Non-intrusive screen data backup and recovery system for display screen

Abstract

The invention discloses a non-intrusive screen data backup and recovery system for a display screen, and relates to the technical field of data processing, and the system comprises a multi-dimensional operation data acquisition module which is used for calling multi-dimensional operation data of target electronic equipment in real time; the operation risk assessment module is used for performing operation risk assessment; the state fluctuation analysis module is used for monitoring the equipment state and the software state and analyzing and predicting the state fluctuation; the non-intrusive data backup module is used for performing non-intrusive screen data backup; a cloud storage module; and the data recovery module is used for carrying out global recovery or partial recovery on the data of the display screen. According to the data backup method and device, the technical problem that the real-time performance and effectiveness of data backup and recovery are affected due to the fact that potential faults or anomalies cannot be found in time in an existing backup method in the prior art is solved, and the technical effects that potential problems are found in time, non-intrusive data backup is triggered, and the real-time performance and effectiveness of data backup and recovery are improved are achieved.

Description

Technical Field

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

[0002] With the popularity of electronic devices, display screens are widely used in various devices as important human-computer interaction interfaces. These display screens not only carry the 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 is particularly important to back up and restore the display screen data in a timely manner.

[0003] Traditional data backup methods usually require the equipment to be shut down or discontinued, affecting the normal operation of the equipment, and are often limited to the backup of static data. There is a lack of sufficient monitoring and evaluation methods for dynamically changing data (such as equipment operating status, main program operation status, etc.), resulting in difficulty in timely discovery and effective recovery of important information when facing equipment abnormalities or data loss. Summary of the invention

[0004] The present application provides a non-invasive screen data backup and recovery system for a display screen, which is used to solve the technical problem that the existing backup methods in the prior art cannot monitor the dynamic state of the device and the program operation status in real time, resulting in the inability to timely detect potential faults or anomalies, affecting the real-time and effectiveness of data backup and recovery.

[0005] The present application provides a non-intrusive screen data backup and recovery system for a display screen, the system comprising: a multi-dimensional operation data acquisition module, used for real-time calling of multi-dimensional operation data of a target electronic device, the multi-dimensional operation data comprising device operation data and main program data, the main program data being program data currently displayed on a main interface of a display screen; an operation risk assessment module, used for performing operation risk assessment based on the multi-dimensional operation data, and starting a corresponding data loss warning level according to a risk assessment result; a state fluctuation analysis module, used for monitoring device state and software state according to the data loss warning level, and performing state fluctuation analysis and prediction to obtain a state trend prediction result; a non-intrusive data backup module, used for generating a data backup instruction based on the state trend prediction result, activating a data backup unit, performing non-intrusive screen data backup, and generating a backup data packet; a cloud storage module, used for uploading the backup data packet to a cloud storage center after dimensionality reduction encryption; and a data recovery module, used for a target user to log in to the cloud storage center, and perform global or partial recovery of 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: The non-intrusive screen data backup and recovery system for display screens provided by the present application relates to the field of data processing technology. It performs risk assessment and early warning by calling multi-dimensional operation data in real time, generates backup instructions and executes data backup according to the analysis results, and uploads the backup data to cloud storage through dimensionality reduction encryption. Users can perform global or partial recovery of data through multi-level interaction. It solves the technical problem that the existing backup method in the prior art cannot monitor the dynamic state of the device and the operation of the program in real time, resulting in the inability to timely detect potential faults or anomalies, affecting the real-time and effectiveness of data backup and recovery. It realizes dynamic monitoring of the device state and program operation, timely detects potential problems and triggers non-intrusive data backup, and improves the real-time and effectiveness of data backup and recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of the structure of a non-intrusive screen data backup and recovery system for a display screen provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for dynamically adjusting the backup granularity in a non-intrusive data backup module of a non-intrusive screen data backup and recovery system for a display screen provided in an embodiment of the present application.

[0009] Explanation of reference numerals: multi-dimensional operation data acquisition module 10 , operation risk assessment module 20 , state fluctuation analysis module 30 , non-intrusive data backup module 40 , cloud storage module 50 , data recovery module 60 . DETAILED DESCRIPTION

[0010] The present application provides a non-invasive screen data backup and recovery system for a display screen, which is used to solve the technical problem that the existing backup methods in the prior art cannot monitor the dynamic state of the device and the program operation status in real time, resulting in the inability to timely detect potential faults or anomalies, affecting the real-time and effectiveness of data backup and recovery.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, 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 "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a non-intrusive screen data backup and recovery system for a display screen, the system comprising: The multi-dimensional operation data acquisition module 10 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. The main program data is the program data currently displayed on the main interface of the display screen.

[0014] 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 to provide necessary information support for subsequent risk assessment, status monitoring and backup strategies, wherein the multi-dimensional operation data refers to a comprehensive data set including the device operation status and the main program operation data.

[0015] First, the system establishes communication with the device's operating system through the hardware interface to obtain the target device's real-time operating data. This data includes the device's memory usage, CPU occupancy, disk space, and network bandwidth. This information can help the system understand the device's resource utilization, especially when the device's resources are approaching a bottleneck, such as when the memory usage reaches the upper limit, the system can respond immediately and issue a data loss warning signal.

[0016] After obtaining the operating 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, text or other interactive content displayed in real time on the display screen. Through the process monitoring mechanism of the operating system, the system can accurately capture the status of the currently running program. For example, in Windows systems, the system can access the running program information in real time through the resource manager or API interface; in Linux systems, the process information can be obtained through the / proc file system to determine the content currently displayed on the display screen.

[0017] When the hardware and main program data are acquired in real time, the system integrates this information and converts it into multi-dimensional operation data. These integrated data not only reflect the performance status of the equipment, but also include the dynamic changes of the display content. The integrated data is transmitted to other modules through efficient communication protocols for subsequent risk assessment and status monitoring. The entire process ensures all-round monitoring from equipment hardware to display program data, and provides accurate and timely basic data for subsequent operation risk assessment and data backup decisions. In this way, comprehensive collection of equipment status is achieved, ensuring that the system can take corresponding early warning and backup measures in time when the equipment response slows down or the display content is abnormal.

[0018] The operation risk assessment module 20 is used to perform an operation risk assessment based on the multi-dimensional operation data, and to initiate a corresponding data loss warning level according to the risk assessment result.

[0019] Furthermore, the operation risk assessment module 20 is also used to perform the following steps: P21: Based on the multi-dimensional operation data, the basic operation data of the target electronic device is extracted, and the basic operation risk level of the device is evaluated; P22: Based on the multi-dimensional operation data, the performance indicators of the main running program and the operation occupancy data of the background running program are respectively extracted, and the program operation risk is evaluated to obtain the program operation risk level; P23: According to the data type of the main running program, the program importance analysis is performed to obtain the main program warning level; P24: The data loss warning level is generated by combining the basic operation risk level of the device, the program operation risk level and the main program warning level.

[0020] Optionally, the operation risk assessment module 20 of the present application is responsible for comprehensively assessing the operation risk of the target electronic device based on 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 capability of the system.

[0021] First, the system extracts the basic operation data of the target electronic device from the multi-dimensional operation data. These data include the hardware resource status of the device, such as memory usage, CPU occupancy, storage space, network bandwidth, etc. The system compares these basic operation data with the preset health threshold 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 assessed as "high". This risk assessment helps to understand whether the device is in a potential resource bottleneck state, thereby providing decision support for subsequent backup operations.

[0022] Next, the system will extract relevant data of the main running program and background running program on the target electronic device, including the performance indicators of the main program (such as response time, frame rate, etc.) and the resource usage of the background program (such as memory, CPU usage ratio). By analyzing these program running data, the system evaluates the running risk level of the main program and background program. For example, if the response time of the main program exceeds the normal range, or the background program occupies too many system resources, the system will evaluate the program running risk as "medium" or "high". This step helps the system identify hidden program failures that may affect display content or performance.

[0023] Furthermore, the system performs 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 auxiliary work. Therefore, the data type of the main program (such as displaying real-time information, user input, key business data, etc.) determines the degree of 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 displays financial transaction data, the system will consider the high risk of data loss and set it to a "high" warning level.

[0024] Finally, the basic operation risk level of the equipment, the program operation risk level and the main program warning level are comprehensively analyzed to generate the final data loss warning level. Comprehensive risk assessment is usually based on weighted average, decision tree or fuzzy logic algorithm to ensure that various risk factors are reasonably balanced. Specifically, the assessment module will combine the weights of each risk level, comprehensively consider the equipment load, program operation status and program importance, and generate a comprehensive warning level. If the comprehensive risk level is high, the system will start more frequent data backup and increase the backup resources and priority; if the comprehensive risk level is low, the backup frequency and resource usage will be reduced accordingly to optimize system performance.

[0025] Through the above steps, the operation risk assessment module realizes a comprehensive analysis of the operating status of the target electronic device, and dynamically adjusts the data backup strategy according to different risk levels to ensure that when the device or program may fail, the system can take backup measures in time to minimize the risk of data loss.

[0026] The state fluctuation analysis module 30 is used to monitor the device state and software state according to the data loss warning level, and to perform state fluctuation analysis and prediction to obtain a state trend prediction result.

[0027] Furthermore, the state fluctuation analysis module 30 is further configured to perform the following steps: P31: According to the data loss warning level, match the status monitoring solution library to obtain the corresponding equipment monitoring solution; P32: According to the equipment monitoring solution, monitor the equipment status and software status, and obtain the equipment performance indicator sequence of the target electronic device and the program performance indicator sequence of the main running program; P33: Based on the equipment performance indicator sequence and the program performance indicator sequence, perform state fluctuation analysis and prediction respectively to obtain the state trend prediction result.

[0028] It should be understood that the state fluctuation analysis module 30 of the present application is responsible for in-depth monitoring of the device state and software state of the target electronic device according to the data loss warning level, and then obtaining the state trend prediction result through state fluctuation analysis prediction. This process includes multiple steps, and the specific execution process is as follows: First, the focus and depth of equipment monitoring are determined based on the data loss warning level. The data loss warning level reflects the potential risk of equipment operation. The higher the warning level, the more sophisticated monitoring is required. The system selects a device monitoring plan that matches the current warning level by matching the status monitoring plan library. For example, if the warning level is "high", more detailed monitoring may be required, including multi-dimensional monitoring of the equipment hardware, software and network status; if the warning level is low, only basic monitoring may be required. The formulation of these monitoring plans depends on the performance requirements of different devices and programs, as well as the response strategies at different warning levels. Among them, the monitoring plan library contains monitoring strategies for different warning levels. Each monitoring plan is formulated based on actual experience and historical data, including performance indicators of different devices, monitoring frequency and other contents.

[0029] Next, based on the device monitoring solution, the system starts to monitor the status of the device in real time. This includes collecting device performance indicator sequences from the hardware level of the device, such as CPU utilization, memory usage, disk read and write speed, network traffic and other data. At the same time, the system also needs to monitor the performance of the main running program, which usually includes program performance indicator sequences such as program response time, frame rate, execution time, etc. These performance indicators can reflect the current running status of the device and program, and provide basic data for subsequent status fluctuation analysis.

[0030] After collecting the device performance indicator series and program performance indicator series, the system will perform state fluctuation analysis and prediction on these data. This analysis process uses historical data, trend prediction models, and real-time monitoring data to predict the state changes that may occur in devices and programs in the future. 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 state trend prediction results, for example, the response time of a key program may increase significantly in the next 10 minutes, or the CPU load of the device may reach a critical value.

[0031] The above steps ensure that the system can make dynamic adjustments based on real-time data, thereby timely identifying potential risks of equipment and software, and making early warnings and adjustments to backup strategies based on status trend prediction results. Through effective status fluctuation analysis, the system can predict possible failures of equipment or programs in advance, and take backup or recovery measures in time, thereby minimizing the risk of data loss.

[0032] Furthermore, step P33 of the embodiment of the present application also includes: P33-1: Based on the equipment performance indicator sequence and the program performance indicator sequence, perform state fluctuation analysis respectively to generate equipment state analysis results and main program state analysis results; P33-2: According to the main program operation mechanism, perform equipment-program correlation impact analysis to generate bidirectional impact factors; P33-3: Based on the bidirectional impact factors, perform state correction on the equipment state analysis results and the main program state analysis results respectively, and perform comprehensive trend prediction based on the corrected equipment state analysis results and the main program state analysis results to obtain the state trend prediction results.

[0033] Optionally, through precise state fluctuation analysis, combined with the performance indicators of the equipment and the main program, deep digging of potential problems that may occur in the system, and through comprehensive correction and trend prediction, ensuring the timeliness and accuracy of data backup.

[0034] First, based on the collected device performance indicator series and program performance indicator series, independent state fluctuation analysis is performed separately. The device performance indicator series usually involves hardware resources (such as CPU usage, memory usage, network bandwidth, etc.), while the program performance indicator series focuses on the running status of the main program, such as response time, frame rate, execution time, etc. The analysis module uses time series analysis methods (such as autoregressive model AR, moving average MA, etc.) to model the performance data of devices and programs, so as to understand their fluctuation patterns and generate device state analysis results and main program state analysis results. These analysis results not only show the current running status of devices and programs, but also reveal possible performance bottlenecks or instability through fluctuation trends. For example, the CPU load of the device may fluctuate violently in the future, while the response time of the program may gradually increase, indicating that the system may be about to enter a resource bottleneck state.

[0035] The running status of devices and programs often affects each other, especially in complex electronic devices, where the running of programs may directly consume the hardware resources of the device, and vice versa. Therefore, the analysis module needs to perform correlation impact analysis between devices and programs and generate bidirectional impact factors 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 a large amount of data input and output is involved, etc. Exemplarily, by establishing a causal relationship model between the device and the program, using regression analysis or other machine learning algorithms, the bidirectional impact factors between the device and the program are 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 surge in CPU usage, thereby affecting the operation 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 be extended.

[0036] Based on the bidirectional impact factors obtained through analysis, the analysis module will make status corrections to the device status analysis results and the main program status analysis results. The goal of this process is to adjust the errors caused by the interaction between the device and the program, so that the fluctuation analysis results are more accurate and a more realistic status trend forecast is generated. By combining the mutual influence of device performance and program operation, the analysis module adjusts the status forecasts of the device and program. For example, if the high load state of the device has caused the program to respond slowly, the module will correct the performance forecast of the program based on the interaction between the device and the program, taking into account the impact of the device bottleneck on the program.

[0037] After the status correction, the corrected device and program status will be summarized to form a comprehensive trend prediction result. This result not only takes into account the fluctuations of individual device or program status, but also integrates the interaction between the two, providing a more accurate basis for the formulation of backup strategies. In this way, the system can not only identify potential device failures or program response delays in advance, but also predict the operating status of devices and programs in the future time period, thereby providing a more accurate data backup and recovery strategy.

[0038] The non-intrusive data backup module 40 is used to generate a data backup instruction based on the state trend prediction result, activate the data backup unit, perform non-intrusive screen data backup, and generate a backup data packet.

[0039] Furthermore, the non-intrusive data backup module 40 is further configured to perform the following steps: P41: Extract system load data based on the state trend prediction results; P42: Collect historical user operation records for analysis to generate user preference data; P43: Introduce an intelligent backup granularity adjustment mechanism to dynamically adjust the backup granularity according to the user preference data and the system load data, and generate data backup instructions.

[0040] It should be understood that the non-intrusive data backup module 40 of the present application is responsible for generating data backup instructions based on the state trend prediction results, activating the data backup unit to perform non-intrusive backup of screen data, and finally generating a backup data packet. In order to ensure that the data backup process is efficient and does not affect the normal operation of the device, the module adopts a multi-dimensional dynamic adjustment mechanism.

[0041] First, the system extracts system load data based on the status trend prediction results. The system load data includes the resource consumption 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 memory or CPU usage is close to saturation), the system may delay or reduce the scale of the backup task to avoid additional equipment burden. Conversely, when the system load is low, a more comprehensive data backup can be performed. In this way, the system can optimize backup timing and resource utilization and reduce the impact on device performance.

[0042] In order to ensure that the backup operation meets user needs and improve backup efficiency, the system also needs to generate user preference data by analyzing historical user operation records. Historical operation records include user usage patterns of the device, frequently operated programs and applications, and user focus on display content during a specific time period. By analyzing these operation data, the system can identify users' frequently used applications and display modes, thereby generating personalized backup strategies. For example, if users use certain important applications during a specific time period on weekdays, the system can prioritize the data of these programs for backup. User preference data provides a personalized basis for backup, making the backup process more in line with users' actual needs and avoiding redundant backup operations.

[0043] After obtaining 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. The backup granularity refers to the scope and accuracy of data backup, which can be full screen, window, specific application, or even a single file or data block. The larger the backup granularity, the more comprehensive the backed up data, but it may also consume more system resources; the smaller the granularity, the less backup data there is, and the lower the system resource consumption.

[0044] For example, when the system load is high, the backup granularity can be appropriately reduced, and only the most critical data can be backed up instead of a full backup; when the system load is low and user preferences are more demanding, the system can increase the backup granularity to ensure more refined data protection. By combining user preferences (which data is most important) and system load (whether there are enough resources for a full backup), the intelligent adjustment mechanism can optimize the backup process to ensure data security without affecting device performance.

[0045] Finally, the system generates data backup instructions based on the adjusted granularity, which not only stipulates the specific data scope of the backup, but also includes the timing, priority and storage target of the backup. The instructions will activate the data backup unit to perform non-intrusive screen data backup. The backup process is usually carried out without interfering with the user's current operations, thus achieving seamless backup.

[0046] 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 needs, 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.

[0047] Further, such as Figure 2 As shown, step P43 of the embodiment of the present application also includes: P43-1: According to the system load data, set the backup granularity search space, where each granularity option corresponds to a backup cost and resource occupancy score; P43-2: Initialize the candidate solutions for the backup granularity, and calculate the individual score value for each backup granularity candidate solution in combination with the granularity scoring function; P43-3: According to the individual score values, perform granularity cross-mutation, continuously generate new backup granularity schemes, and optimize granularity selection. After multiple generations of iterations, select the backup granularity with the best individual score value as the backup execution granularity of the current backup task.

[0048] Wherein, the granularity scoring function is as follows: F(x)=w_1⋅ResourceCost(x)+w_2⋅BackupEffectiveness(x); where ResourceCost(x) is the resource cost of backup granularity x, BackupEffectiveness(x) is the effectiveness of backup granularity x, and w_1 and w_2 are weight coefficients.

[0049] In a possible embodiment of the present application, an intelligent backup granularity adjustment mechanism is further introduced to dynamically select the most appropriate backup granularity through a multi-generation iterative optimization process.

[0050] First, based on the system load data, the system needs to set up a backup granularity search space. The search space includes different backup granularity options, such as full-screen backup, window-level backup, specific application backup, or backup of certain small-range data blocks. Each granularity option has an associated backup cost and resource usage score. The resource usage score usually takes into account 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 a granular search space, the system can explore all possible backup granularity options and provide initial data for the subsequent optimization process.

[0051] Next, after the search space is determined, the non-intrusive data backup module 40 will initialize the candidate solutions of the backup granularity. The candidate solutions are different backup granularity options in the backup granularity space, and each option corresponds to a potential backup solution. Next, the module combines 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. 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.

[0052] The granularity scoring function has the following form: F(x)=w_1⋅ResourceCost(x)+w_2⋅BackupEffectiveness(x).

[0053] 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. The 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. The value range is [0,1]. The higher the value, the higher the backup quality. w_1 and w_2 are weight coefficients.

[0054] After the score calculation is completed, the system will perform granularity crossover and mutation based on the score value, that is, randomly crossover and mutation of the backup granularity candidate solutions to generate a new backup granularity solution.

[0055] First, based on the preliminary calculated individual score 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, which aims 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, resource cost or effectiveness) to generate a new backup granularity scheme. Based on the crossover operation, the system will also randomly adjust certain parameters of the backup granularity to generate a new backup granularity scheme, thereby expanding the search space and avoiding local optimal solutions.

[0056] Through these operations, multiple new backup granularity schemes are generated, and their individual scores are calculated and evaluated through the granularity scoring function. After multiple generations of iterations, the backup granularity scheme with the best individual score 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 optimality of the backup effect, meeting the current load of the device and the user's preferences. This process ensures that the backup operation protects data as accurately and comprehensively as possible without affecting system performance through the granularity scoring function and granularity optimization algorithm.

[0057] The cloud storage module 50 is used to upload the backup data package to the cloud storage center after dimensionality reduction encryption.

[0058] Specifically, in order to ensure the confidentiality and integrity of the backup data and the efficiency of subsequent recovery, the cloud storage module 50 of this embodiment uploads the backup data packet to the cloud storage center through the dimensionality reduction encryption technology.

[0059] Before uploading the backup data package, you first need to perform dimensionality reduction on the backup data. Dimensionality reduction refers to reducing the original dimension of the backup data package through a certain algorithm while retaining the key features and valid information of the data as much as possible. Usually, the backup data may include a large amount of redundant information, which may take up too much storage space and bandwidth, affecting the upload efficiency. The purpose of dimensionality reduction is to retain the efficient and important data parts by compressing the redundant data.

[0060] For example, dimensionality reduction can be performed using a variety of methods, such as principal component analysis (PCA), t-SNE (t-distributed stochastic neighbor embedding) and other data compression techniques, which can compress the data volume without significantly losing information and optimize storage and transmission efficiency.

[0061] In addition to dimensionality reduction, the backup data package also needs to be encrypted to ensure data security during transmission. Data encryption is a key step to prevent data from being illegally accessed and tampered with. Backup data packages can be encrypted using symmetric encryption or asymmetric encryption algorithms. During the encryption process, the generated encryption key can only be decrypted by authorized users, ensuring that after the data is uploaded to the cloud storage center, it cannot be cracked even if it is intercepted by a third party. For example, the system can use AES (Advanced Encryption Standard) for symmetric encryption, or RSA encryption algorithm for asymmetric encryption, to ensure that the data package has a high degree of security during storage and transmission.

[0062] After completing the dimensionality reduction encryption, the system uploads the encrypted backup data package to the cloud storage center through the network protocol. The cloud storage center provides large-scale, distributed data storage and management functions, which can accommodate massive amounts of backup data while ensuring data accessibility and reliability. During the upload process, the system will use an efficient transmission protocol to ensure that data upload is completed in the shortest possible time while reducing the impact on network bandwidth and system resources.

[0063] In the cloud storage center, backup data packages are usually managed in the form of block storage. Each backup data package is stored as an independent data object according to different backup granularities (such as full screen backup, application backup, etc.), and is accompanied by 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 fully protected during the backup and recovery process, and can be quickly and safely restored to the original device.

[0064] 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.

[0065] Optionally, the data recovery module 60 of the present application is mainly responsible for providing flexible data recovery services when the user needs it, including global recovery and partial recovery. The module allows the user to select the recovery method and specific recovery data through multi-level user interaction. This process involves obtaining backup data from the cloud storage center and performing recovery operations according to the user's needs to ensure the efficiency and accuracy of data recovery.

[0066] Before performing data recovery, users first need to log in to the cloud storage center through a secure identity verification process. To ensure data security and user privacy, the system uses multi-factor authentication (such as password, verification code, fingerprint recognition or facial recognition, etc.) to confirm the user's identity. This identity verification process can effectively prevent unauthorized users from accessing backup data and ensure that only authorized users can perform data recovery operations.

[0067] After successful login, the user enters the data recovery interface, which provides users with multiple recovery options. Multi-level user interaction enables the system to provide different operation levels according to user needs: Global recovery, restore the entire backup data package, including all information such as device status, program running status, user preferences, etc. Applicable to scenarios where the entire system needs to be restored, such as device system crash or serious failure. Partial recovery, only restore some specific data or files, such as the settings of a certain program, the operation records of certain users, or the data of a certain time period. Partial recovery can help users accurately restore important data in the event of data damage or loss, avoiding unnecessary redundant recovery.

[0068] At this stage, users can select the scope and data type to be restored through a simple graphical interface. In the data recovery interface, the system not only provides the selection of the recovery scope, but also generates different recovery plans based on the timestamp, file type, importance and other conditions of the backup data. Users can view the detailed information of each plan and select the most appropriate recovery option according to specific needs. In this way, users can preview the contents of the backup data before recovery to ensure that the selected recovery data meets actual needs.

[0069] After the user confirms the recovery plan, the system starts the recovery process. For global recovery, the system will download the entire backup data package from the cloud storage center and restore it to the corresponding location of the target device. During the recovery process, the system will automatically handle data decryption, decompression, and reorganization 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. In order to increase the recovery speed and reduce network bandwidth usage, the system uses intelligent data segmentation transmission technology to transmit the data in the recovery process in batches. After each batch of data is transmitted, the system will automatically verify the integrity of the data to ensure the accuracy and correctness of each recovery operation.

[0070] During the recovery process, users can check the recovery progress at any time through the interface. The system will display the current recovery status in real time, including the amount of data recovered, the estimated completion time, and other information. If an exception occurs, the system will promptly feedback the error information and provide corresponding solutions.

[0071] In summary, the embodiments of the present application have at least the following technical effects: This application conducts operation risk assessment and initiates data loss warning by calling the multi-dimensional operation data of the target electronic device in real time (including device operation data and main program data). According to the warning results, the device and software status are monitored and fluctuations are analyzed, backup instructions are generated, and data is backed up. The backup data packet is uploaded to the cloud storage after dimensionality reduction and encryption. Users can perform global or partial data recovery through a multi-level interactive interface, thereby achieving efficient and intelligent screen data backup and recovery.

[0072] The technical effect of dynamically monitoring the device status and program operation, timely discovering potential problems and triggering non-intrusive data backup has been achieved, thereby improving the real-time and effectiveness of data backup and recovery.

[0073] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0075] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations 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 equivalents, the present application intends to include these modifications and variations.

Claims

1. A non-intrusive screen data backup and recovery system for a display screen, characterized in that: The system is applied to a display screen, and the display screen is used in an electronic device. The system includes: A multi-dimensional operation data acquisition module, used to call the multi-dimensional operation data of the target electronic device in real time, wherein the multi-dimensional operation data includes the device operation data and the main program data, wherein the main program data is the program data currently displayed on the main interface of the display screen; An operation risk assessment module, used to perform an operation risk assessment based on the multi-dimensional operation data, and initiate a corresponding data loss warning level according to the risk assessment result; A state fluctuation analysis module is used to monitor the device state and software state according to the data loss warning level, and to perform state fluctuation analysis and prediction to obtain a state trend prediction result; A non-intrusive data backup module, used to generate a data backup instruction based on the state trend prediction result, activate a data backup unit, perform non-intrusive screen data backup, and generate a backup data packet; A cloud storage module, used to upload the backup data packet to a cloud storage center after dimensionality reduction encryption; The data recovery module is used for the target user to log in to the cloud storage center and perform global or partial recovery of display screen data through multi-level user interaction.

2. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 1, characterized in that: The operational risk assessment module is also used to: Based on the multi-dimensional operation data, extract the basic operation data of the target electronic device, and evaluate and obtain the basic operation risk level of the device; Based on the multi-dimensional operation data, the performance indicators of the main running program and the operation occupancy data of the background running program are respectively extracted to perform program operation risk assessment and obtain the program operation risk level; According to the data type of the main running program, the program importance analysis is carried out to obtain the main program warning level; The data loss warning level is generated by comprehensively considering the basic operation risk level of the equipment, the program operation risk level and the main program warning level.

3. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 2, characterized in that: The state fluctuation analysis module is also used for: According to the data loss warning level, match the status monitoring solution library to obtain the corresponding equipment monitoring solution; According to the device monitoring scheme, monitor the device status and software status, and obtain the device performance indicator sequence of the target electronic device and the program performance indicator sequence of the main running program; Based on the equipment performance indicator sequence and the program performance indicator sequence, state fluctuation analysis and prediction are performed respectively to obtain state trend prediction results.

4. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 3, characterized in that: The state fluctuation analysis module is also used for: Based on the equipment performance indicator sequence and the program performance indicator sequence, respectively, state fluctuation analysis is performed to generate equipment state analysis results and main program state analysis results; According to the main program operation mechanism, conduct equipment-program correlation impact analysis and generate two-way impact factors; Based on the bidirectional influencing factors, the device status analysis results and the main program status analysis results are respectively corrected, and a comprehensive trend prediction is performed based on the corrected device status analysis results and the main program status analysis results to obtain the status trend prediction result.

5. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 1, characterized in that: The non-intrusive data backup module is also used for: Extracting system load data based on the state trend prediction result; Collect historical user operation records for analysis and generate user preference data; An intelligent backup granularity adjustment mechanism is introduced to dynamically adjust the backup granularity according to the user preference data and the system load data, and generate data backup instructions.

6. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 5, characterized in that: The non-intrusive data backup module is also used for: According to the system load data, a backup granularity search space is set, wherein each granularity option corresponds to a backup cost and resource occupancy score; Initialize the candidate solutions of the backup granularity, and calculate the individual score value for each candidate solution of the backup granularity in combination with the granularity scoring function; According to the individual score value, granularity cross-mutation is performed to continuously generate new backup granularity schemes and optimize granularity selection. After multiple generations of iterations, the backup granularity with the best individual score value is selected as the backup execution granularity of the current backup task.

7. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 6, characterized in that: The granularity scoring function is as follows: F(x)=w_1⋅ResourceCost(x)+w_2⋅BackupEffectiveness(x); Among them, 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.

8. The non-intrusive screen data backup and recovery system for a display screen as claimed in claim 6, characterized in that: The non-intrusive data backup module is also used for: Receive the data backup instruction and extract the backup execution granularity; The data backup unit is activated, and real-time screen data collection and program status recording are performed based on the backup execution granularity, and a backup data packet is encapsulated and generated.

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