historical operation and maintenance data of the target special equipment to calculate the real-time dynamic risk score through the dynamic risk profile model. The regulatory level mapping module is communicatively connected to the dynamic risk assessment module and is used to map the real-time dynamic risk score to a preset discrete regulatory level to obtain the target regulatory level. The differentiated supervision execution module is communicatively connected to the supervision level mapping module and is used to automatically trigger and execute the corresponding differentiated supervision instruction set according to the target supervision level. The differentiated supervision instruction set includes at least one of the following: automatically adjusting the inspection cycle, generating and dispatching specific maintenance work orders, and increasing the data monitoring frequency. The system configuration and management module communicates with other modules and is used to configure system parameters, manage user permissions, store historical data, and display monitoring results.

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