A high-safety, wide-temperature range analog correction method, system, device, and medium

Abstract

The present invention relates to electromechanical equipment analog quantity acquisition and correction technology, and discloses a high-safety, wide-temperature range analog quantity correction method, system, device, and medium. The method comprises: obtaining an influencing factor related to temperature drift; acquiring analog quantity data corresponding to different excitation values ​​of an analog quantity acquisition interface at each test temperature to obtain a training data set; training an initial BP neural network using the training data set to obtain a trained BP neural network, storing correction configuration information corresponding to each analog quantity interface and the trained BP neural network in a non-volatile storage area; and correcting the analog quantity data collected by the analog quantity acquisition interface to obtain final analog quantity data based on reading the trained BP neural network and the correction configuration information of each analog quantity interface from the non-volatile storage area. The method of the present invention can effectively correct acquisition errors over a wide temperature range, greatly improving the acquisition performance of the product and effectively improving aircraft control performance.

Description

Technical Field

[0001] The present invention belongs to the field of airborne computers and relates to a technology for collecting and correcting analog quantities of electromechanical equipment, and in particular to a method, system, device and medium for correcting analog quantities with high safety and a wide temperature range. Background Art

[0002] Aircraft electromechanical systems contain multiple sensors for collecting analog quantities such as temperature, pressure, and speed. Analog quantity acquisition is a key functional component of the aircraft electromechanical integrated system. The acquisition accuracy, data processing speed, and data reliability of its hardware interface directly affect the control performance indicators of the electromechanical management system. Therefore, the system has increasingly higher requirements for sensor acquisition accuracy, acquisition speed, and data stability.

[0003] In recent years, the domestic sensor electronic component manufacturing industry has achieved considerable development. However, due to process limitations, the consistency of sensor components is poor, and many key components fall short of system requirements in terms of performance, functionality, and reliability. This is particularly true for avionics equipment, which requires an operating temperature range of -55°C to 125°C. Extreme temperature conditions can cause the performance parameters of some sensor components to drift, failing to meet performance requirements. Therefore, in practical applications, specific processing algorithms are often used to correct the analog values ​​collected from airborne equipment to achieve better acquisition performance while meeting low-cost requirements. Traditional analog correction methods apply a uniform, fixed correction to the collected values, but this approach cannot address the temperature drift characteristics of the product's analog interface during acquisition. Summary of the Invention

[0004] In order to solve the technical problem of low accuracy of correction results due to temperature drift when correcting analog value acquisition values ​​using a unified and fixed correction method when sensor components are used under wide temperature range conditions, the present invention discloses a high-safety wide-temperature range analog value correction method, which includes the following steps:

[0005] S1. Obtaining influencing factors related to temperature drift based on temperature drift characteristics of interface components, wherein the influencing factors include ambient temperature;

[0006] S2. Collect analog data corresponding to different excitation values ​​at each test temperature from each analog acquisition interface to obtain a training data set;

[0007] S3, training the initial BP neural network using the training data set to obtain a trained BP neural network, and storing the modified configuration information and the trained BP neural network in a non-volatile storage area;

[0008] S4. Based on the trained BP neural network and the correction configuration information corresponding to the analog interface read from the non-volatile storage area, the analog data collected by the analog acquisition interface is corrected to obtain the final analog data.

[0009] Furthermore, in step S2, analog data corresponding to different excitation values ​​of each analog acquisition interface at each test temperature is collected to obtain a training data set, including:

[0010] S21. Dividing the operating temperature range of each analog quantity acquisition interface into a plurality of temperature segments according to a preset time;

[0011] S22. In each temperature range, dividing the excitation range of each analog quantity acquisition interface into a plurality of excitation segments according to a preset excitation value;

[0012] S23 , collecting analog values ​​corresponding to each of the excitation segments in each of the temperature segments to obtain a training data set.

[0013] Furthermore, the analog quantity interface includes any one of a voltage signal acquisition interface, a current signal acquisition interface, a resistance signal acquisition interface and a frequency signal acquisition interface.

[0014] Furthermore, the initial BP neural network includes an input layer, multiple hidden layers and an output layer, the input layer inputs the test temperature and the analog data corresponding to each excitation value at the test temperature, the hidden layer uses the ReLU activation function, and the output layer uses the linear activation function to output the corrected analog data.

[0015] Furthermore, in step S3, the initial BP neural network is trained using the training data set to obtain a trained BP neural network, including:

[0016] S31, using a stochastic gradient descent algorithm, with the minimum MMSE loss function as a constraint, training the initial BP neural network through the training data set to obtain a trained BP neural network, and encapsulating the trained BP neural network;

[0017] S32. Storing the packaged trained BP neural network and correction configuration information of each analog interface in a non-volatile storage area, wherein the correction configuration information includes temperature drift correction and non-temperature drift correction.

[0018] Furthermore, in step S4, based on the trained BP neural network and the correction configuration information corresponding to the analog interface read from the non-volatile storage area, the analog data collected by the analog acquisition interface is corrected to obtain the final analog data, including:

[0019] S41, reading the correction configuration information corresponding to the analog interface from the non-volatile storage area, and verifying the correction configuration information;

[0020] S42: When the verification passes and the correction configuration information read is temperature drift correction, the trained BP neural network is used to correct the analog data collected by the analog acquisition interface to obtain the final analog data;

[0021] S43: If the verification fails, or if the verification passes but the correction configuration information read is that the temperature drift is not corrected, the analog data collected by the analog acquisition interface is used as the final analog data.

[0022] An embodiment of the present invention further provides a high-safety, wide-temperature range analog quantity correction system, which includes an impact factor acquisition module, a data acquisition module, a network training module, and an analog quantity correction module.

[0023] The influencing factor acquisition module is used to acquire influencing factors related to temperature drift according to the temperature drift characteristics of the interface components, and the influencing factors include the ambient temperature;

[0024] The data acquisition module is used to collect analog data corresponding to different excitation values ​​of each analog acquisition interface at each test temperature to obtain a training data set;

[0025] The network training module is used to train the initial BP neural network using the training data set to obtain a trained BP neural network, and store the modified configuration information and the trained BP neural network in a non-volatile storage area;

[0026] The analog quantity correction module is used to correct the analog quantity data collected by the analog quantity acquisition interface to obtain final analog quantity data based on the trained BP neural network and the correction configuration information corresponding to the analog quantity interface read from the non-volatile storage area.

[0027] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned high-safety and wide-temperature range analog correction methods to solve the technical problem of low accuracy of correction results due to temperature drift when sensor components are used under wide temperature range conditions and the analog acquisition values ​​are corrected through a unified and fixed correction method.

[0028] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing any of the above-mentioned high-safety and wide-temperature range analog correction methods to solve the technical problem of low accuracy of correction results due to temperature drift when sensor components are used under wide temperature range conditions and the analog acquisition values ​​are corrected through a unified and fixed correction method.

[0029] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above-mentioned technical solutions adopted in the embodiments of this specification include at least the following: the method of the present invention can effectively correct the acquisition errors of various analog interfaces in the product under a wide temperature range, greatly improving the acquisition performance of the product, and effectively improving the aircraft control performance without increasing the design cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 This is a flow chart of the analog correction method with high safety and wide temperature range disclosed in an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a BP neural network disclosed in an embodiment of the present invention;

[0033] Figure 3 The process of writing the modified configuration information in the non-volatile storage area disclosed in the embodiment of the present invention;

[0034] Figure 4 A flow chart of correcting analog data collected by an analog acquisition interface using a trained BP neural network disclosed in an embodiment of the present invention;

[0035] Figure 5 This is an architectural diagram of a high-safety, wide-temperature range analog correction system disclosed in an embodiment of the present invention;

[0036] Among them, 501 is an impact factor acquisition module; 502 is a data acquisition module; 503 is a network training module / 504 is an analog correction module. DETAILED DESCRIPTION

[0037] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0038] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features of the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0039] The embodiment of the present invention discloses a high-safety, wide-temperature range analog correction method. Figure 1 As shown, the method includes the following steps:

[0040] S1. Obtaining influencing factors related to temperature drift based on temperature drift characteristics of interface components, wherein the influencing factors include ambient temperature;

[0041] S2. Collect analog data corresponding to different excitation values ​​at each test temperature from each analog acquisition interface to obtain a training data set;

[0042] S3, training the initial BP neural network using the training data set to obtain a trained BP neural network, and storing the modified configuration information and the trained BP neural network in a non-volatile storage area;

[0043] S4. Based on the trained BP neural network read from the non-volatile storage area and the correction configuration information corresponding to the analog interface, the analog data collected by the analog acquisition interface is corrected to obtain the final analog data.

[0044] Furthermore, the analog interface includes any one of a voltage signal acquisition interface, a current signal acquisition interface, a resistance signal acquisition interface, and a frequency signal acquisition interface. Through experiments, it was found that the acquisition of each type of analog interface has errors, and the errors are different at different temperatures. Therefore, in step S1, the dynamic acquisition error of the interface can be analyzed according to the temperature drift characteristics of the components. The correction target is the input excitation value, and the influencing factor that needs to be corrected is the temperature. For example, the correction formula for the resistance acquisition interface of a certain product can be expressed as:

[0045] g(x,T)=x+f(x,T);

[0046] Where x is the actual collected value, T is the current ambient temperature, f(x, T) is the correction parameter, and g(x, T) is the corrected collected value. The correction result of the interface collected value will be obtained through the algorithm later.

[0047] Furthermore, in step S2, analog data corresponding to different excitation values ​​of each analog acquisition interface at each test temperature is collected to obtain a training data set, including:

[0048] S21. Dividing the operating temperature range of each analog quantity acquisition interface into a plurality of temperature segments according to a preset time;

[0049] S22. In each temperature range, dividing the excitation range of each analog quantity acquisition interface into a plurality of excitation segments according to a preset excitation value;

[0050] S23 , collecting analog values ​​corresponding to each of the excitation segments in each of the temperature segments to obtain a training data set.

[0051] In the specific implementation, it is necessary to use a training data set to select the algorithm for analog correction. The method for obtaining the training data set is as follows: the ambient temperature of the product (that is, the test temperature) such as -55℃ to 125℃ can be segmented, for example, every 2℃ can be divided into a segment. The excitation can also be set in segments within each temperature range. For example: for the PT1000 resistance acquisition interface (assuming its resistance acquisition range is 0-5000Ω), it can be divided into segments of every 10Ω, thereby obtaining a set of acquisition data under each ambient temperature segment, and each acquisition value and excitation value are expressed as (x,y). Where, x=[x1,x2…x n ] is the actual collected value at the current ambient temperature T, y=[y1,y2…y n ] are the n excitation values ​​corresponding to the current ambient temperature T. Then, different (x, y) can be obtained for different ambient temperatures and different excitation values.

[0052] Further, see Figure 2 As shown, the initial BP neural network includes an input layer, multiple hidden layers (the specific number of layers needs to be determined based on the training results) and an output layer. The input layer inputs the test temperature and the analog data corresponding to each excitation value under the test temperature. The hidden layer uses the ReLU activation function, and the output layer uses the linear activation function to output the corrected analog data. Figure 2 As shown in the figure, the input information of the BP neural network is the analog interface at the current ambient temperature and the analog value collected at that temperature [T, x1, ... x n ], the network output information is the corrected analog acquisition value The number of hidden layers can be adjusted during training. For example, for a PT1000 resistor, the ReLU activation function is used for the hidden layer and the linear activation function is used for the output layer. The ReLU function sets output values ​​less than 0 to 0, ensuring that the output values ​​are positive. This function reduces computational complexity and prevents vanishing gradients.

[0053] Furthermore, according to theoretical analysis, the collected values ​​of the interface are related to the ambient temperature and the excitation, but it is difficult to obtain an accurate mapping relationship. Therefore, in order to correct the collected values ​​of the interface, a BP neural network with strong nonlinear fitting capabilities can be used for learning and training. In step S3, the initial BP neural network is trained using the training data set to obtain a trained BP neural network, including:

[0054] S31, using a stochastic gradient descent algorithm, with the minimum MMSE loss function as a constraint, training the initial BP neural network through the training data set to obtain a trained BP neural network, and encapsulating the trained BP neural network;

[0055] S32. Storing the packaged trained BP neural network and correction configuration information of each analog interface in a non-volatile storage area, wherein the correction configuration information includes temperature drift correction and non-temperature drift correction.

[0056] When training the initial BP neural network, the network weights are trained using the stochastic gradient descent algorithm. This algorithm does not require all the data in the training dataset when optimizing the network, and has the advantage of fast calculation speed. The goal of network training is to minimize the mean square error. The loss function can use the MMSE (minimum mean square error) function, which is expressed as:

[0057]

[0058] Among them, w is the weight matrix of BP neural network, b is the bias matrix of BP neural network, y is the training label vector, is the output vector of the BP neural network. During network training, the difference between the actual output of the BP neural network and the training labels is the error. This error information is transmitted backward from the output layer layer by layer along the original path. By gradually selecting the weights of neurons in each layer, the actual output of the BP neural network will become increasingly close to the expected output, thereby reducing the error.

[0059] After the BP neural network is trained, it is packaged so that the device can use it to correct analog value acquisitions. The specific implementation process involves saving the parameters of the trained BP neural network and then packaging it. During the embedded software coding phase, the packaged BP neural network is called to configure the BP neural network within the product. Whether the network is used for parameter correction is determined by the configuration information. In other words, during the software design phase, coders simply use the packaged BP neural network like a standard library function.

[0060] Whether to use the trained BP neural network to correct the temperature drift of analog data is configurable, and the correction configuration information is also read from the non-volatile storage area. Specifically, before the product is officially put into use, the product designer should ensure that the correction configuration information is written to the non-volatile storage area. For the correction configuration information writing, see Figure 3 As shown. For example, if the correction configuration information written is "0X55," it indicates "network correction for temperature drift is used," while any other value indicates "network correction for temperature drift is not used." To ensure that the non-volatile storage area to be written is free of other data, the selected non-volatile storage area can be cleared before writing, or a dedicated area can be allocated for the correction configuration information. After writing the correction configuration information for each analog interface, a checksum should be added to the data area and written to the non-volatile storage area. The checksum is calculated by summing the data to be checked byte by byte and performing a two's complement operation on the result. After writing is complete, the written correction configuration information must be immediately read from the non-volatile storage area and verified to confirm the write success using the above method. If verification is successful, "Parameter configuration successful" will be printed; otherwise, "Parameter configuration failed" will be printed, and the current non-volatile storage area will be cleared.

[0061] Furthermore, in step S4, based on the trained BP neural network and the correction configuration information corresponding to the analog interface read from the non-volatile storage area, the analog data collected by the analog acquisition interface is corrected to obtain the final analog data, including:

[0062] S41, reading the correction configuration information corresponding to the analog interface from the non-volatile storage area, and verifying the correction configuration information;

[0063] S42: When the verification passes and the correction configuration information read is temperature drift correction, the trained BP neural network is used to correct the analog data collected by the analog acquisition interface to obtain the final analog data;

[0064] S43: If the verification fails, or if the verification passes but the correction configuration information read is that the temperature drift is not corrected, the analog data collected by the analog acquisition interface is used as the final analog data.

[0065] Specifically, see Figure 4 As shown in the figure, after the analog interface acquires raw data, it first reads the configurable parameters from the non-volatile storage area and calculates the checksum. If the checksum succeeds, if the correction configuration information read is "0x55," network correction for temperature drift is used; if the correction configuration information is other, network correction for temperature drift is not used. If the checksum fails, the raw acquisition results are directly reported without using the BP neural network for correction. This completes the analog interface acquisition and parameter adjustment.

[0066] The method and solution of the present invention can effectively correct the acquisition errors of various analog interfaces in the product under a wide temperature range, greatly improving the acquisition performance of the product and effectively improving the aircraft control performance without increasing the design cost.

[0067] Based on the same inventive concept, an analog quantity correction system with high safety and wide temperature range is also provided in an embodiment of the present invention, as described in the following embodiments. Since the principle of solving the problem by the analog quantity correction system with high safety and wide temperature range is similar to that of the analog quantity correction method with high safety and wide temperature range, the implementation of the analog quantity correction system with high safety and wide temperature range can refer to the implementation of the analog quantity correction method with high safety and wide temperature range, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements the predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0068] Figure 5 This is a structural block diagram of a high-safety, wide-temperature range analog correction system disclosed in an embodiment of the present invention, such as Figure 5 As shown, it includes an impact factor acquisition module 501, a data acquisition module 502, a network training module 503 and an analog value correction module 504. The structure is described below.

[0069] The influencing factor acquisition module 501 is used to acquire influencing factors related to temperature drift according to the temperature drift characteristics of the interface components, wherein the influencing factors include the ambient temperature;

[0070] The data acquisition module 502 is used to collect analog data corresponding to different excitation values ​​of each analog acquisition interface at each test temperature to obtain a training data set;

[0071] The network training module 503 is used to train the initial BP neural network using the training data set to obtain a trained BP neural network, and store the modified configuration information and the trained BP neural network in a non-volatile storage area;

[0072] The analog correction module 504 is used to correct the analog data collected by the analog acquisition interface to obtain final analog data based on the trained BP neural network and the correction configuration information corresponding to the analog interface read from the non-volatile storage area.

[0073] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned high-safety, wide-temperature range analog correction methods is implemented.

[0074] Specifically, the computer device may be a computer terminal, a server or a similar computing device.

[0075] In this embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program for executing any of the above-mentioned high-safety and wide-temperature range analog correction methods.

[0076] Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include transitory media such as modulated data signals and carrier waves.

[0077] Obviously, those skilled in the art should understand that the various modules or steps of the above-mentioned embodiments of the present invention can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices. Alternatively, they can be implemented using program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or they can be made into separate integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0078] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A high-safety, wide-temperature range analog correction method, characterized in that: include: Obtaining, based on the temperature drift characteristics of the interface components, an influencing factor related to the temperature drift, wherein the influencing factor includes the ambient temperature; Collect analog data corresponding to different excitation values ​​at each test temperature from each analog acquisition interface to obtain a training data set; Training the initial BP neural network using the training data set to obtain a trained BP neural network, and storing the revised configuration information and the trained BP neural network in a non-volatile storage area; Based on the trained BP neural network and the correction configuration information corresponding to the analog interface read from the non-volatile storage area, the analog data collected by the analog acquisition interface is corrected to obtain the final analog data, including: reading the correction configuration information corresponding to the analog interface from the non-volatile storage area, and verifying the correction configuration information; when the verification passes and the read correction configuration information is for temperature drift correction, the trained BP neural network is used to correct the analog data collected by the analog acquisition interface to obtain the final analog data; when the verification fails, or when the verification passes but the read correction configuration information is for non-temperature drift correction, the analog data collected by the analog acquisition interface is used as the final analog data.

2. The analog correction method with high safety and wide temperature range according to claim 1, characterized in that: Collect analog data corresponding to different excitation values ​​at each test temperature from each analog acquisition interface to obtain a training data set, including: Dividing the operating temperature range of each analog quantity acquisition interface into a plurality of temperature segments according to a preset time; In each of the temperature segments, the excitation range of each of the analog quantity acquisition interfaces is divided into a plurality of excitation segments according to a preset excitation value; The analog value corresponding to each of the excitation segments in each of the temperature segments is collected to obtain a training data set.

3. The analog correction method with high safety and wide temperature range according to claim 1, characterized in that: The analog quantity interface includes any one of a voltage signal acquisition interface, a current signal acquisition interface, a resistance signal acquisition interface and a frequency signal acquisition interface.

4. The analog correction method with high safety and wide temperature range according to claim 1, characterized in that: The initial BP neural network includes an input layer, multiple hidden layers and an output layer. The input layer inputs the test temperature and the analog data corresponding to each excitation value under the test temperature. The hidden layer uses the ReLU activation function, and the output layer uses the linear activation function to output the corrected analog data.

5. The analog correction method with high safety and wide temperature range according to claim 1 or 4, characterized in that: The initial BP neural network is trained by the training data set to obtain a trained BP neural network, including: Using a stochastic gradient descent algorithm and taking the minimum MMSE loss function as a constraint, the initial BP neural network is trained using the training data set to obtain a trained BP neural network, and the trained BP neural network is encapsulated; The packaged trained BP neural network and the correction configuration information of each analog interface are stored in a non-volatile storage area, wherein the correction configuration information includes temperature drift correction and non-temperature drift correction.

6. A high-safety, wide-temperature range analog correction system, characterized in that: include: An influence factor acquisition module, configured to acquire an influence factor related to temperature drift based on a temperature drift characteristic of an interface component, wherein the influence factor includes an ambient temperature; A data acquisition module, the data acquisition module is used to collect analog data corresponding to different excitation values ​​of each analog acquisition interface at each test temperature to obtain a training data set; A network training module, the network training module is used to train the initial BP neural network using the training data set to obtain a trained BP neural network, and store the modified configuration information and the trained BP neural network in a non-volatile storage area; An analog quantity correction module is used to correct the analog quantity data collected by the analog quantity acquisition interface to obtain final analog quantity data based on the trained BP neural network and correction configuration information corresponding to the analog quantity interface read from the non-volatile storage area, including: reading the correction configuration information corresponding to the analog quantity interface from the non-volatile storage area and verifying the correction configuration information; when the verification passes and the read correction configuration information is for temperature drift correction, using the trained BP neural network to correct the analog quantity data collected by the analog quantity acquisition interface to obtain the final analog quantity data; when the verification fails, or when the verification passes but the read correction configuration information is for non-temperature drift correction, using the analog quantity data collected by the analog quantity acquisition interface as the final analog quantity data.

7. A computer 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 analog correction method with high safety and wide temperature range according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program for executing the analog correction method for high safety and wide temperature range according to any one of claims 1 to 5.

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