Non-invasive Screen Data Backup and Recovery System for Display Screens

Abstract

The present invention discloses a non-invasive screen data backup and recovery system for a display screen, which relates to the technical field of data processing. The method includes: a multi-dimensional operation data acquisition module for real-time calling of multi-dimensional operation data of a target electronic device; an operation risk assessment module for performing operation risk assessment; a state fluctuation analysis module for monitoring the device state and software state and performing state fluctuation analysis and prediction; a non-invasive data backup module for performing non-invasive screen data backup; a cloud storage module; and a data recovery module for performing global recovery or partial recovery of the display screen data. The present invention solves the technical problem in the prior art that the existing backup method cannot timely detect potential faults or anomalies, affecting the timeliness and effectiveness of data backup and recovery, and achieves the technical effect of timely detecting potential problems and triggering non-invasive data backup, improving the timeliness and effectiveness of data backup and recovery.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a non-invasive screen data backup and recovery system for a display screen. Background Art

[0002] With the popularization of electronic devices, as an important human-computer interaction interface, display screens are widely used in various devices. These display screens not only carry out interactive operations between users and devices, but also store a large amount of program data, user information, and device status information. For many important scenarios (such as production equipment, medical equipment, smart home systems, etc.), the data carried on the display screen is often closely related to the normal operation of the device. Therefore, it has become particularly important to back up and recover display screen data in a timely manner.

[0003] Traditional data backup methods usually require the device to be shut down or the use to be interrupted, which affects the normal operation of the device, and are often limited to the backup of static data. There are insufficient monitoring and evaluation means for dynamically changing data (such as device operation status, main program operation conditions, etc.), resulting in difficulty in timely discovering and effectively recovering important information when the device is abnormal or data is lost. Summary of the Invention

[0004] This application provides a non-invasive screen data backup and recovery system for a display screen, which is used to solve the technical problem that in the prior art, the existing backup method cannot monitor the dynamic state of the device and the program running situation in real time, resulting in the inability to timely discover potential faults or abnormalities, and affecting the timeliness and effectiveness of data backup and recovery.

[0005] This application provides a non-invasive screen data backup and recovery system for a display screen. The system includes: a multi-dimensional operation data acquisition module, which is used to call the multi-dimensional operation data of the target electronic device in real time. The multi-dimensional operation data includes device operation data and main program data, and the main program data is the program data currently displayed on the main interface of the display screen; an operation risk assessment module, which is used to perform an operation risk assessment based on the multi-dimensional operation data, and start the corresponding data loss warning level according to the risk assessment result; a state fluctuation analysis module, which is used to monitor the device state and software state according to the data loss warning level, and perform state fluctuation analysis and prediction to obtain a state trend prediction result; a non-invasive data backup module, which is used to generate a data backup instruction based on the state trend prediction result, activate the data backup unit, perform non-invasive screen data backup, and generate a backup data packet; a cloud storage module, which is used to upload the backup data packet to the cloud storage center after dimension reduction and encryption; a data recovery module, which is used for the target user to log in to the cloud storage center and perform global recovery or partial recovery of the display screen data through multi-level user interaction.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The non-invasive screen data backup and recovery system for a display screen provided in this application relates to the technical field of data processing. It performs risk assessment and early warning by real-time calling multi-dimensional operation data, generates a backup instruction according to the analysis result and executes data backup. The backup data is uploaded to cloud storage through dimensionality reduction encryption. Users can perform global or partial recovery of data through multi-level interaction, solving the technical problem in the prior art that the existing backup methods cannot monitor the dynamic state of the device and the program running situation in real time, resulting in the inability to timely discover potential faults or anomalies, affecting the real-time performance and effectiveness of data backup and recovery. It realizes the dynamic monitoring of the device state and program running, timely discovers potential problems and triggers non-invasive data backup, and improves the technical effects of the real-time performance and effectiveness of data backup and recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0009] Figure 1 It is a schematic structural diagram of the non-invasive screen data backup and recovery system for a display screen provided in the embodiments of this application;

[0010] Figure 2 It is a schematic flow diagram of dynamically adjusting the backup granularity in the non-invasive data backup module of the non-invasive screen data backup and recovery system for a display screen provided in the embodiments of this application.

[0011] Description of the reference numerals: The multi-dimensional operation data acquisition module 10, the operation risk assessment module 20, the state fluctuation analysis module 30, the non-invasive data backup module 40, the cloud storage module 50, and the data recovery module 60. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] This application provides a non-invasive screen data backup and recovery system for a display screen, which is used to solve the technical problem in the prior art that the existing backup methods cannot monitor the dynamic state of the device and the program running situation in real time, resulting in the inability to timely discover potential faults or anomalies, affecting the real-time performance and effectiveness of data backup and recovery.

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 shown, the present application provides a non-intrusive screen data backup and recovery system for a display screen. The system includes:

[0016] A multi-dimensional operation data acquisition module 10, configured to call the multi-dimensional operation data of the target electronic device in real time. The multi-dimensional operation data includes device operation data and main program data, and the main program data is the program data currently displayed on the main interface of the display screen.

[0017] Specifically, the core function of the multi-dimensional operation data acquisition module 10 of the present application is to call the multi-dimensional operation data of the target electronic device in real time, providing necessary information support for subsequent risk assessment, status monitoring, and backup strategies. Among them, the multi-dimensional operation data refers to a comprehensive data set including the device operation status and the main program operation data.

[0018] First, the system establishes communication with the operating system of the device through a hardware interface to obtain the real-time operation data of the target device. These data include the memory usage, CPU occupancy, disk space, and network bandwidth of the device. This information can help the system understand the resource utilization of the device. Especially when the device resources are approaching the bottleneck, such as when the memory usage rate reaches the upper limit, the system can immediately respond and issue a data loss warning signal.

[0019] After obtaining the operation data at the hardware level, the system also needs to obtain the data of the main program in real time, that is, the program content currently displayed on the main interface of the display screen. This part of the data involves the images, texts or other interactive contents displayed on the display screen in real time. Through the process monitoring mechanism of the operating system, the system can accurately capture the current running program status. For example, in the Windows system, the system can access the information of the running programs in real time through the resource manager or API interface; in the Linux system, the information of the processes can be obtained through the / proc file system to determine the content currently displayed on the display screen.

[0020] After the hardware and main program data are obtained in real time, the system integrates and converts this information into multi-dimensional operation data. The integrated data not only reflects the performance status of the device, but also includes the dynamic changes of the display content. The integrated data is transmitted to other modules through an efficient communication protocol for subsequent risk assessment and status monitoring. The whole process ensures the all-round monitoring from the device hardware to the display program data, and provides accurate and timely basic data for subsequent operation risk assessment and data backup decision-making. In this way, the comprehensive collection of the device status is realized, ensuring that the system can take corresponding warning and backup measures in time when the device response becomes slow or the display content is abnormal.

[0021] The operation risk assessment module 20 is used to perform operation risk assessment based on the multi-dimensional operation data and start the corresponding data loss warning level according to the risk assessment result.

[0022] Furthermore, the operation risk assessment module 20 is also used to perform the following steps:

[0023] P21: Based on the multi-dimensional operation data, extract the basic operation data of the target electronic device and evaluate to obtain the basic operation risk level of the device; P22: Based on the multi-dimensional operation data, extract the performance indicators of the main running program and the operation occupancy data of the background running program respectively, perform program operation risk assessment, and obtain the program operation risk level; P23: According to the data type of the main running program, perform program importance analysis to obtain the main program warning level; P24: Integrate the device basic operation risk level, the program operation risk level and the main program warning level to generate the data loss warning level.

[0024] Optionally, the operation risk assessment module 20 of the present application is responsible for comprehensively assessing the operation risk of the target electronic device according to the multi-dimensional operation data, and guiding subsequent backup operations and decisions through the generated data loss warning level. The core goal of this module is to ensure that the probability of data loss is minimized through accurate risk assessment, and to improve the stability and recovery ability of the system.

[0025] First, the system extracts the basic operation data of the target electronic device from multi-dimensional operation data. These data include the hardware resource status of the device, such as memory usage, CPU occupancy rate, storage space, network bandwidth, etc. The system compares these basic operation data with preset health thresholds and calculates the basic operation risk level of the device. For example, when the memory occupancy rate exceeds 80%, the basic operation risk level of the device may be evaluated as "high". This risk assessment helps to understand whether the device is in a potential resource bottleneck state, and thus provides decision support for subsequent backup operations.

[0026] Next, the system extracts the relevant data of the main running program and the background running program on the target electronic device respectively, including the performance indicators of the main program (such as response time, frame rate, etc.) and the resource occupancy of the background program (such as memory and CPU usage ratio). By analyzing these program operation data, the system evaluates the operation risk levels of the main program and the background program. For example, if the response time of the main program exceeds the normal range, or the background program occupies too much system resources, the system will evaluate the program operation risk as "medium" or "high". This step helps the system identify potential program faults that may affect the display content or performance.

[0027] Furthermore, the system conducts an importance analysis based on the data type of the main program. Different programs may have different impact levels. For example, the main interface program of the display screen may involve real-time display of key data or user interaction, while the background program may only be for auxiliary work. Therefore, the data type of the main program (such as displaying real-time information, user input, key business data, etc.) determines its impact on the overall function of the system. If the content involved in the main program is very important, the system will give it a higher warning level. For example, if the display screen shows financial transaction data, the system will consider the high risk of data loss and set it to the "high" warning level.

[0028] Finally, the basic operation risk level of the device, the program operation risk level, and the main program warning level are comprehensively analyzed to generate the final data loss warning level. The comprehensive risk assessment is usually based on weighted average, decision tree, or fuzzy logic algorithms to ensure a reasonable balance of various risk factors. Specifically, the assessment module will combine the weights of each risk level, comprehensively consider the device load, program operation conditions, and program importance, and generate a comprehensive warning level. If the comprehensive risk level is high, the system will start data backup with a higher frequency and increase the backup resources and priorities; if the comprehensive risk level is low, the backup frequency and resource occupancy will be reduced accordingly to optimize the system performance.

[0029] Through the above steps, the operation risk assessment module realizes a comprehensive analysis of the operation status of the target electronic device, and dynamically adjusts the data backup strategy according to different risk levels, ensuring that when the device or program may fail, the system can take backup measures in a timely manner to minimize the risk of data loss.

[0030] The state fluctuation analysis module 30 is used to monitor the device state and software state according to the data loss warning level, and perform state fluctuation analysis and prediction to obtain a state trend prediction result.

[0031] Further, the state fluctuation analysis module 30 is also used to execute the following steps:

[0032] P31: According to the data loss warning level, match the state monitoring scheme library to obtain the corresponding device monitoring scheme; P32: According to the device monitoring scheme, monitor the device state and software state to obtain the device performance index sequence of the target electronic device and the program performance index sequence of the main running program; P33: Based on the device performance index sequence and the program performance index sequence, perform state fluctuation analysis and prediction respectively to obtain a state trend prediction result.

[0033] It should be understood that the state fluctuation analysis module 30 of the present application is responsible for deeply monitoring the device state and software state of the target electronic device according to the data loss warning level, and then obtaining a state trend prediction result through state fluctuation analysis and prediction. This process includes multiple steps, and the specific execution process is as follows:

[0034] First, determine the focus and depth of device monitoring based on the data loss warning level. The data loss warning level reflects the potential risks of device operation, and a higher warning level requires more refined monitoring. The system selects a device monitoring scheme that matches the current warning level by matching the state monitoring scheme library. For example, if the warning level is "high", more detailed monitoring may be required, including multi-dimensional monitoring of the device hardware, software, and network states; if the warning level is low, only basic monitoring may be needed. The formulation of these monitoring schemes depends on the performance requirements of different devices and programs, as well as the response strategies at different warning levels. Among them, the monitoring scheme library contains monitoring strategies for different warning levels. Each monitoring scheme is formulated based on actual experience and historical data, including the performance indicators of different devices, monitoring frequencies, and other contents.

[0035] Next, based on the device monitoring solution, the system starts to monitor the status of the device in real time. This includes collecting sequences of device performance metrics from the hardware level of the device, such as data on CPU utilization, memory occupancy, disk read / write speed, network traffic, etc. At the same time, the system also needs to monitor the performance of the main running program, and these data usually include sequences of program performance metrics such as the program's response time, frame rate, execution duration, etc. These performance metrics can reflect the current running status of the device and the program, providing basic data for subsequent analysis of status fluctuations.

[0036] After collecting the sequences of device performance metrics and program performance metrics, the system will perform an analysis and prediction of status fluctuations on these data. This analysis process uses historical data, trend prediction models, and real-time monitoring data to predict possible status changes of the device and the program in a future period. For example, through time series analysis, regression analysis, or machine learning algorithms, the system can predict whether the device may enter a resource bottleneck state, or whether the program response may gradually slow down. Based on these prediction results, the system can obtain status trend prediction results. For example, the response time of a certain key program may significantly increase within the next 10 minutes, or the CPU load of the device may reach a critical value.

[0037] By the above steps, it is ensured that the system can make dynamic adjustments based on real-time data, thereby timely identifying potential risks of the device and software, and making early warnings and adjustments to backup strategies through the status trend prediction results. Through effective analysis of status fluctuations, the system can predict possible failures of the device or program in advance, and take backup or recovery measures in a timely manner, thereby minimizing the risk of data loss.

[0038] Furthermore, step P33 of the embodiment of the present application further includes:

[0039] P33-1: Based on the sequences of device performance metrics and program performance metrics, perform status fluctuation analysis respectively to generate device status analysis results and main program status analysis results; P33-2: According to the main program running mechanism, perform device-program association impact analysis to generate two-way impact factors; P33-3: Based on the two-way impact factors, respectively correct the status of the device status analysis results and the main program status analysis results, and perform a comprehensive trend prediction according to the corrected device status analysis results and main program status analysis results to obtain the status trend prediction results.

[0040] Optionally, through precise analysis of status fluctuations, combined with the performance metrics of the device and the main program, potential problems that may occur in the system are deeply explored, and through comprehensive correction and trend prediction, the timeliness and accuracy of data backup are ensured.

[0041] First, based on the collected sequences of device performance metrics and program performance metrics, independent state fluctuation analyses are conducted respectively. The sequence of device performance metrics usually involves hardware resources (such as CPU usage, memory occupancy, network bandwidth, etc.), while the sequence of program performance metrics focuses on the running state of the main program, such as response time, frame rate, execution duration, etc. The analysis module uses time series analysis methods (such as autoregressive model AR, moving average MA, etc.) to model the performance data of the device and the program, so as to understand their fluctuation patterns and generate the device state analysis results and the main program state analysis results. These analysis results not only show the current running states of the device and the program, but also reveal possible performance bottlenecks or instabilities through the fluctuation trends. For example, the CPU load of the device may fluctuate violently in the future period, while the response time of the program may gradually increase, indicating that the system may be about to enter a resource bottleneck state.

[0042] The running states of the device and the program often affect each other. Especially in complex electronic devices, the running of the program may directly consume the hardware resources of the device, and vice versa. Therefore, the analysis module needs to conduct an associated impact analysis between the device and the program to generate a two-way impact factor to more comprehensively predict the state trend of the system. The system evaluates the correlation between device resources and program load by analyzing the running mechanism of the main program, such as whether there is multi-threaded processing, whether there is a large amount of data input / output, etc. Exemplarily, by establishing a causal relationship model between the device and the program and using regression analysis or other machine learning algorithms, the two-way impact factor between the device and the program is identified, that is, the impact coefficient of device performance on the program and the impact coefficient of program load on device performance. For example, if the main program is performing a large amount of calculations or frequent I / O operations, it may cause a sharp increase in CPU usage, thereby affecting the running of other programs on the device; conversely, if the memory usage of the device reaches the upper limit, the response time of the main program may also increase accordingly.

[0043] Based on the two-way impact factor obtained from the analysis, the analysis module will perform state correction on the device state analysis results and the main program state analysis results. The goal of this process is to adjust the errors caused by the interaction between the device and the program, make the fluctuation analysis results more accurate, and generate a more realistic state trend prediction. By combining the mutual influence of device performance and program running, the analysis module adjusts the state prediction of the device and the program. For example, if the high load state of the device has caused the program response to slow down, the module will correct the performance prediction of the program according to the interaction between the device and the program, taking into account the impact of the device bottleneck on the program.

[0044] After the status is corrected, the corrected device and program statuses will be summarized to form a comprehensive trend prediction result. This result not only takes into account the fluctuations of individual device or program statuses but also synthesizes the interaction between the two, providing a more accurate basis for formulating backup strategies. Through this method, the system can not only identify potential device failures or program response delays in advance but also predict the operating status of the device and program in future time periods, thus providing more precise data backup and recovery strategies.

[0045] The non-intrusive data backup module 40 is used to generate a data backup instruction based on the status trend prediction result, activate the data backup unit, perform non-intrusive screen data backup, and generate a backup data packet.

[0046] Furthermore, the non-intrusive data backup module 40 is also used to perform the following steps:

[0047] P41: Extract system load data based on the status trend prediction result; P42: Collect historical user operation records for analysis to generate user preference data; P43: Introduce an intelligent backup granularity adjustment mechanism, dynamically adjust the backup granularity according to the user preference data and the system load data, and generate a data backup instruction.

[0048] It should be understood that the non-intrusive data backup module 40 of the present application is responsible for generating a data backup instruction based on the status trend prediction result, activating the data backup unit to perform non-intrusive backup of screen data, and finally generating a backup data packet. To ensure the efficiency of the data backup process and not affect the normal operation of the device, the module adopts a multi-dimensional dynamic adjustment mechanism.

[0049] First, the system extracts system load data according to the status trend prediction result. The system load data includes the resource consumption situation of the device, such as the real-time usage of CPU, memory, storage, and network bandwidth. These data can help the system evaluate the availability of current device resources and determine whether it is suitable for backup operations. For example, if the system load is high (such as the memory or CPU usage rate is close to saturation), the system may delay or reduce the scale of the backup task to avoid additional burden on the device. On the contrary, when the system load is low, more comprehensive data backup can be performed. In this way, the system can optimize the backup timing and resource utilization rate, reducing the impact on device performance.

[0050] To ensure that the backup operation meets user requirements and improves backup efficiency, the system also needs to generate user preference data by analyzing historical user operation records. Historical operation records include the user's usage patterns of the device, frequently operated programs and applications, and the user's focus on the displayed content within a specific time period. By analyzing this operation data, the system can identify the user's frequently used applications and display modes, thereby generating personalized backup strategies. For example, if the user uses certain important application programs during specific time periods on weekdays, the system can prioritize the backup of the data of these programs. User preference data provides a personalized basis for backup, making the backup process more in line with the actual needs of users and avoiding redundant backup operations.

[0051] After obtaining the system load data and user preference data, the system introduces an intelligent backup granularity adjustment mechanism. The core function of this mechanism is to dynamically adjust the backup granularity according to the current device load and user preferences. Backup granularity refers to the scope and precision of data backup, which can be the entire screen, a window, a specific application program, or even a single file or data block. The larger the backup granularity, the more comprehensive the backup data, but it may also consume more system resources; the smaller the granularity, the less backup data volume and lower system resource consumption.

[0052] For example, when the system load is high, the backup granularity can be appropriately reduced, and only the most critical data is selected for backup instead of a full backup; when the system load is low and the user preference requires a higher level, the system can increase the backup granularity to ensure more refined data protection. By combining user preferences (which data is the most important) and system load (whether there are sufficient resources for a full backup), the intelligent adjustment mechanism can optimize the backup process, ensure data security without affecting device performance.

[0053] Finally, the system generates data backup instructions according to the adjusted granularity. These instructions not only specify the specific data range to be backed up but also include the backup timing, priority, and storage target. The instructions will activate the data backup unit to perform non-intrusive screen data backup. The backup process usually occurs without interfering with the user's current operations, thus achieving seamless backup.

[0054] Through the above steps, the non-intrusive data backup module 40 can not only intelligently adjust the backup strategy according to the status trend and user requirements but also minimize the impact of the backup process on system performance, ensuring data security and efficiency. The combination of the intelligent backup granularity adjustment mechanism with real-time system load and user preferences makes the backup process more flexible and accurate, adapting to the needs of different devices and users.

[0055] Furthermore, as Figure 2 shown, step P43 of the embodiment of the present application further includes:

[0056] P43-1: Set a backup granularity search space according to the system load data, where each granularity option corresponds to a backup cost and a resource occupancy score; P43-2: Initialize candidate solutions for the backup granularity, and combine with the granularity scoring function to calculate an individual score value for each backup granularity candidate solution; P43-3: According to the individual score value, perform granularity crossover and mutation, continuously generate new backup granularity schemes, and optimize granularity selection. After multiple generations of iteration, select the backup granularity with the optimal individual score value as the backup execution granularity for the current backup task.

[0057] Among them, the granularity scoring function is as follows:

[0058] F(x)=w_1⋅ResourceCost(x)+w_2⋅BackupEffectiveness(x); where ResourceCost(x) is the resource cost occupied by the backup granularity x, BackupEffectiveness(x) is the effectiveness of the backup granularity x, and w_1 and w_2 are weight coefficients.

[0059] In a possible embodiment of the present application, an intelligent backup granularity adjustment mechanism is further introduced to dynamically select the most suitable backup granularity through an optimization process of multiple generations of iteration.

[0060] First, according to the system load data, the system needs to set a backup granularity search space. This search space includes different backup granularity options, such as full-screen backup, window-level backup, specific application program backup, or backup of some small-range data blocks. Each granularity option has an associated backup cost and a resource occupancy score. The resource occupancy score usually considers the usage of resources such as CPU, memory, and disk I / O, while the backup cost covers the time overhead, system load, and data integrity requirements of each granularity. By constructing the granularity search space, the system can explore all possible backup granularity options and provide initial data for the subsequent optimization process.

[0061] Next, after the search space is determined, the non-intrusive data backup module 40 initializes candidate solutions for the backup granularity. The candidate solutions are different backup granularity options in the backup granularity space, and each option corresponds to a potential backup scheme. Next, the module combines with the granularity scoring function to calculate an individual score value for each candidate solution. This score value reflects the suitability of each backup granularity under the current system load conditions, and the scoring function is mainly calculated based on the resource cost occupied by the backup granularity, the effectiveness of the backup task, and other factors.

[0062] The form of the granularity scoring function is as follows: F(x) = w_1 ⋅ ResourceCost(x) + w_2 ⋅ BackupEffectiveness(x).

[0063] Among them, ResourceCost(x) is the resource cost occupied by the backup granularity x, indicating the degree of occupation of device resources by the backup operation, and its value range is [0, 1]. The lower the value, the smaller the impact on the device. BackupEffectiveness(x) is the effectiveness of the backup granularity x, indicating the degree of data protection of the backup operation, and its value range is [0, 1]. The higher the value, the higher the backup quality. w_1 and w_2 are weight coefficients.

[0064] After the scoring calculation is completed, the system will perform granularity crossover mutation based on the scoring value, that is, randomly cross and mutate the backup granularity candidate solutions to generate new backup granularity schemes.

[0065] First, based on the initially calculated individual scoring values, the system starts to optimize the selection of backup granularity through the granularity crossover mutation process. Granularity crossover mutation is an optimization method based on genetic algorithms, aiming to generate new backup granularity schemes through "crossover" and "mutation" operations, and continuously adjust and improve the granularity selection. Exemplarily, through the crossover operation, two granularity schemes are selected from the existing backup granularity candidate solutions, and some of their features are exchanged (for example, exchanging resource cost or effectiveness) to generate new backup granularity schemes. On the basis of the crossover operation, the system will also randomly adjust some parameters of the backup granularity to generate new backup granularity schemes, thereby expanding the search space and avoiding local optimal solutions.

[0066] Through these operations, multiple new backup granularity schemes are generated, and their individual scoring values are calculated and evaluated respectively through the granularity scoring function. After multiple generations of iteration, the backup granularity scheme with the optimal individual scoring value is selected as the execution granularity of the current backup task. This optimal granularity scheme can not only effectively utilize system resources, but also ensure the best backup effect, meeting the current load conditions of the device and the preferences of users. This process, through the granularity scoring function and the granularity optimization algorithm, ensures that the backup operation can protect data as accurately and comprehensively as possible without affecting system performance.

[0067] The cloud storage module 50 is used to upload the backup data packet to the cloud storage center after dimension reduction encryption.

[0068] Specifically, to ensure the confidentiality, integrity, and subsequent recovery efficiency of the backup data, the cloud storage module 50 of this embodiment uploads the backup data packet to the cloud storage center through dimension reduction encryption technology.

[0069] Before uploading the backup data packet, it is first necessary to perform dimensionality reduction on the backup data. Dimensionality reduction refers to reducing the original dimension of the backup data packet through a certain algorithm while trying to retain the key features and valid information of the data. Usually, the backup data may include a large amount of redundant information, which may occupy too much storage space and bandwidth, affecting the upload efficiency. The purpose of dimensionality reduction processing is to compress the redundant data and retain the efficient and important parts of the data.

[0070] Exemplarily, dimensionality reduction can adopt various methods, such as data compression techniques like principal component analysis (PCA), t-SNE (t-distributed stochastic neighbor embedding), etc., to compress the data volume without significantly losing information and optimize the storage and transmission efficiency.

[0071] In addition to dimensionality reduction, the backup data packet also needs to undergo an encryption operation to ensure data security during transmission. Data encryption is a key step in preventing data from being illegally accessed and tampered with. Symmetric encryption or asymmetric encryption algorithms can be used to encrypt the backup data packet. During the encryption process, the generated encryption key can only be decrypted by authorized users, ensuring that even if the data is intercepted by a third party after being uploaded to the cloud storage center, it still cannot be cracked. For example, the system can use AES (Advanced Encryption Standard) for symmetric encryption or the RSA encryption algorithm for asymmetric encryption to ensure a high level of security for the data packet during storage and transmission.

[0072] After completing dimensionality reduction and encryption, the system uploads the encrypted backup data packet to the cloud storage center through a network protocol. The cloud storage center provides large-scale, distributed data storage and management functions, capable of accommodating a huge amount of backup data while ensuring data accessibility and reliability. During the upload process, the system will use an efficient transmission protocol to ensure that the data upload is completed in the shortest possible time while reducing the impact on network bandwidth and system resources.

[0073] In the cloud storage center, the backup data packets are usually managed in the form of block storage. Each backup data packet will be stored as an independent data object according to different backup granularities (such as full-screen backup, application backup, etc.), along with relevant metadata (such as backup time, data type, file size, etc.) for subsequent retrieval and recovery. After being transmitted to the cloud storage center, the data can be restored at any time, providing a powerful data protection mechanism. Through this mechanism, the user's data is comprehensively protected during the backup and recovery process and can be quickly and securely restored to the original device.

[0074] The data recovery module 60 is used for the target user to log in to the cloud storage center and perform global or partial recovery of the display screen data through multi-level user interaction.

[0075] Optionally, the data recovery module 60 of the present application is mainly responsible for providing flexible data recovery services when needed by the user, including global recovery and partial recovery. Through a multi-level user interaction method, this module allows the user to select the recovery method and specific data to be recovered. This process involves obtaining backup data from the cloud storage center and performing recovery operations according to the user's requirements to ensure the efficiency and accuracy of data recovery.

[0076] Before performing data recovery, the user first needs to log in to the cloud storage center through a secure authentication process. To ensure data security and user privacy, the system uses multi-factor authentication (such as passwords, verification codes, fingerprint recognition, or facial recognition, etc.) to confirm the user's identity. This authentication process can effectively prevent unauthorized users from accessing backup data and ensure that only authorized users can perform data recovery operations.

[0077] After successful login, the user enters the data recovery interface, which provides multiple recovery options for the user. The multi-level user interaction enables the system to provide different operation levels according to the user's needs: global recovery, which restores the entire backup data packet, including all information such as device status, program running status, user preferences, etc. It is applicable to scenarios where the system needs to be restored to the full disk state, such as when the device system crashes or experiences a serious failure. Partial recovery, which only restores some specific data or files, such as the settings of a certain program, the operation records of some users, or the data within a certain time period. Partial recovery can help users accurately restore important data in case of data corruption or loss and avoid unnecessary redundant recovery.

[0078] At this stage, the user can select the recovery scope and data type through a simple graphical interface. In the data recovery interface, the system not only provides the option to select the recovery scope but also generates different recovery schemes based on conditions such as the timestamp, file type, and importance of the backup data. The user can view the detailed information of each scheme and select the most suitable recovery option according to specific needs. In this way, the user can preview the content of the backup data before recovery to ensure that the selected recovered data meets the actual requirements.

[0079] After the user confirms the recovery scheme, the system starts to execute the recovery process. For global recovery, the system will download the entire backup data packet from the cloud storage center and restore it to the corresponding location on the target device. During the recovery process, the system will automatically handle operations such as data decryption, decompression, and recombination to ensure the integrity and consistency of the backup data. For partial recovery, the system only downloads and restores the data files selected by the user. To improve the recovery speed and reduce network bandwidth consumption, the system uses intelligent data segmented transmission technology to transmit the data in batches during the recovery process. After each batch of data transmission is completed, the system will automatically verify the integrity of the data to ensure the accuracy and error-free of each recovery operation.

[0080] During the recovery process, the user can view the recovery progress through the interface at any time. The system will display the current recovery status in real time, including information such as the amount of data already recovered and the estimated completion time. If an exception occurs, the system will promptly feedback error information and provide corresponding solutions.

[0081] In summary, the embodiments of the present application at least have the following technical effects:

[0082] The present application calls the multi-dimensional operation data of the target electronic device (including device operation data and main program data) in real time, conducts operation risk assessment and starts data loss warning. According to the warning results, it monitors the device and software status and conducts fluctuation analysis, generates backup instructions and performs data backup. The backup data packet is uploaded to cloud storage after dimensionality reduction encryption, and the user can perform global or partial data recovery through a multi-level interaction interface, thereby realizing efficient and intelligent screen data backup and recovery.

[0083] It achieves the technical effect of dynamically monitoring the device status and program operation, promptly discovering potential problems and triggering non-intrusive data backup, and improving the real-time performance and effectiveness of data backup and recovery.

[0084] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0085] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0086] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A non-invasive screen data backup and recovery system for a display screen, characterized in that, The system is applied to a display screen, which is used for an electronic device. The system includes: A multi-dimensional operation data acquisition module, which is used to call the multi-dimensional operation data of the target electronic device in real time. The multi-dimensional operation data includes device operation data and main program data, and the main program data is the program data currently displayed on the main interface of the display screen; An operation risk assessment module, which is used to conduct an operation risk assessment based on the multi-dimensional operation data and start the corresponding data loss warning level according to the risk assessment result; A status fluctuation analysis module, which is used to monitor the device status and software status according to the data loss warning level, and conduct status fluctuation analysis and prediction to obtain a status trend prediction result; The status fluctuation analysis module is further used for: Matching the status monitoring scheme library according to the data loss warning level to obtain the corresponding device monitoring scheme; Monitoring the device status and software status according to the device monitoring scheme to obtain the device performance index sequence of the target electronic device and the program performance index sequence of the main running program; Conducting status fluctuation analysis and prediction respectively based on the device performance index sequence and the program performance index sequence to obtain a status trend prediction result; Among them, conducting status fluctuation analysis and prediction respectively based on the device performance index sequence and the program performance index sequence further includes: Conducting status fluctuation analysis respectively based on the device performance index sequence and the program performance index sequence to generate a device status analysis result and a main program status analysis result; Conducting device-program association impact analysis according to the main program operation mechanism to generate a two-way impact factor; Based on the two-way impact factor, respectively correcting the status of the device status analysis result and the main program status analysis result, and conducting comprehensive trend prediction according to the corrected device status analysis result and main program status analysis result to obtain the status trend prediction result; A non-intrusive data backup module, which is used to generate a data backup instruction based on the status trend prediction result, activate the data backup unit, conduct non-intrusive screen data backup, and generate a backup data packet; Among them, generating a data backup instruction based on the status trend prediction result further includes: Extracting system load data based on the status trend prediction result; Collecting and analyzing historical user operation records to generate user preference data; Introducing an intelligent backup granularity adjustment mechanism, dynamically adjusting the backup granularity according to the user preference data and the system load data, and generating a data backup instruction; A cloud storage module, which is used to upload the backup data packet to the cloud storage center after dimensionality reduction encryption; A data recovery module, which is used for the target user to log in to the cloud storage center and conduct global recovery or partial recovery of the display screen data through multi-level user interaction.

2. The non-invasive screen data backup and recovery system for a display screen according to claim 1, wherein The operation risk assessment module is further used for: Extracting the basic operation data of the target electronic device based on the multi-dimensional operation data, and evaluating to obtain the device basic operation risk level; Respectively extracting the performance index of the main running program and the operation occupancy data of the background running program based on the multi-dimensional operation data, and conducting program operation risk assessment to obtain the program operation risk level; Perform program importance analysis based on the data type of the main running program to obtain the warning level of the main program. Generate the data loss warning level by synthesizing the device basic operation risk level, the program operation risk level, and the main program warning level.

3. The non-invasive screen data backup and recovery system for a display screen according to claim 1, wherein The non-intrusive data backup module is further configured to: Set the backup granularity search space according to the system load data, where each granularity option corresponds to a backup cost and a resource occupancy score. Initialize the candidate solutions of the backup granularity, and calculate the individual score values for each backup granularity candidate solution in combination with the granularity scoring function. Perform granularity crossover and mutation according to the individual score values, continuously generate new backup granularity schemes, and optimize the granularity selection. After multiple generations of iteration, select the backup granularity with the optimal individual score value as the backup execution granularity for the current backup task.

4. The non-invasive screen data backup and recovery system for a display screen according to claim 3, wherein The granularity scoring function is as follows: F(x) = w1·Resource Cost(x) + w2· Backup Effectiveness(x); where Resource Cost(x) is the resource cost occupied by the backup granularity x, Backup Effectiveness(x) is the effectiveness of the backup granularity x, and w1 and w2 are weight coefficients.

5. The non-invasive screen data backup and recovery system for a display screen according to claim 4, wherein The non-intrusive data backup module is further configured to: Receive the data backup instruction and extract the backup execution granularity. Activate the data backup unit, perform real-time screen data collection and program status recording based on the backup execution granularity, and encapsulate and generate backup data packets.

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