Intelligent cooperative control method and system for cable twisting

Abstract

The invention discloses an intelligent cooperative control method and system for cable twisting, and relates to the technical field of intelligent control. The method comprises the following steps: carrying out data acquisition on a plurality of cable stranding devices to obtain a plurality of device operation data sets; determining twisting process parameters of the target cable, and performing twisting analysis to obtain a plurality of twisting characteristics; performing control interaction on the plurality of cable twisting devices, and determining cable twisting process demand information according to interaction parameters; traversing a plurality of stranding features to perform cooperative calculation, and formulating a cable stranding cooperative strategy; and performing real-time monitoring on the plurality of cable stranding devices by simulating the execution of the cable stranding cooperation strategy, generating a simulation stranding monitoring result, and dynamically updating the cable stranding cooperation strategy according to the simulation stranding monitoring result to perform intelligent cooperative control on the plurality of cable stranding devices. The technical problems of low stranding efficiency and unstable quality caused by poor collaboration of cable stranding equipment in the prior art are solved, and the technical effect of improving the cable stranding quality and efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent collaborative control method and system for cable twisting. Background Art

[0002] Cable twisting is one of the important processes in the cable production process. Its purpose is to twist multiple conductors or optical fibers according to specific rules to form cable products that meet technical requirements. The twisting quality directly affects the electrical performance, mechanical strength and service life of the cable, and is an important link to ensure product performance. However, the existing cable twisting technology and equipment still face many challenges in practical applications. Traditional cable twisting equipment usually operates in a stand-alone mode, and there is a lack of effective coordinated control between the various devices. Due to the different operating states of the equipment, synchronous operation cannot be achieved, resulting in frequent problems such as twist pitch deviation and tension fluctuation during the twisting process, which affects the quality of the final product. In addition, most equipment relies on fixed parameters to operate, and has weak adaptability to cables of different specifications or process conditions. In actual production, process parameter adjustment often requires manual intervention, which is not only time-consuming and labor-intensive, but also easily limited by operating experience, making it difficult to achieve precise control. Summary of the invention

[0003] The present application provides an intelligent collaborative control method and system for cable twisting, which solves the technical problem in the prior art that the cable twisting equipment has poor coordination, resulting in low twisting efficiency and unstable quality.

[0004] In view of the above problems, the present application provides an intelligent collaborative control method and system for cable twisting.

[0005] In a first aspect of the present application, an intelligent coordinated control method for cable twisting is provided, the method comprising: Data is collected from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets; target cable twisting process parameters are determined based on multiple cable twisting devices, and twisting analysis is performed based on the multiple equipment operation data sets in combination with the target cable twisting process parameters to obtain multiple twisting features; multiple cable twisting devices are controlled and interacted according to the target cable twisting process parameters in combination with the multiple twisting features, and cable twisting process requirement information is determined according to the interaction parameters; multiple twisting features are traversed according to the cable twisting process requirement information to perform collaborative calculations and formulate a cable twisting collaborative strategy; multiple cable twisting devices are simulated to execute the cable twisting collaborative strategy for real-time monitoring to generate simulated twisting monitoring results, and the cable twisting collaborative strategy is dynamically updated according to the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices.

[0006] A second aspect of the present application provides an intelligent coordinated control system for cable twisting, the system comprising: Data acquisition module: collects data from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets; twisting analysis module: determines the target cable twisting process parameters based on multiple cable twisting devices, and performs twisting analysis based on the multiple equipment operation data sets combined with the target cable twisting process parameters to obtain multiple twisting features; interaction module: controls and interacts multiple cable twisting devices according to the target cable twisting process parameters combined with the multiple twisting features, and determines the cable twisting process requirement information according to the interaction parameters; collaborative calculation module: traverses the multiple twisting features according to the cable twisting process requirement information to perform collaborative calculation and formulate a cable twisting collaborative strategy; collaborative control module: simulates the execution of the cable twisting collaborative strategy for multiple cable twisting devices to perform real-time monitoring, generates simulated twisting monitoring results, and dynamically updates the cable twisting collaborative strategy according to the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, data is collected from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets. Next, the target cable twisting process parameters are determined based on multiple cable twisting devices, and twisting analysis is performed based on multiple equipment operation data sets combined with the target cable twisting process parameters to obtain multiple twisting features. Then, multiple cable twisting devices are controlled and interacted according to the target cable twisting process parameters combined with multiple twisting features, and the cable twisting process requirement information is determined according to the interaction parameters. Further, multiple twisting features are traversed according to the cable twisting process requirement information for collaborative calculation, and a cable twisting collaborative strategy is formulated. Finally, multiple cable twisting devices are simulated to execute the cable twisting collaborative strategy for real-time monitoring, and simulated twisting monitoring results are generated. The cable twisting collaborative strategy is dynamically updated according to the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices. The technical problem of poor coordination of cable twisting equipment in the prior art, resulting in low twisting efficiency and unstable quality, is solved, and the technical effect of improving the quality and efficiency of cable twisting is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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.

[0009] Figure 1A schematic flow chart of an intelligent collaborative control method for cable twisting provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an intelligent collaborative control system for cable twisting provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: data acquisition module 11 , twisting analysis module 12 , interaction module 13 , collaborative calculation module 14 , collaborative control module 15 . DETAILED DESCRIPTION

[0011] The present application solves the technical problem in the prior art that cable twisting equipment has poor coordination, resulting in low twisting efficiency and unstable quality by providing an intelligent collaborative control method and system for cable twisting.

[0012] 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.

[0013] It should be noted that the terms "including" and "having" 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 are inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent collaborative control method for cable twisting, wherein the method includes: Data of multiple cable twisting devices are collected through intelligent sensing equipment to obtain multiple equipment operation data sets.

[0015] Intelligent sensing equipment is deployed on the cable twisting production line, including but not limited to temperature sensors, tension sensors, position sensors, speed sensors, etc.; the operating status of multiple cable twisting equipment is monitored using intelligent sensing equipment to obtain multiple equipment operation data sets, where the equipment operation data sets include the speed of the pay-off wheel, the torque and speed of the twisting device, the tension and speed of the take-up wheel, and the operating environment parameters of the equipment (such as temperature, humidity, etc.).

[0016] The target cable twisting process parameters are determined based on a plurality of cable twisting devices, and twisting analysis is performed based on the plurality of device operation data sets in combination with the target cable twisting process parameters to obtain a plurality of twisting features.

[0017] The preset target cable twisting process parameters, including twist direction, twist pitch, tension, twisting speed, etc., are obtained from multiple cable twisting devices. The collected multiple equipment operation data sets are combined with the target cable twisting process parameters to analyze the twisting process and identify multiple twisting characteristics (such as twisting uniformity, tension stability, etc.). These characteristics directly reflect the key performance indicators of the cable twisting process.

[0018] Further, a target cable twisting process parameter is determined based on a plurality of cable twisting devices, twisting analysis is performed based on the plurality of device operation data sets in combination with the target cable twisting process parameter to obtain a plurality of twisting features, the method comprising: Extract multiple initial twisting process parameters based on multiple cable twisting devices, traverse the multiple initial twisting process parameters to perform multi-objective optimization on the multiple cable twisting devices, and set twisting constraints; screen the multiple initial twisting process parameters according to the twisting constraints to determine the target cable twisting process parameters; perform twisting matching on the multiple equipment operation data sets and the target cable twisting process parameters according to a time series to obtain multiple twisting data sets, wherein the multiple twisting data sets include a twisting direction data set and a twisting pitch data set; perform twisting calculation based on the twisting direction data set combined with the twisting pitch data set to obtain multiple twisting features.

[0019] Specifically, multiple initial twisting process parameters are extracted from multiple cable twisting devices, including twisting pitch, tension, twisting speed, etc.; by traversing the multiple extracted initial twisting process parameters, a multi-objective optimization algorithm (such as genetic algorithm or particle swarm optimization) is used to optimize the parameters under the premise of satisfying the twisting constraints, and the constraints include twisting pitch range, tension stability, etc., to ensure that the optimized parameters meet the process requirements and have the best operating performance; multiple initial twisting process parameters are screened according to the optimization results, and parameter combinations that do not meet the constraints are eliminated, and finally the target cable twisting process parameters are determined; the collected equipment operation data set is matched with the target process parameters in time series, and the actual operation status of the equipment is compared with the target parameters point by point along the time axis to generate multiple twisting data sets, including a twisting direction data set and a twisting pitch data set, which respectively reflect the twisting direction change and the actual twisting pitch distribution; based on the twisting direction data set and the twisting pitch data set, the twisting process is calculated in combination with mathematical models and data analysis methods to extract twisting characteristics, such as direction consistency, pitch fluctuation range and tension distribution uniformity.

[0020] The twisting calculation is performed based on the twisting direction data set combined with the twisting pitch data set. Specifically, for each time point in the twisting direction data set, the deviation value between the direction of all wires and the target direction is calculated, and the direction consistency index is quantified by the mean square error formula, so as to evaluate the degree of consistency of the directions between the wires; for each pitch in the twisting pitch data set, the deviation rate between the actual twist pitch and the target twist pitch is calculated, and the maximum deviation rate, the minimum deviation rate and the average deviation rate are statistically calculated to describe the fluctuation range and stability of the twisting pitch; combined with the tension data, the tension uniformity index is obtained by calculating the difference between the maximum and minimum tension values ​​and the mean distribution, which reflects the uniformity of the force applied to the wire during the twisting process.

[0021] According to the target cable twisting process parameters and the multiple twisting characteristics, multiple cable twisting devices are controlled interactively, and the cable twisting process requirement information is determined according to the interactive parameters.

[0022] According to the target cable twisting process parameters combined with multiple twisting characteristics, multiple cable twisting equipment are dynamically controlled and interacted. By adjusting the equipment operating parameters (such as direction, pitch and tension), the equipment feedback data during the interaction process is collected in real time, and the interaction parameters are analyzed and generated to quantify the degree of match between the equipment and the target process. The specific cable twisting process requirement information is determined based on the matching results.

[0023] Furthermore, according to the target cable twisting process parameters and the plurality of twisting characteristics, a plurality of cable twisting devices are controlled interactively, and the cable twisting process requirement information is determined according to the interactive parameters. The method includes: According to the target cable twisting process parameters, the twisting direction data set is mapped to multiple cable twisting devices to determine a first mapping coefficient; according to the target cable twisting process parameters, the twisting pitch data set is mapped to multiple cable twisting devices to determine a second mapping coefficient; the first mapping coefficient and the second mapping coefficient are associated with multiple cable twisting devices to generate associated interaction parameters; the associated interaction parameters are sent to multiple cable twisting devices for process matching calculation to generate multiple process matching coefficients; the target cable twisting process parameters are retrieved using the multiple process matching coefficients as indexes to determine the cable twisting process requirement information.

[0024] Based on the target cable twisting process parameters, the actual direction data in the twisting direction data set are mapped to multiple cable twisting devices one by one, and the proportional relationship between the actual direction and the target direction of each device is calculated to generate a first mapping coefficient for quantifying the device direction adjustment requirements; the actual pitch data in the twisting pitch data set are mapped to multiple cable twisting devices, and the second mapping coefficient is generated according to the ratio of the actual pitch of each device to the target pitch to describe the device pitch adjustment requirements; the first mapping coefficient and the second mapping coefficient are combined according to the device association, and the associated interaction parameters of each device are obtained through weighted calculation, and sent to the corresponding device to perform process matching calculation to generate multiple process matching coefficients. , where the actual state represents the current actual process state of the equipment (such as direction angle, pitch length), the associated interaction parameter represents the expected value, and the process matching coefficient reflects the matching degree between the current process and the associated interaction parameter. The higher the process matching coefficient, the higher the matching degree. The process matching coefficient is used as a retrieval index to match in the target cable twisting process parameter database, and the process parameters corresponding to the current process matching coefficient are located through a preset threshold range or optimal condition. For example, if the process matching coefficient is higher than a certain set value, the corresponding process parameter is directly retrieved as the best match; if the matching coefficient is in a critical range, the trend information of the adjacent parameters is combined to further deduce the adjusted process requirements. During the retrieval process, index matching can be performed one by one according to parameter classification (such as direction, pitch and tension), and finally a target process parameter set matching the current operating state is generated, and the cable twisting process requirement information is extracted, such as the direction range, pitch optimization value, tension compensation amplitude, etc. that the equipment needs to adjust. These cable twisting process requirement information will serve as the guiding basis for the next step of optimization control to ensure that the equipment operation state can dynamically approach the target process requirements and achieve the simultaneous improvement of twisting quality and efficiency.

[0025] The plurality of twisting features are traversed to perform collaborative calculations according to the cable twisting process requirement information, and a cable twisting collaborative strategy is formulated.

[0026] The extracted twisting characteristics (such as directional consistency, pitch fluctuation, tension distribution, etc.) are matched and analyzed one by one with the cable twisting process requirement information, and each eigenvalue is traversed to calculate its influence and weight on the overall process requirement. Then, a collaborative calculation model (such as a multi-objective optimization algorithm or a linear weighted algorithm) is used to comprehensively consider the weight and matching degree of each eigenvalue, analyze the operating relationship between each device, and optimize the interaction parameters and operating status between devices. Finally, a cable twisting collaborative strategy is formulated based on the collaborative calculation results. The strategy includes a synchronous adjustment plan between devices, a tension compensation plan, and a pitch optimization path to ensure that the operating status of each device can achieve efficient collaborative operation while meeting the target process requirements, thereby improving the quality and overall efficiency of cable twisting.

[0027] Furthermore, according to the cable twisting process requirement information, the plurality of twisting features are traversed to perform collaborative calculations to formulate a cable twisting collaborative strategy, the method comprising: Based on the multiple twisting features, data dependency analysis is performed to construct association rules; according to the association rules, the cable twisting process requirement information is mined to obtain data mining results; according to the data mining results, the multiple twisting features are traversed to perform fuzzy evaluation to generate multiple feature fuzzy scores; according to the multiple feature fuzzy scores, the multiple twisting features are graded to determine a priority sequence; according to the priority sequence, the multiple twisting features are collaboratively learned to generate collaborative learning results, collaborative verification is performed based on the collaborative learning results, and the cable twisting collaborative strategy is formulated.

[0028] Based on multiple twisting features (such as directional consistency, pitch fluctuation, tension distribution, etc.), data dependency analysis is performed, and association rules are constructed by mining the correlation between features. For example, the Apriori algorithm is used to analyze the support, confidence and lift between features, and a rule set is generated to clarify the mutual influence relationship between features. Subsequently, data mining is performed on the cable twisting process requirement information according to the constructed association rules to extract the deviation information between the target process parameters and the actual equipment status, such as the directional deviation range, pitch adjustment requirements and tension compensation requirements, and the data mining results are used to reveal the priority of the impact of key features on process requirements. Next, fuzzy mathematical methods are combined to perform fuzzy evaluation on each twisting feature, and the membership function is used to quantify the performance of each feature in meeting the target process requirements, and fuzzy scores are generated, such as directional consistency scores, tension fluctuation scores and pitch stability scores. Based on the fuzzy score, the features are graded and sorted according to the score to form a priority sequence to ensure that key features are processed first, such as tension distribution due to its large impact range. It may be given a higher priority. On this basis, a collaborative learning algorithm is used to collaboratively learn the features. The collaborative learning results are generated by analyzing the collaborative relationship and comprehensive impact effects between the features, and collaborative verification is performed based on this. The effectiveness of the collaborative strategy is evaluated through simulation testing or real-time monitoring. Finally, a cable twisting collaborative strategy is formulated based on the collaborative verification results. The strategy includes equipment synchronization control scheme, tension compensation plan, pitch optimization path, and adjustment suggestions for specific process requirements to ensure efficient coordination of the operating status of multiple cable twisting equipment, while meeting the precise requirements of the target process, thereby improving twisting quality and production efficiency.

[0029] Further, the plurality of twisting features are collaboratively learned according to the priority sequence to generate collaborative learning results, collaborative verification is performed based on the collaborative learning results, and the cable twisting collaborative strategy is formulated, the method comprising: Based on the priority sequence, weights are assigned to the multiple twisted features to generate multiple weight coefficients; the multiple twisted features are integrated according to the multiple weight coefficients to construct multiple twisted spaces, wherein the multiple twisted spaces include twisted action spaces and twisted state spaces; a reward function is introduced to perform reinforcement learning on the twisted action space and the twisted state space to generate reinforcement learning results; based on the reinforcement learning results, continuous collaborative interactive feedback is performed on multiple twisted devices to generate the collaborative learning results.

[0030] Assign weights to multiple twisted features according to a priority sequence, using the formula: Calculate the weight coefficient of each feature to ensure that features with higher priority contribute more to collaborative learning, where: represents the weight coefficient of the i-th feature, represents the priority of feature i, n represents the total number of features, Represents the sum of the priority values ​​of all features. Based on the assigned weight coefficients, multiple twisting features are integrated to construct a twisting space, where the twisting action space is defined as the operational set of the equipment (such as speed adjustment, tension adjustment, pitch optimization, etc.), and the twisting state space records the current operating state parameters of the equipment (such as direction angle, pitch distribution and tension fluctuation, etc.). Next, the reward function is introduced to guide the learning process by quantifying the impact of equipment operation on the target process. The reward function formula is R (s, a) = |target state - actual state|, where s represents the current state of the equipment, a represents the executed action, and R (s, a) is the reward value of action a in state s. The higher the reward value, the closer the operation is to the target process requirements. The twisting action space is combined with the twisting state space, and a reinforcement learning algorithm (such as deep Q learning or policy gradient method) is used to find the optimal operation strategy for equipment operation through repeated trial and error and adjust to generate reinforcement learning results. Based on the results of reinforcement learning, multiple cable twisting devices are continuously interactively feedbacked, the operating parameters of the equipment (such as speed, tension and pitch) are adjusted in real time, the operating status of the equipment is dynamically updated, and the reinforcement learning model is optimized according to the feedback status to form collaborative learning results. Finally, based on the collaborative learning results, the operating status of the equipment is collaboratively verified, and the effectiveness and stability of the collaborative strategy are evaluated through simulation tests or actual operation. Based on the verification results, the cable twisting collaborative strategy is finally formulated, including equipment synchronization control scheme, tension optimization path and pitch adjustment plan, to ensure efficient collaborative operation between equipment, thereby significantly improving the quality and production efficiency of cable twisting.

[0031] The cable twisting coordination strategy is simulated and executed on multiple cable twisting devices to perform real-time monitoring, and simulated twisting monitoring results are generated. The cable twisting coordination strategy is dynamically updated according to the simulated twisting monitoring results to perform intelligent coordinated control on multiple cable twisting devices.

[0032] The cable twisting coordination strategy is simulated and executed on multiple cable twisting devices and real-time monitoring is performed. Specifically, the cable twisting coordination strategy is input into the simulation environment of the device, the state and process parameters (such as tension, directional consistency and pitch fluctuation) of the device in actual operation are simulated, and the operation between the devices is coordinated according to the strategy instructions. During the simulation execution process, the operation data of the equipment is collected in real time through the sensor simulation module to generate simulated twisting monitoring results, including the synchronization between the equipment, the achievement of the target process parameters, and the deviation analysis. According to the monitoring results, the effect of the coordination strategy in the simulation environment is evaluated, the deviation points and deficiencies are identified, and the key parameters of the coordination strategy are dynamically optimized based on this, such as adjusting the equipment running speed, tension or pitch distribution scheme, and redistributing the equipment load to improve the overall coordination effect. Finally, the optimized coordination strategy is applied to the actual equipment, and the operation status of the equipment is adjusted in real time through the intelligent coordination control system, and compared and continuously optimized with the target process requirements to ensure efficient coordination, stable operation and quality improvement of the cable twisting process.

[0033] Furthermore, the cable twisting coordination strategy is simulated and executed on multiple cable twisting devices to perform real-time monitoring to generate simulated twisting monitoring results, and the method includes: Retrieve historical operating environment information of multiple cable twisting devices, and build a simulation execution environment based on the historical operating environment information; based on the simulation execution environment, load the cable twisting coordination strategy to simulate the execution of multiple cable devices and generate multiple simulated twisting parameters; combine the multiple simulated twisting parameters according to the target cable twisting process parameters to determine multiple simulated twisting groups; set an expected twisting threshold, traverse the multiple simulated twisting groups and perform deviation calculation with the expected twisting threshold to generate a simulated twisting deviation value; add the simulated twisting deviation value to the simulated twisting monitoring result.

[0034] First, the historical operating environment information of multiple cable twisting devices (such as the operating speed, tension distribution, pitch deviation and direction consistency of the equipment) is retrieved. By analyzing these historical data, a simulation execution environment that is highly consistent with the actual operating conditions is constructed to simulate the actual operating status of the equipment. Subsequently, the cable twisting coordination strategy is loaded in the constructed simulation execution environment, and multiple cable devices are simulated and executed according to the strategy instructions, and multiple simulated twisting parameters (such as simulation speed, tension adjustment and pitch change, etc.) are generated in real time. These parameters reflect the operating performance of the equipment under the coordination strategy. Then, the generated multiple simulated twisting parameters are combined according to the target cable twisting process parameters to form multiple simulated twisting groups, each of which represents the comprehensive equipment operating status under different process conditions. In order to evaluate the effectiveness of the simulation results, the expected twisting threshold (such as tension stability range, pitch fluctuation range, etc.) is set, and multiple simulated twisting groups are traversed, and they are compared with the expected twisting threshold one by one, and the deviation degree of each group is calculated to generate the simulated twisting deviation value. The smaller the deviation value, the closer the simulation operation is to the target process requirements. Finally, the calculated simulated twisting deviation value is integrated into the simulated twisting monitoring result to comprehensively evaluate the performance of the coordination strategy in a simulated environment, providing important reference data for dynamic optimization and practical application, thereby ensuring the efficiency and applicability of the cable twisting coordination strategy.

[0035] Furthermore, before adding the simulated twisting deviation value to the simulated twisting monitoring result, the method includes: Perform twisting deviation analysis according to the expected twisting threshold, set a preset deviation distance, and determine whether the simulated twisting deviation value is greater than or equal to the preset deviation distance; if the simulated twisting deviation value is less than the preset deviation distance, generate a positive feedback parameter, activate the twisting monitoring instruction through the positive feedback parameter, perform continuous simulation monitoring of multiple twisting devices through the twisting monitoring instruction, and generate the simulated twisting monitoring result; if the simulated twisting deviation value is greater than or equal to the preset deviation distance, generate a negative feedback parameter, activate the twisting abnormality instruction through the negative feedback parameter, trace the abnormality through the twisting abnormality instruction, determine multiple twisting simulation abnormality points, and add the multiple twisting simulation abnormality points to the simulated twisting monitoring result.

[0036] Before adding the simulated twisting deviation value to the simulated twisting monitoring result, the following steps need to be performed: First, analyze the generated simulated twisting deviation value according to the expected twisting threshold, set a preset deviation distance, and use it to determine whether the simulation result deviates from the target process requirements. Specifically, for each simulated twisting deviation value, determine whether it is less than the preset deviation distance. If the deviation value is less than the preset deviation distance, it is considered that the simulated operation state meets the target process requirements, and a positive feedback parameter is generated. The twisting monitoring instruction is activated through the positive feedback parameter, and multiple twisting equipment are continuously simulated and monitored to obtain more accurate equipment operation data and further improve the simulated twisting monitoring result. If the simulated twisting deviation value is greater than or equal to the preset deviation distance, it is considered that there is a large deviation in the equipment operation state, and a negative feedback parameter is generated. The twisting abnormal instruction is activated through the negative feedback parameter, and the abnormal points in the simulation results are traced and analyzed, and multiple twisting simulation abnormal points (such as tension distribution abnormal points, pitch deviation points or directional consistency points) are identified and located. Subsequently, the identified multiple twisting simulation abnormal points are recorded and added to the simulated twisting monitoring result to improve the deviation analysis and guide subsequent strategy optimization. Through this process, the simulated twisting monitoring results can accurately reflect the operating performance of the equipment under the collaborative strategy, providing comprehensive data support and reliable basis for optimizing the cable twisting process and improving equipment coordination.

[0037] In summary, the embodiments of the present application have at least the following technical effects: First, data is collected from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets. Next, the target cable twisting process parameters are determined based on multiple cable twisting devices, and twisting analysis is performed based on multiple equipment operation data sets combined with the target cable twisting process parameters to obtain multiple twisting features. Then, multiple cable twisting devices are controlled and interacted according to the target cable twisting process parameters combined with multiple twisting features, and the cable twisting process requirement information is determined according to the interaction parameters. Further, multiple twisting features are traversed according to the cable twisting process requirement information for collaborative calculation, and a cable twisting collaborative strategy is formulated. Finally, multiple cable twisting devices are simulated to execute the cable twisting collaborative strategy for real-time monitoring, and simulated twisting monitoring results are generated. The cable twisting collaborative strategy is dynamically updated according to the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices. The technical problem of poor coordination of cable twisting equipment in the prior art, resulting in low twisting efficiency and unstable quality, is solved, and the technical effect of improving the quality and efficiency of cable twisting is achieved.

[0038] Embodiment 2, based on the same inventive concept as the intelligent cooperative control method for cable twisting in the previous embodiment, Figure 2 As shown, the present application provides an intelligent collaborative control system for cable twisting, wherein the system includes: Data acquisition module 11: collects data from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets; twisting analysis module 12: determines the target cable twisting process parameters based on multiple cable twisting devices, and performs twisting analysis based on the multiple equipment operation data sets combined with the target cable twisting process parameters to obtain multiple twisting characteristics; interaction module 13: controls and interacts multiple cable twisting devices according to the target cable twisting process parameters combined with the multiple twisting characteristics, and determines the cable twisting process requirement information according to the interaction parameters; collaborative calculation module 14: traverses the multiple twisting characteristics according to the cable twisting process requirement information to perform collaborative calculation and formulate a cable twisting collaborative strategy; collaborative control module 15: simulates the execution of the cable twisting collaborative strategy for multiple cable twisting devices to perform real-time monitoring, generates simulated twisting monitoring results, and dynamically updates the cable twisting collaborative strategy according to the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices.

[0039] Further, the twist analysis module 12 is used to perform the following method: Extract multiple initial twisting process parameters based on multiple cable twisting devices, traverse the multiple initial twisting process parameters to perform multi-objective optimization on the multiple cable twisting devices, and set twisting constraints; screen the multiple initial twisting process parameters according to the twisting constraints to determine the target cable twisting process parameters; perform twisting matching on the multiple equipment operation data sets and the target cable twisting process parameters according to a time series to obtain multiple twisting data sets, wherein the multiple twisting data sets include a twisting direction data set and a twisting pitch data set; perform twisting calculation based on the twisting direction data set combined with the twisting pitch data set to obtain multiple twisting features.

[0040] Furthermore, the interaction module 13 is used to execute the following method: According to the target cable twisting process parameters, the twisting direction data set is mapped to multiple cable twisting devices to determine a first mapping coefficient; according to the target cable twisting process parameters, the twisting pitch data set is mapped to multiple cable twisting devices to determine a second mapping coefficient; the first mapping coefficient and the second mapping coefficient are associated with multiple cable twisting devices to generate associated interaction parameters; the associated interaction parameters are sent to multiple cable twisting devices for process matching calculation to generate multiple process matching coefficients; the target cable twisting process parameters are retrieved using the multiple process matching coefficients as indexes to determine the cable twisting process requirement information.

[0041] Furthermore, the collaborative computing module 14 is used to execute the following method: Based on the multiple twisting features, data dependency analysis is performed to construct association rules; according to the association rules, the cable twisting process requirement information is mined to obtain data mining results; according to the data mining results, the multiple twisting features are traversed to perform fuzzy evaluation to generate multiple feature fuzzy scores; according to the multiple feature fuzzy scores, the multiple twisting features are graded to determine a priority sequence; according to the priority sequence, the multiple twisting features are collaboratively learned to generate collaborative learning results, collaborative verification is performed based on the collaborative learning results, and the cable twisting collaborative strategy is formulated.

[0042] Furthermore, the collaborative computing module 14 is used to execute the following method: Based on the priority sequence, weights are assigned to the multiple twisted features to generate multiple weight coefficients; the multiple twisted features are integrated according to the multiple weight coefficients to construct multiple twisted spaces, wherein the multiple twisted spaces include twisted action spaces and twisted state spaces; a reward function is introduced to perform reinforcement learning on the twisted action space and the twisted state space to generate reinforcement learning results; based on the reinforcement learning results, continuous collaborative interactive feedback is performed on multiple twisted devices to generate the collaborative learning results.

[0043] Furthermore, the collaborative control module 15 is used to execute the following method: Retrieve historical operating environment information of multiple cable twisting devices, and build a simulation execution environment based on the historical operating environment information; based on the simulation execution environment, load the cable twisting coordination strategy to simulate the execution of multiple cable devices and generate multiple simulated twisting parameters; combine the multiple simulated twisting parameters according to the target cable twisting process parameters to determine multiple simulated twisting groups; set an expected twisting threshold, traverse the multiple simulated twisting groups and perform deviation calculation with the expected twisting threshold to generate a simulated twisting deviation value; add the simulated twisting deviation value to the simulated twisting monitoring result.

[0044] Furthermore, the collaborative control module 15 is used to execute the following method: Perform twisting deviation analysis according to the expected twisting threshold, set a preset deviation distance, and determine whether the simulated twisting deviation value is greater than or equal to the preset deviation distance; if the simulated twisting deviation value is less than the preset deviation distance, generate a positive feedback parameter, activate the twisting monitoring instruction through the positive feedback parameter, perform continuous simulation monitoring of multiple twisting devices through the twisting monitoring instruction, and generate the simulated twisting monitoring result; if the simulated twisting deviation value is greater than or equal to the preset deviation distance, generate a negative feedback parameter, activate the twisting abnormality instruction through the negative feedback parameter, trace the abnormality through the twisting abnormality instruction, determine multiple twisting simulation abnormality points, and add the multiple twisting simulation abnormality points to the simulated twisting monitoring result.

[0045] 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 or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0046] 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.

[0047] 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. An intelligent cooperative control method for cable twisting, characterized in that: The method comprises: Collect data from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets; Determining target cable twisting process parameters based on multiple cable twisting devices, performing twisting analysis based on the multiple device operation data sets combined with the target cable twisting process parameters to obtain multiple twisting features; Controlling and interacting a plurality of cable twisting devices according to the target cable twisting process parameters in combination with the plurality of twisting characteristics, and determining the cable twisting process requirement information according to the interaction parameters; According to the cable twisting process requirement information, the plurality of twisting features are traversed to perform collaborative calculations, and a cable twisting collaborative strategy is formulated; The cable twisting coordination strategy is simulated and executed on multiple cable twisting devices to perform real-time monitoring, and simulated twisting monitoring results are generated. The cable twisting coordination strategy is dynamically updated according to the simulated twisting monitoring results to perform intelligent coordinated control on multiple cable twisting devices.

2. The intelligent cooperative control method for cable twisting according to claim 1, characterized in that: Determining target cable twisting process parameters based on multiple cable twisting devices, performing twisting analysis based on the multiple device operation data sets combined with the target cable twisting process parameters, and obtaining multiple twisting features, the method comprising: Extracting multiple initial twisting process parameters based on multiple cable twisting devices, traversing the multiple initial twisting process parameters to perform multi-objective optimization on multiple cable twisting devices, and setting twisting constraint conditions; Screening the plurality of initial twisting process parameters according to the twisting constraint conditions to determine target cable twisting process parameters; Performing twist matching on the multiple equipment operation data sets and the target cable twisting process parameters according to a time series to obtain multiple twisting data sets, wherein the multiple twisting data sets include a twisting direction data set and a twisting pitch data set; Twisting calculation is performed based on the twisting direction data set in combination with the twisting pitch data set to obtain a plurality of twisting features.

3. The intelligent coordinated control method for cable twisting according to claim 2, characterized in that: Controlling and interacting multiple cable twisting devices according to the target cable twisting process parameters combined with the multiple twisting characteristics, and determining the cable twisting process requirement information according to the interaction parameters, the method comprising: Determine a first mapping coefficient by mapping the target cable twisting process parameters to a plurality of cable twisting devices according to the twisting direction data set; Determine a second mapping coefficient by mapping the target cable twisting process parameters to a plurality of cable twisting devices according to the twisting pitch data set; Associating the first mapping coefficient with the second mapping coefficient according to a plurality of cable twisting devices to generate an associated interaction parameter; Sending the associated interaction parameters to multiple cable twisting devices to perform process matching calculations to generate multiple process matching coefficients; The target cable twisting process parameters are retrieved using the multiple process matching coefficients as indexes to determine the cable twisting process requirement information.

4. The intelligent coordinated control method for cable twisting according to claim 1, characterized in that: According to the cable twisting process requirement information, the plurality of twisting features are traversed to perform collaborative calculations, and a cable twisting collaborative strategy is formulated, the method comprising: Performing data dependency analysis based on the plurality of twisted features to construct association rules; Mining the cable twisting process requirement information according to the association rules to obtain data mining results; According to the data mining results, the plurality of twisted features are traversed to perform fuzzy evaluation to generate a plurality of feature fuzzy scores; According to the plurality of feature fuzzy scores, the plurality of twisted features are graded to determine a priority sequence; The plurality of twisting characteristics are collaboratively learned according to the priority sequence to generate collaborative learning results, collaborative verification is performed based on the collaborative learning results, and the cable twisting collaborative strategy is formulated.

5. The intelligent coordinated control method for cable twisting according to claim 4, characterized in that: The plurality of twisting features are collaboratively learned according to the priority sequence to generate collaborative learning results, collaborative verification is performed based on the collaborative learning results, and the cable twisting collaborative strategy is formulated, the method comprising: Assigning weights to the plurality of twisting features based on the priority sequence to generate a plurality of weight coefficients; Integrating the plurality of twisting features according to the plurality of weight coefficients to construct a plurality of twisting spaces, wherein the plurality of twisting spaces include a twisting action space and a twisting state space; A reward function is introduced to perform reinforcement learning on the twisted action space and the twisted state space to generate a reinforcement learning result; Based on the reinforcement learning result, continuous collaborative interactive feedback is performed on multiple twisting devices to generate the collaborative learning result.

6. The intelligent coordinated control method for cable twisting according to claim 1, characterized in that: The method of simulating the execution of the cable twisting coordination strategy on multiple cable twisting devices to perform real-time monitoring and generate simulated twisting monitoring results includes: Retrieving historical operating environment information of multiple cable twisting devices, and constructing a simulation execution environment according to the historical operating environment information; Based on the simulation execution environment, the cable twisting coordination strategy is loaded to simulate and execute multiple cable devices to generate multiple simulation twisting parameters; Combining the multiple simulated twisting parameters according to the target cable twisting process parameters to determine multiple simulated twisting groups; Setting an expected twisting threshold, traversing the plurality of simulated twisting groups and performing deviation calculation with the expected twisting threshold, and generating a simulated twisting deviation value; The simulated twist deviation value is added to the simulated twist monitoring result.

7. The intelligent coordinated control method for cable twisting according to claim 6, characterized in that: Before adding the simulated twist deviation value to the simulated twist monitoring result, the method comprises: Perform twist deviation analysis according to the expected twist threshold, set a preset deviation distance, and determine whether the simulated twist deviation value is greater than or equal to the preset deviation distance; If the simulated twisting deviation value is less than the preset deviation distance, a positive feedback parameter is generated, and a twisting monitoring instruction is activated by the positive feedback parameter. The twisting monitoring instruction is used to continuously simulate monitoring of multiple twisting devices to generate the simulated twisting monitoring result; If the simulated twisting deviation value is greater than or equal to the preset deviation distance, a negative feedback parameter is generated, the twisting abnormal instruction is activated by the negative feedback parameter, the abnormality is traced through the twisting abnormal instruction, a plurality of twisting simulation abnormal points are determined, and the plurality of twisting simulation abnormal points are added to the simulated twisting monitoring result.

8. An intelligent cooperative control system for cable twisting, characterized in that: The system for implementing the intelligent coordinated control method for cable twisting according to any one of claims 1 to 7 comprises: Data acquisition module: collects data from multiple cable twisting devices through intelligent sensing equipment to obtain multiple equipment operation data sets; Twisting analysis module: determining target cable twisting process parameters based on multiple cable twisting devices, performing twisting analysis based on the multiple device operation data sets combined with the target cable twisting process parameters, and obtaining multiple twisting features; Interaction module: controlling and interacting multiple cable twisting devices according to the target cable twisting process parameters and the multiple twisting characteristics, and determining the cable twisting process requirement information according to the interaction parameters; Collaborative calculation module: traverses the plurality of twisting features to perform collaborative calculation according to the cable twisting process requirement information, and formulates a cable twisting collaborative strategy; Collaborative control module: simulates the execution of the cable twisting collaborative strategy on multiple cable twisting devices to perform real-time monitoring, generates simulated twisting monitoring results, and dynamically updates the cable twisting collaborative strategy based on the simulated twisting monitoring results to perform intelligent collaborative control on multiple cable twisting devices.

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