Conductivity sensor pressure compensation test system and method

Abstract

The invention discloses a conductivity sensor pressure compensation test system and method, and the system comprises a pressure environment simulation module which is used for carrying out the dynamic pressure environment simulation of a conductivity sensor through a multi-dimensional pressure test cabin, a data collection module which is connected with the pressure environment simulation module, and a temperature calibration module which is connected with the data collection module. The system comprises a data acquisition module, a temperature calibration module connected with the data acquisition module, a compensation optimization module connected with the temperature calibration module, an error analysis module connected with the data acquisition module, and an execution control module connected with the compensation optimization module. Therefore, high-precision pressure compensation of the conductivity sensor under complex pressure conditions is realized, pressure compensation parameters can be adjusted in real time, the measurement precision and adaptability can be remarkably improved, the operation process is simplified, and the production and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of conductivity sensor testing, and in particular to a conductivity sensor pressure compensation testing system and method. Background Art

[0002] In complex environmental applications such as ocean exploration and industrial process control, the accuracy of conductivity sensor measurements is highly dependent on effective pressure compensation technology. Although existing pressure compensation systems and methods have made some progress in improving the performance of conductivity sensors, their inherent limitations significantly restrict their application in high-precision, multivariable dynamic environments. A specific analysis of current typical technical solutions reveals the following core flaws: Adaptability limitations and insufficient compensation accuracy (compared to CN119689366B solution): Key reliance on a single temperature control point: These solutions typically rely on calibrating and calculating the pressure compensation coefficient at a single (or very few) fixed temperature points. Their core drawback is the difficulty in accurately simulating the widely varying temperature distributions found in real-world applications, leading to widespread inaccuracy in the compensation model under temperature gradients or varying temperature conditions.

[0003] Complicated data fitting and poor real-time performance: The compensation process often requires complex, multiple fitting operations based on static test data. This not only increases data processing complexity but, more importantly, significantly slows compensation response. In real-time measurement scenarios with dynamic pressure / temperature changes (such as profile measurement and dynamic monitoring of industrial processes), this method's compensation lag makes it difficult to meet the requirements of high-precision, real-time output.

[0004] Limitations of environmental simulation: Although calibration is performed using standard equipment (such as seawater constant temperature baths and standard platinum resistance thermometers), its ability to simulate real complex environments (such as multi-physics field coupling and rapid disturbances) is limited, and the compensation model's extrapolation performance to actual working conditions is insufficient.

[0005] Model deviation and process complexity (compared to CN103048085B solution): Model center of gravity deviation: These precision compensation models are often highly optimized for the influence of temperature variables. Their temperature compensation frameworks (e.g., by calculating and soldering resistors to modify bridge characteristics) are often poorly designed for sensitive responses to pressure variables or lack efficient joint compensation algorithms. Consequently, even with excellent temperature compensation, their ability to independently compensate for complex pressure-temperature coupling effects remains weak.

[0006] Compensation networks introduce new errors: Hardware modifications such as additional solder joints and resistor networks introduced for compensation not only increase the complexity and cost of the production process, but more importantly, these non-ideal components themselves may become new sources of drift, noise, or failure points, and may even exhibit nonlinear responses under temperature or pressure changes, causing secondary disturbances in the sensor signal, offsetting or even worsening the effect of the original compensation target.

[0007] Challenges in suitability for batch processing: Although targeted at mass production, it relies heavily on individualized solder joint compensation, making it difficult to ensure calibration consistency and long-term stability, hindering the large-scale improvement of sensor reliability. Summary of the Invention

[0008] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0009] To this end, the purpose of the present invention is to propose a conductivity sensor pressure compensation test system and method, which realizes high-precision pressure compensation of the conductivity sensor under complex pressure conditions. It can not only adjust the pressure compensation parameters in real time, but also significantly improve the measurement accuracy and adaptability, simplify the operation process, and reduce production and maintenance costs.

[0010] To achieve the above-mentioned objectives, the present invention proposes a conductivity sensor pressure compensation test system, comprising: a pressure environment simulation module, used to perform dynamic pressure environment simulation on the conductivity sensor through a multi-dimensional pressure test chamber; a data acquisition module, connected to the pressure environment simulation module, used to collect the output signal of the conductivity sensor under different pressure and temperature conditions in real time; a temperature calibration module, connected to the data acquisition module, used to perform temperature correction on the collected pressure data to form corrected pressure characteristic data; a compensation optimization module, connected to the temperature calibration module, used to construct a multivariate compensation model based on the corrected pressure characteristic data, and optimize the compensation coefficient through an iterative learning algorithm; an error analysis module, connected to the data acquisition module, used to perform multivariate joint modeling on the collected signal, form a dynamic compensation parameter set, and generate an optimal compensation model based on an improved adaptive algorithm; an execution control module, connected to the compensation optimization module, used to apply the optimized compensation coefficient to the conductivity sensor, and detect whether the compensation effect meets the preset accuracy requirements. When the detection result does not meet the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

[0011] In addition, the conductivity sensor pressure compensation test system and method proposed in the application may also have the following additional technical features: Specifically, the pressure environment simulation module includes: a multi-field coupling cavity, which is used to construct a dynamic test environment with multi-physical field coupling, and simultaneously accommodate multiple conductivity sensors and test them; an environmental monitoring unit, which is used to monitor the temperature, humidity and medium flow status in the pressure test chamber in real time to ensure the stability of the test environment.

[0012] Specifically, the data acquisition module includes a high-sensitivity pressure sensor array, a signal conditioning unit and a data storage unit, wherein the high-sensitivity pressure sensor array is used to detect the output signal of the conductivity sensor; the signal conditioning unit filters and amplifies the received signal; and the data storage unit is used to record the pressure data stream collected each time.

[0013] Specifically, the temperature calibration module obtains ambient temperature information through a temperature sensor and corrects the pressure data in combination with a temperature compensation formula. The temperature compensation formula is: in, is the corrected pressure value, is the original collected pressure value, is the temperature drift coefficient, is the current ambient temperature, is the reference temperature.

[0014] Specifically, the compensation optimization module includes: A dynamic adjustment unit is used to construct a multivariate compensation model using a nonlinear regression algorithm, automatically adjust the weight parameters in the compensation model, and recalculate the compensation value when the number of outliers exceeds a preset threshold; A feedback control unit is used to feed back the optimized compensation value to the actual output end of the conductivity sensor to form a closed-loop control system; An alarm prompt unit is used to issue an alarm signal and send abnormal information to the monitoring terminal through the communication interface when the number of abnormal points continues to exceed the threshold and cannot be eliminated by adjusting the compensation parameters; The nonlinear regression algorithm is used to construct a multivariable compensation model. The formula of the nonlinear regression algorithm is: in, is the conductivity value after compensation, is the pressure value, is the temperature value, 、 、 and is the compensation coefficient to be optimized.

[0015] Specifically, the compensation optimization module further includes an optimization compensation unit, which optimizes the compensation coefficient by an iterative learning algorithm based on the least squares method. The iterative learning algorithm formula is: in, is the compensation coefficient vector, for The compensation coefficient vector at time , for The coefficient vector after the update at time, is the learning rate, is the loss function, is the gradient of the loss function, represents the gradient operator.

[0016] Specifically, the error analysis module includes: An error calculation unit, used to calculate the absolute error and relative error between the predicted value of the compensation model and the actual measured value; An error distribution graph generating unit is used to draw an error distribution graph with the error value as the vertical coordinate and the time as the horizontal coordinate; An outlier detection unit is used to count the number of outliers whose error values ​​exceed the threshold according to the error threshold line in the error distribution graph; The calculation formula of the error threshold line is: in, is the error-free threshold, is the total number of samples, For the The error value of the sample, is the standard deviation of the error, is the adjustment coefficient, and its value range is 1.5 to 3.

[0017] Specifically, the method comprises the following steps: S1. Use a multi-dimensional pressure test chamber to simulate the dynamic pressure environment of the conductivity sensor, and use a data acquisition module to collect the output signal of the conductivity sensor under different pressure and temperature conditions in real time; S2. Performing temperature correction on the collected pressure data to generate corrected pressure characteristic data, constructing a multivariable compensation model based on the corrected pressure characteristic data, and optimizing the compensation coefficient through an iterative learning algorithm; S3, performing multivariable joint modeling on the collected signals to form a dynamic compensation parameter set, and generating an optimal compensation model based on an improved adaptive algorithm; S4. Apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. When the detection result meets the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

[0018] In step S1, the multi-dimensional pressure test chamber simulates a continuously changing environment from low pressure to high pressure through an air pressure control system and a liquid medium circulation system, and synchronously records the output signal of the conductivity sensor through a high-precision data acquisition card.

[0019] In step S2, the input variables of the compensation model include pressure value, temperature value and sensor output signal, and the output variable is the compensated conductivity value; the model is constructed based on a nonlinear regression algorithm, and the compensation coefficient is optimized based on an iterative learning algorithm based on the least squares method.

[0020] Compared with the existing technology, the conductivity sensor pressure compensation test system and method of the present invention achieve high-precision pressure compensation of the conductivity sensor under complex pressure conditions. It can not only adjust the pressure compensation parameters in real time, but also significantly improve the measurement accuracy and adaptability, simplify the operation process, and reduce production and maintenance costs.

[0021] At the same time, the temperature calibration module can effectively eliminate the impact of temperature changes on pressure measurement, ensuring that the corrected pressure characteristic data is closer to the true value. In actual scenarios, the temperature calibration module can accurately correct pressure data in high temperature and high pressure environments, thereby improving the accuracy of subsequent compensation.

[0022] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a block diagram of the overall structure of the pressure compensation test system of the present invention; Figure 2 This is a structural block diagram of the pressure environment simulation module of the present invention; Figure 3 This is a functional block diagram of the data acquisition module of the present invention; Figure 4 This is a functional block diagram of the temperature calibration module of the present invention; Figure 5 This is a principle block diagram of the compensation optimization module of the present invention; Figure 6 This is a functional block diagram of the error analysis module of the present invention; Figure 7 This is a functional block diagram of the control module of the present invention. DETAILED DESCRIPTION

[0024] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention and are not to be construed as limiting the present invention. On the contrary, the embodiments of the present invention include all variations, modifications, and equivalents that fall within the spirit and scope of the appended embodiments.

[0025] The following describes a conductivity sensor pressure compensation testing system and method according to embodiments of the present invention with reference to the accompanying drawings.

[0026] The conductivity sensor pressure compensation test system and method of the embodiment of the present invention are described in detail in conjunction with the attached Figure 1 To the attached Figure 7 The overall structure diagram of the system is as follows: Figure 1 As shown in the figure, it includes a pressure environment simulation module, a data acquisition module, a temperature calibration module, a compensation optimization module, an error analysis module, and an execution control module. Through the cooperation of these modules, pressure compensation testing and optimization of the conductivity sensor are completed.

[0027] Specifically, the pressure environment simulation module is used to simulate the dynamic pressure environment of the conductivity sensor through a multi-dimensional pressure test chamber. The data acquisition module is connected to the pressure environment simulation module and is used to collect the output signal of the conductivity sensor under different pressure and temperature conditions in real time. The temperature calibration module is connected to the data acquisition module and is used to perform temperature correction on the collected pressure data to form corrected pressure characteristic data. The compensation optimization module is connected to the temperature calibration module and is used to construct a multivariate compensation model based on the corrected pressure characteristic data and optimize the compensation coefficient through an iterative learning algorithm. The error analysis module is connected to the data acquisition module and is used to perform multivariate joint modeling on the collected signals to form a dynamic compensation parameter set and generate an optimal compensation model based on an improved adaptive algorithm. The execution control module is connected to the compensation optimization module and is used to apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. When the detection result does not meet the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

[0028] It should be noted that the pressure environment simulation module described in this embodiment is the basic module of the entire system. Its core components include a multi-field coupling cavity and an environmental detection unit. The multi-field coupling cavity adopts a pressure chamber design. The pressure chamber adopts a multi-layer sealing structure. Multiple conductivity sensors can be placed in the pressure chamber. Multiple conductivity sensors can be connected at the same time and simulated in multiple environments from low pressure to high pressure. In addition, an air pressure control unit and a liquid medium circulation system are provided in the pressure chamber. The air pressure control device realizes dynamic changes in pressure by adjusting the pressure range of the compressed gas, while the liquid medium circulation system ensures the stability of the fluid state in the pressure chamber by transporting liquid medium through a pump. This design can meet the needs of various industrial scenarios, such as deep-sea exploration or high-pressure chemical reaction environments.

[0029] The data acquisition module is a core component of the entire system. Its primary function is to collect the conductivity sensor's output signals under different pressure conditions in real time and generate corresponding data streams. The module consists of a highly sensitive pressure sensor array and a signal conditioning circuit. The highly sensitive pressure sensor array detects the conductivity sensor's output signals, while the signal conditioning circuit filters and amplifies the received signals.

[0030] Specifically, the signal conditioning circuit uses a low-pass filter to remove high-frequency noise and uses an operational amplifier to adjust the signal gain to ensure the accuracy and stability of the collected pressure data. Furthermore, the data acquisition module is equipped with a data storage unit to record each collected pressure data. This data not only provides reliable support for subsequent analysis but also helps optimize the model construction process. In practical applications, such as chemical production processes, the pressure acquisition module can monitor the output signal of the conductivity sensor in the reactor in real time, providing basic data for subsequent compensation optimization.

[0031] It should be noted that data acquisition utilizes a data acquisition card with a sampling frequency of at least 1,000 times per second to ensure real-time data accuracy. Furthermore, an environmental monitoring unit monitors the temperature, humidity, and fluid flow within the pressure chamber in real time to ensure the stability of the test environment. For example, in one experiment, when the chamber temperature fluctuated by more than ±0.5°, the system automatically adjusted heating or cooling to maintain a constant temperature.

[0032] The temperature calibration module obtains ambient temperature information through the temperature sensor and corrects the pressure data in combination with the temperature compensation formula. The specific correction formula is: ,in, is the corrected pressure value, is the original collected pressure value, is the temperature drift coefficient, is the current ambient temperature, is the reference temperature. Temperature drift coefficient Typically obtained through experimental calibration, this value reflects the pressure sensor's sensitivity to temperature changes. For example, in one experiment, when the ambient temperature rose from 25°C to 40°C, if the original pressure value was 100 kPa, the corrected pressure value would be 100 × (1 + 0.002 × (40 - 25)) = 103 kPa. In this way, the temperature calibration module effectively eliminates the effects of temperature changes on pressure measurement, ensuring that the corrected pressure characteristic data is closer to the true value. In practical scenarios, such as oil production, the temperature calibration module can accurately correct pressure data in high-temperature and high-pressure environments, thereby improving the accuracy of subsequent compensation.

[0033] The core function of the compensation optimization module is to construct a multivariable nonlinear pressure compensation model based on the deviation between the collected pressure data and the standard reference value. This model includes a compensation model construction unit, a dynamic adjustment unit, a feedback control unit, and an alarm prompt unit. The compensation model construction unit includes a data preprocessing unit, a model training unit, and a model verification unit. The data preprocessing unit first filters and normalizes the collected pressure data to remove noise interference and improve data quality. The filtering algorithm uses a low-pass filter with a medium frequency set to 50Hz to effectively eliminate high-frequency noise. Normalization is achieved by mapping the data to the {0,1} interval. The formula is: ,in, is the original data value, and The model training unit builds a compensation model based on the multivariate regression algorithm combined with neural network technology. The input variables include pressure value , temperature value and sensor output signal , the output variable is the conductivity value after compensation The specific expression of the model is: ,in, It is a nonlinear function implemented using a three-layer feedforward neural network with 10 hidden layer nodes and ReLU activation function. The model verification unit evaluates the accuracy and generalization ability of the compensation model through the poor verification method.

[0034] Specifically, the data set is divided into 80% training and 20% validation sets, and the mean square error ( ) to evaluate the model performance. The formula is ,in, is the actual measured value, is the model prediction value, is the total number of samples.

[0035] The formula of the nonlinear regression algorithm for multivariable compensation model construction is: ,in, is the conductivity value after compensation, is the pressure value, is the temperature value, 、 、 and is the compensation coefficient to be optimized. This formula comprehensively considers the nonlinear relationship between pressure, temperature and conductivity, and can more accurately reflect the actual working conditions. In order to optimize the compensation coefficient, the compensation optimization module adopts an iterative learning algorithm based on the least squares method, and its parameter update formula is: ,in, is the compensation coefficient vector, for The compensation coefficient vector at time , for The coefficient vector after the update at time, is the learning rate, is the loss function, is the gradient of the loss function, represents the gradient operator. The loss function is usually defined as the sum of squared errors of the conductivity values ​​before and after compensation, i.e. By continuously iteratively updating the compensation coefficients, the algorithm can quickly converge to the optimal solution, significantly improving compensation accuracy and real-time performance. For example, in one experiment, the initial compensation coefficient vector was {1, 0.5, 0.1, 0}, which was optimized to {1.02, 0.53, 0.11, -0.01} after 10 iterations, significantly improving the compensation effect.

[0036] When the number of abnormal points exceeds the preset threshold, the dynamic adjustment unit automatically adjusts the weight parameters in the compensation model and recalculates the compensation value. The adjustment formula of the weight parameters is: ,in is the current weight value, is the adjusted weight value, is the learning rate, which is set to 0.01. is the error variation. The feedback control unit feeds the optimized compensation value back to the actual output value of the conductivity sensor, forming a closed-loop control system. When the number of abnormal points persists above a threshold and cannot be eliminated by adjusting the compensation parameters, the alarm prompt unit issues an alarm signal and transmits the abnormality information to the monitoring terminal via a communication interface. For example, in one experiment, when the number of abnormal points continuously exceeded 10%, the system automatically triggered an alarm and uploaded the abnormality information to the remote monitoring platform, facilitating timely problem detection.

[0037] The core task of the error analysis module is to set the error threshold and generate an error distribution graph based on the error distribution characteristics between the predicted value calculated by the compensation model and the actual measured value. This module includes an error calculation unit, an error distribution graph generation unit, and an outlier detection unit. The error calculation unit first calculates the absolute error and relative error between the predicted value and the actual value. The absolute error formula is: , the relative error formula is: The error distribution graph generation unit generates an error distribution graph with the error value as the vertical axis and the time as the horizontal axis. The abnormal point detection unit counts the number of abnormal points exceeding the threshold by setting the error threshold line. The calculation formula of the error threshold line is: ,in, is the error-free threshold, is the total number of samples, For the The error value of the sample, is the standard deviation of the error, is the adjustment coefficient, and its value range is 1.5 to 3. In a certain experiment, when =2, the error threshold line successfully distinguishes normal points from abnormal points, and the number of abnormal points accounts for 5% of the total number of samples, which is in line with expectations.

[0038] The function of the execution control module is to apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. The execution control module verifies the optimized compensation coefficient using the compensation effect monitoring device. The detection device calculates the error rate by comparing the conductivity values ​​before and after compensation and determines whether the preset accuracy requirements are met. The error rate calculation formula is: For example, in a certain experiment, the conductivity value before compensation was 10.0mS / cm, and the conductivity value after compensation was 9.98mS / cm. The error rate is If the error rate exceeds a preset threshold (e.g., 1%), the compensation optimization module is triggered to recalculate the compensation coefficient to ensure that the system can continue to provide high-precision measurement results in complex environments.

[0039] The specific implementation steps of the conductivity sensor pressure compensation test method provided by the present invention are as follows: S1. Use a multi-dimensional pressure test chamber to simulate the dynamic pressure environment of the conductivity sensor, and use a data acquisition module to collect the output signal of the conductivity sensor under different pressure and temperature conditions in real time; S2. Performing temperature correction on the collected pressure data to generate corrected pressure characteristic data, constructing a multivariable compensation model based on the corrected pressure characteristic data, and optimizing the compensation coefficient through an iterative learning algorithm; S3, performing multivariable joint modeling on the collected signals to form a dynamic compensation parameter set, and generating an optimal compensation model based on an improved adaptive algorithm; S4. Apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. When the detection result meets the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

[0040] In summary, the conductivity sensor pressure compensation test system and method of the embodiments of the present invention achieve high-precision pressure compensation of the conductivity sensor under complex pressure conditions. It can not only adjust the pressure compensation parameters in real time, but also significantly improve the measurement accuracy and adaptability, simplify the operation process, and reduce production and maintenance costs.

[0041] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and deform the above embodiments within the scope of the present invention.

Claims

1. A conductivity sensor pressure compensation test system, characterized in that: include: Pressure environment simulation module, which simulates the dynamic pressure environment of the conductivity sensor through a multi-dimensional pressure test chamber; A data acquisition module, connected to the pressure environment simulation module, for real-time acquisition of output signals of the conductivity sensor under different pressure and temperature conditions; A temperature calibration module, connected to the data acquisition module, for performing temperature correction on the collected pressure data to form corrected pressure characteristic data; a compensation optimization module, connected to the temperature calibration module, for constructing a multivariable compensation model based on the corrected pressure characteristic data and optimizing the compensation coefficients through an iterative learning algorithm; an error analysis module, connected to the data acquisition module, for performing multivariable joint modeling on the acquired signals, forming a dynamic compensation parameter set, and generating an optimal compensation model based on an improved adaptive algorithm; The execution control module is connected to the compensation optimization module and is used to apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. When the detection result does not meet the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

2. The conductivity sensor pressure compensation test system according to claim 1, characterized in that: The pressure environment simulation module includes: Multi-field coupling cavity, used to build a dynamic test environment for multi-physical field coupling, and to accommodate and test multiple conductivity sensors simultaneously; The environmental monitoring unit is used to monitor the temperature, humidity and medium flow status in the pressure test chamber in real time to ensure the stability of the test environment.

3. The conductivity sensor pressure compensation test system according to claim 1, characterized in that: The data acquisition module includes a high-sensitivity pressure sensor array, a signal conditioning unit and a data storage unit, wherein: The highly sensitive pressure sensor array is used to detect the output signal of the conductivity sensor; The signal conditioning unit performs filtering and amplification processing on the received signal; The data storage unit is used to record the pressure data stream collected each time.

4. The conductivity sensor pressure compensation test system according to claim 3, characterized in that: The temperature calibration module obtains ambient temperature information through a temperature sensor and corrects the pressure data in combination with a temperature compensation formula. The temperature compensation formula is: in, is the corrected pressure value, is the original collected pressure value, is the temperature drift coefficient, is the current ambient temperature, is the reference temperature.

5. The conductivity sensor pressure compensation test system according to claim 1, characterized in that: The compensation optimization module includes: A dynamic adjustment unit is used to construct a multivariate compensation model using a nonlinear regression algorithm, automatically adjust the weight parameters in the compensation model, and recalculate the compensation value when the number of outliers exceeds a preset threshold; A feedback control unit is used to feed back the optimized compensation value to the actual output end of the conductivity sensor to form a closed-loop control system; An alarm prompt unit is used to issue an alarm signal and send abnormal information to the monitoring terminal through the communication interface when the number of abnormal points continues to exceed the threshold and cannot be eliminated by adjusting the compensation parameters; The nonlinear regression algorithm is used to construct a multivariable compensation model. The formula of the nonlinear regression algorithm is: in, is the conductivity value after compensation, is the pressure value, is the temperature value, 、 、 and is the compensation coefficient to be optimized.

6. The conductivity sensor pressure compensation test system according to claim 5, characterized in that: The compensation optimization module further includes an optimization compensation unit, which optimizes the compensation coefficient by an iterative learning algorithm based on the least squares method. The iterative learning algorithm formula is: in, is the compensation coefficient vector, for The compensation coefficient vector at time , for The coefficient vector after the update at time, is the learning rate, is the loss function, is the gradient of the loss function, represents the gradient operator.

7. The conductivity sensor pressure compensation test system according to claim 1, characterized in that: The error analysis module includes: An error calculation unit, used to calculate the absolute error and relative error between the predicted value of the compensation model and the actual measured value; An error distribution graph generating unit is used to draw an error distribution graph with the error value as the vertical coordinate and the time as the horizontal coordinate; An outlier detection unit is used to count the number of outliers whose error values ​​exceed the threshold according to the error threshold line in the error distribution graph; The calculation formula of the error threshold line is: in, is the error-free threshold, is the total number of samples, For the The error value of the sample, is the standard deviation of the error, is the adjustment coefficient, and its value range is 1.5 to 3.

8. The conductivity sensor pressure compensation test method according to claim 1, characterized in that: The method comprises the following steps: S1. Use a multi-dimensional pressure test chamber to simulate the dynamic pressure environment of the conductivity sensor, and use a data acquisition module to collect the output signal of the conductivity sensor under different pressure and temperature conditions in real time; S2. Performing temperature correction on the collected pressure data to generate corrected pressure characteristic data, constructing a multivariable compensation model based on the corrected pressure characteristic data, and optimizing the compensation coefficient through an iterative learning algorithm; S3, performing multivariable joint modeling on the collected signals to form a dynamic compensation parameter set, and generating an optimal compensation model based on an improved adaptive algorithm; S4. Apply the optimized compensation coefficient to the conductivity sensor and detect whether the compensation effect meets the preset accuracy requirements. When the detection result meets the requirements, the compensation optimization module is triggered to recalculate the compensation coefficient.

9. The conductivity sensor pressure compensation test method according to claim 8, characterized in that: In step S1, the multi-dimensional pressure test chamber simulates a continuously changing environment from low pressure to high pressure through an air pressure control system and a liquid medium circulation system, and synchronously records the output signal of the conductivity sensor through a high-precision data acquisition card.

10. The conductivity sensor pressure compensation test method according to claim 8, characterized in that: In step S2, the input variables of the compensation model include pressure value, temperature value and sensor output signal, and the output variable is the conductivity value after compensation; The model is constructed based on a nonlinear regression algorithm, and the compensation coefficient is optimized based on an iterative learning algorithm using the least squares method.

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