Linear rail cooling method, device and equipment applied to machine tool and storage medium

Abstract

The invention relates to a linear guide rail cooling method, device and equipment applied to a machine tool and a storage medium. The method comprises the steps that linear guide rail temperature data are obtained in real time, the linear guide rail temperature data are analyzed based on a fuzzy logic control algorithm, the linear guide rail temperature change trend is obtained, and the temperature threshold value of a cooling system is dynamically adjusted according to the linear guide rail temperature change trend; under the condition that the linear rail temperature data exceed a preset temperature threshold value, the output power of a compressor is dynamically adjusted for preliminary heat dissipation, and the cooling process is optimized through the self-adaptive refrigerant flow speed control technology; and the machine tool coincidence state is obtained, under the condition that the machine tool load state is reduced to the preset machine tool load and / or the linear guide rail temperature data is reduced to the temperature stable value, the future cooling requirement is analyzed through a preset temperature prediction algorithm, and the output power of the compressor is adjusted in advance according to the future cooling requirement. The cooling device has the effect of improving the cooling efficiency of the machine tool linear rail.

Description

Technical Field

[0001] The present application relates to the technical field of machine rail cooling, and in particular to a rail cooling method, device, equipment and storage medium applied to a machine tool. Background Art

[0002] At present, with the continuous improvement of precision and efficiency requirements in mechanical processing, the heat generated by machine tools during long-term high-load operation has become an important factor affecting the processing quality. In particular, the linear rail part of the machine tool, due to continuous friction, the temperature rises, which is easy to cause thermal deformation, thereby affecting the processing accuracy of the workpiece. Existing machine tool linear rail cooling technology mainly uses air cooling or water cooling for cooling. The air cooling method removes heat through airflow, and has a simple structure, but the heat dissipation efficiency is low, especially in space-constrained or high-load processing. It is difficult to effectively cool down. Although the water cooling method has a better cooling effect, it has high requirements for sealing and is prone to short circuits due to leakage, which increases the difficulty of maintenance.

[0003] The above-mentioned existing technical solutions have the following defects: the existing linear rail cooling method lacks intelligent temperature control and cannot dynamically adjust the cooling demand according to the actual load of the machine tool, so there is room for improvement. Summary of the invention

[0004] In order to improve the cooling efficiency of a linear rail of a machine tool, the present application provides a linear rail cooling method, device, equipment and storage medium applied to a machine tool.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A linear rail cooling method applied to a machine tool, the linear rail cooling method applied to a machine tool comprising: Acquire the temperature data of the rail in real time, analyze the temperature data of the rail based on the fuzzy logic control algorithm, obtain the temperature change trend of the rail, and dynamically adjust the temperature threshold of the cooling system according to the temperature change trend of the rail; When the temperature data of the linear track exceeds a preset temperature threshold, the compressor output power is dynamically adjusted for preliminary heat dissipation, and the cooling process is optimized by adaptive refrigerant flow rate control technology; Acquire the compliance status of the machine tool, and when the machine tool load status is reduced to a preset machine tool load and / or the linear rail temperature data is reduced to a temperature stable value, analyze the future cooling demand through a preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; Monitor the status of key components in real time, trigger a component status warning signal when the status of the key components is abnormal, and automatically adjust cooling parameters according to preset emergency processing strategies; Record and store cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, generate optimized cooling parameter settings based on the analysis results, and update the system cooling parameter library.

[0006] By adopting the above technical solution, by analyzing the temperature data of the linear rail in real time based on the fuzzy logic control algorithm and dynamically adjusting the temperature threshold of the cooling system, it is possible to accurately identify the temperature change trend and respond to temperature fluctuations in a timely manner, thereby achieving efficient and intelligent temperature control, ensuring that the machine tool can be maintained within an appropriate temperature range under different working conditions, and avoiding the reduction of processing accuracy due to temperature fluctuations; by dynamically adjusting the compressor output power and optimizing the refrigerant flow rate when the temperature exceeds the preset threshold, it is possible to flexibly adjust the cooling intensity to cope with the rapid temperature increase, optimize the energy consumption performance of the cooling system, thereby reducing energy waste and extending the service life of the equipment while ensuring the heat dissipation effect; by obtaining the load status of the machine tool and predicting future cooling needs when the load status drops to the preset threshold or the temperature tends to stabilize, it is possible to adjust the power in advance to prevent the cooling system from lagging behind due to changes in demand, thereby improving the response speed of the cooling system and the intelligent level of energy consumption management; by real-time monitoring the status of key components of the cooling system and triggering early warning signals, automatically adjusting the cooling parameters when components are abnormal, it is possible to ensure that the cooling system can still operate stably in the event of sudden abnormalities, thereby effectively improving the safety and overall reliability of the system.

[0007] In one example, the present application may be further configured as follows: the fuzzy logic control algorithm is used to analyze the temperature data of the linear rail to obtain a temperature variation trend of the linear rail, and the temperature threshold of the cooling system is dynamically adjusted according to the temperature variation trend of the linear rail, specifically including: Based on the fuzzy logic control algorithm, the temperature data of the linear rail is correlated with the historical temperature data to predict the temperature change trend of the linear rail and determine the corresponding load state of the machine tool; When the machine tool load state is high load, the temperature threshold is increased to adapt to the rapidly rising temperature demand. When the machine tool load state is low load or stable, the temperature threshold is reduced to start the cooling system in advance.

[0008] By adopting the above technical solution, by performing correlation analysis between real-time rail temperature data and historical temperature data and predicting temperature change trends, it is possible to identify temperature demand characteristics under different load states, thereby dynamically adjusting the temperature threshold of the cooling system, so that the cooling system reduces the cooling frequency when the load is high, and starts the cooling system in advance under low load or stable conditions, avoiding increased energy consumption due to frequent startup of the system, improving the operating stability of the cooling system, and enabling the machine tool cooling system to operate effectively under various working conditions, thereby ensuring the temperature stability of the machine tool during long-term processing.

[0009] In one example, the present application may be further configured as follows: the dynamic adjustment of the compressor output power for preliminary heat dissipation and the optimization of the cooling process by the adaptive refrigerant flow rate control technology specifically include: Determining the rising rate of the line rail temperature data based on the line rail temperature change trend, thereby determining the cooling demand of the current working condition, and adjusting the compressor output power according to the cooling demand; The refrigerant temperature and the heat absorption rate of the evaporator are monitored in real time, and the refrigerant flow rate is adjusted based on the adaptive refrigerant flow rate control technology so that the refrigerant flow rate matches the line rail temperature data.

[0010] By adopting the above technical solution, by judging the cooling demand based on the temperature change trend and dynamically adjusting the output power of the compressor, the cooling intensity can be matched with the real-time demand, avoiding energy waste caused by uneven load of the compressor, thereby realizing the power control optimization of the compressor, ensuring that the cooling system can provide sufficient heat dissipation effect when the temperature rises rapidly, and reducing power consumption when the temperature changes steadily, thereby improving the energy efficiency of the cooling system, reducing the overall energy consumption in the cooling process, and further extending the service life of the compressor.

[0011] In one example, the present application may be further configured as follows: analyzing future cooling requirements by a preset temperature prediction algorithm specifically includes: Based on the current temperature change trend of the rail and the load status of the machine tool, the temperature prediction algorithm is used to evaluate the future cooling demand change and determine the cooling demand of the future working condition; When the cooling demand is low, the compressor output power is reduced in advance, and when the cooling demand is high, the compressor output power is increased in advance.

[0012] By adopting the above technical solution, by using the temperature prediction algorithm to analyze the current temperature change trend and machine tool load status to predict future cooling needs, it is possible to achieve forward-looking control of cooling needs, so that the cooling system can actively reduce power output when demand is low, and increase power output in advance when demand is high, thereby reducing energy loss caused by frequent start and stop, improving the control accuracy of the cooling system, ensuring temperature balance under different load conditions, improving the stability and response speed of the system, and ensuring the efficient and stable performance of the cooling system under various operating conditions.

[0013] In one example, the present application may be further configured as follows: the construction of the temperature prediction algorithm specifically includes: Acquire historical temperature data and corresponding machine tool load status data and cooling system parameters, classify the historical temperature data according to different temperature intervals, and generate a model training data set according to the classification results; The model training data set is used to iteratively train the machine learning algorithm, and the impact of each feature on temperature change is analyzed according to the correlation of the multidimensional data features in the model training set to generate the temperature prediction model.

[0014] By adopting the above technical solution, by acquiring historical temperature data, machine tool load status data, and cooling system parameters and classifying them to generate training data sets, the training data sets can comprehensively cover different temperature ranges and load conditions, thereby ensuring that the model can accurately predict cooling requirements under various working conditions. By using the training data sets to iteratively train the machine learning algorithm, the model can identify the correlation between multidimensional data and effectively analyze the impact of each feature on temperature changes. The temperature prediction model finally generated can quickly adapt to temperature change requirements under different load and temperature conditions, thereby realizing intelligent forward-looking control of the cooling system.

[0015] In one example, the present application may be further configured as follows: the cooling parameter data in the cooling system process is recorded and stored, the cooling parameter data is intelligently analyzed using cloud computing analysis, and optimized cooling parameter settings are generated according to the analysis results and the system cooling parameter library is updated, specifically including: Real-time collection of key parameters such as temperature, compressor power, refrigerant flow rate, etc. during the operation of the cooling system as the cooling parameter data; Based on cloud computing technology, the operation mode and energy consumption change trend of the cooling parameter data are analyzed to generate a cooling parameter optimization plan, and the cooling parameter optimization plan is updated to the cooling parameter library.

[0016] By adopting the above technical solution, by collecting the key parameter data of the cooling system in real time, including temperature, compressor power and refrigerant flow rate, and storing and uploading it to the cloud computing platform, the real-time and integrity of the cooling system operation data can be guaranteed, thereby providing high-quality data support for subsequent data analysis; by using cloud computing to analyze the operation mode and energy consumption trend of cooling parameter data, it is possible to generate optimized cooling parameter settings based on multi-dimensional data relationships, optimize the operation status of the cooling system, reduce unnecessary power consumption, realize automatic adjustment of the energy consumption of the cooling system and intelligent improvement of cooling efficiency, and enable the system to continuously learn and optimize by updating the cooling parameter library, thereby ensuring efficient and stable operation of the cooling system under different load conditions.

[0017] The second object of the invention is achieved by the following technical solutions: A linear rail cooling device applied to a machine tool, the linear rail cooling device applied to a machine tool comprising: The temperature monitoring module is used to obtain the temperature data of the linear rail in real time, and analyze the temperature data of the linear rail based on the fuzzy logic control algorithm to obtain the temperature change trend of the linear rail, and dynamically adjust the temperature threshold of the cooling system according to the temperature change trend of the linear rail; A compressor control module, used to dynamically adjust the compressor output power for preliminary heat dissipation when the line rail temperature data exceeds a preset temperature threshold, and optimize the cooling process through adaptive refrigerant flow rate control technology; A load state analysis module is used to obtain the machine tool compliance state, and when the machine tool load state is reduced to a preset machine tool load and / or the rail temperature data is reduced to a temperature stability value, analyze the future cooling demand through a preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; A fault monitoring module is used to monitor the status of key components in real time. When the status of the key components is abnormal, a component status warning signal is triggered, and cooling parameters are automatically adjusted according to a preset emergency processing strategy. The data recording and analysis module is used to record and store the cooling parameter data in the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, and generate optimized cooling parameter settings based on the analysis results and update the system cooling parameter library.

[0018] By adopting the above technical solution, by analyzing the temperature data of the linear rail in real time based on the fuzzy logic control algorithm and dynamically adjusting the temperature threshold of the cooling system, it is possible to accurately identify the temperature change trend and respond to temperature fluctuations in a timely manner, thereby achieving efficient and intelligent temperature control, ensuring that the machine tool can be maintained within an appropriate temperature range under different working conditions, and avoiding the reduction of processing accuracy due to temperature fluctuations; by dynamically adjusting the compressor output power and optimizing the refrigerant flow rate when the temperature exceeds the preset threshold, it is possible to flexibly adjust the cooling intensity to cope with the rapid temperature increase, optimize the energy consumption performance of the cooling system, thereby reducing energy waste and extending the service life of the equipment while ensuring the heat dissipation effect; by obtaining the load status of the machine tool and predicting future cooling needs when the load status drops to the preset threshold or the temperature tends to stabilize, it is possible to adjust the power in advance to prevent the cooling system from lagging behind due to changes in demand, thereby improving the response speed of the cooling system and the intelligent level of energy consumption management; by real-time monitoring the status of key components of the cooling system and triggering early warning signals, automatically adjusting the cooling parameters when components are abnormal, it is possible to ensure that the cooling system can still operate stably in the event of sudden abnormalities, thereby effectively improving the safety and overall reliability of the system.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the linear rail cooling method applied to a machine tool are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the linear rail cooling method applied to a machine tool are implemented.

[0021] In summary, this application includes the following beneficial technical effects: 1. By analyzing the temperature data of the linear rail in real time based on the fuzzy logic control algorithm and dynamically adjusting the temperature threshold of the cooling system, it is possible to accurately identify the temperature change trend and respond to temperature fluctuations in a timely manner, thereby achieving efficient and intelligent temperature control, ensuring that the machine tool can be kept within the appropriate temperature range under different working conditions, and avoiding the reduction of processing accuracy due to temperature fluctuations; by dynamically adjusting the compressor output power and optimizing the refrigerant flow rate when the temperature exceeds the preset threshold, it is possible to flexibly adjust the cooling intensity to cope with the situation of rapid temperature rise, optimize the energy consumption performance of the cooling system, thereby reducing energy waste and extending the service life of the equipment while ensuring the heat dissipation effect; by obtaining the load status of the machine tool and predicting future cooling needs when the load status drops to the preset threshold or the temperature tends to stabilize, it is possible to adjust the power in advance to prevent the cooling system from lagging behind due to changes in demand, thereby improving the response speed of the cooling system and the intelligent level of energy consumption management; by real-time monitoring the status of key components of the cooling system and triggering early warning signals, automatically adjusting the cooling parameters when components are abnormal, it is possible to ensure that the cooling system can still operate stably in the event of sudden abnormalities, thereby effectively improving the safety and overall reliability of the system; 2. By analyzing the correlation between real-time rail temperature data and historical temperature data and predicting the temperature change trend, the temperature demand characteristics under different load conditions can be identified, so as to dynamically adjust the temperature threshold of the cooling system, reduce the cooling frequency of the cooling system under high load, start the cooling system in advance under low load or stable conditions, avoid the system from increasing energy consumption due to frequent startup, improve the operating stability of the cooling system, and enable the machine tool cooling system to operate effectively under various working conditions, thus ensuring the temperature stability of the machine tool during long-term processing; 3. By judging the cooling demand based on the temperature change trend and dynamically adjusting the output power of the compressor, the cooling intensity can be matched with the real-time demand, avoiding energy waste caused by uneven load of the compressor, thereby realizing the power control optimization of the compressor, ensuring that the cooling system can provide sufficient heat dissipation effect when the temperature rises rapidly, and reducing power consumption when the temperature changes steadily, thereby improving the energy efficiency of the cooling system, reducing the overall energy consumption in the cooling process, and further extending the service life of the compressor. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 2 It is a flow chart for implementing step S10 in a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 3 is a flowchart for implementing step S20 in a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 4 It is a flow chart for implementing step S30 in a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 5 It is a flowchart for realizing the construction of a temperature prediction model in a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 6 is a flowchart for implementing step S50 in a linear rail cooling method applied to a machine tool in one embodiment of the present application; Figure 7 It is a principle block diagram of a linear rail cooling device applied to a machine tool in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a linear rail cooling method applied to a machine tool, which specifically includes the following steps: S10: Acquire the temperature data of the line rail in real time, analyze the temperature data of the line rail based on the fuzzy logic control algorithm, obtain the temperature change trend of the line rail, and dynamically adjust the temperature threshold of the cooling system according to the temperature change trend of the line rail.

[0025] Specifically, when obtaining the temperature data of the linear rail, the temperature information is collected in real time through the temperature sensors installed at the key positions of the linear rail. After the temperature data is transmitted to the control module, the fuzzy logic control algorithm is used for analysis. The changing trend of the linear rail temperature is inferred based on the changing rate of the temperature data and the current temperature level. Through this trend, it is determined whether the temperature threshold of the cooling system needs to be dynamically adjusted, so that the cooling system can respond to temperature fluctuations in advance and control temperature changes in time. When the temperature rise trend is significant, the threshold is appropriately increased to prevent frequent cooling startup. When the temperature tends to be stable or changes slowly, the temperature threshold is lowered so that the cooling system can start quickly, thereby improving cooling efficiency and ensuring processing accuracy.

[0026] S20: When the rail temperature data exceeds the preset temperature threshold, the compressor output power is dynamically adjusted for initial heat dissipation, and the cooling process is optimized through adaptive refrigerant flow rate control technology.

[0027] Specifically, when the rail temperature data exceeds the preset temperature threshold, the compressor is started and enters the initial heat dissipation process according to the cooling demand by controlling the power output of the compressor. The cooling intensity is ensured to be adapted to the current temperature conditions by adjusting the compressor power to avoid compressor overload or excessive energy consumption. At the same time, during the cooling process, the refrigerant flow rate in the evaporator is controlled so that the refrigerant can be efficiently vaporized and absorb the heat around the rail. The refrigerant flow rate is adjusted based on the current temperature rise rate and the compressor output power so that the cooling process always maintains the best heat dissipation effect and energy consumption control, thereby ensuring the optimization of cooling efficiency.

[0028] S30: Obtain the machine tool compliance status. When the machine tool load status is reduced to the preset machine tool load and / or the linear rail temperature data is reduced to the temperature stability value, analyze the future cooling demand through the preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand.

[0029] Specifically, by acquiring the load status of the machine tool, the current cooling demand is judged. If the load is reduced to a preset low-load state or the temperature data tends to be stable, the preset temperature prediction algorithm is used in combination with the current temperature data to evaluate the future cooling demand. The power level required by the cooling system is evaluated based on the demand, and the power output of the compressor is reduced in advance to save energy when the actual demand is low, or the compressor power is increased in advance when the future demand is high, to ensure that the cooling system can respond to possible temperature changes in the future at any time. This method can reduce power consumption while maintaining a stable cooling effect, thereby improving the responsiveness and energy consumption control capabilities of the cooling system.

[0030] S40: Monitor the status of key components in real time. If there is an abnormality in the status of key components, trigger the component status warning signal and automatically adjust the cooling parameters according to the preset emergency processing strategy.

[0031] Specifically, during the operation of the cooling system, by monitoring the operating parameters of key components in the cooling device, including the compressor, evaporator, condenser, etc., their temperature, pressure, current and other data are recorded in real time. When the parameters of a component show abnormal values ​​or exceed the preset range, an early warning signal is immediately triggered to notify the potential risk of equipment failure. At the same time, the cooling parameters are automatically adjusted according to the preset processing strategy, including adjusting the power output of the compressor or controlling the flow rate of the refrigerant to ensure that the cooling system can continue to operate effectively under abnormal conditions. This processing strategy improves the safety and stability of the system and avoids cooling failure or other safety hazards caused by component failure.

[0032] S50: Record and store cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, generate optimized cooling parameter settings based on the analysis results, and update the system cooling parameter library.

[0033] Specifically, during the operation of the cooling system, key data of the cooling process are collected in real time, including parameters such as temperature, compressor power and refrigerant flow rate, and the data are stored and uploaded to the data storage module. The cloud computing analysis module is then used to conduct in-depth analysis of the collected data to identify the energy consumption pattern and cooling efficiency of the cooling system under different working conditions. Optimized cooling parameter settings are generated through data analysis, and the optimization results are stored in the cooling parameter library for reference in subsequent cooling processes, thereby further optimizing the energy consumption performance and cooling effect of the cooling system.

[0034] In one embodiment, if Figure 2 As shown, in step S10, the temperature data of the linear rail is analyzed based on the fuzzy logic control algorithm to obtain the temperature change trend of the linear rail, and the temperature threshold of the cooling system is dynamically adjusted according to the temperature change trend of the linear rail, which specifically includes: S11: Based on the fuzzy logic control algorithm, the linear rail temperature data and the historical temperature data are analyzed for correlation, the linear rail temperature change trend is predicted, and the corresponding machine tool load state is determined.

[0035] Specifically, after obtaining real-time temperature data, the current temperature is compared and analyzed with the historical temperature data of the cooling system. The fuzzy logic control algorithm is used to analyze the trend of temperature change, including the rate of temperature rise and fluctuation, so as to determine whether the cooling system needs to adjust the temperature threshold. The temperature rise trend is predicted by analyzing the rate of temperature change. If the temperature rise rate is fast, the temperature threshold is appropriately increased to avoid frequent startup of the cooling system. If the temperature change is relatively stable, the temperature threshold is lowered to allow the cooling system to respond to temperature changes in a timely manner. This threshold adjustment method enables the cooling system to intelligently adapt to changes in temperature demand, thereby more reasonably controlling the upper and lower temperature limits.

[0036] S12: When the machine tool load state is high load, the temperature threshold is increased to adapt to the rapidly rising temperature demand. When the machine tool load state is low load or stable, the temperature threshold is lowered to start the cooling system in advance.

[0037] Specifically, when judging that the current machine tool is in different load states, the temperature threshold is dynamically adjusted according to the real-time temperature data and temperature change trend. When the machine tool is in a high load state, the temperature threshold of the cooling system is increased to avoid the increase in energy consumption caused by frequent startup of the cooling system due to temperature fluctuations. When the machine tool is in a low load or stable state, the temperature threshold is appropriately lowered so that the cooling system can start in advance and control the temperature to ensure that the temperature of the machine tool under different load states is always in a stable range, thereby effectively reducing the impact of temperature fluctuations on processing accuracy and avoiding unnecessary energy consumption.

[0038] In one embodiment, if Figure 3 As shown, in step S20, the compressor output power is dynamically adjusted to perform preliminary heat dissipation, and the cooling process is optimized by adaptive refrigerant flow rate control technology, specifically including: S21: Determine the rising rate of the line rail temperature data based on the line rail temperature change trend, thereby determining the cooling demand of the current working condition, and adjust the compressor output power according to the cooling demand.

[0039] Specifically, after analyzing the rate of change of temperature data, the current cooling demand is identified based on the temperature rising trend. When the temperature rises at a faster rate, the output power of the compressor is gradually increased to accelerate the cooling effect. When the temperature rises slowly, the output power of the compressor is appropriately reduced to ensure that the cooling process always matches the temperature change requirements, avoids excessive consumption of the compressor, and ensures that the rail temperature remains stable within an appropriate range. Through this dynamic power adjustment strategy, the cooling system can quickly respond to temperature change requirements while optimizing energy consumption performance.

[0040] S22: Monitor the refrigerant temperature and the heat absorption rate of the evaporator in real time, and adjust the refrigerant flow rate based on the adaptive refrigerant flow rate control technology to match the refrigerant flow rate with the line rail temperature data.

[0041] Specifically, during the cooling process, the temperature changes and heat absorption rate of the refrigerant in the evaporator are monitored, and the flow rate of the refrigerant is adjusted according to the actual cooling needs. The flow rate of the refrigerant is increased when the temperature rises rapidly to accelerate the vaporization of the refrigerant in the evaporator, thereby absorbing more heat. If the temperature change tends to be stable or decreases, the refrigerant flow rate is reduced to maintain appropriate cooling intensity and reduce unnecessary flow rate losses. Through adaptive adjustment of the refrigerant flow rate, the cooling system is ensured to operate energy-efficiently when the temperature demand is low, and to promptly strengthen cooling when the temperature demand is high, thereby optimizing the cooling efficiency.

[0042] In one embodiment, if Figure 4 As shown, in step S30, the future cooling demand is analyzed by a preset temperature prediction algorithm, specifically including: S31: Based on the current rail temperature change trend and machine tool load status, the temperature prediction algorithm is used to evaluate future cooling demand changes and determine the cooling demand for future working conditions.

[0043] Specifically, after combining the current temperature change trend and machine tool load status, the temperature prediction algorithm is used to evaluate future cooling needs, analyze the similarity between the current temperature change and historical data, and predict the cooling load for a period of time in the future. By comparing historical data with current data, the future cooling demand level is identified. If the future cooling demand is predicted to be low, the compressor power output is reduced when the cooling load is light. If the future cooling demand is predicted to be high, the compressor power is increased to ensure that the cooling system can adapt to future operating conditions. The prediction algorithm can achieve forward-looking cooling demand assessment.

[0044] S32: When the cooling demand is low, the compressor output power is reduced in advance; when the cooling demand is high, the compressor output power is increased in advance.

[0045] Specifically, when the future cooling demand is predicted to be low, the compressor power is adjusted in advance to reduce the output to avoid increased energy consumption due to unnecessary power increase. When the future cooling demand is predicted to be high, the compressor output power is increased to ensure that the cooling system can quickly respond to future temperature changes. Through advance power adjustment, the cooling system can respond to future cooling needs more reasonably, thereby achieving efficient control of system energy consumption while ensuring the cooling effect.

[0046] In one embodiment, if Figure 5 As shown, in step S30, the construction of the temperature prediction algorithm specifically includes: S301: Obtain historical temperature data and corresponding machine tool load status data and cooling system parameters, classify the historical temperature data according to different temperature ranges, and generate a model training data set according to the classification results.

[0047] Specifically, the historical temperature data and load status data of the machine tool are obtained, and combined with the operating parameters of the cooling system, the historical temperature data are classified based on different temperature intervals, and the data of high load, low load and stable state are stored in different temperature intervals respectively. A multidimensional model training data set is generated, and a variety of working condition data are input into the machine learning algorithm for training to learn the characteristic relationship of temperature changes under different working conditions. Through multiple iterative optimizations, a temperature prediction model is generated for subsequent cooling demand prediction, thereby improving the cooling system's forward-looking judgment ability on future temperature changes.

[0048] S302: Iteratively train the machine learning algorithm using the model training data set, analyze the impact of each feature on temperature change based on the correlation of the multidimensional data features in the model training set, and generate a temperature prediction model.

[0049] Specifically, in the process of generating a temperature prediction model, the constructed model training data set is input into the machine learning algorithm for multiple iterative training. By analyzing the correlation between temperature data, load status data and cooling system parameters, the influence of each data feature on temperature change is identified. The model is adjusted in each iteration until the model can accurately predict the temperature change trend. When the training is completed, the model can predict future temperature change trends by identifying key features in real-time data, ensuring that the cooling system can adjust the power and cooling amount in advance according to the results predicted by the model, thereby realizing forward-looking cooling control and energy consumption optimization.

[0050] In one embodiment, if Figure 6 As shown, in step S50, the cooling parameter data in the cooling system process is recorded and stored, the cooling parameter data is intelligently analyzed using cloud computing analysis, and the optimized cooling parameter settings are generated according to the analysis results and the system cooling parameter library is updated, specifically including: S51: collecting key parameters such as temperature, compressor power, and refrigerant flow rate during the operation of the cooling system in real time as cooling parameter data.

[0051] Specifically, during the entire operation of the cooling system, cooling parameter data is continuously collected and recorded, and key parameters such as rail temperature, compressor power, and refrigerant flow rate are uploaded to the data storage module as core data in the cooling process. Each parameter data is marked according to the collection time to facilitate subsequent time series analysis. These data provide a true record of the operating status of the cooling system under different working conditions, while ensuring the real-time and integrity of the data. By aggregating the collected cooling parameter data and uploading it to the cloud computing analysis module, the accuracy and reliability of subsequent data analysis are ensured, providing data support for the optimization of the cooling system.

[0052] S52: Analyze the operation mode and energy consumption change trend of the cooling parameter data based on cloud computing technology, generate a cooling parameter optimization plan, and update the cooling parameter optimization plan to the cooling parameter library.

[0053] Specifically, in the cloud computing analysis module, the cooling parameter data collected in real time is intelligently analyzed, and the operating mode of the cooling system is deeply analyzed using machine learning and data mining techniques. By analyzing the temperature change trend and energy consumption data under different load conditions, the optimal operating parameter configuration of the cooling system is identified, and a cooling parameter optimization plan is generated. The calculated optimization plan can adjust the operating strategy of the cooling system, such as optimizing power output under conditions with high energy consumption and reducing unnecessary energy loss during the cooling process. At the same time, the optimized cooling parameter configuration is stored in the cooling parameter library for reference in subsequent cooling operations, ensuring that the cooling system always maintains a high efficiency state under different load conditions and effectively saves energy.

[0054] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] In one embodiment, a linear rail cooling device for a machine tool is provided, and the linear rail cooling device for a machine tool corresponds to the linear rail cooling method for a machine tool in the above embodiment. Figure 7 As shown, the linear rail cooling device applied to machine tools includes a temperature monitoring module, a compressor control module, a load state analysis module, a fault monitoring module, and a data recording and analysis module. The detailed description of each functional module is as follows: The temperature monitoring module is used to obtain the rail temperature data in real time, analyze the rail temperature data based on the fuzzy logic control algorithm, obtain the rail temperature change trend, and dynamically adjust the temperature threshold of the cooling system according to the rail temperature change trend; The compressor control module is used to dynamically adjust the compressor output power for preliminary heat dissipation when the rail temperature data exceeds the preset temperature threshold, and optimize the cooling process through adaptive refrigerant flow rate control technology; The load state analysis module is used to obtain the machine tool compliance state. When the machine tool load state is reduced to the preset machine tool load and / or the rail temperature data is reduced to the temperature stability value, the future cooling demand is analyzed through the preset temperature prediction algorithm, and the compressor output power is adjusted in advance according to the future cooling demand; Fault monitoring module, used to monitor the status of key components in real time. If there is an abnormality in the status of key components, it will trigger the component status warning signal and automatically adjust the cooling parameters according to the preset emergency processing strategy; The data recording and analysis module is used to record and store the cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, and generate optimized cooling parameter settings based on the analysis results and update the system cooling parameter library.

[0056] Optionally, the temperature monitoring module specifically includes: The trend analysis submodule is used to perform correlation analysis between the rail temperature data and the historical temperature data based on the fuzzy logic control algorithm, predict the rail temperature change trend, and determine the corresponding machine tool load status; The threshold adjustment submodule is used to increase the temperature threshold when the machine tool load state is high load to adapt to the rapidly rising temperature demand, and to reduce the temperature threshold to start the cooling system in advance when the machine tool load state is low load or stable.

[0057] Optionally, the compressor control module specifically includes: The cooling demand judgment submodule is used to judge the rising rate of the line rail temperature data based on the line rail temperature change trend, thereby determining the cooling demand of the current working condition and adjusting the compressor output power according to the cooling demand; The refrigerant flow rate adjustment submodule is used to monitor the refrigerant temperature and the heat absorption rate of the evaporator in real time, and adjust the refrigerant flow rate based on the adaptive refrigerant flow rate control technology to match the refrigerant flow rate with the line rail temperature data.

[0058] Optionally, the load status analysis module specifically includes: The cooling demand prediction module is used to evaluate the future cooling demand changes based on the current rail temperature change trend and machine tool load status using the temperature prediction algorithm and determine the cooling demand for future working conditions; The compressor power pre-adjustment module is used to reduce the compressor output power in advance when the cooling demand is low, and to increase the compressor output power in advance when the cooling demand is high.

[0059] Optionally, the construction of a temperature prediction algorithm includes: A training data set generation module is used to obtain historical temperature data and corresponding machine tool load status data and cooling system parameters, and classify the historical temperature data according to different temperature intervals, and generate a model training data set according to the classification results; The model training module is used to iteratively train the machine learning algorithm using the model training data set, analyze the impact of each feature on temperature change based on the correlation of the multidimensional data features in the model training set, and generate a temperature prediction model.

[0060] Optionally, the data recording and analysis module specifically includes: The data acquisition submodule is used to collect key parameters such as temperature, compressor power, refrigerant flow rate, etc. during the operation of the cooling system in real time as cooling parameter data; The parameter optimization submodule is used to analyze the operating mode and energy consumption trend of cooling parameter data based on cloud computing technology, generate a cooling parameter optimization plan, and update the cooling parameter optimization plan to the cooling parameter library.

[0061] For the specific definition of the linear rail cooling device applied to machine tools, please refer to the definition of the linear rail cooling method applied to machine tools in the above text, which will not be repeated here. Each module in the above-mentioned linear rail cooling device applied to machine tools can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a linear rail cooling method applied to a machine tool is implemented.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire the rail temperature data in real time, analyze the rail temperature data based on the fuzzy logic control algorithm, obtain the rail temperature change trend, and dynamically adjust the temperature threshold of the cooling system according to the rail temperature change trend; When the rail temperature data exceeds the preset temperature threshold, the compressor output power is dynamically adjusted for initial heat dissipation, and the cooling process is optimized through adaptive refrigerant flow rate control technology; Obtain the machine tool compliance status, and when the machine tool load status is reduced to the preset machine tool load and / or the linear rail temperature data is reduced to the temperature stability value, analyze the future cooling demand through the preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; Monitor the status of key components in real time. If there is an abnormality in the status of key components, trigger the component status warning signal and automatically adjust the cooling parameters according to the preset emergency processing strategy; Record and store cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, generate optimized cooling parameter settings based on the analysis results and update the system cooling parameter library.

[0064] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire the rail temperature data in real time, analyze the rail temperature data based on the fuzzy logic control algorithm, obtain the rail temperature change trend, and dynamically adjust the temperature threshold of the cooling system according to the rail temperature change trend; When the rail temperature data exceeds the preset temperature threshold, the compressor output power is dynamically adjusted for initial heat dissipation, and the cooling process is optimized through adaptive refrigerant flow rate control technology; Obtain the machine tool compliance status, and when the machine tool load status is reduced to the preset machine tool load and / or the linear rail temperature data is reduced to the temperature stability value, analyze the future cooling demand through the preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; Monitor the status of key components in real time. If there is an abnormality in the status of key components, trigger the component status warning signal and automatically adjust the cooling parameters according to the preset emergency processing strategy; Record and store cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, generate optimized cooling parameter settings based on the analysis results and update the system cooling parameter library.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0066] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A linear rail cooling method for a machine tool, characterized in that: The linear rail cooling method applied to a machine tool comprises: Acquire the temperature data of the rail in real time, analyze the temperature data of the rail based on the fuzzy logic control algorithm, obtain the temperature change trend of the rail, and dynamically adjust the temperature threshold of the cooling system according to the temperature change trend of the rail; When the temperature data of the linear track exceeds a preset temperature threshold, the compressor output power is dynamically adjusted for preliminary heat dissipation, and the cooling process is optimized by adaptive refrigerant flow rate control technology; Acquire the compliance status of the machine tool, and when the machine tool load status is reduced to a preset machine tool load and / or the linear rail temperature data is reduced to a temperature stable value, analyze the future cooling demand through a preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; Monitor the status of key components in real time, trigger a component status warning signal when the status of the key components is abnormal, and automatically adjust cooling parameters according to preset emergency processing strategies; Record and store cooling parameter data during the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, generate optimized cooling parameter settings based on the analysis results, and update the system cooling parameter library.

2. The linear rail cooling method for machine tools according to claim 1, characterized in that: The fuzzy logic control algorithm is used to analyze the temperature data of the linear rail to obtain the temperature change trend of the linear rail, and the temperature threshold of the cooling system is dynamically adjusted according to the temperature change trend of the linear rail, specifically including: Based on the fuzzy logic control algorithm, the temperature data of the linear rail is correlated with the historical temperature data to predict the temperature change trend of the linear rail and determine the corresponding load state of the machine tool; When the machine tool load state is high load, the temperature threshold is increased to adapt to the rapidly rising temperature demand. When the machine tool load state is low load or stable, the temperature threshold is reduced to start the cooling system in advance.

3. The linear rail cooling method for machine tools according to claim 1, characterized in that: The method of dynamically adjusting the compressor output power for preliminary heat dissipation and optimizing the cooling process by adaptive refrigerant flow rate control technology specifically includes: Determining the rising rate of the line rail temperature data based on the line rail temperature change trend, thereby determining the cooling demand of the current working condition, and adjusting the compressor output power according to the cooling demand; The refrigerant temperature and the heat absorption rate of the evaporator are monitored in real time, and the refrigerant flow rate is adjusted based on the adaptive refrigerant flow rate control technology so that the refrigerant flow rate matches the line rail temperature data.

4. The linear rail cooling method for machine tools according to claim 1, characterized in that: The analyzing future cooling demand by using a preset temperature prediction algorithm specifically includes: Based on the current temperature change trend of the rail and the load status of the machine tool, the temperature prediction algorithm is used to evaluate the future cooling demand change and determine the cooling demand of the future working condition; When the cooling demand is low, the compressor output power is reduced in advance, and when the cooling demand is high, the compressor output power is increased in advance.

5. The linear rail cooling method for machine tools according to claim 4, characterized in that: The construction of the temperature prediction algorithm specifically includes: Acquire historical temperature data and corresponding machine tool load status data and cooling system parameters, classify the historical temperature data according to different temperature intervals, and generate a model training data set according to the classification results; The model training data set is used to iteratively train the machine learning algorithm, and the impact of each feature on temperature change is analyzed according to the correlation of the multidimensional data features in the model training set to generate the temperature prediction model.

6. The linear rail cooling method for machine tools according to claim 1, characterized in that: The recording and storing of cooling parameter data in the cooling system process, performing intelligent analysis on the cooling parameter data using cloud computing analysis, and generating optimized cooling parameter settings and updating the system cooling parameter library according to the analysis results, specifically includes: Real-time collection of key parameters such as temperature, compressor power, refrigerant flow rate, etc. during the operation of the cooling system as the cooling parameter data; Based on cloud computing technology, the operation mode and energy consumption change trend of the cooling parameter data are analyzed to generate a cooling parameter optimization plan, and the cooling parameter optimization plan is updated to the cooling parameter library.

7. A linear rail cooling device for a machine tool, characterized in that: The linear rail cooling device applied to a machine tool comprises: The temperature monitoring module is used to obtain the temperature data of the linear rail in real time, and analyze the temperature data of the linear rail based on the fuzzy logic control algorithm to obtain the temperature change trend of the linear rail, and dynamically adjust the temperature threshold of the cooling system according to the temperature change trend of the linear rail; A compressor control module, used to dynamically adjust the compressor output power for preliminary heat dissipation when the line rail temperature data exceeds a preset temperature threshold, and optimize the cooling process through adaptive refrigerant flow rate control technology; A load state analysis module is used to obtain the machine tool compliance state, and when the machine tool load state is reduced to a preset machine tool load and / or the rail temperature data is reduced to a temperature stability value, analyze the future cooling demand through a preset temperature prediction algorithm, and adjust the compressor output power in advance according to the future cooling demand; A fault monitoring module is used to monitor the status of key components in real time. When the status of the key components is abnormal, a component status warning signal is triggered, and cooling parameters are automatically adjusted according to a preset emergency processing strategy. The data recording and analysis module is used to record and store the cooling parameter data in the cooling system process, use cloud computing analysis to perform intelligent analysis on the cooling parameter data, and generate optimized cooling parameter settings based on the analysis results and update the system cooling parameter library.

8. The linear rail cooling device for machine tools according to claim 7, characterized in that: The temperature monitoring module specifically includes: A trend analysis submodule, for performing correlation analysis on the rail temperature data and historical temperature data based on a fuzzy logic control algorithm, predicting the rail temperature variation trend, and determining the corresponding machine tool load state; The threshold adjustment submodule is used to increase the temperature threshold when the machine tool load state is high load to adapt to the rapidly rising temperature demand, and to reduce the temperature threshold when the machine tool load state is low load or stable to start the cooling system in advance.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the linear rail cooling method applied to a machine tool as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the linear rail cooling method applied to a machine tool as claimed in any one of claims 1 to 6 are implemented.

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