Production process scheduling method, system and equipment based on industrial internet of things, and medium

Abstract

The invention discloses a production process scheduling method, system, equipment and medium based on the industrial Internet of Things, and relates to the technical field of industrial Internet of Things data processing, and the method comprises the steps: obtaining a to-be-scheduled production process, obtaining a preset production queue which is composed of a current production task and a plurality of to-be-processed production tasks, obtaining completion time and production information; obtaining environment trend information, obtaining a dynamic correction index, obtaining a process utilization rate, and obtaining a process utilization index based on the correction model, the process utilization rate and the dynamic correction index; obtaining inventory unit time cost and delivery deadline, and obtaining a scheduling index based on the scheduling model, the completion time, the process utilization index, the inventory unit time cost and the delivery deadline; and sequentially arranging the plurality of production tasks to be processed according to the scheduling indexes from small to large, generating a scheduling queue, and executing the production tasks according to the scheduling queue. The method has the advantages of dynamic environment self-adaption, multi-target collaboration and good scheduling effect.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things data processing, and in particular to a production process scheduling method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] In the field of industrial manufacturing, production process scheduling is the core link to optimize resource allocation, improve equipment utilization and ensure order delivery. Existing production process scheduling methods are all based on static priority rules (such as first-come-first-served, shortest processing time priority, etc.), but such methods have exposed significant defects in actual applications: First, static rules cannot dynamically respond to multi-dimensional disturbances in the production environment, such as real-time fluctuations in equipment operating status (such as load mutations, mechanical wear), abnormal changes in environmental parameters (temperature, electromagnetic field strength) and sudden order demand adjustments, resulting in serious deviations between scheduling results and actual working conditions; second, existing technologies do not provide sufficient support for multi-objective collaborative optimization, and often consider a single indicator in isolation (such as the shortest delivery cycle or the lowest processing cost), lacking a comprehensive balance between equipment utilization, inventory backlog costs and order punctuality, and are prone to local optimization and overall cost increase; third, data... Data collection and scheduling decisions are difficult to respond to dynamic changes in the production line in a timely manner. When in industrial scenarios with high temperature and high electromagnetic interference, environmental parameters will directly affect the processing accuracy and stability of the equipment. At the same time, the existing methods have weak quantification capabilities for environmental interference. For example, they do not consider the nonlinear impact of temperature gradient changes on the heat dissipation efficiency of the equipment and the interference effect of excessive electromagnetic field intensity on the stability of the control signal, resulting in scheduling decisions ignoring environmental risks, increasing equipment failure rate and maintenance costs; finally, there is a conflict between inventory costs and production rhythm. High inventory cost tasks need to be quickly circulated to reduce capital occupation, but high-priority urgent orders may squeeze production capacity resources. Traditional scheduling models lack a dynamic weight allocation mechanism, making it difficult to balance the competitive relationship between the two, resulting in the double loss of inventory backlogs and order delays. Summary of the Invention

[0003] In response to the deficiencies in the prior art, the present invention provides a production process scheduling method, system, equipment and medium based on the Industrial Internet of Things.

[0004] A production process scheduling method based on the industrial Internet of Things includes: obtaining a production process to be scheduled, and obtaining a preset production queue for the production process to be scheduled, wherein the preset production queue consists of a current production task and multiple production tasks to be processed, and obtaining the completion time of the current production task; obtaining environmental trend information corresponding to the completion time based on the completion time and the production information, and obtaining a dynamic correction index of each production task to be processed based on the environmental trend information, and obtaining a process utilization rate of each production task to be processed, and obtaining a process utilization index based on a correction model, the process utilization rate and the dynamic correction index; obtaining an inventory unit time cost and a delivery deadline for each production task to be processed, and obtaining a scheduling index for each production task to be processed based on a scheduling model, the completion time, the process utilization index, the inventory unit time cost and the delivery deadline; arranging multiple production tasks to be processed in ascending order according to the scheduling index to generate a scheduling queue, and executing the production tasks according to the scheduling queue.

[0005] Optionally, obtaining environmental trend information corresponding to the completion time based on the completion time and production information and obtaining dynamic correction indicators of each pending production task based on the environmental trend information includes: obtaining the temperature change sequence of the current production task within the completed time, and predicting the temperature interference trend parameters corresponding to the completion time based on the temperature change sequence; obtaining the electromagnetic intensity change sequence of the current production task within the completed time, and predicting the electromagnetic interference trend parameters corresponding to the completion time based on the electromagnetic intensity change sequence; combining the temperature interference trend parameters and the electromagnetic interference trend parameters into environmental trend information, and obtaining dynamic correction indicators of each pending production task based on the environmental trend information.

[0006] Optionally, the dynamic correction index of each pending production task is obtained based on the environmental trend information and is expressed as: ; in, is the dynamic correction index of the i-th production task to be processed, is the temperature interference trend parameter corresponding to the completion time, is the temperature threshold for the effective operation of the i-th pending production task, is the electromagnetic interference trend parameter corresponding to the completion time, is the electromagnetic interference threshold for the effective operation of the i-th production task to be processed, is the temperature sensitivity weight of the i-th production task to be processed, is the electromagnetic interference sensitivity weight of the i-th production task to be processed.

[0007] Optionally, obtaining process utilization indicators based on the correction model, process utilization rate and dynamic correction indicators includes: obtaining task information of each pending production task, and obtaining the theoretical operating power of each pending production task in the production process to be scheduled based on the task information; obtaining the maximum operating power of the production process to be scheduled; obtaining process utilization indicators based on the correction model, theoretical operating power, maximum operating power and dynamic correction indicators.

[0008] Optionally, the correction model in the indicator acquisition process based on the correction model, theoretical operating power, maximum operating power and dynamic correction index is expressed as: ;in, is the process utilization indicator of the i-th production task to be processed, is the dynamic correction index of the i-th production task to be processed, is the theoretical operating power of the i-th production task to be processed, is the maximum operating power of the production process to be scheduled.

[0009] Optionally, obtaining the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline includes: obtaining the task information of each pending production task, and obtaining the target production amount of each pending production task based on the task information, and obtaining the unit production amount of each pending production task in the production process to be scheduled under the standard utilization rate; obtaining the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount and unit production amount.

[0010] Optionally, the scheduling model in which the scheduling indicators of each pending production task are obtained based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount, and unit production amount is expressed as follows: ;in, is the scheduling index of the i-th production task to be processed, is the target production amount of the i-th production task to be processed, is the unit production amount of the i-th production task to be processed under the standard utilization rate, is the inventory unit time cost of the i-th production task to be processed, is the process utilization indicator of the i-th production task to be processed, is the delivery deadline of the i-th pending production task, For completion time.

[0011] A production process scheduling system based on the industrial Internet of Things is also provided. The system includes a management platform, a sensor network platform and an object platform that are communicatively connected in sequence. The management platform includes: a first acquisition module, which is used to obtain the production process to be scheduled and obtain a preset production queue for the production process to be scheduled, wherein the preset production queue consists of a current production task and multiple production tasks to be processed, and obtains the completion time and production information of the current production task; a second acquisition module, which is used to obtain environmental trend information corresponding to the completion time based on the completion time and production information and obtain dynamic correction indicators of each production task to be processed based on the environmental trend information, and obtain the process utilization rate of each production task to be processed, and obtain the process utilization index based on the correction model, process utilization rate and dynamic correction index; a processing module, which is used to obtain the inventory unit time cost and delivery deadline of each production task to be processed, and obtain the scheduling index of each production task to be processed based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline; a scheduling module, which is used to arrange multiple production tasks to be processed in order from small to large according to the scheduling index and generate a scheduling queue, and execute production tasks according to the scheduling queue.

[0012] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned production process scheduling method based on the industrial Internet of Things.

[0013] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored. When the program is executed by a processor, the above-mentioned production process scheduling method based on the industrial Internet of Things is implemented.

[0014] The beneficial effects of the present invention are embodied in: In the entire production process scheduling method based on the Industrial Internet of Things, first of all, based on the coupling mechanism of environmental trend prediction and dynamic correction indicators, quantitative compensation for nonlinear disturbances such as temperature gradients and electromagnetic interference is realized. For example, by predicting the risk of environmental parameter exceeding the limit at the moment of processing completion, the priority of sensitive tasks is dynamically adjusted to avoid a surge in defective rates due to equipment overheating or signal distortion; further, the calculation of process utilization indicators integrates real-time data of theoretical production capacity, environmental attenuation and equipment load, accurately reflecting the actual efficiency of available resources, so that the accelerated flow of high inventory cost tasks and the delivery pressure of urgent orders are balanced under the dynamic weight distribution model. For example, when the inventory backlog cost conflicts with the delivery cycle, the time cost sensitivity analysis of the scheduling indicators can be used to intelligently avoid the problem of urgent orders squeezing out production capacity. Furthermore, the real-time connection of the entire process data chain supports the dynamic reconstruction capability of the flexible scheduling queue. In the event of sudden equipment failure or interruption of orders, the environmental interference trend prediction and scheduling indicator recalculation can achieve minute-level production sequence optimization. For example, the delay of the painting process caused by high temperature and the queue interruption of urgent orders for new energy battery packs can complete resource reallocation within the same decision cycle. Furthermore, the multi-objective collaborative optimization model breaks through the limitations of traditional single indicators and incorporates key parameters such as equipment utilization rate, inventory turnover rate, and on-time delivery rate into a unified evaluation system. Through the cascade calculation of process utilization indicators and scheduling indicators, the implicit costs such as production capacity loss caused by reduced heat dissipation efficiency and production rhythm dislocation caused by electromagnetic interference are made explicit, ultimately minimizing the overall operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0016] Figure 1 This is a schematic diagram of the steps of the production process scheduling method based on the industrial Internet of Things of the present invention; Figure 2 This is a schematic diagram of part of the steps S2 in the production process scheduling method based on the Industrial Internet of Things of the present invention; Figure 3 This is a schematic diagram of another part of the steps of S2 in the production process scheduling method based on the Industrial Internet of Things of the present invention; Figure 4 Schematic diagram of some steps S3 in the production process scheduling method based on industrial Internet of Things of the present invention; Figure 5 Schematic diagram of the composition of the production process scheduling system based on the Industrial Internet of Things of the present invention; Figure 6 This is a schematic diagram of the composition of the optimized industrial Internet of Things involved in the present invention; Figure 7 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0017] Reference numerals: 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - I / O interface, 705 - communication component. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. In addition, the terms "first," "second," etc. are used only to distinguish the descriptions and are not to be understood as indicating or implying relative importance.

[0021] like Figure 1 As shown, a production process scheduling method based on industrial Internet of Things is provided, including: S1. Obtain a production process to be scheduled, and obtain a preset production queue for the production process to be scheduled, wherein the preset production queue consists of a current production task and multiple pending production tasks, and obtain the completion time and production information of the current production task; S2. Obtaining environmental trend information corresponding to the completion time based on the completion time and the production information, and obtaining a dynamic correction index for each pending production task based on the environmental trend information, and obtaining a process utilization rate for each pending production task, and obtaining a process utilization index based on the correction model, the process utilization rate, and the dynamic correction index; S3. Obtain the inventory unit time cost and delivery deadline of each pending production task, and obtain the scheduling indicator of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline; S4. Arrange multiple pending production tasks in ascending order according to the scheduling indicators and generate a scheduling queue, and execute the production tasks according to the scheduling queue.

[0022] In this embodiment, it should be noted that in S1, the initial data framework required for dynamic scheduling is constructed. Specifically, the production processes to be scheduled can include milling, die-casting, injection molding, and surface treatment, among others. Real-time production line status data from the Manufacturing Execution System (MES) and task queue data from the Order Management System (OMS) are then integrated to obtain a preset production queue. This preset production queue includes not only the currently executing production tasks (such as the finishing process for a specific part model) but also multiple pending production tasks (such as urgent customized product assembly tasks, regular batch production tasks, and repair tasks). The completion time of the current production task is dynamically estimated based on multi-dimensional real-time data. For example, parameters such as spindle load current and tool wear coefficient are collected through equipment sensors. These parameters are then combined with standard working time benchmarks in a historical process database to predict the remaining processing time. Furthermore, the production information acquired must cover both equipment operation (such as spindle speed, feed rate, and production efficiency) and the production environment (temperature and electromagnetic interference), providing basic data support for subsequent environmental disturbance analysis and resource optimization.

[0023] For example, in an automotive parts processing line, the current production task might involve precision milling of an engine block. The completion time prediction takes into account real-time cutting force monitoring data, tool remaining life warning signals, and the operating status of the coolant circulation system to determine the current production task's completion time. The queue of pending production tasks might also include tasks requiring process requirements, such as milling of transmission gears and brake discs. Furthermore, production information for the current production task must be collected synchronously via an industrial IoT terminal. The integration of this data provides a comprehensive, multi-source, and heterogeneous decision-making basis for subsequent environmental trend forecasting and scheduling indicator calculation.

[0024] In S2, a dynamic correlation between environmental interference and equipment performance is established, enabling multi-dimensional dynamic correction of production task priorities. First, the Industrial Internet of Things (IIoT) collects historical environmental data for the current production task in real time (e.g., the spindle box temperature rise curve during machine tool processing and the electromagnetic radiation intensity fluctuations of high-frequency induction heating equipment). Using time series prediction algorithms (such as LSTM or ARIMA), environmental trend parameters at the time of task completion are inferred. Furthermore, considering the process sensitivity of different production tasks (e.g., semiconductor packaging tasks have low tolerance for electromagnetic interference and heat treatment tasks are sensitive to temperature fluctuations), a dynamic correction indicator reflecting each task's anti-interference capability is generated by weightedly integrating the predicted deviations of temperature and electromagnetic interference.

[0025] Furthermore, the calculation of process utilization metrics requires a comprehensive consideration of the equipment's theoretical capacity and environmental degradation effects. The theoretical resource requirements for each task are extracted from the process database, combined with the production line's maximum available resource capacity, and the theoretical value is compensated for by dynamic correction metrics. For example, if a high temperature environment causes the hydraulic system efficiency of an injection molding machine to decrease, the system automatically lowers the task's process utilization metric based on the dynamic correction metrics, thereby reflecting the actual reduction in available capacity in scheduling decisions. This mechanism allows highly environmentally sensitive tasks to be appropriately downgraded during resource competition, avoiding the risk of increased defective product rates or downtime due to equipment overload.

[0026] In S3, a multi-objective collaborative scheduling decision-making system is constructed, balancing the conflicting relationship between inventory costs and order delivery through a dynamic weight allocation mechanism. First, the system extracts the inventory capital utilization factor (such as raw material storage rates and semi-finished product storage costs) for each task from the enterprise resource planning (ERP) system. This factor, combined with the delivery constraints of the order management system (such as delay penalty clauses in customer contracts), quantifies the time cost sensitivity of inventory overstock. Simultaneously, based on the process utilization index calculated in S2 (reflecting actual production efficiency under current environmental disturbances), the system dynamically calculates the scheduling indicators for each task within the remaining production cycle.

[0027] Furthermore, the scheduling index of a pending production task refers to its lag weight. Specifically, the higher the scheduling index of a pending production task, the later it will begin in the scheduled production process. In other words, pending production tasks with the lowest scheduling index are prioritized. The calculation of scheduling indexes requires integrating the spatiotemporal relationship between production rhythm and resource utilization. For example, for gearbox housing processing, when the unit inventory time cost is high and delivery time is sufficient, the higher the priority given to production, the greater the inventory cost. This shifts the production order backwards using the scheduling index. For brake disc heat treatment tasks, on the other hand, when the delivery cycle is close and the unit inventory time cost is low, the scheduling index is also low, shifting the production order forward using the scheduling index. This mechanism effectively addresses the inventory stagnation problem caused by urgent orders squeezing production capacity in traditional methods. It also minimizes overall production costs by coupling environmental interference with delivery pressure.

[0028] In S4, a dynamically variable execution sequence is constructed to achieve adaptive allocation of production line resources and risk avoidance. When sorting tasks according to the scheduling index value, a reverse priority mechanism is adopted - the lower the scheduling index, the higher the priority of execution. This reflects the dynamic balance of the dual costs of inventory backlog and delivery delay. For example, when the scheduling index of a batch of high-precision bearing processing tasks increases significantly due to electromagnetic interference causing a sudden drop in the process utilization index, it will be automatically postponed to a period when the electromagnetic environment of the equipment is stable, and priority will be given to standardized parts processing tasks with high inventory turnover pressure and low environmental sensitivity. At the same time, the sorting mechanism supports real-time dynamic updates. When a sudden equipment failure or an emergency order is inserted on the production line, the scheduling queue is reconstructed in real time by recalculating the scheduling indicators of the newly added tasks and the affected tasks to ensure that resource allocation always fits the real-time working conditions.

[0029] Taking the automobile assembly line as an example, if the scheduling index of a batch of door sheet metal parts increases due to abnormal temperatures in the paint shop, the queue order will be automatically adjusted to prioritize tasks with lower temperature control requirements, such as painting the remaining parts. When a sudden customer urgent order requires early delivery of new energy battery packs, the scheduling index of this urgent task is recalculated and inserted to the front of the queue by collecting the current equipment load, environmental parameters and inventory status in real time. At the same time, tasks in the original queue with lower inventory costs and sufficient delivery buffer periods are dynamically moved back. This flexible scheduling mechanism not only avoids the risk of production line congestion caused by traditional static rules, but also minimizes overall operating costs while ensuring on-time delivery through coupled analysis of environmental interference and order urgency.

[0030] In summary, in the entire production process scheduling method based on the Industrial Internet of Things, first of all, based on the coupling mechanism of environmental trend prediction and dynamic correction indicators, quantitative compensation for nonlinear disturbances such as temperature gradients and electromagnetic interference is realized. For example, by predicting the risk of environmental parameter exceeding the limit at the moment of processing completion, the priority of sensitive tasks is dynamically adjusted to avoid a surge in defective rates due to equipment overheating or signal distortion; further, the calculation of process utilization indicators integrates real-time data of theoretical production capacity, environmental attenuation and equipment load, accurately reflecting the actual efficiency of available resources, so that the accelerated flow of high inventory cost tasks and the delivery pressure of urgent orders are balanced under the dynamic weight distribution model. For example, when the inventory backlog cost conflicts with the delivery cycle, the time cost sensitivity analysis of the scheduling indicators can be used to intelligently avoid the crowding out of urgent orders. The risk of inventory stagnation caused by production capacity; further, the real-time connection of the entire process data chain supports the dynamic reconstruction capability of the flexible scheduling queue. In the event of sudden equipment failure or insertion of orders, the environmental interference trend prediction and scheduling indicator recalculation can achieve minute-level production sequence optimization. For example, the delay of the painting process caused by high temperature and the queue insertion of urgent orders for new energy battery packs can complete resource reallocation within the same decision cycle; further, the multi-objective collaborative optimization model breaks through the limitations of traditional single indicators, and incorporates key parameters such as equipment utilization rate, inventory turnover rate, and on-time order delivery rate into a unified evaluation system. Through the cascade calculation of process utilization indicators and scheduling indicators, the implicit costs such as production capacity loss caused by reduced heat dissipation efficiency and production rhythm dislocation caused by electromagnetic interference are made explicit, ultimately minimizing the overall operating cost.

[0031] like Figure 2 As shown, in one embodiment, obtaining environmental trend information corresponding to the completion time according to the completion time and production information in S2 and obtaining dynamic correction indicators of each pending production task according to the environmental trend information includes: S21. Obtain the temperature change sequence of the current production task within the completed time, and predict the temperature interference trend parameter corresponding to the completion time based on the temperature change sequence; S22. Obtaining a sequence of electromagnetic intensity changes during the completion time of the current production task, and predicting an electromagnetic interference trend parameter corresponding to the completion time based on the sequence of electromagnetic intensity changes; S23. Combining the temperature interference trend parameter and the electromagnetic interference trend parameter into environmental trend information, and obtaining a dynamic correction index for each production task to be processed according to the environmental trend information.

[0032] In this embodiment, it should be noted that in S21, the correlation characteristics between the thermal inertia of the equipment and the environmental disturbance are captured through the temperature change sequence. For example, in a die-casting production line, the temperature change sequence of the mold cooling pipeline is collected in real time (such as the temperature value sampled once per second), the historical temperature data is divided into input sequences according to time windows (such as a 30-minute cycle), and the LSTM model is trained to capture the nonlinear temperature change pattern. Specifically, when the die-casting machine enters the pressure holding stage, the mold temperature shows a trend of first rising and then slowly falling due to the release of latent heat by the solidification of the molten metal. After learning the temperature change patterns of different processing stages (mold closing, injection, pressure holding, cooling), the model inputs the temperature sequence of the current task execution period (such as the first 20 minutes) and predicts the temperature value at the time of task completion (the remaining 10 minutes).

[0033] In S22, the focus is on the dynamic tracing and propagation effects of electromagnetic interference, analyzing the coupling mechanism between equipment operation and the electromagnetic environment through electromagnetic intensity sequences. For example, in a welding workstation, the electromagnetic radiation intensity sequence of the welding robot's servo drive is collected (e.g., magnetic field intensity sampled every 5 seconds). The ARIMA model is used to identify seasonal cycles in the data (e.g., periodic fluctuations in electromagnetic intensity caused by welding pulses triggered every 10 seconds) and trend terms. Specifically, when multiple robots work collaboratively, the superposition effect of electromagnetic interference causes the magnetic field intensity to exhibit a step-like upward trend. The model fits autoregressive parameters and moving average coefficients to the electromagnetic intensity sequence of the completed processing period (e.g., the first 30 minutes of data) to predict the electromagnetic intensity curve for the remaining time of the task (e.g., the last 15 minutes).

[0034] In S23, a dynamic mapping relationship between environmental risk and process tolerance is established, and differentiated environmental sensitivity coefficients are assigned to each task based on the physical characteristics of different processes. For example, taking a semiconductor wafer lithography task as an example, suppose the LSTM predicts that the cleanroom temperature will rise to 22.5°C (exceeding the 22°C threshold for the photoresist development process) at the time a pending production task is completed, and the ARIMA predicts that the electromagnetic radiation intensity of the etching machine will reach 50dBμV / m (exceeding the 45dBμV / m tolerance for the lithography machine's positioning accuracy) at the time a pending production task is completed. By weightedly calculating these two types of environmental risk violations, dynamic correction indicators are obtained for each pending production task.

[0035] In one embodiment, the dynamic correction index of each pending production task is obtained according to the environmental trend information in S23 as follows: ; in, is the dynamic correction index of the i-th production task to be processed, is the temperature interference trend parameter corresponding to the completion time, is the temperature threshold for the effective operation of the i-th pending production task, is the electromagnetic interference trend parameter corresponding to the completion time, is the electromagnetic interference threshold for the effective operation of the i-th production task to be processed, is the temperature sensitivity weight of the i-th production task to be processed, is the electromagnetic interference sensitivity weight of the i-th production task to be processed.

[0036] In this embodiment, it should be noted that obtaining the dynamic correction index The expression design aims to quantify the compound impact of environmental interference on production tasks. Its core is to associate environmental trend parameters with process fault tolerance thresholds through nonlinear functions and introduce sensitive weights to achieve multi-factor coupling evaluation. Among them, the nonlinear form of 1-e^{x} is used to make the environmental parameter exceedance ratio The impact on the correction index is consistent with actual application. When environmental interference such as temperature and electromagnetic field affects the equipment, there is often a threshold effect. For example, when the temperature exceeds the critical value of thermal deformation of the material, the processing accuracy drops sharply. Specifically, the exponential function grows slowly when x is small at the beginning of the over-limit, avoiding misjudgment caused by small fluctuations. When the over-limit value increases, x becomes larger and the index quickly approaches 1, significantly amplifying the risk weight and ensuring the system's rapid response to major environmental risks. Example: Assume that the temperature threshold of a task is =50, predicted temperature =52, then =0.04, and the temperature term contributes to 0.04, indicating that the risk weight of temperature exceeding the limit is 0.04; if the exceeding limit value increases to 20, such as =100, then the temperature term contribution is 0.33, the risk weight of temperature exceeding the limit is greatly increased.

[0037] Further, and The meaning is threshold filtering and directional control. Only when the environmental trend parameter exceeds the threshold and Only when , it contributes to the correction index. and It can eliminate dimensional differences and achieve comparability of cross-task environmental risks; further, it can avoid unnecessary corrections when environmental parameters are within the safe range. For example, when the temperature is below the threshold, even if there is a fluctuation, it will not affect the execution of the task, thereby reducing the waste of resources caused by ineffective scheduling adjustments. Example: Electromagnetic interference threshold of a task =40, if the predicted value =38, then max{0, -0.05}=0, the contribution of electromagnetic interference is 0, and only temperature or other risk factors need to be paid attention to.

[0038] Further, and Achieve differentiated allocation of sensitive weights, that is, dynamically allocate sensitive weights of temperature and electromagnetic interference according to the process characteristics of the task; therefore, different tasks have different sensitive dimensions to environmental interference, such as heat treatment tasks are sensitive to temperature, and chip packaging tasks are sensitive to electromagnetic interference. Weight allocation enables the correction index to accurately match the actual vulnerability of the task. Example: In the production process to be scheduled for semiconductor lithography tasks, the temperature sensitivity weight =0.9, electromagnetic sensitivity weight =0.1 because photoresist is extremely sensitive to temperature changes; and in the production process to be scheduled for laser welding tasks, =0.2, =0.8 Due to high frequency electromagnetic interference, welding positioning may be easily offset. Constraints to ensure the correction of indicators The value range of is [0, 1], which facilitates the unified quantification of indicators in subsequent processes. The normalized output enables horizontal comparison of the impact of different environmental interference types, such as temperature and electromagnetic interference, to avoid assessment bias caused by dimensional differences. For example, a temperature over-limit risk of 0.8 and an electromagnetic over-limit risk of 0.5 can be directly superimposed and weighted without the need for additional normalization.

[0039] For example, the production process to be scheduled is a CNC machining center, and a production task to be processed is a precision bearing turning task. The process requirements in the task content are: temperature threshold =30℃, critical temperature of thermal deformation of lathe spindle; electromagnetic threshold =45dBμV / m, the upper limit of the anti-interference ability of the CNC system; sensitivity weight =0.7, =0.3 Due to the high tolerance requirements of bearings, it is more sensitive to the thermal stability of the lathe. In the environmental trend prediction results, the temperature trend parameter =50℃, predicted to exceed the limit by 20℃; electromagnetic trend parameters =43dBμV / m, the prediction is within the limit. Substitute into the expression and calculate the dynamic correction index =0.34, 0.34 will be used to participate in the correction and obtain the process utilization indicator.

[0040] like Figure 3 As shown, in one embodiment, obtaining the process utilization index based on the correction model, the process utilization rate and the dynamic correction index in S2 includes: S24. Obtain task information of each pending production task, and obtain theoretical operating power of each pending production task in the production process to be scheduled based on the task information; S25. Obtain the maximum operating power of the production process to be scheduled; S26. Obtain process utilization indicators based on the correction model, theoretical operating power, maximum operating power and dynamic correction indicators.

[0041] In this embodiment, it should be noted that in S24, by analyzing the task process parameters and equipment specifications, a baseline value for resource requirements under ideal operating conditions is extracted for each production task. For example, in the case of CNC milling machine processing, the task information clearly specifies the tool feed rate, spindle speed range, and processing time requirements. Based on this, the theoretical power consumption of the task under standard operating conditions is calculated. For example, the product of the spindle motor rated power and the processing cycle time is used to quantify the stable energy supply required per unit time. This theoretical operating power measurement provides a benchmark for subsequent resource utilization assessments.

[0042] The core of S25 lies in defining the global constraint boundaries for production line resources. By integrating equipment nameplate parameters with energy management data, the peak load capacity of the scheduled production process is determined. For example, in a machining center production line, the maximum operating power is calculated by adding the rated power of parallel CNC machining centers. This global resource threshold provides a denominator for evaluating the resource utilization ratio of each task.

[0043] In S26, the calculation of process utilization indicators aligns theoretical values ​​with actual operating conditions through a dynamic correction model. The environmental interference correction coefficient generated in S23 is applied to the theoretical operating power to simulate the resource efficiency degradation caused by environmental disturbances. For example, the theoretical power requirement of a plastic extrusion task is reduced in a high-temperature environment due to the decrease in screw transmission efficiency. The ratio of the corrected actual available power to the maximum operating power represents the resource utilization efficiency of the task in the current environment. This mechanism allows highly environmentally sensitive tasks to be dynamically downgraded in resource competition. Through this dynamic mapping of resource efficiency, intelligent avoidance of equipment load peaks and production capacity optimization are achieved.

[0044] In one embodiment, the correction model in the indicator acquisition process based on the correction model, theoretical operating power, maximum operating power, and dynamic correction index in S26 is expressed as: ;in, is the process utilization indicator of the i-th production task to be processed, is the dynamic correction index of the i-th production task to be processed, is the theoretical operating power of the i-th production task to be processed, is the maximum operating power of the production process to be scheduled.

[0045] In this embodiment, it should be noted that is the ratio of theoretical operating power to maximum power, reflecting the resource utilization ratio of the i-th production task to be processed under ideal working conditions to the scheduling production process. For example, the theoretical power requirement of an injection molding task is =50KW, maximum power of production line =100KW, the theoretical resource utilization rate is 50%.

[0046] Furthermore, the dynamic correction factor Map the environmental interference risk to the actual available power reduction rate. For example, if high temperature causes a task to =0.3, the actual available power efficiency is 70%. Therefore, the traditional scheduling method directly uses the theoretical value, ignoring the equipment performance degradation caused by environmental interference, such as the power loss of the hydraulic system under high temperature, which may cause the risk of resource overload. This problem can be solved. At the same time, equipment power attenuation usually has a nonlinear relationship with environmental interference (for example, if the motor winding temperature rise exceeds the limit by 10°C, the insulation life will decrease by 50%).

[0047] In summary, the entire expression It is used to dynamically quantify the actual resource utilization under environmental interference. It realizes resource overload avoidance. When the cumulative value is close to 1, the system automatically rejects new tasks to avoid overloading the production line; it also reduces the priority of environmentally sensitive tasks, The actual resource utilization of the task decreases Becomes smaller, is delayed in the scheduling order, and is executed first Therefore, the model solves core problems in traditional static scheduling, such as distorted resource estimation and high risk of equipment overload, by quantifying environmental interference and dynamically mapping resource efficiency.

[0048] like Figure 4 As shown, in one embodiment, obtaining the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline in S3 includes: S31. Obtain task information of each pending production task, and obtain the target production amount of each pending production task based on the task information, and obtain the unit production amount of each pending production task under the standard utilization rate in the production process to be scheduled; S32. Obtain scheduling indicators for each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount, and unit production amount.

[0049] In this embodiment, it should be noted that in S31, a quantitative correlation model between task capacity and inventory cost is constructed. Standardized production parameters for each task are extracted from the process specification library, such as the processing time per unit of a certain model component and the raw material consumption quota. Combined with the rated capacity of the equipment (such as the number of molded parts per hour on an injection molding machine), the unit time output efficiency of the task is calculated under standard operating conditions (standard utilization, i.e., the theoretical operating power of the pending production task mentioned above). At the same time, real-time warehouse management data (such as the finished product shelf turnover rate) is integrated to quantify the average daily capital utilization cost after the task is completed (such as the finished product storage fee).

[0050] In S32, multi-objective optimization is achieved through a dynamic capacity simulation model. The expected production cycle for each task is simulated based on process utilization indicators (reflecting actual capacity under environmental interference). Combined with the remaining delivery window (the difference between the delivery deadline and the current time), a composite indicator of inventory backlog cost and delivery delay risk is calculated.

[0051] In one embodiment, the scheduling model in S32 for obtaining the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount, and unit production amount is expressed as follows: ;in, is the scheduling index of the i-th production task to be processed, is the target production amount of the i-th production task to be processed, is the unit production amount of the i-th production task to be processed under the standard utilization rate, is the inventory unit time cost of the i-th production task to be processed, is the process utilization indicator of the i-th production task to be processed, is the delivery deadline of the i-th pending production task, For completion time.

[0052] In this embodiment, it should be noted that It can represent the actual production cycle and pass the environmental interference through Mapping to capacity efficiency attenuation, such as high temperature =0.7, the actual production cycle is extended by 43%. It can represent the remaining time difference. Its core is the remaining delivery buffer time minus the actual production cycle. If the result is a positive value, it means that it is completed ahead of schedule, triggering inventory backlog costs; if the result is a negative value, it means that it cannot be completed, and subsequent measures such as immediate processing can be taken to assist in completion.

[0053] Further, Used to complete inventory cost weighting, when , The larger the value, the greater the total inventory cost. The larger the value is, the later the production task to be processed will be. , The bigger, The smaller the time cost per unit of inventory, the higher the priority of the pending production task. As a scaler, high inventory cost tasks Exponential increase.

[0054] In summary, a multi-objective dynamic trade-off is achieved, solving the problem that traditional methods cannot balance the resource competition between high inventory cost tasks and urgent orders; further, environmental interference compensation is introduced to solve the nonlinear decline in equipment heat dissipation efficiency caused by temperature gradients, and environmental interference is quantified as a capacity attenuation rate, which directly affects scheduling indicators; in short, this model fundamentally solves the problems of resource mismatch and local optimization in traditional static rules, and provides global cost-optimal scheduling decision support for complex industrial scenarios.

[0055] like Figure 5 As shown, a production process scheduling system based on the industrial Internet of Things is also provided. The system includes a management platform, a sensor network platform, and an object platform that are sequentially connected in communication. The management platform includes: A first acquisition module is used to acquire a production process to be scheduled and a preset production queue of the production process to be scheduled, wherein the preset production queue consists of a current production task and multiple pending production tasks, and acquire the completion time and production information of the current production task; A second acquisition module is configured to acquire environmental trend information corresponding to the completion time based on the completion time and the production information, and to acquire a dynamic correction index for each pending production task based on the environmental trend information, and to acquire a process utilization rate for each pending production task, and to acquire a process utilization index based on the correction model, the process utilization rate, and the dynamic correction index; a processing module for obtaining the inventory unit time cost and delivery deadline of each pending production task, and obtaining the scheduling index of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline; The scheduling module is used to arrange multiple pending production tasks in ascending order according to the scheduling indicators and generate a scheduling queue, and execute the production tasks according to the scheduling queue.

[0056] In this embodiment, it should be noted that, regarding the above-mentioned production process scheduling system based on industrial Internet of Things, the specific method of executing operations has been described in detail in the implementation of the production process scheduling method based on industrial Internet of Things, and will not be elaborated here.

[0057] It should also be noted that the entire production process scheduling system based on the Industrial Internet of Things can be applied to the optimized Industrial Internet of Things, such as Figure 6 As shown, the optimized industrial Internet of Things includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform that establish communication in sequence; The user platform is configured to provide front-end services to users; users obtain the required perception service information through the user platform, process the perception service information, and convert it into user perception information; users analyze the user perception information and make corresponding decisions based on their own wishes, and convert the user perception information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0058] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., which realize user-end services through integration with user information system software.

[0059] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0060] The management platform is configured to perform at least one of equipment operation status monitoring and management, data monitoring and management, equipment parameter management, and life cycle management; the management platform is the operation coordination platform of the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0061] The sensor network platform is configured to perform at least one of the following: network management, command management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, parsing, identification, and classification, preventing data from various object platforms from being directly aggregated on the management platform, which would result in data redundancy and inefficient data processing. The physical entities of the object platforms include various gateways, edge computing devices, and other devices.

[0062] The object platform is configured to perform specific production tasks such as production control, detection, and measurement; the physical entities of the production objects include various production equipment, sensors, etc.

[0063] Figure 7 FIG. 1 is a block diagram of an electronic device for a production process scheduling method based on the industrial Internet of Things according to an exemplary embodiment. Figure 7As shown, the electronic device 700 may include: a processor 701 , a memory 702 , and may further include one or more of a multimedia component 703 , an I / O interface 704 (input / output interface), and a communication component 705 .

[0064] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned industrial IoT-based production process scheduling method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data such as contact information, sent and received messages, images, audio, and video. The memory 702 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted via the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, which may include a keyboard, a mouse, buttons, etc. These buttons may be virtual or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G networks, or a combination thereof, is not limited here. Therefore, the communication component 705 may include a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0065] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned industrial Internet of Things-based production process scheduling method.

[0066] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for scheduling a production process based on the Industrial Internet of Things. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to implement the aforementioned method for scheduling a production process based on the Industrial Internet of Things.

[0067] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned production process scheduling method based on the industrial Internet of Things when executed by the programmable device.

[0068] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0069] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0070] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A production process scheduling method based on industrial Internet of Things, characterized in that: include: Obtain the production process to be scheduled, and obtain the preset production queue of the production process to be scheduled, wherein the preset production queue consists of the current production task and multiple pending production tasks, and obtain the completion time and production information of the current production task; Obtain environmental trend information corresponding to the completion time according to the completion time and production information, obtain dynamic correction indicators of each pending production task according to the environmental trend information, obtain process utilization of each pending production task, and obtain process utilization indicators based on the correction model, process utilization and dynamic correction indicators; Obtain the inventory unit time cost and delivery deadline of each pending production task, and obtain the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline; Arrange multiple pending production tasks in ascending order according to the scheduling indicators and generate a scheduling queue, and execute the production tasks according to the scheduling queue.

2. The production process scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of obtaining environmental trend information corresponding to the completion time according to the completion time and the production information and obtaining dynamic correction indicators of each pending production task according to the environmental trend information includes: Obtain the temperature change sequence of the current production task within the completed time, and predict the temperature interference trend parameter corresponding to the completion time based on the temperature change sequence; Obtain the electromagnetic intensity change sequence of the current production task within the completed time, and predict the electromagnetic interference trend parameter corresponding to the completion time based on the electromagnetic intensity change sequence; The temperature interference trend parameter and the electromagnetic interference trend parameter are combined into environmental trend information, and a dynamic correction index of each to-be-processed production task is obtained according to the environmental trend information.

3. The production process scheduling method based on industrial Internet of Things according to claim 2 is characterized in that: The dynamic correction index of each pending production task obtained based on the environmental trend information is expressed as: ； in, is the dynamic correction index of the i-th production task to be processed, is the temperature interference trend parameter corresponding to the completion time, is the temperature threshold for the effective operation of the i-th pending production task, is the electromagnetic interference trend parameter corresponding to the completion time, is the electromagnetic interference threshold for the effective operation of the i-th production task to be processed, is the temperature sensitivity weight of the i-th production task to be processed, is the electromagnetic interference sensitivity weight of the i-th production task to be processed.

4. The production process scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The process utilization index obtained based on the correction model, the process utilization rate and the dynamic correction index includes: Obtaining task information of each pending production task, and obtaining theoretical operating power of each pending production task in the production process to be scheduled based on the task information; Obtain the maximum operating power of the production process to be scheduled; The process utilization index is obtained based on the correction model, theoretical operating power, maximum operating power and dynamic correction index.

5. The production process scheduling method based on industrial Internet of Things according to claim 4 is characterized in that: The correction model in the indicator acquisition process based on the correction model, theoretical operating power, maximum operating power and dynamic correction index is expressed as follows: ;in, is the process utilization indicator of the i-th production task to be processed, is the dynamic correction index of the i-th production task to be processed, is the theoretical operating power of the i-th production task to be processed, is the maximum operating power of the production process to be scheduled.

6. The production process scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The scheduling indicators for each pending production task obtained based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline include: Obtain the task information of each pending production task, and obtain the target production amount of each pending production task based on the task information, and obtain the unit production amount of each pending production task under the standard utilization rate in the production process to be scheduled; Obtain scheduling indicators for each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount and unit production amount.

7. The production process scheduling method based on industrial Internet of Things according to claim 6 is characterized in that: The scheduling model for obtaining the scheduling indicators of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost, delivery deadline, target production amount and unit production amount is expressed as follows: ;in, is the scheduling index of the i-th production task to be processed, is the target production amount of the i-th production task to be processed, is the unit production amount of the i-th production task to be processed under the standard utilization rate, is the inventory unit time cost of the i-th production task to be processed, is the process utilization indicator of the i-th production task to be processed, is the delivery deadline of the i-th pending production task, For completion time.

8. A production process scheduling system based on industrial Internet of Things, characterized by: The system includes a management platform, a sensor network platform, and an object platform that are communicatively connected in sequence, and the management platform includes: A first acquisition module is used to acquire a production process to be scheduled and a preset production queue of the production process to be scheduled, wherein the preset production queue consists of a current production task and multiple pending production tasks, and acquire the completion time and production information of the current production task; A second acquisition module is configured to acquire environmental trend information corresponding to the completion time based on the completion time and the production information, and to acquire a dynamic correction index for each pending production task based on the environmental trend information, and to acquire a process utilization rate for each pending production task, and to acquire a process utilization index based on the correction model, the process utilization rate, and the dynamic correction index; a processing module for obtaining the inventory unit time cost and delivery deadline of each pending production task, and obtaining the scheduling index of each pending production task based on the scheduling model, completion time, process utilization index, inventory unit time cost and delivery deadline; The scheduling module is used to arrange multiple pending production tasks in ascending order according to the scheduling indicators and generate a scheduling queue, and execute the production tasks according to the scheduling queue.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the production process scheduling method based on the industrial Internet of Things as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the production process scheduling method based on the industrial Internet of Things described in any one of claims 1 to 7 is implemented.

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