Method for correcting data of abnormal reading of pressure sensor

Abstract

The invention discloses a method for correcting data of abnormal reading of a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: acquiring sensor readings, constructing a multi-sensor cross validation mechanism, establishing an anomaly detection threshold model, performing anomaly detection based on a time sequence mode, constructing a physical constraint feedback mechanism, performing dynamic drift compensation, processing the acquired readings, and performing self-correction feedback optimization. According to the method for correcting the abnormal reading data of the pressure sensor, through a physical constraint feedback mechanism, the output of the sensor is compared with a theoretical physical model for verification, and the recognition capability of nonlinear abnormality is enhanced; through dynamic drift compensation, linear and exponential drifts generated in long-term use of the sensor are separated and corrected, and accurate correction of the drifts is realized. According to the method, the reliability and the stability of pressure sensor data can be remarkably improved, a more accurate measurement basis is provided for a precise measurement scene, and meanwhile, the risk of system misinformation and failure is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor equipment, and in particular to a method for correcting data of abnormal readings of a pressure sensor. Background Art

[0002] Pressure sensors are widely used in industries such as industry, aviation, medical treatment, and environmental monitoring. The accuracy and stability of their data directly affect the safety and performance of the system. However, the measured values ​​of pressure sensors are often interfered by various factors, including the drift of the sensor itself (such as aging and mechanical fatigue), and nonlinear effects in complex physical environments. These problems may cause abnormal sensor readings, which in turn affects the credibility of the data and the overall performance of the system. To solve these problems, traditional methods usually use simple statistical methods or conventional data cleaning and filtering techniques, but they often have the following shortcomings when dealing with complex dynamic environments:

[0003] Lack of physical constraint verification: Traditional methods focus more on statistical laws and ignore the physical characteristics of the system where the sensor is located, resulting in insufficient ability to identify nonlinear anomalies.

[0004] Unable to effectively deal with long-term drift: Drift problems caused by aging are often ignored or simply handled. There is a lack of targeted dynamic compensation mechanisms, and the measurement accuracy of long-term operation cannot be guaranteed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for correcting data of abnormal readings of a pressure sensor to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for correcting abnormal pressure sensor reading data, comprising the following steps:

[0008] S1, collect sensor readings, and perform noise isolation and signal filtering;

[0009] S2, build a multi-sensor cross-validation mechanism to preliminarily identify abnormal readings;

[0010] S3, establish anomaly detection threshold model, dynamically adjust the threshold range, and identify nonlinear anomalies;

[0011] S4, build anomaly detection based on time series patterns to identify long-term drift anomalies;

[0012] S5. Build a physical constraint feedback mechanism to dynamically adjust the tolerance threshold according to the fluid conditions monitored by the sensor and correct nonlinear anomalies;

[0013] S6. Perform dynamic drift compensation to handle long-term drift anomalies;

[0014] S7, based on the above process, collect sensor readings and process them;

[0015] S8. Perform self-correction feedback optimization to adapt to new abnormal patterns.

[0016] To further optimize the technical solution, in step S1, wavelet transform decomposition or adaptive filtering algorithm is used to separate high-frequency noise and low-frequency interference in the sensor signal;

[0017] The signal is then spectrally analyzed to identify and remove noise interference at specific frequencies while retaining important signal features.

[0018] To further optimize the technical solution, in step S2, the multi-sensor cross-validation mechanism includes:

[0019] Use multiple pressure sensors to measure the target pressure simultaneously and identify outliers by cross-validating data between sensors;

[0020] The reading of a certain sensor deviates significantly from the other sensors, and it is preliminarily determined that the reading of the sensor may be abnormal;

[0021] Based on the weighted average strategy, weights are assigned to each sensor according to its historical stability. The higher the weight, the more credible the sensor data.

[0022] To further optimize the technical solution, in step S3, the anomaly detection threshold model is as follows:

[0023] ;

[0024] in,

[0025] : The historical average value of the pressure sensor, reflecting the expected value under normal working conditions;

[0026] : The standard deviation of the pressure readings, which measures the fluctuation range and dynamically adjusts the width of the threshold;

[0027] : The sensitivity coefficient of anomaly detection, ranging from [2.5-4.0], is used to control the tightness of the detection boundary;

[0028] : Environmental disturbance function, which represents the impact of environmental factors on sensor readings;

[0029] : Historical drift function, used to correct possible systematic deviations of the pressure sensor during long-term use.

[0030] To further optimize the technical solution, in step S4, the abnormality detection includes:

[0031] Use sliding window algorithm or long short-term memory network LSTM to analyze the time series characteristics of sensor data;

[0032] Identify sudden anomalies or long-term drift phenomena based on time series characteristics;

[0033] Identify long-term drift anomalies by detecting gradual trends in data fluctuations over time.

[0034] To further optimize the technical solution, in step S5, the physical constraint feedback mechanism includes:

[0035] When monitoring the fluid pressure in a pipeline, the Bernoulli equation and the continuity equation are combined to calculate the theoretical value and compare it with the sensor output;

[0036] If the deviation exceeds a reasonable range, it is marked as abnormal and fed back to the correction algorithm.

[0037] To further optimize the technical solution, in the physical constraint feedback mechanism, feedback is constructed by combining the difference between the theoretical pressure value calculated by the Bernoulli equation and the continuity equation and the actual measured value:

[0038] ;

[0039] in, is the pressure deviation, is the pressure value output by the sensor, is the theoretical pressure value;

[0040] like , If the tolerance threshold is exceeded, it is marked as abnormal, and the feedback correction mechanism corrects the measured value:

[0041] ;

[0042] in, To calibrate the pressure value, is the correction factor, which is used to avoid error amplification caused by over-correction. .

[0043] To further optimize the technical solution, in step S6, a dynamic drift compensation model is constructed based on the drift problem existing in the long-term use of the sensor. The dynamic drift compensation model is as follows:

[0044] ;

[0045] in,

[0046] : Corrected pressure value, i.e. target output value;

[0047] : Current measurement value of the sensor;

[0048] : Drift initial offset, zero offset calibration based on historical initial data;

[0049] : The linear component of drift, which represents the amount of drift that increases linearly with time, caused by sensor aging or mechanical strain;

[0050] : The exponential decay component of the drift, which is used to describe the rapid drift phenomenon of the sensor in the early stage;

[0051] : Sensor operation time, starting from startup.

[0052] To further optimize the technical solution, in step S7, the processing of the readings includes:

[0053] Data reconstruction fills in abnormal points;

[0054] Data stratification and screening;

[0055] Multi-level data verification.

[0056] To further optimize the technical solution, in step S8, the self-correction feedback optimization includes:

[0057] By collecting sensor and external system feedback in real time, including reference data provided by manual calibration or other measurement equipment;

[0058] Continuously adjust parameters to ensure the accuracy of calibration results, adopt cyclic optimization strategy, regularly self-check and update algorithms to adapt to new abnormal patterns;

[0059] The parameters adjusted include the sensitivity coefficient of the anomaly detection model , and the tolerance threshold of the physical constraint feedback mechanism.

[0060] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a method for correcting abnormal pressure sensor reading data as described in the first aspect of the present invention are implemented.

[0061] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a method for correcting abnormal pressure sensor reading data as described in the first aspect of the present invention are implemented.

[0062] Compared with the prior art, the present invention provides a method for correcting abnormal pressure sensor readings, which has the following beneficial effects:

[0063] This method of correcting abnormal pressure sensor readings overcomes the shortcomings of existing technologies through physical constraint feedback mechanisms and dynamic drift compensation, effectively improving the accuracy of abnormality detection in complex dynamic environments; through the physical constraint feedback mechanism, the sensor output is compared and verified with the theoretical physical model, enhancing the ability to identify nonlinear anomalies; through dynamic drift compensation, the linear and exponential drifts generated by the sensor in long-term use are separated and corrected, achieving accurate drift correction. This method can significantly improve the reliability and stability of pressure sensor data, provide a more accurate measurement basis for various precision measurement scenarios, and reduce the risk of system false alarms and failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 A schematic flow chart of a method for correcting abnormal pressure sensor readings proposed by the present invention;

[0066] Figure 2 A schematic flow chart of an abnormality detection threshold model in a method for correcting abnormal pressure sensor readings proposed by the present invention;

[0067] Figure 3 A flow chart of a physical constraint feedback mechanism in a method for correcting abnormal pressure sensor reading data proposed by the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0070] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0071] Embodiment 1:

[0072] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for correcting abnormal pressure sensor reading data, comprising the following steps:

[0073] S1. Collect sensor readings, perform noise isolation and signal filtering

[0074] In this embodiment, wavelet transform decomposition or adaptive filtering algorithm is used to separate high-frequency noise and low-frequency interference in the sensor signal;

[0075] The signal is then spectrally analyzed to identify and remove noise interference at specific frequencies while retaining important signal features.

[0076] This method can maintain data stability in extreme environments.

[0077] S2. Build a multi-sensor cross-validation mechanism

[0078] In this embodiment, the multi-sensor cross-validation mechanism includes:

[0079] Use multiple pressure sensors to measure the target pressure simultaneously and identify outliers by cross-validating data between sensors;

[0080] The reading of a certain sensor deviates significantly from the other sensors, and it is preliminarily determined that the reading of the sensor may be abnormal;

[0081] Based on the weighted average strategy, weights are assigned to each sensor according to its historical stability. The higher the weight, the more credible the sensor data.

[0082] S3. Establish anomaly detection threshold model

[0083] The model needs to consider the impact of environmental disturbances (temperature, vibration, electromagnetic interference, etc.) that the pressure sensor may be subject to on its readings, and adjust the dynamics of the threshold range by analyzing the correlation between these disturbances and the pressure readings.

[0084] S4. Anomaly detection based on time series patterns

[0085] In this embodiment, anomaly detection includes:

[0086] Use sliding window algorithm or long short-term memory network LSTM to analyze the time series characteristics of sensor data;

[0087] Identify sudden anomalies or long-term drift phenomena based on time series characteristics;

[0088] Long-term drift anomalies are identified by detecting the gradual trend of data fluctuations over a long period of time (i.e., the accumulation of changes in the second-order derivative).

[0089] S5. Constructing physical constraint feedback mechanism

[0090] In this embodiment, the physical constraint feedback mechanism includes:

[0091] When monitoring the fluid pressure in a pipeline, the Bernoulli equation and the continuity equation are combined to calculate the theoretical value and compare it with the sensor output;

[0092] If the deviation exceeds a reasonable range, it is marked as abnormal and fed back to the correction algorithm.

[0093] S6. Perform dynamic drift compensation

[0094] In this embodiment, based on the drift problem existing in the long-term use of the sensor, a dynamic drift compensation model is constructed, and the dynamic drift compensation model is as follows:

[0095] ;

[0096] in,

[0097] : Corrected pressure value, i.e. target output value;

[0098] : Current measurement value of the sensor;

[0099] : Drift initial offset, zero offset calibration based on historical initial data;

[0100] : The linear component of drift, which represents the amount of drift that increases linearly with time, caused by sensor aging or mechanical strain;

[0101] : The exponential decay component of the drift, which is used to describe the rapid drift phenomenon of the sensor in the early stage;

[0102] : Sensor operation time, starting from startup.

[0103] When this model is applied in pipeline pressure monitoring sensors,

[0104] Calculated during initial calibration .

[0105] After running for 1000 seconds, the real-time measurement value is .

[0106] Drift compensation amount:

[0107]

[0108] Calibration pressure:

[0109]

[0110] The final output correction value is 99.0Pa, which compensates for the drift error.

[0111] Through this model, the pressure sensor can achieve real-time drift compensation, ensure data accuracy and reliability in long-term use, and effectively reduce the risk of false alarms caused by drift.

[0112] S7, collect sensor readings and process them

[0113] In this embodiment, the processing of the readings includes:

[0114] Data reconstruction to fill in outliers

[0115] Use data reconstruction algorithms to fill in abnormal points, such as interpolation methods (linear, Lagrangian, polynomial interpolation) or prediction models based on historical data (ARIMA model or deep learning prediction model) to reconstruct possible real readings. In order to improve accuracy, the results of multiple reconstruction algorithms can be combined for weighted fusion to obtain reconstructed data with higher credibility.

[0116] Data stratification screening

[0117] The data are divided into high confidence zone, middle confidence zone and low confidence zone according to the degree of confidence.

[0118] Multi-level data verification

[0119] The data in the high confidence zone can be directly used for downstream analysis, the data in the middle zone needs further evaluation, and the data in the low confidence zone needs to be reconstructed or eliminated. Through this mechanism, the interference of abnormal data on the overall analysis can be reduced and the correction efficiency can be improved.

[0120] S8, self-correction feedback optimization

[0121] In this embodiment, the self-correction feedback optimization includes:

[0122] By collecting sensor and external system feedback in real time, including reference data provided by manual calibration or other measurement equipment;

[0123] Continuously adjust parameters to ensure the accuracy of calibration results, adopt cyclic optimization strategy, regularly self-check and update algorithms to adapt to new abnormal patterns;

[0124] The parameters adjusted include the sensitivity coefficient of the anomaly detection model , and the tolerance threshold of the physical constraint feedback mechanism.

[0125] By the sensitivity coefficient Make adjustments when environmental conditions change. The parameter adjustment can determine the threshold range of anomaly detection. For example, in an environment with large temperature changes, increasing The threshold width can be increased to avoid false positives.

[0126] By adjusting the tolerance threshold of the physical constraint feedback mechanism and dynamically adjusting the tolerance threshold according to the feedback error during the anomaly detection process, the model can adapt to the fault tolerance range under different working conditions, making the anomaly detection mechanism more flexible.

[0127] Embodiment 2:

[0128] Reference Figure 2-3 , which is the second embodiment of the present invention, and this embodiment provides the anomaly detection threshold model and usage process in step S3.

[0129] In this embodiment, the anomaly detection threshold model is as follows:

[0130] ;

[0131] in,

[0132] : The historical average value of the pressure sensor, reflecting the expected value under normal working conditions;

[0133] : The standard deviation of the pressure readings, which measures the fluctuation range and dynamically adjusts the width of the threshold;

[0134] : The sensitivity coefficient of anomaly detection, ranging from [2.5-4.0], is used to control the tightness of the detection boundary;

[0135] : Environmental disturbance function, which indicates the influence of environmental factors on sensor readings. The specific form is:

[0136]

[0137] In the formula,

[0138] : The influence of ambient temperature on pressure (linear or nonlinear, determined based on experimental data).

[0139] The output of the sensor can be measured experimentally at different temperatures. The sensor can be placed in multiple temperature environments, and the deviation between its output and the true pressure can be recorded to analyze the effect of temperature changes on the readings. Regression analysis can be used to determine the relationship between temperature and pressure readings.

[0140] : The influence of vibration intensity on the signal (correlation coefficient is determined by spectrum analysis).

[0141] Through spectrum analysis, the influence of vibrations of different frequencies on the output signal of the sensor is analyzed. First, the sensor is tested for vibration using an accelerometer or a vibration table to collect its output data at different frequencies. Through spectrum analysis (such as fast Fourier transform FFT), the correlation between the vibration frequency and the sensor output is determined.

[0142] : The influence of electromagnetic interference field strength (determined by sensor anti-interference capability calibration experiment).

[0143] Through the electromagnetic anti-interference capability calibration experiment, the changes in sensor output under different electromagnetic field strengths are recorded, and the correlation coefficient between the electromagnetic interference strength and the sensor reading is calculated.

[0144] coefficient Determined by regression analysis or optimization algorithms.

[0145] : Historical drift function, used to correct possible systematic deviations of the pressure sensor in long-term use, expressed as:

[0146]

[0147] In the formula,

[0148] : Sensor usage time.

[0149] : Exponential factor, describing the nonlinear change law of drift, adjusted based on historical test data.

[0150] : Correction amplitude, which is related to the type and working conditions of the sensor.

[0151] When used, the model includes:

[0152] Historical mean and standard deviation calculation: Use the historical operating data of the pressure sensor to calculate the mean and standard deviation according to the time window. For example, select the data of the past 24 hours and update it in real time to get and This allows tracking of the central trend and fluctuation range of pressure under normal working conditions.

[0153] Disturbance factor calculation: real-time monitoring of environmental parameters (such as temperature, vibration, electromagnetic interference), and inputting these parameters into the disturbance function The anomaly detection threshold is dynamically adjusted through real-time feedback from the sensor’s operating environment. For example, in a high-vibration environment, the effect of vibration on the reading may increase significantly, extending the detection range and reducing false alarms.

[0154] Drift compensation: Using the historical operating time and accumulated drift data of the sensor, the parameters of the drift function (such as as well as ). For sensors that have been in operation for a long time, the pressure readings may continue to deviate, and should be calculated by subtracting Compensate for systematic errors. For example, if after 1000 hours of operation, the detected deviation is 0.5 MPa, the compensation amount is deducted from the threshold.

[0155] Calculate dynamic thresholds: Substitute all components into the formula, calculate the upper and lower thresholds in real time, and make abnormal judgments.

[0156] Adaptive adjustment: By introducing a feedback mechanism, real-time adjustment is made based on the accuracy of anomaly detection (such as false alarm rate, missed alarm rate). For example, when the environmental disturbance is large and false alarms are frequent, the system can automatically increase , relax the detection scope.

[0157] The physical constraint feedback mechanism in step S5 is designed and applied.

[0158] In the physical constraint feedback mechanism, the theoretical pressure value formula calculated by combining the Bernoulli equation and the continuity equation is as follows:

[0159] ;

[0160] in, is the pressure value, is the fluid density, is the flow rate, is the acceleration due to gravity, is the fluid height;

[0161] Build feedback based on the difference between theoretical pressure value and actual measured value:

[0162] ;

[0163] like , If the tolerance threshold is exceeded, it is marked as abnormal, and the feedback correction mechanism corrects the measured value:

[0164] ;

[0165] in, is the correction factor, which is used to avoid error amplification caused by over-correction. .

[0166] When used, this mechanism includes:

[0167] Input physical parameters and initial conditions: Collect relevant physical parameters.

[0168] Real-time calculation of theoretical pressure value: Using the real-time flow rate and position height data of the sensor, the theoretical pressure of the target pressure point is calculated through the Bernoulli equation and the continuity equation If the sensor installation position is fixed, static physical parameters can be pre-stored to improve calculation efficiency.

[0169] Comparison of actual pressure and theoretical value: The sensor output pressure value and Compare and calculate pressure deviation If the deviation is within the tolerance range, the measurement value is considered reasonable; otherwise, it is marked as abnormal.

[0170] Abnormal feedback correction: Correct abnormal data points and adjust the deviation proportionally For uncorrectable anomalies (such as a completely failed sensor), an alarm mechanism is triggered and the output data of the sensor is discarded.

[0171] Multi-point data smoothing and verification: If the system consists of multiple sensors (such as installed at multiple locations along the pipeline), the correction results can be further optimized by comparing the data across sensors and verifying the continuity of the fluid. For example, the theoretical value of the downstream sensor can be used as the basis for verification to reduce the error impact of the isolated sensor.

[0172] Dynamically adjust the tolerance threshold: adjust the tolerance threshold in real time according to the fluid working conditions (such as drastic changes in flow rate or external interference) For example, when the flow rate change rate ( ) is large, relax the threshold appropriately to avoid false alarms.

[0173] Embodiment three:

[0174] This embodiment also provides a computer device, which is applicable to a method for correcting data of abnormal pressure sensor readings, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for correcting data of abnormal pressure sensor readings as proposed in the above embodiment.

[0175] This embodiment further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for correcting data of abnormal pressure sensor readings as proposed in the above embodiment.

[0176] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0177] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0179] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0180] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0181] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for correcting abnormal pressure sensor readings, characterized in that: The following steps are involved: S1, collect sensor readings, and perform noise isolation and signal filtering; S2, build a multi-sensor cross-validation mechanism to preliminarily identify abnormal readings; S3, establish anomaly detection threshold model, dynamically adjust the threshold range, and identify nonlinear anomalies; S4, build anomaly detection based on time series patterns to identify long-term drift anomalies; S5. Build a physical constraint feedback mechanism to dynamically adjust the tolerance threshold according to the fluid conditions monitored by the sensor and correct nonlinear anomalies; S6. Perform dynamic drift compensation to handle long-term drift anomalies; S7, based on the above process, collect sensor readings and process them; S8. Perform self-correction feedback optimization to adapt to new abnormal patterns.

2. A method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S1, wavelet transform decomposition or adaptive filtering algorithm is used to separate high-frequency noise and low-frequency interference in the sensor signal; The signal is then spectrally analyzed to identify and remove noise interference at specific frequencies while retaining important signal features.

3. A method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S2, the multi-sensor cross-validation mechanism includes: Use multiple pressure sensors to measure the target pressure simultaneously and identify outliers by cross-validating data between sensors; The reading of a certain sensor deviates significantly from the other sensors, and it is preliminarily determined that the reading of the sensor may be abnormal; Based on the weighted average strategy, weights are assigned to each sensor according to its historical stability. The higher the weight, the more credible the sensor data.

4. A method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S3, the anomaly detection threshold model is as follows: ； in, : The historical average value of the pressure sensor, reflecting the expected value under normal working conditions; : The standard deviation of the pressure readings, which measures the fluctuation range and dynamically adjusts the width of the threshold; : The sensitivity coefficient of anomaly detection, ranging from [2.5-4.0], is used to control the tightness of the detection boundary; : Environmental disturbance function, which represents the impact of environmental factors on sensor readings; : Historical drift function, used to correct possible systematic deviations of the pressure sensor during long-term use.

5. The method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S4, abnormality detection includes: Use sliding window algorithm or long short-term memory network LSTM to analyze the time series characteristics of sensor data; Identify sudden anomalies or long-term drift phenomena based on time series characteristics; Identify long-term drift anomalies by detecting gradual trends in data fluctuations over time.

6. A method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S5, the physical constraint feedback mechanism includes: When monitoring the fluid pressure in a pipeline, the Bernoulli equation and the continuity equation are combined to calculate the theoretical value and compare it with the sensor output; If the deviation exceeds a reasonable range, it is marked as abnormal and fed back to the correction algorithm.

7. A method for correcting abnormal pressure sensor readings according to claim 6, characterized in that: In the physical constraint feedback mechanism, feedback is constructed by combining the difference between the theoretical pressure value calculated by the Bernoulli equation and the continuity equation and the actual measured value: ； in, is the pressure deviation, is the pressure value output by the sensor, is the theoretical pressure value; like , If the tolerance threshold is exceeded, it is marked as abnormal, and the feedback correction mechanism corrects the measured value: ； in, To calibrate the pressure value, is the correction factor, which is used to avoid error amplification caused by over-correction. .

8. The method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S6, based on the drift problem existing in the long-term use of the sensor, a dynamic drift compensation model is constructed, and the dynamic drift compensation model is as follows: ； in, : Corrected pressure value, i.e. target output value; : Current measurement value of the sensor; : Drift initial offset, zero offset calibration based on historical initial data; : The linear component of drift, which represents the amount of drift that increases linearly with time, caused by sensor aging or mechanical strain; : The exponential decay component of the drift, which is used to describe the rapid drift phenomenon of the sensor in the early stage; : Sensor operation time, starting from startup.

9. A method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S7, the processing of the readings includes: Data reconstruction fills in abnormal points; Data stratification and screening; Multi-level data verification.

10. The method for correcting abnormal pressure sensor readings according to claim 1, characterized in that: In step S8, the self-correction feedback optimization includes: By collecting sensor and external system feedback in real time, including reference data provided by manual calibration or other measurement equipment; Continuously adjust parameters to ensure the accuracy of calibration results, adopt cyclic optimization strategy, regularly self-check and update algorithms to adapt to new abnormal patterns; The parameters adjusted include the sensitivity coefficient of the anomaly detection model , and the tolerance threshold of the physical constraint feedback mechanism.

Images