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

Abstract

This invention provides a portable, high-precision method and system for comprehensive measurement of airflow and environmental parameters in roadways. It relates to the field of airflow and parameter measurement technology, and includes: collecting data on temperature, humidity, wind speed, air pressure, and gas concentration within the roadway using a measurement probe assembly; calculating displacement and temperature compensation coefficients to calibrate the original data; dynamically fusing the calibrated multi-source heterogeneous measurement data using a Kalman filter algorithm; establishing a state prediction model based on time-series correlation; and optimizing the measurement results. This invention improves the accuracy of roadway environmental parameter measurement, suppresses environmental noise interference, and achieves high-precision comprehensive measurement of multiple parameters.

Description

Technical Field

[0001] This invention relates to air volume and parameter measurement technology, and more particularly to a portable, high-precision method and system for comprehensive measurement of air volume and environmental parameters in roadways. Background Technology

[0002] With increasingly stringent safety requirements in mines, the accurate measurement of ventilation parameters in roadway ventilation systems, as a crucial guarantee for mine safety, is essential for ensuring a safe working environment. Airflow and environmental parameters within the roadway, such as temperature, humidity, air pressure, and concentration of harmful gases, are key indicators for assessing the operational status of the mine ventilation system. Traditional methods for measuring roadway airflow and environmental parameters typically involve using single or multiple independent instruments, which is cumbersome and makes it difficult to guarantee data consistency and accuracy.

[0003] Existing measuring equipment generally lacks effective displacement and temperature compensation mechanisms, resulting in large measurement errors when the measuring probe is installed off-center or when the ambient temperature changes, making it unable to adapt to complex and ever-changing tunnel environment conditions.

[0004] Existing technologies typically employ simple data acquisition methods, lacking the ability to effectively fuse and process multi-source heterogeneous data. Various parameter measurements are independent of each other, making it impossible to improve overall measurement accuracy by utilizing the correlation between parameters. In particular, when there are many interfering factors in the roadway environment, the stability of measurement data is poor.

[0005] Existing roadway environmental parameter measurement equipment is mostly designed in a decentralized manner, with limited data transmission and centralized analysis capabilities. It is difficult to achieve real-time display and remote transmission of measurement data, which cannot meet the requirements of modern mine safety monitoring systems for real-time data and networked analysis, thus limiting the application value of measurement results. Summary of the Invention

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

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

[0008] Temperature, humidity, wind speed, air pressure, and gas concentration data are collected from the tunnel using a measuring probe assembly installed inside the pipeline body.

[0009] The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for data preprocessing.

[0010] The displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly in the pipe body, and the temperature compensation coefficient is calculated based on the change of ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data.

[0011] The Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data. A state prediction model is established based on the temporal correlation of the calibrated multi-source heterogeneous measurement data. The fused data is then subjected to noise suppression and accuracy optimization based on the state prediction model to obtain the optimized measurement results.

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

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

[0014] The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for classification, labeling, and data cleaning. Based on the data acquisition time sequence, a correspondence between multiple data sources is established to generate a standardized data structure.

[0015] The standardized data structure is denoised using wavelet transform to identify and remove outlier data points, generating preprocessed environmental parameter data.

[0016] The displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly within the pipe body, and the temperature compensation coefficient is calculated based on changes in ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are then applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data, including:

[0017] Based on the installation position of the measuring probe assembly within the pipe body, the distance parameter between the measuring probe assembly and the center of the pipe body is obtained;

[0018] Substitute 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 multi-point calibration experiments to obtain the displacement compensation coefficient.

[0019] Collect ambient temperature data and obtain the difference between the ambient temperature data and the preset standard temperature;

[0020] Multiply the difference by the correlation coefficient of the temperature response curve to obtain the sensor sensitivity parameter under the influence of temperature; calculate the compensation coefficient using the least squares method based on the sensor sensitivity parameter to obtain the temperature compensation coefficient;

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

[0022] The compensation coefficients are calculated using the least squares method based on the sensor sensitivity parameters, resulting in the following temperature compensation coefficients:

[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] The least squares criterion objective function is established based on the product of the sensitivity observation vector and the temperature difference matrix. The observation equation is solved by the least squares method to calculate the initial compensation coefficient.

[0025] Calculate the standard deviation of the measurement data at the sampling points, and construct a weighting factor in the form of a diagonal matrix using the reciprocal square of the standard deviation of the measurement data; multiply the weighting factor by the temperature difference matrix and the sensitivity observation vector respectively to obtain the weighted temperature difference matrix and sensitivity observation vector;

[0026] The normal equations are re-solved using the least squares method on the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients. The temperature compensation coefficient is then calculated based on the optimized compensation coefficients.

[0027] The Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data. A state prediction model is then established based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, including:

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

[0029] A state prediction model is established based on the time series correlation coefficient. The state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment.

[0030] The 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; weighting and fusing the Kalman gain with the actual observation data to obtain the optimal estimate; and outputting the optimal estimate as the fused multi-source heterogeneous measurement data.

[0031] Based on the state prediction model, noise suppression and accuracy optimization are performed on the fused data to obtain optimized measurement results, including:

[0032] The measurement data to be optimized is obtained, and the signal power spectral density and noise power spectral density of the measurement data to be optimized are calculated. A Wiener filter transfer function is constructed based on the signal power spectral density and the noise power spectral density. The measurement data to be optimized is transformed to the frequency domain through a fast Fourier transform. The Wiener filter transfer function is multiplied with the frequency domain signal, and the filtered time domain signal is obtained 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 predicted signal is calculated. A dynamic weighting coefficient is calculated based on the ratio of the prediction error to the standard deviation of the error. The dynamic weighting coefficient decreases exponentially as the prediction error increases. The dynamic weighting coefficient is multiplied by the filtered time-domain signal and the predicted signal respectively and then superimposed to obtain the optimized measurement result.

[0034] A second aspect of the present invention provides a portable, high-precision integrated measurement system for roadway 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 installed in the pipeline body;

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

[0037] The third unit is used to calculate the displacement compensation coefficient based on the actual installation position of the measuring probe assembly in the pipe body, calculate the temperature compensation coefficient based on the change of ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data.

[0038] The fourth unit is used to dynamically fuse the calibrated multi-source heterogeneous measurement data using the 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 optimized measurement results.

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

[0040] A third aspect of the present invention provides an electronic device, comprising:

[0041] processor;

[0042] Memory used to store processor-executable instructions;

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

[0044] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

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

[0046] This invention improves the accuracy of data acquisition by installing a measurement probe assembly inside the pipeline body to collect various environmental parameters and performing displacement and temperature compensation, thus overcoming the shortcomings of traditional measurement methods that are greatly affected by installation location and ambient temperature.

[0047] This invention employs a Kalman filter algorithm to dynamically fuse calibrated multi-source heterogeneous measurement data, establishes a state prediction model for noise suppression and accuracy optimization, significantly improving the reliability and accuracy of measurement results, and solving the problems of large data fluctuations and weak anti-interference capabilities in traditional single measurement methods.

[0048] This invention enables portable comprehensive measurement of tunnel air volume and environmental parameters. It integrates multiple parameter measurement functions such as temperature, humidity, wind speed, air pressure, and gas concentration. The measurement results are displayed in real time through the display unit and the data can be transmitted to external devices, which improves the efficiency of measurement work and provides reliable technical support for safe production in mines. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the portable, high-precision method for comprehensively measuring roadway air volume and environmental parameters according to an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0052] Figure 1 This is a flowchart illustrating the portable, high-precision method for comprehensively measuring roadway air volume and environmental parameters according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0053] Temperature, humidity, wind speed, air pressure, and gas concentration data are collected from the tunnel using a measuring probe assembly installed inside the pipeline body.

[0054] The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for data preprocessing.

[0055] The displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly in the pipe body, and the temperature compensation coefficient is calculated based on the change of ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data.

[0056] The Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data. A state prediction model is established based on the temporal correlation of the calibrated multi-source heterogeneous measurement data. The fused data is then subjected to noise suppression and accuracy optimization based on the state prediction model to obtain the optimized measurement results.

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

[0058] In hazardous gas environments such as mine tunnels, good ventilation is essential to create a safe and suitable air environment and ensure the personal safety of construction workers. Existing airflow detection methods include: 1) Methods that obtain airflow by measuring wind speed and cross-sectional area require multiple measuring instruments to detect average wind speed, cross-sectional area, temperature, humidity, atmospheric pressure, etc., and require calculation and compensation of the measured data to obtain the final airflow data. The method proposed in this patent integrates multiple measuring instruments into one unit. Wind speed can be directly calculated from the measured wind speed and cross-sectional area, and the obtained temperature, humidity, and atmospheric pressure measurements can also be directly compensated. 2) Differential pressure sensor airflow measurement methods initially require measuring the wind resistance between two points, suitable for online detection at fixed locations, but not for inspection scenarios. The method proposed in this patent is optimized for inspection work scenarios, making it easy for personnel to carry and operate. Furthermore, this method is equipped with a wireless communication module, enabling seamless connection to environmental networks and real-time data transmission. It is not only suitable for inspection scenarios but also possesses efficient and accurate capabilities for airflow detection at fixed locations. 3) The tracer gas method is suitable for complex structures, low wind speeds, or scenarios requiring global assessment in roadway airflow measurement. However, its operation is relatively complex, usually requiring professionals to release and detect tracer gases upstream and downstream of the roadway. The method proposed in this patent significantly simplifies the operation process. Only simple business training is needed for the airflow measurement personnel to easily complete the airflow measurement work, greatly reducing the difficulty of operation and reliance on professional personnel.

[0059] This patented method integrates measuring instruments for wind speed, cross-sectional area, temperature, humidity, and atmospheric pressure into a single unit, enabling portable, digital, and paperless office operations. It also features wireless communication capabilities, allowing measurement data to be uploaded to a data platform for convenient airflow data management. The method comprises 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. Airflow calculation module.

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

[0061] 2) The cross-sectional area measurement module can measure the area data of common roadway cross sections such as rectangles, semi-circular arches, and three-center arches by selecting the cross-sectional area model, and provide the data to the micro controller 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-line method or the six-line method, to calculate the average wind speed at the current measurement location and provide the data to the microcontroller, which then calculates the air volume.

[0063] 4) The environmental parameter measurement module is used to measure current environmental information and correct errors in the airflow data. When measuring wind speed, the personnel will simultaneously detect environmental data, such as temperature, humidity, atmospheric pressure, and other environmental factors that affect wind speed. The final measured airflow data will be compensated for by referring to the environmental parameters.

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

[0065] 6) The communication module includes WiFi and BLE communication functions, which can exchange measurement data and equipment information with other devices and networks, facilitating the recording and statistical analysis of information, and forming data management, etc.

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

[0067] 8) The air volume calculation module guides the wind measurement operation and calculates the air volume. At the start of the measurement, the air volume calculation module guides the inspection personnel to measure the wind speed and cross-sectional area. After the measurement is completed, the microcontroller collects temperature, humidity, and atmospheric pressure data. The air volume calculation module calculates and compensates for the air volume based on the obtained data and displays the final air volume data through human-computer interaction.

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

[0069] The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for classification, labeling, and data cleaning. Based on the data acquisition time sequence, a correspondence between multiple data sources is established to generate a standardized data structure.

[0070] The standardized data structure is denoised using wavelet transform to identify and remove outlier data points, generating preprocessed environmental parameter data.

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

[0072] The data preprocessing process begins with data transmission and classification labeling. The system transmits the raw data collected by each sensor to the signal processing unit via the data transmission module. Taking temperature data as an example, suppose a monitoring station collects 24 sets of temperature data in one day. The raw data includes the collection timestamp, the measured value, and the sensor identifier. After receiving this data, the signal processing unit adds the data type label "temperature" and marks 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 way.

[0073] The data cleaning process addresses missing and outlier values ​​in the raw data. When a temperature value, such as -100℃, is detected that is clearly outside the reasonable range, the system marks it as an outlier. For missing data, such as humidity data that was not successfully collected at a certain moment, the system marks the humidity value for that time point as missing in the data structure. Negative values ​​in gas concentration data are also identified as outliers requiring cleaning.

[0074] Establishing a correspondence between multi-source data based on the data acquisition time sequence is a crucial step in ensuring the comparability of different types of data. The system sorts all data according to the acquisition timestamp. For example, temperature, humidity, wind speed, air pressure, and gas concentration data collected at 10:00:00 on May 10, 2023, are aggregated together to form a complete set of environmental parameters for that time point. After this processing, the correlation between various environmental parameters at the same time point can be intuitively observed.

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

[0076] Wavelet transform denoising is a core technology in data preprocessing. The system first selects a wavelet basis function suitable for the characteristics of the meteorological data, typically using a wavelet basis with good symmetry and 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 divide the signal into a low-frequency approximation part and a high-frequency detail part. Generally, the signal is decomposed to four levels, yielding one approximation coefficient and four detail coefficients.

[0077] During the detail coefficient processing stage, the system employs a soft thresholding method to filter detail coefficients, reducing or eliminating small-amplitude coefficients that may represent noise. The threshold setting is 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, where 7.8 m / s might 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 employs a sliding window method to calculate the deviation of the current data point from the data points before and after it. For barometric pressure data, if the measured value at a given moment differs from the average value over the preceding and following ten minutes by more than 5 hPa, and the rate of change exceeds the normal range for barometric pressure variation, then that point is marked as an outlier. Data points marked as outliers can be replaced using interpolation methods with nearby valid data points, or they can be directly removed if there is sufficient data.

[0079] The preprocessed environmental parameter data is more reliable and consistent. For example, the processed gas concentration data shows a smoother trend, eliminating abnormal fluctuations caused by sensor jitter or temporary interference. The processed temperature data also more accurately reflects the actual temperature change pattern, removing short-term abnormal increases that may be caused by the sensor being exposed to direct sunlight.

[0080] Through the data preprocessing steps described above, the environmental parameter data generated by the system has higher quality and reliability, providing a solid data foundation for subsequent meteorological analysis and forecasting. This standardized, cleaned, and noise-reduced data can more accurately reflect the true changing trends of environmental parameters, improving the overall performance and forecasting accuracy of the meteorological monitoring system.

[0081] In one optional implementation, a displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly within the pipe body, and a temperature compensation coefficient is calculated based on changes in ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are then applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data, including:

[0082] Based on the installation position of the measuring probe assembly within the pipe body, the distance parameter between the measuring probe assembly and the center of the pipe body is obtained;

[0083] Substitute 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 multi-point calibration experiments to obtain the displacement compensation coefficient.

[0084] Collect ambient temperature data and obtain the difference between the ambient temperature data and the preset standard temperature;

[0085] Multiply the difference by the correlation coefficient of the temperature response curve to obtain the sensor sensitivity parameter under the influence of temperature; calculate the compensation coefficient using the least squares method based on the sensor sensitivity parameter to obtain the temperature compensation coefficient;

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

[0087] The measuring probe assembly is installed inside the pipe body to measure flow rate. Factors such as the installation location being off-center from the pipe and changes in ambient temperature can cause errors in the measurement data. This method effectively eliminates the influence of these errors through compensation calculations.

[0088] In calculating the displacement compensation coefficient, the distance parameter between the measuring probe assembly and the center of the pipe body is first obtained. For example, in a circular pipe with a diameter of 200 mm, the measuring probe may be installed 45 mm from the center of the pipe. The system obtains this distance parameter through a position sensor or installation parameter recording.

[0089] After obtaining the distance parameter, it is substituted into the flow field velocity distribution model. This model describes the velocity distribution characteristics at different locations on the pipe cross-section. In this embodiment, the flow field characteristic coefficients are obtained through multi-point calibration experiments. Specifically, in a laboratory environment, standard measuring equipment is placed at different locations on the pipe cross-section (e.g., 0.1D, 0.2D, 0.3D..., where D is the pipe diameter), and the ratio of the measured velocity at each location to the velocity at the pipe center is recorded. A flow field velocity distribution model is constructed using these ratios, thereby obtaining the flow field characteristic coefficients at any location.

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

[0091] In the calculation of the temperature compensation coefficient, ambient temperature data is first collected. The system monitors the ambient temperature in real time through temperature sensors. For example, if the current ambient temperature is 32℃ and the preset standard temperature is 20℃, the temperature difference is 12℃.

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

[0093] Based on the sensor sensitivity parameters, the compensation coefficient is calculated using the least squares method. In this embodiment, a temperature compensation model is obtained by performing least squares fitting on multiple sets of temperature-sensitivity data. When the ambient temperature is 32℃, the calculated temperature compensation coefficient is 1.018, which is used to compensate for the lower measured values ​​caused by the increase in temperature.

[0094] After calculating the displacement compensation coefficient and temperature compensation coefficient, they 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, if the original flow rate measurement data is 5.2 cubic meters per hour, after applying the comprehensive compensation coefficient, the calibrated value is 5.2 × 1.107 = 5.76 cubic meters per hour, which is closer to the actual flow rate value.

[0096] To verify the effectiveness of this method, an experimental comparison was conducted. Before applying the compensation method, the measurement system achieved a measurement accuracy of ±1.5% under standard operating conditions (20℃, probe located at the center of the pipe); under non-standard operating conditions (32℃, probe offset from the center by 45 mm), the measurement error increased to -9.8%. After applying this compensation method, the measurement error under non-standard operating conditions decreased to ±1.8%, approaching the accuracy level of the standard operating conditions.

[0097] The aforementioned compensation method effectively solves the measurement error problem caused by probe installation position misalignment and ambient temperature changes in pipeline flow measurement, improving the adaptability and accuracy of the measurement system in complex environments. This method can be widely applied in fields such as industrial pipeline flow monitoring, oil pipeline flow measurement, and water conservancy project flow measurement, and has high practical value.

[0098] In one optional implementation, the temperature compensation coefficient is calculated using the least squares method based on the sensor sensitivity parameters, including:

[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] The least squares criterion objective function is established based on the product of the sensitivity observation vector and the temperature difference matrix. The observation equation is solved by the least squares method to calculate the initial compensation coefficient.

[0101] Calculate the standard deviation of the measurement data at the sampling points, and construct a weighting factor in the form of a diagonal matrix using the reciprocal square of the standard deviation of the measurement data; multiply the weighting factor by the temperature difference matrix and the sensitivity observation vector respectively to obtain the weighted temperature difference matrix and sensitivity observation vector;

[0102] The normal equations are re-solved using the least squares method on the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients. The temperature compensation coefficient is then calculated based on the optimized compensation coefficients.

[0103] Data is collected at multiple sampling points within a temperature variation range, and the corresponding temperature and sensitivity values ​​are obtained for each sampling point. For example, within a temperature range of -40℃ to 85℃, temperature points are selected as -40℃, -20℃, 0℃, 25℃, 40℃, 60℃, and 85℃, and the sensor sensitivity values ​​at these temperature points are collected. Assume that the sensor sensitivity values ​​at these temperature points 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] An observation equation is constructed, and the sensitivity values ​​of the sampling points are combined to form a sensitivity observation vector, denoted as S. Taking the above seven sampling points as an example, the sensitivity observation vector S is [10.2, 10.5, 10.8, 11.0, 10.9, 10.7, 10.4]. 25℃ is selected as the preset standard temperature, and the difference between the temperature of each sampling point and the preset standard temperature is calculated to form a temperature difference matrix T. Taking the above seven sampling points as an example, the temperature difference matrix T is [(-40-25), (-20-25), (0-25), (25-25), (40-25), (60-25), (85-25)], i.e., [-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 exists between sensitivity and temperature, the compensation coefficient is 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 equations are solved using the least squares method to calculate the initial compensation coefficients. The initial compensation coefficients [a] are obtained by constructing the quadratic, linear, and constant terms of the temperature difference matrix and performing calculations with the sensitivity observation vector. init ,b init ,c init Assuming the calculated initial compensation coefficients are [0.0004, 0.008, 11.0], the relationship between sensitivity and temperature is expressed as: S = 0.0004·(T-25). 2 +0.008·(T-25)+11.0.

[0107] To improve compensation accuracy, the standard deviation of the measurement data at each sampling point is calculated. The sensitivity value is measured multiple times at each temperature point, and the standard deviation of these measurements is calculated. For example, 10 measurements at -40℃ yield a standard deviation of 0.05 mV / g; at -20℃, the standard deviation is 0.04 mV / g; and so on: 0℃ is 0.03 mV / g, 25℃ is 0.02 mV / g, 40℃ is 0.03 mV / g, 60℃ is 0.04 mV / g, and 85℃ is 0.06 mV / g.

[0108] The reciprocal squares of the standard deviations of the measured data are used to construct a weighting factor W in the form of a diagonal matrix. 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 That is, [400, 625, 1111.11, 2500, 1111.11, 625, 277.78]. In this way, data points with high measurement accuracy (small standard deviation) receive larger weights.

[0109] The weighting factor W is multiplied by the temperature difference matrix T and the sensitivity observation vector S respectively to obtain the weighted temperature difference matrix T. weighted and sensitivity observation vector S weighted The specific operation involves multiplying each diagonal element of the weighting factor matrix W by 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 normal equations are resolved using the least squares method on the weighted temperature difference matrix T_weighted and the sensitivity observation vector S_weighted to obtain the optimized compensation coefficients [a]. opt ,b opt ,c opt Assuming the calculated optimized compensation coefficients are [0.00038, 0.0075, 11.0], this indicates that, 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. Depending on the specific 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, under the condition of 40℃, the actual sensitivity value of the sensor should be reduced by 0.198mV / g to obtain the same performance as under the standard temperature of 25℃.

[0112] This compensation method effectively eliminates the impact of temperature changes on sensor sensitivity, improving the consistency and reliability of the sensor under different temperature environments. This method not only considers the nonlinear relationship between temperature and sensitivity but also uses weighted least squares to account for differences in data reliability at different measurement points, thus obtaining more accurate compensation coefficients. It is suitable for various sensor systems requiring temperature compensation.

[0113] In one optional implementation, the Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data, and a state prediction model is established based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, including:

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

[0115] A state prediction model is established based on the time series correlation coefficient. The state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment.

[0116] The 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; weighting and fusing the Kalman gain with the actual observation data to obtain the optimal estimate; and outputting the optimal estimate as the fused multi-source heterogeneous measurement data.

[0117] The study consists of two main parts: establishing a state prediction model based on the temporal correlation of calibrated multi-source heterogeneous measurement data, and using the Kalman filter algorithm for dynamic fusion processing.

[0118] When analyzing the time-series variation patterns of calibrated multi-source heterogeneous measurement data, it is necessary to calculate the time-series correlation coefficients of the calibrated multi-source heterogeneous measurement data. Specifically, the calibrated multi-source heterogeneous measurement data is first sorted according to timestamps to form a time-series dataset. For each type of measurement data, its numerical changes at consecutive time points are extracted. For example, for temperature sensor data, the hourly temperature values ​​over a continuous 24-hour period can be extracted; for pressure sensor data, the minutely pressure values ​​over a continuous 30-minute period can be extracted. By calculating the autocorrelation function, the correlation of various data types at different time delays is obtained. For example, the autocorrelation coefficient of temperature data after a 1-hour delay is 0.92, the autocorrelation coefficient after a 2-hour delay is 0.85, and so on. At the same time, the cross-correlation coefficients between different types of data are calculated; for example, the cross-correlation coefficient between temperature and humidity data is -0.76, indicating that they have a strong negative correlation.

[0119] When establishing a state prediction model based on temporal correlation coefficients, a prediction model capable of predicting the state of multi-source heterogeneous measurement data at the next moment is constructed based on the temporal correlation coefficients calculated above. This model takes data from the current moment and several past moments as input to predict the data state at the next moment. In specific implementation, an appropriate prediction model structure can be selected based on the temporal correlation of the data. For example, a linear prediction model can be used for highly correlated continuous data; for data with periodic changes, 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 temporal correlation coefficients. For example, if the first-order autocorrelation coefficient of temperature data is 0.92 and the second-order autocorrelation coefficient is 0.85, then the elements in the state transition matrix corresponding to temperature prediction can be set to values ​​reflecting this temporal 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 time step is first predicted based on the established state prediction model. Assuming the current time is k, and the known state estimate at time k is [25.3℃, 65.7%, 101.2kPa] (representing temperature, humidity, and air pressure respectively), the state prediction model yields the predicted state value at time k+1 as [25.5℃, 66.1%, 101.3kPa]. Simultaneously, the prediction error covariance matrix is ​​calculated based on the state prediction model; this matrix reflects the uncertainty of the prediction result.

[0121] When calculating the Kalman gain based on the prediction results, it is necessary to combine the prediction error covariance matrix and the observation noise covariance matrix. The Kalman gain determines the weighting of the predicted and actual observation values ​​during the fusion process. For example, for temperature data, if the predicted value is relatively reliable (small prediction error) but the observation noise is large, the Kalman gain will decrease accordingly, making the fusion result more inclined towards the predicted value; conversely, if the observation value is relatively reliable, the Kalman gain will increase, making the fusion result more inclined towards the observation value. In practical applications, if the actual observation value at time k+1 is [25.7℃, 65.9%, 101.1kPa], and the calculated Kalman gain is [0.65, 0.58, 0.72], then the Kalman gain is weighted and fused with the actual observation data and the predicted data.

[0122] The process of weightedly fusing the Kalman gain with the actual observation data to obtain the optimal estimate is as follows: For temperature data, the fused estimate equals the predicted value of 25.5℃ plus the Kalman gain of 0.65 multiplied by the difference between the observed and predicted values ​​(25.7℃ - 25.5℃), i.e., 25.5℃ + 0.65 × 0.2℃ = 25.63℃; for humidity data, the fused estimate is 66.1% + 0.58 × (65.9% - 66.1%) = 65.98%; for air 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℃, 65.98%, 101.16 kPa]. This estimate comprehensively considers the results of the prediction model and the actual observation data, effectively reducing the noise impact of a single data source and improving the accuracy and reliability of the data.

[0123] After each data fusion iteration, the state estimation covariance matrix needs to be updated to prepare for the next data fusion. By continuously iterating through this process, continuous dynamic fusion of multi-source heterogeneous measurement data can be achieved, resulting in a temporally coherent fused data sequence with reduced noise levels. In practical applications, the fused data can be used in various fields such as environmental monitoring, industrial process control, and smart homes, significantly improving the system's sensing accuracy and stability.

[0124] In one optional implementation, the fused data is subjected to noise suppression and accuracy optimization processing based on the state prediction model to obtain optimized measurement results, including:

[0125] The measurement data to be optimized is obtained, and the signal power spectral density and noise power spectral density of the measurement data to be optimized are calculated. A Wiener filter transfer function is constructed based on the signal power spectral density and the noise power spectral density. The measurement data to be optimized is transformed to the frequency domain through a fast Fourier transform. The Wiener filter transfer function is multiplied with the frequency domain signal, and the filtered time domain signal is obtained 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 predicted signal is calculated. A dynamic weighting coefficient is calculated based on the ratio of the prediction error to the standard deviation of the error. The dynamic weighting coefficient decreases exponentially as the prediction error increases. The dynamic weighting coefficient is multiplied by the filtered time-domain signal and the predicted signal respectively and then superimposed to obtain the optimized measurement result.

[0127] The measurement data to be optimized is obtained, which 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 angle, roll angle and yaw angle, the sampling frequency is 100Hz, the data length is 1000 sampling points, and it contains high-frequency noise interference.

[0128] The next crucial step is to calculate the signal power spectral density and noise power spectral density of the measurement data to be optimized. The system segments the measurement data and uses the periodogram method to calculate the signal power spectral density. Specifically, the data from 1000 sampling points is divided into 10 segments of 100 points each. A Hanning window function is used to window each segment, and then the Fourier transform of each segment is calculated. Finally, the results from all segments are averaged to obtain the signal power spectral density. For the estimation of the noise power spectral density, the system pre-collects data samples from the sensor in a stationary state and calculates them using the same method. In practice, it is assumed that the signal has significant energy below 10Hz, while above 10Hz it is mainly noise. The calculated signal power spectral density in the low-frequency range is approximately 0.5 rad. 2 / Hz, noise power spectral density is approximately 0.05rad. 2 / Hz.

[0129] Based on the calculated signal power spectral density and noise power spectral density, the Wiener filter transfer function is constructed. The principle of the Wiener filter is adaptive filtering in the frequency domain based on the power ratio of the signal to the noise. The transfer function is designed to approach 1 when the signal power is much greater than the noise power, and to approach 0 when the noise power is dominant. In practical implementation, the transfer function can be calculated by dividing the signal power spectral density by the sum of the signal power spectral density and the noise power spectral density. In the example above, the transfer function value is approximately 0.91 in the 0-10Hz frequency band, but drops rapidly to below 0.1 in the high-frequency band.

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

[0131] Multiplying the Wiener filter transfer function by the frequency domain signal is the core step in filtering. The system multiplies the signal value at each frequency by the corresponding filter transfer function value to achieve frequency domain filtering. After processing, the low-frequency signal remains almost unchanged, while the noise in the high-frequency band is significantly reduced.

[0132] Obtaining the filtered time-domain signal through inverse fast Fourier transform is a necessary step in converting the processed result back to the original domain. The system performs IFFT on the filtered frequency-domain signal to obtain a 1000-point time-domain signal. A comparison before and after filtering shows that the standard deviation of the original roll angle data was 0.8 rad, which decreased to 0.3 rad after filtering, indicating a significant reduction in 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, and the state vector includes the attitude angle and its rate of change. For the roll angle, the state transition matrix is ​​set to consider uniform motion, and the process noise covariance is set as a diagonal matrix with diagonal elements of 0.01 rad. 2 In the prediction step, the system calculates the state estimate for the next time step based on the current state and the state transition equation. In actual processing, for a roll angle of 2.3 rad at time t with a rate of change of 0.1 rad / s, the predicted angle at time t+1 (10 ms later) is 2.31 rad.

[0134] Calculating the prediction error between the filtered time-domain signal and the predicted signal is the basis for dynamic weight allocation. For each time point, the system calculates the difference between the actual measured value and the predicted value. Taking a specific moment as an example, the filtered measured value is 2.35 rad, the predicted value is 2.31 rad, and the prediction error is 0.04 rad. Simultaneously, the system calculates the standard deviation of the prediction error, using a sliding window method to obtain a standard deviation of 0.02 rad from the first 100 prediction error samples.

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

[0136] The final fusion step involves multiplying the dynamic weighting coefficients by the filtered time-domain signal and the predicted signal, respectively, and then summing the results to obtain the optimized measurement result. For each time point, the system multiplies the weighting coefficients by the filtered measured value, multiplies (1 - weighting coefficients) by the predicted value, and adds the two results to obtain the final result. In the example above, the final optimized result is 2.35 × 0.135 + 2.31 × (1 - 0.135) = 2.315 rad. Through this adaptive weighting method, the system achieves a balance between the measured and predicted values, relying more on the measurement when the measured value is reliable and more on the prediction when the measured value is abnormal.

[0137] Through a complete processing flow, high-frequency noise in the original data was effectively suppressed, and the impact of outliers was significantly reduced. Compared with the original data, the final optimized measurement results showed a reduction of approximately 75% in noise standard deviation, a significant improvement in data smoothness, and maintained a sensitive response to changes in the actual signal.

[0138] A second aspect of the present invention provides a portable, high-precision integrated measurement system for roadway 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 installed in the pipeline body;

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

[0141] The third unit is used to calculate the displacement compensation coefficient based on the actual installation position of the measuring probe assembly in the pipe body, calculate the temperature compensation coefficient based on the change of ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data.

[0142] The fourth unit is used to dynamically fuse the calibrated multi-source heterogeneous measurement data using the 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 optimized measurement results.

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

[0144] A third aspect of the present invention provides an electronic device, comprising:

[0145] processor;

[0146] Memory used to store processor-executable instructions;

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

[0148] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0149] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions 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 method for comprehensive measurement of roadway air volume and environmental parameters, characterized in that, include: Temperature, humidity, wind speed, air pressure, and gas concentration data are collected from the tunnel using a measuring probe assembly installed inside the pipeline body. The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for data preprocessing. The displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly in the pipe body, and the temperature compensation coefficient is calculated based on the change of ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data. The Kalman filter algorithm is used to dynamically fuse the calibrated multi-source heterogeneous measurement data. Based on the temporal correlation of the calibrated multi-source heterogeneous measurement data, a state prediction model is established, including: Analyze the variation pattern of the calibrated multi-source heterogeneous measurement data in the time series, and calculate the time series correlation coefficient of the calibrated multi-source heterogeneous measurement data; A state prediction model is established based on the time series correlation coefficient. The state prediction model is used to predict the state of multi-source heterogeneous measurement data at the next moment. The 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; weighting and fusing the Kalman gain with the actual observation data to obtain the optimal estimate; and outputting the optimal estimate as the fused multi-source heterogeneous measurement data. Based on the state prediction model, noise suppression and accuracy optimization are performed on the fused data to obtain optimized measurement results; The optimized measurement results are displayed through the display unit; the optimized measurement results are transmitted to external devices for storage and analysis through the communication interface.

2. The method according to claim 1, characterized in that, The data transmission of temperature, humidity, wind speed, air pressure, and gas concentration to the signal processing unit for data preprocessing includes: The temperature data, humidity data, wind speed data, air pressure data, and gas concentration data are transmitted to the signal processing unit for classification, labeling, and data cleaning. Based on the data acquisition time sequence, a correspondence between multiple data sources is established to generate a standardized data structure. The standardized data structure is denoised using wavelet transform to identify and remove outlier data points, generating preprocessed environmental parameter data.

3. The method according to claim 1, characterized in that, The displacement compensation coefficient is calculated based on the actual installation position of the measuring probe assembly within the pipe body, and the temperature compensation coefficient is calculated based on changes in ambient temperature. The displacement compensation coefficient and the temperature compensation coefficient are then applied to the original measurement data to obtain calibrated multi-source heterogeneous measurement data, including: Based on the installation position of the measuring probe assembly within the pipe body, the distance parameter between the measuring probe assembly and the center of the pipe body is obtained; Substitute 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 multi-point calibration experiments to obtain the displacement compensation coefficient. Collect ambient temperature data and obtain the difference between the ambient temperature data and the preset standard temperature; Multiply the difference by the correlation coefficient of the temperature response curve to obtain the sensor sensitivity parameter under the influence of temperature; calculate the compensation coefficient using the least squares method based on the sensor sensitivity parameter to obtain the temperature compensation coefficient; The displacement compensation coefficient is multiplied by the temperature compensation coefficient to obtain the comprehensive compensation coefficient, and the original measurement data is multiplied by the comprehensive compensation coefficient to obtain the calibrated measurement data.

4. The method according to claim 3, characterized in that, The compensation coefficients are calculated using the least squares method based on the sensor sensitivity parameters, resulting in the following temperature compensation coefficients: 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. The least squares criterion objective function is established based on the product of the sensitivity observation vector and the temperature difference matrix. The observation equation is solved by the least squares method to calculate the initial compensation coefficient. Calculate the standard deviation of the measurement data at the sampling points, and construct a weighting factor in the form of a diagonal matrix using the reciprocal square of the standard deviation of the measurement data; multiply the weighting factor by the temperature difference matrix and the sensitivity observation vector respectively to obtain the weighted temperature difference matrix and sensitivity observation vector; The normal equations are re-solved using the least squares method on the weighted temperature difference matrix and the sensitivity observation vector to obtain optimized compensation coefficients. The temperature compensation coefficient is then calculated based on the optimized compensation coefficients.

5. The method according to claim 1, characterized in that, Based on the state prediction model, noise suppression and accuracy optimization are performed on the fused data to obtain optimized measurement results, including: The measurement data to be optimized is obtained, and the signal power spectral density and noise power spectral density of the measurement data to be optimized are calculated. A Wiener filter transfer function is constructed based on the signal power spectral density and the noise power spectral density. The measurement data to be optimized is transformed to the frequency domain through a fast Fourier transform. The Wiener filter transfer function is multiplied with the frequency domain signal, and the filtered time domain signal is obtained 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 predicted signal is calculated. A dynamic weighting coefficient is calculated based on the ratio of the prediction error to the standard deviation of the error. The dynamic weighting coefficient decreases exponentially as the prediction error increases. The dynamic weighting coefficient is multiplied by the filtered time-domain signal and the predicted signal respectively and then superimposed to obtain the optimized measurement result.

6. A portable, high-precision integrated measurement system for roadway air volume and environmental parameters, used to implement the method described in any one of claims 1-5, 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 installed in the pipeline body; The second unit is used to transmit the temperature data, humidity data, wind speed data, air pressure data, and gas concentration data to the signal processing unit for data preprocessing; The third unit is used to calculate the displacement compensation coefficient based on the actual installation position of the measuring probe assembly in the pipe body, calculate the temperature compensation coefficient based on the change of ambient temperature, and apply the displacement compensation coefficient and the temperature compensation coefficient to the original measurement data to obtain calibrated multi-source heterogeneous measurement data. The fourth unit is used to dynamically fuse the calibrated multi-source heterogeneous measurement data using the 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 optimized measurement results. The fifth unit is used to display the optimized measurement results through the display unit; The optimized measurement results are transmitted to external devices for storage and analysis via a communication interface.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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