Production capacity guarantee degree evaluation method and equipment based on health degree score, and medium

Abstract

The invention discloses a health degree score-based production capacity guarantee degree evaluation method and device and a medium, and the method comprises the steps: obtaining an offline equipment inspection parameter, and carrying out the health index evaluation of the offline equipment inspection parameter, so as to obtain an offline inspection equipment health degree score; obtaining equipment operation state inspection parameters, and performing time sequence health state analysis on the equipment operation state inspection parameters to obtain an operation inspection type equipment health degree score; determining a comprehensive health degree score through multi-level health degree index weight analysis based on the health degree of the offline inspection type equipment and the health degree of the operation inspection type equipment; according to the comprehensive health degree score, determining the current production capacity of the industrial system through production capacity evaluation; and performing system guarantee degree analysis on the current production capacity of the industrial system, and determining the comprehensive guarantee degree of the equipment. According to the method, the technical problem of mutual exclusion of health degree and guarantee degree analysis in an existing industrial system is solved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial system health assessment, and in particular to a production capacity assurance assessment method, device and medium based on health score. Background Art

[0002] In the field of industrial manufacturing, the stability and reliability of production capacity are the core guarantee for enterprises to achieve efficient operation and market competitiveness. As the global manufacturing industry accelerates its transformation towards intelligence and flexibility, the industrial environment is becoming increasingly complex, and production systems are facing challenges from multiple uncertain factors such as equipment failures, supply chain fluctuations, resource constraints, and dynamic order demands. Traditional production capacity assessment methods focus on static indicator analysis (such as equipment utilization, peak capacity, etc.) or statistical models based on historical data. Their limitation is that it is difficult to dynamically reflect the real-time status and potential risks in complex industrial scenarios.

[0003] In industrial systems, health usually refers to the current state and performance level of equipment, systems or the entire production process, which is used to evaluate the operating efficiency, stability and reliability of the system. Health analysis involves monitoring and evaluating system operation data to determine whether the system is operating normally and whether there are signs of performance degradation or potential failures. Assurance in industrial systems refers to the ability of the system to maintain normal operation and complete tasks in the face of various internal and external risks. Compared with health, the most important feature is the evaluation of the performance and responsiveness of the system under specific conditions, that is, the evaluation of relevant factors such as production capacity and resource availability. In existing industrial system research, assurance and health can often only be carried out separately, focusing only on offline inspection or one aspect of online monitoring, and the health scoring indicators of offline analysis are not fully covered. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for evaluating production capacity assurance based on health score, which solves the technical problem of mutual exclusivity between health and assurance analysis in existing industrial systems.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating production capacity assurance based on a health score, characterized in that the method includes: obtaining offline equipment inspection parameters, and performing a health index evaluation on the offline equipment inspection parameters to obtain a health score for the offline inspection equipment; obtaining equipment operation status inspection parameters, and performing a time series health status analysis on the equipment operation status inspection parameters to obtain a health score for the operation inspection equipment; based on the health of the offline inspection equipment and the health of the operation inspection equipment, determining a comprehensive health score through a multi-level health indicator weight analysis; determining the current production capacity of the industrial system through a production capacity evaluation based on the comprehensive health score; performing a system assurance analysis on the current production capacity of the industrial system to determine the comprehensive equipment assurance.

[0006] In one implementation of the present application, a health index evaluation is performed on offline device inspection parameters to obtain a health score for offline inspection devices, specifically including: based on the offline device inspection parameters, determining the device health offline inspection conditions through a health score trigger configuration; wherein the health score trigger configuration includes: device maintenance configuration, equipment repair configuration, and equipment inspection configuration; performing a health evaluation on the device health offline inspection conditions to obtain a health score for the offline inspection device.

[0007] In one implementation of the present application, a time series health state analysis is performed on the equipment operation status check parameters to obtain the health score of the operation check type equipment, specifically including: performing equipment fault history feature preprocessing on the equipment operation status check parameters to obtain an online fault monitoring indicator model library; performing equipment time series state distribution analysis on the online fault monitoring indicator model library to obtain the equipment state benchmark distribution; based on the equipment state benchmark distribution, the JS divergence operation of the equipment operation state is performed to obtain the health score of the operation check type equipment; wherein, the calculation formula of the JS divergence operation of the equipment operation state is:

[0008] in, Score the health of the equipment under operation inspection. It is the prediction output of equipment status detection in the online fault monitoring indicator model library. To monitor the actual value, is an empirical parameter, is the JS divergence.

[0009] In one implementation of the present application, based on the health of offline inspection type equipment and the health of operation inspection type equipment, a comprehensive health score is determined through multi-level health indicator weight analysis, specifically including: obtaining preset evaluation indicators, and constructing a multi-level health scoring model through subjective analysis; training the multi-level health scoring model until the model converges, and inputting the health of offline inspection type equipment and the health of operation inspection type equipment into the trained multi-level health scoring model to determine the comprehensive health score.

[0010] In one implementation of the present application, after inputting the health of offline inspection type equipment and the health of operation inspection type equipment into a trained multi-level health scoring model to determine a comprehensive health score, the method also includes: obtaining product demand timing parameters, and performing data preprocessing on the product demand timing parameters to obtain product demand data to be analyzed; performing product demand forecasting on the product demand data to be analyzed to obtain a product demand forecast amount; based on the comprehensive health score and the product demand forecast amount, determining the equipment maintenance cycle update data through the future production capacity forecast of the equipment.

[0011] In one implementation of the present application, the current production capacity of the industrial system is determined through production capacity assessment according to the comprehensive health score, specifically including: determining the basic performance parameters affecting the production capacity through industrial system performance characteristic analysis; wherein the basic performance parameters include: mechanical performance and electrical performance; based on the comprehensive health score and the basic performance parameters, the current production capacity of the industrial system is determined through production capacity assessment; wherein the calculation formula for production capacity assessment is:

[0012] in, is the device state variable, is the basic performance parameter, Characterize the current production capacity of industrial systems.

[0013] In one implementation of the present application, a system security analysis is performed on the current production capacity of the industrial system to determine the comprehensive security of the equipment, specifically including: performing a production security analysis on the current production capacity of the industrial system to obtain the production capacity security; wherein the calculation formula for the production security analysis is:

[0014] in, is the product demand forecast, is the current production capacity of the industrial system; the total number of devices is obtained, and based on the total number of devices, the device availability is determined through device availability status analysis; wherein the calculation formula for device availability status analysis is:

[0015] in, is the total number of devices, is the actual number of available equipment; the comprehensive equipment security level is determined based on the production capacity security level and equipment availability.

[0016] In one implementation of the present application, after performing a system security analysis on the current production capacity of the industrial system and determining the comprehensive security of the equipment, the method also includes: determining a dynamic maintenance strategy through dynamic maintenance analysis based on the comprehensive health score; wherein the dynamic maintenance strategy includes: preventive maintenance plan, predictive maintenance trigger conditions and maintenance priority sorting rules; based on the comprehensive security of the equipment and the dynamic maintenance strategy, the production plan update data is determined through the optimization of the industrial system production plan.

[0017] In a second aspect, an embodiment of the present application also provides a production capacity assurance assessment device based on a health score, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain offline equipment inspection parameters, and perform a health index assessment on the offline equipment inspection parameters to obtain a health score for the offline inspection equipment; obtain equipment operation status inspection parameters, and perform a time series health status analysis on the equipment operation status inspection parameters to obtain a health score for the operation inspection equipment; based on the health of the offline inspection equipment and the health of the operation inspection equipment, determine a comprehensive health score through a multi-level health indicator weight analysis; determine the current production capacity of the industrial system through a production capacity assessment based on the comprehensive health score; perform a system assurance analysis on the current production capacity of the industrial system to determine the comprehensive equipment assurance.

[0018] In a third aspect, an embodiment of the present application also provides a non-volatile computer storage medium for production capacity assurance assessment based on a health score, storing computer executable instructions, characterized in that the computer executable instructions are set to: obtain offline equipment inspection parameters, and perform a health index assessment on the offline equipment inspection parameters to obtain a health score for the offline inspection equipment; obtain equipment operation status inspection parameters, and perform a time series health status analysis on the equipment operation status inspection parameters to obtain a health score for the operation inspection equipment; based on the health of the offline inspection equipment and the health of the operation inspection equipment, determine a comprehensive health score through a multi-level health indicator weight analysis; determine the current production capacity of the industrial system through a production capacity assessment based on the comprehensive health score; perform a system assurance analysis on the current production capacity of the industrial system to determine the comprehensive equipment assurance.

[0019] The embodiments of the present application provide a production capacity assurance assessment method, device and medium based on health score. Through comprehensive analysis of the health and assurance of industrial systems, the technical problem of mutual exclusivity between health and assurance analysis in existing industrial systems is solved. It can not only accurately assess the health of equipment, but also accurately measure the assurance of production capacity, thereby improving the accuracy of industrial system assessment, overcoming the nonlinear problem caused by the rapid increase in the complexity of industrial systems and the singleness of health assessment, and improving the correlation between variables and fault correlation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a production capacity assurance assessment method based on health score provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a production capacity assurance assessment device based on health scoring provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] The embodiments of the present application provide a production capacity assurance assessment method, device and medium based on health score. Through comprehensive analysis of the health and assurance of industrial systems, the technical problem of mutual exclusivity between health and assurance analysis in existing industrial systems is solved. It can not only accurately assess the health of equipment, but also accurately measure the assurance of production capacity, thereby improving the accuracy of industrial system assessment, overcoming the nonlinear problem caused by the rapid increase in the complexity of industrial systems and the singleness of health assessment, and improving the correlation between variables and fault correlation.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A flow chart of a production capacity assurance assessment method based on health score provided in an embodiment of the present application. Figure 1As shown, a production capacity assurance evaluation method based on health score provided in an embodiment of the present application specifically includes the following steps: Step 101: Obtain offline device inspection parameters, and perform health index evaluation on the offline device inspection parameters to obtain offline inspection device health scores.

[0025] For example, health in industrial systems usually refers to the current state and performance level of equipment, systems, or the entire production process. It is a comprehensive indicator used to evaluate the operating efficiency, stability, and reliability of the system. Health analysis involves monitoring and evaluating system operating data to determine whether the system is operating normally and whether there are signs of performance degradation or potential failure.

[0026] In industrial systems, assurance refers to the ability of a system to maintain normal operation and complete tasks in the face of various internal and external risks. Compared with health, the most important feature is the evaluation of the system's performance and responsiveness under specific conditions, that is, the evaluation of relevant factors such as production capacity and resource availability.

[0027] Offline inspection indicators focus on the impact of maintenance in a shutdown state on health, including three types of operation and maintenance operations: maintenance, repair, and inspection. The health assessment of offline equipment can adapt to the inspection status of the shutdown state of equipment in an industrial environment.

[0028] Specifically, a health index evaluation is performed on offline equipment inspection parameters to obtain a health score for offline inspection equipment, including: based on offline equipment inspection parameters, determining equipment health offline inspection conditions through health score triggering configuration; wherein the health score triggering configuration includes: equipment maintenance configuration, equipment repair configuration, equipment inspection configuration; and performing a health evaluation on equipment health offline inspection conditions to obtain a health score for offline inspection equipment.

[0029] In one embodiment, first, set the trigger condition for offline inspection of the health score indicator. The trigger condition is that the device to be inspected needs to perform a certain operation and maintenance operation under certain conditions to score the health score indicator. When the trigger condition type of "first-level maintenance" is set to "periodic", the time is "every 720 hours of operation", and the effective time is "always valid", the device must be shut down for first-level maintenance every 720 hours of operation. The downtime is not counted in the operating time. Until the equipment is scrapped and removed from the health scoring system, it will continue to detect whether the offline inspection condition of the health indicator of "first-level maintenance" is triggered.

[0030] Set the deduction rules for the offline inspection health score indicator. For the health score indicator of "Level 1 Maintenance", the score range is 0 to 100. If the inspection condition is triggered, the score of this indicator is temporarily 0. Only when the operation and maintenance personnel complete the Level 1 maintenance operation, check the operation and report to the health score system that the "Level 1 Maintenance" is qualified and the equipment is healthy, will the score of this indicator be recorded as 100. If the "Level 1 Maintenance" is ignored and the industrial system continues to run, the health score system will continue to warn the user, and the "Level 1 Maintenance" indicator score will remain 0 until the operator completes the problem and the score returns to 100.

[0031] Then, perform an offline inspection of the health score indicator operation. When the "first-level maintenance" indicator condition is triggered, the corresponding equipment needs to be shut down, cleaned, carefully disassembled and inspected, and parts adjusted. After completion, report to the health score system that the "first-level maintenance" is qualified and the equipment is healthy.

[0032] Finally, the device restarts, and the offline check health score is completed and updated until the next offline check health score indicator trigger condition is triggered.

[0033] Step 102: Acquire equipment operation status check parameters, and perform time series health status analysis on the equipment operation status check parameters to obtain an operation check equipment health score.

[0034] Exemplarily, determining that the offline inspection type equipment health score corresponding to the equipment shutdown state can satisfy the health inspection of the equipment in the non-working state. Similarly, in order to fully cover the equipment health, it is necessary to analyze the health of the equipment in the running state.

[0035] Specifically, a time series health status analysis is performed on the equipment operation status check parameters to obtain the health score of the operation check type equipment, including: performing equipment fault history feature preprocessing on the equipment operation status check parameters to obtain an online fault monitoring indicator model library; performing equipment time series state distribution analysis on the online fault monitoring indicator model library to obtain the equipment state benchmark distribution; based on the equipment state benchmark distribution, the JS divergence operation of the equipment operation status is performed to obtain the health score of the operation check type equipment; wherein, the calculation formula of the JS divergence operation of the equipment operation status is:

[0036] in, Score the health of the equipment under operation inspection. It is the prediction output of equipment status detection in the online fault monitoring indicator model library. To monitor the actual value, is an empirical parameter, is the JS divergence.

[0037] In one embodiment, first, historical data of relevant characteristic parameters are collected according to the set online fault monitoring indicators, the temperature of a certain component in the equipment is selected as the indicator of online fault monitoring, and 14 possible influencing factors are provided in combination with the operation experience of the field staff. According to the correlation degree between the parameters calculated by the Pearson correlation coefficient, four characteristic parameters related to the monitoring parameters are preliminarily screened out, namely, current, motor U-phase coil temperature, oil tank oil temperature, and cooling water return temperature required for motor oil station cooling.

[0038] Then, the collected historical fault data is preprocessed, and a regression model of online fault monitoring indicators is established. After normalization and other preprocessing of the historical time series data of the four characteristic parameters collected under the fault state, a multi-input component temperature online fault monitoring model is established using the BP neural network in deep learning. After selecting multiple online fault monitoring indicators and establishing corresponding calculation models respectively, an online fault monitoring indicator model library can be established.

[0039] Then, the online fault monitoring indicator model library is monitored for time series data, and the similarity detection method is used to determine the similarity with the calculation model in the model library, and the health score of dynamic monitoring is output. The similarity with the fault monitoring regression model is negatively correlated with the equipment health.

[0040] The output of the component temperature online fault monitoring model is used as the benchmark distribution, and the JS divergence is used to measure the distance between the actual value of the fan front bearing temperature and the benchmark distribution, and this gap is defined as the real-time monitoring health of the equipment.

[0041] The Jensen-Shannon divergence (JS divergence) is widely used in information theory and machine learning, especially when measuring the similarity and discrimination between two distributions.

[0042] In this application, based on the device status benchmark distribution, the JS divergence calculation of the device operating status is used to obtain the health score of the operation inspection type device; wherein the JS divergence calculation of the device operating status is explained by the following formula: (1) in, Score the health of the equipment under operation inspection. It is the prediction output of equipment status detection in the online fault monitoring indicator model library. To monitor the actual value, is an empirical parameter, is the JS divergence.

[0043] Furthermore, the empirical parameters It can be set through manual experience, and the default value here is 100.

[0044] Step 103: Based on the health of offline inspection equipment and the health of operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis.

[0045] Exemplarily, after the health of the equipment in both operating and non-operating states can be evaluated, the global comprehensive health assessment of the equipment needs to be carried out through multi-level health indicator weight allocation, so that the weight allocation of offline inspection type equipment health and operating inspection type equipment health under different requirements of different devices meets actual expectations, thereby improving the accuracy of the comprehensive health score and the actual fit.

[0046] Specifically, based on the health of offline inspection equipment and the health of operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis, which specifically includes: obtaining preset evaluation indicators, and building a multi-level health scoring model through subjective analysis; training the multi-level health scoring model until the model converges, and inputting the health of offline inspection equipment and the health of operation inspection equipment into the trained multi-level health scoring model to determine the comprehensive health score.

[0047] In one embodiment, first, a multi-level health rating model is established, allowing users to customize indicators at each level and their corresponding weights. Users can design the hierarchical structure and weight distribution of indicators according to their own needs to ensure that the total weight of each level of indicators reaches 100%.

[0048] Among the first-level indicators, there are three indicators: head, flow rate and power, and their respective weights are 40%, 30% and 30% respectively.

[0049] The flow index is further divided into three secondary indicators: inlet flow, outlet flow and blade flow. The weights of these secondary indicators also need to add up to 100%, that is, the weight of each secondary indicator can be 40%, 30% and 30%.

[0050] It should be noted that the weights of the secondary indicators are actually calculated based on the weights of the primary indicators to which they belong. Therefore, the total weight of the inlet flow is actually the product of the primary indicator flow weight (30%) and the secondary indicator inlet flow weight (40%), which is 12%. Similarly, the total weights of the outlet flow and blade flow are 9% and 9% respectively. In this way, users can accurately calculate the relative importance of each indicator in the overall health assessment, thereby achieving a more refined and personalized equipment health analysis.

[0051] Then, a multi-level health scoring model is constructed through subjective methods to achieve the weight distribution of the health of offline inspection equipment and the health of operation inspection equipment, and then determine the comprehensive health score.

[0052] Furthermore, after completing the scoring of offline inspection and online monitoring indicators in the health scoring model, the comprehensive health score of the system can be obtained according to the weight, and the control strategy of each device in the system can be set according to the health score. At this time, if the health score is too low, you can choose to stop the device, and if the health score is qualified, you can start the device.

[0053] Furthermore, after inputting the health of offline inspection type equipment and the health of operation inspection type equipment into the trained multi-level health scoring model to determine the comprehensive health score, the method also includes: obtaining product demand timing parameters, and performing data preprocessing on the product demand timing parameters to obtain product demand data to be analyzed; performing product demand forecasting on the product demand data to be analyzed to obtain product demand forecast amount; based on the comprehensive health score and the product demand forecast amount, determining the equipment maintenance cycle update data through the future production capacity forecast of the equipment.

[0054] In one embodiment, product demand time series parameters are collected, including but not limited to sales data of different products in the past several years, including sales volume, sales amount, order quantity and other information in different regions and different time periods (such as monthly and quarterly). Data cleaning and feature engineering are performed on the acquired time series data. Feature engineering refers to extracting key features of product demand from raw data.

[0055] Then, for model selection, model training and optimization, you need to divide the data set and train the model. Divide the preprocessed data into training set, validation set and test set. You can divide the data into 70% of the training set, 20% of the validation set and 10% of the test set, and use the training set to train the selected model. According to the results of the validation set, adjust the model's hyperparameters to optimize the model performance.

[0056] Finally, the trained model is evaluated using the test set to calculate evaluation indicators such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. When the model passes the evaluation, new data (such as future macroeconomic forecast data, planned promotional information, etc.) is input into the model to obtain the market demand forecast results of the product. For example, the sales volume of products in different regions in the next quarter is predicted, and companies can formulate production plans, inventory management strategies and marketing activity plans based on these forecast results.

[0057] Step 104: Determine the current production capacity of the industrial system through production capacity assessment based on the comprehensive health score.

[0058] For example, since the comprehensive health score of industrial system equipment can also represent the production capacity of the current process system, and the production capacity indirectly represents the security of the process system. Therefore, in order to be able to evaluate both health and security in the process system evaluation, the analysis of production capacity is required as data support. At the same time, production capacity can also directly represent the shipping capacity of the current industrial system.

[0059] Specifically, according to the comprehensive health score, the current production capacity of the industrial system is determined through production capacity assessment, including: determining the basic performance parameters that affect the production capacity through industrial system performance characteristic analysis; wherein the basic performance parameters include: mechanical performance and electrical performance; based on the comprehensive health score and the basic performance parameters, the current production capacity of the industrial system is determined through production capacity assessment; wherein the calculation formula for production capacity assessment is:

[0060] in, is the device state variable, is the basic performance parameter, Characterize the current production capacity of industrial systems.

[0061] In one embodiment, the production capacity of an industrial system refers to the number of products that can be produced by an enterprise, workshop, work center or the entire industrial system under given organizational and technical conditions within a certain period of time (usually one year). This patent only considers the production capacity related to the equipment in the industrial system when there is sufficient supply of raw materials and personnel. The production capacity assessment is explained by the following formula: (2) in, is the device state variable, is the basic performance parameter, Characterize the current production capacity of industrial systems. It is a 0-1 variable. When it is 0, it means the device is turned off, and when it is 1, it means the device is turned on. The value of is based on the control strategy set by the device health score.

[0062] In each industrial environment, when raw materials and personnel are in sufficient supply, the production capacity of a production unit can be measured by ten basic parameters related to mechanical and electrical properties. The production capacity calculation formula composed of ten basic parameters of mechanical and electrical properties in the i-th production unit is expressed as follows: Rated Value, abbreviated as RV, indicates the standard performance parameters of the equipment under normal working conditions, such as rated voltage, rated power, rated current, etc.

[0063] Maximum Allowable Value: Maximum Allowable Value, abbreviated as MaxAV. It indicates the maximum limit that the device can withstand. Exceeding this value may damage the device or affect its performance, such as the maximum allowable temperature, maximum allowable pressure, etc.

[0064] Minimum Allowable Value: Minimum Allowable Value, abbreviated as MinAV. It indicates the minimum parameter value when the device can work normally, such as minimum starting voltage, minimum flow rate, etc.

[0065] Design Value: Design Value, abbreviated as DV. It indicates the parameter set when designing the equipment. It may be the same as the rated value, or a certain margin may be left for safety reasons.

[0066] Actual Operating Value: AOV for short. It refers to the data measured during the actual operation of the device, such as actual power and actual temperature.

[0067] Safety Value: Safety Value, abbreviated as SV. It indicates some safety parameters set to ensure the safety of equipment and operators, such as the pressure setting value of the safety valve.

[0068] Maintenance Value: Maintenance Value, abbreviated as MV. It indicates the parameters of the equipment when maintenance or replacement of parts is required, such as the replacement cycle of lubricating oil and the cleaning cycle of filters.

[0069] Environmental Adaptability Value: Environmental Adaptability Value, abbreviated as EAV. It indicates the parameters that enable the device to work normally under different environmental conditions, such as humidity and temperature range.

[0070] Performance Parameters: Performance Parameters, abbreviated as PP. In addition to the rated value, it may also include performance-related parameters such as efficiency, response time, accuracy, etc.

[0071] Life Parameters: Life Parameters, abbreviated as LP, indicate the expected service life of a device or its components, such as the number of battery charge cycles, the wear limit of mechanical components, etc.

[0072] Step 105: Perform a system security analysis on the current production capacity of the industrial system to determine the comprehensive security of the equipment.

[0073] For example, production capacity assurance is an important evaluation indicator of process system assurance. At the same time, in order to further improve the accuracy of assurance, equipment availability is introduced and combined with production capacity assurance to analyze and judge the system assurance.

[0074] Specifically, a system security analysis is performed on the current production capacity of the industrial system to determine the comprehensive equipment security, including: performing a production security analysis on the current production capacity of the industrial system to obtain the production capacity security; wherein the calculation formula for the production security analysis is:

[0075] in, is the product demand forecast, is the current production capacity of the industrial system; the total number of devices is obtained, and based on the total number of devices, the device availability is determined through device availability status analysis; wherein the calculation formula for device availability status analysis is:

[0076] in, is the total number of devices, is the actual number of available equipment; the comprehensive equipment security level is determined based on the production capacity security level and equipment availability.

[0077] In one embodiment, the production assurance analysis is explained by the following formula: (3) in, is the product demand forecast, Current production capacity of industrial systems.

[0078] The production assurance index is used to measure the size of production capacity relative to product demand. When the index is greater than 1, it means that the production capacity can meet product demand, and usually a certain margin needs to be left. Generally speaking, only when the value reaches 1.1 or above can it properly guarantee the normal and quality production of all products required by the market. If the index is less than 1, it means that the production capacity cannot meet market demand and the assurance is poor.

[0079] The total number of devices is obtained, and based on the total number of devices, the device availability is determined through device availability status analysis; wherein the calculation formula for device availability status analysis is: (4) in, is the total number of devices, The actual number of available devices.

[0080] Equipment availability refers to the ratio of the number of devices that are actually available for use to the total number of devices according to the health score control strategy. High availability indicates that most of the equipment can operate normally and provide stable support for production activities.

[0081] Finally, the comprehensive equipment security level is determined based on the production capacity security level and equipment availability.

[0082] Furthermore, after performing a system security analysis on the current production capacity of the industrial system and determining the comprehensive security of the equipment, the method also includes: determining a dynamic maintenance strategy through dynamic maintenance analysis based on the comprehensive health score; wherein the dynamic maintenance strategy includes: preventive maintenance plan, predictive maintenance trigger conditions and maintenance priority sorting rules; based on the comprehensive security of the equipment and the dynamic maintenance strategy, the production plan update data is determined through the optimization of the industrial system production plan.

[0083] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a production capacity assurance evaluation device based on health score, and its structure is as follows: Figure 2 shown.

[0084] Figure 2 This is a schematic diagram of the internal structure of a production capacity assurance assessment device based on health score provided in an embodiment of the present application. Figure 2 As shown, the device includes: at least one processor 201; and, a memory 202 communicatively connected to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to: Obtain offline equipment inspection parameters, and perform health index evaluation on the offline equipment inspection parameters to obtain the offline inspection equipment health score; obtain equipment operation status inspection parameters, and perform time series health status analysis on the equipment operation status inspection parameters to obtain the operation inspection equipment health score; based on the health of offline inspection equipment and the health of operation inspection equipment, determine the comprehensive health score through multi-level health indicator weight analysis; based on the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; perform system security analysis on the current production capacity of the industrial system to determine the comprehensive security of the equipment.

[0085] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for production capacity assurance evaluation based on health score, storing computer executable instructions, wherein the computer executable instructions are set as: Obtain offline equipment inspection parameters, and perform health index evaluation on the offline equipment inspection parameters to obtain the offline inspection equipment health score; obtain equipment operation status inspection parameters, and perform time series health status analysis on the equipment operation status inspection parameters to obtain the operation inspection equipment health score; based on the health of offline inspection equipment and the health of operation inspection equipment, determine the comprehensive health score through multi-level health indicator weight analysis; based on the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; perform system security analysis on the current production capacity of the industrial system to determine the comprehensive security of the equipment.

[0086] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0087] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0094] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0096] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A production capacity assurance evaluation method based on health score, characterized in that: The method comprises: Obtaining offline device inspection parameters, and performing health index evaluation on the offline device inspection parameters to obtain offline inspection device health scores; Obtaining equipment operation status inspection parameters, and performing time series health status analysis on the equipment operation status inspection parameters to obtain a health score of the operation inspection type equipment; Based on the health of the offline inspection equipment and the health of the operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis; Determine the current production capacity of the industrial system through production capacity assessment based on the comprehensive health score; Conduct a system security analysis on the current production capacity of the industrial system and determine the comprehensive security of the equipment.

2. According to the method for evaluating production capacity assurance based on health score in claim 1, it is characterized in that: The health index evaluation is performed on the offline device inspection parameters to obtain the offline inspection device health score, specifically including: Based on the offline equipment inspection parameters, the equipment health offline inspection conditions are determined through the health score trigger configuration; wherein the health score trigger configuration includes: equipment maintenance configuration, equipment repair configuration, and equipment inspection configuration; A health evaluation is performed on the offline inspection condition of the device to obtain a health score of the offline inspection type device.

3. According to the method for evaluating production capacity assurance based on health score in claim 1, it is characterized in that: Performing a time series health status analysis on the equipment operation status check parameters to obtain the health score of the operation check equipment, specifically including: Preprocessing the equipment operation status inspection parameters based on equipment fault history characteristics to obtain an online fault monitoring indicator model library; Performing equipment time series state distribution analysis on the online fault monitoring indicator model library to obtain equipment state benchmark distribution; Based on the equipment status benchmark distribution, the health score of the operation inspection equipment is obtained by performing a JS divergence operation on the equipment operation status; wherein the calculation formula for the JS divergence operation on the equipment operation status is: in, Score the health of the operation inspection equipment. is the predicted output of the equipment status detection in the online fault monitoring indicator model library, To monitor the actual value, is an empirical parameter, is the JS divergence.

4. The production capacity assurance evaluation method based on health score according to claim 1 is characterized in that: Based on the health of the offline inspection equipment and the health of the operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis, specifically including: Obtain the preset evaluation indicators and build a multi-level health scoring model through subjective analysis; The multi-level health scoring model is trained until the model converges, and the health of the offline inspection type equipment and the health of the operation inspection type equipment are input into the trained multi-level health scoring model to determine a comprehensive health score.

5. A production capacity assurance evaluation method based on health score according to claim 4, characterized in that: After inputting the offline inspection equipment health and the operational inspection equipment health into the trained multi-level health scoring model to determine a comprehensive health score, the method further includes: Acquire product demand timing parameters, and perform data preprocessing on the product demand timing parameters to obtain product demand data to be analyzed; Performing product demand forecasting on the product demand data to be analyzed to obtain a product demand forecast amount; Based on the comprehensive health score and the product demand forecast, equipment maintenance cycle update data is determined through equipment future production capacity forecast.

6. A production capacity assurance evaluation method based on health score according to claim 1, characterized in that: Based on the comprehensive health score, the current production capacity of the industrial system is determined through production capacity assessment, including: By analyzing the performance characteristics of the industrial system, the basic performance parameters that affect the production capacity are determined; wherein the basic performance parameters include: mechanical properties and electrical properties; Based on the comprehensive health score and the basic performance parameters, the current production capacity of the industrial system is determined through production capacity evaluation; wherein the calculation formula for the production capacity evaluation is: in, is the device state variable, is the basic performance parameter, Characterize the current production capacity of the industrial system.

7. The production capacity assurance evaluation method based on health score according to claim 1 is characterized in that: Conduct system security analysis on the current production capacity of the industrial system and determine the comprehensive security of the equipment, including: Perform production assurance analysis on the current production capacity of the industrial system to obtain production capacity assurance; wherein the calculation formula for the production assurance analysis is: in, is the product demand forecast, The current production capacity of the industrial system; The total number of devices is obtained, and based on the total number of devices, the device availability is determined by analyzing the device availability status; wherein the calculation formula for the device availability status analysis is: in, is the total number of devices, is the actual number of available devices; The comprehensive equipment security level is determined based on the production capacity security level and the equipment availability.

8. The production capacity assurance evaluation method based on health score according to claim 1 is characterized in that: After performing a system security analysis on the current production capacity of the industrial system and determining the comprehensive security of the equipment, the method further includes: According to the comprehensive health score, a dynamic maintenance strategy is determined through dynamic maintenance analysis; wherein the dynamic maintenance strategy includes: preventive maintenance plan, predictive maintenance trigger conditions and maintenance priority sorting rules; Based on the comprehensive equipment security and the dynamic maintenance strategy, production plan update data is determined through industrial system production plan optimization.

9. A production capacity assurance assessment device based on health score, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtaining offline device inspection parameters, and performing health index evaluation on the offline device inspection parameters to obtain offline inspection device health scores; Obtaining equipment operation status inspection parameters, and performing time series health status analysis on the equipment operation status inspection parameters to obtain a health score of the operation inspection type equipment; Based on the health of the offline inspection equipment and the health of the operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis; Determine the current production capacity of the industrial system through production capacity assessment based on the comprehensive health score; Conduct a system security analysis on the current production capacity of the industrial system and determine the comprehensive security of the equipment.

10. A non-volatile computer storage medium for evaluating production capacity assurance based on health score, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Obtaining offline device inspection parameters, and performing health index evaluation on the offline device inspection parameters to obtain offline inspection device health scores; Obtaining equipment operation status inspection parameters, and performing time series health status analysis on the equipment operation status inspection parameters to obtain a health score of the operation inspection type equipment; Based on the health of the offline inspection equipment and the health of the operation inspection equipment, a comprehensive health score is determined through multi-level health indicator weight analysis; Determine the current production capacity of the industrial system through production capacity assessment based on the comprehensive health score; Conduct a system security analysis on the current production capacity of the industrial system and determine the comprehensive security of the equipment.

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