Smart card smart weighing method

Abstract

The application relates to the technical field of intelligent weighing, and particularly discloses an intelligent card intelligent weighing method, which comprises the following steps: step S1: acquiring a main weighing point and collecting a pressure analog signal; a plurality of micro vibration collection points are arranged in a ring array around the main weighing point; step S2: converting the pressure analog signal and each vibration analog signal into a pressure digital signal and a vibration digital signal respectively; extracting a vibration feature vector of each vibration digital signal; step S3: performing time-space alignment on the vibration digital signals, comparing the vibration digital signals with each other, obtaining a consistency index, and generating a weighing correction factor; correcting the pressure digital signal based on the weighing correction factor to obtain a true weighing value; the application realizes high performance while maintaining the simplicity of the structure, and effectively solves the environmental interference problem that has long plagued the field of precision weighing.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of intelligent weighing, and particularly relates to an intelligent card intelligent weighing method. BACKGROUND

[0002] With the rapid development of the Internet of Things and intelligent logistics systems, intelligent cards have been widely applied to the fields of postal parcel charging, warehouse logistics sorting, intelligent retail settlement and the like. As key charging basis, the weight data of the intelligent cards directly affects the accuracy and fairness of system operation. However, in the actual application environment, the weighing system generally faces complex environmental vibration interference, including ground mechanical vibration, structure conduction vibration caused by personnel walking, air pressure fluctuation caused by air flow and the like. The interference signals and the real weight signals overlap in the frequency band, and the traditional filtering method cannot effectively separate them.

[0003] Especially in the small-range weighing application of the intelligent card, the measured object is light in weight, and the relative error caused by the environmental vibration is more significant. The traditional solution often improves the stability through a mechanical vibration isolation platform or a multi-sensor average value algorithm, but this not only increases the system cost and complexity, but also reduces the response speed, and it is difficult to meet the dual requirements of real-time performance and economy of the modern intelligent system. SUMMARY

[0004] The application aims to provide an intelligent card intelligent weighing method, and solve the following technical problems.

[0005] The application can be achieved by the following technical solutions.

[0006] An intelligent card intelligent weighing method comprises the following steps:

[0007] Step S1: a main weighing point is acquired, the main weighing point is used to acquire the weight of the intelligent card and collect a pressure analog signal; a plurality of micro vibration collection points are arranged based on a ring array around the main weighing point, the micro vibration collection points are used to collect vibration analog signals at the positions, and the vibration analog signals include vibration caused by ground shaking, air flow and personnel walking;

[0008] Step S2: the pressure analog signal and each vibration analog signal are respectively converted into a pressure digital signal and a vibration digital signal; vibration feature vectors of each vibration digital signal are extracted, and the vibration feature vectors include waveforms, propagation directions and intensities;

[0009] Step S3: the vibration digital signals of each micro vibration collection point are time-space aligned and compared with each other, a consistency index is obtained based on the comparison result, and a weighing correction factor is generated according to the consistency index; the pressure digital signal is corrected based on the weighing correction factor, and a real weighing value is obtained.

[0010] As a further scheme of the present application: the main scale focus is based on a weighing sensing device, a sensing element of the weighing sensing device is a metal strain gauge in a Wheatstone bridge structure; the micro-vibration collection point is based on a micro-vibration sensing device, the micro-vibration sensing device is a micro-electro-mechanical system accelerometer, a frequency response range of the micro-vibration sensing device covers [0.1 Hz, 1000 Hz].

[0011] As a further scheme of the present application: the arrangement process of the micro-vibration collection point includes:

[0012] The number of the micro-vibration collection points is greater than or equal to 3; taking the main scale focus as a center and a preset distance as a radius, a circular plane area is obtained according to the center and the radius, an edge line of the circular plane area is obtained, and each micro-vibration collection point is arranged on the edge line at equal intervals.

[0013] As a further scheme of the present application: the process of converting into the pressure digital signal and the vibration digital signal includes:

[0014] The pressure analog signal and each vibration analog signal are recorded as analog signals, the analog signals are amplified and filtered by a signal conditioning circuit, the analog signals are adjusted to a preset amplitude, and noise is removed; a sampling rate is set, the sampling rate is greater than or equal to twice a highest frequency in the analog signals; the analog signals are synchronously sampled and held by a multi-channel synchronous analog-to-digital converter with a unified clock, voltage instantaneous values at the same time are captured, and the multi-channel synchronous analog-to-digital converter at each time is mapped to a 24-bit binary digital code, to obtain a digital signal corresponding to the analog signal.

[0015] As a further scheme of the present application: the extraction process of the vibration feature vector includes:

[0016] Based on a digital filtering algorithm, a plurality of environmental vibration components are separated from the vibration digital signal, frequencies of the environmental vibration components are obtained, a frequency threshold is set, the environmental vibration components higher than the frequency threshold are removed, and a separated vibration digital signal is obtained, recorded as a clean vibration digital signal;

[0017] Optionally, one of the micro-vibration collection points is taken as a reference point, and a time delay of a net vibration digital signal of the reference point reaching the remaining micro-vibration collection points is obtained based on a cross-correlation function; for the net vibration digital signal of any micro-vibration collection point, denoted as a to-be-measured signal, an interval between each adjacent two micro-vibration collection points is obtained, a path difference of the to-be-measured signal reaching the remaining micro-vibration collection points is obtained based on the time delay and a propagation speed of the to-be-measured signal in the medium, and an azimuth angle of the to-be-measured signal relative to the master point, denoted as a propagation direction, is obtained based on a triangulation method according to the radius.

[0018] As a further scheme of the application, the space-time alignment process comprises:

[0019] The time stamps of the master point and the micro-vibration collection points during collection are synchronized, and a coordinate system is established with the center of the circle as an origin to obtain position coordinates of the micro-vibration collection points in the coordinate system.

[0020] As a further scheme of the application, the generation process of the weighing correction factor comprises:

[0021] An error estimation model is constructed based on an adaptive filtering algorithm, the error estimation model takes the vibration feature vector collected in real time as a reference input, takes the original reading of the master point as a main input, and takes an error between the original reading and the true weight of the smart card as an output; the weights of the filter are iterated continuously, so that the difference between the output of the error estimation model and the true error reaches a minimum under the same reference input and main input, and finally an error model is obtained; the vibration feature vector collected currently and the current original reading of the master point are input into the error model, and the output obtained is denoted as a weighing correction factor.

[0022] The application has the following beneficial effects:

[0023] The application arranges micro-vibration sensors in an annular manner around the master point, constructs a perception network similar to a biological lateral line, and realizes multi-dimensional and all-around perception of environmental vibration; such spatial distributed monitoring can not only capture intensity information of vibration, but also accurately identify the propagation direction and waveform characteristics of vibration, thereby providing a rich data basis for intelligent compensation; the multi-sensor data comparison and fusion technology can effectively distinguish global vibration interference from local transient interference; the consistency index of vibration signals of each measurement point is calculated to intelligently judge the nature and influence range of vibration interference, thereby generating a targeted weighing correction factor; the compensation strategy based on consistency analysis overcomes the limitations of traditional single-sensor vibration compensation, and significantly improves the weighing stability under complex working conditions.

[0024] The application keeps the simplicity of structure while realizing high performance, effectively solves the problem of environmental interference in the field of precision weighing by optimizing the sensor layout and algorithm architecture, and does not excessively increase the hardware cost. BRIEF DESCRIPTION OF DRAWINGS

[0025] The application will be further described below with reference to the drawings.

[0026] Figure 1 is a step schematic diagram of an intelligent card intelligent weighing method of the application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0028] The intelligent card involved in the application is a high-security card integrating a chip and printed information, mainly used in the fields of finance, social security, Internet of Things, and access control; and the core features and production detection requirements are as follows.

[0029] 1. Each card contains physical printed information (such as a barcode, a pattern) and chip internal data (such as a BLOCK code), and it is necessary to ensure that the two are strictly bound and consistent without errors, so as to prevent mismatch or tampering.

[0030] 2. On the production line, visual positioning, barcode recognition, chip reading and writing, and defect detection are automatically completed by industrial cameras, code scanners, card readers, and other equipment, replacing traditional manual visual inspection, and improving precision and efficiency.

[0031] 3. The system recognition speed needs to reach ≥50 cards / minute, and the misjudgment rate is <0.05%, so as to realize rapid and reliable batch production and data traceability.

[0032] 4. The application is widely used in the fields of financial card or social security card manufacturing, Internet of Things SIM card, and high-security access control card, and has high requirements for anti-cloning and anti-data mismatch.

[0033] Referring to Figure 1 The application is an intelligent card intelligent weighing method, comprising the following steps:

[0034] Step S1: acquiring a master focus point, the master focus point is used to acquire the weight of the smart card and collect a pressure analog signal; a plurality of micro-vibration collection points are arranged based on an annular array around the master focus point, the micro-vibration collection points are used to collect vibration analog signals at the positions, the vibration analog signals include vibrations caused by ground shaking, air flow and personnel walking;

[0035] As a preferred embodiment of the present application, the master focus point is based on a weighing sensing device, the sensing element of the weighing sensing device is a metal strain gauge in a Wheatstone bridge structure; the micro-vibration collection point is based on a micro-vibration sensing device, the micro-vibration sensing device is a micro-electro-mechanical system accelerometer, the frequency response range of the micro-vibration sensing device covers [0.1 Hz, 1000 Hz];

[0036] As a preferred embodiment of the present application, the arrangement process of the micro-vibration collection point includes:

[0037] The number of micro-vibration collection points is greater than or equal to 3; taking the master focus point as the center and a preset distance as the radius, a circular plane area is obtained according to the center and the radius, an edge line of the circular plane area is acquired, and each micro-vibration collection point is arranged on the edge line at equal intervals;

[0038] Step S2: converting the pressure analog signal and each vibration analog signal into a pressure digital signal and a vibration digital signal respectively; extracting a vibration feature vector of each vibration digital signal, the vibration feature vector includes waveform, propagation direction and intensity;

[0039] As a preferred embodiment of the present application, the process of converting into a pressure digital signal and a vibration digital signal includes:

[0040] The pressure analog signal and each vibration analog signal are recorded as analog signals, the analog signals are amplified and filtered through a signal conditioning circuit, the analog signals are adjusted to a preset amplitude, and noise is removed; a sampling rate is set, the sampling rate is greater than or equal to twice the highest frequency in the analog signal; the analog signals are synchronously sampled and held through a multi-channel synchronous analog-to-digital converter with a unified clock, voltage instantaneous values at the same time are captured, and the multi-channel synchronous analog-to-digital converter at each time is mapped to a 24-bit binary digital code, to obtain a digital signal corresponding to the analog signal;

[0041] As a preferred embodiment of the present application, the extraction process of the vibration feature vector includes:

[0042] Separating several environmental vibration components from the vibration digital signal based on a digital filtering algorithm, obtaining the frequency of each environmental vibration component, setting a frequency threshold, eliminating the environmental vibration component higher than the frequency threshold, obtaining the separated vibration digital signal, denoted as the net vibration digital signal;

[0043] Optionally, one of the micro-vibration collection points is a reference point, and the time delay of the net vibration digital signal of the reference point reaching the remaining micro-vibration collection points is obtained based on the cross-correlation function; for the net vibration digital signal of any micro-vibration collection point, denoted as the to-be-tested signal, the interval between each adjacent two micro-vibration collection points is obtained, the path difference of the to-be-tested signal reaching the remaining micro-vibration collection points is obtained based on the time delay and the propagation speed of the to-be-tested signal in the medium, and the azimuth of the to-be-tested signal relative to the main reference point is obtained based on the triangular positioning method according to the radius, denoted as the propagation direction; the propagation direction is fused with the waveform and intensity of the to-be-tested signal to obtain the vibration feature vector of the to-be-tested signal;

[0044] Specifically, in theory, since the annular array is symmetrical, the vibration direction result finally calculated should be consistent regardless of selecting any micro-vibration collection point as the reference point; therefore, in all micro-vibration collection points, one of the micro-vibration collection points is selected as the reference point, and the time difference required for the net vibration digital signal to propagate from the reference point to each of the other micro-vibration collection points, i.e., the time delay, is calculated;

[0045] For the collection point and any micro-vibration collection point, the net vibration digital signals of the collection point and the micro-vibration collection point are obtained respectively, in order to align the two net vibration digital signals, one of the net vibration digital signals is moved to the left or to the right, and the distance of the movement is the time delay;

[0046] Step S3: The vibration digital signals of the micro-vibration collection points are time-space aligned and compared with each other, a consistency index is obtained based on the comparison result, and a weight correction factor is generated according to the consistency index; the pressure digital signal is corrected based on the weight correction factor to obtain the true weight value;

[0047] As a preferred embodiment of the present application, the time-space alignment process includes:

[0048] The time stamps of the main reference point and the micro-vibration collection points during collection are synchronized; and a coordinate system is established with the center of the circle as the origin to obtain the position coordinates of the micro-vibration collection points in the coordinate system;

[0049] As a preferred embodiment of the present application, the consistency index obtaining process includes:

[0050] combining all the micro-vibration collection points two by two, obtaining a plurality of combinations, for the net vibration digital signals of any two micro-vibration collection points in a combination, obtaining the waveform similarity of the two net vibration digital signals in the time domain, and obtaining the standard deviation of the waveform similarity of all combinations, denoted as a consistency index;

[0051] The waveform similarity obtaining process comprises: obtaining the digital signal sequences corresponding to the two net vibration digital signals respectively, and obtaining the correlation coefficient between the two digital signal sequences, denoted as the waveform similarity;

[0052] Specifically, when the waveform similarity is equal to 1, the waveform shapes of the two net vibration digital signals are completely consistent, and the amplitudes are in a fixed proportion, that is, the vibrations detected by the two micro-vibration collection points are exactly the same; when the waveform similarity is equal to 0, the fluctuation modes of the net vibration digital signals have no linear relationship and are completely random, that is, the vibration source is very local; when the waveform similarity is equal to -1, the waveform shapes of the two net vibration digital signals are completely opposite, which is rarely seen in practice but can occur in specific symmetric vibration modes; the closer the value of the waveform similarity is to 1, the higher the similarity of the two net vibration digital signals is, and the closer the value of the waveform similarity is to -1, the higher the degree of the waveform of the two net vibration digital signals is opposite;

[0053] It can be understood that when calculating the cross-correlation coefficient of the net vibration digital signals of the two micro-vibration collection points, if the result is close to 1, it means that the vibration events perceived by the two micro-vibration collection points are highly consistent, which points to global interference; if the result is very low, it means that the vibration modes of the two micro-vibration collection points are very different, which strongly suggests the existence of local interference;

[0054] As a preferred embodiment of the present application, the generation process of the weighing correction factor comprises:

[0055] An error estimation model is constructed based on an adaptive filtering algorithm, the error estimation model takes the vibration feature vector collected in real time as a reference input, takes the original reading of the master scale point as a main input, and takes the error between the original reading and the true weight of the smart card as an output; by continuously iterating the weight of the filter, the difference between the output of the error estimation model and the true error under the same reference input and main input is minimized, and finally an error model is obtained; the vibration feature vector collected at present and the current original reading of the master scale point are input into the error model, and the output obtained is denoted as a weighing correction factor;

[0056] Specifically, the generation process of the weighing correction factor is based on an adaptive filtering algorithm to construct a dynamic error estimation model: the adaptive filtering algorithm takes the real-time collected vibration feature vector intensity time series as the reference input (i.e. the interference observation signal) and takes the original reading of the weighing sensor as the disturbed main input signal; the filter weight is calculated through continuous iteration, so that the algorithm output can infinitely approach the real error component caused by the vibration in the weighing signal; finally, the optimal estimation value output by the algorithm in real time is the weighing correction factor, which will be directly deducted from the original weighing reading to obtain the compensated net weight value;

[0057] As a preferred embodiment of the present application, the process of correcting the pressure digital signal based on the weighing correction factor is G=G0-K, where G0 is the current original reading and K is the weighing correction factor;

[0058] The above describes one embodiment of the present application in detail, but the content is only a preferred embodiment of the present application and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the scope of the present application.

Claims

1. A smart card intelligent weighing method, characterized in that, Includes the following steps: Step S1: Obtain the main weighing point, which is used to obtain the weight of the smart card and collect pressure simulation signals; arrange several micro vibration acquisition points in a ring array around the main weighing point, which are used to collect vibration simulation signals at their location, including vibrations caused by ground shaking, airflow and people walking. Step S2: Convert the pressure analog signal and each vibration analog signal into pressure digital signal and vibration digital signal respectively; extract the vibration feature vector of each vibration digital signal, the vibration feature vector including waveform, propagation direction and intensity; Step S3: Spatiotemporally align the vibration digital signals from each micro vibration acquisition point and compare them with each other. Obtain the consistency index based on the comparison results and generate a weighing correction factor based on the consistency index. The pressure digital signal is corrected based on the weighing correction factor to obtain the true weighing value; In step S1, the process of arranging the micro vibration acquisition points includes: The number of micro vibration acquisition points is greater than or equal to 3; with the main point as the center and the preset distance as the radius, a circular planar area is obtained according to the center and the radius, the edge line of the circular planar area is obtained, and each micro vibration acquisition point is arranged at equal intervals on the edge line; In step S2, the process of extracting the vibration feature vector includes: Based on the digital filtering algorithm, several environmental vibration components are separated from the vibration digital signal. The frequency of each environmental vibration component is obtained, a frequency threshold is set, and environmental vibration components with a frequency higher than the frequency threshold are removed to obtain the separated vibration digital signal, which is denoted as the net vibration digital signal. Select one micro-vibration acquisition point as a reference point. Based on the cross-correlation function, obtain the time delay of the net vibration digital signal from the reference point to the other micro-vibration acquisition points. For any micro-vibration acquisition point, the net vibration digital signal is denoted as the signal to be measured. Obtain the interval between each two adjacent micro-vibration acquisition points. Based on the time delay and the propagation speed of the signal to be measured in the medium, obtain the path difference of the signal to be measured to the other micro-vibration acquisition points. Based on the radius, obtain the azimuth angle of the signal to be measured relative to the main point of reference using the triangulation method, and denot it as the propagation direction. Combine the propagation direction with the waveform and intensity of the signal to be measured to obtain the vibration feature vector of the signal to be measured.

2. The smart card intelligent weighing method according to claim 1, characterized in that, In step S1, the main weighing point is based on a weighing sensor, the sensing element of which is a metal strain gauge with a Wheatstone bridge structure; the micro vibration acquisition point is based on a micro vibration sensor, which is a microelectromechanical system accelerometer, and the frequency response range of which covers [0.1Hz, 1000Hz].

3. The smart card intelligent weighing method according to claim 1, characterized in that, In step S2, the process of converting the signals into digital pressure signals and digital vibration signals includes: The pressure simulation signal and each vibration simulation signal are both recorded as analog signals. The analog signals are amplified and filtered by a signal conditioning circuit to adjust them to a preset amplitude and remove noise. A sampling rate is set, which is greater than or equal to twice the highest frequency in the analog signal. The analog signals are synchronously sampled and held by a multi-channel synchronous analog-to-digital converter with a unified clock to capture the instantaneous voltage value at the same moment. The multi-channel synchronous analog-to-digital converter at each moment is mapped to a 24-bit binary digital code to obtain the digital signal corresponding to the analog signal.

4. The smart card intelligent weighing method according to claim 1, characterized in that, In step S3, the spatiotemporal alignment process includes: The timestamps of the main sampling point and each micro vibration sampling point are synchronized during the sampling process; and a coordinate system is established with the center of the circle as the origin to obtain the position coordinates of each micro vibration sampling point on the coordinate system.

5. The smart card intelligent weighing method according to claim 1, characterized in that, In step S3, the process of obtaining the consistency index includes: All micro-vibration acquisition points are paired to obtain several combinations. For the net vibration digital signals of two micro-vibration acquisition points in any combination, the waveform similarity of the two net vibration digital signals in the time domain is obtained, and the standard deviation of the waveform similarity of all combinations is obtained, which is denoted as the consistency index.

6. The smart card intelligent weighing method according to claim 1, characterized in that, In step S3, the process of generating the weighing correction factor includes: An error estimation model is constructed based on an adaptive filtering algorithm. The error estimation model takes the real-time acquired vibration feature vector as the reference input, the original reading of the main weighing point as the main input, and the error between the original reading and the actual weight of the smart card as the output. By continuously iterating the weight of the filter, the difference between the output of the error estimation model and the actual error is minimized under the same reference input and main input, and the error model is finally obtained. The currently acquired vibration feature vector and the current original reading of the main weighing point are input into the error model, and the obtained output is recorded as the weighing correction factor.

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