Environmental control method and system for preparing aluminum nitride vacuum detectors

Abstract

The present invention discloses an environmental control method and system for the preparation of aluminum nitride vacuum detectors, which relates to the technical field of vacuum detector preparation. The method comprises: obtaining the characteristics of the aluminum nitride vacuum detector preparation environment to construct a monitoring system, monitoring and filtering key parameters in real time, and storing data in a database. Key parameters are predicted based on the database, the deviation between the predicted value and the actual value is calculated, the deviation is input into the optimization control model to obtain a strategy, and the preparation environment is adaptively controlled according to the strategy. The technical problems in the prior art of inaccurate environmental monitoring of aluminum nitride vacuum detectors, lack of effective prediction and control, and difficulty in adapting to complex environmental changes are solved. By realizing accurate monitoring, effective prediction and adaptive control of the aluminum nitride vacuum detector preparation environment, the technical effects of improving the preparation quality and stability of the detector and improving the efficiency and controllability of the preparation process are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of vacuum detector preparation, and in particular to an environmental control method and system for preparing aluminum nitride vacuum detectors. Background Art

[0002] In the preparation scenario of aluminum nitride vacuum detectors, the problem of precise control of the preparation environment is particularly prominent, and the demand for advanced environmental control methods is also more prominent. Achieving efficient environmental control and better meeting the needs of high-quality detector preparation have become crucial links in the preparation of aluminum nitride vacuum detectors. Traditional aluminum nitride vacuum detector preparation environmental control is often relatively simple and localized, lacking a comprehensive and accurate environmental parameter monitoring system, insufficient overall control of the preparation environment, lacking in-depth analysis and utilization of historical data on key environmental parameters, making it difficult to accurately predict environmental change trends. There are unreasonable situations in the allocation of environmental control resources, resulting in an inability to adapt to the complex and changing preparation environment. The formulation of environmental control plans is relatively fixed and cannot cope well with the ever-changing actual conditions during the preparation process.

[0003] At present, relevant technologies have the following technical problems: inaccurate environmental monitoring in the preparation of aluminum nitride vacuum detectors, lack of effective prediction and control, and difficulty in adapting to complex environmental changes. Summary of the Invention

[0004] This application provides an environmental control method and system for the preparation of aluminum nitride vacuum detectors. This system uses a monitoring system to obtain the environmental characteristics of aluminum nitride vacuum detector preparation, monitor and filter key parameters in real time, and store the data in a database. Key parameters are predicted based on the database, the deviation between the predicted value and the actual value is calculated, and the deviation is input into an optimization control model to obtain a strategy. The preparation environment is adaptively controlled based on the strategy. By accurately monitoring, effectively predicting, and adaptively controlling the preparation environment of aluminum nitride vacuum detectors, the technical effects of improving the preparation quality and stability of the detectors and increasing the efficiency and controllability of the preparation process are achieved.

[0005] The present application provides an environmental control method for preparing an aluminum nitride vacuum detector, comprising:

[0006] Acquire the characteristics of the aluminum nitride vacuum detector preparation environment, and construct an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector preparation environment; use the environmental parameter monitoring system to monitor and filter the key parameters of the detector preparation environment in real time, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in a preparation environment database; predict the key parameters of the detector preparation environment based on the preparation environment database to obtain predicted values ​​of the key parameters of the detector preparation environment within a variable time window; calculate the numerical deviation between the predicted values ​​of the key parameters of the detector preparation environment and the actual values ​​of the key parameters of the detector preparation environment; input the numerical deviation into a preset optimization control model for analysis to obtain an optimization control strategy for the detector preparation environment, wherein the optimization control strategy includes optimization control parameters of the key parameters of the detector preparation environment; and adaptively control the aluminum nitride vacuum detector preparation environment according to the optimization control strategy.

[0007] The present application also provides an environmental control system for preparing an aluminum nitride vacuum detector, comprising:

[0008] An environmental parameter monitoring system construction module is used to obtain the characteristics of the aluminum nitride vacuum detector preparation environment, and to construct an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector preparation environment; a preparation environment database storage module is used to perform real-time monitoring and data filtering on the key parameters of the detector preparation environment through the environmental parameter monitoring system, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in the preparation environment database; a key parameter prediction module is used to predict the key parameters of the detector preparation environment based on the preparation environment database, and obtain variable time The predicted value of the key parameters of the detector preparation environment within the window; a numerical deviation calculation module, the numerical deviation calculation module is used to calculate the numerical deviation between the predicted value of the key parameters of the detector preparation environment and the actual value of the key parameters of the detector preparation environment; an optimization control strategy acquisition module, the optimization control strategy acquisition module is used to input the numerical deviation into a preset optimization control model for analysis, and obtain the optimization control strategy of the detector preparation environment, the optimization control strategy including the optimization control parameters of the key parameters of the detector preparation environment; an adaptive control module, the adaptive control module is used to perform adaptive control on the aluminum nitride vacuum detector preparation environment according to the optimization control strategy.

[0009] The environmental control method and system for aluminum nitride vacuum detector preparation proposed in this application first acquires the characteristics of the aluminum nitride vacuum detector preparation environment and constructs a monitoring system. Key parameters are monitored and filtered in real time, and the data is stored in a database. Key parameters are predicted based on the database, and the deviation between the predicted and actual values ​​is calculated. This deviation is input into an optimized control model to obtain a strategy. Based on this strategy, the preparation environment is adaptively controlled. By achieving precise monitoring, effective prediction, and adaptive control of the aluminum nitride vacuum detector preparation environment, the technical effects of improving the preparation quality and stability of the detector and enhancing the efficiency and controllability of the preparation process are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic flow chart of an environmental control method for preparing an aluminum nitride vacuum detector provided in an embodiment of the present application;

[0012] Figure 2 This is a schematic diagram of the structure of an environmental control system for preparing an aluminum nitride vacuum detector provided in an embodiment of the present application.

[0013] Explanation of the accompanying symbols: environmental parameter monitoring system construction module 10, preparation environment database storage module 20, key parameter prediction module 30, numerical deviation calculation module 40, optimization control strategy acquisition module 50, adaptive control module 60. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present application provides an environmental control method for preparing an aluminum nitride vacuum detector, such as Figure 1 As shown, the method includes:

[0018] Step S100, obtain the characteristics of the aluminum nitride vacuum detector preparation environment, and construct an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector preparation environment. Specifically, in the process of constructing the environmental parameter monitoring system for the preparation of aluminum nitride vacuum detectors, first obtain the preparation environment characteristics, including attribute data characteristics, event data characteristics and service data characteristics. For attribute data, clarify the environmental parameters and equipment attribute types, understand their range of variation and their impact on detector performance; for event data, cover monitoring, abnormal and control events, and grasp their occurrence frequency, impact level and early warning signals; for service data, evaluate the methods, goals and effects of data collection, analysis and control services, select appropriate sensors and data acquisition equipment according to the characteristics, establish reliable data transmission channels and storage solutions, develop monitoring system software to realize real-time monitoring, data analysis and control functions, and finally optimize and improve through testing to improve the stability and reliability of the system and provide strong guarantees for detector preparation.

[0019] Step S200, the environmental parameter monitoring system is used to monitor and filter the key parameters of the detector preparation environment in real time, obtain the historical data and real-time data of the key parameters of the detector preparation environment, and store them in the preparation environment database. Specifically, the environmental parameter monitoring system monitors the key parameters of the detector preparation environment in real time, uses the box plot method to filter the data, groups the key parameter data in chronological order, calculates the lower, middle, and upper quartiles and the interquartile range to determine the range of outliers, removes outlier data points, and after filtering, stores the historical data and real-time data of the key parameters in the preparation environment database. The database classifies and stores attribute data (including environmental parameter attribute data and equipment attribute data), event data (monitoring event data, abnormal event data, and control event data), and service data (data acquisition service, data analysis service, and control service) for subsequent targeted analysis and processing, providing a basis for problem investigation and optimization control, and improving the level of preparation environment management.

[0020] In one possible implementation, the environmental parameter monitoring system is used to monitor and filter the key parameters of the detector preparation environment in real time, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in a preparation environment database. Step S200 further includes step S210, grouping the key parameters of the detector preparation environment according to time sequence to obtain multiple groups of key parameter grouping data. Specifically, during the detector preparation process, the environmental parameter monitoring system continuously collects key parameter data. Since data is continuously generated over time, in order to better analyze the distribution and change patterns of the data, the key parameters are grouped according to time sequence, and the data is divided into multiple time period groups according to certain time intervals, such as every hour, every day, etc., to facilitate subsequent separate analysis of the data in each time period, and also to better observe the changes in data on different time scales.

[0021] Step S220 , for the multiple groups of key parameter grouped data, calculate the lower quartile, median, and upper quartile of each group of key parameter grouped data. Specifically, for each group of key parameter grouped data, it is necessary to calculate its lower quartile, median and upper quartile. The statistics can reflect the distribution of the data. The lower quartile represents the median of the lower part of the data. When calculating the lower quartile, first sort the group of data from small to large, and then determine the value at one-quarter of the data. If the number of data is odd, the lower quartile is the value of the (n+1) / 4th data after sorting. If the number of data is even, the lower quartile is the average of the n / 4th and (n / 4+1)th data after sorting. The median is the middle value of the data. After sorting the data from small to large, if the number of data is odd, the median is the middle data value after sorting. If the number of data is even, the median is the average of the two middle data after sorting. The upper quartile represents the median of the higher part of the data. The calculation method is similar to the lower quartile, and the value at three-quarters of the data is determined.

[0022] Step S230: Obtain the interquartile range of the key parameter grouped data, where the interquartile range is the upper quartile minus the lower quartile. Specifically, after calculating the upper quartile and lower quartile of each group of key parameter grouped data, the interquartile range is obtained by subtracting the upper quartile and lower quartile from each other. The interquartile range reflects the degree of dispersion of the data. A smaller interquartile range indicates that the data is more concentrated, while a larger interquartile range indicates that the data distribution is more dispersed.

[0023] Step S240, determine the outlier range, determine whether the data point is within the outlier range, and if so, mark the data point as an outlier data point, and remove the outlier data point from the key parameter grouping data. Specifically, the outlier range is determined based on the interquartile range, and the outlier range is set to be less than the lower quartile minus 1.5 times the interquartile range, or greater than the upper quartile plus 1.5 times the interquartile range. For each data point, determine whether it is within this outlier range. If the value of the data point is less than the lower quartile minus 1.5 times the interquartile range, or greater than the upper quartile plus 1.5 times the interquartile range, the data point is marked as an outlier data point. Once the outlier data point is determined, it is removed from the key parameter grouping data to remove abnormal data that may be caused by sensor failure, sudden interference, etc., thereby improving the quality and reliability of the data and allowing for more accurate subsequent analysis and processing.

[0024] In one possible implementation, the key parameters of the detector preparation environment are monitored and filtered in real time by the environmental parameter monitoring system, and the historical data and real-time data of the key parameters of the detector preparation environment are obtained and stored in the preparation environment database. Step S200 further includes step S250, which obtains the attribute data of the key parameters of the detector preparation environment, classifies the attribute data according to the attribute type, obtains the environmental parameter attribute data and the equipment attribute data, and stores the environmental parameter attribute data and the equipment attribute data separately. Specifically, the attribute data of the key parameters of the detector preparation environment are obtained, and the key parameters in the detector preparation environment are clarified, including environmental related parameters such as ambient temperature, humidity, vacuum degree, and equipment attribute parameters such as equipment model and location. These attribute data are collected in real time by setting corresponding sensors and monitoring equipment in the preparation environment. For example, temperature and humidity sensors can be installed in the preparation workshop to obtain ambient temperature and humidity data. At the same time, the model and location information of each detector preparation equipment are recorded. The attribute data are classified according to the attribute type, and the acquired attribute data are divided into two categories according to their nature: environmental parameter attribute data and equipment attribute data. The environmental parameter attribute data mainly includes The data includes parameters that reflect environmental conditions, such as temperature, humidity, and vacuum degree; device attribute data covers device-related information such as device model and location. For example, data obtained through temperature and humidity sensors is classified as environmental parameter attribute data, while information such as the manufacturer and model of the device is classified as device attribute data. Environmental parameter attribute data and device attribute data are stored separately. For ease of management and query, environmental parameter attribute data and device attribute data are stored in different database tables or files. For example, create a table called "Environmental Parameter Table" to store data such as temperature, humidity, and vacuum degree, and then create a "Device Information Table" to store information such as device model and location. At the same time, for fields that are frequently used for query and filtering, such as device model and temperature range, indexes are established to improve query efficiency.

[0025] Step S260: Acquire event data for key parameters of the detector preparation environment. The event data includes monitoring event data, abnormal event data, and control event data, and the monitoring event data, abnormal event data, and control event data are stored separately. Specifically, the event data for key parameters of the detector preparation environment are acquired. The event data mainly includes monitoring event data, abnormal event data, and control event data. Monitoring event data is the routine monitoring result of the detector preparation environment; abnormal event data is data generated when an abnormal situation occurs in the environment; and control event data is data generated when the environment is adjusted and controlled. For example, by continuously monitoring the ambient temperature, each temperature reading can be used as a monitoring event data. If the temperature exceeds the set range, abnormal event data will be generated. When measures are taken to adjust the temperature, such as turning on the air conditioner or adjusting the ventilation system, control event data will be generated. The monitoring event data, abnormal event data, and control event data are stored separately. The three types of event data are stored in different database tables. For monitoring event data, the key parameter values ​​of each monitoring can be stored with the timestamp as the primary key. For example, a "monitoring event table" is created, which includes a timestamp field and key parameter fields such as temperature, humidity, and vacuum degree. Each monitored data is stored in the table in chronological order. For abnormal event data, in addition to the timestamp and abnormality type, the degree of abnormality and related key parameter values ​​need to be recorded in detail. For example, when the temperature is too high and causes an abnormality, the timestamp, the abnormality type is "temperature is too high", the degree of abnormality (such as the specific value beyond the normal temperature range) and the temperature value at that time are recorded in the "Abnormal Event Table". For control event data, the time, method and adjusted key parameter values ​​of the control operation are stored. For example, when the temperature is lowered by turning on the air conditioner, the time and method of the control operation are recorded in the "Control Event Table". The method is "turn on cooling" and the adjusted temperature value.

[0026] Step S270, obtain the service data of the key parameters of the detector preparation environment, the service data includes data collection service, data analysis service and control service, and store the data collection service, data analysis service and control service separately. Specifically, obtain the service data of the key parameters of the detector preparation environment, the service data includes data collection service, data analysis service and control service, the data collection service is responsible for collecting key parameter data of the environment; the data analysis service analyzes and processes the collected data; the control service adjusts and controls the environment according to the analysis results, for example, the data collection service collects environmental temperature, humidity and other data through sensors at regular intervals; the data analysis service uses a specific algorithm to analyze the collected data to determine whether the environment is normal; the control service decides whether to turn on cooling, adjust humidity, etc. according to the analysis results, and stores the data collection service, data analysis service and control service separately. For the data collection service, store its collection frequency, data source and other configurations Information, for example, creating a data collection service table, recording information such as the collection frequency (such as once every 5 minutes), data source (such as the specific sensor number or name), etc., recording the algorithm used by the data analysis service, the time range of the analysis, and the results. For example, in the data analysis service table, record the name of the algorithm used (such as the mean algorithm, trend analysis algorithm, etc.), the time range of the analysis (such as the past 24 hours), and the analysis results (such as the judgment of whether the environment is normal or abnormal). Save the control policy generation rules and execution history of the control service. For example, in the control service table, record the control policy generation rules (such as turning on the air conditioner when the temperature exceeds a certain value) and the execution history of each control operation, including information such as the execution time and control method.

[0027] Step S300, based on the preparation environment database, predict the key parameters of the detector preparation environment to obtain the predicted values ​​of the key parameters of the detector preparation environment within a variable time window. Specifically, based on the preparation environment database, the historical operation data and real-time operation data of the detector preparation environment are sorted out, and predictions are made using deep learning algorithms such as recurrent neural networks (RNN) and long short-term memory networks (LSTM). The historical operation data are input into the model for training to learn the change rules of key parameters, and then the real-time operation data are input into the trained model to obtain the predicted values ​​of the environmental parameters in the future. The length of the time window affects the prediction accuracy. Generally, the shorter the time window, the higher the prediction accuracy, because the changes in environmental parameters in the short term are relatively stable; the longer the time window, the greater the influence of uncertainty factors, and the relatively lower the prediction accuracy. In actual applications, it is necessary to determine the appropriate time window according to specific needs to improve the preparation quality and efficiency of the detector.

[0028] In one possible implementation, based on the preparation environment database, the key parameters of the detector preparation environment are predicted to obtain the predicted values ​​of the key parameters of the detector preparation environment within a variable time window. Step S300 further includes step S310, which constructs a key parameter prediction model. The key parameter prediction model is constructed based on the historical data of the key parameters of the aluminum nitride vacuum detector preparation environment. Specifically, the range of the key parameters of the aluminum nitride vacuum detector preparation environment is clarified. The key parameters include temperature, humidity, vacuum degree, gas composition, etc. A large amount of historical data is collected. The historical data is recorded by the environmental parameter monitoring system during the past detector preparation process. For the construction of the model, based on machine learning algorithms, especially time series analysis algorithms, considering the time series characteristics of the data, an algorithm suitable for processing time series data is selected, such as a long short-term memory network. The long short-term memory network model is constructed by first cleaning and preprocessing the collected historical data to remove outliers and noise. Statistical methods, such as the box plot method, are used to determine the range of outliers and eliminate outlier data points. The data is normalized and the data values ​​are mapped to a specific interval so that the model can better learn the data. Features, design the structure of the long short-term memory network, including determining the number of nodes in the input layer, hidden layer and output layer. The number of nodes in the input layer usually corresponds to the number of key parameters. The hidden layer can be set to multiple levels according to the complexity of the data and the prediction requirements. Each level contains a certain number of long short-term memory network units. The number of nodes in the output layer is usually the number of key parameters that need to be predicted. The preprocessed historical data is input into the designed long short-term memory network for training. During the training process, the network weights and biases are continuously adjusted to minimize the error between the predicted value and the actual value. The back propagation algorithm and optimization algorithm, such as the stochastic gradient descent method, are used to update the network parameters. The training process usually requires multiple iterations until the model converges or reaches the preset number of training times.

[0029] Step S320, using the key parameter prediction model to predict the key parameters of the detector preparation environment based on the real-time data, and obtaining the predicted values ​​of the key parameters of the detector preparation environment within the variable time window. Specifically, after the key parameter prediction model is constructed, the model is used in combination with real-time data to predict the key parameters of the detector preparation environment, and real-time data is obtained. The values ​​of the key parameters of the detector preparation environment collected in real time by the environmental parameter monitoring system are input into the trained key parameter prediction model. The model predicts the key parameters of the detector preparation environment within the variable time window based on the real-time data and the learned historical data pattern. The length of the variable time window can be adjusted according to actual needs. A shorter time window may provide a more accurate short-term prediction, while a longer time window can provide a rough estimate of future trends. The model outputs the predicted values ​​of the key parameters of the detector preparation environment within the variable time window. The predicted values ​​can provide an important reference basis for environmental control during the detector preparation process, helping operators to adjust environmental parameters in a timely manner to ensure the preparation quality and stability of the detector.

[0030] In a possible implementation, based on the preparation environment database, the key parameters of the detector preparation environment are predicted to obtain the predicted values ​​of the key parameters of the detector preparation environment within a variable time window. Step S300 further includes step S330, which obtains the attribute data, event data, and service data from the preparation environment database. Specifically, the various required data are extracted from the already constructed preparation environment database. The attribute data include environmental parameter attribute data (such as temperature, humidity, vacuum degree, etc.) and equipment attribute data (such as equipment model, location, etc.). The event data covers monitoring event data (recording key parameter values ​​for routine monitoring), abnormal event data (recording abnormal conditions and related parameter values) and control event data (recording key parameter values ​​for control operations and adjustments). The service data includes data acquisition service data (such as configuration information such as acquisition frequency and data source), data analysis service data (recording the algorithm used, the time range and results of the analysis), and control service data (saving control strategy generation rules and execution history records).

[0031] Step S340: Determine the reference range of the variable time window based on the data characteristics of the attribute data. Specifically, the data characteristics of the attribute data are analyzed, including trends, importance, sensitivity, change speed, and amplitude. For example, if the change trend of a key parameter is relatively stable, with a slow change speed and a small amplitude, then a relatively long time window reference range can be preliminarily determined. If the parameter changes frequently, with a large amplitude and high sensitivity, then a shorter time window reference range may be required. By analyzing the characteristics of different attribute data, an approximate reference range of the variable time window can be comprehensively determined.

[0032] Step S350, obtaining sub-event data in the event data, associating data features of the sub-event data with actual event features, and obtaining event constraint conditions, wherein the sub-event data includes monitoring event data, abnormal event data, and control event data. Specifically, for monitoring event data, analyze the parameter changes and detection frequency of its data characteristics. If the monitoring frequency is high and the parameter changes are relatively slow, then a longer time range can be considered when determining the time window, because frequent monitoring can capture parameter changes in a timely manner. On the contrary, if the monitoring frequency is low, the parameter changes may change significantly between two monitorings. At this time, a shorter time window is required to ensure timely response; for abnormal event data, pay attention to the frequency of abnormal occurrence. If the frequency of abnormal events is high, it means that the environment is unstable, and a shorter time window may be required to promptly discover and handle abnormal situations. If the frequency of abnormal events is low, the time window can be appropriately relaxed; for control event data, consider the time delay between control instructions and parameter changes. If the parameters change quickly after the control instructions are issued and the time delay is short, a shorter time window can be selected to achieve more timely control. If the time delay is long, a longer time window is required to ensure the effectiveness of the control. By analyzing the correlation between these sub-event data characteristics and actual event characteristics, event constraints are obtained.

[0033] Step S360: Obtain sub-service data from the service data, associate the data features of the sub-service data with the actual service features, and obtain service constraints. The sub-service data includes data acquisition service data, data analysis service data, and control service data. Specifically, for data acquisition service data, consider the acquisition frequency. If the acquisition frequency is high, it can provide more real-time data, supporting a shorter time window for more accurate analysis and prediction. If the acquisition frequency is low, data updates are slow, requiring a longer time window. For data analysis service data, analyze the adaptability of the key parameter prediction model to variable time windows. If the model shows good prediction accuracy and stability within a shorter time window, a shorter time window can be prioritized. If the model can better capture trend changes under a longer time window, a longer time window can be considered. For control service data, analyze its responsiveness to different variable time windows. If the control service can quickly respond and adjust environmental parameters within a shorter time window, a shorter time window can be selected. If the response speed is slow, a longer time window may be required to make control preparations in advance. Service constraints are obtained by analyzing the association between the sub-service data features and the actual service features.

[0034] In step S370, the reference range of the variable time window is constrained based on the event constraint and the service constraint, and multiple time window lengths are obtained. Specifically, the previously determined reference range of the variable time window is further constrained by comprehensively considering the event constraint and the service constraint. Based on different combinations of constraints, multiple possible time window lengths can be obtained. For example, a shorter time window length can be obtained by combining a higher frequency of abnormal occurrences in the event constraint and a faster data collection frequency and control response capability in the service constraint. On the other hand, a longer time window length may be obtained if the frequency of abnormal occurrences in the event constraint is lower and the model in the service constraint performs better under longer time windows.

[0035] Step S380: Determine the actual effectiveness of the multiple time window lengths and determine the optimal time window length. Specifically, the actual effectiveness of the multiple time window lengths is determined. Through simulation experiments, historical data analysis, and other methods, the prediction accuracy and control effectiveness of different time window lengths are evaluated. For example, in a simulated detector production environment, different time window lengths are used for prediction and control, and the parameter changes and detector production quality are observed. Based on the evaluation results, the time window length that achieves the best balance between prediction accuracy, control effectiveness, and response speed is selected as the optimal time window length.

[0036] In one possible implementation, the actual effects of the multiple time window lengths are judged to determine the optimal time window length. Step S380 further includes step S381, where the optimal time window length is regularly evaluated to calculate a prediction error index. Specifically, after determining the optimal time window length, it is necessary to regularly evaluate it and select a suitable prediction error index, such as a root mean square error, a mean absolute error, etc. The index can measure the accuracy of the prediction. The prediction error index is calculated by comparing the difference between the predicted value and the actual value. The actual environmental parameter value is compared with the predicted value under the current optimal time window length using historical data and real-time data in the prepared environment database, and the calculation is performed according to the calculation formula of the error index.

[0037] Step S382 compares the prediction error index with a preset prediction accuracy threshold. If the prediction error index is less than the preset prediction accuracy threshold, a window time length adjustment strategy is formulated to optimize the solution. Specifically, a preset prediction accuracy threshold is set, which represents an acceptable prediction error range. The calculated prediction error index is compared with the preset threshold. If the prediction error index is less than the preset prediction accuracy threshold, it indicates that the current optimal time window length is within the acceptable range and the prediction accuracy is high. If the prediction error index is greater than the preset threshold, it indicates that the prediction accuracy has decreased and the time window length needs to be adjusted.

[0038] Step S383: Adjust the optimal solution of the strategy according to the window time length and re-determine the optimal window time length. Specifically, according to different trigger conditions, the corresponding adjustment strategy is selected. If the prediction accuracy decreases, you can consider shortening the time window to capture parameter changes more timely, because a long time window may lead to untimely response to recent parameter changes. Shortening the time window can improve the sensitivity to rapidly changing environmental parameters. If the control effect is not good, you can try to adjust the time window to better adapt to the control needs. For example, if the response time of the control instruction under the current time window is too long or the control effect is not obvious, you can appropriately adjust the time window length to find a time range that is more suitable for control. For new periodic changes in environmental parameters, you can adjust the time window according to the cycle length. If new periodic fluctuations in environmental parameters are found, analyze the cycle length and then adjust the time window length accordingly to match the cycle length in order to better predict and control environmental parameters. In order to avoid the instability caused by large adjustments to the time window, a small gradual adjustment method is adopted. For example, each time a certain proportion of the time window length is adjusted to gradually explore a more suitable time window length without causing drastic fluctuations in the system. Before the actual application of the adjusted time window, historical data or simulation data are used to simulate prediction and control effect verification. A part of historical data is selected from the prepared environmental database. Historical data, or use the simulation data generator to generate simulation data. In the simulation environment, use the adjusted time window for prediction and control, and observe the prediction results and control effects. If the simulation results show that the adjusted time window can improve the prediction accuracy and control effect, continue to apply it; otherwise, adjust the strategy and try again; apply the adjusted time window to the actual environment control and continuously monitor the effect. If the simulation verification passes, apply the adjusted time window to the actual aluminum nitride vacuum detector preparation environment control, continuously monitor the changes in environmental parameters and control effects, and collect actual operation data to timely discover problems and make adjustments. Feedback on actual results is used to continuously optimize and adjust strategies, enabling the system to better adapt to various environmental changes. Based on feedback from actual applications, the system analyzes whether the adjusted time window has truly improved prediction accuracy and control effectiveness. If the results are positive, the current adjustment strategy is continued. If new problems arise or the results are poor, the causes are re-analyzed, the strategy is adjusted, and system performance is continuously optimized. The process and results of each adjustment are recorded for subsequent analysis and learning, improving the system's adaptability. Throughout the adjustment process, detailed information such as the time window length, triggering conditions, adjustment strategy, simulation verification results, and actual application results of each adjustment is recorded. These records can provide valuable experience for subsequent analysis and learning, helping the system continuously improve its adaptability and better respond to various environmental changes.

[0039] Step S400, calculate the numerical deviation between the predicted value of the key parameter of the detector preparation environment and the actual value of the key parameter of the detector preparation environment. Specifically, in the process of calculating the numerical deviation between the predicted value and the actual value of the key parameter of the detector preparation environment, first determine the key parameters including temperature, humidity, vacuum degree, etc., obtain the actual value through the environmental parameter monitoring system, and at the same time use the prediction model to obtain the predicted value. The deviation can be calculated using methods such as absolute deviation, relative deviation, and root mean square error. For example, for the temperature parameter, at a certain moment, compare the actual measured value with the predicted value, and obtain the deviation value through a specific calculation method. The size of the numerical deviation can evaluate the accuracy of the prediction model, guide the environmental control strategy, and continuously improve the performance of the environmental parameter monitoring system and the prediction model.

[0040] Step S500, input the numerical deviation into a preset optimization control model for analysis, and obtain an optimization control strategy for the detector preparation environment, wherein the optimization control strategy includes optimization control parameters for key parameters of the detector preparation environment. Specifically, in the scheme, the key parameters of the detector preparation environment are first clarified, the actual values ​​are collected through the environmental parameter monitoring system, and the predicted values ​​are obtained using the prediction model, and the numerical deviation between the two is calculated. The deviation is input into a preset optimization control model, which comprehensively considers factors such as environmental conditions, equipment operation conditions, and preparation process requirements, analyzes the deviation, determines the optimization control parameters including specific values, as well as control methods and timing, etc., forms an optimization control strategy and outputs it, provides specific guidance for high-quality preparation of detectors, and improves the performance and quality of detectors by adjusting environmental parameters to make them close to the ideal state.

[0041] Step S600, according to the optimization control strategy, the aluminum nitride vacuum detector preparation environment is adaptively controlled. Specifically, in the scheme for adaptively controlling the aluminum nitride vacuum detector preparation environment, an optimization control strategy is obtained by presetting an optimization control model, which includes optimization control parameters of key parameters and control method timing, etc., to identify different stages in the preparation process, such as material growth, device processing and packaging, etc., each stage has different process requirements, and the system makes judgments by monitoring factors such as specific parameter combinations, change trends, and equipment operating status. According to the optimization control strategy, adaptive control is performed for specific stage requirements, and the corresponding control equipment is adjusted to meet the environmental parameter requirements. At the same time, continuous real-time monitoring is carried out. If parameter deviations are found, feedback is immediately adjusted. As the preparation process proceeds, the system continuously accumulates data experience, optimizes its own control strategy, and can also interact with the outside to learn new methods and improve adaptive capabilities, thereby improving the quality and efficiency of detector preparation.

[0042] In one possible implementation, the preparation environment of the aluminum nitride vacuum detector is adaptively controlled according to the optimization control strategy, and step S600 further includes step S610, establishing a preparation stage feature library, wherein the preparation stage feature library includes environmental parameter characteristics of different preparation stages. Specifically, the preparation process of the aluminum nitride vacuum detector is deeply analyzed and divided into different preparation stages, mainly including material growth, device processing and packaging stages. For each stage, relevant environmental parameter characteristics are collected and sorted. For the material growth stage, the environmental parameter characteristics of this stage are determined by analyzing previous preparation data and communicating with experts. The typical range of environmental parameters may include a specific temperature range, a certain vacuum range, and a specific gas component ratio. For example, the temperature may need to be maintained in a relatively stable high temperature range to promote material growth, and the vacuum degree also needs to reach a certain value to ensure the purity of the growth environment. The change trend of the key parameters may be that the temperature gradually increases and then stabilizes at a specific value as the growth time goes by, and the vacuum degree needs to remain relatively stable throughout the growth process. The operating status of a specific device may include the power output of the growth device and the flow stability of the gas supply device. etc. For the device processing stage, the characteristics of the environmental parameters will be different. The typical range may be more precise temperature control and lower humidity levels. The changing trend of the key parameters may be that during the processing, the temperature needs to be fine-tuned according to different processing steps, and the humidity must always be kept at a low level to prevent the device from being corroded. The operating status of specific equipment may include the precision adjustment of the processing equipment, the operating efficiency of the cooling system, etc. For the packaging stage, the characteristics of the environmental parameters change again. The typical range may include a strictly controlled humidity range and specific cleanliness requirements. The changing trend of the key parameters is that during the packaging process, the humidity must be quickly reduced to an extremely low level, and the cleanliness must always be maintained at a high level. The operating status of specific equipment includes the sealing of the packaging equipment, the operating status of the air purification equipment, etc. The environmental parameter characteristics of different preparation stages are sorted out and summarized, and a preparation stage feature library is established to provide a basis for subsequent optimization target determination and control strategy formulation.

[0043] Step S620: determining the optimization targets of different preparation stages in the preparation stage feature library, and formulating environmental parameter control indicators for the different preparation stages. Specifically, based on actual needs and past experience, the optimization goals of different preparation stages are determined. For the material growth stage, the optimization goal is to obtain high-quality material crystal structure and improve the purity and performance of the material. It is required that the environmental parameters in this stage can promote material growth to the greatest extent and remain stable. For example, ensure that parameters such as temperature, vacuum degree and gas composition are always in the most favorable state for material growth to avoid parameter fluctuations from having adverse effects on the growth process. For the device processing stage, the optimization goal may be to achieve high-precision device processing and improve the performance and reliability of the device. It is necessary to strictly control the environmental parameters in this stage to ensure processing accuracy and stability. For example, control the temperature within an extremely small fluctuation range to prevent the influence of thermal expansion and contraction on processing accuracy; maintain a low humidity level to prevent the device from being corroded by moisture. For the packaging stage, the optimization goal may be to ensure the sealing and reliability of the package and improve the service life and stability of the detector. It is required to strictly control the environmental parameters in this stage to meet the special requirements of the package. For example, control the humidity at an extremely low level to prevent moisture from entering the package; maintain high cleanliness to prevent dust and impurities from affecting the packaging quality.

[0044] Step S630, formulate an adaptive control strategy based on the environmental parameter characteristics and environmental parameter control indicators of different preparation stages. Specifically, for different preparation stages, specific environmental parameter control indicators are formulated according to their optimization goals and environmental parameter characteristics. For the material growth stage, the formulated environmental parameter control indicators include the allowable temperature fluctuation range, such as within plus or minus a few degrees Celsius; the target value of vacuum degree, such as reaching a specific Pascal value; the ratio requirement of gas components, such as the content of a certain gas within a certain percentage range, etc. For the device processing stage, the environmental parameter control indicators include a stricter temperature control range, such as within plus or minus a few tenths of a degree; lower humidity requirements, such as relative humidity below a certain percentage; specific air cleanliness indicators, etc. For the packaging stage, the environmental parameter control indicators may include extremely strict humidity control ranges, such as relative humidity close to zero; high cleanliness requirements, such as the content of a certain gas in the air. For example, the number of particles is within a specific range. Based on the environmental parameter characteristics and environmental parameter control indicators of different preparation stages, adaptive control strategies are developed. The key environmental parameters and their changing trends in each preparation stage are analyzed. For the material growth stage, temperature and vacuum may be key parameters, and corresponding control strategies need to be developed based on their changing trends. For example, if the temperature tends to rise, the cooling system should be adjusted in advance to prevent the temperature from overheating. For the device processing stage, higher precision requirements may be required, and the control strategy needs to be adjusted promptly based on the operating status of the equipment and slight changes in environmental parameters. For example, if the precision of the processing equipment deviates, the environmental parameters should be adjusted promptly to ensure processing accuracy. For the packaging stage, humidity and cleanliness are key parameters, and the control strategy needs to be adjusted promptly based on environmental changes during the packaging process. For example, if the humidity suddenly increases, the dehumidification equipment should be immediately activated. Combined with the environmental parameter control indicators, specific control methods and timing should be determined. For example, what control measures should be taken when the temperature exceeds the allowable fluctuation range? When the vacuum level does not reach the target value, how should the vacuum pump and other equipment be adjusted?

[0045] Step S640, according to the adaptive control strategy, the preparation environment of the aluminum nitride vacuum detector is adaptively controlled. Specifically, in the actual preparation process, according to the developed adaptive control strategy, the preparation environment of the aluminum nitride vacuum detector is controlled in real time, and the environmental parameter monitoring system is used to continuously monitor the changes in the environmental parameters. When it is detected that the environmental parameters deviate from the control indicators, the relevant equipment is automatically adjusted according to the adaptive control strategy to ensure that the environmental parameters are always within the target range. For example, if the temperature rise is detected to exceed the allowable fluctuation range of the material growth stage, the cooling equipment is automatically started to cool down; if the humidity is found to rise during the packaging stage, the dehumidification equipment is immediately started to reduce the humidity. The adaptive control strategy is continuously optimized and adjusted according to the actual control effect. If it is found that some control measures are not effective, the reasons are analyzed and the strategy is adjusted in time to improve the accuracy and efficiency of the control. Through continuous adaptive control, it is ensured that the aluminum nitride vacuum detector is in the best environmental conditions at different preparation stages, thereby improving the preparation quality and performance of the detector.

[0046] The embodiment of the present application uses a monitoring system to obtain the characteristics of the aluminum nitride vacuum detector preparation environment, monitors and filters key parameters in real time, and stores the data in a database. Based on the database, key parameters are predicted, the deviation between the predicted value and the actual value is calculated, and the deviation is input into the optimization control model to obtain a strategy. The preparation environment is adaptively controlled based on the strategy. By achieving accurate monitoring, effective prediction, and adaptive control of the aluminum nitride vacuum detector preparation environment, the technical effect of improving the preparation quality and stability of the detector and enhancing the efficiency and controllability of the preparation process is achieved.

[0047] In the above, refer to Figure 1 The environmental control method for preparing an aluminum nitride vacuum detector according to an embodiment of the present invention is described in detail. Figure 2 An environmental control system for preparing an aluminum nitride vacuum detector according to an embodiment of the present invention is described.

[0048] The environmental control system for aluminum nitride vacuum detector preparation according to an embodiment of the present invention is used to solve the technical problems in the prior art of inaccurate environmental monitoring of aluminum nitride vacuum detector preparation, lack of effective prediction and control, and difficulty in adapting to complex environmental changes. By achieving accurate monitoring, effective prediction, and adaptive control of the aluminum nitride vacuum detector preparation environment, the technical effects of improving the preparation quality and stability of the detector and increasing the efficiency and controllability of the preparation process are achieved. The environmental control system for aluminum nitride vacuum detector preparation includes: an environmental parameter monitoring system construction module 10, a preparation environment database storage module 20, a key parameter prediction module 30, a numerical deviation calculation module 40, an optimization control strategy acquisition module 50, and an adaptive control module 60.

[0049] The environmental parameter monitoring system construction module 10 is used to obtain the characteristics of the aluminum nitride vacuum detector manufacturing environment and construct an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector manufacturing environment;

[0050] The preparation environment database storage module 20 is used to monitor and filter the key parameters of the detector preparation environment in real time through the environmental parameter monitoring system, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in the preparation environment database;

[0051] The key parameter prediction module 30 is used to predict the key parameters of the detector preparation environment based on the preparation environment database, and obtain the predicted values ​​of the key parameters of the detector preparation environment within a variable time window;

[0052] The numerical deviation calculation module 40 is used to calculate the numerical deviation between the predicted value of the key parameter of the detector preparation environment and the actual value of the key parameter of the detector preparation environment;

[0053] The optimization control strategy acquisition module 50 is used to input the numerical deviation into a preset optimization control model for analysis to obtain an optimization control strategy for the detector preparation environment, wherein the optimization control strategy includes optimization control parameters of key parameters of the detector preparation environment;

[0054] The adaptive control module 60 is used to adaptively control the aluminum nitride vacuum detector preparation environment according to the optimization control strategy.

[0055] The specific configuration of the preparation environment database storage module 20 will be described in detail below. As described above, the environmental parameter monitoring system is used to monitor and filter the key parameters of the detector preparation environment in real time, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in the preparation environment database. The preparation environment database storage module 20 further includes: a parameter grouping unit, which is used to group the key parameters of the detector preparation environment according to time sequence and obtain multiple groups of key parameter group data; a group data calculation unit, which is used to calculate the lower quartile, median, and upper quartile of each group of the key parameter group data for the multiple groups of key parameter group data; an interquartile range acquisition unit, which is used to obtain the interquartile range of the key parameter group data, and the interquartile range is the upper quartile minus the lower quartile; an outlier range determination unit, which is used to determine the outlier range and determine whether the data point is within the outlier range. If so, the data point is marked as an outlier data point and the outlier data point in the key parameter group data is removed.

[0056] Among them, the preparation environment database storage module 20 further includes: an attribute data storage unit, the attribute data storage unit is used to obtain the attribute data of the key parameters of the detector preparation environment, classify the attribute data according to the attribute type, obtain the environment parameter attribute data and the equipment attribute data, and store the environment parameter attribute data and the equipment attribute data separately; an event data storage unit, the event data storage unit is used to obtain the event data of the key parameters of the detector preparation environment, the event data includes monitoring event data, abnormal event data and control event data, and the monitoring event data, abnormal event data and the control event data are stored separately; a service data storage unit, the service data storage unit is used to obtain the service data of the key parameters of the detector preparation environment, the service data includes data acquisition service, data analysis service and control service, and the data acquisition service, data analysis service and control service are stored separately.

[0057] The specific configuration of the key parameter prediction module 30 will be described in detail below. As described above, based on the preparation environment database, the key parameters of the detector preparation environment are predicted to obtain predicted values ​​of the key parameters of the detector preparation environment within a variable time window. The key parameter prediction module 30 further includes: a prediction model construction unit, which is used to construct a key parameter prediction model, and the key parameter prediction model is constructed based on the historical data of the key parameters of the aluminum nitride vacuum detector preparation environment; and a prediction value acquisition unit, which is used to use the key parameter prediction model to predict the key parameters of the detector preparation environment based on the real-time data and obtain the predicted values ​​of the key parameters of the detector preparation environment within the variable time window.

[0058] Among them, the key parameter prediction module 30 further includes: a data acquisition unit, the data acquisition unit is used to acquire the attribute data, event data, and service data from the preparation environment database; a reference range determination unit, the reference range determination unit is used to determine the reference range of the variable time window according to the data characteristics of the attribute data; an event constraint condition acquisition unit, the event constraint condition acquisition unit is used to acquire sub-event data in the event data, associate the data characteristics of the sub-event data with the actual event characteristics, and acquire event constraints, the sub-event data including monitoring event data, abnormal event data, and control event data; a service constraint condition acquisition unit The service constraint condition acquisition unit is used to obtain sub-service data in the service data, associate the data characteristics of the sub-service data with the actual service characteristics, and obtain service constraints. The sub-service data includes data acquisition service data, data analysis service data, and control service data; the time window length acquisition unit is used to constrain the reference range of the variable time window according to the event constraints and the service constraints, and obtain multiple time window lengths; the optimal time window length determination unit is used to judge the actual effects of the multiple time window lengths and determine the optimal time window length.

[0059] Among them, the actual effects of the multiple time window lengths are judged to determine the optimal time window length. The optimal time window length determination unit further includes: a prediction error index calculation subunit, the prediction error index calculation subunit is used to regularly evaluate the optimal time window length and calculate the prediction error index; an adjustment strategy optimal solution formulation subunit, the adjustment strategy optimal solution formulation subunit is used to compare the prediction error index with a preset prediction accuracy threshold, if the prediction error index is less than the preset prediction accuracy threshold, formulate a window time length adjustment strategy optimal solution; an optimal window time length resetting subunit, the optimal window time length resetting subunit is used to re-determine the optimal window time length according to the window time length adjustment strategy optimal solution.

[0060] The specific configuration of the adaptive control module 60 will be described in detail below. As described above, according to the optimization control strategy, the aluminum nitride vacuum detector preparation environment is adaptively controlled. The adaptive control module 60 further includes: a preparation stage feature library construction unit, which is used to establish a preparation stage feature library, and the preparation stage feature library includes environmental parameter characteristics of different preparation stages; an optimization target determination unit, which is used to determine the optimization targets of different preparation stages in the preparation stage feature library and formulate environmental parameter control indicators for the different preparation stages; an adaptive control strategy formulation unit, which is used to formulate an adaptive control strategy based on the environmental parameter characteristics and environmental parameter control indicators of different preparation stages; and an adaptive control unit, which is used to adaptively control the aluminum nitride vacuum detector preparation environment according to the adaptive control strategy.

[0061] The environmental control system for preparing aluminum nitride vacuum detectors provided in an embodiment of the present invention can execute the environmental control method for preparing aluminum nitride vacuum detectors provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0062] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0063] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An environmental control method for preparing an aluminum nitride vacuum detector, characterized in that: The method comprises: Acquiring characteristics of an aluminum nitride vacuum detector preparation environment, and constructing an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector preparation environment; Real-time monitoring and data filtering of key parameters of the detector preparation environment are performed through the environmental parameter monitoring system, and historical and real-time data of the key parameters of the detector preparation environment are obtained and stored in the preparation environment database; Predicting key parameters of the detector preparation environment based on the preparation environment database to obtain predicted values ​​of the key parameters of the detector preparation environment within a variable time window; Calculating a numerical deviation between a predicted value of a key parameter of the detector preparation environment and an actual value of the key parameter of the detector preparation environment; Inputting the numerical deviation into a preset optimization control model for analysis to obtain an optimization control strategy for the detector preparation environment, wherein the optimization control strategy includes optimization control parameters of key parameters of the detector preparation environment; Adaptively controlling the aluminum nitride vacuum detector preparation environment according to the optimization control strategy; Determining the variable time window includes: Acquire attribute data, event data, and service data from the preparation environment database; determining a reference range of the variable time window according to data characteristics of the attribute data; Obtaining sub-event data from the event data, correlating data features of the sub-event data with actual event features, and obtaining event constraint conditions, wherein the sub-event data includes monitoring event data, abnormal event data, and control event data; Obtaining sub-service data from the service data, associating data features of the sub-service data with actual service features, and obtaining service constraints, wherein the sub-service data includes data collection service data, data analysis service data, and control service data; Constraining a reference range of the variable time window according to the event constraint and the service constraint to obtain multiple time window lengths; The actual effects of the multiple time window lengths are judged to determine the optimal time window length.

2. The environmental control method for preparing an aluminum nitride vacuum detector according to claim 1, characterized in that: The data filtering includes: Grouping the key parameters of the detector preparation environment according to time sequence to obtain multiple groups of key parameter grouping data; For the multiple groups of key parameter grouped data, calculating the lower quartile, median, and upper quartile of each group of key parameter grouped data; Obtaining the interquartile range of the key parameter grouped data, where the interquartile range is the upper quartile minus the lower quartile; Determine an outlier range, determine whether a data point is within the outlier range, and if so, mark the data point as an outlier data point, and remove the outlier data point from the key parameter grouped data.

3. The environmental control method for preparing an aluminum nitride vacuum detector according to claim 1, characterized in that: The storing in the preparation environment database includes: Acquiring attribute data of key parameters of the detector preparation environment, classifying the attribute data according to attribute type, obtaining environment parameter attribute data and device attribute data, and storing the environment parameter attribute data and the device attribute data separately; Acquire event data of key parameters of the detector preparation environment, the event data including monitoring event data, abnormal event data and control event data, and store the monitoring event data, abnormal event data and control event data separately; Obtain service data of key parameters of the detector preparation environment, wherein the service data includes data acquisition service, data analysis service and control service, and store the data acquisition service, data analysis service and control service separately.

4. The environmental control method for preparing an aluminum nitride vacuum detector according to claim 3, characterized in that: Based on the preparation environment database, key parameters of the detector preparation environment are predicted, including: Constructing a key parameter prediction model, wherein the key parameter prediction model is constructed based on historical data of key parameters of the aluminum nitride vacuum detector preparation environment; The key parameter prediction model is used to predict the key parameters of the detector preparation environment according to the real-time data, and the predicted values ​​of the key parameters of the detector preparation environment within the variable time window are obtained.

5. The environmental control method for preparing an aluminum nitride vacuum detector according to claim 1, characterized in that: include: Regularly evaluating the optimal time window length and calculating a forecast error indicator; Comparing the prediction error index with a preset prediction accuracy threshold, and formulating an optimal solution for a time window length adjustment strategy if the prediction error index is less than the preset prediction accuracy threshold; The optimal solution of the time window length adjustment strategy is determined based on the time window length, and the optimal time window length is re-determined.

6. The environmental control method for preparing an aluminum nitride vacuum detector according to claim 1, characterized in that: Adaptively controlling the aluminum nitride vacuum detector preparation environment includes: Establishing a preparation stage feature library, wherein the preparation stage feature library includes environmental parameter features of different preparation stages; Determining optimization targets for different preparation stages in the preparation stage feature library, and formulating environmental parameter control indicators for the different preparation stages; Formulate adaptive control strategies based on the environmental parameter characteristics and environmental parameter control indicators at different preparation stages; According to the adaptive control strategy, the aluminum nitride vacuum detector preparation environment is adaptively controlled.

7. An environmental control system for the preparation of aluminum nitride vacuum detectors, characterized in that: The system is used to implement the environmental control method for preparing an aluminum nitride vacuum detector according to any one of claims 1 to 6, and the system comprises: An environmental parameter monitoring system construction module, the environmental parameter monitoring system construction module is used to obtain characteristics of the aluminum nitride vacuum detector preparation environment and construct an environmental parameter monitoring system based on the characteristics of the aluminum nitride vacuum detector preparation environment; A preparation environment database storage module is used to monitor and filter the key parameters of the detector preparation environment in real time through the environmental parameter monitoring system, obtain historical data and real-time data of the key parameters of the detector preparation environment, and store them in the preparation environment database; A key parameter prediction module, configured to predict key parameters of the detector preparation environment based on the preparation environment database, and obtain predicted values ​​of the key parameters of the detector preparation environment within a variable time window; A numerical deviation calculation module, configured to calculate a numerical deviation between a predicted value of a key parameter of the detector preparation environment and an actual value of the key parameter of the detector preparation environment; An optimization control strategy acquisition module, wherein the optimization control strategy acquisition module is used to input the numerical deviation into a preset optimization control model for analysis to obtain an optimization control strategy for the detector preparation environment, wherein the optimization control strategy includes optimization control parameters for key parameters of the detector preparation environment; An adaptive control module is used to adaptively control the aluminum nitride vacuum detector preparation environment according to the optimization control strategy.

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