Method and system for calibrating a multi-channel pressure sensor

Abstract

The application relates to the technical field of equipment calibration, in particular to a calibration method and system of a multi-channel pressure sensor, which comprises the following steps: constructing a flexible pressure touch response platform, performing channel calibration according to the flexible pressure touch response platform, and performing preliminary deviation modeling; generating a nonlinear response correction factor according to the output result of the preliminary deviation modeling, and performing local regression adjustment on the channels according to the nonlinear response correction factor; in response to the completion of the local regression adjustment of each channel, performing unified response surface fusion processing; in response to the completion of the unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. The application overcomes the limitations of traditional methods which depend on linear models and static single-point calibration, and solves the error accumulation problem caused by the interaction interference and delayed response among channels.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device calibration, in particular to a calibration method and system of a multi-channel pressure sensor. BACKGROUND

[0002] Multi-channel pressure sensors are widely used in flexible electronics, aerospace, medical monitoring, intelligent manufacturing and other fields due to their ability to simultaneously obtain pressure distribution information of multiple spatial points. However, in actual applications, due to the existence of structural, response characteristics and material differences between channels, different channels may have response deviations under the same pressure, leading to inconsistent or even distorted measurement results. Therefore, high-precision calibration of multi-channel pressure sensors is a key prerequisite to ensure their measurement reliability.

[0003] Currently, there are calibration methods for multi-channel pressure sensors in the prior art. For example, an invention patent with publication number CN115901084A, published on April 4, 2023, discloses an integrated multi-protocol multi-channel pressure sensor calibration device and its calibration method, which includes a human-computer interface module, a main control module, a voltage measurement module, a constant current source module, a programmable power supply module, a multi-protocol communication module, a primary switching module, a secondary switching module, and an auxiliary module.

[0004] Although the technical solution in the above patent file can realize calibration and analog measurement of voltage, current and resistance in one or more measured products, and can switch different communication protocols to meet the problem of product incompatibility, it still has drawbacks, such as ignoring the nonlinear characteristics of channel responses in flexible materials or composite structures, and being unable to effectively identify and correct signal shifts under dynamic or stress dispersion conditions. In addition, the existing calibration methods for multi-channel pressure sensors generally use rigid plates or fixed pressure sources for static single-point or double-point calibration, and usually correct the mean value of each channel output based on the linear assumption. This type of method still has deficiencies, specifically lacking a recognition mechanism for potential interactive interference between channels, which can easily lead to response coupling between adjacent channels that is not identified, affecting the calibration independence and precision.

[0005] Therefore, there is an urgent need to design a calibration method and system for a multi-channel pressure sensor. SUMMARY

[0006] Therefore, it is necessary to provide a calibration method and system for a multi-channel pressure sensor that can overcome the limitations of traditional methods relying on linear models and static single-point calibration, and solve the error accumulation problem caused by interactive interference and delayed response between channels.

[0007] The technical solution of the present application is as follows:

[0008] A method for calibrating a multi-channel pressure sensor, the method comprising:

[0009] constructing a flexible pressure touch response platform, calibrating channels according to the flexible pressure touch response platform, and performing preliminary deviation modeling;

[0010] generating a non-linear response correction factor according to the results output by the preliminary deviation modeling, and performing local regression adjustment on the channels according to the non-linear response correction factor;

[0011] in response to completing the local regression adjustment of each channel, performing unified response surface fusion processing;

[0012] in response to completing the unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function.

[0013] Specifically, constructing a flexible pressure touch response platform, calibrating channels according to the flexible pressure touch response platform, and performing preliminary deviation modeling; comprising:

[0014] constructing a flexible pressure touch response platform, dividing the entire calibration surface into a plurality of pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone;

[0015] after ensuring that each sub-zone is uniformly loaded, entering a calibration stage of activating channels one by one, and performing preliminary deviation modeling.

[0016] Specifically, constructing a flexible pressure touch response platform, dividing the entire calibration surface into a plurality of pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone, comprising:

[0017] constructing a flexible pressure touch response platform based on a preset pressure touch unit and a stepping driver, and dividing the entire calibration surface into a plurality of pressure touch sub-zones based on the flexible pressure touch response platform;

[0018] constructing a loading uniformity factor representation model according to the flexible pressure touch response platform and the pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone based on the loading uniformity factor representation model.

[0019] Specifically, after ensuring that each sub-zone is uniformly loaded, entering a calibration stage of activating channels one by one, and performing preliminary deviation modeling, comprising:

[0020] after ensuring that each sub-zone is uniformly loaded, entering a calibration stage of activating channels one by one, activating a single pressure touch sub-zone, and obtaining an original output electrical signal value of a sensor channel corresponding to the activated pressure touch sub-zone;

[0021] performing preliminary deviation modeling according to the original output electrical signal value and a preset standard pressure.

[0022] Specifically, a non-linear response correction factor is generated according to the result output by the preliminary deviation modeling, and local regression adjustment is performed on the channel according to the non-linear response correction factor; comprising:

[0023] A non-linear response correction model is constructed according to the result output by the preliminary deviation modeling, and a non-linear response correction factor is generated according to the non-linear response correction model;

[0024] The channel is subjected to voltage-controlled fine tuning according to the non-linear response correction factor, so as to complete the local regression adjustment of each channel.

[0025] Specifically, in response to completing the local regression adjustment of each channel, unified response surface fusion processing is performed, comprising:

[0026] In response to completing the local regression adjustment of each channel, the overall reference average value of the average output value of each channel is obtained;

[0027] A channel equalization correction model is constructed according to the overall reference average value and the average output value of the sensor channel on the standard pressure segment, and unified response surface fusion processing is performed according to the channel equalization correction model.

[0028] Specifically, in response to completing the unified response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function, comprising:

[0029] In response to completing the unified response surface fusion processing, a stability comprehensive criterion function is constructed;

[0030] A stability comprehensive criterion factor is output according to the stability comprehensive criterion function, and a calibration result is generated according to the stability comprehensive criterion factor.

[0031] Specifically, a calibration system for a multi-channel pressure sensor is also provided, the system comprising:

[0032] A deviation modeling construction module is configured to construct a flexible pressure touch response platform, perform channel calibration according to the flexible pressure touch response platform, and perform preliminary deviation modeling;

[0033] A local regression adjustment module is configured to generate a non-linear response correction factor according to the result output by the preliminary deviation modeling, and perform local regression adjustment on the channel according to the non-linear response correction factor;

[0034] A response fusion processing module is configured to perform unified response surface fusion processing in response to completing the local regression adjustment of each channel;

[0035] A calibration result generation module is configured to construct a stability comprehensive criterion function in response to completing the unified response surface fusion processing, and generate a calibration result according to the stability comprehensive criterion function.

[0036] Specifically, the bias modeling and constructing module is further configured to: construct a flexible pressure touch response platform, divide the entire calibration surface into a plurality of pressure touch sub-zones, and detect the uniform loading degree of each pressure touch sub-zone; after ensuring that each sub-zone is uniformly loaded, enter a calibration stage of activating channels one by one, and perform preliminary bias modeling.

[0037] Specifically, the bias modeling and constructing module is further configured to: construct a flexible pressure touch response platform based on a preset pressure touch unit and a stepping driver, divide the entire calibration surface into a plurality of pressure touch sub-zones based on the flexible pressure touch response platform, construct a loading uniformity factor characterization model according to the flexible pressure touch response platform and the pressure touch sub-zones, and detect the uniform loading degree of each pressure touch sub-zone based on the loading uniformity factor characterization model.

[0038] Specifically, the bias modeling and constructing module is further configured to: after ensuring that each sub-zone is uniformly loaded, enter a calibration stage of activating channels one by one, activate a single pressure touch sub-zone, and obtain an original output electrical signal value of a sensor channel corresponding to the activated pressure touch sub-zone; and perform preliminary bias modeling according to the original output electrical signal value and a preset standard pressure.

[0039] Specifically, the local regression adjustment module is further configured to: construct a non-linear response correction model according to the result output by the preliminary bias modeling, generate a non-linear response correction factor according to the non-linear response correction model, and perform pressure control fine tuning on the channels according to the non-linear response correction factor to complete local regression adjustment of each channel.

[0040] Specifically, the response fusion processing module is further configured to: in response to completion of the local regression adjustment of each channel, obtain an overall reference average value of average output values of the channels; construct a channel equalization correction model according to the overall reference average value and average output values of the sensor channels in a standard pressure segment, and perform unified response surface fusion processing according to the channel equalization correction model.

[0041] Specifically, the calibration result generating module is further configured to: in response to completion of the unified response surface fusion processing, construct a stability comprehensive criterion function; output a stability comprehensive criterion factor according to the stability comprehensive criterion function, and generate a calibration result according to the stability comprehensive criterion factor.

[0042] Optionally, a computer device is also provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the calibration method of the multi-channel pressure sensor when executing the computer program.

[0043] Optionally, a computer readable storage medium is also provided, and the computer readable storage medium has stored thereon a computer program, and the computer program is executed by a processor to implement the steps of the calibration method of the multi-channel pressure sensor.

[0044] The present application achieves the following technical effects:

[0045] (1) The calibration method and system of the multi-channel pressure sensor sequentially pass through the construction of a flexible pressure touch response platform, calibrate the channels according to the flexible pressure touch response platform, and perform preliminary deviation modeling; generate a nonlinear response correction factor according to the output result of the preliminary deviation modeling, and perform local regression adjustment on the channels according to the nonlinear response correction factor; in response to the completion of the local regression adjustment of each channel, perform unified response surface fusion processing; in response to the completion of the unified response surface fusion processing, construct a stability comprehensive criterion function, and generate a calibration result according to the stability comprehensive criterion function; by establishing models such as a loading uniformity factor, an initial deviation response factor, a nonlinear response correction factor, a regression adjustment factor, a channel equalization correction coefficient, and a comprehensive criterion function, a complete calibration process of systematization, dynamic feedback, and multi-layer correction is formed, which not only overcomes the limitations of traditional methods relying on linear models and static single-point calibration, but also effectively solves the error accumulation problem caused by the interaction and delay response between channels.

[0046] (2) By constructing a flexible pressure touch response platform, the entire calibration surface is divided into several pressure touch partitions, which solves the problem that traditional multi-channel pressure sensor calibration mostly uses rigid platforms or fixed standard loading devices for calibration, only considers isobaric input in a static and rigid plane environment, and is difficult to truly restore the dynamic response characteristics of flexible sensors in actual non-uniform stress scenarios.

[0047] (3) By implementing pressure control fine tuning for channels with large deviations, local regression adjustment of each channel is completed, which solves the problem that existing multi-channel sensor calibration methods mostly rely on one-time set compensation coefficients, or use hardware pre-adjustment to approximately correct the output differences of each channel during the manufacturing process, and are passive and inflexible when facing flexible substrates, non-uniform strain distribution, and changes in channel characteristics caused by long-term use, ensuring the improvement of single-channel precision while realizing the consistency and stability of the system level, and providing solid technical support for the engineering application of multi-channel pressure sensors in high-precision scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a flowchart of the calibration method of the multi-channel pressure sensor in one embodiment;

[0049] Figure 2 It is a structure block diagram of the financial data acquisition system based on artificial intelligence in one embodiment;

[0050] Figure 3 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0052] It is to be understood that the terminology "includes", "has", "holds", "contains" or "comprising", "including", "having" and the like, when used in the present specification and in the accompanying claims, is taken to specify the presence of stated features, integers, steps, operations, elements, or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0053] It is also to be understood that the terminology "and / or" when used in the present specification and in the accompanying claims, refers to both conjunctive and disjunctive senses of the term, such that it includes any and all combinations of one or more of the associated listed items.

[0054] As used in the present specification and in the accompanying claims, the term "if' can be interpreted as meaning "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon detecting [the described condition or event]" or "in response to detecting [the described condition or event]", depending on the context.

[0055] In addition, the terms "first", "second", "third", etc. in the description of the present specification and in the accompanying claims are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0056] Reference in the specification to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places in the specification are not necessarily all referring to the same embodiment, although it can. The terms "including", "containing", "comprising", "having" and variations thereof in the specification are meant to encompass the item listed thereafter, but do not exclude the presence of one or more other items.

[0057] In one embodiment, a terminal is provided, configured to: construct a flexible pressure touch response platform, perform channel calibration according to the flexible pressure touch response platform, and perform preliminary deviation modeling; generate a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and perform local regression adjustment on the channels according to the nonlinear response correction factor; in response to completion of the local regression adjustment on each channel, perform unified response surface fusion processing; in response to completion of the unified response surface fusion processing, construct a stability comprehensive criterion function, and generate a calibration result according to the stability comprehensive criterion function.

[0058] The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.

[0059] In one embodiment, as shown in Figure 1 A calibration method of a multi-channel pressure sensor is provided, and the method comprises:

[0060] Step S100: constructing a flexible pressure touch response platform, performing channel calibration according to the flexible pressure touch response platform, and performing preliminary deviation modeling;

[0061] Step S200: generating a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and performing local regression adjustment on the channels according to the nonlinear response correction factor;

[0062] Step S300: in response to completion of the local regression adjustment on each channel, performing unified response surface fusion processing;

[0063] Step S400: in response to completion of the unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function.

[0064] In this embodiment, by constructing a flexible pressure touch response platform, performing channel calibration according to the flexible pressure touch response platform, and performing preliminary deviation modeling, a nonlinear response correction factor is generated according to the result output by the preliminary deviation modeling, and local regression adjustment is performed on the channels according to the nonlinear response correction factor. In response to completion of the local regression adjustment on each channel, unified response surface fusion processing is performed. In response to completion of the unified response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function. By establishing models such as a uniformity factor, an initial deviation response factor, a nonlinear response correction factor, a regression adjustment factor, a channel equalization correction coefficient, and a comprehensive criterion function, a complete calibration process of systematic, dynamic feedback, and multi-layer correction is formed, which not only overcomes the limitations of traditional methods relying on linear models and static single-point calibration, but also effectively solves the error accumulation problem caused by the interaction interference and delay response among channels.

[0065] In one embodiment, the step S100 of constructing a flexible pressure touch response platform, calibrating channels according to the flexible pressure touch response platform, and performing preliminary deviation modeling comprises:

[0066] The step S110 of constructing a flexible pressure touch response platform, dividing the entire calibration surface into a plurality of pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone.

[0067] The step S120 of ensuring uniform loading of each sub-zone, entering the calibration stage of activating channels one by one, and performing preliminary deviation modeling.

[0068] In this embodiment, in order to ensure that the pressure response of each channel can be accurately simulated in the actual working scene, a flexible pressure touch response platform is constructed. Specifically, the flexible pressure touch response platform is constructed, the entire calibration surface is divided into a plurality of pressure touch sub-zones, and the uniform loading degree of each pressure touch sub-zone is detected. Then, after ensuring uniform loading of each sub-zone, the calibration stage of activating channels one by one is entered, and preliminary deviation modeling is performed.

[0069] In one embodiment, the step S110 of constructing a flexible pressure touch response platform, dividing the entire calibration surface into a plurality of pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone comprises:

[0070] The step S111 of constructing a flexible pressure touch response platform based on a preset pressure touch unit and a step motor, and dividing the entire calibration surface into a plurality of pressure touch sub-zones based on the flexible pressure touch response platform.

[0071] The step S112 of constructing a loading uniformity factor representation model according to the flexible pressure touch response platform and the pressure touch sub-zones, and detecting the uniform loading degree of each pressure touch sub-zone based on the loading uniformity factor representation model.

[0072] In this embodiment, in order to solve the problem that the existing multi-channel pressure sensor calibration is usually calibrated by rigid platform or fixed standard loading device, and the calibration method usually only considers the isobaric input in the static and rigid plane environment, and it is difficult to truly restore the dynamic response characteristics of the flexible sensor in the actual non-uniform stress field. Therefore, the flexible pressure touch response platform described in the present application is composed of a plurality of independently liftable pressure touch units, each unit is accurately controlled in loading position and force by a stepping driver, and is used for applying a standardized in-plane pressure area. That is, the flexible pressure touch platform only needs to be composed of a plurality of stepping drivers, each driver controls the lifting of the corresponding pressure touch unit, realizes the accurate loading of the standard pressure, and does not need additional complex structure. Therefore, on the basis of simple structure, not only the controllable loading of spatial multi-point pressure is realized, but also the non-uniform distribution of the pressure state of each channel corresponding area in the real working condition is effectively simulated through the fine partition control and independent adjustment ability of the pressure touch unit, and the calibration accuracy and the actual adaptability of the channel response are significantly improved.

[0073] Specifically, the entire calibration surface is divided into a plurality of pressure touch partitions, each region is numbered as , and is one-to-one corresponding to the sensor channel ; in order to quantify the response stability of each partition under the reference pressure (standard pressure) , a loading uniformity factor is constructed to represent the uniformity of the partition loading state.

[0074] The loading uniformity factor representation model is as follows:

[0075]

[0076] , wherein is the number of virtual test nodes set in the mth partition. is the actual applied pressure at the qth node. is the standard pressure set by the calibration system, and the standard pressure is a set of typical working points set according to the full range of the sensor, which is usually selected at 0%, 25%, 50%, 75%, and 100% of the full range, for example, for a sensor with a range of 100kPa, 0kPa, 25kPa, 50kPa, 75kPa and 100kPa.

[0077] The loading uniformity factor representation model is used to detect the uniform loading of each pressure touch partition on the platform, and if exceeds the set threshold (such as 5%), the partition re-pressure adjustment instruction is automatically triggered to ensure that the subsequent calibration is carried out in an approximately ideal isobaric environment. The loading uniformity factor representation model has high practicability and engineering operability. In each partition a plurality of virtual nodes are arranged inside , the actual applied pressure for sampling the local loading point , the standard pressure , the absolute deviation average of all nodes is calculated for judging whether the partition loading is uniform enough. When the calculated exceeds the set threshold value of the system, the system will automatically issue a "voltage adjustment feedback command" to the partition, adjust the loading amplitude of the corresponding pressure touch unit, until the pressure on each virtual node tends to be balanced, and the isobaric state is ensured as the premise of subsequent channel response collection.

[0078] In one embodiment, step S120: after ensuring that each partition is loaded uniformly, enter the calibration phase of activating channels one by one, and perform preliminary deviation modeling, including:

[0079] Step S121: after ensuring that each partition is loaded uniformly, enter the calibration phase of activating channels one by one, activate a single pressure touch partition, and obtain the original output electrical signal value of the sensor channel corresponding to the activated pressure touch partition;

[0080] Step S122: preliminary deviation modeling is performed according to the original output electrical signal value and the preset standard pressure.

[0081] In this embodiment, the calibration of the traditional multi-channel sensor is usually based on full-channel simultaneous collection and mean filtering, which ignores the response deviation caused by the inherent manufacturing error, welding stress or structural strain residual of each channel under the initial loading condition, so it is difficult to compensate for systematic deviation in subsequent adjustment. In this embodiment, after ensuring that each partition is loaded uniformly, the calibration phase of activating channels one by one is entered, a single pressure touch partition is activated, and the original output electrical signal value of the sensor channel corresponding to the activated pressure touch partition is obtained; preliminary deviation modeling is performed according to the original output electrical signal value and the preset standard pressure.

[0082] Specifically, the preliminary deviation modeling activates only a single partition by the control platform, and records the original output electrical signal value of the sensor channel corresponding to the partition. An initial deviation response factor is constructed, which is in volts and represents the difference between the original output electrical signal value and the standard pressure , and is used for error compensation reference.

[0083] The initial deviation response factor reference model is as follows:

[0084]

[0085] wherein, is the initial electrical signal reading of the mth channel. is the theoretical sensitivity constant (Volt per Pascal (V / Pa)) of the sensor system, used to convert the standard pressure to the ideal output. The system parameter is obtained by repeated loading tests under standard pressure environment before the sensor leaves the factory. Specifically, a plurality of standard pressure points (such as 5kPa, 10kPa, 15kPa, etc.) are selected, and the stable electrical signal output of the sensor under these pressure conditions is recorded respectively. The least square fitting or linear regression method is used to obtain the average slope value between pressure and output signal as the sensitivity coefficient. is the standard pressure.

[0086] If the absolute value of is large, the system will mark this channel as "need to be adjusted" and assign a higher disturbance density or use multiple rounds of calibration in the subsequent steps; if is close to zero, it means that the initial state of the channel is good, and a low-weight adjustment strategy can be used subsequently. Thus, a differentiated calibration path for the channels is formed, realizing true channel-by-channel optimization processing. The initial deviation response factor reference model takes as the reference for subsequent channel calibration, representing the initial output deviation of the channel under the current working condition, and is used to evaluate whether the current error level of each channel has a systematic drift or sensitivity deviation problem.

[0087] In one embodiment, step S200: generating a non-linear response correction factor according to the output result of the preliminary deviation modeling, and performing local regression adjustment on the channel according to the non-linear response correction factor; comprising:

[0088] Step S210: constructing a non-linear response correction model according to the output result of the preliminary deviation modeling, and generating a non-linear response correction factor according to the non-linear response correction model;

[0089] In this embodiment, a non-linear response correction factor is constructed based on the output result of the preliminary deviation modeling, and the non-linear response correction factor is used to reflect the degree of non-linear response of each channel.

[0090] Specifically, traditional pressure sensor calibration is mostly based on single-point or double-point calibration, and relies on linear assumption to set the mapping relationship between signal and pressure. This simplified process is acceptable under ideal working conditions, but in actual applications, especially in multi-channel pressure sensors, the physical response curve of each channel often presents different degrees of non-linear characteristics, such as hysteresis, drift or stress concentration points. If this non-linearity is not identified and corrected during the calibration stage, it will directly affect the overall accuracy of the system.

[0091] Based on the obtained The sensor channel A multi-point pressure scan is performed at each pressure level The corresponding signal output is recorded A non-linear response correction model of the channel is fitted, which outputs a non-linear response correction factor Specifically as follows:

[0092]

[0093] Wherein, is the number of standard pressure levels scanned.

[0094] is the set pressure value at the i-th level. By sequentially applying standard pressure values on the flexible pressure platform, a number of representative points within the sensor range are usually selected, such as 0%, 25%, 50%, 75%, and 100% full-scale pressure, which are applied by a step motor driver in steps, ensuring that each applied pressure value has traceability and high stability. is the output signal of the m-th channel at the i-th pressure level. is the initial deviation response factor obtained in step S112.

[0095] Non-linear response correction factor Reflects the deviation sum of squares of the output curve of each channel from the ideal state, i.e. the degree of non-linear response. The degree of non-linearity of the channel is calculated by the deviation sum of squares. Instead of simply relying on the "ideal linear model" assumption, the degree of deviation of each channel response from the theoretical linear expectation is actually quantified. If is large, it indicates that there is a significant non-linear mismatch in the channel, which requires the introduction of perturbation regression adjustment. If is small, it indicates that the response curve of the channel is approximately in the ideal linear state, and a light correction path can be used.

[0096] Specifically, the square operation of the signal in the formula of the non-linear response correction model is to amplify the influence of the output error on the overall evaluation and avoid the mutual offset of positive and negative errors; the two terms in the parentheses represent the difference between the actual output signal and the theoretical linear prediction value, which is used to measure the degree of non-linear deviation of the channel at this pressure point.

[0097] Step S220: Implementing pressure-controlled fine tuning of the channel according to the non-linear response correction factor to complete the local regression adjustment of each channel.

[0098] In this embodiment, the existing multi-channel sensor calibration method mostly relies on the one-time set compensation coefficient, or the output difference of each channel is approximately corrected by hardware pre-adjustment in the manufacturing process. This way is passive and inflexible when facing flexible substrates, non-uniform distribution of strain, and changes in channel characteristics caused by long-term use, and it is difficult to meet the needs of high-precision applications under dynamic conditions.

[0099] The pressure-controlled fine adjustment specifically includes: adjusting the pressure touch partition corresponding to the channel applying multiple rounds of micro-disturbance pressure, recording the signal output change after each round of disturbance, and establishing a regression adjustment factor of the channel for the disturbance input for evaluating the output regression trend of the channel after fine adjustment. Among them, applying multiple rounds of micro-disturbance pressure includes adjusting the displacement of the pressure device by a stepping driver in the flexible pressure touch platform, such as adjusting 0.1 mm each time, and then controlling the corresponding pressure touch unit to apply multiple small increments, such as a disturbance pressure of 2kPa per level, and continuously performing multiple rounds (for example, 3-5 rounds) of pressure and signal reading process.

[0100] The regression adjustment factor quantization model is as follows:

[0101]

[0102] Among them, is the number of disturbance rounds. is the pressure amplitude applied by the jth disturbance. is the channel signal before disturbance. is the channel signal after disturbance.

[0103] The model obtains the signal change rate under unit pressure disturbance by calculating the ratio of the response difference before and after disturbance to the pressure difference; if tends to be stable and the error is within an acceptable range, it means that the local regression adjustment of the channel has been completed. The output can be used as an efficiency index of the channel response in the pressure-controlled adjustment process, and when the value is stable and tends to approach the reference range, it indicates that the channel response state has entered a convergent trend. If there is a sharp fluctuation or far away from the expected sensitive interval, the system will automatically extend the disturbance rounds of the channel or appropriately amplify the disturbance amplitude to obtain more comprehensive response information. It realizes the transition from "quantitative disturbance" to "dynamic feedback correction", makes the channel adjustment process have stronger process controllability and error approaching ability, and is a calibration and adjustment mechanism with dynamic response identification, nonlinear compensation, and channel behavior adaptive optimization function, which is significantly better than the traditional static correction method.

[0104] In one embodiment, step S300: in response to completing the local regression adjustment of each channel, a unified response surface fusion processing is performed, including:

[0105] Step S310: In response to completing the local regression adjustment of each channel, obtaining the overall reference average value of the average output value of each channel;

[0106] Step S320: constructing a channel equalization correction model according to the overall reference average value and the average output value of the sensor channel on the standard pressure segment, and performing unified response surface fusion processing according to the channel equalization correction model.

[0107] In the embodiment, in most existing multi-channel pressure sensor calibration technologies, even if each channel is adjusted respectively during the calibration process, the overall output may still have a "relative deviation", that is, the outputs of different channels under the same pressure are inconsistent. This difference is usually caused by systematic deviations such as device processing errors, packaging stress differences, and circuit layout interference between channels, and these factors often cannot be completely covered by single-channel adjustment.

[0108] Therefore, if further unified processing is not performed at the system level, the spatial consistency of the multi-channel measurement results of the sensor cannot be guaranteed in actual use, which seriously affects the overall measurement accuracy. In the embodiment, unified response surface fusion processing is performed to eliminate system-level errors caused by response differences between multi-channels, and a channel equalization correction coefficient is constructed for linear correction of the output value of each channel to make it consistent under standard pressure. The channel equalization correction coefficient is obtained through a channel equalization correction model, and the calculation method of the channel equalization correction model is as follows:

[0109]

[0110] wherein, is the average output value of the sensor channel on the standard pressure segment. It is obtained by repeatedly sampling the output signals of the channel at multiple known standard pressure points (such as ) and taking the average. The specific process is as follows: a predetermined pressure value is applied to the pressure touch area corresponding to the channel in turn; at each , multiple sets of stable state sensor output values are collected; and the average of the response signals at all pressure levels is calculated to obtain the overall average response value of the channel.

[0111] is the overall reference average value of the average output value of all channels. It is a system reference value obtained by aggregating and calculating the average output values of all channels under the same standard pressure segment. The acquisition process is as follows: for each standard pressure value , the average output values of all channels (numbered ) the output value under this pressure; first, the average value of each channel is calculated, and then the average output of all channels is averaged to obtain. The output signal of each channel will be multiplied by the corresponding in the final application, so that the outputs of all channels under the same pressure tend to be the same, eliminating the errors between channels caused by processing differences and response drift.

[0112] In the final application stage, the correction coefficient will be used as a signal adjustment factor to linearly correct the original output of each channel, so that it tends to uniform response under the same pressure condition. This approach has two advantages: first, it unifies the measurement reference of each channel under standard conditions from the signal level, improving the consistency and comparability of the overall measurement space of the sensor; second, it can be used as a long-term monitoring basis for channel output changes. If there is a significant deviation in future use , the system can determine whether the channel has deteriorated or aged out of alignment based on this. If the deviation from the ideal value 1 exceeds a certain threshold (such as ±5%), it is considered that the channel has a significant deviation and needs to be recalibrated or performance tested.

[0113] In one embodiment, step S400: in response to completing the uniform response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function, including:

[0114] Step S410: in response to completing the uniform response surface fusion processing, a stability comprehensive criterion function is constructed;

[0115] Step S420: output a stability comprehensive criterion factor according to the stability comprehensive criterion function, and generate a calibration result according to the stability comprehensive criterion factor.

[0116] In the prior art, the judgment of channel calibration completion is mostly subjective or qualitative standard, such as error reaching a certain proportion, signal being stable without obvious fluctuation, etc. These judgment standards not only have poor operability and are difficult to quantify, but also cannot effectively integrate multiple calibration indicators, thereby easily leading to insufficient or excessive adjustment, affecting efficiency and stability. In this embodiment, the stability comprehensive criterion function is constructed based on the nonlinear response correction factor , the regression adjustment factor , and the channel equalization correction coefficient , and the value thereof is used to represent whether the overall calibration of the mth channel is completed.

[0117] The stability comprehensive criterion function is as follows:

[0118]

[0119] wherein, ​is a fixed weighting coefficient preset by the system, which is obtained by simulation evaluation or historical calibration data fitting optimization. Specifically, it is determined by simulation experiment and historical calibration data analysis, and the purpose is to reflect the actual weight of different indicators on the calibration quality. In the setting process, first, a simulation data set is established based on real sensor samples, the deviation data of multiple channels in the three dimensions of nonlinear response, disturbance adjustment and equalization correction are recorded, and their calibration effects (such as “up to standard” or “not up to standard”) are manually marked. Then use the minimum error classification method or the logistic regression method to train, find the most accurate combination of weights that can distinguish between qualified and unqualified samples under known calibration state.

[0120] is the optimal disturbance response value in the system memory. Specifically, the system obtains the reference value from long-term calibration history data or initial calibration template samples, which is used to represent the signal change rate of “ideal channel” under unit disturbance. The setting process is as follows: select a batch of stable performance and minimum error sensor channels, record their average sensitivity response value in the disturbance round under the standard calibration process, and then average these values.

[0121] When is less than the system preset threshold, the setting of the system preset threshold should be based on the joint decision of the comprehensive error tolerance range and the misadjustment risk tolerance. Generally, through statistical analysis: in the calibration success samples, calculate the maximum value of , and slightly relax it combined with the safety margin, that is, the threshold value can be set, which means that the channel has reached the standard in the three dimensions of nonlinear correction, disturbance regression and equalization correction, and the calibration process of the channel can be ended. Otherwise, automatically return to step S200 for the next round of adjustment. The upgrade from qualitative experience to quantitative threshold is realized, so that the calibration termination standard is repeatable and verifiable; the influence of different error sources is automatically balanced to avoid single indicator misjudgment of system state; it has closed-loop control logic, which can be directly linked with system execution logic to support intelligent calibration automation process.

[0122] In another embodiment, in the step S120, the original output electrical signal value At this time, the system does not immediately read the electrical signal value. Under the condition of flexible material or low stiffness substrate, the sensor body often has phenomena such as elastic hysteresis, material buffer response, adhesion hysteresis or stress dispersion, which causes the instantaneous signal to often not truly reflect the output state after the stress field stabilizes. This will cause the deviation model constructed subsequently to deviate from the actual working state, forming error accumulation. Therefore, the initial touch pressure is delayed for a period of time, and a plurality of different delay points are set for multi-point reading. A set of delay windows, such as 10 ms, 30 ms, 100 ms, etc., are set, and a plurality of groups of data are read at different time points, and the signal stable state is determined through response curve comparison. Finally, the reading at the minimum first-order derivative fluctuation region of the system dynamic response curve is selected as the output. Therefore, through the delay sampling strategy, short-term response abnormalities can be effectively filtered out, thereby significantly improving the physical authenticity of the initial deviation estimation.

[0123] In one embodiment, as shown in FIG. 1, a calibration system for a multi-channel pressure sensor is also provided, and the system comprises: Figure 2 A deviation modeling construction module is configured to construct a flexible pressure touch response platform, calibrate channels according to the flexible pressure touch response platform, and perform preliminary deviation modeling.

[0124] A local regression adjustment module is configured to generate a non-linear response correction factor according to the result output by the preliminary deviation modeling, and to perform local regression adjustment on the channels according to the non-linear response correction factor.

[0125] A response fusion processing module is configured to perform unified response surface fusion processing in response to completing the local regression adjustment of each channel.

[0126] A calibration result generation module is configured to construct a stability comprehensive criterion function in response to completing the unified response surface fusion processing, and to generate a calibration result according to the stability comprehensive criterion function.

[0127] In one embodiment, the deviation modeling construction module is further configured to: construct a flexible pressure touch response platform, divide the entire calibration surface into a plurality of pressure touch sub-zones, and detect the uniform loading degree of each pressure touch sub-zone; after ensuring that each sub-zone is uniformly loaded, enter a calibration phase of activating the channels one by one, and perform preliminary deviation modeling.

[0128] In one embodiment, the deviation modeling construction module is further configured to: construct a flexible pressure touch response platform based on a preset pressure touch unit and a step driver, divide the entire calibration surface into a plurality of pressure touch sub-zones based on the flexible pressure touch response platform, construct a loading uniformity factor representation model according to the flexible pressure touch response platform and the pressure touch sub-zones, and detect the uniform loading degree of each pressure touch sub-zone based on the loading uniformity factor representation model.

[0129] In one embodiment, the deviation modeling construction module is further configured to: construct a flexible pressure touch response platform based on a preset pressure touch unit and a step driver, divide the entire calibration surface into a plurality of pressure touch sub-zones based on the flexible pressure touch response platform, construct a loading uniformity factor representation model according to the flexible pressure touch response platform and the pressure touch sub-zones, and detect the uniform loading degree of each pressure touch sub-zone based on the loading uniformity factor representation model.

[0130] In one embodiment, the bias modeling module is further configured to: after ensuring that each partition is loaded evenly, enter a calibration phase of activating channels one by one, activate a single pressure touch partition, and obtain a raw output electrical signal value of a sensor channel corresponding to the activated pressure touch partition; and perform preliminary bias modeling according to the raw output electrical signal value and a preset standard pressure.

[0131] In one embodiment, the local regression adjustment module is further configured to: construct a non-linear response correction model according to a result output by the preliminary bias modeling, generate a non-linear response correction factor according to the non-linear response correction model, and perform voltage-controlled fine tuning on a channel according to the non-linear response correction factor to complete local regression adjustment of each channel.

[0132] In one embodiment, the response fusion processing module is further configured to: in response to completion of local regression adjustment of each channel, obtain an overall reference average value of average output values of the channels; construct a channel equalization correction model according to the overall reference average value and average output values of the sensor channels in a standard pressure segment, and perform unified response surface fusion processing according to the channel equalization correction model.

[0133] In one embodiment, the calibration result generation module is further configured to: in response to completion of unified response surface fusion processing, construct a stability comprehensive criterion function; output a stability comprehensive criterion factor according to the stability comprehensive criterion function, and generate a calibration result according to the stability comprehensive criterion factor.

[0134] In one embodiment, as shown in Figure 3 In one embodiment, a computer device is also provided, which includes a memory and a processor, the memory stores a computer program and an operating system, and the processor implements the steps of the above-mentioned machine vision-based separation detection method for a separation screen surface when executing the computer program. The computer device further includes a system bus, an internal memory, a network structure, a display screen, an input device, and the like.

[0135] In one embodiment, a computer readable storage medium is also provided, which stores a computer program, and the computer program implements the steps of the above-mentioned calibration method for a multi-channel pressure sensor when executed by a processor.

[0136] It should be noted that the information interaction and execution process between the above-mentioned modules, since based on the same concept as the method embodiments, the specific functions and the technical effects brought by the modules can be referred to the method embodiments part, and will not be repeated here.

[0137] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0138] It should be noted that the information interaction, execution process and the like between the above modules are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by them can be referred to the method embodiments part. Here, it will not be repeated.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0140] The embodiments of the present application also provide a network device, which comprises at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above method embodiments when executing the computer program.

[0141] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is executable by a processor to implement the steps in any of the above method embodiments.

[0142] The embodiments of the present application provide a computer program product, which, when running on a mobile terminal, causes the mobile terminal to perform the steps in the above-mentioned various method embodiments.

[0143] The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiments by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium. The computer program, when executed by a processor, can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0144] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0145] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0146] In the embodiments of the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other manners. For example, the described apparatus / network device embodiments are merely illustrative. For example, the division of the modules or units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0147] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0148] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

[0149] An embodiment of the present application further provides a computer device, which comprises at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described embodiments.

[0150] The computer device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above description is an example of the computer device, and does not constitute a limitation on the computer device, and can include more or fewer components than the above description, or combine some components, or different components, for example, can also include input / output devices, network access devices, etc.

[0151] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0152] The memory can be an internal storage unit of the computer device in some embodiments, for example, a hard disk or a memory of the computer device. The memory can also be an external storage device of the computer device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, for example, program codes of the computer program, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0153] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0154] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, however, it should not be understood as a limitation on the scope of the present application. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of calibrating a multi-channel pressure sensor, characterized by, The method comprises: The method comprises: According to the results output by the preliminary deviation modeling, a non-linear response correction factor is generated, which reflects the deviation square sum of the output curve of each channel relative to the ideal state, i.e. the degree of non-linear response, and the channel is locally adjusted according to the non-linear response correction factor, comprising: constructing a non-linear response correction model according to the results output by the preliminary deviation modeling, and generating a non-linear response correction factor according to the non-linear response correction model; performing pressure control fine tuning on the channel according to the non-linear response correction factor; the pressure control fine tuning specifically comprises: applying a plurality of rounds of micro disturbance pressure to the pressure touch sub-area corresponding to the channel, recording the signal output change after each round of disturbance, establishing a regression adjustment factor for quantizing the disturbance input of the channel, which is used to evaluate the output regression trend of the channel after fine tuning; by calculating the ratio of the response difference before and after disturbance to the pressure difference, the signal change rate under unit pressure disturbance is obtained; if the regression adjustment factor tends to be stable and the error is within an acceptable range, it is indicated that the local regression adjustment of the channel has been completed; In response to completing the local regression adjustment of each channel, unified response surface fusion processing is performed, comprising: obtaining the overall reference average value of the average output value of each channel; constructing a channel equalization correction model according to the overall reference average value and the average output value of the sensor channel in the standard pressure segment, and performing unified response surface fusion processing according to the channel equalization correction model; In response to completing the unified response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function.

2. The method of calibrating a multi-channel pressure sensor of claim 1, wherein, The flexible pressure touch response platform is constructed, and the entire calibration surface is divided into a plurality of pressure touch sub-areas, and the uniform loading degree of each pressure touch sub-area is detected, comprising: Based on the preset pressure touch unit and the step motor, a flexible pressure touch response platform is constructed, and based on the flexible pressure touch response platform, the entire calibration surface is divided into a plurality of pressure touch sub-areas; According to the flexible pressure touch response platform and the pressure touch sub-area, a loading uniformity factor representation model is constructed, and the uniform loading degree of each pressure touch sub-area is detected based on the loading uniformity factor representation model.

3. The method of calibrating a multi-channel pressure sensor of claim 1, wherein, In response to completing the unified response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function, comprising: In response to completing the unified response surface fusion processing, a stability comprehensive criterion function is constructed; According to the stability comprehensive criterion function, a stability comprehensive criterion factor is output, and a calibration result is generated according to the stability comprehensive criterion factor.

4. A calibration system for a multi-channel pressure sensor, characterized by, The system is used to execute the steps of the method according to any one of claims 1 to 3, and the system comprises: The bias modeling construction module is configured to construct a flexible pressure touch response platform, perform channel calibration according to the flexible pressure touch response platform, and perform preliminary bias modeling. The local regression adjustment module is configured to generate a nonlinear response correction factor according to a result output by the preliminary bias modeling, and perform local regression adjustment on the channel according to the nonlinear response correction factor. The response fusion processing module is configured to perform unified response surface fusion processing in response to completion of the local regression adjustment of each channel. The calibration result generation module is configured to construct a stability comprehensive criterion function in response to completion of the unified response surface fusion processing, and generate a calibration result according to the stability comprehensive criterion function. 5.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-4 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 3.

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