A production process optimization method and system for multi-channel optical microwave modules

Abstract

The present application relates to the field of optical microwave technology, and provides a method and system for optimizing the production process of a multi-channel optical microwave module. The method includes: determining a multifunctional unit based on the optical microwave function; constructing an adaptive optimization model; optimizing the single-channel production configuration and determining a first optimization strategy; locating and adjusting the strategy points of the first optimization strategy to determine the production optimization strategy; identifying the production optimization strategy, configuring the host device and building a programmable gate array to generate a host device unit; and performing production optimization management on the multi-channel optical microwave module. The present application solves the technical problem that it is difficult to quickly optimize the production configuration according to specific functional requirements in the traditional production process, resulting in low production efficiency, high cost, and limited module function expansion. It realizes the effect of clarifying the multifunctional unit through functional partitioning, quickly locating and adjusting the production strategy, improving production efficiency, reducing costs, and enhancing the flexibility and scalability of module functions.

Description

Technical Field

[0001] The present application relates to the field of optical microwave technology, and in particular to a production process optimization method and system for a multi-channel optical microwave module. Background Art

[0002] With the continuous advancement of science and technology and the increasing complexity of industrial production, multi-channel optical microwave modules, as key components, play an important role in the fields of communications, radar, satellite navigation, etc. However, the traditional production process of multi-channel optical microwave modules is unable to cope with the diversified and customized functional requirements. Specifically, the traditional production process often lacks systematic functional partitioning and flexible production configuration strategies, which makes it difficult to quickly optimize the production configuration when facing changes in specific functional requirements, resulting in low production efficiency and high production costs, and limiting the further expansion and upgrading of module functions. Summary of the invention

[0003] The present application provides a method and system for optimizing the production process of a multi-channel optical microwave module, aiming to solve the technical problems in the traditional production process that it is difficult to quickly optimize the production configuration according to specific functional requirements, resulting in low production efficiency, high cost, and limited module function expansion.

[0004] In view of the above problems, the present application provides a method and system for optimizing the production process of a multi-channel optical microwave module.

[0005] The first aspect disclosed in the present application provides a method for optimizing the production process of a multi-channel optical microwave module, the method comprising: performing functional partitioning based on optical microwave functions, determining a multifunctional unit, wherein the multifunctional unit at least includes a light source-optical modulation-optical reception-microwave source-microwave processing, and is scalable; constructing an adaptive optimization model, wherein the adaptive optimization model includes a hardware structure branch and a software system branch, and there is branch cross-linking; initializing the adaptive optimization model based on the multifunctional unit, performing single-channel production configuration optimization, and determining a first optimization strategy; determining the number of channels and channel distinguishing features, locating and adjusting the strategy points of the first optimization strategy, and determining a production optimization strategy; identifying the production optimization strategy, configuring a host device and building a programmable gate array based on a lightweight standard, and generating a host device unit, wherein the programmable gate array corresponds to the number of channels; combining the host device unit with the production optimization strategy, performing production optimization management on the multi-channel optical microwave module.

[0006] Another aspect disclosed in the present application provides a production process optimization system for a multi-channel optical microwave module, the system comprising: a functional partitioning component, the functional partitioning component is used to perform functional partitioning based on optical microwave functions and determine a multifunctional unit, wherein the multifunctional unit at least includes a light source-optical modulation-optical reception-microwave source-microwave processing and has scalability; a model building component, the model building component is used to build an adaptive optimization model, the adaptive optimization model includes a hardware structure branch and a software system branch, and there is branch cross-linking; a single-channel production configuration optimization component, the single-channel production configuration optimization component is used to initialize the adaptive optimization model based on the multifunctional unit, Perform single-channel production configuration optimization to determine the first optimization strategy; a strategy point positioning and adjustment component, the strategy point positioning and adjustment component is used to determine the number of channels and channel distinguishing features, perform strategy point positioning and adjustment on the first optimization strategy, and determine the production optimization strategy; a host unit generation component, the host unit generation component is used to identify the production optimization strategy, configure the host and build a programmable gate array based on lightweight standards, and generate a host unit, wherein the programmable gate array corresponds to the number of channels; a production optimization management component, the production optimization management component is used to combine the host unit with the production optimization strategy to perform production optimization management on the multi-channel optical microwave module.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The above-mentioned method for optimizing the production process of a multi-channel optical microwave module performs detailed functional partitioning according to the optical microwave function, and clearly defines the multifunctional units including the core links such as light source, optical modulation, optical reception, microwave source and microwave processing. These units not only operate independently, but also have high scalability, leaving room for the addition or adjustment of future functions. Subsequently, in order to realize the intelligent optimization of production configuration, an adaptive optimization model is constructed. This model integrates the hardware structure branch and the software system branch, and realizes the close collaboration between hardware and software through the cross-linking between the branches. This design enables the model to flexibly adjust the strategy according to the actual production situation and quickly respond to changes in production demand. In the initialization stage of the model, the optimization of the single-channel production configuration is carried out based on the multifunctional unit. Through simulation and data analysis, the first optimization strategy is determined to provide a reference for subsequent multi-channel production. Afterwards, the number of channels and the distinguishing characteristics of each channel are determined according to actual needs, and the first optimization strategy is refined. The strategic point positioning and adjustment are carried out to ensure that the production strategy can accurately match the specific needs of different channels, thereby realizing the customization of the production optimization strategy. In terms of hardware implementation, the host unit is configured based on the lightweight standard, and a programmable gate array corresponding to the number of channels is built. These hardware components not only meet the needs of production optimization, but also ensure the stability and reliability of the system. Then, the host unit is combined with the production optimization strategy to carry out all-round production optimization management of multi-channel optical microwave modules, closely combining intelligent control with production practice to achieve significant improvement in production efficiency and effective control of production costs.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 The figure is a flow chart of a method for optimizing the production process of a multi-channel optical microwave module in one embodiment.

[0012] Figure 2 A system architecture diagram for optimizing the production process of a multi-channel optical microwave module in one embodiment.

[0013] Explanation of the reference numerals: functional partitioning component 1, model building component 2, single-channel production configuration optimization component 3, strategic point positioning and adjustment component 4, upper unit generation component 5, production optimization management component 6. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method and system for optimizing the production process of a multi-channel optical microwave module to solve the technical problem that it is difficult to quickly optimize the production configuration according to specific functional requirements in the traditional production process, resulting in low production efficiency, high cost, and limited module function expansion.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment 1, as Figure 1 As shown, the present application provides a method for optimizing the production process of a multi-channel optical microwave module, the method comprising:

[0018] Based on the optical microwave function, functional partitioning is performed to determine a multifunctional unit, wherein the multifunctional unit at least includes a light source-optical modulation-optical reception-microwave source-microwave processing and has scalability.

[0019] In the embodiment of the present application, the system terminal determines an expandable unit containing multiple functions based on the specific functions of the optical microwave. In order to make the multifunctional unit have good scalability, the system terminal adopts a modular design concept, and each function is designed as an independent unit, which is connected to each other through a standard interface. In this way, different functional modules can be flexibly selected and combined according to actual needs to construct an optical microwave that meets specific application requirements. The multifunctional unit includes multiple key modules such as light source, light modulation, light reception, microwave source, and microwave processing. Among them, the light source module provides a stable and high-quality optical signal, and a laser diode (LD) or a light-emitting diode (LED) can be selected as the input light source, and the wavelength and power can be adjusted according to application requirements. The optical modulation module modulates the optical signal emitted by the light source and changes certain characteristics of the optical signal, such as phase, intensity, frequency, etc., according to the input electrical signal or data signal. The optical receiving module receives the modulated and transmitted optical signal and converts it into an electrical signal or data signal. The microwave source module can generate a stable microwave signal for microwave processing or as a reference signal for other internal modules. The microwave processing module can amplify, filter, mix, and other processes on the microwave signal to meet the needs of internal or external applications.

[0020] An adaptive optimization model is constructed, wherein the adaptive optimization model includes a hardware structure branch and a software system branch, and there are branch cross-links.

[0021] In one embodiment, building an adaptive optimization model is a complex process that aims to cope with changing environments or task requirements through dynamic adjustment and optimization strategies. This model integrates hardware structure branches and software system branches, and achieves a higher level of coordination and adaptive capabilities through cross-linking between branches. For hardware structure branches, the system terminal selects appropriate hardware components such as high-performance processors, high-precision sensors, programmable logic controllers, etc. according to the plan. Design the hardware architecture, including the layout, connection method, power supply, etc. of the components. Ensure that the hardware structure branch can meet the basic operating requirements of the system and has certain scalability and reconfigurability. Then design standard interfaces for the hardware structure branch to cross-link with the software system branch. These interfaces include data transmission interfaces, control signal interfaces, etc. After the interface is set, the system terminal constructs the hardware structure branch. For the software system branch, the system terminal analyzes the capabilities and limitations of the hardware structure branch to determine the functions and interfaces that the software needs to support. Design the overall architecture of the software system, including the hierarchy, module division, and interaction between components. Determine the core functional modules, such as data acquisition modules, data processing modules, control decision modules, etc. Design the interface of the software system branch to cross-link with the hardware structure branch. After the interface is set, the system terminal constructs the software system branch. After obtaining the hardware structure branch and the software system branch, the system terminal cross-links the two branches, that is, connects the interface of the hardware structure branch with the interface of the software system branch to ensure the smooth transmission of data and control signals. Then formulate a cross-linking protocol to clarify the format, timing, and verification method of data exchange to ensure the accuracy and reliability of data transmission. Subsequently, the interface of the cross-linked hardware structure branch and the software system branch are cross-linked to test the collaborative working ability between the hardware structure branch and the software system branch, including the real-time, accuracy, and stability of data transmission. If the verification result meets the expected expectations, the system terminal encapsulates the cross-linked hardware structure branch and the software system branch to construct an adaptive optimization model. Otherwise, adjust the structure of the hardware structure branch and the software system branch, and redesign the interface.

[0022] The adaptive optimization model is initialized based on the multifunctional unit, single-channel production configuration optimization is performed, and a first optimization strategy is determined.

[0023] In one embodiment, the system terminal uses the multifunctional unit as a reference to start the process of initializing the adaptive optimization model. This initialization stage is intended to provide a basis for the subsequent optimization of the single-channel production configuration. In the optimization of the single-channel production configuration, the system terminal first initializes the hardware structure branch according to the characteristics of the multifunctional unit to ensure that the modules such as the light source, light modulation, light reception, microwave source and microwave processing work normally and can communicate smoothly with each other. At the same time, the software system branch will also be initialized, and modules such as data acquisition, processing and control will be set to monitor various parameters in the production process in real time. Subsequently, the optimization process of the single-channel production configuration is started using the initialized adaptive optimization model. This process will perform multi-channel parallel analysis on the initialized adaptive optimization model to determine the first optimization strategy. This strategy is automatically generated by the adaptive optimization model based on the current production configuration and performance data, and has certain pertinence and practicality.

[0024] Further, the present application provides the method of initializing the adaptive optimization model to perform single-channel production configuration optimization, including:

[0025] The multifunctional unit is traversed to determine the model domain division standard, wherein the model domain division standard includes processing domain division and resource allocation; based on the model domain division standard, the hardware structure branch and the software system branch are initialized respectively to determine the initialized parallel branch; based on the initialized parallel branch, multi-path parallel analysis is performed to determine the first optimization strategy, including intra-branch collision and inter-branch collision.

[0026] Preferably, during the optimization process, the system terminal performs functional analysis on the multifunctional unit, identifies each functional module in the multifunctional unit one by one, such as the light source module, the light modulation module, the light receiving module, the microwave source module and the microwave processing module, etc., collects technical documents, specifications, interface standards and other information about these functional modules, and constructs a general model framework. The system terminal constructs a highly abstract general model framework based on the basic characteristics and common functions of the multifunctional unit. For example, the light source generation of the light source module, the signal modulation and coding of the light modulation module, and the microwave signal generation of the microwave source module. This framework can accommodate different functional modules while maintaining the clarity and scalability of the structure. Standardized interfaces and communication protocols are defined in the general model to facilitate seamless connection and data exchange between different functional modules. These interfaces are flexible enough to accommodate possible future functional expansions. After constructing the general model framework, the system terminal traverses these functional modules based on the general model framework and records the basic information of each module, such as functional description, input and output interfaces, performance indicators, etc. Subsequently, according to the recorded content, the specific functions of each module are analyzed to understand its role and importance in the entire multifunctional unit, and the dependencies and interaction methods between the modules are determined, including the transmission of data flow and control flow. Then the performance of each module is evaluated, including key indicators such as processing power, response time, and stability, to identify potential performance bottlenecks or limiting factors, and provide a basis for subsequent processing domain division and resource allocation. After that, based on the results of functional analysis and performance evaluation, the multifunctional unit is divided into several processing domains. Each processing domain contains a set of closely related functional modules that work together to complete specific processing tasks. There are clear boundaries and interactive interfaces between the processing domains to ensure that they can work together. Then, hardware and software resources are allocated according to the needs and characteristics of each processing domain. This is based on the selection, layout and connection methods of hardware devices, as well as the architecture and computing resources of the software system, so as to improve the applicability of the model. The division of processing domains is to clearly define the different functional areas in the multifunctional unit to ensure that each area can perform its tasks independently and efficiently. Resource allocation is to reasonably allocate hardware and software resources according to the needs of each processing domain to achieve the optimization of overall performance. The system terminal determines the model domain division standard by summarizing the obtained processing domain division and resource configuration. Then, based on the determined model domain division standard, the system terminal initializes the hardware structure branch and the software system branch respectively. The initialization of the hardware structure branch involves the configuration, connection and debugging of the physical devices to ensure that they can work together in the predetermined manner. The initialization of the software system branch includes the installation, configuration and testing of the software program, as well as the establishment of data acquisition, processing and control to monitor and adjust the production process in real time. After the initialization is completed, the system terminal obtains multiple parallel branches, which represent different processing flows and resource allocation schemes in the multifunctional unit.The system terminal performs multi-path parallel analysis based on these initialized parallel branches to understand intra-branch collisions, i.e. conflicts or bottlenecks between different components in the same branch, and inter-branch collisions, i.e. mutual influence between different branches due to resource sharing or timing issues. By comprehensively evaluating the performance of each parallel branch, the system terminal determines the first optimization strategy. This strategy aims to solve or alleviate the collision problem within and between branches, and improve the overall efficiency and stability of the production process by adjusting resource allocation, optimizing processing flow, etc.

[0027] Furthermore, the present application provides a method for performing multi-path parallel analysis based on the initialized parallel branches, including:

[0028] Identify the multifunctional units, and determine the optimization targets based on the functional baseline, wherein each functional unit corresponds to at least one optimization target; based on the initialized parallel branches, perform unit independence parallel optimization based on the optimization targets, and determine multiple unit optimization strategies; based on the intra-branch coordination, the co-frequency between branches, and the non-intermittent collision of branches, integrate and correct the multiple unit optimization strategies to determine the first optimization strategy.

[0029] Optionally, in the process of optimizing the multifunctional units, the system terminal comprehensively identifies these units and understands their respective functional baselines, that is, the capabilities or characteristics they originally possess. Subsequently, based on these functional baselines, at least one optimization goal is set for each functional unit, which is intended to improve the performance, efficiency or compatibility of the unit. In order to efficiently achieve these optimization goals, the system terminal randomly selects an initial solution, that is, a set of parameter values, for each functional unit in its parameter space, and sets an initial temperature to control the heat of the algorithm search range. Based on the current solution, a new solution, that is, a new combination of parameter values, is generated through random perturbations. This new solution is located in the neighborhood of the current solution. Afterwards, the objective function values ​​of the new solution and the current solution, that is, the performance indicators corresponding to the optimization target, are calculated. If the new solution is better, it is unconditionally accepted as the new current solution; if the new solution is not as good as the current solution, it is decided whether to accept the new solution based on the current temperature and the Metropolis criterion. This probability gradually decreases as the temperature decreases, so that the algorithm can widely explore the solution space in the early stage of the search, and gradually converge to the optimal solution in the later stage of the search. Then update the current temperature according to the linear temperature reduction, and repeat the above process of neighborhood generation, acceptance criteria and temperature update until the number of iterations reaches the upper limit. During the optimization process, the optimal solution after each iteration and its corresponding objective function value are recorded. Then, the system terminal summarizes the optimization results of each parallel branch. Each branch corresponds to the optimization strategy of one or more functional units. After obtaining multiple unit optimization strategies, the system terminal integrates and corrects multiple unit optimization strategies based on intra-branch coordination, inter-branch frequency co-ordination, and branch non-or-collision. Among them, intra-branch coordination is used to evaluate the interaction and dependency between different functional modules in the same branch, such as light source module, optical modulation module, etc. Inter-branch frequency co-ordination refers to the ability of different branches to maintain synchronization or coordination when processing similar or related tasks. Since multiple parallel branches may simultaneously process tasks that are closely related to the overall performance of the system, their collaborative work is essential. Branch non-or-collision refers to the situation where the optimization strategies of different branches may interfere or conflict with each other, including functional coordination collision and software and hardware collision. Functional coordination collision is used to identify functional conflicts or overlaps that may occur in the coordination process of different functional modules within a branch. Software and hardware collision is used to analyze the allocation and use of software and hardware resources between branches and identify possible collision problems. The system terminal conducts actual tests on multiple unit optimization strategies to verify whether multiple unit optimization strategies are effective and stable in different scenarios, whether they can eliminate co-frequency interference or resource conflicts, integrate and correct multiple unit optimization strategies, and determine the first optimization strategy to ensure that the overall optimization plan can not only meet the optimization goals of each functional unit, but also maintain overall coordination and stability.

[0030] Determine the number of channels and channel distinguishing features, locate and adjust the strategic points of the first optimization strategy, and determine the production optimization strategy.

[0031] In one embodiment, in the process of determining the production optimization strategy, the number of channels refers to the number of different paths that can process optical signals and microwave signals in parallel. These channels each undertake different functions, such as light source emission, optical signal modulation, optical signal reception, microwave signal generation, microwave signal processing, etc. The system terminal determines the number of channels based on the design requirements, performance requirements and cost considerations of the module, combined with the overall architecture of the module, functional partitioning and the interaction between the multifunctional units. Although more channels improve the parallel processing capability and flexibility, they will also increase the complexity and cost of the system. Because each type of channel has its own unique processing capabilities and performance characteristics, such as processing speed, bandwidth, noise level, power consumption, etc., the system terminal determines the channel distinguishing features corresponding to each type of channel. After determining the number of channels and distinguishing features, the system terminal locates and adjusts the strategy points of the first optimization strategy obtained by the adaptive optimization model, that is, each strategy point in the first optimization strategy is adjusted in a targeted manner. The system terminal identifies the strategy points corresponding to each function in the first optimization strategy, extracts these strategy points, and then maps the extracted multiple strategy points with the corresponding number of channels and channel distinguishing features. Subsequently, through actual simulation, we understand the performance of each strategy point in analyzing these strategy points in the corresponding channels and find out the possible bottlenecks or deficiencies. Then, we fine-tune or reconstruct the relevant parameters in these strategy points based on the analysis results. For example, if a channel has a performance bottleneck when processing a specific type of optical signal, it is necessary to adjust the light source parameters of the channel or enhance the signal processing capability; if there is a conflict or redundancy in the interaction between different channels, it is necessary to redesign the cross-linking method between channels or introduce a new coordination mechanism. After the positioning and adjustment of the above strategy points, the system terminal forms a set of production optimization strategies that are more in line with the actual production needs of multi-channel optical microwave modules. This strategy will comprehensively consider the number of channels, the distinguishing characteristics of the channels, and the advantages and disadvantages of the first optimization strategy, and optimize the module production efficiency, performance and quality through refined adjustment and optimization.

[0032] The production optimization strategy is identified, and based on the lightweight standard, a host device is configured and a programmable gate array is built to generate a host device unit, wherein the programmable gate array corresponds to the number of channels.

[0033] In one embodiment, during the production process optimization of the multi-channel optical microwave module, the system terminal identifies the determined production optimization strategy and determines the specific content of the production optimization strategy, including but not limited to the optimization of the production process, the adjustment of resource allocation, the setting of key parameters, etc. Subsequently, a lightweight standard is obtained, which is set in advance according to professional knowledge and actual needs, and is to minimize the complexity, volume, power consumption and cost of the system under the premise of ensuring functional integrity. After that, the system terminal clusters the software system class strategy in the production optimization strategy according to this lightweight standard to build a host unit for optimizing and adjusting the overall configuration. At the same time, the configuration is adjusted and a programmable gate array is generated according to the upper call, defuzzification and channel distinction characteristics, and these programmable gate arrays correspond to the number of channels. Then, the system terminal integrates the configured host with the built programmable gate array to form a complete control host unit, which integrates multiple functions such as control logic, data processing and signal transmission, and can accurately control and manage the multi-channel optical microwave module according to the requirements of the production optimization strategy.

[0034] Furthermore, the present application provides the configuration of the host device and the construction of a programmable gate array based on the lightweight standard to generate a host device unit, including:

[0035] Based on the lightweight standard, the production optimization strategy is traversed, the software system class strategy is extracted and clustered, and the clustering result is determined; the clustering result is traversed to divide the channel autonomy part and the upper management part, wherein the division standard is the preset number of clusters.

[0036] Optionally, the system terminal first traverses the determined production optimization strategies and extracts all software system-class strategies as the objects of subsequent clustering processing, such as software architecture, algorithm, software module division, etc. Subsequently, each software system-class strategy is regarded as a separate cluster, and a feature vector containing lightweight related indicators is constructed for each software system-class strategy. This vector contains the quantitative values ​​of indicators such as code volume, CPU usage, memory usage, response time, etc. The cosine similarity is then used to calculate the cosine value of the angle between two feature vectors to evaluate their similarity. The larger the value, the more similar it is. After that, the AGNES (bottom-up) merging strategy is adopted, starting from the bottom layer and gradually merging upwards. In each iteration, the system terminal calculates the cosine similarity between all cluster pairs and selects the two clusters with the highest similarity for merging. After merging, the system terminal updates the feature vector of the synthesized cluster by taking the average value, as well as the information of the intra-cluster strategy and the inter-cluster distance. Repeat the above merging process until the maximum similarity between all cluster pairs exceeds the preset similarity threshold, then stop merging and output the current clustering results. Then, the system terminal traverses the clustering results, counts the number of data points in each cluster, and compares it with the division standard. This division standard is the number of data points in the cluster set based on actual needs, data characteristics and historical experience, and is used to determine whether a cluster belongs to the channel autonomous part or the upper management part. Among them, the channel autonomous part stores algorithms, models, etc. specific to a certain function, has high independence, and can be managed and optimized independently. The upper management part stores reusable algorithms, models, etc., which require higher-level management and coordination. By comparing the size relationship between the number of data points in each cluster and the preset number of clusters, the system terminal stores the clusters with less than or equal to the preset number of data points in the cluster to the channel autonomous part, and stores the clusters with more than the preset number of data points in the cluster to the upper management part. By dividing algorithms, models, etc., computing tasks that may have been scattered in various channels can be processed centrally, reducing the data exchange and synchronization requirements between channels, thereby improving response speed and stability. At the same time, it can more effectively coordinate the work of each channel and achieve optimal allocation of resources.

[0037] Identify the upper management part, perform fuzzy processing on the intra-cluster mechanism, and determine the upper management strategy; and build the upper device based on the upper management strategy.

[0038] Preferably, after obtaining the upper management part through clustering processing, the internal members of each cluster in this upper management part have a high degree of functional similarity, but there are still subtle differences or specific needs. In order to formulate a management mechanism that can be widely applied to each member in the cluster, the system terminal identifies various specific mechanisms, rules, processes, etc. in each cluster, which are the basis for realizing the functions and services in the cluster. Subsequently, the mechanism differences between the members in the cluster are analyzed, including differences in function implementation methods, performance parameters, constraints, etc. These differences are the objects that need to be focused on during fuzzy processing. Then, through methods such as abstraction, generalization, and induction, the mechanism differences of each member in the cluster are fuzzified to extract the core elements. This step aims to eliminate or weaken non-core individual differences and form a universal mechanism framework. Afterwards, based on the fuzzy mechanism framework and overall goals, the upper management strategy is formulated. This strategy clarifies key elements such as management goals, management scope, management means, and management processes. Through fuzzification, the management complexity caused by individual differences can be reduced, and the flexibility and adaptability of management strategies can be improved. Then, the system terminal designs and builds the host device according to the determined upper management strategy. As the control center, the host device is responsible for executing the management strategy, monitoring and adjusting the status and behavior of each member in the cluster. It has powerful data processing and decision-making capabilities, as well as good scalability and maintainability.

[0039] Furthermore, the present application provides the construction of a programmable gate array, comprising:

[0040] Identify the upper management strategy and the upper management part, and determine the channel differentiation characteristics; adjust to the underlying logic based on the upper call, defuzzification and channel differentiation characteristics, configure multiple programmable channels, and generate a programmable gate array; based on the programmable gate array, establish a connection between the upper device unit and the channel autonomous region block.

[0041] Optionally, the system terminal specifies the specific content of the upper management strategy, including the unified management and control methods for each part. At the same time, the key parts that need to be managed by the upper management are identified. Subsequently, the differences in transmission speed, data format, security requirements and other characteristics between different channels are identified, and the channel distinguishing characteristics of each channel are determined. After that, various mechanisms are called according to the upper management strategy. At the same time, defuzzification operations are performed on the fuzzy processing mechanism to restore the individual characteristics of a specific channel or component. This step is to ensure that the upper management strategy can take into account both universality and particularity during implementation. Then, according to the distinguishing characteristics of the channel, the underlying logic is adjusted and optimized accordingly. This includes configuring appropriate transmission protocols, data processing algorithms, security mechanisms, etc. to ensure that each channel can operate efficiently and safely. Then, based on the results of the above logic configuration and channel adjustment, the system terminal configures multiple programmable channels. These channels can be flexibly configured and modified according to actual needs to adapt to changes in the internal and external environment of the system. Then, using FPGA (field programmable gate array) technology, the above configured logic and channel information are embedded into the hardware to generate a programmable gate array. This programmable gate array is highly flexible and reconfigurable, and can quickly implement complex logic functions and data processing algorithms. Finally, the system terminal integrates the generated programmable gate array with the built host device to form a host device unit. This host device unit is then used to establish connections with each channel autonomous region block. These connections ensure that the upper management strategy can be smoothly transmitted to each part and realize unified management and control of multiple channels. At the same time, each channel autonomous region block can also autonomously adjust and optimize according to the upper instructions to improve the overall performance and efficiency of the system.

[0042] The host unit is combined with the production optimization strategy to perform production optimization management on the multi-channel optical microwave module.

[0043] In one embodiment, the system terminal uses the host unit as the core control unit of the entire production optimization management, which is responsible for receiving and executing the resource allocation, process flow and quality control of production optimization. The production optimization strategy is a series of strategies and measures aimed at improving production efficiency, reducing costs and improving product quality. The system terminal determines the target production process by combining the host unit with the production optimization strategy, and traverses this target production process to locate and divide the production risk points. Then, according to these production risk points, the corresponding adjustment information and early warning information are obtained, and production optimization management is carried out to improve production efficiency, reduce costs and improve product quality.

[0044] Further, the present application provides the production optimization management, including:

[0045] Based on the upper device unit and the production optimization strategy, a target production process is determined; the target production process is traversed to locate production risk points, and the production risk points are determined based on preset risk levels; the production risk points are divided to perform production optimization management.

[0046] Preferably, the system terminal uses the data and functions in the upper unit, combined with the obtained production optimization strategy, to select a production process from the production process record library that best meets the current production needs and production goals as the target production process. Subsequently, this target production process is traversed to find out the risk points therein. These risk points involve multiple aspects such as equipment failure and raw material quality. Each risk point will be assigned a preset risk level according to its potential impact for subsequent management and response. After identifying all risk points, the system terminal classifies and divides them so that more targeted optimization management measures can be formulated to carry out production optimization management.

[0047] Furthermore, the present application provides for dividing the production risk points and performing production optimization management, including:

[0048] Based on the production risk points, controllable risk points are determined, and control accuracy expansion processing is performed based on the risk level to determine first adjustment information, wherein the risk level is positively correlated with the expansion step; uncontrollable risk points are determined, risk precursor characteristics are determined, and second warning information is generated based on the risk precursor characteristics, wherein the risk precursor characteristics are initial visualization characteristics that meet the risk occurrence rate; and production optimization management is performed based on the first adjustment information and the second warning information.

[0049] Optionally, in production optimization management, the system terminal will distinguish between controllable and uncontrollable production risk points that have been located. This process aims to allocate resources and formulate response strategies more effectively. For controllable risk points, such as some sensitive control points, these points are easily affected by external factors in actual production. In order to reduce the potential impact of these risk points on the production process, the system terminal will perform external expansion of control accuracy based on the risk level. Specifically, the higher the risk level, the larger the external expansion step of the control accuracy, which means that the system terminal will take more stringent control measures and higher accuracy requirements to ensure that these control points are within the normal standard. In this way, even if there is an immediate external impact, the production process can be kept within the controllable range to prevent the risk from further expanding. For these controllable risk points, the system terminal will eventually determine the first adjustment information to guide specific optimization and adjustment measures. For uncontrollable risk points, the system terminal focuses on early warning and advance preparation. By determining the precursor features of these risk points, that is, those visual or quantifiable initial features that indicate that risks are about to occur but have not yet directly led to the occurrence of risk events. These precursor features are crucial for early identification of risks and formulation of early warning strategies. Based on these precursor characteristics, the system terminal will generate a second warning information so that operators can take measures in advance to reduce losses and impacts when risks occur. Finally, the system terminal comprehensively optimizes production management based on the first adjustment information and the second warning information. On the one hand, by implementing the optimization adjustment measures in the first adjustment information, the control and management of controllable risk points are strengthened; on the other hand, by paying attention to the second warning information, potential uncontrollable risk points are responded to in a timely manner to ensure the stability and safety of the production process.

[0050] In summary, the embodiments of the present application have at least the following technical effects:

[0051] The embodiment of the present application performs functional partitioning based on optical microwave functions and determines multifunctional units, which are not only fully functional but also scalable. Subsequently, an adaptive optimization model including hardware structure branches and software system branches with branch cross-linking is constructed. By initializing this model and optimizing the production configuration for a single channel, the optimization strategy is preliminarily determined. Afterwards, the preliminary optimization strategy is positioned and adjusted according to the number of channels and distinguishing features to form a production optimization strategy. In order to implement this strategy, the host device is configured based on the lightweight standard, and a programmable gate array corresponding to the number of channels is built to form a host device unit. In this process, by traversing the optimization strategy, clustering processing, dividing the autonomous and management parts, and fuzzy processing, it is ensured that the host device unit can efficiently and accurately manage each channel. Then, by traversing the target production process, the production risk points are located and divided according to the preset risk level. For controllable risk points, the first adjustment information is determined by controlling the precision expansion processing, in which the risk level is positively correlated with the expansion stride. For uncontrollable risk points, the risk precursor characteristics are determined, and the second warning information is generated based on these characteristics so that measures can be taken in advance to prevent the occurrence of risks. Finally, the production process of multi-channel optical microwave modules is comprehensively optimized and managed by combining the first adjustment information with the second warning information, aiming to improve production efficiency, reduce production costs and improve product quality. These technical effects jointly solve the technical problems in traditional production processes, such as the difficulty in quickly optimizing production configuration according to specific functional requirements, resulting in low production efficiency, high costs, and limited module function expansion. It realizes the effect of clarifying multifunctional units through functional zoning, quickly locating and adjusting production strategies, improving production efficiency, reducing costs, and enhancing the flexibility and scalability of module functions.

[0052] Embodiment 2, based on the same inventive concept as the method for optimizing the production process of a multi-channel optical microwave module in the above embodiment, Figure 2 As shown, the present application provides a production process optimization system for a multi-channel optical microwave module, the system comprising:

[0053] Functional partitioning component 1: the functional partitioning component 1 is used to perform functional partitioning based on optical microwave functions and determine multifunctional units, wherein the multifunctional units at least include light source-optical modulation-optical reception-microwave source-microwave processing and are scalable;

[0054] Model building component 2: the model building component 2 is used to build an adaptive optimization model, the adaptive optimization model includes a hardware structure branch and a software system branch, and there are branch cross-links;

[0055] Single-channel production configuration optimization component 3: The single-channel production configuration optimization component 3 is used to initialize the adaptive optimization model based on the multifunctional unit, perform single-channel production configuration optimization, and determine the first optimization strategy;

[0056] Strategy point positioning and adjustment component 4: the strategy point positioning and adjustment component 4 is used to determine the number of channels and channel distinguishing features, perform strategy point positioning and adjustment on the first optimization strategy, and determine the production optimization strategy;

[0057] Host unit generation component 5: The host unit generation component 5 is used to identify the production optimization strategy, configure the host and build a programmable gate array based on the lightweight standard, and generate a host unit, wherein the programmable gate array corresponds to the number of channels;

[0058] Production optimization management component 6: The production optimization management component 6 is used to combine the host unit and the production optimization strategy to perform production optimization management on the multi-channel optical microwave module.

[0059] Furthermore, the single-channel production configuration optimization component 3 is also used to execute the following method:

[0060] The multifunctional unit is traversed to determine the model domain division standard, wherein the model domain division standard includes processing domain division and resource allocation; based on the model domain division standard, the hardware structure branch and the software system branch are initialized respectively to determine the initialized parallel branch; based on the initialized parallel branch, multi-path parallel analysis is performed to determine the first optimization strategy, including intra-branch collision and inter-branch collision.

[0061] Furthermore, the single-channel production configuration optimization component 3 is also used to execute the following method:

[0062] The multifunctional unit is traversed to determine the model domain division standard, wherein the model domain division standard includes processing domain division and resource allocation; based on the model domain division standard, the hardware structure branch and the software system branch are initialized respectively to determine the initialized parallel branch; based on the initialized parallel branch, multi-path parallel analysis is performed to determine the first optimization strategy, including intra-branch collision and inter-branch collision.

[0063] Furthermore, the host unit generation component 5 is also used to execute the following method:

[0064] Based on the lightweight standard, the production optimization strategy is traversed, the software system class strategy is extracted and clustered to determine the clustering result; the clustering result is traversed to divide the channel autonomy part and the upper management part, wherein the division standard is the preset number of clusters; the upper management part is identified, the cluster mechanism is fuzzy processed, and the upper management strategy is determined; based on the upper management strategy, the upper device is built.

[0065] Furthermore, the host unit generation component 5 is also used to execute the following method:

[0066] Identify the upper management strategy and the upper management part, and determine the channel differentiation characteristics; adjust to the underlying logic based on the upper call, defuzzification and channel differentiation characteristics, configure multiple programmable channels, and generate a programmable gate array; based on the programmable gate array, establish a connection between the upper device unit and the channel autonomous region block.

[0067] Furthermore, the production optimization management component 6 is also used to execute the following method:

[0068] Based on the upper device unit and the production optimization strategy, a target production process is determined; the target production process is traversed to locate production risk points, and the production risk points are determined based on preset risk levels; the production risk points are divided to perform production optimization management.

[0069] Furthermore, the production optimization management component 6 is also used to execute the following method:

[0070] Based on the production risk points, controllable risk points are determined, and control accuracy expansion processing is performed based on the risk level to determine first adjustment information, wherein the risk level is positively correlated with the expansion step; uncontrollable risk points are determined, risk precursor characteristics are determined, and second warning information is generated based on the risk precursor characteristics, wherein the risk precursor characteristics are initial visualization characteristics that meet the risk occurrence rate; and production optimization management is performed based on the first adjustment information and the second warning information.

[0071] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0073] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the production process of a multi-channel optical microwave module, characterized in that: The method comprises: Based on the optical microwave function, functional partitioning is performed to determine the multifunctional unit, wherein the multifunctional unit at least includes light source-optical modulation-optical reception-microwave source-microwave processing and has scalability; Constructing an adaptive optimization model, wherein the adaptive optimization model includes a hardware structure branch and a software system branch, and there are branch cross-links; Taking the multifunctional unit as a benchmark, initializing the adaptive optimization model, performing single-channel production configuration optimization, and determining a first optimization strategy; Determine the number of channels and channel distinguishing features, locate and adjust the strategic points of the first optimization strategy, and determine the production optimization strategy; Identify the production optimization strategy, configure the host device and build a programmable gate array based on the lightweight standard, and generate a host device unit, wherein the programmable gate array corresponds to the number of channels; The host unit is combined with the production optimization strategy to perform production optimization management on the multi-channel optical microwave module.

2. The method for optimizing the production process of a multi-channel optical microwave module according to claim 1, characterized in that: Initializing the adaptive optimization model to perform single-channel production configuration optimization includes: Traversing the multifunctional unit to determine a model domain division standard, wherein the model domain division standard includes processing domain division and resource allocation; Based on the model domain demarcation standard, the hardware structure branch and the software system branch are respectively initialized to determine the initialized parallel branch; Based on the initialized parallel branches, multi-path parallel analysis is performed to determine the first optimization strategy, including intra-branch collision and inter-branch collision.

3. The method for optimizing the production process of a multi-channel optical microwave module according to claim 2, characterized in that: Performing multi-path parallel analysis based on the initialized parallel branches includes: Identify the multifunctional units, and determine optimization objectives based on the functional baselines, wherein each functional unit corresponds to at least one optimization objective; Based on the initialized parallel branch, performing unit independence parallel optimization based on the optimization target, and determining multiple unit optimization strategies; Based on the coordination within the branch, the co-frequency between the branches, and the non-inter-branch collision, the multiple unit optimization strategies are integrated and corrected to determine the first optimization strategy.

4. The method for optimizing the production process of a multi-channel optical microwave module according to claim 1, characterized in that: Based on the lightweight standard, the host device is configured and a programmable gate array is built to generate a host device unit, including: Based on the lightweight standard, the production optimization strategy is traversed, the software system strategy is extracted and clustered, and the clustering result is determined; Traversing the clustering results, dividing the channel autonomous part and the upper management part, wherein the division standard is the number of preset clusters; Identify the upper management part, perform fuzzy processing on the intra-cluster mechanism, and determine the upper management strategy; Based on the upper management strategy, build the upper device.

5. The method for optimizing the production process of a multi-channel optical microwave module according to claim 4, characterized in that: The construction of a programmable gate array comprises: Identify the upper management strategy and the upper management part, and determine channel distinguishing characteristics; Based on the upper call, defuzzification and channel distinction characteristics, the underlying logic is adjusted, multiple programmable channels are configured, and a programmable gate array is generated; Based on the programmable gate array, a connection is established between the host unit and the channel autonomous region block.

6. The method for optimizing the production process of a multi-channel optical microwave module according to claim 1, characterized in that: The production optimization management includes: Determining a target production process based on the host unit and the production optimization strategy; Traversing the target production process and locating production risk points, wherein the production risk points are determined based on a preset risk level; Divide the production risk points and conduct production optimization management.

7. The method for optimizing the production process of a multi-channel optical microwave module according to claim 6, characterized in that: Divide the production risk points and conduct production optimization management, including: Based on the production risk point, a controllable risk point is determined, and a control precision expansion process is performed based on the risk level to determine first adjustment information, wherein the risk level is positively correlated with the expansion step; Determine an uncontrollable risk point, determine a risk precursor feature, and generate second warning information based on the risk precursor feature, wherein the risk precursor feature is an initial visualization feature that satisfies the risk occurrence rate; Production optimization management is performed based on the first adjustment information and the second warning information.

8. A production process optimization system for a multi-channel optical microwave module, characterized in that: The steps for implementing the method for optimizing the production process of a multi-channel optical microwave module according to any one of claims 1 to 7 include: Functional partitioning component: Based on the optical microwave function, functional partitioning is performed to determine the multifunctional unit, wherein the multifunctional unit at least includes light source-optical modulation-optical reception-microwave source-microwave processing and has scalability; Model building component: building an adaptive optimization model, wherein the adaptive optimization model includes a hardware structure branch and a software system branch, and there are branch cross-links; Single-channel production configuration optimization component: based on the multifunctional unit, the adaptive optimization model is initialized, single-channel production configuration optimization is performed, and a first optimization strategy is determined; Strategic point positioning and adjustment component: determine the number of channels and channel distinguishing features, perform strategic point positioning and adjustment on the first optimization strategy, and determine the production optimization strategy; A host unit generation component: identifying the production optimization strategy, configuring the host and building a programmable gate array based on the lightweight standard, and generating a host unit, wherein the programmable gate array corresponds to the number of channels; Production optimization management component: combining the host unit with the production optimization strategy to perform production optimization management on the multi-channel optical microwave module.

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