Temperature and pressure integrated transmission measurement method and system based on nonlinear compensation

Abstract

The application discloses a temperature and pressure integrated transmission measurement method and system based on nonlinear compensation, and relates to the technical field of measurement. The method comprises the following steps: synchronously collecting temperature original signals and pressure original signals of a target environment to perform cross-influence analysis, and constructing a temperature-pressure cross-influence matrix; performing nonlinear segmented compensation on the temperature original signals and the pressure original signals according to the temperature-pressure cross-influence matrix to generate temperature compensation signals and pressure compensation signals; fusing the temperature compensation signals and the pressure compensation signals to generate initial fusion measurement values for data change diagnosis; performing multistage verification according to a diagnosis result to determine temperature and pressure integrated transmission measurement values. The technical problem that temperature and pressure signals interfere with each other in the temperature and pressure measurement process in the prior art, cannot be effectively compensated and corrected, and thus measurement inaccuracy is caused is solved, and the technical effect of improving temperature and pressure integrated transmission measurement precision is achieved.

Description

Technical Field

[0001] The present invention relates to the field of measurement technology, and in particular to a temperature and pressure integrated transmission measurement method and system based on nonlinear compensation. Background Art

[0002] The integrated temperature and pressure transmitter is a multi-parameter integrated transmitter that combines temperature measurement and pressure measurement functions. By integrating temperature sensors and pressure sensors, the device can synchronously collect the temperature and pressure parameters of the measured medium. However, the existing integrated temperature and pressure transmitters still face many technical bottlenecks in practical applications. Due to the coupling relationship between temperature and pressure sensing elements in structure and signal channels, temperature changes may cause zero-point drift or sensitivity deviation of pressure sensitive elements, and pressure changes may also cause indirect interference to the temperature measurement accuracy, thereby causing cross-influence between the two signals and affecting measurement accuracy. In addition, traditional compensation methods are mostly based on linear models or empirical formulas, which make it difficult to effectively characterize the nonlinear change law of temperature-pressure coupling under complex working conditions, and cannot achieve high-precision compensation and dynamic correction, which further limits the performance of the integrated temperature and pressure transmitter. Summary of the Invention

[0003] The present application provides a temperature and pressure integrated transmission measurement method and system based on nonlinear compensation, which solves the technical problem in the prior art that temperature and pressure signals interfere with each other during temperature and pressure measurement and cannot be effectively compensated and corrected, resulting in inaccurate measurements.

[0004] In a first aspect of the present application, a temperature and pressure integrated transmission and measurement method based on nonlinear compensation is provided, the method comprising:

[0005] The original temperature signal and the original pressure signal of the target environment are synchronously collected for cross-influence analysis to construct a temperature-pressure cross-influence matrix; nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal; the temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis, and multi-level verification is performed based on the diagnosis result to determine the temperature and pressure integrated transmission measurement value.

[0006] A second aspect of the present application provides a temperature and pressure integrated transmission and measurement system based on nonlinear compensation, the system comprising:

[0007] Analysis module: synchronously collects the original temperature signal and the original pressure signal of the target environment for cross-influence analysis and constructs a temperature-pressure cross-influence matrix; compensation module: performs nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal; verification module: fuses the temperature compensation signal and the pressure compensation signal to generate an initial fusion measurement value for data change diagnosis, performs multi-level verification based on the diagnosis results, and determines the temperature and pressure integrated transmission measurement value.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the original temperature signal and the original pressure signal of the target environment are synchronously collected for cross-influence analysis, and a temperature-pressure cross-influence matrix is ​​constructed. Then, nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate temperature compensation signal and pressure compensation signal. Finally, the temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis. Based on the diagnosis results, multi-level verification is performed to determine the temperature and pressure integrated transmission measurement value. This solves the technical problem in the prior art that the temperature and pressure signals interfere with each other during the temperature and pressure measurement process and cannot be effectively compensated and corrected, resulting in inaccurate measurements, and achieves the technical effect of improving the measurement accuracy of the temperature and pressure integrated transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic flow chart of a temperature and pressure integrated transmission and measurement method based on nonlinear compensation provided in an embodiment of the present application;

[0012] Figure 2 This is a schematic diagram of the structure of the temperature and pressure integrated transmission and measurement system based on nonlinear compensation provided in an embodiment of the present application.

[0013] Description of reference numerals: analysis module 11 , compensation module 12 , verification module 13 . DETAILED DESCRIPTION

[0014] The present application solves the technical problem in the prior art that temperature and pressure signals interfere with each other during temperature and pressure measurement and cannot be effectively compensated and corrected, resulting in inaccurate measurements, by providing a temperature and pressure integrated transmission measurement method and system based on nonlinear compensation.

[0015] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Example 1, as Figure 1 As shown, the present application provides a temperature and pressure integrated transmission measurement method based on nonlinear compensation, wherein the method includes:

[0018] The original temperature and pressure signals of the target environment are collected synchronously for cross-impact analysis, and a temperature-pressure cross-impact matrix is ​​constructed.

[0019] Through temperature sensors and pressure sensors, the original temperature signals and pressure signals of the target environment are collected and cross-impact analysis is performed to analyze the interaction between temperature and pressure, thereby constructing a temperature-pressure cross-impact matrix. The temperature-pressure cross-impact matrix quantifies the cross-impact.

[0020] Furthermore, the original temperature signal and the original pressure signal of the target environment are synchronously collected for cross-impact analysis, and a temperature-pressure cross-impact matrix is ​​constructed. The method includes:

[0021] Set standard temperature and pressure environmental parameters, perform temperature sampling through a standard temperature generator according to the standard temperature and pressure environmental parameters to obtain a temperature sample signal; perform sliding average filtering on the temperature sample signal to generate a temperature original signal; perform pressure sampling through a standard pressure generator according to the standard temperature and pressure environmental parameters to obtain a pressure sample signal; perform time domain alignment processing on the pressure sample signal to generate a pressure original signal; perform deviation analysis based on the temperature original signal, the pressure original signal, the temperature standard value, and the temperature standard value to generate a temperature deviation data set and a pressure deviation data set; perform coupling mapping based on the temperature deviation data set and the pressure deviation data set to construct a temperature-pressure coupling relationship mapping table; perform cross-impact analysis based on the temperature-pressure coupling relationship mapping table to construct the temperature-pressure cross-impact matrix.

[0022] Preferably, standard temperature and pressure environmental parameters are set for measurement and calibration. These parameters consist of a combination of multiple temperature and pressure values ​​to fully cover the common operating ranges of measurement applications. Based on these standard temperature and pressure environmental parameters, multiple sampling experiments are conducted using a standard temperature generator and a standard pressure generator.

[0023] During the temperature sampling process, a standard temperature generator outputs a preset temperature value, driving the temperature sensor for real-time sampling, generating the corresponding temperature sample signal. A sliding average filter is applied to the temperature sample signal to remove high-frequency interference and short-term sudden fluctuations, generating a stable original temperature signal sequence.

[0024] During the pressure sampling process, a standard pressure generator provides a synchronous pressure output, which is continuously sampled by the pressure sensor to generate a pressure sample signal. The pressure sample signal is then time-aligned to generate the original pressure signal, ensuring that the original pressure signal is synchronized with the original temperature signal on the time axis.

[0025] The original temperature signal is compared with the corresponding set temperature standard value, and the temperature deviation value is calculated to form a temperature deviation data set; the original pressure signal is compared with the corresponding set pressure standard value, and the pressure deviation value is calculated to form a pressure deviation data set.

[0026] After obtaining the temperature and pressure deviation datasets, a coupled mapping analysis was performed based on their respective deviation characteristics: temperature deviation data was categorized into positive and negative deviations, while pressure deviation data was categorized into linear response, sudden change, or trend deviation. Based on this, a temperature-pressure coupled relationship mapping table was established, recording the corresponding relationships and impact paths between various deviation combinations.

[0027] Cross-impact analysis is performed based on the temperature-pressure coupling relationship mapping table: by setting multiple fixed temperature intervals, observing the trend of pressure deviation changing with temperature in each interval, and calculating its corresponding temperature impact factor; at the same time, setting multiple fixed pressure intervals, observing the trend of temperature deviation changing with pressure in each interval, and calculating its corresponding pressure impact factor.

[0028] The temperature influence factor is used as the secondary diagonal element, and the pressure influence factor is used as the main diagonal element to fill in the two-dimensional temperature-pressure cross-influence matrix; the non-diagonal elements are interpolated or fitted according to the cross terms in the mapping table to complete the temperature-pressure cross-influence matrix.

[0029] Further, according to the temperature deviation data set, the pressure deviation data set is coupled and mapped, a temperature-pressure coupling relationship mapping table is constructed, cross-influence analysis is performed according to the temperature-pressure coupling relationship mapping table, and the temperature-pressure cross-influence matrix is constructed. The method comprises:

[0030] Based on the temperature deviation data set, the temperature positive deviation data and the temperature negative deviation data are obtained according to the temperature positive and negative characteristics; based on the pressure deviation data set, the pressure positive deviation data and the pressure negative deviation data are obtained according to the interference mode; according to the temperature positive deviation data, the temperature negative deviation data, the pressure positive deviation data and the pressure negative deviation data, the coupling path analysis is performed, and the multi-class coupling path is divided; according to the multi-class coupling path, the data is filled, and the temperature-pressure coupling relationship mapping table is constructed; the fixed value pressure interval is set, the influence intensity analysis is performed on the pressure deviation data set according to the temperature change, and the temperature influence factor is generated; the fixed value temperature interval is set, the influence intensity analysis is performed on the temperature deviation data set according to the pressure change, and the pressure influence factor is generated; according to the temperature-pressure coupling relationship mapping table, the temperature influence factor is stored as a sub-diagonal line, and according to the temperature-pressure coupling relationship mapping table, the pressure influence factor is stored as a main diagonal line, and the temperature-pressure cross-influence matrix is constructed.

[0031] Based on the temperature deviation data set, the deviation characteristics of the temperature signal are classified and processed, and the temperature deviation values are divided into temperature positive deviation data and temperature negative deviation data according to their positive and negative polarities, wherein the temperature positive deviation data represents that the temperature measurement value is higher than the standard value, and the temperature negative deviation data represents that the temperature measurement value is lower than the standard value. Based on the pressure deviation data set, the deviation mode of the pressure signal is identified and classified, specifically including defining the pressure anomaly caused by temperature negative drift as pressure positive deviation data, and defining the pressure anomaly caused by temperature positive drift as pressure negative deviation data.

[0032] Based on the temperature positive deviation data, the temperature negative deviation data, the pressure positive deviation data and the pressure negative deviation data, the coupling path analysis between temperature and pressure is performed, that is, by analyzing the influence trend of temperature deviation on pressure deviation and the influence effect of pressure deviation on temperature measurement feedback, a plurality of typical coupling paths are summarized and divided, including: the first type of path: temperature positive deviation leads to pressure negative deviation; the second type of path: temperature negative deviation leads to pressure positive deviation; the third type of path: pressure positive deviation aggravates temperature measurement negative deviation; the fourth type of path: pressure negative deviation aggravates temperature measurement positive deviation.

[0033] Based on the above coupling path relationship, the corresponding deviation data pairs are filled into the temperature-pressure coupling relationship mapping table according to the path type to form a complete coupling path data set. Each unit in the table records the interaction performance characteristics of a certain type of path under specific temperature and pressure combination conditions.

[0034] Several fixed pressure intervals are set, the temperature deviation data at different pressure levels are grouped, and the pressure response intensity caused by temperature changes in each pressure interval is evaluated. The temperature impact factor is quantified to characterize the degree of influence of temperature on pressure. Similarly, fixed temperature intervals are set, the pressure deviation data at different temperature levels are grouped, and the feedback intensity of pressure changes on temperature signals is calculated to obtain the pressure impact factor to characterize the degree of interference of pressure on temperature measurement.

[0035] Optionally, based on the pressure variation range of the target environment, the pressure values ​​corresponding to the pressure deviation data set are divided into several discrete pressure intervals, and each interval can be set to a constant step size (for example, one interval is divided every 10 kPa), forming multiple fixed-value pressure interval segments. Each pressure interval segment contains several groups of temperature variation data associated with the pressure level. In each pressure interval, the variation range, variation trend and variation frequency of the temperature deviation data in the interval are statistically analyzed to analyze the disturbance amplitude caused by the temperature deviation on the pressure response under the condition of keeping the pressure approximately constant. Furthermore, a quantitative index of the impact intensity (such as the average pressure offset caused by a unit temperature deviation) is used to evaluate the degree of influence of temperature on pressure in the interval to obtain the corresponding temperature influence factor. The temperature influence factors corresponding to each fixed pressure interval are summarized in the order of the pressure interval to form a complete temperature influence factor sequence, which is used to fill the data in the sub-diagonal area of ​​the temperature-pressure cross-influence matrix.

[0036] In constructing the cross-influence matrix, the temperature influence factor is stored as a secondary diagonal element in the matrix, indicating the direction and intensity of the impact of temperature changes on pressure. The pressure influence factor is stored as a primary diagonal element in the matrix, indicating the interference effect of pressure changes on temperature measurement. By integrating the temperature-pressure coupling relationship mapping table with various influencing factors, the temperature-pressure cross-influence matrix is ​​constructed.

[0037] Nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal.

[0038] The collected temperature raw signal is matched one-to-one with the pressure raw signal at the corresponding moment in time, according to the time series. The specific coupling block of the temperature-pressure cross-influence matrix is ​​located. Based on the current temperature and pressure ranges, the corresponding temperature and pressure influence factors in this coupling block are extracted. A nonlinear piecewise compensation mechanism is used to perform interference cancellation compensation on the current pressure raw signal based on the temperature influence factor to obtain a pressure-compensated signal; and interference cancellation compensation is performed on the current temperature raw signal based on the pressure influence factor to obtain a temperature-compensated signal. Nonlinear piecewise compensation involves using piecewise function modeling of the raw signal within each specific temperature and pressure range, setting different correction factors and response curves to improve the accuracy and dynamic adaptability of the compensation.

[0039] Furthermore, the method of performing nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal includes:

[0040] The temperature interval to which the original temperature signal belongs is determined based on the original pressure signal, and the pressure interval to which the original pressure signal belongs is determined; the row index of the temperature-pressure cross-influence matrix is ​​traversed according to the temperature interval to obtain the temperature interval number, and the column index of the temperature-pressure cross-influence matrix is ​​traversed according to the pressure interval to obtain the pressure interval number; a row-column cross analysis is performed according to the temperature interval number and the pressure interval number to determine multiple row-column intersection points, and multiple types of compensation coefficient groups are determined according to the multiple row-column intersection points; nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal based on the multiple types of compensation coefficient groups to generate the temperature compensation signal and the pressure compensation signal.

[0041] Based on the collected temperature raw signal and the interval division rules of the temperature deviation data, the current temperature interval is determined. Based on the corresponding pressure raw signal and the interval division rules of the pressure deviation data, the current pressure interval is determined. The temperature interval is used as the row index in the temperature-pressure cross-influence matrix, and the row number corresponding to the temperature interval is obtained by traversing the matrix row labels. The pressure interval is used as the column index in the matrix, and the column number corresponding to the pressure interval is obtained by traversing the matrix column labels. The row and column numbers jointly point to specific cells in the cross-influence matrix, indicating the strength of the cross-influence between the temperature and pressure signals under the conditions of the combination of temperature and pressure intervals.

[0042] Furthermore, based on the current temperature and pressure interval numbers, a matrix row-column cross-analysis is performed to locate the intersection point associated with multiple historical samples. The corresponding compensation coefficient group (e.g., the pressure influence coefficient on the main diagonal, the temperature influence coefficient on the secondary diagonal, and other possible higher-order coupling terms) is extracted from the matrix structure. This allows the construction of multiple compensation coefficient groups that match the current operating conditions. Based on these multiple compensation coefficient groups, nonlinear segmented compensation is performed on the original temperature and pressure signals to generate temperature- and pressure-compensated signals.

[0043] Furthermore, the method of performing nonlinear piecewise compensation on the original temperature signal and the original pressure signal based on the multiple compensation coefficient groups to generate the temperature compensated signal and the pressure compensated signal includes:

[0044] The temperature original signal and the pressure original signal are automatically calibrated based on the multiple compensation coefficient groups, and the multiple compensation coefficient groups are updated according to the calibration results to generate multiple compensation optimization coefficients; the operating conditions are identified according to the temperature original signal and the pressure original signal, and multiple operating condition sections are divided; the multiple operating condition sections are matched with the multiple compensation optimization coefficients, and the temperature compensation optimization coefficient and the pressure compensation optimization coefficient are determined by compensation separation according to the matching results; the temperature original signal is subjected to nonlinear compensation correction according to the temperature compensation optimization coefficient to generate the temperature compensation signal; the pressure original signal is subjected to nonlinear compensation correction according to the pressure compensation optimization coefficient to generate the pressure compensation signal.

[0045] Based on the multi-class compensation coefficient group, the temperature original signal and the pressure original signal are automatically calibrated respectively; based on the calibration results, the multi-class compensation coefficient group is updated to generate multi-class compensation optimization coefficients.

[0046] The operating condition is identified based on the changing trends of the original temperature signal and the original pressure signal, and multiple operating condition sections are divided. Specifically: a temperature change threshold and a pressure change threshold are set to judge the change amplitude of the temperature and pressure signals respectively; when the temperature change amplitude and the pressure change amplitude are both lower than their respective thresholds, it is divided into a stable operating condition section; when the temperature change amplitude exceeds the temperature threshold and the pressure change amplitude is lower than the pressure threshold, it is divided into a temperature-dominated operating condition section; when the pressure change amplitude exceeds the pressure threshold and the temperature change amplitude is lower than the temperature threshold, it is divided into a pressure-dominated operating condition section; when the temperature change amplitude and the pressure change amplitude exceed their respective thresholds, it is divided into a coupled disturbance operating condition section.

[0047] The above-mentioned multiple operating condition sections are matched with the updated multiple types of compensation optimization coefficients, and the compensation optimization coefficient set is separated and extracted according to the matching results to determine the temperature compensation optimization coefficient and the pressure compensation optimization coefficient under the current operating condition respectively.

[0048] After obtaining the working condition matching compensation coefficient, the original temperature signal is subjected to nonlinear compensation correction according to the temperature compensation optimization coefficient. This is done by directly superimposing or multiplying the temperature original signal with the temperature optimization coefficient as the adjustment increment or adjustment ratio to generate a temperature compensation signal. Similarly, the original pressure signal is subjected to nonlinear compensation correction according to the pressure compensation optimization coefficient to generate the corresponding pressure compensation signal.

[0049] Furthermore, the temperature original signal and the pressure original signal are automatically calibrated based on the multiple compensation coefficient groups, and the multiple compensation coefficient groups are updated according to the calibration results to generate multiple compensation optimization coefficients. The method includes:

[0050] Retrieve multiple reference conditions of the integrated temperature and pressure transmitter, wherein the multiple reference conditions include zero pressure condition and constant temperature condition; perform temperature acquisition based on the zero pressure condition, and draw a temperature reference curve according to the temperature acquisition result; perform pressure acquisition based on the constant temperature condition, and draw a pressure reference curve according to the pressure acquisition result; when the environmental parameters meet the zero pressure condition, trigger the first automatic calibration mode, update and replace the multiple compensation coefficient groups according to the temperature reference curve, and generate the multiple compensation optimization coefficients; when the environmental parameters meet the constant temperature condition, trigger the second automatic calibration mode, update and replace the multiple compensation coefficient groups according to the pressure reference curve, and generate the multiple compensation optimization coefficients; when the environmental parameters meet the zero pressure condition and the constant temperature condition, trigger the third automatic calibration mode, update and replace the multiple compensation coefficient groups according to the temperature reference curve and the pressure reference curve, and generate the multiple compensation optimization coefficients.

[0051] Retrieve multiple reference conditions of the preset temperature and pressure integrated transmitter, including but not limited to zero pressure condition (referring to the external pressure input being approximately atmospheric pressure or a preset reference pressure value) and constant temperature condition (referring to the ambient temperature being constantly maintained within the set reference value range).

[0052] Temperature acquisition is performed based on zero-pressure conditions. In a static and stable state without pressure disturbance, the original temperature signal is periodically sampled. During the acquisition process, sliding filtering and anomaly rejection strategies are used to process the sampling results. The processing results are used to draw a temperature reference curve, which can reflect the temperature signal response characteristics at the ideal pressure zero point. Pressure acquisition is performed based on constant temperature conditions. The original pressure signal is measured at equal intervals under the working condition that the temperature remains constant. A pressure reference curve is constructed based on the collected samples. This pressure reference curve is used to reflect the standard behavior of the pressure sensor response when there is no temperature disturbance.

[0053] When the environmental parameters meet the zero-pressure condition, the first automatic calibration mode is triggered. The system calls the preset temperature reference curve as a correction reference, identifies the difference between the current temperature signal and the reference, and replaces or incrementally corrects the original multi-category compensation coefficient group based on the difference, thereby generating updated multi-category compensation optimization coefficients.

[0054] When the environmental parameters meet the constant temperature conditions, the second automatic calibration mode is triggered. The system determines the offset trend of the current pressure signal based on the pressure reference curve, constructs an error correction factor based on constant temperature, and also performs dynamic adjustments on multiple types of compensation coefficient groups to obtain multiple types of compensation optimization coefficients.

[0055] When the environmental parameters meet both the zero-pressure and constant-temperature conditions, the third automatic calibration mode is triggered. The system uses the temperature reference curve and the pressure reference curve as the basis for joint calibration, and performs joint fitting and bidirectional updates on the temperature compensation coefficient group and the pressure compensation coefficient group respectively, ensuring that both temperature and pressure compensation can obtain optimal compensation performance in an interference-free reference environment, and ultimately outputs highly optimized multiple types of compensation optimization coefficients.

[0056] The temperature compensation signal and the pressure compensation signal are fused to generate an initial fusion measurement value for data change diagnosis, and multi-level verification is performed based on the diagnosis result to determine the temperature and pressure integrated transmission measurement value.

[0057] Furthermore, the temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis, and the method includes:

[0058] A credibility analysis is performed based on the temperature compensation signal and the pressure compensation signal to generate a temperature credibility factor and a pressure credibility factor; the temperature credibility factor and the pressure credibility factor are normalized, and a weighted calculation is performed on the temperature compensation signal and the pressure compensation signal according to the processing results to determine a plurality of weight coefficients; the temperature compensation signal and the pressure compensation signal are fused according to the plurality of weight coefficients to generate an initial fused measurement value; fluctuation change identification is performed based on the initial fused measurement value to determine a plurality of fluctuation patterns, an abnormality analysis is performed based on the plurality of fluctuation patterns to generate an abnormality analysis result; the plurality of fluctuation patterns are abnormally marked based on the abnormality analysis result, and data change diagnosis is performed based on the abnormality marking parameters to generate the diagnosis result.

[0059] Credibility analysis is performed based on the temperature- and pressure-compensated signals. By calculating each signal's stability index, signal-to-noise ratio, historical error statistics, and real-time anomaly detection parameters, temperature and pressure credibility factors, reflecting the signal reliability, are generated. Normalization of the temperature and pressure credibility factors can be performed using methods such as maximum-minimum value normalization and Z-score standardization to convert credibility factors of different dimensions and scales into a unified numerical range.

[0060] Based on the normalized confidence factors, multiple weight coefficients are calculated to weight the contribution ratios of the temperature compensation signal and the pressure compensation signal. The weight coefficients for the temperature compensation signal are obtained by dividing the normalized temperature confidence factor by the sum of the normalized temperature confidence factor and the normalized pressure confidence factor. Similarly, the weight coefficients for the pressure compensation signal are obtained by dividing the normalized pressure confidence factor by the sum of the normalized temperature confidence factor and the normalized pressure confidence factor.

[0061] The temperature compensation signal and the pressure compensation signal are weightedly fused using the aforementioned weight coefficients, and a weighted average is used to generate an initial fused measurement value. Based on the initial fused measurement value, fluctuation change identification is performed to analyze the dynamic change characteristics of the measurement data. Time series analysis methods such as sliding window differencing, rate of change detection, and spectrum analysis are used to identify various fluctuation characteristics, including continuous fluctuation rise patterns, step mutation patterns, and periodic oscillation patterns. Based on the identified fluctuation patterns, combined with system operating parameters and a historical fault database, anomaly analysis is performed to determine whether the fluctuation is abnormal, infer the possible anomaly type and root cause, and generate anomaly analysis results. Finally, based on the anomaly analysis results, different fluctuation patterns are marked as abnormal, forming an anomaly marking parameter set.

[0062] Furthermore, the method includes: performing fluctuation change identification based on the initial fused measurement value, determining multiple fluctuation patterns, performing anomaly analysis based on the multiple fluctuation patterns, and generating anomaly analysis results.

[0063] Based on the initial fused measurement value, fluctuation rise identification is performed to determine a continuous fluctuation rise pattern; based on the initial fused measurement value, fluctuation mutation identification is performed to determine a step fluctuation mutation pattern; based on the initial fused measurement value, fluctuation period identification is performed to determine a fluctuation oscillation period pattern; based on the continuous fluctuation rise pattern, temperature power supply circuit anomaly detection is performed to generate a first anomaly analysis result; based on the step fluctuation mutation pattern, pressure zero point position anomaly detection is performed to generate a second anomaly analysis result; based on the fluctuation oscillation period pattern, equipment mechanical vibration anomaly detection is performed to generate a third anomaly analysis result.

[0064] For the initial fused measurement values, time series analysis technology is used to identify fluctuation increases; by calculating the incremental change rate and trend judgment of consecutive data points, the continuous fluctuation increase pattern of the data is identified, that is, the measurement values ​​show a continuous upward trend over a period of time and the fluctuation amplitude gradually increases.

[0065] Based on the initial fused measurements, we identify sudden fluctuations. Using differential analysis, threshold triggering, and sudden change detection algorithms, we identify step fluctuation patterns, which are characterized by sudden jumps in measurement data that persist for extended periods at abnormal levels.

[0066] Based on the initial fused measurements, we perform fluctuation period identification, using frequency domain analysis methods (such as Fourier transform and wavelet transform) and autocorrelation functions to detect periodic oscillation patterns in the measurement data. Periodic fluctuations often indicate mechanical vibrations of the equipment, piping resonances, or periodic environmental disturbances.

[0067] Based on the identified continuous fluctuation and rising pattern, the system conducts temperature power supply circuit anomaly detection. Combining circuit monitoring parameters and temperature variation characteristics, it analyzes possible anomalies such as unstable power supply and poor cable contact, generating the first anomaly analysis result.

[0068] Based on the step fluctuation mutation pattern, the pressure zero position anomaly detection is performed. By analyzing the pressure measurement zero drift trend and combining it with the pressure calibration records, it is confirmed whether there is a zero position offset and a second anomaly analysis result is generated.

[0069] Detect mechanical vibration anomalies in equipment based on fluctuating oscillation patterns. Combined with vibration sensor data and vibration spectrum analysis, determine whether mechanical failure or environmental vibration influences, and generate a third anomaly analysis result.

[0070] Furthermore, a multi-level verification is performed based on the diagnosis results to determine the temperature and pressure integrated transmitter measurement value. The method includes:

[0071] The abnormal marking parameter is parsed to obtain a first abnormal marking parameter, a second abnormal marking parameter, and a third abnormal marking parameter, wherein the first abnormal marking parameter, the second abnormal marking parameter, and the third abnormal marking parameter correspond to the first abnormal analysis result, the second abnormal analysis result, and the third abnormal analysis result; a hardware verification result is generated by combining the first abnormal marking parameter with the diagnosis result; a data verification result is generated by combining the second abnormal marking parameter with the diagnosis result; a mechanical verification result is generated by combining the third abnormal marking parameter with the diagnosis result; when the hardware verification result, the data verification result, and the mechanical verification result are all verified, the temperature and pressure integrated transmitter is determined according to the initial fusion measurement value. measurement value; when any one of the hardware verification results, the data verification results, and the mechanical verification results fails the verification, the initial fusion measurement value is attenuated and calculated to determine the temperature-pressure integrated transmission measurement value; when any two of the hardware verification results, the data verification results, and the mechanical verification results fail the verification, the initial fusion measurement value is attenuated and superimposed with offset verification to determine the temperature-pressure integrated transmission measurement value; when the hardware verification results, the data verification results, and the mechanical verification results all fail the verification, the initial fusion measurement value is treated as discarded data, the reverse re-measurement instruction is executed, the initial fusion measurement value is updated and iterative verification is performed again until the verification passes, and the temperature-pressure integrated transmission measurement value is determined.

[0072] The abnormal marking parameters are parsed to obtain the first abnormal marking parameter, the second abnormal marking parameter and the third abnormal marking parameter, wherein the three abnormal marking parameters correspond to the first abnormal analysis result, the second abnormal analysis result and the third abnormal analysis result, respectively, forming a one-to-one correspondence. Secondly, according to the diagnostic results combined with the first abnormal marking parameter, hardware verification is performed; hardware verification includes sensor circuit status detection, signal acquisition module integrity verification and connection reliability check, and finally generates a hardware verification result to determine whether the hardware equipment is working normally. Subsequently, combined with the second abnormal marking parameter, data verification is performed, wherein the data verification content covers the rationality verification, consistency check and data integrity assessment of the measurement data, and the data verification result is generated based on the data abnormality pattern judgment. Then, combined with the third abnormal marking parameter, mechanical verification is carried out; mechanical verification determines whether there is mechanical abnormality or vibration interference by analyzing the equipment mechanical vibration sensor data and environmental impact assessment, and forms a mechanical verification result.

[0073] When the hardware verification results, data verification results and mechanical verification results all show that the verification has passed, the initial fusion measurement value is directly determined as the final temperature and pressure integrated transmission measurement value.

[0074] When any of the hardware verification results, data verification results, and mechanical verification results fails to pass verification, attenuation calculation processing is performed on the initial fused measurement value to reduce the impact of abnormal data on the measurement value and generate an adjusted temperature and pressure integrated transmission measurement value.

[0075] If any two verification results fail, attenuation calculation is performed on the initial fusion measurement value and offset correction is superimposed to further correct the measurement value to ensure the stability and reliability of the measurement result.

[0076] If hardware, data, and mechanical verification fail, the initial fused measurement is considered discarded, triggering a reverse remeasurement. The system resamples and re-fuse the measurement process, updates the initial fused measurement, and iterates through multiple levels of verification until verification passes, ultimately determining a reliable temperature and pressure measurement.

[0077] In summary, the embodiments of the present application have at least the following technical effects:

[0078] First, the original temperature signal and the original pressure signal of the target environment are synchronously collected for cross-influence analysis, and a temperature-pressure cross-influence matrix is ​​constructed. Then, nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate temperature compensation signal and pressure compensation signal. Finally, the temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis. Based on the diagnosis results, multi-level verification is performed to determine the temperature and pressure integrated transmission measurement value. This solves the technical problem in the prior art that the temperature and pressure signals interfere with each other during the temperature and pressure measurement process and cannot be effectively compensated and corrected, resulting in inaccurate measurements, and achieves the technical effect of improving the measurement accuracy of the temperature and pressure integrated transmission.

[0079] The second embodiment is based on the same inventive concept as the temperature and pressure integrated transmission measurement method based on nonlinear compensation in the above embodiment. Figure 2 As shown, the present application provides a temperature and pressure integrated transmission and measurement system based on nonlinear compensation, wherein the system includes:

[0080] Analysis module 11: synchronously collects the original temperature signal and the original pressure signal of the target environment for cross-influence analysis and constructs a temperature-pressure cross-influence matrix; compensation module 12: performs nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal; verification module 13: fuses the temperature compensation signal with the pressure compensation signal to generate an initial fusion measurement value for data change diagnosis, performs multi-level verification based on the diagnosis result, and determines the temperature and pressure integrated transmission measurement value.

[0081] Furthermore, the analysis module 11 is used to perform the following method:

[0082] Set standard temperature and pressure environmental parameters, perform temperature sampling through a standard temperature generator according to the standard temperature and pressure environmental parameters to obtain a temperature sample signal; perform sliding average filtering on the temperature sample signal to generate a temperature original signal; perform pressure sampling through a standard pressure generator according to the standard temperature and pressure environmental parameters to obtain a pressure sample signal; perform time domain alignment processing on the pressure sample signal to generate a pressure original signal; perform deviation analysis based on the temperature original signal, the pressure original signal, the temperature standard value, and the temperature standard value to generate a temperature deviation data set and a pressure deviation data set; perform coupling mapping based on the temperature deviation data set and the pressure deviation data set to construct a temperature-pressure coupling relationship mapping table; perform cross-impact analysis based on the temperature-pressure coupling relationship mapping table to construct the temperature-pressure cross-impact matrix.

[0083] Furthermore, the analysis module 11 is used to perform the following method:

[0084] Based on the temperature deviation data set, the temperature is classified according to the positive and negative temperature characteristics to obtain positive temperature deviation data and negative temperature deviation data; based on the pressure deviation data set, the pressure is classified according to the interference mode to obtain positive pressure deviation data and negative pressure deviation data; coupling path analysis is performed based on the positive temperature deviation data, the negative temperature deviation data, the positive pressure deviation data, and the negative pressure deviation data to obtain multiple types of coupling paths; data is filled according to the multiple types of coupling paths to construct a temperature-pressure coupling relationship mapping table; a fixed value pressure interval is set to perform an influence intensity analysis on the pressure deviation data set according to temperature changes to generate a temperature influence factor; a fixed value temperature interval is set to perform an influence intensity analysis on the temperature deviation data set according to pressure changes to generate a pressure influence factor; the temperature influence factor is stored as a secondary diagonal according to the temperature-pressure coupling relationship mapping table, and the pressure influence factor is stored as a main diagonal according to the temperature-pressure coupling relationship mapping table to construct the temperature-pressure cross-influence matrix.

[0085] Furthermore, the compensation module 12 is configured to perform the following method:

[0086] determining a temperature interval based on the temperature raw signal and a pressure interval based on the pressure raw signal; traversing the temperature-pressure cross-influence matrix according to the temperature interval to locate a row index and obtain a temperature interval number, and traversing the temperature-pressure cross-influence matrix according to the pressure interval to locate a column index and obtain a pressure interval number; performing row-column cross analysis according to the temperature interval number and the pressure interval number to determine a plurality of row-column cross points, and determining a plurality of compensation coefficient sets according to the plurality of row-column cross points; and performing nonlinear segmented compensation on the temperature raw signal and the pressure raw signal based on the plurality of compensation coefficient sets to generate the temperature compensation signal and the pressure compensation signal.

[0087] Further, the compensation module 12 is configured to perform the following method:

[0088] Further, the compensation module 12 is configured to perform the following method:

[0089] Further, the compensation module 12 is configured to perform the following method:

[0090] Further, the compensation module 12 is configured to perform the following method:

[0091] Further, the compensation module 12 is configured to perform the following method:

[0092] A credibility analysis is performed based on the temperature compensation signal and the pressure compensation signal to generate a temperature credibility factor and a pressure credibility factor; the temperature credibility factor and the pressure credibility factor are normalized, and a weighted calculation is performed on the temperature compensation signal and the pressure compensation signal according to the processing results to determine a plurality of weight coefficients; the temperature compensation signal and the pressure compensation signal are fused according to the plurality of weight coefficients to generate an initial fused measurement value; fluctuation change identification is performed based on the initial fused measurement value to determine a plurality of fluctuation patterns, an abnormality analysis is performed based on the plurality of fluctuation patterns to generate an abnormality analysis result; the plurality of fluctuation patterns are abnormally marked based on the abnormality analysis result, and data change diagnosis is performed based on the abnormality marking parameters to generate the diagnosis result.

[0093] Furthermore, the verification module 13 is used to perform the following method:

[0094] Based on the initial fused measurement value, fluctuation rise identification is performed to determine a continuous fluctuation rise pattern; based on the initial fused measurement value, fluctuation mutation identification is performed to determine a step fluctuation mutation pattern; based on the initial fused measurement value, fluctuation period identification is performed to determine a fluctuation oscillation period pattern; based on the continuous fluctuation rise pattern, temperature power supply circuit anomaly detection is performed to generate a first anomaly analysis result; based on the step fluctuation mutation pattern, pressure zero point position anomaly detection is performed to generate a second anomaly analysis result; based on the fluctuation oscillation period pattern, equipment mechanical vibration anomaly detection is performed to generate a third anomaly analysis result.

[0095] Furthermore, the verification module 13 is used to perform the following method:

[0096] The abnormal marking parameter is parsed to obtain a first abnormal marking parameter, a second abnormal marking parameter, and a third abnormal marking parameter, wherein the first abnormal marking parameter, the second abnormal marking parameter, and the third abnormal marking parameter correspond to the first abnormal analysis result, the second abnormal analysis result, and the third abnormal analysis result; a hardware verification result is generated by combining the first abnormal marking parameter with the diagnosis result; a data verification result is generated by combining the second abnormal marking parameter with the diagnosis result; a mechanical verification result is generated by combining the third abnormal marking parameter with the diagnosis result; when the hardware verification result, the data verification result, and the mechanical verification result are all verified, the temperature and pressure integrated transmitter is determined according to the initial fusion measurement value. measurement value; when any one of the hardware verification results, the data verification results, and the mechanical verification results fails the verification, the initial fusion measurement value is attenuated and calculated to determine the temperature-pressure integrated transmission measurement value; when any two of the hardware verification results, the data verification results, and the mechanical verification results fail the verification, the initial fusion measurement value is attenuated and superimposed with offset verification to determine the temperature-pressure integrated transmission measurement value; when the hardware verification results, the data verification results, and the mechanical verification results all fail the verification, the initial fusion measurement value is treated as discarded data, the reverse re-measurement instruction is executed, the initial fusion measurement value is updated and iterative verification is performed again until the verification passes, and the temperature-pressure integrated transmission measurement value is determined.

[0097] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0099] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation is characterized by: The method comprises: Synchronously collect the target environment's original temperature and pressure signals for cross-impact analysis and construct a temperature-pressure cross-impact matrix. performing nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal; The temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis, and multi-level verification is performed based on the diagnosis result to determine the temperature and pressure integrated transmission measurement value; Synchronously collect the target environment's original temperature and pressure signals for cross-impact analysis and construct a temperature-pressure cross-impact matrix, including: Setting standard temperature and pressure environmental parameters, performing temperature sampling through a standard temperature generator according to the standard temperature and pressure environmental parameters, and obtaining a temperature sample signal; Performing a sliding average filter on the temperature sample signal to generate an original temperature signal; Performing pressure sampling through a standard pressure generator according to the standard temperature and pressure environmental parameters to obtain a pressure sample signal; Performing time domain alignment processing on the pressure sample signal to generate an original pressure signal; Perform deviation analysis based on the original temperature signal, the original pressure signal, the temperature standard value, and the temperature standard value to generate a temperature deviation data set and a pressure deviation data set; Perform coupling mapping based on the temperature deviation data set and the pressure deviation data set to construct a temperature-pressure coupling relationship mapping table, perform cross-impact analysis based on the temperature-pressure coupling relationship mapping table to construct the temperature-pressure cross-impact matrix; Performing coupling mapping according to the temperature deviation data set and the pressure deviation data set to construct a temperature-pressure coupling relationship mapping table, performing cross-impact analysis according to the temperature-pressure coupling relationship mapping table to construct the temperature-pressure cross-impact matrix, including: Classifying the temperature deviation data set according to positive and negative temperature characteristics to obtain positive temperature deviation data and negative temperature deviation data; Classifying the pressure deviation data set according to the interference pattern to obtain positive pressure deviation data and negative pressure deviation data; Perform coupling path analysis based on the positive temperature deviation data, the negative temperature deviation data, the positive pressure deviation data, and the negative pressure deviation data to obtain multiple types of coupling paths; Filling data according to the multiple coupling paths to construct a temperature-pressure coupling relationship mapping table; Setting a fixed value pressure interval to perform an impact intensity analysis on the pressure deviation data set according to temperature changes, and generating a temperature impact factor; Setting a fixed temperature interval to perform an impact intensity analysis on the temperature deviation data set according to pressure changes, and generating a pressure impact factor; According to the temperature-pressure coupling relationship mapping table, the temperature influence factor is stored as a secondary diagonal, and according to the temperature-pressure coupling relationship mapping table, the pressure influence factor is stored as a main diagonal to construct the temperature-pressure cross-influence matrix.

2. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 1, characterized in that: The method includes performing nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal. Determine the temperature range based on the original temperature signal, and determine the pressure range based on the original pressure signal; Traverse the temperature-pressure cross-influence matrix according to the temperature interval to locate the row index and obtain the temperature interval number; traverse the temperature-pressure cross-influence matrix according to the pressure interval to locate the column index and obtain the pressure interval number; Performing row-column cross analysis according to the temperature interval number and the pressure interval number to determine a plurality of row-column cross points, and determining a plurality of compensation coefficient groups according to the plurality of row-column cross points; Nonlinear segmented compensation is performed on the original temperature signal and the original pressure signal based on the multiple compensation coefficient groups to generate the temperature compensated signal and the pressure compensated signal.

3. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 2, characterized in that: The method includes performing nonlinear segmented compensation on the original temperature signal and the original pressure signal based on the multiple compensation coefficient groups to generate the temperature compensated signal and the pressure compensated signal. Automatically calibrating the temperature original signal and the pressure original signal based on the multiple compensation coefficient groups, and updating the multiple compensation coefficient groups according to the calibration results to generate multiple compensation optimization coefficients; Identify the working condition according to the original temperature signal and the original pressure signal, and divide the working condition into multiple sections; Matching the multiple operating conditions with the multiple types of compensation optimization coefficients, and performing compensation separation to determine the temperature compensation optimization coefficient and the pressure compensation optimization coefficient according to the matching results; Performing nonlinear compensation correction on the original temperature signal according to the temperature compensation optimization coefficient to generate the temperature compensation signal; The pressure original signal is subjected to nonlinear compensation correction according to the pressure compensation optimization coefficient to generate the pressure compensation signal.

4. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 3 is characterized in that: Automatically calibrating the temperature original signal and the pressure original signal based on the multiple compensation coefficient groups, updating the multiple compensation coefficient groups according to the calibration results, and generating multiple compensation optimization coefficients, the method includes: Retrieving multiple reference conditions of the temperature and pressure integrated transmitter, wherein the multiple reference conditions include a zero pressure condition and a constant temperature condition; Performing temperature acquisition based on the zero-pressure condition, and drawing a temperature reference curve according to the temperature acquisition result; Performing pressure collection based on the constant temperature condition, and drawing a pressure reference curve according to the pressure collection result; When the environmental parameters meet the zero-pressure condition, a first automatic calibration mode is triggered, and the multiple compensation coefficient groups are updated and replaced according to the temperature reference curve to generate the multiple compensation optimization coefficients; When the environmental parameters meet the constant temperature condition, triggering a second automatic calibration mode, updating and replacing the multiple compensation coefficient groups according to the pressure reference curve, and generating the multiple compensation optimization coefficients; When the environmental parameters meet the zero pressure condition and the constant temperature condition, the third automatic calibration mode is triggered, and the multiple types of compensation coefficient groups are updated and replaced according to the temperature reference curve and the pressure reference curve to generate the multiple types of compensation optimization coefficients.

5. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 1, characterized in that: The temperature compensation signal and the pressure compensation signal are fused to generate an initial fused measurement value for data change diagnosis, the method comprising: Performing a credibility analysis based on the temperature compensation signal and the pressure compensation signal to generate a temperature credibility factor and a pressure credibility factor; Normalizing the temperature credibility factor and the pressure credibility factor, performing weighted calculation on the temperature compensation signal and the pressure compensation signal according to the processing result, and determining a plurality of weight coefficients; fusing the temperature compensation signal and the pressure compensation signal according to the multiple weight coefficients to generate an initial fused measurement value; performing fluctuation change identification based on the initial fused measurement value, determining a plurality of fluctuation patterns, performing anomaly analysis based on the plurality of fluctuation patterns, and generating anomaly analysis results; The plurality of fluctuation patterns are marked as abnormal according to the abnormal analysis result, and data change diagnosis is performed according to the abnormal marking parameters to generate the diagnosis result.

6. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 5, characterized in that: The method includes: identifying fluctuation changes based on the initial fused measurement value, determining multiple fluctuation patterns, performing anomaly analysis based on the multiple fluctuation patterns, and generating anomaly analysis results. performing fluctuation rise identification based on the initial fused measurement value to determine a continuous fluctuation rise pattern; performing fluctuation mutation identification based on the initial fusion measurement value to determine a step fluctuation mutation pattern; performing fluctuation period identification based on the initial fused measurement value to determine a fluctuation oscillation periodic pattern; Performing abnormality detection on the temperature power supply circuit according to the continuous fluctuation and rising pattern to generate a first abnormality analysis result; Performing pressure zero point position abnormality detection according to the step fluctuation mutation pattern to generate a second abnormality analysis result; Anomaly detection of mechanical vibration of the equipment is performed based on the wave oscillation period pattern to generate a third anomaly analysis result.

7. The temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to claim 6, characterized in that: Perform multi-level verification based on the diagnostic results to determine the temperature and pressure integrated transmitter measurement value. The methods include: Parsing the abnormality marking parameter to obtain a first abnormality marking parameter, a second abnormality marking parameter, and a third abnormality marking parameter, wherein the first abnormality marking parameter, the second abnormality marking parameter, and the third abnormality marking parameter correspond to the first abnormality analysis result, the second abnormality analysis result, and the third abnormality analysis result; Performing verification based on the diagnosis result and the first abnormality flag parameter to generate a hardware verification result; Perform verification based on the diagnosis result in combination with the second abnormality mark parameter to generate a data verification result; Performing verification based on the diagnosis result in combination with the third abnormality flag parameter to generate a mechanical verification result; When the hardware verification result, the data verification result, and the mechanical verification result are all verified to be passed, determining the temperature and pressure integrated transmission measurement value according to the initial fusion measurement value; When any one of the hardware verification result, the data verification result, and the mechanical verification result fails the verification, performing attenuation calculation on the initial fusion measurement value to determine the temperature and pressure integrated transmission measurement value; When any two of the hardware verification results, the data verification results, and the mechanical verification results fail verification, performing attenuation calculation on the initial fusion measurement value and superimposing offset verification to determine the temperature and pressure integrated transmission measurement value; When the hardware verification result, the data verification result, and the mechanical verification result all fail the verification, the initial fused measurement value is treated as discarded data, and a reverse re-measurement instruction is executed to update the initial fused measurement value and re-iterate verification until the verification passes, thereby determining the temperature and pressure integrated transmission measurement value.

8. The temperature and pressure integrated transmission and measurement system based on nonlinear compensation is characterized by: A system for implementing the temperature and pressure integrated transmission and measurement method based on nonlinear compensation according to any one of claims 1 to 7, comprising: Analysis module: Synchronously collects the original temperature and pressure signals of the target environment for cross-impact analysis and constructs a temperature-pressure cross-impact matrix; Compensation module: performing nonlinear segmented compensation on the original temperature signal and the original pressure signal according to the temperature-pressure cross-influence matrix to generate a temperature compensation signal and a pressure compensation signal; Verification module: fuses the temperature compensation signal and the pressure compensation signal to generate an initial fusion measurement value for data change diagnosis, performs multi-level verification based on the diagnosis result, and determines the temperature and pressure integrated transmission measurement value.

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