An error monitoring method and system adapted to a balance with a precision of one ten-thousandth

Abstract

The present invention relates to an error monitoring method and system for a balance with a precision of one ten-thousandth, belonging to the technical field of error monitoring. The method comprises acquiring multimodal balance sensor signals, performing spatiotemporal alignment verification based on the multimodal balance sensor signals to obtain a multimodal balance environmental parameter matrix; obtaining balance dynamic tensor coupling information based on the multimodal balance environmental parameter matrix through a physical constraint tensor network; obtaining a balance dynamic error interval based on the balance dynamic tensor coupling information through a multi-field coupling equation; obtaining balance error correction information based on the balance dynamic error interval through multimodal environmental interference analysis; and outputting a balance error correction visualization graph based on the balance error correction information, thereby achieving dynamic probability assessment of the balance error interval and visualization of the balance error correction information.

Description

Technical Field

[0001] The present invention belongs to the technical field of error monitoring, and in particular relates to an error monitoring method and system adapted for a balance with a precision of one ten-thousandth. Background Art

[0002] As high-precision weighing tools, 1 / 10,000th precision balances are widely used in fields such as drug development, precious metal testing, and precision chemical experiments. The reliability of their measurement results directly impacts the validity of experimental data. The extremely high measurement accuracy of 1 / 10,000th precision balances is affected by external factors such as vibration, airflow, and humidity.

[0003] In order to reduce external interference and make the balance measurement more accurate, the existing technology reduces interference through physical isolation means, and increases the inertial mass of the system by increasing the mass of the operating table and adding a shock-absorbing structure, thereby reducing external interference. However, the lack of intuitive feedback and error correction functions makes it difficult to determine whether the balance load-bearing results are accurate or the error range. In addition, the existing technology lacks the ability to quantitatively analyze the coupling effects of multiple interferences, making it difficult to predict the weight of their combined impact on the final weighing value. At the same time, the existing technology lacks a dynamic error compensation mechanism. When the environmental interference exceeds the load threshold of the vibration isolation system, the balance can only indicate instability through numerical jumps, but cannot automatically correct the deviation or calibrate the error confidence interval, forcing the experimenter to repeatedly perform no-load calibration and sample retesting, seriously reducing the detection efficiency.

[0004] Therefore, it is urgent to develop an error monitoring method and system suitable for balances with a precision of one ten-thousandth in order to break through the bottleneck of high-precision weighing technology. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes an error monitoring method and system adapted for a balance with a precision of one ten-thousandth.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An error monitoring method adapted for a balance with a precision of one ten-thousandth, comprising:

[0008] S1: Acquire a multimodal balance sensor signal, and obtain a multimodal balance environment parameter matrix through a spatiotemporal alignment check based on the multimodal balance sensor signal;

[0009] S2: Obtaining balance dynamic tensor coupling information through a physical constraint tensor network according to the multimodal balance environment parameter matrix;

[0010] S3: Obtaining a balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0011] S4: obtaining balance error correction information through multimodal environmental interference analysis according to the balance dynamic error interval, and outputting a balance error correction visual graph according to the balance error correction information.

[0012] Preferably, the process of constructing the multimodal balance environmental parameter matrix in step S1 specifically includes:

[0013] S101: Obtaining a multimodal balance sensor time synchronization signal through time deviation compensation according to the multimodal balance sensor signal;

[0014] S102: Obtain a multimodal balance environmental parameter matrix through chi-square anomaly detection according to the multimodal balance sensor time synchronization signal.

[0015] Preferably, the acquisition of the balance dynamic tensor coupling information in step S2 specifically includes:

[0016] S201: The balance dynamic tensor coupling information includes a balance dynamic weight tensor and a balance interference coupling coefficient matrix;

[0017] S202: Obtaining a balance dynamic weight tensor through physical constraint tensor network training according to the multimodal balance environment parameter matrix;

[0018] S203: Obtaining a balance interference coupling coefficient matrix through sliding window covariance calculation according to the balance dynamic weight tensor and the multimodal balance environment parameter matrix.

[0019] Preferably, the structure of the physical constraint tensor network in step S202 specifically includes:

[0020] S202-1: The physical constraint tensor network includes an input layer, a hidden layer, and an output layer. The input layer receives the multimodal balance environment parameter matrix. The hidden layer extracts the balance multi-field coupling characteristics through a third-order tensor convolution kernel. The balance dynamic weight tensor is updated based on the balance multi-field coupling characteristics through an adaptive moment estimation optimizer.

[0021] S202-2: The mathematical expression of the loss function of the physical constraint tensor network is:

[0022] ,

[0023] Among them, L is the physical constraint tensor network loss term, m is the physical constraint weight coefficient, S T is the temperature field Laplace operator, k is the thermal diffusivity, is the rate of change of temperature with respect to time, ||.|| F is the Frobenius norm of the multimodal balance environmental parameter matrix.

[0024] Preferably, the calculation process of the balance dynamic error interval in step S3 includes:

[0025] S301: Obtaining a standard deviation of the total balance error through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0026] S302: Calculating a dynamic error interval of the balance according to the standard deviation of the total error of the balance through the dynamic boundary of the confidence interval.

[0027] Preferably, the mathematical expression of the multi-field coupling equation in step S301 is:

[0028] ,

[0029] Where H is the standard deviation of the total error of the balance, W i is the weight of the i-th environmental parameter, E i is the value of the i-th environmental parameter, P ij is the coupling coefficient between the i-th environmental parameter and the j-th environmental parameter, and n is the number of environmental parameters.

[0030] Preferably, the process of generating the balance error correction information in step S4 specifically includes:

[0031] S401: Obtaining environmental interference error information through multimodal environmental interference degree analysis according to the dynamic error interval of the balance, wherein the environmental interference error information includes an environmental interference error coefficient and an environmental stability index;

[0032] S402: Preset an error correction control rule library, and generate balance error correction information through the error correction control rule library according to the environmental interference error information.

[0033] An error monitoring system adapted for a 1 / 10,000 precision balance, applied to the above-mentioned error monitoring method, comprising a multi-modal balance environmental parameter matrix acquisition module, a balance dynamic tensor coupling module, a balance dynamic error interval calculation module, and a balance error correction visualization module;

[0034] The multimodal balance environment parameter matrix acquisition module is used to acquire the multimodal balance sensor signal and obtain the multimodal balance environment parameter matrix through time-space alignment verification;

[0035] The balance dynamic tensor coupling module is used to obtain balance dynamic tensor coupling information through a physical constraint tensor network according to the multi-modal balance environment parameter matrix;

[0036] The balance dynamic error interval calculation module is used to obtain the balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0037] The balance error correction visualization module is used to obtain balance error correction information and output a balance error correction visualization diagram based on the balance dynamic error interval through multimodal environmental interference analysis.

[0038] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the error monitoring method described above is implemented.

[0039] A storage medium comprising computer executable instructions, wherein the computer executable instructions are used to perform the above error monitoring method when executed by a computer processor.

[0040] The beneficial effects of the present invention are:

[0041] (1) By acquiring the multimodal balance sensor signals and obtaining the multimodal balance environmental parameter matrix through spatiotemporal alignment verification, the spatiotemporal baseline inconsistency problem of multi-source sensor signals is solved, the fusion error caused by sampling frequency differences and sensor layout heterogeneity is eliminated, and the data alignment accuracy is improved.

[0042] (2) The dynamic tensor coupling information of the balance is obtained through the physical constraint tensor network, which integrates deep learning and physical constraints. Compared with the pure data-driven model, the model generalization ability is improved to a certain extent.

[0043] (3) The dynamic error range of the balance is obtained through the multi-field coupling equation, and the dynamic error quantitative modeling is realized. The simulation model is constructed through the multi-field coupling equation, which can calculate the impact of complex environmental factors such as vibration interference, temperature drift, and airflow disturbance on the measurement accuracy in real time, and realize the dynamic probability evaluation of the error range.

[0044] (4) The balance error correction information is obtained through multimodal environmental interference analysis and a visual diagram of the balance error correction is output. The error correction information is fed back visually, making the complex environment-error coupling relationship concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0046] Figure 1 The figure is a flow chart of an error monitoring method adapted for a balance with a precision of one ten-thousandth of an inch according to the present invention. DETAILED DESCRIPTION

[0047] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0048] See also Figure 1 , an error monitoring method adapted for a balance with a precision of one ten-thousandth, comprising:

[0049] S1: Acquire a multimodal balance sensor signal, and obtain a multimodal balance environment parameter matrix through a spatiotemporal alignment check based on the multimodal balance sensor signal;

[0050] S2: Obtaining balance dynamic tensor coupling information through a physical constraint tensor network according to the multimodal balance environment parameter matrix;

[0051] S3: Obtaining a balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0052] S4: obtaining balance error correction information through multimodal environmental interference analysis according to the balance dynamic error interval, and outputting a balance error correction visual graph according to the balance error correction information.

[0053] In this embodiment, the step of acquiring the multimodal balance sensor signal and obtaining the multimodal balance environment parameter matrix through spatiotemporal alignment verification according to the multimodal balance sensor signal is specifically implemented by the following steps:

[0054] S101: The multimodal balance sensor signal includes external micro-vibration data, external airflow vector data, and external humidity data;

[0055] S101-1: Acquire external micro-vibration data through micro-vibration sensors. The micro-vibration sensors are deployed in four quadrants of the balance base, with one set each, and two sets deployed on the core load-bearing beam, for a total of six sets of sensors.

[0056] S101-2: Acquire external airflow vector data using a laser, wherein the laser generates an orthogonal grating and obtains the external airflow vector data through Doppler frequency shift detection;

[0057] The mathematical expression of the Doppler shift detection is:

[0058] ,

[0059] Among them, V air is the external airflow vector data, Δf is the Doppler frequency shift, α is the laser wavelength, and β is the laser incident angle.

[0060] S101-3: Acquire external humidity data through a nanopore humidity sensor, where the structure of the nanopore humidity sensor is a graphene nanopore array.

[0061] S102: Obtaining a multimodal balance sensor time synchronization signal through time deviation compensation according to the multimodal balance sensor signal;

[0062] S103: Obtaining a multimodal balance environmental parameter matrix through chi-square anomaly detection according to the multimodal balance sensor time synchronization signal.

[0063] In this embodiment, obtaining the balance dynamic tensor coupling information through the physical constraint tensor network according to the multimodal balance environment parameter matrix is ​​specifically implemented by the following steps:

[0064] S201: The balance dynamic tensor coupling information includes a balance dynamic weight tensor and a balance interference coupling coefficient matrix;

[0065] S202: Obtaining a balance dynamic weight tensor through physical constraint tensor network training according to the multimodal balance environment parameter matrix;

[0066] S202-1: The physical constraint tensor network includes an input layer, a hidden layer, and an output layer. The input layer receives the multimodal balance environment parameter matrix. The hidden layer extracts the balance multi-field coupling characteristics through a third-order tensor convolution kernel. The balance dynamic weight tensor is updated based on the balance multi-field coupling characteristics through an adaptive moment estimation optimizer.

[0067] S202-2: The mathematical expression of the loss function of the physical constraint tensor network is:

[0068] ,

[0069] Among them, L is the physical constraint tensor network loss term, m is the physical constraint weight coefficient, S T is the temperature field Laplace operator, k is the thermal diffusivity, is the rate of change of temperature with respect to time, ||.|| F is the Frobenius norm of the multimodal balance environmental parameter matrix.

[0070] S203: Obtaining a balance interference coupling coefficient matrix through sliding window covariance calculation according to the balance dynamic weight tensor and the multimodal balance environment parameter matrix.

[0071] The mathematical expression of the balance interference coupling coefficient matrix is:

[0072] ,

[0073] Among them, P ij is the coupling coefficient between the i-th environmental parameter and the j-th environmental parameter, Cov(E i ,E j ) is the covariance between the i-th environmental parameter and the j-th environmental parameter, var(E i ) is the standard deviation of the i-th environmental parameter, var(E j) is the standard deviation of the jth environmental parameter.

[0074] In this embodiment, obtaining the balance dynamic error interval through the multi-field coupling equation according to the balance dynamic tensor coupling information is specifically implemented by the following steps:

[0075] S301: Obtaining a standard deviation of the total balance error through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0076] The mathematical expression of the multi-field coupling equation is:

[0077] ,

[0078] Where H is the standard deviation of the total error of the balance, W i is the weight of the i-th environmental parameter, E i is the value of the i-th environmental parameter, P ij is the coupling coefficient between the i-th environmental parameter and the j-th environmental parameter, and n is the number of environmental parameters;

[0079] S302: Calculating a dynamic error interval of the balance according to the standard deviation of the total error of the balance through the dynamic boundary of the confidence interval.

[0080] The mathematical expression of the dynamic boundary of the confidence interval is:

[0081] ,

[0082] Where Z(t) is the half-width of the confidence interval of the balance error at time t, z is the Z value corresponding to the preset confidence level, and ||T|| is the norm of the temperature gradient.

[0083] In this embodiment, the balance error correction information is obtained by analyzing the multimodal environment interference degree according to the balance dynamic error interval through the following steps:

[0084] S401: Obtaining environmental interference error information through multimodal environmental interference degree analysis according to the dynamic error interval of the balance, wherein the environmental interference error information includes an environmental interference error coefficient and an environmental stability index;

[0085] S401-1: The mathematical expression of the environmental interference error coefficient is:

[0086] ,

[0087] ,

[0088] ,

[0089] in, is the environmental interference error coefficient, is the single environment interference error factor, is the multi-environment interference coupling error factor, W k is the kth environmental parameter weight, E k is the value of the kth environmental parameter.

[0090] S401-2: The mathematical expression of the environmental stability index is:

[0091] ,

[0092] Among them, S(t) is the environmental stability index at time t, W i is the weight of the i-th environmental parameter, E i is the value of the i-th environmental parameter, E i,max is the maximum allowed value of the i-th environmental parameter.

[0093] S402: Preset an error correction control rule library, and generate balance error correction information through the error correction control rule library according to the environmental interference error information.

[0094] An error monitoring system adapted to a 1 / 10,000 precision balance includes a multi-modal balance environmental parameter matrix acquisition module, a balance dynamic tensor coupling module, a balance dynamic error interval calculation module, and a balance error correction visualization module;

[0095] The multimodal balance environment parameter matrix acquisition module is used to acquire the multimodal balance sensor signal and obtain the multimodal balance environment parameter matrix through time-space alignment verification;

[0096] The balance dynamic tensor coupling module is used to obtain balance dynamic tensor coupling information through a physical constraint tensor network according to the multi-modal balance environment parameter matrix;

[0097] The balance dynamic error interval calculation module is used to obtain the balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information;

[0098] The balance error correction visualization module is used to obtain balance error correction information and output a balance error correction visualization diagram based on the balance dynamic error interval through multimodal environmental interference analysis.

[0099] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), ... computer-readable storage medium. ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this document, computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus or device.

[0100] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0101] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF or the like, or any suitable combination thereof. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, and the programming language includes an object-oriented programming language such as Java, Smalltalk, C++, and also includes a conventional procedural programming language such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, utilizing an Internet service provider to connect through the Internet).

[0102] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for error monitoring adapted to a balance with a precision of one ten-thousandth, characterized in that: include: S1: Acquire a multimodal balance sensor signal, and obtain a multimodal balance environmental parameter matrix through a spatiotemporal alignment check based on the multimodal balance sensor signal; S2: Obtaining balance dynamic tensor coupling information through a physical constraint tensor network according to the multimodal balance environment parameter matrix; The acquisition of the balance dynamic tensor coupling information specifically includes: S201: The balance dynamic tensor coupling information includes a balance dynamic weight tensor and a balance interference coupling coefficient matrix; S202: Obtaining a balance dynamic weight tensor through physical constraint tensor network training according to the multimodal balance environment parameter matrix; S203: Obtaining a balance interference coupling coefficient matrix through sliding window covariance calculation according to the balance dynamic weight tensor and the multimodal balance environment parameter matrix; S3: Obtaining a balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information; The calculation process of the dynamic error range of the balance includes: S301: Obtaining a standard deviation of the total balance error through a multi-field coupling equation according to the balance dynamic tensor coupling information; S302: Calculating a dynamic error interval of the balance according to the standard deviation of the total error of the balance through the dynamic boundary of the confidence interval; S4: obtaining balance error correction information through multimodal environmental interference analysis according to the balance dynamic error interval, and outputting a balance error correction visual graph according to the balance error correction information; The generation process of the balance error correction information specifically includes: S401: Obtaining environmental interference error information through multimodal environmental interference degree analysis according to the dynamic error interval of the balance, wherein the environmental interference error information includes an environmental interference error coefficient and an environmental stability index; S402: Preset an error correction control rule library, and generate balance error correction information through the error correction control rule library according to the environmental interference error information.

2. The error monitoring method according to claim 1, characterized in that: The process of constructing the multimodal balance environmental parameter matrix in step S1 specifically includes: S101: Obtaining a multimodal balance sensor time synchronization signal through time deviation compensation according to the multimodal balance sensor signal; S102: Obtain a multimodal balance environmental parameter matrix through chi-square anomaly detection according to the multimodal balance sensor time synchronization signal.

3. The error monitoring method according to claim 1, characterized in that: The structure of the physical constraint tensor network in step S202 specifically includes: S202-1: The physical constraint tensor network includes an input layer, a hidden layer, and an output layer. The input layer receives the multimodal balance environment parameter matrix. The hidden layer extracts the balance multi-field coupling characteristics through a third-order tensor convolution kernel. The balance dynamic weight tensor is updated based on the balance multi-field coupling characteristics through an adaptive moment estimation optimizer. S202-2: The mathematical expression of the loss function of the physical constraint tensor network is: ， Among them, L is the physical constraint tensor network loss term, m is the physical constraint weight coefficient, S T is the temperature field Laplace operator, k is the thermal diffusivity, is the rate of change of temperature with time, is the Frobenius norm of the multimodal balance environmental parameter matrix.

4. An error monitoring system adapted for a 1 / 10,000 precision balance, applied to the error monitoring method according to any one of claims 1 to 3, characterized in that: It includes a multi-modal balance environmental parameter matrix acquisition module, a balance dynamic tensor coupling module, a balance dynamic error interval calculation module, and a balance error correction visualization module; The multimodal balance environment parameter matrix acquisition module is used to acquire the multimodal balance sensor signal and obtain the multimodal balance environment parameter matrix through time-space alignment verification; The balance dynamic tensor coupling module is used to obtain balance dynamic tensor coupling information through a physical constraint tensor network according to the multi-modal balance environment parameter matrix; The balance dynamic error interval calculation module is used to obtain the balance dynamic error interval through a multi-field coupling equation according to the balance dynamic tensor coupling information; The balance error correction visualization module is used to obtain balance error correction information based on the balance dynamic error interval through multimodal environmental interference analysis and output a balance error correction visualization diagram.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the error monitoring method according to any one of claims 1 to 3 is implemented.

6. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions are used to perform the error monitoring method according to any one of claims 1 to 3 when executed by a computer processor.

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