Intelligent production method and device based on directed acyclic graph, equipment and storage medium

Abstract

The invention provides an intelligent production method and device based on a directed acyclic graph, equipment and a storage medium. The limitation of the performance of the production equipment is determined by constructing an equipment model. An actual production process is simulated by constructing an intelligent factory model, the intelligent factory model is solved by taking the minimum cost as a target, an optimal transmitting power distribution proportion and an optimal unloading decision are obtained, and the energy cost and the time cost of the intelligent factory can be minimum through the optimal transmitting power distribution proportion and the optimal unloading decision. The intelligent factory model limits parameters such as transmitting power distribution proportion, energy consumption of production equipment, production time and the like, so that the optimal production scheme is ensured not to exceed the time limit of production tasks, and the condition of production stagnation caused by insufficient energy is avoided; and the production cost is minimized, and meanwhile, it is ensured that each production device has enough energy support and meets the time limit of production.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an intelligent production method, device, equipment and storage medium based on a directed acyclic graph. Background Art

[0002] With the rise of smart manufacturing, the industrial Internet has developed rapidly, and the vision of digitalization, intelligence, and networking has gradually become a reality. In the actual smart factory scenario, the research on the dependency relationship between tasks of production equipment mainly focuses on the data sharing ratio. However, in practice, the premise of data sharing requires the support of transmission power. In addition, in the research on production costs, the relevant technical solutions often do not take into account the energy of the production equipment itself and the time limit of each production task, which will lead to insufficient energy and stagnant production. Summary of the invention

[0003] In view of this, the purpose of this application is to propose an intelligent production method, device, equipment and storage medium based on a directed acyclic graph, which can complete the intelligent production task at the lowest cost within the specified time while ensuring sufficient energy.

[0004] Based on the above objectives, the present application provides an intelligent production method based on a directed acyclic graph, comprising:

[0005] Determine the external dependencies between different production equipment and the internal dependencies of the same production equipment;

[0006] Constructing a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship;

[0007] Building a device model based on device performance parameters of each production device and server performance parameters of each edge server;

[0008] Building a smart factory model according to the combined directed acyclic graph, the equipment model and the task information of each production equipment;

[0009] Taking the minimum cost as the goal, the smart factory model is solved by a linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision;

[0010] An optimal production plan is determined according to the optimal transmission power allocation ratio and the optimal unloading decision, and intelligent production is performed according to the optimal production plan.

[0011] Based on the same inventive concept, the present disclosure also provides an intelligent production device based on a directed acyclic graph, comprising:

[0012] The dependency determination module is configured to: determine the external dependency between different production equipment and the internal dependency of the same production equipment;

[0013] A combined directed acyclic graph module is configured to: construct a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship;

[0014] The device model building module is configured to: build a device model according to the device performance parameters of each production device and the server performance parameters of each edge server;

[0015] A factory model building module is configured to: build a smart factory model according to the combined directed acyclic graph and the task information of each production equipment;

[0016] The model optimal solution module is configured to: solve the smart factory model by a linear target programming algorithm with the goal of minimizing cost, and obtain an optimal transmission power allocation ratio and an optimal unloading decision;

[0017] The production plan generation module is configured to: determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan.

[0018] Based on the same inventive concept, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0019] Based on the same inventive concept, the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method as described above.

[0020] From the above, it can be seen that the intelligent production method, device, equipment and storage medium based on directed acyclic graph provided by the present application determine the external dependencies between different production equipment and the internal dependencies of the same production equipment; construct a combined directed acyclic graph according to the external dependencies and internal dependencies; construct a device model according to the equipment performance parameters of each production equipment and the server performance parameters of each edge server; construct an intelligent factory model according to the combined directed acyclic graph, the equipment model and the task information of each production equipment; with the minimum cost as the goal, the intelligent factory model is solved by the linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision; determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan. By constructing a combined directed acyclic graph according to internal dependencies and external dependencies, unified management of the dependencies between production tasks is achieved, and a dependency basis is provided for the reasonable allocation of transmission power resources between sending data to the edge server and sending data to other production equipment. And the limitations of the performance of the production equipment itself are determined by constructing a device model. And by building a smart factory model to simulate the actual production process, and solving the smart factory model with the minimum cost as the goal, the optimal transmission power allocation ratio and the optimal unloading decision are obtained. The optimal transmission power allocation ratio and the optimal unloading decision can minimize the energy cost and time cost of the smart factory. The smart factory model itself limits the parameters such as the transmission power allocation ratio, the energy consumption of the production equipment itself, and the production time, ensuring that the optimal production plan will not exceed the time limit of the production task, and will not cause production stagnation due to insufficient energy. While minimizing the production cost, it ensures that each production equipment has sufficient energy support and meets the production time limit. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of an intelligent production method based on a directed acyclic graph according to an embodiment of the present application;

[0023] Figure 2 A schematic diagram of a smart factory model according to an embodiment of the present application;

[0024] Figure 3 A schematic diagram of a directed five-ring graph combined with an embodiment of the present application;

[0025] Figure 4A flowchart for constructing a smart factory model for an embodiment of the present application;

[0026] Figure 5 This is a flowchart of solving the smart factory model by a linear goal programming algorithm with the goal of minimizing cost in the embodiment of the present application;

[0027] Figure 6 This is a schematic diagram of the structure of an intelligent production device based on a directed acyclic graph according to an embodiment of the present application;

[0028] Figure 7 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0030] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0031] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0032] Based on the description of the above background technology, the related technology also has the following situations:

[0033] 1) Intelligent Manufacturing: Intelligent Manufacturing is a human-machine integrated intelligent system composed of intelligent machines and humans, which aims to realize supervised intelligent activities in the manufacturing process through analysis, reasoning, judgment and decision-making. Through the cooperation between humans and machines, the physical and mental activities of human experts in the manufacturing process are expanded, extended and partially replaced, and the concept of manufacturing automation is extended to flexibility, integration and overall intelligence.

[0034] 2) Industrial Internet: Industrial Internet of Things (IIoT) is a new type of infrastructure, application model and industrial ecology that is a deep integration of the new generation of information and communication technology and industrial economy. By fully connecting people, machines, objects, systems, etc., it builds a new manufacturing and service system covering the entire industrial chain and the entire value chain, providing a way to realize the digitalization, networking and intelligent development of industry and even industries.

[0035] 3) Mobile edge computing: Mobile edge computing (MEC) is an edge computing model that uses network edge nodes to process and analyze data. As the heterogeneity and complexity of communication network architectures gradually increase, the inherent defects of traditional cloud computing, such as limited computing resources and large processing delays, have also emerged. The introduction of MEC can effectively share the computing pressure of the central processor, thereby effectively improving the performance of the network architecture.

[0036] 4) Task offloading: Task offloading is a key technology in edge computing. Computation offloading in edge computing is to offload the computing tasks of mobile terminals to the edge cloud environment, solving the shortcomings of the device in terms of resource storage, computing performance, and energy efficiency.

[0037] 5) Directed Acyclic Graph: Directed Acyclic Graph (DAG) is a graph data structure consisting of vertices and directed edges, where each edge has a direction and there are no loops. DAG is often used to describe the dependencies between a series of tasks or events, such as the task flow of an engineering project, code dependencies in an encoder, etc. Since there are no loops, DAG has strong computability and reliability, and can efficiently perform operations such as topological sorting.

[0038] In the smart manufacturing factory in the related technology, the technical solution for task scheduling of production equipment in the smart factory under the industrial Internet scenario either considers a single directed acyclic graph or only considers one edge server. However, in a real smart factory, there are various production equipment, and their production tasks are naturally different, so they need to be represented by different directed acyclic graphs. In addition, as the number of production equipment increases, the number of production tasks also increases. Using only one edge server is not enough to support the execution of all tasks, which may cause task queue congestion and increase time costs.

[0039] Question 1: In the actual smart factory scenario, the external dependencies of production equipment will affect the allocation of transmission power. In a smart manufacturing factory, on the one hand, the production tasks of some production equipment require data on the execution results of production tasks of other production equipment. For example, some production tasks require certain sensor devices to provide temperature, humidity and other data, that is, there is data sharing between the tasks of different production equipment, and the premise of data sharing is to allocate corresponding transmission power. On the other hand, because during the execution of the task, the task may be offloaded to the edge server, the data transmission of this process also requires corresponding transmission power.

[0040] Problem 2: In the smart factory scenario, the constraints of the limited energy of the production equipment and the limited running time of the production tasks are not taken into account. The problem of minimizing production costs in the smart manufacturing factory scenario often does not take into account the energy limits of the production equipment itself. However, in actual smart factories, the energy of each production equipment itself is limited, and they all tend to complete their own production tasks first, and the remaining energy is used to share data with other production tasks. At the same time, if the running time of each production task is not constrained, the running time of some production tasks will be too long, causing production to stagnate and increasing time costs.

[0041] The intelligent production method, device, equipment and storage medium based on directed acyclic graph provided by the embodiment of the present application determine the external dependencies between different production equipment and the internal dependencies of the same production equipment; construct a combined directed acyclic graph according to the external dependencies and the internal dependencies; construct a device model according to the equipment performance parameters of each production equipment and the server performance parameters of each edge server; construct an intelligent factory model according to the combined directed acyclic graph, the equipment model and the task information of each production equipment; with the minimum cost as the goal, the intelligent factory model is solved by the linear target programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision; determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan. By constructing a combined directed acyclic graph according to the internal dependencies and the external dependencies, the dependencies between the production tasks are uniformly managed, and the dependency basis is provided for the reasonable allocation of transmission power resources between sending data to the edge server and sending data to other production equipment. And the limitations of the performance of the production equipment itself are determined by constructing the equipment model. And by building a smart factory model to simulate the actual production process, and solving the smart factory model with the minimum cost as the goal, the optimal transmission power allocation ratio and the optimal unloading decision are obtained. The optimal transmission power allocation ratio and the optimal unloading decision can minimize the energy cost and time cost of the smart factory. The smart factory model itself limits the parameters such as the transmission power allocation ratio, the energy consumption of the production equipment itself, and the production time, ensuring that the optimal production plan will not exceed the time limit of the production task, and will not cause production stagnation due to insufficient energy. While minimizing the production cost, it ensures that each production equipment has sufficient energy support and meets the production time limit.

[0042] The following describes in detail the directed acyclic graph-based intelligent production method provided by the embodiments of the present application in conjunction with the accompanying drawings.

[0043] In some embodiments, Figure 1 As shown, an intelligent production method based on a directed acyclic graph includes:

[0044] Step 101: Determine the external dependencies between different production equipment and the internal dependencies of the same production equipment.

[0045] In specific implementation, for a smart factory including N manufacturing equipment (ME), ME i ,i∈{1,2,...,N} represents the i-th production equipment in the smart factory.

[0046] The execution task of each production equipment is composed of production tasks with dependencies, that is, the execution task includes at least one production task. The internal dependency of the same production equipment is the order of execution between different production tasks of the same production equipment. When the production equipment is running, the production tasks are executed in the order of internal dependencies. For example, the production equipment ME is executed in sequence. i The first production task, the second production task, the third production task, until the production equipment ME is executed i The last launch mission, production equipment ME i End the operation or execute the next cycle of production tasks.

[0047] The external dependency between different production equipment is the execution order relationship between the production tasks of different production equipment. For example, the production equipment ME i After the second production task is completed, it is necessary to continue to execute the production equipment ME i At the same time, another production equipment ME needs to be executed i′ The fifth production task is that when the smart factory is carrying out production and manufacturing, different production equipment needs to cooperate with each other to complete the manufacturing of the corresponding products, so there is a certain external dependency between different production equipment.

[0048] Step 102: Construct a combined directed acyclic graph according to the external dependency and the internal dependency.

[0049] In the specific implementation, for the first directed acyclic Figure 1 →2→3→4→5→6→7 can be directly determined based on the internal dependency relationship. After executing the third task of the first directed acyclic graph, the fourth production task is continued according to the internal dependency relationship. However, according to the external dependency relationship, it can be seen that while continuing to execute the fourth production task of its own production equipment, it is necessary to continue to execute the fourth production task of other connected production equipment. Therefore, according to the internal dependency relationship, multiple single directed acyclic graphs are constructed, and different single directed acyclic graphs are combined according to the external dependency relationship to obtain the corresponding Figure 2 The combined directed acyclic graph shown in FIG. The combined directed acyclic graph represents the execution sequence between all production tasks of N production equipment.

[0050] In some embodiments, each production device needs to perform at least one production task; a combined directed acyclic graph is constructed according to the external dependency relationship and the internal dependency relationship, including:

[0051] Step 1011: Determine an internal task set and an internal dependency set according to the internal dependency relationship, define the start task and the exit task in the internal task set as virtual tasks, and define the intermediate tasks in the internal task set as real tasks.

[0052] In the specific implementation, the internal task set and the internal dependency set are determined according to the internal dependency relationship, among which the production equipment ME i The corresponding internal task set is Among them, the first production task q executed in the internal task set i,0 Determined as an incoming task, the last production task executed in the internal task set Determined as an exit task, since the entry task and exit task are in the production equipment ME i The computational effort during task execution is zero, so the entry task and exit task are defined as virtual tasks, and all production tasks except virtual tasks in the internal task set are real tasks. i Indicates production equipment ME i The number of production tasks, and the number of real tasks in the internal task set. When defining production tasks, for any production task q i,j For example, the i in the subscript is defined to represent the i-th production equipment, and j is defined to represent the j-th production task.

[0053] Then use ID i Represents an internal dependency set, and the production equipment ME i The corresponding elements in the internal dependency set can be expressed as {id(q i,j ,q i,k )|j∈{0,1,2,...,D i},q i,k ∈suc(q i,j )}, indicating that from the production task q i,j To production task q i,k The size of the data transmitted is Indicates that in the production task q i,j After the execution is completed, you need to continue to execute the production task q i,k , and the amount of data transmitted between the two production tasks is

[0054] Step 1012: Determine an external dependency set according to the external dependency relationship.

[0055] In specific implementation, the external dependency set is determined according to the external dependency relationship, where ED i Display and production equipment ME i Corresponding dependency set, external dependency set ED iThe elements in can be expressed as {ed(q i,j ,q i′,j′ )|i≠i′,j∈{0,1,2,...,D i},j′∈{0,1,2,...,D i′}}, indicating that from the production task q i,j To task q i′,j′ The size of the data transmitted is Indicates that in the production task q i,j After the execution is completed, it is necessary to continue to execute the production tasks of other production equipment. i′,j′ , and the amount of data transmitted between the two production tasks is Then a single production equipment ME i A single directed non-commutation graph can be defined as G i =(Q i ,ID i ,ED i ).

[0056] Step 1013: Construct a combined directed acyclic graph according to the internal dependency set, the external dependency set and the internal task set.

[0057] In specific implementation, the production tasks of a single production equipment can be determined based on a single internal task set, and the internal task set of all production equipment includes all production tasks that need to be performed by the smart factory. The internal dependency set includes the dependencies between all production tasks in a single internal task set corresponding to the same production equipment, while the external dependency set includes the dependencies between production tasks of different production equipment. By integrating the internal dependency set, the external dependency set and the internal task set, it is possible to understand all production tasks and the dependencies between production tasks. Therefore, multiple single directed acyclic graphs are constructed based on the internal dependencies, and virtual tasks and real tasks in the single directed acyclic graph are defined based on the internal task set. Different single directed acyclic graphs are combined according to the external dependencies to obtain the corresponding Figure 2 The combined directed acyclic graph shown in the figure, where the number between different production tasks represents the amount of data transmitted for data transmission. The combined directed acyclic graph is used to describe the dependency relationship between all production tasks in the smart factory. Since the combined directed acyclic graph does not have loops, it has strong computability and reliability, and can efficiently perform operations such as topological sorting, providing a sufficient dependency relationship basis for the subsequent model building process.

[0058] By constructing a combined directed acyclic graph based on internal dependencies and external dependencies, unified management of dependencies between production tasks is achieved, providing a dependency basis for the reasonable allocation of transmission power resources between sending data to edge servers and sending data to other production equipment.

[0059] Step 103: construct a device model according to the device performance parameters of each production device and the server performance parameters of each edge server.

[0060] In specific implementation, all production tasks in the smart factory and the dependencies between production tasks can be understood based on the combined directed acyclic graph. As the main body of executing production tasks, the production equipment and edge servers have their own constraints. Because in actual use, without replacing the equipment or server, the equipment performance parameters of the production equipment and the server performance parameters of the edge server are fixed, so first of all, it is necessary to build a device model based on the equipment performance parameters of each production equipment and the server performance parameters of each edge server to determine the performance of the production equipment and edge server, and provide basic model data of equipment performance for the subsequent construction of the smart factory model.

[0061] For example, N production equipment and M edge servers (ES) are randomly arranged in a smart factory. The production equipment can be represented as ME i ,i∈{1,2,...,N}. Production equipment ME i The computing power is f i , working power is The transmission power is The received power is Standby power is And its available energy is The edge server can be represented as ES m ,m∈{1,2,...,M}. Edge Server ES m The computing power is f m Each edge server can execute multiple production tasks at the same time, and the production operation of each production device starts at the same time. Therefore, the performance limit of the production device itself is determined by building a device model. For example, the production device ME i The maximum energy that can be used cannot exceed its own available energy. The maximum transmission power when sending production task data cannot exceed the transmission power The transmit power limit of the production task data cannot exceed the receiving power of The receiving power limit of the standby power cannot exceed the standby power. The standby power limit.

[0062] Step 104: Construct a smart factory model based on the combined directed acyclic graph, the equipment model, and the task information of each production equipment.

[0063] In the specific implementation, the energy of the production equipment, the running time of the production task and the computing power of the edge server are comprehensively considered, and the smart factory is abstracted as follows: Figure 3 The smart factory model shown in the figure is a two-layer wireless communication network structure, which is the upper edge layer (Edge Layer) and the lower equipment layer (EquipmentLayer). The smart factory model includes equipment model, task model, communication model, task execution model, and external dependency evaluation model, so the process of building a smart factory model can be divided into the process of building equipment model, task model, communication model, task execution model, and external dependency evaluation model. Then, the built equipment model, task model, communication model, task execution model, and external dependency evaluation model are integrated to obtain the smart factory model.

[0064] Step 105: With the minimum cost as the goal, the smart factory model is solved by a linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision.

[0065] In the specific implementation, the cost objective function is first constructed with the minimum cost as the goal, and then it is solved by the linear goal programming algorithm based on the smart factory model. The smart factory model is a simulation of the actual smart factory, so the smart factory model can determine various constraints for solving the cost objective function. Therefore, the smart factory model is solved by the linear goal programming algorithm, and the optimal transmission power allocation ratio and the optimal unloading decision obtained are the optimal control parameters that can be actually used. Because the solution process is completed under the constraints of the smart factory model, the optimal transmission power allocation ratio and the optimal unloading decision will not exceed the constraints of the smart factory model, thereby ensuring the effectiveness of the optimal transmission power allocation ratio and the optimal unloading decision.

[0066] Step 106: Determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan.

[0067] In specific implementation, the optimal production plan is determined according to the optimal transmission power allocation ratio and the optimal offloading decision, and when intelligent production is carried out according to the optimal production plan, reasonable energy allocation is carried out for production tasks with different dependencies (internal task dependency, external task dependency). The optimal transmission power allocation ratio and the optimal offloading decision can reasonably allocate transmission power for the two situations of data sharing between tasks and task offloading to the edge, which can make full use of the transmission power of production equipment and effectively allocate transmission power resources. The smart factory model implements the constraints of the production equipment's own energy and the running time of each production task, ensuring that when intelligent production is carried out according to the optimal production plan, each production task can have sufficient energy support and can be completed within the specified time, ensuring that the equipment in the entire smart factory can complete a production operation and minimize its production cost.

[0068] In summary, the intelligent production method based on directed acyclic graph provided by the embodiment of the present application realizes unified management of the dependencies between production tasks by constructing a combined directed acyclic graph according to internal dependencies and external dependencies, and provides a dependency basis for the reasonable allocation of transmission power resources between sending data to edge servers and sending data to other production equipment. And the performance limitations of the production equipment itself are determined by building a device model. And by building a smart factory model to simulate the actual production process, and solving the smart factory model with the minimum cost as the goal, the optimal transmission power allocation ratio and the optimal unloading decision are obtained. The energy cost and time cost of the smart factory can be minimized by the optimal transmission power allocation ratio and the optimal unloading decision, and the smart factory model itself limits the parameters such as the transmission power allocation ratio, the energy consumption of the production equipment itself, and the production time, ensuring that the optimal production plan will not exceed the time limit of the production task, nor will it cause insufficient energy and production stagnation. While minimizing the production cost, it ensures that each production equipment has sufficient energy support and meets the production time limit.

[0069] In some embodiments, Figure 4 As shown in FIG. 1 , a smart factory model is constructed based on the combined directed acyclic graph, the equipment model and the task information of each production equipment, including:

[0070] Step 401: Determine a task model based on the combined directed acyclic graph and the task information of each production equipment.

[0071] In specific implementation, the task model is determined according to the combined directed acyclic graph and the task information of each production equipment, including:

[0072] Step 4011: Determine the predecessor task of each production task according to the combined directed acyclic graph to obtain a predecessor task set.

[0073] Among them, according to the combined directed acyclic graph, all production tasks in the smart factory and the dependencies between the production tasks can be understood, and the predecessor tasks of each production task can be determined according to the combined directed acyclic graph. For example, Figure 2 As shown, the production task q 2,7 The predecessor tasks include the production task q 2,4 , production task q 2,5 and production task q 2,6 , and the production task q 2,7 , production task q 2,4 , production task q 2,5 and production task q 2,6 All of them belong to the same production equipment and can be determined based on the internal dependencies in the combined directed acyclic graph; production task q 1,6 The predecessor tasks include the production task q 2,7 and production task q 1,5 , where the production task q 1,5 and production task q 1,6 Belonging to the same production equipment, it can be determined based on the internal dependencies in the combined directed acyclic graph; production task q 1,6 and production task q 2,7 Belonging to different production equipment, it can be determined based on the external dependencies in the combined directed acyclic graph.

[0074] It can be seen that the production task in the combined directed acyclic graph can be used as the input end of multiple external dependencies, or as the output end of multiple external dependencies, so the production task q is defined as i,j The set of predecessor tasks is ψ i,j = {q l,s ∈q i′,j′ ∪q i,k |ed(q i′,j′ ,q i,j )∈ED i ,id(q i,k ,q i,j )∈ID i}, indicating all predecessor tasks of production task qi,j.

[0075] Step 4012: Determine the offloading decision of each production task in the combined directed acyclic graph according to the task information.

[0076] Production task i,j The task information includes three elements, denoted as (x i,j ,o i,j ,z i,j ), where x i,j is the task execution calculation amount, indicating the execution of production task q i,j The amount of calculation required when i,jFor production task q i,j The amount of data returned when the task is executed on the edge server. If the task is offloaded to the edge server for execution, the production task is considered completed only when the calculation result data is returned to the production device; i,j For production task q i,j The uninstall decision when z i,j = 0, the production task q i,j By production equipment ME i Self-execution, the unloading destination is the production equipment ME i itself. Otherwise, the production task q i,j The offload is executed to the edge server, and the offload destination is any edge server. i,j The uninstall decision is as follows:

[0077]

[0078] Step 4013: Determine the unloading destination of the production task in the combined directed acyclic graph in the edge server and the production equipment corresponding to the production task according to the unloading decision, integrate the previous generation task set, the combined directed acyclic graph and the unloading destination of each production task, and obtain the task model.

[0079] In the specific implementation, it should be noted that the virtual task can only be executed by the production equipment itself, so the unloading decision of starting the task and exiting the task is 0, that is, By integrating the previous generation task set, combining the directed acyclic graph and the unloading destination of each production task, the relationship between production tasks, the execution order and the task unloading destination can be determined, all model data related to the production tasks can be determined, and the task model can be obtained.

[0080] Step 402: Determine a transmission power allocation decision for a target production device according to the task model, and determine a first transmission rate between production devices and a second transmission rate between an edge server and the production device according to the device model.

[0081] In specific implementation, when the production equipment ME i To production equipment ME i′ Send production task q i,j When the data is the transmit power Used to produce equipment ME i and production equipment ME i′ The first distribution ratio of data sent between Indicates the transmit power Used to produce equipment ME i The second distribution ratio of data between the edge server ES. Denotes the production task qi,j No external dependencies, production tasks i,j By production equipment ME i If the production task q i,j If there is external dependency, the transmission power must be Assign. Then use As a transmit power allocation decision.

[0082] Through orthogonal frequency division multiplexing, each production device uses an orthogonal channel to communicate with the edge server, ensuring that there is no interference between communication links. i,j Need to be offloaded to the edge server ES m When executed, the production equipment ME i To make production tasks i,j The required data is transferred to ES m , according to Shannon's theory, the achievable uplink transmission rate is the second transmission rate:

[0083]

[0084] Where w is the channel bandwidth between the production equipment and the edge server, δ 2 is the channel noise between the production device and the edge server, h i,m For production equipment ME i With edge server ES m The channel gain between the two depends on the production equipment ME i With edge server ES m The distance between i,m ,Right now Where ρ is the path loss exponent. Assuming that each wireless channel is symmetrical, the achievable downlink transmission rate is the same as the uplink transmission rate.

[0085] Due to the existence of external dependencies, data will also be transmitted between production equipment, using channels in different frequency bands to avoid interference. i′ The production task q in i′,j′ Need G i The production task q in i,j When the data is i,j The data is transmitted to the production task q i′,j′ , the transmission rate at this time is the second transmission rate:

[0086]

[0087] Among them, w0 is the channel bandwidth for communication between production equipment ME, is the channel noise for communication between production equipment ME, h i,i′For production equipment ME i and production equipment ME i′ The channel gain for communication between the two depends on the production equipment ME i and production equipment ME i′ The distance between i,i′ ,Right now Where ρ is the path loss exponent.

[0088] Step 403: Construct a communication model according to the transmit power allocation decision, the first transmission rate and the second transmission rate.

[0089] In a specific implementation, a communication model is constructed according to the transmit power allocation decision, the first transmission rate, and the second transmission rate, including:

[0090] Step 4031: Determine the task dependency relationship between the target task and the target predecessor task of the target task according to the task model.

[0091] In specific implementation, q i,j Represents the target task, q l,s Indicates the target predecessor task of the target task. If the directed edge between the target task and the target predecessor task belongs to the internal dependency set ID i , determine the task dependency relationship between the target task and the target predecessor task of the target task as an internal dependency relationship. If the directed edge between the target task and the target predecessor task belongs to the external dependency set ED i , determine the task dependency between the target task and the target predecessor task of the target task as an external dependency.

[0092] Step 4032: In response to the task dependency being an internal dependency, edge transmission delay is determined according to the first transmission rate and the transmission data volume of the target task, and the edge transmission delay is determined as the task transmission delay.

[0093] Step 4033: In response to the task dependency being an external dependency, the edge transmission delay is determined according to the first transmission rate and the transmission data volume of the target task, the external transmission delay is determined according to the second transmission rate and the transmission data volume of the target task, and the sum of the external transmission delay and the edge transmission delay is determined as the task transmission delay.

[0094] In the specific implementation, because there are different situations between the task dependency relationship between the target task and the target predecessor task of the target task, the target task q i,j The required data is determined by its target predecessor task q l,s The transmission delay of a task to its execution location can be calculated as:

[0095]

[0096] Among them, when the target predecessor task q l,s With the target task q i,j It is an internal dependency, that is, id(q l,s ,q i,j )∈ID l , then the data transmission delay only considers whether the target task q i,j Offload to the edge server ES, only calculate the edge transmission delay If the target predecessor task q l,s With the target task q i,j is an external dependency, i.e. ed(q l,s ,q i,j )∈ED l When , there is a target predecessor task q l,s With the target task q i,j External transmission delay between We also consider whether to set the target task q i,j Edge transmission delay offloaded to edge server ES

[0097] Among them, 1 {#} is an indicator function, that is, if event # is true, the function value is 1, and if event # is false, the function value is 0. and If the uninstall decision is z i,j =0, indicating that the production task q i,j The task is executed by the production device itself, and there is no process of uploading task data to the edge server. The event is false. i,j =m, indicating that the production task q i,j By edge device ES m To execute, the task data needs to be uploaded to the edge server, and the event is true.

[0098] Step 4034: Determine the task receiving delay according to the return data volume of the target task and the second transmission rate.

[0099] In specific implementation, when event # is true, when the target task q i,j On edge server ES m After the execution is completed, the results of the execution need to be returned to the production equipment ME i , then the transmission delay of receiving the execution result is:

[0100]

[0101] Step 4035: Determine the communication delay based on the task reception delay and the task transmission delay.

[0102] In specific implementation, the communication delay is 0 when the event is false. When the event is true, the sum of the task reception delay and the task transmission delay is determined as the communication delay. The communication delay represents the delay caused by task data transmission when executing a production task.

[0103] Step 4036: Determine the transmission energy consumption of the target task according to the transmission power allocation decision, the transmission power of the target production equipment, the receiving power of the target production equipment and the amount of transmission data.

[0104] In specific implementation, the target task q i,j The required data is transferred to the process where it is executed, the production equipment ME i The transmission energy consumption can be calculated as:

[0105]

[0106] in, When there is no external dependency, the target task q i,j Energy consumption for transmission to edge servers. It represents the energy consumption of transmitting external dependencies and the energy consumption of transmitting tasks to edge servers when there are external dependencies.

[0107] Step 4037: Determine the receiving energy consumption according to the amount of returned data, the second transmission rate and the receiving power of the target production device.

[0108] In specific implementation, when the target task q i,j On edge server ES m After the execution is completed, the results need to be returned to the production equipment ME i , then the receiving energy consumption of receiving the execution result is:

[0109]

[0110] Step 4038: Determine the communication energy consumption based on the receiving energy consumption and the transmitting energy consumption.

[0111] In specific implementation, depending on the offloading decision, the event may be true or false. When the event is false, only the transmission energy consumption needs to be considered, and the transmission energy consumption is determined as the communication energy consumption. When the event is true, the sum of the receiving energy consumption and the transmission energy consumption is determined as the communication energy consumption.

[0112] Step 4039: Integrate the communication delay and communication energy consumption to obtain a communication model.

[0113] In specific implementation, the communication model includes the communication delay and communication energy consumption for data transmission between production equipment and between production equipment and edge servers. The communication model can be obtained by integrating the communication delay and communication energy consumption.

[0114] Step 404: Determine the first computing capability and energy consumption parameters of the target production equipment and the second computing capability of the edge server according to the equipment model.

[0115] In specific implementation, the first computing capability of the target production equipment is f i , the second computing power of the edge server is f m The energy consumption parameters of the target production equipment include working power The transmission power is The received power is Standby power is And its available energy is

[0116] Step 405: Construct a task execution model according to the first computing capability, the energy consumption parameter, and the second computing capability.

[0117] In specific implementation, the energy consumption parameters include working power and standby power; and a task execution model is constructed according to the first computing capability, the energy consumption parameters, and the second computing capability, including:

[0118] Step 4051: Determine the task completion time of the target predecessor task of the target task according to the task model, and determine the task transmission delay between the target task and the target predecessor task according to the communication model, and determine the sum of the task completion time and the task transmission delay as the calculation start time.

[0119] In the specific implementation, the internal and external dependencies between tasks increase the complexity of execution delay analysis. Indicates the execution of the target task q i,j The task start time, Indicates the execution of the target task q i,j The running time, Indicates the execution of the target task q i,j completion time.

[0120] Target task q i,j The calculation start time of the target predecessor task q l,s ∈ψ i,j Completion time and the target predecessor task q l,s ∈ψ i,j The data is transferred to task q i,j The task transmission delay of the execution location of

[0121] Step 4052: Determine the maximum calculation start time among the calculation start times corresponding to different predecessor tasks as the task start time of the target task.

[0122] In specific implementation, the target task q i,j The start time of the task is the target predecessor task q l,s ∈ψ i,j Completion time and the target predecessor task q l,s ∈ψ i,j The data is transferred to task q i,j The task transmission delay of the execution location The maximum value of the sum is obtained because only when the last predecessor task is transmitted can it be ensured that all the data executed by the target task has been obtained. Therefore, the maximum calculation start time among the calculation start times corresponding to different predecessor tasks is determined as the task start time of the target task, and the task start time is expressed as:

[0123]

[0124] Step 4053: Determine the target unloading destination of the target task according to the task model.

[0125] In specific implementation, the offloading decision of the task model is z i,j = 0, the target task q i,j By production equipment ME i The target offloading destination is the target production equipment corresponding to the target task. The offloading decision of the task model is z i,j = m, the target task q i,j Offload to edge server ES m If executed, the target offloading destination is the edge server.

[0126] Step 4054: In response to the target unloading destination being the target production equipment corresponding to the target task, the internal execution delay is determined according to the task execution calculation amount of the target task and the first computing capacity, the product of the internal execution delay and the working power is determined as the internal computing energy consumption, the internal computing delay is determined as the task execution delay, and the internal computing energy consumption is determined as the task execution energy consumption.

[0127] Step 4055: In response to the target offloading destination being an edge server, the external execution delay is determined according to the task execution computing amount and the second computing capacity of the target task, the product of the external execution delay and the standby power is determined as the internal standby energy consumption, and the sum of the external computing delay and the task receiving delay is determined as the task execution delay, and the sum of the internal standby energy consumption and the receiving energy consumption is determined as the task execution energy consumption.

[0128] In specific implementation, when the target task q i,j By the target production equipment ME i Self-execution, i.e. z i,j=0, the internal execution delay can be expressed as follows according to the task execution calculation amount of the target task and the first computing capability:

[0129]

[0130] Similarly, when the target task q i,j Offload to edge server ES m Execution, i.e. z i,j = m, the external execution delay can be expressed as follows according to the task execution calculation amount of the target task and the second computing capability:

[0131]

[0132] When the target task q i,j By the target production equipment ME i Self-execution, i.e. z i,j = 0, only the energy consumption of production equipment is considered, so when task q i,j By production equipment ME i When executing itself, the product of the internal execution delay and the working power needs to be determined as the internal computing energy consumption. The internal computing energy consumption can be expressed as:

[0133]

[0134] Although the computing energy consumption of the edge server ES is not considered when the target task is offloaded to the edge server, the target production equipment is in standby state during the execution of the target task by the edge server, ready to receive the computing results from the edge server at any time. Therefore, the standby energy consumption of the target production equipment also needs to be considered, and the product of the external execution delay and the standby power is determined as the internal standby energy consumption, which can be expressed as:

[0135]

[0136] Therefore, when the target offloading destination is the target production equipment corresponding to the target task, the internal computing delay is determined as the task execution delay, and the internal computing energy consumption is determined as the task execution energy consumption. When the target offloading destination is the edge server, the external computing delay is determined as the task execution delay, and the internal standby energy consumption and data receiving energy consumption are determined as the task execution energy consumption.

[0137] Then the task execution delay (also called running time) is expressed as:

[0138]

[0139] The task execution energy consumption (also called operation energy consumption) is expressed as:

[0140]

[0141] Step 4056: Determine the target completion time of the target task based on the task start time and the task execution delay.

[0142] During specific implementation, the target completion time of the target task is determined based on the task start time and task execution delay:

[0143]

[0144] Step 4057: In response to the target task being the final production task, the target completion time is determined as the equipment production completion time of the target production equipment, and the maximum value of the equipment production completion times corresponding to different production equipment is determined as the total production time.

[0145] In specific implementation, when the target production task is the last production task of the target production equipment, that is, j = D i Target completion time Equipment completion time with target production equipment Equal, that is The total production time of a production operation of the production equipment It is the equipment completion time of the last production task in the single directed acyclic graph corresponding to the production equipment. Then there is For the entire smart factory, only when the last production equipment completes production can it be determined that a complete production operation has been completed. The total production time for all production equipment in the smart factory to perform a production operation is the maximum value of the equipment production completion time corresponding to different production equipment. The total production time is expressed as:

[0146]

[0147] Step 4058: Determine the single-device energy consumption of the target production equipment based on the task execution energy consumption and the transmission energy consumption, and determine the sum of the single-device energy consumption of all production equipment as the total energy consumption.

[0148] During the specific implementation, the production equipment ME i The energy consumption of a single device for a production operation can be expressed as:

[0149]

[0150] The total energy consumption of a smart factory to perform a production operation is the sum of the energy consumption of all production equipment, and the total energy consumption is expressed as:

[0151]

[0152] Step 4059: Integrate the total energy consumption and the total production time to obtain the task execution model.

[0153] In specific implementation, the task execution model includes the equipment production completion time and energy consumption of a single device, as well as the total energy consumption and total production time of the entire smart factory. The equipment production completion time is a component of the total production time, and the energy consumption of a single device is a component of the total energy consumption. The task execution model can be obtained by integrating the total energy consumption and the total production time.

[0154] Step 406: Determine the transmission power of the production equipment according to the equipment model, and construct an external dependency evaluation model according to the transmission power and the transmission power allocation decision.

[0155] In specific implementation, the external dependency relationship between production equipment can improve production efficiency and save some production costs. However, due to the limited resources of production equipment, the available energy and transmission power of the equipment will be consumed in the process of external dependency data sharing. Therefore, it is necessary to reasonably evaluate the energy consumption of production equipment and the distribution of transmission power. An external dependency evaluation model is proposed for evaluation. The external dependency evaluation model can be expressed as:

[0156]

[0157] As the amount of shared data in external dependencies increases, the advantage of data sharing gradually weakens or even becomes negligible, so using To evaluate the impact of the amount of shared data, the total evaluation of external dependencies is expressed as:

[0158]

[0159] Step 407: Integrate the device model, task model, communication model, task execution model and external dependency evaluation model to obtain a smart factory model.

[0160] In specific implementation, by integrating the equipment model, task model, communication model, task execution model and external dependency evaluation model, all functions of the smart factory can be fully executed, the simulation of the smart factory can be realized, and the smart factory model can be obtained.

[0161] In some embodiments, Figure 5 As shown in the figure, with the minimum cost as the goal, the smart factory model is solved by the linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision, including:

[0162] Step 501: With the goal of minimizing cost, a cost objective function is constructed based on the total production time, total energy consumption and total external dependency evaluation of the smart factory model.

[0163] In specific implementation, the cost objective function with the goal of minimizing cost can be expressed as:

[0164]

[0165] Among them, E represents the total energy consumption, T represents the total production time, Eva represents the total external dependency evaluation, α, β, and γ are weight parameters. Θ and Z are variables of the optimization problem, Θ represents the transmission power allocation ratio, and Z represents the offloading decision.

[0166] Step 502: Determine the task offloading constraint according to the smart factory model and the number of edge servers.

[0167] Step 503: Determine energy constraints based on the smart factory model and available energy of production equipment.

[0168] Step 504: Determine the time constraint according to the smart factory model and the preset maximum operating time.

[0169] Step 505: Determine the transmit power allocation ratio range constraint according to a preset ratio range.

[0170] Step 506: Under the constraint set, the cost objective function is solved for the minimum value by a linear goal programming algorithm to obtain the optimal transmit power allocation ratio and the optimal offloading decision; wherein the constraint set includes task offloading constraints, energy constraints, time constraints and preset transmit power allocation ratio range constraints.

[0171] In specific implementation, the constraint set is expressed as:

[0172]

[0173] Among them, the transmission power allocation ratio range constraint C1 represents the range of the transmission power allocation ratio of data sharing in the external dependency. Only when the allocation ratio is within the preset ratio range (0,1) does it indicate that the transmission power is allocated. The task offloading constraint includes the offloading range constraint C2 and the virtual task offloading constraint C3. The smart factory model limits the choice of task offloading destination, that is, it can only be the production equipment itself and the edge server, and the number of edge servers constrains the selection range of the edge server, thus obtaining the offloading range constraint C2. In the smart factory model, entry and exit tasks can only be executed on the production equipment itself, thus obtaining the virtual task offloading constraint C3. The energy constraint C4 is determined based on the smart factory model and the available energy of the production equipment. It is the energy constraint of each production equipment ME itself, ensuring that the available energy provides data sharing energy for the allocated transmission power while meeting the energy consumption of a single device that executes its own tasks. Time constraint C5 limits the total production time of the smart factory task to not exceed the maximum running time

[0174] Under the constraints of the constraint set, the cost objective function is minimized by the linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision, that is, Θ when the cost objective function is minimized is the optimal transmission power allocation ratio, and Z when the cost objective function is minimized is the optimal unloading decision. The optimal transmission power allocation ratio is determined by changing the transmission power input, and the optimal unloading decision determines whether each production device performs the production task by itself or unloads the production task to the edge server, and selects the most suitable edge server when unloading to the edge server based on energy consumption and delay, while ensuring low energy consumption and low delay. The optimal transmission power allocation ratio can achieve the most efficient use of available energy to avoid energy shortage.

[0175] By building a smart factory model to simulate the actual production process, and solving the smart factory model with the minimum cost as the goal, the optimal transmission power allocation ratio and the optimal unloading decision are obtained. The optimal transmission power allocation ratio and the optimal unloading decision can minimize the energy cost and time cost of the smart factory. The smart factory model itself limits parameters such as the transmission power allocation ratio, the energy consumption of the production equipment itself, and the production time, ensuring that the optimal production plan will not exceed the time limit of the production task, and will not cause production stagnation due to insufficient energy. While minimizing the production cost, it ensures that each production equipment has sufficient energy support and meets the production time limit.

[0176] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0177] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0178] In summary, in response to problem 1, the embodiment of the present application proposes a model for determining the optimal transmit power allocation ratio, firstly reasonably allocating power to task pairs with different dependencies (internal dependency, external dependency); then reasonably allocating transmit power for the two situations of data sharing between tasks and task offloading to the edge. The transmit power of production equipment can be fully utilized and transmit power resources can be effectively allocated.

[0179] In response to question 2, the embodiment of the present application sets the available energy and maximum operating time of the production equipment itself to ensure that each production task has sufficient energy support, and can ensure that all equipment in the entire smart factory can complete a production operation within the specified time and minimize its production cost.

[0180] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an intelligent production device based on a directed acyclic graph.

[0181] refer to Figure 6 , an intelligent production device based on a directed acyclic graph, including:

[0182] The dependency determination module 10 is configured to: determine the external dependencies between different production equipment and the internal dependencies of the same production equipment;

[0183] The combined directed acyclic graph module 20 is configured to: construct a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship;

[0184] The device model building module 30 is configured to: build a device model according to the device performance parameters of each production device and the server performance parameters of each edge server;

[0185] The factory model building module 40 is configured to: build a smart factory model according to the combined directed acyclic graph and the task information of each production equipment;

[0186] The model optimal solution module 50 is configured to: solve the smart factory model by a linear target programming algorithm with the goal of minimizing the cost, and obtain the optimal transmission power allocation ratio and the optimal unloading decision;

[0187] The production plan generation module 60 is configured to: determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan.

[0188] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0189] The device of the above embodiment is used to implement the corresponding intelligent production method based on a directed acyclic graph in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0190] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the intelligent production method based on the directed acyclic graph described in any of the above embodiments is implemented.

[0191] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0192] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0193] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0194] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0195] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0196] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0197] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0198] The electronic device of the above embodiment is used to implement the corresponding intelligent production method based on a directed acyclic graph in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0199] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the intelligent production method based on the directed acyclic graph as described in any of the above embodiments.

[0200] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0201] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the intelligent production method based on the directed acyclic graph as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0202] Based on the same concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0203] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0204] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0205] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0206] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0207] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0208] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0209] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0210] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent production method based on a directed acyclic graph, characterized in that: include: Determine the external dependencies between different production equipment and the internal dependencies of the same production equipment; Constructing a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship; Building a device model based on device performance parameters of each production device and server performance parameters of each edge server; Building a smart factory model according to the combined directed acyclic graph, the equipment model and the task information of each production equipment; Taking the minimum cost as the goal, the smart factory model is solved by a linear goal programming algorithm to obtain the optimal transmission power allocation ratio and the optimal unloading decision; An optimal production plan is determined according to the optimal transmission power allocation ratio and the optimal unloading decision, and intelligent production is performed according to the optimal production plan.

2. The method according to claim 1, characterized in that in, Each production device needs to perform at least one production task; the step of constructing a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship includes: Determine an internal task set and an internal dependency set according to the internal dependency relationship, define a start task and an exit task in the internal task set as virtual tasks, and define an intermediate task in the internal task set as a real task; Determine an external dependency set according to the external dependency relationship; The combined directed acyclic graph is constructed according to the internal dependency set, the external dependency set and the internal task set.

3. The method according to claim 1, characterized in that The step of constructing a smart factory model according to the combined directed acyclic graph, the equipment model and the task information of each production equipment includes: Determine a task model according to the combined directed acyclic graph and the task information of each production equipment; Determine a transmit power allocation decision for a target production device according to the task model, and determine a first transmission rate between production devices and a second transmission rate between an edge server and the production device according to the device model; constructing a communication model according to the transmit power allocation decision, the first transmission rate, and the second transmission rate; Determine, according to the device model, a first computing capability and energy consumption parameter of the target production device and a second computing capability of the edge server; Building a task execution model according to the first computing capability, the energy consumption parameter and the second computing capability; determining a transmission power of a production device according to a device model, and constructing an external dependency evaluation model according to the transmission power and the transmission power allocation decision; The device model, the task model, the communication model, the task execution model and the external dependency evaluation model are integrated to obtain the smart factory model.

4. The method according to claim 3, characterized in that Determining the task model according to the combined directed acyclic graph and the task information of each production equipment includes: Determine the predecessor task of each production task according to the combined directed acyclic graph to obtain a predecessor task set; Determine an offloading decision for each production task in the combined directed acyclic graph according to the task information; The unloading destination of the production task in the combined directed acyclic graph is determined in the edge server and the production equipment corresponding to the production task according to the unloading decision, and the previous generation task set, the combined directed acyclic graph and the unloading destination of each production task are integrated to obtain the task model.

5. The method according to claim 3, characterized in that: The constructing a communication model according to the transmit power allocation decision, the first transmission rate, and the second transmission rate includes: Determine a task dependency relationship between a target task and a target predecessor task of the target task according to the task model; In response to the task dependency being an internal dependency, determining an edge transmission delay according to the first transmission rate and a transmission data volume of the target task, and determining the edge transmission delay as the task transmission delay; In response to the task dependency being an external dependency, determining an edge transmission delay according to the first transmission rate and the amount of transmission data of the target task, determining an external transmission delay according to the second transmission rate and the amount of transmission data of the target task, and determining a sum of the external transmission delay and the edge transmission delay as the task transmission delay; Determine the task receiving delay according to the amount of returned data of the target task and the second transmission rate; Determine the communication delay according to the task reception delay and the task transmission delay; Determining the transmission energy consumption of the target task according to the transmission power allocation decision, the transmission power of the target production equipment, the receiving power of the target production equipment and the amount of transmission data; determining the receiving energy consumption according to the amount of returned data, the second transmission rate and the receiving power of the target production equipment; Determine the communication energy consumption according to the receiving energy consumption and the transmitting energy consumption; The communication delay and the communication energy consumption are integrated to obtain the communication model.

6. The method according to claim 3, characterized in that The energy consumption parameter includes working power and standby power; and constructing a task execution model according to the first computing capability, the energy consumption parameter and the second computing capability includes: Determine the task completion time of the target predecessor task of the target task according to the task model, determine the task transmission delay between the target task and the target predecessor task according to the communication model, and determine the sum of the task completion time and the task transmission delay as the calculation start time; Determine the maximum calculation start time among the calculation start times corresponding to different predecessor tasks as the task start time of the target task; determining a target unloading destination for the target task according to the task model; In response to the target offloading destination being a target production device corresponding to the target task, determining an internal execution delay according to the task execution calculation amount of the target task and the first computing capacity, determining the product of the internal execution delay and the working power as the internal computing energy consumption, determining the internal computing delay as the task execution delay, and determining the internal computing energy consumption as the task execution energy consumption; In response to the target offloading destination being an edge server, determining an external execution delay according to the task execution calculation amount of the target task and the second computing capacity, determining the product of the external execution delay and the standby power as the internal standby energy consumption, determining the sum of the external calculation delay and the task reception delay as the task execution delay, and determining the sum of the internal standby energy consumption and the reception energy consumption as the task execution energy consumption; Determine the target completion time of the target task according to the task start time and the task execution delay; In response to the target task being the final production task, determining the target completion time as the equipment production completion time of the target production equipment, and determining the maximum value of the equipment production completion times corresponding to different production equipment as the total production time; Determine the single-device energy consumption of the target production equipment according to the task execution energy consumption and the transmission energy consumption, and determine the sum of the single-device energy consumption of all production equipment as the total energy consumption; The total energy consumption and the total production time are integrated to obtain the task execution model.

7. The method according to claim 1, characterized in that The method of solving the smart factory model by a linear target programming algorithm with the goal of minimizing the cost to obtain the optimal transmission power allocation ratio and the optimal unloading decision includes: Taking cost minimization as the goal, a cost objective function is constructed according to the total production time, total energy consumption and total external dependency evaluation of the smart factory model; Determining a task offloading constraint based on the smart factory model and the number of servers of the edge server; determining an energy constraint based on the smart factory model and available energy of production equipment; Determine the time constraint according to the smart factory model and the preset maximum operating time; Determine the transmit power allocation ratio range constraint according to a preset ratio range; The cost objective function is minimized by a linear goal programming algorithm under a constraint set to obtain the optimal transmit power allocation ratio and the optimal unloading decision; wherein the constraint set includes the task unloading constraint, the energy constraint, the time constraint and a preset transmit power allocation ratio range constraint.

8. An intelligent production device based on a directed acyclic graph, characterized in that: include: The dependency determination module is configured to: determine the external dependency between different production equipment and the internal dependency of the same production equipment; A combined directed acyclic graph module is configured to: construct a combined directed acyclic graph according to the external dependency relationship and the internal dependency relationship; The device model building module is configured to: build a device model according to the device performance parameters of each production device and the server performance parameters of each edge server; A factory model building module is configured to: build a smart factory model according to the combined directed acyclic graph and the task information of each production equipment; The model optimal solution module is configured to: solve the smart factory model by a linear target programming algorithm with the goal of minimizing cost, and obtain an optimal transmission power allocation ratio and an optimal unloading decision; The production plan generation module is configured to: determine the optimal production plan according to the optimal transmission power allocation ratio and the optimal unloading decision, and perform intelligent production according to the optimal production plan.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

Images