Industrial big data graph computation algorithm

Abstract

The present application relates to the field of industrial data analysis, and more particularly to an industrial big data graph computing algorithm, the present application constructs a runtime sequence diagram for industrial equipment by obtaining equipment application data corresponding to a plurality of tasks in a predetermined period of an industrial plant, identifies a heavy operation time domain segment to extract a running state feature corresponding to the heavy operation time domain segment, evaluates a time sequence compactness representation value of the industrial equipment in combination with the running state feature and an average physical distance between the industrial equipment to divide the running state of the industrial plant, collects equipment working features of the heavy operation time domain segment according to the running state, and adaptively evaluates and analyzes the operation scheduling of the industrial plant. The present application improves the accuracy of the running state evaluation of the industrial equipment. At the same time, the present application can adapt to complex scene requirements, dynamically adapt the operation scheduling, realize dynamic balance of the operation efficiency and resource efficiency under different load scenes, and improve the continuity and economy of industrial production.

Description

Technical Field

[0001] This invention relates to the field of industrial data analysis, and in particular to an industrial big data graph calculation algorithm. Background Technology

[0002] Against the backdrop of industrial production transforming towards intelligent and large-scale operations, the number of equipment, task complexity, and operational interoperability in industrial plants have significantly increased. The multi-dimensional, high-frequency operational data generated by massive amounts of equipment during continuous production constitutes the core carrier of industrial big data. The operating status of industrial equipment directly determines production continuity, product quality stability, and resource utilization efficiency. Since equipment within industrial plants often works collaboratively in the form of assembly lines and upstream / downstream linkages, forming complex interconnected networks, an anomaly or improper scheduling of a single piece of equipment can trigger a chain reaction, leading to production interruptions, surges in energy consumption, and other problems.

[0003] In current industrial production scenarios, the operating status of industrial equipment is affected by multiple factors such as task load fluctuations, physical layout constraints, and equipment aging and wear, exhibiting dynamic characteristics. On the one hand, the production workload varies significantly across different time periods, with some periods requiring multiple devices to operate intensively and continuously for extended periods, forming heavy operating time periods; while other periods are in a low-load, smooth operating state. The requirements for equipment stability and scheduling flexibility differ significantly under different states. On the other hand, the physical distance between connected devices directly affects the efficiency of linkage response and the risk of fault propagation. The collaborative operation of devices in close proximity relies on higher state synchronization, while devices in distant proximity need to balance resource allocation and response latency.

[0004] Therefore, it is necessary to construct an industrial big data processing and scheduling evaluation scheme that can integrate spatiotemporal characteristics and adapt to different operating scenarios, so as to achieve accurate identification and dynamic scheduling optimization of the operating status of industrial equipment.

[0005] Chinese Patent Application Publication No. CN114817739A discloses an industrial big data processing system based on artificial intelligence algorithms, comprising: a data acquisition module, a data storage module, a data analysis module, and a data management module. The data acquisition module collects data from industrial equipment to obtain industrial big data, and transmits this data to the data analysis module and the data storage module. The data analysis module analyzes and processes the industrial big data transmitted by the data acquisition module using artificial intelligence algorithms to obtain industrial big data analysis results. The data management module handles information exchange related to industrial big data processing and transmits the industrial big data analysis results. The data storage module stores the industrial big data obtained by the data acquisition module and provides data information to the data analysis module. This invention uses artificial intelligence algorithms to process industrial big data, efficiently and quickly extracting valuable information from massive amounts of industrial big data for use and management, effectively improving the value of industrial big data.

[0006] However, the following problems still exist in the existing technology. Many assessments of the overall operational status of industrial plants rely solely on single-dimensional data, such as the runtime of isolated equipment or the number of tasks. This neglects the interrelationships between industrial equipment and the impact of spatial layout constraints, making it impossible to accurately distinguish the differences between various load scenarios. This reduces the accuracy of industrial equipment operational status assessments and consequently affects the rationality of subsequent operational scheduling decisions. Summary of the Invention

[0007] To address this, the present invention provides an industrial big data graph calculation algorithm to overcome the problem that existing technologies often assess the overall operating status of industrial plants based solely on single-dimensional data, neglecting the influence of the linkage between industrial equipment and spatial layout constraints. This results in the inability to accurately distinguish the differences between different load scenarios, reduces the accuracy of industrial equipment operating status assessment, and consequently affects the rationality of subsequent operation scheduling decisions.

[0008] To achieve the above objectives, the present invention provides an industrial big data graph calculation algorithm, which includes: Obtain equipment application data corresponding to several tasks within a predetermined period in an industrial plant, construct a runtime sequence diagram for industrial equipment, identify heavy operating time domain segments, and extract the operating state features corresponding to the heavy operating time domain segments. By combining the aforementioned operational status characteristics with the average physical spacing between connected industrial equipment, the temporal compactness characterization value of the industrial equipment is evaluated to classify the operational status of the industrial plant area. Based on the operating status, the operating characteristics of the equipment during the peak operating time period are collected, and the operation scheduling of the industrial plant is evaluated and analyzed, including: Based on the equipment's operating characteristics and continuous operating delay, the operational stability characterization parameters of the industrial equipment are calculated to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; Alternatively, the acquisition frequency of the device's operating characteristics may be adjusted based on the time-compact characterization value; The operating status characteristics include the number of industrial devices connected in operation and the average operating time of the connected industrial devices. The equipment operating characteristics include the frequency of abnormal noise and the amplitude of energy consumption fluctuations.

[0009] Furthermore, the process of identifying heavy time-domain segments includes: If the number of running tasks in any time domain segment exceeds the threshold for the number of running tasks, and the number of industrial devices in operation exceeds the threshold for the number of industrial devices, then the time domain segment is identified as the heavy-duty time domain segment.

[0010] Furthermore, the process of evaluating the timing compactness characterization value of the industrial equipment includes: The sum of the ratio of the number of connected industrial devices in operation to the threshold of the number of connected industrial devices and the ratio of the average running time of connected industrial devices to the threshold of the average running time is used as the first time-series compact feature. The ratio of the average physical spacing threshold to the average physical spacing of the connected industrial equipment is used as the second temporal compactness feature; The first time-compact feature and the second time-compact feature are weighted and summed to determine the time-compact representation value.

[0011] Furthermore, the operational status of the industrial plant area is categorized, including: If the time-compactness representation value is greater than the time-compactness representation threshold, the industrial plant area will be classified as a densely operating state. If the time-compact characterization value is less than or equal to the time-compact characterization threshold, the industrial plant area is classified as a smooth operation state.

[0012] Furthermore, the operation scheduling of the industrial plant area is evaluated and analyzed, including: If the industrial plant is in a state of intensive operation, the operational stability characterization parameters of the industrial equipment are calculated based on the equipment's working characteristics and continuous operation delay to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; If the industrial plant is in a stable operating state, the sampling frequency of the equipment's operating characteristics is adjusted based on the time-compact characterization value.

[0013] Furthermore, the process of calculating the operational stability characterization parameters of the industrial equipment includes: The sum of the ratio of abnormal noise frequency to abnormal noise frequency threshold and the ratio of energy consumption fluctuation amplitude to energy consumption fluctuation amplitude threshold is taken as the first operational stability feature. The ratio of runtime duration to runtime duration threshold is used as the second runtime stability feature; The weighted sum of the first operational stability feature and the second operational stability feature is used to determine the operational stability characterization parameter.

[0014] Further, determining the operational stability level of the industrial equipment includes: The correspondence between the operational stability level and the predetermined range of operational stability characterization parameters is pre-defined; Determine the range of operational stability characterization parameters to which the corresponding operational stability characterization parameters of the industrial equipment belong; Set the operating stability level corresponding to the range of operating stability characterization parameters as the operating stability level of the industrial equipment; Among them, the operational stability level corresponds one-to-one with the range of operational stability characterization parameters.

[0015] Furthermore, identifying anomalous industrial equipment includes: If any industrial equipment meets the abnormal operating conditions, then the industrial equipment is identified as the abnormal industrial equipment. The abnormal operating conditions include a low operating stability level and a position in the runtime sequence diagram that is less than a position threshold.

[0016] Further, determining whether the abnormal transmission criteria are met includes: If the abnormal interval duration of adjacent industrial equipment is less than the abnormal interval duration threshold, and the operating energy consumption deviation between the abnormal industrial equipment and the adjacent industrial equipment is greater than the operating energy consumption deviation threshold, then it is determined that the abnormal transmission benchmark is not met.

[0017] Furthermore, adjusting and switching the operation of industrial equipment includes: The operating status of similar equipment in an unloaded operating state corresponding to the heterogeneous industrial equipment is determined based on the runtime sequence diagram. If any device of the same type meets the operation scheduling benchmark, then switch the device of the same type to the running state and replace the abnormal industrial device to continue running; The operation scheduling benchmark includes the remaining runtime of the same type of idle equipment and the remaining runtime of the different type of industrial equipment. The surplus remaining runtime is greater than the surplus remaining runtime threshold, and the location distance between the same type of equipment and the different type of industrial equipment is less than the location distance threshold.

[0018] Compared with existing technologies, this invention acquires equipment application data corresponding to several tasks within a predetermined period in an industrial plant, constructs a runtime sequence diagram for the industrial equipment, identifies heavy-duty time periods, and extracts the corresponding operational status characteristics. Combining these operational status characteristics with the average physical spacing between connected industrial equipment, it evaluates the temporal compactness representation value of the industrial equipment to classify the operational status of the industrial plant. Based on the operational status, it collects the equipment working characteristics of the heavy-duty time periods and adaptively evaluates and analyzes the operation scheduling of the industrial plant. This invention improves the accuracy of operational status assessment for industrial equipment. Simultaneously, it can adapt to complex scenario requirements, dynamically adapting the operation scheduling to achieve a dynamic balance between operational efficiency and resource efficiency under different load scenarios, thereby improving the continuity and economy of industrial production.

[0019] In particular, this invention establishes a dual-dimensional (time and space) industrial equipment operation status assessment model. In actual industrial production, industrial equipment does not operate in isolation but is interconnected and linked in numerous ways, and the physical distance between equipment directly affects the linkage efficiency and load transmission. Based on this, this invention integrates both temporal correlation characteristics and spatial constraint characteristics. The number of interconnected industrial equipment reflects the linkage density, the average runtime reflects the continuous load intensity, and the average physical distance reflects the compactness of the equipment layout. This provides a more realistic and comprehensive reflection of the actual operating load of the industrial plant, accurately distinguishing the load pressure differences between multiple devices operating in close proximity for extended periods and multiple devices operating in distant proximity for short periods, avoiding misjudgments of the operation status due to one-sided assessments. Therefore, this invention comprehensively considers the temporal correlation and spatial constraints of industrial equipment operation, adapting to the "equipment linkage and fixed layout" characteristics of industrial production. Compared to generalized status assessment methods, it better aligns with the actual operating logic of industrial scenarios, and the assessment results have higher guiding value for industrial scheduling. Furthermore, the temporal compactness characterization value represents the linkage density, continuous load intensity, and overall compactness under spatial layout constraints of industrial equipment operation, providing data support for subsequent division of the operation status of industrial plants. Furthermore, it provides accurate and reliable decision-making basis for scenario-based scheduling strategies, namely prioritizing stable operation in intensive states and optimizing data collection efficiency in calm states, ensuring that scheduling actions are highly matched with actual operational needs, promoting the coordinated optimization of industrial plant operation efficiency and resource utilization, and forming a closed loop of "accurate assessment - dynamic scheduling - efficient operation".

[0020] In particular, this invention covers two categories of indicators: immediate hardware anomalies and stable energy utilization, as well as long-term fatigue status, forming a comprehensive evaluation system. Noise frequency directly reflects immediate anomalies related to hardware failures in industrial equipment; energy consumption fluctuation reflects the stability of energy utilization aspects such as power output and load adaptation, and indirectly reflects abnormal control accuracy or operating load. In practice, a gradual increase in noise frequency and an abnormal expansion of energy consumption fluctuation are often early signs of equipment failure. Furthermore, the cumulative duration of operation delay reflects the fatigue level of industrial equipment and can provide early warning of gradual failures caused by prolonged high loads. The fusion of these three indicators accurately captures precursor signals of industrial equipment failures. Furthermore, operational stability parameters are calculated to characterize the degree of anomalies in the immediate working state and the cumulative risk under long-term fatigue operation, comprehensively evaluating the operational stability of the industrial equipment and providing data support for subsequently determining the corresponding operational stability level and differentiated scheduling of industrial plants. This invention is adapted to the operating characteristics of industrial equipment, improving the practicality of equipment operation evaluation scenarios.

[0021] In particular, this invention combines operational stability levels and runtime sequence diagrams to identify abnormal industrial equipment, pinpointing high-risk devices and providing precise targeting for subsequent fault handling, avoiding ineffective scheduling. A core characteristic of industrial production is the close interoperability of equipment; a change in one part can have far-reaching consequences. For example, in the coordinated operation of production lines and chemical reaction units, an anomaly in a single industrial device can spread throughout the entire system through interconnected relationships. Therefore, to align with the collaborative operation characteristics of industrial equipment, this invention quantifies the risk of interconnected transmission between industrial devices by measuring the interval between anomalies in adjacent industrial devices. The deviation in operating energy consumption between abnormal and adjacent industrial devices reflects the tightness of the interconnected relationships between them, while also reflecting the potential probability and scope of anomaly transmission. Simultaneously, by using runtime sequence diagrams to understand the interconnected relationships of industrial equipment, this invention ensures the compatibility of switched industrial equipment with the original abnormal industrial equipment, avoiding problems such as poor equipment integration and production process disruptions after switching. This invention can match the actual operational needs of industrial scenarios and strengthen the control over the overall production system. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of the industrial big data graph calculation algorithm in an embodiment of the invention. Figure 2 A logic decision diagram for dividing the operating states of an industrial plant area according to an embodiment of the invention; Figure 3 A logic decision diagram for identifying abnormal industrial equipment in an embodiment of the invention; Figure 4 This is a logic diagram for determining whether an abnormal propagation benchmark is met in an embodiment of the invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 The diagram shown illustrates the steps of the industrial big data graph calculation algorithm according to an embodiment of the present invention. The industrial big data graph calculation algorithm according to an embodiment of the present invention includes: Step S1: Obtain equipment application data corresponding to several tasks within a predetermined period in the industrial plant area, construct a runtime sequence diagram for the industrial equipment, identify the heavy operation time domain segment, and extract the operation status features corresponding to the heavy operation time domain segment. Step S2: Combining the operating status characteristics and the average physical spacing between connected industrial equipment, evaluate the temporal compactness characterization value of the industrial equipment to classify the operating status of the industrial plant area. Step S3: Based on the operating status, collect the equipment operating characteristics during the peak operating time period, and evaluate and analyze the operation scheduling of the industrial plant, including... Based on the equipment's operating characteristics and continuous operating delay, the operational stability characterization parameters of the industrial equipment are calculated to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; Alternatively, the acquisition frequency of the device's operating characteristics may be adjusted based on the time-compact characterization value; The operating status characteristics include the number of industrial devices connected in operation and the average operating time of the connected industrial devices. The equipment operating characteristics include the frequency of abnormal noise and the amplitude of energy consumption fluctuations.

[0028] Specifically, the equipment application data includes the number of running tasks, the number of industrial devices in operation, operating status characteristics, the average physical distance between connected industrial devices, equipment operating characteristics, operating duration, abnormal interval duration of adjacent industrial devices corresponding to heterogeneous industrial devices, operating energy consumption deviation between heterogeneous and adjacent industrial devices, remaining operating time of similar unloaded devices and the remaining operating time of heterogeneous industrial devices, and the location distance between similar devices and heterogeneous industrial devices.

[0029] Specifically, the method for collecting relevant equipment application data is not specifically limited. The number of running tasks and the number of industrial devices in operation can be collected in real time through the industrial plant's production scheduling system and equipment management platform. Then, the equipment management platform identifies the target device in operation and, combined with the equipment linkage relationship list, counts the total number of operating industrial devices directly / indirectly connected to the target device, determining the number of connected industrial devices in operation. The equipment controller records the start time and current time or end time of each connected industrial device, calculates the runtime of a single connected industrial device, and then averages the runtimes of all connected industrial devices to obtain the average runtime of the connected industrial devices. Furthermore, the equipment management platform records the continuous running time of the industrial devices from startup to the end of a predetermined cycle to determine the running duration delay. If the industrial device does not stop during the cycle, this time is the running duration delay; if there is a short-term stop, the running duration delay is calculated based on the cumulative running time.

[0030] The production task plan for the industrial plant is obtained from the production scheduling system. The total duration of the tasks currently undertaken by the non-standard industrial equipment is determined, and the running time is subtracted to obtain the remaining running time of the non-standard industrial equipment. At the same time, the same type of equipment that is idle is queried through the equipment management platform. Combined with the equipment maintenance records and aging and wear data, the maximum duration for which the industrial equipment can be continuously idled and started at any time is evaluated. This is the remaining running time of the same type of idle equipment. The difference between the two is used to determine the surplus remaining time. By filtering through the equipment association list, all connected industrial equipment to the target equipment is identified. The physical coordinates of these connected industrial equipment are extracted from the industrial plant's physical layout archives, such as CAD drawings. The actual distance between each pair of connected industrial equipment is calculated, and the average physical distance of all connected industrial equipment is obtained. Furthermore, by extracting the physical coordinates of similar unloaded equipment and dissimilar industrial equipment, the straight-line distance or actual travel distance within the plant area between the two points is calculated using these coordinates to determine the positional distance between similar and dissimilar equipment.

[0031] Sound sensors are deployed in key parts of industrial equipment, such as bearings, motors, and transmission mechanisms, to collect sound signals during equipment operation in real time. An audio analysis module identifies abnormal noise and counts the number of times abnormal noise occurs within a predetermined period, which is the abnormal noise frequency. Energy consumption data per unit time is collected in real time using energy metering sensors in the industrial equipment, such as smart meters and energy consumption monitoring modules. The difference between the maximum and minimum energy consumption values ​​within a predetermined period is calculated to obtain the energy consumption fluctuation range.

[0032] By combining the equipment management platform and sensor data, abnormal states of adjacent industrial equipment are identified, such as excessive noise, abnormal energy consumption, and shutdown signals. The timestamps of the abnormal occurrences are recorded. Then, the time difference between the time of the abnormal occurrence of the abnormal industrial equipment and the time of the abnormal occurrence of the adjacent industrial equipment is calculated, which is the abnormal interval of the adjacent industrial equipment. If no abnormality occurs in the adjacent industrial equipment, a predetermined period is taken as the abnormal interval.

[0033] Among them, the production activities in industrial plants generally follow the characteristics of periodic load fluctuations, which is consistent with the time-domain load pattern of industrial production. At the same time, to balance data integrity and scheduling real-time performance, the predetermined period is set to 24 hours.

[0034] It is understandable that "connecting industrial equipment" refers to equipment in the industrial production process that has a direct or indirect functional collaboration or task connection with the target equipment. "Adjacent industrial equipment" refers to equipment in the physical layout of an industrial plant that is spatially adjacent to the target equipment and has a direct linkage relationship, such as direct material transfer, signal exchange, or energy transfer.

[0035] Specifically, the process of identifying heavy time-domain segments includes: If the number of running tasks in any time domain segment exceeds the threshold for the number of running tasks, and the number of industrial devices in operation exceeds the threshold for the number of industrial devices, then the time domain segment is identified as the heavy-duty time domain segment.

[0036] In this embodiment, the purpose of setting the threshold for the number of running tasks and the threshold for the number of industrial equipment is to filter the high-incidence time period of equipment failure and abnormal transmission. By obtaining historical equipment application data corresponding to several tasks within a predetermined period in the industrial plant, calling historical data on the number of running tasks and historical data on the number of industrial equipment in operation, the average number of running tasks and the average number of industrial equipment are calculated and used as the benchmark value under normal conditions. Based on the purpose of setting the above two thresholds, the threshold for the number of running tasks is determined as the product of the average number of running tasks and the task deviation coefficient, and the threshold for the number of industrial equipment is determined as the product of the average number of industrial equipment and the equipment deviation coefficient. The task deviation coefficient is selected in the interval [1.2, 1.4], preferably 1.2 in the implementation, and the equipment deviation coefficient is selected in the interval [1.2, 1.5], preferably 1.2 in the implementation.

[0037] Specifically, the process of evaluating the time-compact characterization value of the industrial equipment includes: The sum of the ratio of the number of connected industrial devices in operation to the threshold of the number of connected industrial devices and the ratio of the average running time of connected industrial devices to the threshold of the average running time is used as the first time-series compact feature. The ratio of the average physical spacing threshold to the average physical spacing of the connected industrial equipment is used as the second temporal compactness feature; The first time-compact feature and the second time-compact feature are weighted and summed to determine the time-compact representation value.

[0038] Specifically, the core purpose of constructing a time-series compactness representation value is to quantify the compactness of the operational load in an industrial plant. The essence of operational load is the busyness and continuous pressure of equipment linkage. Operational status characteristics reflect the core load indicators in the time dimension. Among them, the number of connected industrial equipment in operation reflects the equipment linkage density. The more connections, the more equipment involved in coordinated operation, and the greater the load transmission pressure, which is the core indicator of intensive operation. The average running time of connected industrial equipment reflects the continuous load intensity. The longer the running time, the higher the equipment fatigue wear and failure risk, which is a key characteristic of heavy operation. The combination of the above two characteristics determines whether the industrial plant is in a "high load, high pressure" state, which is the core basis for classifying the operational status. The average physical distance between connected industrial equipment reflects the auxiliary influence of spatial layout on load. Its core role is to correct the accuracy of load assessment. For example, the load transmission risk of closely spaced equipment linkage is higher, but it is not a decisive factor in load intensity. Therefore, in the weighted summation, the first temporal compactness feature, calculated based on the operating state characteristics, namely the number of connected industrial equipment in operation and the average operating time of the connected industrial equipment, is given a higher weight coefficient, set to 0.6. Correspondingly, the weight coefficient of the second temporal compactness feature, calculated based on the average physical spacing of the connected industrial equipment, is set to 0.4.

[0039] In this embodiment, the purpose of setting the threshold for the number of connected industrial equipment is to characterize the situation where the linkage density of industrial equipment operation is high, the purpose of setting the threshold for the average running time is to characterize the situation where the continuous load intensity is high, and the purpose of setting the threshold for the average physical distance is to characterize the situation where the overall compactness under spatial layout constraints is strong. Thus, the overall characterization of the situation where the operating load compactness of the industrial plant area is high. By acquiring historical equipment application data corresponding to several tasks within a predetermined period in an industrial plant, and by calling historical data on the number of connected industrial equipment in operation, the historical data on the average running time of connected industrial equipment, and the historical data on the average physical distance between connected industrial equipment, the average number of connected industrial equipment, the average running time, and the average physical distance are calculated and used as the benchmark values ​​under normal conditions. Based on the purpose of setting the above three thresholds, the threshold for the number of connected industrial equipment is determined as the product of the average number of connected industrial equipment and a first deviation coefficient; the threshold for the average running time is determined as the product of the average running time and a second deviation coefficient; and the threshold for the average physical distance is determined as the product of the average physical distance and a third deviation coefficient. The first deviation coefficient is selected within the interval [1.2, 1.4], preferably 1.2 in practice; the second deviation coefficient is selected within the interval [1.3, 1.5], preferably 1.3 in practice; and the third deviation coefficient is selected within the interval [0.9, 0.95], preferably 0.9 in practice.

[0040] Specifically, please refer to Figure 2 As shown, this is a logic decision diagram for dividing the operating status of an industrial plant area according to an embodiment of the present invention. The division of the operating status of the industrial plant area includes: If the time-compactness representation value is greater than the time-compactness representation threshold, the industrial plant area will be classified as a densely operating state. If the time-compact characterization value is less than or equal to the time-compact characterization threshold, the industrial plant area is classified as a smooth operation state.

[0041] The time-compact representation threshold is predetermined. The time-compact representation value calculated is determined when the number of connected industrial equipment in operation is equal to the industrial equipment connection number threshold, the average running time of connected industrial equipment is equal to the average running time threshold, and the average physical distance threshold is equal to the average physical distance of connected industrial equipment.

[0042] Specifically, the operation scheduling of the industrial plant area is evaluated and analyzed, including: If the industrial plant is in a state of intensive operation, the operational stability characterization parameters of the industrial equipment are calculated based on the equipment's working characteristics and continuous operation delay to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; If the industrial plant is in a stable operating state, the sampling frequency of the equipment's operating characteristics is adjusted based on the time-compact characterization value.

[0043] Specifically, this invention establishes a dual-dimensional (time and space) industrial equipment operation status assessment model. In actual industrial production, industrial equipment does not operate in isolation but is interconnected in numerous ways, such as assembly line equipment and upstream and downstream processing equipment. The physical distance between equipment directly affects the efficiency of the interconnection and load transmission; for example, equipment connected in close proximity faces a greater risk of failure propagation and resource competition. Therefore, this invention integrates both temporal correlation characteristics and spatial constraint characteristics. The number of connections reflects the equipment interconnection density, the average runtime reflects the continuous load intensity, and the average physical distance reflects the compactness of the equipment layout. This provides a more realistic and comprehensive reflection of the actual operating load of the industrial plant, accurately distinguishing the load pressure differences between multiple devices operating in close proximity for extended periods and multiple devices operating in distant proximity for short periods, avoiding misjudgments of the operating status due to one-sided assessments. Therefore, this invention comprehensively considers the temporal correlation and spatial constraints of industrial equipment operation, adapting to the "equipment interconnection and fixed layout" characteristics of industrial production. Compared to generalized status assessment methods, it better aligns with the actual operating logic of industrial scenarios, and the assessment results have higher guiding value for industrial scheduling. Furthermore, by using time-series compactness representation values ​​to characterize the linkage density, continuous load intensity, and overall compactness under spatial layout constraints of industrial equipment operation, data support is provided for subsequent division of the operational status of industrial plant areas. This, in turn, provides accurate and reliable decision-making basis for scenario-based scheduling strategies—prioritizing stable operation during intensive periods and optimizing data collection efficiency during calmer periods—ensuring a high degree of matching between scheduling actions and actual operational needs. This promotes the coordinated optimization of industrial plant operational efficiency and resource utilization, forming a closed loop of "accurate assessment - dynamic scheduling - efficient operation."

[0044] Specifically, the process of calculating the operational stability characterization parameters of the industrial equipment includes: The sum of the ratio of abnormal noise frequency to abnormal noise frequency threshold and the ratio of energy consumption fluctuation amplitude to energy consumption fluctuation amplitude threshold is taken as the first operational stability feature. The ratio of runtime duration to runtime duration threshold is used as the second runtime stability feature; The weighted sum of the first operational stability feature and the second operational stability feature is used to determine the operational stability characterization parameter.

[0045] Specifically, the core purpose of setting operational stability parameters is to quantify the real-time operational stability of industrial equipment. The core criterion for judging the stability of industrial equipment is the presence of immediate precursors to failures and operational anomalies. The composition of equipment operating characteristics can reflect the degree of anomalies in the equipment's immediate operation. The frequency of abnormal noise corresponds to immediate fault signals in the mechanical structure of industrial equipment, such as bearing wear, component loosening, and housing resonance. These are the most direct physical manifestations of hardware failures. The higher the frequency of abnormal noise, the more urgent the risk of equipment failure. The amplitude of energy consumption fluctuation corresponds to the stability of energy utilization in industrial equipment, such as power output imbalance, load mismatch, and control system deviation. This indirectly reflects the operational accuracy and load matching degree of industrial equipment. The larger the fluctuation amplitude, the more unstable the operating state of industrial equipment, which is prone to process deviations or failures. The superposition of the aforementioned two characteristics can capture whether industrial equipment is currently abnormal and to what extent, which is the core basis for judging whether the equipment is in a high-risk state. The continuous delay in operation reflects the cumulative risk of long-term fatigue operation of industrial equipment. Its core role is to supplement the assessment of potential failure risks of industrial equipment, such as gradual losses caused by component aging and coil overheating due to long-term operation, but it is not a decisive factor in the immediate stable state. Therefore, when performing weighted summation, the first operational stability feature, calculated based on the equipment's operating characteristics, namely the frequency of abnormal noise and the amplitude of energy consumption fluctuations, is given a higher weight coefficient, set to 0.6. Correspondingly, the weight coefficient of the second operational stability feature, calculated based on the duration of operation delay, is set to 0.4.

[0046] In this embodiment, the purpose of setting thresholds for abnormal noise frequency, energy consumption fluctuation amplitude, and operating duration delay is to characterize situations where industrial equipment exhibits high abnormality in its immediate operating state, significant cumulative risk under long-term fatigue operation, and low operational stability. This is achieved by acquiring historical equipment application data corresponding to several tasks within a predetermined period in the industrial plant, calling historical data on abnormal noise frequency, energy consumption fluctuation amplitude, and operating duration delay during periods of heavy operation, and calculating the average abnormal noise frequency, average energy consumption fluctuation amplitude, and average operating duration delay. These average values ​​are then used as baseline values ​​under normal conditions. The purpose of setting the above three thresholds is to determine the noise frequency threshold as the product of the average noise frequency and the noise deviation coefficient, the energy consumption fluctuation amplitude threshold as the product of the average energy consumption fluctuation amplitude and the energy consumption deviation coefficient, and the operating duration delay threshold as the product of the average operating duration delay and the delay deviation coefficient. The noise deviation coefficient is selected within the interval [1.3, 1.5], preferably 1.3 in practice; the energy consumption deviation coefficient is selected within the interval [1.2, 1.4], preferably 1.2 in practice; and the delay deviation coefficient is selected within the interval [1.25, 1.35], preferably 1.25 in practice.

[0047] Specifically, determining the operational stability level of the industrial equipment includes: The correspondence between the operational stability level and the predetermined range of operational stability characterization parameters is pre-defined; Determine the range of operational stability characterization parameters to which the corresponding operational stability characterization parameters of the industrial equipment belong; Set the operating stability level corresponding to the range of operating stability characterization parameters as the operating stability level of the industrial equipment; Among them, the operational stability level corresponds one-to-one with the range of operational stability characterization parameters.

[0048] In this embodiment, the operational stability level of the industrial equipment is determined in the following manner: The operational stability characterization parameters are divided into three preset intervals, and three levels of operational stability are set. If the operational stability characterization parameters of the industrial equipment are within the first preset range (0, S0), then it is set to a high operational stability level. If the operational stability characterization parameters corresponding to the industrial equipment are within the second preset range [S0, 1.2S0), then the level is set to medium operational stability. If the operational stability characterization parameters of the industrial equipment are within the third preset range [1.2S0, +∞), then the operation stability level is set to low.

[0049] The threshold S0 of the operation stability characterization parameter is predetermined. The operation stability characterization parameter calculated under the condition that the abnormal noise frequency is equal to the abnormal noise frequency threshold, the energy consumption fluctuation amplitude is equal to the energy consumption fluctuation amplitude threshold, and the operation duration delay is equal to the operation duration delay threshold is determined as the operation stability characterization parameter threshold.

[0050] Specifically, this invention covers two categories of indicators: immediate hardware anomalies and stable energy utilization, as well as long-term fatigue conditions, forming a comprehensive evaluation system. Noise frequency directly reflects immediate anomalies related to hardware failures such as mechanical wear and loose components in industrial equipment. Energy consumption fluctuation reflects the stability of energy utilization aspects such as power output and load adaptation, and indirectly reflects abnormalities in control accuracy or operating load. In practice, a gradual increase in noise frequency and an abnormal expansion of energy consumption fluctuation are often early signs of equipment failure; for example, initial bearing wear may only produce slight noise before operational interruptions occur. Furthermore, the cumulative duration of operational delay reflects the fatigue level of industrial equipment and can provide early warning of progressive failures caused by prolonged high loads, such as coil overheating after continuous full-load operation of a motor. The fusion of these three indicators accurately captures precursor signals of industrial equipment failures. Furthermore, operational stability parameters are calculated to characterize the degree of anomalies in the immediate operating state and the cumulative risk under long-term fatigue operation, comprehensively assessing the operational stability of industrial equipment and providing data support for determining the corresponding operational stability level and differentiated scheduling of industrial plants. This invention is adapted to the operating characteristics of industrial equipment, improving the practicality of equipment operation assessment scenarios.

[0051] Specifically, please refer to Figure 3 As shown, it is a logic decision diagram for identifying abnormal industrial equipment according to an embodiment of the present invention. Identifying abnormal industrial equipment includes: If any industrial equipment meets the abnormal operating conditions, then the industrial equipment is identified as the abnormal industrial equipment. The abnormal operating conditions include a low operating stability level and a position in the runtime sequence diagram that is less than a position threshold.

[0052] In this embodiment, the purpose of setting the ranking threshold is to screen out core industrial equipment that has a critical impact on production and a high risk of fault propagation based on a low level of operational stability. By obtaining historical equipment application data corresponding to several tasks within a certain predetermined period in the industrial plant, calling the historical ranking data of abnormal industrial equipment in the runtime sequence diagram, solving the ranking average, and determining the ranking average as the ranking threshold based on the purpose of setting the ranking threshold.

[0053] Specifically, the position of industrial equipment in the sequence diagram is essentially a quantitative representation of the importance and priority of industrial equipment in the linked process. Higher-ranking industrial equipment is typically the starting point of the production line, core processing equipment, or key link between upstream and downstream processes, such as welding robots in automobile manufacturing or reaction vessels in chemical production. This type of equipment directly determines the operating rhythm of subsequent equipment and is a critical node in the production process. Lower-ranking industrial equipment is mostly auxiliary or end-point equipment, such as material handling auxiliary equipment or finished product inspection end-point equipment. Their operating status has a relatively limited impact on the overall production process, and the risk of transmission to linked equipment after a failure is low. Therefore, this invention focuses on critical node equipment, only classifying a low-stability-level equipment as an abnormal device when it is also a core device in the linked process. This avoids the misconception of "equal intervention for all low-stability equipment" and accurately identifies the core risk source with the greatest impact on the production system.

[0054] Specifically, please refer to Figure 4 As shown, this is a logic diagram for determining whether an abnormal propagation criterion is met according to an embodiment of the present invention. The determination of whether an abnormal propagation criterion is met includes: If the abnormal interval duration of adjacent industrial equipment is less than the abnormal interval duration threshold, and the operating energy consumption deviation between the abnormal industrial equipment and the adjacent industrial equipment is greater than the operating energy consumption deviation threshold, then it is determined that the abnormal transmission benchmark is not met.

[0055] In this embodiment, the purpose of setting the abnormal interval duration threshold and the operating energy consumption deviation threshold is to characterize situations where the linkage risk correlation between abnormal industrial equipment and adjacent equipment is high. By acquiring historical equipment application data corresponding to several tasks within a predetermined period in the industrial plant, calling historical data on the operating energy consumption deviation of abnormal industrial equipment and adjacent industrial equipment, as well as historical data on the abnormal interval duration of adjacent industrial equipment, the average abnormal interval duration and the average operating energy consumption deviation are calculated and used as the benchmark values ​​under normal conditions. Based on the purpose of setting the above two thresholds, the abnormal interval duration threshold is determined as the product of the average abnormal interval duration and the interval deviation coefficient, and the operating energy consumption deviation threshold is determined as the product of the average operating energy consumption deviation and the operating deviation coefficient. The interval deviation coefficient is selected within the interval [0.9, 0.95], preferably 0.9 in practice, and the operating deviation coefficient is selected within the interval [1.3, 1.4], preferably 1.3 in practice.

[0056] Specifically, the deviation in operating energy consumption between the non-standard industrial equipment and the adjacent industrial equipment refers to the degree of difference between the real-time operating energy consumption of the non-standard industrial equipment and the real-time operating energy consumption of the adjacent industrial equipment. A larger difference indicates weaker energy consumption synchronization and lower inter-equipment linkage between the two devices. Provided the abnormal interval duration of the adjacent industrial equipment meets the judgment requirements, the non-standard industrial equipment can be safely switched without affecting its operation. Conversely, a smaller difference indicates stronger energy consumption synchronization and higher inter-equipment linkage between the two devices. Even if the abnormal interval duration of the adjacent industrial equipment meets the requirements, the non-standard industrial equipment should not be switched blindly, otherwise it may trigger a chain reaction of failures in the adjacent industrial equipment. The operational energy consumption deviation can be determined by calculating the relative deviation rate of real-time energy consumption between the anomalous industrial equipment and adjacent industrial equipment, which will not be elaborated further here.

[0057] Specifically, adjusting and switching the operation of industrial equipment includes: The operating status of similar equipment in an unloaded operating state corresponding to the heterogeneous industrial equipment is determined based on the runtime sequence diagram. If any device of the same type meets the operation scheduling benchmark, then switch the device of the same type to the running state and replace the abnormal industrial device to continue running; The operation scheduling benchmark includes the remaining runtime of the same type of idle equipment and the remaining runtime of the different type of industrial equipment. The surplus remaining runtime is greater than the surplus remaining runtime threshold, and the location distance between the same type of equipment and the different type of industrial equipment is less than the location distance threshold.

[0058] In this embodiment, the purpose of setting the remaining time threshold and the location distance threshold is to establish a dual rigid standard of "time feasibility + spatial adaptability" for equipment switching, so as to ensure that the same type of idle equipment that replaces the abnormal industrial equipment can seamlessly take over the task, without interrupting the production process and without causing new linkage risks. By acquiring historical equipment application data corresponding to several tasks within a predetermined period in an industrial plant, and calling historical data on the remaining runtime of idle equipment of the same type and the remaining runtime of industrial equipment of different types, as well as historical data on the location distance between equipment of the same type and industrial equipment of different types, the average value of the remaining runtime and the average value of the location distance are calculated and used as the benchmark value under normal circumstances. Based on the purpose of setting the above two thresholds, the remaining runtime threshold is determined as the product of the average remaining runtime and the surplus deviation coefficient, and the location distance threshold is determined as the product of the average location distance and the location deviation coefficient. The surplus deviation coefficient is selected in the interval [1.4, 1.5], preferably 1.4 in practice, and the location deviation coefficient is selected in the interval [0.85, 0.95], preferably 0.85 in practice.

[0059] Specifically, the remaining runtime of similar idle equipment versus the remaining runtime of heterogeneous industrial equipment refers to the remaining time that similar equipment in an idle state can maintain idle status and be ready to start and take over the task at any time, minus the remaining runtime required for the heterogeneous industrial equipment to complete the current task. This is the excess redundant time. This indicator is a key indicator for judging whether similar equipment can seamlessly replace heterogeneous industrial equipment. The purpose is to ensure that the replacement standby equipment has sufficient time to support the completion of the original task and avoid production interruptions due to insufficient idle time of the standby equipment itself.

[0060] Specifically, this invention combines operational stability levels and runtime sequence diagram ranking to identify abnormal industrial equipment, pinpointing high-risk equipment. For example, insufficient stability in core production line equipment directly impacts overall production, and such equipment is prioritized for identification as abnormal industrial equipment. This provides precise targeting for subsequent fault handling and avoids ineffective scheduling. A core characteristic of industrial production is the close interoperability of equipment; a problem in one part can have far-reaching consequences. For instance, in the coordinated operation of production lines and chemical reaction units, an anomaly in a single piece of industrial equipment can spread throughout the entire system through interconnected relationships. Therefore, to align with the coordinated operation characteristics of industrial equipment, this invention quantifies the risk of interconnected transmission between industrial equipment by measuring the interval between anomalies in adjacent equipment. The deviation in operating energy consumption between abnormal and adjacent industrial equipment reflects the tightness of the interconnected relationships between industrial equipment, while also reflecting the potential probability and scope of anomaly transmission. Simultaneously, by using runtime sequence diagrams to understand the interconnected relationships of industrial equipment, this invention ensures the compatibility of switched industrial equipment with the original abnormal equipment, preventing problems such as poor equipment integration and production process disruptions after switching. This invention can match the actual operational needs of industrial scenarios and strengthen the control over the overall production system.

[0061] If the industrial big data graph calculation algorithm of the present invention is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An industrial big data graph calculation algorithm, characterized in that, include: Obtain equipment application data corresponding to several tasks within a predetermined period in an industrial plant, construct a runtime sequence diagram for industrial equipment, identify heavy operating time domain segments, and extract the operating state features corresponding to the heavy operating time domain segments. By combining the aforementioned operational status characteristics with the average physical spacing between connected industrial equipment, the temporal compactness characterization value of the industrial equipment is evaluated to classify the operational status of the industrial plant area. Based on the operating status, the operating characteristics of the equipment during the peak operating time period are collected, and the operation scheduling of the industrial plant is evaluated and analyzed, including: Based on the equipment's operating characteristics and continuous operating delay, the operational stability characterization parameters of the industrial equipment are calculated to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; Alternatively, the acquisition frequency of the device's operating characteristics may be adjusted based on the time-compact characterization value; The operating status characteristics include the number of industrial devices connected in operation and the average operating time of the connected industrial devices. The equipment operating characteristics include the frequency of abnormal noise and the amplitude of energy consumption fluctuations.

2. The industrial big data graph calculation algorithm according to claim 1, characterized in that, The process of identifying heavy time-domain segments includes: If the number of running tasks in any time domain segment exceeds the threshold for the number of running tasks, and the number of industrial devices in operation exceeds the threshold for the number of industrial devices, then the time domain segment is identified as the heavy-duty time domain segment.

3. The industrial big data graph calculation algorithm according to claim 1, characterized in that, The process of evaluating the time-compact characterization values ​​of the industrial equipment includes: The sum of the ratio of the number of connected industrial devices in operation to the threshold of the number of connected industrial devices and the ratio of the average running time of connected industrial devices to the threshold of the average running time is used as the first time-series compact feature. The ratio of the average physical spacing threshold to the average physical spacing of the connected industrial equipment is used as the second temporal compactness feature; The first time-compact feature and the second time-compact feature are weighted and summed to determine the time-compact representation value.

4. The industrial big data graph calculation algorithm according to claim 3, characterized in that, The operational status of the industrial plant area is defined, including: If the time-compactness representation value is greater than the time-compactness representation threshold, the industrial plant area will be classified as a densely operating state. If the time-compact characterization value is less than or equal to the time-compact characterization threshold, the industrial plant area is classified as a smooth operation state.

5. The industrial big data graph calculation algorithm according to claim 4, characterized in that, The operation scheduling of the industrial plant area is evaluated and analyzed, including: If the industrial plant is in a state of intensive operation, the operational stability characterization parameters of the industrial equipment are calculated based on the equipment's working characteristics and continuous operation delay to determine the operational stability level of the industrial equipment. Identify abnormal industrial equipment and determine whether it meets the abnormal transmission benchmark based on the abnormal interval duration of adjacent industrial equipment corresponding to the abnormal industrial equipment and the deviation of the operating energy consumption between the abnormal industrial equipment and the adjacent industrial equipment. In response to non-compliance with the anomaly propagation benchmark, the runtime sequence diagram is invoked to adjust and switch the operation of the industrial equipment; If the industrial plant is in a stable operating state, the sampling frequency of the equipment's operating characteristics is adjusted based on the time-series compact characterization value.

6. The industrial big data graph calculation algorithm according to claim 1, characterized in that, The process of calculating the operational stability characterization parameters of the industrial equipment includes: The sum of the ratio of abnormal noise frequency to abnormal noise frequency threshold and the ratio of energy consumption fluctuation amplitude to energy consumption fluctuation amplitude threshold is taken as the first operational stability feature. The ratio of runtime duration to runtime duration threshold is used as the second runtime stability feature; The weighted sum of the first operational stability feature and the second operational stability feature is used to determine the operational stability characterization parameter.

7. The industrial big data graph calculation algorithm according to claim 6, characterized in that, Determining the operational stability level of the industrial equipment includes: The correspondence between the operational stability level and the predetermined range of operational stability characterization parameters is pre-defined; Determine the range of operational stability characterization parameters to which the corresponding operational stability characterization parameters of the industrial equipment belong; Set the operating stability level corresponding to the range of operating stability characterization parameters as the operating stability level of the industrial equipment; Among them, the operational stability level corresponds one-to-one with the range of operational stability characterization parameters.

8. The industrial big data graph calculation algorithm according to claim 1, characterized in that, Identifying abnormal industrial equipment, including: If any industrial equipment meets the abnormal operating conditions, then the industrial equipment is identified as the abnormal industrial equipment. The abnormal operating conditions include a low operating stability level and a position in the runtime sequence diagram that is less than a position threshold.

9. The industrial big data graph calculation algorithm according to claim 1, characterized in that, Determining whether the abnormal transmission criteria are met includes: If the abnormal interval duration of adjacent industrial equipment is less than the abnormal interval duration threshold, and the operating energy consumption deviation between the abnormal industrial equipment and the adjacent industrial equipment is greater than the operating energy consumption deviation threshold, then it is determined that the abnormal transmission benchmark is not met.

10. The industrial big data graph calculation algorithm according to claim 1, characterized in that, Adjusting and switching the operation of industrial equipment, including: The operating status of similar equipment in an unloaded operating state corresponding to the heterogeneous industrial equipment is determined based on the runtime sequence diagram. If any device of the same type meets the operation scheduling benchmark, then switch the device of the same type to the running state and replace the abnormal industrial device to continue running; The operation scheduling benchmark includes the remaining runtime of the same type of idle equipment and the remaining runtime of the different type of industrial equipment. The surplus remaining runtime is greater than the surplus remaining runtime threshold, and the location distance between the same type of equipment and the different type of industrial equipment is less than the location distance threshold.

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