Multi-channel pressure sensor calibration method and system

Abstract

The invention relates to the technical field of equipment calibration, in particular to a multi-channel pressure sensor calibration method and system, and the method comprises the steps: constructing a flexible pressure response platform, carrying out the channel calibration according to the flexible pressure response platform, and carrying out the preliminary deviation modeling; generating a nonlinear response correction factor according to a result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; in response to completion of local regression adjustment of each channel, performing unified response surface fusion processing; and in response to completion of unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. According to the method, the limitation that a traditional method depends on a linear model and static single-point calibration is overcome, and the problem of error accumulation caused by inter-channel interaction interference and delay response is solved.

Description

Technical Field

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

[0002] Multi-channel pressure sensors are widely used in flexible electronics, aerospace, medical monitoring, and intelligent manufacturing due to their ability to simultaneously acquire pressure distribution information at multiple points in space. However, in practical applications, slight variations in channel structure, response characteristics, and materials can lead to response deviations between different channels under the same pressure, resulting in inconsistent or even distorted measurement results. Therefore, high-precision calibration of multi-channel pressure sensors is a critical prerequisite for ensuring their measurement reliability.

[0003] At present, there are calibration methods for multi-channel pressure sensors in the prior art. For example, the invention patent with the publication date of April 4, 2023 and the publication number CN115901084A discloses an integrated multi-protocol multi-channel pressure sensor calibration device and its calibration method, including: a human-machine 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 solutions in the above patent documents can realize the calibration and analog measurement of voltage, current, and resistance in one or more products under test, and can arbitrarily switch between products with different communication protocols to solve the problem of product incompatibility, they still have disadvantages, such as ignoring the nonlinear characteristics of channel response in flexible materials or composite structures, and being unable to effectively identify and correct signal offsets under dynamic or stress diffusion conditions. In addition, the existing calibration methods of multi-channel pressure sensors generally use rigid plates or fixed pressure sources for static single-point or double-point calibration, and usually perform mean correction on the output of each channel based on the linear assumption. This type of method still has shortcomings, specifically the lack of an identification mechanism for potential mutual interference between channels, which can easily lead to response coupling between adjacent channels without being identified, affecting the independence and accuracy of calibration.

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

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

[0007] The technical solutions of the present invention are as follows: A method for calibrating a multi-channel pressure sensor, the method comprising: Constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; generating a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; In response to completing the local regression adjustment of each channel, a unified response surface fusion process is performed; In response to completing the unified response surface fusion process, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function.

[0008] Specifically, a flexible pressure-touch response platform is constructed, channel calibration is performed based on the flexible pressure-touch response platform, and preliminary deviation modeling is performed; including: Construct a flexible pressure-touch response platform, divide the entire calibration surface into several pressure-touch zones, and test the uniform loading level of each pressure-touch zone; After ensuring that each partition is loaded evenly, we enter the calibration phase where we activate channels one by one and perform preliminary bias modeling.

[0009] Specifically, a flexible pressure-touch response platform is constructed, the entire calibration surface is divided into several pressure-touch zones, and the uniform loading degree of each pressure-touch zone is tested, including: Constructing a flexible pressure-sensitive response platform based on a preset pressure-sensitive unit and a stepping driver, and dividing the entire calibration surface into a plurality of pressure-sensitive zones based on the flexible pressure-sensitive response platform; A loading uniformity factor characterization model is constructed according to the flexible pressure touch response platform and the pressure touch partitions, and the uniform loading degree of each pressure touch partition is detected based on the loading uniformity factor characterization model.

[0010] Specifically, after ensuring that each partition is evenly loaded, we enter the calibration phase where we activate each channel one by one and perform preliminary bias modeling, including: After ensuring that each partition is loaded evenly, the calibration phase of activating each channel one by one begins. A single pressure-touch partition is activated, and the raw output electrical signal value of the sensor channel corresponding to the activated pressure-touch partition is obtained. Preliminary deviation modeling is performed based on the original output electrical signal value and a preset standard pressure.

[0011] Specifically, a nonlinear response correction factor is generated according to the result output by the preliminary deviation modeling, and a local regression adjustment is performed on the channel according to the nonlinear response correction factor; including: constructing a nonlinear response correction model based on the results output by the preliminary deviation modeling, and generating a nonlinear response correction factor based on the nonlinear response correction model; The voltage control fine-tuning is performed on the channel according to the nonlinear response correction factor to complete the local regression adjustment of each channel.

[0012] Specifically, in response to completing the local regression adjustment of each channel, a unified response surface fusion process is performed, including: In response to completing the local regression adjustment of each channel, obtaining an overall reference average value of the average output value of each channel; A channel balance correction model is constructed according to the overall reference average value and the average output value of the sensor channel in the standard pressure section, and a unified response surface fusion process is performed according to the channel balance correction model.

[0013] Specifically, in response to completing the unified response surface fusion process, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function, including: In response to completing the unified response surface fusion process, a comprehensive stability criterion function is constructed; 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.

[0014] Specifically, a calibration system for a multi-channel pressure sensor is also provided, the system comprising: a deviation modeling construction module, for constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; A local regression adjustment module is used to generate a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and perform local regression adjustment on the channel according to the nonlinear response correction factor; A response fusion processing module, configured to perform unified response surface fusion processing in response to completing local regression adjustment of each channel; The calibration result generating module is used 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.

[0015] Specifically, the deviation modeling construction module is also used to: construct a flexible pressure touch response platform, divide the entire calibration surface into several pressure touch partitions, and detect the uniform loading degree of each pressure touch partition; after ensuring that each partition is loaded evenly, enter the calibration stage of activating channels one by one and perform preliminary deviation modeling.

[0016] Specifically, the deviation modeling construction module is also used to: construct a flexible pressure-sensitive response platform based on a preset pressure-sensitive unit and a stepping driver, and divide the entire calibration surface into several pressure-sensitive zones based on the flexible pressure-sensitive response platform; construct a loading uniformity factor characterization model based on the flexible pressure-sensitive response platform and the pressure-sensitive zones, and detect the uniform loading degree of each pressure-sensitive zone based on the loading uniformity factor characterization model.

[0017] Specifically, the deviation modeling construction module is also used to: after ensuring that each partition is loaded evenly, enter the calibration stage 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; perform preliminary deviation modeling based on the original output electrical signal value and the preset standard pressure.

[0018] Specifically, the local regression adjustment module is also used to: construct a nonlinear response correction model based on the results of the preliminary deviation modeling output, generate a nonlinear response correction factor based on the nonlinear response correction model; and perform voltage control fine-tuning on the channel according to the nonlinear response correction factor to complete the local regression adjustment of each channel.

[0019] Specifically, the response fusion processing module is also used to: obtain the overall reference average value of the average output value of each channel in response to completing the local regression adjustment of each channel; construct a channel balance correction model based on the overall reference average value and the average output value of the sensor channel in the standard pressure segment, and perform unified response surface fusion processing based on the channel balance correction model.

[0020] Specifically, the calibration result generation module is further used to: construct a stability comprehensive criterion function in response to completing the unified response surface fusion processing; 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.

[0021] Optionally, a computer device is further provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned multi-channel pressure sensor calibration method when executing the computer program.

[0022] Optionally, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the calibration method of the multi-channel pressure sensor are implemented.

[0023] The present invention achieves the following technical effects: (1) The calibration method and system of the multi-channel pressure sensor are as follows: constructing a flexible pressure-touch response platform, calibrating the channels according to the flexible pressure-touch response platform, and performing preliminary deviation modeling; generating a nonlinear response correction factor according to the output 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 a unified response surface fusion process; in response to the completion of the unified response surface fusion process, constructing a stability comprehensive criterion function, and generating 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 with systematic, dynamic feedback, and multi-layer correction is formed, which not only overcomes the limitations of the traditional method that relies on linear models and static single-point calibration, but also effectively solves the error accumulation problem caused by mutual interference and delayed response between channels; (2) By constructing a flexible pressure-touch response platform, the entire calibration surface is divided into several pressure-touch zones, solving the problem that traditional multi-channel pressure sensor calibration mostly uses a rigid platform or a fixed standard loading device for calibration, only considering the isobaric input in a static, rigid plane environment, and it is difficult to truly restore the dynamic response characteristics of the flexible sensor under actual non-uniform force scenarios; (3) By implementing pressure control fine-tuning for channels with large deviations, local regression adjustment of each channel is completed, solving the problem that most existing multi-channel sensor calibration methods rely on a one-time set compensation coefficient, or approximately correct the output difference of each channel through hardware pre-adjustment during the manufacturing process, which is passive and inflexible when facing flexible substrates, non-uniform strain distribution, and changes in channel characteristics caused by long-term use. While ensuring the improvement of single-channel accuracy, system-level consistency and stability are achieved, providing solid technical support for the engineering application of multi-channel pressure sensors in high-precision scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 1 is a flow chart of a calibration method for a multi-channel pressure sensor according to an embodiment; Figure 2 is a structural block diagram of a financial data collection system based on artificial intelligence in one embodiment; Figure 3 FIG. 1 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate 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 may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0027] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] As used in this specification and the appended claims, the term "if" can be interpreted as "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 [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0029] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] In one embodiment, a terminal is provided, which is used to: construct a flexible pressure-touch response platform, perform channel calibration based on the flexible pressure-touch response platform, and perform preliminary deviation modeling; generate a nonlinear response correction factor based on the results output by the preliminary deviation modeling, and perform local regression adjustment on the channel based on the nonlinear response correction factor; in response to completing the local regression adjustment of each channel, perform unified response surface fusion processing; in response to completing the unified response surface fusion processing, construct a stability comprehensive criterion function, and generate a calibration result based on the stability comprehensive criterion function.

[0032] The terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices.

[0033] In one embodiment, Figure 1 As shown, a calibration method for a multi-channel pressure sensor is provided, the method comprising: Step S100: constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; 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 channel according to the nonlinear response correction factor; Step S300: in response to completing the local regression adjustment of each channel, performing unified response surface fusion processing; Step S400: In response to the completion of the unified response surface fusion process, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function.

[0034] In this embodiment, a flexible pressure-touch response platform is constructed, channel calibration is performed based on the flexible pressure-touch response platform, and preliminary deviation modeling is performed; a nonlinear response correction factor is generated based on the result of the preliminary deviation modeling output, and the channel is locally regressed and adjusted based on the nonlinear response correction factor; in response to the completion of the local regression adjustment of each channel, a unified response surface fusion processing is performed; in response to the completion of the unified response surface fusion processing, a stability comprehensive criterion function is constructed, and a calibration result is generated based on the stability comprehensive criterion function. By establishing models such as loading uniformity factor, initial deviation response factor, nonlinear response correction factor, regression adjustment factor, channel equalization correction coefficient and comprehensive criterion function, a complete calibration process with systematic, dynamic feedback and multi-layer correction is formed, which not only overcomes the limitations of traditional methods that rely on linear models and static single-point calibration, but also effectively solves the error accumulation problem caused by mutual interference and delayed response between channels.

[0035] In one embodiment, step S100: constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; includes: Step S110: constructing a flexible pressure-touch response platform, dividing the entire calibration surface into a number of pressure-touch zones, and detecting the uniform loading degree of each pressure-touch zone; Step S120: After ensuring that each partition is loaded evenly, enter the calibration phase of activating channels one by one and perform preliminary deviation modeling.

[0036] In this embodiment, in order to ensure that the pressure response of each channel can be accurately simulated in the actual working scenario, a flexible pressure touch response platform is constructed. Specifically, a flexible pressure touch response platform is constructed, the entire calibration surface is divided into several pressure touch zones, and the uniform loading degree of each pressure touch zone is detected; then, after ensuring that each zone is loaded uniformly, the calibration stage of activating the channels one by one is entered, and preliminary deviation modeling is performed.

[0037] In one embodiment, step S110: constructing a flexible pressure touch response platform, dividing the entire calibration surface into a plurality of pressure touch zones, and detecting the uniform loading degree of each pressure touch zone, includes: Step S111: 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 zones based on the flexible pressure-touch response platform; Step S112: constructing a loading uniformity factor characterization model according to the flexible pressure touch response platform and the pressure touch partitions, and detecting the uniform loading degree of each pressure touch partition based on the loading uniformity factor characterization model.

[0038] In this embodiment, in order to solve the problem that the calibration of multi-channel pressure sensors in the prior art mostly uses a rigid platform or a fixed standard loading device for calibration, this calibration method usually only considers isobaric input in a static, rigid plane environment, and it is difficult to truly restore the dynamic response characteristics of the flexible sensor under actual non-uniform force scenarios. Therefore, the flexible pressure touch response platform described in this application is composed of multiple independently liftable pressure touch units, each unit accurately controls the loading position and force through a stepper driver, and is used to apply a standardized in-plane pressure area. That is, the flexible pressure touch platform only needs to be composed of multiple stepper drivers, each driver controls the lifting and lowering of the corresponding pressure touch unit respectively, to achieve accurate loading of standard pressure without the need for additional complex structures. Therefore, on the basis of a simple structure, not only is controllable loading of multi-point pressure in space achieved, but also through the fine partitioning control and independent adjustment capability of the pressure touch unit, the non-uniform distribution of the pressure state of the corresponding area of ​​each channel under real working conditions is effectively simulated, significantly improving the calibration accuracy and the actual adaptability of the channel response.

[0039] Specifically, this embodiment divides the entire calibration surface into several pressure-touch zones, each of which is numbered and with the sensor channel One-to-one correspondence; to quantify each partition at the reference pressure (standard pressure) Response stability under load uniformity factor , used to characterize the uniformity of partition loading status.

[0040] The loading uniformity factor characterization model is as follows:

[0041] in, is the number of virtual test nodes set in the mth partition. is the actual applied pressure at the qth node. It is the standard pressure set by the calibration system. A set of typical operating points is set according to the full scale range of the sensor, usually selected at 0%, 25%, 50%, 75%, and 100% of the full scale position. For example, for a sensor with a range of 100kPa, Can be set to 0kPa, 25kPa, 50kPa, 75kPa and 100kPa.

[0042] The loading uniformity factor characterization model is used to detect the uniform loading of each pressure-touch zone on the platform. If the set threshold (e.g. 5%) is exceeded, the partition re-pressure adjustment instruction is automatically triggered to ensure that subsequent calibration is carried out in an approximately ideal isobaric environment. The loading uniformity factor characterization model is highly practical and engineering operability. Several virtual nodes are deployed inside , used to sample the actual applied pressure at the local loading point , and standard pressure Compare and calculate the average absolute deviation of all nodes to determine whether the partition load is uniform enough. If the pressure exceeds the set threshold set by the system, the system will automatically issue a "pressure adjustment feedback command" to the partition, adjusting the loading amplitude of the corresponding pressure touch unit until the pressure on each virtual node tends to be balanced, ensuring that the premise for subsequent channel response collection is a quasi-isobaric state.

[0043] In one embodiment, step S120: after ensuring that each partition is loaded evenly, enter the calibration phase of activating each channel one by one and perform preliminary deviation modeling, including: Step S121: After ensuring that each partition is loaded evenly, 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; Step S122: performing preliminary deviation modeling based on the original output electrical signal value and a preset standard pressure.

[0044] In this embodiment, traditional multi-channel sensor calibration typically relies on simultaneous acquisition and mean filtering of all channels, ignoring the inherent manufacturing errors, welding stress, or residual structural strain present in each channel under initial loading conditions. This makes it difficult to specifically compensate for systematic deviations during subsequent adjustments. This embodiment, after ensuring uniform loading in each zone, enters a channel-by-channel calibration phase. This activates a single pressure-touch zone and obtains the raw output electrical signal value of the sensor channel corresponding to the activated pressure-touch zone. Preliminary deviation modeling is performed based on this raw output electrical signal value and a preset standard pressure.

[0045] Specifically, the initial deviation modeling is performed by controlling the platform to activate only a single partition , and record the sensor channel corresponding to the partition The original output electrical signal value . Constructing the initial deviation response factor , in volts, indicating With standard pressure The difference between them is used as a reference for error compensation.

[0046] The initial deviation response factor benchmark model is as follows:

[0047] in, is the initial electrical signal reading of channel m. is the theoretical sensitivity constant of the sensor system (volts per Pascal (V / Pa)) used to convert the standard pressure Convert to ideal output. System parameters are obtained by repeatedly performing load tests on the sensor under standard pressure conditions before shipment. Specifically, multiple standard pressure points (such as 5kPa, 10kPa, and 15kPa) are selected, and the stable electrical signal output of the sensor under these pressure conditions is recorded. Using least squares fitting or linear regression, the average slope between pressure and output signal is calculated as the sensitivity coefficient. is the standard pressure.

[0048] like If the absolute value of is large, the system will mark the channel as "needing key adjustment" and assign a higher perturbation density or use multiple rounds of calibration in the subsequent steps; If the value is close to zero, it means that the initial state of the channel is good and a low-weight adjustment strategy can be used later. This forms a channel-differentiated calibration path and achieves true channel-by-channel optimization. As a benchmark reference for subsequent channel calibration, it indicates the initial output deviation of the channel under the current working conditions and is used to evaluate whether the current error level of each channel has systematic drift or sensitivity offset problems.

[0049] In one embodiment, step S200: generating a nonlinear response correction factor based on the output of the preliminary deviation modeling, and performing local regression adjustment on the channel based on the nonlinear response correction factor; includes: Step S210: constructing a nonlinear response correction model according to the output of the preliminary deviation modeling, and generating a nonlinear response correction factor according to the nonlinear response correction model; In this embodiment, a nonlinear response correction factor is constructed based on the result output by the preliminary deviation modeling. The nonlinear response correction factor is used to reflect the nonlinear response degree of each channel.

[0050] Specifically, traditional pressure sensor calibration is often based on single- or two-point calibration, relying on a linear assumption to establish the signal-pressure mapping relationship. This simplification is acceptable under ideal operating conditions, but in real-world applications, especially in multi-channel pressure sensors, the physical response curve of each channel often exhibits varying degrees of nonlinearity, such as hysteresis, drift, or stress concentration points. If this nonlinearity is not identified and corrected during the calibration phase, it will directly affect the overall accuracy of the system.

[0051] Based on the information obtained in step S112 , for sensor channels Perform multi-point pressure scans at each pressure level The following records the corresponding signal output , the nonlinear response correction model of the channel is obtained by fitting, and the nonlinear response correction model outputs the nonlinear response correction factor , as follows:

[0052] in, is the number of standard pressure levels scanned.

[0053] is the set pressure value at level i. The standard pressure values ​​applied sequentially on the flexible pressure touch platform are usually selected at several representative points within the sensor's range, such as 0%, 25%, 50%, 75%, and 100% full-scale pressure, at equal intervals or in a dense distribution. These pressure values ​​are applied step by step by the stepper driver, ensuring that each pressure value applied is traceable and highly stable. is the output signal of the mth channel at the i-th pressure level. is the initial deviation response factor obtained in step S112.

[0054] Nonlinear response correction factor Reflects the square sum of the deviations of the output curve of each channel relative to the ideal state, that is, the degree of nonlinear response. The channel is calculated by the square sum of the deviations. The degree of nonlinearity is no longer simply based on the assumption of an "ideal linear model", but rather on the actual quantification of the degree to which each channel response deviates from the theoretical linear expectation. If the value is large, it indicates that there is a significant nonlinear mismatch in the channel and a disturbance regression adjustment is required. If it is small, it means that the channel response curve is close to the ideal linear state, and a lightweight correction path can be adopted.

[0055] Specifically, the square operation of the signal in the formula of the nonlinear response correction model is to amplify the impact of the output error on the overall evaluation and avoid the mutual cancellation of positive and negative errors; the two terms in the brackets represent the differences between the actual output signal and the theoretical linear prediction value, which are used to measure the degree of nonlinear deviation of the channel at this pressure point.

[0056] Step S220: performing voltage control fine-tuning on the channels according to the nonlinear response correction factor to complete local regression adjustment of each channel.

[0057] In this embodiment, existing multi-channel sensor calibration methods mostly rely on a one-time set compensation coefficient, or approximately correct the output difference of each channel through hardware pre-adjustment during the manufacturing process. This method is passive and inflexible when facing flexible substrates, non-uniform strain distribution, 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.

[0058] The implementation of pressure control fine-tuning specifically includes: the pressure touch partition corresponding to the channel Apply multiple rounds of slight perturbation pressure, record the signal output changes after each round of perturbation, and establish the channel's quantitative regression adjustment factor for the perturbation input , used to evaluate the output regression trend of the channel after fine-tuning. Applying multiple rounds of micro-perturbation pressure involves using a stepper driver in the flexible pressure-touch platform to gradually fine-tune the displacement of the pressure-applying device, such as by 0.1 mm at a time. This then controls the corresponding pressure-touch unit to apply multiple small increments of perturbation pressure, such as 2 kPa per level, and continuously performs multiple rounds (e.g., 3-5 rounds) of pressure application and signal reading.

[0059] The regression adjustment factor quantification model is as follows:

[0060] in, is the number of perturbation rounds. is the pressure amplitude applied by the jth disturbance. is the channel signal before disturbance. is the channel signal after perturbation.

[0061] 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 If the error is within an acceptable range, it means that the channel has completed the local regression adjustment. It can be used as an indicator of the channel's response efficiency during the voltage control process. When the value is stable and close to the reference range, it indicates that the channel response state is in a convergence trend. If there are sharp fluctuations or the system moves beyond the expected sensitive range, it will automatically extend the perturbation cycles for that channel or appropriately amplify the perturbation amplitude to obtain more comprehensive response information. This shift from "quantitative perturbation" to "dynamic feedback correction" provides the channel adjustment process with greater process controllability and error approximation capabilities. This calibration and adjustment mechanism features dynamic response identification, nonlinear compensation, and adaptive optimization of channel behavior, significantly outperforming traditional static correction methods.

[0062] In one embodiment, step S300: in response to completing the local regression adjustment of each channel, performing unified response surface fusion processing includes: 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; Step S320: constructing a channel balance correction model according to the overall reference average value and the average output value of the sensor channel in the standard pressure section, and performing unified response surface fusion processing according to the channel balance correction model.

[0063] In this embodiment, in most existing multi-channel pressure sensor calibration technologies, even if each channel is individually adjusted during the calibration process, the overall output may still exhibit "relative deviation," meaning that different channels may experience inconsistent outputs under the same pressure. This discrepancy typically stems from systematic deviations between channels, such as device manufacturing errors, package stress differences, and wiring interference. These factors are often not fully accounted for by single-channel calibration.

[0064] Therefore, if further unified processing is not performed at the system level, the sensor will not be able to ensure the spatial consistency of multi-channel measurement results in actual use, seriously affecting its overall measurement accuracy. In this embodiment, unified response surface fusion processing is performed to eliminate the system-level error caused by the response differences between multiple channels and construct the channel equalization correction coefficient. , used to perform linear correction on the output value of each channel so that it tends to be consistent under standard pressure conditions. The channel balance correction coefficient is obtained through the channel balance correction model, and the calculation method of the channel balance correction model is as follows:

[0065] in, Is the sensor channel The average output value in the standard pressure section. The specific process is as follows: The corresponding pressure touch area applies each preset pressure value; At this point, multiple sets of sensor output values ​​under stable conditions are collected; the response signals under all pressure levels are averaged to obtain the overall average response value of the channel.

[0066] It is the overall reference average value of the average output value of all channels. It is the system reference benchmark value obtained by summarizing the average output value of all channels under the same standard pressure range. The acquisition process is as follows: For each standard pressure value , collect all channels (numbered ) output value at this pressure; first find the output value of each channel The output signal of each channel will be multiplied by the corresponding , thereby achieving convergence of outputs of all channels at the same pressure, eliminating inter-channel errors caused by processing differences and response drift.

[0067] In the final application stage, this correction factor will be used as a signal adjustment factor to perform linear correction on the original output of each channel, so that it tends to respond uniformly under the same pressure conditions. This approach has two advantages: first, it unifies the measurement benchmark of each channel under standard conditions at the signal level, improving the consistency and comparability of the sensor's overall measurement space; second, it can serve as a long-term monitoring basis for channel output changes. If it is used in the future, If a significant deviation occurs, the system can determine whether the channel has experienced performance degradation or aging. If the deviation from the ideal value 1 exceeds a set threshold (such as ±5%), the channel is considered to have deviated significantly and requires recalibration or performance testing.

[0068] In one embodiment, step S400: in response to completing the unified response surface fusion process, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function, includes: Step S410: in response to completing the unified response surface fusion process, constructing a comprehensive stability criterion function; Step S420: Outputting a stability comprehensive criterion factor according to the stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion factor.

[0069] In this embodiment, in the existing technical solutions, the judgment of the end of channel calibration is mostly subjective or qualitative standards, such as the error reaching a certain ratio, the signal being stable and without obvious fluctuations, etc. These judgment standards are not only poor in operability and difficult to quantify, but also cannot effectively integrate multiple calibration indicators, which can easily lead to insufficient calibration or over-adjustment, affecting efficiency and stability. In this embodiment, the stability comprehensive criterion function is based on the nonlinear response correction factor , the regression adjustment factor , the channel equalization correction coefficient The value of the calibration is used to indicate whether the overall calibration of the mth channel is completed.

[0070] The comprehensive stability criterion function is as follows:

[0071] in, This is a system-preset weighting factor, a fixed weighting factor derived through simulation evaluation or historical calibration data fitting optimization. Specifically, it is determined through simulation experiments and analysis of historical calibration data, aiming to reflect the actual weights of different indicators influencing calibration quality. During the setup process, a simulation dataset is first established based on real sensor samples. Deviation data for multiple channels in three dimensions—nonlinear response, disturbance regulation, and equalization correction—is recorded, and the calibration results are manually labeled (e.g., "meets standard" or "does not meet standard"). Minimum error classification or logistic regression is then used for training to find the weighted combination that most accurately distinguishes between qualified and unqualified samples under known calibration conditions.

[0072] This is the optimal disturbance response value stored in the system's memory. Specifically, the system uses long-term calibration history data or initial calibration template samples to determine a baseline reference value. This value represents the expected signal rate of change for an "ideal channel" under a unit disturbance. The process for setting this value is to select a group of sensor channels with stable performance and minimal error, record their average sensitivity response values ​​over a series of disturbance cycles using a standard calibration process, and then average these values.

[0073] when When the error is less than the system preset threshold, the system preset threshold should be determined based on the comprehensive error tolerance range and the misadjustment risk tolerance. Generally, statistical analysis is performed: in the calibration success sample, calculate The maximum value of is then combined with a slightly relaxed safety margin to set a threshold, indicating that the channel has met the standards for nonlinear correction, disturbance regression, and equalization correction, and the calibration process for that channel can be terminated. Otherwise, the system automatically returns to step S200 for the next round of adjustments. This upgrade from qualitative experience to quantitative thresholds makes the calibration termination criteria repeatable and verifiable; it automatically balances the influence of different error sources to avoid misjudging the system status with a single indicator; and it features closed-loop control logic that can be directly linked to the system execution logic, supporting intelligent calibration automation.

[0074] In another embodiment, in step S120, after extracting the original output electrical signal value, , the system does not read the electrical signal value immediately. Under the conditions of flexible materials or low-rigidity substrates, the sensor body often has elastic hysteresis, material buffer response, adhesion hysteresis or stress diffusion, resulting in the instantaneous signal often not being able to 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, resulting in error accumulation. Therefore, the initial touch pressure is delayed for a period of time, and several different delay points are set for multi-point reading. A set of delay windows is set, such as 10ms, 30ms, 100ms, etc., and multiple sets of data are read at different time points. The signal stabilization state is determined by comparing the response curves. Finally, the reading in the minimum first-order derivative fluctuation area of ​​the system dynamic response curve is selected as the output. Therefore, the delayed sampling strategy can effectively filter out short-term response anomalies, thereby significantly improving the physical authenticity of the initial deviation estimation.

[0075] In one embodiment, Figure 2 As shown, a calibration system for a multi-channel pressure sensor is also provided, the system comprising: a deviation modeling construction module, for constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; A local regression adjustment module is used to generate a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and perform local regression adjustment on the channel according to the nonlinear response correction factor; A response fusion processing module, configured to perform unified response surface fusion processing in response to completing local regression adjustment of each channel; The calibration result generating module is used 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.

[0076] In one embodiment, the deviation modeling construction module is also used to: construct a flexible pressure touch response platform, divide the entire calibration surface into several pressure touch zones, and detect the uniform loading degree of each pressure touch zone; after ensuring that each zone is loaded uniformly, enter the calibration stage of activating channels one by one, and perform preliminary deviation modeling.

[0077] In one embodiment, the deviation modeling construction module is also used to: construct a flexible pressure-sensitive response platform based on a preset pressure-sensitive unit and a stepping driver, and divide the entire calibration surface into several pressure-sensitive zones based on the flexible pressure-sensitive response platform; construct a loading uniformity factor characterization model based on the flexible pressure-sensitive response platform and the pressure-sensitive zones, and detect the uniform loading degree of each pressure-sensitive zone based on the loading uniformity factor characterization model.

[0078] In one embodiment, the deviation modeling construction module is also used to: after ensuring that each partition is loaded evenly, enter the calibration stage 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; perform preliminary deviation modeling based on the original output electrical signal value and the preset standard pressure.

[0079] In one embodiment, the local regression adjustment module is further used to: construct a nonlinear response correction model based on the results of the preliminary deviation modeling output, generate a nonlinear response correction factor based on the nonlinear response correction model; and perform voltage control fine-tuning on the channel according to the nonlinear response correction factor to complete the local regression adjustment of each channel.

[0080] In one embodiment, the response fusion processing module is also used to: obtain the overall reference average value of the average output value of each channel in response to completing the local regression adjustment of each channel; construct a channel balance correction model based on the overall reference average value and the average output value of the sensor channel in the standard pressure segment, and perform unified response surface fusion processing based on the channel balance correction model.

[0081] In one embodiment, the calibration result generation module is further used to: construct a stability comprehensive criterion function in response to completing the unified response surface fusion processing; 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.

[0082] In one embodiment, Figure 3 A computer device is also provided, including a memory and a processor. The memory stores a computer program and an operating system, and the processor executes the computer program to implement the steps of the above-mentioned machine vision-based separator screen surface separation detection method. The computer device also includes a system bus, internal memory, a network structure, a display screen, and an input device.

[0083] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the calibration method of the multi-channel pressure sensor are implemented.

[0084] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0086] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0088] An embodiment of the present application also provides a network device, which includes: 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 of any of the above-mentioned method embodiments when executing the computer program.

[0089] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0090] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0091] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0092] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0096] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

[0097] An embodiment of the present application also provides a computer device, which includes: 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 embodiment of the above method when executing the computer program.

[0098] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above description is an example of a computer device and does not limit the computer device. The computer device may include more or fewer components than described above, or a combination of certain components, or different components. For example, the computer device may also include input / output devices, network access devices, etc.

[0099] The processor may be a central processing unit (CPU), and the processor 0 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0100] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped with the computer device. Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0101] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are intended to fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A calibration method for a multi-channel pressure sensor, characterized in that: The method comprises: Constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; generating a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; In response to completing the local regression adjustment of each channel, a unified response surface fusion process is performed; In response to completing the unified response surface fusion process, a stability comprehensive criterion function is constructed, and a calibration result is generated according to the stability comprehensive criterion function.

2. The calibration method of a multi-channel pressure sensor according to claim 1, characterized in that: Constructing a flexible pressure-touch responsive platform, performing channel calibration based on the flexible pressure-touch responsive platform, and performing preliminary deviation modeling; including: Construct a flexible pressure-touch response platform, divide the entire calibration surface into several pressure-touch zones, and test the uniform loading level of each pressure-touch zone; After ensuring that each partition is loaded evenly, we enter the calibration phase where we activate channels one by one and perform preliminary bias modeling.

3. The calibration method of a multi-channel pressure sensor according to claim 2, characterized in that: Construct a flexible pressure-touch response platform, divide the entire calibration surface into several pressure-touch zones, and test the uniform loading level of each pressure-touch zone, including: Constructing a flexible pressure-sensitive response platform based on a preset pressure-sensitive unit and a stepping driver, and dividing the entire calibration surface into a plurality of pressure-sensitive zones based on the flexible pressure-sensitive response platform; A loading uniformity factor characterization model is constructed according to the flexible pressure touch response platform and the pressure touch partitions, and the uniform loading degree of each pressure touch partition is detected based on the loading uniformity factor characterization model.

4. The calibration method of a multi-channel pressure sensor according to claim 2, characterized in that: After ensuring that each partition is evenly loaded, we enter the calibration phase where we activate each channel one by one and perform preliminary bias modeling, including: After ensuring that each partition is loaded evenly, the calibration phase of activating each channel one by one begins. A single pressure-touch partition is activated, and the raw output electrical signal value of the sensor channel corresponding to the activated pressure-touch partition is obtained. Preliminary deviation modeling is performed based on the original output electrical signal value and a preset standard pressure.

5. The calibration method of a multi-channel pressure sensor according to claim 1, characterized in that: Generating a nonlinear response correction factor according to the output of the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; comprising: constructing a nonlinear response correction model based on the results output by the preliminary deviation modeling, and generating a nonlinear response correction factor based on the nonlinear response correction model; The voltage control fine-tuning is performed on the channel according to the nonlinear response correction factor to complete the local regression adjustment of each channel.

6. The calibration method of a multi-channel pressure sensor according to claim 1, characterized in that: In response to completing the local regression adjustment of each channel, a unified response surface fusion process is performed, including: In response to completing the local regression adjustment of each channel, obtaining an overall reference average value of the average output value of each channel; A channel balance correction model is constructed according to the overall reference average value and the average output value of the sensor channel in the standard pressure section, and a unified response surface fusion process is performed according to the channel balance correction model.

7. The calibration method of a multi-channel pressure sensor according to claim 1, characterized in that: In response to completing the unified response surface fusion process, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function, including: In response to completing the unified response surface fusion process, a comprehensive stability criterion function is constructed; 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.

8. A calibration system for a multi-channel pressure sensor, characterized in that: The system comprises: a deviation modeling construction module, for constructing a flexible pressure-touch response platform, performing channel calibration based on the flexible pressure-touch response platform, and performing preliminary deviation modeling; A local regression adjustment module is used to generate a nonlinear response correction factor according to the result output by the preliminary deviation modeling, and perform local regression adjustment on the channel according to the nonlinear response correction factor; A response fusion processing module, configured to perform unified response surface fusion processing in response to completing local regression adjustment of each channel; The calibration result generating module is used 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.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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