Production business mode construction method based on industrial internet platform

Abstract

The invention provides a production business mode construction method based on an industrial internet platform, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing of the multi-source data; calculating the adaptation degree of the equipment and the task; dynamic influence parameters are extracted, and operation parameters in the equipment data are adjusted in combination with the adaptation result; constructing a weighted multi-objective function in combination with a completion time limit, a resource demand and a priority factor in the task data; generating a task priority sequence by solving the multi-objective function, and generating a task scheduling strategy according to the task priority sequence; in combination with the real-time state parameters in the equipment data, calculating the utilization efficiency of the equipment resources; performing priority ranking on the equipment according to the equipment resource use efficiency, and generating a resource allocation scheme through a segmented dynamic optimization algorithm; generating a production business mode according to the task scheduling strategy and the resource allocation scheme; according to the invention, the autonomy and accuracy of the production business mode are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for constructing a production business model based on an industrial Internet platform. Background Art

[0002] Industrial Internet platforms are widely used in modern manufacturing to integrate production equipment, industrial control systems and management systems to achieve digital and intelligent construction of production operations. In existing technologies, platforms usually provide enterprises with services such as production process optimization, resource allocation decisions and predictive maintenance by collecting equipment operation data, production parameters and environmental information in real time, combined with big data analysis technology. However, in actual applications, these technologies are usually optimized for a single production environment and are difficult to adapt to dynamically changing production scenarios, especially when multiple scenarios and multiple tasks are running simultaneously, the flexibility and adaptability of existing platforms are insufficient.

[0003] For example, in a smart workshop, production lines need to be adjusted frequently to meet the needs of rapid delivery of personalized orders. Although existing technologies can collect and analyze data for a single production line through a fixed process, when faced with multiple production lines that are dynamically adjusted, they lack the ability to dynamically adapt to data flows and control logic, which can easily lead to delays in real-time data processing or the inability to update control strategies in a timely manner. This technical defect may cause a decrease in the coordination of the dynamically adjusted production lines, and may even cause conflicts in production tasks or waste of resources, thereby limiting the flexible construction and efficient operation of the production business model. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a production business model based on an industrial Internet platform, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A method for constructing a production business model based on an industrial Internet platform, the method comprising:

[0007] Collect multi-source data, pre-process it, and generate raw data, which includes equipment data, task data, and environmental data;

[0008] According to the operating parameters in the equipment data and the required parameters in the task data, matching is performed according to the functional requirements, the degree of adaptation between the equipment and the task is calculated, and the adaptation result data is generated;

[0009] Extract dynamic impact parameters from environmental data and combine them with adaptation result data to adjust the operating parameters in the equipment data to generate dynamic adjustment parameters;

[0010] According to the adjustment value in the dynamic adjustment parameter, combined with the completion time limit, resource demand and priority factor in the task data, a weighted multi-objective function is constructed, wherein the weighted multi-objective function includes a task time weight, a resource consumption weight and a real-time dynamic environment weight;

[0011] Generate task priority ranking by solving multi-objective functions, and generate task scheduling strategy according to the task priority ranking, the task scheduling strategy task execution order and device allocation data;

[0012] Calculate the efficiency of equipment resource utilization based on the task scheduling strategy and the real-time status parameters in the equipment data;

[0013] Prioritize the devices according to their resource utilization efficiency, and generate a resource allocation plan through a segmented dynamic optimization algorithm, wherein the resource allocation plan includes the device operation time interval, operation frequency, and task allocation ratio allocated to each task;

[0014] Dynamically verify resource allocation plans based on historical production data and simulation scenarios, generate verification feedback data, and adjust task scheduling strategies and resource allocation plans based on the verification feedback data to generate production business models;

[0015] The production business model is loaded onto the industrial Internet platform, and real-time task scheduling and equipment control are performed according to its task scheduling strategy and resource allocation plan.

[0016] Preferably, the matching is performed according to the functional requirements based on the operating parameters in the device data and the requirement parameters in the task data, the degree of adaptation between the device and the task is calculated, and the adaptation result data is generated, including:

[0017] Extract key operating parameters from equipment data and generate a set of equipment operating characteristics;

[0018] Extract execution requirement parameters from task data and generate a set of task requirement features;

[0019] Calculate the degree of adaptation between the device and the task according to the device operation feature set and the task requirement feature set, eliminate the device and task combinations below the preset matching degree threshold, and generate adaptation result data, wherein the adaptation result data includes the device identifier, the task identifier and the adaptation degree value;

[0020] The calculation formula for the degree of adaptation between the device and the task is: ,in, For equipment With the task The adaptation degree value, is the characteristic parameter The weight of For equipment In the characteristic parameters The running performance value on For equipment In the characteristic parameters The execution requirement value on is the number of feature parameters.

[0021] Preferably, extracting the dynamic impact parameter from the environmental data and combining it with the adaptation result data to adjust the operating parameters in the device data to generate the dynamic adjustment parameter includes:

[0022] Extract real-time environmental status parameters and historical environmental change trend parameters from environmental data to generate an environmental feature set;

[0023] According to the operating parameters in the equipment operating characteristic set, determine the dynamic factors in the environmental characteristic set that have the greatest impact on the equipment operating parameters, and generate dynamic impact parameters;

[0024] The dynamic impact parameter is combined with the adaptation degree value in the adaptation result data, and the adjustment value of the equipment operation parameter is generated according to the dynamic weight calculation rule;

[0025] The equipment operation parameters are optimized through adjustment values ​​to generate dynamic adjustment parameters, wherein the dynamic adjustment parameters include an operation speed adjustment value, a power adjustment value and an operation accuracy adjustment value.

[0026] Preferably, the calculation formula of the weighted multi-objective function is: ,in, For the task The priority value of For the task The remaining time limit for completion, For the task The resource demand, is the maximum amount of system resources available, Dynamic environment parameters The weight of For the task In dynamic environment parameters The sensitivity of Dynamic environment parameters The real-time status value of Dynamic environment parameters The reference value of is the number of dynamic environment parameters, , and is the weight coefficient;

[0027] in, , For the task Dynamic environmental parameters The target expected value, Dynamic environment parameters The current state value of Dynamic environment parameters range of change.

[0028] Preferably, generating task priority ranking by solving a multi-objective function comprises:

[0029] Calculate the priority value of each task according to the weighted multi-objective function;

[0030] According to the priority value of the task and the preset priority threshold, the task priority is hierarchically sorted to generate task hierarchical sorting data, which includes high priority tasks, medium priority tasks and low priority tasks, and the tasks in each layer are arranged from large to small according to the value;

[0031] According to the task hierarchical sorting data and the adaptation degree value between the device and the task, the device calling order in the high priority task is readjusted to generate the task priority sorting data, wherein the task priority sorting data includes the execution order of the task and the corresponding device calling order.

[0032] Preferably, the task scheduling strategy is generated by sorting the task priorities, including:

[0033] Sort the data according to task priorities and extract the device call combination for each task;

[0034] Filter the equipment operation status parameters in the equipment call combination, remove the equipment that is currently in overload or maintenance status, and generate adaptation equipment data;

[0035] According to the adaptation degree value between the device and the task and the current availability parameter of the device, the device calling sequence in the adaptation device data is adjusted to generate the device calling priority data;

[0036] A task scheduling strategy is generated based on the task priority sorting data, the device call priority data and the resource requirements of the task on the device, wherein the task scheduling strategy includes the execution order of the tasks, the corresponding device allocation data and the device call time interval.

[0037] Preferably, the calculating of the equipment resource utilization efficiency according to the task scheduling strategy and in combination with the real-time status parameters in the equipment data includes:

[0038] Extract real-time status parameters from equipment data, including the current running time, current load value, historical usage frequency and failure rate of the equipment, and generate a set of equipment status features;

[0039] According to the device allocation data in the task scheduling strategy, the resource utilization rate of each device in the current task execution is calculated. The resource utilization rate is the ratio of the current load of the device to the available load capacity.

[0040] The resource utilization rate is calculated together with the historical usage frequency and failure rate in the equipment status feature set to generate the equipment resource utilization efficiency value;

[0041] The calculation formula of the equipment resource utilization efficiency value is: , is the equipment resource utilization efficiency value, For equipment The current resource utilization of For equipment The average frequency of use, For equipment The failure rate, For equipment The remaining resource capacity, For equipment The current task allocation complexity is, where , For equipment The total number of currently assigned tasks, For the task On the device The degree of occupancy.

[0042] Preferably, generating a resource allocation scheme according to the device resource utilization efficiency includes:

[0043] Sort all equipment resource utilization efficiency values ​​to generate equipment resource efficiency ranking data;

[0044] According to the device resource efficiency sorting data, select high resource utilization efficiency devices and assign corresponding task execution time periods to generate device operation time interval data;

[0045] Analyze the frequency of equipment operation and generate operation frequency data based on the equipment's real-time status parameters and the operating parameters of the equipment's performance requirements;

[0046] According to the task priority sorting data and the real-time status of the remaining resources of the equipment, the resource allocation ratio of the task is analyzed and the task allocation ratio data is generated;

[0047] Generate a resource allocation plan based on the equipment operation time interval data, operation frequency data and task allocation ratio data.

[0048] Preferably, the resource allocation scheme is dynamically verified based on historical production data and simulation scenarios to generate verification feedback data, including:

[0049] Extract the historical records of equipment operation time interval, operation frequency and task allocation ratio from historical production data to generate historical equipment performance data;

[0050] Generate a task execution simulation set based on the historical performance data of the device and the dynamic environment parameters in the simulation scenario, wherein the task execution simulation set includes the device operation status simulation data and the task execution status simulation data;

[0051] Through the equipment operation status simulation data, analyze whether the equipment operation time interval in the resource allocation plan meets the historical performance trend of the equipment and the requirements of dynamic environmental parameters;

[0052] Through task execution status simulation data, analyze whether the task allocation ratio matches the task requirement parameters, and evaluate whether the completion time of the current task execution meets the requirements of task priority sorting;

[0053] Based on the analysis results of the equipment operation status simulation data and the task execution status simulation data, optimization suggestions are made for the equipment operation time interval, operation frequency and task allocation ratio in the resource allocation plan, and verification feedback data is generated, wherein the verification feedback data includes equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions for equipment and tasks.

[0054] Preferably, adjusting the task scheduling strategy and resource allocation scheme according to the verification feedback data to generate a production business model includes:

[0055] Extract equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions from verification feedback data to generate feedback adjustment parameters;

[0056] According to the equipment resource utilization efficiency in the feedback adjustment parameters, the equipment allocation data in the task scheduling strategy is dynamically adjusted to generate an optimized task scheduling strategy;

[0057] According to the task completion time and resource allocation optimization suggestions in the feedback adjustment parameters, combined with the equipment operation time interval and task allocation ratio in the resource allocation plan, the equipment operation time interval and task allocation ratio are optimized in real time to generate an optimized resource allocation plan;

[0058] The optimized task scheduling strategy and the optimized resource allocation plan are combined to generate a production business model, wherein the production business model includes task execution sequence, equipment allocation data, optimized equipment operation time interval and task allocation ratio.

[0059] The above solution of the present invention includes at least the following beneficial effects:

[0060] This method collects and preprocesses multi-source data, and classifies key data in complex production environments into three categories: equipment operation parameters, task requirement parameters, and environmental status parameters, thereby making data flow organization more efficient and laying a solid foundation for subsequent data processing and task scheduling. This multi-source data fusion capability can dynamically adapt to the complex needs of multiple production lines or multi-task environments, effectively solving the problem of single data and inability to fully capture multi-dimensional information in existing technologies.

[0061] In the process of matching equipment and tasks, by calculating the degree of adaptation, the adaptability of equipment operating parameters and task requirement parameters can be scientifically evaluated. Based on the degree of adaptation, inefficient combinations are eliminated to generate highly adaptable equipment task combinations, thereby improving resource utilization and task execution efficiency. In addition, by extracting dynamic influencing parameters from environmental data and combining them with the adaptation result data, the equipment operating parameters are adjusted to optimize the equipment's operating performance in a real-time environment. This dynamic adaptation capability can significantly solve the problem of the lack of flexible response capabilities in the existing technology when facing dynamic adjustments to production scenarios, especially when multiple production lines are adjusted quickly, ensuring the stability of equipment operation and the efficiency of task allocation.

[0062] This method constructs a weighted multi-objective function, comprehensively considers multiple factors such as task completion time limit, resource requirements and dynamic environment weight, sorts the task priorities, and generates a task scheduling strategy based on the sorting. This scheduling method based on multi-dimensional optimization can effectively balance the resource allocation requirements in a multi-task environment and avoid resource waste and task conflict problems. In addition, the task scheduling strategy can respond to the dynamic changes of the production environment in real time, provide an efficient solution for multi-task parallel processing, and greatly improve the operating efficiency of the production business model.

[0063] In the resource allocation process, by dynamically adjusting the equipment operation time interval, operation frequency and task allocation ratio, the generated resource allocation plan can accurately allocate equipment resources for each task. Combined with the calculation and sorting of equipment resource utilization efficiency, this method gives priority to high-efficiency equipment and optimizes the equipment operation status to ensure the smooth completion of the task. This precise optimization of resource allocation can effectively cope with the complex needs of frequent adjustments in dynamic production scenarios, and avoid the decline in coordination and waste of resources caused by fixed allocation plans in the prior art.

[0064] In the process of generating production business models, by combining historical production data and simulation scenarios, the resource allocation plan is dynamically verified, potential coordination problems can be discovered and optimization suggestions can be generated. Finally, the task scheduling strategy and resource allocation plan are dynamically adjusted according to the verification feedback to build an efficient production business model that adapts to production needs in real time. This closed-loop design of verification and optimization ensures that the system can be continuously optimized in actual operation, making up for the lack of real-time performance and difficulty in adjustment caused by fixed control logic in existing technologies.

[0065] Through dynamic adaptation, task scheduling and resource optimization of the entire process, this method demonstrates excellent flexibility and adaptability in a dynamic production environment where multiple scenarios and multiple tasks are running simultaneously. For example, in a smart workshop, this method can adjust the production line in real time to meet the rapid delivery needs of personalized orders, avoiding the problems of real-time data processing delays or policy failures caused by the lack of dynamic adaptability to data flow and control logic in existing technologies. In contrast, this method can effectively coordinate tasks on multiple production lines, improve production coordination and resource utilization efficiency, avoid resource waste, and achieve efficient production task execution.

[0066] This method significantly improves the flexibility and adaptability of the platform through data collection, dynamic adaptation, intelligent scheduling and resource optimization in the construction of the production business model of the industrial Internet platform. Especially in the dynamic production scenario, through full-process optimization, the pain point problem of the inability to respond dynamically in the existing technology is solved, making the adjustment of the production line more efficient, the task scheduling more scientific, and the resource utilization more accurate, which comprehensively improves the intelligence level and production efficiency of the industrial Internet platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flowchart of a method for constructing a production business model based on an industrial Internet platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0069] like Figure 1 As shown, an embodiment of the present invention proposes a method for constructing a production business model based on an industrial Internet platform, the method comprising:

[0070] S100, collecting multi-source data, pre-processing it, and generating raw data, where the raw data includes equipment data, task data, and environment data;

[0071] S200, according to the operating parameters in the device data and the requirement parameters in the task data, matching is performed according to the functional requirements, the degree of adaptation between the device and the task is calculated, and adaptation result data is generated;

[0072] S300, extracting dynamic impact parameters from environmental data and combining them with adaptation result data, adjusting operating parameters in device data, and generating dynamic adjustment parameters;

[0073] S400, constructing a weighted multi-objective function according to the adjustment value in the dynamic adjustment parameter, combined with the completion time limit, resource demand and priority factor in the task data, wherein the weighted multi-objective function includes a task time weight, a resource consumption weight and a real-time dynamic environment weight;

[0074] S500, generating task priority ranking by solving a multi-objective function, and generating a task scheduling strategy according to the task priority ranking, the task scheduling strategy task execution order and device allocation data;

[0075] S600, calculating the utilization efficiency of device resources according to the task scheduling strategy and in combination with the real-time status parameters in the device data;

[0076] S700, prioritizing devices according to their resource utilization efficiency, and generating a resource allocation plan through a segmented dynamic optimization algorithm, wherein the resource allocation plan includes a device operation time interval, an operation frequency, and a task allocation ratio allocated to each task;

[0077] S800, dynamically verify the resource allocation plan based on historical production data and simulation scenarios, generate verification feedback data, and adjust the task scheduling strategy and resource allocation plan based on the verification feedback data to generate a production business model;

[0078] S900, loads the production business model to the industrial Internet platform, and performs real-time task scheduling and equipment control according to its task scheduling strategy and resource allocation plan.

[0079] In an embodiment of the present invention, by collecting and preprocessing multi-source data, classifying device data, task data and environmental data into independent data sets, the efficiency of data organization and management can be greatly improved, and a clear input basis is provided for subsequent analysis and processing. The operating parameters of the device data can intuitively reflect the current working status of the device, including device operating time, power and load, etc., which play a vital role in task allocation. The demand parameters of the task data provide specific requirements during task execution, such as resource consumption, time constraints and task priorities. Environmental data can capture dynamic environmental change information during device operation, including key influencing factors such as temperature, humidity and power fluctuations.

[0080] After classifying these data, by matching the equipment operating parameters and task requirement parameters, and using the calculation method of the degree of adaptation, the equipment and tasks are accurately matched one-to-one or many-to-one. The technical effect of this process is that it can effectively eliminate equipment and task combinations that do not meet the requirements, thereby ensuring the rationality of resource allocation. Combined with the dynamic influencing parameters in the environmental data, the equipment operating parameters are adjusted in real time to further improve the reliability and stability of equipment operation. Through the weighted multi-objective function, multi-dimensional factors such as task completion time limit, resource consumption and real-time dynamic environment are comprehensively analyzed to generate task priority ranking, making task scheduling flexible and accurate.

[0081] In the process of generating resource allocation plans, by combining parameters such as equipment operation time interval, operation frequency and task allocation ratio, the maximum utilization value of equipment resources can be fully utilized. Finally, by verifying historical data and simulating dynamic scenarios for resource allocation plans, the applicability and robustness of the plan are further improved, providing a reliable foundation for real-time task scheduling on the industrial Internet platform. The entire process achieves efficient coordination of equipment resources and task execution through multi-level and multi-stage data analysis and optimization.

[0082] More specifically, the multi-source data is collected and pre-processed to generate raw data, which includes equipment data, task data and environment data, including:

[0083] Equipment data is collected in real time by connecting sensors, controllers and industrial IoT gateways on production equipment. These equipment data include equipment operating status (such as power, speed, temperature, etc.), equipment load conditions, and equipment operation history records. The collection process can use existing industrial communication protocols such as Modbus, OPC-UA or MQTT to achieve standardized transmission of equipment data.

[0084] Secondly, task data usually comes from the production management system (MES) or enterprise resource planning system (ERP), including the completion time limit of the production task, the resources required for the task, the task priority, and the upstream and downstream process information related to the task. These data are obtained through the interface interaction with the industrial Internet platform and updated in real time.

[0085] Environmental data is the real-time monitoring result of the production site environment, including temperature, humidity, vibration, noise, power fluctuation and other information. These data can be collected by deploying environmental monitoring equipment (such as temperature and humidity sensors, noise sensors, etc.). Environmental data collection is usually uploaded to the industrial Internet platform through wireless communication technology (such as Zigbee, LoRa or Wi-Fi).

[0086] The preprocessing stage is to unify the format, clean the noise, detect outliers and classify the collected data. For example:

[0087] Format unification: Convert data from different sources into a standardized data format that the platform can process, such as JSON or table format.

[0088] Noise cleaning: Use filtering algorithms to remove invalid data caused by equipment failure or signal interference during the acquisition process.

[0089] Outlier detection: Use statistical threshold methods (such as upper and lower limit detection) or machine learning-based anomaly detection models (such as the isolation forest algorithm) to identify and exclude outliers.

[0090] Classification and integration: The pre-processed data is classified into equipment data, task data, and environmental data, and stored in the platform database for subsequent calls.

[0091] This data collection and preprocessing process can ensure that the data obtained by the platform is accurate, standardized and available, providing basic support for subsequent task scheduling and resource optimization.

[0092] In a preferred embodiment of the present invention, the matching is performed according to the functional requirements based on the operating parameters in the device data and the requirement parameters in the task data, the degree of adaptation between the device and the task is calculated, and the adaptation result data is generated, including:

[0093] Extract key operating parameters from equipment data and generate a set of equipment operating characteristics;

[0094] Extract execution requirement parameters from task data and generate a set of task requirement features;

[0095] Calculate the degree of adaptation between the device and the task according to the device operation feature set and the task requirement feature set, eliminate the device and task combinations below the preset matching degree threshold, and generate adaptation result data, wherein the adaptation result data includes the device identifier, the task identifier and the adaptation degree value;

[0096] The calculation formula for the degree of adaptation between the device and the task is: ,in, For equipment With the task The adaptation degree value, is the characteristic parameter The weight of For equipment In the characteristic parameters The running performance value on For equipment In the characteristic parameters The execution requirement value on is the number of feature parameters.

[0097] In an embodiment of the present invention, by extracting key operating parameters from device data to generate a device operating feature set, the performance and operating capability of the device can be quantified to form a structured feature description. These operating parameters include information such as the device's operating power, load status, response time, and operating frequency, which fully reflect the current performance status of the device. By extracting execution requirement parameters from task data to generate a task requirement feature set, the performance requirements of the task can be clearly expressed, such as the execution time limit, the type of resources required, and the minimum requirements for device performance. These task requirement parameters provide a clear basis for device selection.

[0098] The equipment operation feature set is mapped to the task requirement feature set, and the matching degree between the equipment and the task is scientifically evaluated through the calculation method of the degree of adaptation. In the calculation formula of the degree of adaptation, the weight of the feature parameters is dynamically adjusted according to the importance of the task, so that the matching process can more accurately meet the task requirements. By eliminating the equipment and task combinations below the preset fitness threshold, highly adaptable adaptation result data is generated, which can not only effectively improve the utilization efficiency of equipment resources, but also avoid the problem of equipment overload or resource waste. Finally, the generated adaptation result data clearly identifies the equipment and tasks of efficient combination, providing a reliable basis for subsequent task scheduling.

[0099] In a preferred embodiment of the present invention, the extracting of dynamic influencing parameters from environmental data and combining with adaptation result data to adjust the operating parameters in the equipment data to generate dynamic adjustment parameters includes:

[0100] Extract real-time environmental status parameters and historical environmental change trend parameters from environmental data to generate an environmental feature set;

[0101] According to the operating parameters in the equipment operating characteristic set, determine the dynamic factors in the environmental characteristic set that have the greatest impact on the equipment operating parameters, and generate dynamic impact parameters;

[0102] The dynamic impact parameter is combined with the adaptation degree value in the adaptation result data, and the adjustment value of the equipment operation parameter is generated according to the dynamic weight calculation rule;

[0103] The equipment operation parameters are optimized through adjustment values ​​to generate dynamic adjustment parameters, wherein the dynamic adjustment parameters include an operation speed adjustment value, a power adjustment value and an operation accuracy adjustment value.

[0104] In an embodiment of the present invention, by extracting real-time environmental status parameters from environmental data, such as temperature, humidity, power fluctuations and other dynamic environmental information, an environmental feature set can be generated for real-time analysis of the impact of the environment on the operating performance of the device. Combined with key parameters in the device operating feature set, such as device load capacity, operating frequency and energy consumption, it is possible to further identify the dynamic factors in the environmental features that have the greatest impact on the operation of the device. For example, in some scenarios, temperature changes may cause device power fluctuations, while humidity may affect the precision operation of the device.

[0105] By combining dynamic impact parameters with equipment operation characteristic data, adjustment values ​​can be generated according to dynamic weight calculation rules to optimize equipment operation parameters. The adjusted equipment operation parameters include optimized values ​​for operating speed, power, and operating accuracy, ensuring that the equipment can maintain efficient operation under dynamic environmental conditions. The resulting dynamic adjustment parameters not only improve the equipment's ability to adapt to environmental changes, but also effectively ensure the accuracy and stability of task execution.

[0106] More specifically, determining the dynamic factors in the environmental feature set that have the greatest impact on the device operating parameters based on the operating parameters in the device operating feature set and generating the dynamic impact parameters includes:

[0107] Extract equipment operating parameters: Extract key operating parameters from the equipment operating feature set, such as the equipment's current operating temperature, power, speed, and load. These parameters can directly reflect the equipment's operating status.

[0108] Analyze the relationship between equipment operating parameters and environmental parameters: Perform correlation analysis between equipment operating parameters and environmental parameters in the environmental feature set (such as temperature and humidity, vibration, power fluctuations, etc.). Existing technologies can use correlation analysis methods (such as Pearson correlation coefficient) or causal analysis tools (such as Granger causality analysis) to identify the degree of influence of environmental parameters on equipment operating parameters. For example, if it is found that the correlation between equipment temperature and ambient temperature and humidity is high, it can be considered that temperature and humidity are the main dynamic factors affecting equipment temperature.

[0109] Screening dynamic influencing factors: According to the analysis results, select dynamic environmental factors that have a significant impact on the equipment operating parameters. For example, when multiple environmental parameters affect the equipment speed at the same time, you can sort them according to the degree of influence and select 1-3 factors with the greatest influence as dynamic influencing parameters.

[0110] Through the above method, the generated dynamic impact parameters can accurately reflect the dynamic impact of the production environment on the equipment operation, and provide data support for the subsequent optimization of equipment operation parameters.

[0111] More specifically, the step of combining the dynamic impact parameter with the adaptation degree value in the adaptation result data and generating the adjustment value of the device operation parameter according to the dynamic weight calculation rule includes:

[0112] Introduction of dynamic influencing parameters: The generated dynamic influencing parameters (such as temperature and humidity, voltage fluctuation, etc.) are associated with the equipment operation parameters to analyze the degree of interference of the dynamic environment on the equipment operation status. For example, when the ambient temperature and humidity increase, the equipment temperature may rise abnormally, thus affecting the normal operation of the equipment.

[0113] Optimize equipment operating parameters in combination with the adaptation degree value: The adaptation degree value in the adaptation result data reflects the matching between the equipment and the task requirements. When adjusting the equipment operating parameters, it is necessary to comprehensively consider the dynamic influencing parameters and the adaptation degree value. For example:

[0114] If the device has a high degree of adaptability, when adjusting the device operating parameters, it is preferred to maintain its matching state to avoid adjustments that may cause a decrease in adaptability.

[0115] If the device adaptability is low, the operating parameters are adjusted through dynamic weight calculation rules to improve its adaptability to task requirements.

[0116] Adjust equipment operating parameters based on dynamic weights: Dynamic weight calculation rules can assign weights based on the priority of equipment operating parameters and the degree of influence of dynamic influencing parameters. For example, when the speed of equipment is greatly affected by environmental vibration, a higher weight is assigned to adjust the speed, while a lower weight is assigned to the power parameter that is less affected. The adjustment value can be generated by linear interpolation, historical data regression, or rule-based control algorithm calculation.

[0117] Through the above method, the adjusted equipment operating parameters can maintain stable and efficient operation in a complex dynamic environment while ensuring the adaptability of task execution.

[0118] In a preferred embodiment of the present invention, the calculation formula of the weighted multi-objective function is: ,in, For the task The priority value of For the task The remaining time limit for completion, For the task The resource demand, is the maximum amount of system resources available, Dynamic environment parameters The weight of For the task In dynamic environment parameters The sensitivity of Dynamic environment parameters The real-time status value of Dynamic environment parameters The reference value of is the number of dynamic environment parameters, , and is the weight coefficient;

[0119] in, , For the task Dynamic environmental parameters The target expected value, Dynamic environment parameters The current state value of Dynamic environment parameters range of change.

[0120] In an embodiment of the present invention, when constructing a weighted multi-objective function, by assigning different weights to task completion deadlines, resource requirements, and real-time dynamic environmental parameters, a comprehensive evaluation of multi-dimensional priorities can be achieved. For example, the time weight gives priority to the urgency of the task, the resource consumption weight focuses on the load balancing of the equipment, and the dynamic environment weight is used to deal with the potential impact of environmental changes on task execution. Through the comprehensive sorting of task priorities, it can be ensured that in a scenario where multiple tasks are executed simultaneously, urgent tasks are given priority in resource allocation, while ordinary tasks use the remaining resources, further improving resource utilization efficiency.

[0121] The solution of the weighted multi-objective function can dynamically adapt to changes in equipment status and task requirements in complex production scenarios. Through the calculation of this function, the priority value of each task is generated, and the sorted results provide a scientific basis for the generation of task scheduling strategies. Compared with the traditional task priority algorithm, this method can better balance the efficiency of task execution and the rationality of resource allocation, ensuring the maximization of resource utilization and task completion rate.

[0122] In a preferred embodiment of the present invention, generating task priority ranking by solving a multi-objective function includes:

[0123] Calculate the priority value of each task according to the weighted multi-objective function;

[0124] According to the priority value of the task and the preset priority threshold, the task priority is hierarchically sorted to generate task hierarchical sorting data, which includes high priority tasks, medium priority tasks and low priority tasks, and the tasks in each layer are arranged from large to small according to the value;

[0125] According to the task hierarchical sorting data and the adaptation degree value between the device and the task, the device calling order in the high priority task is readjusted to generate the task priority sorting data, wherein the task priority sorting data includes the execution order of the task and the corresponding device calling order.

[0126] In the embodiment of the present invention, based on the task priority values ​​generated by the weighted multi-objective function, the tasks are divided into three categories: high priority, medium priority and low priority, and the tasks are hierarchically sorted according to the priority values. By hierarchically sorting the data, the execution order of the tasks can be clearly distinguished, so that the execution of the tasks meets the priority requirements of the overall production plan. For example, high-priority tasks can directly call high-efficiency equipment, while medium and low-priority tasks can reasonably arrange execution time according to resource allocation strategies.

[0127] By combining the compatibility values ​​of devices and tasks, the device call order of high-priority tasks is dynamically optimized to ensure that critical tasks are given priority support from devices with the highest compatibility. At the same time, by dynamically adjusting the device call order, resource conflicts between tasks can be reduced and the overall task execution efficiency can be improved. The introduction of hierarchical sorting data and the optimization of the device call order make task scheduling more accurate and resource allocation more reasonable, effectively improving the overall production efficiency of the system.

[0128] More specifically, the task hierarchical sorting data is combined with the adaptation degree value of the device and the task to readjust the device calling order in the high priority task to generate task priority sorting data, wherein the task priority sorting data includes the execution order of the task and the corresponding device calling order, including:

[0129] Extract high-priority task data: Extract high-priority tasks from the task hierarchical ranking data. These tasks are usually calculated by weighted multi-objective functions based on factors such as task completion time limit and resource requirements.

[0130] Analyze the adaptability value and device resource status: Combine the device and task adaptability values ​​of high-priority tasks to evaluate whether the current device can meet the task requirements. For example, when a task has high requirements for the operating accuracy of the device, give priority to devices with higher adaptability values. If the adaptability of the current device decreases due to changes in the operating status (such as increased load, failure), adjust the device selection in time.

[0131] Optimize the order of device calls: Based on the device selection, readjust the order of device calls according to the current operating status of the device (such as load, available time period, etc.). Give priority to the device with the best operating status and the highest degree of adaptability, and ensure the balance of device allocation to avoid device overload or idle resources.

[0132] Generate new task priority data: After adjusting the device call sequence, the optimized task priority data is generated based on the task execution requirements and device resource status. This data includes the task execution sequence and device call plan, providing precise guidance for subsequent task scheduling.

[0133] Through the above operations, this step can significantly improve the execution efficiency of high-priority tasks, while ensuring the rational use of device resources and avoiding resource conflicts or delays during task execution.

[0134] In a preferred embodiment of the present invention, a task scheduling strategy is generated by sorting the task priorities, including:

[0135] Sort the data according to task priorities and extract the device call combination for each task;

[0136] Filter the equipment operation status parameters in the equipment call combination, remove the equipment that is currently in overload or maintenance status, and generate adaptation equipment data;

[0137] According to the adaptation degree value between the device and the task and the current availability parameter of the device, the device calling sequence in the adaptation device data is adjusted to generate the device calling priority data;

[0138] A task scheduling strategy is generated based on the task priority sorting data, the device call priority data and the resource requirements of the task on the device, wherein the task scheduling strategy includes the execution order of the tasks, the corresponding device allocation data and the device call time interval.

[0139] In an embodiment of the present invention, by extracting the device call combination of each task from the task priority sorting data and filtering out the devices that are currently overloaded or in maintenance status, the stability and reliability of the device call can be ensured. On this basis, the call order of the adapted devices is dynamically adjusted in combination with the adaptation degree value of the device and the task and the current availability parameters of the device to generate the device call priority data. The dynamic adjustment process takes into account the real-time changes in the operating status of the device and the importance of the task requirements, and can balance the resource allocation of the device in the case of multi-tasking execution.

[0140] The resulting task scheduling strategy not only includes the task execution order and equipment allocation data, but also covers the specific time interval of the equipment, providing clear operational guidance for task execution. Through this process, the production system can dynamically adjust task allocation based on real-time data, improve the flexibility and execution efficiency of task scheduling, especially in scenarios where complex tasks are executed in parallel.

[0141] More specifically, the device calling sequence in the adapted device data is adjusted according to the adaptation degree value between the device and the task and the current availability parameter of the device to generate the device calling priority data, including:

[0142] Obtain the compatibility value of the device and the task from the adapted device data. The compatibility value reflects the matching of the device's capabilities under specific task requirements, such as the degree of consistency between the power range and operating accuracy and the task requirements. Devices with higher compatibility values ​​are selected as the preferred resources for task execution.

[0143] Availability parameters include the current operating status of the equipment, load conditions, remaining available time, and maintenance plan. For example, if a device is currently performing other tasks or is approaching a maintenance cycle, its availability is low. By monitoring the status of the equipment in real time (such as real-time load data collected through the Industrial Internet of Things gateway), select devices with high current availability.

[0144] Based on the adaptability value and availability parameters, the calling order of the devices is rearranged. During the adjustment process, high adaptability and high availability devices are assigned to priority tasks, while low adaptability or low availability devices are delayed or reserved. The adjustment of the calling order can be combined with the urgency of the task and the execution time window to ensure that the task requirements are efficiently met.

[0145] The adjusted device call sequence is recorded as device call priority data, which clarifies the best device allocation plan for each task under current resource conditions. This data not only ensures the accuracy of task scheduling, but also dynamically responds to real-time changes in device status.

[0146] Through this process, the method can optimize the efficiency of device allocation and reduce resource conflicts in task execution, especially in the case of resource constraints or multi-task parallelization, thereby improving the reliability and efficiency of overall task execution.

[0147] In a preferred embodiment of the present invention, the calculation of the equipment resource utilization efficiency according to the task scheduling strategy and in combination with the real-time status parameters in the equipment data includes:

[0148] Extract real-time status parameters from equipment data, including the current running time, current load value, historical usage frequency and failure rate of the equipment, and generate a set of equipment status features;

[0149] According to the device allocation data in the task scheduling strategy, the resource utilization rate of each device in the current task execution is calculated. The resource utilization rate is the ratio of the current load of the device to the available load capacity.

[0150] The resource utilization rate is calculated together with the historical usage frequency and failure rate in the equipment status feature set to generate the equipment resource utilization efficiency value;

[0151] The calculation formula of the equipment resource utilization efficiency value is: , is the equipment resource utilization efficiency value, For equipment The current resource utilization of For equipment The average frequency of use, For equipment The failure rate, For equipment The remaining resource capacity, For equipment The current task allocation complexity is, where , For equipment The total number of currently assigned tasks, For the task On the device The degree of occupancy.

[0152] In the embodiment of the present invention, when calculating the efficiency of device resource utilization, a device state feature set is generated by extracting the real-time state parameters of the device, including the current operating time, the current load value, the historical usage frequency, and the failure rate. The current operating time and load value of the device reflect the working intensity of the device in the current task allocation, while the historical usage frequency can reflect the average performance status of the device during long-term operation. The failure rate further provides an important reference for the reliability analysis of the device.

[0153] By combining the equipment allocation data in the task scheduling strategy, the equipment resource utilization is calculated. The resource utilization is the ratio of the current load of the equipment to the maximum load capacity of the equipment. The utilization calculation can not only reflect the current resource consumption of the equipment, but also provide a basis for further evaluating whether the equipment has the ability to continue to perform tasks. The resource utilization rate is analyzed together with the historical usage frequency and failure rate in the equipment status feature set to generate the equipment resource utilization efficiency value. The equipment resource utilization efficiency value can comprehensively reflect the working capacity of the equipment by comprehensively considering the current working status and historical operating performance, providing a scientific basis for subsequent resource allocation optimization.

[0154] In addition, this calculation process provides a clear standard for prioritizing multiple devices, which helps to achieve optimal allocation of device resources in complex task allocation scenarios, thereby effectively improving task execution efficiency and equipment utilization.

[0155] In a preferred embodiment of the present invention, generating a resource allocation scheme according to the device resource utilization efficiency includes:

[0156] Sort all equipment resource utilization efficiency values ​​to generate equipment resource efficiency ranking data;

[0157] According to the device resource efficiency sorting data, select high resource utilization efficiency devices and assign corresponding task execution time periods to generate device operation time interval data;

[0158] Analyze the frequency of equipment operation and generate operation frequency data based on the equipment's real-time status parameters and the operating parameters of the equipment's performance requirements;

[0159] According to the task priority sorting data and the real-time status of the remaining resources of the equipment, the resource allocation ratio of the task is analyzed and the task allocation ratio data is generated;

[0160] Generate a resource allocation plan based on the equipment operation time interval data, operation frequency data and task allocation ratio data.

[0161] In the embodiment of the present invention, by sorting all the device resource utilization efficiency values, device resource efficiency sorting data can be generated, thereby clarifying the priority order of the devices in the current task allocation. Under the guidance of the sorting data, high resource utilization efficiency devices are preferentially selected, and the corresponding task execution time period is allocated, thereby generating device operation time interval data. This process can ensure the rationality of resource allocation and improve the accuracy of task execution.

[0162] Combining the real-time status parameters of the equipment, such as the current load, operating power, and remaining resource capacity of the equipment, with the operating parameters of the task's performance requirements for the equipment, such as the task's requirements for the equipment's operating frequency, the equipment's operating frequency can be further analyzed and optimized. The optimization process of operating frequency combines the priority of task requirements with the current availability of the equipment to ensure the stability and efficiency of the equipment in task execution.

[0163] At the same time, by combining the task priority sorting data and the real-time status of the remaining resources of the equipment, the resource allocation ratio of the task is analyzed and the task allocation ratio data is generated. This step makes resource utilization more flexible and efficient by dynamically optimizing the resource allocation ratio. Finally, a resource allocation scheme is generated through the comprehensive design of the equipment operation time interval, operation frequency and task allocation ratio. This resource allocation scheme can minimize resource conflicts when multiple tasks are executed in parallel, while improving equipment utilization efficiency and task completion rate.

[0164] More specifically, the sorting data according to the device resource efficiency, selecting the device with high resource utilization efficiency and allocating the corresponding task execution time period, and generating the device operation time interval data include:

[0165] Get the device priority ranking from the previously calculated device resource efficiency data. Devices with high resource efficiency usually have lower load, lower failure rate and higher historical usage frequency. The ranking data can provide a clear basis for the task allocation of devices.

[0166] According to the time window requirements of the task and the idle time interval of the device, a suitable device operation time period is allocated to the task. For example, if a task is required to be completed within a specific time period, the device that is available and has the highest resource efficiency during that time period is given priority.

[0167] The allocated time interval of each device is clearly recorded to form the device operation time interval data. This data is used to guide task execution and ensure a high degree of match between task time requirements and device resource utilization.

[0168] More specifically, the analysis of the frequency of equipment operation based on the real-time status parameters of the equipment and the operating parameters of the task performance requirements of the equipment to generate the operation frequency data includes:

[0169] Real-time status parameters include the current load, operating power, response speed, etc. of the equipment, which can dynamically reflect the operating capacity of the equipment. For example, through the real-time collected load data, it is possible to evaluate whether the equipment is capable of meeting the operating requirements of the task.

[0170] Extract specific requirements for the equipment's operating frequency from the task demand data, such as the minimum requirements for the equipment's processing speed or working cycle. Combined with the equipment's current status, determine whether the operating frequency needs to be adjusted.

[0171] By analyzing the gap between task requirements and the real-time status of the equipment, the operating frequency of the equipment can be optimized. For example, when the load is light, the operating frequency can be increased to speed up the task completion, while when the equipment is close to the maximum load, the frequency can be reduced to ensure the stability of the equipment operation. The generated operating frequency data can dynamically guide the actual operation of the equipment.

[0172] More specifically, analyzing the resource allocation ratio of the task according to the task priority ranking data and the real-time status of the remaining resources of the device to generate the task allocation ratio data includes:

[0173] The task priority sorting data clarifies the execution order of each task, while the device remaining resource status provides real-time availability information of the device. For example, the remaining resource status can include the current available power, remaining computing power, and idle time periods.

[0174] According to the demand for equipment resources of high-priority tasks, a larger proportion of resources is allocated to ensure that key tasks are completed first. Low-priority tasks use the remaining resources and their allocation ratio is dynamically adjusted according to the actual resource utilization.

[0175] According to the actual demand of the task for equipment resources, the task allocation ratio data is formed. For example, 80% of the equipment resources are allocated to high-priority tasks, while only 20% are allocated to low-priority tasks. These ratio data dynamically guide the reasonable allocation of resources and avoid the problem of resource waste or insufficient allocation.

[0176] In a preferred embodiment of the present invention, the resource allocation scheme is dynamically verified based on historical production data and simulation scenarios to generate verification feedback data, including:

[0177] Extract the historical records of equipment operation time interval, operation frequency and task allocation ratio from historical production data to generate historical equipment performance data;

[0178] Generate a task execution simulation set based on the historical performance data of the device and the dynamic environment parameters in the simulation scenario, wherein the task execution simulation set includes the device operation status simulation data and the task execution status simulation data;

[0179] Through the equipment operation status simulation data, analyze whether the equipment operation time interval in the resource allocation plan meets the historical performance trend of the equipment and the requirements of dynamic environmental parameters;

[0180] Through task execution status simulation data, analyze whether the task allocation ratio matches the task requirement parameters, and evaluate whether the completion time of the current task execution meets the requirements of task priority sorting;

[0181] Based on the analysis results of the equipment operation status simulation data and the task execution status simulation data, optimization suggestions are made for the equipment operation time interval, operation frequency and task allocation ratio in the resource allocation plan, and verification feedback data is generated, wherein the verification feedback data includes equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions for equipment and tasks.

[0182] In an embodiment of the present invention, when dynamically verifying the resource allocation scheme, the historical performance data of the equipment is generated by extracting the historical records of the equipment operation time interval, operation frequency and task allocation ratio in the historical production data. These historical data can reflect the performance of the equipment in the previous task execution and provide a reference basis for subsequent verification. Combined with the dynamic environmental parameters in the simulation scenario, a task execution simulation set is generated. The task execution simulation set includes equipment operation status simulation data and task execution status simulation data, which can accurately restore the operating status and task execution process in actual production.

[0183] During the verification process, the equipment operation status simulation data can be used to analyze whether the equipment operation time interval in the resource allocation plan meets the historical performance trend of the equipment and the requirements of dynamic environmental parameters. For example, when the simulation finds that the equipment operation time is too long and exceeds the historical data threshold, it can prompt the need to optimize the equipment time allocation. Through the task execution status simulation data, it is possible to further analyze whether the task allocation ratio matches the task requirement parameters and evaluate whether the completion time of the current task execution meets the requirements of task priority sorting.

[0184] Finally, by analyzing the results of the equipment operation status simulation data and the task execution status simulation data, optimization suggestions are put forward for the equipment operation time interval, operation frequency and task allocation ratio in the resource allocation plan. These optimization suggestions are summarized as verification feedback data, which clarifies the equipment resource utilization efficiency, task completion time and adjustment direction for resource allocation, providing a basis for the subsequent optimization of task scheduling strategies.

[0185] More specifically, the simulation method can be selected in the following ways:

[0186] Industrial simulation technology: Use industrial simulation software (such as MATLAB / Simulink, Arena, or AnyLogic) to simulate the operating status of equipment and tasks, which is particularly suitable for performance analysis in complex dynamic environments.

[0187] Big data analysis and prediction: Use big data platforms (such as Apache Spark and Hadoop) to analyze historical equipment performance data, and combine machine learning algorithms (such as LSTM and random forest) to predict equipment operating status.

[0188] Environmental simulation and scenario testing: Use environmental simulation tools (such as Fluent and COMSOL Multiphysics) to simulate the impact of external environmental changes such as temperature, humidity, and voltage fluctuations on the device.

[0189] Task execution simulation: Use task scheduling and resource allocation tools (such as Plant Simulation, FlexSim) to verify the resource adaptability of task execution.

[0190] More specifically, the task execution simulation set is generated based on the historical performance data of the device and the dynamic environment parameters in the simulation scenario. The task execution simulation set includes the device operation status simulation data and the task execution status simulation data, including:

[0191] The equipment's operating time, power load, fault records and other performance data are extracted from the historical database and integrated to form the equipment's historical performance data. This data can reflect the state change rules of the equipment in long-term operation.

[0192] The dynamic environmental parameters in the simulation scenario include temperature and humidity fluctuations, power load changes, etc. By setting different simulation parameters, the applicability of the resource allocation scheme under various environmental conditions can be verified.

[0193] Combine the historical performance data of the equipment with the dynamic environment parameters to build a task execution simulation collection, which includes equipment operation status simulation data (equipment expected load, running time, etc.) and task execution status simulation data (task expected completion time, task resource usage, etc.).

[0194] More specifically, the device operation status simulation data is used to analyze whether the device operation time interval in the resource allocation plan meets the historical performance trend of the device and the requirements of the dynamic environmental parameters, including:

[0195] By simulating the operation status of the equipment, we can verify whether the equipment operation time interval in the resource allocation plan is reasonable. For example, if a certain equipment has shown an overload tendency in the historical records, the simulation can predict whether it will have the same problem in the current allocation time interval.

[0196] Analyze the adaptability of the equipment operation time interval in combination with dynamic environmental parameters (such as voltage fluctuations). For example, in the case of unstable power, extending the equipment operation time may cause operation failure, thus prompting the need to reallocate the time interval.

[0197] More specifically, through the task execution status simulation data, analyze whether the task allocation ratio matches the task requirement parameters, and evaluate whether the completion time of the current task execution meets the requirements of task priority sorting, including:

[0198] Simulate the task execution status to verify whether the ratio of device resources allocated to the task meets the task requirements. For example, if a high-priority task does not receive sufficient resource support, it will not be completed on time, prompting you to optimize the allocation ratio.

[0199] Based on the simulation data, evaluate whether the expected completion time of the task meets the requirements of task priority sorting. If high-priority tasks are expected to be delayed, adjust the allocated resources or re-plan the priority sorting.

[0200] More specifically, according to the analysis results of the equipment operation status simulation data and the task execution status simulation data, optimization suggestions are made for the equipment operation time interval, operation frequency and task allocation ratio in the resource allocation plan, and verification feedback data is generated, including:

[0201] Extract device operation status simulation data from the task execution simulation set, including the device's expected operation time, load level, power consumption trend, and device reliability analysis. These data are generated by combining historical performance data with dynamic environmental parameters, and can reflect the performance of the device during task execution. For example, in the case of multiple tasks running in parallel, simulate whether the device's operation time exceeds its normal load threshold, or whether certain dynamic environmental parameters (such as voltage fluctuations) may cause device abnormalities.

[0202] Extract task execution status simulation data, including the estimated completion time of the task, resource usage, and accuracy of task execution. These data can evaluate the adaptability of the current resource allocation scheme to task completion. For example, whether high-priority tasks can be completed within the specified time window, and whether low-priority tasks are delayed due to insufficient resources.

[0203] Analyze the rationality of the equipment operation time interval based on the operation status simulation data: If the simulation results show that the equipment operation time is too long or the load is too high, adjustments can be made by shortening the operation time interval or reducing the number of load tasks.

[0204] Analyze the task allocation ratio based on the task execution status simulation data: When the resource ratio allocated to a task is insufficient, resulting in a delay in completion time, resources can be allocated from other low-priority tasks to ensure task completion time.

[0205] Operating frequency optimization can dynamically adjust the device operating frequency based on simulation results. For example, lower the device frequency to reduce power consumption, or increase the frequency to speed up task completion.

[0206] Optimization suggestions are output in the form of verification feedback data, including suggestions for adjusting the device operation time interval, operation frequency adjustment, and task allocation ratio optimization. These feedback data provide a basis for subsequent optimization of task scheduling logic and resource allocation solutions.

[0207] In a preferred embodiment of the present invention, the step of adjusting the task scheduling strategy and resource allocation scheme according to the verification feedback data to generate the production business model includes:

[0208] Extract equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions from verification feedback data to generate feedback adjustment parameters;

[0209] According to the equipment resource utilization efficiency in the feedback adjustment parameters, the equipment allocation data in the task scheduling strategy is dynamically adjusted to generate an optimized task scheduling strategy;

[0210] According to the task completion time and resource allocation optimization suggestions in the feedback adjustment parameters, combined with the equipment operation time interval and task allocation ratio in the resource allocation plan, the equipment operation time interval and task allocation ratio are optimized in real time to generate an optimized resource allocation plan;

[0211] The optimized task scheduling strategy and the optimized resource allocation plan are combined to generate a production business model, wherein the production business model includes task execution sequence, equipment allocation data, optimized equipment operation time interval and task allocation ratio.

[0212] In the embodiment of the present invention, based on the device resource utilization efficiency and task completion time information in the verification feedback data, the applicability of the existing task scheduling strategy and resource allocation scheme can be comprehensively evaluated. Under the guidance of the verification feedback data, by generating feedback adjustment parameters, the device allocation data in the task scheduling strategy is dynamically adjusted in combination with the device resource utilization efficiency, thereby optimizing the execution process of the task.

[0213] According to the task completion time and resource allocation optimization suggestions in the feedback adjustment parameters, combined with the equipment operation time interval and task allocation ratio in the resource allocation plan, the allocation of equipment operation time interval can be further optimized to ensure that the task can be completed within the specified time limit. At the same time, real-time optimization of the task allocation ratio can dynamically balance the resource allocation between tasks and equipment, improve equipment resource utilization, and reduce resource waste.

[0214] Through the optimized task scheduling strategy and resource allocation scheme, a more efficient production business model can be generated. The optimized production business model clarifies the execution order of tasks, equipment allocation data, equipment operation time interval and task allocation ratio, and has higher execution efficiency and adaptability in a multi-task complex environment. At the same time, this model can dynamically respond to changes in the actual production environment, ensure the continuity and reliability of the production process, and thus significantly improve the overall efficiency of the production system.

[0215] More specifically, the device resource utilization efficiency in the feedback adjustment parameter is adjusted, the device allocation data in the task scheduling strategy is dynamically adjusted, and the optimized task scheduling strategy is generated, including:

[0216] Equipment resource utilization efficiency data and related task demand indicators are extracted from the verification feedback data. Equipment resource utilization efficiency is a comprehensive evaluation value obtained by integrating equipment load conditions, availability parameters and historical performance, which can reflect the priority level of equipment use.

[0217] According to the feedback of equipment resource utilization efficiency data, the equipment allocation data is dynamically adjusted. High-efficiency equipment is allocated to high-priority tasks first, and low-efficiency equipment is delayed or used as a standby. When adjusting the order, the current load status and available time period of the equipment are combined to ensure the scientific nature of the allocation. For example:

[0218] For high-load devices, their tasks can be transferred to low-load devices to balance resources.

[0219] For high-priority tasks, the device with the highest adaptability is allocated to ensure task execution efficiency.

[0220] The adjusted equipment allocation data is recorded as the optimized task scheduling logic, which includes the order of task execution and the specific plan of equipment call. This logic provides clear guidance for the allocation of subsequent tasks.

[0221] More specifically, the task completion time and resource allocation optimization suggestions in the feedback adjustment parameters are combined with the device operation time interval and task allocation ratio in the resource allocation plan to optimize the device operation time interval and task allocation ratio in real time to generate an optimized resource allocation plan, including:

[0222] The task completion time in the feedback data reflects the expected completion status of the task under the current resource allocation, and the resource allocation optimization suggestion provides the direction for adjusting the task allocation ratio. For example, if a task takes too long to complete, the feedback suggests increasing the resource allocation ratio or extending the equipment operation time interval.

[0223] Combine the actual operation status of the equipment and the task requirement parameters to optimize the equipment's operation time interval. For example, when the equipment's operation time is insufficient to complete the task, the operation time interval can be appropriately extended while reducing the time occupied by other tasks. If the equipment's operation time is too long, resulting in low efficiency or overload, the equipment resources can be released by shortening the time interval.

[0224] Adjust the task allocation ratio based on the task completion time and task requirement parameters. For example, allocate more resources to high-priority tasks and reduce the resource usage of low-priority tasks. Dynamic adjustment of task allocation ratio can ensure the completion of critical tasks while preventing low-priority tasks from occupying too many resources.

[0225] The optimized resource allocation plan includes the device's operating time interval, task allocation ratio, and adjusted resource allocation plan. These data are used to guide the dynamic coordination of devices and tasks to ensure efficient use of resources.

[0226] More specifically, the optimization task scheduling strategy and the optimization resource allocation scheme are combined to generate a production business model, wherein the production business model includes the task execution sequence, equipment allocation data, optimized equipment operation time interval and task allocation ratio, including:

[0227] The optimized task scheduling logic and optimized resource allocation scheme are dynamically adjusted from the two levels of task execution and equipment allocation. After integrating the two, a complete production business model is formed to ensure the coordination between task execution sequence and equipment allocation data.

[0228] The production business model is task-centric and clarifies the execution order, assigned equipment, equipment operation time interval and task allocation ratio of each task. For example:

[0229] High-priority tasks are given priority in the execution order, allocated high-efficiency equipment, and are allocated sufficient running time intervals and resource ratios.

[0230] Low-priority tasks are flexibly adjusted based on remaining resources to maximize resource utilization.

[0231] The production business model can respond to changes in the production environment in real time, such as fluctuations in equipment status or adjustments in task requirements. By combining optimized logic with solutions, the production business model has a high degree of dynamic adaptability and execution efficiency.

[0232] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for constructing a production business model based on an industrial Internet platform, characterized in that: The method comprises: Collect multi-source data, pre-process it, and generate raw data, which includes equipment data, task data, and environmental data; According to the operating parameters in the equipment data and the required parameters in the task data, matching is performed according to the functional requirements, the degree of adaptation between the equipment and the task is calculated, and the adaptation result data is generated; Extract dynamic impact parameters from environmental data and combine them with adaptation result data to adjust the operating parameters in the equipment data to generate dynamic adjustment parameters; According to the adjustment value in the dynamic adjustment parameter, combined with the completion time limit, resource demand and priority factor in the task data, a weighted multi-objective function is constructed, wherein the weighted multi-objective function includes a task time weight, a resource consumption weight and a real-time dynamic environment weight; Generate task priority ranking by solving multi-objective functions, and generate task scheduling strategy according to the task priority ranking, the task scheduling strategy task execution order and device allocation data; Calculate the efficiency of equipment resource utilization based on the task scheduling strategy and the real-time status parameters in the equipment data; Prioritize the devices according to their resource utilization efficiency, and generate a resource allocation plan through a segmented dynamic optimization algorithm, wherein the resource allocation plan includes the device operation time interval, operation frequency, and task allocation ratio allocated to each task; Dynamically verify resource allocation plans based on historical production data and simulation scenarios, generate verification feedback data, and adjust task scheduling strategies and resource allocation plans based on the verification feedback data to generate production business models; The production business model is loaded onto the industrial Internet platform, and real-time task scheduling and equipment control are performed according to its task scheduling strategy and resource allocation plan.

2. The method for constructing a production business model based on an industrial Internet platform according to claim 1 is characterized in that: The matching is performed according to the functional requirements based on the operating parameters in the device data and the requirement parameters in the task data, the degree of adaptation between the device and the task is calculated, and the adaptation result data is generated, including: Extract key operating parameters from equipment data and generate a set of equipment operating characteristics; Extract execution requirement parameters from task data and generate a set of task requirement features; Calculate the degree of adaptation between the device and the task according to the device operation feature set and the task requirement feature set, eliminate the device and task combinations below the preset matching degree threshold, and generate adaptation result data, wherein the adaptation result data includes the device identifier, the task identifier and the adaptation degree value; The calculation formula for the degree of adaptation between the device and the task is: ,in, For equipment With the task The adaptation degree value, is the characteristic parameter The weight of For equipment In the characteristic parameters The running performance value on For equipment In the characteristic parameters The execution requirement value on is the number of feature parameters.

3. The method for constructing a production business model based on an industrial Internet platform according to claim 2 is characterized in that: The extracting of dynamic impact parameters from the environmental data and combining the adaptation result data to adjust the operating parameters in the equipment data to generate dynamic adjustment parameters includes: Extract real-time environmental status parameters and historical environmental change trend parameters from environmental data to generate an environmental feature set; According to the operating parameters in the equipment operating characteristic set, determine the dynamic factors in the environmental characteristic set that have the greatest impact on the equipment operating parameters, and generate dynamic impact parameters; The dynamic impact parameter is combined with the adaptation degree value in the adaptation result data, and the adjustment value of the equipment operation parameter is generated according to the dynamic weight calculation rule; The equipment operation parameters are optimized through adjustment values ​​to generate dynamic adjustment parameters, wherein the dynamic adjustment parameters include an operation speed adjustment value, a power adjustment value and an operation accuracy adjustment value.

4. The method for constructing a production business model based on an industrial Internet platform according to claim 3 is characterized in that: The calculation formula of the weighted multi-objective function is: ,in, For the task The priority value of For the task The remaining time limit for completion, For the task The resource demand, is the maximum amount of system resources available, Dynamic environment parameters The weight of For the task In dynamic environment parameters The sensitivity of Dynamic environment parameters The real-time status value of Dynamic environment parameters The reference value of is the number of dynamic environment parameters, , and is the weight coefficient; in, , For the task Dynamic environmental parameters The target expected value, Dynamic environment parameters The current state value of Dynamic environment parameters range of change.

5. The method for constructing a production business model based on an industrial Internet platform according to claim 4 is characterized in that: The generating task priority ranking by solving the multi-objective function includes: Calculate the priority value of each task according to the weighted multi-objective function; According to the priority value of the task and the preset priority threshold, the task priority is hierarchically sorted to generate task hierarchical sorting data, which includes high priority tasks, medium priority tasks and low priority tasks, and the tasks in each layer are arranged from large to small according to the value; According to the task hierarchical sorting data and the adaptation degree value between the device and the task, the device calling order in the high priority task is readjusted to generate the task priority sorting data, wherein the task priority sorting data includes the execution order of the task and the corresponding device calling order.

6. The method for constructing a production business model based on an industrial Internet platform according to claim 5 is characterized in that: Generate task scheduling strategies by sorting task priorities, including: Sort the data according to task priorities and extract the device call combination for each task; Filter the equipment operation status parameters in the equipment call combination, remove the equipment that is currently in overload or maintenance status, and generate adaptation equipment data; According to the adaptation degree value between the device and the task and the current availability parameter of the device, the device calling sequence in the adaptation device data is adjusted to generate the device calling priority data; A task scheduling strategy is generated based on the task priority sorting data, the device call priority data and the resource requirements of the task on the device, wherein the task scheduling strategy includes the execution order of the tasks, the corresponding device allocation data and the device call time interval.

7. The method for constructing a production business model based on an industrial Internet platform according to claim 6 is characterized in that: The calculation of the equipment resource utilization efficiency according to the task scheduling strategy and in combination with the real-time status parameters in the equipment data includes: Extract real-time status parameters from equipment data, including the current running time, current load value, historical usage frequency and failure rate of the equipment, and generate a set of equipment status features; According to the device allocation data in the task scheduling strategy, the resource utilization rate of each device in the current task execution is calculated. The resource utilization rate is the ratio of the current load of the device to the available load capacity. The resource utilization rate is calculated together with the historical usage frequency and failure rate in the equipment status feature set to generate the equipment resource utilization efficiency value; The calculation formula of the equipment resource utilization efficiency value is: , is the equipment resource utilization efficiency value, For equipment The current resource utilization of For equipment The average frequency of use, For equipment The failure rate, For equipment The remaining resource capacity, For equipment The current task allocation complexity is, where , For equipment The total number of currently assigned tasks, For the task On the device The degree of occupancy.

8. The method for constructing a production business model based on an industrial Internet platform according to claim 7 is characterized in that: Generating a resource allocation scheme according to the device resource utilization efficiency includes: Sort all equipment resource utilization efficiency values ​​to generate equipment resource efficiency ranking data; According to the device resource efficiency sorting data, select high resource utilization efficiency devices and assign corresponding task execution time periods to generate device operation time interval data; Analyze the frequency of equipment operation and generate operation frequency data based on the equipment's real-time status parameters and the operating parameters of the equipment's performance requirements; According to the task priority sorting data and the real-time status of the remaining resources of the equipment, the resource allocation ratio of the task is analyzed and the task allocation ratio data is generated; Generate a resource allocation plan based on the equipment operation time interval data, operation frequency data and task allocation ratio data.

9. The method for constructing a production business model based on an industrial Internet platform according to claim 8, characterized in that: The resource allocation scheme is dynamically verified based on historical production data and simulation scenarios to generate verification feedback data, including: Extract the historical records of equipment operation time interval, operation frequency and task allocation ratio from historical production data to generate historical equipment performance data; Generate a task execution simulation set based on the historical performance data of the device and the dynamic environment parameters in the simulation scenario, wherein the task execution simulation set includes the device operation status simulation data and the task execution status simulation data; Through the equipment operation status simulation data, analyze whether the equipment operation time interval in the resource allocation plan meets the historical performance trend of the equipment and the requirements of dynamic environmental parameters; Through task execution status simulation data, analyze whether the task allocation ratio matches the task requirement parameters, and evaluate whether the completion time of the current task execution meets the requirements of task priority sorting; Based on the analysis results of the equipment operation status simulation data and the task execution status simulation data, optimization suggestions are made for the equipment operation time interval, operation frequency and task allocation ratio in the resource allocation plan, and verification feedback data is generated, wherein the verification feedback data includes equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions for equipment and tasks.

10. The method for constructing a production business model based on an industrial Internet platform according to claim 9, characterized in that: The step of adjusting the task scheduling strategy and resource allocation plan according to the verification feedback data to generate a production business model includes: Extract equipment resource utilization efficiency, task completion time and resource allocation optimization suggestions from verification feedback data to generate feedback adjustment parameters; According to the equipment resource utilization efficiency in the feedback adjustment parameters, the equipment allocation data in the task scheduling strategy is dynamically adjusted to generate an optimized task scheduling strategy; According to the task completion time and resource allocation optimization suggestions in the feedback adjustment parameters, combined with the equipment operation time interval and task allocation ratio in the resource allocation plan, the equipment operation time interval and task allocation ratio are optimized in real time to generate an optimized resource allocation plan; The optimized task scheduling strategy and the optimized resource allocation plan are combined to generate a production business model, wherein the production business model includes task execution sequence, equipment allocation data, optimized equipment operation time interval and task allocation ratio.

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