Cloud data processing system for processor environment verification

Abstract

The invention, which relates to the technical field of cloud data processing, discloses a cloud data processing system for processor environment verification, comprising a system login module, a data acquisition module, a resource evaluation module, a demand prediction module, a processing resource allocation module, a dynamic adjustment module and an environment verification module. The data acquisition module is used for acquiring system resource data and user data, and the user data comprises user member information, historical resource use information, historical use time information, operation quantity information and operation diversity information. According to the method, the use habit is evaluated by combining historical resource use and time information, the initial resource demand of a user is accurately predicted through demand prediction and resource allocation, the allocation flexibility and the user experience are improved, after allocation, the dynamic adjustment module performs real-time adjustment according to resource differences, the performance bottleneck and waste are avoided, and the resource utilization rate and the cloud service quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud data processing, and in particular to a cloud data processing system for processor environment verification. Background Art

[0002] Cloud data is a general term for the technologies and platforms used in cloud computing business models for data integration, data analysis, data consolidation, data distribution, and data early warning. Technically, cloud data involves multiple aspects, including data integration, data analysis, data consolidation, data distribution, and data early warning. It leverages the powerful computing power of cloud computing to efficiently process and analyze massive amounts of data, extracting valuable information and insights. Cloud data has been widely adopted across various industries and holds a crucial position in modern information technology. With the advent of the big data era, data has become a core value asset across all industries, making the management of processor resources increasingly complex and critical.

[0003] Currently, traditional processor resource management methods rely on static or preset resource allocation strategies, which make it difficult to adjust resource allocation according to user usage needs. The accuracy and flexibility of resource allocation are poor, which reduces the user experience. At the same time, they cannot adapt to changes in resource demand in the cloud environment and cannot achieve real-time adjustment of resource allocation, which reduces resource utilization efficiency and affects the overall quality and reliability of cloud services. Summary of the Invention

[0004] The purpose of the present invention is to provide a cloud data processing system for processor environment verification, which solves the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a cloud data processing system for processor environment verification, comprising a system login module, a data acquisition module, a resource assessment module, a demand forecasting module, a processing resource allocation module, a dynamic adjustment module, and an environment verification module;

[0006] The data acquisition module is used to obtain system resource data and user data, and the user data includes user membership information, historical resource usage information, historical usage time information, operation quantity information and operation diversity information;

[0007] The resource evaluation module includes a system evaluation unit and a user evaluation unit. The system evaluation unit is used to analyze the system in combination with the system resource data to obtain the amount of system resources used;

[0008] The user evaluation unit first combines the operation quantity information and the operation diversity information to obtain the user operation complexity characteristics, and then combines the user operation complexity characteristics with the historical resource usage information and the historical usage time information to obtain the historical behavior complexity score;

[0009] The demand prediction module combines historical usage time information and user membership information to obtain a user priority score, and combines the user priority score with the historical behavior complexity score to obtain the user's initial demand characteristics;

[0010] The processing resource allocation module allocates processing resources to the user by combining the system resource data, the amount of system resources used and the user's initial demand characteristics to obtain an initial resource allocation amount;

[0011] The dynamic adjustment module obtains the actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and combines the actual resource consumption, resource fluctuation and initial resource allocation to obtain the resource adjustment amount.

[0012] Optionally, the system evaluation unit obtains CPU usage information, memory usage information, disk occupancy information and bandwidth usage information from the resource data, and weightedly adds the CPU usage information, memory usage information, disk occupancy information and bandwidth usage information to obtain the amount of resources used by the system. The higher the amount of resources used by the system, the fewer resources the system currently has available, and vice versa.

[0013] Optionally, the user evaluation unit performs the following evaluation process:

[0014] The operation quantity information includes the number of operation steps and the maximum number of operation steps, and the number of operation steps is compared with the maximum number of operation steps to obtain an operation quantity score;

[0015] The operation diversity information includes the number of operation types and the total number of operation types. The number of operation types is compared with the total number of operation types to obtain an operation type score. The operation number score and the operation type score are weightedly added to obtain a user operation complexity feature.

[0016] The historical resource usage information includes the historical average resource usage and the historical maximum resource usage, and the historical average resource usage and the historical maximum resource usage are compared to obtain a resource usage score;

[0017] The historical usage time information includes the historical average duration and the historical maximum duration, and the historical average duration and the historical maximum duration are compared to obtain a usage time score;

[0018] The historical behavior complexity score is obtained by weighted addition of the resource usage score, the time score, and the user operation complexity feature.

[0019] Optionally, the demand forecasting module performs the following forecasting process:

[0020] The demand forecasting module sets unit time information, extracts the total usage time per unit time from the historical usage time information in combination with the unit time information, and compares the total usage time per unit time with the unit time information to obtain a user activity score;

[0021] The user membership information includes the user membership level and the maximum membership level, and the user membership level and the maximum membership level are compared to obtain a user level score;

[0022] The user priority score is obtained by weighted addition of the user activity score and the user level score;

[0023] The demand prediction module combines the user priority score and the historical behavior complexity score for analysis to obtain the user's initial demand characteristics.

[0024] Optionally, the processing resource allocation module allocates processing resources to the user in combination with the system resource data and the user's initial demand characteristics;

[0025] The processing resource allocation module extracts the total amount of system resources from the system resource data, sets the resource reservation amount, and subtracts the resource reservation amount and the system used resources from the total amount of system resources to obtain the allocatable resource amount, then combines the user initial demand characteristics of all users to obtain the total initial demand characteristics, compares the user initial demand characteristics with the total initial demand characteristics, and then multiplies them by the allocatable resource amount to obtain the initial resource allocation amount.

[0026] Optionally, the dynamic adjustment module obtains actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and obtains the resource adjustment amount by combining and analyzing the actual resource consumption, resource fluctuation and initial resource allocation;

[0027] The dynamic adjustment module monitors the usage of the initial resource allocation amount per unit time of the user, obtains the actual resource consumption and the resource fluctuation, subtracts the actual resource consumption from the initial resource allocation amount and multiplies the resultant amount by the resource fluctuation to obtain a resource usage difference value, calculates the resource usage difference value used by all users to obtain a total resource usage difference value, compares the resource usage difference value with the total resource usage difference value to obtain a difference ratio feature, multiplies the difference ratio feature by the resource reservation amount to obtain a resource adjustment amount, adjusts the user's initial resource allocation amount according to the resource adjustment amount, sets a difference threshold for the resource usage difference value, and adjusts the user when the resource usage difference value is greater than the difference threshold.

[0028] Optionally, the environment verification module includes a system management interface, BIOS and hardware sensors.

[0029] Optionally, the system login module includes a user login unit and a user verification unit, the user login unit is used to construct an account registration and login interface, and the user verification unit is used to verify the user identity.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention can standardize the complexity score of each user by comparing the historical data of each user with the maximum value of the historical data of all users, thereby facilitating the system to compare the historical behavior complexity of each user, and then combine the historical behavior complexity score with the historical resource usage information and the historical usage time information for analysis, so as to evaluate the system usage habits of each user, and then combine the user priority score and the historical behavior complexity score through the demand prediction module to obtain the user's initial demand characteristics, and finally allocate processing resources to the user through the processing resource allocation module in combination with the system resource data, the amount of system resources used and the user's initial demand characteristics, which fully considers the historical resource usage of each user, can more accurately predict the user's initial resource demand, and improve the flexibility of resource allocation and user experience.

[0032] 2. After each user makes initial resource allocation, the present invention analyzes the user's resource usage through a dynamic adjustment module to obtain the actual resource consumption and resource fluctuation. The resource fluctuation represents the difference between the user's actual resource consumption and the initial resource allocation. The user's resources are adjusted in real time according to the size of the difference, increasing or decreasing resources to avoid performance bottlenecks or resource waste, thereby ensuring the stability of system use and enabling the system to adapt to changes in resource demand in the cloud environment. This achieves real-time adjustment of resource allocation, improves resource utilization efficiency, and ensures the overall quality and reliability of cloud services. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a block diagram of the system module of the present invention;

[0034] Figure 2 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] Example 1:

[0037] See also Figure 1 and Figure 2 ,This implementation provides a cloud data processing system for processor environment verification, including a system login module, a data acquisition module, a resource evaluation module, a demand prediction module, a processing resource allocation module, a dynamic adjustment module, and an environment verification module;

[0038] The system login module includes a user login unit and a user verification unit. The user login unit is used to build an account registration and login interface, and the user verification unit is used to verify the user's identity;

[0039] The data acquisition module is used to obtain system resource data and user data, and the user data includes user membership information, historical resource usage information, historical usage time information, operation quantity information and operation diversity information;

[0040] The resource evaluation module includes a system evaluation unit and a user evaluation unit. The system evaluation unit is used to analyze the system in combination with the system resource data to obtain the amount of system resources used;

[0041] The user evaluation unit first combines the operation quantity information and the operation diversity information to obtain the user operation complexity characteristics, and then combines the user operation complexity characteristics with the historical resource usage information and the historical usage time information to obtain the historical behavior complexity score;

[0042] The demand prediction module combines historical usage time information and user membership information to obtain a user priority score, and combines the user priority score with the historical behavior complexity score to obtain the user's initial demand characteristics;

[0043] The processing resource allocation module allocates processing resources to the user by combining the system resource data, the amount of system resources used and the user's initial demand characteristics to obtain an initial resource allocation amount;

[0044] The dynamic adjustment module obtains actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and obtains resource adjustment by combining and analyzing the actual resource consumption, resource fluctuation and initial resource allocation.

[0045] More specifically, in this embodiment: first, after obtaining system resource data and user data through the data acquisition module, the system evaluation unit in the resource evaluation module analyzes the resources currently used by the system to obtain the amount of system resources used. Since each system needs to continue to run some system components to maintain normal operation of the system after startup, which will occupy part of the system resources, the amount of system resources used includes this part, thereby improving the accuracy of subsequent resource allocation. Then, the user evaluation unit first combines the operation quantity information and the operation diversity information to obtain the user operation complexity characteristics, and then combines the user operation complexity characteristics with the historical resource usage information and the historical usage time information for analysis, and analyzes the system usage habits of each user. If some users use a lot of resources, more resources need to be allocated when allocating resources. If some users use less resources, fewer resources need to be allocated, so that subsequent resources can be allocated on demand, which can adapt to the needs of different users and improve the accuracy of resource allocation.

[0046] Afterwards, the demand prediction module combines the historical usage time information and user membership information to obtain the user priority score. Users with high user priority scores will be allocated more resources because the user priority score combines the historical usage time information and user membership information. The historical usage time information analyzes the user's activity. The higher the activity, the longer the user's recent usage time. The user membership information analyzes the user's total usage time or recharge status. The longer the user's usage time, the more accurate the system's prediction of their resource needs will be. In order to ensure the user experience of users with high user priority scores, it is necessary to appropriately increase the allocated resources. The user priority score and the historical behavior complexity score are combined and analyzed to obtain the user's initial demand characteristics. Finally, the processing resource allocation module combines the system resource data, the system's used resources and the user's initial demand characteristics to allocate processing resources to the user to obtain the initial resource allocation amount. This fully considers the historical resource usage of each user, and can more accurately predict the user's initial resource demand, thereby improving the flexibility of resource allocation and user experience.

[0047] After each user is initially allocated resources, the user's resource usage is analyzed through the dynamic adjustment module to obtain the actual resource consumption and resource fluctuation. The resource fluctuation represents the difference between the user's actual resource consumption and the initial resource allocation. According to the size of the difference, the user's resources are adjusted in real time to increase or decrease resources to avoid performance bottlenecks or resource waste, ensure the stability of system use, enable the system to adapt to changes in resource demand in the cloud environment, achieve real-time adjustment of resource allocation, improve resource utilization efficiency, and ensure the overall quality and reliability of cloud services.

[0048] Furthermore, the system evaluation unit obtains CPU usage information, memory usage information, disk usage information, and bandwidth usage information from the resource data, and performs weighted addition of the CPU usage information, the memory usage information, the disk usage information, and the bandwidth usage information to obtain the amount of system resources used. The evaluation process of the system evaluation unit is as follows:

[0049] R au =W1×C+W2×M+W3×P+W4×D

[0050] where R au Indicates the amount of resources used by the system;

[0051] C represents the CPU usage, W1 represents the CPU impact coefficient;

[0052] M represents the memory usage, and W2 represents the disk usage impact coefficient;

[0053] P represents disk usage, and W3 represents disk usage impact coefficient;

[0054] D represents bandwidth utilization, W4 represents bandwidth impact coefficient, W1+W2+W3+W4=1;

[0055] Specifically, the amount of system resources used R au Indicates the overall resource usage of the system, the amount of resources used by the system S au The higher the value, the less resources the cloud data processing system can currently allocate. Conversely, the more resources can be allocated. After each system is started, in order to maintain normal operation of the system, it is necessary to continue to run some system components, which will occupy some system resources. By calculating the amount of used resources R au , which can facilitate the subsequent allocation of resources and improve the accuracy of resource allocation.

[0056] Furthermore, the operation quantity information includes the number of operation steps and the maximum number of operation steps, and the number of operation steps is compared with the maximum number of operation steps to obtain an operation quantity score;

[0057] The operation diversity information includes the number of operation types and the total number of operation types. The number of operation types is compared with the total number of operation types to obtain an operation type score. The operation number score and the operation type score are weightedly added to obtain a user operation complexity feature.

[0058] The historical resource usage information includes the historical average resource usage and the historical maximum resource usage, and the historical average resource usage and the historical maximum resource usage are compared to obtain a resource usage score;

[0059] The historical usage time information includes the historical average duration and the historical maximum duration, and the historical average duration and the historical maximum duration are compared to obtain a usage time score;

[0060] The historical behavior complexity score is obtained by weighted addition of the resource usage score, the time score and the user operation complexity feature.

[0061] The evaluation process for the User Assessment Unit is as follows:

[0062]

[0063] Among them B i represents the complexity score of user i’s historical behavior;

[0064] H avg,i The historical average resource usage of user i is calculated by dividing the total resource usage of the user by the number of times the system is used. This represents the average resource usage of the user each time they log in to the system.

[0065] H max Indicates the historical maximum resource usage;

[0066] W5 represents the resource usage impact coefficient;

[0067] T avg,i The historical average usage time of user i is calculated by dividing the total usage time of the user by the number of times the system is used;

[0068] H max Indicates the historical maximum usage time;

[0069] W6 represents the resource usage impact coefficient;

[0070] F i User i's operation complexity score;

[0071] W7 represents the operation complexity influence coefficient, W5+W6+W7=1;

[0072] User i's operation complexity score F i The process is as follows:

[0073]

[0074] Among them L avg,i Indicates the historical average number of operation steps for user i. The operation steps indicate the total number of steps required for the user to complete the requirement.

[0075] L max Indicates the historical maximum number of operation steps, that is, the maximum number of operation steps taken by all users in the process of using cloud data to process business;

[0076] β1 represents the influence coefficient of the operation step;

[0077] L avg,i Indicates the historical average number of operation types performed by user i, such as data entry, file download, file upload, and system query after logging into the system;

[0078] L max Indicates the historical maximum number of operation types, that is, the maximum number of operation types among all users;

[0079] β2 represents the influence coefficient of operation type, β1+β2=1;

[0080] Specifically, the historical behavior complexity score B of user i i The larger the value, the more complex the user's cloud data business operations are, and the more time and system resources are required. Conversely, the simpler the user's business needs are, the less time and system resources are required. By comparing each user's historical data with the maximum value of all users' historical data, the complexity score of each user can be standardized, making it easier for the system to compare the complexity of each user's historical behavior, analyze each user's system usage habits, and improve the accuracy of subsequent resource allocation.

[0081] Furthermore, the demand forecasting module sets unit time information, which includes a time interval ΔT, extracts the total usage time per unit time from the historical usage time information in combination with the unit time information, and compares the total usage time per unit time with the unit time information to obtain a user activity score;

[0082] The user membership information includes the user membership level and the maximum membership level, and the user membership level and the maximum membership level are compared to obtain a user level score;

[0083] The user priority score is obtained by weighted addition of the user activity score and the user level score;

[0084] The demand prediction module combines the user priority score and the historical behavior complexity score for analysis to obtain the user's initial demand characteristics.

[0085] The forecasting process of the demand forecasting module is as follows:

[0086] Q i =B i ×(1+G i )

[0087]

[0088] where Q i represents the initial demand score of user i;

[0089] B i represents the complexity score of user i’s historical behavior;

[0090] G i represents the priority score of user i;

[0091] T all,i It represents the total usage time of user i per unit time, that is, the total usage time of the user who logged into the system per unit time in the past;

[0092] ΔT represents the time interval in days;

[0093] The total usage time of user i in unit time is T all,i Compared with the time interval ΔT, it shows the user's activity in the past time period;

[0094] γ1 represents the activity influence coefficient, ranging from 0 to 1;

[0095] E i Indicates the membership level of user i;

[0096] E max Indicates the maximum membership level, that is, the highest level among all users;

[0097] Compare user i's membership level with the maximum membership level E max For comparison, it shows the current user's level performance in the overall level;

[0098] γ2 represents the member level influence coefficient, ranging from 0 to 1;

[0099] Specifically, the user membership level is determined by the system settings. For example, the system sets experience thresholds for different levels. When a user exceeds the experience threshold, he or she will be upgraded. The experience value can be obtained by the time the user uses the cloud data processing system or by recharging. The higher the user membership level, the more resources are allocated. The higher the level of the user, the more accurate the system's analysis of its historical data, and thus the smaller the error in resource allocation. By introducing the user membership level factor, the system stability and response time can be avoided from being affected by the influx of requests from a large number of low-level users.

[0100] User i’s initial demand score Q i The higher the value, the more system resources should be allocated, and vice versa. Therefore, initial resources are allocated to each user based on the user's initial demand score. This ensures that in the early stages of system operation, users can obtain resources that match their needs and ensure the normal development of business.

[0101] Furthermore, the processing resource allocation module allocates processing resources to the user based on the system resource data and the user's initial demand characteristics;

[0102] The resource allocation processing module extracts the total amount of system resources from the system resource data, sets a resource reservation, and subtracts the resource reservation and the system used resources from the total amount of system resources to obtain the allocatable resource amount. The module then combines the initial user demand characteristics of all users to obtain the total initial demand characteristics. The initial user demand characteristics are compared with the total initial demand characteristics and then multiplied by the allocatable resource amount to obtain the initial resource allocation amount. The resource allocation processing module performs the following allocation process:

[0103]

[0104] Among them A i represents the initial resource allocation of user i, which indicates the amount of resources allocated to the user per unit time;

[0105] Q i represents the initial demand score of user i;

[0106] Indicates the total score of initial demand;

[0107] N represents the number of users;

[0108] R tot Indicates the total amount of system resources;

[0109] R res Indicates the amount of resource reservation, which is determined by the system settings. If 10% is required to be reserved, the total system resource R tot Multiply by 10% to get the resource reservation R res ;

[0110] (R tot -R res -R au ) represents the total amount of allocable resources;

[0111] Specifically, user i's initial resource allocation amount A i Indicates the resources allocated to users by the system in unit time, such as 10GB of network bandwidth resources per hour. By dividing the initial demand score of user i by the total initial demand score, the proportion of user i's resource demand in the overall demand can be evaluated. This proportion can clearly reflect the degree of resource demand of each user relative to other users, so that the system can ensure that resources are allocated according to demand. Users with a large demand ratio will obtain relatively more resources, and users with a small demand ratio will obtain fewer resources, thereby ensuring that system resource allocation matches the actual needs of users, improving the accuracy and flexibility of resource allocation, and improving user experience. The initial resource allocation amount A for user i is iThe more accurate it is, the fewer users will need to be dynamically adjusted later, thereby reducing system resource loss and improving system performance.

[0112] Furthermore, the dynamic adjustment module obtains the actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and combines the actual resource consumption, resource fluctuation and initial resource allocation to obtain the resource adjustment amount.

[0113] The dynamic adjustment module monitors the usage of the initial resource allocation amount of the user per unit time, obtains the actual resource consumption and the resource fluctuation amount, subtracts the actual resource consumption from the initial resource allocation amount and multiplies the result by the resource fluctuation amount to obtain a resource usage difference value, calculates the resource usage difference value used by all users to obtain a total resource usage difference value, compares the resource usage difference value with the total resource usage difference value to obtain a difference ratio feature, multiplies the difference ratio feature by the resource reservation amount to obtain a resource adjustment amount, adjusts the user's initial resource allocation amount according to the resource adjustment amount, sets a difference threshold for the resource usage difference value, and adjusts the user when the resource usage difference value is greater than the difference threshold. The adjustment process of the dynamic adjustment module is as follows:

[0114]

[0115]

[0116] where R usage,i The resource usage difference value of user i is used to measure the difference between the user's actual resource consumption and the initial resource allocation, and takes into account the volatility factor. The larger the resource usage difference value of user i, the greater the gap between the user's actual resource consumption and the initial allocation, and vice versa.

[0117] When user i’s resource usage difference value R usage,i When it is a positive number, the user's resources need to be increased;

[0118] When user i’s resource usage difference value R usage,i When it is a negative number, the user's resources need to be reduced, and the reduced resources are added to the total system resources;

[0119] R acti Indicates the actual resource consumption of user i, indicating the actual resource loss of the user per unit time;

[0120] A i represents the initial resource allocation of user i;

[0121] Indicates resource fluctuation, which indicates the fluctuation range of the user's resource usage at different times. The larger the resource fluctuation, the more unstable the user's resource usage, and the smaller the resource fluctuation, the more stable the user's resource usage.

[0122] R acti,avg represents the average resource consumption of user i;

[0123] ΔR i represents the resource allocation of user i;

[0124] R res Indicates the resource reservation amount, which is determined by system settings;

[0125] According to the experiment, the resource usage difference value R of user i is set usage,i The difference threshold is Y1, and the difference value R is used for user i’s resources. usage,i Take the absolute value, that is, when |R usage,i When |> difference threshold Y1, it means that the gap between the actual resources of the current user and the initially allocated resources is too large, which may cause resource bottleneck or resource waste. At this time, it needs to be adjusted according to the resource allocation amount ΔR of user i. i , through the processing resource allocation module to adjust the user's resource quantity in real time, if |R usage,i |≤difference threshold Y1, no adjustment is made.

[0126] Specifically, by calculating the difference between the actual resource usage of the user and the initial allocated resources, and analyzing the fluctuation of resource usage, and by comparing the resource usage difference value of each user with the sum of the resource usage difference values ​​of all users, the resource usage difference value of each user can be standardized, and the resource allocation of the user can be dynamically adjusted according to the actual resource usage difference of the user, which can improve the resource utilization rate, avoid resource waste, ensure that the system resources are reasonably configured, and enable the system to adapt to the changes in resource demand in the cloud environment. res Dynamic adjustments are made to ensure that the operation of key system components is not affected, thereby improving the overall quality and reliability of cloud services.

[0127] Furthermore, the environment verification module includes a system management interface, BIOS, and hardware sensors, which are used to verify the hardware environment and software configuration of the processor.

[0128] Specifically, in terms of hardware environment, basic hardware information of the processor is collected through the system management interface, BIOS and hardware sensors. Basic hardware information includes processor model, number of cores, main frequency, cache size, hardware serial number, etc. The integrity of the processor hardware is verified using the hardware trust root, such as TPM. TPM can store the encrypted hash value of the hardware. The verification engine generates a hash value for the currently collected hardware information and compares it with the hash value stored in TPM. If the comparison results are consistent, it means that the hardware has not been tampered with. If they are inconsistent, an alarm message is issued to remind staff to handle it.

[0129] In terms of software, the applications installed on the processor are verified, including the version, source and configuration of the application, whether the application is downloaded from a trusted software source and whether it has been digitally signed. At the same time, the application configuration file is checked to see if it complies with the security policy, and reminders are given according to the verification results. Through comprehensive verification of the processor hardware, software configuration and operating environment, a multi-level security protection system is built, which can effectively resist various security threats such as hardware tampering, software vulnerability attacks, malicious process intrusion, etc., thereby improving the system security level.

[0130] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cloud data processing system for processor environment verification, characterized in that: It includes system login module, data acquisition module, resource assessment module, demand forecasting module, processing resource allocation module, dynamic adjustment module and environment verification module; The data acquisition module is used to obtain system resource data and user data, and the user data includes user membership information, historical resource usage information, historical usage time information, operation quantity information and operation diversity information; The resource evaluation module includes a system evaluation unit and a user evaluation unit. The system evaluation unit is used to analyze the system in combination with the system resource data to obtain the amount of system resources used; The user evaluation unit first combines the operation quantity information and the operation diversity information to obtain the user operation complexity characteristics, and then combines the user operation complexity characteristics with the historical resource usage information and the historical usage time information to obtain the historical behavior complexity score; The demand prediction module combines historical usage time information and user membership information to obtain a user priority score, and combines the user priority score with the historical behavior complexity score to obtain the user's initial demand characteristics; The processing resource allocation module allocates processing resources to the user by combining the system resource data, the amount of system resources used and the user's initial demand characteristics to obtain an initial resource allocation amount; The dynamic adjustment module obtains actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and obtains resource adjustment by combining and analyzing the actual resource consumption, resource fluctuation and initial resource allocation.

2. The cloud data processing system for processor environment verification according to claim 1, wherein: The system evaluation unit obtains CPU usage information, memory usage information, disk occupancy information and bandwidth usage information from the resource data, and weightedly adds the CPU usage information, memory usage information, disk occupancy information and bandwidth usage information to obtain the amount of system resources used. The higher the amount of system resources used, the fewer resources the system currently has available, and vice versa.

3. The cloud data processing system for processor environment verification according to claim 2, wherein: The evaluation process of the user evaluation unit is as follows: The operation quantity information includes the number of operation steps and the maximum number of operation steps, and the number of operation steps is compared with the maximum number of operation steps to obtain an operation quantity score; The operation diversity information includes the number of operation types and the total number of operation types. The number of operation types is compared with the total number of operation types to obtain an operation type score. The operation number score and the operation type score are weightedly added to obtain a user operation complexity feature. The historical resource usage information includes the historical average resource usage and the historical maximum resource usage, and the historical average resource usage and the historical maximum resource usage are compared to obtain a resource usage score; The historical usage time information includes the historical average duration and the historical maximum duration, and the historical average duration and the historical maximum duration are compared to obtain a usage time score; The historical behavior complexity score is obtained by weighted addition of the resource usage score, the time score, and the user operation complexity feature.

4. The cloud data processing system for processor environment verification according to claim 3, wherein: The forecasting process of the demand forecasting module is as follows: The demand forecasting module sets unit time information, extracts the total usage time per unit time from the historical usage time information in combination with the unit time information, and compares the total usage time per unit time with the unit time information to obtain a user activity score; The user membership information includes the user membership level and the maximum membership level, and the user membership level and the maximum membership level are compared to obtain a user level score; The user priority score is obtained by weighted addition of the user activity score and the user level score; The demand prediction module combines the user priority score and the historical behavior complexity score for analysis to obtain the user's initial demand characteristics.

5. The cloud data processing system for processor environment verification according to claim 4, wherein: The processing resource allocation module allocates processing resources to users based on system resource data and user initial demand characteristics; The processing resource allocation module extracts the total amount of system resources from the system resource data, sets the resource reservation amount, and subtracts the resource reservation amount and the system used resources from the total amount of system resources to obtain the allocatable resource amount, then combines the user initial demand characteristics of all users to obtain the total initial demand characteristics, compares the user initial demand characteristics with the total initial demand characteristics, and then multiplies them by the allocatable resource amount to obtain the initial resource allocation amount.

6. The cloud data processing system for processor environment verification according to claim 5, wherein: The dynamic adjustment module obtains the actual resource consumption and resource fluctuation by monitoring the user's initial resource allocation, and obtains the resource adjustment amount by combining and analyzing the actual resource consumption, resource fluctuation and initial resource allocation; The dynamic adjustment module monitors the usage of the initial resource allocation amount per unit time of the user, obtains the actual resource consumption and the resource fluctuation, subtracts the actual resource consumption from the initial resource allocation amount and multiplies the resultant amount by the resource fluctuation to obtain a resource usage difference value, calculates the resource usage difference value used by all users to obtain a total resource usage difference value, compares the resource usage difference value with the total resource usage difference value to obtain a difference ratio feature, multiplies the difference ratio feature by the resource reservation amount to obtain a resource adjustment amount, adjusts the user's initial resource allocation amount according to the resource adjustment amount, sets a difference threshold for the resource usage difference value, and adjusts the user when the resource usage difference value is greater than the difference threshold.

7. The cloud data processing system for processor environment verification according to claim 1, wherein: The environment verification module includes a system management interface, BIOS and hardware sensors.

8. The cloud data processing system for processor environment verification according to claim 1, wherein: The system login module includes a user login unit and a user verification unit. The user login unit is used to build an account registration and login interface, and the user verification unit is used to verify the user identity.

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