Network upgrading method based on bare computer program of ZYNQ chip

Abstract

The invention discloses a network upgrading method based on a ZYNQ chip bare computer program, which relates to the technical field of chips and comprises the following steps: S1, constructing an upgrading mirror image for network upgrading and a user mirror image containing the ZYNQ bare computer program based on original chip parameters; s2, starting a target ZYNQ chip, and receiving a new version of the user mirror image through a network to update the new version to the user mirror image; s3, jumping back the version to the user mirror image based on a rollback judgment mechanism if the upgrade mirror image is attacked when the upgrade mirror image is used; and S4, when the updating time reaches the work cycle, stopping updating, if the updating of the user mirror image is not completed, judging that abnormal attacks exist, and notifying manual processing. Compared with the prior art, the method has the advantages that the upgrading mirror image and the user mirror image are constructed by using the original chip parameters to perform alternate self-inspection and verification work, the system can quickly roll back to the security state when attacked, and the method has the advantages of enhancing the system security and realizing data protection.

Description

Technical Field

[0001] The present invention relates to the field of chip technology, and in particular to a network upgrade method based on a ZYNQ chip bare metal program. Background Art

[0002] In embedded systems, network upgrade is an important technology. Through network upgrade, developers can quickly respond to security threats or functional requirements and ensure that the device always runs the latest firmware version. In the bare metal program development based on ZYNQ, the ZYNQ series devices combine the ARM Cortex-A9 processor and FPGA, that is, in terms of software, the ZYNQ device integrates the processing system PS and programmable logic PL, which can provide powerful processing power and flexibility for embedded applications, and provide flexible hardware and software collaboration capabilities for network upgrades.

[0003] At present, ZYNQ chips also have potential security risks during regular network upgrades and remote upgrades. During the firmware transmission process, network attacks may attempt to tamper with firmware data, causing the device to load malicious code, and can intercept and modify upgrade data through man-in-the-middle attacks, disguising as a legitimate server. Attacks may cause abnormal resource consumption, such as a surge in CPU or memory usage, affecting system performance; network attacks may also cause network delays, packet loss, etc., resulting in tampering of downloaded firmware or software packages, thereby affecting the normal operation of the chip and the communication quality with external devices. The existing ZYNQ chip platform currently has a high cost in network upgrade security protection, mainly involving the introduction of dedicated hardware encryption modules, the implementation of complex security protocols and security mechanisms, requiring more software development resources, and increasing development time and maintenance costs; at the same time, the currently commonly used low-cost remote security upgrade method is usually carried out in a single mirror upgrade method between the upper and lower computers, but sending upgrade files directly through the upper computer may increase security risks, and the single mirror construction method may cause unverified files to be sent to the lower computer, increasing the possibility of malicious attacks, and is not conducive to the construction of a low-cost security upgrade platform. Summary of the invention

[0004] The present invention provides a network upgrade method based on a ZYNQ chip bare metal program, which solves the problem that it is difficult to simultaneously take into account both data security protection and design cost reduction during network upgrade of the existing ZYNQ chip.

[0005] The present invention is achieved through the following technical solutions: A network upgrade method based on a ZYNQ chip bare metal program, the method comprising: Step S1: Record the execution data of the target ZYNQ chip and mark it as the original chip parameters, build an upgrade image for network upgrade and a user image containing the ZYNQ bare metal program based on the original chip parameters, and use Vitis to compile the image task; Step S2: Start the target ZYNQ chip, load the upgrade image to enter the network upgrade mode, use the upgrade image to perform task self-check and verification to confirm integrity, and then receive the new version of the user image through the network to update to the user image; Step S3: Construct a rollback determination mechanism. If the upgrade image is attacked, the upgrade image data will be temporarily stored based on the rollback determination mechanism. After the attack stops, the upgrade image will be jumped to the breakpoint of the upgrade image to continue updating. Step S4: a fixed-duration working cycle is set for the updating process of the upgrade image. When the updating time reaches the working cycle, the updating is stopped. If the upgrade image has not completed the user image update at this time, it is determined that an abnormal network attack exists and a manual processing is notified.

[0006] The existing ZYNQ chip platform has a high cost in network upgrade security protection, which mainly involves the introduction of dedicated hardware encryption modules, the implementation of complex security protocols and security mechanisms, and requires more software development resources, which increases development time and maintenance costs; at the same time, the currently commonly used low-cost remote security upgrade method is usually carried out in a single mirror upgrade method between the upper and lower computers, but sending the upgrade file directly through the upper computer is prone to increase security risks, and the single mirror construction method may cause unverified files to be sent to the lower computer, increasing the possibility of malicious attacks, and is not conducive to the construction of a low-cost security upgrade platform. Based on this, the present invention provides a network upgrade method based on the ZYNQ chip bare metal program to solve the problem that the existing ZYNQ chip is difficult to take into account both data security protection and design cost reduction during network upgrades.

[0007] Furthermore, the content of the rollback determination mechanism includes: setting a floating threshold range for each original chip parameter of the ZYNQ chip to indicate the safety of the operating value; when it is monitored that the operating value of the original chip parameter exceeds the floating threshold range, it is determined that the current ZYNQ chip has been subjected to an abnormal attack; when it is monitored that the operating value that exceeds the floating threshold range returns to the floating threshold range, it is determined that the abnormal attack suffered by the current ZYNQ chip has stopped.

[0008] Furthermore, in the process of monitoring the operating values ​​of the original chip parameters exceeding and returning to the floating threshold range, the Savi-Golay filtering method is used to perform smoothing filtering on all the operating values; the smoothing filtering process includes: setting a time series for each operating value along the length of the working cycle, setting a number of sliding windows of the same length that are an odd number based on the time series, performing low-order polynomial fitting on the operating values ​​in each sliding window, calculating the filter coefficient corresponding to each sliding window based on the polynomial fitting results of all sliding windows, using the Savi-Golay filtering to perform weighted calculation on all the filter coefficients for the original data points of the operating values ​​to generate a final smoothing result value, and using the smoothing result value as the judgment value of the operating value to compare with the floating threshold range.

[0009] Furthermore, the weighted calculation of the original data points based on the filter coefficient includes: assuming that the smoothed result value of the running value of the original chip parameter at the current moment is represented by y[t], assuming that the moment value in the time series is represented by t, assuming that the number of original data points in the window is N, assuming that the filter coefficient is represented by C k , let the original data point be x, and the time value of the adjacent data point of each original data point be k, The formula for weighted calculation of the original data points is expressed as: .

[0010] Furthermore, the least squares method is used to perform error minimization fitting on the original data points of the running values ​​in each sliding window to find the optimal polynomial, and the found optimal polynomial is used to perform low-order polynomial fitting on the running values ​​in each sliding window to calculate the filter coefficient.

[0011] Furthermore, the calculation process of the filter coefficient includes: constructing a design matrix X for each data point in the sliding window, which includes terms of each order of the polynomial; constructing a response vector Y, which includes the original running values ​​in the sliding window; assuming that the polynomial coefficient vector representing the filter coefficient is denoted by C, The calculation formula of the filter coefficient matrix is ​​expressed as: , The values ​​in vector C represent all filter coefficients in the current sliding window.

[0012] Furthermore, a fast Fourier transform is applied to the running numerical signal in the time series of the time domain to convert the signal into the frequency domain, and the standard deviation of the amplitude spectrum is obtained to set the critical values ​​of the upper and lower boundaries of the floating threshold range.

[0013] Furthermore, after loading the upgrade image, the TLS protocol is used to encrypt the data, the hash algorithm is used to verify the integrity of the upgrade image, and a random salt value is introduced when calculating the hash value, and the calculated hash value is digitally signed.

[0014] Furthermore, the public key is used to verify the signature during the upgrade image verification phase to ensure that the hash value has not been tampered with.

[0015] Furthermore, after loading the upgraded image, the lightweight mbed TLS library in the ZYNQ chip is used to complete the execution when using the TLS protocol.

[0016] Compared with the prior art, the present invention utilizes the original chip parameters to construct an upgrade image and a user image for alternating self-checking and verification, and can quickly roll back to a safe state when attacked, thereby ensuring that the upgrade image is not tampered with during transmission, reducing system risks, and having the advantages of enhancing system security and achieving the beneficial effect of data protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention. Example

[0019] like Figure 1 As shown, this embodiment is a network upgrade method based on a ZYNQ chip bare metal program, the method comprising: Step S1: Record the execution data of the target ZYNQ chip and mark it as the original chip parameters, build an upgrade image for network upgrade and a user image containing the ZYNQ bare metal program based on the original chip parameters, and use Vitis to compile the image task; Step S2: Start the target ZYNQ chip, load the upgrade image to enter the network upgrade mode, use the upgrade image to perform task self-check and verification to confirm integrity, and then receive the new version of the user image through the network to update to the user image; Step S3: Construct a rollback determination mechanism. If the upgrade image is attacked, the upgrade image data will be temporarily stored based on the rollback determination mechanism. After the attack stops, the upgrade image will be jumped to the breakpoint of the upgrade image to continue updating. Step S4: a fixed-duration working cycle is set for the updating process of the upgrade image. When the updating time reaches the working cycle, the updating is stopped. If the upgrade image has not completed the user image update at this time, it is determined that an abnormal network attack exists and a manual processing is notified.

[0020] Real-time monitoring of the target ZYNQ chip's status information during runtime, including memory usage, CPU load, peripheral status, etc., such as CPU load, running time and usage of each core, RAM and cache usage, including available memory and memory allocation statistics. Data can be obtained through the debug interface, system log or specific monitoring tools. The process of building upgrade images and user images based on the original chip parameters is mainly based on conventional technical means. The process of building the upgrade image mainly includes: filtering out important data related to network upgrades and bare metal programs from the recorded original chip parameters, such as configuration registers, peripheral status and system performance indicators, and standardizing data from different sources; selecting functional modules that need to be included in the upgrade image according to the original parameters, such as drivers, library files and core system components; packaging all related files and functional modules into a complete upgrade image to ensure that it can be transmitted and installed on the network. The process of building the user image mainly includes: integrating the ZYNQ bare metal program and its related dependent files into the base image, and customizing the base image according to specific application requirements and functional testing. The Vitis compile image task is used to convert high-level source code into an executable image suitable for the ZYNQ chip to ensure that the code can run correctly on specific hardware. The upgrade image communicates with the server through a stable network connection to receive the latest version of the user image; after verifying the integrity of the new version, it is stored in the chip to replace the old user image. When the rollback judgment mechanism determines that there is an attack, the system immediately starts the rollback program to restore to the state of the user image to ensure system security. The current upgrade image data is temporarily stored, and a temporary storage area or cache can be used to quickly restore after the attack stops. The working cycle refers to the time limit set during the network upgrade process, which is usually determined by the system designer based on actual needs and network conditions. A fixed working cycle helps to standardize the upgrade process, ensure that the update task is completed within the specified time, and avoid resource waste and security risks caused by long-term unresponsiveness. When the update time reaches the set working cycle, the system will automatically stop the current upgrade operation to avoid unlimited waiting and prevent system resource occupation due to network problems or attacks during the upgrade process. After stopping the update, the system will check the status of the current upgrade image. If the user image update is not completed at the end of the work cycle, it is determined that there may be an abnormal situation, such as a network attack or system failure, and a notification is sent to relevant personnel (such as the operation and maintenance team). Timely notification and manual processing can avoid the system being in an unsafe state, ensure rapid response and problem solving, and thus reduce potential losses and risks.

[0021] Furthermore, as a feasible implementation method, the content of the rollback determination mechanism includes: setting a floating threshold range for each original chip parameter of the ZYNQ chip to indicate the safety of the operating value; when it is monitored that the operating value of the original chip parameter exceeds the floating threshold range, it is determined that the current ZYNQ chip is under abnormal attack; when it is monitored that the operating value that exceeds the floating threshold range returns to the floating threshold range, it is determined that the abnormal attack suffered by the current ZYNQ chip has stopped.

[0022] The original chip parameters include key operating indicators such as CPU utilization, memory usage, temperature, voltage, etc., which can reflect the status and health of the chip. For each original chip parameter, a floating threshold range for safe operation is set. In a specific implementation, the floating threshold range can be obtained based on data analysis and historical records of the ZYNQ chip under normal operating conditions. The operating value refers to the specific value obtained when these original chip parameters are measured at a specific moment. For example, at a certain moment, the CPU utilization is 75%, then "75%" is the operating value at that moment. The system continuously monitors each original parameter of the ZYNQ chip and compares the current operating value with the set floating threshold range. Once the operating value of a parameter is detected to exceed the preset floating threshold range, the system immediately determines that the current ZYNQ chip may be subject to abnormal attacks and implements real-time alarms. After being determined to be in an abnormal state, the system will automatically perform a rollback operation and switch back to the user image to avoid possible security risks. When the operating value that exceeds the floating threshold range is detected to return to the safe floating threshold range, the system will determine that the abnormal attack has stopped. As a specific application, in the specific implementation, a cooling time can be set to ensure that the parameters remain in a stable state for a period of time before normal operation is allowed to resume, to prevent misjudgment caused by short-term fluctuations. Through monitoring and evaluation, the system can quickly recover to a safe state after being attacked, reducing the risk of data leakage or damage.

[0023] Furthermore, as a feasible implementation method, in the process of monitoring the operating values ​​of the original chip parameters exceeding and returning to the floating threshold range, the Savi-Golay filtering method is used to perform smoothing filtering on all the operating values; the smoothing filtering process includes: setting a time series for each operating value along the length of the working cycle, setting a number of sliding windows of the same length that are an odd number based on the time series, performing low-order polynomial fitting on the operating values ​​in each sliding window, calculating the filter coefficient corresponding to each sliding window based on the polynomial fitting results of all sliding windows, using the Savi-Golay filtering to perform weighted calculation on all the filter coefficients for the original data points of the operating values ​​to generate a final smoothing result value, and using the smoothing result value as the judgment value of the operating value to compare with the floating threshold range.

[0024] The Savitzky-Golay filtering method is a nonlinear filtering technique for data smoothing and noise reduction. It performs smoothing by constructing a polynomial around a set of data points while retaining the trend of the data. Each running value is set as a time series along the working cycle, and an odd number of sliding windows are set to ensure symmetry. A low-order polynomial is fitted to the running values ​​in each sliding window, and a quadratic or cubic polynomial is usually selected to capture the local change trend of the data. According to the polynomial fitting results of the sliding window, the filter coefficient corresponding to each window is calculated. These coefficients will be used for weighted calculation to generate the final smoothing result value. The original data point refers to the running value corresponding to each moment in the time series. These data points are obtained through real-time monitoring, usually collected continuously, to form a time series. The filter coefficient is applied to the original data point using the Savitzky-Golay filtering method to generate a smoothing result value of the smoothed running value, and it will be used as a judgment value for comparison with the floating threshold range. The smoothing process is used to reduce noise caused by the environment or operation and improve the identifiability of the signal. Through sliding windows and polynomial fitting, data changes can be captured more accurately and the probability of false positives and negatives can be reduced.

[0025] Further, as a feasible implementation, the weighted calculation of the original data points based on the filter coefficient includes: assuming that the smoothed result value of the running value of the original chip parameter at the current moment is represented by y[t], assuming that the moment value in the time series is represented by t, assuming that the number of original data points in the window is N, assuming that the filter coefficient is represented by C k , let the original data point be x, and the time value of the adjacent data point of each original data point be k, The formula for weighted calculation of the original data points is expressed as: .

[0026] The smoothing result value is set to be expressed as y[t], which is the smoothing running value at the current time t, reflecting the smoothing trend of the original parameter. t represents the current time, as a point in the time series, all subsequent calculations are based on this time. N represents the number of original data points in the sliding window. The size of the window determines the scope of calculation and the degree of data smoothing. The filter coefficient C kIndicates the weighting coefficient calculated for the original data points of each window during the polynomial fitting process. The weighting coefficient is used to reflect the influence of each data point on the final smoothing result. The original data point represents the running value at each moment in the time series, specifically x[t+k], where k represents the relative position before or after the current moment t. The filter coefficient represents the filter coefficient (weight), which is the coefficient obtained by polynomial fitting and is used to weight the contribution of each data point. Each C k Corresponding to the data point x[t+k] in the window, x[t+k] represents the data point in the original monitoring data sequence with index t+k. This is a neighboring data point around the current data point t, depending on the value of k. For k=0, x[t+k] is exactly the current point x[t]; for other values, they are the previous or next data points, respectively.

[0027] Furthermore, as a feasible implementation method, the least squares method is used to perform error minimization fitting on the original data points of the running values ​​in each sliding window to find the optimal polynomial, and the found optimal polynomial is used to perform low-order polynomial fitting on the running values ​​in each sliding window to calculate the filter coefficient. The least squares method is a statistical method that aims to find the best fitting curve by minimizing the sum of squares of errors between observed values ​​and model predicted values; the running values ​​in each sliding window are fitted with low-order polynomials by the least squares method to obtain the best polynomial expression. The process mainly involves extracting the original data points, establishing a polynomial, calculating the error value and then minimizing the error value, and finally extracting the coefficients from the fitted polynomial as the required filter coefficients. The least squares method can effectively capture the trend of the data, thereby providing a more accurate model and reducing interference caused by noise.

[0028] As a feasible implementation method, in a specific application, a polynomial is found by minimizing the fitting error so that the polynomial can best fit the original data in each sliding window. That is, the constructed polynomial form can be set to: P(x)=c0+c1x+c2x 2 +…+c p x ρ , where c0, c1, c1, …, c p is the filter coefficient to be determined, and ρ is the order of the polynomial.

[0029] Further, as a feasible implementation, the calculation process of the filter coefficient includes: constructing a design matrix X for each data point in the sliding window, which includes terms of each order of the polynomial; constructing a response vector Y, which includes the original running value in the sliding window; assuming that the polynomial coefficient vector representing the filter coefficient is denoted by C, The calculation formula of the filter coefficient matrix is ​​expressed as: , The values ​​in vector C represent all filter coefficients in the current sliding window.

[0030] The design matrix X contains the polynomial terms of each order for each data point in the sliding window. For example, for a quadratic polynomial fit, the matrix X can be expressed as: , where x k represents the original data point in the sliding window; the response vector Y contains the original running value in the sliding window, which is expressed as: , these values ​​are the data to be fitted and represent the target values ​​within the sliding window. The key step in calculating the least squares solution is to obtain the effective polynomial coefficients. The response vector is multiplied by the transpose of the design matrix, and the result is the adjusted response value. Combined with the above results, the filter coefficient vector C is calculated. Through matrix operations, the optimal polynomial coefficients can be efficiently solved, making the data fitting more accurate; the application of the least squares method can effectively reduce errors and ensure a good fitting effect under different data conditions.

[0031] Furthermore, as a feasible implementation method, a fast Fourier transform is applied to the running numerical signal in the time series of the time domain to convert the signal into the frequency domain, and the standard deviation of the amplitude spectrum is obtained to set the critical values ​​of the upper and lower boundaries of the floating threshold range; after loading the upgrade image, the TLS protocol is used to encrypt the data, the hash algorithm is used to verify the integrity of the upgrade image, and a random salt value is introduced when calculating the hash value, and the calculated hash value is digitally signed; in the upgrade image verification stage, the signature is verified using the public key to ensure that the hash value has not been tampered with; when using the TLS protocol after loading the upgrade image, the lightweight mbed TLS library in the ZYNQ chip is used to complete the execution.

[0032] FFT converts the running numerical signal in the time domain into the frequency domain in order to analyze its frequency components. By calculating the standard deviation of the amplitude spectrum, the fluctuation range of the signal can be determined; the upper and lower boundaries of the floating threshold range are set according to the standard deviation so that abnormal fluctuations can be detected in time in subsequent monitoring. After loading the upgrade image, the TLS protocol is used to encrypt the data to ensure the confidentiality and integrity of the data during transmission. Furthermore, the lightweight mbed TLS library in the ZYNQ chip is used to execute the TLS protocol, so that secure communication can be carried out even with limited resources. The upgrade image is hashed and a random salt value is introduced in the hash calculation to prevent rainbow table attacks and increase the randomness of the hash result. The calculated hash value is digitally signed to generate signature information to provide data integrity and source verification. The hash and digital signature mechanism ensures that the upgrade image has not been tampered with and provides data integrity protection.

[0033] In a specific implementation, preferably, the process of setting the floating threshold range may be: collecting the operating numerical signals of the ZYNQ chip within a certain time range to form time series data; denoising and normalizing the collected signals, and using the FFT algorithm to convert the time domain signals into frequency domain signals; then calculating the amplitude spectrum of the frequency domain signal, usually by taking the absolute value of the FFT result; finally, calculating the standard deviation of the amplitude spectrum, and setting the upper and lower boundaries of the floating threshold according to the standard deviation.

[0034] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A network upgrade method based on ZYNQ chip bare metal program, characterized in that: The method includes: Step S1: Record the execution data of the target ZYNQ chip and mark it as the original chip parameters, build an upgrade image for network upgrade and a user image containing the ZYNQ bare metal program based on the original chip parameters, and use Vitis to compile the image task; Step S2: Start the target ZYNQ chip, load the upgrade image to enter the network upgrade mode, use the upgrade image to perform task self-check and verification to confirm integrity, and then receive the new version of the user image through the network to update to the user image; Step S3: Construct a rollback determination mechanism. If the upgrade image is attacked, the upgrade image data will be temporarily stored based on the rollback determination mechanism. After the attack stops, the upgrade image will be jumped to the breakpoint of the upgrade image to continue updating. Step S4: a fixed-duration working cycle is set for the updating process of the upgrade image. When the updating time reaches the working cycle, the updating is stopped. If the upgrade image has not completed the user image update at this time, it is determined that an abnormal network attack exists and a manual processing is notified.

2. According to claim 1, a network upgrade method based on ZYNQ chip bare metal program is characterized in that: The content of the rollback determination mechanism includes: setting a floating threshold range for each original chip parameter of the ZYNQ chip to indicate the safety of the operating value; when it is monitored that the operating value of the original chip parameter exceeds the floating threshold range, it is determined that the current ZYNQ chip has been subjected to an abnormal attack; when it is monitored that the operating value that exceeds the floating threshold range returns to the floating threshold range, it is determined that the abnormal attack suffered by the current ZYNQ chip has stopped.

3. A network upgrade method based on ZYNQ chip bare metal program according to claim 2, characterized in that: In the process of monitoring the running values ​​of the original chip parameters exceeding and returning to the floating threshold range, the Savvy-Golay filtering method is used to perform smoothing filtering on all the running values; The smoothing filter processing process includes: setting a time series for each operating value along the length of the working cycle, setting a number of sliding windows of the same length that are an odd number based on the time series, performing low-order polynomial fitting on the operating values ​​in each sliding window, calculating the filter coefficient corresponding to each sliding window according to the polynomial fitting results of all sliding windows, using Savi-Golay filtering to perform weighted calculation on all the filter coefficients for the original data points of the operating values ​​to generate a final smoothing result value, and using the smoothing result value as a judgment value of the operating value to compare with the floating threshold range.

4. According to claim 3, a network upgrade method based on ZYNQ chip bare metal program is characterized in that: The weighted calculation of the original data points based on the filter coefficient includes: assuming that the smoothed result value of the running value of the original chip parameter at the current moment is represented by y[t], the moment value in the time series is represented by t, the number of original data points in the window is N, and the filter coefficient is represented by C k , let the original data point be x, and the time value of the adjacent data point of each original data point be k, The formula for weighted calculation of the original data points is expressed as: .

5. A network upgrade method based on ZYNQ chip bare metal program according to any one of claims 3-4, characterized in that: The least square method is used to perform error minimization fitting on the original data points of the running values ​​in each sliding window to find the optimal polynomial, and the found optimal polynomial is used to perform low-order polynomial fitting on the running values ​​in each sliding window to calculate the filter coefficient.

6. A network upgrade method based on ZYNQ chip bare metal program according to claim 5, characterized in that: The calculation process of the filter coefficient includes: constructing a design matrix X for each data point in the sliding window, which includes terms of each order of the polynomial; constructing a response vector Y, which includes the original running values ​​in the sliding window; assuming that the polynomial coefficient vector representing the filter coefficient is denoted by C, The calculation formula of the filter coefficient matrix is ​​expressed as: , The values ​​in vector C represent all filter coefficients in the current sliding window.

7. A network upgrade method based on ZYNQ chip bare metal program according to any one of claims 3 to 6, characterized in that: Fast Fourier transform is applied to the running numerical signal in the time series of the time domain to convert the signal into the frequency domain, and the standard deviation of the amplitude spectrum is obtained to set the critical values ​​of the upper and lower boundaries of the floating threshold range.

8. A network upgrade method based on ZYNQ chip bare metal program according to claim 1, characterized in that: After loading the upgrade image, use the TLS protocol to encrypt the data, use the hash algorithm to check the integrity of the upgrade image, introduce a random salt value when calculating the hash value, and digitally sign the calculated hash value.

9. A network upgrade method based on ZYNQ chip bare metal program according to claim 8, characterized in that: During the upgrade image verification phase, the public key is used to verify the signature to ensure that the hash value has not been tampered with.

10. A network upgrade method based on ZYNQ chip bare metal program according to claim 8, characterized in that: After loading the upgrade image, the lightweight mbed TLS library in the ZYNQ chip is used to complete the execution when using the TLS protocol.

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