A method and system for operation and maintenance management of process industry equipment

Abstract

The application relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management method and system for process industrial equipment, the method comprising the following steps: coupling multi-modal features of time sequence operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment; quantifying fluctuation trends of the fusion feature set to obtain a degradation feature index of the process industrial equipment; performing evolution evaluation on the degradation feature index to obtain a health quantitative value of the process industrial equipment; constructing operation and maintenance decision boundary conditions of the process industrial equipment; performing collaborative mapping on the fusion feature set and the health quantitative value to obtain an optimized operation and maintenance strategy of the process industrial equipment; and associating and integrating the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment; and the application can improve the operation and maintenance management efficiency of the process industrial equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance, and particularly relates to a process industrial equipment operation and maintenance method and system. BACKGROUND

[0002] The existing process industrial equipment operation and maintenance technology has significant limitations in processing multi-source heterogeneous data, and cannot effectively perform multi-modal feature fusion on time sequence operation parameters and field state information. Due to the lack of systematic cross-modal correlation analysis and feature coupling mechanism, the extracted equipment operation features are one-sided and scattered, and cannot fully reflect the actual operation state of the equipment, thereby affecting the early identification and judgment of the equipment degradation trend. Meanwhile, the existing technology is not accurate enough in quantitatively analyzing the feature fluctuation trend, and does not fully combine process condition data and historical operation and maintenance records to construct a scientific benchmark reference system, so that the calculation of the degradation feature index lacks rationality and cannot accurately represent the degradation degree of the equipment.

[0003] In the operation and maintenance decision and strategy making link, the existing technology cannot realize the deep cooperation of operation and maintenance cost threshold, maintenance resource constraint and production plan, and the decision boundary condition constructed lacks flexibility and practicality, is prone to constraint conflicts and cannot be effectively coordinated. In addition, when matching the operation and maintenance strategy, the existing technology cannot accurately cooperate the equipment fusion feature set and the health quantitative value, and lacks targeted adjustment in calling the preset operation and maintenance strategy library, so that the generated operation and maintenance strategy has low adaptation degree with the actual health state of the equipment and the field constraint condition, cannot form an optimized operation and maintenance scheme, and finally causes low operation and maintenance efficiency, which is difficult to meet the actual needs of efficient operation and maintenance of process industrial equipment. SUMMARY

[0004] The present application provides a process industrial equipment operation and maintenance method and system to solve the problems raised in the background technology.

[0005] To achieve the above-mentioned purpose, the present application provides a process industrial equipment operation and maintenance method, which comprises:

[0006] S1, coupling multi-modal features of time sequence operation parameters and field state information of process industrial equipment to obtain a fusion feature set of the process industrial equipment;

[0007] S2, quantifying fluctuation trend of the fusion feature set based on process condition data and historical operation and maintenance records of the process industrial equipment to obtain a degradation feature index of the process industrial equipment;

[0008] S3, performing evolution evaluation on the degradation feature index to obtain a health quantitative value of the process industrial equipment;

[0009] S4, constructing an operation and maintenance decision boundary condition of the process industrial equipment according to the operation and maintenance cost threshold range, maintenance resource constraints and the production plan of the process industrial equipment;

[0010] S5, based on a preset operation and maintenance strategy library, the fusion feature set and the health quantization value are cooperatively mapped to obtain an optimized operation and maintenance strategy of the process industrial equipment;

[0011] S6, the operation and maintenance decision boundary condition is associated and integrated with the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment.

[0012] In a preferred embodiment, the multi-modal feature coupling of the time sequence operation parameters and the field state information of the process industrial equipment obtains a fusion feature set of the process industrial equipment, which comprises:

[0013] The time sequence operation parameters and the field state information of the process industrial equipment are time sequence normalized to obtain standardized time sequence data and standardized state data of the process industrial equipment;

[0014] The standardized time sequence data and the standardized state data are cross-modal associated analyzed to obtain a feature correlation strength between the time sequence operation parameters and the field state information;

[0015] Based on the feature correlation strength, the standardized time sequence data and the standardized state data are coupled to obtain an initial fusion feature set of the process industrial equipment;

[0016] According to the historical operation and maintenance records of the process industrial equipment, the initial fusion feature set is weighted and adjusted to obtain a fusion feature set of the process industrial equipment.

[0017] In a preferred embodiment, the fluctuation trend quantization of the fusion feature set based on the process working condition data and the historical operation and maintenance records of the process industrial equipment obtains a degradation feature index of the process industrial equipment, which comprises:

[0018] Reading the process working condition data of the process industrial equipment;

[0019] Based on the process working condition data, the fusion feature set is time sequence segmented to obtain a time period feature subset of the process industrial equipment;

[0020] The rate change analysis of the time period feature subset obtains a trend quantization sequence of the time period feature subset;

[0021] The time period feature subset is matched and mapped with the historical operation and maintenance records of the process industrial equipment, and according to the mapping result, a historical reference trend boundary of the process industrial equipment is determined.

[0022] The deviation of the trend quantification sequence is compared with the historical benchmark trend boundary to obtain the degradation characteristic index of the process industry equipment.

[0023] In a preferred embodiment, the formula for calculating the degradation characteristic index is as follows:

[0024] ;

[0025] In the formula, The degradation characteristic index, This represents the total number of evaluation dimensions for key trend features in the trend quantification sequence. The ordinal number of the feature dimension index. For the first Dynamic weights of dimensions For the trend quantization sequence, the first... Dimensional trend eigenvalues The median of healthy operation within the historical benchmark trend boundary. For the first Historical benchmark span of dimensional The preset global weighting coefficients for volatility. For the first The incremental trend fluctuation.

[0026] In a preferred embodiment, the step of performing an evolutionary assessment of the degradation characteristic indicators to obtain a quantitative health value for the process industry equipment includes:

[0027] The degradation characteristic index is subjected to time-series smoothing to obtain the time-series smoothed sequence of the degradation characteristic index;

[0028] By performing trend extrapolation on the time-series smoothed sequence, the development direction and rate of change of the degradation characteristic indicators are identified, and the degradation evolution trend of the process industry equipment is obtained.

[0029] Based on similar data representing the health status of equipment in the historical operation and maintenance records, a baseline health threshold for the process industry equipment is constructed.

[0030] Based on the baseline health threshold and the degradation evolution trend, the deviation degree of the process industry equipment is calculated, wherein the calculation formula for the deviation degree is as follows:

[0031] ;

[0032] In the formula, The degree of deviation, To find the maximum value function, This is an indicator of the current state's degradation characteristics. a reference health threshold value, a preset trend influence coefficient, a momentary slope of the deterioration evolution trend;

[0033] fusing the time sequence smoothing sequence and the deviation degree to obtain a health quantification value of the process industrial equipment.

[0034] In a preferred embodiment, the constructing of the operation and maintenance decision boundary condition of the process industrial equipment according to the operation and maintenance cost threshold range, maintenance resource constraint and production plan in the process industrial equipment comprises:

[0035] structurally and associatively analyzing the operation and maintenance cost threshold range, maintenance resource constraint and production plan in the process industrial equipment to obtain a cost upper limit condition, a resource availability condition and a production continuity condition of the process industrial equipment;

[0036] detecting conflicts of the cost upper limit condition, the resource availability condition and the production continuity condition based on preset conflict detection rules to obtain a constraint conflict pair of the process industrial equipment;

[0037] coordinating the constraint conflict pair according to a priority strategy of a process working condition in the process industrial equipment to obtain a compromise constraint rule set of the cost upper limit condition and the resource availability condition;

[0038] restricting and recombining the cost upper limit condition, the resource availability condition and the production continuity condition according to the compromise constraint rule set to construct the operation and maintenance decision boundary condition of the process industrial equipment.

[0039] In a preferred embodiment, the coordinating the constraint conflict pair according to a priority strategy of a process working condition in the process industrial equipment to obtain a compromise constraint rule set of the cost upper limit condition and the resource availability condition comprises:

[0040] obtaining a priority strategy of a current running stage in the process working condition;

[0041] dynamically allocating weights to different constraint conditions in the constraint conflict pair based on the priority strategy to obtain a weight-adjusted constraint conflict pair of the process industrial equipment;

[0042] adjusting the cost upper limit condition and the resource availability condition according to the weight-adjusted constraint conflict pair to obtain a candidate coordination scheme of the process industrial equipment;

[0043] Based on the historical operation and maintenance records, feasibility simulation evaluation is performed on the candidate coordination scheme, and an optimal coordination scheme of the candidate coordination scheme is selected according to the simulation evaluation result.

[0044] The optimal coordination scheme is subjected to rule form transformation, and a compromise constraint rule set of the cost upper limit condition and the resource availability condition is obtained.

[0045] In a preferred embodiment, based on the preset operation and maintenance strategy library, the fusion feature set and the health quantitative value are synergistically mapped to obtain the optimized operation and maintenance strategy of the process industrial equipment, including:

[0046] According to the health state interval of the health quantitative value, adaptive retrieval is performed in the preset operation and maintenance strategy library to obtain a candidate operation and maintenance strategy template of the health state interval.

[0047] The key feature subset related to the equipment failure mode in the fusion feature set is subjected to matching degree evaluation with the trigger condition and action parameter defined in the candidate operation and maintenance strategy template to obtain a matching evaluation result of the candidate operation and maintenance strategy template.

[0048] Based on the matching evaluation result, the specific numerical value of the fusion feature set is instantiated and assigned to the adjustable parameter in the candidate operation and maintenance strategy template to obtain the optimized operation and maintenance strategy of the process industrial equipment.

[0049] In a preferred embodiment, the operation and maintenance decision boundary condition is associated and integrated with the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment, including:

[0050] The operation and maintenance decision boundary condition and the optimized operation and maintenance strategy are subjected to association analysis to obtain a corresponding relationship between the operation and maintenance decision boundary condition and the optimized operation and maintenance strategy.

[0051] According to the corresponding relationship, an operation and maintenance report basic framework of the process industrial equipment is constructed.

[0052] The execution steps, required resources and time sequence arrangement of the optimized operation and maintenance strategy are synergistically explained with the fusion feature set and the health quantitative value to obtain a strategy detailed description and decision basis part of the optimized operation and maintenance strategy.

[0053] The key limitation clauses in the operation and maintenance decision boundary condition and the compromise constraint rule set are used as an implementation prerequisite and risk prompt part of the optimized operation and maintenance strategy.

[0054] According to the preset report template, the operation and maintenance report basic framework, the strategy details and decision basis part and the implementation premise and risk prompt part are structurally integrated and formatted output, and the operation and maintenance management report of the process industrial equipment is obtained.

[0055] To solve the above problems, the application also provides an operation and maintenance management system of process industrial equipment, the system comprises:

[0056] A multi-modal feature coupling module is configured to couple multi-modal features of time-series operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment.

[0057] A fluctuation trend quantification module is configured to quantize fluctuation trends of the fusion feature set based on process condition data and historical operation and maintenance records of the process industrial equipment to obtain a degradation feature index of the process industrial equipment.

[0058] A health evolution evaluation module is configured to evaluate the degradation feature index to obtain a health quantization value of the process industrial equipment.

[0059] A decision boundary construction module is configured to construct operation and maintenance decision boundary conditions of the process industrial equipment according to an operation and maintenance cost threshold range, maintenance resource constraints and a production plan in the process industrial equipment.

[0060] A strategy coordination mapping module is configured to perform coordinated mapping of the fusion feature set and the health quantization value based on a preset operation and maintenance strategy library to obtain an optimized operation and maintenance strategy of the process industrial equipment.

[0061] A strategy association integration module is configured to associate and integrate the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment.

[0062] Compared with the prior art, the application has the following beneficial effects:

[0063] 1. The application integrates and optimizes time-series operation parameters and field state information of the process industrial equipment through multi-modal feature coupling technology to form a precise fusion feature set, and then combines process condition data and historical operation and maintenance records to perform fluctuation trend quantization, accurately calculates degradation feature indexes, and finally evaluates health quantization values through time-series smoothing and trend extrapolation. This series of technical operations realizes multi-dimensional and deep analysis of the equipment operation state, accurately captures the equipment degradation rules and health change trend, provides comprehensive and reliable state basis for operation and maintenance work, and improves the accuracy and comprehensiveness of the equipment operation state cognition.

[0064] 2.The application constructs scientific operation and maintenance decision boundary conditions according to operation and maintenance cost thresholds, maintenance resource constraints and production plans, matches and optimizes adaptive operation and maintenance schemes from a pre-set operation and maintenance strategy library through the collaborative mapping of a feature set and health quantitative values, and finally integrates structured operation and maintenance management reports containing implementation basis, execution steps, resource requirements and risk prompts. This technical path makes the formulation of operation and maintenance strategies more targeted and operable, and the operation and maintenance management reports can better meet the actual application requirements, effectively improving the rationality of operation and maintenance decisions and the efficiency of operation and maintenance execution, and providing strong support for the stable and efficient operation of process industrial equipment. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 A flowchart of a process industrial equipment operation and maintenance management method provided by an embodiment of the application is shown in the figure.

[0066] Figure 2 A functional module diagram of a process industrial equipment operation and maintenance management system provided by an embodiment of the application is shown in the figure.

[0067] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0069] The embodiment of the application provides a process industrial equipment operation and maintenance management method. The execution subject of the process industrial equipment operation and maintenance management method includes but is not limited to at least one of electronic devices such as a server and a terminal which can be configured to execute the method provided by the embodiment of the application. In other words, the process industrial equipment operation and maintenance management method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc. Basic cloud computing services.

[0070] Referring to Figure 1 A flowchart of a process industrial equipment operation and maintenance management method provided by an embodiment of the application is shown in the figure. In this embodiment, the process industrial equipment operation and maintenance management method includes:

[0071] S1, coupling multi-modal features of time sequence operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment;

[0072] In the embodiment of the application, the coupling of the multi-modal features of the time sequence operation parameters and the field state information of the process industrial equipment to obtain the fusion feature set of the process industrial equipment comprises:

[0073] The time sequence operation parameters and the field state information of the process industrial equipment are subjected to time sequence normalization to obtain standardized time sequence data and standardized state data of the process industrial equipment.

[0074] The standardized time sequence data and the standardized state data are subjected to cross-modal correlation analysis to obtain a feature correlation strength between the time sequence operation parameters and the field state information.

[0075] Based on the feature correlation strength, the standardized time sequence data and the standardized state data are subjected to feature vector coupling to construct an initial fusion feature set of the process industrial equipment.

[0076] According to the historical operation and maintenance records of the process industrial equipment, the initial fusion feature set is subjected to weighted adjustment to obtain the fusion feature set of the process industrial equipment.

[0077] In the time sequence normalization of the time sequence operation parameters and the field state information of the process industrial equipment, the rated operation parameter interval of the process industrial equipment is taken as the normalization reference of the time sequence operation parameters, and the factory-calibrated field state parameter threshold interval is taken as the normalization reference of the field state information. For the real-time collected value of each feature dimension of the time sequence operation parameters, it is mapped to a fixed interval of 0 to 1, and the mapping rule is that the rated minimum value of the feature dimension corresponds to 0 and the rated maximum value corresponds to 1. For the collected value exceeding the rated interval, the corresponding boundary value is directly assigned. For the real-time collected value of each feature dimension of the field state information, the same mapping operation is performed according to the factory-calibrated threshold interval. Finally, the standardized time sequence data and the standardized state data of the process industrial equipment are obtained respectively.

[0078] When the standardized time series data and the standardized state data are subjected to cross-modal correlation analysis, for each feature dimension combination of the standardized time series data and the standardized state data, the change directions of the two in the same continuous collection period are counted one by one, the proportion of the number of the same change direction in the total collection period is the feature correlation strength between the corresponding feature dimension combination, the proportion value ranges from 0 to 1, 0 represents that the two feature dimensions are not correlated, and 1 represents that the two feature dimensions are completely correlated, and finally the feature correlation strength matrix between the time series running parameters and the field state information is obtained through statistical calculation of all feature dimension combinations.

[0079] Based on the feature correlation strength, when the standardized time series data and the standardized state data are subjected to feature vector coupling, the threshold of the preset feature correlation strength participating in coupling is 0.2, only the feature dimension combinations with a feature correlation strength greater than or equal to 0.2 are selected for coupling operation, for each feature dimension combination meeting the condition, the feature vector elements of the corresponding feature dimensions of the standardized time series data and the feature vector elements of the corresponding feature dimensions of the standardized state data are multiplied by the feature correlation strength of the feature dimension combination and then added, to complete the superposition operation of the two feature vectors, and the feature vector set formed after coupling of all feature dimension combinations meeting the condition is used to construct the initial fusion feature set of the process industrial equipment.

[0080] According to the historical operation and maintenance records of the process industrial equipment, the initial fusion feature set is subjected to weighted adjustment, feature vector data corresponding to all faults occurring before are extracted from the historical operation and maintenance records, the proportion of the number of faults caused after the value of each feature vector dimension in the initial fusion feature set becomes abnormal in the total number of faults is calculated, the proportion is the adjustment weight of the corresponding feature vector dimension, the adjustment weight ranges from 0 to 1.5, 0 represents that the feature vector dimension has no contribution to fault warning, and 1.5 represents that the feature vector dimension has the highest contribution to fault warning, the value of each feature vector dimension in the initial fusion feature set is multiplied by the corresponding adjustment weight, the weighted adjustment of the initial fusion feature set is completed, and finally the fusion feature set of the process industrial equipment is obtained.

[0081] S2, based on the process working condition data and the historical operation and maintenance records of the process industrial equipment, the fusion feature set is subjected to fluctuation trend quantification, and a degradation feature index of the process industrial equipment is obtained.

[0082] In the embodiment of the application, the fusion feature set is subjected to fluctuation trend quantification based on the process working condition data and the historical operation and maintenance records of the process industrial equipment, and a degradation feature index of the process industrial equipment is obtained, which comprises the following steps:

[0083] reading process condition data of the process industry equipment;

[0084] performing time series segmentation on the fusion feature set based on the process condition data to obtain a period feature subset of the process industry equipment;

[0085] performing rate change analysis on the period feature subset to obtain a trend quantization sequence of the period feature subset;

[0086] mapping the period feature subset and historical operation and maintenance records of the process industry equipment, and determining a historical benchmark trend boundary of the process industry equipment according to a mapping result;

[0087] performing deviation comparison between the trend quantization sequence and the historical benchmark trend boundary to obtain a degradation feature index of the process industry equipment.

[0088] The degradation feature index is calculated according to the following formula:

[0089] ;

[0090] In the formula, is the degradation feature index, is a total number of evaluation dimensions of a key trend feature in the trend quantization sequence, is a feature dimension index serial number, is a dynamic weight of the i-th dimension, is an i-th trend feature value in the trend quantization sequence, is a healthy operation median value in the historical benchmark trend boundary, is a historical benchmark span of the i-th dimension, is a preset fluctuation global weight coefficient, is a fluctuation increment of the i-th trend,

[0091] The total number of evaluation dimensions of the degradation feature index is a total number of key trend feature dimensions selected from the trend quantization sequence. The selection is based on the fact that the feature dimension is in a stable operation stage of the equipment and a correlation degree with equipment failure determined by cross-modal correlation analysis is not less than 0.2. Only the feature dimension meeting the two conditions is counted in the total number.

[0092] The dynamic weight is derived from the historical operation and maintenance records of the process industry equipment. The number of times of triggering failure after the abnormal appearance of the feature value of the dimension is calculated, and the total number of failures in the historical operation and maintenance records is calculated by ratio. The result is the dynamic weight of the dimension. The weight can reflect the contribution degree of the corresponding feature dimension to the equipment degradation evaluation. ​​​​

[0093] The trend characteristic value is a specific value in the trend quantization sequence obtained by rate change analysis on the time period characteristic subset, which reflects the change rate of the corresponding characteristic dimension in a specific time period.

[0094] The healthy running median is derived from the feature change trend data of the equipment in a normal running state in the historical operation and maintenance records that completely match the current process condition. The median is the average of the maximum normal change value and the minimum normal change value in the historical benchmark trend boundary. The median is the standard reference value of the corresponding characteristic dimension in the healthy running state of the equipment.

[0095] The historical benchmark span is the difference between the maximum normal change value and the minimum normal change value in the historical benchmark trend boundary, which reflects the normal fluctuation range of the corresponding characteristic dimension in the healthy running state of the equipment.

[0096] The preset fluctuation global weight coefficient is determined based on the industry technical standards of process industry equipment and the historical running statistical data of similar equipment in the past five years, and the value is fixed at 0.3. The value is verified by comparing the normal running and fault state of similar equipment, and can balance the influence of trend deviation and fluctuation increment on the degradation evaluation result.

[0097] The calculation method of the trend fluctuation increment is to extract the characteristic values of the corresponding dimension at two adjacent time points in the trend quantization sequence, calculate the absolute value of the difference between the two, and then calculate the average value of the absolute value of the difference between all adjacent time points of the dimension. Then, the historical average fluctuation value of the dimension under the same process condition is called, and the difference between the two is the trend fluctuation increment.

[0098] The significance of the formula is to comprehensively consider the degree of deviation of each key characteristic dimension from the healthy running state and the influence of trend fluctuation. By calculating the proportion of the absolute deviation of the trend characteristic value and the healthy running median of each characteristic dimension to the historical benchmark span, multiplying the dynamic weight of the dimension, and adding the product of the fluctuation global weight coefficient and the trend fluctuation increment of the dimension, the sum of the calculation results of all characteristic dimensions is obtained. The final result is the index that can accurately represent the overall degradation degree of the process industry equipment, which not only reflects the different contributions of each characteristic dimension to the degradation, but also takes into account the potential impact of trend fluctuation, ensuring the comprehensiveness and accuracy of the degradation evaluation result.

[0099] When reading the process condition data of the process industrial equipment, process-related data corresponding to the time dimension of the fusion feature set is extracted from the condition data collection system of the equipment, specifically including core condition information such as the current production load level of the equipment, the raw material input type, the operation pressure interval, and the production stage identifier. During the extraction process, it is necessary to ensure that each piece of process condition data is accompanied by a unique timestamp that accurately matches the collection timestamp of each feature data in the fusion feature set to avoid time misalignment. Finally, complete process condition data of the process industrial equipment synchronized with the time of the fusion feature set is obtained.

[0100] Based on the process condition data, when performing time series segmentation on the fusion feature set, the production stage identifier in the process condition data is used as the segmentation basis, and the preset production stage division standard is the start-up stage, the stable operation stage, and the shutdown preparation stage. The determination conditions for each stage are as follows: the start-up stage corresponds to the process of increasing the production load from 0 to 80% of the rated load, the stable operation stage corresponds to the process of maintaining the production load at 80%-100% of the rated load, and the shutdown preparation stage corresponds to the process of decreasing the production load from 80% of the rated load to 0. The time starting point and ending point of each production stage are determined according to the change of the production load in the process condition data. Then, the time series data of the fusion feature set is cut into multiple continuous data blocks according to these time nodes, and each data block corresponds to a complete production stage. Finally, the time period feature subset of the process industrial equipment is obtained.

[0101] When performing rate change analysis on the time period feature subset, for each feature dimension in the time period feature subset, the feature values corresponding to two adjacent collection time points are selected in chronological order, the difference between the feature value at the latter time point and the feature value at the former time point is calculated, and then the difference is divided by the time interval between the two time points to obtain the change rate of the feature dimension in the adjacent time interval. The change rates corresponding to each adjacent time interval are arranged in chronological order to form a continuous rate sequence for the corresponding feature dimension of the time period feature subset. After all feature dimensions complete the above operation, the trend quantization sequence of the time period feature subset is formed.

[0102] When the time period feature subset is matched and mapped with the historical operation and maintenance record of the process industrial equipment, first, record entries containing process condition information, historical feature data and corresponding equipment running state are extracted from the historical operation and maintenance record, the process condition parameters corresponding to the current time period feature subset are taken as the matching reference, the entries in which the process condition parameters are completely consistent with the current time period are screened out, the mapping relationship between the historical feature data corresponding to the screened historical entries and the current time period feature subset is established, the feature change trend data of the equipment in the normal running state is extracted from the mapped historical data, the fluctuation range of the normal trend data is counted, the maximum normal change value and the minimum normal change value of each feature dimension at different time points are determined, and the historical reference trend boundary of the process industrial equipment is formed.

[0103] When the trend quantization sequence is compared with the historical reference trend boundary, the rate value of each time point in the trend quantization sequence is compared with the maximum normal change value and the minimum normal change value of the corresponding time point in the historical reference trend boundary one by one, the difference value between the rate value and the reference boundary of the corresponding time point is calculated, if the rate value is between the maximum and minimum normal change values, the deviation value is 0, if the rate value is greater than the maximum normal change value, the deviation value is the rate value minus the maximum normal change value, if the rate value is less than the minimum normal change value, the deviation value is the minimum normal change value minus the rate value, and the deviation values of all time points are summarized to obtain the total deviation value of each feature dimension, and the total deviation values jointly constitute the degradation feature index of the process industrial equipment.

[0104] S3, evolution evaluation is performed on the degradation feature index to obtain a health quantization value of the process industrial equipment.

[0105] In the embodiment of the present application, the evolution evaluation on the degradation feature index to obtain the health quantization value of the process industrial equipment comprises:

[0106] The time sequence smoothing sequence of the degradation feature index is obtained by performing time sequence smoothing processing on the degradation feature index.

[0107] The development direction and the change rate of the degradation feature index are identified by performing trend extrapolation on the time sequence smoothing sequence, and the degradation evolution trend of the process industrial equipment is obtained.

[0108] The reference health threshold of the process industrial equipment is constructed according to the same type data representing the equipment health state in the historical operation and maintenance record.

[0109] The deviation degree of the process industrial equipment is obtained by performing deviation degree calculation based on the reference health threshold and the degradation evolution trend, and the calculation formula of the deviation degree is as follows:

[0110] ;

[0111] wherein, is the deviation degree, is a maximum function, is a degradation characteristic index of the current state, is the reference health threshold, is a preset trend influence coefficient, is an instantaneous slope of the degradation evolution trend;

[0112] The time sequence smoothing sequence is fused with the deviation degree to obtain a health quantification value of the process industrial equipment.

[0113] When the degradation characteristic index is time sequence smoothed, a sliding window with a fixed length of 5 consecutive collection periods is adopted, the degradation characteristic indexes in the window are selected in time sequence, the arithmetic mean of all indexes in the window is calculated, the mean value is taken as the smoothed index value of the middle position time point in the window, and for the part with less than 5 collection periods at the beginning and end, the index values at the beginning and end are filled to ensure that each time point can correspond to a smoothed data, and finally the time sequence smoothing sequence of the degradation characteristic index is obtained.

[0114] When the time sequence smoothing sequence is trend extrapolated, the data of the last 30 consecutive time points in the time sequence smoothing sequence are extracted, the difference between the smoothed values of adjacent two time points is calculated in time sequence to obtain 30 difference data, the arithmetic mean of the 30 difference data is calculated, and the mean value is the change rate of the degradation characteristic index. If the mean value is greater than 0, the development direction is degradation aggravation; if the mean value is equal to 0, the development direction is stable maintenance; and if the mean value is less than 0, the development direction is degradation relief. The change rate and the development direction are combined to obtain the degradation evolution trend of the process industrial equipment.

[0115] When the reference health threshold is constructed according to the same type of data representing the equipment health state in the historical operation and maintenance record, the records of the equipment in the normal operation state are screened from the historical operation and maintenance record, the screening standard is that there is no fault repair during the record period and the core performance parameter is maintained in the rated interval, the degradation characteristic index data corresponding to the records are extracted, the arithmetic mean of all data is calculated, and the mean value is determined as the reference health threshold of the process industrial equipment, so as to ensure that the threshold can accurately reflect the degradation characteristic index standard of the equipment in the healthy operation state.

[0116] The deviation degree of the process industry equipment is obtained by adding the first part of the value and the second part of the value, wherein the first part of the value is obtained by: calculating the difference between the current degradation characteristic index and the benchmark health threshold value, and then dividing the difference by the absolute value of the benchmark health threshold value, and if the calculation result is negative, taking 0; the second part of the value is obtained by: judging whether the instantaneous slope of the degradation evolution trend is negative, if the instantaneous slope of the degradation evolution trend is negative, taking 0, multiplying the obtained non-negative value by the preset trend influence coefficient, and then multiplying the obtained non-negative value by the current degradation characteristic index.

[0117] The deviation degree is a quantitative result obtained by comprehensively considering the current equipment degradation state and the degradation evolution trend, and reflecting the deviation of the equipment from the health running state.

[0118] The current degradation characteristic index is obtained by comparing the trend quantization sequence and the historical benchmark trend boundary, and the index has comprehensively considered the degree of deviation of each key characteristic dimension from the health running state and the influence of the trend fluctuation.

[0119] The benchmark health threshold value is derived from the degradation characteristic index data of the equipment in the normal running state in the historical operation and maintenance record, and the screening standard is that there is no fault repair and the core performance parameter is maintained in the rated interval during the record period, and the arithmetic mean of the data after screening is taken as the threshold value.

[0120] The preset trend influence coefficient is determined by statistically analyzing the contribution proportion of the degradation evolution trend to the fault occurrence in the historical fault data of the similar equipment in the past five years, and the value is fixed as 0.4. It is verified that the value can reasonably balance the weight of the trend factor in the deviation degree evaluation.

[0121] The instantaneous slope of the degradation evolution trend is the change rate obtained by trend extrapolation on the time series smoothing sequence, and the arithmetic mean of the difference between the adjacent time point smoothing values is obtained by extracting the data of the last 30 continuous time points in the time series smoothing sequence. The value reflects the change speed of the degradation characteristic index.

[0122] The maximum function is used to retain only non-negative values for calculation, that is, when the calculation result is negative, the value is directly taken as 0, so as to avoid the unreasonable influence of negative data on the deviation degree evaluation.

[0123] The formula has the meaning of considering the influence of current degradation state and degradation development trend on equipment health at the same time, first calculating the difference between the current degradation characteristic index and the benchmark health threshold value, and the proportion of the absolute value of the benchmark health threshold value, and retaining the non-negative value as the current deviation from the foundation, then multiplying the preset trend influence coefficient, the non-negative instantaneous slope, and the result of adding 1 to the current deviation from the foundation, to obtain the additional influence of the trend on the deviation degree, and adding the two results to obtain the quantitative value which can comprehensively reflect the deviation of the equipment from the health running state, and provide an accurate basis for the calculation of the subsequent health quantitative value.

[0124] When the time sequence smoothing sequence and the deviation degree are cooperatively fused, the fusion weight of the time sequence smoothing sequence is 0.6, and the fusion weight of the deviation degree is 0.4. The weight distribution is based on the fact that the time sequence smoothing sequence reflects the current stable degradation state of the equipment, and the deviation degree reflects the deviation of the equipment from the health benchmark. The health evaluation of the same type of equipment verifies that the two can balance the representation of the health state. For the value of each time point in the time sequence smoothing sequence, multiply the weight of 0.6, and then multiply the weight of 0.4 of the deviation degree corresponding to the time point. The two results are added to obtain a fusion intermediate value. Subsequently, all the fusion intermediate values are mapped to the interval of 0-100. The mapping rule is that the minimum value in all the fusion intermediate values corresponds to 0, the maximum value corresponds to 100, and the remaining intermediate values are converted according to the difference ratio of the minimum value and the maximum value. Finally, the health quantitative value of the process industrial equipment is obtained.

[0125] S4, according to the operation and maintenance cost threshold range, maintenance resource constraint and production plan in the process industrial equipment, the operation and maintenance decision boundary condition of the process industrial equipment is constructed;

[0126] In the embodiment of the application, the operation and maintenance decision boundary condition of the process industrial equipment is constructed according to the operation and maintenance cost threshold range, maintenance resource constraint and production plan in the process industrial equipment, comprising:

[0127] The operation and maintenance cost threshold range, maintenance resource constraint and production plan in the process industrial equipment are structurally associated and analyzed to obtain the cost upper limit condition, resource availability condition and production continuity condition of the process industrial equipment;

[0128] Based on the preset conflict detection rule, the cost upper limit condition, the resource availability condition and the production continuity condition are subjected to conflict detection to obtain the constraint conflict pair of the process industrial equipment.

[0129] According to the priority strategy of the process working condition in the process industrial equipment, the constraint conflict pair is subjected to coordination processing to obtain a compromise constraint rule set of the cost upper limit condition and the resource availability condition.

[0130] According to the compromise constraint rule set, the cost upper limit condition, the resource availability condition and the production continuity condition are limited and reorganized to construct the operation and maintenance decision boundary condition of the process industrial equipment.

[0131] According to the priority strategy of the process condition in the process industrial equipment, the compromise constraint rule set of the cost upper limit condition and the resource availability condition is obtained by coordinating the constraint conflict pair, including:

[0132] The priority strategy of the current running stage in the process condition is obtained.

[0133] Based on the priority strategy, the dynamic weight distribution is performed on different constraint conditions in the constraint conflict pair to obtain the weight-adjusted constraint conflict pair of the process industrial equipment.

[0134] According to the weight-adjusted constraint conflict pair, the cost upper limit condition and the resource availability condition are adjusted in stages to obtain the candidate coordination scheme of the process industrial equipment.

[0135] Based on the historical operation and maintenance record, the feasibility simulation evaluation is performed on the candidate coordination scheme, and the optimal coordination scheme of the candidate coordination scheme is selected according to the simulation evaluation result.

[0136] The optimal coordination scheme is subjected to rule form conversion to obtain the compromise constraint rule set of the cost upper limit condition and the resource availability condition.

[0137] When the operation and maintenance cost threshold range, maintenance resource constraint and production plan of the process industrial equipment are structurally associated and analyzed, the operation and maintenance cost threshold range is taken from the enterprise annual equipment operation and maintenance budget allocation file, which clearly marks the total operation and maintenance fee that can be borne by the equipment throughout the year, and the total fee is determined as the core value of the cost upper limit condition.

[0138] The maintenance resource constraint is extracted from the enterprise equipment management system, specifically including the number of adjustable maintenance personnel, professional skill level, available period of maintenance tools, number of spare parts inventory and replenishment cycle, and these information is sorted according to the association relationship of “personnel-tool-spare parts” to form the resource availability condition, which clearly marks the resource supply boundary corresponding to different maintenance tasks.

[0139] The production plan is derived from the annual production scheduling file formulated by the production scheduling department of the enterprise, and the planned running period, yield target and key process period of the equipment that cannot be interrupted are extracted, and these information is converted into the production continuity condition to clearly mark the production time range that cannot be disturbed by operation and maintenance activities, and finally the cost upper limit condition, resource availability condition and production continuity condition of the process industrial equipment are obtained.

[0140] The preset conflict detection rule includes three types when the cost upper limit condition, the resource availability condition and the production continuity condition are detected for conflicts. The first type is a cost conflict rule, that is, the required fee of an operation and maintenance scheme exceeds the core value of the cost upper limit condition. The second type is a resource conflict rule, that is, the required maintenance personnel, tools or spare parts of an operation and maintenance scheme cannot be met in the planned implementation period. The third type is a production conflict rule, that is, the implementation period of an operation and maintenance scheme completely overlaps with the non-interruptible key process period in the production continuity condition and there is no alternative production arrangement. The adaptability between the three conditions is compared one by one. Any two condition combinations that trigger any one of the above rules are determined as a constraint conflict pair. Finally, the constraint conflict pair of the process industry equipment is obtained.

[0141] According to the priority strategy of the process condition in the process industry equipment, the constraint conflict pair is processed. The priority strategy of the process condition is preset to three levels. The first priority is production continuity, the second priority is resource availability, and the third priority is cost upper limit. The principle of high priority condition being satisfied first is followed in the coordination processing.

[0142] When the production continuity condition and the resource availability condition conflict, the deployment scheme of the resource is adjusted, such as replacing the maintenance personnel shift period, enabling the standby maintenance tool or urgently deploying the spare parts, to ensure that the operation and maintenance activities avoid the key process period in the production continuity condition. When the resource availability condition and the cost upper limit condition conflict, under the premise of not reducing the maintenance quality, a more cost-effective spare part replacement scheme or an optimized maintenance process is selected to reduce the labor cost, while the total cost after adjustment is controlled to be not more than 10% of the core value of the cost upper limit condition. When the production continuity condition and the cost upper limit condition conflict, the production continuity is prioritized, and the part exceeding the cost upper limit is included in the special operation and maintenance budget application channel, which is executed after being approved. After the above coordination processing is completed for all constraint conflict pairs, the adjustment standards and execution requirements are arranged to obtain the compromise constraint rule set of the cost upper limit condition and the resource availability condition.

[0143] According to the compromise constraint rule set, the cost upper limit condition, the resource availability condition and the production continuity condition are restricted and reorganized. According to the adjustment standards in the compromise constraint rule set, the core value of the cost upper limit condition is updated, and the allowed fee floating range is determined. The deployment period, alternative scheme and supplementary process of personnel, tools and spare parts in the resource availability condition are refined. The non-key process period that can be temporarily interrupted and the longest allowed implementation time of operation and maintenance activities in the production continuity condition are determined. The updated three conditions are logically integrated to ensure that there is no conflict between the conditions and they are mutually adapted. A set of clear restriction standards covering the three dimensions of cost, resource and production is formed. Finally, the operation and maintenance decision boundary condition of the process industry equipment is constructed.

[0144] When the priority strategy of the current running stage in the process condition is acquired, a production stage identifier is extracted from the process condition data of the process industrial equipment, the identifier corresponds to one of three types of starting stage, stable running stage and shutdown preparation stage, and the enterprise has preset fixed priority standards for the three types of stages, wherein the production continuity priority of the stable running stage is the highest, the resource availability priority is the second, and the cost upper limit priority is the lowest; the production continuity and resource availability priorities of the starting stage and the shutdown preparation stage are the same, and are higher than the cost upper limit; according to the extracted production stage identifier, the corresponding preset priority standard is matched, and the priority strategy of the current running stage is obtained.

[0145] Based on the priority strategy, when the dynamic weight distribution is performed on different constraint conditions in the constraint conflict pair, the weight distribution standard of the stable running stage is preset as production continuity 0.6, resource availability 0.3 and cost upper limit 0.1, the weight distribution standard of the starting stage and the shutdown preparation stage is production continuity 0.4, resource availability 0.4 and cost upper limit 0.2, for each constraint conflict pair, the constraint conditions corresponding to the conflict parties are determined, each constraint condition is given a corresponding weight according to the weight distribution standard of the current running stage, the constraint conflict pair and the distributed weight are associated and bound, and finally the weight adjustment constraint conflict pair of the process industrial equipment is obtained.

[0146] According to the weight adjustment constraint conflict pair, when the cost upper limit condition and the resource availability condition are adjusted in stages, the core principle of the staged adjustment is that the constraint condition with high weight is preferentially satisfied, and the constraint condition with low weight is adaptively adjusted; when the constraint conflict pair is production continuity and resource availability, the production continuity is preferentially guaranteed, and the resource availability condition is adjusted, specifically, the maintenance personnel are dispatched to the non-critical process period allowed by the production continuity condition, all spare maintenance tools are enabled, and the spare part replenishment period is compressed to within 48 hours; when the constraint conflict pair is resource availability and cost upper limit, the resource availability is preferentially guaranteed, and the cost upper limit condition is adjusted, the original cost upper limit value is floated by 10%, and a higher cost performance replacement scheme of spare parts is selected, the quality of the replacement spare parts meets the industry standard and the price is lower than that of the original spare parts by 20%, each constraint conflict pair is adapted to a set of specific adjustment measures, and all adjustment measures are combined to form the candidate coordination scheme of the process industrial equipment.

[0147] Based on historical operation and maintenance records, when conducting feasibility simulation evaluations of candidate coordination schemes, cases that perfectly match the current process conditions and constraints are selected from the historical operation and maintenance records. The operation and maintenance implementation data, failure rate, cost consumption, and resource usage in these cases are extracted as evaluation benchmarks. The implementation process of each candidate coordination scheme is simulated to evaluate whether all constraints are met, whether the failure rate after operation and maintenance is lower than 10% of the historical benchmark cases, whether the actual cost is within the adjusted cost limit, and whether resource allocation is feasible. Each candidate coordination scheme is verified one by one, and schemes that meet all evaluation requirements are counted. If multiple schemes meet the requirements, the scheme with the lowest failure rate and the lowest cost consumption is selected; if only one scheme meets the requirements, it is directly determined as the optimal one. Finally, the optimal coordination scheme among the candidate coordination schemes is selected.

[0148] When transforming the optimal coordination scheme into rule form, the applicable scenarios, adjustment objects, adjustment boundaries, and execution standards in the optimal coordination scheme are expressed in clear rule language. For example, "When the equipment is in a stable operation phase, and production continuity and resource availability form a constraint conflict pair, the maintenance resource allocation period is limited to 00:00-02:00 per day, spare maintenance tools must be 100% available, and the spare parts replenishment cycle shall not exceed 48 hours." "When resource availability and cost ceiling form a constraint conflict pair, the cost ceiling can be increased to 110% of the original value, and spare parts replacement must meet industry quality standards and the price must be reduced by more than 20% compared to the original spare parts." All transformed rules are classified and organized according to constraint type to ensure that each rule clearly defines the applicable conditions, adjustment content, and execution threshold, and finally obtains the compromise constraint rule set of the cost ceiling condition and the resource availability condition.

[0149] S5. Based on the preset operation and maintenance strategy library, the fusion feature set and the health quantification value are collaboratively mapped to obtain the optimized operation and maintenance strategy of the process industry equipment.

[0150] In this embodiment of the invention, the optimized operation and maintenance strategy for the process industry equipment is obtained by co-mapping the fused feature set and the health quantification value based on a preset operation and maintenance strategy library, including:

[0151] Based on the health status range of the health quantification value, an adaptation search is performed in the preset operation and maintenance strategy library to obtain the candidate operation and maintenance strategy template for the health status range.

[0152] The key feature subset related to the equipment failure mode in the fused feature set is evaluated for matching degree with the triggering conditions and action parameters defined in the candidate operation and maintenance strategy template to obtain the matching evaluation result of the candidate operation and maintenance strategy template.

[0153] Based on the matching evaluation results, the specific values ​​of the fusion feature set are instantiated and assigned values ​​to the adjustable parameters in the candidate operation and maintenance strategy template to obtain the optimized operation and maintenance strategy for the process industry equipment.

[0154] Based on the health status range of the health quantification value, when performing an adaptation search in the preset operation and maintenance strategy library, the health status range of the health quantification value is preset to four fixed ranges: 80-100 corresponds to a healthy state, 50-79 corresponds to a slightly deteriorated state, 20-49 corresponds to a moderately deteriorated state, and 0-19 corresponds to a severely deteriorated state. The preset operation and maintenance strategy library is constructed based on historical successful operation and maintenance cases of similar process industry equipment, industry operation and maintenance technical standards, and equipment manufacturer recommended solutions. Each operation and maintenance strategy template in the library is clearly marked with the appropriate health status range. During the search, the currently obtained health quantification value is directly mapped to one of the above four ranges, and the operation and maintenance strategy template uniquely corresponding to that range is extracted, finally obtaining the candidate operation and maintenance strategy template for the health status range.

[0155] When evaluating the matching degree of a subset of key features related to equipment failure modes in the fused feature set with the triggering conditions and action parameters defined in the candidate operation and maintenance strategy template, the key feature subset is selected from the fused feature set. The selection criteria are that the correlation degree between the feature and equipment failure determined by cross-modal correlation analysis is not less than 0.2, and it is included in the key trend feature evaluation dimension in the calculation of degradation feature indicators. The triggering conditions in the candidate operation and maintenance strategy template are the specific numerical ranges of each key feature, and the action parameters are the operation and maintenance operation standards under the corresponding triggering conditions. The matching degree evaluation needs to check whether the specific value of each feature in the key feature subset falls within the numerical range of the triggering conditions. The ratio of the number of features that meet the conditions to the total number of features in the key feature subset is counted. This ratio is the matching evaluation result. The ratio ranges from 0 to 1, and it is only considered to be a qualified match when the ratio is not less than 0.8.

[0156] Based on the matching evaluation results, when instantiating and assigning values ​​to the specific values ​​of the fused feature set and the adjustable parameters in the candidate operation and maintenance strategy template, the assignment operation is only performed when the matching evaluation result is not lower than 0.8. The adjustable parameters in the candidate operation and maintenance strategy template include maintenance interval duration, detection frequency, spare parts replacement priority, and operation and maintenance operation duration limit. These parameters are all set with association rules corresponding to the key feature values. For example, when the vibration trend feature value in the key feature subset is between 5 and 8, the maintenance interval duration is assigned to 7 days, the detection frequency is assigned to once a day, the spare parts replacement priority is assigned to level two, and the operation and maintenance operation duration limit is assigned to 4 hours. According to this association rule, the specific values ​​of the key features in the fused feature set are mapped one by one to the adjustable parameters. After all the parameters are assigned, a set of implementable solutions containing specific operation requirements, time nodes, and execution standards is formed, and finally the optimized operation and maintenance strategy of the process industry equipment is obtained.

[0157] S6, the operation and maintenance decision boundary condition is associated with the optimization operation and maintenance strategy, and the operation and maintenance management report of the process industrial equipment is obtained.

[0158] In the embodiment of the application, the operation and maintenance decision boundary condition is associated with the optimization operation and maintenance strategy, and the operation and maintenance management report of the process industrial equipment is obtained, comprising:

[0159] The operation and maintenance decision boundary condition is associated with the optimization operation and maintenance strategy, and the corresponding relationship between the operation and maintenance decision boundary condition and the optimization operation and maintenance strategy is obtained.

[0160] According to the corresponding relationship, the operation and maintenance report basic framework of the process industrial equipment is constructed.

[0161] The execution steps, required resources and time sequence arrangement of the optimization operation and maintenance strategy are cooperatively explained with the fusion feature set and the health quantitative value, and the strategy details and decision basis part of the optimization operation and maintenance strategy are obtained.

[0162] The key restriction clauses in the operation and maintenance decision boundary condition and the compromise constraint rule set are taken as the implementation premise and risk prompt part of the optimization operation and maintenance strategy.

[0163] According to the preset report template, the operation and maintenance report basic framework, the strategy details and decision basis part and the implementation premise and risk prompt part are structurally integrated and formatted output, and the operation and maintenance management report of the process industrial equipment is obtained.

[0164] When the operation and maintenance decision boundary condition and the optimization operation and maintenance strategy are associated, the cost upper limit, the resource available period and the production uninterrupted period are extracted from the operation and maintenance decision boundary condition, and the execution steps, the resource demand type and quantity, and the time sequence arrangement are extracted from the optimization operation and maintenance strategy. The adaptation relationship between each strategy element and boundary condition dimension is compared one by one, the execution time length of the optimization operation and maintenance strategy needs to fit the limitation of the production uninterrupted period, the resource demand needs to be within the supply range of the resource availability condition, and the cost consumption needs to be within the numerical value of the cost upper limit condition, and each strategy element corresponding to the specific boundary condition clause is labeled, and finally the corresponding relationship between the operation and maintenance decision boundary condition and the optimization operation and maintenance strategy is obtained.

[0165] According to the correspondence, when constructing the operation and maintenance report framework of the process industry equipment, the framework needs to include the logical association between the report core module and the module. The core module is set to six parts: report abstract, strategy core content, decision basis, implementation premise, risk prompt, and execution plan. Among them, the report abstract module is used to summarize the equipment health status, core operation and maintenance strategy, and key limitation conditions. The strategy core content module corresponds to the specific content of the optimized operation and maintenance strategy. The decision basis module corresponds to the support information of the fusion feature set and the health quantitative value. The implementation premise module corresponds to the operation and maintenance decision boundary condition. The risk prompt module corresponds to the impact after the boundary condition is broken. The execution plan module corresponds to the timing arrangement and resource allocation of the strategy. The module order is arranged in the order of "abstract-strategy-basis-premise-risk-plan", ensuring that the framework can completely carry all subsequent content, and finally obtaining the operation and maintenance report framework.

[0166] When the execution steps, required resources, and timing arrangement of the optimized operation and maintenance strategy are cooperatively explained with the fusion feature set and the health quantitative value, the execution steps need to be combined with the key fault association features in the fusion feature set for explanation. For example, if the vibration trend feature value in the fusion feature set exceeds 15% of the historical benchmark span, a special step of increasing vibration detection is added in the corresponding strategy. The required resources need to be configured reasonably according to the state description of the health quantitative value. For example, if the health quantitative value is 65 points in a mild deterioration state, 1 senior maintenance personnel and basic detection tools can meet the demand. The timing arrangement needs to set the time node according to the change trend of the health quantitative value. For example, if the health quantitative value decreases by 3 points per month, the maintenance interval is set to 30 days. At the same time, the specific values of each key feature in the fusion feature set, the specific scores of the health quantitative value, and how these data support the formulation of each execution detail in the strategy are explicitly listed, and finally the strategy details and decision basis part of the optimized operation and maintenance strategy are obtained.

[0167] When the key limitation clauses and compromise constraint rule set in the operation and maintenance decision boundary condition are used as the implementation premise and risk prompt part of the optimized operation and maintenance strategy, the key limitation clauses need to explicitly list the specific numerical value of the cost upper limit, the specific time period of resource availability and the number of personnel and tools, and the specific process period of production that cannot be interrupted. For example, the cost upper limit is not more than 100,000 yuan, the resource allocation time period is limited to 00:00-02:00 every day, and the production interruption period is 8:00-20:00 every day. The compromise constraint rule set needs to fully present the coordination standard of cost and resource. For example, spare parts replacement needs to meet the industry quality standard and the price needs to be reduced by more than 20% compared with the original spare parts, and cost overrun needs to be no more than 10% of the original upper limit. The risk prompt part needs to explain the specific consequences if the limitation clauses are broken. For example, implementing operation and maintenance beyond the available resource period will cause production process interruption, and cost overrun of more than 10% will not be approved by the budget, and finally the implementation premise and risk prompt part of the optimized operation and maintenance strategy are formed.

[0168] In the process of structuring and formatting the output of the operation and maintenance report basic framework, the strategy details and decision basis part, and the implementation premise and risk prompt part according to the preset report template, the preset report template adopts an industry standard operation and maintenance report format, which clearly specifies the font, font size, line spacing and data presentation method of each module. Among them, numerical data needs to be presented in the form of a table, execution steps need to be presented in the form of a numbered list, and risk prompts need to be highlighted in bold font. According to the module order of the basic framework, fill in the contents of each part into the corresponding module, ensure that the report abstract is no more than 500 words, the strategy details are clear and non-overlapping, the decision basis data is accurate and the source is labeled, the implementation premise clause is clear and unambiguous, and the risk prompt is specific and quantifiable. After filling in, check the logical coherence of each part to ensure that the strategy content and the decision basis, implementation premise are completely matched and there is no conflicting information, and finally obtain the operation and maintenance management report of the process industrial equipment.

[0169] As shown in Figure 2 , it is a functional module diagram of an operation and maintenance management system of process industrial equipment provided by an embodiment of the present application.

[0170] The operation and maintenance management system 100 of the process industrial equipment can be installed in an electronic device. According to the implemented functions, the operation and maintenance management system 100 of the process industrial equipment can include a demand multi-modal feature coupling module 101, a fluctuation trend quantification module 102, a health evolution evaluation module 103, a decision boundary construction module 104, a strategy coordination mapping module 105, and a strategy correlation integration module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0171] In this embodiment, the functions of each module / unit are as follows:

[0172] The multi-modal feature coupling module 101 is used for multi-modal feature coupling of the time series operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment;

[0173] The fluctuation trend quantification module 102 is used for fluctuation trend quantification of the fusion feature set based on the process working condition data and historical operation and maintenance records of the process industrial equipment to obtain a degradation feature index of the process industrial equipment;

[0174] The health evolution evaluation module 103 is used for evolution evaluation of the degradation feature index to obtain a health quantitative value of the process industrial equipment;

[0175] The decision boundary construction module 104 is configured to construct an operation and maintenance decision boundary condition of the process industrial equipment according to a threshold range of operation and maintenance cost, a maintenance resource constraint and a production plan of the process industrial equipment.

[0176] The strategy coordination mapping module 105 is configured to perform coordination mapping on the fusion feature set and the health quantification value based on a preset operation and maintenance strategy library to obtain an optimized operation and maintenance strategy of the process industrial equipment.

[0177] The strategy association integration module 106 is configured to associate and integrate the operation and maintenance decision boundary condition and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment.

[0178] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and actual implementation can have another division mode.

[0179] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0180] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

[0181] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0182] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system for using digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain optimal results.

[0183] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for operation and maintenance management of process industry equipment, characterized in that, The method includes: S1. Multimodal feature coupling is performed on the time-series operating parameters and field status information of the process industry equipment to obtain the fused feature set of the process industry equipment. S2. Based on the process operating condition data and historical maintenance records of the process industry equipment, the fluctuation trend of the fused feature set is quantified to obtain the degradation characteristic indicators of the process industry equipment, including: Read the process condition data of the process industry equipment; Based on the process condition data, the fused feature set is segmented into time series to obtain the time-period feature subset of the process industry equipment; Rate change analysis is performed on the time period feature subset to obtain the trend quantification sequence of the time period feature subset; The time period feature subset is matched and mapped with the historical operation and maintenance records of the process industry equipment. Based on the mapping results, the historical benchmark trend boundary of the process industry equipment is determined. The deviation of the trend quantification sequence is compared with the historical benchmark trend boundary to obtain the deterioration characteristic index of the process industry equipment. S3. Evolution assessment of the aforementioned degradation characteristic indicators to obtain the quantitative health value of the process industry equipment, including: The degradation characteristic index is subjected to time-series smoothing to obtain a time-series smoothed sequence of the degradation characteristic index; By performing trend extrapolation on the time-series smoothed sequence, the development direction and rate of change of the degradation characteristic indicators are identified, and the degradation evolution trend of the process industry equipment is obtained. Based on similar data representing the health status of equipment in the historical operation and maintenance records, a baseline health threshold for the process industry equipment is constructed. Based on the baseline health threshold and the degradation evolution trend, the deviation degree of the process industry equipment is calculated, wherein the calculation formula for the deviation degree is as follows: ； In the formula, The degree of deviation, To find the maximum value function, This is an indicator of the current state's degradation characteristics. The baseline health threshold is... This is the preset trend influence coefficient. The instantaneous slope of the deterioration trend; The time-series smoothing sequence and the degree of deviation are synergistically fused to obtain the health quantification value of the process industry equipment; S4. Based on the threshold range of operation and maintenance costs, maintenance resource constraints and production plans of the process industry equipment, construct the boundary conditions for operation and maintenance decisions of the process industry equipment. S5. Based on a preset operation and maintenance strategy library, perform a collaborative mapping between the fused feature set and the health quantification value to obtain an optimized operation and maintenance strategy for the process industry equipment, including: Based on the health status range of the health quantification value, an adaptation search is performed in the preset operation and maintenance strategy library to obtain the candidate operation and maintenance strategy template for the health status range. The key feature subset related to the equipment failure mode in the fused feature set is evaluated for matching degree with the triggering conditions and action parameters defined in the candidate operation and maintenance strategy template to obtain the matching evaluation result of the candidate operation and maintenance strategy template. Based on the matching evaluation results, the specific values ​​of the fusion feature set are instantiated and assigned to the adjustable parameters in the candidate operation and maintenance strategy template to obtain the optimized operation and maintenance strategy for the process industry equipment. S6. The operation and maintenance decision boundary conditions are correlated and integrated with the optimized operation and maintenance strategy to obtain the operation and maintenance management report of the process industry equipment.

2. The operation and maintenance management method for process industry equipment as described in claim 1, characterized in that, The process industrial equipment's time-series operating parameters and on-site status information are coupled using multimodal features to obtain a fused feature set, including: The time-series operating parameters and field status information of process industry equipment are time-series normalized to obtain standardized time-series data and standardized status data of the process industry equipment. Cross-modal correlation analysis is performed on the standardized time-series data and the standardized state data to obtain the characteristic correlation strength between the time-series operating parameters and the field state information. Based on the aforementioned feature correlation strength, feature vector coupling is performed on the standardized time-series data and the standardized state data to construct an initial fusion feature set for the process industry equipment. Based on the historical operation and maintenance records of the process industry equipment, the initial fusion feature set is weighted and adjusted to obtain the fusion feature set of the process industry equipment.

3. The operation and maintenance management method for process industry equipment as described in claim 1, characterized in that, The formula for calculating the degradation characteristic index is as follows: ； In the formula, The degradation characteristic index, This represents the total number of evaluation dimensions for key trend features in the trend quantification sequence. The ordinal number of the feature dimension index. For the first Dynamic weights of dimensions For the trend quantization sequence, the first... Dimensional trend eigenvalues The median of healthy operation within the historical benchmark trend boundary. For the first Historical benchmark span of dimensional The preset global weighting coefficients for volatility. For the first The incremental trend fluctuation.

4. The operation and maintenance management method for process industry equipment as described in claim 1, characterized in that, The step of constructing the operation and maintenance decision boundary conditions for the process industry equipment based on the threshold range of operation and maintenance costs, maintenance resource constraints, and production plans includes: A structured correlation analysis was performed on the operation and maintenance cost threshold range, maintenance resource constraints, and production plan of the process industry equipment to obtain the cost upper limit conditions, resource availability conditions, and production continuity conditions of the process industry equipment. Based on preset conflict detection rules, conflict detection is performed on the cost ceiling condition, the resource availability condition, and the production continuity condition to obtain the constraint conflict pairs of the process industry equipment. Based on the priority strategy of the process conditions in the process industry equipment, the constraint conflict pairs are coordinated to obtain a set of compromise constraint rules between the cost ceiling condition and the resource availability condition. Based on the compromise constraint rule set, the cost ceiling condition, the resource availability condition, and the production continuity condition are restricted and reorganized to construct the operation and maintenance decision boundary conditions for the process industry equipment.

5. The operation and maintenance management method for process industry equipment as described in claim 4, characterized in that, The step of coordinating the conflicting constraints based on the priority strategy of the process conditions in the process industry equipment to obtain a set of compromise constraint rules between the cost ceiling condition and the resource availability condition includes: Obtain the priority strategy for the current operating stage in the process condition; Based on the priority strategy, dynamic weight allocation is performed on different constraints in the constraint conflict pair to obtain the weight adjustment constraint conflict pair of the process industry equipment. Based on the weight adjustment constraint conflict pair, the cost ceiling condition and the resource availability condition are adjusted in a graded manner to obtain the candidate coordination schemes for the process industry equipment. Based on the historical operation and maintenance records, a feasibility simulation evaluation is performed on the candidate coordination schemes, and the optimal coordination scheme is selected based on the simulation evaluation results. The optimal coordination scheme is transformed into a rule form to obtain a set of compromise constraint rules between the cost ceiling condition and the resource availability condition.

6. The operation and maintenance management method for process industry equipment as described in claim 4, characterized in that, The step of associating and integrating the operation and maintenance decision boundary conditions with the optimized operation and maintenance strategy to obtain the operation and maintenance management report of the process industry equipment includes: A correlation analysis is performed on the boundary conditions of the operation and maintenance decision and the optimized operation and maintenance strategy to obtain the correspondence between the boundary conditions of the operation and maintenance decision and the optimized operation and maintenance strategy; Based on the aforementioned correspondence, a basic framework for the operation and maintenance report of the process industry equipment is constructed. The execution steps, required resources, and timing arrangements of the optimized operation and maintenance strategy are interpreted in conjunction with the fused feature set and the health quantification value to obtain the strategy details and decision basis of the optimized operation and maintenance strategy. The key limiting clauses in the boundary conditions of the operation and maintenance decision and the set of compromise constraint rules are used as the premise and risk warning for the implementation of the optimized operation and maintenance strategy. Based on the preset report template, the basic framework of the operation and maintenance report, the strategy details and decision basis section, and the implementation prerequisites and risk warning section are structured, integrated, and formatted for output to obtain the operation and maintenance management report of the process industry equipment.

7. A maintenance and management system for process industry equipment, characterized in that, The system is used to implement the operation and maintenance management method for process industry equipment as described in claim 1, the system comprising: The multimodal feature coupling module is used to perform multimodal feature coupling on the time-series operating parameters and field status information of process industry equipment to obtain the fused feature set of the process industry equipment. The fluctuation trend quantification module is used to quantify the fluctuation trend of the fused feature set based on the process condition data and historical maintenance records of the process industry equipment, to obtain the degradation characteristic indicators of the process industry equipment, including: Read the process condition data of the process industry equipment; Based on the process condition data, the fused feature set is segmented into time series to obtain the time-period feature subset of the process industry equipment; Rate change analysis is performed on the time period feature subset to obtain the trend quantification sequence of the time period feature subset; The time period feature subset is matched and mapped with the historical operation and maintenance records of the process industry equipment. Based on the mapping results, the historical benchmark trend boundary of the process industry equipment is determined. The deviation of the trend quantification sequence is compared with the historical benchmark trend boundary to obtain the deterioration characteristic index of the process industry equipment. A health evolution assessment module is used to perform evolution assessment on the degradation characteristic indicators to obtain the health quantification value of the process industry equipment, including: The degradation characteristic index is subjected to time-series smoothing to obtain a time-series smoothed sequence of the degradation characteristic index; By performing trend extrapolation on the time-series smoothed sequence, the development direction and rate of change of the degradation characteristic indicators are identified, and the degradation evolution trend of the process industry equipment is obtained. Based on similar data representing the health status of equipment in the historical operation and maintenance records, a baseline health threshold for the process industry equipment is constructed. Based on the baseline health threshold and the degradation evolution trend, the deviation degree of the process industry equipment is calculated, wherein the calculation formula for the deviation degree is as follows: ； In the formula, The degree of deviation, To find the maximum value function, This is an indicator of the current state's degradation characteristics. The baseline health threshold is... This is the preset trend influence coefficient. The instantaneous slope of the deterioration trend; The time-series smoothing sequence and the degree of deviation are synergistically fused to obtain the health quantification value of the process industry equipment; The decision boundary construction module is used to construct the operation and maintenance decision boundary conditions of the process industry equipment based on the operation and maintenance cost threshold range, maintenance resource constraints and production plan in the process industry equipment. The strategy coordination mapping module is used to coordinately map the fused feature set and the health quantification value based on a preset operation and maintenance strategy library to obtain the optimized operation and maintenance strategy for the process industry equipment, including: Based on the health status range of the health quantification value, an adaptation search is performed in the preset operation and maintenance strategy library to obtain the candidate operation and maintenance strategy template for the health status range. The key feature subset related to the equipment failure mode in the fused feature set is evaluated for matching degree with the triggering conditions and action parameters defined in the candidate operation and maintenance strategy template to obtain the matching evaluation result of the candidate operation and maintenance strategy template. Based on the matching evaluation results, the specific values ​​of the fusion feature set are instantiated and assigned to the adjustable parameters in the candidate operation and maintenance strategy template to obtain the optimized operation and maintenance strategy for the process industry equipment. The strategy association and integration module is used to associate and integrate the operation and maintenance decision boundary conditions with the optimized operation and maintenance strategy to obtain the operation and maintenance management report of the process industry equipment.

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