Atmospheric sensing system internal pressure self-calibration method

Abstract

The invention provides an atmospheric sensing system internal pressure self-calibration method, which comprises the following steps of: acquiring an original pressure measurement value (actual pressure measurement value) of multiple paths of pressure when a system is electrified and started by constructing a self-calibration model of the internal multiple paths of pressure; based on a self-calibration implementation scheme of original pressure anomaly detection and an iterative mean alignment mathematical fitting model, system deviations, gain errors or drifts of all paths of pressure sensors are estimated and corrected through data fusion and iterative optimization by utilizing a consistency relationship among a plurality of pressure sensors; error compensation and mutual calibration between the pressure sensors are realized. By means of the technical scheme, the technical problems that in the prior art, access type calibration needs to depend on special equipment and special gas circuit tools, and the calibration process is time-consuming and labor-consuming are solved.

Description

Technical Field

[0001] This invention relates to the field of pressure sensing technology, and in particular to a method for self-calibrating internal pressure in an atmospheric sensing system. Background Technology

[0002] Atmospheric sensing systems employ multiple pressure sensors for pressure measurement. However, these sensors are susceptible to unforeseen factors such as environmental conditions, internal stress within the elastic body, malfunctions, and external interference, leading to systematic errors and affecting measurement accuracy. Traditional calibration methods for the internal pressure sensors of atmospheric sensing systems typically involve access-based calibration. When calibrating the system, a higher-precision standard pressure measuring device must be manually installed on a dedicated pneumatic measurement fixture. The pressure generated by the standard device serves as the pressure source, and the output of both the standard pressure source and the pressure measured by the atmospheric sensing system are recorded. Subsequent analysis of the standard and measured pressure values ​​yields the calibration result for the calibrated pressure measurement unit. This calibration process is time-consuming, labor-intensive, and requires specialized equipment and pneumatic fixtures, failing to meet the requirements of simplicity, practicality, and ease of implementation. Summary of the Invention

[0003] This invention provides a method for self-calibrating the internal pressure of an atmospheric sensing system, which can solve the technical problems of existing technologies where access calibration requires special equipment and special gas path fixtures, and the calibration process is time-consuming and labor-intensive.

[0004] According to one aspect of the present invention, a method for self-calibrating internal pressure of an atmospheric sensing system is provided. The atmospheric sensing system includes N pressure sensors, and the method includes:

[0005] S1, Establish a pressure self-calibration model based on additive bias;

[0006] S2, acquire the N actual pressure measurement values ​​output by the N pressure sensors at the current moment;

[0007] S3, calculate the mean and standard deviation of the N actual pressure measurements at the current moment;

[0008] S4. Calculate the error of each pressure sensor at the current moment based on the actual pressure measurement value of each channel and the mean and standard deviation of the actual pressure measurement values ​​of N channels. Determine whether the error of each pressure sensor is greater than the preset error threshold. If not, determine that the corresponding pressure sensor is abnormal and proceed to S5. If yes, determine that the corresponding pressure sensor is normal and output the actual pressure measurement value.

[0009] S5, assign an initial value to the additive bias, and use the actual pressure measurement value of the current N pressure sensors as the initial pressure reading in the pressure self-calibration model to obtain the initialized pressure self-calibration model;

[0010] S6, calculate the average of the current pressure readings of the N pressure sensors as the true value of the group perception in this round, and calculate the average difference between the true value of the group perception in this round and the pressure reading of each abnormal pressure sensor.

[0011] S7. Assign the average difference obtained in S6 to the additive bias to obtain the updated additive bias. Based on the pressure self-calibration model, use the updated additive bias to self-calibrate the current pressure reading of each abnormal pressure sensor to obtain the pressure reading of each abnormal pressure sensor after this round of self-calibration.

[0012] S8. Calculate the change in additive bias of each abnormal pressure sensor before and after this round of update, and determine whether the change in additive bias is less than the preset change threshold. If not, go to S9; if yes, go to S10.

[0013] S9, take the pressure reading of the corresponding abnormal pressure sensor after this round of self-calibration as the updated pressure reading, and then switch to S6;

[0014] S10, the corresponding abnormal pressure sensor stops iterating and outputs the pressure reading after this round of self-calibration.

[0015] Furthermore, the established pressure self-calibration model is as follows:

[0016] y i (t)=x i (t)+b i +ε i (t),

[0017] In the above formula, y i (t) represents the pressure reading of the i-th pressure sensor at time t, x(t) represents the actual pressure measurement value of the i-th pressure sensor at time t, and b i For the additive bias of the i-th pressure sensor, ε i (t) represents the measurement random noise of the i-th pressure sensor at time t.

[0018] Furthermore, the mean and standard deviation of the N actual pressure measurements at the current moment are calculated using the following formula:

[0019]

[0020] In the above formula, X is the average of the actual pressure measurements from N channels. i Let σ be the actual pressure measurement value of the i-th pressure sensor, and σ be the standard deviation of the actual pressure measurement values ​​of the N-th sensor.

[0021] Furthermore, the error of each pressure sensor at the current moment is calculated using the following formula, based on the current actual pressure measurement value of each channel and the mean and standard deviation of the N channels' actual pressure measurements:

[0022]

[0023] In the above formula, Y i This represents the error of the i-th pressure sensor.

[0024] Furthermore, the average current pressure readings of the N pressure sensors are calculated using the following formula:

[0025]

[0026] In the above formula, Let y be the average of the actual pressure measurements from N pressure sensors at time t after the k-th iteration. i (k) (t) represents the actual pressure measurement value of the i-th pressure sensor at time t, and the pressure reading after the k-th iteration.

[0027] Furthermore, the average difference between the current group perception value and the pressure reading of each abnormal pressure sensor is calculated using the following formula:

[0028]

[0029] In the above formula, Let T represent the additive bias of the i-th abnormal pressure sensor in round k+1, and let T represent the number of time sampling points.

[0030] Furthermore, the current pressure reading of each abnormal pressure sensor is self-calibrated using the updated additive bias based on the pressure self-calibration model using the following formula:

[0031]

[0032] In the above formula, y i (k+1) (t) represents the actual pressure measurement value of the i-th abnormal pressure sensor at time t, and the pressure reading after the (k+1)-th iteration.

[0033] Furthermore, the change in additive bias of each abnormal pressure sensor before and after this round of updates is calculated using the following formula:

[0034]

[0035] In the above formula, Δ (k) This represents the change between the additive bias in the (k+1)th iteration and the additive bias in the kth iteration. This represents the additive bias of the i-th abnormal pressure sensor k-th wheel.

[0036] This invention provides a method for self-calibrating internal pressure in an atmospheric sensing system. This method constructs a self-calibration model for multiple internal pressure channels, acquiring the original pressure measurements (actual pressure measurements) of these channels upon system startup. Based on a self-calibration scheme using original pressure anomaly detection and iterative mean alignment mathematical fitting model, it leverages the consistency relationships between multiple pressure sensors. Through data fusion and iterative optimization, it estimates and corrects the system deviation, gain error, or drift of each pressure sensor, achieving error compensation and mutual calibration between pressure sensors. This method can automatically identify, estimate, compensate for, and correct system errors in the pressure measurement results of the atmospheric sensing system. Particularly useful when a pressure sensor's measurement accuracy deviates significantly due to some reason during system startup, it can perform mutual calibration even without an external reference standard (no "gold standard" sensor), further improving the product's measurement accuracy and reliability. It is simple, practical, easy to implement, effective, and highly feasible, meeting the requirements for convenient system application. Attached Figure Description

[0037] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0038] Figure 1 A flowchart illustrating an internal pressure self-calibration method for an atmospheric sensing system according to a specific embodiment of the present invention is shown. Detailed Implementation

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0042] According to a specific embodiment of the present invention, a method for self-calibrating internal pressure of an atmospheric sensing system is provided. The atmospheric sensing system includes N pressure sensors. Since the atmospheric sensing system operates under ambient atmospheric pressure, the multiple sensors measure the ambient atmospheric pressure (most pressure sensors in the system are functioning normally). Therefore, the present invention considers utilizing the data consistency relationship between the multiple pressure sensors for anomaly detection and self-calibration. Specifically, the method includes:

[0043] S1, Establish a pressure self-calibration model based on additive bias;

[0044] S2, acquire the N actual pressure measurement values ​​output by the N pressure sensors at the current moment;

[0045] S3, calculate the mean and standard deviation of the N actual pressure measurements at the current moment;

[0046] S4. Calculate the error of each pressure sensor at the current moment based on the actual pressure measurement value of each channel and the mean and standard deviation of the actual pressure measurement values ​​of N channels. Determine whether the error of each pressure sensor is greater than the preset error threshold. If not, determine that the corresponding pressure sensor is abnormal and proceed to S5. If yes, determine that the corresponding pressure sensor is normal and output the actual pressure measurement value.

[0047] S5, assign an initial value to the additive bias, and use the actual pressure measurement value of the current N pressure sensors as the initial pressure reading in the pressure self-calibration model to obtain the initialized pressure self-calibration model;

[0048] S6, calculate the average of the current pressure readings of the N pressure sensors as the true value of the group perception in this round, and calculate the average difference between the true value of the group perception in this round and the pressure reading of each abnormal pressure sensor.

[0049] S7. Assign the average difference obtained in S6 to the additive bias to obtain the updated additive bias. Based on the pressure self-calibration model, use the updated additive bias to self-calibrate the current pressure reading of each abnormal pressure sensor to obtain the pressure reading of each abnormal pressure sensor after this round of self-calibration.

[0050] S8. Calculate the change in additive bias of each abnormal pressure sensor before and after this round of update, and determine whether the change in additive bias is less than the preset change threshold. If not, go to S9; if yes, go to S10.

[0051] S9, take the pressure reading of the corresponding abnormal pressure sensor after this round of self-calibration as the updated pressure reading, and then switch to S6;

[0052] S10, the corresponding abnormal pressure sensor stops iterating and outputs the pressure reading after this round of self-calibration.

[0053] The specific values ​​of the preset error threshold and the preset change threshold are determined based on the actual situation. For example, the preset error threshold is set to 3 times the standard deviation, and the preset change threshold is set to 10. -6 .

[0054] This configuration provides a self-calibration method for internal pressure in an atmospheric sensing system. This method constructs a self-calibration model for multiple internal pressure channels, acquiring the raw pressure measurements (actual pressure measurements) of these channels upon system startup. Based on a self-calibration scheme using raw pressure anomaly detection and iterative mean alignment mathematical fitting model, it leverages the consistency relationships between multiple pressure sensors. Through data fusion and iterative optimization, it estimates and corrects the system deviations, gain errors, or drift of each pressure sensor, achieving error compensation and mutual calibration between pressure sensors. This method can automatically identify, estimate, compensate for, and correct system errors in the pressure measurement results of the atmospheric sensing system. Especially when a pressure sensor's measurement accuracy deviates significantly due to some reason during system startup, it can perform mutual calibration even without an external reference standard (no "gold standard" sensor), further improving the product's measurement accuracy and reliability. It is simple, practical, easy to implement, effective, and highly feasible, meeting the requirements for convenient system application. Compared with existing technologies, this invention solves the technical problem that access-based calibration requires dedicated equipment and gas path fixtures, and the calibration process is time-consuming and labor-intensive.

[0055] To facilitate a clearer understanding of the self-calibration method provided by this invention, the following will use practical application examples to illustrate each of the above processes in detail. Those skilled in the art will understand that these examples are only for the purpose of facilitating a clearer understanding of the self-calibration method provided by this invention and do not constitute any technical limitation.

[0056] After the atmospheric sensing system (containing N internal pressure sensors) is powered on, it first acquires the raw pressure values ​​(actual pressure measurements) output by the N internal pressure sensors. Then, it performs anomaly detection on the acquired N raw pressure values. The anomaly detection method for each raw pressure sensor is as follows:

[0057] 1) Calculate the average value of the actual pressure measurements output by N pressure sensors at the same time.

[0058] in: X is the average of the actual pressure measurements from N channels. i This represents the actual pressure measurement value of the i-th pressure sensor.

[0059] 2) Calculate the standard deviation σ of the actual pressure measurements output by the N pressure sensors at the same time:

[0060]

[0061] Where: σ is the standard deviation of the actual pressure measurements of N channels.

[0062] 3) Calculate the error Y of the i-th pressure sensor.i :

[0063]

[0064] Where: Y i This represents the error of the i-th pressure sensor.

[0065] When Y i If the error exceeds 3 times the standard deviation (i.e., 3σ), the data output by the pressure sensor is considered abnormal, and a pressure self-calibration operation is required.

[0066] First, a pressure self-calibration model is established for the output of each pressure sensor:

[0067] y i (t)=x i (t)+b i +ε i (t) (4)

[0068] In the above formula, y i x(t) represents the pressure reading of the i-th pressure sensor at time t, and x(t) represents the actual pressure measurement value (unknown) of the i-th pressure sensor at time t. i For the additive bias of the i-th pressure sensor, ε i (t) represents the random noise (zero mean) of the i-th pressure sensor at time t.

[0069] The number of pressure sensors is N, and the number of time sampling points is T. Therefore, the matrix of raw data (actual pressure measurements) after T time sampling points is Y∈R. N×T Let Y be an N x T matrix, where each row corresponds to one sensor and each column corresponds to one time sampling point. Each element of the matrix is ​​Y. it =y i (t).

[0070] The pressure self-calibration method is as follows:

[0071] First, complete the self-calibration model initialization, and initialize the i-th pressure sensor with initial additive bias. for:

[0072]

[0073] Initial calibration data:

[0074] Then the data is iterated, and the k-th iteration process is as follows:

[0075] 1) Calculate the "Crowd Perception True Value": For each sampling time point t, calculate the average of the current readings of the N pressure sensors, which is taken as the "Crowd Perception True Value" at time t. In other words, this invention calculates the average of the current pressure readings of the N pressure sensors using the following formula:

[0076]

[0077] In the above formula, Let y be the average of the actual pressure measurements from N pressure sensors at time t after the k-th iteration. i (k) (t) represents the actual pressure measurement value of the i-th pressure sensor at time t, and the pressure reading after the k-th iteration.

[0078] 2) Update bias estimation: The bias of each pressure sensor should be equal to the average difference between the pressure reading after the k-th iteration and the "collective perception true value" of the k-th iteration. That is, this invention calculates the average difference between the current collective perception true value and the current pressure reading of each abnormal pressure sensor using the following formula:

[0079]

[0080] In the above formula, Let T represent the additive bias of the i-th abnormal pressure sensor in round k+1, and let T represent the number of time sampling points.

[0081] 3) Update calibrated data: Recalibrate all readings using the new bias. That is, this invention performs self-calibration on the current pressure readings of each abnormal pressure sensor using the updated additive bias based on the pressure self-calibration model according to the following formula:

[0082]

[0083] In the above formula, y i (k+1) (t) represents the actual pressure measurement value of the i-th abnormal pressure sensor at time t, and the pressure reading after the (k+1)-th iteration.

[0084] 4) Check convergence: Calculate the bias change. That is, this invention calculates the additive bias change of each abnormal pressure sensor before and after the current update using the following formula:

[0085]

[0086] In the above formula, Δ (k) This represents the change between the additive bias in the (k+1)th iteration and the additive bias in the kth iteration. This represents the additive bias of the i-th abnormal pressure sensor k-th wheel.

[0087] If Δ (k) <10 -6 If the condition is met, stop iterating; otherwise, continue iterating using the method described above.

[0088] After iteration, the final bias estimate The calibrated data matrix is

[0089] Among them, 1 T Let T be a column vector of all 1s.

[0090] At this point, the outputs of all pressure sensors become consistent, completing the internal self-calibration of the pressure sensors within the atmospheric sensing system.

[0091] In summary, the main steps of the self-calibration method for internal pressure of the atmospheric sensing system provided by this invention can be referred to... Figure 1 The block diagram and the specific implementation methods of each step have been described in detail in the foregoing embodiments, and will not be described in detail here. Those skilled in the art will understand that this example is merely one application of the atmospheric sensing system internal pressure self-calibration method provided by this invention, and does not constitute any limitation thereof.

[0092] The self-calibration method for internal pressure of an atmospheric sensing system provided by this invention constructs a self-calibration model for multiple internal pressure channels. Upon system power-on startup, it acquires the original pressure measurements (actual pressure measurements) of multiple channels. Based on a self-calibration scheme using original pressure anomaly detection and iterative mean alignment mathematical fitting model, it leverages the consistency relationship between multiple pressure sensors. Through data fusion and iterative optimization, it estimates and corrects the system deviation, gain error, or drift of each pressure sensor, achieving error compensation and mutual calibration between pressure sensors. This method can automatically identify, estimate, compensate for, and correct system errors in the pressure measurement results of the atmospheric sensing system. Especially when the pressure measurement accuracy of a certain pressure sensor deviates significantly due to some reason during system startup, it can perform mutual calibration even without an external reference standard (no "gold standard" sensor), further improving the measurement accuracy and reliability of the product. It is simple, practical, easy to implement, effective, and highly feasible, meeting the requirements for convenient system application. Compared with existing technologies, the technical solution of this invention solves the technical problem that access calibration in existing technologies requires dedicated equipment and special gas path tooling, and the calibration process is time-consuming and labor-intensive.

[0093] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0094] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of atmospheric sensing system internal pressure self-calibration, characterized in that, The atmospheric sensing system internally comprises N pressure sensors, and the method comprises the following steps: S1, establishing a pressure self-calibration model based on an additive bias; S2, obtaining N actual pressure measurement values output by the N pressure sensors at the current time; S3, calculating the mean value and standard deviation of the N actual pressure measurement values at the current time; S4, calculating the error of each pressure sensor at the current time according to the actual pressure measurement value of each channel at the current time and the mean value and standard deviation of the N actual pressure measurement values, and determining whether the error of each pressure sensor is greater than a preset error threshold, if not, determining that the corresponding pressure sensor is abnormal, and turning to S5, if yes, determining that the corresponding pressure sensor is normal, and outputting the actual pressure measurement value; S5, assigning an initial value to the additive bias, and taking the actual pressure measurement values of the current N pressure sensors as the initial pressure readings in the pressure self-calibration model, to obtain an initialized pressure self-calibration model; S6, calculating the mean value of the current pressure readings of the current N pressure sensors as the group perception real value of this round, and calculating the average difference between the group perception real value of this round and the pressure readings of each abnormal pressure sensor; S7, assigning the average difference obtained in S6 to the additive bias to obtain an updated additive bias, and performing self-calibration on the current pressure readings of each abnormal pressure sensor based on the pressure self-calibration model and the updated additive bias, to obtain the pressure readings of each abnormal pressure sensor after self-calibration in this round; S8, calculating the change amount of the additive bias of each abnormal pressure sensor before and after updating in this round, and determining whether the change amount of the additive bias is less than a preset change amount threshold, if not, turning to S9, if yes, turning to S10; S9, taking the pressure readings of the corresponding abnormal pressure sensor after self-calibration in this round as the updated pressure readings, and turning to S6; S10, stopping iteration of the corresponding abnormal pressure sensor, and outputting the pressure readings after self-calibration in this round.

2. The method of claim 1, wherein, The established pressure self-calibration model is: y i (t) = x i (t) + b i + ε i (t), In the above equation, y i (t) is the pressure reading of the i-th pressure sensor at time t, x(t) is the true pressure measurement of the i-th pressure sensor at time t, b i is the additive bias of the i-th pressure sensor, ε i (t) is the measurement random noise of the i-th pressure sensor at time t.

3. The method of claim 2, wherein, The mean value and standard deviation of the N actual pressure measurement values at the current time are calculated by the following formula: In the above formula, is the mean of the N actual pressure measurements, X i is the actual pressure measurement of the i-th pressure sensor, and σ is the standard deviation of the N actual pressure measurements.

4. The method of claim 3, wherein, The error of each pressure sensor at the current time is calculated according to the actual pressure measurement value of each channel at the current time and the mean value and standard deviation of the N actual pressure measurement values by the following formula: In the above formula, Y i represents the error of the i-th pressure sensor.

5. The method of claim 4, wherein, The mean value of the current pressure readings of the current N pressure sensors is calculated by the following formula: In the above formula, is the mean value of the pressure readings after the kth iteration of the actual pressure measurement of the ith pressure sensor at time t, y i (k) (t) is the mean value of the pressure readings after the kth iteration of the actual pressure measurement of the ith pressure sensor at time t, y 6. The method of claim 5, wherein, The average difference between the group perception real value of this round and the pressure readings of each abnormal pressure sensor is calculated by the following formula: In the above formula, represents the additive bias of the i-th abnormal pressure sensor in the k+1th round, and T represents the number of time sampling points.

7. The method of claim 6, wherein, The current pressure readings of each abnormal pressure sensor are self-calibrated based on the pressure self-calibration model and the updated additive bias by the following formula: In the above formula, y i (k+1) (t) is the actual pressure measurement value of the i-th abnormal pressure sensor at time t after the pressure reading of the k+1th iteration.

8. The method according to any one of claims 1 to 7, characterized in that, The change amount of the additive bias of each abnormal pressure sensor before and after updating in this round is calculated by the following formula: In the above formula, Δ (k) represents the change between the additive bias of the k+1th iteration and the additive bias of the kth iteration, represents the additive bias of the kth iteration of the ith abnormal pressure sensor.

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