A method, system, device and storage medium for zero-point tracking self-calibration of differential pressure sensor

Abstract

The present invention relates to the field of measurement and testing technology, and specifically, to a differential pressure sensor zero-point tracking self-calibration method, system, device and storage medium. The differential pressure sensor zero-point tracking self-calibration method comprises the following steps: Environmental data acquisition: obtaining current environmental parameters through a detection module. The present invention saves manpower and time costs and improves the user experience of the product by setting an automatic zeroing and self-calibration mechanism without the need for manual intervention by the user. It also effectively maintains the long-term stability and accuracy of the sensor by real-time monitoring of the drift caused by temperature and humidity and performing compensation. At the same time, by using a filtering algorithm for calculation, it can completely eliminate the noise and drift generated by the sensor during long-term use, protect and extend the service life of the chip. In addition, the status of the device can be fed back in real time through the display module, so that the user can clearly understand the current working status of the system, thereby improving the confidence and satisfaction in the use of the device.

Description

Technical Field

[0001] The present invention relates to the field of measurement and testing technology, and in particular to a zero-point tracking self-calibration method, system, device and storage medium for a differential pressure sensor. Background Art

[0002] Differential pressure sensors are widely used in industrial equipment, gas management, medical equipment and other fields. Due to environmental factors, such as temperature and humidity changes, the sensor's detection value will drift, that is, the standard value will be offset. This phenomenon is called "temperature drift", which causes the sensor to have inaccurate measurement problems in actual application.

[0003] In the prior art, differential pressure sensors usually need to be calibrated before leaving the factory, but during long-term use, this calibration may be affected, resulting in reduced accuracy. To solve this problem, many systems use manual calibration or regular calibration to restore the accuracy of the sensor, but in actual applications, due to cumbersome operations and high time costs, the user experience is poor.

[0004] Therefore, there is an urgent need for a differential pressure sensor that can automatically perform zero-point tracking and self-calibration to maintain high accuracy and high reliability of the sensor without human intervention. Summary of the invention

[0005] The purpose of the present invention is to provide a differential pressure sensor zero-point tracking self-calibration method, system, device and storage medium to solve the problem that the prior art proposed in the above background technology uses manual calibration or periodic calibration to restore the accuracy of the sensor, which is cumbersome and time-consuming, resulting in a poor user experience.

[0006] In a first aspect, in order to solve the above technical problems, an embodiment of the present invention provides a differential pressure sensor zero tracking self-calibration method, comprising the following steps:

[0007] S1, Environmental data collection: obtain current environmental parameters through the detection module, including real-time collection of temperature and humidity data;

[0008] S2, drift detection: read the standard value and compare it with the environmental parameters collected in real time to determine whether there is drift;

[0009] S3, self-calibration trigger: if drift is found, the self-calibration procedure is triggered and the calibration algorithm is executed by the central processor;

[0010] S4, filtering: filtering the influence of temperature and humidity on the standard value to form a correction value;

[0011] S5, standard value update: update the standard value and store the corrected value in the storage medium;

[0012] S6, status feedback: complete a zero point reset process and output the current status to the display module.

[0013] In an optional implementation manner, the specific operation steps for collecting environmental data in step S1 are: S11, starting the temperature sensor and humidity sensor inside the detection module;

[0014] S12, configure sensors to read temperature and humidity data;

[0015] S13, set the sampling frequency, once per second or once every ten seconds;

[0016] S14. Store the acquired temperature and humidity data as initial environmental parameters.

[0017] In an optional implementation manner, the specific operation steps of performing drift detection in step S2 are:

[0018] S21, reading the current standard value from the storage medium;

[0019] S22, performing mathematical comparison with the acquired environmental parameters to calculate the drift value;

[0020] S23, determining whether the drift value exceeds a set threshold;

[0021] S24. If the drift value exceeds the threshold, it is recorded as a drift state and is ready for self-calibration.

[0022] In an optional implementation manner, the specific operation steps of performing the self-calibration trigger in step S3 are: S31, receiving the drift detection result by the central processor;

[0023] S32, determining whether the condition for performing self-calibration is met, that is, whether the drift amplitude reaches a preset value;

[0024] S33, starting the self-calibration algorithm, including selecting required calculation parameters;

[0025] S34. Record the timestamp of the start of the calibration, and evaluate the calibration effect later.

[0026] In an optional implementation manner, the specific operation steps of performing filtering processing in step S4 are:

[0027] S41. Collect temperature and humidity data for the past ten days;

[0028] S42, applying a moving average filter algorithm or a Kalman filter algorithm to smooth the data;

[0029] S43, calculating the detailed impact of temperature and humidity on the standard value, and generating a correction value;

[0030] S44, passing the filtered correction value to the central processing unit for updating.

[0031] In an optional implementation manner, the specific operation steps for updating the standard value in step S5 are: S51, updating the corrected value and the current standard value;

[0032] S52, writing the corrected value into a storage medium through a central processing unit;

[0033] S53, confirming that the data has been successfully written, and performing data verification to ensure accuracy;

[0034] S54. Update the timestamp of the corrected standard value for subsequent query and analysis.

[0035] In an optional implementation manner, the specific operation steps of performing status feedback in step S6 are:

[0036] S61, executing the zero point clearing procedure to reset the relevant calculation internal parameters;

[0037] S62, generating feedback information, including current standard value, status and whether self-calibration is completed;

[0038] S63, sending the feedback information to the display module through the driving unit;

[0039] S64, updating the status in real time on the display module to ensure that the user can visualize the current status and calibration results.

[0040] In a second aspect, an embodiment of the present invention further provides a differential pressure sensor zero-point tracking self-calibration system, including a display module, a detection module, a central processing unit, a drive unit, and a power module;

[0041] The detection module is used to collect environmental parameters in real time, including temperature and humidity data, to support subsequent drift detection and calibration processes. The output end of the detection module is connected to the central processor to transmit the collected environmental parameters to the central processor;

[0042] The central processing unit is used to process the real-time data collected by the detection module, compare it with the stored standard value, determine the drift situation, and execute the self-calibration algorithm and other calculation tasks. The output end of the central processing unit is connected to the drive unit to transmit the processing result to the drive unit;

[0043] The drive unit is used to transmit the processing results and status feedback of the central processor to the display module. The output end of the drive unit is connected to the display module to ensure that the user can see the working status and calibration results of the system in time, and is used to transmit the processing results to the display module and feedback the working status of the system;

[0044] The display module is used to intuitively present the current status of the system, standard values ​​and calibration process information, so that users can easily monitor the operating status of the sensor;

[0045] The power module is used to provide a stable power supply for the entire system to ensure the normal operation of each module. The output end of the power module is respectively connected to the display module, the detection module, the central processing unit and the drive unit to provide power.

[0046] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps included in any one of the implementations in the first aspect when executing the computer program.

[0047] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps included in any one of the implementations in the first aspect are implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. In the present invention, by setting up automatic zeroing and self-calibration mechanisms, there is no need for manual intervention by the user, which saves manpower and time costs, improves the user experience of the product, and is conducive to practical applications.

[0050] 2. The present invention effectively maintains the long-term stability and accuracy of the sensor by real-time monitoring of the drift caused by temperature and humidity and performing compensation. At the same time, by using a filtering algorithm for calculation, the noise and drift generated by the sensor during long-term use can be completely eliminated, thereby protecting and extending the service life of the chip.

[0051] 3. In the present invention, by setting a display module, the status of the device can be fed back in real time through the display module, so that the user can clearly understand the working status of the current system, thereby improving the confidence and satisfaction in the use of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the steps of the zero-point tracking self-calibration method of the differential pressure sensor of the present invention.

[0053] Figure 2 It is a schematic diagram of the operation flow of the zero-point tracking self-calibration method of the differential pressure sensor of the present invention.

[0054] Figure 3 It is a structural schematic diagram of the zero-point tracking self-calibration system of the differential pressure sensor of the present invention.

[0055] Figure 4 It is a schematic diagram of the overall structure of the present invention.

[0056] Figure 5 It is a front view of the internal parts connection structure of the present invention.

[0057] Figure 6 It is an exploded view of the connection structure of the second spring, the slide groove, the slide plate and other parts in the present invention.

[0058] Figure 7 for Figure 6 Enlarged view of point A in the middle.

[0059] In the figure: 1. Equipment body; 110. Mounting plate; 111. Pointer; 112. Silicone plate; 113. Fixing plate; 2. Anti-collision assembly; 210. Mounting box; 211. First connecting plate; 212. Second connecting plate; 213. First spring; 214. Rubber ring; 215. Slide groove; 216. Second spring; 217. Silicone plate; 218. Bottom plate; 219. Slider. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a differential pressure sensor zero point tracking self-calibration method, comprising the following steps:

[0062] Step 1: Environmental data collection: Obtain current environmental parameters through the detection module, including real-time collection of temperature and humidity data.

[0063] 1. Start the temperature sensor and humidity sensor inside the detection module.

[0064] A start signal is sent to a control component inside the detection module to activate an internal temperature sensor (such as a thermocouple, RTD or digital temperature sensor) and a humidity sensor (such as a resistive sensor).

[0065] The detection module performs a self-test procedure to confirm that the sensor is working properly and reads the initial status information.

[0066] Record the start time of the sensor for subsequent data processing and time calibration.

[0067] 2. Configure the sensor to read temperature and humidity data.

[0068] Set the sensor's operating mode, such as high-precision mode or fast-response mode, based on application requirements.

[0069] Configure the output format of the sensor (for example, digital or analog, temperature in degrees Celsius (°C) or Fahrenheit (°F), humidity in % relative humidity).

[0070] Initialize the measurement range of the sensor to ensure that it can adapt to the current environmental conditions (such as setting the temperature range to -20℃ to 85℃ and the humidity range to 0%RH to 100%RH).

[0071] 3. Set the sampling frequency to once per second or once every ten seconds.

[0072] Enter the desired sampling frequency and select an appropriate sampling interval (e.g. 1 second or 10 seconds).

[0073] A timer is set in the central processing unit to ensure that the data collection program is automatically triggered at set time intervals.

[0074] Record the timestamp of each sampling to facilitate subsequent analysis of data trends and drift.

[0075] 4. Store the acquired temperature and humidity data as initial environmental parameters.

[0076] When the sampling period arrives, the sensor reading function is called to read the current temperature (T) and humidity (H) data.

[0077] The formula is:

[0078] [T=f_{temp}(data)\quad(T is the current temperature)]

[0079] [H=f_{hum}(data)\quad (H is the current humidity)]

[0080] The temperature and humidity data are saved in memory to form the initial environmental parameters. For example, use data structure storage:

[0081] [Environment_Data={T_{initial},H_{initial}}]

[0082] Confirm that the data is stored successfully and record the status, such as storage time, storage location, and other information for subsequent review and verification.

[0083] The above steps help to achieve efficient and accurate environmental data collection.

[0084] Step 2: Drift detection: Read the standard value and compare it with the environmental parameters collected in real time to determine whether there is drift.

[0085] 1. Read the current standard value from the storage medium.

[0086] Access the storage medium (such as EEPROM or Flash storage) to obtain previously saved standard values, which are usually reference values ​​under calibrated normal working conditions.

[0087] Get the standard values ​​(S_T) (temperature part) and (S_H) (humidity part) through the corresponding read functions, for example:

[0088] [S_T=read_standard_value("temperature")]

[0089] [S_H=read_standard_value("humidity")]

[0090] Ensure that the read is successful and record the read timestamp for future tracing.

[0091] 2. Make mathematical comparison with the acquired environmental parameters and calculate the drift value.

[0092] Get the current environmental parameters from the above steps and set them as current values ​​(C_T) (current temperature) and (C_H) (current humidity).

[0093] Calculate the drift value (D_T) and (D_H):

[0094] [D_T=C_T-S_T]

[0095] [D_H=C_H-S_H]

[0096] Record the calculated drift value and save it for later judgment. It can be stored in the form of a dictionary or array:

[0097] [Drift_Values ​​= {D_T, D_H}]

[0098] 3. Determine whether the drift value exceeds the set threshold.

[0099] The drift value thresholds (T_T) (temperature drift threshold) and (T_H) (humidity drift threshold) are set, and these thresholds are determined based on system requirements and acceptable errors.

[0100] Compare the drift value with the threshold to determine whether any drift value exceeds the set threshold:

[0101] [\text{if}|D_T|>T_T\text{or}|D_H|>T_H:]

[0102] If the drift value is found to exceed the threshold, a flag is set to record the current status for subsequent processing.

[0103] 4. If the drift value exceeds the threshold, it is recorded as drift status and ready for self-calibration.

[0104] When the drift value exceeds the set threshold, the system status is recorded as "drift status" and saved in the event log or database for tracking and analysis.

[0105] Set the necessary parameters for self-calibration, such as the start flag (Calibration_Needed = True) to trigger the subsequent calibration process.

[0106] A notification or signal is sent to the user interface module to inform the user that the system has entered a drift state and is ready to perform self-calibration.

[0107] Prepare for the self-calibration process, including environmental parameters at the response time, drift values, and related timestamps.

[0108] Through the above steps, the drift detection procedure can be more clearly understood and the accuracy and effectiveness of the detection can be ensured.

[0109] Step 3: Self-calibration trigger: If drift is found, the self-calibration procedure is triggered and the calibration algorithm is executed by the central processor.

[0110] 1. The central processing unit receives the drift detection result.

[0111] The central processing unit obtains the results returned by the drift detection module through the data bus or the communication interface, including the drift values ​​(D_T) and (D_H).

[0112] Check the drift status flag to ensure that the drift detection module has completed the detection and the result is valid. Ensure that the received drift value is within the expected range.

[0113] The drift value is stored in a specified memory area for subsequent processing and analysis, which can be defined as:

[0114] [Drift_Result={D_T,D_H,\text{status:"drift detected"}}].

[0115] 2. Determine whether the conditions for performing self-calibration are met, that is, whether the drift amplitude reaches the preset value.

[0116] The conditional thresholds (C_T) (temperature drift threshold) and (C_H) (humidity drift threshold) for self-calibration can be set according to system performance requirements and development standards.

[0117] Compare the drift value with the threshold to determine whether the self-calibration conditions are met:

[0118] [\text{Calibrate_Needed}=(|D_T|>C_T),\text{or},(|D_H|>C_H)]

[0119] If the conditions are met, the system will set the flag to enter the self-calibration state and prepare to execute the subsequent steps. If the conditions are not met, the self-calibration process will be skipped and wait for the next detection.

[0120] 3. Start the self-calibration algorithm, including selecting the required calculation parameters.

[0121] Once it is confirmed that the self-calibration conditions are met, the central processing unit immediately starts the self-calibration algorithm and calls the corresponding self-calibration function or module.

[0122] According to the system requirements, select the required calculation parameters, such as calibration gain, offset, etc. You can define parameters:

[0123] [Calibration_Parameters={gain:K,offset:B}]

[0124] Initialize the algorithm parameters, such as the influencing factors of temperature and humidity, sampling frequency, and the maximum number of iterations of self-calibration, to ensure that the algorithm can run accurately and efficiently.

[0125] 4. Record the timestamp from the start of calibration to evaluate the calibration effect later.

[0126] At the beginning of starting the self-calibration algorithm, the current system time is obtained in time, and usually the system clock or real-time clock (RTC) is used to obtain an accurate timestamp.

[0127] Save the timestamp of the start of self-calibration (T_{start}):

[0128] [T_{start}=get_current_timestamp()]

[0129] Store the timestamp together with the self-calibration parameters to facilitate subsequent evaluation of the calibration effect. For example, record in the log:

[0130] [Calibration_Log={start_timestamp:T_{start},parameters:Calibration_Parameters}]

[0131] Through the above steps, the entire self-calibration process is ensured to proceed smoothly, and the necessary information for subsequent evaluation of the effect is obtained.

[0132] Step 4: Filtering: Filter the impact of temperature and humidity on the standard value to form a correction value.

[0133] 1. Collect temperature and humidity data for the past ten days.

[0134] Define a data storage structure (such as an array or list) to store the temperature (T_{past}) and humidity (H_{past}) data for the past ten days.

[0135] Through the sensor data interface or historical data storage module, the temperature (T_i) and humidity (H_i) data are extracted periodically (such as every hour) and saved to the corresponding storage structure:

[0136] [T_{past}[i]=T_i\quad\text{(i=0,1,...,239)}\quad\text{(assuming sampling every hour, a total of 240 samples)}]

[0137] [H_{past}[i]=H_i]

[0138] The time of data collection was recorded to ensure that the time series of the data was accurate and used in subsequent processing.

[0139] 2. Apply a moving average filter algorithm or a Kalman filter algorithm to smooth the data.

[0140] Moving average filtering: Defines the window size (n), usually 7 or 14, to smooth the data.

[0141] For each time point (i), calculate the moving average:

[0142] [M_T[i]=\frac{1}{n}\sum_{j=0}^{n-1}T_{past}[ij]]

[0143] [M_H[i]=\frac{1}{n}\sum_{j=0}^{n-1}H_{past}[ij]]

[0144] Kalman filter:

[0145] Initialize the state variables (x) (such as current temperature and humidity), the covariance matrix (P), and the measurement equations.

[0146] After obtaining each new measurement value, the prediction and correction steps are updated as follows:

[0147] Status prediction:

[0148] [x_{pred}=A\cdot x_{prev}+B\cdot u]

[0149] Calculate the Kalman gain:

[0150] [K=\frac{P_{pred}}{P_{pred}+R}]

[0151] Update status:

[0152] [x_{new}=x_{pred}+K\cdot(zH\cdot x_{pred})]

[0153] Where (z) is the measured value and (R) is the measurement noise matrix.

[0154] Update the covariance:

[0155] [P=(IK\cdot H)\cdot P_{pred}].

[0156] 3. Calculate the detailed impact of temperature and humidity on the standard value and generate the correction value.

[0157] Calculate the deviation of temperature and humidity and obtain the correction values ​​(C_T) and (C_H):

[0158] [C_T=M_T[i]-S_T]

[0159] [C_H=M_H[i]-S_H]

[0160] Analyzing the impact of these revisions on the standard values ​​may require the use of a weighting algorithm to take into account the impact of recent data:

[0161] [R_T=\alpha\cdot C_T+(1-\alpha)\cdot R_T\quad\text{(where}0<\alpha<1\text{is a weighting factor)}]

[0162] [R_H=\alpha\cdot C_H+(1-\alpha)\cdot R_H]

[0163] The final generated correction value is recorded for subsequent updating and comparison.

[0164] 4. Pass the filtered correction value to the central processing unit for updating.

[0165] Encapsulate the calculated correction values ​​(R_T) and (R_H) into a data structure, such as a dictionary or object:

[0166] [Correction_Data = {R_T, R_H}]

[0167] Use the communication interface (such as I2C, SPI or UART, etc.) to send the correction value to the central processor and call the corresponding communication function:

[0168] [send_to_CPU(Correction_Data)]

[0169] Ensure successful communication and log when confirmation is received for subsequent monitoring and debugging of the calibration process.

[0170] Through the above steps, the accuracy and stability of the data are ensured, thereby improving the measurement accuracy of the system.

[0171] Step 5: Update standard value: Update the standard value and store the corrected value in the storage medium.

[0172] 1. Update the corrected value with the current standard value.

[0173] Read the current standard values ​​(S_T) (standard temperature) and (S_H) (standard humidity) from the storage medium and load them into the corresponding variables.

[0174] Update the standard value according to the correction values ​​(R_T) and (R_H):

[0175] [S_T^{new}=S_T+R_T]

[0176] [S_H^{new}=S_H+R_H]

[0177] The updated standard values ​​(S_T^{new}) and (S_H^{new}) need to be stored for use in future operations.

[0178] 2. Write the corrected value to the storage medium through the central processing unit.

[0179] The CPU selects a suitable storage medium (such as EEPROM, flash memory or other non-volatile memory).

[0180] Package the modified standard value into a data structure for writing, for example:

[0181] [New_Standard_Values={S_T^{new},S_H^{new}}]

[0182] Through the corresponding communication and writing functions, the data is written to the storage medium. The sample pseudo code is as follows:

[0183] [write_to_storage(New_Standard_Values)]

[0184] Ensure that the write operation has started and monitor its progress to prevent data loss.

[0185] 3. Confirm that the data has been written successfully and perform data verification.

[0186] Read the data in the storage medium to ensure that the write operation was successful by:

[0187] [Retrieved_Values=read_from_storage()]

[0188] Compare the read value with the expected value for verification:

[0189] [Validated=(Retrieved_Values[0]==S_T^{new}),\text{and},(Retrieved_Values[1]==S_H^{new})]

[0190] If the verification is successful, the data written is confirmed to be valid. If the verification fails, the error information should be recorded and corresponding measures should be taken, such as rewriting or alarming.

[0191] 4. Update the timestamp of the corrected standard value.

[0192] Get the current system timestamp (T_{update}) to record the specific time of the update. The system clock or real-time clock can be used as the time source:

[0193] [T_{update}=get_current_timestamp()]

[0194] Associate the timestamp with the corrected standard value and store it in the following structure:

[0195] [Timestamp_Record={S_T^{new},S_H^{new},T_{update}}]

[0196] Write this record to a storage medium or log file for later query and tracking. For example, create a log entry:

[0197] [log_entry={"Standard Values ​​Updated",Timestamp_Record}]

[0198] If necessary, send the update log to a monitoring system or user interface to provide operational feedback.

[0199] By following the above steps, it is ensured that the data is kept consistent and accurate throughout the self-calibration scheme.

[0200] Step 6: Status feedback: Complete a zero point reset process and output the current status to the display module.

[0201] 1. Execute the zero point reset procedure to reset the relevant calculation internal parameters.

[0202] Start the zero point reset procedure and prepare for parameter reset.

[0203] Reset all relevant internal parameters in the program, including but not limited to:

[0204] Current measured values ​​(M_T) and (M_H), initialized to zero or default values.

[0205] The calculated deviations (C_T) and (C_H) should also be set to zero:

[0206] [M_T=0,\quad M_H=0]

[0207] [C_T=0,\quad C_H=0]

[0208] Update the status flag to indicate that clear mode has been entered.

[0209] After the zeroing is complete, stop the associated measurements and confirm that the parameters have been successfully reset.

[0210] 2. Generate feedback information, including current standard value, status and whether self-calibration is completed.

[0211] Collect the current standard values ​​(S_T^{new}) and (S_H^{new}):

[0212] [Standard_Values={S_T^{new},S_H^{new}}]

[0213] Create a feedback information structure, including the following:

[0214] Current standard value; device status (for example, running, standby, clearing, etc.); a flag indicating whether self-calibration is completed (generally represented by a Boolean value).

[0215] Confirm that the feedback information generated is complete and accurate and suitable for subsequent dispatch.

[0216] 3. Send the feedback information to the display module through the drive unit.

[0217] Write data transmission functions to ensure that feedback information can be transmitted accurately.

[0218] Call the data transmission function written above to send the generated feedback information to the display module.

[0219] Ensure that possible abnormal situations are handled during the transmission process and ensure the integrity of the feedback information.

[0220] 4. Update the status in real time on the display module.

[0221] Receives feedback from the CPU and parses it to ensure that the format is correct and the information is complete.

[0222] The current standard value, status and self-calibration completion indicator are displayed via the graphical user interface or display.

[0223] Set the frequency of display updates to ensure that users can obtain status information in real time.

[0224] At the same time, ensure that the displayed content is clear and concise, and can effectively convey the current status of the system and any possible problems to improve the user experience.

[0225] Through the above steps, it is ensured that the system can provide timely feedback on its status during the calibration process, thereby improving the user's understanding and control of the equipment operation status.

[0226] Based on the same inventive concept, an embodiment of the present invention also provides a differential pressure sensor zero-point tracking self-calibration system, which can realize the functions corresponding to the aforementioned differential pressure sensor zero-point tracking self-calibration method. The differential pressure sensor zero-point tracking self-calibration system can be a hardware structure, a software module, or a hardware structure plus a software module. The differential pressure sensor zero-point tracking self-calibration system can be implemented by a chip system, which can be composed of chips or include chips and other discrete devices. Please refer to Figure 3 , is a structural schematic diagram of a differential pressure sensor zero-point tracking self-calibration system provided in an embodiment of the invention. The differential pressure sensor zero-point tracking self-calibration system includes a display module, a detection module, a central processing unit, a drive unit and a power supply module.

[0227] Among them, the detection module is used to collect environmental parameters in real time, including temperature and humidity data, to support the subsequent drift detection and calibration process. The output end of the detection module is connected to the central processor for transmitting the collected environmental parameters to the central processor. The central processor is used to process the real-time data collected by the detection module, compare it with the stored standard value, judge the drift situation, and execute the self-calibration algorithm and other calculation tasks. The output end of the central processor is connected to the drive unit for transmitting the processing results to the drive unit. The drive unit is used to transmit the processing results and status feedback of the central processor to the display module. The output end of the drive unit is connected to the display module to ensure that the user can see the working status and calibration results of the system in time, and is used to transmit the processing results to the display module and feedback the working status of the system. The display module is used to intuitively present the current status of the system, standard values ​​and calibration process information, so that the user can easily monitor the operating status of the sensor. The power module is used to provide a stable power supply for the entire system to ensure the normal operation of each module. The output end of the power module is connected to the display module, the detection module, the central processor and the drive unit respectively to provide power.

[0228] In actual application, Figure 2 As shown, first, the environmental parameters are collected through the detection module, and then the collected environmental parameters are transmitted to the central processing unit. The collected environmental parameters are processed by the central processing unit and compared with the stored standard values ​​to determine the drift situation. If no drift is found, the operation is terminated. If drift is found, the self-calibration program is triggered and the self-calibration algorithm is executed to filter the influence of temperature and humidity on the standard value to form a correction value, and the standard value is updated at the same time. Then, the processing result and status feedback of the central processing unit are transmitted to the display module through the driving unit, and then the display module is used to intuitively present the current status of the system, the standard value and the information of the calibration process.

[0229] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the functions corresponding to the aforementioned differential pressure sensor zero point tracking self-calibration method when executing the computer program.

[0230] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the functions corresponding to the aforementioned differential pressure sensor zero point tracking self-calibration method are implemented.

[0231] In summary, the present invention saves manpower and time costs and improves the user experience of the product by setting up an automatic zeroing and self-calibration mechanism, without the need for manual intervention by the user. It also effectively maintains the long-term stability and accuracy of the sensor by real-time monitoring of the drift caused by temperature and humidity and compensating for it. At the same time, by adopting a filtering algorithm for calculation, it can completely eliminate the noise and drift generated by the sensor during long-term use, thereby protecting and extending the service life of the chip. In addition, the status of the device can be fed back in real time through the display module, so that the user can clearly understand the current working status of the system, thereby improving confidence and satisfaction in the use of the device.

[0232] like Figure 4-7 As shown, in view of the problems in the prior art, the present invention provides a calibration device, including an equipment body 1, the anti-collision component 2 is fixedly installed inside the equipment body 1, the anti-collision component 2 includes an installation box 210, a first connecting plate 211, a second connecting plate 212, a first spring 213, a rubber ring 214, a slide groove 215, a second spring 216, a slide plate 217, a bottom plate 218 and a slider 219, the installation box 210 is fixedly installed inside the equipment body 1, the first connecting plate 211 is fixedly connected to the inside of the installation box 210, and both ends of the first connecting plate 211 are fixedly connected to the equipment body The outer wall of the device body 1, the second connecting plate 212 is fixedly connected to the interior of the installation box 210, the two ends of the second connecting plate 212 are fixedly connected to the outer wall of the device body 1, the center position of the second connecting plate 212 is fixedly connected to the center position of the first connecting plate 211, the slider 219 is slidably connected to the inside of the first connecting plate 211 and the second connecting plate 212, one end of the plurality of groups of the first springs 213 is fixedly connected to the outer wall of the device body 1, the inner wall of the rubber ring 214 is fixedly connected to one end of the slider 219, and the inner wall of the rubber ring 214 is fixedly connected to the other end of the plurality of groups of the first springs 213.

[0233] Preferably, a group of slide grooves 215 are opened inside the rubber ring 214, and multiple groups of second springs 216 are fixedly connected in the slide grooves 215. The upper end of the second spring 216 is fixedly connected to one side of a slide plate 217. The slide plate 217 is slidably connected to the inside of the slide groove 215. The upper and middle parts of the slide plate 217 are located at the periphery of the slide groove 215. A group of bottom plates 218 are fixedly connected to the upper end of the slide plate 217. There is a certain gap between the bottom plate 218 and the fixed plate 113. A group of mounting plates 110 are fixedly opened on one side of the device body 1. A group of pointers 111 are fixedly installed inside the mounting plate 110. A group of fixing plates 113 are fixedly installed on the rear side of the device body 1.

[0234] (1) In the present invention, when the side of the device body 1 is hit, the rubber ring 214 will be stressed, and the slider 219 and the first spring 213 will also be manually contracted. The first spring 213 will absorb part of the energy caused by the impact. In addition, a group of silicone plates 112 are fixedly installed inside the device body 1, which will also absorb part of the energy caused by the impact. This can effectively prevent the parts inside the device body 1 from being damaged by the energy caused by the impact, and improve the overall anti-collision effect of the device body 1.

[0235] (2) The present invention has a group of slide grooves 215 formed inside the rubber ring 214, and a plurality of groups of second springs 216 are fixedly installed inside the slide grooves 215. The second springs 216 and the slide plate 217 are fixedly connected, and the slide plate 217 is also slidably connected inside the slide grooves 215. When the bottom plate 218 is hit, the bottom plate 218 will drive the slide plate 217 to completely enter the inside of the slide grooves 215. At this time, the slide plate 217 will squeeze the second springs 216. The second springs 216 are compressed and absorb part of the energy caused by the impact. In addition, the bottom plate 218 is made of rubber, which can play a certain anti-collision effect, thereby effectively preventing the parts inside the device body 1 from being damaged due to impact or falling.

[0236] Working principle: When the side of the device body 1 is hit, the rubber ring 214 will be stressed, and the slider 219 and the first spring 213 will also be manually contracted. The first spring 213 will absorb part of the energy caused by the impact, and a group of silicone plates 112 are fixedly installed inside the device body 1, which will also absorb part of the energy caused by the impact, which can effectively prevent the parts inside the device body 1 from being damaged by the energy caused by the impact, and improve the overall anti-collision effect of the device body 1. A group of slide grooves 215 are opened inside the rubber ring 214, and the inside of the slide groove 215 is fixed. Multiple sets of second springs 216 are installed, and the second springs 216 and the slide plate 217 are fixedly connected. The slide plate 217 is also slidably connected to the inside of the slide groove 215. When the bottom plate 218 is hit, the bottom plate 218 will drive the slide plate 217 to completely enter the inside of the slide groove 215. At this time, the slide plate 217 will squeeze the second spring 216. The second spring 216 is compressed and will absorb part of the energy caused by the impact. The bottom plate 218 is made of rubber, which can play a certain anti-collision effect, thereby effectively preventing the parts inside the device body 1 from being damaged due to collision or falling.

[0237] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A zero-point tracking self-calibration method for a differential pressure sensor, characterized in that: The following steps are involved: S1, Environmental data collection: obtain current environmental parameters through the detection module, including real-time collection of temperature and humidity data; S2, drift detection: read the standard value and compare it with the environmental parameters collected in real time to determine whether there is drift; S3, self-calibration trigger: if drift is found, the self-calibration procedure is triggered and the calibration algorithm is executed by the central processor; S4, filtering: filtering the influence of temperature and humidity on the standard value to form a correction value; S5, standard value update: update the standard value and store the corrected value in the storage medium; S6, status feedback: complete a zero point reset process and output the current status to the display module.

2. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps for collecting environmental data in step S1 are: S11, start the temperature sensor and humidity sensor inside the detection module; S12, configure sensors to read temperature and humidity data; S13, set the sampling frequency, once per second or once every ten seconds; S14. Store the acquired temperature and humidity data as initial environmental parameters.

3. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps of performing drift detection in step S2 are: S21, reading the current standard value from the storage medium; S22, performing mathematical comparison with the acquired environmental parameters to calculate the drift value; S23, determining whether the drift value exceeds a set threshold; S24. If the drift value exceeds the threshold, it is recorded as a drift state and is ready for self-calibration.

4. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps of performing the self-calibration trigger in step S3 are: S31, the central processing unit receives the drift detection result; S32, determining whether the condition for performing self-calibration is met, that is, whether the drift amplitude reaches a preset value; S33, starting the self-calibration algorithm, including selecting required calculation parameters; S34. Record the timestamp of the start of the calibration, and evaluate the calibration effect later.

5. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps of the filtering process in step S4 are: S41. Collect temperature and humidity data for the past ten days; S42, applying a moving average filter algorithm or a Kalman filter algorithm to smooth the data; S43, calculating the detailed impact of temperature and humidity on the standard value, and generating a correction value; S44, passing the filtered correction value to the central processing unit for updating.

6. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps for updating the standard value in step S5 are: S51, updating the corrected value and the current standard value; S52, writing the corrected value into a storage medium through a central processing unit; S53, confirming that the data has been written successfully, and performing data verification; S54. Update the timestamp of the corrected standard value.

7. The zero-point tracking self-calibration method for a differential pressure sensor according to claim 1, characterized in that: The specific operation steps for performing status feedback in step S6 are: S61, executing the zero point clearing procedure to reset the relevant calculation internal parameters; S62, generating feedback information, including current standard value, status and whether self-calibration is completed; S63, sending the feedback information to the display module through the driving unit; S64. Update the status on the display module in real time.

8. A differential pressure sensor zero-point tracking self-calibration system, characterized in that: It includes a display module, a detection module, a central processing unit, a drive unit and a power module; The detection module is used to collect environmental parameters in real time, including temperature and humidity data. The output end of the detection module is connected to the central processor to transmit the collected environmental parameters to the central processor; The central processing unit is used to process the real-time data collected by the detection module, compare it with the stored standard value, determine the drift situation, and execute the self-calibration algorithm and other calculation tasks. The output end of the central processing unit is connected to the drive unit to transmit the processing result to the drive unit; The driving unit is used to transmit the processing result and status feedback of the central processor to the display module, and the output end of the driving unit is connected to the display module to transmit the processing result to the display module and feedback the working status of the system; The display module is used to intuitively present the current status, standard value and calibration process information of the system; The power module is used to provide a stable power supply for the entire system, and the output ends of the power module are respectively connected to the display module, the detection module, the central processing unit and the driving unit for providing power.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the step of zero-point tracking self-calibration of the differential pressure sensor as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of zero-point tracking and self-calibration of the differential pressure sensor as claimed in any one of claims 1 to 7 are implemented.

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