Multi-sensor linkage heat flux density-temperature real-time acquisition and error compensation method

Abstract

The invention relates to the technical field of thermotechnical measurement and signal processing, in particular to a multi-sensor linkage heat flux density-temperature real-time acquisition and error compensation method, which comprises the following steps: S1, constructing an embedded sensing array on a surface to be measured; s2, synchronously acquiring original measurement values of the sensors by taking 10ms as a period, and generating an original data matrix with a timestamp identifier; s3, extracting a three-dimensional temperature gradient field, and calculating to obtain a convective disturbance intensity coefficient of each measurement point; s4, establishing a dynamic error compensation model, and performing point-by-point error correction on the original data matrix; s5, cross validation is carried out, and credibility evaluation parameters are generated; and S6, carrying out weighted fusion, and outputting a final heat flux density-temperature coupling measurement value. According to the invention, through multi-sensor array cooperative acquisition, dynamic error compensation and credibility weighted fusion, high-precision, strong-anti-interference and high-credibility output of heat flux density and temperature measurement is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal measurement and signal processing, and in particular to a multi-sensor linked heat flux density-temperature real-time acquisition and error compensation method. Background Art

[0002] In fields such as industrial thermal processes, environmental monitoring, and heat transfer performance evaluation, the precise acquisition of heat flux and temperature is the basis for energy transfer analysis and thermal management optimization. With the widespread emergence of non-steady-state thermal environments in complex application scenarios, a single type of sensor often finds it difficult to cope with measurement deviations caused by interference from multiple factors.

[0003] Currently, joint measurements often involve the coordinated deployment of heat flux and temperature sensors. However, improper deployment, asynchronous acquisition cycles, and a lack of effective error control mechanisms often lead to data offset, distortion, and even failure. Furthermore, environmental disturbances such as uneven convection and thermal coupling effects can significantly affect sensor response stability, reducing the reliability of thermal parameter measurements. Traditional methods are particularly unable to accurately distinguish and correct measurement errors when spatial temperature gradients vary dramatically or when environmental interference is significant, resulting in insufficiently reliable measurement results. Therefore, a multi-sensor coordinated heat flux and temperature real-time acquisition and error compensation method is urgently needed to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a multi-sensor linked heat flux-temperature real-time acquisition and error compensation method.

[0005] The multi-sensor linkage heat flux-temperature real-time acquisition and error compensation method includes the following steps: S1: Build an embedded sensor array on the surface to be measured. The array contains at least three heat flux density sensors and six temperature sensors. The temperature sensors are distributed in a concentric circle topology, and the heat flux density sensors are arranged in an equilateral triangle. S2: Start the synchronous acquisition module to synchronously obtain the original measurement values ​​of each sensor with a period of 10ms and generate a raw data matrix with a timestamp; S3: Analyze environmental interference factors, extract the three-dimensional temperature gradient field based on the spatial distribution characteristics of temperature sensors, and calculate the convective disturbance intensity coefficient at each measurement point; S4: Establish a dynamic error compensation model, generate a compensation weight matrix based on the convective disturbance intensity coefficient obtained in S3, and perform point-by-point error correction on the original data matrix; S5: Perform cross-validation, invert the temperature field based on the spatial distribution of the heat flux density sensor, and compare the difference with the measured temperature field to generate credibility assessment parameters; S6: Perform weighted fusion on the compensated data based on the credibility assessment parameters and output the final heat flux-temperature coupled measurement value.

[0006] Optionally, the S1 specifically includes: S11: Determine the center point of the measurement area on the surface to be measured, and draw three concentric circles with diameters of 60 mm, 100 mm, and 140 mm with the center point as the center; S12: Three heat flux density sensors are evenly distributed around the circumference of a concentric circle with a diameter of 100 mm. The circumferential interval angle between each sensor is fixed at 120°, forming an equilateral triangle layout; S13: Distribute the six temperature sensors on two concentric circles with diameters of 60 mm and 140 mm, respectively. Three temperature sensors are evenly spaced on each concentric circle, ensuring that the angle between any two adjacent temperature sensors is 120°. S14: Use a high thermal conductivity adhesive to fix the heat flux density sensor and temperature sensor to corresponding positions on the surface of the measurement area, ensuring that the sensors are tightly attached to the surface and the contact thermal resistance is less than 0.01 m²·K / W. S15: Use embedded wires to connect each heat flux sensor and temperature sensor to the synchronous acquisition module.

[0007] Optionally, the S2 specifically includes: S21: Start the synchronous acquisition module to generate a periodic trigger pulse signal with a fixed sampling period of 10ms; S22: using the trigger pulse signal to simultaneously activate the analog-to-digital conversion circuits of all heat flux density sensors and temperature sensors in the array, and obtaining the voltage output signal of each sensor in real time; S23: Convert the voltage output signal into the corresponding heat flux density value and temperature value respectively through the built-in calibration relationship, and retain two decimal places; S24: Using the high-speed register group in the synchronous acquisition module and the sensor serial number as the index, the values ​​obtained by each sensor in the current sampling period are stored in the corresponding position in a fixed order; S25: After each 10ms sampling cycle ends, a unified timestamp generation unit is used to record the standard time of the current cycle, and write it into the identification field of the data matrix in the format of year-month-day hour: minute: second. millisecond to generate the original data matrix with a timestamp identification.

[0008] Optionally, the S3 specifically includes: S31: Based on the spatial layout of the temperature sensors in S1, a rectangular coordinate system with the center point of the measurement area as the origin is constructed to accurately represent the position coordinates of each temperature sensor as three-dimensional coordinates (x, y, z); S32: Based on the original data matrix with time stamp obtained in S2, extract the real-time measurement value of each temperature sensor at the same time stamp, and establish the corresponding relationship between the measurement value of each temperature sensor and the spatial position coordinate; S33: The temperature measurement values ​​of each temperature sensor are processed using a spatial interpolation algorithm, and a three-dimensional temperature gradient field is constructed with the measurement area as the boundary. The temperature gradient components of the temperature field in the x, y, and z directions in the rectangular coordinate system are obtained, which are recorded as 、 、 ; S34: Using the three-dimensional temperature gradient components obtained in S33, calculate the temperature gradient vector modulus at each measurement point. The formula is: , where G is the vector modulus of temperature gradient; S35: Using the calculation formula for the convective disturbance intensity coefficient, combined with the temperature gradient vector modulus G at each measurement point, the corresponding convective disturbance intensity coefficient is calculated. The formula is: , where C is the convective disturbance intensity coefficient; G is the temperature gradient vector modulus at the corresponding measurement point; and k is the environmental heat transfer characteristic constant.

[0009] Optionally, the S4 specifically includes: S41: Based on the convective disturbance intensity coefficient C obtained in S3, a mapping relationship between the sensor position and the disturbance degree is constructed, and a disturbance intensity threshold range is set to distinguish high disturbance, medium disturbance, and low disturbance areas; S42: According to the disturbance partitioning result, a compensation weight is assigned to the measurement point corresponding to each sensor, and a compensation weight matrix W with the same structure as the original data matrix is ​​constructed; S43: Determine the error amplitude required to correct the measurement value of each sensor based on the weight coefficients included in the compensation weight matrix W, and establish a dynamic error correction model that evolves over time in combination with the acquisition timestamp information; S44: Apply the constructed dynamic error compensation model to the original data matrix obtained in S2, perform error correction operations point by point according to the correspondence between matrix elements, and obtain a compensated corrected data matrix, retaining the data accuracy to two decimal places.

[0010] Optionally, the S42 specifically includes: S421: Set the corresponding basic compensation weight values ​​for high disturbance, medium disturbance and low disturbance areas, respectively. , which satisfies ; S422: For each sensor measurement point, assign a corresponding basic weight value according to its corresponding disturbance level. , and calculate the final compensation weight: , where P is the final compensation weight; is the basic weight coefficient; is a dynamic regulatory factor; The difference between the current measurement value and the measurement value of the previous cycle; is the reference measurement value; S423: Based on the calculation result of S422, the compensation weight P of each sensor is written into the two-dimensional matrix W in the order of its fixed position in the array. The row and column numbers of the matrix are consistent with the original data matrix.

[0011] Optionally, the S43 specifically includes: S431: Extract the compensation weight coefficient of each sensor corresponding to the position from the compensation weight matrix W obtained in S42, and determine the measurement error correction amplitude of the corresponding sensor in the current sampling period according to the weight coefficient. ; S432: Using the collected timestamp information as the time axis reference, the error correction amplitude corresponding to each sensor position is calculated. Store them sequentially according to the time series to establish the error correction time series at the sensor position; S433: Using the sliding window time series analysis method, the error correction amplitude at the current moment Based on the historical time series data, the time decay coefficient is calculated ; S434: Correct the error Corresponding time attenuation coefficient Combined with the above, a dynamic error correction model that evolves over time is established, and its expression is: , where is the sensor measurement value after dynamic error correction; The raw data measured by the sensor in the current sampling period; It is the error correction amplitude of the measured value in the current sampling period.

[0012] Optionally, the S5 specifically includes: S51: Based on the spatial position layout of the heat flux density sensors in S1, a spatial coordinate system with the center of the array as the origin is constructed, and the spatial coordinates of each heat flux density sensor are recorded; S52: extracting the real-time measurement data of each heat flux density sensor from the corrected data matrix obtained in S4, and calculating the inverted temperature value at the corresponding position of each heat flux density sensor based on Fourier's heat conduction law and heat flow inversion algorithm; S53: constructing an inverted temperature field based on the spatial layout coordinate relationship of the heat flux density sensor using all the temperature values ​​inverted in S52; S54: extracting the measured temperature values ​​of each temperature sensor at the same time stamp from the modified data matrix in S4, and constructing the measured temperature field based on the spatial position of the temperature sensor; S55: By calculating the temperature field difference, the inverted temperature field and the measured temperature field are analyzed point by point, and the temperature field difference value D is calculated as the mean square temperature difference between the two fields; S56: Generate a credibility evaluation parameter R based on the temperature field difference value D; the formula is: , where R is the credibility evaluation parameter; The maximum allowable temperature difference set for the system.

[0013] Optionally, the S52 specifically includes: S521: extracting the heat flux value measured in real time by each heat flux sensor from the corrected data matrix of S4; S522: Set the temperature sensor measurement value at the center of the array to the reference temperature, which is recorded as , and record the vertical distance from each heat flux density sensor to the center point of the array, recorded as d; S523: Obtaining the thermal conductivity k of the material of the measurement surface; S524: Based on Fourier's law of heat conduction, the inverted temperature value at the corresponding position is inferred using the heat flux density measurement value. The calculation formula is: , where is the temperature value obtained by inversion; q is the heat flux density value measured by the sensor; k is the thermal conductivity of the material; S525: Execute the above calculations on all heat flux density sensors in the corrected data matrix in sequence to obtain the inverted temperature value at the corresponding position of each sensor.

[0014] Optionally, the S6 specifically includes: S61: extracting the compensated heat flux and temperature data of the corresponding measurement position of each sensor from the correction data matrix obtained in S4, and recording them as a correction measurement value set; S62: Obtain the credibility evaluation parameter corresponding to each measurement position from S5 as a weight coefficient; S63: Perform weighted fusion on the corrected measurement values ​​of each measurement position and the credibility evaluation parameters of the corresponding position to obtain the fused heat flux density measurement value and temperature measurements ; S64: The final measured value of the heat flux after fusion and the final measured value of temperature The results are associated with the corresponding timestamp information to generate unified coupling measurement results and stored in a structured data format.

[0015] Beneficial effects of the present invention: The present invention realizes the spatial collaborative measurement of thermal parameters by constructing an embedded array of heat flux density sensors and temperature sensors distributed at multiple points, and introduces a synchronous acquisition mechanism with a period of 10ms to ensure the consistency of all measurement data in the time dimension. Furthermore, by combining temperature field gradient analysis with disturbance intensity assessment, a dynamic error compensation model is established, which effectively solves the problem of measurement error fluctuation caused by environmental disturbances and improves the stability and accuracy of data in complex thermal field environments.

[0016] The present invention generates a credibility assessment parameter by comparing the spatial differences between the temperature field inverted based on heat flux density and the measured temperature field, and uses the parameter as a weight to perform data screening and synthesis in the terminal weighted fusion process, thereby obtaining a heat flux-temperature coupled measurement value at the output end, achieving the unity of high spatial resolution, high temporal accuracy and high data credibility, and meeting the practical needs of dynamic thermal field monitoring in the fields of industrial measurement and control and environmental research. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of a method for real-time heat flux-temperature acquisition and error compensation according to an embodiment of the present invention; Figure 2 Schematic diagram of the process of generating an original data matrix according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] like Figure 1-Figure 2 As shown, the multi-sensor linkage heat flux-temperature real-time acquisition and error compensation method includes the following steps: S1: Build an embedded sensor array on the surface to be measured. The array contains at least three heat flux density sensors and six temperature sensors. The temperature sensors are distributed in a concentric circle topology, and the heat flux density sensors are arranged in an equilateral triangle. S2: Start the synchronous acquisition module to synchronously obtain the original measurement values ​​of each sensor with a period of 10ms and generate a raw data matrix with a timestamp; S3: Analyze environmental interference factors, extract the three-dimensional temperature gradient field based on the spatial distribution characteristics of temperature sensors, and calculate the convective disturbance intensity coefficient at each measurement point; S4: Establish a dynamic error compensation model, generate a compensation weight matrix based on the convective disturbance intensity coefficient obtained in S3, and perform point-by-point error correction on the original data matrix; S5: Perform cross-validation, invert the temperature field based on the spatial distribution of the heat flux density sensor, and compare the difference with the measured temperature field to generate credibility assessment parameters; S6: Perform weighted fusion on the compensated data based on the credibility assessment parameters and output the final heat flux-temperature coupled measurement value.

[0021] S1 specifically includes: S11: Determine the center point of the measurement area on the surface to be measured, and draw three concentric circles with diameters of 60 mm, 100 mm, and 140 mm with the center point as the center; S12: Three heat flux density sensors are evenly distributed around the circumference of a concentric circle with a diameter of 100 mm. The circumferential interval angle between each sensor is fixed at 120°, forming an equilateral triangle layout; S13: Distribute the six temperature sensors on two concentric circles with diameters of 60 mm and 140 mm, respectively. Three temperature sensors are evenly spaced on each concentric circle, ensuring that the angle between any two adjacent temperature sensors is 120°. S14: Use a high thermal conductivity adhesive to fix the heat flux density sensor and temperature sensor to corresponding positions on the surface of the measurement area, ensuring that the sensors are tightly attached to the surface and the contact thermal resistance is less than 0.01 m²·K / W. S15: Use embedded wires to connect each heat flux density sensor and temperature sensor to the synchronous acquisition module respectively, to achieve stable transmission of the internal circuit signal of the array, and complete the construction of the embedded sensor array; through the above method, accurately construct the embedded sensor array on the surface to be measured, to achieve precise positioning and reliable fixation of various sensors, and ensure the accuracy and stability of subsequent temperature and heat flux density data acquisition.

[0022] S2 specifically includes: S21: Start the synchronous acquisition module to generate a periodic trigger pulse signal with a fixed sampling period of 10ms; S22: using the trigger pulse signal to simultaneously activate the analog-to-digital conversion circuits of all heat flux density sensors and temperature sensors in the array, and obtaining the voltage output signal of each sensor in real time; S23: Convert the voltage output signal into the corresponding heat flux density value and temperature value respectively through the built-in calibration relationship, and retain two decimal places; S24: Using the high-speed register group in the synchronous acquisition module and the sensor serial number as the index, the values ​​obtained by each sensor in the current sampling period are stored in the corresponding position in a fixed order; S25: After each 10ms sampling cycle, a unified timestamp generation unit is used to record the standard time of the current cycle, and it is written into the identification field of the data matrix in the format of year-month-day hour: minute: second. millisecond to generate the original data matrix with a timestamp identification. Through the above technical solution, the synchronous acquisition of sensor data and accurate timestamp identification are achieved, which ensures the temporal consistency of the data matrix and effectively supports the subsequent data analysis and error compensation accuracy.

[0023] S3 specifically includes: S31: Based on the spatial layout of the temperature sensors in S1, a rectangular coordinate system with the center point of the measurement area as the origin is constructed to accurately represent the position coordinates of each temperature sensor as three-dimensional coordinates (x, y, z); S32: Based on the original data matrix with time stamp obtained in S2, extract the real-time measurement value of each temperature sensor at the same time stamp, and establish the corresponding relationship between the measurement value of each temperature sensor and the spatial position coordinate; S33: The temperature measurement values ​​of each temperature sensor are processed using a spatial interpolation algorithm, and a three-dimensional temperature gradient field is constructed with the measurement area as the boundary. The temperature gradient components of the temperature field in the x, y, and z directions in the rectangular coordinate system are obtained, which are recorded as 、 、 ; S34: Using the three-dimensional temperature gradient components obtained in S33, calculate the temperature gradient vector modulus at each measurement point. The formula is: , where G is the vector modulus of temperature gradient; S35: Using the calculation formula for the convective disturbance intensity coefficient, combined with the temperature gradient vector modulus G at each measurement point, the corresponding convective disturbance intensity coefficient is calculated. The formula is: , where C is the convective disturbance intensity coefficient; G is the vector modulus of the temperature gradient at the corresponding measurement point; and k is the environmental heat transfer characteristic constant. Through the above technical solution, the environmental interference factor can be accurately analyzed, the spatial temperature gradient characteristics can be quantitatively described, and a clear interference quantification basis can be provided for the subsequent error compensation model, effectively improving the accuracy and stability of the measurement data.

[0024] S4 specifically includes: S41: Based on the convective disturbance intensity coefficient C obtained in S3, a mapping relationship between sensor location and disturbance degree is constructed, and a disturbance intensity threshold range is set to distinguish high disturbance, medium disturbance, and low disturbance areas, which is used to guide the weight allocation strategy. S41 specifically includes: S411: Extract the set of convective disturbance intensity coefficients C corresponding to all measurement points obtained in S3 and calculate their average value and standard deviation ; S412: Centered, combined The disturbance level intervals are divided into: High disturbance area: the corresponding C value satisfies ; Medium disturbance area: the corresponding C value satisfies ; Low disturbance area: the corresponding C value satisfies .

[0025] S42: According to the disturbance partitioning result, a compensation weight is assigned to the measurement point corresponding to each sensor, and a compensation weight matrix W with the same structure as the original data matrix is ​​constructed, where each element in W corresponds one-to-one to the data at the same position in the original data matrix; S43: Determine the error amplitude required to correct the measurement value of each sensor based on the weight coefficients included in the compensation weight matrix W, and establish a dynamic error correction model that evolves over time in combination with the acquisition timestamp information. The weight coefficients in the model can be dynamically adjusted as the disturbance intensity changes. S44: Apply the constructed dynamic error compensation model to the original data matrix obtained in S2, and perform error correction operations point by point according to the correspondence between matrix elements to obtain the compensated corrected data matrix, retaining the data accuracy to two decimal places; through the above steps, a dynamic error compensation mechanism is constructed that can automatically adjust with the disturbance intensity and time changes, ensuring that the sensor data still has high credibility and accuracy under complex environmental conditions, providing a reliable foundation for subsequent data fusion processing.

[0026] S42 specifically includes: S421: Set the corresponding basic compensation weight values ​​for high disturbance, medium disturbance and low disturbance areas, respectively. , which satisfies , the unit is the dimensionless weight coefficient; S422: For each sensor measurement point, assign a corresponding basic weight value according to its corresponding disturbance level. , and calculate the final compensation weight: , where P is the final compensation weight; is the basic weight coefficient; is a dynamic regulatory factor; The difference between the current measurement value and the measurement value of the previous cycle; As the reference measurement value, take the median of the historical measurement data; S423: Based on the calculation results of S422, the compensation weight P of each sensor is written into the two-dimensional matrix W in the order of its fixed position in the array. The row and column numbers of the matrix are consistent with the original data matrix, ensuring that each element in the W matrix corresponds to the corresponding sensor measurement value in the original data matrix. Through the above steps, the compensation weight matrix W is established on the basis of clarifying the disturbance level and response weight, so that the weight can be adaptively adjusted according to the disturbance degree and the dynamic characteristics of the measurement, providing a precise and controllable weighted support mechanism for dynamic error compensation.

[0027] S43 specifically includes: S431: Extract the compensation weight coefficient P of each sensor corresponding to the position from the compensation weight matrix W obtained in S42, and determine the measurement error correction amplitude of the corresponding sensor in the current sampling period according to the weight coefficient. , the specific calculation formula is as follows: , where is the error correction amplitude of the measured value, and the unit is the same as the sensor measurement value; P is the compensation weight coefficient; It is the original measurement value obtained by the sensor in the current sampling period; S432: Using the collected timestamp information as the time axis reference, the error correction amplitude corresponding to each sensor position is calculated. Store them sequentially according to the time series to establish the error correction time series at the sensor position; S433: Using the sliding window time series analysis method, the error correction amplitude at the current moment Based on the historical time series data, the time decay coefficient is calculated , the specific expression is: , where is the time attenuation coefficient; t is the current sampling time, in seconds; is the sampling time of the previous cycle; is the error decay time constant; S434: Correct the error Corresponding time attenuation coefficient Combined with the above, a dynamic error correction model that evolves over time is established, and its expression is: , where is the sensor measurement value after dynamic error correction; The raw data measured by the sensor in the current sampling period; is the error correction amplitude of the measured value in the current sampling period; through the above steps, the dynamic error correction model can combine time information and historical data characteristics to track and correct sensor measurement errors in real time, thereby improving the accuracy of measurement data and environmental adaptability.

[0028] S5 specifically includes: S51: Based on the spatial position layout of the heat flux density sensors in S1, a spatial coordinate system with the center of the array as the origin is constructed, and the spatial coordinates of each heat flux density sensor are recorded; S52: extracting the real-time measurement data of each heat flux density sensor from the corrected data matrix obtained in S4, and calculating the inverted temperature value at the corresponding position of each heat flux density sensor based on Fourier's heat conduction law and heat flow inversion algorithm; S53: All temperature values ​​inverted in S52 are used to construct an inverted temperature field according to the spatial layout coordinate relationship of the heat flux density sensor, and a spatial interpolation method is used to obtain a continuous temperature distribution within the array range; S54: extracting the measured temperature values ​​of each temperature sensor at the same time stamp from the modified data matrix in S4, and constructing the measured temperature field based on the spatial position of the temperature sensor; S55: Through temperature field difference calculation, the inverted temperature field and the measured temperature field are analyzed point by point, and the temperature field difference value D is calculated as the mean square temperature difference between the two fields. The specific calculation formula is: , where D is the temperature field difference value; Invert the temperature value for the i-th measurement point; is the measured temperature value of the i-th measurement point; N is the number of measurement points; S56: Generate a credibility evaluation parameter R based on the temperature field difference value D; the formula is: , where R is the credibility evaluation parameter; The maximum allowable temperature difference value set by the system. The credibility assessment parameter is used to reflect the degree of consistency between the inverted temperature field and the measured temperature field. The closer the value is to 1, the higher the credibility. Through the above steps, the spatial difference analysis between the inverted temperature field and the measured temperature field can be accurately realized, and clear credibility assessment parameters can be obtained, which effectively guarantees the reliability of the measurement results and provides accurate data support for subsequent data fusion.

[0029] S52 specifically includes: S521: extracting the heat flux value measured in real time by each heat flux sensor from the corrected data matrix of S4; S522: Set the temperature sensor measurement value at the center of the array to the reference temperature, which is recorded as , and record the vertical distance from each heat flux density sensor to the center point of the array, recorded as d; S523: Obtain the thermal conductivity k of the material on the measurement surface, which is obtained through a previous material calibration experiment and recorded; S524: Based on Fourier's law of heat conduction, the inverted temperature value at the corresponding position is inferred using the heat flux density measurement value. The calculation formula is: , where is the temperature value obtained by inversion; q is the heat flux density value measured by the sensor; k is the thermal conductivity of the material; S525: Execute the above calculations for all heat flux density sensors in the corrected data matrix in turn to obtain the inverted temperature value at the corresponding position of each sensor, which is used to construct a continuous inverted temperature field. Through the above clear inversion steps, the heat flux density measurement value can be effectively used to infer the temperature information, thereby improving the accuracy and spatial resolution of the inverted temperature field and providing reliable data support for subsequent temperature field difference analysis.

[0030] S6 specifically includes: S61: extracting the compensated heat flux and temperature data of the corresponding measurement position of each sensor from the correction data matrix obtained in S4, and recording them as a correction measurement value set; S62: Obtain the credibility evaluation parameter corresponding to each measurement position from S5 as a weight coefficient; S63: Perform weighted fusion on the corrected measurement values ​​of each measurement position and the credibility evaluation parameters of the corresponding position to obtain the fused heat flux density measurement value and temperature measurements , the specific weighted fusion calculation formula is as follows: ; , where is the final measured value of heat flux after fusion; is the final measured value of the temperature after fusion; is the heat flux measurement value after position correction of the i-th heat flux sensor; is the temperature measurement value after the j-th temperature sensor position correction; are the credibility assessment parameters corresponding to the measurement positions; is the number of heat flux sensors; M is the number of temperature sensors; S64: The final measured value of the heat flux after fusion and the final measured value of temperature The data is associated with the corresponding timestamp information to generate a unified coupled measurement result, which is stored in a structured data format. Through the weighted fusion technology mentioned above, multi-sensor data and credibility assessment parameters are effectively fused, significantly improving the reliability of the final measurement data and ensuring the accuracy of data output.

[0031] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0032] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A multi-sensor linked heat flux-temperature real-time acquisition and error compensation method, characterized in that: The following steps are involved: S1: Build an embedded sensor array on the surface to be measured. The array contains at least three heat flux density sensors and six temperature sensors. The temperature sensors are distributed in a concentric circle topology, and the heat flux density sensors are arranged in an equilateral triangle. S2: Start the synchronous acquisition module to synchronously obtain the original measurement values ​​of each sensor with a period of 10ms and generate a raw data matrix with a timestamp; S3: Analyze environmental interference factors, extract the three-dimensional temperature gradient field based on the spatial distribution characteristics of temperature sensors, and calculate the convective disturbance intensity coefficient at each measurement point; S4: Establish a dynamic error compensation model, generate a compensation weight matrix based on the convective disturbance intensity coefficient obtained in S3, and perform point-by-point error correction on the original data matrix; S5: Perform cross-validation, invert the temperature field based on the spatial distribution of the heat flux density sensor, and compare the difference with the measured temperature field to generate credibility assessment parameters; S6: Perform weighted fusion on the compensated data based on the credibility assessment parameters and output the final heat flux-temperature coupled measurement value.

2. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1 is characterized in that: Said S1 specifically includes: S11: Determine the center point of the measurement area on the surface to be measured, and draw three concentric circles with diameters of 60 mm, 100 mm, and 140 mm with the center point as the center; S12: Three heat flux density sensors are evenly distributed around the circumference of a concentric circle with a diameter of 100 mm. The circumferential interval angle between each sensor is fixed at 120°, forming an equilateral triangle layout; S13: Distribute the six temperature sensors on two concentric circles with diameters of 60 mm and 140 mm, respectively. Three temperature sensors are evenly spaced on each concentric circle, ensuring that the angle between any two adjacent temperature sensors is 120°. S14: Use a high thermal conductivity adhesive to fix the heat flux density sensor and temperature sensor to corresponding positions on the surface of the measurement area, ensuring that the sensors are tightly attached to the surface and the contact thermal resistance is less than 0.01 m²·K / W. S15: Use embedded wires to connect each heat flux sensor and temperature sensor to the synchronous acquisition module.

3. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1, characterized in that: The S2 specifically includes: S21: Start the synchronous acquisition module to generate a periodic trigger pulse signal with a fixed sampling period of 10ms; S22: using the trigger pulse signal to simultaneously activate the analog-to-digital conversion circuits of all heat flux density sensors and temperature sensors in the array, and obtaining the voltage output signal of each sensor in real time; S23: Convert the voltage output signal into the corresponding heat flux density value and temperature value respectively through the built-in calibration relationship, and retain two decimal places; S24: Using the high-speed register group in the synchronous acquisition module and the sensor serial number as the index, the values ​​obtained by each sensor in the current sampling period are stored in the corresponding position in a fixed order; S25: After each 10ms sampling cycle, a unified timestamp generation unit is used to record the standard time of the current cycle, and write it into the identification field of the data matrix in the format of year-month-day hour: minute: second. millisecond to generate the original data matrix with a timestamp identification.

4. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1, characterized in that: The S3 specifically includes: S31: Based on the spatial layout of the temperature sensors in S1, a rectangular coordinate system with the center point of the measurement area as the origin is constructed to accurately represent the position coordinates of each temperature sensor as three-dimensional coordinates (x, y, z); S32: Based on the original data matrix with time stamp obtained in S2, extract the real-time measurement value of each temperature sensor at the same time stamp, and establish the corresponding relationship between the measurement value of each temperature sensor and the spatial position coordinate; S33: The temperature measurement values ​​of each temperature sensor are processed using a spatial interpolation algorithm, and a three-dimensional temperature gradient field is constructed with the measurement area as the boundary. The temperature gradient components of the temperature field in the x, y, and z directions in the rectangular coordinate system are obtained, which are recorded as 、 、 ; S34: Using the three-dimensional temperature gradient components obtained in S33, calculate the temperature gradient vector modulus at each measurement point. The formula is: , where G is the vector modulus of temperature gradient; S35: Using the calculation formula for the convective disturbance intensity coefficient, combined with the temperature gradient vector modulus G at each measurement point, the corresponding convective disturbance intensity coefficient is calculated. The formula is: , where C is the convective disturbance intensity coefficient; G is the temperature gradient vector modulus at the corresponding measurement point; and k is the environmental heat transfer characteristic constant.

5. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1, characterized in that: The S4 specifically includes: S41: Based on the convective disturbance intensity coefficient C obtained in S3, a mapping relationship between the sensor position and the disturbance degree is constructed, and a disturbance intensity threshold range is set to distinguish high disturbance, medium disturbance, and low disturbance areas; S42: According to the disturbance partitioning result, a compensation weight is assigned to the measurement point corresponding to each sensor, and a compensation weight matrix W with the same structure as the original data matrix is ​​constructed; S43: Determine the error amplitude required to correct the measurement value of each sensor based on the weight coefficients included in the compensation weight matrix W, and establish a dynamic error correction model that evolves over time in combination with the acquisition timestamp information; S44: Apply the constructed dynamic error compensation model to the original data matrix obtained in S2, perform error correction operations point by point according to the correspondence between matrix elements, and obtain a compensated corrected data matrix, retaining the data accuracy to two decimal places.

6. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 5, characterized in that: The S42 specifically includes: S421: Set the corresponding basic compensation weight values ​​for high disturbance, medium disturbance and low disturbance areas, respectively. , which satisfies ; S422: For each sensor measurement point, assign a corresponding basic weight value according to its corresponding disturbance level. , and calculate the final compensation weight: , where P is the final compensation weight; is the basic weight coefficient; is a dynamic regulatory factor; The difference between the current measurement value and the measurement value of the previous cycle; is the reference measurement value; S423: Based on the calculation result of S422, the compensation weight P of each sensor is written into the two-dimensional matrix W in the order of its fixed position in the array. The row and column numbers of the matrix are consistent with the original data matrix.

7. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 6, characterized in that: The S43 specifically includes: S431: Extract the compensation weight coefficient of each sensor corresponding to the position from the compensation weight matrix W obtained in S42, and determine the measurement error correction amplitude of the corresponding sensor in the current sampling period according to the weight coefficient. ; S432: Using the collected timestamp information as the time axis reference, the error correction amplitude corresponding to each sensor position is calculated. Store them sequentially according to the time series to establish the error correction time series at the sensor position; S433: Using the sliding window time series analysis method, the error correction amplitude at the current moment Based on the historical time series data, the time decay coefficient is calculated ; S434: Correct the error Corresponding time attenuation coefficient Combined with the above, a dynamic error correction model that evolves over time is established, and its expression is: , where is the sensor measurement value after dynamic error correction; The raw data measured by the sensor in the current sampling period; It is the error correction amplitude of the measured value in the current sampling period.

8. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1, characterized in that: The S5 specifically includes: S51: Based on the spatial position layout of the heat flux density sensors in S1, a spatial coordinate system with the center of the array as the origin is constructed, and the spatial coordinates of each heat flux density sensor are recorded; S52: extracting the real-time measurement data of each heat flux density sensor from the corrected data matrix obtained in S4, and calculating the inverted temperature value at the corresponding position of each heat flux density sensor based on Fourier's heat conduction law and heat flow inversion algorithm; S53: constructing an inverted temperature field based on the spatial layout coordinate relationship of the heat flux density sensor using all the temperature values ​​inverted in S52; S54: extracting the measured temperature values ​​of each temperature sensor at the same time stamp from the modified data matrix in S4, and constructing the measured temperature field based on the spatial position of the temperature sensor; S55: By calculating the temperature field difference, the inverted temperature field and the measured temperature field are analyzed point by point, and the temperature field difference value D is calculated as the mean square temperature difference between the two fields; S56: Generate a credibility evaluation parameter R based on the temperature field difference value D; the formula is: , where R is the credibility evaluation parameter; The maximum allowable temperature difference set for the system.

9. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 8, characterized in that: The S52 specifically includes: S521: extracting the heat flux value measured in real time by each heat flux sensor from the corrected data matrix of S4; S522: Set the temperature sensor measurement value at the center of the array to the reference temperature, which is recorded as , and record the vertical distance from each heat flux density sensor to the center point of the array, recorded as d; S523: Obtaining the thermal conductivity k of the material of the measurement surface; S524: Based on Fourier's law of heat conduction, the inverted temperature value at the corresponding position is inferred using the heat flux density measurement value. The calculation formula is: , where is the temperature value obtained by inversion; q is the heat flux density value measured by the sensor; k is the thermal conductivity of the material; S525: Execute the above calculations on all heat flux density sensors in the corrected data matrix in sequence to obtain the inverted temperature value at the corresponding position of each sensor.

10. The multi-sensor linked heat flux-temperature real-time acquisition and error compensation method according to claim 1, characterized in that: The S6 specifically includes: S61: extracting the compensated heat flux and temperature data of the corresponding measurement position of each sensor from the correction data matrix obtained in S4, and recording them as a correction measurement value set; S62: Obtain the credibility evaluation parameter corresponding to each measurement position from S5 as a weight coefficient; S63: Perform weighted fusion on the corrected measurement values ​​of each measurement position and the credibility evaluation parameters of the corresponding position to obtain the fused heat flux density measurement value and temperature measurements ; S64: The final measured value of the heat flux after fusion and the final measured value of temperature The results are associated with the corresponding timestamp information to generate unified coupling measurement results and stored in a structured data format.

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