Laboratory humiture measuring and recording display

Abstract

The invention relates to the field of environment monitoring and automatic control, and discloses a laboratory temperature and humidity measuring and recording display, which comprises a temperature and humidity sensor module used for acquiring temperature and humidity data in a laboratory environment in a distributed manner; the intelligent self-adaptive calibration module is used for dynamically adjusting calibration parameters of the sensor according to historical data and real-time data changes; the data processing and display module is used for displaying the temperature and humidity data in real time and providing historical data storage, analysis and visualization functions; the Internet of Things module is used for uploading the collected temperature and humidity data to a remote server through the Internet of Things; and the environment control module is linked with laboratory environment control equipment. An intelligent adaptive calibration algorithm (such as a recursive least square method and Kalman filtering) is adopted by the system, so that the measurement error of the sensor can be automatically calibrated after long-time use, data drift is avoided, the frequency of manual calibration is greatly reduced, and the long-term precision and stability of measurement are ensured.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring and automation control, and specifically to a laboratory temperature and humidity measurement, recording and display device. Background Art

[0002] In a modern laboratory environment, accurate monitoring and control of temperature and humidity are crucial. Especially when it comes to high-precision experiments in the fields of chemistry, biology, medicine, etc., even slight changes in environmental factors may lead to errors or failures in experimental results. Therefore, many laboratories are equipped with temperature and humidity monitoring devices to ensure the stability of the experimental environment. However, in the existing technology, there are still some obvious deficiencies in temperature and humidity measurement, recording and display devices, making it difficult to meet the requirements of laboratories for high precision, stability and intelligent management.

[0003] Poor sensor accuracy and long-term stability: Existing temperature and humidity sensors will drift during long-term use, and the measurement accuracy gradually decreases. In this case, frequent manual calibration is required, which consumes a large amount of manpower, and there is also the problem that errors cannot be detected in time. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a laboratory temperature and humidity measurement, recording and display device, which solves the problem that existing temperature and humidity sensors will drift during long-term use and the measurement accuracy gradually decreases.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A laboratory temperature and humidity measurement, recording and display device, comprising:

[0006] A temperature and humidity sensor module for distributedly collecting temperature and humidity data in the laboratory environment. The sensor module includes multiple sensors installed at different monitoring points, and the sensors integrate multi-point monitoring data through a weighted average algorithm;

[0007] An intelligent adaptive calibration module for dynamically adjusting the calibration parameters of the sensor according to historical data and real-time data changes. The calibration module includes an error detection unit, an adaptive calibration algorithm unit and a calibration feedback unit;

[0008] A data processing and display module for real-time displaying temperature and humidity data, and providing functions of historical data storage, analysis and visualization. The module includes a data storage unit, a data analysis unit and a multi-functional display unit;

[0009] An Internet of Things module for uploading the collected temperature and humidity data to a remote server through the Internet of Things. The module includes a data upload unit, a remote monitoring unit and an alarm push unit;

[0010] The environmental control module is linked with the laboratory environmental control equipment. When the temperature and humidity exceed the preset range, it automatically adjusts the air conditioner and humidifier.

[0011] Preferably, the temperature and humidity sensor module includes:

[0012] Multi-point distributed sensor unit: Used to arrange multiple temperature and humidity sensors in different areas of the laboratory to ensure the collection of environmental data at different positions and form the monitoring of the temperature and humidity distribution in the space;

[0013] Data preprocessing unit: Processes the collected temperature and humidity data through the Kalman filter algorithm to filter out short-term fluctuations during the measurement process;

[0014] Sensor integration unit: After integrating the data collected at multiple points, uses the weighted average algorithm to output the real-time average values of the overall temperature and humidity.

[0015] Preferably, the intelligent adaptive calibration module includes:

[0016] Error detection unit: Detects the measurement error of the sensor by comparing with the reference sensor or historical standard data, and identifies the drift phenomenon of the sensor;

[0017] Adaptive calibration algorithm unit: Based on the recursive least squares method, corrects the drift of the sensor and adjusts the calibration parameters of the sensor in real time;

[0018] Calibration feedback unit: Performs secondary verification on the calibrated sensor data and feeds back the calibration data to the data processing module.

[0019] Preferably, the data processing and display module includes:

[0020] Data storage unit: Built-in time series database, used to store historical temperature and humidity data, and supports fast query of data according to time, event or condition;

[0021] Data analysis unit: Adopts the linear regression algorithm to perform trend analysis on historical data, providing real-time trend prediction and statistical report generation functions;

[0022] Multi-functional display unit: Real-time displays the temperature and humidity data through the LCD screen, and provides alarm information, historical data trend charts and environmental status reports.

[0023] Preferably, the Internet of Things module includes:

[0024] Data upload unit: Responsible for uploading the temperature and humidity data to the cloud server through the MQTT protocol or HTTP protocol, and synchronizing the data in real time according to the preset upload frequency;

[0025] Remote monitoring unit: Allows users to remotely view laboratory environmental data through mobile devices or PCs and provides a real-time monitoring interface;

[0026] Alarm push unit: When the temperature and humidity data exceed the set range, the system pushes alarm notifications to relevant personnel through the Internet of Things, supporting alarm methods such as SMS, email, or mobile applications.

[0027] Preferably, the environmental control module includes:

[0028] Control instruction generation unit: Generates an automatic adjustment instruction based on real-time temperature and humidity data through the PID control algorithm for controlling air conditioners and humidifiers in the laboratory;

[0029] Environmental equipment interface unit: Supports linkage control with environmental equipment through Modbus or BACnet control protocols, enabling the equipment to respond and adjust in a timely manner;

[0030] Feedback monitoring unit: Monitors the operating status of environmental equipment in real time and adjusts the control strategy based on the feedback data of the equipment to keep the environmental conditions stable within the set range.

[0031] Preferably, the weighted average algorithm formula is as follows:

[0032]

[0033] Where T avg and H avg are the weighted average values of temperature and humidity respectively, T i and H i are the temperature and humidity data of the i-th sensor, and w i is the data weight of each sensor;

[0034] The Kalman filter algorithm of the data preprocessing unit is as follows:

[0035] x k = Ax k-1 + Bu k + w k

[0036] z k = Hx k + v k

[0037] Where x k is the true temperature and humidity state at the current moment, z k is the temperature and humidity data measured at the current moment, A is the state transition matrix, H is the observation matrix, and w k and v k are the system noise and measurement noise respectively.

[0038] Preferably, the recursive least squares method of the adaptive calibration algorithm unit can update the calibration parameters online, reduce the long-term deviation, and the update formula of the RLS algorithm is as follows:

[0039]

[0040] where, θ k is the calibration parameter vector, K k is the Kalman gain, λ is the forgetting factor, P k is the covariance matrix, and y k is the observed value.

[0041] Preferably, the data analysis unit uses linear regression analysis to predict the trend of historical data. The linear regression model fits the change of temperature and humidity data by the least squares method, and the regression equation is as follows:

[0042] T predicted = α + βt, H predicted = γ + δt

[0043] where, t is the time, α and γ are the intercepts, and β and δ are the regression coefficients;

[0044] The alarm rule of the alarm push unit is based on the threshold detection algorithm:

[0045]

[0046] Preferably, the control instruction generation unit enables the controller to calculate the adjustment amount according to the following formula:

[0047]

[0048] This formula is the PID control algorithm, and K p , K i , K d are the proportional, integral, and differential control parameters.

[0049] The present invention provides a laboratory temperature and humidity measurement and recording display. It has the following beneficial effects:

[0050] 1. By adopting an intelligent adaptive calibration algorithm (such as recursive least squares method and Kalman filtering) in the system, the present invention can automatically calibrate the measurement error of the sensor after long-term use, avoid data drift, greatly reduce the frequency of manual calibration, and ensure the long-term accuracy and stability of the measurement.

[0051] 2. Through the design of multi-point distributed temperature and humidity sensors, the system of the present invention can simultaneously monitor the temperature and humidity data in different areas of the laboratory, providing all-round environmental monitoring. This multi-point monitoring method can help laboratory managers understand the temperature and humidity distribution in the laboratory and ensure that the environment in each area is in an ideal state.

[0052] 3. The system of the present invention can not only monitor the temperature and humidity data in real time, but also perform trend prediction and in-depth analysis based on historical data. Combined with the automatic control module, when the temperature and humidity deviate from the set range, the system will automatically adjust devices such as air conditioners and humidifiers to maintain the stability of the laboratory environment and reduce manual intervention.

[0053] 4. The present invention realizes the remote monitoring function through the Internet of Things module. Experimental personnel can remotely view the temperature and humidity data of the laboratory through mobile devices or PCs. The system also has an alarm push function. When the experimental environment is abnormal, the system will push alarms through text messages, emails or application push to ensure that managers can handle problems in a timely manner.

[0054] 5. The system of the present invention is built with a time series database, which can store historical data for several months or even years, supporting large-capacity data storage and complex queries. Users can generate historical data trend charts through the system to help analyze the impact of environmental changes on experimental results, and can export data for subsequent analysis and reporting. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the system architecture diagram of the laboratory temperature and humidity measurement and recording display of the present invention;

[0056] Figure 2 It is the architecture diagram of the temperature and humidity sensor module of the present invention;

[0057] Figure 3 It is the architecture diagram of the intelligent adaptive calibration module of the present invention;

[0058] Figure 4 It is the architecture diagram of the data processing and display module of the present invention;

[0059] Figure 5 It is the architecture diagram of the Internet of Things module of the present invention;

[0060] Figure 6 It is the architecture diagram of the environmental control module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0061] Next, in combination with the accompanying drawings of the present invention, the technical solutions of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Please refer to the attached Figure 1 - attached Figure 6 , the embodiment of the present invention relates to a laboratory temperature and humidity measurement and recording display system, which includes a temperature and humidity sensor module, an intelligent adaptive calibration module, a data processing and display module, an Internet of Things module, and an environmental control module. Through the collaborative work of these modules, the system can realize real-time monitoring, intelligent calibration, data storage and display, remote control, and automatic environmental adjustment of temperature and humidity in the laboratory environment.

[0063] 1. Temperature and humidity sensor module

[0064] This module is used to deploy multiple temperature and humidity sensors at different positions in the laboratory to achieve multi-point distributed monitoring of the laboratory environment. The data collected by each sensor is processed through the following units:

[0065] Multi-point distributed sensor unit:

[0066] Multiple sensors are installed in different areas of the laboratory to collect temperature and humidity data of the local environment respectively. Through these sensors, the temperature and humidity distribution in different areas of the laboratory can be captured in real time.

[0067] Underlying technology: The sensor uses a thin-film capacitive sensor, which has the characteristics of high precision (temperature ±0.1°C, humidity ±0.1%RH) and is suitable for accurate monitoring of the laboratory environment. The sensor is connected to the main control unit through the I2C or SPI communication bus to ensure the stability and speed of data transmission.

[0068] Underlying logic: The data collected by each sensor will be marked with the location and timestamp, and the system constructs a spatial distribution map of the laboratory temperature and humidity based on these metadata. After these data are summarized by the main control unit, they enter the subsequent data processing stage.

[0069] Data preprocessing unit:

[0070] The collected raw data is processed by the Kalman filter algorithm to reduce interference factors in the measurement process and ensure the accuracy and stability of the temperature and humidity data.

[0071] Underlying technology: The Kalman filter algorithm is used to preprocess the original data, filtering out noise and short-term fluctuations. The Kalman filter algorithm can provide real-time state estimation in a dynamic environment and is particularly suitable for sensor noise processing and data smoothing. The prediction-correction two-step process of the Kalman filter ensures the stability of the data:

[0072] x k = Ax k-1 + Bu k + w k

[0073] z k = Hx k + v k

[0074] where x k is the true temperature and humidity state at the current moment, z k is the temperature and humidity data measured at the current moment, A is the state transition matrix, H is the observation matrix, w k and v k are the system noise and measurement noise respectively.

[0075] Underlying logic: After the data of each sensor is processed by filtering, the system integrates the multi-point data to provide more accurate input for subsequent temperature and humidity data processing.

[0076] Sensor integration unit:

[0077] The temperature and humidity data of each monitoring point are integrated through the weighted average algorithm to calculate the real-time temperature and humidity average value of the overall laboratory environment, so as to provide unified reference data for subsequent calibration and control.

[0078] Underlying technology: The system uses the weighted average algorithm to integrate the temperature and humidity data of different regions to ensure the representativeness of the overall environmental data. The weighted average formula is as follows:

[0079]

[0080] where T avg and H avg are the weighted average values of temperature and humidity respectively, T i and H i are the temperature and humidity data of the i-th sensor, and w i is the data weight of each sensor.

[0081] Underlying logic: By weighted average, the response sensitivity of the system to the overall laboratory environment can be improved, and the weight can be set according to the importance of the location where the sensor is located to ensure that the data in different regions have different influences.

[0082] 2. Intelligent adaptive calibration module

[0083] To ensure the accuracy of the sensor during long-term use, this module can automatically calibrate the measurement data of the sensor to avoid errors caused by data drift.

[0084] Error detection unit: By comparing with the data of the reference sensor or historical standard data, it can detect the measurement error of the sensor in real time. When it is found that the sensor deviates from the standard, the system will trigger the calibration procedure;

[0085] Underlying technology: The system calculates the error of the sensor by comparing the real-time data of the sensor with the reference sensor or historical standard data. The error formula is as follows:

[0086] ΔT = T sensor - T standard ,ΔH = H sensor - H standard

[0087] Underlying logic: The error detection unit continuously monitors the sensor. When the detected error exceeds the preset threshold, it triggers the calibration process. The reference data can be provided by a known standard environment or a sensor calibrated with high precision.

[0088] Adaptive calibration algorithm unit: Using the recursive least squares method (RLS), this unit can adaptively adjust the measurement error of the sensor. According to the error detection results, it dynamically adjusts the calibration parameters of the sensor to eliminate the drift phenomenon during the long-term operation of the sensor;

[0089] Underlying technology: The system uses the recursive least squares method (RLS) to dynamically adjust the error of the sensor. This algorithm updates the current calibration parameters based on historical data, and the update formula is:

[0090]

[0091] where θ k is the calibration parameter vector, K k is the Kalman gain, λ is the forgetting factor, P k is the covariance matrix, and y k is the observed value.

[0092] Underlying logic: The system can make adaptive adjustments according to the error trend of the sensor to ensure that the measurement accuracy of the sensor can still be maintained within the ideal range even during long-term use.

[0093] Calibration feedback unit: After the calibration operation is completed, the system will perform secondary verification on the calibrated data to ensure the reliability of the calibration results. The calibrated data will be fed back to the data processing module for subsequent monitoring and display;

[0094] Underlying technology: The calibration feedback unit compares the adjusted sensor data with the reference data again to ensure the effectiveness of calibration. If the error continues to exceed the standard, the system will readjust the calibration parameters until the error meets the requirements.

[0095] Underlying logic: This unit ensures the stability and effectiveness of the calibration process through a closed-loop feedback control mechanism, avoiding the situation where the error remains uneliminated after multiple calibrations.

[0096] 3. Data Processing and Display Module

[0097] This module is responsible for storing, analyzing, and displaying the temperature and humidity data in the laboratory, ensuring that users can conveniently view the current environmental status and historical data.

[0098] Data storage unit: The system has a built-in time series database for storing the historical temperature and humidity data of the laboratory. The data is stored in chronological order, supporting data queries by time period or specific events, and can be exported as reports in various formats;

[0099] Underlying technology: The system uses a time series database (such as InfluxDB or TimescaleDB) to store historical temperature and humidity data. The time series database can handle a large amount of time-related data and provides efficient retrieval and storage capabilities.

[0100] Underlying logic: The data is stored in a time series, and users can query historical data by date or event. The storage structure ensures that each record contains a timestamp, sensor location, and data type for subsequent analysis.

[0101] Data analysis unit: The system analyzes and processes the historical data through a linear regression algorithm to generate a prediction model for the temperature and humidity change trend, helping users understand future environmental changes and providing suggestions for environmental management;

[0102] Underlying technology: Trend prediction of historical data is carried out through linear regression and time series analysis to generate a trend report on environmental changes. The linear regression equation is:

[0103] T predicted =α + βt, H predicted =γ + δt

[0104] where t is time, α and γ are intercepts, and β and δ are regression coefficients;

[0105] Underlying logic: The system can predict possible environmental changes in advance based on the historical data trend and provide an analysis report for users' reference to help optimize laboratory environmental management.

[0106] Multifunctional Display Unit: Through the LCD screen, the system real-time displays the current temperature and humidity values, including the minimum value, maximum value, average value, etc. At the same time, users can view the historical trend graph through the screen and receive system alarm information;

[0107] Underlying Technology: The high-definition LCD display screen is used to display real-time temperature and humidity data, historical trend graphs, and alarm information. Users can switch the display mode through the touch screen or buttons to view the current environmental status or historical records.

[0108] Underlying Logic: The display unit provides a user-friendly interaction interface. Users can select to view real-time data, historical data, or system alarm information according to their needs, and can export the data for subsequent analysis.

[0109] 4. Internet of Things Module

[0110] This module enables the system to have remote monitoring and control functions, facilitating users to understand the laboratory environment status anytime and anywhere and take corresponding measures when necessary.

[0111] Data Upload Unit: Through Internet of Things protocols (such as MQTT or HTTP), the system uploads the collected temperature and humidity data to the cloud server. The data upload frequency can be set according to experimental requirements to ensure real-time synchronization of cloud data and local data;

[0112] Underlying Technology: Use MQTT or HTTP protocols to upload temperature and humidity data to the cloud server. MQTT is suitable for low-power, high-frequency real-time data transmission, and HTTP is suitable for periodic transmission of large quantities of data.

[0113] Underlying Logic: The data upload frequency can be flexibly adjusted according to experimental requirements to ensure that key data can be synchronized to the cloud in a timely manner, and users can remotely access this data.

[0114] Remote Monitoring Unit: Users can remotely log in to the system through a PC or mobile device to view the temperature and humidity data of the laboratory and the system operation status, and support remote operation and control;

[0115] Underlying Technology: Users remotely access the system interface through a PC or mobile device and use RESTAPI or WebSocket for real-time monitoring. The system uses SSL encryption to ensure the security of data transmission.

[0116] Underlying Logic: The remote monitoring function allows users to view the current temperature and humidity data at any time and supports remotely issuing adjustment instructions to ensure the stability of the laboratory environment.

[0117] Alarm Push Unit: When the temperature and humidity in the laboratory exceed the preset safe range, the system will automatically send alarm notifications to relevant personnel. The alarm information can be pushed via SMS, email, or application to ensure that problems are dealt with promptly;

[0118] Underlying Technology: When the temperature and humidity exceed the preset range, the system sends alarm notifications via SMS, email, or mobile application. The alarm rules are based on preset thresholds:

[0119]

[0120] Underlying Logic: Alarm push ensures that laboratory managers can handle abnormal situations in a timely manner and prevent environmental conditions from affecting experimental results.

[0121] 5. Environmental Control Module

[0122] This module is responsible for linking with the environmental conditioning equipment in the laboratory (such as air conditioners, humidifiers, dehumidifiers, etc.) to ensure that the laboratory environmental parameters always remain within the set range.

[0123] Control Instruction Generation Unit: Based on real-time temperature and humidity data, the system generates automatic adjustment instructions through the PID control algorithm. When the environmental data exceeds the set threshold, the system will automatically control devices such as air conditioners and humidifiers for adjustment;

[0124] Underlying Technology: The system generates control instructions through the PID control algorithm to adjust devices such as air conditioners and humidifiers. The PID control equation is:

[0125]

[0126] K p 、K i 、K d are the proportional, integral, and derivative control parameters.

[0127] Underlying Logic: The control algorithm continuously monitors environmental data and automatically adjusts the device output to ensure that the laboratory environmental parameters always remain within the set range.

[0128] Environmental Equipment Interface Unit: This unit supports data exchange and instruction transmission with the environmental conditioning equipment in the laboratory. The system connects to external devices through protocols such as Modbus and BACnet to achieve automated linkage with the laboratory environmental equipment;

[0129] Underlying Technology: Communicate with laboratory air conditioners, humidifiers, dehumidifiers, etc. through protocols such as Modbus and BACnet to achieve automated control.

[0130] Underlying Logic: The system can adjust the control strategy according to device feedback to ensure that the device working state is consistent with laboratory requirements.

[0131] Feedback monitoring unit: This unit is responsible for monitoring the operating status of environmental equipment and adjusting control strategies based on the feedback data of the equipment to ensure the timeliness and accuracy of environmental regulation operations.

[0132] Underlying technology: The system monitors the operating status of environmental equipment in real time and ensures that the equipment response meets expectations through closed-loop control. Through the data feedback loop, the system can dynamically adjust control parameters to adapt to environmental changes.

[0133] Underlying logic: The feedback monitoring unit ensures that the equipment responds to system instructions in a timely manner during execution through two-way communication with the equipment, maintaining environmental stability.

[0134] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Laboratory temperature and humidity measurement record display, characterized in that, include: The temperature and humidity sensor module is used for distributed collection of temperature and humidity data in the laboratory environment. The sensor module includes multiple sensors installed at different monitoring points. The sensors integrate multi-point monitoring data through a weighted average algorithm. An intelligent adaptive calibration module, used to dynamically adjust the calibration parameters of the sensor according to historical data and real-time data changes, the calibration module includes an error detection unit, an adaptive calibration algorithm unit and a calibration feedback unit; A data processing and display module, which is used to display temperature and humidity data in real time and provide historical data storage, analysis and visualization functions. The module includes a data storage unit, a data analysis unit and a multi-function display unit; The Internet of Things module is used to upload the collected temperature and humidity data to a remote server through the Internet of Things. The module includes a data uploading unit, a remote monitoring unit and an alarm pushing unit; The environmental control module is linked with the laboratory environmental control equipment to automatically adjust the air conditioner and humidifier when the temperature and humidity exceed the preset range.

2. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The temperature and humidity sensor module comprises: Multi-point distributed sensor unit: used to arrange multiple temperature and humidity sensors in different areas of the laboratory to ensure the collection of environmental data at different locations and form temperature and humidity distribution monitoring in the space; Data preprocessing unit: Use Kalman filtering algorithm to process the noise of the collected temperature and humidity data and filter out short-term fluctuations during the measurement process; Sensor integration unit: After integrating the data collected at multiple points, a weighted average algorithm is used to output the real-time average value of the overall temperature and humidity.

3. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The intelligent adaptive calibration module comprises: Error detection unit: detects the measurement error of the sensor and identifies the drift of the sensor by comparing it with the reference sensor or historical standard data; Adaptive calibration algorithm unit: corrects the drift of the sensor based on the recursive least squares method and adjusts the calibration parameters of the sensor in real time; Calibration feedback unit: performs secondary verification on the calibrated sensor data and feeds the calibration data back to the data processing module.

4. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The data processing and display module includes: Data storage unit: built-in time series database, used to store historical temperature and humidity data, and supports fast query of data by time, event or condition; Data analysis unit: uses linear regression algorithm to perform trend analysis on historical data, providing real-time trend forecasting and statistical report generation functions; Multi-function display unit: Displays temperature and humidity data in real time through the LCD screen, and provides alarm information, historical data trend charts and environmental status reports.

5. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The Internet of Things module includes: Data upload unit: responsible for uploading temperature and humidity data to the cloud server through MQTT protocol or HTTP protocol, and synchronizing data in real time according to the preset upload frequency; Remote monitoring unit: allows users to remotely view laboratory environment data via mobile devices or PCs, and provides a real-time monitoring interface; Alarm push unit: When the temperature and humidity data exceed the set range, the system pushes alarm notifications to relevant personnel through the Internet of Things, and supports alarm methods such as SMS, email or mobile applications.

6. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The environmental control module comprises: Control instruction generation unit: Generates automatic adjustment instructions based on real-time temperature and humidity data through PID control algorithm to control the air conditioner and humidifier in the laboratory; Environmental equipment interface unit: supports linkage control with environmental equipment through Modbus or BACnet control protocols, so that the equipment can respond and adjust in time; Feedback monitoring unit: monitors the operating status of environmental equipment in real time and adjusts the control strategy based on the feedback data of the equipment to keep the environmental conditions stable within the set range.

7. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The weighted average algorithm formula is as follows: Among them, T avg and H avg are the weighted average values ​​of temperature and humidity, T i and H i is the temperature and humidity data of the i-th sensor, w i The data weight for each sensor; The Kalman filter algorithm of the data preprocessing unit is as follows: x k =Ax k-1 +Bu k +w k z k =Hx k +v k Among them, x k is the actual temperature and humidity status at the current moment, z k is the temperature and humidity data measured at the current moment, A is the state transfer matrix, H is the observation matrix, and w k and v k point Especially system noise and measurement noise.

8. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The recursive least squares method of the adaptive calibration algorithm unit can update the calibration parameters online to reduce long-term deviations. The update formula of the RLS algorithm is: Among them, θ k is the calibration parameter vector, K k is the Kalman gain, λ is the forgetting factor, P k is the covariance matrix, y k is the observed value.

9. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The data analysis unit uses linear regression analysis to predict the trend of historical data. The linear regression model fits the temperature and humidity data changes by the least squares method. The regression equation is: T predicted =α+βt,H predicted =γ+δt Among them, t is time, α and γ are intercepts, and β and δ are regression coefficients; The alarm rule of the alarm push unit is based on a threshold detection algorithm:

10. The laboratory temperature and humidity measurement and recording display according to claim 1, characterized in that: The control instruction generating unit enables the controller to calculate the adjustment amount according to the following formula: This formula is the PID control algorithm, K p , K i , K d are the proportional, integral and derivative control parameters.

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