Ultrafast laser heat dissipation state real-time monitoring system based on cloud computing

Abstract

The invention provides an ultrafast laser heat dissipation state real-time monitoring system based on cloud computing, and relates to the technical field of laser heat dissipation, and the system comprises a laser monitoring module which is used for transmitting ultrafast laser and is internally provided with a temperature sensor so as to monitor the working temperature of a laser in real time; the data acquisition module is used for being connected with the laser monitoring module, acquiring temperature data and key parameters, including laser power and working current, of the temperature sensor in real time, and uploading the temperature data and the key parameters to the cloud server; and the cloud analysis module is used for receiving and analyzing the temperature data and the key parameter data, identifying an abnormal temperature mode, predicting a potential overheating risk and calculating heat dissipation efficiency to obtain an analysis result. According to the invention, comprehensive, real-time and intelligent management of the heat dissipation state of the laser is realized, the operation stability and safety of the laser are effectively improved, the overheating risk is reduced, the heat dissipation efficiency is optimized, and remote monitoring and data analysis services are provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of laser heat dissipation, and in particular to a real-time monitoring system for the heat dissipation state of an ultrafast laser based on cloud computing. Background Art

[0002] Traditional technologies rely on locally deployed sensors and monitoring equipment, which limits the monitoring range by physical connections and geographic locations. For widely distributed or remotely deployed lasers, comprehensive monitoring becomes difficult and costly. Data processing capabilities are limited and it is difficult to cope with large amounts of high-frequency heat dissipation status data. This leads to delays and accuracy issues in data processing, and it is impossible to provide accurate heat dissipation status information and analysis results in real time.

[0003] Traditional monitoring systems do not have remote access capabilities, and users need to be on-site or connected through a dedicated network to view monitoring data. This limits user flexibility and convenience, especially in scenarios where remote monitoring or simultaneous monitoring of multiple locations is required. The hardware and software architecture of traditional monitoring systems is often relatively fixed and difficult to flexibly expand according to actual needs. As the number of lasers increases or monitoring requirements change, the system may require a large number of hardware upgrades and software modifications, increasing maintenance costs and complexity.

[0004] In addition, in traditional systems, the transmission of alarm information often relies on manual inspection or simple alarm mechanisms. This may lead to slow alarm response and failure to take timely measures to deal with potential heat dissipation problems, thereby increasing the risk of equipment damage or safety hazards. The lack of powerful data management and analysis tools makes it difficult to effectively manage and deeply analyze historical data accumulated over a long period of time. This limits users' comprehensive understanding of the heat dissipation status of the laser and the mining of optimization potential. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a real-time monitoring system for the heat dissipation status of an ultrafast laser based on cloud computing, which provides a comprehensive, efficient and convenient solution for the heat dissipation status monitoring of an ultrafast laser.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: The real-time monitoring system of ultrafast laser heat dissipation status based on cloud computing includes: Laser monitoring module, used to emit ultrafast lasers and with a built-in temperature sensor to monitor the operating temperature of the laser in real time; The data acquisition module is used to connect with the laser monitoring module to obtain the temperature data and key parameters of the temperature sensor in real time, including laser power and working current, and upload the temperature data and key parameters to the cloud server; A cloud analysis module is used to receive and analyze temperature data and key parameter data, identify abnormal temperature patterns, predict potential overheating risks, and calculate heat dissipation efficiency to obtain analysis results; Adaptive heat dissipation control module, which is used to automatically adjust the working parameters of the laser according to the analysis results of the cloud processing module, so that the laser operates within the preset temperature range; The early warning module is used to issue early warnings for potential heat dissipation problems based on the analysis results of the cloud analysis module, predict the future state of the laser heat dissipation system, and automatically plan preventive measures; The user interaction module is used for remote access, allowing users to view the cooling status of the laser in real time through the web page, receive alarm information, view historical data reports and analyze results.

[0007] Further, the temperature data and key parameter data are received and analyzed, abnormal temperature patterns are identified, potential overheating risks are predicted, and heat dissipation efficiency is calculated to obtain analysis results, including: Receive the temperature data and key parameter data uploaded by the data acquisition module, and perform preprocessing to obtain processed temperature data; Extract features from the processed temperature data, including mean, standard deviation, and temperature trend, and Identify abnormal temperature patterns; Build an overheating risk prediction model based on abnormal temperature patterns and historical data, input real-time temperature data and key parameters into the risk prediction model, calculate the current overheating risk value, and obtain the risk assessment result; According to the risk assessment results, a heat dissipation efficiency calculation model is established, and the real-time temperature data, key parameters and the operating status of the heat dissipation system, including fan speed and coolant flow, are input into the heat dissipation efficiency calculation model to calculate the current heat dissipation efficiency; Combine abnormal temperature patterns, overheating risk predictions, and cooling efficiency as analysis results Furthermore, features are extracted from the processed temperature data, including mean, standard deviation, and temperature variation trend, and abnormal temperature patterns are identified, including: Arrange the temperature data in chronological order to form a time series data set; Traverse the time series data set to obtain the mean and standard deviation of the temperature data, and fit the time series data set to obtain a trend model of temperature changes over time; According to the trend model of temperature changes over time, the average value and standard deviation of temperature data, the threshold range is set, and the real-time temperature data is compared with the threshold to identify temperature patterns that are beyond the normal range, have abnormal fluctuations or abnormal trends, and mark them as abnormal temperature patterns.

[0008] Furthermore, the current overheating risk value is calculated as follows: ; in, Indicates the current overheating risk value; represents the weight coefficient; Indicates the current operating temperature of the laser; Indicates the temperature threshold; Indicates the maximum temperature difference; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the ambient reference temperature; Indicates the number of temperature sampling points; Indicates temperature index; Indicates The temperature value of each temperature sampling point; Indicates the average temperature value of all temperature sampling points; Indicates the temperature fluctuation influence coefficient; Indicates at time Temperature value at ; Indicates a time interval; Indicates the temperature change rate influence coefficient; Indicates real-time power; represents the power influence coefficient; Indicates rated power; Indicates real-time current; represents the current influence coefficient; Indicates rated current.

[0009] Furthermore, the current heat dissipation efficiency calculation formula is: ; in, Indicates the current heat dissipation efficiency; Indicates the actual heat dissipation; Preset heat dissipation; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the reference temperature; Indicates the power influence coefficient; Indicates the real-time power of the laser; Indicates the maximum power of the laser; Indicates real-time current; Indicates the maximum current of the laser; Indicates the fan speed influence coefficient; Indicates the actual speed of the fan; Indicates the coolant flow rate influence coefficient; Indicates the actual flow rate of coolant; represents the humidity influence coefficient; Indicates ambient humidity; Indicates the maximum ambient humidity; represents the pollutant impact coefficient; Indicates the concentration value of environmental pollutants; Indicates the baseline value of pollutant concentration in a clean environment; represents the pollutant concentration threshold; Indicates the influence coefficient of radiator material; Indicates the heat sink material type.

[0010] Furthermore, according to the analysis results of the cloud processing module, the operating parameters of the laser are automatically adjusted so that the laser operates within a preset temperature range, including: Define the parameter settings of the bat population and initialize the position of the bat population. The position of each bat represents a set of laser operating parameters, including laser power, operating current, and pulse frequency. Parse the analysis results of the cloud processing module to extract key information, including the current temperature status, whether it exceeds the preset temperature range, overheating risk level and recommended cooling measures; Based on the key information, a fitness function is defined to evaluate whether the heat dissipation state under the current laser operating parameters meets the preset requirements to obtain the evaluation results; According to the evaluation results, during the iteration process, each bat transmits ultrasonic waves according to its current position and speed, and receives echoes, which contain information about the heat dissipation status under the current working parameters, including temperature and overheating risk level; According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts local search. When the number of iterations reaches the preset value, the final working parameters are determined. According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts a local search. When the number of iterations reaches the preset value, the final working parameters are determined from the bat population according to the bat's fitness value; Sending adjustment instructions to the laser to change the operating parameters to final operating parameters to update the operating parameter settings in the laser control system; The process of continuously receiving analysis results from the cloud, parsing key information, and evaluating the current status allows the laser to operate within the preset temperature range.

[0011] Furthermore, the parameter settings of the bat population include population size, frequency range, pulse emission rate, loudness, and pulse volume attenuation coefficient parameters.

[0012] Furthermore, the calculation formula of the fitness function is: ; in, represents the fitness value; Represents the weight coefficient of temperature deviation; Indicates the current operating temperature of the laser; Indicates the preset temperature midpoint; Indicates the temperature range in which the laser is allowed to operate; Indicates the influence coefficient of temperature change rate; Indicates the current operating temperature of the laser Small changes in The weight coefficient representing the overheating risk; Indicates the maximum temperature the laser is allowed to reach; The weight coefficient representing the temperature change rate in the overheating risk; A non-linear index indicating the level of overheating risk; The weight coefficient representing the deviation of the working parameters; Indicates the current working parameters; Indicates the working parameter value reached by the preset laser; Indicates the maximum deviation range allowed for working parameters; Represents the penalty index for deviation of operating parameters.

[0013] Furthermore, based on the analysis results of the cloud analysis module, early warnings are issued for potential heat dissipation problems, the future state of the laser heat dissipation system is predicted, and preventive measures are automatically planned, including: Receive the analysis results of the cloud analysis module, including abnormal temperature patterns, overheating risk prediction values, and cooling efficiency data, and evaluate the current cooling status according to the preset warning thresholds and conditions to obtain the evaluation results; if the evaluation results show that there is a potential cooling problem and it reaches the warning level, immediately send a warning signal through the user interaction module; According to the evaluation results and the early warning signals sent, the overheating risk prediction model and the heat dissipation efficiency calculation model are used to predict the heat dissipation status of the laser in the future, including the temperature change trend and the probability of increased overheating risk, so as to obtain the prediction results; Based on the prediction results, preventive measures are automatically planned, including adjusting the operating parameters of the laser, increasing the cooling capacity of the heat dissipation system, and scheduling maintenance.

[0014] Furthermore, remote access allows users to view the cooling status of the laser in real time through the web page, receive alarm information, view historical data reports and analyze results, including: Collect the heat dissipation status data of the laser in real time, including temperature, pressure, and flow rate indicators, and integrate historical data reports, analysis results, and alarm information; The heat dissipation status data of the laser is sent to the user end in real time through the remote access interface, and when new alarm information is generated or historical data reports and analysis results are updated, the information is sent to the user through the remote access interface; The web interface displays the laser's heat dissipation status data in real time, including temperature curves and heat dissipation efficiency indicators, and allows users to view historical data reports and analysis results through the interface.

[0015] The above solution of the present invention includes at least the following beneficial effects: Through the temperature sensor built into the laser monitoring module, the system can monitor the operating temperature of the laser in real time to ensure the accuracy and timeliness of the data. Combined with the data acquisition module, the system can also simultaneously obtain key parameters such as laser power and operating current, providing a solid foundation for comprehensive evaluation of the heat dissipation status of the laser.

[0016] The cloud analysis module uses powerful cloud computing capabilities to quickly and accurately analyze the collected temperature data and key parameters. By identifying abnormal temperature patterns and predicting potential overheating risks, the system can promptly detect potential cooling problems and issue early warnings through the early warning module, providing users with the opportunity to intervene in advance and effectively prevent equipment damage and safety hazards.

[0017] The adaptive heat dissipation control module automatically adjusts the laser's operating parameters, such as laser power and operating current, based on the analysis results of the cloud analysis module to ensure that the laser operates stably within the preset temperature range. This intelligent control method not only improves the performance stability of the laser, but also extends the service life of the equipment.

[0018] The user interaction module provides remote access, allowing users to view the cooling status of the laser in real time, receive alarm information, and view historical data reports and analysis results through the web page anytime and anywhere. This convenient management method greatly improves the user's work efficiency and flexibility, especially in scenarios where multiple locations are monitored simultaneously or remote monitoring is required.

[0019] The early warning module can not only issue immediate warnings, but also predict the future status of the laser cooling system and automatically plan preventive measures. This helps users to make maintenance plans in advance, optimize the cooling system, avoid production interruptions or equipment damage caused by sudden failures, and thus reduce maintenance costs and production risks. The cloud computing-based architecture makes the system scalable and upgradeable. As the number of lasers increases or monitoring requirements change, users can easily expand the system scale or upgrade system functions without a lot of hardware upgrades and software modifications. This reduces the long-term maintenance cost of the system and improves the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of a real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing provided by an embodiment of the present invention.

[0021] Figure 2 The present invention provides a flow chart of a real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing, which receives and analyzes temperature data and key parameter data, identifies abnormal temperature patterns, predicts potential overheating risks, and calculates heat dissipation efficiency to obtain analysis results. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] like Figure 1 As shown, an embodiment of the present invention proposes a real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing, including: The laser monitoring module 11 is used to emit ultrafast laser and has a built-in temperature sensor to monitor the operating temperature of the laser in real time; The data acquisition module 12 is used to connect with the laser monitoring module to obtain the temperature data and key parameters of the temperature sensor in real time, including the laser power and the working current, and upload the temperature data and key parameters to the cloud server; The cloud analysis module 13 is used to receive and analyze temperature data and key parameter data, identify abnormal temperature patterns, predict potential overheating risks, and calculate heat dissipation efficiency to obtain analysis results; The adaptive heat dissipation control module 14 is used to automatically adjust the working parameters of the laser according to the analysis results of the cloud processing module so that the laser operates within a preset temperature range; The early warning module 15 is used to issue early warnings for potential heat dissipation problems based on the analysis results of the cloud analysis module, predict the future state of the laser heat dissipation system, and automatically plan preventive measures; The user interaction module 16 is used for remote access, so that the user can view the heat dissipation status of the laser in real time through the web page, receive alarm information, check historical data reports and analyze results.

[0024] In the embodiment of the present invention, through the built-in temperature sensor, the module can monitor the operating temperature of the laser in real time, provide an accurate data basis for subsequent heat dissipation status analysis, and ensure the stable operation of the laser.

[0025] The data acquisition module 12 can obtain the temperature data of the laser and key parameters such as laser power and operating current in real time, providing comprehensive data support for cloud analysis and helping to more accurately evaluate the heat dissipation status of the laser. By uploading the data to the cloud server in real time, remote storage and sharing of the data are achieved, making it easy for users to access and analyze at any time.

[0026] The cloud analysis module 13 uses the powerful computing power of cloud computing to quickly analyze temperature data and key parameters, identify abnormal temperature patterns, predict potential overheating risks, and provide a scientific basis for heat dissipation control. By calculating the heat dissipation efficiency, users can intuitively understand the heat dissipation performance of the laser and provide data support for optimizing the heat dissipation design.

[0027] The adaptive heat dissipation control module 14 can automatically adjust the operating parameters of the laser, such as laser power, operating current, etc., according to the results of cloud analysis, so that the laser can operate stably within the preset temperature range, effectively avoiding performance degradation or damage caused by overheating. It can respond to temperature changes in real time and dynamically adjust the heat dissipation strategy to ensure the continuous and stable operation of the laser.

[0028] Early warning module 15, by predicting potential heat dissipation problems, can issue early warnings, provide users with valuable early intervention time, and effectively prevent equipment failures. Based on the early warning results, the module can automatically plan preventive measures, such as adjusting the heat dissipation system, reducing workload, etc., to reduce the probability of heat dissipation failures.

[0029] User interaction module 16, users can remotely access the system through the web page, view the heat dissipation status of the laser in real time, and receive alarm information, which improves the convenience and efficiency of management. Through the web interface, users can intuitively view historical data reports and analysis results to provide strong support for decision-making. Supporting multiple users to access at the same time, it is convenient for information sharing and collaboration among team members, and improves overall work efficiency.

[0030] In a preferred embodiment of the present invention, ultrafast laser is emitted, and a temperature sensor is built in to monitor the working temperature of the laser in real time; the method may include: In the embodiment of the present invention, ultrafast laser refers to a laser with an extremely short pulse width (such as nanosecond, picosecond or even femtosecond level). A high-precision temperature sensor is integrated at an appropriate position of the laser (such as a laser crystal, a heat sink or a laser cavity). The selection of the temperature sensor should take into account its measurement range, accuracy, response time and compatibility with the working environment of the laser. The temperature sensor should be closely attached to the surface of the object to be measured or embedded inside it to ensure the accuracy of the measurement. At the same time, the thermal inertia of the sensor needs to be considered to avoid affecting the measurement accuracy due to the heating of the sensor itself.

[0031] Design the signal transmission path between the temperature sensor and the data acquisition module. This includes signal lines, interface circuits, and necessary signal conditioning circuits (such as amplification, filtering, etc.). Ensure the stability and anti-interference of signal transmission to avoid errors in temperature measurement caused by external interference. Develop the software part of the data acquisition module to read the output signal of the temperature sensor in real time and convert it into digital temperature data. Implement data preprocessing functions such as denoising and calibration to improve the accuracy and reliability of temperature data. Integrate real-time monitoring functions in the data acquisition module to continuously obtain temperature data in a loop or event-driven manner and update it to the monitoring system interface or database in real time. Design a feedback mechanism that can immediately trigger an alarm or adjust the operating parameters of the laser when the temperature exceeds the preset threshold to ensure that the laser operates within a safe temperature range. Integrate the data acquisition module with the cloud server to realize remote uploading and storage of temperature data.

[0032] In a preferred embodiment of the present invention, the laser monitoring module is connected to obtain the temperature data and key parameters of the temperature sensor in real time, including the laser power and the operating current, and the temperature data and key parameters are uploaded to the cloud server, which may include: In an embodiment of the present invention, a connecting line (such as a cable, an optical fiber, etc.) is used to connect the laser monitoring module to the data acquisition module. Ensure that the type, length, and interface of the connecting line are compatible with both. If the laser monitoring module and the data acquisition module support wireless connection (such as Wi-Fi, Bluetooth, etc.), pair and connect them according to the standard process of wireless connection. According to the output interface of the laser monitoring module (such as an analog interface, a digital interface, a serial interface, etc.), configure the corresponding input interface on the data acquisition module. Set interface parameters such as baud rate, data bit, stop bit, check bit, etc. to ensure the accuracy and stability of data transmission. Write or configure the software of the data acquisition module to initialize its communication with the laser monitoring module. Set the frequency of data acquisition to ensure that temperature data and key parameters can be obtained in real time.

[0033] The data acquisition module continuously sends requests to the laser monitoring module to obtain the temperature data of the temperature sensor and the key parameters of the laser (such as laser power and operating current). The laser monitoring module responds to the request and sends the temperature data and key parameters to the data acquisition module in an agreed format. The data acquisition module preprocesses the received data, such as denoising, calibration, format conversion, etc. Configure the network connection of the data acquisition module to ensure that it can access the cloud server. Package the preprocessed temperature data and key parameters into a data packet, and add necessary identification information (such as timestamp, device ID, etc.). Encrypt the data packet to ensure the security of data transmission. The data acquisition module uploads the encrypted data packet to the cloud server through the network. The cloud server receives the data packet uploaded by the data acquisition module, decrypts and parses it. Store the parsed temperature data and key parameters in the database.

[0034] In a preferred embodiment of the present invention, receiving and analyzing temperature data and key parameter data, identifying abnormal temperature patterns, predicting potential overheating risks, and calculating heat dissipation efficiency to obtain analysis results may include: Receive the temperature data and key parameter data uploaded by the data acquisition module, and perform preprocessing to obtain processed temperature data; Extract features from the processed temperature data, including mean, standard deviation, and temperature trend, and Identify abnormal temperature patterns; Build an overheating risk prediction model based on abnormal temperature patterns and historical data, input real-time temperature data and key parameters into the risk prediction model, calculate the current overheating risk value, and obtain the risk assessment result; According to the risk assessment results, a heat dissipation efficiency calculation model is established, and the real-time temperature data, key parameters and the operating status of the heat dissipation system, including fan speed and coolant flow, are input into the heat dissipation efficiency calculation model to calculate the current heat dissipation efficiency; Combine abnormal temperature patterns, overheating risk predictions, and cooling efficiency as analysis results.

[0035] In an embodiment of the present invention, a cloud server sets up a dedicated data receiving port and continuously listens to network requests from the data acquisition module. When receiving a data packet sent by the data acquisition module, the server verifies the integrity and source of the data packet to ensure the security of the data. The server unpacks the received data packet and extracts the temperature data and key parameters (such as laser power and operating current). The extracted data is formatted and converted into the format required for internal processing of the server. The temperature data is cleaned to remove invalid or abnormal data points. The temperature data is smoothed to reduce the noise caused by data fluctuations. The key parameters are verified to ensure that their values ​​are within a reasonable range. The pre-processed temperature data and key parameters are stored in the database.

[0036] Calculate the average value of the temperature data to reflect the overall operating temperature level of the laser. Calculate the standard deviation of the temperature data to reflect the degree of dispersion of the temperature data. Analyze the time series of the temperature data to identify the trend of temperature changes (such as rising, falling, and stable). Set the temperature threshold and the rate of change threshold to determine whether the temperature is abnormal. Traverse the temperature data to check whether there are data points exceeding the set threshold or the rate of change exceeds the set threshold. When an abnormal temperature pattern is detected, record the time, temperature value, and change trend of the abnormality. Collect historical temperature data and overheating event records as training data sets. Select the logistic regression algorithm and use the training data set to train the overheating risk prediction model. Verify and tune the model to ensure its prediction accuracy. Input the real-time temperature data and key parameters as input into the overheating risk prediction model.

[0037] The model calculates the current overheating risk value based on the input data and outputs the risk assessment results (such as low risk, medium risk, and high risk).

[0038] Analyze the working principle and influencing factors of the cooling system and determine the calculation formula for cooling efficiency. Consider the impact of factors such as fan speed, coolant flow, and ambient temperature on cooling efficiency and incorporate them into the model. Input real-time temperature data, key parameters, and the operating status of the cooling system (such as fan speed and coolant flow) into the cooling efficiency calculation model. The model calculates the current cooling efficiency based on the input data and outputs the cooling efficiency value. Summarize the identified abnormal temperature patterns, overheating risk prediction values, and cooling efficiency values. Generate analysis reports or charts to intuitively display the analysis results.

[0039] Assume that the temperature range of a laser during normal operation is 20-40°C. The temperature data uploaded by the data acquisition module shows that at a certain moment, the temperature of the laser suddenly rises to 45°C and continues to rise. The system recognizes this abnormal temperature pattern and triggers the overheating risk prediction model. Based on key parameters such as real-time temperature data, laser power, and operating current, the model calculates that the current overheating risk value is high risk. At the same time, the heat dissipation efficiency calculation model calculates that the current heat dissipation efficiency is low based on data such as fan speed and coolant flow. The system integrates this information and generates an analysis report to prompt the user that the laser is at risk of overheating and recommends taking corresponding measures to improve the heat dissipation efficiency.

[0040] The system can monitor the temperature data and key parameters of the laser in real time, identify abnormal temperature patterns in time, and predict potential overheating risks. This provides users with valuable advance intervention time and effectively prevents equipment failures and safety accidents. By calculating the heat dissipation efficiency, users can intuitively understand the heat dissipation performance of the laser. The system can automatically adjust the heat dissipation strategy according to the risk assessment results and the operating status of the heat dissipation system, improve the heat dissipation efficiency, and ensure that the laser operates at the optimal operating temperature. The system can record the historical temperature data and overheating events of the laser, providing strong support for equipment maintenance. Users can formulate a more reasonable maintenance plan based on these data to extend the service life of the laser. By generating analysis reports and charts, the system can intuitively display the heat dissipation status and risk assessment results of the laser. This enables users to manage laser equipment more conveniently and improve management efficiency. At the same time, the remote access function also enables users to view the equipment status anytime and anywhere, improving work convenience.

[0041] In another preferred embodiment of the present invention, extracting features from the processed temperature data, including the mean value, standard deviation, and temperature variation trend, and identifying abnormal temperature patterns may include: Arrange the temperature data in chronological order to form a time series data set; Traverse the time series data set to obtain the mean and standard deviation of the temperature data, and fit the time series data set to obtain a trend model of temperature changes over time; According to the trend model of temperature changes over time, the average value and standard deviation of temperature data, the threshold range is set, and the real-time temperature data is compared with the threshold to identify temperature patterns that are beyond the normal range, have abnormal fluctuations or abnormal trends, and mark them as abnormal temperature patterns.

[0042] In an embodiment of the present invention, preprocessed temperature data is read from a database. According to the timestamp information in the data, the temperature data is sorted in chronological order. An ordered time series data set is formed, in which each data point contains a timestamp and a corresponding temperature value. The time series data set is traversed to obtain the average value and standard deviation of the temperature data. An accumulator and a counter are initialized to calculate the sum and quantity of the temperature data. Each data point in the time series data set is traversed, the temperature value is accumulated in the accumulator, and the counter is increased. The average value of the temperature data is calculated, that is, the sum in the accumulator is divided by the value of the counter. A square difference accumulator is initialized again. The time series data set is traversed again, the square of the difference between each temperature value and the average value is calculated, and it is accumulated in the square difference accumulator. The standard deviation of the temperature data is calculated, that is, the value of the square difference accumulator is divided by the value of the counter and then squared. The time series data set is fitted to obtain a trend model of temperature change over time. Linear regression is selected to fit the temperature data. A suitable fitting method is selected according to the characteristics of the time series data set (such as linearity, nonlinearity, periodicity, etc.). For data where the temperature changes over time, linear regression is selected. Extract timestamps and corresponding temperature values ​​from the time series dataset to form a training dataset. Perform the fitting process using the selected fitting method and the training dataset. Use a portion of the data (such as a holdout set or a cross-validation set) to verify the performance of the fitted model. This can help evaluate the generalization ability of the model, that is, how well the model performs on new data. Calculate some performance metrics such as mean squared error, root mean squared error, coefficient of determination, etc. to evaluate the fit of the model. Once the model is verified to be valid and performs well, store the model parameters and related information. This can be a file, database record, object in memory, etc.

[0043] Set the threshold range based on the trend model of temperature changes over time, the average value and standard deviation of temperature data. Calculate the upper and lower limits of the normal temperature range (such as the average value plus or minus two standard deviations) based on the average value and standard deviation of temperature data. Combined with the trend model of temperature changes over time, dynamic thresholds can be further set to adjust the normal temperature range by considering the trend of temperature changes over time. Compare real-time temperature data with thresholds to identify abnormal temperature patterns. Continuously receive new real-time temperature data. For each new temperature data point, compare it with the set threshold range. If the temperature data exceeds the normal temperature range, fluctuates abnormally (such as a sudden large increase or decrease), or has an abnormal trend (such as a large difference from the value predicted by the trend model), mark this data point as an abnormal temperature pattern.

[0044] Suppose that the temperature of an industrial equipment is being monitored. The temperature data is arranged in chronological order to form a time series data set. The mean value of the temperature data is calculated to be 30°C and the standard deviation is 5°C. By fitting the time series data set, a linear trend model is obtained, indicating that the temperature rises slowly over time. The normal temperature range is set to 20°C to 40°C (mean plus or minus two standard deviations). At the same time, according to the trend model, the temperature at the next moment is predicted to be 31°C. When a new real-time temperature data is received, if this data is 35°C, it is still within the normal temperature range and will not be marked as abnormal. However, if this data suddenly jumps to 50°C, or suddenly drops to 10°C, or is very different from the value predicted by the trend model (such as predicted to be 31°C, but the actual is 25°C), then these situations are marked as abnormal temperature patterns.

[0045] By refining the feature extraction and anomaly detection process of temperature data, abnormal temperature patterns can be more accurately identified and the accuracy of monitoring can be improved. Temperature data can be received and processed in real time. Once an abnormal temperature pattern is found, an early warning signal can be immediately issued to provide timely feedback and decision support for operators.

[0046] Continuous monitoring and analysis of temperature data can help operators understand the operating status and potential problems of the equipment, so as to formulate more reasonable maintenance plans and measures and extend the service life of the equipment. Timely identification and handling of abnormal temperature patterns can avoid equipment failures and production interruptions, ensure the continuity and stability of the production process, and thus improve production efficiency.

[0047] In a preferred embodiment of the present invention, the calculation formula of the current overheating risk value is: ; in, Indicates the current overheating risk value; represents the weight coefficient; Indicates the current operating temperature of the laser; Indicates the temperature threshold; Indicates the maximum temperature difference; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the ambient reference temperature; Indicates the number of temperature sampling points; Indicates temperature index; Indicates The temperature value of each temperature sampling point; Indicates the average temperature value of all temperature sampling points; Indicates the temperature fluctuation influence coefficient; Indicates at time Temperature value at ; Indicates a time interval; It represents the influence coefficient of temperature change rate; Indicates real-time power; Indicates the power influence coefficient; Indicates rated power; Indicates real-time current; represents the current influence coefficient; Indicates rated current.

[0048] In an embodiment of the present invention, the real-time laser operating temperature ( )、Ambient temperature( )、Real-time power( ) and real-time current ( ). Read preset parameter values, including temperature thresholds ( )、Maximum temperature difference( )、Ambient reference temperature( )、Rated power( )、Rated current( ) and each influence coefficient ( ) and weight coefficient ( ). Calculate the proportion of temperature exceeding the threshold , and considering the influence of ambient temperature, the coefficient Adjustment. Calculate the average temperature value of all temperature sampling points ( ), then calculate the square of the deviation of each temperature sampling point from the average temperature, sum them up, take the average and square root, get the standard deviation of temperature fluctuation, and then use the coefficient Adjust. Get the temperature value at the previous time point ( ), calculate the rate of change of the current temperature and the temperature at the previous time point, and use the coefficient Adjustment. Calculate the deviation ratio between the real-time power and the rated power, and use the coefficient Adjustment. Calculate the deviation ratio between the real-time current and the rated current, and adjust it by the coefficient Adjustment.

[0049] Multiply the risk values ​​of each part calculated above by the corresponding weight coefficient ( ), and then add them together to get the final overheating risk value ( ). The calculated overheating risk value ( ) output to the display screen or sent to the monitoring system.

[0050] By comprehensively considering multiple factors such as temperature, temperature fluctuation, temperature change rate, power and current, the overheating risk of the laser can be assessed more accurately, and early warnings can be issued in time to avoid equipment damage or casualties. According to the size and change trend of the overheating risk value, a reasonable maintenance plan can be formulated. For example, when the risk value is high, the frequency of equipment inspection and maintenance can be increased to ensure the safe operation of the equipment. By real-time monitoring and assessment of overheating risks, operators can be helped to discover and deal with potential problems in a timely manner, reduce the occurrence of equipment failures and production interruptions, and thus improve production efficiency. The overheating risk value can provide strong decision-making support for operators and management. More reasonable production arrangements and resource allocation decisions can be made based on the size and changes of the risk value.

[0051] In a preferred embodiment of the present invention, the current heat dissipation efficiency is calculated as follows: ; in, Indicates the current heat dissipation efficiency; Indicates the actual heat dissipation; Preset heat dissipation; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the reference temperature; represents the power influence coefficient; Indicates the real-time power of the laser; Indicates the maximum power of the laser; Indicates real-time current; Indicates the maximum current of the laser; Indicates the fan speed influence coefficient; Indicates the actual speed of the fan; Indicates the coolant flow rate influence coefficient; Indicates the actual flow rate of coolant; represents the humidity influence coefficient; Indicates ambient humidity; Indicates the maximum ambient humidity; represents the pollutant impact coefficient; Indicates the concentration value of environmental pollutants; Indicates the baseline value of pollutant concentration in a clean environment; represents the pollutant concentration threshold; Indicates the influence coefficient of radiator material; Indicates the heat sink material type.

[0052] In the embodiment of the present invention, real-time data is obtained from the sensor, including the actual heat dissipation ( )、Ambient temperature( )、Real-time laser power( )、Real-time current( ), the actual speed of the fan ( ), the actual flow rate of the coolant ( )、Ambient humidity( ) and environmental pollutant concentration values ​​( ). Read preset parameters from the configuration file, including preset heat dissipation ( )、reference temperature( )、Maximum laser power( )、Maximum laser current( ), baseline value of pollutant concentration in a clean environment ( ), pollutant concentration threshold ( )、Maximum ambient humidity( ) and each influence coefficient ( ) and the heat sink material influence coefficient ( ) corresponding value. According to the ambient temperature ( ) and reference temperature ( ), calculate the adjustment item of the ambient temperature influence coefficient ( ).

[0053] According to the real-time laser power ( ) and maximum power ( ), calculate the adjustment item of power influence coefficient ( ).

[0054] According to the real-time current ( ) and the maximum current ( ), calculate the adjustment item of the current influence coefficient ( ). According to the actual speed of the fan ( ), calculate the adjustment item of the fan speed influence coefficient ( ). According to the actual flow rate of coolant ( ), calculate the adjustment item of coolant flow rate influence coefficient ( ). According to the ambient humidity ( ) and maximum ambient humidity ( ), calculate the adjustment term of humidity influence coefficient ( ). According to the environmental pollutant concentration value ( ), baseline value of pollutant concentration in a clean environment ( ) and pollutant concentration thresholds ( ), calculate the adjustment item of the pollutant impact coefficient ( ). According to the radiator material type ( ), find the corresponding radiator material influence coefficient from the preset material influence coefficient table ( ) of the adjustment item ( ). Multiply the adjustment coefficients obtained from the above calculations and add them to the actual heat dissipation ( ) and preset heat dissipation ( ) ratio to obtain the final heat dissipation efficiency ( ).

[0055] By comprehensively considering the impact of multiple factors (such as ambient temperature, power, current, fan speed, coolant flow, humidity, pollutant concentration, and radiator material) on heat dissipation efficiency, the heat dissipation performance of the laser can be evaluated more accurately. The heat dissipation design of the laser can be optimized based on the calculation results of the heat dissipation efficiency. For example, adjust the fan speed, coolant flow, or improve the radiator material to improve the heat dissipation efficiency. By real-time monitoring of the heat dissipation efficiency, it is possible to promptly detect the decline in heat dissipation performance and issue an early warning to prevent laser failure due to overheating. Improving heat dissipation efficiency helps to reduce the operating temperature of the laser and reduce the impact of thermal stress on the equipment, thereby improving the reliability and service life of the equipment.

[0056] In a preferred embodiment of the present invention, according to the analysis result of the cloud processing module, automatically adjusting the working parameters of the laser so that the laser operates within a preset temperature range may include: Define the parameter settings of the bat population and initialize the position of the bat population. The position of each bat represents a set of laser operating parameters, including laser power, operating current, and pulse frequency. Parse the analysis results of the cloud processing module to extract key information, including the current temperature status, whether it exceeds the preset temperature range, overheating risk level and recommended cooling measures; Based on the key information, a fitness function is defined to evaluate whether the heat dissipation state under the current laser operating parameters meets the preset requirements to obtain the evaluation results; According to the evaluation results, during the iteration process, each bat transmits ultrasonic waves according to its current position and speed, and receives echoes, which contain information about the heat dissipation status under the current working parameters, including temperature and overheating risk level; According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts local search. When the number of iterations reaches the preset value, the final working parameters are determined. According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts a local search. When the number of iterations reaches the preset value, the final working parameters are determined from the bat population according to the bat's fitness value; Sending adjustment instructions to the laser to change the operating parameters to final operating parameters to update the operating parameter settings in the laser control system; The process of continuously receiving analysis results from the cloud, parsing key information, and evaluating the current status allows the laser to operate within the preset temperature range.

[0057] In an embodiment of the present invention, space is allocated for the bat population, and parameters such as the population size (i.e., the number of bats), the maximum number of iterations, the initial speed of the bats, and the frequency range are set. The operating parameters (laser power, operating current, pulse frequency) of each group of lasers are randomly generated as the initial position of each bat. These parameters are stored in an array, corresponding to each bat in the bat population. The analysis result data sent by the cloud processing module is received through the network interface. The data is parsed to extract key information such as the temperature state of the current laser, whether it exceeds the preset temperature range, the overheating risk level, and the recommended heat dissipation measures, and the information is stored in the corresponding variables. Based on the extracted key information, a fitness function is defined. The function evaluates whether the heat dissipation state under the current working parameters meets the preset requirements based on factors such as the temperature state of the current laser and the overheating risk level. The output of the fitness function is a numerical value indicating the degree of pros and cons of the current working parameters. The higher the value, the better the heat dissipation state and the better the working parameters. According to the evaluation results, in the iteration process, each bat emits ultrasonic waves according to its current position and speed, and receives echoes: In each iteration, the behavior of each bat emitting ultrasonic waves according to its current position and speed is simulated. This is actually simulated by calculating the heat dissipation state under the current working parameters. Receive "echoes", that is, calculate the heat dissipation state information such as temperature and overheating risk level under the current working parameters, and associate this information with each bat. According to the echo information (i.e., heat dissipation state information), calculate the new position and speed for each bat. The new position and speed are stored in the corresponding array for the next iteration. After each position update, simulate the behavior of each bat to perform a local search near its new position. This can be achieved by adding some small perturbations to the current working parameters and evaluating the heat dissipation state of these perturbed working parameters.

[0058] Select the final operating parameters obtained in the local search as the position of the next iteration of the bat. Check whether the number of iterations reaches the preset value. If so, stop the iteration. Determine the final operating parameters from the bat population based on the fitness value of each bat (i.e., the evaluation result of the heat dissipation status). Send an adjustment instruction to the laser through the communication interface, which contains the final operating parameters (laser power, operating current, pulse frequency). After receiving the instruction, the laser updates the operating parameter settings in its control system and enters a loop to ensure that the laser always operates within the preset temperature range.

[0059] Assume that a laser is being controlled and the parameter settings for the bat population have been defined. During the initialization phase, a set of operating parameters is randomly generated for each bat, such as a laser power of 100W, an operating current of 20A, and a pulse frequency of 10kHz. Then, the analysis results of the cloud processing module are received, and the current laser temperature is analyzed to be 50°C (within the preset temperature range), the overheating risk level is low, and no cooling measures are required.

[0060] In the next iteration, the behavior of the bat emitting ultrasonic waves and receiving echoes is simulated. For example, for a bat, the heat dissipation status at its current position (working parameters) is evaluated as good (temperature is 48°C, overheating risk level is very low). Based on this echo information, the position and speed of the bat are updated to move it closer to an area with better heat dissipation status.

[0061] After multiple iterations, the final operating parameters were determined from the bat population based on the fitness values ​​of the bats, and adjustment instructions were sent to the laser. After receiving the instructions, the laser adjusted its operating parameters to the final values, thus ensuring stable operation within the preset temperature range.

[0062] By continuously optimizing the operating parameters of the laser, it is possible to ensure that the laser operates in the best heat dissipation state, thereby improving heat dissipation efficiency and reducing the risk of overheating. Since the cloud analysis results can be received in real time and the operating parameters can be adjusted, the laser can operate more stably within the preset temperature range, reducing failures and downtime caused by temperature fluctuations. The entire adjustment process is completed automatically without human intervention. This reduces the requirements for operators and improves production efficiency and management level. The operating parameters of the laser can be flexibly adjusted according to the analysis results and key information provided by the cloud processing module. This enables the system to adapt to different working environments and conditions, enhancing its adaptability and flexibility. By continuously monitoring and evaluating the heat dissipation status of the laser, overheating failures can be detected and prevented in a timely manner, thereby extending the service life of the laser and reducing maintenance costs.

[0063] In another preferred embodiment of the present invention, the parameter settings of the bat population include population size, frequency range, pulse emission rate, loudness, and pulse volume attenuation coefficient parameters, which may include: In the embodiment of the present invention, a population size is set, that is, the number of bats, which is determined according to the complexity of the problem and computing resources.

[0064] Frequency range: Set the frequency range of the ultrasonic waves emitted by the bat. This range determines the size of the parameter space that the bat can explore during the search process.

[0065] Pulse emission rate: defines the pulse emission rate of the bats, that is, the number of times each bat emits ultrasound in each iteration cycle. This affects the level of detail and computational cost of the search.

[0066] Loudness: Set the initial loudness of the ultrasonic waves emitted by the bat, which represents the "intensity" of the bat's exploration of the parameter space during the search process.

[0067] Pulse volume attenuation coefficient: defines the attenuation coefficient of the bat's pulse volume during the search process. It determines how the bat gradually reduces the search range during the iteration process, thereby adjusting the working parameters more finely.

[0068] Randomly generate N sets of laser operating parameters (laser power, operating current, pulse frequency) as the initial position of the bat population. These parameters are stored in an N×3 array, with each row representing the position of a bat.

[0069] In a preferred embodiment of the present invention, the calculation formula of the fitness function is: ; in, represents the fitness value; Represents the weight coefficient of temperature deviation; Indicates the current operating temperature of the laser; Indicates the preset temperature midpoint; Indicates the temperature range in which the laser is allowed to operate; Indicates the influence coefficient of temperature change rate; Indicates the current operating temperature of the laser Small changes in The weight coefficient representing the overheating risk; Indicates the maximum temperature the laser is allowed to reach; The weight coefficient representing the temperature change rate in the overheating risk; A non-linear index indicating the level of overheating risk; The weight coefficient representing the deviation of the working parameters; Indicates the current working parameters; Indicates the working parameter value reached by the preset laser; Indicates the maximum deviation range allowed for working parameters; Represents the penalty index for deviation of operating parameters.

[0070] In the embodiment of the present invention, all necessary parameters and variables are initialized, including weight coefficients ( )、Preset temperature midpoint( )、The temperature range in which the laser can operate ( ), the influence coefficient of temperature change rate ( ), the maximum temperature that the laser can reach ( ), weight coefficient of temperature change rate in overheating risk ( ), nonlinear index of overheating risk level ( )、The maximum deviation range allowed by working parameters( ), penalty index for working parameter deviation ( ) etc. At the same time, initialize the current operating temperature of the laser ( ), current working parameters (uc) and the working parameter values ​​of the preset laser ( By monitoring the operating temperature of the laser, its small changes are calculated ( ), and then the temperature change rate ( ). This is done by comparing the current temperature with the temperature at the last moment. Using the temperature deviation part of the formula, calculate the deviation between the current operating temperature and the midpoint of the preset temperature, taking into account the effect of the temperature change rate. The specific calculation is . Calculate the overheating risk based on the current operating temperature, the maximum temperature allowed by the laser, and the temperature change rate. The specific calculation is: Compare the current working parameters with the preset working parameter values, calculate the working parameter deviation, and consider the penalty index of the deviation. The specific calculation is . Add the results of the above three parts to get the final fitness value ( ). This value reflects the heat dissipation status and working performance of the laser under the current working parameters. During the iteration process, the working parameters (such as laser power, working current, pulse frequency, etc.) are continuously adjusted and the fitness value is recalculated. By comparing the fitness values ​​under different working parameters, the working parameters are adjusted to the final value.

[0071] By comprehensively considering temperature deviation, temperature change rate and overheating risk, the operating temperature of the laser can be controlled more accurately to ensure stable operation within the preset temperature range. The optimized operating parameters make the laser perform better in heat dissipation, reduce the risk of overheating, and extend the service life of the laser.

[0072] By minimizing the penalty of working parameter deviation, the final parameter combination can be found, which improves the output efficiency and stability of the laser. The whole process is completed automatically without manual intervention, which reduces the difficulty and cost of operation and improves production efficiency and management level.

[0073] In a preferred embodiment of the present invention, based on the analysis results of the cloud analysis module, an early warning is issued for potential heat dissipation problems, the future state of the laser heat dissipation system is predicted, and preventive measures are automatically planned, which may include: Receive the analysis results of the cloud analysis module, including abnormal temperature patterns, overheating risk prediction values, and cooling efficiency data, and evaluate the current cooling status according to the preset warning thresholds and conditions to obtain the evaluation results; if the evaluation results show that there is a potential cooling problem and it reaches the warning level, immediately send a warning signal through the user interaction module; According to the evaluation results and the early warning signals sent, the overheating risk prediction model and the heat dissipation efficiency calculation model are used to predict the heat dissipation status of the laser in the future, including the temperature change trend and the probability of increased overheating risk, so as to obtain the prediction results; Based on the prediction results, preventive measures are automatically planned, including adjusting the operating parameters of the laser, increasing the cooling capacity of the heat dissipation system, and scheduling maintenance.

[0074] In an embodiment of the present invention, a connection is established with a cloud analysis module through a network interface to receive analysis data in real time. The data includes abnormal temperature patterns (such as abnormal temperature rise, excessive temperature fluctuation, etc.), overheating risk prediction values ​​(future overheating possibility predicted based on historical data and machine learning models) and heat dissipation efficiency data (such as radiator working efficiency, cooling medium flow and temperature, etc.). The received data is parsed and verified to ensure the integrity and accuracy of the data. Preset warning thresholds and conditions, such as temperature upper limit, temperature change rate threshold, overheating risk warning level, etc. The received analysis data is compared with the preset thresholds and conditions to determine whether the current heat dissipation state is abnormal or there is a potential risk. The evaluation result is output in the form of a numerical value or status identifier. When the evaluation result reaches or exceeds the warning threshold, the warning mechanism is triggered. A warning signal is sent to the user through a user interaction module (such as a display screen, sound alarm, email, text message, etc.), including a specific description of the heat dissipation problem, the warning level and the recommended preliminary response measures. The sending time and content of the warning signal are recorded.

[0075] Assume that the data received from the cloud analysis module shows that the temperature of the laser has risen abnormally in a certain period of time, and the overheating risk prediction value has reached 80% (the preset threshold is 75%). The current heat dissipation status is immediately evaluated, and it is found that the temperature exceeds the preset upper limit value, and the temperature change rate also exceeds the threshold. Therefore, it is judged that there is a potential heat dissipation problem and it has reached the warning level. Subsequently, the warning message is displayed through the user interaction module (such as the display screen): "The laser temperature has risen abnormally, and the risk of overheating is high. Please check the cooling system immediately!" At the same time, emails and text messages are sent to users to remind them to pay attention and take corresponding countermeasures.

[0076] By receiving and analyzing cloud data in real time, potential heat dissipation problems can be discovered in time and early warning signals can be issued, allowing users to take measures in advance to reduce the risk of laser overheating. Intelligent evaluation of the current heat dissipation status based on preset thresholds and conditions avoids the subjectivity and errors of human judgment and improves the accuracy and reliability of the evaluation. The timely transmission of early warning signals enables users to respond quickly and take measures to solve heat dissipation problems, thereby avoiding losses such as equipment damage and production interruptions caused by laser overheating. Automatic planning and execution of preventive measures, such as adjusting working parameters and increasing cooling capacity, can optimize the heat dissipation management of the laser and improve its working efficiency and stability. At the same time, this also reduces the need for manual intervention and reduces management costs. Continuously receiving and analyzing cloud data provides data support for the continuous optimization and improvement of the heat dissipation system. Through continuous learning and adjustment, the accuracy of early warnings and the effectiveness of preventive measures can be gradually improved, providing guarantee for the long-term stable operation of the laser.

[0077] In a preferred embodiment of the present invention, remote access is performed to allow users to view the heat dissipation status of the laser in real time through a web page, receive alarm information, view historical data reports and analyze results, which may include: Collect the heat dissipation status data of the laser in real time, including temperature, pressure, and flow rate indicators, and integrate historical data reports, analysis results, and alarm information; The heat dissipation status data of the laser is sent to the user end in real time through the remote access interface, and when new alarm information is generated or historical data reports and analysis results are updated, the information is sent to the user through the remote access interface; The web interface displays the laser's heat dissipation status data in real time, including temperature curves and heat dissipation efficiency indicators, and allows users to view historical data reports and analysis results through the interface.

[0078] In an embodiment of the present invention, temperature sensors, pressure sensors and flow rate sensors monitor the heat dissipation system of the laser in real time, and collect key indicators such as temperature, pressure and flow rate respectively. The collected data is preliminarily processed by the internal data processing module, such as data cleaning, format conversion, etc., to ensure the accuracy and readability of the data. The processed data is integrated into a unified data storage system, which also contains historical data reports, analysis results and alarm information for subsequent data processing and remote access. Through the remote access interface, the heat dissipation status data of the laser is sent to the user end in real time: the remote access interface supports a variety of communication protocols (such as HTTP, WebSocket, etc.) for data transmission with the user end. When the new data is collected and processed, the data is sent to the user end in real time through the remote access interface. This includes real-time values ​​of temperature, pressure and flow rate, as well as any new alarm information. At the same time, when the historical data report, analysis results or alarm information is updated, these update information will also be sent to the user end through the remote access interface to ensure that the user can obtain the latest data and analysis results. The user end accesses a specific web page through a browser, which communicates with the remote access interface to obtain the heat dissipation status data of the laser. The web interface is designed with a real-time temperature curve chart to show the trend of laser temperature changes over time. At the same time, it also displays heat dissipation efficiency indicators, such as the working efficiency of the radiator, the flow rate of the cooling medium, etc. Users can view historical data reports and analysis results through the query function on the web interface. These reports and analysis results are displayed in the form of charts, tables or texts to help users gain a deeper understanding of the heat dissipation status and performance of the laser.

[0079] Suppose a user is remotely monitoring the cooling status of a laser through a web interface. The sensor collects the temperature, pressure, and flow rate data of the laser in real time, and sends this data to the user through the remote access interface. The user sees the real-time temperature curve on the web interface and finds that the temperature has risen, but it is still within the normal range. At the same time, the user also checks the cooling efficiency index and finds that the flow rate of the cooling medium has dropped slightly. At this time, a new alarm message is generated, indicating that the cooling medium flow rate is insufficient, which may affect the cooling effect. This alarm message is immediately sent to the user through the remote access interface. After receiving the alarm message on the web interface, the user immediately takes measures to adjust the flow rate of the cooling medium, thereby avoiding potential cooling problems.

[0080] By collecting and sending the heat dissipation status data of the laser in real time, users can timely understand the heat dissipation of the laser and take prompt measures when problems occur to avoid equipment damage or production interruption. Users can remotely access the heat dissipation status data of the laser through the web interface without going to the site in person, which improves the convenience and efficiency of monitoring. The real-time temperature curve graph and heat dissipation efficiency index on the web interface enable users to intuitively understand the heat dissipation status and performance of the laser, which is convenient for users to make decisions and analysis. Users can view historical data reports and analysis results through the web interface, conduct in-depth analysis and evaluation of the heat dissipation status and performance of the laser, and provide data support for equipment maintenance and optimization. It can automatically generate alarm information and send it to the user end through the remote access interface, helping users to promptly discover and prevent potential heat dissipation risks and ensure the safe and stable operation of the laser.

[0081] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing, characterized in that: include: Laser monitoring module, used to emit ultrafast lasers and with a built-in temperature sensor to monitor the operating temperature of the laser in real time; The data acquisition module is used to connect with the laser monitoring module to obtain the temperature data and key parameters of the temperature sensor in real time, including laser power and working current, and upload the temperature data and key parameters to the cloud server; A cloud analysis module is used to receive and analyze temperature data and key parameter data, identify abnormal temperature patterns, predict potential overheating risks, and calculate heat dissipation efficiency to obtain analysis results; Adaptive heat dissipation control module, which is used to automatically adjust the working parameters of the laser according to the analysis results of the cloud processing module, so that the laser operates within the preset temperature range; The early warning module is used to issue early warnings for potential heat dissipation problems based on the analysis results of the cloud analysis module, predict the future state of the laser heat dissipation system, and automatically plan preventive measures; The user interaction module is used for remote access, allowing users to view the cooling status of the laser in real time through the web page, receive alarm information, view historical data reports and analyze results.

2. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 1 is characterized in that: Receive and analyze temperature data and key parameter data, identify abnormal temperature patterns, predict potential overheating risks, and calculate cooling efficiency to obtain analytical results, including: Receive the temperature data and key parameter data uploaded by the data acquisition module, and perform preprocessing to obtain processed temperature data; Extract features from the processed temperature data, including mean, standard deviation, and temperature trend, and Identify abnormal temperature patterns; Build an overheating risk prediction model based on abnormal temperature patterns and historical data, input real-time temperature data and key parameters into the risk prediction model, calculate the current overheating risk value, and obtain the risk assessment result; According to the risk assessment results, a heat dissipation efficiency calculation model is established, and the real-time temperature data, key parameters and the operating status of the heat dissipation system, including fan speed and coolant flow, are input into the heat dissipation efficiency calculation model to calculate the current heat dissipation efficiency; Combine abnormal temperature patterns, overheating risk predictions, and cooling efficiency as analysis results.

3. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 2 is characterized in that: Extract features from processed temperature data, including mean, standard deviation, and temperature trend, and identify abnormal temperature patterns, including: Arrange the temperature data in chronological order to form a time series data set; Traverse the time series data set to obtain the mean and standard deviation of the temperature data, and fit the time series data set to obtain a trend model of temperature changes over time; According to the trend model of temperature changes over time, the average value and standard deviation of temperature data, the threshold range is set, and the real-time temperature data is compared with the threshold to identify temperature patterns that are beyond the normal range, have abnormal fluctuations or abnormal trends, and mark them as abnormal temperature patterns.

4. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 3 is characterized in that: The current overheating risk value is calculated as: ； in, Indicates the current overheating risk value; represents the weight coefficient; Indicates the current operating temperature of the laser; Indicates the temperature threshold; Indicates the maximum temperature difference; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the ambient reference temperature; Indicates the number of temperature sampling points; Indicates temperature index; Indicates The temperature value of each temperature sampling point; Indicates the average temperature value of all temperature sampling points; Indicates the temperature fluctuation influence coefficient; Indicates at time Temperature value at ; Indicates a time interval; Indicates the temperature change rate influence coefficient; Indicates real-time power; represents the power influence coefficient; Indicates rated power; Indicates real-time current; represents the current influence coefficient; Indicates rated current.

5. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 4 is characterized in that: The current heat dissipation efficiency is calculated as: ； in, Indicates the current heat dissipation efficiency; Indicates the actual heat dissipation; Preset heat dissipation; Indicates the environmental temperature influence coefficient; Indicates the ambient temperature; Indicates the reference temperature; represents the power influence coefficient; Indicates the real-time power of the laser; Indicates the maximum power of the laser; Indicates real-time current; Indicates the maximum current of the laser; Indicates the fan speed influence coefficient; Indicates the actual speed of the fan; Indicates the coolant flow rate influence coefficient; Indicates the actual flow rate of coolant; represents the humidity influence coefficient; Indicates ambient humidity; Indicates the maximum ambient humidity; represents the pollutant impact coefficient; Indicates the concentration value of environmental pollutants; Indicates the baseline value of pollutant concentration in a clean environment; represents the pollutant concentration threshold; Indicates the influence coefficient of radiator material; Indicates the heat sink material type.

6. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 5 is characterized in that: According to the analysis results of the cloud processing module, the operating parameters of the laser are automatically adjusted to make the laser operate within the preset temperature range, including: Define the parameter settings of the bat population and initialize the position of the bat population. The position of each bat represents a set of laser operating parameters, including laser power, operating current, and pulse frequency. Parse the analysis results of the cloud processing module to extract key information, including the current temperature status, whether it exceeds the preset temperature range, overheating risk level and recommended cooling measures; Based on the key information, a fitness function is defined to evaluate whether the heat dissipation state under the current laser operating parameters meets the preset requirements to obtain the evaluation results; According to the evaluation results, during the iteration process, each bat transmits ultrasonic waves according to its current position and speed, and receives echoes, which contain information about the heat dissipation status under the current working parameters, including temperature and overheating risk level; According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts local search. When the number of iterations reaches the preset value, the final working parameters are determined. According to the echo information, the bat updates its position and speed. After updating the position, the bat conducts a local search. When the number of iterations reaches the preset value, the final working parameters are determined from the bat population according to the bat's fitness value; Sending adjustment instructions to the laser to change the operating parameters to final operating parameters to update the operating parameter settings in the laser control system; The process of continuously receiving analysis results from the cloud, parsing key information, and evaluating the current status allows the laser to operate within the preset temperature range.

7. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 6, characterized in that: The parameter settings of the bat population include population size, frequency range, pulse emission rate, loudness, and pulse volume attenuation coefficient parameters.

8. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 7, characterized in that: The calculation formula of the fitness function is: ； in, represents the fitness value; Represents the weight coefficient of temperature deviation; Indicates the current operating temperature of the laser; Indicates the preset temperature midpoint; Indicates the temperature range in which the laser is allowed to operate; Indicates the influence coefficient of the temperature change rate; Indicates the current operating temperature of the laser Small changes in The weight coefficient representing the overheating risk; Indicates the maximum temperature the laser is allowed to reach; The weight coefficient representing the temperature change rate in the overheating risk; A non-linear index indicating the level of overheating risk; The weight coefficient representing the deviation of the working parameters; Indicates the current working parameters; Indicates the working parameter value reached by the preset laser; Indicates the maximum allowable deviation range of working parameters; Represents the penalty index for deviation of operating parameters.

9. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 8, characterized in that: Based on the analysis results of the cloud analysis module, early warnings are issued for potential heat dissipation problems, the future status of the laser cooling system is predicted, and preventive measures are automatically planned, including: Receive the analysis results of the cloud analysis module, including abnormal temperature patterns, overheating risk prediction values, and cooling efficiency data, and evaluate the current cooling status according to the preset warning thresholds and conditions to obtain the evaluation results; if the evaluation results show that there is a potential cooling problem and it reaches the warning level, immediately send a warning signal through the user interaction module; According to the evaluation results and the early warning signals sent, the overheating risk prediction model and the heat dissipation efficiency calculation model are used to predict the heat dissipation status of the laser in the future, including the temperature change trend and the probability of increased overheating risk, so as to obtain the prediction results; Based on the prediction results, preventive measures are automatically planned, including adjusting the operating parameters of the laser, increasing the cooling capacity of the heat dissipation system, and scheduling maintenance.

10. The real-time monitoring system for ultrafast laser heat dissipation status based on cloud computing according to claim 9, characterized in that: Remote access allows users to view the laser's cooling status, receive alarm information, view historical data reports and analyze results in real time through the web page, including: Collect the heat dissipation status data of the laser in real time, including temperature, pressure, and flow rate indicators, and integrate historical data reports, analysis results, and alarm information; The heat dissipation status data of the laser is sent to the user end in real time through the remote access interface, and when new alarm information is generated or historical data reports and analysis results are updated, the information is sent to the user through the remote access interface; The web interface displays the laser's heat dissipation status data in real time, including temperature curves and heat dissipation efficiency indicators, and allows users to view historical data reports and analysis results through the interface.

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