Preparation process optimization method and system for tensile flame-retardant fireproof power cable

Abstract

The invention discloses a preparation process optimization method and system for a tensile flame-retardant fireproof power cable, and relates to the technical field of intelligent control and optimization systems.The method comprises the step that optimization of the preparation process of the power cable is achieved by building an intelligent control platform. The method comprises the following steps: firstly, analyzing a preparation process, creating a twin process database, carrying out balance optimization according to preparation requirements, and establishing an optimization fitting scheme; and the preparation process is optimized through inter-process association identification and linkage response network. And the platform executes production based on the fitting scheme, collects data in real time and feeds back the data to the central processing center. And the central processing center generates real-time linkage response after processing the data, and establishes a self-optimization feedback mechanism. The technical problems of insufficient process control precision and unstable production process in the existing power cable preparation process are solved, and the technical effects of improving the process control precision and enhancing the stability of the production process are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent control and optimization systems, and in particular to a method and system for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable. Background Art

[0002] With the continuous improvement of the quality requirements for power cables in the fields of electricity, communications, transportation, etc., the traditional power cable preparation process faces problems such as insufficient process control accuracy, unstable production process, information islands and feedback lag. Traditional process adjustment relies more on manual experience and fixed rules, lacks precise automated control, which leads to fluctuations in the production process, affecting the quality and production efficiency of the cables. In order to solve these problems, intelligent process control systems have been gradually applied to the production process in recent years, and automated, precise and intelligent process control has been achieved through modern information technology, sensing technology and data analysis. In response to this demand, this technical solution proposes to improve the level of automated control in the preparation process of power cables through intelligent control platforms, real-time data feedback and process optimization algorithms, ensure the stability of the production process and the consistency of product quality, thereby optimizing production efficiency, reducing costs, and promoting the intelligent upgrade of modern industrial production.

[0003] In the current related technologies, there are technical problems such as insufficient process control accuracy and unstable production process in the preparation of power cables. Summary of the invention

[0004] The present application provides a method and system for optimizing the preparation process of tensile flame-retardant and fire-resistant power cables, and optimizes the preparation process of power cables by building an intelligent control platform. The platform integrates a central processing center and an edge point sensor group. The sensor group is configured in layers according to the preparation process of the power cable and communicates digitally with the central processing center. First, the preparation process is analyzed, a twin process database is created, and a balanced optimization is performed according to the preparation requirements to establish an optimal fitting scheme. Then, the preparation process is optimized through inter-process association identification and linkage response network. The platform executes production based on the fitting scheme and collects data in real time and feeds it back to the central processing center. After processing the data, the central processing center generates a real-time linkage response and establishes a self-optimization feedback mechanism, which ultimately realizes self-update of the optimization scheme and preparation compensation, completes the continuous optimization of the preparation process, and achieves the technical effect of improving process control accuracy and enhancing the stability of the production process.

[0005] The present application provides a method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable, comprising: An intelligent control platform is constructed, wherein the intelligent control platform integrates a central processing center and an edge point sensor group, wherein the edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is hierarchically configured according to the preparation process of the power cable; a process analysis is performed on the preparation process, a twin process database is created according to the process analysis results, preparation balance optimization is performed based on the twin process database and the preparation requirements, and an optimal fitting scheme is established; the association between processes is identified using the optimal fitting scheme and the process analysis results, and a linkage response network is configured based on the association between processes; the preparation of the power cable is performed based on the optimal fitting scheme using the intelligent control platform, and the edge point sensors are simultaneously activated to perform data collection, a real-time sensing data set is established, and the real-time sensing data set is fed back to the central processing center; after the real-time sensing data set is processed by the central processing center, it is synchronized to the linkage response network, a real-time linkage response result is generated, and a self-optimization feedback is established; the optimal fitting scheme is self-updated according to the self-optimization feedback, and preparation compensation is performed through the real-time linkage response result, and the preparation process optimization is completed based on the preparation compensation and the self-update result.

[0006] The present application also provides a preparation process optimization system for a tensile flame retardant and fire resistant power cable, comprising: An intelligent control platform building module, the intelligent control platform building module is used to build an intelligent control platform, the intelligent control platform integrates a central processing center and an edge point sensor group, the edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is hierarchically configured according to the preparation process of the power cable; a preparation balance optimization module, the preparation balance optimization module is used to perform process analysis on the preparation process, create a twin process database according to the process analysis results, perform preparation balance optimization based on the twin process database and preparation requirements, and establish an optimization fitting scheme; an association identification module, the association identification module is used to use the optimization fitting scheme and process analysis results to identify the association between processes, and configure a linkage response network based on the association between processes; A real-time sensing data set establishment module, the real-time sensing data set establishment module is used to perform preparation and execution of power cables based on an optimal fitting scheme with an intelligent control platform, and synchronously activate edge point sensors to perform data collection, establish a real-time sensing data set, and feed the real-time sensing data set back to a central processing center; a linkage response network synchronization module, the linkage response network synchronization module is used to synchronize the real-time sensing data set to a linkage response network after processing it with a central processing center, generate a real-time linkage response result, and establish a self-optimization feedback; a preparation compensation module, the preparation compensation module is used to self-update the optimal fitting scheme according to the self-optimization feedback, and perform preparation compensation through the real-time linkage response result, and complete the preparation process optimization based on the preparation compensation and self-update results.

[0007] It is proposed that a method and system for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable proposed in this application will first optimize the preparation process of power cables by building an intelligent control platform. The platform integrates a central processing center and an edge point sensor group. The sensor group is configured in layers according to the preparation process of power cables and communicates digitally with the central processing center. First, the preparation process is analyzed, a twin process database is created, and a balanced optimization is performed according to the preparation requirements to establish an optimal fitting scheme. Then, the preparation process is optimized through inter-process association identification and linkage response network. The platform executes production based on the fitting scheme and collects data in real time and feeds it back to the central processing center. After processing the data, the central processing center generates a real-time linkage response and establishes a self-optimization feedback mechanism, which ultimately realizes self-update of the optimization scheme and preparation compensation, completes the continuous optimization of the preparation process, and achieves the technical effect of improving process control accuracy and enhancing the stability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0009] Figure 1 A schematic diagram of a process for optimizing a preparation process of a tensile flame-retardant and fire-resistant power cable provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a system for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: intelligent control platform building module 10, preparation balance optimization module 20, association identification module 30, real-time sensor data set establishment module 40, linkage response network synchronization module 50, preparation compensation module 60. DETAILED DESCRIPTION

[0011] 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.

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations 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 clearly listed, but may include other steps or modules that are not clearly 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0014] The present application embodiment provides a method for optimizing the preparation process of a tensile flame retardant and fire resistant power cable. Figure 1 As shown, the method includes: Step S100, build an intelligent control platform, the intelligent control platform integrates a central processing center and an edge point sensor group, the edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is hierarchically configured according to the preparation process of the power cable. Specifically, to build an intelligent control platform, its basic architecture must be determined, that is, the central processing center and the edge point sensor group are integrated. The central processing center is responsible for receiving, processing and analyzing data and issuing instructions, and the edge point sensor group is distributed at key positions in the preparation process of the power cable to collect information. Then, a stable digital communication link is established between the edge point sensor group and the central processing center, and the electromagnetic interference factors of the preparation environment must be analyzed to ensure reliable communication. Then, the edge point sensor group is configured in layers according to the power cable preparation process. First, the preparation process is studied and decomposed into multiple layers, including raw material preparation layer, cable core manufacturing layer, insulation layer and sheath layer processing layer, and finished product inspection layer. For the raw material preparation layer, humidity, weight and other sensors are configured in the storage area and key points of the feed channel to monitor parameters such as raw material humidity and feed volume. Then, the appropriate type of sensor is selected according to the requirements of different process levels. For example, high-precision temperature sensors are selected for temperature-sensitive links, and pressure sensors are installed in pressure control links. Sensors are reasonably arranged at key positions of each process level. For example, tension, speed and other sensors are installed near wire drawing machines, stranding machines and other equipment in the cable core manufacturing layer to monitor tension changes and equipment operating speed. Finally, sensors at each level are connected to the network to make them compatible with the communication link, reduce data transmission delay and packet loss, optimize the network, set appropriate nodes and transmission paths, and centralize data to the central processing center, laying the foundation for intelligent control and optimization of the preparation process.

[0015] Step S200, perform process analysis on the preparation process, create a twin process database based on the process analysis results, perform preparation balance optimization based on the twin process database and preparation requirements, and establish an optimal fitting solution. Specifically, a comprehensive analysis of the power cable preparation process requires the establishment of a professional team including process engineers, material experts, electrical engineers, etc., to sort out the various steps from raw material input to finished product output from the perspective of process flow, such as raw material pretreatment, cable core manufacturing, insulation and sheath layer laying, and special treatment procedures, and clarify the operation and parameters of each step, including the material pretreatment method and parameters, the impact of equipment operating parameters on product quality, and the impact of the connection relationship between steps on quality and efficiency; based on the analysis results, use advanced database management systems and data modeling technology to create a twin process database, establish a process step model to store step details, and establish a parameter table for the process parameters to record their names, value ranges, set values, and units. In addition, a correlation model between process steps is established to describe input and output, sequence and mutual influence; preparation balance optimization is carried out in combination with preparation requirements covering dimensions such as cost, quality, production efficiency and environmental protection, and the influence of different processes and parameters on various cost elements, key quality indicators, production efficiency and environmental protection is analyzed. Mathematical models and optimization algorithms are used to search and optimize in the process parameter space, and multi-objective functions are comprehensively analyzed to find the optimal parameter combination; an optimal fitting scheme is established through optimization, and the optimal operating parameters and execution sequence of each process step under the preparation requirements are clearly defined, and the key parameter setting values ​​of each step and the connection method and time arrangement between steps are listed to provide guidance for the preparation process to achieve the optimal balance and improve product competitiveness.

[0016] In one possible implementation, a process analysis is performed on the preparation process, a twin process database is created based on the process analysis results, preparation balance optimization is performed based on the twin process database and preparation requirements, and an optimization fitting scheme is established. Step S200 further includes step S210, analyzing the preparation requirements and establishing a balance coefficient, wherein the balance coefficient includes a preparation cost balance coefficient, a cable quality balance coefficient, and an environmental sustainability balance coefficient. Specifically, the analysis of preparation needs requires the collaborative participation of professionals from multiple fields, such as cost accounting experts, quality engineers and environmental experts. Cost accounting experts analyze the cost structure of the entire process from raw material procurement to finished product packaging, and clarify the impact weight of each link on the total cost, so as to establish the preparation cost balance coefficient. If the raw material cost accounts for a high proportion of the total cost, its weight in this coefficient will increase accordingly. Quality engineers determine the key factors affecting cable quality, such as tensile strength, flame retardant properties, insulation properties, etc., based on power cable industry standards and customer-specific requirements, and construct the cable quality balance coefficient through quantitative analysis and comprehensive evaluation. If tensile strength is the key quality indicator, its proportion in the quality balance coefficient is prominent. Environmental experts determine the environmental sustainability balance coefficient from the dimensions of raw material environmental properties (such as recyclability, environmental hazards), production waste emissions, energy consumption, etc. The use of degradable materials and energy-saving production processes are positively reflected in this coefficient.

[0017] Step S220, establish a balance fitness function with the balance coefficient, create an initial solution set based on the twin process database, and evaluate the fitness of the initial solution set based on the balance fitness function, and establish a fitness evaluation result. Specifically, a balance fitness function is constructed based on the preparation cost balance coefficient, the cable quality balance coefficient, and the environmental sustainability balance coefficient. The function is a mathematical expression that comprehensively reflects the relationship and importance of different balance coefficients in the overall preparation process. Then, an initial solution set is created based on the twin process database. The twin process database stores a large amount of process parameters, steps and their relationship information. Such data is used to generate a set of initial solutions through specific algorithms and rules, such as combining different raw material combinations, process parameter value ranges and other information in the database to form multiple initial solutions. Then, the initial solution set is evaluated for fitness based on the balance fitness function, and each initial solution is substituted into the balance fitness function calculation to obtain the comprehensive evaluation results of each solution in the three dimensions of cost, quality and environmental protection, that is, to establish a fitness evaluation result.

[0018] Step S230, the scheme of the initial scheme set is updated with the fitness evaluation result to perform multi-objective balanced optimization iteration. Specifically, the initial scheme set is updated according to the fitness evaluation result, and for the schemes with low fitness, its defects in cost, quality or environmental protection are analyzed, and improved by adjusting process parameters, changing raw material selection or optimizing process steps. If the cost of a certain scheme is too high, more economical raw materials or more efficient processing technology are found to replace the corresponding part of the original scheme. In this way, multi-objective balanced optimization iteration is performed, and the scheme is continuously adjusted in each iteration to promote the scheme to gradually achieve a better balance between the three goals of preparation cost, cable quality and environmental sustainability. This process is a cycle of repeated adjustment and optimization, and each iteration makes the scheme closer to the optimal solution.

[0019] Step S240, establish an optimal fitting scheme according to the iteration result. Specifically, after multiple iterations, establish an optimal fitting scheme according to the final iteration result. The optimal fitting scheme is a set of optimal schemes after comprehensive consideration of all factors, and clarifies the specific process steps, process parameters and raw material selection under the conditions of meeting the balance of preparation cost, cable quality and environmental sustainability. For example, the scheme specifies the types of raw materials, the setting values ​​of equipment parameters in each process step, etc., to provide guidance for the actual preparation process of power cables.

[0020] In a possible implementation, the solutions of the initial solution set are updated based on the fitness evaluation results to perform multi-objective balance optimization iteration, and step S230 further includes step S231, creating a solution search space based on the twin process database. Specifically, the twin process database stores a large amount of information related to the preparation process of power cables, including but not limited to the characteristic parameters of various raw materials, detailed parameter ranges of different process steps (such as temperature, pressure, time, etc.), operating parameter ranges of equipment, and logical relationships between different process steps. A solution search space is created based on data information. For example, for the selection of raw materials, the database records a variety of metal materials and insulating materials that can be used for cable production, and different combinations of materials constitute the part of the solution search space in the raw material dimension. In terms of process step parameters, if the temperature range of a certain process step (such as extruded insulation layer) is recorded in the database from 100℃-200℃, and the pressure range is 1-5MPa, then the two-dimensional parameter space formed by the parameter values ​​is part of the process step in the solution search space. By combining the search spaces of various dimensions (raw materials, process step parameters, equipment parameters, etc.), a complete, high-dimensional solution search space is formed. This space covers all possible combinations of process solutions and provides a basis for subsequent search and evaluation.

[0021] Step S232, the distribution evaluation of the initial solution set in the solution search space is performed to establish a weak search space and a dense search space. Specifically, an initial solution set is obtained, wherein each solution is a power cable preparation solution generated based on certain rules in the previous step, and the initial solution is placed in the created solution search space for analysis. The distribution of the initial solution in the entire search space is evaluated by a distribution evaluation algorithm. For example, a distance metric-based method can be used to calculate the distance between each solution and other solutions in the search space. If the initial solutions are sparsely distributed in a certain area, that is, the distance between the solutions is large, then the area is determined to be a weak search space, reflecting that there are fewer solutions to choose from and explore in this area, and there may be potential high-quality solutions that have not yet been fully explored. On the contrary, if the initial solutions are concentrated together in a certain area, and the distance between the solutions is small, it means that the area is a dense search space. In this area, many solutions have been analyzed, but this may also lead to excessive search of a local area, while ignoring other possible solutions. Through distribution evaluation, the weak search space and the dense search space are clearly divided, providing a basis for subsequent targeted search and optimization.

[0022] Step S233, using the fitness evaluation results, calculate the search trust of the weak search space and the dense search space respectively. Specifically, first, review the fitness evaluation results, which are a comprehensive evaluation of the initial solution set in terms of meeting the balance of multiple objectives such as preparation cost, cable quality and environmental sustainability. Each solution has a corresponding fitness value. For the weak search space, when calculating its search trust, analyze the fitness values ​​and the number of solutions in the space. If there are few solutions in the weak search space, but there are some solutions with higher fitness values, it means that this space has certain potential, and its search trust will be improved accordingly. For example, if a weak search space has only a few solutions, but some of them perform well in cost control and quality assurance, Then the search trust of the weak search space in these two target dimensions will increase. For the dense search space, the calculation of its search trust is also based on the fitness value of the internal solution. Since there are a large number of solutions in this space, more attention is paid to the average fitness value and the distribution of the fitness value. If the average fitness value of the solutions in the dense search space is high, and the distribution of the fitness values ​​is more concentrated at a higher level, then the search trust of the dense search space is high. On the contrary, if the fitness values ​​are uneven, further analysis and adjustment of the search strategy may be required, and its search trust will be affected. Reasonable search trust is calculated for the weak search space and the dense search space respectively.

[0023] Step S234, performing iterative search based on the search confidence and the weak search space and the dense search space to complete the solution update. Specifically, the iterative search process is guided according to the calculated search confidence of the weak search space and the dense search space. For the weak search space with high search confidence, the search intensity in the space is appropriately increased in the iterative search. For example, the search algorithm parameters can be adjusted to make the search process more inclined to explore new solutions in the weak search space. It is possible to try to make local adjustments to the existing solutions in the space, such as fine-tuning the process parameters or changing a small part of the composition of the raw materials, to generate new solutions in the hope of finding a better solution. For the dense search space, it is determined whether to continue the in-depth search according to its search confidence. If the confidence is high, the solutions in the space can be further optimized, and some other algorithms, such as simulated annealing algorithm, crossover and mutation operations in genetic algorithms, can be used to improve the solutions in the dense search space to improve the quality of the solutions. If the confidence is low, the search resource investment in the space can be appropriately reduced, and more attention can be shifted to the weak search space or other potential areas. Through iterative search based on search confidence in the weak search space and the dense search space, new solutions are continuously generated, the initial solution set is updated, the solutions are gradually optimized, and the solutions are updated, providing a solution basis for the subsequent optimization of the power cable preparation process.

[0024] In a possible implementation, an iterative search is performed based on the search trust and the weak search space and the dense search space to complete the solution update, and step S234 further includes step S2341, which determines whether the current iteration stage is the early stage of the iteration. Specifically, in the iterative search process, it is necessary to clarify the current iteration stage, and the judgment can be achieved by recording the number of iterations or setting a specific stage identifier. For example, a threshold is set in advance. If the number of iterations is less than the threshold, it is determined that the current iteration is in the early stage; or it can be determined based on whether certain specific stage conditions are met, such as whether the preliminary exploration of certain key parameter spaces has been completed, etc., to accurately determine whether the current iteration is in the early exploration stage or has entered the subsequent in-depth optimization stage.

[0025] Step S2342, if the current iteration stage is the initial stage of the iteration, a weak search enhancement factor is established based on the number of iterations. Specifically, when it is determined that the current iteration stage is the initial stage, the weak search enhancement factor is established. The establishment of the factor is closely related to the number of iterations. Since the number of iterations is relatively small in the initial stage, as the number of iterations increases, it means that the search process gradually moves away from the initial stage, and a weak search enhancement factor is established. For example, the weak search enhancement factor is set to F, the number of iterations is n, and the maximum number of iterations is N. F=1-n / N can be set in the initial stage of iteration, n is smaller, and the value of F is larger. This makes it possible to provide more search weights for the weak search space in the early stage of the search, expand the search range, explore possible solutions in a wider area, and avoid falling into a local optimal solution too early.

[0026] Step S2343, after the weak search space is enhanced by the weak search enhancement factor, the search attention is reconstructed based on the search confidence, and the iterative search is completed. Specifically, the weak search space is enhanced by the established weak search enhancement factor, and the search strategy is adjusted in the weak search space. For example, if new search points are originally generated in a uniformly distributed manner in the weak search space, after the enhancement, the number of search points can be increased or the distribution law of the search points can be changed according to the weak search enhancement factor, so that the search in the space is more intensive and comprehensive, and the search attention is reconstructed based on the search confidence calculated previously. The search confidence reflects the potential value of different search spaces (including the weak search space and other possible search areas). Search resources are reallocated and the search direction is adjusted according to the search confidence. If the search confidence of a certain area is high, even when the weak search space is enhanced by the weak search enhancement factor, part of the attention may be appropriately transferred to this high-confidence area to obtain more potential solutions, and an iterative search is completed. The special treatment of the weak search space in the iterative stage is considered, and the search confidence is combined to optimize the entire search process, so that the search can explore valuable solutions in a wider range, especially in the early stage of the iteration to avoid excessive concentration on local areas.

[0027] Step S300, using the optimal fitting scheme and process analysis results to identify the correlation between processes, and configuring the linkage response network based on the correlation between processes. Specifically, the process steps are determined using the optimal fitting scheme, and the process analysis results are called to analyze the correlation between each process step, including parameter correlation (such as the conductor wire drawing step parameters affect the twisting step parameters, and the insulation layer extrusion step parameters affect the parameters of the previous and next processes) and time dependence (such as the wire must be twisted within a specified time after wire drawing, and the sheath layer must be extruded within a certain time after the insulation layer is extruded), and the correlation analysis results are established (presented in the form of a matrix, directed graph, etc.), and then based on this and the optimal fitting scheme, the process parameter linkage rules are created (specifying the adjustment method of other related step parameters when a process step parameter changes), and the linkage rules are used as the process association, and the linkage response network is created in conjunction with the prediction and maintenance layer (when a process step parameter changes or is predicted to change, the relevant parameters are adjusted according to the linkage rules and prediction results to ensure production stability and product quality).

[0028] In one possible implementation, the correlation between processes is identified using the optimal fitting scheme and the process analysis results, and a linkage response network is configured based on the correlation between processes. Step S300 further includes step S310, in which the process steps are determined based on the optimal fitting scheme, and the process analysis results are called by the process steps to perform correlation analysis on each process step, and a correlation analysis result is established, wherein the correlation analysis result includes parameter correlation and time dependence.Specifically, the optimal fitting solution is the best solution obtained after optimizing the balance of the power cable preparation process, which contains a series of orderly process operation instructions. Each clear process step is sorted out from the optimal fitting solution. The steps constitute the process framework of power cable production, for example, including raw material preparation, conductor processing (such as wire drawing, stranding, etc.), insulation layer processing (such as extrusion of insulation material), sheath layer processing and final finished product testing. Each link can be subdivided into multiple specific operation steps. The orderly combination of the above steps forms a complete production process. The process analysis result is a data set for an in-depth analysis of each process step, covering various information such as process parameters and the impact of process conditions on product quality. The process steps are analyzed, and the correlation analysis is carried out using the process analysis results to analyze the relationship between the parameters in different process steps. In the conductor processing link, the parameters of the wire drawing step (such as wire drawing speed, die size, etc.) will affect the diameter and surface quality of the drawn conductor, and the above parameters are related to the parameters in the stranding step (such as stranding pitch, number of stranding layers, etc.), because the diameter and surface quality of the conductor will determine the tightness and uniformity of the stranded wire. If the conductor diameter after wire drawing is uneven, it will cause local over-tightening or over-loosening during the stranding process, affecting the electrical and mechanical properties of the cable. In the process of insulating layer and sheath layer processing, the parameters of insulation layer extrusion (such as extrusion temperature, pressure, speed, etc.) will affect the thickness, uniformity and density of the insulation layer. These characteristics will affect the parameters of the sheath layer extrusion (such as the flow characteristics of the sheath layer material, extrusion pressure, etc.). For example, if the extrusion thickness of the insulating layer is uneven, the extrusion pressure of the sheath layer needs to be adjusted to ensure that the sheath layer can completely cover the insulating layer and the thickness is uniform. Analyze the relationship between the process steps in the time dimension. In the entire production process, there are strict time sequence and time interval requirements between certain process steps. For example, after the conductor processing is completed, the insulation layer needs to be treated as soon as possible to prevent the conductor surface from being oxidized or contaminated, affecting the insulation effect. If the time interval between the conductor processing and the insulation layer treatment is too long, the conductor surface may need to be cleaned or pre-treated again, increasing production costs and production cycles. Similarly, the insulation There is also a time limit between the layer processing and the sheath layer processing. If the interval time is inappropriate, it may affect the adhesion between the two and reduce the overall quality of the cable. Through the above analysis of parameter correlation and time dependence, the results are organized and expressed in a systematic way to establish the correlation analysis results. For parameter correlation, a parameter correlation matrix can be constructed. The rows and columns of the matrix correspond to different process steps, and the elements in the matrix represent the degree of correlation between the parameters of the two process steps (which can be determined by experimental data, theoretical models or empirical formulas). For time dependence, it can be represented by a directed graph. The nodes represent the process steps, and the directed edges represent the order of the process steps and the maximum allowed time interval or optimal time range.

[0029] Step S320, based on the correlation analysis results and the optimal fitting scheme, the linkage rules of the process parameters are created, and the linkage rules are used as the association between processes to create a linkage response network in conjunction with the prediction and maintenance layer, which is a data processing layer for predicting parameter change trends. Specifically, according to the established correlation analysis results and optimal fitting scheme, linkage rules are formulated for the parameters of each process step. The linkage rules clarify how the parameters of other related process steps should be adjusted when the parameters of a certain process step change, so as to ensure the stability of the entire production process and the consistency of product quality. For example, if in the wire drawing step, the wire drawing speed needs to be reduced due to changes in the characteristics of the raw materials, according to the linkage rules, the drawing die size or the twisting pitch of the subsequent twisting step and other parameters need to be adjusted accordingly. If the extrusion temperature of the insulation layer rises slightly due to equipment failure, the linkage rules will indicate the adjustment of the extrusion pressure, speed and related parameters of the subsequent sheath layer extrusion to ensure that the quality of the insulation layer and the sheath layer is not affected. The linkage rules are based on the analysis of the intrinsic relationship between the process steps and the optimal parameter combination in the optimal fitting scheme. The predictive maintenance layer, as a data processing layer dedicated to predicting parameter change trends, can use historical data, real-time data and data analysis algorithms to predict possible changes in process parameters in advance. The linkage rules created above are used as the correlation information between processes and are compatible with the predictive maintenance layer. Combined to create a linkage response network, in this network, when the predictive maintenance layer detects that the parameters of a certain process step are about to change (such as through real-time monitoring and analysis of equipment operation data, it is predicted that the key parameters of a certain equipment may exceed the normal range) or have changed, the linkage response network quickly calculates the impact on other related process steps based on the linkage rules and prediction results, and adjusts the relevant parameters in time. For example, if the predictive maintenance layer predicts that the speed of the stranding machine may decrease due to equipment aging in a certain period of time in the future, the linkage response network will adjust the parameters of the subsequent insulation layer extrusion steps in advance according to the linkage rules, such as adjusting the extrusion speed to match the changes in the stranded wire, thereby ensuring that the entire production process is not affected, realizing real-time and automatic control of the production process, and improving production efficiency and product quality.

[0030] Step S400, the intelligent control platform performs the preparation of power cables based on the optimal fitting scheme, and simultaneously activates the edge point sensors to perform data collection, establishes a real-time sensor data set, and feeds the real-time sensor data set back to the central processing center. Specifically, the intelligent control platform starts the preparation of power cables based on the optimal fitting scheme, sends precise control instructions to each device of the production line, such as commanding the conveying system to put in an appropriate amount of materials in the raw material delivery link, sending instructions containing specific parameters to the wire drawing machine, stranding machine, etc. in the conductor processing link, setting appropriate parameters for the extruder in the insulation and sheath layer extrusion link, and activating the edge point sensors configured in key positions according to the preparation process to collect data, the weight and material identification sensors in the raw material delivery area, the tension, speed, and temperature sensors in the conductor processing area, and the pressure, flow, and thickness sensors in the insulation and sheath layer extrusion area collect information from different angles, and the large amount of data collected is aggregated into a dynamic real-time sensor data set that has been preliminarily verified and preprocessed, and fed back to the central processing center through a stable and efficient communication link. The center further organizes and classifies the data and stores it to provide support for subsequent analysis and decision-making.

[0031] Step S500, after the central processing center processes the real-time sensor data set, it is synchronized to the linkage response network, generates a real-time linkage response result, and establishes a self-optimization feedback. Specifically, after receiving the real-time sensor data set, the central processing center first cleans the data to remove noise and outliers, then classifies and integrates the data, structures it by source and type, then extracts and analyzes the data features, and uses mathematical models and statistical analysis methods to grasp the data rules and internal connections. The processed data set is synchronized to the linkage response network through a stable communication mechanism in a prescribed format and protocol. The network integrates it with the process parameter linkage rules and predictive maintenance information, and generates a real-time linkage response result based on the integrated information when the process link parameters change abnormally, such as adjusting the relevant parameters when the drawing tension increases abnormally or the insulation layer extrusion temperature increases abnormally. At the same time, a self-optimization feedback mechanism is established based on the real-time sensor data and linkage response situation, and the analysis data and results are recorded, and the process parameter settings are adjusted and optimized according to them.

[0032] Step S600, self-update the optimal fitting scheme according to the self-optimization feedback, and make preparation compensation through the real-time linkage response results, and complete the preparation process optimization based on the preparation compensation and self-update results. Specifically, by analyzing the self-optimization feedback information, including process parameter deviation, process step execution difference, product quality fluctuation and linkage response performance, the update direction of the optimal fitting scheme is determined, and the process parameters (such as raw material ratio, processing temperature, pressure, speed, etc.) and process steps (such as increasing or decreasing or adjusting the detection steps, changing the process sequence) are adjusted; at the same time, the real-time linkage response results are interpreted, and the parameters in the preparation process (such as the adjustment of relevant parameters after flow fluctuations during insulation layer extrusion) and process links (such as taking compensation measures in the insulation layer extrusion link when the strands are uneven) are compensated according to them; finally, the preparation compensation and self-update results are integrated and applied to production to improve product quality, increase production efficiency, control costs, and form a closed loop to continuously optimize the preparation process.

[0033] In a possible implementation, the optimal fitting scheme is self-updated according to the self-optimization feedback, and preparation compensation is performed through the real-time linkage response results. The preparation process optimization is completed based on the preparation compensation and the self-update results. Step S600 further includes step S610, establishing a balanced bias of the balanced fitness function based on the self-optimization feedback. Specifically, the self-optimization feedback covers many aspects of information in the preparation process of the power cable, including but not limited to the deviation between the actual and theoretical values ​​of the parameters in each process step, the fluctuation of the quality of different batches of products, the resource consumption in the production process, and the effect of the linkage response measures. For example, in the conductor processing link, it may be found that the actual tension value in the wire drawing process often deviates from the set value, or the twist pitch of the stranded wire fluctuates to a certain extent; in terms of quality, the insulation resistance of a batch of cables may not meet the standard requirements, or the tensile strength may change abnormally; in terms of resource consumption, it may be found that the waste of raw materials increases or the energy consumption is too high at a certain stage; at the same time, for the linkage response measures, the actual effect of each adjustment on the production process is recorded. Real impact, such as whether the adjustment of the extrusion temperature at a certain time effectively solved the problem of uneven thickness of the insulation layer. According to the analysis results of the self-optimization feedback, clarify the adjustment direction of the factors related to the balance coefficient in the balance fitness function. For the preparation cost balance coefficient, if the problem of raw material waste is found, it is necessary to pay more attention to the control of raw material costs and appropriately increase the weight of raw material costs in the total cost calculation; if the energy consumption is too high, increase the proportion of energy cost-related factors in the cost balance coefficient. For the cable quality balance coefficient, if key quality indicators such as insulation resistance or tensile strength frequently have problems, increase the weight of these quality indicators in the quality balance coefficient accordingly to highlight their importance in evaluating the pros and cons of process solutions. In terms of the environmental sustainability balance coefficient, if waste emissions exceed expectations or the implementation of environmental protection measures is not effective, adjust the weights of relevant factors such as waste treatment costs and environmental protection equipment operating costs in the coefficient. Based on the adjustment direction determined above, establish a balance bias of the balance fitness function. The balance bias is a targeted adjustment to the original balance state of the balance fitness function. For example, if it is decided to increase the weight of tensile strength in the mass balance coefficient, the amount of change in the weight and the way it is changed are the manifestation of the balance bias in the mass dimension.

[0034] Step S620, reconstruct the preparation balance optimization according to the balance bias to perform self-update processing of the optimization fitting scheme. Specifically, in the process of reconstructing the preparation balance optimization, the optimization strategy is adjusted according to the established balance bias. For the preparation cost balance coefficient adjusted based on the balance bias, if the weight of the raw material cost increases, in the optimization process, the search algorithm will pay more attention to finding a lower-cost raw material substitute or optimizing the process parameter combination of the raw material usage. For example, expand the search scope of raw material information in different price ranges in the twin process database, and analyze the relationship between the amount of different raw materials and product quality in more depth to find the optimal solution to reduce the cost of raw materials while meeting the quality requirements. For the balance bias caused by the adjustment of the mass balance coefficient, if the weight of the tensile strength in the mass balance coefficient is increased, the optimization process will focus more on exploring the process parameter space closely related to the tensile strength. In the wire drawing and stranding processes, the optimal value range of parameters such as the wire drawing speed, die size, and stranding pitch will be searched more finely, and the search density within these parameter value ranges will be increased. More parameter combinations will be tried to find process parameters that can significantly improve the tensile strength. number setting, when the environmental sustainable balance coefficient changes due to the balance bias, if the weight related to waste emission increases, the optimization process will focus on the process scheme to reduce waste generation, including finding more environmentally friendly raw materials that generate less waste during the production process; or optimizing process steps, such as improving the processing technology to make the raw material utilization rate higher and reduce the generation of waste such as scraps. Through the above-mentioned preparation balance optimization process adjusted based on the balance bias, the optimization fitting scheme is self-updated. During the updating process, the process steps, process parameters and raw material selection in the optimization fitting scheme will be adjusted according to the new optimization strategy. For example, according to the optimization results of raw material cost control, the optimization fitting scheme may select a new raw material with lower price but similar performance; based on the optimization of improving tensile strength, the process parameters of wire drawing and twisting are adjusted; in order to reduce waste emissions, the operation mode of a certain process step is changed or a waste recycling and treatment link is added.

[0035] In one possible implementation, the optimal fitting scheme is self-updated according to self-optimization feedback, and preparation compensation is performed through real-time linkage response results. Preparation process optimization is completed based on preparation compensation and self-update results. Step S600 further includes step S630, building an early warning center on the intelligent control platform, and the early warning center is created based on the optimal fitting scheme. Specifically, the optimal fitting scheme is the optimal scheme obtained after multiple rounds of optimization of the power cable preparation process. The scheme specifies the parameters and execution order of each process step. The optimal fitting scheme is analyzed to understand the parameter range of each process step during normal operation. For example, in the conductor processing link, the speed range, tension range, and twist pitch range of the stranded wire are clarified; in the insulation layer and sheath layer processing link, the reasonable range of parameters such as extrusion temperature, pressure, and speed are mastered, and the connection relationship and time requirements between different process steps are clarified. According to the parameter range and process relationship determined by the optimal fitting scheme, the architecture of the early warning center is built. The early warning center includes a data receiving module for receiving data from the central processing center; a parameter comparison module for storing the standard parameter range in the optimal fitting scheme for comparison with the received data; an early warning rule setting module for formulating early warning rules based on process characteristics and parameter importance; and an early warning information output module for outputting early warning information. For example, for the parameter comparison module, it compares the wire drawing speed in the received real-time sensor data with the wire drawing speed range in the optimal fitting scheme to determine whether it exceeds the normal range. The warning rule setting module sets warning rules of different levels according to the criticality of the process steps and the impact of the parameters on product quality. For example, a slight deviation of a key parameter from the range may be a low-level warning, while a serious deviation may be a high-level warning. The warning center is connected to other key parts of the intelligent control platform, and a stable data transmission channel is established with the central processing center to ensure that the processed real-time sensor data set can be received in time. An interactive channel is established with the linkage response network. When the warning center issues a warning message, the linkage response network can respond quickly according to the warning information and connect to the production equipment control system so that in the event of a serious warning situation, the production equipment can be urgently controlled, such as suspending equipment operation, to prevent the problem from further deteriorating.

[0036] Step S640, when the real-time sensor data set flows back to the central processing center at any time, the real-time sensor data set is synchronized to the warning center to perform warning identification. Specifically, during the preparation of the power cable, the real-time sensor data set is continuously collected from the edge point sensor and transmitted to the central processing center. The real-time sensor data set is transmitted to the warning center in real time through a pre-established synchronization mechanism. The synchronization mechanism ensures the timeliness and integrity of the data and avoids data transmission delay or loss. For example, a high-speed data bus or network communication protocol is used to send data from the central processing center to the data receiving module of the warning center in a certain data format. After the warning center receives the real-time sensor data set, the data is first pre-processed, including data decoding, format conversion and other operations, so that the data can be correctly identified and processed by the parameter comparison module. For example, if the data transmitted by the sensor is encrypted or in a specific encoding format, it needs to be decrypted and format converted in the warning center to convert it into data consistent with the standard parameter format stored in the parameter comparison module. The pre-processed data is compared with the standard parameter range in the optimal fitting scheme using the parameter comparison module. For each parameter of each process step, check whether it is within the normal range. For example, in the extrusion process of the insulation layer, compare the real-time extrusion temperature, pressure, and flow rate data with the corresponding temperature range, pressure range, and flow rate range in the optimal fitting solution. If a parameter exceeds the range, further analyze its degree of deviation and duration. Combined with the rules in the early warning rule setting module, analyze the importance of the parameter in the process and determine whether to trigger an early warning. For example, if the extrusion temperature is slightly higher than the normal range but returns to normal within a short time, it may not trigger an early warning or may trigger a low-level early warning according to the rules; but if the temperature continues to be too high and exceeds the set critical value, a high-level early warning will be triggered.

[0037] Step S650: Report the abnormality according to the warning identification result. Specifically, the level and type of the abnormality are determined according to the analysis results in the warning identification process. The abnormality level is divided according to the degree of parameter deviation, duration, and criticality of the process step. For example, a slight deviation that can be recovered in a short time is a low-level abnormality, and a serious deviation or a long-term deviation from the critical parameter is a high-level abnormality. The abnormality type is determined according to which parameter of which process step has a problem, such as abnormal tension in conductor processing, or abnormal temperature in insulation layer extrusion. For example, if the tension in the wire drawing process continues to exceed the normal range by a large margin, this is a high-level abnormality of conductor processing tension abnormality. According to the determined abnormality level and type, the warning is used to identify the abnormality. The alarm information output module reports abnormalities. For low-level abnormalities, prompt information can be displayed on the operation interface of the intelligent control platform to remind operators to pay attention, but it will not affect the continued production. For high-level abnormalities, in addition to displaying eye-catching warning information on the operation interface, relevant personnel can also be notified through sound alarms, SMS notifications, etc., and the abnormal information can be sent to the linkage response network and the production equipment control system. For example, a strong alarm sound is issued, a text message containing detailed information of the abnormality is sent to the production supervisor, and the abnormal information is passed to the linkage response network so that it can start the emergency handling procedure, and at the same time notify the production equipment control system to suspend production to avoid greater losses.

[0038] The embodiment of the present application uses an intelligent control platform to optimize the preparation process of power cables. The platform integrates a central processing center and an edge point sensor group. The sensor group is configured in layers according to the preparation process of power cables and communicates digitally with the central processing center. First, the preparation process is analyzed, a twin process database is created, and a balanced optimization is performed according to the preparation requirements to establish an optimal fitting scheme. Then, the preparation process is optimized through inter-process association identification and linkage response network. The platform executes production based on the fitting scheme and collects data in real time and feeds it back to the central processing center. After processing the data, the central processing center generates a real-time linkage response, and establishes a self-optimization feedback mechanism, which ultimately realizes self-update of the optimization scheme and preparation compensation, completes the continuous optimization of the preparation process, and achieves the technical effect of improving process control accuracy and enhancing the stability of the production process.

[0039] In the above, refer to Figure 1 A method for optimizing the preparation process of a tensile flame retardant and fire resistant power cable according to an embodiment of the present invention is described in detail. Figure 2 A preparation process optimization system for a tensile flame-retardant and fire-resistant power cable according to an embodiment of the present invention is described.

[0040] A preparation process optimization system for a tensile flame retardant and fire resistant power cable according to an embodiment of the present invention is used to solve the technical problems of insufficient process control accuracy and unstable production process in the existing power cable preparation process, so as to achieve the technical effect of improving process control accuracy and enhancing the stability of the production process. A preparation process optimization system for a tensile flame retardant and fire resistant power cable includes: an intelligent control platform building module 10, a preparation balance optimization module 20, an association identification module 30, a real-time sensor data set establishment module 40, a linkage response network synchronization module 50, and a preparation compensation module 60.

[0041] The intelligent control platform building module 10 is used to build an intelligent control platform, which integrates a central processing center and an edge point sensor group. The edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is layered according to the preparation process of the power cable.

[0042] The preparation balance optimization module 20 is used to perform process analysis on the preparation process, create a twin process database according to the process analysis results, perform preparation balance optimization based on the twin process database and preparation requirements, and establish an optimization fitting plan.

[0043] The association identification module 30 is used to identify the associations between processes by using the optimal fitting scheme and the process analysis results, and configure a linkage response network based on the associations between processes.

[0044] The real-time sensing data set establishment module 40 is used to prepare and execute the power cable based on the optimal fitting solution using the intelligent control platform, and simultaneously activate the edge point sensors to perform data collection, establish the real-time sensing data set, and feed back the real-time sensing data set to the central processing center.

[0045] The linkage response network synchronization module 50 is used to synchronize the real-time sensor data set to the linkage response network after processing it by the central processing center, generate a real-time linkage response result, and establish a self-optimization feedback.

[0046] The preparation compensation module 60 is used to self-update the optimal fitting scheme according to the self-optimization feedback, and to perform preparation compensation through real-time linkage response results, and to complete the preparation process optimization based on the preparation compensation and self-update results.

[0047] Below, the specific configuration of the preparation balance optimization module 20 will be described in detail. As described above, the preparation process is analyzed, a twin process database is created according to the process analysis results, and a preparation balance optimization is performed based on the twin process database and the preparation requirements, and an optimization fitting scheme is established. The preparation balance optimization module 20 further includes: a balance coefficient establishment unit, the balance coefficient establishment unit is used to analyze the preparation requirements and establish a balance coefficient, and the balance coefficient includes a preparation cost balance coefficient, a cable quality balance coefficient, and an environmental sustainability balance coefficient; a balance fitness function establishment unit, the balance fitness function establishment unit is used to establish a balance fitness function with a balance coefficient, create an initial solution set based on the twin process database, and perform fitness evaluation of the initial solution set based on the balance fitness function, and establish a fitness evaluation result; a solution update unit, the solution update unit is used to update the solution of the initial solution set with the fitness evaluation result to perform multi-objective balance optimization iteration; an optimization fitting scheme establishment unit, the optimization fitting scheme establishment unit is used to establish an optimization fitting scheme according to the iteration result.

[0048] Among them, the scheme of the initial scheme set is updated based on the fitness evaluation result to perform multi-objective balanced optimization iteration, and the scheme updating unit further includes: a scheme search space creation subunit, the scheme search space creation subunit is used to create a scheme search space according to the twin process database; a distribution evaluation subunit, the distribution evaluation subunit is used to perform distribution evaluation on the initial scheme set within the scheme search space, and establish a weak search space and a dense search space; a search trust calculation subunit, the search trust calculation subunit is used to use the fitness evaluation result to calculate the search trust of the weak search space and the dense search space respectively; an iterative search subunit, the iterative search subunit is used to perform iterative search based on the search trust and the weak search space and the dense search space to complete the scheme update.

[0049] Among them, an iterative search is performed based on the search trust and the weak search space and the dense search space to complete the solution update, and the iterative search sub-unit further includes: an iterative stage judgment micro-unit, the iterative stage judgment micro-unit is used to judge whether the current iterative stage is the initial stage of iteration; a weak search enhancement factor establishment micro-unit, the weak search enhancement factor establishment micro-unit is used to establish a weak search enhancement factor based on the number of iterations if the current iterative stage is the initial stage of iteration; a search attention reconstruction micro-unit, the search attention reconstruction micro-unit is used to enhance the weak search space with the weak search enhancement factor, and then perform search attention reconstruction based on the search trust to complete the iterative search.

[0050] The specific configuration of the association identification module 30 will be described in detail below. As described above, the association between processes is identified using the optimal fitting scheme and the process analysis results, and a linkage response network is configured based on the association between processes. The association identification module 30 further includes: a process step determination unit, the process step determination unit is used to determine the process steps based on the optimal fitting scheme, and use the process steps to call the process analysis results to perform correlation analysis on each process step, and establish correlation analysis results, the correlation analysis results include parameter correlation and time dependence; a linkage response network creation micro-unit, the linkage response network creation micro-unit is used to create linkage rules for process parameters based on the correlation analysis results and the optimal fitting scheme, and the linkage rules are used as the association between processes, and the linkage response network is created in conjunction with the prediction maintenance layer, the prediction maintenance layer is a data processing layer for predicting parameter change trends.

[0051] The specific configuration of the preparation compensation module 60 will be described in detail below. As described above, the optimal fitting scheme is self-updated according to the self-optimization feedback, and the preparation compensation is performed through the real-time linkage response result. The preparation process optimization is completed based on the preparation compensation and self-update results. The preparation compensation module 60 further includes: a balance bias establishment unit, the balance bias establishment unit is used to establish a balance bias of the balance fitness function based on the self-optimization feedback; a balance optimization reconstruction unit, the balance optimization reconstruction unit is used to reconstruct the preparation balance optimization according to the balance bias to perform the self-update processing of the optimal fitting scheme.

[0052] Among them, the preparation compensation module 60 further includes: an early warning center building unit, the early warning center building unit is used to build an early warning center on the intelligent control platform, and the early warning center is created based on the optimal fitting solution; an early warning identification execution unit, the early warning identification execution unit is used to synchronize the real-time sensor data set to the early warning center when the real-time sensor data set flows back to the central processing center at any time, and execute early warning identification; an abnormal reporting unit, the abnormal reporting unit is used to report abnormalities according to the early warning identification results.

[0053] A preparation process optimization system for a tensile flame retardant and fire resistant power cable provided in an embodiment of the present invention can execute a preparation process optimization method for a tensile flame retardant and fire resistant power cable provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0055] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art 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 the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable, characterized in that: The method comprises: Building an intelligent control platform, the intelligent control platform integrates a central processing center and an edge point sensor group, the edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is hierarchically configured according to the preparation process of the power cable; Conduct process analysis on the preparation process, create a twin process database based on the process analysis results, perform preparation balance optimization based on the twin process database and preparation requirements, and establish an optimal fitting solution; Use the optimal fitting scheme and process analysis results to identify the correlation between processes, and configure the linkage response network based on the correlation between processes; The intelligent control platform is used to prepare the power cables based on the optimal fitting solution, and the edge point sensors are activated simultaneously to perform data collection, establish a real-time sensor data set, and feed the real-time sensor data set back to the central processing center; After the real-time sensor data set is processed by the central processing center, it is synchronized to the linkage response network to generate real-time linkage response results and establish self-optimizing feedback; The optimal fitting scheme is self-updated according to the self-optimization feedback, and preparation compensation is performed through real-time linkage response results. The preparation process optimization is completed based on the preparation compensation and self-update results.

2. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 1, characterized in that: The method of performing preparation balance optimization based on the twin process database and preparation requirements and establishing an optimization fitting scheme also includes: Analyze the preparation requirements and establish a balance coefficient, which includes a preparation cost balance coefficient, a cable quality balance coefficient, and an environmental sustainability balance coefficient; A balance fitness function is established with a balance coefficient, an initial solution set is created based on the twin process database, and the fitness of the initial solution set is evaluated based on the balance fitness function to establish a fitness evaluation result; The initial solution set is updated based on the fitness evaluation results to perform multi-objective balance optimization iteration; An optimal fitting solution is established based on the iterative results.

3. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 2, characterized in that: The updating of the solution of the initial solution set based on the fitness evaluation result also includes: Create a solution search space based on the twin process database; Performing a distribution evaluation on the initial solution set in the solution search space to establish a weak search space and a dense search space; The fitness evaluation results are used to calculate the search confidence of the weak search space and the dense search space respectively; An iterative search is performed based on the search confidence and the weak search space and the dense search space to complete the solution update.

4. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 3, characterized in that: The iterative search based on the search confidence and the weak search space and the dense search space to complete the scheme update also includes: Determine whether the current iteration stage is the initial stage of iteration; If the current iteration stage is the initial stage of iteration, a weak search enhancement factor is established based on the number of iterations; After the weak search space is enhanced by using the weak search enhancement factor, the search attention is reconstructed based on the search confidence to complete the iterative search.

5. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 1, characterized in that: The method of identifying the correlation between processes by using the optimal fitting scheme and the process analysis results, and configuring the linkage response network based on the correlation between processes, further includes: Determine the process steps based on the optimal fitting scheme, and use the process steps to call the process analysis results to perform correlation analysis on each process step, and establish the correlation analysis results, wherein the correlation analysis results include parameter correlation and time dependence; Based on the correlation analysis results and the optimal fitting scheme, linkage rules of process parameters are created. The linkage rules are used as the association between processes, and a linkage response network is created in conjunction with the prediction and maintenance layer. The prediction and maintenance layer is a data processing layer for predicting parameter change trends.

6. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 2, characterized in that: The self-updating of the optimal fitting scheme according to the self-optimization feedback also includes: Establishing a balanced bias of the balanced fitness function based on self-optimizing feedback; A balanced optimization is prepared according to the balanced bias reconstruction to perform a self-updating process of the optimization fitting scheme.

7. The method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to claim 1, characterized in that: The method further comprises: Building an early warning center on the intelligent control platform, wherein the early warning center is created based on an optimal fitting solution; When the real-time sensor data set flows back to the central processing center at any time, the real-time sensor data set is synchronized to the warning center to perform warning identification; Report abnormalities based on the warning identification results.

8. A preparation process optimization system for tensile flame retardant and fire resistant power cables, characterized in that: The system is used to implement the method for optimizing the preparation process of a tensile flame-retardant and fire-resistant power cable according to any one of claims 1 to 7, and the system comprises: An intelligent control platform building module, wherein the intelligent control platform building module is used to build an intelligent control platform, wherein the intelligent control platform integrates a central processing center and an edge point sensor group, wherein the edge point sensor group digitally communicates with the central processing center, and the edge point sensor group is hierarchically configured according to the preparation process of the power cable; A preparation balance optimization module, which is used to perform process analysis on the preparation process, create a twin process database according to the process analysis results, perform preparation balance optimization based on the twin process database and preparation requirements, and establish an optimization fitting scheme; A correlation identification module, which is used to identify correlations between processes using an optimal fitting solution and process analysis results, and configure a linkage response network based on the correlations between processes; A real-time sensing data set establishment module, which is used to prepare and execute power cables based on an optimal fitting solution using an intelligent control platform, and to simultaneously activate edge point sensors to perform data collection, establish a real-time sensing data set, and feed the real-time sensing data set back to a central processing center; A linkage response network synchronization module, which is used to synchronize the real-time sensor data set to the linkage response network after the central processing center processes it, generate real-time linkage response results, and establish self-optimization feedback; A preparation compensation module is used to self-update the optimal fitting scheme according to the self-optimization feedback, and to perform preparation compensation through real-time linkage response results, and to complete the preparation process optimization based on the preparation compensation and self-update results.

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