A method and system for backing up BIOS data of micro host computer

Abstract

The present application relates to the field of data processing technology, and specifically to a method and system for backing up BIOS data of a micro-host computer, the method comprising: obtaining the monitoring data of each hardware parameter of the micro-host computer at each moment in real time; forming a response monitoring matrix at each moment; obtaining multiple sub-matrices corresponding to each hardware parameter at each moment; determining the joint trend coefficient of any sub-matrix; obtaining the state responsiveness of each hardware parameter at each moment; predicting the monitoring data of each hardware parameter at each moment and all moments before it, and obtaining the trigger coefficient of the micro-host computer at each moment; predicting the trigger coefficient at the current moment and all moments before it, and evaluating the data backup operation of the BIOS data. The present application improves the accuracy of the evaluation of the state of the micro-host computer, and can respond to the BIOS data backup in a timely manner.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for backing up BIOS data of a micro-host computer. Background Art

[0002] Micro-host computers have the advantages of small size and strong portability. BIOS (Basic Input / Output System) is the key link between the hardware and software of micro-host computers, carrying important data such as system boot program and hardware configuration information. When BIOS encounters failures such as incorrect settings, virus attacks, and upgrade failures, backup data can help it quickly restore to its previous normal state, effectively avoiding the situation where the micro-host computer cannot operate normally due to BIOS backup problems, thereby reducing maintenance costs and time consumption.

[0003] Micro-host computers often need to use special software or tools when backing up the BIOS. The installation process of the tools is relatively complicated and there is a risk of incompatibility with the operating system or hardware of the micro-host computer. Although the current BIOS has an automatic backup function, there are deficiencies in the implementation of emergency data backup based on hardware monitoring. The poor monitoring accuracy may cause the BIOS backup to be not timely, resulting in the failure to effectively back up data in a timely manner when an abnormality occurs in the micro-host computer, which may have an adverse impact on the stable operation and maintenance of the micro-host computer. Summary of the invention

[0004] In order to solve the above technical problems, a micro host computer BIOS data backup method and system are provided to solve the existing problems.

[0005] The solution to the technical problem of the present application is to provide a micro host computer BIOS data backup method and system, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a method for backing up BIOS data of a micro host computer, the method comprising the following steps:

[0007] Obtain the monitoring data of each hardware parameter of the micro host computer at each moment in real time;

[0008] The monitoring data of all hardware parameters at all times before each time are used to form a response monitoring matrix at each time; the response monitoring matrix is ​​divided by taking the column where the abnormal data of the row element corresponding to each hardware parameter in the response monitoring matrix is ​​located as the dividing point, so as to obtain multiple sub-matrices corresponding to each hardware parameter at each time;

[0009] Analyze the similarity of the change trends between any submatrix corresponding to each hardware parameter at each moment and the remaining submatrices, as well as the differences between the ranks of the submatrices, to determine the joint trend coefficient of any submatrix; based on the joint trend coefficient, obtain the state responsiveness of each hardware parameter at each moment;

[0010] Predicting the monitoring data of each hardware parameter at each moment and all previous moments, analyzing the deviation between the predicted value of each hardware parameter and its preset abnormal threshold, combining the joint trend coefficient and the state responsiveness, and obtaining the trigger coefficient of the micro host computer at each moment;

[0011] Predict the trigger coefficients at the current moment and all previous moments, compare the trigger coefficients at the current moment with the prediction results, or compare the monitoring data of each hardware parameter at the current moment with the preset abnormal threshold, and evaluate the data backup operation of the BIOS data.

[0012] Preferably, the hardware parameters include: CPU temperature, GPU temperature, motherboard voltage and fan speed.

[0013] Preferably, the abnormal data is obtained by performing an abnormality detection on any row element in the response monitoring matrix through a LOF abnormality detection algorithm.

[0014] Preferably, determining the joint trend coefficient of any submatrix includes:

[0015] Obtain the trend statistics of each row element in each submatrix, and form a trend response sequence with the trend statistics of all rows in each submatrix; calculate the similarity of the trend response sequence between any submatrix corresponding to each hardware parameter at each time and the remaining submatrices;

[0016] Calculate the difference between the rank of any submatrix corresponding to each hardware parameter at each time and the ranks of the remaining submatrices, and record it as the rank difference;

[0017] Calculate the ratio of the similarity to the rank difference, record it as the first ratio, and take the average of the first ratios of any submatrix corresponding to each hardware parameter at each moment and all other submatrices as the joint trend coefficient of any submatrix corresponding to each hardware parameter at each moment.

[0018] Preferably, the similarity degree is the cosine similarity of the trend response sequence between any submatrix and the remaining submatrices.

[0019] Preferably, obtaining the state responsiveness of each hardware parameter at each moment includes:

[0020] The joint trend coefficients of all sub-matrices corresponding to each hardware parameter at each time are used to form a response trend sequence corresponding to each hardware parameter at each time;

[0021] Through the comprehensive evaluation algorithm, the response trend sequences corresponding to all hardware parameters at each moment are comprehensively evaluated, and the comprehensive evaluation results of each hardware parameter at each moment are used as the state responsiveness of each hardware parameter at each moment.

[0022] Preferably, the method for determining the deviation is:

[0023] Calculate the difference between the predicted value corresponding to each hardware parameter and the preset abnormal threshold;

[0024] The deviation is a result of an exponential function with a natural constant as the base and the difference as the exponent.

[0025] Preferably, obtaining the trigger coefficient of the micro host computer at each moment includes:

[0026] Calculate the average value of the joint trend coefficients of all sub-matrices corresponding to each hardware parameter at each time;

[0027] The normalized result of the mean value of all hardware parameters at each moment, the mean value of the product of the state responsiveness and the deviation amount is used as the trigger coefficient of the micro host computer at each moment.

[0028] Preferably, the evaluating of the data backup operation of the BIOS data includes:

[0029] Through the prediction model, the trigger coefficients of the current moment and all previous moments are predicted to obtain the estimated trigger coefficient of the next moment;

[0030] If the trigger coefficient at the current moment is greater than the estimated trigger coefficient, or the monitoring data of any hardware parameter at the current moment is greater than the preset abnormal threshold of any hardware parameter, the intelligent backup engine starts the backup operation, otherwise, the backup operation is not started.

[0031] In a second aspect, an embodiment of the present application also provides a micro-host computer BIOS data backup system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned micro-host computer BIOS data backup methods when executing the computer program.

[0032] This application has at least the following beneficial effects:

[0033] The present application analyzes the abnormal data in the monitoring data of each hardware parameter, divides the abnormal data of each hardware parameter into different stages, and calculates the joint trend coefficient. The beneficial effect of the application is that the relevant conditions of the changing trends of the monitoring data of different stages are taken into account to reflect the joint response changes of different stages, and further illustrate the consistency of the responses of different stages to the state changes of the micro-host computer when the stages are divided by different hardware parameters; the state responsiveness is calculated, and the beneficial effect of the application is that the significance of the response of the corresponding hardware parameter to the state changes of the micro-host computer when the data stages are divided based on a hardware parameter is taken into account, so as to illustrate that the corresponding hardware parameter can reflect the state of the micro-host computer; the trigger coefficient of the micro-host computer at each moment is obtained, and the beneficial effect of the application is that the monitoring data of each hardware parameter is used to determine the trigger coefficient of the micro-host computer. Make predictions, analyze the deviations between the predicted values ​​and the corresponding preset abnormal thresholds, and comprehensively analyze the joint change trends of all hardware parameters and the response of each hardware parameter to the state of the microhost computer to reflect the possibility of abnormal state changes in the microhost computer, and then predict the trigger coefficient, compare the trigger coefficient at the current moment with the predicted result, or compare the monitoring data of each hardware parameter at the current moment with the preset abnormal threshold, evaluate the data backup operation of the BIOS data, and judge whether it is necessary to trigger the backup mechanism in time. The beneficial effect is that by judging the state of the microhost computer, the BIOS data backup is responded to in time according to the state of the microhost computer in real time, thereby improving the accuracy of the evaluation of the state of the microhost computer and being able to respond to the BIOS data backup in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The following is a further detailed description of a micro host computer BIOS data backup method of the present application in conjunction with the accompanying drawings.

[0035] Figure 1 A flowchart of a method for backing up BIOS data of a micro-host computer provided in an embodiment of the present application;

[0036] Figure 2 A flowchart of the steps of a method for obtaining the joint trend coefficient of any sub-matrix corresponding to each hardware parameter at each moment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clear, the following is a further detailed description of a micro host computer BIOS data backup method and system proposed in the present application in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] See also Figure 1 , which shows a flowchart of a method for backing up BIOS data of a micro host computer provided by an embodiment of the present application, the method comprising the following steps:

[0040] Step 1, obtaining the monitoring data of each hardware parameter of the micro host computer at each moment in real time.

[0041] BIOS (Basic Input / Output System) is a basic input / output system. It is a set of program codes that are fixed on the computer chip. The main functions of BIOS are power-on self-test, hardware detection at startup, hardware device initialization, and creation of BIOS interrupts. When the micro-host computer starts, the system kernel will automatically load the hardware monitoring driver; after the hardware monitoring driver is loaded, it will establish a communication connection with the hardware monitoring chip on the motherboard, such as the SuperI / O chip, and carry out initialization operations; the BIOS driver can manage and monitor the hardware status of the computer. During the hardware monitoring process, the CPU temperature, GPU temperature, motherboard voltage, fan speed and other hardware parameters of the micro-host computer are monitored in real time, and the collected data is stored in the buffer. By starting the background service process, during the BIOS data backup process, when interacting with the backup engine, the status data of the hardware device parameters is continuously monitored, thereby ensuring that the entire system can work together in terms of hardware monitoring and data backup, and realizing effective monitoring and data protection of the micro-host computer BIOS data backup.

[0042] Therefore, the monitoring data of each hardware parameter of the micro host computer at each moment is obtained, wherein the hardware parameters include: CPU temperature, GPU temperature, motherboard voltage and fan speed.

[0043] In this embodiment, the frequency of data collection is once every 5 seconds. As other implementation methods, the implementer can set it according to the actual situation.

[0044] At this point, the monitoring data of each hardware parameter at each time is obtained.

[0045] Step 2, analyze the similarity of the changing trends between any submatrix corresponding to each hardware parameter at each moment and the remaining submatrices, as well as the differences between the ranks of each submatrix, and determine the joint trend coefficient of any submatrix; based on the joint trend coefficient, obtain the state responsiveness of each hardware parameter at each moment.

[0046] In the conventional strategy of BIOS data backup of micro-host computers, intelligent backup tasks are often performed by setting fixed backup time intervals or specific trigger conditions. However, when the host is actually running, due to the lack of sufficient timeliness in monitoring hardware status changes, data may not be backed up in time before the hardware is damaged; and if there is a delay in the backup trigger, data loss is very likely to occur.

[0047] Therefore, in order to ensure the timely backup of BIOS data, it is necessary to build a high-precision backup trigger mechanism that can start the backup operation in time when the hardware status is abnormal, so as to effectively avoid data loss. By monitoring and analyzing the abnormal response of the real-time status of the hardware parameters in the micro host computer, it is determined whether to trigger the backup, and then the backup mechanism is accurately started.

[0048] Based on the above analysis, the collaborative response correlation characteristics between different hardware parameters are analyzed through the change trends of different hardware parameters of the micro host computer, that is, the mutual influence of different hardware parameters during the change process, and the joint trend coefficient is determined. The step flow chart of the method for obtaining the joint trend coefficient of any sub-matrix corresponding to each hardware parameter at each time provided in the embodiment of the present application is as follows: Figure 2 As shown, specifically including:

[0049] The monitoring data of each hardware parameter at all times before each time is used as a row element in the response monitoring matrix at each time in chronological order;

[0050] Using the LOF anomaly detection algorithm, anomaly detection is performed on any row element in the response monitoring matrix to obtain abnormal data;

[0051] It should be noted that the LOF anomaly detection algorithm is a well-known technology and will not be described in detail here.

[0052] Taking the column where the abnormal data of any row is located as a dividing point, the response monitoring matrix is ​​divided into a plurality of sub-matrices to obtain the sub-matrices corresponding to each hardware parameter at each time;

[0053] It can be understood that, assuming that there are 3 abnormal data in the i-th row, the column where the abnormal data in the i-th row is located is used as the split point, and the response monitoring matrix can be divided into at most 4 sub-matrices according to the 3 split points in the i-th row; assuming that there are 5 abnormal data in the j-th row, the column where the abnormal data in the j-th row is located is used as the split point, and the response monitoring matrix can be divided into at most 6 sub-matrices according to the 5 split points in the j-th row. Therefore, there are multiple sub-matrices for each hardware parameter represented by the elements in each row.

[0054] It should be noted that the columns corresponding to the abnormal data are divided into the sub-matrix where they were at the previous moment.

[0055] Calculate the rank of each submatrix; obtain the trend statistics of each row element in each submatrix, and combine the trend statistics of all rows in each submatrix into a trend response sequence;

[0056] In this embodiment, the trend statistics of each row of elements are calculated by using the MK trend test algorithm, wherein the MK trend test algorithm is a well-known technology and will not be described in detail here.

[0057] It should be noted that the calculation of the rank of the matrix is ​​a well-known technology and will not be elaborated here; the larger the rank of the sub-matrix, the more significant the stage-by-stage joint response characteristics after the division based on the abnormal response characteristics of each hardware parameter; the larger the trend statistic, the more significant the stage-by-stage hardware parameter response during the state change of the microhost computer.

[0058] Calculate the cosine similarity of the trend response sequence between any sub-matrix corresponding to each hardware parameter at each time and the remaining sub-matrices;

[0059] It should be noted that the calculation of cosine similarity is a well-known technique and will not be described in detail here.

[0060] Calculate the difference between the rank of any submatrix corresponding to each hardware parameter at each time and the ranks of the remaining submatrices, and record it as the rank difference;

[0061] In this embodiment, the absolute value of the difference between the rank of any submatrix corresponding to each hardware parameter at each moment and the ranks of the remaining submatrices is calculated, and the calculation result of the exponential function with a natural constant as the base and the absolute value as the exponent is recorded as the rank difference.

[0062] Calculate the ratio of the similarity to the rank difference, record it as a first ratio, and take the average of the first ratios of any submatrix corresponding to each hardware parameter at each moment and all other submatrices as the joint trend coefficient of any submatrix corresponding to each hardware parameter at each moment;

[0063] In this embodiment, Hardware parameters in The time corresponding to Taking the sub-matrix as an example, the calculation method of its joint trend coefficient is:

[0064]

[0065] in, For the Hardware parameters in The time corresponding to The joint trend coefficient of the sub-matrices is For the Hardware parameters in The time corresponding to The sub-matrix and The similarity of the trend response sequences between the sub-matrices, For the Hardware parameters in The time corresponding to The sub-matrix and The rank difference between the sub-matrices is For the Hardware parameters in The number of all sub-matrices corresponding to the moment.

[0066] It should be noted that the larger the joint trend coefficient is, the When dividing the data into stages based on the response characteristics of the hardware parameters of different stages, the higher the consistency of the joint response characteristics and trend changes of the hardware parameters in the divided stages, the better the The more consistent the response characteristics of the hardware parameters are with the associated responses of the microhost computer.

[0067] Further, based on the joint trend coefficient, the significance of each hardware parameter's response to the state change of the micro host computer is evaluated to determine the state responsiveness, specifically:

[0068] The joint trend coefficients of all sub-matrices corresponding to each hardware parameter at each time are used to form a response trend sequence corresponding to each hardware parameter at each time;

[0069] Through the comprehensive evaluation algorithm, the response trend sequence corresponding to all hardware parameters at each moment is comprehensively evaluated, and the comprehensive evaluation result of each hardware parameter at each moment is used as the state responsiveness of each hardware parameter at each moment;

[0070] In this embodiment, a Topsis algorithm based on an entropy weight method is used for comprehensive evaluation, wherein the Topsis algorithm based on an entropy weight method is a well-known technology and will not be described in detail here.

[0071] It should be noted that the higher the state responsiveness, the more significant the response of the corresponding hardware parameter to the state change of the microhost computer when dividing the data stages based on a hardware parameter, that is, the corresponding hardware parameter can better reflect the state of the microhost computer.

[0072] At this point, the state responsiveness of each hardware parameter at each moment is obtained.

[0073] Step 3, predict the monitoring data of each hardware parameter at each moment and all previous moments, analyze the deviation between the predicted value of each hardware parameter and its preset abnormal threshold, combine the joint trend coefficient and the state responsiveness, and obtain the trigger coefficient of the micro host computer at each moment.

[0074] Furthermore, in order to integrate the response characteristics of different hardware parameters compared to the state changes of the micro host computer, each hardware parameter is predicted and the abnormal deviation of the predicted value is analyzed, specifically:

[0075] Through the prediction model, the monitoring data of each hardware parameter at all times before each moment is predicted to obtain the predicted value of each hardware parameter at the next moment;

[0076] In this embodiment, an exponential moving smoothing algorithm is used for prediction, wherein the exponential moving smoothing algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers can adopt other prediction models of the prior art, such as the ARIMA model, etc. This embodiment does not impose any special restrictions on this.

[0077] The difference between the predicted value of each hardware parameter at the next moment and the preset abnormal threshold of each hardware parameter is used as the result of an exponential function with a natural constant as the base and the difference as the exponent, as the estimated deviation of each hardware parameter at each moment;

[0078] In this embodiment, the preset abnormal threshold value of the CPU temperature is 80°C, the preset abnormal threshold value of the GPU temperature is 90°C, the preset abnormal threshold value of the motherboard voltage is 1.5V, and the preset abnormal threshold value of the fan speed is 2500 revolutions per minute. As other implementation methods, the implementer can set them according to actual conditions.

[0079] It should be noted that the larger the abnormal deviation is, the more likely the micro host computer is in an abnormal state.

[0080] Further, based on the joint trend coefficient, the state responsiveness and the estimated deviation, a trigger coefficient is determined, specifically:

[0081] Calculate the average value of the joint trend coefficients of all sub-matrices corresponding to each hardware parameter at each time;

[0082] Normalizing the average value of all hardware parameters at each moment, the mean value of the product of the state responsiveness and the estimated deviation as the trigger coefficient of the micro host computer at each moment;

[0083] In this embodiment, the calculation method of the trigger coefficient of the micro host computer at each time is:

[0084]

[0085] in, For micro-host computers The trigger coefficient at the moment, For the Hardware parameters in The state responsiveness at the moment, For the Hardware parameters in The average value of the joint trend coefficients of all sub-matrices corresponding to the time, For the Hardware parameters in The estimated deviation at time, is an exponential function with a natural constant as base, is the number of all hardware parameters, is a normalization function. In this embodiment, a sigmoid function is used for normalization processing. The sigmoid function is a well-known technology and will not be described in detail here.

[0086] It should be noted that the larger the trigger coefficient is, the greater the possibility that the micro host computer will experience abnormal state changes based on the changes in all hardware parameters, and the backup mechanism needs to be triggered in time.

[0087] At this point, the trigger coefficient of the micro host computer at each moment is obtained.

[0088] Step 4, predict the trigger coefficients at the current moment and all previous moments, compare the trigger coefficients at the current moment with the predicted results, or compare the monitoring data of each hardware parameter at the current moment with the preset abnormal threshold, and evaluate the data backup operation of the BIOS data.

[0089] Further, based on the trigger coefficient, whether to send a trigger instruction is evaluated, specifically:

[0090] Through the prediction model, the trigger coefficients of the current moment and all previous moments are predicted to obtain the estimated trigger coefficient of the next moment;

[0091] In this embodiment, an exponential moving smoothing algorithm is used to predict the trigger coefficients at the current moment and all previous moments, wherein the exponential moving smoothing algorithm is a well-known technology and will not be described in detail here.

[0092] If the trigger coefficient at the current moment is greater than the estimated trigger coefficient, or the monitoring data of any hardware parameter at the current moment is greater than the preset abnormal threshold of any hardware parameter, a trigger signal is sent to the intelligent backup engine to start the backup operation, otherwise, the backup operation is not started;

[0093] After receiving the trigger signal, the intelligent backup engine first calls the system information reading module and uses the WMI interface of the Windows operating system to collect the hardware device information of the microhost computer, including CPU model, memory capacity, hard disk specifications, etc., and organizes it into a structured text file.

[0094] Then, the backup engine directly communicates with the BIOS chip through the BIOS access interface provided by the motherboard chipset, reads the key data area in the BIOS, including the boot sequence settings, hardware initialization parameters and BIOS version information; and reads these data into a temporary buffer in the memory.

[0095] The collected text file of the hardware device information and the key BIOS data in the temporary buffer are merged and processed, and they are combined into a data block to be backed up in the format of writing the hardware device information first and then writing the BIOS data.

[0096] An encryption algorithm is used to encrypt the data blocks to be backed up; wherein, the encryption key can be set by the user when the smart backup system is first installed on the micro host computer, and the key is securely stored in the local TPM chip. In this embodiment, the AES encryption algorithm is used to encrypt the data blocks to be backed up. The AES encryption algorithm is a well-known technology and will not be described in detail here.

[0097] The encrypted data blocks are compressed by a compression algorithm to obtain a compressed backup file, so as to reduce storage space occupancy and improve the efficiency of backup file transmission. In this embodiment, the compression algorithm can adopt ZIP and 7ZIP algorithms, wherein ZIP and 7ZIP algorithms are well-known technologies and will not be described in detail here.

[0098] The compressed backup files are first stored in the local hidden partition of the micro-host computer; when partitioning the hard disk, a hidden partition is reserved specifically for storing important backup files of the system. This partition uses the NTFS (New Technology File System) format and is set with hidden attributes and permission restrictions. Only system processes with specific permissions can access it.

[0099] Based on the same inventive concept as the above method, an embodiment of the present application also provides a micro-host computer BIOS data backup system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned micro-host computer BIOS data backup methods are implemented.

[0100] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

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

[0102] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A method for backing up BIOS data of a micro host computer, characterized in that: The method comprises the following steps: Obtain the monitoring data of each hardware parameter of the micro host computer at each moment in real time; The monitoring data of all hardware parameters at all times before each time are used to form a response monitoring matrix at each time; the response monitoring matrix is ​​divided by taking the column where the abnormal data of the row element corresponding to each hardware parameter in the response monitoring matrix is ​​located as the dividing point, so as to obtain multiple sub-matrices corresponding to each hardware parameter at each time; The trend statistics of all rows in each submatrix are used to form a trend response sequence; the similarity of the trend response sequence between any submatrix corresponding to each hardware parameter at each moment and the remaining submatrices is calculated; the difference between the rank of any submatrix corresponding to each hardware parameter at each moment and the rank of the remaining submatrices is calculated, and recorded as the rank difference; the average of the ratio of the similarity and rank difference between any submatrix corresponding to each hardware parameter at each moment and all the remaining submatrices is used as the joint trend coefficient of any submatrix corresponding to each hardware parameter at each moment; the joint trend coefficient of all submatrices corresponding to each hardware parameter at each moment is used to form a response trend sequence corresponding to each hardware parameter at each moment; the response trend sequence is comprehensively evaluated by a comprehensive evaluation algorithm, and the evaluation result is used as the state responsiveness of each hardware parameter at each moment; Predict the monitoring data of each hardware parameter at each moment and all previous moments, analyze the deviation between the predicted value of each hardware parameter and its preset abnormal threshold, and use the normalized result of the mean of the product of the deviation, the average value of the joint trend coefficients of all sub-matrices and the state responsiveness as the trigger coefficient of the micro host computer at each moment; The trigger coefficients at the current moment and all previous moments are predicted to obtain an estimated trigger coefficient. If the trigger coefficient at the current moment is greater than the estimated trigger coefficient, the intelligent backup engine starts the backup operation.

2. A micro host computer BIOS data backup method as claimed in claim 1, characterized in that: The hardware parameters include: CPU temperature, GPU temperature, motherboard voltage and fan speed.

3. A micro host computer BIOS data backup method as claimed in claim 1, characterized in that: The abnormal data is detected by performing abnormality detection on any row element in the response monitoring matrix through the LOF abnormality detection algorithm to obtain abnormal data.

4. A micro host computer BIOS data backup method as claimed in claim 1, characterized in that: The similarity degree is the cosine similarity of the trend response sequence between any submatrix and the remaining submatrices.

5. A micro host computer BIOS data backup method as claimed in claim 1, characterized in that: The method for determining the deviation is: Calculate the difference between the predicted value corresponding to each hardware parameter and the preset abnormal threshold; The deviation is a result of an exponential function with a natural constant as the base and the difference as the exponent.

6. A micro host computer BIOS data backup system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a micro host computer BIOS data backup method as described in any one of claims 1-5 are implemented.

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