Multi-equipment adaptive collaborative control methods, equipment and media in CNC workshops

Abstract

This invention relates to the technical field of lathe auxiliary devices, specifically including a multi-equipment adaptive collaborative control method, equipment, and medium for CNC workshops. The method includes: combining machining environment parameters for adversarial multimodal enhancement, configuring first, second, third, and fourth collaborative control vectors; analyzing equipment start-up and shutdown timing and energy consumption allocation, and marking competing conflict points; and, based on the first, second, third, and fourth collaborative control vectors and the competing conflict points, centrally controlling the dust collection equipment, adaptive dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment in the CNC workshop. This solves the technical problem of independent operation of various equipment, lack of unified coordination, and low collaborative efficiency in the machining environment control of CNC lathes. It achieves collaborative operation among dust collection equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment, ensuring the cleanliness and suitable humidity of the machining environment, reducing machining errors caused by environmental factors, and improving the machining stability of CNC lathes.

Description

Technical Field

[0001] This invention relates to the technical field of lathe auxiliary devices, specifically to a multi-device adaptive collaborative control method, equipment, and medium for CNC workshops. Background Technology

[0002] In the CNC lathe machining environment, the machining process is often accompanied by a large amount of dust generation, temperature and humidity changes, and equipment heat dissipation problems, which affect machining accuracy and may even damage the machine tool and its electronic components. Especially in the field of precision machining, such as the machining of materials with high thermal expansion coefficients like aluminum alloys and titanium alloys, the requirements for the machining environment are even more stringent. Environmental control, workpiece preheating, dust extraction, and heat dissipation in CNC workshops are usually operated independently without unified coordination, resulting in low machining efficiency, increased energy consumption, and difficulty in achieving ideal machining quality and environmental standards.

[0003] In summary, existing technologies suffer from the technical problem that the various devices in the machining environment control of CNC lathes operate independently, lack unified coordination, and have low collaborative efficiency. Summary of the Invention

[0004] This application provides a multi-device adaptive collaborative control method, equipment, and medium for CNC workshops, aiming to solve the technical problems in the existing CNC lathe machining environment control where each device operates independently, lacks unified coordination, and has low collaborative efficiency.

[0005] In view of the above problems, the technical solution to achieve the present application is as follows:

[0006] In a first aspect, the present invention provides a multi-device adaptive collaborative control method for CNC workshops, wherein the multi-device collaborative control network is communicatively connected to a dust collection device, a dehumidification device, a workpiece preheating device, and a heat dissipation device, including: acquiring dust collection operation parameters, dehumidification operation parameters, workpiece preheating operation parameters, and heat dissipation operation parameters; performing adversarial multimodal enhancement in conjunction with processing environment parameters; configuring a first collaborative control vector associated with dust collection efficiency, a second collaborative control vector associated with dehumidification efficiency, a third collaborative control vector associated with heating rate, and a fourth collaborative control vector associated with cooling efficiency; aggregating the status data of the dust collection device and the dehumidification device, and the status data of the workpiece preheating device and the heat dissipation device, analyzing the device start-up and shutdown sequence and energy consumption allocation, and marking competition and conflict points; and performing centralized control of the dust collection device, the dehumidification device, the workpiece preheating device, and the heat dissipation device in the CNC workshop based on the first collaborative control vector, the third collaborative control vector, the fourth collaborative control vector, and the competition and conflict points.

[0007] Secondly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described multi-device adaptive collaborative control method for CNC workshops.

[0008] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the above-described multi-device adaptive collaborative control method for CNC workshops.

[0009] In summary, one or more technical solutions provided in this application solve the technical problems of independent operation of various devices, lack of unified coordination, and low collaborative efficiency in the machining environment control of CNC lathes. They realize the collaborative operation of dust collection equipment, adaptive dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment, ensuring the cleanliness and suitable humidity of the machining environment, reducing machining errors caused by environmental factors, and improving the machining stability of CNC lathes through multi-device energy efficiency optimization and conflict avoidance. Attached Figure Description

[0010] Figure 1 This application provides a flowchart illustrating a multi-device adaptive collaborative control method for CNC workshops.

[0011] Figure 2 This application provides a flowchart illustrating the execution steps for determining the first load balancing operating cycle and the first load balancing operating power in the dust collection collaborative control module of the multi-equipment adaptive collaborative control method for CNC workshops.

[0012] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0013] Explanation of reference numerals in the attached drawings: Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus Interface 305. Detailed Implementation Example 1

[0014] The present application will now be described in detail with reference to the accompanying drawings, such as... Figure 1 As shown, a multi-equipment adaptive collaborative control method for a CNC workshop includes a multi-equipment collaborative control network that is communicatively connected to dust collection equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment, comprising:

[0015] S1: Obtain dust collection operation parameters, dehumidification operation parameters, workpiece preheating operation parameters, and heat dissipation operation parameters, and combine them with processing environment parameters to perform adversarial multimodal enhancement, and configure a first collaborative control vector associated with dust collection efficiency, a second collaborative control vector associated with dehumidification efficiency, a third collaborative control vector associated with heating rate, and a fourth collaborative control vector associated with cooling efficiency.

[0016] Specifically, the multi-device collaborative control network is communicatively connected to the vacuuming equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment. Simply put, the communication connection is established through signal transmission and interaction between the multi-device collaborative control network and the vacuuming equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment. Preferably, for metals with high thermal expansion coefficients (such as aluminum alloys and titanium alloys), appropriate preheating before processing can reduce thermal deformation during processing, improve processing accuracy and surface quality, and make the workpiece temperature more uniform, avoiding dimensional changes and deformations caused by local overheating.

[0017] Adversarial multimodal augmentation is a technique that integrates data from multiple modalities (such as operating parameters of equipment like vacuuming, dehumidification, preheating, and heat dissipation, as well as processing environment parameters) and uses adversarial training to improve the data feature representation ability and model performance. This allows the model to better adapt to complex processing environments and improve the accuracy of equipment operation status assessment. Cooperative control vectors are directed parameters used to control the cooperative work of equipment, determined based on the relationship between the operating parameters of each device and environmental parameters. Each vector corresponds to the specific operating efficiency or effectiveness of different devices. By adjusting the values ​​of these vectors, precise control of the equipment can be achieved to achieve the purpose of cooperative work.

[0018] In one feasible implementation, the operating parameters of the dust collection equipment, such as suction power and vacuum rate, are acquired through data acquisition devices such as sensors; the operating parameters of the dehumidification equipment, such as dehumidification frequency and dehumidification intensity, are acquired; the operating parameters of the workpiece preheating equipment, such as preheating power and preheating rate, are acquired; and the operating parameters of the heat dissipation equipment, such as heat dissipation efficiency and heat dissipation rate, are acquired.

[0019] Simultaneously, processing environment parameters such as temperature, humidity, and dust concentration are collected. These parameters are then input into adversarial multimodal augmentation processing. Through adversarial training, the data features of different modalities complement and enhance each other, resulting in a more comprehensive and accurate representation of the equipment and environmental conditions. Based on these feature representations, a first collaborative control vector associated with dust collection efficiency, a second collaborative control vector associated with dehumidification efficiency, a third collaborative control vector associated with heating rate, and a fourth collaborative control vector associated with cooling efficiency are configured.

[0020] Preferably, based on dust collection data, including the temperature, humidity, air pressure of the CNC workshop, and dust collection operation parameters (such as suction power settings, running time, etc.), the current dust collection efficiency of the dust collection equipment is analyzed, and the dust collection effect is evaluated by combining historical data and real-time environmental parameters; a first collaborative control vector associated with the dust collection efficiency is configured, and the first collaborative control vector takes the dust collection efficiency data, environmental parameters, and equipment status parameters as input variables; optimization objectives are set, such as minimizing dust residue, maximizing air cleanliness, or optimizing energy consumption.

[0021] In practical applications, this also includes using fuzzy logic control or machine learning algorithms to dynamically adjust parameters such as suction power and running time of the vacuum cleaner based on the environmental vacuuming dataset; implementing control strategies, such as increasing suction power during periods of high dust concentration or large particle size, and reducing energy consumption during periods of low dust; and monitoring the vacuuming effect and equipment status in real time, adjusting the control strategy based on feedback to ensure that vacuuming efficiency matches environmental requirements.

[0022] Preferably, based on dehumidification data, including humidity, temperature, dust particle size, and dehumidification operating parameters (such as dehumidification frequency and intensity) in the CNC workshop, the current dehumidification efficiency of the dehumidification equipment is analyzed, and the dehumidification effect is evaluated by combining historical data and real-time environmental parameters. A second collaborative control vector associated with the dehumidification efficiency is configured, and the second collaborative control vector takes the dehumidification efficiency data, environmental parameters, and equipment status parameters as input variables. Optimization objectives are set, such as minimizing environmental humidity fluctuations, maintaining a suitable humidity level, or balancing dehumidification energy consumption.

[0023] In practical applications, this also includes using fuzzy logic control or machine learning algorithms to dynamically adjust parameters such as the dehumidification frequency and intensity of the dehumidification equipment based on the environmental fusion dehumidification dataset; implementing control strategies, such as increasing dehumidification intensity during periods of high humidity or small dust particle size, and reducing energy consumption during periods of low humidity; and monitoring the dehumidification effect and equipment status in real time, adjusting the control strategy based on feedback to ensure that the dehumidification efficiency matches the environmental requirements.

[0024] Preferably, based on workpiece preheating data, the influence of current environmental parameters on the preheating effect (such as the effect of humidity on preheating speed) is analyzed; the workpiece thermal expansion coefficient, material properties, and the current heating rate and target temperature of the workpiece preheating equipment are analyzed; a third collaborative control vector associated with the heating rate is configured. The third collaborative control vector considers environmental factors, workpiece characteristics, and equipment capabilities, with the goal of ensuring that the workpiece is uniformly preheated to the optimal processing temperature.

[0025] In practical applications, this also includes dynamically adjusting the heating rate using an adaptive control algorithm based on the workpiece material characteristics to reduce thermal deformation and improve machining accuracy; continuously fine-tuning the heating strategy to maintain the high efficiency and stability of the preheating process, taking into account heat transfer efficiency and changes in the workshop environment; and monitoring the preheating process and adjusting the heating parameters in real time based on the workpiece temperature fed back by sensors to ensure that the preheating process is neither overheated nor underheated.

[0026] Preferably, based on heat dissipation data, combined with the real-time heat dissipation of key parts of the machine tool and the current working status of the heat dissipation equipment, the cooling efficiency of the heat dissipation equipment is analyzed, taking into account the influence of ambient temperature and humidity on the heat dissipation effect; a fourth collaborative control vector associated with the cooling efficiency is configured, and the third collaborative control vector aims to maintain a suitable temperature for the machine tool and workpiece, while optimizing energy use.

[0027] In practical applications, this also includes predicting the heat dissipation of the machine tool in the future and adjusting the cooling intensity and mode of the heat dissipation equipment; taking into account the dynamic changes in the processing process, making timely adjustments to cope with short-term heat peaks and avoid processing errors caused by overheating; coordinating the dust extraction and preheating modules to ensure that heat dissipation and environmental regulation are coordinated, for example, enhancing heat dissipation during the preheating process to prevent local overheating; implementing control strategies, monitoring and providing feedback adjustments in real time to ensure that the temperature of the processing environment remains stable within the optimal range.

[0028] In the above steps, based on the operating parameters of the dust collection equipment and the dust concentration in the processing environment, a fuzzy logic control algorithm is used to determine the specific value of the first cooperative control vector to optimize dust collection efficiency. In the above steps, by acquiring comprehensive equipment operating parameters and environmental parameters and performing adversarial multimodal enhancement processing, a precise basis can be provided for subsequent equipment cooperative control. By adopting adversarial multimodal enhancement technology, the accuracy of equipment operating status assessment is improved, and the configured cooperative control vector can accurately reflect the cooperative relationship between various devices, laying a solid foundation for realizing adaptive cooperative control of multiple devices in CNC workshops, and effectively improving the intelligent level of workshop environmental control and energy utilization efficiency.

[0029] S2: Aggregate the status data of the dust collection equipment and dehumidification equipment, and the status data of the workpiece preheating equipment and heat dissipation equipment, analyze the start-up and shutdown sequence and energy consumption distribution of the equipment, and mark the competition and conflict points; S3: Based on the first collaborative control vector, the third collaborative control vector, the fourth collaborative control vector, and the competition and conflict points, centrally control the dust collection equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment in the CNC workshop.

[0030] Specifically, aggregating the status data of the dust collection and dehumidification equipment, and the status data of the workpiece preheating and heat dissipation equipment, refers to collecting and integrating the operating status information of each device to form a comprehensive data set, facilitating subsequent overall analysis; analyzing the equipment start-up and shutdown sequence and energy consumption distribution refers to conducting in-depth analysis of the start-up and shutdown time sequence of the equipment and the distribution of energy consumption to identify existing problems and optimization opportunities; marking competition and conflict points refers to identifying and marking points or situations where mutual competition and conflict may occur between devices during operation during the analysis process; and centralized control of the CNC workshop equipment based on the collaborative control vector and the competition and conflict points refers to the centralized management and control of multiple devices in the CNC workshop based on the previously configured collaborative control vector and the marked competition and conflict points, in order to achieve coordinated and efficient operation between devices, improve overall processing efficiency and environmental control effects.

[0031] In one feasible implementation, a data acquisition system collects real-time status data of vacuuming equipment, dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment. This data includes information such as the equipment's operating power, operating frequency, and operating mode. Data analysis algorithms are used to process and analyze this data to determine the equipment's start-up and shutdown sequence and energy consumption distribution. For example, analyzing the energy consumption data of vacuuming and dehumidification equipment reveals that simultaneous startup generates a significant electrical load, affecting the stability of the workshop's power supply. Points of potential competition and conflict are marked, such as power supply competition and cooling demand conflicts between equipment. Based on the first, third, and fourth collaborative control vectors, combined with the marked competition and conflict points, a centralized control algorithm is used to uniformly control and schedule the equipment, optimizing its operating status, avoiding competition and conflict, and improving the equipment's collaborative efficiency.

[0032] It should be noted that, based on the requirements of CNC lathe machining process and material characteristics, standard machining environment parameters are set, including target temperature, humidity range, and air pressure value. These standard machining environment parameters comply with relevant standards such as GB / T 41093-2021 "Machine Tool Safety - Lathe" and GB / T 16462.2-2017 "Inspection Conditions for CNC Lathes and Turning Centers" to ensure that the standard machining environment parameters are conducive to improving machining accuracy and efficiency, while ensuring operational safety.

[0033] Preferably, by aggregating equipment status data and analyzing start-up and shutdown timing and energy consumption distribution, a comprehensive understanding of the equipment's operating status and existing problems can be achieved, reducing equipment failure rates, significantly improving the processing environment, enhancing the machining accuracy and stability of CNC lathes, effectively solving the challenges in multi-equipment collaborative control, and providing strong support for realizing intelligent CNC workshop management.

[0034] Furthermore, the multi-equipment adaptive collaborative control method in the CNC workshop is also used to execute the following methods:

[0035] Collect dust particle size; based on the dust particle size, use fuzzy logic to control the suction power and vacuum rate, and send the data to the dust collection device.

[0036] In a preferred embodiment, it is confirmed that the dust particle size detection instrument (such as a laser particle size analyzer, piezoelectric crystal sensor, etc.) has been correctly installed and calibrated; dust samples from the environment are collected using an appropriate sampling method (such as isokinetic sampling, fixed-point sampling, etc.); the collected dust samples are placed in the detection instrument, and particle size is measured according to the instrument's operating instructions; the measured dust particle size is recorded, typically including parameters such as particle size distribution and average particle size.

[0037] The system connects the vacuum cleaner and the dust particle size detection instrument. Further, based on the requirements of the vacuum cleaner and dust characteristics, relevant module parameters are set, such as suction range and response time. Dust particle size data is converted into a fuzzy set representation, involving the definition of fuzzy sets (e.g., small, medium, large) and membership functions. Based on expert experience or experimental data, fuzzy control rules related to dust particle size are established. For example, when the dust particle size is large, the suction power needs to be increased to enhance suction and improve the vacuuming effect. Based on the current dust particle size data and the fuzzy rule base, fuzzy inference is performed to calculate the required suction adjustment. Based on the suction adjustment obtained from fuzzy inference, a control signal is sent to the vacuum cleaner through the vacuuming collaborative control module to adjust the suction power and vacuum rate. Based on the changes in vacuuming effect and dust particle size, the fuzzy control rules are continuously optimized to improve vacuuming efficiency.

[0038] Preferably, during the entire process of dust removal in the processing, direct contact with harmful dust should be avoided; dust particle size measuring instruments and dust collection equipment should be maintained and calibrated regularly to ensure their accuracy and reliability; relevant data during the dust collection process, such as suction power and dust collection effect, should be recorded and analyzed to optimize control strategies; and the emissions of the dust collection equipment should be ensured to meet relevant environmental protection requirements to avoid secondary pollution.

[0039] Furthermore, such as Figure 2 As shown, the method further includes:

[0040] Based on the aforementioned vacuuming equipment, an adaptive dehumidification device is configured, the dehumidification device including an adaptive dehumidification device; dust concentration information is collected, and the humidity in the processing environment parameters is uploaded to the adaptive dehumidification device; combining the working status of the adaptive dehumidification device with the vacuuming efficiency of the vacuuming equipment, the first load balancing operation cycle and the first load balancing operation power are determined.

[0041] Specifically, the compatible dehumidifier is connected to the vacuum cleaner co-control module via a suitable interface (such as a data cable or control bus) to ensure smooth communication between the vacuum cleaner and the compatible dehumidifier. The compatible dehumidifier refers to the dehumidifier selected or adjusted according to the characteristics of the vacuum cleaner and the requirements of the processing environment, so that it can work in conjunction with the vacuum cleaner, better adapt to the processing environment, and improve the overall environmental control effect. The compatible dehumidifier is initialized and configured, including setting parameters such as working mode, power range, and response time, to adapt to different working environments and requirements.

[0042] The first load balancing operation cycle refers to the rational arrangement of the operating time and workload of the vacuum cleaner and the adapted dehumidifier during operation, so that the vacuum cleaner and the adapted dehumidifier undertake appropriate work tasks in different time periods, thereby improving the utilization rate and operating efficiency of the vacuum cleaner and the adapted dehumidifier. The first load balancing operation power refers to the rational allocation of power output according to the load and operating needs of the vacuum cleaner and the adapted dehumidifier during operation, so as to maximize their performance and reduce energy consumption while ensuring normal operation.

[0043] In a preferred embodiment, a dust concentration sensor is installed in the workshop to ensure that the sensor can accurately and in real time monitor the dust concentration in the environment; the dust concentration sensor collects dust concentration data in the environment in real time and transmits the data to the dust collection and control module; a humidity sensor collects humidity information of the processing environment; the collected humidity information is uploaded and used as a reference for determining the first load balancing operation cycle and the first load balancing operation power.

[0044] Based on the vacuuming equipment, a suitable dehumidification device is configured. Furthermore, considering the working characteristics of the vacuuming equipment and the humidity requirements of the processing environment, a suitable dehumidification device is selected, and necessary parameter settings and adjustments are made. Specifically, since the vacuuming equipment is mainly used to handle environments with high dust concentrations, the dehumidification device needs to have corresponding high-performance dehumidification capabilities to ensure that the ambient humidity can be effectively controlled during the vacuuming process.

[0045] Dust concentration information is collected, and the humidity in the processing environment parameters is uploaded to the compatible dehumidifier. Furthermore, the dust concentration and humidity parameters are monitored in real time by sensors installed in the processing environment, and these data are transmitted to the control system of the dehumidifier. Combining the working status of the compatible dehumidifier with the dust collection efficiency of the vacuum cleaner, the first load balancing operation cycle and the first load balancing operation power are determined.

[0046] Based on parameters such as the operating power and dehumidification rate of the dehumidifier and the dust collection efficiency of the vacuum cleaner, intelligent algorithms or control systems are used to analyze and calculate the optimal load-balanced operating cycle and power settings. Specifically, when the dust concentration is high, it is necessary to appropriately increase the operating power and time of the vacuum cleaner, and at the same time adjust the operating parameters of the dehumidifier accordingly to ensure that the ambient humidity is effectively controlled during the efficient dust collection process.

[0047] In the above steps, by rationally configuring and adapting dehumidifying equipment, and dynamically adjusting the equipment's operating cycle and power based on parameters such as dust concentration and humidity, collaborative work between the dust collection and dehumidification equipment can be achieved, optimizing the control effect of the processing environment. This not only helps improve equipment operating efficiency and processing quality, but also reduces energy consumption and equipment failure rate, effectively enhancing the processing stability and environmental adaptability of CNC lathes.

[0048] Preferably, ensure compatibility between the compatible dehumidifier and the vacuum cleaner to avoid communication failures or control malfunctions; during data transmission and storage, pay attention to data security to prevent data leakage or tampering; ensure that the emissions of the compatible dehumidifier and vacuum cleaner meet relevant environmental protection requirements to avoid secondary pollution to the environment; regularly maintain and calibrate the compatible dehumidifier, vacuum cleaner and related sensors to ensure the stability and reliability of the equipment.

[0049] Furthermore, by combining the operating status of the adapted dehumidifier with the dust collection efficiency of the vacuum cleaner, the first load balancing operation cycle and the first load balancing operation power are determined. The method also includes:

[0050] Based on the working status of the adapted dehumidifier and the dust collection efficiency of the vacuum cleaner, the dehumidification workload and the dust collection workload are determined; the dehumidification operating axis is set according to the dehumidification workload, the dehumidification frequency and the dehumidification intensity of the adapted dehumidifier; the dust collection operating axis is set according to the dust collection workload, the dust collection frequency and the dust collection intensity of the vacuum cleaner; the dehumidification operating axis and the dust collection operating axis are merged, and load balancing analysis is performed to determine the first load balancing operating cycle and the first load balancing operating power.

[0051] In a preferred embodiment, the current dehumidification workload is determined based on the current ambient humidity, the target humidity, and the dehumidification capacity (e.g., dehumidification amount per unit time) of the adapted dehumidification equipment; the load calculation involves parameters such as the rate of change of ambient humidity and the dehumidification efficiency of the adapted dehumidification equipment; the current dust collection workload is determined based on the current dust concentration, the dust collection efficiency of the dust collection equipment, and the dust collection requirements; the load calculation involves parameters such as the rate of change of dust concentration and the suction power of the dust collection equipment.

[0052] To set the dehumidification operation axis, specifically, determine the operating frequency of the adapted dehumidification equipment based on factors such as the dehumidification workload, the maximum dehumidification capacity of the adapted dehumidification equipment, and its service life. The operating frequency involves the duration of each operation of the adapted dehumidification equipment and the interval between two operations. Based on the dehumidification workload and the performance parameters of the adapted dehumidification equipment, set the dehumidification intensity of the adapted dehumidification equipment, such as adjusting the power or fan speed of the adapted dehumidification equipment.

[0053] Set the vacuuming operation axis. Specifically, determine the operating frequency of the vacuuming equipment based on the vacuuming workload, the vacuuming efficiency of the vacuuming equipment, and the dust removal target. The operating frequency involves the duration of each vacuuming operation and the interval between two operations. Set the vacuuming intensity of the vacuuming equipment based on the vacuuming workload and the performance parameters of the vacuuming equipment, such as adjusting the suction power of the vacuuming equipment.

[0054] The dehumidification and vacuuming operation axes (including dehumidification frequency and intensity) are merged. The merged operation axes are then analyzed to ensure that the dehumidifier and vacuuming equipment do not interfere with each other or generate excessive energy consumption during operation. The operating time and power of the dehumidifier and vacuuming equipment need to be adjusted to achieve load balancing. Based on the load balancing analysis results, the first load balancing operating cycle and the first load balancing operating power of the dehumidifier and vacuuming equipment are determined. The first load balancing operating cycle and the first load balancing operating power should ensure that energy consumption and equipment wear are minimized while meeting the dust removal and dehumidification requirements.

[0055] In practical applications, this also includes converting parameters such as the determined first load balancing operation cycle and the first load balancing operation power into specific control commands and sending them to the compatible dehumidifier and vacuum cleaner; after receiving the control commands, the compatible dehumidifier and vacuum cleaner execute the corresponding operations according to the command requirements; during the operation of the compatible dehumidifier and vacuum cleaner, environmental parameters (such as humidity and dust concentration) and equipment status (such as power and running time) are continuously monitored; based on the real-time monitored data, the operating status of the compatible dehumidifier and vacuum cleaner is dynamically adjusted to ensure that the compatible dehumidifier and vacuum cleaner always operate in a load-balanced state, thereby achieving load balancing control based on the working status of the compatible dehumidifier and the vacuum cleaner's dust collection efficiency.

[0056] Furthermore, the dehumidification operating axis and the vacuuming operating axis are combined, and a load balancing analysis is performed to determine the first load balancing operating cycle and the first load balancing operating power. The method includes:

[0057] The dehumidification and vacuuming operating axes are merged to identify a set of conflict points and determine multiple critical operating cycles at the beginning and end of the cycle. Based on these conflict points, critical operating cycles, and cycle numbers, an alternating load balancing analysis is performed to determine the initial first load balancing operating cycle. Based on these conflict points, critical operating cycles, and cycle numbers, a power balancing analysis is performed over different time periods to determine the initial first load balancing operating power. Based on the initial first load balancing operating cycle and the initial first load balancing operating power, the first load balancing operating cycle and the first load balancing operating power are determined.

[0058] In a preferred embodiment, the dehumidification operation axis (including dehumidification frequency, dehumidification intensity, etc.) and the vacuuming operation axis (including vacuuming frequency, vacuuming intensity, etc.) are merged. The merged axis forms a comprehensive operating timeline, indicating the operating status of the adapted dehumidification and vacuuming equipment at each time period. On the merged timeline, periods where the adapted dehumidification and vacuuming equipment need to operate simultaneously but may conflict are identified. These conflicts may arise from overlapping or mutual interference in the operating requirements of the equipment. These conflict points are categorized and grouped into a conflict point set, providing a basis for subsequent adjustments.

[0059] Within the set of conflict points, identify the time periods in the first half that require priority operation for the suitable dehumidifier or vacuum cleaner. These time periods constitute the critical operating cycle of the first half. Analyze the start and end times of each critical operating cycle of the first half, as well as the corresponding operating requirements of the equipment. Similarly, identify the time periods in the second half that require priority operation within the set of conflict points, and these constitute the critical operating cycle of the second half. Analyze the start and end times of each critical operating cycle of the second half, as well as the corresponding operating requirements of the equipment.

[0060] Based on the critical operating cycles of the front and rear segments, possible alternating operating modes are designed to ensure that the dehumidifier and vacuum cleaner can operate alternately at different times to reduce conflicts. By simulating and testing different alternating operating modes, an initial first load balancing operating cycle is found that can meet the dehumidification and vacuuming needs while reducing conflicts. The initial first load balancing operating cycle can balance the operating time of the dehumidifier and vacuum cleaner.

[0061] Based on the set of conflict points and the critical operating cycles of the first and second phases, analyze the power requirements of the adapted dehumidifier and vacuum cleaner in different time periods. Pay special attention to the fact that near the conflict points, the power of the equipment needs to be adjusted to avoid overload or mutual interference. Based on the power demand analysis, determine the appropriate operating power for each time period to ensure that the adapted dehumidifier and vacuum cleaner can operate efficiently and stably while maintaining a balanced load. The initial first load balancing operating power should take into account factors such as equipment performance, energy consumption, and maintenance costs.

[0062] A comprehensive evaluation is conducted, taking into account the feasibility, effectiveness, and economy of the initial first load balancing operation cycle and power. Based on the evaluation results, the final first load balancing operation cycle and power are determined. These parameters should minimize conflicts, improve equipment efficiency, and meet the requirements for dust removal and dehumidification.

[0063] In practical applications, this also includes converting the determined first load balancing operation cycle and power parameters into specific control commands and sending them to the compatible dehumidifiers and vacuum cleaners for execution; monitoring the operating status of the equipment and changes in environmental parameters in real time and making adjustments as needed; and continuously optimizing the load balancing strategy based on actual operating results to improve the overall performance and efficiency of the system.

[0064] Furthermore, based on the initial first load balancing operation cycle and the initial first load balancing operation power, the method further includes determining the first load balancing operation cycle and the first load balancing operation power, and includes:

[0065] Obtain the model parameters of the adapted dehumidifier and establish a parameter adaptation model; configure the parameter adaptation model and conduct compatibility and stability tests using a gray-box testing strategy; after the compatibility and stability tests are passed, automatically adjust the parameters based on the model parameters of the adapted dehumidifier according to the parameter adaptation model.

[0066] In a preferred embodiment, the model parameters of the dehumidifier are collected, including dehumidification capacity, power range, operating frequency, and fan speed level; this forms the basis for subsequent parameter adaptation and automatic adjustment. Based on the model parameters of the dehumidifier, a parameter adaptation model is defined. This model needs to be able to receive parameter input from the dehumidifier and output corresponding operating cycles and power adjustment suggestions. Within the parameter adaptation model, algorithm logic is determined to calculate and adjust the operating cycle and power based on the input model parameters. This algorithm logic may involve empirical rules or machine learning algorithms.

[0067] Based on the model parameters of the compatible dehumidifier, set the parameter range in the model to limit and adjust the model's output, ensuring that the output's operating cycle and power are within a reasonable range; initialize the parameters in the model, including the initial first load balancing operating cycle and the initial first load balancing operating power, and use the initial first load balancing operating cycle and the initial first load balancing operating power as the starting point for adjustment.

[0068] Based on the model parameters and parameter adaptation model of the compatible dehumidifier, test cases are designed to cover different operating scenarios and parameter combinations. A gray-box testing strategy is used to execute the designed test cases. Gray-box testing allows for understanding some internal logic but does not require in-depth knowledge of all details. During testing, the model's output and the actual performance of the compatible dehumidifier are recorded, and compatibility and stability issues are checked. The test results are analyzed to evaluate the model's compatibility and stability. If problems are found, corresponding adjustments and optimizations are required.

[0069] After passing compatibility and stability tests, the system receives input parameters from actual applications, including the model parameters of the compatible dehumidifiers. Based on the input parameters, it performs calculations according to the preset algorithm logic and outputs the corresponding first load balancing operation cycle and power adjustment suggestions. The adjustment suggestions output by the parameter adaptation model are converted into specific control commands and sent to the compatible dehumidifiers and vacuum cleaners for execution. During the adjustment process, dynamic adjustments and optimizations can be made according to actual needs.

[0070] In practical applications, the operating status of the adapted dehumidifiers and vacuum cleaners is monitored in real time; feedback data from actual applications is collected, including environmental parameters and equipment status; based on the feedback data, the parameter adaptation model is continuously optimized to improve its accuracy and adaptability. Based on the model parameters of the adapted dehumidifiers, the parameter adaptation model automatically adjusts the first load balancing operation cycle and power to achieve more efficient and stable coordinated control of dehumidification and vacuuming.

[0071] Furthermore, the method also includes:

[0072] The thermal expansion coefficient and material properties of the workpiece are collected; based on the thermal expansion coefficient and material properties of the workpiece, the preheating power and preheating rate are adaptively controlled and sent to the workpiece preheating equipment.

[0073] In a preferred embodiment, a professional measuring device or tool, such as a thermal expansion meter, is used to measure the coefficient of thermal expansion of the workpiece at different temperatures; the data is recorded and organized to establish a temperature-expansion coefficient data table or curve of the workpiece's thermal expansion coefficient; and the physical and chemical properties of the workpiece material, such as thermal conductivity, specific heat capacity, melting point, etc., are collected, as these material properties will affect the heat conduction and temperature distribution of the workpiece during the preheating process.

[0074] A safe temperature limit is set for the workpiece preheating equipment to prevent material instability due to overheating. At the same time, the air pressure in the workshop is monitored and controlled to reduce the accumulation of flammable gases. Based on this, the relevant parameters of the preheating collaborative control module are configured according to the specific needs of the workpiece and the capabilities of the preheating equipment, including temperature range, preheating time, and target temperature. The collected workpiece thermal expansion coefficient and material property data are loaded into the preheating collaborative control module to ensure that the workpiece preheating collaborative control module can access this data for real-time adjustments during the preheating process.

[0075] Based on the characteristics of the preheating equipment and the requirements of the workpiece, a suitable adaptive control algorithm is selected. Common adaptive control algorithms include fuzzy control, neural network control, and genetic algorithms. According to the requirements of the adaptive control algorithm, relevant parameters are set, such as learning rate, number of iterations, and objective function. These parameters will affect the convergence speed and control accuracy of the algorithm. Temperature sensors are used to monitor the temperature changes of the workpiece in real time. The temperature data is transmitted in real time to the preheating collaborative control module and the adaptive control algorithm. The adaptive control algorithm dynamically calculates and adjusts the preheating power and preheating rate based on the temperature changes of the workpiece (deviation from the target temperature, rate of temperature change), coefficient of thermal expansion, and material properties. The adjusted preheating power and preheating rate are converted into specific control commands, which are then sent to the workpiece preheating equipment to achieve real-time control of the workpiece preheating equipment.

[0076] In practical applications, this also includes real-time monitoring of the operating status of the preheating equipment and the temperature changes of the workpiece to ensure that the preheating process proceeds according to the preset trajectory and meets the requirements of the workpiece; collecting various data during the preheating process, such as temperature data, heating power data, and time data; optimizing and adjusting the adaptive control algorithm based on the collected feedback data; and continuously improving the accuracy and adaptability of the control algorithm to meet the needs of different workpieces and preheating equipment.

[0077] Furthermore, the multi-equipment adaptive collaborative control method in the CNC workshop is also used to execute the following methods:

[0078] Based on the workpiece preheating equipment, an adaptive heat dissipation device is configured, the heat dissipation device including the adaptive heat dissipation device; the workpiece surface temperature information is collected, and the temperature in the processing environment parameters is uploaded to the adaptive heat dissipation device; combined with the working status of the adaptive heat dissipation device and the preheating power of the workpiece preheating equipment, the second load balancing operation cycle and the second load balancing operation power are determined.

[0079] Specifically, the matching heat dissipation equipment refers to selecting or adjusting the heat dissipation equipment according to the characteristics of the workpiece preheating equipment and the processing requirements, so that it can work in conjunction with the workpiece preheating equipment to ensure that the temperature of the workpiece is effectively controlled during preheating and processing, and to prevent the workpiece from overheating or the temperature from changing too quickly, which would affect the processing quality. The second load balancing operation cycle and the second load balancing operation power refer to the reasonable arrangement of the equipment's operating time and power output for the workpiece preheating equipment and the matching heat dissipation equipment, taking into account their coordinated operation. This allows the equipment to achieve efficient and stable operation while ensuring accurate control of the workpiece temperature, avoiding equipment overload or frequent start-stop, extending the equipment's service life, and optimizing energy utilization efficiency.

[0080] In a preferred embodiment, a suitable heat dissipation device is configured based on the workpiece preheating device. Furthermore, taking into account factors such as the preheating power and preheating rate of the workpiece preheating device, as well as the material characteristics and size of the workpiece, a heat dissipation device with appropriate specifications and performance is selected, and corresponding parameters are set and connected. Specifically, for workpieces made of materials with high thermal expansion coefficients, such as aluminum alloys and titanium alloys, it is necessary to accurately control the temperature rise rate and final temperature during the preheating process. Therefore, it is necessary to configure efficient and responsive heat dissipation devices, such as coolant circulation heat dissipation systems or air-cooled heat dissipation systems.

[0081] The system collects workpiece surface temperature information and uploads the temperature from the processing environment parameters to the compatible heat dissipation equipment. Furthermore, it monitors the workpiece temperature in real time using temperature sensors installed on or near the workpiece surface and transmits the data to the control system of the heat dissipation equipment. Simultaneously, it collects and uploads temperature data from the processing environment so that the heat dissipation equipment can adjust its heat dissipation strategy based on the overall temperature situation. Combining the working status of the compatible heat dissipation equipment with the preheating power of the workpiece preheating equipment, it determines the second load balancing operation cycle and the second load balancing operation power.

[0082] Based on parameters such as the power output of the workpiece preheating equipment, the time requirements of the preheating stage, the heat dissipation capacity of the heat dissipation equipment, and the current working status, the optimal load balancing operation cycle and power setting are calculated and analyzed using intelligent control algorithms or preset control logic. Specifically, in the early stage of workpiece preheating, the preheating power is relatively large, and the heat dissipation equipment may need to operate at a lower power. As the preheating process progresses and the workpiece temperature approaches the target temperature, the preheating power is appropriately reduced, while the operating power of the heat dissipation equipment is increased to accurately control the workpiece temperature and prevent overheating.

[0083] In the above steps, by reasonably configuring and adapting heat dissipation equipment, and dynamically adjusting the operating cycle and power of the equipment according to the workpiece surface temperature and ambient temperature, the synergistic work between the workpiece preheating equipment and the heat dissipation equipment is realized, the workpiece temperature change is precisely controlled, the stability of the processing process and the processing quality are ensured, and at the same time, the processing error and equipment failure caused by temperature factors are effectively reduced, and the accuracy and efficiency of CNC lathes in processing workpieces with high thermal expansion coefficients are improved.

[0084] In summary, the beneficial effects of the embodiments of this application are:

[0085] This application employs an adversarial multimodal enhancement method by acquiring dust collection operating parameters, dehumidification operating parameters, workpiece preheating operating parameters, and heat dissipation operating parameters, combined with processing environment parameters. It configures a first collaborative control vector associated with dust collection efficiency, a second collaborative control vector associated with dehumidification efficiency, a third collaborative control vector associated with heating rate, and a fourth collaborative control vector associated with cooling efficiency. It aggregates the status data of the dust collection equipment and the adapted dehumidification equipment, as well as the status data of the workpiece preheating equipment and the heat dissipation equipment, analyzes the equipment start-up and shutdown sequence and energy consumption distribution, and marks competing conflict points. Based on the first, third, and fourth collaborative control vectors, and combined with the competing conflict points, it centrally controls the dust collection equipment, adapted dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment in the CNC workshop. It enables collaborative operation among dust collection equipment, adaptive dehumidification equipment, workpiece preheating equipment, and heat dissipation equipment, ensuring the cleanliness and suitable humidity of the processing environment, reducing processing errors caused by environmental factors, and improving the processing stability of CNC lathes through multi-equipment energy efficiency optimization and conflict avoidance.

[0086] Example 2: Based on the same inventive concept as the multi-device adaptive collaborative control method in the CNC workshop in Example 1, the present invention also provides an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method described in Example 1.

[0087] like Figure 3As shown, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, connecting various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0088] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-device adaptive collaborative control method for a numerically controlled workshop, characterized in that, The multi-device cooperative control network is in communication connection with a dust collection device, a dehumidification device, a workpiece preheating device, and a heat dissipation device, and comprises: Obtaining dust collection operation parameters, dehumidification operation parameters, workpiece preheating operation parameters, and heat dissipation operation parameters, and performing antagonistic multi-modal enhancement in combination with machining environment parameters to configure a first cooperative control vector associated with dust collection efficiency, a second cooperative control vector associated with dehumidification efficiency, a third cooperative control vector associated with heating rate, and a fourth cooperative control vector associated with cooling efficiency; Aggregating state data of the dust collection device and the dehumidification device, and state data of the workpiece preheating device and the heat dissipation device, analyzing device start-stop timing and energy consumption distribution, and marking competitive conflict points; Performing centralized control on the dust collection device, the dehumidification device, the workpiece preheating device, and the heat dissipation device of the numerical control workshop in combination with the competitive conflict points according to the first cooperative control vector, the second cooperative control vector, the third cooperative control vector, and the fourth cooperative control vector; Based on the workpiece preheating device, an adaptive heat dissipation device is configured, and the heat dissipation device comprises the adaptive heat dissipation device; Collecting workpiece surface temperature information, and uploading temperature in the machining environment parameters to the adaptive heat dissipation device to determine a second load balancing operation period and a second load balancing operation power in combination with a working state of the adaptive heat dissipation device and a preheating power of the workpiece preheating device.

2. The method of claim 1, wherein, Collecting dust particle size; According to the dust particle size, the suction power and the vacuum rate are controlled by fuzzy logic control, and are sent to the dust collection device.

3. The method of adaptive multi-device collaborative control of a numerically controlled job shop of claim 2, wherein, Further comprising: Based on the dust collection device, an adaptive dehumidification device is configured, and the dehumidification device comprises the adaptive dehumidification device; Collecting dust concentration information, and uploading humidity in the machining environment parameters to the adaptive dehumidification device to determine a first load balancing operation period and a first load balancing operation power in combination with a working state of the adaptive dehumidification device and a dust collection efficiency of the dust collection device.

4. The method of adaptive multi-device collaborative control of a numerically controlled job shop of claim 3, wherein, Determining a first load balancing operation period and a first load balancing operation power in combination with a working state of the adaptive dehumidification device and a dust collection efficiency of the dust collection device comprises: Determining a dehumidification working load and a dust collection working load based on the working state of the adaptive dehumidification device and the dust collection efficiency of the dust collection device; Setting a dehumidification working operation axis through the dehumidification working load, a dehumidification frequency, and a dehumidification intensity of the adaptive dehumidification device; Setting a dust collection working operation axis through the dust collection working load, a dust collection frequency, and a dust collection intensity of the dust collection device; Combining the dehumidification working operation axis and the dust collection working operation axis, identifying a conflict point set, and determining a plurality of front critical operation periods and a plurality of rear critical operation periods; 5. The method of adaptive multi-device collaborative control of a computer numerically controlled job shop of claim 4, wherein, Combining the dehumidification working operation axis and the dust collection working operation axis, identifying a conflict point set, and determining a plurality of front critical operation periods and a plurality of rear critical operation periods; ​ Based on the conflict point set, a plurality of front critical operation periods, a plurality of rear critical operation periods, alternating work balance analysis is carried out to determine the initial first load balance operation period; Based on the conflict point set, a plurality of front critical operation periods, a plurality of rear critical operation periods, running power balance analysis in different time periods is carried out to determine the initial first load balance operation power; Based on the initial first load balance operation period and the initial first load balance operation power, the first load balance operation period and the first load balance operation power are determined.

6. The method of multi-device adaptive co-ordination control of a numerically controlled workshop according to claim 5, characterized in that, Based on the initial first load balance operation period and the initial first load balance operation power, the first load balance operation period and the first load balance operation power are determined, which further comprises: Obtain the model parameters of the adaptive dehumidification equipment, and establish a parameter adaptation model; Configure the parameter adaptation model, and use a gray box test strategy to test compatibility and stability; After the compatibility and stability test is passed, the parameter adaptation model is automatically adjusted based on the model parameters of the adaptive dehumidification equipment.

7. The method of adaptive multi-device collaborative control of a computer numerically controlled job shop of claim 2, wherein, Collect the workpiece thermal expansion coefficient and material properties; According to the workpiece thermal expansion coefficient and material properties, the self-adaptive control preheating power and preheating rate are adopted, and are sent to the workpiece preheating equipment. 8.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the multi-device adaptive cooperative control method of the numerical control workshop in any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the multi-device adaptive cooperative control method of the numerical control workshop in any one of claims 1-7.

Images