Production Capacity Assurance Degree Evaluation Method, Equipment and Medium Based on Health Score

Abstract

The present application discloses a production capacity guarantee degree evaluation method, device and medium based on health score. The method includes: obtaining inspection parameters of offline devices, and performing a health index evaluation on the inspection parameters of offline devices to obtain a health score of offline inspection devices; obtaining inspection parameters of the device operation status, and performing a sequential health status analysis on the inspection parameters of the device operation status to obtain a health score of operation inspection devices; based on the health degree of offline inspection devices and the health degree of operation inspection devices, determining a comprehensive health score through multi-level health index weight analysis; according to the comprehensive health score, determining the current production capacity of the industrial system through production capacity evaluation; performing a system guarantee degree analysis on the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree. The present application solves the technical problem of mutual exclusivity existing in the analysis of health degree and guarantee degree in the existing industrial system through the above method.

Description

Technical Field

[0001] This application relates to the technical field of industrial system health assessment, and particularly to a production capacity guarantee degree 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 guarantees for enterprises to achieve efficient operation and market competitiveness. With the accelerating transformation of the global manufacturing industry 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 mostly focus on static index analysis (such as equipment utilization rate, production capacity peak, etc.) or statistical models based on historical data. Their limitation lies in the difficulty of dynamically reflecting the real-time state and potential risks in complex industrial scenarios.

[0003] Health in an industrial system generally refers to the current state and performance level of equipment, systems, or the entire production process, and is used to evaluate the operating efficiency, stability, and reliability of the system. Health analysis involves monitoring and evaluating the operating data of the system to determine whether the system is operating normally and whether there are signs of performance degradation or potential failures. Guarantee degree in an industrial system 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 assessment of the system's performance and response ability under specific conditions, that is, the assessment of relevant factors such as production capacity and resource availability. In existing industrial system research, guarantee degree and health are often analyzed separately, only focusing on one aspect of offline inspection or online monitoring, and the health score indicators for offline analysis are not comprehensive enough. Summary of the Invention

[0004] The embodiments of this application provide a production capacity guarantee degree assessment method, device, and medium based on health score, solving the technical problem of mutual exclusivity between health and guarantee degree analysis in existing industrial systems.

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

[0006] In an implementation manner of the present application, performing a health index evaluation on the inspection parameters of offline devices to obtain the health score of offline inspection devices specifically includes: based on the inspection parameters of offline devices, determining the offline inspection conditions of the device health through health score trigger configuration; where the health score trigger configuration includes: device maintenance configuration, device repair configuration, and device inspection configuration; performing a health degree evaluation on the offline inspection conditions of the device health to obtain the health score of offline inspection devices.

[0007] In an implementation manner of the present application, performing a sequential health status analysis on the inspection parameters of the device operation status to obtain the health score of operation inspection devices specifically includes: performing preprocessing on the historical characteristics of device failures of the inspection parameters of the device operation status to obtain an online fault monitoring index model library; performing an analysis of the sequential state distribution of the device on the online fault monitoring index model library to obtain the reference distribution of the device state; based on the reference distribution of the device state, obtaining the health score of operation inspection devices through the JS divergence operation of the device operation status; where the calculation formula for the JS divergence operation of the device operation status is:

[0008]

[0009] where is the health score of operation inspection devices, is the predicted output of device state detection in the online fault monitoring index model library, is the monitored actual value, is the empirical parameter, is the JS divergence.

[0010] In an implementation manner of the present application, based on the health degree of the offline inspection type equipment and the health degree of the operation inspection type equipment, through the multi-level health degree index weight analysis, the comprehensive health degree score is determined, which specifically includes: obtaining the preset evaluation indexes, and constructing a multi-level health score model through subjective method analysis; training the multi-level health score model until the model converges, and inputting the health degree of the offline inspection type equipment and the health degree of the operation inspection type equipment into the trained multi-level health score model to determine the comprehensive health degree score.

[0011] In an implementation manner of the present application, after inputting the health degree of the offline inspection type equipment and the health degree of the operation inspection type equipment into the trained multi-level health score model to determine the comprehensive health degree score, the method further includes: obtaining the product demand time series parameters, and performing data preprocessing on the product demand time series parameters to obtain the product demand data to be analyzed; performing product demand prediction on the product demand data to be analyzed to obtain the product demand prediction quantity; based on the comprehensive health degree score and the product demand prediction quantity, determining the equipment maintenance cycle update data through equipment future production capacity prediction.

[0012] In an implementation manner of the present application, according to the comprehensive health degree score, through production capacity evaluation, the current production capacity of the industrial system is determined, which specifically includes: determining the performance basic parameters affecting the production capacity through the analysis of the performance characteristics of the industrial system; wherein, the performance basic parameters include: mechanical performance, electrical performance; based on the comprehensive health degree score and the performance basic parameters, determining the current production capacity of the industrial system through production capacity evaluation; wherein, the calculation formula of the production capacity evaluation is:

[0013]

[0014] Wherein, is the equipment state variable, is the performance basic parameter, characterizes the current production capacity of the industrial system.

[0015] In an implementation manner of the present application, the system support degree of the current production capacity of the industrial system is analyzed to determine the comprehensive equipment support degree, which specifically includes: analyzing the production support degree of the current production capacity of the industrial system to obtain the production capacity support degree; wherein, the calculation formula of the production support degree analysis is:

[0016]

[0017] Wherein, is the product demand prediction quantity, is the current production capacity of the industrial system; obtaining the total number of equipment, and based on the total number of equipment, determining the equipment availability through the analysis of the equipment available state; wherein, the calculation formula of the equipment available state analysis is:

[0018]

[0019] Among them, is the total number of devices, is the actual available number of devices; according to the production capacity guarantee degree and the equipment availability, the comprehensive equipment guarantee degree is determined.

[0020] In an implementation manner of the present application, after analyzing the system guarantee degree of the current production capacity of the industrial system and determining the comprehensive equipment guarantee degree, the method further includes: determining a dynamic maintenance strategy through dynamic maintenance analysis according to the comprehensive health score; wherein, the dynamic maintenance strategy includes: a preventive maintenance plan, predictive maintenance trigger conditions, and maintenance priority sorting rules; based on the comprehensive equipment guarantee degree and the dynamic maintenance strategy, determining production plan update data through industrial system production plan optimization.

[0021] In a second aspect, an embodiment of the present application further provides a production capacity guarantee degree evaluation device based on a health score, which is 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 to enable the at least one processor to: obtain off-line device inspection parameters, and perform a health index evaluation on the off-line device inspection parameters to obtain a health score of the off-line inspection device; obtain device operation status inspection parameters, and perform a time-series health status analysis on the device operation status inspection parameters to obtain a health score of the operation inspection device; based on the health of the off-line inspection device and the health of the operation inspection device, determine a comprehensive health score through multi-level health index weight analysis; according to the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; analyze the system guarantee degree of the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree.

[0022] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for production capacity guarantee degree evaluation based on a health score, storing computer-executable instructions, which are characterized in that the computer-executable instructions are set to: obtain off-line device inspection parameters, and perform a health index evaluation on the off-line device inspection parameters to obtain a health score of the off-line inspection device; obtain device operation status inspection parameters, and perform a time-series health status analysis on the device operation status inspection parameters to obtain a health score of the operation inspection device; based on the health of the off-line inspection device and the health of the operation inspection device, determine a comprehensive health score through multi-level health index weight analysis; according to the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; analyze the system guarantee degree of the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree.

[0023] The embodiment of the present application provides a method, device and medium for evaluating the production capacity guarantee degree based on the health score. Through the comprehensive analysis of the health degree and guarantee degree of the industrial system, the technical problem of mutual exclusivity in the analysis of health degree and guarantee degree in the existing industrial system is solved. It can not only accurately evaluate the health degree of the equipment, but also accurately measure the guarantee degree of the production capacity, improve the accuracy of the industrial system evaluation, overcome the non-linear problem brought by the sharp increase in the complexity of the industrial system and the singularity of the health degree evaluation, and improve the correlation between variables and the correlation of faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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 and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0025] Figure 1 It is a flowchart of a method for evaluating the production capacity guarantee degree based on the health score provided by the embodiment of the present application;

[0026] Figure 2 It is a schematic diagram of the internal structure of a device for evaluating the production capacity guarantee degree based on the health score provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0028] The embodiment of the present application provides a method, device and medium for evaluating the production capacity guarantee degree based on the health score. Through the comprehensive analysis of the health degree and guarantee degree of the industrial system, the technical problem of mutual exclusivity in the analysis of health degree and guarantee degree in the existing industrial system is solved. It can not only accurately evaluate the health degree of the equipment, but also accurately measure the guarantee degree of the production capacity, improve the accuracy of the industrial system evaluation, overcome the non-linear problem brought by the sharp increase in the complexity of the industrial system and the singularity of the health degree evaluation, and improve the correlation between variables and the correlation of faults.

[0029] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the drawings.

[0030] Figure 1 It is a flowchart of a method for evaluating the production capacity guarantee degree based on the health score provided by the embodiment of the present application. AsFigure 1 As shown in Figure 1 , an evaluation method for production capacity guarantee degree based on health score provided by an embodiment of the present application specifically includes the following steps:

[0031] Step 101: Obtain the inspection parameters of offline devices, and evaluate the health index of the inspection parameters of offline devices to obtain the health score of offline inspection devices.

[0032] Exemplarily, in an industrial system, health usually refers to the current state and performance level of a device, a system, or the entire production process. It is a comprehensive indicator used to evaluate the operating efficiency, stability, and reliability of a system. Health analysis involves monitoring and evaluating the operating data of a system to determine whether the system is operating normally and whether there are signs of performance degradation or potential failures.

[0033] Guarantee degree in an industrial system 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 response ability of the system under specific conditions, that is, the evaluation of relevant factors such as production capacity and resource availability.

[0034] The offline inspection indicators focus on the impact of maintenance in the shutdown state on health, specifically including three types of operation and maintenance operations: maintenance, repair, and inspection. The health evaluation of offline devices can adapt to the inspection status of the shutdown state of devices in an industrial environment.

[0035] Specifically, evaluating the health index of the inspection parameters of offline devices to obtain the health score of offline inspection devices includes: based on the inspection parameters of offline devices, determining the offline inspection conditions of the device's health through the trigger configuration of the health score; wherein, the trigger configuration of the health score includes: device maintenance configuration, device repair configuration, and device inspection configuration; evaluating the health of the offline inspection conditions of the device to obtain the health score of offline inspection devices.

[0036] In one embodiment, first, set the trigger conditions for the health score index of offline inspection. The trigger condition is that the device to be detected needs to be scored for the health score index related to a certain operation and maintenance operation under certain conditions. Under the condition that the trigger condition type of "primary maintenance" is set to "periodic", the time is set to "every 720 hours of operation", and the effective time is set to "always effective", it means that the device needs to stop for primary maintenance every 720 hours of operation, and the shutdown time is not included in the operation time. Until the device is scrapped and removed from the health score system, it will continuously detect whether the offline inspection conditions of the health index of "primary maintenance" are triggered.

[0037] Set the deduction rules for the health score of the offline inspection. For the health score index of "first-level maintenance", the score range is from 0 to 100 points. If the inspection condition is triggered, the score of this index is temporarily 0 points. Only when the operation and maintenance personnel complete the first-level maintenance operation, the inspection operation is qualified and report to the health score system that the "first-level maintenance" is qualified and the equipment is healthy, will the score of this index be recorded as 100 points. If the "first-level maintenance" is ignored and the industrial system continues to run, the health score system will keep warning the user, and the score of the "first-level maintenance" index will always be 0 points until the operator solves the problem and the score changes back to 100 points.

[0038] Then, perform the operation of the health score index of the offline inspection. After the condition of the "first-level maintenance" index is triggered, the corresponding equipment needs to be shut down, and the equipment is cleaned, carefully disassembled and inspected, and the parts are adjusted. After completion, report to the health score system that the "first-level maintenance" is qualified and the equipment is healthy.

[0039] Finally, the equipment restarts to run, and the offline inspection health score is completed and updated until the trigger condition of the next offline inspection health score index is triggered.

[0040] Step 102: Obtain the inspection parameters of the equipment operation status, and perform the time-series health status analysis on the inspection parameters of the equipment operation status to obtain the health score of the equipment for the operation inspection category.

[0041] Exemplarily, determining the health score of the offline inspection equipment corresponding to the equipment shutdown state can meet the health inspection of the equipment in the non-working state. Similarly, in order to achieve full coverage of the equipment health, it is necessary to analyze the health of the equipment in the running state.

[0042] Specifically, performing the time-series health status analysis on the inspection parameters of the equipment operation status to obtain the health score of the equipment for the operation inspection category includes: performing preprocessing on the historical characteristics of equipment failures on the inspection parameters of the equipment operation status to obtain the online fault monitoring index model library; performing the analysis of the time-series state distribution of the online fault monitoring index model library to obtain the reference distribution of the equipment state; based on the reference distribution of the equipment state, obtaining the health score of the equipment for the operation inspection category through the JS divergence operation of the equipment operation status; where the calculation formula for the JS divergence operation of the equipment operation status is:

[0043]

[0044] Among them, is the health score of the equipment for the operation inspection category, is the predicted output of the equipment state detection in the online fault monitoring index model library, is the monitored actual value, is the empirical parameter, is the Jensen-Shannon divergence.

[0045] In one embodiment, first, historical data of relevant characteristic parameters is collected according to the set online fault monitoring indicators. The temperature of a certain component in the device is selected as the online fault monitoring indicator, and 14 possible influencing factors are provided in combination with the operation experience of on-site staff. According to the degree of correlation between parameters calculated by the Pearson correlation coefficient, 4 characteristic parameters related to the monitoring parameters are preliminarily screened out, namely current, motor U-phase coil temperature, oil tank oil temperature, and return water temperature of cooling water required for the motor oil station.

[0046] Then, the collected historical fault data is preprocessed, and a regression model of the online fault monitoring indicator is established. After preprocessing such as normalization of the historical time-series data of the four characteristic parameters collected under the fault state, a multi-input online fault monitoring model for component temperature is established using the BP neural network in deep learning. When multiple online fault monitoring indicators are selected and corresponding calculation models are established respectively, an online fault monitoring indicator model library can be established.

[0047] Then, the time-series data of the online fault monitoring indicator model library is monitored, and the similarity determination is carried out with the calculation models in the model library using the similarity detection method, and a health score of dynamic monitoring is output. The degree of similarity with the fault monitoring regression model has a negative correlation with the device health.

[0048] Taking the output of the online fault monitoring model for component temperature as the reference distribution, the Jensen-Shannon divergence (JS divergence) is used to measure the distance between the actual value of the monitored front bearing temperature of the fan and the reference distribution, and this gap is defined as the real-time monitoring health of the device.

[0049] The Jensen-Shannon divergence (JS divergence) is widely used in information theory and machine learning, especially when measuring the similarity and distinguishability between two distributions.

[0050] In this application, based on the reference distribution of the device state, through the JS divergence operation of the device operation state, a health score of the operation inspection type device is obtained; among them, the JS divergence operation of the device operation state is explained by the following formula:

[0051] (1)

[0052] Wherein, is the health score of the operation inspection type device, is the predicted output of the device state detection in the online fault monitoring indicator model library, is the monitored actual value, is the empirical parameter, is the JS divergence.

[0053] Further, the empirical parameter can be set according to manual experience, and the preset value here is 100.

[0054] Step 103: Based on the health degrees of off-line inspection type devices and operation inspection type devices, determine the comprehensive health degree score through multi-level health degree index weight analysis.

[0055] Exemplarily, after the health degrees of both the device running state and the non-running state can be evaluated, for the global device comprehensive health degree evaluation, it is necessary to allocate weights of multi-level health degree indexes, so that under different requirements of different devices, the weight allocations of the health degrees of off-line inspection type devices and operation inspection type devices conform to the actual expectations, thereby improving the accuracy and actual fitting degree of the comprehensive health degree score.

[0056] Specifically, based on the health degrees of off-line inspection type devices and operation inspection type devices, determine the comprehensive health degree score through multi-level health degree index weight analysis, which specifically includes: obtaining the preset evaluation indexes, and constructing a multi-level health score model through subjective method analysis; training the multi-level health score model until the model converges, and inputting the health degrees of off-line inspection type devices and operation inspection type devices into the trained multi-level health score model to determine the comprehensive health degree score.

[0057] In one embodiment, first, a multi-level health degree score model is established, allowing users to customize each level of indexes and their corresponding weights. Users can design the hierarchical structure and weight allocation of the indexes according to their own needs, ensuring that the sum of the weights of each level of indexes reaches 100%.

[0058] Among the first-level indexes, there are three indexes: head, flow rate, and power, and their respective weights are 40%, 30%, and 30% respectively.

[0059] The flow rate index is further divided into three secondary indexes: inlet flow rate, outlet flow rate, and vane flow rate. Then, the weights of these secondary indexes also need to add up to 100%, that is, the weight of each secondary index can be 40%, 30%, and 30%.

[0060] It should be noted that the weights of the secondary indexes are actually calculated based on the weights of their respective first-level indexes. Therefore, the total weight of the inlet flow rate is actually the product of the first-level index flow rate weight (30%) and the secondary index inlet flow rate weight (40%), that is, 12%. Similarly, the total weights of the outlet flow rate and the vane flow rate are 9% and 9% respectively. In this way, users can accurately calculate the relative importance of each index in the overall health degree evaluation, so as to realize more refined and personalized device health degree analysis.

[0061] Then, a multi-level health score model of the model is constructed through a subjective method, the weight distribution of the health of offline inspection devices and the health of operation inspection devices is realized, and then the comprehensive health score is determined.

[0062] Furthermore, after completing the scoring of the offline inspection class and online monitoring class indicators in the health score 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 detected health score is too low, the device can be selected to stop, and if the health is qualified, the device can be started.

[0063] Furthermore, after inputting the health of offline inspection devices and the health of operation inspection devices into the trained multi-level health scoring model to determine the comprehensive health score, the method further includes: obtaining the product demand time series parameters, and performing data preprocessing on the product demand time series parameters to obtain the product demand data to be analyzed; performing product demand prediction on the product demand data to be analyzed to obtain the product demand prediction volume; based on the comprehensive health score and the product demand prediction volume, determining the device maintenance cycle update data through the future production capacity prediction of the device.

[0064] In one embodiment, the product demand time series parameters are collected, including but not limited to the sales data of different products in the past several years, including information such as sales volume, sales amount, and order quantity in different regions and different time periods (such as monthly and quarterly). Data cleaning and feature engineering processing are performed on the obtained time series data, and feature engineering refers to extracting key product demand features from the original data.

[0065] Then, for model selection, model training and optimization, the data set needs to be divided and the model is trained. The preprocessed data is divided into a training set, a validation set, and a test set. It can be divided according to the ratio of 70% of the data as the training set, 20% as the validation set, and 10% as the test set, and the selected model is trained using the training set. According to the results of the validation set, the hyperparameters of the model are adjusted to optimize the model performance.

[0066] Finally, the trained model is evaluated using the test set, and evaluation metrics such as mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) are calculated. When the model evaluation passes, new data (such as future macroeconomic forecast data, planned promotion activity information, etc.) is input into the model to obtain the market demand prediction results of the product. For example, predicting the product sales volume in different regions in the next quarter, enterprises can formulate production plans, inventory management strategies, and marketing activity plans based on these prediction results.

[0067] Step 104: Determine the current production capacity of the industrial system through production capacity evaluation according to the comprehensive health score.

[0068] Exemplarily, since the comprehensive health score of industrial system equipment can also characterize the production capacity of the current process system, and the production capacity indirectly characterizes the security level of the process system. Therefore, in order to fully evaluate the health and security levels in the process system assessment, the analysis of production capacity is required as data support. At the same time, the production capacity can also directly characterize the shipping capacity of the current industrial system.

[0069] Specifically, according to the comprehensive health score, through production capacity assessment, the current production capacity of the industrial system is determined, including: by analyzing the performance characteristics of the industrial system, the performance basic parameters affecting the production capacity are determined; among them, the performance basic parameters include: mechanical performance, electrical performance; based on the comprehensive health score and performance basic parameters, through production capacity assessment, the current production capacity of the industrial system is determined; among them, the calculation formula for production capacity assessment is:

[0070]

[0071] Among them, is the equipment status variable, is the performance basic parameter, characterizes the current production capacity of the industrial system.

[0072] In one embodiment, the production capacity in an industrial system refers to the quantity of products that an enterprise, workshop, work center, or the entire industrial system can produce under the given organizational and technical conditions within a certain period (usually one year). In this patent, only the production capacity related to the equipment in the industrial system under the condition of sufficient raw material and personnel supply is considered. The production capacity assessment is explained by the following formula:

[0073] (2)

[0074] Among them, is the equipment status variable, is the performance basic parameter, characterizes the current production capacity of the industrial system. is a 0-1 variable, when it is 0, it means the equipment is off, and when it is 1, it means the equipment is on. The value of is based on the control strategy set according to the equipment health score.

[0075] In each industrial environment, under the condition of sufficient raw material and personnel supply, the production capacity of the production unit can be measured by ten basic parameters related to mechanical performance and electrical performance. The calculation formula for the production capacity composed of ten mechanical performance and electrical performance basic parameters in the i-th production unit is as follows. These ten basic parameters are specifically:

[0076] Rated Value, abbreviated as RV. It represents the standard performance parameters of a device under normal operating conditions, such as rated voltage, rated power, rated current, etc.

[0077] Maximum Allowable Value, abbreviated as MaxAV. It represents the maximum limit value that a device can withstand. Exceeding this value may damage the device or affect its performance, such as maximum allowable temperature, maximum allowable pressure, etc.

[0078] Minimum Allowable Value, abbreviated as MinAV. It represents the minimum parameter value for a device to operate normally, such as minimum startup voltage, minimum flow rate, etc.

[0079] Design Value, abbreviated as DV. It represents the parameters set during the design of a device, which may be the same as the rated value or may have a certain margin for safety considerations.

[0080] Actual Operating Value, abbreviated as AOV. It represents the data measured during the actual operation of a device, such as actual power, actual temperature, etc.

[0081] Safety Value, abbreviated as SV. It represents some safety parameters set to ensure the safety of the device and operators, such as the pressure setting value of a safety valve.

[0082] Maintenance Value, abbreviated as MV. It represents the parameters of a device when maintenance or component replacement is required, such as the replacement cycle of lubricating oil, the cleaning cycle of filters, etc.

[0083] Environmental Adaptability Value, abbreviated as EAV. It represents the parameters for a device to operate normally under different environmental conditions, such as humidity, temperature range, etc.

[0084] 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.

[0085] Life Parameters, abbreviated as LP. It represents the expected service life of a device or its components, such as the number of charge cycles of a battery, the wear limit of mechanical components, etc.

[0086] Step 105: Conduct a system supportability analysis on the current production capacity of the industrial system to determine the comprehensive supportability of the equipment.

[0087] Exemplarily, the production capacity guarantee degree is an important evaluation index of the process system guarantee degree. At the same time, in order to further improve the accuracy of the guarantee degree, the equipment availability is introduced, and combined with the production capacity guarantee to analyze and judge the system guarantee degree.

[0088] Specifically, perform a system guarantee degree analysis on the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree, including: performing a production guarantee degree analysis on the current production capacity of the industrial system to obtain the production capacity guarantee degree; among them, the calculation formula for the production guarantee degree analysis is:

[0089]

[0090] Among them, is the predicted demand quantity of the product, is the current production capacity of the industrial system; obtain the total number of equipment, and based on the total number of equipment, determine the equipment availability through the analysis of the equipment available status; among them, the calculation formula for the equipment available status analysis is:

[0091]

[0092] Among them, is the total number of equipment, is the number of actually available equipment; determine the comprehensive equipment guarantee degree according to the production capacity guarantee degree and the equipment availability.

[0093] In one embodiment, the production guarantee degree analysis is explained by the following formula:

[0094] (3)

[0095] Among them, is the predicted demand quantity of the product, is the current production capacity of the industrial system.

[0096] The production guarantee degree index is used to measure the degree of the production capacity relative to the product demand. When this index is greater than 1, it means that the production capacity can meet the product demand, and usually a certain margin needs to be reserved. Generally speaking, only when this value reaches 1.1 or more can it properly ensure the normal production of all products required by the market with quality and quantity guaranteed. If this index is less than 1, it indicates that the production capacity cannot meet the market demand and the guarantee degree is poor.

[0097] Obtain the total number of equipment, and based on the total number of equipment, determine the equipment availability through the analysis of the equipment available status; among them, the calculation formula for the equipment available status analysis is:

[0098] (4)

[0099] Among them, is the total number of devices, is the actual available number of devices.

[0100] The device availability refers to the ratio of the actually available devices to the total number of devices according to the health score control strategy. A high availability indicates that most devices can operate normally and provide stable support for production activities.

[0101] Finally, according to the production capacity guarantee degree and the device availability, the comprehensive device guarantee degree is determined.

[0102] Furthermore, after analyzing the system guarantee degree of the current production capacity of the industrial system and determining the comprehensive device guarantee degree, the method further includes: determining the dynamic maintenance strategy through dynamic maintenance analysis according to 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 device guarantee degree and the dynamic maintenance strategy, determining the production plan update data through the optimization of the industrial system production plan.

[0103] The above is the method embodiment proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a production capacity guarantee degree evaluation device based on health score, and its structure is as Figure 2 shown.

[0104] Figure 2 This is a schematic diagram of the internal structure of a production capacity guarantee degree evaluation device based on health score provided by the embodiment of this application. As Figure 2 shown, the device includes:

[0105] At least one processor 201;

[0106] And a memory 202 communicatively connected to at least one processor;

[0107] Wherein, the memory 202 stores instructions executable by at least one processor, and the instructions are executed by at least one processor 201 so that at least one processor 201 can:

[0108] Obtain the inspection parameters of offline devices, and evaluate the health index of the inspection parameters of offline devices to obtain the health score of offline inspection devices; obtain the inspection parameters of the device operation status, and perform time-series health status analysis on the inspection parameters of the device operation status to obtain the health score of operation inspection devices; based on the health of offline inspection devices and the health of operation inspection devices, determine the comprehensive health score through multi-level health index weight analysis; according to the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; analyze the system guarantee degree of the current production capacity of the industrial system to determine the comprehensive device guarantee degree.

[0109] Some embodiments of the present application provide a non-volatile computer storage medium corresponding to Figure 1 a production capacity guarantee degree evaluation based on health score, storing computer-executable instructions, and the computer-executable instructions are set as follows:

[0110] Obtain the inspection parameters of offline devices, and evaluate the health index of the inspection parameters of offline devices to obtain the health score of offline inspection devices; obtain the inspection parameters of the device operation status, and perform a timing health status analysis on the inspection parameters of the device operation status to obtain the health score of operation inspection devices; based on the health of offline inspection devices and the health of operation inspection devices, determine the comprehensive health score through multi-level health index weight analysis; according to the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; perform a system guarantee degree analysis on the current production capacity of the industrial system to determine the comprehensive device guarantee degree.

[0111] The embodiments in the present application are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the partial description of the method embodiments for the relevant parts.

[0112] The systems, media and methods provided by the embodiments of the present application are in one-to-one correspondence. Therefore, the systems and media also have beneficial technical effects similar to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0114] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0117] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0118] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0119] 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.

[0120] 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.

[0121] 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 guarantee degree evaluation method based on health degree scoring, characterized in that The method includes: Obtaining the inspection parameters of offline devices, and performing a health index assessment on the inspection parameters of offline devices to obtain the health score of offline inspection devices; Obtaining the inspection parameters of the device operation status, and performing a time-series health status analysis on the inspection parameters of the device operation status to obtain the health score of operation inspection devices; Based on the health of offline inspection devices and the health of operation inspection devices, determining the comprehensive health score through multi-level health index weight analysis; According to the comprehensive health score, determining the current production capacity of the industrial system through production capacity assessment; Performing a system guarantee degree analysis on the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree; Performing a time-series health status analysis on the inspection parameters of the device operation status to obtain the health score of operation inspection devices, specifically including: Performing preprocessing on the historical characteristics of device failures of the inspection parameters of the device operation status to obtain an online fault monitoring index model library; Performing an analysis of the time-series state distribution of the online fault monitoring index model library to obtain the device state reference distribution; Based on the device state reference distribution, obtaining the health score of the operation inspection devices through the JS divergence operation of the device operation status; where the calculation formula for the JS divergence operation of the device operation status is: Among them, is the health score of the operation inspection class equipment, is the predicted output of the equipment status detection in the online fault monitoring index model library, is the monitoring actual value, is the empirical parameter, is the JS divergence; According to the comprehensive health score, determining the current production capacity of the industrial system through production capacity assessment, specifically including: Determining the performance basic parameters affecting the production capacity through the analysis of the performance characteristics of the industrial system; where the performance basic parameters include: mechanical performance, electrical performance; Based on the comprehensive health score and the performance basic parameters, determining the current production capacity of the industrial system through production capacity assessment; where the calculation formula for the production capacity assessment is: Among them, is the device status variable, is the performance basic parameter, characterizing the current production capacity of the industrial system; Performing a system guarantee degree analysis on the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree, specifically including: Performing a production guarantee degree analysis on the current production capacity of the industrial system to obtain the production capacity guarantee degree; where the calculation formula for the production guarantee degree analysis is: Among them, is the predicted demand volume of the product, is the current production capacity of the industrial system; Obtaining the total number of devices, and based on the total number of devices, determining the device availability through the analysis of the device available state; where the calculation formula for the device available state analysis is: Among them, is the total number of said devices, is the actual available number of devices; Determining the comprehensive equipment guarantee degree according to the production capacity guarantee degree and the device availability.

2. The production capacity guarantee degree evaluation method based on health score according to claim 1, characterized in that Performing a health index assessment on the inspection parameters of offline devices to obtain the health score of offline inspection devices, specifically including: Based on the inspection parameters of offline devices, determining the offline inspection conditions of the device health through the trigger configuration of the health score; where the trigger configuration of the health score includes: device maintenance configuration, device repair configuration, device inspection configuration; Performing a health degree assessment on the offline inspection conditions of the device health to obtain the health score of the offline inspection devices.

3. The production capacity guarantee degree evaluation method based on health score according to claim 1, characterized in that, Based on the health of offline inspection devices and the health of operation inspection devices, determining the comprehensive health score through multi-level health index weight analysis, specifically including: Obtain preset evaluation indicators, and construct a multi-level health scoring model through subjective analysis; Train the multi-level health scoring model until the model converges, and input the health degree of the offline inspection class equipment and the health degree of the operation inspection class equipment into the trained multi-level health scoring model to determine the comprehensive health degree score.

4. The production capacity guarantee degree evaluation method based on health score according to claim 3, characterized in that, After inputting the health degree of the offline inspection class equipment and the health degree of the operation inspection class equipment into the trained multi-level health scoring model to determine the comprehensive health degree score, the method further includes: Obtain the product demand time series parameters, and perform data preprocessing on the product demand time series parameters to obtain the product demand data to be analyzed; Perform product demand prediction on the product demand data to be analyzed to obtain the predicted product demand volume; Based on the comprehensive health degree score and the predicted product demand volume, determine the equipment maintenance cycle update data through equipment future production capacity prediction.

5. The production capacity guarantee degree evaluation method based on health score according to claim 1, wherein, After analyzing the system guarantee degree of the current production capacity of the industrial system and determining the comprehensive equipment guarantee degree, the method further includes: Determine the dynamic maintenance strategy according to the comprehensive health degree score through dynamic maintenance analysis; wherein, the dynamic maintenance strategy includes: preventive maintenance plan, predictive maintenance trigger conditions, and maintenance priority ranking rules; Based on the comprehensive equipment guarantee degree and the dynamic maintenance strategy, determine the production plan update data through industrial system production plan optimization.

6. A production capacity guarantee degree evaluation device based on 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 to enable the at least one processor to: Obtain the inspection parameters of the offline equipment, and evaluate the health index of the inspection parameters of the offline equipment to obtain the health degree score of the offline inspection class equipment; Obtain the inspection parameters of the equipment operation status, and perform time series health status analysis on the inspection parameters of the equipment operation status to obtain the health degree score of the operation inspection class equipment; Based on the health degree of the offline inspection class equipment and the health degree of the operation inspection class equipment, determine the comprehensive health degree score through multi-level health degree index weight analysis; Determine the current production capacity of the industrial system according to the comprehensive health degree score through production capacity evaluation; Analyze the system guarantee degree of the current production capacity of the industrial system to determine the comprehensive equipment guarantee degree; Performing time series health status analysis on the inspection parameters of the equipment operation status to obtain the health degree score of the operation inspection class equipment specifically includes: Perform preprocessing on the historical characteristics of equipment failures of the inspection parameters of the equipment operation status to obtain an online fault monitoring index model library; Perform equipment time series state distribution analysis on the online fault monitoring index model library to obtain the equipment state reference distribution; Based on the equipment state reference distribution, obtain the health degree score of the operation inspection class equipment through the JS divergence operation of the equipment operation status; wherein, the calculation formula of the JS divergence operation of the equipment operation status is: Among them, is the health score of the operation inspection class equipment, is the predicted output of the equipment status detection in the online fault monitoring index model library, is the monitored actual value, is the empirical parameter, is the JS divergence; Based on the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation, specifically including: Determine the performance basic parameters affecting production capacity through the analysis of the performance characteristics of the industrial system; among them, the performance basic parameters include: mechanical performance, electrical performance; Based on the comprehensive health score and the performance basic parameters, determine the current production capacity of the industrial system through production capacity evaluation; among them, the calculation formula for the production capacity evaluation is: Among them, is the device status variable, is the performance basic parameter, characterizing the current production capacity of the industrial system; Conduct a system supportability analysis on the current production capacity of the industrial system to determine the comprehensive equipment supportability, specifically including: Conduct a production supportability analysis on the current production capacity of the industrial system to obtain the production capacity supportability; among them, the calculation formula for the production supportability analysis is: Among them, is the predicted demand for products, is the current production capacity of the industrial system; Obtain the total number of devices, and based on the total number of devices, determine the device availability through the analysis of the device available state; among them, the calculation formula for the device available state analysis is: Among them, is the total number of said devices, is the number of actually available devices; Determine the comprehensive equipment supportability according to the production capacity supportability and the device availability.

7. A non - volatile computer storage medium for evaluating the production capacity guarantee degree based on the health score, storing computer - executable instructions, characterized in that, The computer-executable instructions are set to: Obtain the inspection parameters of offline devices, and conduct a health index evaluation on the inspection parameters of offline devices to obtain the health score of offline inspection devices; Obtain the device operation status inspection parameters, and conduct a time-series health status analysis on the device operation status inspection parameters to obtain the health score of operation inspection devices; Based on the health of offline inspection devices and the health of operation inspection devices, determine the comprehensive health score through multi-level health index weight analysis; Based on the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation; Conduct a system supportability analysis on the current production capacity of the industrial system to determine the comprehensive equipment supportability; Conduct a time-series health status analysis on the device operation status inspection parameters to obtain the health score of operation inspection devices, specifically including: Preprocess the device failure history characteristics of the device operation status inspection parameters to obtain an online fault monitoring index model library; Conduct a device time-series state distribution analysis on the online fault monitoring index model library to obtain the device state reference distribution; Based on the device state reference distribution, obtain the health score of the operation inspection devices through the JS divergence operation of the device operation status; among them, the calculation formula for the JS divergence operation of the device operation status is: Among them, is the health score of the operation inspection class device, is the predicted output of the device status detection in the online fault monitoring index model library, is the monitoring actual value, is the empirical parameter, is the JS divergence; Based on the comprehensive health score, determine the current production capacity of the industrial system through production capacity evaluation, specifically including: Determine the performance basic parameters affecting production capacity through the analysis of the performance characteristics of the industrial system; among them, the performance basic parameters include: mechanical performance, electrical performance; Based on the comprehensive health score and the performance basic parameters, determine the current production capacity of the industrial system through production capacity evaluation; among them, the calculation formula for the production capacity evaluation is: Among them, is the device status variable, is the performance basic parameter, characterizing the current production capacity of the industrial system; Conduct a system supportability analysis on the current production capacity of the industrial system to determine the comprehensive equipment supportability, specifically including: Analyze the production guarantee degree of the current production capacity of the industrial system to obtain the production capacity guarantee degree; among them, the calculation formula for the production guarantee degree analysis is: Among them, is the predicted demand for products, is the current production capacity of the industrial system; Obtain the total number of devices, and based on the total number of devices, determine the device availability through the analysis of the device available state; among them, the calculation formula for the device available state analysis is: Among them, is the total number of the devices, is the number of actually available devices; Determine the comprehensive guarantee degree of the device according to the production capacity guarantee degree and the device availability.

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