A dynamic scheduling method for monitoring abnormal events based on an inter-vehicle communication system

Abstract

The application discloses a kind of based on workshop thing contact system monitoring abnormal event dynamic scheduling method, the information of production process is monitored by the means of Internet of Things, the information monitored will be uploaded to workshop network;After the basic information of production and manufacturing is extracted from workshop network, the three-target scheduling model of maximum completion time, single machine load and machine total processing time is established, and the basic scheduling method is provided by JAYA algorithm;After workshop Internet of Things system monitoring has abnormal event, the scheduling plan of already scheduling is adjusted locally using rescheduling strategy;After adjusting original scheduling, the maximum completion time and machine deviation degree are evaluated, and the feasibility of conforming to actual processing environment is continued to complete the remaining scheduling operation.The application uses the mode of workshop Internet of Things to improve the response capability to dynamic disturbance, and the dynamic scheduling strategy is committed to improving workshop original scheduling scheme in two aspects of processing efficiency and stability, to obtain effective workshop scheduling plan.

Description

Technical Field

[0001] This invention relates to the field of workshop operation scheduling technology, and in particular to a dynamic scheduling method based on monitoring abnormal events using a workshop IoT system. Background Technology

[0002] Currently, intelligent manufacturing is the mainstream trend in industrial manufacturing development. In the past, open production models resulted in significant waste, excessively long inventory turnover times, and stagnant inventory, making it increasingly difficult for such enterprises to survive. Small-batch and precision manufacturing is becoming a trend, aiming to minimize ineffective costs. Shop floor scheduling is usually done manually, relying almost entirely on experienced employees, which cannot achieve stability or optimal performance. Numerous intelligent scheduling methods researched in academia transform actual processing data into mathematical models, and the resulting scheduling plans are far superior to manual scheduling, achieving lower processing costs, faster speeds, and more substantial overall benefits. However, most existing scheduling methods address scheduling problems in the theoretically uninterrupted scenario. Once the original scheduling plan is officially implemented, the actual processing is highly likely to be interrupted by abnormal events, preventing subsequent processes from continuing according to the original plan. Some rescheduling strategies aim to solve these problems, but they suffer from poor stability, excessively long completion times, and high implementation costs. Excessive completion times in rescheduling naturally lead to poor economic efficiency, and numerous modifications to the processing equipment in the original scheduling plan result in poor stability in rescheduling. This will render the original equipment, routes, and personnel in the processing workshop ineffective, greatly increasing the actual processing cost. Some methods with excessively high implementation costs are only applicable to specific situations. Summary of the Invention

[0003] In view of the aforementioned shortcomings of existing technologies, the technical problem to be solved by this invention is that existing workshop scheduling methods rely on experienced senior employees, which cannot achieve stability and optimal performance. Existing intelligent algorithm-based scheduling methods can only be used for interference-free scheduling processes, resulting in low practicality. Limited by the complexity of abnormal events, existing intelligent scheduling methods cannot balance the requirements of stability and completion time, and their implementation costs are too high, their running time is too long, and they cannot generate new scheduling plans in a timely manner. This invention provides a dynamic scheduling method based on a workshop IoT system for monitoring abnormal events. It utilizes the workshop IoT system to monitor and eliminate abnormal downtime events in workshop processing in a timely manner. It has an effective static scheduling scheme under interference-free conditions and forms a feasible dynamic scheduling strategy under interference conditions. The workshop IoT system responds quickly to abnormal events, and the dynamic scheduling strategy can be flexibly adjusted based on the original scheduling. This comprehensive dynamic scheduling method is highly flexible and can achieve the goal of reducing workshop production costs for enterprises.

[0004] To achieve the above objectives, this invention provides a dynamic scheduling method based on monitoring abnormal events using a workshop IoT system, comprising the following steps:

[0005] Extract basic processing information from the workshop manufacturing platform and establish a three-objective scheduling mathematical model that includes completion time, single machine load, and total machine processing time;

[0006] Preliminary scheduling results were obtained by optimizing the three-objective scheduling mathematical model using the Pareto method and the JAYA algorithm.

[0007] Based on the preliminary scheduling results, production is scheduled in the workshop, and during the processing, the physical condition of the workshop is monitored using the workshop Internet of Things system;

[0008] When downtime occurs, the machine that stopped abnormally is marked as abnormal equipment, and the abnormal equipment is suspended from processing; the process that the abnormal equipment is currently processing is added to the abnormal process set to wait for assignment; the process that the equipment was originally scheduled to process later is marked as suspended, and other unaffected equipment continues to process.

[0009] Based on the downtime event, analyze the alarm information in the workshop IoT system. If it is a general downtime event, remotely control the equipment to start through the workshop IoT system. If it is a complex downtime event, notify maintenance personnel to troubleshoot the fault. Finally, control the relevant equipment to start through the workshop IoT system and resume processing of the abnormal equipment.

[0010] Based on the original scheduling, a rescheduling strategy is used to mitigate the impact of downtime caused by abnormal events. Equipment that was originally abnormal is restarted. After assessing the efficiency and stability of the new scheduling scheme and determining its feasibility under actual conditions, production continues as planned until all processes are completed.

[0011] Furthermore, basic processing information, including processing equipment information, process information, and personnel configuration information, is extracted from the workshop manufacturing platform.

[0012] Furthermore, a three-objective scheduling mathematical model is established, encompassing completion time, single machine load, and total machine processing time, specifically including:

[0013] Based on the scheduling plan, establish a workshop completion time scheduling model.

[0014] Based on the processing time at each piece of equipment, establish a single machine load model for the workshop;

[0015] Establish a model of the total processing time for all equipment in the workshop.

[0016] Furthermore, the three-objective scheduling mathematical model was optimized using the JAYA algorithm to obtain preliminary scheduling results. This mainly utilizes the integer encoding method of processes and jobs, specifically including: first, initializing the population, then performing encoding and decoding operations; using Pareto layering to determine the best and worst reference solutions for each layer; optimizing using the JAYA algorithm, merging parent and child populations to select the best individual from the population; and obtaining the initial scheduling plan when the loop reaches the maximum number of iterations.

[0017] Furthermore, general downtime events include downtime events caused by human error, while complex downtime events include downtime events caused by equipment failure.

[0018] Furthermore, if a downtime is caused by a complex event, the abnormal process will be replaced with another machine that can process it; other subsequent processes will be arranged in sequence to form a dynamic scheduling plan.

[0019] Furthermore, the previously malfunctioning equipment is reactivated. After assessing the feasibility of the new scheduling scheme in terms of efficiency and stability under actual conditions, production continues as planned until all processes are completed. The assessment of the feasibility of the new scheduling scheme in terms of efficiency and stability under actual conditions includes evaluating the minimum completion time and machine offset rate; the machine offset rate represents the stability of dynamic scheduling.

[0020] Furthermore, the formula for calculating the machine offset rate is as follows:

[0021]

[0022] This indicates the number of processing steps dynamically adjusted on machine number s, m e It represents the total number of all machines whose processing procedures have been adjusted.

[0023] Furthermore, the rescheduling strategy takes into account the recovery time of both types of abnormal devices.

[0024] Furthermore, the two scenarios for abnormal equipment recovery time include: First, short-term recovery of abnormal equipment, where the abnormal process will be delayed, and the new start time will be the recovery time. Subsequent affected processes will not have their original processing order and equipment changed; only the start time of processing will be rearranged. Second, short-term unavailability of abnormal equipment, where the proposed rescheduling method will transfer the abnormal process to another machine sequence for processing. The new start time of the abnormal process will be defined as the earliest available start time in that sequence. Subsequent affected processes will not have their original processing order and equipment changed; only the start time will be altered. In the new scheduling plan, the processing machines of the directly affected processes will be sacrificed.

[0025] Technical effect

[0026] This invention proposes a dynamic scheduling method based on monitoring abnormal events using a workshop IoT system. This method combines a workshop IoT system with a rescheduling strategy to address situations where the original scheduling plan cannot continue to be executed due to interference from abnormal events and a rescheduling is urgently needed. Considering that abnormal events with different recovery times have a significant impact on the rescheduling results, the value of the workshop IoT system lies in enabling some abnormal events to recover in a short period of time, which is beneficial to the rescheduling process. The subsequent rescheduling strategy has the advantage of balancing completion time and stability and is applicable to most industrial scheduling scenarios.

[0027] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention of a dynamic scheduling method based on monitoring abnormal events in a workshop IoT system.

[0029] Figure 2 This is an initial scheduling Gantt chart using the JAYA method, representing a preferred embodiment of a dynamic scheduling method for monitoring abnormal events in a workshop IoT system according to the present invention.

[0030] Figure 3 This is a Gantt chart of a short-term interference dynamic scheduling strategy based on a dynamic scheduling method for monitoring abnormal events in a workshop IoT system, which is a preferred embodiment of the present invention.

[0031] Figure 4 This is a Gantt chart of a long-term interference dynamic scheduling strategy based on a dynamic scheduling method for monitoring abnormal events in a workshop IoT system, which is a preferred embodiment of the present invention. Detailed Implementation

[0032] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0033] In the following description, specific details, such as particular internal procedures and techniques, are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will appreciate that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of the invention with unnecessary detail.

[0034] like Figure 1As shown, this embodiment of the invention provides a dynamic scheduling method based on monitoring abnormal events in a workshop IoT system, including the following steps:

[0035] Step 100: Extract basic processing information from the workshop manufacturing platform and establish a three-objective scheduling mathematical model including completion time, single machine load, and total machine processing time. Before formal processing, it is necessary to confirm the absence of equipment-related alarms and the normal personnel configuration. The extracted basic processing information includes processing equipment information, process information, and personnel configuration information, etc. As shown in Table 1: There are 5 production machines in the workshop used to process two products. Each process has a set of available machines and a corresponding processing time. The processes within each workpiece are constrained by sequence, and each process will be performed on one machine.

[0036] Table 1 Basic Information on Product Processing

[0037]

[0038] Scheduling is performed using the extracted basic information, with the optimization objective of minimizing the maximum completion time, maximum single machine load, and maximum total machine processing time. The description is as follows:

[0039]

[0040]

[0041]

[0042] This represents the deadline for the k-th process of the j-th workpiece in the i-th product. `max` represents the deadline for the latest process. `f1` represents the latest completion time among all equipment. This indicates the processing time for each step. f1 represents each process step. f2 represents the longest processing time on a single piece of equipment. f3 represents the total processing time across all equipment.

[0043] The above objectives satisfy the following constraints:

[0044] 1) The preparation, transportation, and installation time for each process are included in the processing time;

[0045] 2) Each process can only be processed on one machine at a time;

[0046] 3) Each piece of equipment can only process one process at a time;

[0047] 4) The processes in each workpiece need to be arranged in sequence;

[0048] 5) Processing must not be stopped unless there are abnormal interference events.

[0049] In step 100, a three-objective scheduling mathematical model is established, which includes completion time, single machine load, and total machine processing time. Specifically, this includes:

[0050] 1. Establish a workshop completion time scheduling model;

[0051] According to the scheduling plan, each processing machine will be assigned a processing step in sequence. The completion time for each machine is not uniform. When the last processing step is completed, the entire workpiece is considered finished. The calculation formula is as follows.

[0052]

[0053] This represents the deadline for the k-th process of the j-th workpiece in the i-th product. `max` represents the deadline for the latest process.

[0054] 2. Establish a single-machine load model;

[0055] In a scheduling plan, each piece of equipment has processing time and idle time. The processing time for each piece of equipment is not uniform. If the processing time of one piece of equipment is significantly longer than that of other equipment, a problematic situation can arise: this piece of equipment may fail first, leading to instability in the processing flow. The formula for the maximum load of workshop equipment is as follows:

[0056]

[0057] This indicates the processing time for each step. This indicates each process step.

[0058] 3. Establish a model of the total processing time for all equipment in the workshop;

[0059] Each piece of equipment in the processing stage represents a portion of the company's costs. The best scheduling outcome is to minimize the total processing time, which maximizes economic efficiency. The formula for the total processing time of all equipment is as follows:

[0060]

[0061] Furthermore, the previously malfunctioning equipment is reactivated. After assessing the feasibility of the new scheduling scheme in terms of efficiency and stability under actual conditions, production continues as planned until all processes are completed. The assessment of the feasibility of the new scheduling scheme in terms of efficiency and stability under actual conditions includes evaluating the minimum completion time and machine offset rate; the machine offset rate represents the stability of dynamic scheduling.

[0062] Furthermore, the formula for calculating the machine offset rate is as follows:

[0063]

[0064] PN s 'Indicates the number of processing steps dynamically adjusted on machine number s, m e It represents the total number of all machines whose processing procedures have been adjusted.

[0065] Step 200: Optimize the three-objective scheduling mathematical model using the Pareto method and the JAYA algorithm to obtain preliminary scheduling results. This optimization primarily utilizes integer encoding of processes and jobs, specifically including: initializing the population, followed by encoding and decoding operations; using Pareto layering to determine the best and worst reference solutions for each layer; optimizing using the JAYA algorithm, merging parent and child populations to select the best individuals; and obtaining the initial scheduling plan when the loop reaches its maximum iteration count. Specifically: Assign integers to processes and machines for encoding. In the process encoding, the tens digit represents the product number, and the units digit represents the job number. For example, the integer 00 represents job number 1 for product number 1. In machine encoding, the machine number is directly used as the integer. The length of the encoding is the total number of processes. Initialize and generate an initial population of 100 individuals; use a greedy decoding method to decode each process. The principle is that the processing machine and processing time of an individual can be determined, and the start time of the processing sequence should be as early as possible. In the processing machine sequence, idle processing intervals are searched from front to back. If the processing time is exactly less than this interval, the start time of the process can be determined. According to the three-objective formula, each individual is evaluated as a three-vector solution. After decoding, each individual is Pareto sorted, and the population is divided into numerous Pareto layers. The maximum value of each of the three objectives is found, and all individuals are divided by this value for normalization. Based on the niche principle, the two adjacent individuals in each Pareto layer are determined, and their straight-line distances are calculated and sorted. In each Pareto layer, the individual with the longest straight-line distance between adjacent individuals is considered the optimal solution, and the shortest is the worst solution. The JAYA algorithm uses the optimal and worst individuals in each layer as reference solutions to generate new individuals. The process operator is used to optimize the process vector X, and the worst individual X is selected as the optimal solution. worst and random individual X t1 The code at each position is matched, and the same code at the same position is recorded and placed in X. t X is obtained by removing X. t2 The optimal individual X is extracted sequentially. beat The encoding attempt at each position is in X t2If a matching code is found, it will be deleted; the others will be retained as a sequence S. The codes in sequence S will then be sequentially placed into X. t2 The position without encoding, X t2 Becoming a new individual X new ;Optimize the machine vector Y using machine operators, and select the worst individual Y worst and random individual Y t1 The code at each position is matched, and the same code at the same position is recorded and added to Y. t Remove Y during the process to obtain Y t2 The optimal individual Y best and Y t2 Each position corresponds to Y t2 The missing code in the middle position will be provided by Y. best The corresponding position's encoding is supplemented to obtain Y. new Each new individual mutates with a probability of 0.1. The new population formed in each iteration is merged with the old population and reordered. Individuals with lower Pareto levels and the top 100 individuals from niches are retained. After 500 iterations, the loop exits, and the optimal solution in the lowest Pareto level is used as the scheduling result. The JAYA method improves the overall convergence performance of the algorithm by utilizing the reference solution and escapes potential local optima through the mutation step, exhibiting good optimization performance. Figure 2 The image shows the initial scheduling plan. The horizontal axis represents the processing time, and the vertical axis represents the machine number. The completion time shown in the image is 1750s.

[0066] Step 300: Based on the preliminary scheduling results, production is scheduled in the workshop, and during processing, the workshop's physical conditions are monitored using the workshop's IoT system; specifically, according to... Figure 1 The process follows the flowchart shown, proceeding according to the initial scheduling plan. During the actual processing, the workshop IoT system will issue alarms for temperature, current, pressure, and vibration signals. If the issue is due to human error, it can be detected through video monitoring. If no abnormalities are detected, the processing continues.

[0067] Step 400: When a downtime occurs, the machine that stopped abnormally is marked as abnormal equipment, and the abnormal equipment is suspended from processing; the process that the abnormal equipment is currently processing is added to the abnormal process set to wait for allocation; the process that the equipment was originally scheduled to process later is marked as suspended, and other unaffected equipment continues processing;

[0068] Step 500: Based on the shutdown event, analyze the alarm information in the workshop IoT system. If it is a general shutdown event, remotely control the equipment to start it up through the workshop IoT system. If it is a complex shutdown event, notify maintenance personnel to troubleshoot the fault. Finally, control the relevant equipment to start up through the workshop IoT system and resume processing of the abnormal equipment. Specifically, by analyzing the alarm source of the workshop IoT system, and based on the human-machine interface of the workshop IoT system, remote operation and debugging are used to troubleshoot general shutdown situations. For example, if personnel violate regulations and cause the relevant equipment to stop running, there is no need for a complex equipment repair process; remote control can start the equipment to ensure production continues in a timely manner. Or, when the equipment makes abnormal noises and workshop machine 4 stops directly, the workshop IoT system detects multiple abnormal data such as current and vibration, and judges it as a complex shutdown accident that cannot be easily resolved. Relevant personnel are then contacted to troubleshoot the abnormality.

[0069] Step 600: Based on the original scheduling, the rescheduling strategy is used to mitigate the impact of abnormal events and the equipment that was originally abnormal is restarted. After assessing the efficiency and stability of the new scheduling scheme and its feasibility under actual conditions, production continues as planned until all processes are completed.

[0070] When a workshop anomaly occurs, the following model can be used to simulate its occurrence, including simulations in three aspects: the first aspect is the selection of the abnormal machine M. s The first is the product of the number of machines and a random decimal between [0,1]. `int()` represents rounding down, meaning a machine with a certain number cannot process normally. The second is the time when the abnormal situation occurs, which will be specified in BT. s , is the product of the completion time of the original scheduling phase and a random decimal number between [0,1]. The third is TN, which can simulate two recovery durations: int(TN[0]) corresponds to the short-term recovery duration anomaly, and int(TN[1]) corresponds to the long-term recovery duration anomaly. The specific simulation model is as follows:

[0071] M s =int(m*rand()), where m is the number of machines, rand() represents a random decimal number between [0,1], and int represents rounding;

[0072] in, Indicates the completion time;

[0073] T e ∈[0,0.2],T g ∈(0.2,1).

[0074] The original scheduling plan is Figure 2As shown in the example, suppose that a machine downtime occurs during the actual processing in the workshop at 100s. The downtime occurs on machine number 4. At this moment, process P0J3T0 cannot start processing as planned at 335s. Without a new scheduling plan, processes P1J3T2, P0J4T0, and P1J4T0, which were originally scheduled to continue processing, also cannot continue processing.

[0075] The details of the rescheduling scheme in this invention will be described, which takes into account the recovery time of two types of abnormal devices.

[0076] In the first scenario, where the abnormal equipment recovers quickly, the affected processes will be rescheduled with a new start time. Subsequent affected processes will not have their original processing order or equipment changed; only their start times will be rearranged. In this new scheduling plan, most affected processes will not have their processing equipment altered, and the extended processing time will be kept within a short recovery timeframe. Assuming machine number 4 recovers in 450 seconds... Figure 3 The situation is as follows: Operation P0J3T0 was directly affected and stopped at 100s. Operations P1J3T2, P0J4T0, and P1J4T0 could not continue processing according to the original plan due to the delayed processing of operation P0J3T0. Until machine 4 resumed operation at 450s, the proposed rescheduling strategy delayed operation P0J3T0 on machine 4 until that time, an arrangement consistent with the actual workshop conditions. According to the basic information in Table 1, the processing time of operation P0J3T0 on machine 4 is 440s, allowing operation P1J3T2 to be scheduled for processing at the end time of operation P0J3T0, 890s. In Table 1, the processing time of operation P0J4T0 on machine 5 is 750s, and the processing time of operation P1J4T0 on machine 5 is 225s. Operation P0J4T0 will be scheduled to begin processing at 890s after operation P0J3T0, and operation P1J4T0 will be scheduled to begin processing at 1640s after operation P0J4T0. Figure 3 The data shows a rescheduled completion time of 1865 seconds. To ensure short-term recovery of faulty equipment, all processing equipment for all processes remains in its original state, therefore PN... s =PN s Then the machine offset rate f4 is 0.

[0077] The second scenario involves equipment malfunctioning and becoming temporarily unavailable. The proposed rescheduling method transfers the malfunctioning process to another machine sequence. The new start time for the malfunctioning process will be defined as the earliest available start time in that sequence. Subsequent affected processes will not change their original processing order or equipment; only their start times will be altered. The new scheduling plan will sacrifice the processing machines directly affected by the changes. Since malfunctions cannot be resolved quickly, the workshop previously delayed processing all affected processes, significantly extending completion time and resulting in incalculable losses. The proposed rescheduling method increases the machine offset rate to substantially reduce the delayed completion time. Assuming a 100s malfunction occurs, process P0J3T0 on machine 4 is directly affected and cannot continue processing. This type of event is resolved by transferring the process to another available machine. According to Table 1, process P0J3T0 will be assigned to machine 3 for processing. Figure 4 As shown, operation P0J3T0 can be scheduled as early as the end time of P0J2T1, 492s. At this time, operation P0J3T0 is inserted into the processing sequence of machine 3, while the order of other operations in the processing sequence of machine 3 is set to remain unchanged. According to the information in Table 1, the processing time of operation P0J3T0 on machine 3 is 400s. Due to this change, the start times of operations P0J2T2, P1J1T0, and P1J2T0 on machine 3 will be delayed by 400s, to 932s, 1183s, and 1333s respectively. Assume the faulty equipment recovers at 570s. In the original scheduling, the original start time of operation P1J3T2 in the processing sequence of machine 4 was 775s. Since the faulty machine is activated at 570s, the preceding operation P0J3T0 is no longer in this processing sequence, and the start time of operation P1J3T2 will be advanced to 570s. The start time of process P0J4T0 will be delayed until the new end time of process P0J2T2, which is 1183s. Process P1J4T0 will be scheduled to start processing 1933s after process P0J4T0. Figure 4 The data shows a rescheduled completion time of 2158 seconds. Regarding the case where machine 4, the faulty equipment, requires a long recovery period, one processing unit from a faulty operation was rescheduled, therefore |PN s '-PN s If |=1, then the machine offset rate is (0.5+0.2) / 5=0.14. The new scheduling plan reduces the economic losses caused by the inability of some processes to resume processing in the short term due to abnormal events by changing the processing equipment of one process. This rescheduling method considers both shorter completion time and stability.

[0078] Figure 3The minimum completion time for medium-heavy scheduling is 1865s, and the machine offset rate is 0. This scheduling plan can proceed with processing. Figure 4 The medium-heavy scheduling result is a minimum completion time of 2158s and a machine offset rate of 0.14. The new dynamic scheduling plan is in line with the actual situation and is feasible. The scheduling plan is updated and production is scheduled until all processes are completed.

[0079] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A dynamic scheduling method for monitoring abnormal events based on an inter-plant object system, characterized by, The method comprises the following steps: extracting basic information of processing from the workshop manufacturing platform, establishing a three-objective scheduling mathematical model including the finished product time, single machine load and total machine processing time; extracting basic information of processing from the workshop manufacturing platform, including processing equipment information, process information and personnel configuration information; establishing a three-objective scheduling model of maximum finished product time, single machine load and total machine processing time; optimizing the three-objective scheduling mathematical model using the pareto method and using the JAYA algorithm to obtain a preliminary scheduling result; The integer coding mode of process and workpiece is mainly used, including: firstly, population initialization is performed, then coding and decoding operations are performed; the pareto layer is used to determine the best reference solution and the worst reference solution of each layer; the JAYA algorithm is used for optimization, and the parent and child populations are selected to obtain the individual of the best population; when the loop reaches the maximum iteration number, the initial scheduling plan is obtained; the integer coding of process and machine allocation is performed, the ten digits in the process coding represent the product number, and the individual digit represents the operation number; the length of the coding is the number of all processes; 100 initial individuals are generated in the initial population; the greedy decoding method is used to decode each process; the idle processing interval is searched from front to back in the processing machine sequence, and when the processing time is exactly less than the interval, the starting processing time of the process is determined; according to the formula of three targets, each individual will be evaluated as a three-vector solution, and each individual is sorted after decoding, and the population will be divided into numerous pareto layers; the maximum value of the three targets is found respectively, all individuals are divided by the maximum value, and then normalized; according to the niche principle, the adjacent two individuals in each individual in each pareto layer are determined, the straight line distance between them is calculated, and they are sorted; in each pareto layer, the individual with the longest straight line distance between adjacent individuals is considered as the optimal solution, and the shortest one is the worst solution; the JAYA algorithm uses the optimal and worst individuals in each layer as reference solutions to generate new individuals; the process operator is used to optimize the process vector X, and the encoding of each position of the worst individual X worst and the random individual X t1 is matched, the same encoding in the same position is recorded, and the encoding in Y t is removed to obtain Y t2 ,the optimal individual Y best and Y t2 are matched in each position, and the missing encoding in the position of Y t2 is supplemented by the encoding in the corresponding position of Y best to obtain Y new . worst t1 t t2 beat t2 t2 t2 new worst t1 t t2 best t2 t2 best new ; each new individual is mutated with a probability of 0.1, the new population formed in each iteration is combined with the old population and reordered, the individuals in the lower pareto layer and the top 100 individuals in the niching layer are reserved; the loop is exited after 500 iterations, and the optimal solution in the lowest pareto layer is taken as the scheduling result; scheduling production according to the preliminary scheduling result, and monitoring the physical situation of the workshop during processing using the workshop Internet of Things system; when downtime occurs, the abnormal machine is marked as an abnormal device, the abnormal device suspends processing, the process being processed by the abnormal device enters an abnormal process set to wait for allocation, and the subsequent processes of the device are marked as suspended, and other devices continue to process; according to the downtime event, analyzing the alarm information in the workshop Internet of Things system, if it is a general downtime event, starting the device remotely through the workshop Internet of Things system; if it is a complex downtime event, notifying maintenance personnel to troubleshoot, and finally, controlling the related device to start through the workshop Internet of Things system to restore the processing of the abnormal device; on the basis of the original scheduling, using a rescheduling strategy to reduce the impact of abnormal event downtime, the originally abnormal device is re-enabled, and after evaluating the work efficiency and stability of the new scheduling scheme, the actual feasibility is continued according to the plan, until all processes are completed.

2. The dynamic scheduling method for monitoring abnormal events based on the inter-vehicle system according to claim 1, wherein, establishing a three-objective scheduling mathematical model including the finished product time, single machine load and total machine processing time, specifically including: establishing a workshop finished product time scheduling model according to the scheduling plan, establishing a workshop single machine load model according to the length of time each device is processing; establishing a total machine processing time model of all devices in the workshop.

3. The dynamic scheduling method for monitoring abnormal events based on the inter-vehicle system according to claim 1, wherein, General downtime events include downtime events caused by personnel misoperation, and complex downtime events include downtime events caused by device failure.

4. The dynamic scheduling method for monitoring abnormal events based on the inter-vehicle system according to claim 3, wherein, If the downtime is caused by a complex event, replace the abnormal process with other machines that can process; other subsequent processes are arranged in turn to form a dynamic scheduling plan.

5. The dynamic scheduling method for monitoring abnormal events based on the inter-vehicle system according to claim 1, wherein, The originally abnormal device is re-enabled, and after evaluating the work efficiency and stability of the new scheduling scheme, the actual feasibility is continued according to the plan, until all processes are completed, and evaluating the work efficiency and stability of the new scheduling scheme includes evaluating the minimum finished product time and machine deviation rate; the machine deviation rate represents the stability of dynamic scheduling.

6. The dynamic scheduling method of monitoring abnormal events based on an inter-vehicle system according to claim 5, wherein, The calculation formula of the machine deviation rate is as follows: ， represents the number of processing steps adjusted dynamically on the s-th machine, is the number of all machines on which processing step adjustment has occurred.

7. The dynamic dispatch method of monitoring abnormal events based on an inter-vehicle system according to claim 5, wherein, The rescheduling strategy considers two abnormal device recovery time length conditions.

8. The dynamic scheduling method of monitoring abnormal events based on an inter-vehicle system according to claim 7, wherein, Two abnormal equipment recovery time length cases include, one is abnormal equipment short-term recovery case, abnormal process will be delayed arrangement, the new start time is the recovery time, the subsequent affected process will not change the original processing order and processing equipment, only the start time of processing is rearranged; Two is that the abnormal equipment cannot be started for a short time, the rescheduling method proposed will transfer the abnormal process to another machine sequence for processing, the new start time of the abnormal process will be defined as the earliest start time that can be arranged in the sequence, the subsequent affected process will not change the original processing order and processing equipment, only the start time is changed. In the new scheduling plan, the processing machine of the directly affected process will be sacrificed.

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