Time sensor scheduling method based on 5G deterministic network

Abstract

The invention discloses a time sensor scheduling method based on a 5G deterministic network, and relates to the technical field of communication engineering, and the method comprises the steps: enabling an automatic equipment group to be wirelessly connected with a time sensor through a 5G network, and obtaining the real-time operation time, the remaining processing time and the maximum operation time of each equipment; calculating a load value according to the real-time operation time and the maximum operation time, judging the load condition of the equipment according to the load value, marking categories and sorting, and carrying out rest scheduling according to the category and sorting; task load division, preferential distribution of emergency tasks, reasonable distribution of conventional tasks and elastic tasks are carried out by combining the theoretical residual capacity of the equipment, the tasks are stored in a buffer pool for specific equipment application, the task completion condition is monitored in real time, and task load stripping and equipment category adjustment are carried out when necessary; according to the method, automatic equipment scheduling and task allocation can be optimized, the production efficiency is improved, and the equipment fault risk is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of communication engineering, and particularly relates to a time sensor scheduling method based on a 5G deterministic network. BACKGROUND

[0002] In the current field of automatic production, the device scheduling system relies on traditional wired connection or unstable wireless network, and data acquisition and transmission often have delays and packet loss, so it is difficult to accurately obtain key data such as real-time running, remaining and maximum processing time of the device; load evaluation is also relatively extensive, and load levels are simply divided according to preset thresholds, and cannot be dynamically adjusted according to the real-time state of the device; task allocation is mostly roughly performed according to the type of the device or the urgency of the task, without comprehensively considering the real-time production capacity and load of the device, so that the high-load device is easy to continuously overwork, accelerating wear and tear, and the low-load device is idle, and task allocation is also not accurate, affecting production efficiency and quality.

[0003] Based on the above, the application provides a time sensor scheduling method based on a 5G deterministic network, which uses a time sensor to collect data in real time, and uses the high reliability and low delay characteristics of 5G to ensure the accuracy and timeliness of data transmission; accurately judges the load of the device and dynamically classifies it, and finely schedules the device rest and task allocation according to the classification; compared with the prior art, the method overcomes the shortcomings of unstable data transmission, extensive load judgment, and unscientific task allocation, realizes rational use of device resources and efficient production, and significantly improves the overall efficiency of automatic production and the service life of the device. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a time sensor scheduling method based on a 5G deterministic network, which solves the problems of unstable data transmission, extensive load judgment and unscientific task allocation in the field of automatic devices in the prior art.

[0005] The purpose of the application can be achieved by the following technical solutions: The time sensor scheduling method based on a 5G deterministic network comprises the following steps: Step 1: using a pre-constructed 5G network to wirelessly connect any one of the automatic devices in the automatic device group to the time sensor, and obtaining the real-time running time, remaining processing time and maximum running time of any one of the automatic devices in the automatic device group through the time sensor; Step 2: calculating the load value associated with the automatic device based on the real-time running time and maximum running time of each automatic device in the automatic device group, judging the load condition of the automatic device based on the load value, marking the category and sorting, and scheduling the rest based on the load condition of the sorted automatic device; Step three, extract the remaining processing time of each automation equipment in the automation equipment group, the historical average processing rate combined with the classification marked by the corresponding automation equipment correct the theoretical remaining capacity of the automation equipment, and schedule the task quantity based on the corrected theoretical remaining capacity of the automation equipment.

[0006] As a further scheme of the present application, the automation equipment group in step one is composed of a plurality of automation equipment, and the plurality of automation equipment are connected through a 5G network for data acquisition and transmission. The collected data includes real-time running time, remaining processing time and maximum running time of the automation equipment.

[0007] As a further scheme of the present application, in step one, the specific way of real-time obtaining the real-time running time and the remaining processing time of any automation equipment in the automation equipment group through the time sensor is: Select the automation equipment group A as the collection target, extract j automation equipment in A, and mark them as automation equipment sequence according to the extraction order: , wherein j is the total number of automation equipment in A; Real-time extract the real-time running time and the remaining processing time of the j automation equipment in the automation equipment sequence through the time sensor, and sort them as real-time running time sequence and remaining processing time sequence according to the order of the automation equipment sequence; Real-time running time sequence: ; Remaining processing time sequence: ; Extract the automation equipment Associated real-time running time , remaining processing time , wherein i is the count index, and the value range is 1 to j.

[0008] As a further scheme of the present application, the specific way of judging the load condition of the automation equipment based on the load value and marking the category and sorting is: S41, based on the real-time running time , remaining processing time Associated with the automation equipment Determine the load value Associated with the automation equipment ; S42, further determine the load value of all automation equipment in the automation equipment sequence: , and sort them in ascending order according to the numerical value of the load value, and mark them as automation equipment load sequence ; S43, The 25th percentile 75th percentile , load value Automated equipment is categorized as Low; load value And load value Automated equipment is categorized as Med; load value Automated equipment is categorized as High.

[0009] As a further aspect of the present invention, the specific method for rest scheduling based on the load status of the sorted automated equipment is as follows: Extract the automated equipment marked as High, generate a rest instruction, add it to the forced rest queue in descending order of load value, and issue a shutdown instruction. The prerequisite for adding it to the forced rest queue is that the task currently being performed by the automated equipment has been completed. Then, extract any automated device marked Med, use W = 75th percentile - load value to get the difference W, obtain the difference threshold E preset by the operator, and compare the difference W with the difference threshold E. If W E, then add the corresponding automated equipment to the flexible rest queue, if W E then sets the corresponding automated equipment to remain operational and monitor it in real time. For automated equipment marked as "Low", extract its remaining processing time and generate dynamic task filling instructions, and synchronously add processing tasks of the same type.

[0010] As a further aspect of the present invention, the specific method for rest scheduling based on the load status of the sorted automated equipment also includes: When the automated equipment resources are released from the forced rest queue, all automated equipment of the same type as the released automated equipment resources are extracted from the elastic rest queue and summarized as the elastic rest equipment sequence R. The difference between the load value of all automated equipment in R and the 75th percentile is determined, and the automated equipment with the smallest difference is locked as the replacement target. Replace the automated equipment resources released from the forced rest queue with the replacement target.

[0011] As a further aspect of the present invention, the specific method for scheduling the task volume based on the corrected theoretical remaining capacity of the automated equipment in step three is as follows: S71. Determine the automated equipment Historical average processing rate Remaining processing time ,use Quantitative automation equipment Theoretical surplus capacity Among them, automated equipment Historical average processing rate By automated equipment Historical average processing rate and All of these are parameter adjustments. Greater than 0 and Less than 0, and The specific values ​​are determined by the operators based on the actual situation; S72, based on The category it belongs to affects the theoretical surplus capacity. Make corrections; like At High, adopt Theoretical surplus capacity was obtained by correction. ; like In Med, using Theoretical surplus capacity was obtained by correction. ; like In Low, adopt Theoretical surplus capacity was obtained by correction. ; in, The overload attenuation coefficient preset for operators has a value range of [0.5, 1]. The preset elasticity coefficient for operators has a value range of []. ,1]、 The expansion coefficient preset for operators has a range of values ​​( ,1.2]; Repeat this step to obtain the theoretical remaining capacity of all automated equipment in the automated equipment sequence, and then summarize them to obtain the set of theoretical remaining capacity. ; S73. Extract the delivery deadline of each subtask from the total task volume, and sort them in ascending order of delivery deadline to obtain the delivery deadline sequence. ,in, This represents the total number of subtasks in the total task load, and locks the delivery deadline of any subtask in the delivery deadline time sequence, denoted as . ; S74. Obtain the minimum delivery deadline threshold preset by the operator. Normal delivery deadline And each delivery deadline in the delivery deadline time series is compared with the minimum delivery deadline threshold. Normal delivery deadline Perform a comparison; like Then The associated subtasks are classified as urgent tasks; like Then The associated subtasks are classified as regular tasks; like Then The associated subtasks are classified as elastic tasks; S75. Prioritize assigning emergency tasks to the top k automated devices with the largest theoretical remaining capacity in the Low category, where k is the number of emergency tasks. S76. Extracting the theoretical surplus capacity set With theoretical surplus capacity The associated automated equipment Perform example processing; according to Determining the ratio between theoretical remaining capacity and the theoretical remaining capacity of other automated equipment The associated task allocation weights are obtained by repeating the above steps to get the task allocation weights associated with each of the j automated devices. These weights are then normalized and recorded as follows: ; Determine the normal workload as ; The regular task load associated with each of the j automated devices is allocated according to the weighted distribution of the tasks associated with each device. The associated regular task volume And so on; S77. Store elastic tasks in a shared task pool and allow automated devices in the Low, Med, and High categories to claim additional tasks after completing their assigned tasks.

[0012] As a further aspect of the present invention, the specific method for scheduling the workload based on the corrected theoretical remaining capacity of automated equipment also includes: Monitor the task completion status of j automated devices. If any one of the automated devices... The assigned workload exceeds that of automated equipment. The associated theoretical spare capacity If so, task volume stripping will be triggered; If the automated equipment If task stripping is triggered p times consecutively, the automated equipment will be automatically removed. Labeled as category High, where p is a preset value for the operator.

[0013] As a further scheme of the present application, the specific way of the task amount stripping is: exceeding the theoretical residual capacity of the automated equipment part of the task amount is stored in the task amount sharing buffer pool.

[0014] The present application has the following beneficial effects: The present application realizes real-time dynamic precise scheduling of the automated equipment group, effectively improves the overall efficiency of the production system, and through the pre-constructed 5G network connection between the automated equipment group and the time sensor, the real-time running time, residual processing time and maximum running time of the equipment and other key data can be obtained in real time. Based on these data, the load value calculated can accurately judge the load state of the equipment and perform category labeling and sorting. For high-load equipment, rest instructions are generated in time and added to the forced rest queue; for medium-load equipment, whether to join the elastic rest queue is determined according to the difference from the 75% percentile; and for low-load equipment, the same type of processing task is dynamically added. This on-demand allocation of rest time strategy not only avoids equipment failure due to excessive running, but also fully utilizes the idle capacity of low-load equipment, ensuring that the entire production system is running efficiently while extending the service life of the equipment and reducing maintenance costs. The present application improves the rationality and execution efficiency of task allocation by dynamically correcting the theoretical residual capacity and combining the task urgency. When determining the theoretical residual capacity of the automated equipment, not only the real-time running time and residual processing time are considered, but also the historical average processing rate is taken into account. According to the category of the equipment, different correction coefficients are used to adjust the theoretical residual capacity, so as to obtain more realistic residual capacity data. At the same time, each sub-task in the total task amount is classified according to the delivery deadline, divided into urgent tasks, regular tasks and flexible tasks. According to the urgency of the task and the corrected residual capacity of the equipment, the urgent tasks are preferentially allocated to low-load equipment with larger residual capacity, the regular tasks are reasonably allocated according to the task amount allocation weight, and the flexible tasks are stored in the shared buffer pool for equipment to claim. This task allocation method not only fully considers the actual running capacity of the equipment, but also takes into account the urgency of the task, so that the task can be processed more timely and efficiently, and the task execution efficiency and resource utilization of the production system are improved as a whole. ​This application introduces a task stripping and sharing mechanism to further enhance the flexibility and robustness of production scheduling. During task allocation, the task completion status of automated equipment is monitored in real time. If the task assigned to a piece of equipment exceeds its theoretical remaining capacity, the task stripping mechanism is triggered, storing the excess task in a shared task buffer pool. Furthermore, if a piece of equipment triggers task stripping multiple times consecutively, the system automatically marks it as a high-load category for subsequent scheduling adjustments. This mechanism provides additional security for production scheduling, effectively preventing equipment overload or task backlog caused by unreasonable task allocation. After completing its assigned tasks, equipment can also proactively claim flexible tasks from the shared buffer pool, thereby fully utilizing idle time, further improving resource utilization, and enhancing the flexibility and adaptability of the entire production process. Attached Figure Description

[0015] The present invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart illustrating the method described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 2 of the present invention; Figure 3 This is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 A time-aware scheduling method based on 5G deterministic networks, such as... Figure 1 As shown, it specifically includes the following: Step 1: Use the pre-built 5G network to wirelessly connect any automated device in the automated equipment group to the time sensor. The time sensor can obtain the real-time running time, remaining processing time, and maximum running time of any automated device in the automated equipment group. Specifically, the time sensor, as a key data acquisition terminal, starts to continuously monitor the various time parameters of the device once it is connected to the automated equipment. The acquisition of the remaining processing time in each time parameter involves deep tracking of the current task progress of the equipment. The time sensor interacts with the control system of the equipment, combines the total processing time of the task and the amount of work completed, accurately calculates the remaining processing time, and updates this data in real time to optimize resource allocation and production process for subsequent processes.

[0019] The monitoring of the maximum operating time provides an important reference for the protection and maintenance of the equipment. According to the design parameters and actual operating conditions of the equipment, a safety threshold is set. When the equipment operating time approaches or reaches this maximum value, the system will immediately issue a warning signal to remind the operator, thereby maximizing the service life of the equipment and reducing production losses and safety risks caused by equipment failure.

[0020] Step two, based on the real-time operating time of each automation equipment in the determined automation equipment group, the maximum operating time of the automation equipment is calculated, the load value of the automation equipment is calculated, the load condition of the automation equipment is judged and marked, the category and sorting are performed, and the rest scheduling is performed based on the load condition of the sorted automation equipment. Specifically, the real-time operating time data of the automation equipment and the corresponding maximum operating time parameters of the equipment are collected to calculate the load value associated with the current automation equipment in real time. The load value can directly reflect the load condition of the current automation equipment. Based on the load value, the automation equipment is classified, and the classified automation equipment is sorted. The sorted result will be the data basis for subsequent rest scheduling.

[0021] Step three, extract the remaining processing time of each automation equipment in the automation equipment group, combine the corresponding automation equipment to correct the theoretical remaining capacity of the automation equipment, and divide the task quantity based on the corrected theoretical remaining capacity of the automation equipment. Specifically, the historical average processing rate of each automation equipment in the automation equipment group is acquired and recorded in real time by the time sensor, and the theoretical remaining capacity of the current equipment is quantified by combining the remaining processing time of the current automation equipment. Further extract the category of the current automation equipment, which is Low: low load state, Med: medium load state, and High: high load state. Combine different categories to further correct the quantified theoretical remaining capacity of the automation equipment, and distribute the total task quantity based on the corrected theoretical remaining capacity.

[0022] The purpose of the embodiment is to connect the automation equipment with the time sensor through the use of the 5G network, to accurately monitor the running time, residual processing time, maximum running time and other key parameters of the equipment in real time. Based on these data, the load value of the equipment is calculated and sorted, to accurately evaluate the load situation of the equipment, and then reasonably arrange the rest schedule of the equipment. At the same time, combined with the residual processing time, historical average processing rate and load classification of the equipment, the theoretical residual capacity of the equipment is corrected, and on this basis, the task scheduling is optimized, the flexibility and adaptability of production scheduling are improved, and the automation equipment group runs in an efficient and stable state, fully utilizes the capacity of each equipment, and meets the production demand.

[0023] Embodiment 2 The embodiment discloses a method for rest scheduling of automation equipment, as shown in Figure 2 , which specifically comprises the following steps: Based on the content described in embodiment 1, the real-time running time and residual processing time of any automation equipment in the automation equipment group can be extracted in real time by the time sensor, based on which the following steps are performed: First, select any automation equipment group A as the collection target, which contains several automation equipments, and the total number of the several automation equipments is denoted as j, that is, there are j automation equipments in the automation equipment group A. The j automation equipments in the automation equipment group A are sorted in the order of counting, and the sorted result is denoted as the automation equipment sequence, which is represented as , where j is the total number of automation equipments in the automation equipment group A, and is determined. As any automation equipment in the automation equipment sequence, and is the counting index, whose value range is 1 to j.

[0024] For the real-time running time and residual processing time of each automation equipment collected in real time from the automation equipment sequence by the time sensor, the real-time running time sequence and the residual processing time sequence are respectively denoted according to the sorting order of the automation equipment sequence. , the residual processing time sequence is represented as .

[0025] Based on the automation equipment determined in the above steps, the real-time running time and the residual processing time associated with the automation equipment are further determined.

[0026] According to the formula Further determine the automation equipment The associated load value Wherein the values involved in the formula is the calculation weight of real-time running time, is the calculation weight of remaining processing time, and are determined by the operator in combination with the actual situation, and and need to satisfy + =1.

[0027] Repeat the method of calculating the load value associated with the automation equipment Further determine the sequence of the automation equipment: All the load values associated with the automation equipment in the sequence, and sort them in order from small to large according to the numerical value of the load value, and record the sorted result as the automation equipment load sequence, denoted as By sorting the load values of the automation equipment, the automation equipment with low to high load status is obtained.

[0028] From the determined automation equipment load sequence Extract the 25th percentile as and the 75th percentile as Based on the determined and Lock three intervals, the load value of the automation equipment in the first interval is less than , mark this interval as category Low, indicating that the automation equipment in this category is in a low load state; The load value of the automation equipment in the second interval is greater than or equal to and less than or equal to , mark this interval as category Med, indicating that the automation equipment in this category is in a medium load state; The load value of the automation equipment in the third interval is greater than , mark this interval as category High, indicating that the automation equipment in this category is in a high load state.

[0029] After completing the classification of the automation equipment in the automation equipment group, different rest scheduling strategies need to be executed for automation equipment in different load states, as described below: ​Extract automated equipment marked as High from the automated equipment load sequence, and generate rest instructions for all automated equipment marked as High. When an automated equipment receives a rest instruction, it will perform a self-check to determine whether the current task has been completed. If it has not been completed, it will continue to complete the task. If it has been completed, it will be added to the forced rest queue in order of load value from high to low. The number of automated equipment that can rest in the rest queue is determined by the operator based on the actual situation. The automated equipment in the rest queue will receive a shutdown command and perform a shutdown operation. The shutdown command is issued by the operator through a pre-built 5G network.

[0030] At this point, the rest scheduling for automated equipment categorized as "High" is complete. Next, the automated equipment categorized as "Med" is processed. Any automated equipment categorized as "Med" is extracted from the automated equipment load sequence, and its load value is calculated and its 75th percentile is determined. The difference between them (using the 75th percentile) Subtract the load value of the automated equipment to obtain the difference. The load value of automated equipment categorized as Med will not exceed the 75th percentile. The calculated difference is denoted as W. Then, the operator's preset difference threshold E is extracted to detect the difference W. The difference W is compared with the difference threshold E. If the difference W is less than or equal to the difference threshold E, the corresponding automated equipment is added to the flexible rest queue and works intermittently according to the operator's preset short rest time. If the difference W is greater than the difference threshold E, the corresponding automated equipment is set to keep working and monitored in real time. This completes the rest scheduling for the automated equipment categorized as Med.

[0031] For automated equipment marked as "Low", extract its remaining processing time and generate dynamic task filling instructions, and synchronously add the same type of processing tasks through the 5G network.

[0032] After all the above operations are completed, the rest scheduling of the automated equipment group is complete. However, the automated equipment resources in the forced rest queue will begin to be released. That is, the rest of the automated equipment previously classified as High is complete and can be put back into work. At this time, firstly, all automated equipment of the same type as the released automated equipment resources are identified from the elastic rest queue, and this type of equipment is recorded as the elastic rest equipment sequence R. Then, the difference between the load value and the 75th percentile of all automated equipment in the elastic rest equipment sequence R is determined in turn, and the automated equipment with the smallest difference is locked as the replacement target and the automated equipment is replaced.

[0033] Example 3 The embodiment continues to disclose a method for scheduling task quantity based on the theoretical residual capacity of the automated equipment after correction, based on embodiments 1 and 2, as shown, specifically comprising the following steps: Figure 3 Before scheduling the task quantity, the total task quantity allocated to the corresponding automated equipment group this time needs to be determined first, and after determining the total task quantity, the theoretical residual capacity of each automated equipment in the automated equipment group needs to be further obtained, and the task quantity is allocated in combination with the theoretical residual capacity of each automated equipment, specifically as follows: Based on the determined sequence of automated equipment: , select any one of them for example analysis, and the rest of the automated equipment is processed according to the processing mode of the automated equipment and processed accordingly; Obtain the historical processing rate of the automated equipment in the past normal working state, and then average process the obtained historical processing rate to obtain the historical average processing rate of the automated equipment , denoted as , and further obtain the residual processing time of the current automated equipment , according to the formula: The theoretical residual capacity of the corresponding automated equipment can be quantified, wherein and are adjustment parameters, is greater than 0 and is less than 0, and The specific values of the parameters are adjusted and determined by the operator in combination with the actual situation.

[0034] Based on embodiment 2, further obtain the category in which the automated equipment is located, and give the category to correct the theoretical residual capacity of the automated equipment ; If the automated equipment is marked as category High, the theoretical residual capacity of the automated equipment after dynamic correction is calculated by using ; If the automated equipment is marked as category Med, the theoretical residual capacity of the automated equipment after dynamic correction is calculated by using ;​ If the automation device is marked as the category Low, then by adopting the calculation of the theoretical residual capacity of the automation device is obtained ; wherein, is the overload attenuation coefficient preset by the operator, the value range is [0.5, 1], is the elasticity coefficient preset by the operator, the value range is [ , 1], is the expansion coefficient preset by the operator, the value range is ( , 1.2]; Repeat the above steps, and the same processing is performed on all automation devices in the automation device sequence: , and finally the theoretical residual capacity corresponding to each automation device in the automation device sequence is obtained, and by summarizing the obtained theoretical residual capacities, the theoretical residual capacity set is obtained.

[0035] At this point, the theoretical residual capacity of the automation device group is processed, and the total task quantity needs to be processed next. The delivery deadline of each subtask in the total task quantity (the delivery deadline is regarded as a known value) is extracted, and the delivery deadline is sorted in order from short to long. The sorted result is recorded as the delivery deadline sequence, which is represented as: , wherein the delivery deadline of any subtask in the delivery deadline sequence is recorded as , , and the subtask corresponding to the delivery deadline in the delivery deadline sequence is the most urgent, and the urgency of the subsequent subtasks decreases in turn.

[0036] Then the minimum delivery deadline threshold determined by the operator in combination with the actual situation is obtained, which represents the longest delivery deadline of the corresponding subtask. The delivery time of the corresponding subtask cannot exceed the minimum delivery deadline threshold ; Then the normal delivery deadline determined by the operator in combination with the actual situation is determined, which represents the time that the corresponding subtask can normally complete and deliver under normal workload allocation; Then the delivery deadline of each subtask associated with the delivery deadline sequence: is compared with the minimum delivery deadline threshold and the normal delivery deadline performing a comparison operation; locking any one of the delivery deadline in the delivery deadline sequence performing an example process: if the delivery deadline is less than or equal to the minimum delivery deadline threshold , the delivery deadline associated sub-tasks are classified as urgent tasks; if the delivery deadline is greater than the minimum delivery deadline threshold and less than or equal to the normal delivery deadline , the delivery deadline associated sub-tasks are classified as regular tasks; if the delivery deadline is greater than the normal delivery deadline , the delivery deadline associated sub-tasks are classified as flexible tasks.

[0037] After processing the total task amount, three types of tasks can be obtained, which are: urgent tasks, regular tasks, and flexible tasks. Extract all urgent tasks and record the total number of urgent tasks as k. Assign the k urgent tasks to the first k automated devices in the Low category with the largest theoretical residual capacity. If there are not enough k automated devices in the Low category, expand to the Med category. At this point, the urgent task assignment is complete. extract the revised theoretical residual capacity set again , the automated device associated with the theoretical residual capacity performs an example process by using: the task amount distribution weight associated with the automated device is calculated , and this calculation step is repeated to obtain the task amount distribution weight associated with each of the j automated devices in the automated device group, and is sequentially recorded as , using: the task amount distribution weight corresponding to the automated device is calculated , and the calculation step is repeated to obtain the task amount distribution weight associated with each of the j automated devices, and is sequentially recorded as , is normalized, and the normalized weight value is re-assigned to the corresponding task amount distribution weight; determine the regular task amount, denoted as ; According to the weight distribution of the task amount associated with each of the j automation devices, the conventional task amount associated with each automation device is distributed, that is, the automation device is associated with the conventional task amount , and so on, until the conventional task amount associated with each of the j automation devices is processed. The remaining flexible tasks are stored in the task amount sharing buffer pool, and the automation devices in the Low, Med and High categories are allowed to actively apply for flexible tasks for processing through the 5G network after completing the assigned task amount. The task amount sharing buffer pool is used to save unassigned task amounts.

[0038] The task amount completion of the j automation devices in the automation device group is monitored in real time, and if any automation device The task amount after distribution exceeds the theoretical remaining capacity associated with the automation device , the task amount stripping is triggered. If the automation device continuously triggers the task amount stripping p times, it means that the load of the automation device is high, and the automation device is automatically marked as the High category, wherein p is a value preset by the operator according to the actual situation.

[0039] The part of the task amount that exceeds the theoretical remaining capacity of the automation device is stored in the task amount sharing buffer pool, and the automation devices in the Med and High categories are allowed to actively apply for flexible tasks for processing through the 5G network after completing the assigned task amount.

[0040] Some of the data in the formulas described above are dimensionless numerical calculations, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0041] The above is only an example and description of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the invention or exceed the scope defined by the present claims, and they should belong to the protection scope of the present application.

[0042] It needs to be declared that all user data collected in this application is collected with the consent and authorization of the user. The use of user data is legal and compliant, and the use and processing of user data comply with relevant laws, regulations and standards in the relevant region.

Claims

1. A time-aware scheduler method based on 5G deterministic networks, characterized in that, The method comprises the following steps: Step one, use the pre-constructed 5G network to wirelessly connect any one of the automation device group to the time sensor, and obtain the real-time running time, remaining processing time and maximum running time of any one of the automation device group through the time sensor; Step two, calculate the load value associated with the automation device based on the real-time running time and maximum running time of each automation device in the automation device group, judge the load condition of the automation device based on the load value, mark the category and sort, and schedule the rest based on the load condition of the sorted automation device; Step three, extract the remaining processing time and historical average processing rate of each automation device in the automation device group, correct the theoretical residual capacity of the corresponding automation device based on the marked category, and schedule the task quantity based on the corrected theoretical residual capacity of the automation device. 2.The 5G deterministic network based time-aware scheduler method of claim 1, wherein, The automation device group in step one is composed of several automation devices connected by 5G network for data collection and transmission; The collected data includes real-time running time, remaining processing time and maximum running time of the automation device. 3.The 5G deterministic network based time-aware scheduler method of claim 1, wherein, In step one, the specific way to obtain the real-time running time and remaining processing time of any one of the automation device group through the time sensor is: Selecting the automation device group A as the collection target, extracting j automation devices in A, and recording the extracted automation devices in sequence as an automation device sequence: wherein j is the total number of automation devices in A; Extract the real-time running time and remaining processing time of each of the j automation devices in the automation device sequence through the time sensor in real time, and sort them in the order of the automation device sequence as the real-time running time sequence and the remaining processing time sequence; Real-time run-time series: ; Residual processing time sequence: ; Extraction automation apparatus Associated real-time runtimes Remaining processing time where i is a count index ranging from 1 to j.

4. The 5G deterministic network based time-aware scheduler method of claim 3, wherein, The specific way to judge the load condition of the automation device based on the load value and mark the category and sort is: S41, based on the automation device the associated real-time runtime , the remaining processing time determining the automation device the associated load value ; S42, further determining the sequence of automation devices: all the load values associated with the respective automation devices and sorting the load values in ascending order of their numerical values, denoted as the sequence of automation device load values ; S43、 25th percentile 75th percentile automated equipment with a load value of less than 1000 is marked as category Low; Load values and load values of the automation device are marked as category Med; Load values of the automation device are marked as category High.

5. The 5G deterministic network based time-aware scheduler method of claim 4, wherein, The specific way to schedule the rest based on the load condition of the sorted automation device is: Extract the automation device marked as High, generate a rest instruction, and add it to the forced rest queue in the order of load value from high to low, and issue a shutdown instruction, provided that the current automation device is completing a task; Then extract any automation device marked as Med, use W=75% quantile-load value to get the difference W, get the operator's preset difference threshold E, and compare the difference W with the difference threshold E; If W E, the corresponding automation device is added to the flexible rest queue, if W E, the corresponding automation device is set to remain in the working state and is monitored in real time; For the automation device marked as Low, extract its remaining processing time and generate a dynamic task filling instruction, and simultaneously add the same type of processing task.

6. The 5G deterministic network based time-aware scheduler method of claim 5, wherein, The specific way to schedule the rest based on the load condition of the sorted automation device also includes: When the automation device resource in the forced rest queue is released, extract all automation devices in the flexible rest queue that are the same type as the released automation device resource, and summarize them as the flexible rest device sequence R, determine the difference between the load value and the 75% quantile of all automation devices in R, and lock the automation device with the smallest difference as the replacement target; Replace the released automation device resource in the forced rest queue with the replacement target.

7. The 5G deterministic network based time-aware scheduler method of claim 6, wherein, In step three, the specific way to schedule the task quantity based on the corrected theoretical residual capacity of the automation device is: S71, determining the historical average processing rate of the automation equipment the specific values of the parameters a and b are determined by the operator in combination with the actual situation.​​​​​​​​​​​​​​ S72、based on The category in which the theory residual capacity is corrected; If High, use Corrected theoretical residual capacity ; If at Med, adopt corrected to theoretical residual capacity ; If Low, the following Theoretical remaining capacity ; in, The overload attenuation coefficient preset for operators has a value range of [0.5, 1]. The preset elasticity coefficient for operators has a value range of []. ,1]、 The expansion coefficient preset for operators has a range of values ​​( ,1.2]; Repeat this step to obtain the theoretical residual capacity of all automation devices in the automation device sequence, and obtain a theoretical residual capacity set by aggregation ; S73, extract the delivery deadline of each subtask in the total task amount again, and sort them in the order from short to long, to obtain a delivery deadline sequence , wherein, represents the total number of subtasks in the total task amount, and the delivery deadline of any one subtask in the locked delivery deadline sequence is recorded as ; S74, obtaining a minimum delivery deadline threshold preset by an operator , normal delivery deadline , and comparing each delivery deadline in the delivery deadline sequence with the minimum delivery deadline threshold , normal delivery deadline respectively; If then the associated subtasks are classified as urgent tasks; associated subtasks are classified as urgent tasks; If then the associated subtasks are classified as routine tasks; associated subtasks are classified as routine tasks; If then the associated subtasks are classified as elastic tasks; associated subtasks are classified as elastic tasks; S75, the emergency task is preferentially assigned to the first k automation devices with the largest theoretical residual capacity in the category Low, wherein k is the number of emergency tasks; S76, extract the theoretical residual capacity set with the theoretical residual capacity the associated automation device perform the example process; According to The ratio between the theoretical residual capacity and the theoretical residual capacity of the remaining automation equipment determines The associated task quantity allocation weight, repeat the above steps to obtain the associated task quantity allocation weight of each of the j automation equipment, and normalize the processing, and the processed are sequentially recorded as ; determining a regular task volume as ; The routine task volume associated with each automation device is allocated a weight in accordance with the amount of tasks associated with each automation device, i.e. automation devices associated with each automation device and so on. S77, the flexible task is stored in the task quantity shared buffer pool, and the automation devices in the categories Low, Med and High are allowed to actively apply for the assigned task quantity after completing the assigned task quantity. 8.The 5G deterministic network based time-aware scheduler method of claim 7, wherein, The specific manner of scheduling the task quantity based on the corrected theoretical residual capacity of the automation devices further includes: monitoring the task volume completion of the j automation devices, if the task volume of any one automation device exceeds the automation device associated theoretical residual capacity after distribution, then triggering task volume stripping; If the automation device is triggered p times in succession, the automation device is automatically marked as category High, where p is a value preset by the operator. 9.The 5G deterministic network based time-aware scheduler method of claim 8, wherein, The specific manner of the task quantity stripping is that: Will exceed automated equipment Theoretical surplus capacity A portion of the task volume is stored in the shared task volume buffer pool.

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