Portable high-precision comprehensive measurement method and system for roadway air volume and environmental parameters

Abstract

The invention provides a portable high-precision comprehensive measurement method and system for roadway air volume and environmental parameters, and relates to the technical field of air volume and parameter measurement, and the method comprises the steps: collecting temperature, humidity, air speed, air pressure and gas concentration data in a roadway through a measurement probe assembly, and calculating a displacement compensation coefficient and a temperature compensation coefficient to calibrate original data; and performing dynamic fusion on the calibrated multi-source heterogeneous measurement data by adopting a Kalman filtering algorithm, establishing a state prediction model based on time sequence relevance, and optimizing a measurement result. According to the invention, the measurement precision of the environmental parameters of the roadway is improved, the environmental noise interference is inhibited, and the high-precision comprehensive measurement of multiple parameters is realized.

Description

Technical Field

[0001] The present invention relates to air volume and parameter measurement technology, and in particular to a portable high-precision comprehensive measurement method and system for tunnel air volume and environmental parameters. Background Art

[0002] With increasing safety requirements in mines, accurate measurement of ventilation parameters in tunnel ventilation systems, a crucial element in ensuring safe working environments, is crucial for ensuring a safe mining environment. Tunnel air volume and environmental parameters such as temperature, humidity, air pressure, and hazardous gas concentrations are key indicators for evaluating the operational status of mine ventilation systems. Traditionally, tunnel air volume and environmental parameter measurements have been performed using single or multiple independent instruments, resulting in cumbersome operations and difficulty ensuring data consistency and accuracy.

[0003] Existing measuring equipment generally lacks effective displacement compensation and temperature compensation mechanisms, resulting in large measurement errors when the measuring probe is installed away from the center position or when the ambient temperature changes, and is unable to adapt to the complex and changing tunnel environmental conditions.

[0004] Existing technologies usually adopt a simple data collection method, lack the ability to effectively integrate and process multi-source heterogeneous data. The measurements of various parameters are independent of each other, and the correlation between parameters cannot be used to improve the overall measurement accuracy. Especially when there are many interference factors in the tunnel environment, the measurement data stability is poor.

[0005] Most of the existing tunnel environmental parameter measurement equipment is of decentralized design, with limited data transmission and centralized analysis capabilities, making it difficult to achieve real-time display and remote transmission of measurement data. It cannot meet the requirements of modern mine safety monitoring systems for data real-time and network analysis, limiting the application value of the measurement results. Summary of the Invention

[0006] The embodiments of the present invention provide a portable high-precision comprehensive measurement method and system for tunnel air volume and environmental parameters, which can solve the problems in the prior art.

[0007] A first aspect of an embodiment of the present invention provides a portable high-precision comprehensive measurement method for roadway air volume and environmental parameters, comprising:

[0008] The temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel are collected through the measuring probe assembly arranged in the pipeline body;

[0009] transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing;

[0010] calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly in the pipeline body, calculating a temperature compensation coefficient based on the ambient temperature change, and applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data;

[0011] Dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and precision optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result;

[0012] The optimized measurement results are displayed on a display unit; and the optimized measurement results are transmitted to an external device through a communication interface for storage and analysis.

[0013] Transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing includes:

[0014] The temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data are transmitted to a signal processing unit for classification and labeling and data cleaning, and a multi-source data correspondence is established based on the data acquisition time sequence to generate a standardized data structure;

[0015] The standardized data structure is subjected to noise reduction processing by using wavelet transform, abnormal data points are identified and eliminated, and pre-processed environmental parameter data are generated.

[0016] Calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly in the pipeline body, calculating a temperature compensation coefficient based on the ambient temperature change, applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data, and obtaining calibrated multi-source heterogeneous measurement data includes:

[0017] According to the installation position of the measuring probe assembly in the pipeline body, obtaining a distance parameter between the measuring probe assembly and the center of the pipeline body;

[0018] Substituting the distance parameter into the flow field velocity distribution model to calculate the flow field characteristic coefficient, the flow field characteristic coefficient is obtained through a multi-point calibration experiment, and a displacement compensation coefficient is obtained;

[0019] Collecting ambient temperature data and obtaining the difference between the ambient temperature data and a preset standard temperature;

[0020] Multiplying the difference value by the relationship coefficient of the temperature response curve to obtain a sensor sensitivity parameter under the influence of temperature; calculating a compensation coefficient by a least square method based on the sensor sensitivity parameter to obtain a temperature compensation coefficient;

[0021] The displacement compensation coefficient is multiplied by the temperature compensation coefficient to obtain a comprehensive compensation coefficient, and the original measurement data is multiplied by the comprehensive compensation coefficient to obtain calibrated measurement data.

[0022] The compensation coefficient is calculated by the least square method according to the sensor sensitivity parameter, and the temperature compensation coefficient is obtained, which includes:

[0023] An observation equation is constructed based on multiple sampling points within the temperature variation range, the sensitivity values of the sampling points are combined into a sensitivity observation vector, and the difference between the temperature corresponding to the sampling point and the preset standard temperature is combined into a temperature difference matrix;

[0024] Establishing a least squares criterion objective function according to the product of the sensitivity observation vector and the temperature difference matrix, solving the observation equation for the least squares criterion objective function using the least squares method, and calculating an initial compensation coefficient;

[0025] Calculating the standard deviation of the measurement data of the sampling points, constructing the reciprocal square value of the standard deviation of the measurement data into a weighting factor in the form of a diagonal matrix; multiplying the weighting factor with the temperature difference matrix and the sensitivity observation vector respectively to obtain a weighted temperature difference matrix and a sensitivity observation vector;

[0026] The least square method is used to resolve the normal equations for the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients, and the temperature compensation coefficients are calculated based on the optimized compensation coefficients.

[0027] Dynamically fusing the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, and establishing a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data includes:

[0028] Analyzing the variation pattern of the calibrated multi-source heterogeneous measurement data in a time series, and calculating the time series correlation coefficient of the calibrated multi-source heterogeneous measurement data;

[0029] Establishing a state prediction model based on the time series correlation coefficient, wherein the state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment;

[0030] A Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data, including: predicting the data state at the next moment according to the state prediction model; calculating the Kalman gain based on the prediction result, weightedly fusing the Kalman gain with the actual observation data to obtain an optimal estimate, and outputting the optimal estimate as the fused multi-source heterogeneous measurement data.

[0031] The fused data is subjected to noise suppression and precision optimization processing according to the state prediction model, and the optimized measurement results obtained include:

[0032] Acquire measurement data to be optimized, calculate the signal power spectral density and the noise power spectral density of the measurement data to be optimized; construct a Wiener filter transfer function based on the signal power spectral density and the noise power spectral density, and convert the measurement data to be optimized into the frequency domain through a fast Fourier transform; multiply the Wiener filter transfer function by the frequency domain signal, and obtain a filtered time domain signal through an inverse fast Fourier transform;

[0033] The filtered time domain signal is predicted based on the state prediction model, and the prediction error between the filtered time domain signal and the prediction signal is calculated; the dynamic weight coefficient is calculated according to the ratio of the prediction error to the error standard deviation, and the dynamic weight coefficient decays exponentially with the increase of the prediction error; the dynamic weight coefficient is multiplied and superimposed with the filtered time domain signal and the prediction signal respectively to obtain an optimized measurement result.

[0034] A second aspect of an embodiment of the present invention provides a portable high-precision comprehensive measurement system for tunnel air volume and environmental parameters, comprising:

[0035] The first unit is used to collect temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel through a measuring probe assembly arranged in the pipeline body;

[0036] A second unit is configured to transmit the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing;

[0037] a third unit, configured to calculate a displacement compensation coefficient based on an actual installation position of the measurement probe assembly within the pipeline body, calculate a temperature compensation coefficient based on a change in ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to original measurement data to obtain calibrated multi-source heterogeneous measurement data;

[0038] a fourth unit, configured to dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and accuracy optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result;

[0039] The fifth unit is configured to display the optimized measurement result through a display unit; and transmit the optimized measurement result to an external device through a communication interface for storage and analysis.

[0040] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0041] processor;

[0042] a memory for storing processor-executable instructions;

[0043] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0044] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0045] The beneficial effects of this application are as follows:

[0046] The present invention collects a variety of environmental parameters by arranging a measuring probe assembly in the pipeline body and performs displacement compensation and temperature compensation, thereby effectively improving the accuracy of data collection and overcoming the shortcomings of traditional measurement methods that are greatly affected by the installation position and ambient temperature.

[0047] The present invention adopts the Kalman filtering algorithm to dynamically fuse the calibrated multi-source heterogeneous measurement data, establishes a state prediction model to suppress noise and optimize accuracy, significantly improves the reliability and accuracy of the measurement results, and solves the problems of large data fluctuations and weak anti-interference ability of the traditional single measurement method.

[0048] The present invention realizes portable comprehensive measurement of tunnel air volume and environmental parameters, integrates multi-parameter measurement functions such as temperature, humidity, wind speed, air pressure and gas concentration, displays measurement results in real time through a display unit and can transmit data to external equipment, thereby improving measurement efficiency and providing reliable technical support for mine safety production. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a portable high-precision comprehensive measurement method for tunnel air volume and environmental parameters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0051] The technical solution of the present invention is described in detail below with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0052] Figure 1 FIG. 1 is a flow chart of a portable high-precision comprehensive measurement method for tunnel air volume and environmental parameters according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0053] The temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel are collected through the measuring probe assembly arranged in the pipeline body;

[0054] transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing;

[0055] calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly in the pipeline body, calculating a temperature compensation coefficient based on the ambient temperature change, and applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data;

[0056] Dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and precision optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result;

[0057] The optimized measurement results are displayed on a display unit; and the optimized measurement results are transmitted to an external device through a communication interface for storage and analysis.

[0058] In hazardous gas environments such as mine tunnels, ventilation is required to create a safe and suitable air environment and ensure the personal safety of construction workers. Among the existing wind volume detection methods: 1) The method of obtaining wind volume by measuring wind speed and cross-sectional area requires a variety of measuring instruments to detect average wind speed, cross-sectional area, temperature, humidity, atmospheric pressure and other data, and the measured data needs to be calculated and compensated to finally obtain wind volume data. The method proposed in this patent integrates a variety of measuring instruments into one, and the wind speed can be directly calculated by measuring wind speed and cross-sectional area. The obtained temperature, humidity and atmospheric pressure measurement data can also be directly compensated. 2) The differential pressure sensor wind volume measurement method initially needs to measure the wind resistance between two points. It is suitable for online detection at fixed locations, but not suitable for inspection scenarios. The method proposed in this patent is optimized for inspection work scenarios and is easy for personnel to carry and operate. At the same time, the method is also equipped with a wireless communication module that can seamlessly connect to the environmental network to realize real-time data transmission. It is not only suitable for inspection scenarios, but also has the ability to efficiently and accurately detect wind volume at fixed locations. 3) The tracer gas method is suitable for measuring air volume in tunnels, particularly in complex structures, low wind speeds, or when global assessments are required. However, its operation is complex, typically requiring specialized personnel to release and detect tracer gas upstream and downstream of the tunnel. The method proposed in this patent significantly simplifies the process, requiring only minimal training for wind measurement personnel to easily complete air volume measurement tasks, significantly reducing operational difficulty and reliance on specialized personnel.

[0059] The method proposed in this patent integrates measuring instruments for wind speed, cross-sectional area, temperature, humidity, and atmospheric pressure, achieving portable, digital, and paperless office work. It also has wireless communication capabilities, allowing the measured data to be uploaded to a data platform, facilitating wind volume data management. The method proposed in this patent is divided into eight modules: 1. Microcontroller module; 2. Cross-sectional area measurement module; 3. Wind speed measurement module; 4. Environmental parameter measurement module; 5. Human-computer interaction module; 6. Communication module; 7. Data storage module; and 8. Wind volume calculation module.

[0060] 1) The microcontroller module is the control core of this method. It is responsible for obtaining the environmental information detected by the measurement module, calculating the wind volume data that can be directly read, communicating with external devices, and managing and controlling other internal modules to achieve wind volume measurement.

[0061] 2) The cross-sectional area measurement module can measure the area data of common tunnel cross-sections such as rectangle, semicircular arch, and three-center arch by selecting the cross-sectional area model, and provide the data to the microcontroller to calculate the air volume.

[0062] 3) The wind speed measurement module is used to measure wind speed. When measuring wind speed, the surveyor selects different measurement methods, such as the four-wire method and the six-wire method, to calculate the average wind speed at the current measurement location and provide the data to the microcontroller, which then calculates the wind volume.

[0063] 4) The environmental parameter measurement module is used to measure current environmental information and correct errors in wind volume data. While measuring wind speed, surveyors will simultaneously check environmental data such as temperature, humidity, and atmospheric pressure, which can affect wind speed. The final measured wind volume data will be compensated for these environmental parameters.

[0064] 5) The human-computer interaction module is used for human-computer interaction and is divided into display and input. The display section displays measurement data and device information, making it easy for operators to observe measurement data. The input section is used to operate and run the device. Users can control the device's operating status and change device information.

[0065] 6) The communication module includes WiFi and BLE communication functions, which can exchange measurement data and device information with other devices and networks, facilitate recording and statistical information, and form data management.

[0066] 7) The data storage module uses a memory chip for data storage, which can save device information, measurement data, etc.

[0067] 8) The air volume calculation module guides wind measurement operations and calculates air volume. At the start of a measurement, the module guides inspectors in measuring wind speed and cross-sectional area. After the measurement is complete, the microcontroller collects temperature, humidity, and atmospheric pressure data. The module then calculates and compensates air volume based on this data, and displays the final air volume data through human-computer interaction.

[0068] In an optional embodiment, transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing includes:

[0069] The temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data are transmitted to a signal processing unit for classification and labeling and data cleaning, and a multi-source data correspondence is established based on the data acquisition time sequence to generate a standardized data structure;

[0070] The standardized data structure is subjected to noise reduction processing by using wavelet transform, abnormal data points are identified and eliminated, and pre-processed environmental parameter data are generated.

[0071] Data preprocessing is a key step in ensuring the quality of meteorological data. The temperature, humidity, wind speed, air pressure, and gas concentration data collected by the system need to undergo a series of processing before they can be used for subsequent analysis.

[0072] The data preprocessing process begins with data transmission and classification and labeling. The system transmits the raw data collected by each sensor to the signal processing unit via the data transmission module. For example, suppose a monitoring station collects 24 sets of temperature data in a single day. The raw data includes the acquisition timestamp, measurement value, and sensor identifier. Upon receiving this data, the signal processing unit adds the data type label "temperature" and identifies the data collection location coordinates, such as longitude 116.123 and latitude 39.456. Humidity data, wind speed data, air pressure data, and gas concentration data are labeled in the same manner.

[0073] The data cleaning phase addresses missing values and outliers in the raw data. If a value, such as -100°C, is detected in the temperature data and is clearly outside the acceptable range, the system will mark it as an outlier. In the case of missing data, such as if humidity data at a certain moment was not successfully collected, the system will mark the humidity value at that point in time as missing in the data structure. Negative values in gas concentration data are also identified as outliers that require cleaning.

[0074] Establishing a correspondence between multi-source data based on the data collection time sequence is a crucial step in ensuring the comparability of different types of data. The system sorts all data by collection timestamp. For example, the temperature, humidity, wind speed, air pressure, and gas concentration data collected at 10:00:00 on May 10, 2023, are aggregated to form a complete set of environmental parameters at that point in time. This process allows for intuitive observation of the correlation between environmental parameters at the same point in time.

[0075] The generation of a standardized data structure allows for unified management and analysis of different types of data. This standard data structure contains the following fields: timestamp, location, data type, measurement value, unit, and quality flag. For example, a complete record might look like: {Timestamp: 2023-05-10 10:00:00, Location: (116.123, 39.456), Type: Temperature, Value: 25.6, Unit: Celsius, Quality: Normal}. This structured data format facilitates subsequent data processing and analysis.

[0076] Wavelet transform noise reduction is a core technology in data preprocessing. The system first selects a wavelet basis function that is appropriate for the characteristics of the meteorological data, typically one with good symmetry and high smoothness. When processing temperature data, the system treats 24 consecutive hours of temperature data as a signal sequence and uses multi-level wavelet decomposition to separate the signal into a low-frequency approximation component and a high-frequency detail component. The signal is typically decomposed to four levels, resulting in one approximation coefficient and four detail coefficients.

[0077] During the detail coefficient processing phase, the system uses a soft thresholding method to screen detail coefficients, reducing or eliminating small-amplitude coefficients that may represent noise. The threshold is set based on the statistical characteristics of the data, such as the median absolute deviation of the detail coefficients. For example, for wind speed data, if the original data sequence is [3.2, 3.5, 7.8, 3.6, 3.4, 3.3, 3.5, 3.4] m / s, 7.8 m / s may be an outlier. After wavelet transform processing, this outlier is identified and smoothed to approximately 3.7 m / s, making the entire sequence more coherent and reasonable.

[0078] Identifying and removing outlier data points is crucial for ensuring data quality. The system uses a sliding window approach to calculate the deviation between the current data point and the data points before and after it. For barometric pressure data, if the measured value at a given moment differs by more than 5 hPa from the average value over the preceding and following ten minutes, and the rate of change exceeds the normal pressure range, the point is marked as an outlier. Data points marked as outliers can be replaced by interpolating values from nearby valid data points, or, if sufficient data is available, directly removed.

[0079] Preprocessed environmental parameter data is more reliable and consistent. For example, processed gas concentration data exhibits smoother trends, eliminating abnormal fluctuations caused by sensor jitter or temporary interference. Processed temperature data also more accurately reflects actual temperature fluctuations, eliminating short-term abnormal increases that may occur when the sensor is exposed to direct sunlight.

[0080] Through this data preprocessing process, the system generates environmental parameter data of higher quality and reliability, providing a reliable data foundation for subsequent meteorological analysis and forecasting. This standardized, cleaned, and noise-reduced data more accurately reflects the true changing trends of environmental parameters, improving the overall performance and forecasting accuracy of the meteorological monitoring system.

[0081] In an optional embodiment, calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly within the pipeline body, calculating a temperature compensation coefficient based on ambient temperature changes, and applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data includes:

[0082] According to the installation position of the measuring probe assembly in the pipeline body, obtaining a distance parameter between the measuring probe assembly and the center of the pipeline body;

[0083] Substituting the distance parameter into the flow field velocity distribution model to calculate the flow field characteristic coefficient, the flow field characteristic coefficient is obtained through a multi-point calibration experiment, and a displacement compensation coefficient is obtained;

[0084] Collecting ambient temperature data and obtaining the difference between the ambient temperature data and a preset standard temperature;

[0085] Multiplying the difference value by the relationship coefficient of the temperature response curve to obtain a sensor sensitivity parameter under the influence of temperature; calculating a compensation coefficient by a least square method based on the sensor sensitivity parameter to obtain a temperature compensation coefficient;

[0086] The displacement compensation coefficient is multiplied by the temperature compensation coefficient to obtain a comprehensive compensation coefficient, and the original measurement data is multiplied by the comprehensive compensation coefficient to obtain calibrated measurement data.

[0087] The measuring probe assembly is installed inside the pipe body to measure flow. Factors such as deviation from the pipe center and ambient temperature fluctuations can cause measurement errors. This method effectively eliminates these errors through compensation calculations.

[0088] The calculation of the displacement compensation coefficient begins by determining the distance between the measuring probe assembly and the center of the pipe. For example, in a circular pipe with a diameter of 200 mm, the measuring probe might be installed 45 mm from the pipe center. The system obtains this distance using a position sensor or installation parameter records.

[0089] After obtaining the distance parameter, the parameter is substituted into the flow field velocity distribution model. This model describes the flow velocity distribution characteristics at different positions on the pipe cross section. In this embodiment, the flow field characteristic coefficient is obtained through a multi-point calibration experiment. Specifically, in a laboratory environment, standard measuring equipment is placed at different positions of the pipe cross section (such as 0.1D, 0.2D, 0.3D..., where D is the pipe diameter), and the ratio of the flow velocity measured at each position to the flow velocity at the center of the pipe is recorded. The flow field velocity distribution model is constructed using these ratios, and the flow field characteristic coefficient at any position is obtained.

[0090] For example, when the probe is 45 mm from the pipe center (approximately 0.45 times the pipe radius), the corresponding flow field characteristic coefficient is 0.92 according to the pre-calibrated flow field characteristic coefficient table. This means that the flow velocity at this location is approximately 92% of the flow velocity at the pipe center. Therefore, the displacement compensation coefficient is 1.087 (i.e., 1 / 0.92) to compensate for the lower measurement value caused by the probe's off-center position.

[0091] When calculating the temperature compensation coefficient, the system first collects ambient temperature data. The system uses a temperature sensor to monitor the ambient temperature in real time. For example, if the current ambient temperature is 32°C and the preset standard temperature is 20°C, the temperature difference is 12°C.

[0092] Multiply the temperature difference by the coefficient of the temperature response curve. The temperature response curve is obtained through temperature calibration experiments and describes the effect of temperature changes on sensor sensitivity. In this example, the temperature coefficient of a flow sensor is 0.0015 / °C, indicating that for every 1°C increase in temperature, the sensor sensitivity decreases by 0.15%. Therefore, a 12°C temperature difference results in a change in sensor sensitivity parameter of 0.0015 × 12 = 0.018, a 1.8% decrease in sensitivity.

[0093] The compensation coefficient is calculated using the least squares method based on the sensor sensitivity parameters. In this embodiment, a temperature compensation model is derived by performing a least squares fit on multiple sets of temperature-sensitivity data. When the ambient temperature is 32°C, the calculated temperature compensation coefficient is 1.018, which is used to compensate for the low measurement value caused by the temperature increase.

[0094] After calculating the displacement compensation coefficient and the temperature compensation coefficient, the two are multiplied together to obtain the comprehensive compensation coefficient. In this embodiment, the comprehensive compensation coefficient is 1.087 × 1.018 = 1.107. Finally, the original measurement data is multiplied by the comprehensive compensation coefficient to obtain the calibrated measurement data.

[0095] For example, the original data of a flow measurement is 5.2 cubic meters / hour. After applying the comprehensive compensation coefficient, the calibration value is 5.2×1.107=5.76 cubic meters / hour, which is closer to the actual flow value.

[0096] To validate the effectiveness of this method, a comparative experiment was conducted. Before applying the compensation method, the measurement system achieved a ±1.5% accuracy under standard operating conditions (20°C, probe located at the center of the pipe). Under non-standard operating conditions (32°C, probe 45 mm off-center), the measurement error increased to -9.8%. After applying the compensation method, the measurement error under non-standard conditions was reduced to ±1.8%, approaching the accuracy level under standard operating conditions.

[0097] This compensation method effectively addresses measurement errors caused by probe position offset and ambient temperature fluctuations in pipeline flow measurement, improving the adaptability and accuracy of the measurement system in complex environments. This method has high practical value and can be widely applied in fields such as industrial pipeline flow monitoring, oil pipeline flow metering, and water conservancy project flow measurement.

[0098] In an optional embodiment, the compensation coefficient is calculated by least square method according to the sensor sensitivity parameter to obtain the temperature compensation coefficient, which includes:

[0099] An observation equation is constructed based on multiple sampling points within the temperature variation range, the sensitivity values of the sampling points are combined into a sensitivity observation vector, and the difference between the temperature corresponding to the sampling point and the preset standard temperature is combined into a temperature difference matrix;

[0100] Establishing a least squares criterion objective function according to the product of the sensitivity observation vector and the temperature difference matrix, solving the observation equation for the least squares criterion objective function using the least squares method, and calculating an initial compensation coefficient;

[0101] Calculating the standard deviation of the measurement data of the sampling points, constructing the reciprocal square value of the standard deviation of the measurement data into a weighting factor in the form of a diagonal matrix; multiplying the weighting factor with the temperature difference matrix and the sensitivity observation vector respectively to obtain a weighted temperature difference matrix and a sensitivity observation vector;

[0102] The least square method is used to resolve the normal equations for the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients, and the temperature compensation coefficients are calculated based on the optimized compensation coefficients.

[0103] Collect data at multiple sampling points within the temperature range, obtaining the corresponding temperature and sensitivity values for each sampling point. For example, within the temperature range of -40°C to 85°C, select the temperature points of -40°C, -20°C, 0°C, 25°C, 40°C, 60°C, and 85°C, and collect the sensor sensitivity values at these temperature points. Assume that at these temperature points, the sensor sensitivity values are 10.2mV / g, 10.5mV / g, 10.8mV / g, 11.0mV / g, 10.9mV / g, 10.7mV / g, and 10.4mV / g, respectively.

[0104] Construct the observation equation and combine the sensitivity values of the sampling points into a sensitivity observation vector, denoted as S. Taking the seven sampling points mentioned above as an example, the sensitivity observation vector S is [10.2, 10.5, 10.8, 11.0, 10.9, 10.7, 10.4]. Select 25°C as the preset standard temperature and calculate the difference between the temperature of each sampling point and the preset standard temperature to form the temperature difference matrix T. Taking the seven sampling points mentioned above as an example, the temperature difference matrix T is [(-40-25), (-20-25), (0-25), (25-25), (40-25), (60-25), (85-25)], that is, [-65, -45, -25, 0, 15, 35, 60].

[0105] A least-squares objective function is established based on the sensitivity observation vector S and the temperature difference matrix T. The objective function is expressed as minimizing the sum of squared errors between the predicted and actual sensitivity values. The predicted sensitivity value is calculated using the relationship between temperature and the compensation coefficient. Assuming a quadratic relationship between sensitivity and temperature, the compensation coefficients are set to [a, b, c], where a is the quadratic coefficient of the sensitivity-temperature relationship, b is the linear coefficient, and c is the constant coefficient.

[0106] The observation equation is solved by the least square method to calculate the initial compensation coefficient. The initial compensation coefficient [a init ,b init ,c init Assuming the calculated initial compensation coefficient is [0.0004, 0.008, 11.0], the relationship between sensitivity and temperature is: S = 0.0004 (T-25) 2 +0.008·(T-25)+11.0.

[0107] To improve compensation accuracy, calculate the standard deviation of the measured data at each sampling point. Repeat the sensitivity measurement multiple times at each temperature point and calculate the standard deviation of these measurements. For example, at -40°C, performing 10 measurements yields a standard deviation of 0.05mV / g; at -20°C, the standard deviation is 0.04mV / g. Similarly, at 0°C, the standard deviation is 0.03mV / g, 25°C is 0.02mV / g, 40°C is 0.03mV / g, 60°C is 0.04mV / g, and 85°C is 0.06mV / g.

[0108] The reciprocal square value of the standard deviation of the measured data is constructed as a diagonal matrix weighting factor W. Taking the seven sampling points mentioned above as an example, the weighting factor W is a diagonal matrix with diagonal elements of (1 / 0.05) 2 、(1 / 0.04) 2 、(1 / 0.03) 2 、(1 / 0.02) 2 、(1 / 0.03) 2 、(1 / 0.04) 2 、(1 / 0.06) 2 , i.e. [400, 625, 1111.11, 2500, 1111.11, 625, 277.78]. In this way, data points with high measurement accuracy (small standard deviation) are given larger weights.

[0109] Multiply the weighting factor W with the temperature difference matrix T and the sensitivity observation vector S respectively to obtain the weighted temperature difference matrix T weighted and the sensitivity observation vector S weighted The specific operation is to multiply each diagonal element of the weighting factor matrix W with the corresponding element of the temperature difference matrix T to obtain T weight ed ; Multiply each diagonal element of the weighting factor matrix W by the corresponding element of the sensitivity observation vector S to obtain S weighted .

[0110] The least squares method is used to re-solve the normal equations for the weighted temperature difference matrix T_weighted and the sensitivity observation vector S_weighted to obtain the optimized compensation coefficient [a opt ,b opt ,c opt Assuming that the calculated optimized compensation coefficient is [0.00038, 0.0075, 11.0], it means that after considering data reliability, the relationship between sensitivity and temperature is: S = 0.00038 (T-25) 2 +0.0075·(T-25)+11.0.

[0111] The temperature compensation coefficient is calculated based on the optimized compensation coefficient. According to the actual application requirements, the compensation coefficient can be converted into a specific temperature compensation formula. For example, when the sensor operates at temperature T, its sensitivity correction value is: ΔS = 0.00038 (T-25) 2 +0.0075·(T-25). Taking 40℃ as an example, the sensitivity correction value is: ΔS=0.00038·(40-25) 2 +0.0075·(40-25)=0.00038·225+0.0075·15=0.0855+0.1125=0.198mV / g. That is, at 40°C, the actual sensitivity of the sensor should be subtracted by 0.198mV / g to achieve the same performance as at the standard temperature of 25°C.

[0112] This compensation method effectively eliminates the effects of temperature changes on sensor sensitivity, improving the sensor's consistency and reliability across varying temperature environments. This method not only accounts for the nonlinear relationship between temperature and sensitivity but also incorporates differences in data reliability at different measurement points through weighted least squares analysis, resulting in a more accurate compensation coefficient. This approach is suitable for all sensor systems requiring temperature compensation.

[0113] In an optional embodiment, dynamically fusing the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, and establishing a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data includes:

[0114] Analyzing the variation pattern of the calibrated multi-source heterogeneous measurement data in a time series, and calculating the time series correlation coefficient of the calibrated multi-source heterogeneous measurement data;

[0115] Establishing a state prediction model based on the time series correlation coefficient, wherein the state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment;

[0116] A Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data, including: predicting the data state at the next moment according to the state prediction model; calculating the Kalman gain based on the prediction result, weightedly fusing the Kalman gain with the actual observation data to obtain an optimal estimate, and outputting the optimal estimate as the fused multi-source heterogeneous measurement data.

[0117] The method mainly consists of establishing a state prediction model based on the time series correlation of calibrated multi-source heterogeneous measurement data and performing dynamic fusion processing using the Kalman filter algorithm.

[0118] To analyze the temporal variation patterns of calibrated multi-source heterogeneous measurement data, it is necessary to calculate the temporal correlation coefficient of the calibrated multi-source heterogeneous measurement data. Specifically, the calibrated multi-source heterogeneous measurement data is first sorted by timestamp to form a time series dataset. For each measurement data type, its numerical variation at consecutive time points is extracted. For example, for temperature sensor data, hourly temperature values can be extracted for 24 consecutive hours; for pressure sensor data, minutely pressure values can be extracted for 30 consecutive minutes. By calculating the autocorrelation function, the correlation between each type of data at different time delays is determined. For example, the autocorrelation coefficient of temperature data with a 1-hour delay is 0.92, the autocorrelation coefficient of temperature data with a 2-hour delay is 0.85, and so on. Furthermore, the cross-correlation coefficient between different data types is calculated. For example, the cross-correlation coefficient of temperature and humidity data is -0.76, indicating a strong negative correlation.

[0119] When establishing a state prediction model based on the time series correlation coefficient, the time series correlation coefficients calculated above are used to construct a prediction model capable of predicting the state of multi-source heterogeneous measurement data at the next moment. This model takes data from the current moment and several past moments as input and predicts the data state at the next moment. In specific implementation, the appropriate prediction model structure can be selected based on the time series correlation of the data. For example, for highly correlated continuous data, a linear prediction model can be used; for data with periodic variations, a periodic term can be introduced for modeling. The state vector of the state prediction model contains the measurement values of various sensors, and the state transition matrix is determined based on the time series correlation coefficients. For example, if the first-order autocorrelation coefficient of the temperature data is 0.92 and the second-order autocorrelation coefficient is 0.85, the element corresponding to the temperature prediction in the state transition matrix can be set to a value that reflects this time series relationship.

[0120] When using the Kalman filter algorithm to dynamically fuse calibrated multi-source heterogeneous measurement data, the state of the data at the next moment is first predicted based on the state prediction model established above. Assuming the current moment is k, and the estimated state values at time k are known to be [25.3°C, 65.7%, 101.2 kPa] (temperature, humidity, and pressure, respectively), the state prediction model yields the predicted state value at time k+1 as [25.5°C, 66.1%, 101.3 kPa]. Simultaneously, the prediction error covariance matrix is calculated based on the state prediction model, reflecting the uncertainty of the prediction results.

[0121] When calculating the Kalman gain based on the prediction results, the prediction error covariance matrix and the observation noise covariance matrix must be combined. The Kalman gain determines the weighting of the predicted and observed values during the fusion process. For example, for temperature data, if the prediction is reliable (small prediction error) but the observation noise is large, the Kalman gain will be reduced, making the fusion result more inclined towards the predicted value. Conversely, if the observed value is reliable, the Kalman gain will be increased, making the fusion result more inclined towards the observed value. In a practical application, if the actual observation value at time k+1 is [25.7°C, 65.9%, 101.1kPa], and the calculated Kalman gain is [0.65, 0.58, 0.72], the Kalman gain is weighted with the observed and predicted data.

[0122] The optimal estimate is obtained by weighted fusion of the Kalman gain and the actual observation data as follows: For the temperature data, the fused estimate equals the predicted value of 25.5°C plus the Kalman gain of 0.65 multiplied by the difference between the observed and predicted values (25.7°C - 25.5°C), i.e., 25.5°C + 0.65 × 0.2°C = 25.63°C. For the humidity data, the fused estimate is 66.1% + 0.58 × (65.9% - 66.1%) = 65.98%. For the pressure data, the fused estimate is 101.3 kPa + 0.72 × (101.1 kPa - 101.3 kPa) = 101.16 kPa. Therefore, the optimal estimate at time k+1 is [25.63°C, 65.98%, 101.16 kPa]. This estimate comprehensively considers the results of the prediction model and the actual observation data, effectively reducing the influence of noise from a single data source and improving data accuracy and reliability.

[0123] After each data fusion cycle, the state estimation covariance matrix must be updated to prepare for the next data fusion step. By continuously iterating this process, we can achieve continuous dynamic fusion of multi-source heterogeneous measurement data, generating a temporally coherent fused data sequence with reduced noise. In practical applications, the fused data can be used in a variety of fields, including environmental monitoring, industrial process control, and smart homes, significantly improving the system's perception accuracy and stability.

[0124] In an optional embodiment, noise suppression and precision optimization processing are performed on the fused data according to the state prediction model to obtain optimized measurement results including:

[0125] Acquire measurement data to be optimized, calculate the signal power spectral density and the noise power spectral density of the measurement data to be optimized; construct a Wiener filter transfer function based on the signal power spectral density and the noise power spectral density, and convert the measurement data to be optimized into the frequency domain through a fast Fourier transform; multiply the Wiener filter transfer function by the frequency domain signal, and obtain a filtered time domain signal through an inverse fast Fourier transform;

[0126] The filtered time domain signal is predicted based on the state prediction model, and the prediction error between the filtered time domain signal and the prediction signal is calculated; the dynamic weight coefficient is calculated according to the ratio of the prediction error to the error standard deviation, and the dynamic weight coefficient decays exponentially with the increase of the prediction error; the dynamic weight coefficient is multiplied and superimposed with the filtered time domain signal and the prediction signal respectively to obtain an optimized measurement result.

[0127] Obtain the measurement data to be optimized. This data may come from the results of multi-source sensor fusion. Taking a set of attitude measurement data as an example, assume that the acquired raw data includes pitch, roll, and yaw angles, with a sampling frequency of 100 Hz and a data length of 1000 sampling points, which contains high-frequency noise interference.

[0128] Calculating the signal power spectrum density and noise power spectrum density of the measurement data to be optimized is the next key processing step. The system processes the measurement data in segments and uses the periodogram method to calculate the power spectrum density of the signal. Specifically, the data of 1000 sampling points are divided into 10 segments, each with 100 points. The Hanning window function is used to perform windowing on each segment of data, and then the Fourier transform of each segment of data is calculated. Finally, the results of all segments are averaged to obtain the signal power spectrum density. For the estimation of the noise power spectrum density, the system collects data samples in the static state of the sensor in advance and calculates it in the same way. In actual operation, it is assumed that the signal has significant energy below 10Hz and is mainly noise above 10Hz. The calculated signal power spectrum density is about 0.5rad in the low frequency band. 2 / Hz, the noise power spectrum density is about 0.05rad 2 / Hz.

[0129] Based on the calculated signal power spectral density and noise power spectral density, the system constructs the Wiener filter transfer function. The Wiener filter performs adaptive filtering in the frequency domain based on the ratio of signal to noise power. The transfer function is designed to approach 1 when the signal power is significantly greater than the noise power and approach 0 when the noise power dominates. In practice, the transfer function is calculated by dividing the signal power spectral density by the sum of the signal power spectral density and the noise power spectral density. For the above example, the transfer function value is approximately 0.91 in the 0-10 Hz frequency band, but it quickly drops below 0.1 at higher frequencies.

[0130] Converting the measured data to the frequency domain via Fast Fourier Transform (FFT) is a preparatory step for applying Wiener filtering. The system performs an FFT on a 1000-point sequence of measured data to obtain a frequency-domain representation. For example, after FFT conversion, the amplitude of the roll angle data in the low-frequency range (0-10Hz) is approximately 2.5 rad, while the amplitude in the high-frequency range (>10Hz) drops to below 0.3 rad.

[0131] Multiplying the Wiener filter transfer function with the frequency-domain signal is the core filtering step. Frequency-domain filtering is achieved by multiplying the signal value at each frequency by the filter transfer function value at that frequency. After processing, the low-frequency signal remains virtually unchanged, while the noise in the high-frequency band is significantly reduced.

[0132] Obtaining a filtered time-domain signal through an inverse fast Fourier transform is a necessary step for converting the processed results back to the original domain. The system performs an IFFT operation on the filtered frequency-domain signal, generating a 1000-point time-domain signal. A comparison before and after filtering shows that the standard deviation of the original roll angle data, which was 0.8 rad, drops to 0.3 rad after filtering, significantly reducing high-frequency jitter.

[0133] Predicting the filtered time-domain signal based on a state prediction model is key to further improving accuracy. The system uses a Kalman filter as the state prediction model, with the state vector consisting of the attitude angle and its rate of change. For the roll angle, the state transfer matrix is set to a model that considers uniform motion, and the process noise covariance is set to a diagonal matrix with diagonal elements of 0.01 rad. 2 In the prediction step, the system calculates an estimated state at the next moment based on the current state and the state transition equation. In practice, for a roll angle of 2.3 rad at time t and a rate of change of 0.1 rad / s, the predicted angle at time t+1 (10ms later) is 2.31 rad.

[0134] Calculating the prediction error between the filtered time-domain signal and the predicted signal forms the basis for dynamic weight assignment. At each time point, the system calculates the difference between the actual measured value and the predicted value. For example, at a specific moment, the filtered measured value is 2.35 rad, the predicted value is 2.31 rad, and the prediction error is 0.04 rad. The system also calculates the standard deviation of the prediction error. Using a sliding window method, the system takes the first 100 prediction error samples and calculates a standard deviation of 0.02 rad.

[0135] The core of adaptive fusion is calculating a dynamic weight coefficient based on the ratio of prediction error to error standard deviation. The system employs an exponential decay function, which exponentially decreases the weight coefficient as the ratio of prediction error to standard deviation increases. In practice, the weight coefficient is calculated as the negative exponential function e, where the exponent is the square of the ratio of prediction error to standard deviation multiplied by a constant of 0.5. In the example above, the ratio of prediction error to standard deviation is 2, resulting in a calculated weight coefficient of approximately 0.135.

[0136] The final fusion step involves multiplying the dynamic weight coefficients with the filtered time-domain signal and the predicted signal, respectively, and then superimposing them to obtain the optimized measurement result. At each time point, the system multiplies the weight coefficients with the filtered measurement value, and multiplies (1-weight coefficient) with the predicted value, adding the two together to obtain the final result. In the above example, the final optimized result is 2.35 × 0.135 + 2.31 × (1-0.135) = 2.315 rad. This adaptive weighting approach allows the system to strike a balance between measured and predicted values, relying more on measurements when they are reliable and more on predictions when they are anomalous.

[0137] After a complete processing flow, high-frequency noise in the raw data is effectively suppressed, and the impact of outliers is significantly mitigated. The final optimized measurement results show a noise standard deviation reduction of approximately 75% compared to the original data, significantly improving data smoothness while maintaining a sensitive response to actual signal changes.

[0138] A second aspect of an embodiment of the present invention provides a portable high-precision comprehensive measurement system for tunnel air volume and environmental parameters, comprising:

[0139] The first unit is used to collect temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel through a measuring probe assembly arranged in the pipeline body;

[0140] A second unit is configured to transmit the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing;

[0141] a third unit, configured to calculate a displacement compensation coefficient based on an actual installation position of the measurement probe assembly within the pipeline body, calculate a temperature compensation coefficient based on a change in ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to original measurement data to obtain calibrated multi-source heterogeneous measurement data;

[0142] a fourth unit, configured to dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and accuracy optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result;

[0143] The fifth unit is configured to display the optimized measurement result through a display unit; and transmit the optimized measurement result to an external device through a communication interface for storage and analysis.

[0144] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0145] processor;

[0146] a memory for storing processor-executable instructions;

[0147] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0148] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0149] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A portable high-precision comprehensive measurement method for tunnel air volume and environmental parameters, characterized in that: include: The temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel are collected through the measuring probe assembly arranged in the pipeline body; transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing; calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly in the pipeline body, calculating a temperature compensation coefficient based on the ambient temperature change, and applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data; Dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and precision optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result; Displaying the optimized measurement results via a display unit; The optimized measurement results are transmitted to an external device via a communication interface for storage and analysis.

2. The method according to claim 1, characterized in that Transmitting the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing includes: The temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data are transmitted to a signal processing unit for classification and labeling and data cleaning, and a multi-source data correspondence is established based on the data acquisition time sequence to generate a standardized data structure; The standardized data structure is subjected to noise reduction processing by using wavelet transform, abnormal data points are identified and eliminated, and pre-processed environmental parameter data are generated.

3. The method according to claim 1, characterized in that Calculating a displacement compensation coefficient based on the actual installation position of the measurement probe assembly in the pipeline body, calculating a temperature compensation coefficient based on the ambient temperature change, applying the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data, and obtaining calibrated multi-source heterogeneous measurement data includes: According to the installation position of the measuring probe assembly in the pipeline body, obtaining a distance parameter between the measuring probe assembly and the center of the pipeline body; Substituting the distance parameter into the flow field velocity distribution model to calculate the flow field characteristic coefficient, the flow field characteristic coefficient is obtained through a multi-point calibration experiment, and a displacement compensation coefficient is obtained; Collecting ambient temperature data and obtaining the difference between the ambient temperature data and a preset standard temperature; Multiplying the difference value by the relationship coefficient of the temperature response curve to obtain a sensor sensitivity parameter under the influence of temperature; calculating a compensation coefficient by a least square method based on the sensor sensitivity parameter to obtain a temperature compensation coefficient; The displacement compensation coefficient is multiplied by the temperature compensation coefficient to obtain a comprehensive compensation coefficient, and the original measurement data is multiplied by the comprehensive compensation coefficient to obtain calibrated measurement data.

4. The method according to claim 3, characterized in that The compensation coefficient is calculated by the least square method according to the sensor sensitivity parameter, and the temperature compensation coefficient is obtained, which includes: An observation equation is constructed based on multiple sampling points within the temperature variation range, the sensitivity values of the sampling points are combined into a sensitivity observation vector, and the difference between the temperature corresponding to the sampling point and the preset standard temperature is combined into a temperature difference matrix; Establishing a least squares criterion objective function according to the product of the sensitivity observation vector and the temperature difference matrix, solving the observation equation for the least squares criterion objective function using the least squares method, and calculating an initial compensation coefficient; Calculating the standard deviation of the measurement data of the sampling points, constructing the reciprocal square value of the standard deviation of the measurement data into a weighting factor in the form of a diagonal matrix; multiplying the weighting factor with the temperature difference matrix and the sensitivity observation vector respectively to obtain a weighted temperature difference matrix and a sensitivity observation vector; The least square method is used to resolve the normal equations for the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients, and the temperature compensation coefficients are calculated based on the optimized compensation coefficients.

5. The method according to claim 1, wherein Dynamically fusing the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, and establishing a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data includes: Analyzing the variation pattern of the calibrated multi-source heterogeneous measurement data in a time series, and calculating the time series correlation coefficient of the calibrated multi-source heterogeneous measurement data; Establishing a state prediction model based on the time series correlation coefficient, wherein the state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment; A Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data, including: predicting the data state at the next moment according to the state prediction model; calculating the Kalman gain based on the prediction result, weightedly fusing the Kalman gain with the actual observation data to obtain an optimal estimate, and outputting the optimal estimate as the fused multi-source heterogeneous measurement data.

6. The method according to claim 1, characterized in that The fused data is subjected to noise suppression and precision optimization processing according to the state prediction model, and the optimized measurement results obtained include: Acquire measurement data to be optimized, calculate the signal power spectral density and the noise power spectral density of the measurement data to be optimized; construct a Wiener filter transfer function based on the signal power spectral density and the noise power spectral density, and convert the measurement data to be optimized into the frequency domain through a fast Fourier transform; multiply the Wiener filter transfer function by the frequency domain signal, and obtain a filtered time domain signal through an inverse fast Fourier transform; The filtered time domain signal is predicted based on the state prediction model, and the prediction error between the filtered time domain signal and the prediction signal is calculated; the dynamic weight coefficient is calculated according to the ratio of the prediction error to the error standard deviation, and the dynamic weight coefficient decays exponentially with the increase of the prediction error; the dynamic weight coefficient is multiplied and superimposed with the filtered time domain signal and the prediction signal respectively to obtain an optimized measurement result.

7. A portable high-precision comprehensive measurement system for tunnel air volume and environmental parameters, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to collect temperature data, humidity data, wind speed data, air pressure data and gas concentration data in the tunnel through a measuring probe assembly arranged in the pipeline body; A second unit is configured to transmit the temperature data, the humidity data, the wind speed data, the air pressure data, and the gas concentration data to a signal processing unit for data preprocessing; a third unit, configured to calculate a displacement compensation coefficient based on an actual installation position of the measurement probe assembly within the pipeline body, calculate a temperature compensation coefficient based on a change in ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to original measurement data to obtain calibrated multi-source heterogeneous measurement data; a fourth unit, configured to dynamically fuse the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm, establish a state prediction model based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, and perform noise suppression and accuracy optimization processing on the fused data according to the state prediction model to obtain an optimized measurement result; A fifth unit, configured to display the optimized measurement result via a display unit; The optimized measurement results are transmitted to an external device via a communication interface for storage and analysis.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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