Distributed platform manufacturing service scheduling algorithm test method and system

Abstract

The invention relates to a distributed platform manufacturing service scheduling algorithm test method and system, and belongs to the technical field of industrial internet, and the method comprises the following steps: platform information perception, which is used for describing platform dynamic characteristics and perceiving manufacturing task attributes and service node real-time information; the platform state evaluation is used for evaluating the urgency degree of the manufacturing task and the load state and the shortage degree of the service node; the manufacturing service scheduling algorithm test is used for constructing constraint conditions and objective functions of platform manufacturing task allocation and manufacturing service scheduling problems, and further screening a proper scheduling algorithm for testing; the algorithm evaluation is used for evaluating the operation result of the manufacturing service scheduling algorithm and the performance of the algorithm; and the algorithm recommendation is used for analyzing the current platform operation state and carrying out algorithm recommendation in combination with the algorithm evaluation data. According to the method, the efficiency of platform manufacturing task allocation and service scheduling is effectively improved, and the occurrence of service queue congestion situations is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial Internet, and specifically relates to a distributed platform manufacturing service scheduling algorithm testing method and system. Background Art

[0002] With the continuous penetration of the digital economy ecosystem in the manufacturing field, the industrial Internet platform that realizes the sharing of socialized manufacturing resources / capabilities is an important carrier for promoting the digital transformation and intelligent upgrading of the manufacturing industry. The manufacturing demand submitted to the industrial Internet platform is characterized as a manufacturing task with clear demand attributes; the manufacturing resources / capabilities are digitally encapsulated as a manufacturing service access platform, and different manufacturing services can be aggregated and collaborated on demand to complete a complex manufacturing task. The manufacturing tasks received by the industrial Internet platform have the characteristics of multiple functional composite requirements, large scale, heterogeneity, and randomness. The platform manufacturing services are heterogeneous and the number of collaborative services is limited. Different manufacturing services with similar functions often have differences in service capabilities. As a result, under the influence of the dual uncertainty of supply and demand in the actual operation of the platform, the two indicators of maximizing the service throughput of similar services and the fairness of service utilization are conflicting in the long term, and it is inevitable that some service queues will be congested, thus affecting the continuous and stable operation of the platform.

[0003] In response to the above problems, there is an urgent need for a distributed platform manufacturing service scheduling algorithm testing method and system. The system includes a platform information perception module, a platform status evaluation module, a manufacturing service scheduling algorithm testing module, an algorithm evaluation module, and an algorithm recommendation module. Through the collaboration of various modules, real-time perception of manufacturing platform information can be achieved, and a recommended algorithm can be given according to the platform operation status, so as to realize platform manufacturing task allocation and manufacturing service scheduling algorithm testing and evaluation. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a distributed platform manufacturing service scheduling algorithm testing method and system, which includes five steps: platform information perception, platform status evaluation, manufacturing service scheduling algorithm testing, algorithm evaluation, and algorithm recommendation. The present invention can perceive the state of the manufacturing platform in real time, evaluate the adaptability of multiple algorithms in specific task scenarios, test and compare multiple algorithms, select the optimal algorithm to regulate the task allocation and service scheduling between service nodes, achieve the balance between maximizing the time average throughput of the platform service community and utilizing fairness, and reduce the occurrence of service queue congestion, providing support for the study of the continuous and stable operation of the industrial Internet platform.

[0005] The present invention provides a distributed platform manufacturing service scheduling algorithm testing method, comprising the following steps:

[0006] Step 1: Platform information perception, including: based on the established manufacturing service aggregation network topology, describing the dynamic characteristics of manufacturing service node clustering and combined collaboration relationships within and between different manufacturing service communities on the platform; perceiving the manufacturing task attributes after receiving the manufacturing task arrival signal; monitoring the status of the platform's manufacturing service nodes in real time and obtaining real-time information of the service nodes;

[0007] Step 2: Platform status evaluation, including: evaluating the urgency of manufacturing tasks based on the perceived attributes of manufacturing tasks; evaluating the load status and shortage of manufacturing service nodes based on the perceived status information of manufacturing service nodes;

[0008] Step 3: Manufacturing service scheduling algorithm testing, including: constructing the constraints of platform manufacturing task allocation and manufacturing service scheduling problems according to the status of platform manufacturing service nodes; constructing an optimization objective function, which is further decomposed into two sub-problems for task allocation and service scheduling; based on the objective function and constraints, selecting appropriate scheduling algorithms from the pre-built algorithm library for testing according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements;

[0009] Step 4: Algorithm evaluation, including: based on the operation results of the manufacturing service scheduling algorithm, evaluate the scheduling solutions generated by different algorithms under different operating conditions in terms of resource utilization, response rate, efficiency, and load balancing; evaluate the performance of different algorithms themselves in terms of time complexity, memory consumption, and scalability;

[0010] Step 5: Algorithm recommendation, including: in-depth analysis of the current platform operation status, evaluating the current platform operation status by sensing the manufacturing task attributes and manufacturing service status; algorithm recommendation based on the platform operation status and the algorithm evaluation data obtained by the algorithm evaluation.

[0011] The present invention also provides a distributed platform manufacturing service scheduling algorithm testing system, comprising:

[0012] Platform information perception module: Based on the established manufacturing service aggregation network topology, the platform information perception module describes the dynamic characteristics of the clustering and combined collaboration relationship of manufacturing service nodes within and between different manufacturing service communities on the platform; after receiving the manufacturing task arrival signal, it perceives the manufacturing task attributes; monitors the status of the platform's manufacturing service nodes in real time and obtains real-time information of the service nodes;

[0013] Platform status assessment module: The platform status assessment module assesses the urgency of manufacturing tasks based on the perceived attributes of manufacturing tasks; and assesses the load status and shortage of manufacturing service nodes based on the perceived status information of manufacturing service nodes;

[0014] Manufacturing service scheduling algorithm testing module: The manufacturing service scheduling algorithm testing module constructs the constraints of platform manufacturing task allocation and manufacturing service scheduling problems according to the status of platform manufacturing service nodes; constructs an optimization objective function, which is further decomposed into two sub-problems for task allocation and service scheduling; based on the objective function and constraints, selects appropriate scheduling algorithms from the pre-built algorithm library for testing according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements;

[0015] Algorithm evaluation module: Based on the operation results of the manufacturing service scheduling algorithm, the algorithm evaluation module evaluates the scheduling schemes generated by different algorithms under different operating conditions in terms of resource utilization, response rate, efficiency, and load balancing; and evaluates the performance of different algorithms themselves in terms of time complexity, memory consumption, and scalability;

[0016] Algorithm recommendation module: The algorithm recommendation module conducts an in-depth analysis of the current platform operation status, and evaluates the current platform operation status by sensing the manufacturing task attributes and manufacturing service status; based on the platform operation status, the algorithm evaluation data obtained by the algorithm evaluation is combined to make algorithm recommendations.

[0017] The advantages of the present invention compared with the prior art are:

[0018] (1) The platform status evaluation step proposed in the present invention can effectively evaluate the actual dynamic operation status of the platform, the urgency of manufacturing tasks, and the shortage of service nodes based on the perceived manufacturing task information and service node information.

[0019] (2) The manufacturing service scheduling algorithm test steps proposed in the present invention can effectively regulate the task allocation and service scheduling among service nodes during the platform operation process, achieving a balance between maximizing the time-averaged throughput of the platform service community and utilization fairness.

[0020] (3) The algorithm recommendation step proposed in the present invention can comprehensively evaluate the performance indicators of different algorithms and automatically recommend the most appropriate scheduling algorithm by analyzing the current platform operation status, thereby effectively improving the efficiency of platform manufacturing task allocation and service scheduling and greatly reducing the occurrence of service queue congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above-mentioned purpose, the present invention adopts the following technical scheme.

[0023] The present invention is described in further detail below in conjunction with the accompanying drawings.

[0024] The present invention proposes a distributed platform manufacturing service scheduling algorithm testing method and system. The method includes five steps: platform information perception, platform status evaluation, manufacturing service scheduling algorithm testing, algorithm evaluation, and algorithm recommendation. The system includes five modules: platform information perception module, platform status evaluation module, manufacturing service scheduling algorithm testing module, algorithm evaluation module, and algorithm recommendation module.

[0025] The flow chart of the present invention is as follows Figure 1 As shown, the specific implementation is as follows:

[0026] Step 1: Platform information perception, including: based on the established manufacturing service aggregation network topology, describing the dynamic characteristics of manufacturing service node clustering and combined collaboration relationships within and between different manufacturing service communities on the platform; perceiving the manufacturing task attributes after receiving the manufacturing task arrival signal; monitoring the status of the platform's manufacturing service nodes in real time and obtaining real-time information of the service nodes; the specific implementation methods are as follows:

[0027] Step 1.1: To improve the efficiency of platform information perception and task processing, the platform manufacturing resources and manufacturing capabilities are abstracted and encapsulated into manufacturing service nodes, while considering the functional and non-functional attributes of the nodes (Quality of Service, QoS for short); based on graph theory, the topological structure of the manufacturing service aggregation network is described as a graph ,satisfy ,in Represents the collection of all service nodes in the network, To serve the community The nodes, To serve the community The number of nodes in is the number of all service communities in the network, ; It is the set of all edges in the network, representing the clustering / combination collaboration relationship between nodes, that is, clustering / combination collaboration edges. The number of nodes in different service communities is the same. Each service node is the smallest service unit and has one or more functional attributes. After node function decomposition and replication, it belongs to different service communities: within the same service community, they are clustering collaboration edges and are undirected; between different service communities, they are combination collaboration edges and are directed; several nodes in the same service community can cluster and collaborate to complete a large-scale task, and nodes in different service communities can combine and collaborate to complete a task with multiple functional requirements;

[0028] Step 1.2: When the system receives the manufacturing task arrival signal, it perceives the information of the manufacturing task. Divide a period of time during the platform operation into several moments , T represents the length of a specific time period, and every two adjacent moments and The unit time length between .exist At this moment, the manufacturing task that reaches the platform is decomposed according to the functional requirements and divided into different subtask sets, representing the first The subtask set of class functional requirements is , the number of subtasks that arrive is , the number of completed (i.e., left) subtasks is .exist At this moment, Represents a subtask set The mth subtask in the task, the specific task attributes can be described as .in, Indicates the functional requirement type. Represents the load of each subtask. The subset of candidate service nodes that meet the QoS requirements is ; According to the carrying capacity limit of the platform operation process, the number of tasks that reach the subset The maximum number of tasks allowed should not be exceeded . Represents a manufacturing subtask Deadline: express Moment subtask set Manufacturing subtasks in Total execution time; express Moment subtask set Manufacturing subtasks in Length of time it has been executed; express Moment subtask set Manufacturing subtasks in The remaining execution time of ; Indicates that the manufacturing subtask is completed The number of service nodes required in each service cluster, To complete the manufacturing subtask The number of service nodes required in the jth service cluster. The number of nodes required to be called in each service cluster should not exceed the total number of nodes in the cluster. . express Moment subtask set Manufacturing subtasks in Arrival or not, =1 means the task has arrived, =0 means not arrived; express Moment subtask set Manufacturing subtasks in Will it be completed on time? =1 means the task will be completed on time, =0 means the task cannot be completed on time; express Moment subtask set Manufacturing subtasks in Is it being executed? =1 means the task is being executed. =0 means the task has not been executed. Task types include regular manufacturing tasks, emergency manufacturing tasks, and special manufacturing tasks. Different types of manufacturing tasks have different requirements for manufacturing resources and time. By sensing task information, service nodes can be allocated and scheduled according to the specific situation of the manufacturing task to ensure efficient execution of the task;

[0029] Step 1.3: Monitor the status of the platform manufacturing service nodes in real time and obtain real-time information of the service nodes. At this moment, the service node The available status is , Indicates that the service node is available. Indicates that the node is unavailable. If and only if the service node is available, the service node can be matched with subtasks with the same functional requirements; service node The matching is , Indicates that the service node matches a subtask. Indicates that the service node does not match any subtask; represent Assigned to the service node at the moment The number of subtasks, There must be By sensing the status information of service nodes, it provides strong data support for the rational allocation of manufacturing resources and ensures the efficient operation of the manufacturing platform.

[0030] Step 2: Platform status evaluation, including: evaluating the urgency of manufacturing tasks based on perceived manufacturing task attributes; evaluating the load status and shortage of manufacturing service nodes based on perceived manufacturing service node status information; the specific implementation method is as follows:

[0031] Step 2.1: Based on the perceived manufacturing task information, evaluate the urgency of the manufacturing task. To quantify the urgency of the manufacturing task and determine the priority of task allocation and service scheduling based on the urgency of the manufacturing task, the deadline of the manufacturing task is comprehensively considered. , Remaining execution time Total execution time , the number of service nodes required , Arrival status , Execution Status The calculation formula for the comprehensive evaluation index of manufacturing task urgency is constructed based on factors such as:

[0032] (1)

[0033] in, , , It is the weight coefficient of each factor, indicating the relative importance of different factors to the task urgency.

[0034] When a manufacturing task arrives at the platform ( =1), the urgency of the manufacturing task is measured by formula (1) to determine whether the task needs to be handled urgently or has buffer time;

[0035] Step 2.2: Based on the perceived service node status information, evaluate the load status and shortage level of the service node.

[0036] (1) At this moment, each service node Load status Can be assessed as:

[0037] , , (2)

[0038] in, yes The waiting queue length of the service node at this moment, is the maximum number of tasks allowed to be carried by the node;

[0039] (2) In At this moment, consider the number of service nodes available on the platform And the load of each service node , Free time and task completion The calculation formula for the comprehensive evaluation index of manufacturing service node shortage is constructed based on factors such as:

[0040] (3)

[0041] in, Is a service node exist The number of tasks completed at any time and satisfying ; Service node per unit time The task load is the execution rate; , , , It is the weight coefficient of each factor, indicating the relative importance of different factors to the task urgency.

[0042] By constructing the above evaluation formula, we can measure the shortage of manufacturing service nodes and determine whether manufacturing resources are in a state of scarcity and difficulty in supporting the execution of manufacturing tasks or are relatively abundant and can be flexibly allocated.

[0043] Step 3: Manufacturing service scheduling algorithm testing, including: constructing the constraints of platform manufacturing task allocation and manufacturing service scheduling problems according to the status of platform manufacturing service nodes; constructing an optimization objective function, which is further decomposed into two sub-problems for task allocation and service scheduling; based on the objective function and constraints, according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements, selecting a suitable scheduling algorithm from the pre-built algorithm library for testing; the specific implementation methods are as follows:

[0044] Step 3.1: According to the platform manufacturing task attributes and service node status, construct the constraints of platform manufacturing task allocation and manufacturing service scheduling problem:

[0045] (1) When matching and executing subtasks through service nodes, The first subtask QoS attribute value Should meet , , ,in For subtask QoS attribute requirements, A set of QoS indicators, including manufacturing service availability , execution rate , Load Status ,reliability And other indicators;

[0046] (2) Considering the limited number of manufacturing service nodes, in order to reduce service queue congestion, each service queue in the service cluster should satisfy the time average stability constraint:

[0047] (4)

[0048] in, For service nodes The average waiting queue length is For service nodes The maximum allowed upper bound of the average waiting queue length is E, which is the expected value. is the set of positive real numbers;

[0049] Step 3.2: To ensure the long-term throughput performance of the platform under the dynamic operation environment, a multi-objective function is constructed that considers the throughput of different nodes and the fairness of service utilization. Consider two decision variables and The independence of , the optimization problem can be further decomposed into two sub-optimization problems with low complexity, namely, the sub-optimization problem for task allocation and the sub-optimization problem for service scheduling. On the basis of ensuring the long-term throughput performance of the platform, other optimization objective functions are further set according to the type of manufacturing tasks and the status of service nodes. For example, in a scenario where manufacturing tasks are relatively tight, in order to ensure that all tasks can be completed in the shortest time, a minimum total task completion time function can be constructed ; In the scenario where service nodes are relatively scarce, in order to ensure that limited resources are used most fully and efficiently, a function that maximizes resource utilization can be constructed ;

[0050] Step 3.3: Based on the objective function and constraints, select the appropriate scheduling algorithm from the pre-built algorithm library for testing according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements. The algorithm library includes a variety of algorithms for solving scheduling problems, such as simple optimization algorithms, heuristic optimization algorithms, intelligent optimization algorithms, etc. When the number of manufacturing tasks arriving at the platform is small and the service node load is relatively light, a simple traditional scheduling algorithm may be selected; when the manufacturing task demand is high and the service node load is heavy, a heuristic or intelligent optimization algorithm that is good at handling multi-constrained nonlinear complex optimization problems tends to be selected. After selecting a suitable scheduling algorithm based on the platform operation status and manufacturing task requirements, the above sub-optimization problem is solved for each service community in the manufacturing service aggregation network to obtain the decision variables. The optimal solution , ,according to The value of generates the platform manufacturing task allocation and service scheduling sequence table, for example, for the found ,if , then it is a service node Assign subtasks, otherwise do not assign them. Configure service nodes for manufacturing tasks according to the sequence table, so as to promote the service scheduling optimization process for different service nodes in the service community.

[0051] Step 4: Algorithm evaluation, including: based on the operation results of the manufacturing service scheduling algorithm, evaluate the scheduling solutions generated by different algorithms under different operating conditions from the perspectives of resource utilization, response rate, efficiency, and load balancing; evaluate the performance of different algorithms themselves from the perspectives of time complexity, memory consumption, and scalability; the specific implementation methods are as follows:

[0052] Step 4.1: Based on the operation results of the manufacturing service scheduling algorithm, evaluate the scheduling schemes generated by different algorithms under different operating conditions from the aspects of resource utilization, response rate, efficiency, and load balancing. Evaluate the resource utilization of the scheme by calculating the ratio of the number of utilized manufacturing service nodes to the total number of aggregated network service nodes at a specific time; evaluate the response rate and efficiency of the scheme by calculating the waiting time of each manufacturing task arriving at the platform at a specific time; and evaluate the load balancing of the service nodes under the scheme by comparing the occupancy time of each service node and the number of allocated manufacturing tasks in a specific period of time.

[0053] Step 4.2: Based on the operation results of the manufacturing service scheduling algorithm, the performance of different algorithms is evaluated from the aspects of time complexity, memory consumption, and scalability. By analyzing the operation steps of each part of the algorithm, the time complexity of the scheduling algorithm is calculated. The lower the complexity, the more efficient the algorithm is; by measuring the memory space used by the algorithm at different manufacturing task scales, especially large-scale manufacturing task allocation and service scheduling, the memory consumption of the algorithm is evaluated. The smaller the memory consumption, the higher the resource utilization efficiency of the system; by increasing the number of manufacturing tasks and service nodes and comparing the changes in algorithm running time and memory consumption, the scalability of the algorithm is evaluated. The better the scalability, the stronger the performance stability of the algorithm.

[0054] Step 5: Algorithm recommendation, including: in-depth analysis of the current platform operation status, evaluating the current platform operation status by sensing the manufacturing task attributes and manufacturing service status; algorithm recommendation based on the platform operation status and the algorithm evaluation data obtained by the algorithm evaluation; the specific implementation methods are as follows:

[0055] Step 5.1: Conduct an in-depth analysis of the current platform operation status, and evaluate the current platform operation status by sensing the manufacturing task attributes and manufacturing service status, which is usually divided into normal status and emergency status. In the normal status, the manufacturing task demand and service node load status remain relatively stable, the task allocation is relatively balanced, and the service node utilization rate is moderate; in the emergency status, the manufacturing task demand increases suddenly, the service node load is close to saturation, and the resources are relatively tight;

[0056] Step 5.2: Based on the platform operation status, the algorithm evaluation data of the algorithm evaluation module is combined to make algorithm recommendations. For example, in normal operation, it is necessary to maintain efficient use of resources and ensure the smooth execution of various tasks. An efficient and stable scheduling algorithm is recommended to ensure the smooth allocation of manufacturing tasks and service scheduling. In emergency operation, it is necessary to prioritize the processing of emergency manufacturing tasks. A scheduling algorithm that can respond quickly is recommended, and the interruption and delay of manufacturing tasks are minimized through dynamic resource allocation, load balancing, etc. Through timely perception and analysis of different operation states, combined with algorithm evaluation data, the most appropriate algorithm is selected from the algorithm library to regulate task allocation and service scheduling between service nodes.

[0057] In summary, the present invention discloses a distributed platform manufacturing service scheduling algorithm testing system. The system includes five modules: platform information perception module, platform status evaluation module, manufacturing service scheduling algorithm testing module, algorithm evaluation module, and algorithm recommendation module. The present invention can perceive the state of the manufacturing platform in real time, evaluate the adaptability of multiple algorithms under different platform operation states, test and compare multiple algorithms, select the optimal algorithm to regulate the task allocation and service scheduling between service nodes, achieve the balance between maximizing the time average throughput of the platform service community and utilizing fairness, and reduce the occurrence of service queue congestion, providing support for the study of the continuous and stable operation of the industrial Internet platform.

[0058] The contents not described in detail in the specification of the present invention belong to the prior art known to the professional and technical personnel in the field. The above is only the preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A distributed platform manufacturing service scheduling algorithm testing method, characterized in that: The following steps are involved: Step 1: Platform information perception, including: based on the established manufacturing service aggregation network topology, describing the dynamic characteristics of manufacturing service node clustering and combined collaboration relationships within and between different manufacturing service communities on the platform; perceiving the manufacturing task attributes after receiving the manufacturing task arrival signal; monitoring the status of the platform's manufacturing service nodes in real time and obtaining real-time information of the service nodes; Step 2: Platform status evaluation, including: evaluating the urgency of manufacturing tasks based on the perceived attributes of manufacturing tasks; evaluating the load status and shortage of manufacturing service nodes based on the perceived status information of manufacturing service nodes; Step 3: Manufacturing service scheduling algorithm testing, including: constructing the constraints of platform manufacturing task allocation and manufacturing service scheduling problems according to the status of platform manufacturing service nodes; constructing an optimization objective function, which is further decomposed into two sub-problems for task allocation and service scheduling; based on the objective function and constraints, selecting appropriate scheduling algorithms from the pre-built algorithm library for testing according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements; Step 4: Algorithm evaluation, including: based on the operation results of the manufacturing service scheduling algorithm, evaluate the scheduling solutions generated by different algorithms under different operating conditions in terms of resource utilization, response rate, efficiency, and load balancing; evaluate the performance of different algorithms themselves in terms of time complexity, memory consumption, and scalability; Step 5: Algorithm recommendation, including: in-depth analysis of the current platform operation status, evaluating the current platform operation status by sensing the manufacturing task attributes and manufacturing service status; algorithm recommendation based on the platform operation status and the algorithm evaluation data obtained by the algorithm evaluation.

2. A distributed platform manufacturing service scheduling algorithm testing method according to claim 1, characterized in that: The step 1 comprises: Step 1.1: Abstract and encapsulate the platform manufacturing resources and manufacturing capabilities into manufacturing service nodes, and describe the manufacturing service aggregation network topology as a graph. ,satisfy ,in Represents the set of all manufacturing service nodes in the network, Manufacturing service community The nodes, Manufacturing service community The number of nodes in The number of all manufacturing service clusters in the network, , ; It is the set of all edges in the network, representing the clustering or combined collaboration relationship between manufacturing service nodes, that is, clustering or combined collaboration edges. The number of manufacturing service nodes in different service communities is the same. Each manufacturing service node is the smallest service unit and has one or more functional attributes. After node function decomposition and replication, it belongs to different manufacturing service communities: within the same manufacturing service community, all are clustered collaboration edges and are undirected, and between different manufacturing service communities, all are combined collaboration edges and are directed; several nodes in the same manufacturing service community can cluster and collaborate to complete a certain large-scale task, and nodes in different manufacturing service communities can combine and collaborate to complete a task with multiple functional requirements; Step 1.2: After receiving the manufacturing task signal, the attributes of the manufacturing task are sensed and a certain period of time during the platform operation is divided into several moments. , T represents the length of the time period, and every two adjacent moments and The unit time length between ,exist At the moment, the manufacturing tasks arriving at the platform are decomposed according to the functional requirements and divided into different subtask sets to represent the current moment The following has The subtask set of class functional requirements is , the number of subtasks that arrive is , the number of subtasks that are completed and left is ,exist At this moment, Represents a subtask set The mth subtask in the task, the specific task attributes are described as ,in, Indicates the functional requirement type. Represents the load of each subtask. The subset of candidate service nodes that meet the QoS requirements is ; According to the carrying capacity limit of the platform operation process, the number of tasks that reach the subset The maximum number of tasks allowed should not be exceeded , Represents a manufacturing subtask Deadline: express Moment subtask set Manufacturing subtasks in Total execution time; express Moment subtask set Manufacturing subtasks in Length of time it has been executed; express Moment subtask set Manufacturing subtasks in The remaining execution time of ; Indicates that the manufacturing subtask is completed The number of manufacturing service nodes required in each service cluster, To complete the manufacturing subtask The number of manufacturing service nodes required in the jth service cluster. The number of nodes required to be called in each manufacturing service cluster does not exceed the total number of nodes in the cluster. , express Moment subtask set Manufacturing subtasks in Arrival or not, =1 means the task has arrived, =0 means not arrived; express Moment subtask set Manufacturing subtasks in Will it be completed on time? =1 means the task will be completed on time, =0 means the task cannot be completed on time; express Moment subtask set Manufacturing subtasks in Is it being executed? =1 means the task is being executed. =0 means the task has not been executed. The types of manufacturing tasks include regular manufacturing tasks and emergency manufacturing tasks. By sensing the attributes of manufacturing tasks, service nodes are allocated and scheduled according to the specific circumstances of manufacturing tasks to ensure efficient execution of tasks; Step 1.3: Monitor the status of the manufacturing service nodes on the platform in real time and obtain real-time information of the manufacturing service nodes. At this moment, manufacturing service nodes The available status is , Indicates that the manufacturing service node is available. Indicates that the manufacturing service node is unavailable. If and only if the manufacturing service node is available, the manufacturing service node matches the subtask with the same functional requirements. The matching is , Indicates that the manufacturing service node matches a subtask. Indicates that the manufacturing service node is not matched with any subtask; represent Assigned to the manufacturing service node at the moment The number of subtasks, Time .

3. A distributed platform manufacturing service scheduling algorithm testing method according to claim 2, characterized in that: The step 2 comprises: Step 2.1: Based on the perceived manufacturing task attributes, evaluate the urgency of the manufacturing task, quantify the urgency of the manufacturing task, and determine the priority of task allocation and service scheduling based on the urgency of the manufacturing task, taking into account the deadline of the manufacturing task , Remaining execution time Total execution time , the number of service nodes required , Arrival status , Execution Status Each factor is used to construct the calculation formula for the comprehensive evaluation index of manufacturing task urgency: （1） in, , , is the weight coefficient of each factor, indicating the relative importance of different factors to the task urgency; When a manufacturing task arrives at the platform =1, the urgency of the manufacturing task is measured by formula (1) to determine whether the task needs to be handled urgently or has buffer time; Step 2.2: Based on the perceived manufacturing service node status information, evaluate the load status and shortage level of the service node; including: (1) At this moment, each manufacturing service node Load status Evaluates to: , , （2） in, yes The waiting queue length of the manufacturing service node at the moment, is the maximum number of tasks allowed to be carried by the manufacturing service node; (2) In At this moment, consider the number of service nodes available on the platform And the load of each service node , Free time and task completion The calculation formula for the comprehensive evaluation index of manufacturing service node shortage is constructed based on various factors: （3） in, Is a manufacturing service node exist The number of tasks completed at any time and satisfying ; Service node per unit time The task load is the execution rate; , , , is the weight coefficient of each factor, indicating the relative importance of different factors to the task urgency; By constructing the above evaluation formula, we can measure the shortage of manufacturing service nodes and determine whether manufacturing resources are in a state of scarcity and difficulty in supporting the execution of manufacturing tasks or are relatively abundant and can be flexibly allocated.

4. A distributed platform manufacturing service scheduling algorithm testing method according to claim 3, characterized in that: The step 3 comprises: Step 3.1: According to the platform manufacturing task attributes and manufacturing service node status, construct the platform manufacturing task allocation and manufacturing service scheduling problem constraints, including: (1) When matching and executing subtasks through manufacturing service nodes, The first subtask QoS attribute value Should meet , , ,in For subtask QoS attribute requirements, A set of QoS indicators, including manufacturing service availability , execution rate , Load Status ,reliability Each indicator; (2) Considering the limited number of manufacturing service nodes, in order to reduce service queue congestion, each service queue in the manufacturing service community should satisfy the time average stability constraint: （4） in, For service nodes The average waiting queue length is For service nodes The maximum allowed upper bound of the average waiting queue length is E, which is the expected value. is the set of positive real numbers; Step 3.2: Construct a multi-objective function that considers different node throughput and service utilization fairness: ; Consider two decision variables and Based on the independence of the manufacturing tasks, the optimization problem is further decomposed into two sub-optimization problems with low complexity, namely, the sub-optimization problem for task allocation and the sub-optimization problem for service scheduling. According to the types of manufacturing tasks and the status of manufacturing service nodes, other optimization objective functions are further set. In the scenario where the manufacturing tasks are relatively tight, in order to ensure that all tasks can be completed in the shortest time, the minimum total task completion time function is constructed. ; In the scenario where service nodes are relatively scarce, in order to ensure that limited resources are used most efficiently, a function to maximize resource utilization is constructed ; Step 3.3: Based on the objective function and constraints, select appropriate scheduling algorithms from the pre-built algorithm library for testing according to the platform operation status and manufacturing task requirements perceived and evaluated in real time; after selecting the appropriate scheduling algorithm according to the platform operation status and manufacturing task requirements, solve the above sub-optimization problems for each service community in the manufacturing service aggregation network to obtain the decision variables The optimal solution , ,according to The value of generates the platform manufacturing task allocation and service scheduling sequence table. For the found ,if , then it is the manufacturing service node Assign subtasks, otherwise do not assign them, and configure manufacturing service nodes for manufacturing tasks according to the sequence table.

5. A distributed platform manufacturing service scheduling algorithm testing method according to claim 4, characterized in that: The step 4 comprises: Step 4.1: Based on the operation results of the manufacturing service scheduling algorithm, evaluate the scheduling schemes generated by different algorithms under different operating conditions from four aspects: resource utilization, response rate, efficiency, and load balancing; evaluate the resource utilization of the scheme by calculating the ratio of the number of manufacturing service nodes utilized at a specific time to the total number of manufacturing service nodes in the aggregation network; evaluate the response rate and efficiency of the scheme by calculating the waiting time of each manufacturing task arriving at the platform at time t; evaluate the load balancing of the manufacturing service nodes under the scheme by comparing the occupancy time of each service node and the number of allocated manufacturing tasks in time period t; Step 4.2: Based on the running results of the manufacturing service scheduling algorithm, evaluate the performance of different algorithms from three aspects: time complexity, memory consumption, and scalability; calculate the time complexity of the scheduling algorithm by analyzing the operation steps of each part of the algorithm; measure the memory space used by the algorithm when running under different manufacturing task scales to determine the manufacturing task allocation and service scheduling under different scales, and evaluate the algorithm's memory consumption; evaluate the scalability of the algorithm by increasing the number of manufacturing tasks and manufacturing service nodes and comparing the changes in algorithm running time and memory consumption.

6. A distributed platform manufacturing service scheduling algorithm testing method according to claim 5, characterized in that: The step 5 comprises: Step 5.1: Conduct an in-depth analysis of the current platform operation status. By sensing the manufacturing task attributes and the manufacturing service node status, the current platform operation status is evaluated and divided into normal status and emergency status. In the normal status, the manufacturing task demand and service node load status remain relatively stable, the task distribution is balanced, and the service node utilization rate is moderate. In the emergency status, the manufacturing task demand increases suddenly, the service node load is close to saturation, and the resources are relatively tight. Step 5.2: Based on the platform operation status, the algorithm evaluation data obtained by the algorithm evaluation is combined to make algorithm recommendations; including: under normal operation status, recommending efficient and stable scheduling algorithms to ensure the smooth implementation of manufacturing task allocation and service scheduling; under emergency operation status, recommending scheduling algorithms that can respond quickly; through timely perception and analysis of different operation statuses, combined with algorithm evaluation data, selecting the most appropriate algorithm from the algorithm library to regulate task allocation and service scheduling between service nodes.

7. A distributed platform manufacturing service scheduling algorithm testing system, characterized in that: include: Platform information perception module; The platform information perception module is based on the established manufacturing service aggregation network topology structure to describe the dynamic characteristics of the clustering and combined collaboration relationships of manufacturing service nodes within and between different manufacturing service communities on the platform. After receiving the manufacturing task arrival signal, the manufacturing task attributes are sensed; Monitor the status of the platform's manufacturing service nodes in real time and obtain real-time information about the service nodes; Platform status assessment module; The platform status assessment module assesses the urgency of manufacturing tasks based on the perceived attributes of manufacturing tasks; and assesses the load status and shortage of manufacturing service nodes based on the perceived status information of manufacturing service nodes; Manufacturing service scheduling algorithm testing module: The manufacturing service scheduling algorithm testing module constructs the constraints of platform manufacturing task allocation and manufacturing service scheduling problems according to the status of platform manufacturing service nodes; constructs an optimization objective function, which is further decomposed into two sub-problems for task allocation and service scheduling; based on the objective function and constraints, selects appropriate scheduling algorithms from the pre-built algorithm library for testing according to the real-time perception and evaluation of the platform operation status and manufacturing task requirements; Algorithm evaluation module: Based on the operation results of the manufacturing service scheduling algorithm, the algorithm evaluation module evaluates the scheduling schemes generated by different algorithms under different operating conditions in terms of resource utilization, response rate, efficiency, and load balancing; and evaluates the performance of different algorithms themselves in terms of time complexity, memory consumption, and scalability; Algorithm recommendation module: The algorithm recommendation module conducts an in-depth analysis of the current platform operation status, and evaluates the current platform operation status by sensing the manufacturing task attributes and manufacturing service status; based on the platform operation status, the algorithm evaluation data obtained by the algorithm evaluation is combined to make algorithm recommendations.

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