Pressure sensor data precision compensation method and system for industrial control

Abstract

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a pressure sensor data accuracy compensation method and system for industrial control. Background Art

[0002] In the process of industrial control, pressure sensors play a vital role. The accuracy of their measurement data directly affects the stability of industrial production, product quality and the reliability of the entire system. With the continuous improvement of industrial automation, the requirements for the accuracy of pressure sensor data are becoming increasingly stringent.

[0003] The traditional data processing method of pressure sensors is relatively simple, usually relying only on the fixed calibration parameters set by the sensor itself when it leaves the factory to correct the measurement data. This method does not fully consider the impact of complex and changeable factors in the actual industrial environment on the measurement accuracy of pressure sensors. In actual industrial scenarios, the environment in which the pressure sensor is located is often very complex. Factors such as temperature fluctuations, humidity changes, and mechanical vibrations may cause deviations in the pressure signal output by the sensor, which in turn causes errors in industrial control based on these inaccurate signals. In severe cases, it may even cause production accidents or reduce product quality. Summary of the invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a pressure sensor data accuracy compensation method for industrial control, the method comprising: Acquire an original pressure signal sequence output by a target pressure sensor within a preset acquisition period, wherein the original pressure signal sequence includes a plurality of pressure signals arranged by timestamps; Detecting a set of real-time environmental parameters of the target pressure sensor within the acquisition period, wherein the set of real-time environmental parameters includes a temperature parameter, a humidity parameter, and a mechanical vibration parameter; Inputting the real-time environmental parameter set into a pre-trained compensation parameter generation model to generate a dynamic compensation parameter set corresponding to the original pressure signal sequence; According to the dynamic compensation parameter set, each pressure signal in the original pressure signal sequence is adjusted step by step to obtain a compensated pressure signal sequence; The compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an actuator of the industrial control system.

[0005] On the other hand, an embodiment of the present invention also provides a pressure sensor data accuracy compensation system for industrial control, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0006] Based on the above aspects, after obtaining the original pressure signal sequence output by the target pressure sensor within the preset acquisition period and the real-time environmental parameter set (covering temperature parameters, humidity parameters and mechanical vibration parameters), the embodiment of the present application introduces a pre-trained compensation parameter generation model, which can generate a dynamic compensation parameter set corresponding to the original pressure signal sequence. Secondly, the generated dynamic compensation parameter set is used to adjust each pressure signal in the original pressure signal sequence step by step, and the deviation characteristics of each pressure signal under different environmental parameters are deeply explored, which can effectively eliminate the pressure signal error caused by various environmental factors such as temperature, humidity and mechanical vibration, so that the compensated pressure signal sequence has extremely high accuracy, which greatly improves the reliability and effectiveness of pressure sensor data in industrial control. Finally, the compensated high-precision pressure signal sequence is output to the signal processing module of the industrial control system to drive the actuator, which can significantly improve the control accuracy and stability of the industrial control system, make the action of the actuator more accurate and efficient, avoid the problems of misoperation and increased defective rate in the industrial production process caused by inaccurate pressure signals, and thus improve the quality and efficiency of the entire industrial production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the execution flow of a pressure sensor data accuracy compensation method for industrial control provided by an embodiment of the present invention.

[0008] Figure 2 It is a schematic diagram of the hardware architecture of a pressure sensor data accuracy compensation system for industrial control provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 1 is a flow chart of a pressure sensor data accuracy compensation method for industrial control provided by an embodiment of the present invention. The pressure sensor data accuracy compensation method for industrial control is introduced in detail below.

[0010] Step S110, obtaining an original pressure signal sequence output by the target pressure sensor within a preset acquisition period, wherein the original pressure signal sequence includes a plurality of pressure signals arranged by timestamps.

[0011] In this embodiment, in the industrial control scenario, taking the reactor in the chemical production workshop as an example, in order to ensure the safety and stability of the reaction process, it is necessary to monitor the pressure in the reactor at all times. The target pressure sensor can be installed in the key part of the reactor. The preset acquisition cycle can be set to collect data every 10 seconds, so that within a longer period of time, such as 10 minutes, a number of original pressure signals arranged by timestamp can be obtained, and the original pressure signal reflects the change of pressure in the reactor over time. For example, at the beginning of the reaction, due to the addition of reactants and the initial reaction, the pressure may gradually rise. The pressure sensor will record the pressure values ​​at different time points in this rising process to form an original pressure signal sequence, which contains the real-time status information of the pressure in the reactor.

[0012] Step S120 , detecting a set of real-time environmental parameters of the target pressure sensor within the acquisition period, wherein the set of real-time environmental parameters includes temperature parameters, humidity parameters, and mechanical vibration parameters.

[0013] The inventors have found through research that, for a target pressure sensor installed on a reactor, the environmental parameters surrounding the target pressure sensor have a significant impact on the accuracy of the pressure signal.

[0014] First, the chemical reaction process is often accompanied by the generation or absorption of heat, which causes the temperature around the reactor to change. The temperature distribution data in the space where the target pressure sensor is located is collected through a temperature sensor array. Assuming that one side of the reactor is close to the heating device and the temperature is higher, while the other side is relatively low, the temperature sensor array can detect this temperature difference. These temperature distribution data are then processed by spatial weighted averaging to obtain a comprehensive temperature parameter. For example, the sensor data close to the heating device is multiplied by a larger weight, because the temperature here may have a greater impact on the pressure sensor, while the sensor data far from the heating device is multiplied by a smaller weight, and finally a parameter that can represent the overall temperature condition around the pressure sensor is obtained.

[0015] In addition, the humidity in the chemical workshop may change due to the water vapor generated in the chemical reaction or the ventilation conditions of the workshop. The humidity distribution data in the space where the target pressure sensor is located is collected using a humidity sensor array. There may be some water vapor leakage or condensation near the reactor, which will cause high local humidity. After collecting the humidity distribution data, time series filtering is performed to remove some abnormal data caused by short-term sensor fluctuations or noise interference, and obtain accurate humidity parameters.

[0016] In addition, the stirring device in the reactor will generate mechanical vibrations during operation, which may be transmitted to the pressure sensor and affect the accuracy of the pressure signal. The mechanical vibration waveform data of the target pressure sensor installation position is collected by the vibration sensor. For example, uneven rotation speed of the stirring device or slight wear of mechanical parts may cause irregular changes in the vibration waveform. The collected mechanical vibration waveform data is subjected to frequency domain energy analysis, and the energy peak in the preset frequency band is extracted as the mechanical vibration parameter. The energy peak can reflect the key characteristics of the impact of mechanical vibration on the pressure sensor. In addition, the data acquisition timestamps of temperature parameters, humidity parameters and mechanical vibration parameters are synchronized and aligned with the timestamps of the original pressure signal sequence, which can ensure the subsequent accurate compensation of the pressure signal.

[0017] Step S130: input the real-time environmental parameter set into a pre-trained compensation parameter generation model to generate a dynamic compensation parameter set corresponding to the original pressure signal sequence.

[0018] In the control system of the chemical production workshop, the pre-trained compensation parameter generation model has been trained based on a large amount of experimental data and actual operation data. After obtaining the real-time environmental parameter set, the temperature parameters, humidity parameters and mechanical vibration parameters must first be timestamp aligned and verified. For example, due to some minor delays or failures in the data acquisition system, the timestamps of individual environmental parameter data may deviate. If the deviation exceeds the preset synchronization threshold, such as 0.1 seconds, these data need to be eliminated to generate a synchronized and calibrated temperature parameter sequence, humidity parameter sequence and mechanical vibration parameter sequence.

[0019] Then, the synchronously calibrated temperature parameter sequence is input into the temperature feature extraction channel of the compensation parameter generation model, which consists of three layers of convolutional networks. The first layer of convolutional networks can capture the low-frequency trend components of temperature parameters, such as the slow temperature change trend of the reactor during long-term operation due to the effect of the overall temperature control system of the workshop. The second layer of convolutional networks can extract the medium-frequency disturbance components of temperature fluctuations, such as temperature fluctuations caused by periodic heat release or absorption during the reaction process. The third layer of convolutional networks can identify transient impact components of temperature mutations, such as temperature mutations when the heating device of the reactor suddenly fails or performs emergency cooling operations. Through the processing of these three layers of convolutional networks, the temperature influence feature vector is finally extracted.

[0020] Next, the synchronously calibrated humidity parameter sequence is input into the humidity feature extraction channel of the compensation parameter generation model, which uses a bidirectional recurrent network to generate a humidity impact feature vector. During the forward propagation process, the cumulative penetration effect of humidity parameters on sensor materials can be captured. For example, over time, the high humidity environment in a chemical workshop may gradually penetrate into the interior of the pressure sensor and affect its performance. During the reverse propagation process, the hysteresis compensation requirements caused by sudden changes in humidity can be identified. For example, when the workshop is suddenly ventilated and the humidity drops sharply, the response of the pressure sensor may have a certain lag. The bidirectional recurrent network can detect this hysteresis and generate the corresponding humidity impact feature vector.

[0021] The synchronously calibrated mechanical vibration parameter sequence is then input into the vibration feature extraction channel of the compensation parameter generation model. The time-frequency hybrid network of the vibration feature extraction channel will decompose the vibration influence feature vector. The time domain analysis unit extracts the envelope characteristics of the vibration amplitude, such as the change trend of the vibration amplitude of the stirring device in different time periods. This change may have different degrees of impact on the signal of the pressure sensor. The frequency domain analysis unit separates the distribution ratio of vibration energy at different resonant frequencies. For example, certain specific speeds of the stirring device will excite vibrations of specific frequencies. The distribution of vibration energy at these frequencies is crucial for analyzing the degree of interference of the pressure sensor.

[0022] After that, the temperature effect eigenvector, humidity effect eigenvector and vibration effect eigenvector are fused, and the coupling strength matrix of the three is calculated through the feature cross layer of the compensation parameter generation model. The temperature-humidity coupling strength is represented by the dot product value of the two-way eigenvector. For example, if the changing trends of temperature and humidity are correlated, the dot product value will reflect the comprehensive impact of this correlation on the pressure sensor. The humidity-vibration coupling strength is calculated by the covariance within the sliding window, which can reflect the synergistic effect between humidity change and vibration change on the pressure sensor. The temperature-vibration coupling strength is evaluated by the maximum mutual information entropy, which can reflect the complex relationship between temperature change and vibration change.

[0023] The compensation weight coefficients are dynamically allocated according to the coupling strength matrix, and the real-time proportional combination of temperature compensation weight, humidity compensation weight and vibration compensation weight is generated by the weight allocator of the compensation parameter generation model. The temperature compensation weight is positively correlated with the temperature-humidity coupling strength, that is, the stronger the coupling between temperature and humidity, the greater the temperature compensation weight. The humidity compensation weight is exponentially related to the humidity-vibration coupling strength, which means that a small change in the humidity-vibration coupling strength may lead to a large change in the humidity compensation weight. The vibration compensation weight and the temperature-vibration coupling strength form a dynamic feedback adjustment, and the vibration compensation weight is dynamically adjusted according to the relationship between the two.

[0024] Finally, the temperature compensation weight, humidity compensation weight and vibration compensation weight are input into the multi-layer compensation parameter generator of the compensation parameter generation model. The basic offset of the temperature compensation coefficient is generated by the first compensation layer, and the basic offset can correct the baseline offset of the pressure sensor caused by temperature change. The second compensation layer superimposes the sensitivity adjustment of the humidity compensation coefficient, because humidity may affect the sensitivity of the pressure sensor to pressure changes. The third compensation layer injects the phase correction of the vibration compensation coefficient to compensate for the phase change of the pressure signal caused by mechanical vibration. The final output includes a dynamic compensation parameter set containing temperature compensation coefficient, humidity compensation coefficient and vibration compensation coefficient. At the same time, the physical feasibility boundary of the dynamic compensation parameter set must be verified, and the parameter constraint module of the compensation parameter generation model must be used to detect whether each compensation coefficient exceeds the safety threshold of the sensor range. If a compensation coefficient exceeds the safety threshold, for example, the temperature compensation coefficient is too large, it may cause the compensated pressure signal to exceed the reasonable range. It is necessary to truncate the compensation coefficient that exceeds the safety threshold and generate a compensation parameter correction flag, and match and align the corrected dynamic compensation parameter set with the timestamp of the original pressure signal sequence to ensure the temporal correspondence between the compensation parameter and the pressure signal.

[0025] Step S140: adjusting each pressure signal in the original pressure signal sequence step by step according to the dynamic compensation parameter set to obtain a compensated pressure signal sequence.

[0026] In a chemical production workshop, the original pressure signal sequence obtained from the reactor pressure sensor must first be processed by time domain filtering to obtain a preprocessed pressure signal sequence. For example, due to the presence of various electrical equipment and complex electromagnetic environment in the workshop, the original pressure signal may be interfered by some high-frequency noise. Through time domain filtering, these high-frequency noises can be removed, making the pressure signal smoother and more stable.

[0027] Then, according to the temperature compensation coefficient in the dynamic compensation parameter set, the baseline offset correction is performed on the preprocessed pressure signal sequence. Assume that due to the increase in the temperature around the reactor, the baseline of the pressure sensor has shifted upward, making the pressure signal overall higher. According to the temperature compensation coefficient, the offset that needs to be adjusted can be calculated to restore the baseline of the pressure signal to a normal level.

[0028] Next, the sensitivity of the pressure signal sequence after baseline offset correction is scaled according to the humidity compensation coefficient in the dynamic compensation parameter set. For example, in a high humidity environment, the sensitivity of the pressure sensor may decrease, resulting in the measured pressure value being smaller than the actual value. Through the humidity compensation coefficient, the pressure signal can be amplified or reduced accordingly to restore its correct sensitivity.

[0029] Then, according to the vibration compensation coefficient in the dynamic compensation parameter set, the phase synchronization calibration of the pressure signal sequence after sensitivity scaling adjustment is performed. The vibration frequency component corresponding to the mechanical vibration parameter is extracted, and a reference sinusoidal waveform with the same period as the vibration frequency component is generated. For example, if the vibration frequency of the stirring device is 50Hz, a 50Hz reference sinusoidal waveform is generated. The local extreme point detection is performed on the pressure signal sequence after sensitivity scaling adjustment to determine the phase offset of the signal waveform. It is assumed that due to the interference of vibration, the phase of the pressure signal has advanced or lagged. According to the phase offset and the vibration compensation coefficient in the dynamic compensation parameter set, the reference sinusoidal waveform is phase compensated to generate an anti-vibration interference template. Finally, the anti-vibration interference template is convolved with the pressure signal sequence after sensitivity scaling adjustment to suppress the signal distortion component caused by vibration and obtain the compensated pressure signal sequence.

[0030] Step S150: output the compensated pressure signal sequence to a signal processing module of the industrial control system to drive an actuator of the industrial control system.

[0031] In the industrial control system of the chemical production workshop, the compensated pressure signal sequence is output to the signal processing module, which will further analyze and process the pressure signal, such as comparing it with the set pressure threshold to determine whether the pressure in the reactor is within the safe range. If the pressure is too high or too low, the signal processing module will generate corresponding control instructions according to the preset control strategy to drive the actuator to operate. The actuator may be a motor that adjusts the opening of the reactor inlet and outlet valves, or a controller that controls the power of the heating or cooling device. For example, when the compensated pressure signal shows that the pressure in the reactor is too high, the signal processing module will send instructions to the motor that controls the valve opening to increase the valve opening, thereby reducing the pressure in the reactor and ensuring that the chemical reaction is carried out under safe and stable pressure conditions. The entire process ensures the normal operation of the chemical production workshop and the stability of product quality through precise pressure monitoring and control.

[0032] For another example, taking the EGR blower throttling system of a certain ship manufacturing as an example, the target pressure sensor is installed in the key part of the throttling system to monitor the internal pressure of the system. In the above steps, the preset acquisition cycle can be set to collect data every 5 seconds, thereby forming an original pressure signal sequence. For example, when the marine engine is started, the EGR blower throttling system starts to work. With the change of air flow, the change of the operating state of internal components and other factors, the pressure will continue to change. The pressure values ​​at different time points are recorded by the pressure sensor to form an original pressure signal sequence.

[0033] In the above environment, environmental parameters have a certain impact on the pressure sensor. For example, in terms of temperature, since there are many devices running in the ship's engine room, such as the main engine, generator equipment, etc., they will emit heat, resulting in an uneven distribution of the surrounding temperature. The temperature near these heat-generating devices is higher, which may affect the accuracy of the pressure sensor. Moreover, when the ship switches to different speeds during navigation, the temperature will also change significantly. For example, when the main engine runs from high speed to low speed, the rapid change in temperature will interfere with the normal operation of the pressure sensor. Similarly, humidity can be affected by many factors. For example, if the air conditioning system in the cabin fails or in humid seasons, the humidity may increase significantly. For pressure sensors, high humidity may cause the electronic components inside the sensor to get damp, which in turn affects the accuracy of the pressure signal. For example, in the rainy season, the humidity in the cabin remains at a high level for a long time, and the pressure signal collected by the pressure sensor may be deviated due to the influence of humidity.

[0034] As for mechanical vibration factors, the rotation of the impeller and the operation of the motor in the EGR blower throttling system will generate mechanical vibrations, and the vibrations of other large equipment in the cabin may also be transmitted through the ground or air. For example, the strong vibrations generated by the host nearby during operation will be transmitted to the pressure sensor, and these vibrations may cause the pressure signal collected by the pressure sensor to fluctuate or distort.

[0035] In such a scenario, the compensation parameter generation model integrates environmental parameters such as temperature, humidity and mechanical vibration to generate a set of dynamic compensation parameters to compensate the original pressure signal sequence. The compensated pressure signal sequence is output to the signal processing module of the industrial control system, which can determine whether the system pressure is normal based on the compensated pressure signal. If the pressure is too high, for example, because a valve in the EGR blower throttling system is blocked and the pressure rises, the signal processing module will issue a command to the actuator (such as a motor that adjusts the valve opening) to increase the valve opening, thereby reducing the system pressure and ensuring that the EGR blower throttling system operates stably at an appropriate pressure, thereby ensuring the normal operation of the EGR blower throttling system and avoiding equipment failure or reduced production efficiency due to pressure problems.

[0036] Based on the above steps, after obtaining the original pressure signal sequence output by the target pressure sensor within the preset acquisition period and the real-time environmental parameter set (covering temperature parameters, humidity parameters and mechanical vibration parameters), the embodiment of the present application introduces a pre-trained compensation parameter generation model, which can generate a dynamic compensation parameter set corresponding to the original pressure signal sequence. Secondly, the generated dynamic compensation parameter set is used to adjust each pressure signal in the original pressure signal sequence step by step, and the deviation characteristics of each pressure signal under different environmental parameters are deeply explored, which can effectively eliminate the pressure signal error caused by various environmental factors such as temperature, humidity and mechanical vibration, so that the compensated pressure signal sequence has extremely high accuracy, which greatly improves the reliability and effectiveness of pressure sensor data in industrial control. Finally, the compensated high-precision pressure signal sequence is output to the signal processing module of the industrial control system to drive the actuator, which can significantly improve the control accuracy and stability of the industrial control system, make the action of the actuator more accurate and efficient, avoid the problems of misoperation and increased defective rate in the industrial production process caused by inaccurate pressure signals, and thus improve the quality and efficiency of the entire industrial production process.

[0037] In a possible implementation, step S120 includes: Step S121 , collecting temperature distribution data in the space where the target pressure sensor is located through a temperature sensor array, and performing spatial weighted average processing on the temperature distribution data to obtain the temperature parameter.

[0038] In this embodiment, the temperature environment around the reactor has an important influence on the measurement accuracy of the pressure sensor. The temperature distribution data in the space where the target pressure sensor is located is collected by arranging a temperature sensor array. Since the temperature of different parts of the reactor may be different, for example, the temperature of the area close to the heating jacket of the reactor is higher, while the temperature of the area far away from the heating source and close to the workshop vent is relatively low. The sensors in the temperature sensor array are distributed at different positions around the target pressure sensor to fully obtain temperature information. The above sensors transmit the temperature data detected by each sensor back to form temperature distribution data. Then these temperature distribution data are processed by spatial weighted average to obtain temperature parameters. In this process, the weight is determined according to the relationship between the position of each sensor and the target pressure sensor and the degree of influence that the temperature at the position may have on the pressure sensor. For example, the sensor data of the area close to the target pressure sensor and where the temperature change has a greater impact on the pressure sensor is given a larger weight, while the sensor data of the area far away and where the impact is relatively small is given a smaller weight. The temperature parameter obtained after weighted average calculation can accurately reflect the overall temperature condition around the target pressure sensor. The temperature parameter is closely related to the working state of the pressure sensor and is an important basis for subsequent accurate compensation of the pressure signal.

[0039] Step S122, collecting humidity distribution data in the space where the target pressure sensor is located through a humidity sensor array, and performing time series filtering processing on the humidity distribution data to obtain the humidity parameter.

[0040] The humidity situation in the chemical workshop is relatively complex, and changes in humidity can affect the performance of the pressure sensor. The humidity distribution data in the space where the target pressure sensor is located is collected using a humidity sensor array. The humidity around the reactor may be different due to the dissipation of water vapor during the chemical reaction or the ventilation conditions of the workshop. For example, near the material feed port of the reactor, the humidity may be relatively high due to the moisture carried by the material or the water vapor generated in the early stage of the reaction; while the humidity is relatively low in areas with good air circulation in the workshop. The humidity distribution data detected by the humidity sensor array may contain some noise data caused by short-term fluctuations of the sensor itself or minor interference from the local environment. In order to obtain accurate humidity parameters, the humidity distribution data needs to be filtered for time series. This filtering process can remove abnormal data that does not conform to the humidity change trend, thereby obtaining a humidity parameter that can truly reflect the humidity condition around the target pressure sensor, which is also indispensable for accurately compensating the pressure signal of the pressure sensor.

[0041] Step S123, collecting mechanical vibration waveform data of the target pressure sensor installation position through a vibration sensor, and performing frequency domain energy analysis on the mechanical vibration waveform data, extracting the energy peak in a preset frequency band as the mechanical vibration parameter. The data collection timestamps of the temperature parameter, humidity parameter and mechanical vibration parameter are synchronized with the timestamp of the original pressure signal sequence.

[0042] The stirring device in the reactor will generate mechanical vibration during operation, which will be transmitted to the target pressure sensor installed on the reactor, thereby affecting the accuracy of the pressure signal. The mechanical vibration waveform data of the target pressure sensor installation position is collected by the vibration sensor. Due to the influence of factors such as the mechanical structure, rotation speed and load of the stirring device, the mechanical vibration waveform data has complex characteristics. For example, the wear and imbalance of the stirring blades or the uneven distribution of materials in the reactor may cause changes in the vibration waveform. The collected mechanical vibration waveform data is subjected to frequency domain energy analysis. In this process, the vibration waveform is converted from the time domain to the frequency domain to analyze the energy distribution under different frequency components. By extracting the energy peak in the preset frequency band as the mechanical vibration parameter, the frequency band where the energy peak is located may be a frequency band related to the natural frequency of the stirring device or the key vibration frequency during its operation. Extracting this energy peak can highlight the key features of the impact of mechanical vibration on the pressure sensor, thereby providing an important basis for accurately considering the impact of mechanical vibration in the subsequent compensation parameter generation model. In addition, during the entire acquisition process, the data acquisition timestamps of the temperature parameters, humidity parameters and mechanical vibration parameters are synchronized and aligned with the timestamps of the original pressure signal sequence. This is a very critical point, because only by aligning the timestamps synchronously can we ensure that in the subsequent compensation process, the environmental parameters are accurately associated with the corresponding pressure signals, thereby achieving accurate compensation for the pressure signal based on environmental influences, ensuring the accuracy and reliability of pressure monitoring in the chemical reactor, and thus ensuring the stability and safety of the entire chemical production process.

[0043] In a possible implementation, step S130 includes: Step S131, performing timestamp alignment verification on the temperature parameters, humidity parameters and mechanical vibration parameters in the real-time environmental parameter set, eliminating environmental parameter data whose timestamp deviation exceeds a preset synchronization threshold, and generating a temperature parameter sequence, humidity parameter sequence and mechanical vibration parameter sequence after synchronization calibration.

[0044] In the actual scenario of chemical production, due to the complexity of the data acquisition system, there may be deviations in the timestamps of data collected by different sensors. For example, when the temperature sensor array, humidity sensor array, and vibration sensor are collecting data, the timestamps of the collected temperature parameters, humidity parameters, and mechanical vibration parameters may not be completely consistent due to their respective collection frequencies, transmission delays, or slight differences in the internal clocks. The preset synchronization threshold is set according to the system's requirements for data synchronization, for example, it is set to 0.05 seconds. When it is found that the timestamp deviation of a certain environmental parameter data exceeds this preset synchronization threshold, it needs to be eliminated. Through such processing, a synchronously calibrated temperature parameter sequence, humidity parameter sequence, and mechanical vibration parameter sequence can be generated to ensure that the data processed subsequently has strict consistency in time.

[0045] Step S132, input the temperature parameter sequence after synchronous calibration into the temperature feature extraction channel of the compensation parameter generation model, and extract the temperature influence feature vector through the three-layer convolution network of the temperature feature extraction channel, wherein the first layer of convolution network captures the low-frequency trend component of the temperature parameter, the second layer of convolution network extracts the medium-frequency disturbance component of the temperature fluctuation, and the third layer of convolution network identifies the transient impact component of the temperature mutation.

[0046] In this embodiment, the temperature feature extraction channel includes three layers of convolutional networks, each of which has its specific function. The first layer of convolutional network is used to capture the low-frequency trend component of the temperature parameter. During the operation of the chemical reactor, the temperature around the reactor will be affected by the overall temperature control system of the workshop, resulting in a slow change trend. For example, in order to maintain the overall temperature stability, the cooling system of the workshop will continue to refrigerate the workshop environment, which makes the temperature around the reactor show a slow downward trend over a long period of time. This low-frequency trend component reflects the basic change law of temperature over a long period of time, which is crucial for the subsequent accurate evaluation of the impact of temperature on the pressure sensor. The role of the second layer of convolutional network is to extract the intermediate frequency disturbance component of temperature fluctuations. During the reaction process, the chemical reaction in the reactor will release or absorb heat in stages, resulting in fluctuations in the temperature around the reactor. For example, in the exothermic stage of a chemical reaction, the temperature around the reactor will rise rapidly, and then gradually decrease when the reaction tends to stabilize. This temperature fluctuation is the intermediate frequency disturbance component. The second layer of convolutional network can accurately capture this fluctuation and provide a basis for analyzing the dynamic impact of temperature on the pressure sensor. The third convolutional network is responsible for identifying the transient impact component of temperature mutation. In chemical production, some emergencies may occur, such as failure of the heating device or emergency cooling operation. For example, the heating element of the heating device suddenly short-circuits, causing the heating power to drop instantly, and the temperature around the reactor will change suddenly. The third convolutional network can accurately identify the transient impact component of this temperature mutation, comprehensively obtain the various impact characteristics of temperature parameters on the pressure sensor, and finally extract the temperature impact feature vector.

[0047] Step S133, input the synchronously calibrated humidity parameter sequence into the humidity feature extraction channel of the compensation parameter generation model, and generate a humidity influence feature vector through the bidirectional recurrent network of the humidity feature extraction channel, wherein the forward propagation process captures the cumulative penetration effect of the humidity parameters on the sensor material, and the reverse propagation process identifies the hysteresis compensation requirements caused by humidity mutations.

[0048] In a chemical workshop, in an environment with high humidity, water vapor will gradually penetrate into the internal materials of the pressure sensor over time. For example, there may be tiny gaps in the outer shell of the sensor, and water vapor will slowly enter the sensor through these gaps, affecting the electrical or mechanical properties of the sensor. The forward propagation bidirectional recurrent network can accurately capture this cumulative penetration effect based on the changing trend of the humidity parameter. In the reverse propagation process, the bidirectional recurrent network can identify the hysteresis compensation requirements caused by the sudden change of humidity. For example, when the workshop is suddenly ventilated, the humidity in the workshop will drop sharply. However, due to its own thermal inertia or the moisture absorption-desorption characteristics of the material, the pressure sensor may have a certain lag in its response to humidity changes. The reverse propagation process can detect this hysteresis phenomenon and generate the corresponding humidity impact feature vector, thereby providing accurate humidity-related information for subsequent compensation processing.

[0049] Step S134, input the synchronously calibrated mechanical vibration parameter sequence into the vibration feature extraction channel of the compensation parameter generation model, and decompose the vibration influence feature vector through the time-frequency hybrid network of the vibration feature extraction channel, wherein the time domain analysis unit extracts the envelope characteristics of the vibration amplitude, and the frequency domain analysis unit separates the distribution ratio of the vibration energy at different resonant frequency points.

[0050] In a chemical reactor, the operating state of the stirring device will directly affect the mechanical vibration. For example, the stirring blades may be worn or unbalanced during long-term operation, which will cause the vibration amplitude to change. The time domain analysis unit can extract the change trend of the vibration amplitude in different time periods to form the envelope characteristics of the vibration amplitude. The envelope characteristics reflect the overall change law of mechanical vibration in the time domain, which is of great significance for analyzing the impact of vibration on the pressure sensor. The frequency domain analysis unit separates the distribution ratio of vibration energy at different resonant frequencies. During the operation of the stirring device, due to its own mechanical structure and working principle, a large vibration energy will be generated at a specific resonant frequency. For example, when the speed of the stirring device reaches a certain value, a certain resonant frequency of the reactor structure will be excited, resulting in a significant increase in the vibration energy at this frequency. The frequency domain analysis unit can accurately separate the distribution ratio of vibration energy at different resonant frequencies, thereby comprehensively obtaining the impact characteristics of mechanical vibration on the pressure sensor.

[0051] Step S135, fusing the temperature influence eigenvector, humidity influence eigenvector and vibration influence eigenvector, and calculating the coupling strength matrix of the three through the characteristic cross layer of the compensation parameter generation model, wherein the temperature-humidity coupling strength is represented by the dot product value of the two-way eigenvector, the humidity-vibration coupling strength is calculated by the covariance within the sliding window, and the temperature-vibration coupling strength is evaluated by the maximum mutual information entropy.

[0052] For example, the strength of the temperature-humidity coupling is characterized by the dot product value of the two-way eigenvector. In the environment of a chemical workshop, there may be a complex relationship between temperature and humidity. For example, when the temperature is high, the evaporation rate of water vapor is accelerated, which may cause the humidity in the workshop to decrease; conversely, when the temperature is low, water vapor may condense more easily, resulting in an increase in humidity. The impact of this correlation between temperature and humidity on the pressure sensor is not a simple superposition, but the dot product value accurately reflects the combined impact of the coupling of the two on the pressure sensor. The strength of the humidity-vibration coupling is calculated by the covariance within the sliding window. In actual situations, changes in humidity may affect the friction coefficient between mechanical parts, thereby affecting the vibration characteristics of the stirring device. For example, in a high humidity environment, mechanical parts may rust or the surface may become more lubricated, which will change the vibration of the stirring device. By calculating the covariance within the sliding window, the strength of the humidity-vibration coupling can be accurately measured, reflecting the degree of influence of this coupling on the pressure sensor. The strength of the temperature-vibration coupling is evaluated by the maximum mutual information entropy. In a chemical reactor system, changes in temperature may cause thermal expansion or contraction of the reactor material, thereby changing the mechanical structural characteristics of the reactor, and thus affecting the vibration of the stirring device. The maximum mutual information entropy can accurately evaluate the intensity of temperature-vibration coupling from complex relationships, providing a basis for comprehensively analyzing the comprehensive impact of environmental parameters on pressure sensors.

[0053] Step S136, dynamically allocating compensation weight coefficients according to the coupling intensity matrix, generating a real-time proportional combination of temperature compensation weight, humidity compensation weight and vibration compensation weight through the weight allocator of the compensation parameter generation model, wherein the temperature compensation weight is positively correlated with the temperature-humidity coupling intensity, the humidity compensation weight is exponentially related to the humidity-vibration coupling intensity, and the vibration compensation weight forms a dynamic feedback adjustment with the temperature-vibration coupling intensity.

[0054] The temperature compensation weight is positively correlated with the strength of the temperature-humidity coupling. This means that when the temperature-humidity coupling strength is large, it means that the mutual influence of temperature and humidity has a large combined impact on the pressure sensor, and a larger weight needs to be given to the temperature compensation. For example, if the trends of temperature and humidity in the workshop are closely related to each other and have a significant impact on the measurement accuracy of the pressure sensor, the temperature compensation weight will increase accordingly. The humidity compensation weight is exponentially related to the humidity-vibration coupling strength. In actual scenarios, a small change in the humidity-vibration coupling strength may have a large impact on the pressure sensor. For example, when the humidity-vibration coupling strength increases slightly, due to the exponential relationship, the humidity compensation weight will be greatly increased to adapt to the impact of this coupling on the pressure sensor. The vibration compensation weight forms a dynamic feedback adjustment with the temperature-vibration coupling strength. When the temperature-vibration coupling strength changes, the vibration compensation weight will be dynamically adjusted according to this change. For example, as the temperature of the reactor changes, the vibration characteristics of the stirring device change, and the vibration compensation weight will be adjusted accordingly to accurately compensate for the impact of vibration on the pressure sensor.

[0055] Step S137, input the temperature compensation weight, humidity compensation weight and vibration compensation weight into the multi-layer compensation parameter generator of the compensation parameter generation model, generate the basic offset of the temperature compensation coefficient through the first compensation layer, superimpose the sensitivity adjustment amount of the humidity compensation coefficient on the second compensation layer, and inject the phase correction amount of the vibration compensation coefficient into the third compensation layer, and finally output a dynamic compensation parameter set including the temperature compensation coefficient, the humidity compensation coefficient and the vibration compensation coefficient.

[0056] In the environment of a chemical reactor, the measurement baseline of the pressure sensor may shift due to the influence of temperature. For example, when the temperature rises, the electrical characteristics of the pressure sensor may change, resulting in a fixed offset in the measured pressure value. The basic offset of the temperature compensation coefficient generated by the first compensation layer is to correct the baseline offset caused by temperature change. The sensitivity adjustment amount of the humidity compensation coefficient is superimposed on the second compensation layer. In a high humidity environment, the sensitivity of the pressure sensor may decrease, that is, the responsiveness to pressure changes is weakened. The sensitivity adjustment amount of the humidity compensation coefficient can adjust the pressure signal accordingly according to the degree of influence of humidity on the sensor sensitivity to restore the normal sensitivity of the sensor. The third compensation layer injects the phase correction amount of the vibration compensation coefficient. The vibration of the stirring device can cause the phase of the pressure signal to change. For example, the vibration may cause the waveform of the pressure signal to shift on the time axis, and the phase correction amount of the vibration compensation coefficient injected by the third compensation layer is to compensate for the phase change caused by vibration. The final output includes a set of dynamic compensation parameters including the temperature compensation coefficient, the humidity compensation coefficient and the vibration compensation coefficient.

[0057] Step S138, verifying the physical feasibility boundary of the dynamic compensation parameter set, detecting whether each compensation coefficient exceeds the safety threshold of the sensor range through the parameter constraint module of the compensation parameter generation model, truncating the compensation coefficient that exceeds the safety threshold and generating a compensation parameter correction flag, and matching and aligning the corrected dynamic compensation parameter set with the timestamp of the original pressure signal sequence.

[0058] In chemical production, pressure sensors have a specified measuring range. If the compensation coefficient is too large, the compensated pressure signal may exceed the measuring range of the sensor, resulting in an erroneous measurement result. For example, if the temperature compensation coefficient is too large, the pressure signal that was originally within the measuring range may exceed the upper limit of the measuring range after compensation. When it is detected that a compensation coefficient exceeds the safety threshold, the excess part needs to be truncated and a compensation parameter correction flag is generated. The corrected dynamic compensation parameter set is then matched and aligned with the timestamp of the original pressure signal sequence to ensure that the temporal correspondence between the compensation parameters and the original pressure signal is accurate, thereby providing a reliable basis for the subsequent accurate compensation processing of the original pressure signal sequence according to the dynamic compensation parameter set, and ensuring the accuracy and reliability of pressure monitoring and control in chemical reactors.

[0059] In a possible implementation manner, the training step of the compensation parameter generation model includes: Step S210 , collecting a pressure signal sample set and a corresponding standard pressure signal set of the target pressure sensor under conditions of a plurality of different environmental parameter combinations.

[0060] The environment of a chemical production workshop is complex and changeable, and different combinations of environmental parameters will have different effects on the measurement of pressure sensors. For example, in different operating stages of the reactor, factors such as the intensity of the reaction, the rate of heating or cooling, and the flow rate of materials will cause changes in the temperature, humidity, and mechanical vibration of the surrounding environment. In order to fully cover various possible situations, data collection is required under a variety of different combinations of environmental parameters. For the pressure signal sample set, this is the pressure signal in the reactor measured by the target pressure sensor under various environmental conditions during the actual chemical production process. The standard pressure signal set is the signal corresponding to the actual pressure value in the reactor obtained by more accurate and reliable measurement means or strictly calibrated measurement equipment. For example, a high-precision pressure measuring instrument can be used, which has a much higher accuracy than the target pressure sensor, and after professional calibration and verification, it can provide accurate pressure standard values ​​as a standard pressure signal set. The collection of these data needs to be carried out over a long period of time to ensure that enough data samples of different situations can be obtained, thereby providing a rich data foundation for subsequent model training.

[0061] Step S220: For each combination of environmental parameters, calculate an error distribution matrix between the pressure signal sample set and the standard pressure signal set.

[0062] In the actual scenario of chemical production, since the target pressure sensor is affected by environmental factors, there will be an error between its measured value and the standard value. For each specific combination of environmental parameters, each sample in the pressure signal sample set is compared with the sample with the corresponding timestamp in the standard pressure signal set, and the error value between them is calculated. For example, at a certain moment, when the ambient temperature is high, the humidity is moderate, and the mechanical vibration is small, the difference between the pressure value measured by the target pressure sensor and the pressure value obtained by the standard pressure measuring instrument is an error value. Arranging all these error values ​​in chronological order and the corresponding environmental parameter combinations forms an error distribution matrix, which describes in detail the distribution of pressure signal measurement errors under different environmental parameter combinations, and provides a key basis for the subsequent determination of the parameter mapping relationship in the compensation parameter generation model.

[0063] Step S230, performing nonlinear regression fitting on the environmental parameter combination and the error distribution matrix to determine a parameter mapping relationship in the compensation parameter generation model.

[0064] Step S240, iteratively optimizing the weight coefficients in the parameter mapping relationship by a back propagation algorithm until the matching degree between the predicted compensation parameters output by the compensation parameter generation model and the error distribution matrix reaches a preset threshold.

[0065] In a possible implementation, step S230 includes: Step S231, performing three-dimensional surface fitting on the temperature parameter, humidity parameter and mechanical vibration parameter in the environmental parameter combination and the error amplitude of the corresponding timestamp in the error distribution matrix, to generate an initial parameter mapping relationship including a temperature compensation surface, a humidity compensation surface and a vibration compensation surface.

[0066] In the reactor environment of a chemical workshop, the effects of temperature, humidity and mechanical vibration on pressure sensors are nonlinear. For example, the effect of temperature on pressure sensors may show a change trend in a certain temperature range, and a different change trend in another temperature range. Through three-dimensional surface fitting, the complex relationship between temperature parameters and corresponding error amplitudes can be represented by temperature compensation surfaces. Similarly, humidity compensation surfaces and vibration compensation surfaces also reflect the relationship between humidity parameters, mechanical vibration parameters and error amplitudes, respectively. These surfaces together constitute the initial parameter mapping relationship, which preliminarily describes the mapping relationship between environmental parameters and pressure signal errors.

[0067] Step S232, calculating the error change rate of each pressure signal sample in a continuous acquisition cycle, and generating a dynamic gradient vector of the error distribution matrix.

[0068] In the chemical production process, the error of the pressure sensor is not static. As the reactor runs for a long time, the error will change dynamically due to the continuous effect of environmental factors and the changes in the characteristics of the sensor itself. For example, as the reaction proceeds, the temperature in the reactor gradually rises, the humidity may increase due to the water vapor produced by the chemical reaction, and the mechanical vibration may also change due to the wear of the stirring device. These factors will cause the error of the pressure sensor to have different rates of change in different acquisition cycles. The error change rate of each pressure signal sample in the continuous acquisition cycle is arranged in a certain order to form a dynamic gradient vector of the error distribution matrix. The dynamic gradient vector reflects the trend of error changes over time and environmental factors, and provides important dynamic information for subsequent multivariate coupling analysis.

[0069] Step S233, performing multivariate coupling analysis on the temperature parameter change rate, humidity parameter change rate and mechanical vibration parameter change rate in the environmental parameter combination and the dynamic gradient vector to generate a dynamic compensation coefficient set including temperature gradient weight, humidity gradient weight and vibration gradient weight.

[0070] In chemical reactor systems, there are complex relationships between temperature, humidity and mechanical vibration, and the effects of their change rates on pressure sensor errors are not independent. For example, a rapid rise in temperature may lead to faster changes in humidity, and may also affect the mechanical vibration characteristics of the stirring device, which in turn jointly affect the error change rate of the pressure sensor. Through multivariate coupling analysis, the combined influence of the change rate of temperature, humidity and mechanical vibration parameters on the error change rate can be accurately measured, thereby generating temperature gradient weights, humidity gradient weights and vibration gradient weights. These weights reflect the relative importance of each environmental parameter to the change in pressure sensor error under different environmental parameter changes, and constitute a set of dynamic compensation coefficients.

[0071] Step S234, performing point-by-point multiplication operations on the temperature compensation surface, humidity compensation surface and vibration compensation surface in the initial parameter mapping relationship and the corresponding gradient weights in the dynamic compensation coefficient set, respectively, to generate an enhanced parameter mapping relationship that integrates static error features and dynamic error features.

[0072] The error of the pressure sensor includes both the static error caused by the static value of the environmental parameters and the dynamic error caused by the change of the environmental parameters. For example, the basic temperature level around the reactor will cause a certain static error in the pressure sensor, and the rate of change of temperature will introduce dynamic errors. Through the above point-by-point product operation, the parts of the temperature compensation surface, humidity compensation surface and vibration compensation surface that reflect the static error characteristics are combined with the gradient weights that reflect the dynamic error characteristics in the dynamic compensation coefficient set, so that the enhanced parameter mapping relationship can more comprehensively and accurately describe the relationship between the environmental parameters and the pressure sensor error, taking into account both the static influence of the environmental parameters and the influence of their dynamic changes.

[0073] Step S235, constructing a neural network model with a multi-layer hidden structure, inputting the temperature compensation surface data, humidity compensation surface data and vibration compensation surface data in the enhanced parameter mapping relationship into three independent input channels of the neural network model respectively, performing nonlinear feature fusion on the data of the three input channels through the fully connected layer of the neural network model, and generating a final parameter mapping relationship with a cross-compensation effect.

[0074] The effects of temperature, humidity and mechanical vibration on pressure sensors are not simple linear superpositions, but rather complex cross-influence relationships. For example, temperature and humidity may work together to affect the electrical properties of pressure sensors, and humidity and mechanical vibration may synergistically change the mechanical structural stability of pressure sensors, thereby affecting their measurement accuracy. The multi-layer hidden structure of the neural network model has a strong nonlinear fitting capability and can learn this complex cross-influence relationship. By inputting the temperature compensation surface data, humidity compensation surface data and vibration compensation surface data into three independent input channels respectively, the fully connected layer can perform nonlinear feature fusion on these data and comprehensively process the data from different channels, so that the final parameter mapping relationship generated can reflect the cross-compensation effect between temperature, humidity and mechanical vibration, and more accurately reflect the complex relationship between environmental parameters and pressure sensor errors.

[0075] Step S236, optimize the output accuracy of the final parameter mapping relationship by performing residual back propagation calculation on the output layer data of the neural network model and the standard pressure signal set, and adjust the node connection weights of the fully connected layer in the neural network model until the prediction error value of the final parameter mapping relationship reaches a preset accuracy threshold.

[0076] In order to ensure that the compensation parameter generation model can accurately predict the compensation parameters, the output accuracy of the final parameter mapping relationship needs to be optimized. The output layer data of the neural network model is compared with the standard pressure signal set, and the residual between the two is calculated. The residual reflects the gap between the prediction result of the current final parameter mapping relationship and the true standard value. Through the residual back propagation calculation, the node connection weights of the fully connected layer in the neural network model are adjusted according to the size and direction of the residual. For example, if the residual between the predicted pressure signal and the standard pressure signal is large under a certain combination of environmental parameters, it means that there is a deviation in the current parameter mapping relationship, and the node connection weights need to be adjusted to reduce this deviation. Repeat this process continuously and gradually adjust the node connection weights until the prediction error value of the final parameter mapping relationship reaches the preset accuracy threshold, ensuring that the final parameter mapping relationship can accurately reflect the relationship between the environmental parameters and the pressure sensor error, thereby providing a reliable basis for generating accurate compensation parameters.

[0077] Step S237, solidifying the optimized node connection weights in the neural network model, generating a data structure of executable parameter mapping relationships in the compensation parameter generation model, wherein the data structure includes a cross-query interface for a temperature compensation surface index table, a humidity compensation surface index table, and a vibration compensation surface index table.

[0078] In detail, once the node connection weights of the neural network model are optimized to meet the preset accuracy requirements, these weights need to be solidified to form a data structure of executable parameter mapping relationships in the compensation parameter generation model. The cross-query interface of the temperature compensation surface index table, humidity compensation surface index table, and vibration compensation surface index table in the data structure is of great significance. For example, when it is necessary to find the corresponding compensation parameters according to the current environmental parameter combination in the actual pressure signal compensation process, the cross-query interface of these index tables can be used to quickly and accurately obtain the compensation surface information corresponding to temperature, humidity, and mechanical vibration, and then determine the appropriate compensation parameters, so as to achieve accurate compensation of the pressure sensor signal and ensure the accuracy and reliability of pressure monitoring in the chemical reactor.

[0079] In a possible implementation, step S140 includes: Step S141, performing time domain filtering processing on the original pressure signal sequence to obtain a preprocessed pressure signal sequence.

[0080] For example, the original pressure signal sequence is collected by the pressure sensor installed in the key part of the reactor. However, the original signal sequence may be affected by a variety of interference factors. For example, there are various electrical equipment in the workshop, which may generate electromagnetic interference during operation, resulting in high-frequency noise components mixed in the pressure signal. In addition, due to the start-stop operation of various equipment in the chemical production process or the instability of material flow, some irregular fluctuations may also be introduced into the pressure signal. Through time domain filtering, these interference components can be effectively removed. Time domain filtering technology can identify and filter out high-frequency noise and irregular fluctuations that do not conform to the normal change law of pressure signals based on the characteristics of the signal in the time domain. For example, a low-pass filter is used, which allows pressure signal components below a certain cutoff frequency to pass, while noise and interference components above the cutoff frequency are filtered out. After such time domain filtering, the pre-processed pressure signal sequence obtained is smoother and more stable, and can more accurately reflect the actual pressure changes in the reactor, providing a more reliable basis for subsequent adjustment operations.

[0081] Step S142: performing baseline offset correction on the preprocessed pressure signal sequence according to the temperature compensation coefficient in the dynamic compensation parameter set.

[0082] In the environment of chemical workshops, temperature has an important impact on pressure sensors. During the operation of the reactor, the temperature around the pressure sensor will change due to the exothermic or endothermic process of the internal chemical reaction and the fluctuation of the ambient temperature. For example, in some stages of intense exothermic reactions, the temperature around the reactor may rise significantly. The change in temperature will cause the electrical or mechanical properties of the pressure sensor to change, which will in turn cause the baseline offset of the pressure signal. This baseline offset is manifested as the overall upward or downward shift of the pressure signal by a certain value. The temperature compensation coefficient in the dynamic compensation parameter set is based on the previous accurate modeling and analysis of the relationship between temperature and pressure sensor. Through this temperature compensation coefficient, the baseline offset correction required for the pressure signal can be accurately calculated. For example, if the temperature rise causes the baseline of the pressure signal to shift upward by a certain value, a reverse adjustment amount can be determined based on the temperature compensation coefficient to restore the baseline of the pressure signal to a normal level, so that the pressure signal can accurately reflect the actual pressure situation in the reactor and avoid the pressure measurement error caused by the baseline offset caused by temperature.

[0083] Step S143: performing sensitivity scaling adjustment on the pressure signal sequence after the baseline offset correction according to the humidity compensation coefficient in the dynamic compensation parameter set.

[0084] The humidity environment of the chemical workshop is relatively complex, and changes in humidity can affect the sensitivity of the pressure sensor. During the operation of the reactor, water vapor may be generated due to chemical reactions, or changes in the ventilation conditions of the workshop may change the humidity of the environment in which the pressure sensor is located. For example, when the workshop is poorly ventilated, the humidity around the reactor may increase. The increase in humidity may cause some sensitive components inside the pressure sensor to become damp, thereby reducing its sensitivity to pressure changes. This means that under the same pressure change, the amplitude of the signal change output by the pressure sensor may become smaller. The humidity compensation coefficient in the dynamic compensation parameter set reflects the degree of influence of humidity on the sensitivity of the pressure sensor. According to this humidity compensation coefficient, the sensitivity scaling adjustment can be performed on the pressure signal sequence after the baseline offset correction. If the humidity causes the sensor sensitivity to decrease, an amplification factor can be calculated through the humidity compensation coefficient to amplify the amplitude of the pressure signal accordingly so that it can accurately reflect the actual pressure change. Conversely, if the humidity change causes the sensitivity to increase, a corresponding reduction adjustment is made to ensure that the sensitivity of the pressure signal matches the actual pressure change, thereby improving the accuracy of the pressure measurement.

[0085] Step S144: performing phase synchronization calibration on the pressure signal sequence after the sensitivity scaling adjustment according to the vibration compensation coefficient in the dynamic compensation parameter set.

[0086] Step S145: using the signal sequence after the phase synchronization calibration as the compensated pressure signal sequence.

[0087] Wherein, step S144 includes: Step S1441, extracting the vibration frequency component corresponding to the mechanical vibration parameter, and generating a reference sinusoidal waveform with the same period as the vibration frequency component.

[0088] In a chemical reactor, the operation of the stirring device will generate mechanical vibration, which will be transmitted to the pressure sensor and affect the phase of the pressure signal. The mechanical vibration parameters reflect the characteristics of this vibration. By performing frequency domain analysis on the mechanical vibration parameters, the vibration frequency component can be accurately extracted. For example, if the operating frequency of the stirring device is 50Hz, then the corresponding vibration frequency component is 50Hz. Based on this vibration frequency component, a reference sine waveform with the same period can be generated. The reference sine waveform has the same frequency characteristics as the vibration, providing a benchmark for subsequent phase compensation.

[0089] Step S1442, performing local extreme point detection on the pressure signal sequence after the sensitivity scaling adjustment to determine the phase offset of the signal waveform.

[0090] Due to the interference of mechanical vibration, the phase of the pressure signal may shift. By detecting the local extreme points in the pressure signal waveform, such as the position of the peak and the trough, the phase shift of the pressure signal relative to the normal situation can be analyzed. For example, when there is no vibration interference, the peak of the pressure signal should appear at a certain time point, but due to the influence of vibration, the peak may appear earlier or later, and the amount of time of the advance or lag reflects the phase shift of the signal waveform.

[0091] Step S1443: performing phase compensation on the reference sinusoidal waveform according to the phase offset and the vibration compensation coefficient in the dynamic compensation parameter set to generate an anti-vibration interference template.

[0092] The vibration compensation coefficient is derived from the previous modeling and analysis of the relationship between vibration and pressure sensors, and reflects the degree to which the phase shift caused by vibration needs to be compensated. Based on the magnitude and direction of the phase shift, as well as the vibration compensation coefficient, the reference sine waveform can be accurately phase-adjusted. For example, if the phase shift is positive, indicating that the pressure signal is phase-leading, then based on the vibration compensation coefficient, the phase of the reference sine waveform can be adjusted back a certain amount to generate an anti-vibration interference template that has characteristics that match the phase characteristics that the pressure signal should have when there is no vibration interference.

[0093] Step S1444: performing a convolution operation on the anti-vibration interference template and the pressure signal sequence after sensitivity scaling adjustment to suppress signal distortion components caused by vibration.

[0094] Convolution operation is an effective signal processing method that can integrate the characteristics of the anti-vibration interference template into the pressure signal sequence. In the environment of a chemical reactor, due to the interference of vibration, the pressure signal may have waveform distortion, such as changes in the shape of the peaks and troughs, or unstable fluctuations in the amplitude of the signal in different cycles. Through the convolution operation, the anti-vibration interference template can suppress these distorted components in the pressure signal sequence. For example, when the anti-vibration interference template is convolved with the pressure signal sequence, it will weighted average or correct the distorted part of the pressure signal according to its own phase and amplitude characteristics, so that the final signal sequence is closer to the ideal pressure signal without vibration interference, thereby improving the accuracy and reliability of the pressure signal. The signal sequence after phase synchronization calibration is used as the compensated pressure signal sequence. The compensated pressure signal sequence can more accurately reflect the actual pressure situation in the reactor, providing reliable data support for pressure monitoring and control in the chemical production process.

[0095] In a possible implementation, the method further includes: Step S310, monitoring the change gradient of the environmental parameters of the target pressure sensor within a continuous acquisition cycle.

[0096] For example, the environmental parameters around the reactor, such as temperature, humidity, and mechanical vibration, are constantly changing. Taking temperature as an example, the chemical reaction in the reactor will continuously release or absorb heat, which will cause the temperature around the reactor to change. In a continuous collection cycle, it may be found that the temperature rises slowly in a few cycles and then drops in the next cycle. This rate of change of temperature over time is the temperature change gradient. Similarly, humidity will also change due to factors such as the generation of water vapor during the reaction process and the operation of the workshop ventilation system. Its changes in the continuous collection cycle constitute the humidity change gradient. In terms of mechanical vibration, the operating state of the stirring device, the wear of mechanical parts, and the stirring resistance of the material will cause changes in the mechanical vibration. These changes form a mechanical vibration change gradient in the continuous collection cycle.

[0097] Step S320: When the environmental parameter change gradient exceeds a preset fluctuation threshold, the current environmental parameter set is re-collected and input into the compensation parameter generation model to generate an updated dynamic compensation parameter set.

[0098] For example, the preset temperature fluctuation threshold is 5°C every 10 minutes. If the temperature change gradient is found to exceed this threshold in a certain continuous acquisition cycle, this may mean that some abnormal situation has occurred in the reactor, such as reaction out of control or heating-cooling system failure. At this time, it is necessary to immediately re-collect the current set of environmental parameters including temperature, humidity and mechanical vibration. For the re-collection of temperature, to ensure the accuracy and timeliness of the collection, it may be necessary to increase the collection frequency of the temperature sensor or use more accurate temperature collection equipment. For the collection of humidity, the dynamic changes of water vapor around the reactor should be taken into account, and the humidity distribution data should be re-acquired and processed to obtain humidity parameters. In terms of mechanical vibration, the mechanical vibration waveform data of the stirring device and the overall structure of the reactor in the current state should be accurately collected, and accurate mechanical vibration parameters should be obtained through frequency domain energy analysis and other means. The re-collected complete set of environmental parameters is input into a pre-trained compensation parameter generation model. The compensation parameter generation model is built based on a large amount of previous data and complex algorithms, and can accurately generate an updated dynamic compensation parameter set according to the new environmental parameter set. The updated dynamic compensation parameter set includes the temperature compensation coefficient, humidity compensation coefficient, vibration compensation coefficient, etc. required to compensate the original pressure signal for the current environmental changes.

[0099] Step S330 , performing sliding average fusion on the updated dynamic compensation parameter set and the historical compensation parameter set to generate a compensation parameter sequence with a smooth transition.

[0100] Step S340: Use the smooth transition compensation parameter sequence to progressively adjust the subsequently collected original pressure signal.

[0101] In the chemical reactor system, the original pressure signal is continuously collected and reflects the real-time changes in the pressure in the reactor. When the original pressure signal is adjusted using a smooth transition compensation parameter sequence, since the compensation parameter sequence is carefully integrated and updated, the impact of the dynamic changes of environmental parameters on the pressure signal can be more accurately considered. For example, when the ambient temperature in the reactor gradually increases, the humidity also fluctuates to a certain extent, and the mechanical vibration changes at the same time, the smooth transition compensation parameter sequence can make a progressive adjustment to the original pressure signal according to these environmental changes. The progressive adjustment will not cause a sudden jump in the pressure signal, but gradually adjust the pressure signal according to the gradual change of the environmental parameters. For the application of the temperature compensation coefficient, the pressure signal deviation caused by the temperature increase can be gradually corrected; the humidity compensation coefficient can smoothly adjust the sensitivity of the pressure signal with the fluctuation of humidity; and the vibration compensation coefficient continuously calibrates the phase and amplitude changes of the pressure signal caused by mechanical vibration. Through this progressive adjustment, it can ensure that the pressure signal always maintains a high accuracy under complex and changeable environmental conditions, provide reliable data support for pressure monitoring and control in the chemical production process, and ensure the safety, stability and efficiency of chemical production.

[0102] In a possible implementation, step S330 includes: Step S331, setting the time window length, and arranging the historical compensation parameter set and the updated dynamic compensation parameter set in chronological order within the time window.

[0103] For example, a time window of 30 minutes can be set. In this time window, the historical compensation parameter set and the updated dynamic compensation parameter set are arranged in chronological order. The historical compensation parameter set is the compensation parameters generated and saved at different time points before, which reflects the parameters of the pressure sensor signal compensation under different environmental conditions in the past. After the two sets of compensation parameters are arranged in chronological order in the time window, a weight coefficient related to the timestamp is assigned to each compensation parameter in the time window, and the weight coefficient of the latest parameter is the largest. This is because the latest compensation parameter can better reflect the current environmental state and the actual situation of the pressure sensor. For example, for the compensation parameter that has just been updated, since it is recalculated based on the current environmental parameters, a larger weight coefficient is given, while for the older compensation parameters in the time window, its weight coefficient gradually decreases as time goes by. Then the weighted average of all compensation parameters in the window is calculated, and the calculation process of the weighted average fully considers the weight of each compensation parameter and its corresponding timestamp information. For example, assuming that there are 5 compensation parameters within a 30-minute time window, the latest compensation parameter weight coefficient is 0.5, and the weight coefficients of the other 4 historical compensation parameters are 0.1, 0.1, 0.2 and 0.1 respectively. Multiply each compensation parameter by its corresponding weight coefficient and add them together. The result is the current output value of the smoothly transitioned compensation parameter sequence.

[0104] Step S332, assigning a weight coefficient related to the timestamp to each compensation parameter in the time window, wherein the weight coefficient of the latest parameter is the largest.

[0105] Step S333: Calculate the weighted average value of all compensation parameters in the window as the current output value of the smooth transition compensation parameter sequence.

[0106] Step S334: when a new parameter enters the time window, the oldest parameter in the window is removed and the weighted average value is recalculated to implement rolling update of the compensation parameter and generate a compensation parameter sequence with a smooth transition.

[0107] As the chemical production process continues, new compensation parameters will be continuously generated. When a new compensation parameter enters the set time window, the earliest compensation parameter in the time window needs to be removed to keep the number of compensation parameters in the time window unchanged. Then the weighted average is recalculated according to the previous weight distribution principle. For example, when a new compensation parameter enters, the earliest compensation parameter in the window is removed, and the weight coefficient of the new compensation parameter is 0.4. The weight coefficients of the remaining compensation parameters are adjusted according to their relative newness, such as 0.1, 0.1, 0.2 and 0.2 respectively. The weighted average is calculated again to obtain a new smooth transition compensation parameter sequence. This rolling update mechanism enables the compensation parameter sequence to adapt to the dynamic changes of environmental parameters in a timely manner, and ensures the smooth transition of the compensation parameters during the update process, avoiding the problem of inaccurate pressure signal compensation caused by sudden changes in compensation parameters.

[0108] In a possible implementation, the method further includes: Step S410: In a steady-state operation phase of the industrial control system, a compensated pressure signal output by the target pressure sensor is collected.

[0109] Step S420, synchronously obtaining an actual action feedback signal of the actuator.

[0110] In chemical production, the steady-state working stage means that the chemical reaction in the reactor is carried out stably according to the established process parameters, such as temperature, pressure, material flow rate and other parameters are in a relatively stable state. At this time, the target pressure sensor continuously monitors the pressure in the reactor and outputs a pressure signal after a series of compensation processes. The compensated pressure signal reflects the pressure situation after considering the influence of environmental factors such as temperature, humidity, and mechanical vibration. The actuators, such as the valve motor that controls the inlet and outlet of the material in the reactor or the heating-cooling device controller that adjusts the temperature of the reactor, will act according to the instructions of the control system, and these actuators have their own feedback mechanism, which can generate actual action feedback signals. For example, the encoder of the valve motor can feedback the actual opening of the valve, and the heating-cooling device controller can feedback the actual heating or cooling power. These actual action feedback signals are obtained in the same time frame as the compensated pressure signal.

[0111] Step S430: performing correlation analysis on the compensated pressure signal and the actual action feedback signal to calculate a signal matching index.

[0112] Step S440, when the signal matching index is lower than a preset safety threshold, a retraining instruction of the compensation parameter generation model is triggered, and a system alarm log is generated.

[0113] In chemical production, the preset safety threshold is set according to the requirements of the production process and past experience, for example, it is set to 0.8. If the calculated signal matching index is lower than 0.8, this may mean that there is a problem with the current compensation parameters, resulting in a deviation in the correlation between the pressure signal and the actual action of the actuator. This deviation may affect the pressure control in the reactor, and thus affect the stability of the chemical reaction and the quality of the product. At this time, the system will automatically trigger the retraining instruction of the compensation parameter generation model. At the same time, in order to facilitate the operator to understand the abnormal situation of the system, a system alarm log will be generated to record the time when the signal matching index is lower than the safety threshold, the current compensated pressure signal value, the actual action feedback signal value and other related information.

[0114] Step S450: re-collect environmental parameter samples and pressure signal samples according to the retraining instruction, and update the parameter mapping relationship of the compensation parameter generation model.

[0115] When the retraining instruction is triggered, data needs to be recollected to re-evaluate the relationship between environmental parameters and pressure signals. In the chemical reactor environment, recollecting environmental parameter samples includes accurately measuring the temperature, humidity and mechanical vibration around the reactor again. For temperature collection, it is necessary to ensure that the collection points cover all areas around the target pressure sensor that may affect the temperature, and increase the collection frequency to obtain more detailed temperature changes. The collection of humidity should take into account the dynamic changes of water vapor generation in the reactor and workshop ventilation. It may be necessary to increase the number of humidity sensors or use more precise humidity measurement equipment. The collection of mechanical vibration requires comprehensive vibration detection of the stirring device, reactor support structure, etc. to obtain accurate mechanical vibration waveform data. At the same time, the pressure signal samples of the target pressure sensor should also be recollected. These pressure signal samples should cover pressure values ​​under different reaction stages and different environmental conditions. Then, using these newly collected environmental parameter samples and pressure signal samples, according to the training steps of the compensation parameter generation model described above, the relationship between environmental parameters such as temperature, humidity, mechanical vibration and pressure signal errors is re-determined, thereby updating the parameter mapping relationship of the compensation parameter generation model to improve the accuracy of the compensation parameters and ensure a good match between the pressure signal and the actual action of the actuator.

[0116] Wherein, step S430 includes: Step S431, normalizing the compensated pressure signal and the actual action feedback signal to eliminate dimensional differences.

[0117] In a chemical reactor system, the unit of the compensated pressure signal may be Pascal (Pa), while the actual action feedback signal of the actuator may be different dimensions such as the percentage of valve opening or the watt (W) of heating-cooling power. Through normalization, these data of different dimensions are converted into dimensionless relative values ​​so that they can be compared on the same mathematical scale. For example, for the pressure signal, its value can be mapped to between 0 and 1, and for the valve opening feedback signal, it can also be mapped to between 0 and 1.

[0118] Step S432, calculating the correlation coefficient matrix between the compensated pressure signal and the actual action feedback signal in the same time window, and determining the maximum correlation coefficient and its corresponding delay.

[0119] In the chemical production process, due to factors such as signal transmission delay and actuator response time, there may be a certain time difference between the compensated pressure signal and the actual action feedback signal. For example, when the pressure in the reactor increases, the control system issues a command to open the valve to reduce the pressure based on the compensated pressure signal, but there is a short delay from the valve receiving the command to the actual opening, which will be reflected in the signal delay. By calculating the mutual correlation coefficient matrix, the degree of correlation between the two signals under different time delays can be found. For example, assuming that the time window is set to 10 minutes, the mutual correlation coefficient is calculated at a time interval of 1 second, and a mutual correlation coefficient matrix corresponding to different time delays within 10 minutes can be obtained. In this mutual correlation coefficient matrix, there will be a maximum correlation coefficient, and the corresponding time delay is the time difference between the two signals for the best match.

[0120] Step S433: construct a signal consistency evaluation function according to the maximum correlation coefficient and the delay information, and use the output value of the signal consistency evaluation function as the signal matching index.

[0121] The signal consistency evaluation function is constructed based on the characteristics of the chemical production process and the requirements for signal matching. For example, it can be a function that takes into account the maximum correlation coefficient and the delay weight, such as the signal matching index = maximum correlation coefficient × (1-delay weight × absolute value of delay). Among them, the delay weight is set according to the requirements of chemical production for signal real-time performance. If the real-time requirements are very high, the delay weight will be larger. The signal matching index comprehensively reflects the degree of matching between the compensated pressure signal and the actual action feedback signal. Its value is between 0 and 1. The closer it is to 1, the higher the matching degree of the two signals.

[0122] In a possible implementation, the method further includes: Step S510: monitor the signal output stability index of the target pressure sensor in real time.

[0123] In the complex environment of a chemical production workshop, the signal output stability of the target pressure sensor is affected by many factors. For example, the electronic components of the sensor itself may age or fail due to long-term operation, the surrounding electromagnetic environment interference may affect the signal transmission stability, and environmental factors such as temperature, humidity and mechanical vibration may also cause the performance of the sensor to fluctuate. These potential problems can be discovered in time by real-time monitoring of the signal output stability index, which can be a comprehensive index based on factors such as the signal amplitude fluctuation range and frequency stability.

[0124] Step S520: when it is detected based on the signal output stability indicator that the signal output is interrupted or the amplitude exceeds the physical range, the data acquisition channel of the standby pressure sensor is started.

[0125] In a chemical reactor system, if the signal output of the target pressure sensor is interrupted, this may be due to a sensor line fault, power supply problem, or a serious fault inside the sensor. The amplitude exceeding the physical range may be due to abnormal high pressure in the reactor or measurement deviation of the sensor itself. Once this situation is detected, in order to ensure the continuity of pressure monitoring, the system will immediately start the data acquisition channel of the backup pressure sensor. The backup pressure sensor exists as a redundant device, and its installation location and measurement principle are similar to those of the target pressure sensor, and it has been calibrated in advance.

[0126] Step S530: the output signal of the backup pressure sensor is connected to the signal processing module of the industrial control system after being processed by the same compensation process.

[0127] The output signal of the backup pressure sensor will also be affected by environmental factors such as temperature, humidity and mechanical vibration, so it needs to go through the same compensation process as the target pressure sensor. For example, first obtain the original pressure signal sequence of the backup pressure sensor within the preset acquisition period, then detect the real-time environmental parameter set around it, including temperature, humidity and mechanical vibration parameters, input these environmental parameters into the pre-trained compensation parameter generation model to generate a dynamic compensation parameter set, and then adjust the original pressure signal step by step according to the dynamic compensation parameter set to obtain the compensated pressure signal. Finally, the pressure signal of the compensated backup pressure sensor is connected to the signal processing module of the industrial control system to replace the signal of the target pressure sensor to ensure the normal operation of the pressure monitoring and control system.

[0128] Step S540, synchronously record the abnormal status information of the target pressure sensor and generate a device maintenance request instruction.

[0129] After detecting an abnormality in the target pressure sensor, it is necessary to record the relevant abnormal status information in detail, such as the time of signal interruption, the specific value of the amplitude exceeding the range, the signal fluctuation in the previous period of time, etc. This information helps maintenance personnel to quickly locate the problem. At the same time, a device maintenance request instruction is generated to notify the relevant maintenance personnel to repair the target pressure sensor.

[0130] Step S550, after the equipment maintenance is completed, the target pressure sensor is subjected to a full-range calibration test, and the validity of the compensation parameter generation model is verified and then reactivated.

[0131] For example, equipment maintenance may involve repairing the sensor, replacing parts, or recalibrating it. After the maintenance is completed, the target pressure sensor needs to be calibrated and tested for the full range. In a chemical reactor environment, the full-scale calibration test requires the use of a high-precision pressure source. Within the entire measurement range of the target pressure sensor, a known standard pressure value is gradually applied from the minimum to the maximum value. At the same time, the output signal of the sensor is recorded and compared with the standard pressure value to calculate the error. If the error is within an acceptable range, it means that the sensor has resumed normal working conditions. Then the target pressure sensor is reconnected to the system, and its output pressure signal is collected again. Compensation processing is performed according to the normal process, and the compensated pressure signal is correlated with the actual action feedback signal of the actuator to verify the effectiveness of the compensation parameter generation model. If the verification is successful, it means that the compensation parameter generation model can accurately compensate for the signal of the target pressure sensor. At this time, the target pressure sensor can be officially reactivated for normal pressure monitoring.

[0132] Figure 2 A schematic diagram of exemplary hardware and software components of a pressure sensor data accuracy compensation system 100 for industrial control that can implement the concept of the present application is shown in some embodiments of the present application. For example, the processor 120 can be used in the pressure sensor data accuracy compensation system 100 for industrial control and used to perform the functions in the present application.

[0133] The pressure sensor data accuracy compensation system 100 for industrial control can be a general server or a special-purpose server, both of which can be used to implement the pressure sensor data accuracy compensation method for industrial control of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0134] For example, the pressure sensor data accuracy compensation system 100 for industrial control may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in different forms, such as disks, ROMs, or RAMs, or any combination thereof. Exemplarily, the pressure sensor data accuracy compensation system 100 for industrial control may also include program instructions stored in ROMs, RAMs, or other types of non-temporary storage media, or any combination thereof. The method of the present application may be implemented according to these program instructions. The pressure sensor data accuracy compensation system 100 for industrial control also includes an input / output (I / O) interface 150 between a computer and other input / output devices.

[0135] For ease of explanation, only one processor is described in the pressure sensor data accuracy compensation system 100 for industrial control. However, it should be noted that the pressure sensor data accuracy compensation system 100 for industrial control in the present application may also include multiple processors, so the steps performed by one processor described in the present application may also be performed jointly or individually by multiple processors. For example, if the processor of the pressure sensor data accuracy compensation system 100 for industrial control executes step A and step B, it should be understood that step A and step B may also be executed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0136] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer executable instructions are preset. When a processor executes the computer executable instructions, the pressure sensor data accuracy compensation method for industrial control as described above is implemented.

[0137] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, various features are sometimes combined into one embodiment, drawing or description thereof.

Claims

1. A pressure sensor data accuracy compensation method for industrial control, characterized in that: The method comprises: Acquire an original pressure signal sequence output by a target pressure sensor within a preset acquisition period, wherein the original pressure signal sequence includes a plurality of pressure signals arranged by timestamps; Detecting a set of real-time environmental parameters of the target pressure sensor within the acquisition period, wherein the set of real-time environmental parameters includes a temperature parameter, a humidity parameter, and a mechanical vibration parameter; Inputting the real-time environmental parameter set into a pre-trained compensation parameter generation model to generate a dynamic compensation parameter set corresponding to the original pressure signal sequence; According to the dynamic compensation parameter set, each pressure signal in the original pressure signal sequence is adjusted step by step to obtain a compensated pressure signal sequence; The compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an actuator of the industrial control system.

2. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The detecting a set of real-time environmental parameters of the target pressure sensor within the acquisition period includes: The temperature distribution data in the space where the target pressure sensor is located is collected by a temperature sensor array, and a spatial weighted average processing is performed on the temperature distribution data to obtain the temperature parameter; Collecting humidity distribution data in the space where the target pressure sensor is located through a humidity sensor array, and performing time series filtering processing on the humidity distribution data to obtain the humidity parameter; The mechanical vibration waveform data of the target pressure sensor installation position is collected by a vibration sensor, and the frequency domain energy analysis is performed on the mechanical vibration waveform data to extract the energy peak value within a preset frequency band as the mechanical vibration parameter; The data acquisition timestamps of the temperature parameter, humidity parameter and mechanical vibration parameter are synchronized and aligned with the timestamp of the original pressure signal sequence.

3. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The step of inputting the real-time environmental parameter set into a pre-trained compensation parameter generation model to generate a dynamic compensation parameter set corresponding to the original pressure signal sequence includes: Performing timestamp alignment verification on the temperature parameters, humidity parameters and mechanical vibration parameters in the real-time environmental parameter set, eliminating environmental parameter data whose timestamp deviation exceeds a preset synchronization threshold, and generating a temperature parameter sequence, a humidity parameter sequence and a mechanical vibration parameter sequence after synchronization calibration; Input the synchronously calibrated temperature parameter sequence into the temperature feature extraction channel of the compensation parameter generation model, and extract the temperature impact feature vector through the three-layer convolution network of the temperature feature extraction channel, wherein the first layer of convolution network captures the low-frequency trend component of the temperature parameter, the second layer of convolution network extracts the medium-frequency disturbance component of the temperature fluctuation, and the third layer of convolution network identifies the transient impact component of the temperature mutation; Inputting the synchronously calibrated humidity parameter sequence into the humidity feature extraction channel of the compensation parameter generation model, generating a humidity impact feature vector through the bidirectional recurrent network of the humidity feature extraction channel, wherein the forward propagation process captures the cumulative penetration effect of humidity parameters on the sensor material, and the reverse propagation process identifies the hysteresis compensation demand caused by humidity mutation; The synchronously calibrated mechanical vibration parameter sequence is input into the vibration feature extraction channel of the compensation parameter generation model, and the vibration influence feature vector is decomposed through the time-frequency hybrid network of the vibration feature extraction channel, wherein the time domain analysis unit extracts the envelope characteristics of the vibration amplitude, and the frequency domain analysis unit separates the distribution ratio of the vibration energy at different resonant frequency points; The temperature influence feature vector, humidity influence feature vector and vibration influence feature vector are integrated, and the coupling strength matrix of the three is calculated through the feature cross layer of the compensation parameter generation model, wherein the temperature-humidity coupling strength is characterized by the dot product value of the two-way feature vector, the humidity-vibration coupling strength is calculated by the covariance in the sliding window, and the temperature-vibration coupling strength is evaluated by the maximum mutual information entropy; Dynamically allocating compensation weight coefficients according to the coupling strength matrix, generating a real-time proportional combination of temperature compensation weight, humidity compensation weight and vibration compensation weight through the weight allocator of the compensation parameter generation model, wherein the temperature compensation weight is positively correlated with the temperature-humidity coupling strength, the humidity compensation weight is exponentially related to the humidity-vibration coupling strength, and the vibration compensation weight forms a dynamic feedback adjustment with the temperature-vibration coupling strength; The temperature compensation weight, humidity compensation weight and vibration compensation weight are input into the multi-layer compensation parameter generator of the compensation parameter generation model, a basic offset of the temperature compensation coefficient is generated through the first compensation layer, a sensitivity adjustment amount of the humidity compensation coefficient is superimposed on the second compensation layer, and a phase correction amount of the vibration compensation coefficient is injected into the third compensation layer, and finally a dynamic compensation parameter set including the temperature compensation coefficient, the humidity compensation coefficient and the vibration compensation coefficient is output; Verify the physical feasibility boundary of the dynamic compensation parameter set, detect whether each compensation coefficient exceeds the safety threshold of the sensor range through the parameter constraint module of the compensation parameter generation model, truncate the compensation coefficient that exceeds the safety threshold and generate a compensation parameter correction flag, and match and align the corrected dynamic compensation parameter set with the timestamp of the original pressure signal sequence.

4. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The training step of the compensation parameter generation model includes: Under the conditions of multiple different environmental parameter combinations, collecting a pressure signal sample set of the target pressure sensor and a corresponding standard pressure signal set; For each combination of environmental parameters, calculating an error distribution matrix between the pressure signal sample set and the standard pressure signal set; Performing nonlinear regression fitting on the environmental parameter combination and the error distribution matrix to determine a parameter mapping relationship in the compensation parameter generation model; Iteratively optimizing the weight coefficients in the parameter mapping relationship by a back propagation algorithm until the matching degree between the predicted compensation parameters output by the compensation parameter generation model and the error distribution matrix reaches a preset threshold; The step of performing nonlinear regression fitting on the environmental parameter combination and the error distribution matrix to determine the parameter mapping relationship in the compensation parameter generation model includes: Performing three-dimensional surface fitting on the temperature parameter, humidity parameter and mechanical vibration parameter in the environmental parameter combination and the error amplitude of the corresponding time stamp in the error distribution matrix, respectively, to generate an initial parameter mapping relationship including a temperature compensation surface, a humidity compensation surface and a vibration compensation surface; Calculating the error change rate of each pressure signal sample in a continuous acquisition cycle to generate a dynamic gradient vector of the error distribution matrix; Performing a multivariate coupling analysis on the temperature parameter change rate, humidity parameter change rate and mechanical vibration parameter change rate in the environmental parameter combination and the dynamic gradient vector to generate a dynamic compensation coefficient set including a temperature gradient weight, a humidity gradient weight and a vibration gradient weight; Performing point-by-point multiplication operations on the temperature compensation surface, the humidity compensation surface and the vibration compensation surface in the initial parameter mapping relationship and the corresponding gradient weights in the dynamic compensation coefficient set, respectively, to generate an enhanced parameter mapping relationship integrating static error features and dynamic error features; A neural network model with a multi-layer hidden structure is constructed, and the temperature compensation surface data, humidity compensation surface data, and vibration compensation surface data in the enhanced parameter mapping relationship are respectively input into three independent input channels of the neural network model, and nonlinear feature fusion is performed on the data of the three input channels through the fully connected layer of the neural network model to generate a final parameter mapping relationship with a cross-compensation effect; Optimizing the output accuracy of the final parameter mapping relationship by performing residual back propagation calculation on the output layer data of the neural network model and the standard pressure signal set, and adjusting the node connection weights of the fully connected layer in the neural network model until the prediction error value of the final parameter mapping relationship reaches a preset accuracy threshold; The optimized node connection weights in the neural network model are solidified to generate a data structure of executable parameter mapping relationships in the compensation parameter generation model, wherein the data structure includes a cross-query interface for a temperature compensation surface index table, a humidity compensation surface index table, and a vibration compensation surface index table.

5. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The step of adjusting each pressure signal in the original pressure signal sequence step by step according to the dynamic compensation parameter set to obtain a compensated pressure signal sequence includes: Performing time domain filtering on the original pressure signal sequence to obtain a preprocessed pressure signal sequence; performing baseline offset correction on the preprocessed pressure signal sequence according to the temperature compensation coefficient in the dynamic compensation parameter set; Performing sensitivity scaling adjustment on the pressure signal sequence after the baseline offset correction according to the humidity compensation coefficient in the dynamic compensation parameter set; performing phase synchronization calibration on the pressure signal sequence after the sensitivity scaling adjustment according to the vibration compensation coefficient in the dynamic compensation parameter set; Using the signal sequence after phase synchronization calibration as the compensated pressure signal sequence; The step of performing phase synchronization calibration on the pressure signal sequence after the sensitivity scaling adjustment according to the vibration compensation coefficient in the dynamic compensation parameter set includes: Extracting the vibration frequency component corresponding to the mechanical vibration parameter, and generating a reference sinusoidal waveform having the same period as the vibration frequency component; Performing local extreme point detection on the pressure signal sequence after the sensitivity scaling adjustment to determine the phase offset of the signal waveform; Performing phase compensation on the reference sinusoidal waveform according to the phase offset and the vibration compensation coefficient in the dynamic compensation parameter set to generate an anti-vibration interference template; The anti-vibration interference template is convolved with the pressure signal sequence after the sensitivity scaling adjustment to suppress the signal distortion component caused by vibration.

6. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The method further comprises: Monitoring the change gradient of the environmental parameters of the target pressure sensor during a continuous acquisition cycle; When the environmental parameter change gradient exceeds a preset fluctuation threshold, the current environmental parameter set is re-collected and input into the compensation parameter generation model to generate an updated dynamic compensation parameter set; Perform sliding average fusion of the updated dynamic compensation parameter set and the historical compensation parameter set to generate a compensation parameter sequence with a smooth transition; The smooth-transition compensation parameter sequence is used to progressively adjust the subsequently collected original pressure signal.

7. The pressure sensor data accuracy compensation method for industrial control according to claim 6, characterized in that: The step of performing sliding average fusion on the updated dynamic compensation parameter set and the historical compensation parameter set to generate a compensation parameter sequence with a smooth transition includes: Setting a time window length, and arranging the historical compensation parameter set and the updated dynamic compensation parameter set in chronological order within the time window; Assigning a timestamp-related weight coefficient to each compensation parameter within the time window, wherein the weight coefficient of the latest parameter is the largest; Calculating a weighted average value of all compensation parameters in the window as the current output value of the compensation parameter sequence for the smooth transition; When a new parameter enters the time window, the oldest parameter in the window is removed and the weighted average is recalculated to achieve a rolling update of the compensation parameter and generate a smooth transition compensation parameter sequence.

8. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The method further comprises: In a steady-state operation phase of the industrial control system, collecting a compensated pressure signal output by the target pressure sensor; Synchronously obtaining an actual action feedback signal of the actuator; Performing correlation analysis on the compensated pressure signal and the actual action feedback signal to calculate a signal matching index; When the signal matching index is lower than a preset safety threshold, a retraining instruction of the compensation parameter generation model is triggered, and a system alarm log is generated; Recollect environmental parameter samples and pressure signal samples according to the retraining instruction, and update the parameter mapping relationship of the compensation parameter generation model; The step of performing correlation analysis on the compensated pressure signal and the actual action feedback signal to calculate a signal matching index includes: Normalizing the compensated pressure signal and the actual action feedback signal to eliminate dimensional differences; Calculating the mutual correlation coefficient matrix of the compensated pressure signal and the actual action feedback signal in the same time window, and determining the maximum correlation coefficient and its corresponding delay; A signal consistency evaluation function is constructed according to the maximum correlation coefficient and the time delay information, and an output value of the signal consistency evaluation function is used as the signal matching index.

9. The pressure sensor data accuracy compensation method for industrial control according to claim 1, characterized in that: The method further comprises: Real-time monitoring of the signal output stability index of the target pressure sensor; When it is detected based on the signal output stability indicator that the signal output is interrupted or the amplitude exceeds the physical range, the data acquisition channel of the standby pressure sensor is started; The output signal of the backup pressure sensor is processed by the same compensation process and then connected to the signal processing module of the industrial control system; synchronously recording abnormal status information of the target pressure sensor and generating an equipment maintenance request instruction; After the equipment maintenance is completed, the target pressure sensor is subjected to a full-scale calibration test to verify the validity of the compensation parameter generation model and then reactivated.

10. A pressure sensor data accuracy compensation system for industrial control, characterized in that: The pressure sensor data accuracy compensation system for industrial control includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the pressure sensor data accuracy compensation method for industrial control described in any one of claims 1 to 9.

Images