Production workshop-oriented multi-device wireless communication optimization method and device

Abstract

The invention discloses a production workshop-oriented multi-device wireless communication optimization method and device, and relates to the related field of wireless communication networks, and the method comprises the steps: executing real-time task calling and interpreting a production service intention through the interaction between a standard interface and a workshop service system, and carrying out the QoS parameter coding to generate a communication KPI; according to the communication KPI, assisting a slice manager embedded in a communication center controller, dynamically generating a network slice, performing protocol element reading coupling under multiple communication protocols, and determining a communication element condition set; and for the communication element condition set, dimension joint optimization based on network resource scheduling is carried out, a resource configuration strategy is determined, communication management is carried out on task-related equipment interaction terminals, temporary communication isolation is established between non-associated task terminals, and a loose channel network is used as alternative configuration. The technical problem that existing production workshop multi-device wireless communication is insufficient in stability and reliability is solved, and the technical effect of improving the stability and reliability of communication is achieved.

Description

Technical Field

[0001] This application relates to the field of wireless communication networks, and in particular to a method and device for optimizing wireless communication between multiple devices in a production workshop. Background Technology

[0002] In modern production workshops, the stable and efficient operation of multi-device wireless communication directly affects the smoothness of production processes, the improvement of production efficiency, and the stability of product quality, serving as a key support for achieving intelligent manufacturing. Currently, the main approach to solving the problem of multi-device wireless communication in production workshops is the traditional fixed network configuration and single communication protocol management method. This involves pre-setting a fixed network resource allocation scheme for workshop equipment and managing communication interactions between devices based on a specific communication protocol. This method, due to its fixed network resource configuration, cannot dynamically adjust resource allocation according to the real-time changing task requirements of the production workshop. Furthermore, a single communication protocol is difficult to adapt to the communication characteristics and needs of various types of equipment within the workshop. This leads to problems such as unreasonable communication resource allocation, frequent communication conflicts between devices, and low communication efficiency when facing complex and ever-changing production tasks, ultimately affecting the overall operational efficiency of the production workshop.

[0003] Currently, wireless communication between multiple devices in a production workshop suffers from insufficient stability and reliability. Summary of the Invention

[0004] This application provides a method and device for optimizing multi-device wireless communication in production workshops. Through interaction with the workshop's business system via a standard interface, it retrieves and interprets real-time tasks to clarify production business intentions. Communication KPIs are generated using QoS parameter encoding. Based on these KPIs, a slice manager embedded in the communication control center dynamically generates network slices and reads coupled multi-communication protocol elements to determine the communication element condition set. For this condition set, a resource allocation strategy is determined through joint optimization using network resource scheduling. The application also manages communication between task-related device terminals and establishes temporary communication isolation for unrelated task terminals, using a relaxed channel network as an alternative configuration. These technical means address the technical problems of insufficient stability and reliability in existing multi-device wireless communication in production workshops, achieving the technical effect of improving communication stability and reliability.

[0005] This application provides a method for optimizing wireless communication of multiple devices in a production workshop, comprising: interacting with the workshop business system through a standard interface, executing real-time task retrieval and interpretation of production business intent, performing QoS parameter encoding, and generating communication KPIs; based on the communication KPIs, assisting a slice manager embedded in the communication control center to dynamically generate network slices and perform protocol element reading and coupling under multiple communication protocols to determine a set of communication element conditions; for the set of communication element conditions, performing dimensional joint optimization based on network resource scheduling to determine resource configuration strategies, and managing communication between task-related device interaction terminals, wherein temporary communication isolation is established between non-associated task terminals, with a relaxed channel network as an alternative configuration.

[0006] In a possible implementation, a real-time task is executed to retrieve and interpret the production business intent, encode QoS parameters, generate communication KPIs, and perform the following processing: retrieve a real-time task set, wherein the real-time task set is a multi-threaded industrial control task; interpret the real-time task set to determine the task intent set; and, based on a lightweight encoder, encode the task intent set and fit it with the task QoS parameter sequence to determine the communication KPIs.

[0007] In a possible implementation, the task intent set is encoded and fitted with a task QoS parameter sequence using a lightweight encoder, and the following processing is performed: a set of industrial control elements of the target workshop and a QoS parameter set based on the communication network are obtained; mutual mapping between the industrial control element set and the QoS parameter set is performed to build a lightweight encoder; the task intent set is encoded and converted using the lightweight encoder to determine the task QoS parameter sequence; and the task QoS parameter sequence is clustered and aggregated within each cluster based on communication patterns to generate the communication KPI.

[0008] In a possible implementation, network slices are dynamically generated, and the following processes are performed: driving the slice manager, writing the communication mode, performing slice type matching, and determining the target slice type; and performing communication status writing based on the target slice type according to the communication KPI to generate network slices.

[0009] In a possible implementation, protocol element reading coupling under multiple communication protocols is performed to determine the communication element condition set, and the following processing is executed: at the communication protocol layer, protocol condition reading and coupling are performed on the network slice to determine the communication element condition set, wherein the communication element condition set is a normalized standard; wherein, protocol condition reading includes: determining the first communication protocol, performing protocol element reading on the first network slice to determine the first communication element; performing analysis of the Nth communication protocol on the Mth network slice until the N×Mth communication element is determined; integrating the first communication element up to the N×Mth communication element.

[0010] In a possible implementation, joint optimization based on network resource scheduling is performed, and the following processes are executed: setting scheduling dimensions for network resource scheduling, wherein the scheduling dimensions include at least spectrum resource allocation, time slot / air interface resource allocation, routing selection and transmit power modulation; for the set of communication element conditions, joint optimization calculation based on scheduling dimensions is performed to determine the resource allocation strategy.

[0011] In a possible implementation, for the set of communication element conditions, joint optimization calculation based on the scheduling dimension is performed, and the following processes are performed: a communication space is constructed using available communication networks and a second channel quality heatmap; a task space is constructed using the set of communication element conditions and a first communication requirement; an optimization module is generated based on the communication space and the task space; and for the optimization module, the resource allocation strategy is generated by combined iterative optimization based on the scheduling dimension.

[0012] In a possible implementation, before generating the optimization module based on the communication space and the task space, the following processes are performed: obtaining real-time channel status, performing virtual reconstruction of the available network in the communication network, and determining the available communication network, wherein the available communication network includes a first idle channel network and a second relaxed channel network, the second relaxed channel network being a candidate channel network; predicting a first communication demand by sensing the workshop's operational intention; and determining a second channel quality heatmap by performing quality prediction on the available communication network, wherein the channel quality is defined at least based on channel quality changes and potential interference conflicts.

[0013] In a possible implementation, communication management is performed on task-related device interaction terminals, and the following processes are executed: for the real-time task set, the device interaction terminals are determined, and communication isolation is established between non-associated task terminals; communication management between the device interaction terminals is performed using the resource configuration strategy and the communication isolation.

[0014] This application also provides an electronic device, including: a memory for storing executable instructions; and a processor for implementing a multi-device wireless communication optimization method for a production workshop when executing the executable instructions stored in the memory.

[0015] The proposed method and device for multi-device wireless communication optimization in production workshops, as described in this application, first interacts with the workshop business system through a standard interface to execute real-time task retrieval and interpretation of production business intentions, encode QoS parameters, and generate communication KPIs. Then, based on these KPIs, a slice manager embedded in the communication control center dynamically generates network slices and performs protocol element reading and coupling under multiple communication protocols to determine the communication element condition set. Finally, based on the communication element condition set, dimensional joint optimization based on network resource scheduling is performed to determine resource allocation strategies and manage communication between task-related device interaction terminals. Temporary communication isolation is established between non-associated task terminals, with a relaxed channel network as an alternative configuration. This achieves the technical effect of improving communication stability and reliability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating a multi-device wireless communication optimization method for production workshops, provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0022] This application provides a method for optimizing wireless communication between multiple devices in a production workshop, such as... Figure 1 As shown, the method includes: Step S100: Through interaction with the workshop business system via the standard interface, execute real-time tasks to retrieve and interpret production business intentions, encode QoS parameters, and generate communication KPIs.

[0023] Specifically, a standard interface is a predefined communication connection method that specifies the format, protocol, and rules for data exchange between different devices or systems, ensuring that all parties can accurately transmit and receive data. A shop floor business system is an information system used to manage shop floor production activities, including multiple functional modules such as production planning, material management, production process monitoring, and quality management. It can record and process various production-related data and information in real time.

[0024] The communication management system interacts with the workshop business system through a standard interface. When executing real-time task retrieval, it utilizes the API provided by the workshop business system to obtain the production task information that needs to be processed from the system according to preset rules and permissions. Semantic analysis algorithms from natural language processing technology are used to interpret the production business intent. By parsing the task description text, key information such as the type, quantity, and delivery date of the products to be produced is extracted, thereby determining the specific requirements of the production business.

[0025] QoS parameters refer to Quality of Service parameters, used to measure and describe the service performance indicators provided by a communication system to users, including bandwidth, latency, jitter, packet loss rate, etc. Different services have different requirements for QoS parameters. Communication KPIs are key performance indicators for communication, which are quantitative evaluation indicators of communication system performance, used to measure the effectiveness and efficiency of the communication system in meeting service requirements.

[0026] Based on different business needs and service types, QoS parameters are encoded, converting them into binary code that computers can recognize and process according to specific encoding rules. For example, for tasks with high real-time requirements, latency parameters are encoded with lower values; for tasks with high data accuracy requirements, packet loss rate parameters are encoded with extremely low values. Ultimately, communication KPIs are generated, which comprehensively reflect the performance level that the communication system should achieve in meeting production business needs, such as data transmission rate, latency, and reliability.

[0027] In one possible implementation, the execution of real-time tasks involves retrieving and interpreting production business intentions, encoding QoS parameters, and generating communication KPIs. Step S100 further includes step S110, retrieving a real-time task set, wherein the real-time task set is a multi-threaded industrial control task. Specifically, the real-time task set is retrieved through a communication interface with the industrial control system, such as the OPC UA protocol interface. The OPC UA protocol provides a unified data access and communication mechanism, ensuring accurate acquisition of task information from different manufacturers and different types of industrial control systems. The real-time task set originates from various industrial control systems within the workshop, such as programmable logic controllers and distributed control systems. These systems generate a series of industrial control tasks based on production plans, equipment status, and other information, such as equipment start / stop tasks, parameter adjustment tasks, and data acquisition tasks. These industrial control tasks exist in a multi-threaded manner, meaning they can be executed simultaneously or alternately to improve system processing efficiency. For example, in an automobile production workshop, multiple workstations perform different operations simultaneously; the equipment control task at each workstation is an independent thread, and together they constitute the real-time task set.

[0028] Step S120: For the real-time task set, perform task interpretation to determine the task intent set. Specifically, a method combining natural language processing (NLP) technology and industry domain knowledge is used to interpret the real-time task set. NLP technology can analyze and understand the task description text and extract key information; industry domain knowledge is used to supplement and verify this information to ensure the accuracy of the interpretation. By analyzing information such as task type, goal, and parameters, the intent of each task is determined. For example, a device start task may have the intent to start producing a certain product; a data acquisition task may have the intent to acquire equipment operating status data for monitoring and analysis. The interpretation results of all tasks are summarized to form a task intent set. Each element in the task intent set corresponds to the intent description of a real-time task, such as "Start the welding equipment on production line 1, with the goal of completing the welding task of 100 products, and the welding speed parameter is 10 products / minute".

[0029] Step S130: Based on a lightweight encoder, the task intent set is encoded and fitted with a task QoS parameter sequence to determine the communication KPI. Specifically, a lightweight encoder, such as a hash-based encoder, is selected. This encoder features low computational complexity and fast encoding speed, reducing system resource consumption while ensuring encoding accuracy. Each intent in the task intent set is encoded using the lightweight encoder, converting it into a fixed-length binary sequence. For example, the intent "start the welding equipment on production line 1" is encoded as a 128-bit binary sequence. Based on the task type and intent, and combined with predefined QoS parameter rules, a corresponding QoS parameter sequence is determined for each task. QoS parameters may include latency, bandwidth, reliability, etc. For example, for equipment control tasks with high real-time requirements, the latency parameter must be within 10ms; for data acquisition tasks, the reliability parameter must reach 99.9% or higher. The task intent encoding and task QoS parameter sequence are comprehensively processed to generate the communication KPI. The communication KPI is a comprehensive indicator used to measure the performance requirements of a task during communication. This implementation generates communication KPIs based on the QoS parameter sequence of the task, which can optimize communication parameters according to the characteristics of different tasks, ensuring that the performance requirements of the task are met during the communication process, thereby improving the stability and reliability of the entire industrial automation system.

[0030] In one possible implementation, the task intent set is encoded and fitted with a task QoS parameter sequence using a lightweight encoder. Step S130 further includes step S131, obtaining the industrial control element set of the target workshop and the QoS parameter set based on the communication network, performing mutual mapping between the industrial control element set and the QoS parameter set, and building a lightweight encoder. Specifically, the industrial control element set of the target workshop refers to various elements related to industrial control within the workshop, including but not limited to equipment, production processes, and control commands. This element information can be obtained from the workshop's industrial control system database, equipment configuration files, and production process documents. The obtained industrial control elements are classified and organized, for example, according to equipment type (e.g., electrical control equipment, mechanical transmission equipment); and according to production process (e.g., raw material processing process, finished product packaging process).

[0031] QoS parameter data in the communication network can be collected in real time using network monitoring tools such as network analyzers and traffic monitoring software. Alternatively, QoS parameter sets can be obtained by referring to performance indicator documents provided by communication equipment manufacturers. The inherent relationship between industrial control elements and QoS parameters should be analyzed to formulate mapping rules. For example, for devices requiring real-time control, such as high-speed robotic arms, the requirements for communication latency are high; therefore, industrial control elements related to such devices are mapped to low-latency QoS parameters. For data acquisition tasks, the requirements for bandwidth are relatively high, so industrial control elements related to data acquisition are mapped to high-bandwidth QoS parameters. Based on the formulated mapping rules, each element in the industrial control element set is associated with the corresponding parameter in the QoS parameter set. For example, the industrial control element "robotic arm motion control command" can be mapped to the QoS parameter "latency ≤ 5ms".

[0032] Based on the results of the mutual mapping, a lightweight encoder architecture is designed. The lightweight encoder can employ a neural network structure, such as a single-layer perceptron, or a rule-based encoding method, ensuring efficient encoding with low computational resource consumption. The parameters in the encoder are initialized; these parameters are used in subsequent encoding and transformation processes. For example, in a neural network encoder, weights and bias parameters are initialized; in a rule-based encoder, rule parameters from the rule base are initialized.

[0033] Step S132: The task intent set is encoded and converted using the lightweight encoder to determine the task QoS parameter sequence. Specifically, each task intent in the task intent set is used as input and fed into the established lightweight encoder. The lightweight encoder encodes and converts the task intents according to the established mapping relationship between industrial control elements and QoS parameters, as well as its own encoding rules. For example, for the task intent "start the filling equipment on production line 1," the encoder will convert it into a combination of QoS parameters related to starting the equipment, such as "latency ≤ 10ms, bandwidth ≥ 1Mbps," based on the mapping relationship. The QoS parameter combinations obtained after encoding and conversion are arranged in a certain order to form a task QoS parameter sequence. This sequence reflects the specific QoS parameter requirements of each task during communication.

[0034] Step S133: Perform clustering and intra-cluster aggregation based on communication patterns on the task QoS parameter sequence to generate the communication KPI. Specifically, analyze the communication patterns between different tasks within the target workshop. Communication patterns refer to the characteristics and requirements of different tasks during communication, including communication frequency, data volume, real-time requirements, and reliability requirements. For example, real-time control tasks have high real-time and high reliability requirements, with a higher communication frequency and relatively smaller data volume; video surveillance tasks, on the other hand, have higher bandwidth requirements, a relatively lower communication frequency, but a larger data volume. Select a clustering algorithm, such as K-means clustering, to cluster the task QoS parameter sequence. The clustering is based on the similarity of communication patterns between tasks, grouping QoS parameter sequences with similar communication patterns into one category. For example, group all tasks requiring low-latency communication into one category, and all tasks with high bandwidth requirements into another category.

[0035] Within each cluster, aggregation rules are defined to comprehensively process the QoS parameters of all tasks within the cluster. For example, for latency parameters, the minimum latency requirement of all tasks within the cluster is taken as the aggregated latency parameter for that cluster; for bandwidth parameters, the maximum bandwidth requirement of all tasks within the cluster is taken as the aggregated bandwidth parameter for that cluster. Based on the defined aggregation rules, the QoS parameter sequences of tasks within each cluster are aggregated to obtain the comprehensive QoS parameters for each cluster.

[0036] Communication KPIs are generated based on aggregated comprehensive QoS parameters. For example, weighted averaging can be used to calculate the aggregated comprehensive QoS parameters, assigning different weights to parameters such as latency, bandwidth, and jitter, and then calculating the weighted average as the communication KPI. The communication KPI reflects the overall performance level of the communication system in meeting the communication needs of various tasks within the workshop. This implementation method uses communication pattern-based clustering and intra-cluster aggregation, which can rationally integrate the QoS parameters of different tasks, avoid wasting communication resources, and improve the resource utilization rate of the communication system.

[0037] Step S200: Based on the communication KPI, the slice manager embedded in the communication control center is assisted in dynamically generating network slices and performing protocol element reading coupling under multiple communication protocols to determine the communication element condition set.

[0038] Specifically, the communication control center is the core control unit of the workshop communication system, responsible for managing and coordinating the operation of the entire communication network. The communication control center integrates multiple functional modules, such as a network slice manager and a resource scheduler, enabling real-time monitoring of network status and dynamic adjustment of network configuration based on business needs. The slice manager, embedded within the communication control center, is responsible for the generation, management, and maintenance of network slices. Based on communication KPI requirements, such as specific bandwidth and latency, the slice manager divides the physical network into multiple logically independent network slices. Each slice has an independent resource allocation and management strategy; that is, each network slice can be customized according to specific business needs, possessing independent network topology, resource allocation, and transmission characteristics, thus meeting the differentiated network requirements of various businesses.

[0039] Identify the communication protocols used by different devices within the workshop, such as Modbus and Profinet. Parse multiple communication protocols, extracting key elements such as data frame structure, address information, and control commands. Through protocol conversion and mapping techniques, couple the elements of different protocols to enable transmission and interaction within a unified network environment. Finally, determine the communication element condition set, which includes relevant network slicing parameters and coupled protocol element information, comprehensively reflecting the various conditions required by the communication system to meet business needs.

[0040] In one possible implementation, network slices are dynamically generated. Step S200 further includes step S210, which drives the slice manager, writes the communication mode, performs slice type matching, and determines the target slice type. Specifically, the network slice generation process is initiated by sending a drive command to the slice manager. This can be done through a software interface or a hardware trigger signal. For example, in an industrial control system, when a new task request is received, the control system sends a drive command to the slice manager via API, informing it that a new network slice needs to be generated. Pre-analyzed communication mode information is written to the slice manager. For example, for real-time control tasks, the communication mode is written as "high real-time performance, high reliability, communication frequency of 100 times per second, and average data volume of 100 bytes per transmission." Slice type matching rules are formulated based on the characteristics of the communication mode. Network slice types include dedicated control slices, mobile broadband slices, and massive connection slices. Dedicated control slices are mainly used in industrial control scenarios with extremely high real-time and reliability requirements; mobile broadband slices are suitable for scenarios requiring high bandwidth transmission of video, images, and other data; massive connection slices are used to connect a large number of sensors and devices, with special requirements for resource consumption and protocol efficiency. For example, if the communication mode has extremely high real-time and reliability requirements, it is matched as a dedicated control slice; if the communication mode has high bandwidth requirements, it is matched as a mobile broadband slice; if the communication mode involves a large number of device connections, such as ≥1000 devices, it is matched as a large-scale connection slice. The slice manager matches slice types based on the written communication mode information and pre-defined matching rules to determine the target slice type. For example, for the communication mode of the real-time control task mentioned above, the slice manager matches it as a dedicated control slice. After determining the target slice type, the specific characteristics and configuration requirements of that slice type are further confirmed. For example, a dedicated control slice requires dedicated resources and TDMA scheduling to ensure ultra-low latency and ultra-high reliability; a mobile broadband slice requires large bandwidth resources to carry data such as video backhaul; a large-scale connection slice requires lightweight protocols to access a massive number of sensors and other devices. The determined target slice type and its characteristic information are recorded in the slice manager's database for network slice generation and configuration.

[0041] Step S220: Based on the communication KPIs, perform communication status writing based on the target slice type to generate a network slice. Specifically, communication status refers to various parameters and configuration information of the network slice during operation, including resource allocation, scheduling strategies, protocol selection, etc. This status information affects the performance and functionality of the network slice. According to the determined target slice type and communication KPIs, the corresponding communication status information is written to the network slice. For example, for a dedicated control slice, dedicated resource allocation information and TDMA scheduling strategy parameters are written; for a mobile broadband slice, high-bandwidth resource allocation information and video backhaul-related protocol configurations are written; for a large-scale connection slice, lightweight protocol selection and configuration information are written. After completing the communication status writing, the slice manager configures the network slice based on the written information, including setting the network slice topology, allocating network resources, and configuring network protocols. For example, for a dedicated control slice, the network connection between it and industrial control equipment is configured to ensure that dedicated resources can be correctly allocated and used; for a mobile broadband slice, the network connection between it and video acquisition equipment and video servers is configured to achieve efficient transmission of video data. After configuration, the network slice is activated and put into operation. During activation, the slice manager initializes and tests network slices to ensure they function correctly. For example, it performs latency and reliability tests on dedicated control slices to verify that they meet ultra-low latency and ultra-high reliability requirements; and it performs bandwidth and video transmission quality tests on mobile broadband slices to verify that they can handle video backhaul tasks.

[0042] This implementation method, through communication pattern matching and communication KPI analysis, can accurately determine the target slice type and write communication status according to actual needs, thereby generating network slices that meet the communication requirements of different tasks. Furthermore, the dynamic generation of network slices allows the communication system to quickly adapt to changes in different tasks and scenarios. When new task requirements arise, corresponding network slices can be generated promptly without requiring large-scale modifications to the entire physical network.

[0043] In one possible implementation, protocol element reading coupling is performed under multiple communication protocols to determine the communication element condition set. Step S200 further includes step S230, whereby, at the communication protocol layer, protocol condition reading and coupling are performed on the network slice to determine the communication element condition set, wherein the communication element condition set is a normalized standard; wherein, the protocol condition reading includes: determining a first communication protocol, performing protocol element reading on the first network slice to determine a first communication element; performing analysis of the Nth communication protocol on the Mth network slice until the N×Mth communication element is determined; and integrating the first communication element up to the N×Mth communication element.

[0044] Specifically, due to the heterogeneity between different protocols, a task may depend on multiple protocols, and only some key protocol points are considered in each protocol. Therefore, the core objective of step S230 is to overcome the obstacles caused by protocol heterogeneity, extract the communication elements closely related to the current task from the complex protocol system, and normalize them to form a set of communication element conditions, providing a unified and standardized basis for subsequent communication management and optimization.

[0045] Based on the characteristics and requirements of the task, and the available communication protocols in the network, a communication protocol is selected as the current analysis object, namely the first communication protocol. Relevant documents and specifications for the first communication protocol are obtained. According to the protocol specifications, key protocol elements are read from the first network slice, including the source address, destination address, message type, data payload length, timestamp, and control flags. For example, in the HTTP protocol, elements such as request method, URL, and request header information are read. The read key protocol elements are organized and recorded to form the first communication elements. For the first network slice, each protocol used is analyzed sequentially, and the corresponding elements are read. After completing the analysis of the first network slice and all its protocols, the next network slice is selected, and the process of determining the protocol, reading elements, and forming an element set is repeated. This process is continuously looped until all relevant communication protocols for all network slices are analyzed, ultimately determining multiple communication element sets. Assuming there are M network slices in total, and each network slice analyzes N protocols, then N×M communication elements will be obtained.

[0046] Integrate all communication elements, retaining only those closely related to the current task and filtering out irrelevant elements. For example, in a task focused solely on data transmission speed, speed-independent elements like data encryption methods can be ignored. For elements with similar functions but different names across different protocols, standardize their naming and definition. For instance, the "message number" element from different protocols is uniformly named "Message_Number," ensuring semantic and format consistency in the integrated communication elements. For elements with different data formats across different protocols, perform data conversion and normalization. For example, timestamps in different protocols may use different time formats; convert them to the standard UTC time format. Combine all integrated and normalized communication elements into a set, namely the communication element condition set.

[0047] This implementation integrates and normalizes communication elements from different protocols into a set of communication element conditions. The communication management system can manage and monitor communication elements in a unified manner under a multi-protocol environment, avoiding complex management problems caused by protocol heterogeneity and improving management efficiency.

[0048] Step S300: For the set of communication element conditions, perform dimensional joint optimization based on network resource scheduling to determine the resource configuration strategy and perform communication management on the task-related device interaction terminals. Temporary communication isolation is established between non-associated task terminals, with a relaxed channel network as an alternative configuration.

[0049] Specifically, a multi-dimensional resource scheduling model is constructed, integrating factors such as network bandwidth, computing resources, and storage resources. Optimization algorithms, such as genetic algorithms and particle swarm optimization, are used to solve the model to find a resource allocation strategy that maximizes resource utilization while satisfying the set of communication element conditions. When managing communication between task-related device terminals, corresponding network resources, such as bandwidth and channels, are allocated to each device according to the determined resource allocation strategy. Temporary communication isolation is established between unrelated task terminals; that is, communication between unrelated task terminals is restricted or blocked for a specific period of time to avoid communication interference and improve network performance and security. This can be achieved by setting access control lists or virtual LANs. A relaxed channel network is used as a backup configuration. When the primary network fails or becomes congested, the system can automatically switch to the relaxed channel network to ensure communication continuity and reliability.

[0050] In one possible implementation, joint optimization based on network resource scheduling is performed. Step S300 further includes step S310, setting the scheduling dimensions of network resource scheduling. These scheduling dimensions include at least spectrum resource allocation, time slot / air interface resource allocation, routing selection, and transmit power modulation. Specifically, spectrum is the wireless frequency resource used for information transmission in a communication network. Spectrum resource allocation involves rationally allocating limited spectrum resources to various services based on different service requirements and network conditions. Rational spectrum resource allocation can improve spectrum utilization and avoid spectrum resource waste and conflicts. For example, through dynamic spectrum allocation technology, spectrum allocation is adjusted according to real-time service traffic, allowing services with high spectrum demand to obtain more spectrum resources, thereby improving the network's transmission capacity.

[0051] A time slot is a unit of time resource. In systems like Time Division Multiple Access (TDMA), time is divided into multiple time slots, and different services communicate in different time slots. Air interface resources include the physical channel resources of the air interface, such as code channels and subcarriers. Time slot / air interface resource allocation involves the rational allocation of these time and physical channel resources. A reasonable allocation of time slot / air interface resources can reduce interference between services and improve communication reliability and efficiency.

[0052] Routing refers to selecting the transmission path for data packets from the source node to the destination node in a communication network. When multiple possible paths exist in the network, the optimal path needs to be selected according to certain rules and algorithms. Reasonable routing can optimize network traffic distribution, reduce network congestion, and improve network transmission performance and reliability. For example, by selecting a path with sufficient bandwidth and low latency to transmit data, data transmission time can be shortened, improving user experience.

[0053] Transmit power modulation refers to dynamically adjusting the transmit power of a transmitting device based on the communication environment and service requirements. The transmit power affects the signal transmission distance and coverage, and can also interfere with other devices. For example, in wireless sensor networks, sensor nodes adjust their transmit power based on their distance from the sink node and the surrounding ambient noise level. Appropriate transmit power modulation can reduce device power consumption and minimize interference to other users while ensuring communication quality. For instance, adaptive power control technology adjusts the transmit power in real time based on channel quality, minimizing transmit power while meeting the signal-to-noise ratio requirements of the receiver, thus extending device battery life.

[0054] In real-world networks, these four scheduling dimensions are interconnected and mutually influential. For example, spectrum allocation affects the use of time slots / air interface resources because different frequency bands have different time slot structures and air interface resource characteristics; routing selection interacts with transmit power modulation, and different routes require different transmit powers to ensure communication quality. Therefore, when scheduling network resources, these four scheduling dimensions should be considered comprehensively, and the optimal resource allocation strategy should be determined through joint optimization of these dimensions.

[0055] Step S320: For the set of communication element conditions, perform joint optimization calculation based on the scheduling dimension to determine the resource allocation strategy. Specifically, the set of communication element conditions provides specific inputs and constraints for the joint optimization calculation, enabling the optimization calculation to be tailored to actual communication needs. Based on the set of scheduling dimensions and communication element conditions, an optimization model that comprehensively considers multiple dimensions is established. This model uses network performance indicators such as throughput, latency, and bit error rate as optimization objectives, variables of the scheduling dimension as decision variables, and various conditions as constraints, such as total spectrum resource limits, time slot resource limits, and transmit power range limits. For example, an optimization model can be established with maximizing network throughput as the objective function, spectrum allocation, time slot allocation, routing selection, and transmit power as decision variables, and total spectrum resources, number of time slots, and transmit power range as constraints.

[0056] The specific data of the communication element condition set are substituted into the optimization model, and optimization algorithms are used for calculation. During the calculation process, the algorithm continuously adjusts the values ​​of decision variables according to the optimization objective and constraints to find the optimal resource allocation strategy. For example, in a genetic algorithm, new resource allocation schemes are continuously generated, their network performance indicators are calculated, and selection, crossover, and mutation operations are performed based on the network performance indicators to gradually approach the optimal solution. After optimization calculation, a set of decision variable values ​​that satisfy the optimization objective and constraints is obtained. These values ​​correspond to the optimal resource allocation strategy, including how spectrum resources are allocated, how time slot / air interface resources are allocated, which route is selected, and what the transmit power should be set to, etc. This implementation method, by comprehensively optimizing multiple scheduling dimensions through joint optimization, can comprehensively optimize the allocation of network resources and improve overall network performance.

[0057] In one possible implementation, for the set of communication element conditions, joint optimization calculation based on the scheduling dimension is performed. Step S320 further includes step S321, constructing a communication space using the available communication network and the second channel quality heatmap, constructing a task space using the set of communication element conditions and the first communication requirement, and generating an optimization module based on the communication space and the task space. Specifically, the available communication network represents various communication infrastructures and frequency band resources available in the current environment. The second channel quality heatmap is a tool for displaying the distribution of channel quality. By representing the channel quality of different regions, such as signal-to-noise ratio and bit error rate, with colors or other visualization methods, it reflects the channel quality at various locations in the communication network. For example, in the heatmap, red areas can indicate poor channel quality with strong interference or signal fading; green areas can indicate good channel quality with stable signal transmission. Combining the available communication network with the second channel quality heatmap to construct the communication space provides a reference for resource allocation and optimization. For example, when allocating spectrum resources, high-quality frequency bands can be prioritized for areas with poor channel quality based on the channel quality of different regions in the communication space, thereby improving the overall communication quality; when selecting routes, areas with poor channel quality can be avoided, and paths with better channel quality can be selected for data transmission.

[0058] The communication element condition set reflects the specific characteristics and requirements of the communication task. The first communication requirement determines the performance objective of the communication task. A task space is constructed using the communication element condition set and the first communication requirement. This space integrates and quantifies the specific characteristics and performance requirements of the communication task, forming a unified task description framework that provides objectives and constraints for optimization computation.

[0059] The optimization module combines the actual situation of the communication space with the requirements of the task space, providing a computational model and optimization framework for combinatorial iterative optimization. Based on network resources and channel quality information in the communication space, as well as the characteristics and performance requirements of communication tasks in the task space, the optimization module generates a series of possible resource allocation schemes and selects the optimal scheme from them using optimization algorithms.

[0060] Step S322: For the optimization module, a combined iterative optimization based on scheduling dimensions is used to generate the resource allocation strategy. Specifically, scheduling dimensions affect network resource allocation and communication performance from different aspects. Combined iterative optimization integrates these scheduling dimensions and finds the optimal resource allocation scheme by continuously iterating and adjusting the parameters of each dimension. Specifically, an initial resource allocation scheme is generated based on information from the communication space and task space. This initial scheme can be random or generated based on certain empirical rules. For example, spectrum resources and time slot resources can be initially allocated based on the channel quality of each region in the communication space, a simple route can be selected for data transmission, and an initial transmit power value can be set. The performance of the initial scheme is evaluated, its communication performance indicators are calculated, and compared with the performance requirements in the task space. If the performance of the current scheme does not meet the requirements, the parameters of each scheduling dimension are adjusted according to the optimization algorithm. For example, if the throughput of the current scheme is low, the amount of spectrum resources allocated can be increased or the time slot allocation scheme can be optimized; if the latency is high, the route can be reselected or the transmit power can be adjusted to speed up signal transmission. The process of performance evaluation and parameter adjustment is repeated for multiple iterative optimizations. In each iteration, the resource allocation scheme is continuously improved so that its performance gradually approaches the requirements of the task space. When the performance of the scheme meets the requirements of the task space or the predetermined number of iterations is reached after multiple iterations, the iteration stops, and the resource allocation scheme obtained at this point is the optimal scheme.

[0061] In one possible implementation, before generating the optimization module based on the communication space and the task space, step S321 further includes step S3211: acquiring real-time channel status, performing virtual reconstruction of the available network in the communication network, and determining the available communication network. The available communication network includes a first idle channel network and a second relaxed channel network, with the second relaxed channel network serving as a candidate channel network. Specifically, sensors and monitoring devices are deployed in the communication network to collect relevant channel data in real time. These devices can periodically send the collected data to the central control system, which processes and analyzes the data to obtain real-time channel status information. The real-time channel status reflects the actual transmission status of each channel in the current communication network, including key indicators such as signal strength, signal-to-noise ratio, and bit error rate. Based on the acquired real-time channel status information, combined with the topology and device configuration of the communication network, a virtual reconstruction algorithm is used to simulate the communication network. That is, without changing the physical communication network structure, software algorithms and models are used to simulate and optimize the communication network to determine which network resources and channels are available at the current moment. Based on the results of virtual reconstruction, channel networks that meet availability criteria are selected and divided into a first idle channel network and a second relaxed channel network. The first idle channel network refers to channels that are not currently occupied by any communication tasks. These channels have high availability and can be directly allocated to new communication tasks. For example, in a factory's wireless network, some frequency bands may be idle because no equipment is currently transmitting data. The second relaxed channel network serves as a backup channel network. Although these channels may be partially occupied at present, some resources can be freed up for other communication tasks through adjustments and optimizations. For example, some channels may be occupied by low-priority communication tasks; when encountering high-priority tasks, some channel resources can be freed up by adjusting the transmission parameters of these low-priority tasks.

[0062] Step S3212 involves predicting the first communication demand by sensing the workshop's operational intentions. Specifically, workshop operational intentions are sensed through various means, such as integrating with the workshop's production management system to obtain production plans and equipment scheduling information; and deploying sensors to monitor environmental parameters and equipment operating status within the workshop to analyze potential changes in production activities. Workshop operational intentions reflect the operating status and development trends of various equipment and systems within the workshop, including information on production plans, equipment scheduling, and material transportation. By sensing these intentions, changes in communication demand can be predicted in advance, providing forward-looking guidance for resource allocation. For example, when the workshop plans large-scale production, it can be predicted that related communication tasks such as equipment monitoring and data acquisition will increase significantly. Based on the sensed workshop operational intentions, combined with historical communication data and statistical analysis methods, future communication demands are predicted. For example, a predictive model can be established based on communication traffic data from similar past production plans to predict the type, data volume, and real-time requirements of various communication tasks under the current production plan. The predicted first communication demand is used to provide objectives and constraints for resource allocation optimization. For example, if it is predicted that video surveillance communication tasks will increase significantly within a certain period of time, then sufficient bandwidth and time slot resources need to be allocated for these tasks during resource configuration.

[0063] Step S3213 involves determining a second channel quality heatmap by performing quality prediction on the available communication network. The channel quality is defined at least based on channel quality changes and potential interference conflicts. Specifically, channel quality prediction refers to predicting the quality change trend of each channel in the available communication network over a future period. Machine learning algorithms, time series analysis, and other methods can be used to predict channel quality. For example, a neural network model can be used to establish a prediction model based on historical channel quality data and relevant influencing factors, such as device location and interference source distribution, to predict future channel quality.

[0064] Based on the quality prediction results, the quality of each channel in the available communication network is assessed and quantified. Then, according to certain rules, the channel quality is mapped to different color regions of a heatmap, generating a second channel quality heatmap. For example, in the heatmap, green areas indicate good channel quality that will remain stable for a period of time, while red areas indicate poor channel quality that may be affected by potential interference. Channel quality is defined at least based on channel quality changes and potential interference. Channel quality changes reflect the trend of channel quality over time, such as the possibility of the channel quality gradually improving or deteriorating; potential interference considers other factors that may affect channel quality, such as interference from other communication devices and environmental noise.

[0065] In one possible implementation, communication management is performed on the task-related device interaction terminals. Step S300 further includes step S330, which involves determining the device interaction terminals for the real-time task set and establishing communication isolation between unrelated task terminals. Specifically, based on the specific content of the real-time task set, tools such as task flowcharts and data flow diagrams are used to identify the device interaction terminals directly related to each task. A device interaction terminal is a device capable of participating in task communication, performing task operations, or providing the data required for the task. For example, in a raw material handling task, the device interaction terminal may include a handling robot, a warehouse management system terminal, sensors, etc.; in a machine processing task, it may include a machine tool, a CNC system terminal, a tool monitoring device, etc.

[0066] Non-associative task terminals refer to device interaction terminals within the current real-time task set that do not require direct data interaction or collaborative work between their tasks. For example, in a workshop with multi-variety, small-batch production, raw material handling task terminals and finished product packaging task terminals are non-associative task terminals. They perform different tasks at different production stages and do not need to exchange data in real time. Establishing communication isolation between non-associative task terminals reduces communication interference, improves the utilization rate and quality of communication channels, avoids unnecessary communication between non-associative task terminals consuming valuable communication resources, and reduces the possibility of communication conflicts and errors, while ensuring smooth communication between associated task terminals. Specifically, at the software level, access control mechanisms in the communication protocol stack restrict communication permissions between non-associative task terminals. For example, firewall technology is used at the network layer to set access rules and prohibit data packet transmission between non-associative task terminals; at the application layer, permission management through application programming interfaces restricts data access and interaction between non-associative task terminals; at the hardware level, physical isolation is used to connect non-associative task terminals to different communication subnets or channels. For example, in wireless communication networks, different frequency bands or time slots are allocated to different task terminals, allowing unrelated task terminals to operate independently on different communication resources and avoiding mutual interference.

[0067] Step S340: Communication management between the device interaction terminals is performed using the resource configuration strategy and communication isolation. Specifically, communication between the device interaction terminals is scheduled according to the resource configuration strategy and communication isolation requirements. For example, in terms of time, communication time slots are arranged for different device interaction terminals according to task priority and resource allocation to ensure that associated task terminals can communicate at appropriate times, while avoiding communication conflicts between unassociated task terminals. In terms of frequency, associated and unassociated task terminals are allocated to different frequency bands according to communication isolation requirements to reduce interference. During data transmission, the data transmission rate, format, and security are controlled according to the resource configuration strategy and communication isolation rules. Simultaneously, the communication isolation mechanism prevents unassociated task terminals from acquiring or interfering with the data transmission of associated task terminals. During communication management, the performance of the communication network and task execution status are monitored in real time, and the resource configuration strategy and communication isolation measures are dynamically adjusted according to the actual situation. For example, if a decrease in the communication quality of an associated task terminal is detected, it may be due to insufficient resource allocation or interference from unassociated task terminals. In this case, resource configuration can be adjusted to increase the communication resources of that terminal and further strengthen communication isolation to ensure that its communication returns to normal.

[0068] This application embodiment interacts with the workshop business system through a standard interface, retrieves and interprets real-time tasks to clarify production business intentions, generates communication KPIs through QoS parameter encoding, and dynamically generates network slices based on the communication KPIs. It also reads coupled multi-communication protocol elements to determine the communication element condition set. For the communication element condition set, it jointly optimizes and determines the resource configuration strategy through network resource scheduling. It manages the communication of task-related device interaction terminals, and establishes temporary communication isolation for non-associated task terminals, using relaxed channel networks as alternative configurations. These technical means solve the technical problems of insufficient stability and reliability in existing multi-device wireless communication in production workshops, and achieve the technical effect of improving communication stability and reliability.

[0069] Based on the foregoing embodiments, this application also provides an electronic device. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 201, a processor 202, a memory 203, and an output device 204. The processor 202 may be one or more; the memory 203 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0070] The memory 203 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the multi-device wireless communication optimization method for production workshops in this embodiment of the invention. The processor 202 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 203, thereby realizing the above-mentioned multi-device wireless communication optimization method for production workshops.

[0071] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for optimizing wireless communication between multiple devices in a production workshop, characterized in that: The method includes: Through interaction with the workshop business system via standard interfaces, real-time tasks are executed to retrieve and interpret production business intentions, encode QoS parameters, and generate communication KPIs. Based on the communication KPIs, the slice manager embedded in the communication control center is used to dynamically generate network slices and perform protocol element reading and coupling under multiple communication protocols to determine the communication element condition set. For the set of communication element conditions, a joint optimization based on network resource scheduling is performed to determine the resource allocation strategy and to manage the communication of task-related device interaction terminals. Temporary communication isolation is established between non-associated task terminals, with a relaxed channel network as an alternative configuration.

2. The multi-device wireless communication optimization method for production workshops as described in claim 1, characterized in that, Execute real-time tasks to retrieve and interpret production business intentions, encode QoS parameters, and generate communication KPIs, including: Retrieve a real-time task set, wherein the real-time task set is a multi-threaded industrial control task; For the set of real-time tasks, perform task interpretation to determine the set of task intents; The task intent set is encoded and fitted with a task QoS parameter sequence using a lightweight encoder to determine the communication KPI.

3. The multi-device wireless communication optimization method for production workshops as described in claim 2, characterized in that, The task intent set is encoded and fitted with a sequence of task QoS parameters based on a lightweight encoder, including: Obtain the set of industrial control elements of the target workshop and the set of QoS parameters based on the communication network, perform mutual mapping between the set of industrial control elements and the set of QoS parameters, and build a lightweight encoder; The task intent set is encoded and converted according to the lightweight encoder to determine the task QoS parameter sequence; The communication KPI is generated by performing clustering and intra-cluster aggregation based on communication patterns on the sequence of QoS parameters for the task.

4. The multi-device wireless communication optimization method for production workshops as described in claim 1, characterized in that, Dynamically generating network slices, including: Drive the slice manager, write the communication mode, perform slice type matching, and determine the target slice type; Based on the communication KPI, perform communication state writing based on the target slice type to generate a network slice.

5. The multi-device wireless communication optimization method for production workshops as described in claim 4, characterized in that, Couple the reading of protocol elements under multiple communication protocols to determine the set of communication element conditions, including: At the communication protocol layer, the network slice is read and coupled according to protocol conditions to determine the communication element condition set, wherein the communication element condition set is a normalization standard; The protocol conditions read include: Determine the first communication protocol, read the protocol elements of the first network slice, and determine the first communication element; The analysis of the Nth communication protocol is completed for the Mth network slice until the N×Mth communication elements are determined; Integrate the first communication element up to the N×Mth communication element.

6. The multi-device wireless communication optimization method for production workshops as described in claim 1, characterized in that, Joint optimization based on network resource scheduling includes: Define the scheduling dimensions for network resource scheduling, wherein the scheduling dimensions include at least spectrum resource allocation, time slot / air interface resource allocation, routing selection, and transmit power modulation; For the set of communication element conditions, perform joint optimization calculations based on the scheduling dimension to determine the resource allocation strategy.

7. The multi-device wireless communication optimization method for production workshops as described in claim 6, characterized in that, For the set of communication element conditions, perform joint optimization calculations based on the scheduling dimension, including: A communication space is constructed using the available communication network and the second channel quality heatmap; a task space is constructed using the communication element condition set and the first communication requirement; and an optimization module is generated based on the communication space and the task space. For the optimization module, the resource configuration strategy is generated by combined iterative optimization based on the scheduling dimension.

8. The multi-device wireless communication optimization method for production workshops as described in claim 7, characterized in that, Before generating the optimization module based on the communication space and the task space, the following steps are included: The real-time channel status is obtained, and the communication network is virtually reconstructed to determine the available communication network. The available communication network includes a first idle channel network and a second relaxed channel network, and the second relaxed channel network is a candidate channel network. By sensing the intention of workshop operations, predict the primary communication needs; A second channel quality heatmap is determined by performing quality prediction on the available communication network, wherein the channel quality is defined based at least on channel quality variations and potential interference conflicts.

9. The multi-device wireless communication optimization method for production workshops as described in claim 2, characterized in that, Communication management of task-related device interaction terminals, including: For the set of real-time tasks, identify the device interaction terminals and establish communication isolation between unrelated task terminals; Communication management between the device interaction terminals is performed using the resource configuration strategy and communication isolation.

10. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the multi-device wireless communication optimization method for production workshops as described in any one of claims 1 to 9.

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