Coprocessing system for encrypting and quickly decrypting data of solid state disk

Abstract

The invention relates to the technical field of data encryption and decryption, in particular to a solid state disk data encryption and rapid decryption co-processing system which comprises a key management module, a task scheduling distribution module, a cache optimization module, an identity verification module and a controller monitoring module. According to the method, the unique random seed is generated by combining the user identity characteristic parameters and the SSD controller state, the key attribute is dynamically adjusted by periodically disturbing the data, the key complexity and safety are improved, cross matching is carried out according to the concurrent task priority and the data safety level in the task scheduling link, and the complexity and safety of the key are improved. The method comprises the following steps: distributing special channels and buffer resources, ensuring that a key task has a higher execution priority, performing cache optimization, dynamically dividing a multi-level cache region, realizing priority scheduling and processing of a high-frequency data block according to the access frequency of the data block, fusing authentication parameters and equipment and environment information in an identity authentication link, realizing multi-identity verification, and realizing high-efficiency and high-efficiency scheduling of the high-frequency data block according to the access frequency of the high-frequency data block. And the data decryption authority is strictly controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption and decryption, and in particular to a collaborative processing system for solid-state hard disk data encryption and rapid decryption. Background Art

[0002] Data encryption and decryption technology is mainly a method of protecting information security during storage, transmission and processing. It converts plaintext data into ciphertext through various encryption algorithms to prevent unauthorized access and tampering, and restores the original data through the corresponding decryption algorithm. This field covers a variety of technical means such as symmetric encryption, asymmetric encryption, hash algorithms, digital signatures, etc., and is widely used in computer systems, network communications, data storage, mobile terminals and other scenarios.

[0003] Among them, the collaborative processing system for solid-state drive data encryption and fast decryption is a data security protection solution developed for new storage media such as solid-state drives (SSDs). Its main purpose is to encrypt data stored in solid-state drives in real time, and to decrypt data efficiently and quickly when needed, ensuring the security and efficiency of data during storage, reading and transmission. It can be widely used in enterprise data centers, personal privacy protection, government information security and other fields, which not only improves the security level of data, but also meets the actual needs of high-speed access.

[0004] Existing technologies lack dynamic changes and diversity in the key generation process, are easily speculated by external parties, and pose security risks. Task allocation is routine, and it is difficult to balance security levels and priorities, affecting the efficient processing of high-risk tasks. Cache management lacks hierarchical scheduling for high-frequency and sensitive data, resulting in response delays and difficulty in meeting the processing needs of critical data. Identity authentication is limited to single information verification and fails to combine multiple environmental information, increasing the risk of illegal access. The controller resource scheduling method is rigid and does not respond in a timely manner when dealing with high concurrency and load changes. The system is prone to performance bottlenecks, limiting the system's adaptability and security assurance to complex application scenarios. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a collaborative processing system for solid state hard disk data encryption and rapid decryption.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a collaborative processing system for solid-state hard drive data encryption and fast decryption, the system comprising: The key management module removes noise interference based on user identity characteristic parameters and SSD controller status, performs XOR and shift operations, creates a unique random seed, generates a key, and periodically inserts perturbation data to obtain a dynamic key generation result; The task scheduling and allocation module analyzes the task priority and data block security level based on the dynamic key generation result, selects key tasks and allocates dedicated channels and buffers, allocates common resources to other tasks, dynamically adjusts the encryption and decryption task paths, and obtains the optimized execution path configuration; The cache optimization module monitors access patterns and analyzes data block access frequencies based on the execution path optimization configuration, schedules high-frequency data blocks to the first-level cache and prioritizes computing resources, and schedules low-frequency data blocks to the second-level or third-level cache, thereby obtaining a cache scheduling optimization plan. The identity authentication module compares the user identity authentication parameters, device and environment information based on the cache scheduling optimization plan, identifies access rights, determines whether to continue to perform the encryption and decryption tasks, and obtains an identity authentication confirmation result.

[0007] The improvements of the present invention are that the dynamic key generation result includes seed parameters, diversified key sequences, and disturbance factors; the execution path optimization configuration includes task allocation structure, priority index, and security level identification; the cache scheduling optimization plan includes high-frequency data mapping, cache hierarchy structure, and resource allocation parameters; and the identity authentication confirmation result includes access authorization status, authentication credentials, and environment verification information.

[0008] The present invention is improved in that the key management module includes: The identity feature extraction submodule is based on user identity feature parameters and SSD controller status, combined with their respective feature sequences. By detecting abnormal changes and regular interference in the data stream, it calculates the correlation distribution between identity and device data, optimizes the noise processing process, and obtains the identity controller mapping group; The disturbance factor construction submodule optimizes the embedding order of disturbance data based on the identity controller mapping group, adjusts the XOR and shift operation structures, filters the interference fragments in the signal stream, and determines the abnormal distribution area in the data stream to obtain the disturbance coupling path set; The key sequence generation submodule analyzes the distribution state of each disturbance sequence according to the disturbance coupling path set, selects data segments with related features, adjusts the data sequence in each key generation path, optimizes the interaction rules between multi-channel data, and obtains dynamic key generation results.

[0009] The present invention is improved in that the task scheduling and allocation module includes: The priority sorting submodule optimizes the arrangement of each task based on the dynamic key generation result, combined with the concurrent identification, time sequence and resource occupancy of the task, determines the criticality of each task in the resource distribution, and obtains the task criticality priority sequence; The security level screening submodule analyzes the security sensitivity factors, historical access anomalies and behavioral characteristics of the data blocks associated with each task based on the task criticality priority sequence, identifies tasks that are critical in both security and priority, and obtains a high security criticality task group; Based on the high-security critical task group, the execution path adjustment submodule analyzes the channel call frequency, cache allocation frequency and concurrent interference of each task, obtains the channel resource conflict level parameters, optimizes the allocation path of each task in the channel and buffer, and obtains the execution path optimization configuration.

[0010] The present invention is improved in that the cache optimization module includes: The access frequency identification submodule analyzes the continuous access records of the data blocks based on the execution path optimization configuration, calculates the proportion of the number of accesses of each data block in the statistical period, identifies the high-frequency access segments and the common segments, and obtains the access frequency labels; The cache hierarchical scheduling submodule calls the access frequency label, determines the access label of each type of data block, optimizes the distribution of each data block in the cache hierarchy, assigns high-frequency access labels to the first-level cache area, and hierarchically assigns the remaining data blocks to the second-level or third-level cache area to obtain a cache mapping distribution structure; The resource priority configuration submodule compares the data block allocation tag with the task priority, security level and cache occupancy according to the cache mapping distribution structure, obtains the resource configuration sorting index, optimizes the computing resources of the first-level cache area data block, and obtains the cache scheduling optimization plan.

[0011] The present invention is improved in that the identity verification module includes: The identity parameter analysis submodule analyzes the user identity authentication parameters based on the cache scheduling optimization plan, compares the password information structure, screens the authentication elements with consistent fields, determines the integrity and correctness of each authentication element, and obtains the identity matching structure characteristics; The device environment comparison submodule compares the identity matching structure characteristics with the device identification information, analyzes the operating system, network connection and geographic location parameters, selects the device and environmental factors that are consistent with the cache scheduling record, determines the information matching situation, and obtains the device environment consistency characteristics; The permission confirmation judgment submodule analyzes the cache task status and access credential accuracy according to the device environment consistency characteristics, determines whether to continue to execute the encryption and decryption task, and obtains the identity authentication confirmation result.

[0012] The present invention is improved in that the system further comprises: The controller monitoring module analyzes the SSD controller I / O load and working status in real time based on the identity verification result, monitors the data block access frequency, dynamically adjusts the controller resource configuration, and obtains the controller status adjustment result; The controller state adjustment result includes I / O load parameters, resource allocation items, and working mode configuration.

[0013] The present invention is improved in that the controller monitoring module includes: The status acquisition submodule analyzes the current I / O request frequency, operating voltage, current, storage channel occupancy, and task queue length of the SSD controller based on the authentication confirmation result, compares the status parameters with the normal operating standards of the system, determines whether the controller is in a sustainable working state, and obtains the operating state range; The access pattern analysis submodule screens data blocks associated with encryption and decryption tasks based on the operating state interval, analyzes the total number of accesses to the data blocks, the channel switching frequency, and the access distribution within time, determines the criticality of changes in the access density of the data blocks, and obtains an access density distribution group; The resource configuration adjustment submodule adjusts the controller's cache allocation and task scheduling sequence according to the access density distribution group, analyzes the current resource consumption status, optimizes processor thread allocation and data channel utilization, and obtains a controller state adjustment result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, a unique random seed is generated by combining user identity feature parameters with the SSD controller status, and the key attributes are dynamically adjusted through periodic disturbance data, so that the key complexity and security are improved. In the task scheduling link, cross-matching is performed according to the concurrent task priority and data security level, and dedicated channels and buffer resources are allocated to ensure that critical tasks have a higher execution priority. In terms of cache optimization, multi-level cache areas are dynamically divided, and high-frequency data blocks are prioritized and processed according to the data block access frequency. The identity authentication link integrates authentication parameters, equipment and environmental information to achieve multiple identity verification and strictly control data decryption permissions. Controller resource allocation relies on real-time I / O load and dynamic access adjustment to ensure efficient continuity of encryption and decryption tasks in high-concurrency scenarios, thereby improving the system's protection capabilities for sensitive data, data access response speed, and the intelligence level of task resource configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flowchart of the key management module in the present invention; Figure 3 This is a flowchart of the task scheduling and allocation module in the present invention; Figure 4 This is a flow chart of the cache optimization module in the present invention; Figure 5 This is a flow chart of the identity authentication module in the present invention; Figure 6 This is a flow chart of the controller monitoring module in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] Example See also Figure 1 The present invention provides a technical solution: a collaborative processing system for solid-state hard drive data encryption and fast decryption includes: The key management module removes noise interference from the data based on user identity characteristic parameters and SSD controller status. It combines the user identity and controller status, performs XOR and shift operations, creates a unique random seed, and generates a key. At the same time, it periodically inserts perturbation data to increase key diversity and obtain dynamic key generation results. The task scheduling module analyzes the priorities of concurrent tasks and the security levels of data blocks based on dynamic key generation results, sorts task priorities, and then cross-checks them with the security levels. It selects tasks with both critical priorities and security levels, assigns them to dedicated channels and buffers, and allocates other tasks to ordinary channels and caches based on their priorities. It dynamically adjusts the execution paths of encryption and decryption tasks to achieve optimized execution path configuration. The cache optimization module optimizes the configuration based on the execution path, monitors access patterns, analyzes the access frequency of each data block, filters out frequently accessed data blocks, schedules them to the first-level cache, and allocates computing resources to process them first. Low-frequency data blocks are scheduled to the second or third-level cache, and encryption and decryption tasks are performed in sequence to obtain a cache scheduling optimization plan. The authentication module analyzes user authentication parameters based on the cache scheduling optimization plan, compares them with device and environment information, identifies whether the user identity meets the access requirements, ensures that only authorized users can perform decryption operations, determines whether to continue the encryption and decryption tasks, and obtains the authentication confirmation result; Based on the authentication confirmation results, the controller monitoring module analyzes the I / O load and working status of the SSD controller in real time, monitors the access mode and frequency of associated data blocks, determines whether the current state of the SSD controller is suitable for performing encryption and decryption tasks, dynamically adjusts the controller resource configuration, and obtains the controller state adjustment result.

[0019] The dynamic key generation results include seed parameters, diversified key sequences, and disturbance factors; the execution path optimization configuration includes task allocation structure, priority index, and security level identification; the cache scheduling optimization plan includes high-frequency data mapping, cache hierarchy structure, and resource allocation parameters; the identity authentication confirmation results include access authorization status, authentication credentials, and environmental verification information; and the controller state adjustment results include I / O load parameters, resource allocation items, and working mode configuration.

[0020] In module 1, user identity feature parameters refer to biometric features or device identifiers used to uniquely identify a user, such as fingerprints, facial recognition, passwords, etc., to help confirm the user's identity; SSD controller status refers to the current working status information of the SSD controller, including but not limited to temperature, voltage, I / O load, memory health status, etc. The status affects the execution efficiency and security of encryption and decryption tasks; XOR and shift operations XOR (XOR) and shift are commonly used cryptographic operations. During the key generation process, XOR operations can mix binary bits of different data, while shift operations move bits to new positions, which can enhance the complexity and unpredictability of the key; perturbation data Perturbation data is additional data that introduces random changes to the original data to increase the security of the system. By periodically inserting perturbation data, regularity in the key generation process can be prevented, the diversity of the key can be increased, and it can be prevented from being cracked.

[0021] In Module 2, cross-comparison refers to the comparison of multiple factors (such as priority and security level) in task scheduling, and the sorting and allocation of tasks based on the comparison results. Cross-comparison ensures that tasks are allocated not only based on a single standard, but also based on multi-dimensional considerations of task processing requirements; dedicated channels and buffers Dedicated channels refer to independent data transmission channels allocated for high-priority or high-security level tasks to ensure that task processing does not conflict with other tasks, and buffers are used to store data in task processing to ensure efficient execution of tasks; ordinary channels and caches Ordinary channels are channels allocated to lower-priority tasks, usually sharing resources, while caches are used to store data to ensure that the system can provide sufficient response speed even if data is frequently accessed.

[0022] In module 3, the first-level cache area (L1 cache) refers to a very fast small cache area inside the processor. High-frequency access data is scheduled to the first-level cache area, which can be quickly obtained and processed during the encryption and decryption process to reduce latency; the second or third-level cache area refers to the second-level cache (L2 cache) and the third-level cache (L3 cache), which usually refers to a larger but slightly slower cache with a larger storage capacity than the first-level cache. It is usually located outside the processor and is suitable for processing lower-frequency data access requests.

[0023] In Module 4, device and environmental information: Device information refers to the unique identifier of the user's device, such as device ID, model, operating system, etc.; environmental information includes the user's geographic location, login time, network environment, etc. This information can be used to determine whether the user is an authorized user to increase security.

[0024] In Module 5, the associated data block refers to the storage data block related to the encryption and decryption task, which is the part of the encrypted file or the data part in the decryption process. The data block is the object of controller monitoring and optimization; adjusting the controller resource configuration means that the SSD controller dynamically adjusts its resource usage, such as memory, processing power, I / O channels, etc., according to factors such as load conditions and performance requirements to ensure that the encryption and decryption tasks are carried out smoothly and efficiently.

[0025] See also Figure 2 , the key management module includes: The identity feature extraction submodule is based on user identity feature parameters and SSD controller status, combined with their respective feature sequences. By detecting abnormal changes and regular interference in the data stream, it calculates the correlation distribution between identity and device data, optimizes the noise processing process, and obtains the identity controller mapping group; A feature sequence is extracted by combining user identity parameter parameters (such as device ID and password information) with SSD controller status information (including temperature, voltage, and I / O load). After user identity features are collected, they are converted into digital features that can be used for calculations. SSD controller status data, such as temperature and voltage, is collected through the controller's real-time monitoring function. This data changes continuously during operation. Potential abnormal changes in the data stream are detected and threshold ranges are set to compare whether there are abnormal fluctuations. For example, when the temperature exceeds the normal fluctuation range of 5%, or the I / O load exceeds the standard deviation of 10%, it is determined to be an abnormal fluctuation. The regularity of the changes is then analyzed to identify interference patterns. For example, high temperature can affect the stability of the data stream, resulting in reduced encryption and decryption task efficiency. By calculating the correlation distribution between identity and device data, the feature sequences are matched to form a mapping group for the identity controller. Based on the mapping group, the noise processing process is optimized to filter out feature data that is highly correlated with user identity and device status, and remove low-correlation and noisy data, forming high-quality feature data for encryption and decryption tasks.

[0026] The disturbance factor construction submodule optimizes the embedding order of disturbance data based on the identity controller mapping group, adjusts the XOR and shift operation structures, filters the interference fragments in the signal stream, and determines the abnormal distribution area in the data stream to obtain the disturbance coupling path set; First, interference fragments are screened out from the data stream. The fragments are usually composed of high-frequency fluctuations (such as rapid changes in voltage and I / O load) and low-frequency fluctuations (such as gradual temperature increases). Through a series of signal processing techniques, the interference fragments are analyzed and their impact on the stability of the data stream is evaluated. For each interference fragment, it is determined whether it is an abnormal distribution area. For example, if the temperature in a certain data stream is found to continue to rise and exceed the normal range, this area is identified as an abnormal area. Subsequently, the interference data is embedded into the target data stream, and the structure of the XOR and shift operations is adjusted to optimize the introduction method of the disturbance factor. Through this process, it is ensured that the introduction of disturbance data will not have unnecessary impact on the normal encryption and decryption process, while increasing the security of the encryption and decryption tasks. Finally, a set of disturbance coupling paths is obtained, each path representing a disturbance pattern. The path will help optimize the security of the encryption and decryption operations and the stability of the data stream.

[0027] The key sequence generation submodule analyzes the distribution of each perturbation sequence based on the perturbation coupling path set, selects data segments with related features, adjusts the data order in each key generation path, optimizes the interaction rules between multi-channel data, and obtains dynamic key generation results; The distribution state of each perturbation sequence is analyzed, and based on the perturbation path set analysis, characteristic data fragments related to high-security tasks are screened out. The characteristic sequences of the fragments are highly correlated with the controller status and user identity, and therefore will become the key data for generating keys. On this basis, the data order in the key generation path is adjusted to improve the randomness and security of the key. By changing the transmission order of the data, the repetition or regularity in the data stream is effectively avoided, and the key security in the encryption and decryption process is enhanced. In addition, the interaction rules between multi-channel data are optimized to ensure that the key generation task can be carried out smoothly without being affected by resource conflicts. For example, under high I / O load conditions, dedicated channels are given priority for encryption and decryption operations to ensure the timely execution of high-priority tasks. After the optimized data order and path adjustment, a dynamic key sequence with high randomness and security is generated, ensuring that the key used each time is not easy to be cracked or predicted, thereby ensuring the security of the encryption and decryption tasks.

[0028] See also Figure 3 , the task scheduling module includes: The priority sorting submodule optimizes the arrangement of each task based on the dynamic key generation result, combined with the concurrent identification, time sequence and resource occupancy of the task, determines the criticality of each task in the resource distribution, and obtains the task criticality priority sequence; Each task is sorted by combining its concurrency flag, time order, and resource usage. The concurrency flag refers to whether the task can be executed in parallel with other tasks, the time order refers to the execution time requirement of the task, and the resource usage refers to the amount of computing, storage, I / O, and other resources required by each task during execution. During analysis, the priority of each task is determined based on its resource requirements, and whether it can be processed concurrently with other tasks is determined. For tasks that require high resources or take a long time to process, idle resources are allocated first based on their resource usage, and the execution of low-priority tasks is postponed. Then, based on the task's resource requirements and available resources, the task arrangement is optimized to ensure that high-priority tasks are processed in a timely manner and avoid delays caused by resource conflicts. For example, suppose task A requires a large number of I / O operations and task B is compute-intensive. Task A is scheduled for execution first when there is no conflict, and task B is scheduled to execute after the I / O operations are completed. Finally, based on the criticality of the tasks, a task criticality priority sequence is formed, which is the order of importance of each task in the resource distribution.

[0029] The security level screening submodule analyzes the security sensitivity factors, historical access anomalies, and behavioral characteristics of the data blocks associated with each task based on the mission criticality priority sequence, identifies tasks that are both security-critical and priority-critical, and obtains a high-security criticality task group; During the task security level screening process, the data block security sensitivity factors, historical access anomalies, and behavioral characteristics associated with each task are further analyzed based on the mission criticality priority sequence. Data block security sensitivity factors refer to security risks associated with data blocks, such as sensitive data content or frequently accessed files. Historical access anomalies refer to whether a data block has been repeatedly accessed without authorization or subjected to abnormal operations. Behavioral characteristics refer to abnormal behaviors during task execution, such as those that do not conform to conventional resource consumption or data flow patterns. By analyzing tasks based on these factors, data blocks associated with high security risks are first screened out. Data blocks usually involve sensitive information or have experienced abnormal access behavior. Next, based on the priority and security requirements of the tasks, tasks that require both high priority processing and high security requirements are identified. Tasks usually contain confidential data or have special security requirements and must be handled with extreme caution during execution. Ultimately, a high-security criticality task group is obtained. Tasks will be given the highest priority and more stringent security measures in the subsequent scheduling process to ensure that they are fully protected during the encryption and decryption process.

[0030] The execution path adjustment submodule analyzes the channel call frequency, cache allocation frequency, and concurrent interference of each task based on the high-security critical task group, using the formula: ; Get channel resource conflict level parameters , used to measure the degree of resource conflict generated by each task in the resource allocation process, optimize the allocation path of each task in the channel and buffer, and obtain the execution path optimization configuration, where, Indicates the total number of tasks included in the statistics, Indicates the The channel calling frequency of a task, that is, the frequency of a task scheduling the data channel during the encryption and decryption process, Indicates the average call frequency of all task channels, which is used to compare the overall level of each task call channel. Indicates the The cache allocation frequency of a task, that is, the frequency with which the task is allocated cache resources, Indicates the The concurrent interference detected by a task during the scheduling cycle is quantified by the number of concurrent accesses or conflicts monitored. Indicates the The number of channel categories currently involved in a task, which is used to reflect the number of different channels that the task needs to schedule simultaneously.

[0031] The channel resource conflict level parameter refers to the comprehensive level parameter used to measure the channel and cache resource competition and conflict caused by factors such as resource allocation, scheduling conflicts, and concurrent interference during the channel and buffer allocation process of concurrent tasks in the collaborative processing of SSD data encryption and decryption. There are three tasks involved in the execution path adjustment parameters as follows: Task 2: Channel call frequency , cache allocation frequency , concurrent interference situation , number of channel categories ; Task 3: Channel call frequency , cache allocation frequency , concurrent interference situation , number of channel categories ; Task 1: Channel call frequency , cache allocation frequency , concurrent interference situation , number of channel categories ; First, calculate the mean channel call frequency of all tasks : ; Then, using this mean and the relevant parameters of each task, the channel resource conflict level parameter of each task is calculated one by one ; For Task 2: ; First calculate each term: ; ; therefore, ; For Task 3: ; First calculate each term: ; ; therefore, ; For Task 1: ; First calculate each term: ; ; therefore, ; According to the calculation results, the resource conflict level parameters of the three tasks are obtained: Task 2: , Task 3: , Task 1: ; When optimizing the execution path, priority is given to tasks with the smallest resource conflict level parameters to ensure efficient use of channels and buffers. Therefore, Task 3 has the lowest conflict level and is allocated resources first. Through the above calculations, the allocation paths of tasks in channels and buffers can be dynamically adjusted based on each task's channel call frequency, cache allocation frequency, and concurrent interference, thereby achieving optimized execution path configuration.

[0032] See also Figure 4 , the cache optimization module includes: The access frequency identification submodule is based on the execution path optimization configuration, analyzes the continuous access records of data blocks, calculates the proportion of access times of each data block in the statistical period, identifies high-frequency access segments and common segments, and obtains access frequency labels; First, the continuous access records of the data blocks are analyzed. The records include all access operations of each data block within a certain time period. By calculating the access ratio of each data block in this statistical period, the access frequency of each data block is obtained. Specifically, if the monitoring period is 1 hour, for a specific data block, the number of times it is accessed within that hour is counted. Assuming that the data block is accessed 100 times and the total number of accesses to other data blocks in the same period is 1000 times, then the access ratio of this data block is 100 / 1000=0.1. Next, the access frequency is classified. According to the access ratio, the data blocks are divided into high-frequency access segments and common segments. For example, a segment with an access ratio greater than 0.05 is considered a high-frequency segment, and a segment with an access ratio less than 0.05 is a common segment. Finally, the access frequency of the data block is assigned to each data block as a label to obtain the access frequency label. The label will provide a key reference for subsequent cache tiered scheduling, helping the system identify which data blocks need to be cached first.

[0033] The cache hierarchical scheduling submodule calls the access frequency tag to determine the access tag of each type of data block, optimizes the distribution of each data block in the cache hierarchy, assigns high-frequency access tags to the first-level cache area, and hierarchically assigns the remaining data blocks to the second-level or third-level cache area to obtain the cache mapping distribution structure; The access frequency label is called, and the access frequency label of each data block is used to determine whether it belongs to the high-frequency access segment or the common segment. Specifically, the access label of each data block is classified. If the access frequency label is high, it is preferentially assigned to the first-level cache to ensure that the data can be accessed quickly. For data blocks in the common segment, they are assigned to the second or third-level cache. The cache is relatively large but slow, suitable for infrequently accessed data blocks. Through this classification, the data distribution in the cache hierarchy is optimized, ensuring that frequently accessed data blocks are always located in the cache with the fastest access speed, thereby improving the response efficiency of the entire system. For example, assuming that the access frequency label of data block A is 0.1, which meets the high-frequency access standard, it is assigned to the first-level cache. While the access frequency label of data block B is 0.02, which belongs to the common segment, it is assigned to the second-level cache. Through this optimization strategy, a cache mapping distribution structure is obtained, in which high-frequency data blocks are concentrated in the first-level cache, and other data blocks are distributed in the second or third-level cache, thus achieving optimal allocation of cache resources.

[0034] The resource priority configuration submodule compares the data block allocation tag with the task priority, security level and cache occupancy according to the cache mapping distribution structure, using the formula: ; Obtain resource configuration ranking indicators, optimize the computing resources of the first-level cache data blocks, and obtain the cache scheduling optimization plan, where: Indicates the The resource allocation ranking index of each data block, Indicates the The task priority of each data block, Indicates the The security level identifier of each data block, Indicates the The access frequency of a data block in a unit statistical period, Indicates the The current occupancy ratio of data blocks in the cache, Indicates the The real-time I / O usage of the SSD controller used by each data block, Indicates the maximum I / O load limit of the SSD controller.

[0035] The resource allocation ranking index refers to a quantitative reference index used to arrange the allocation order of each data block during the cache optimization process, which is calculated by comprehensively considering multiple key parameters such as task priority, security level, access frequency, cache occupancy, and controller I / O load. It reflects the degree of priority and the order in which each data block should be processed in the overall resource scheduling and computing resource allocation process. First, data blocks are sorted according to task priority (such as task urgency), security level (such as data sensitivity) and cache occupancy (such as current cache space utilization). Task priority and security level are quantified by preset rules. Data blocks with high task priority will receive higher resource allocation, while data blocks with high security level require more computing resources to ensure their security. Then, based on cache occupancy, the cache resources currently occupied by each data block are evaluated. When resources are relatively tight, more computing resources will be allocated to data blocks with less cache and higher priority. For example, the task priority of a data block ( ) is 5, the security level ( ) is 3, the access frequency ( ) is 0.8, the cache occupancy ratio ( ) is 0.5, the real-time I / O usage ( ) is 60%, the controller's maximum I / O load limit ( ) is 80%. Based on the parameters, we calculate the resource allocation ranking index and substitute the above values: ; Calculate the square root part: ; ; ; Substituting into the formula: ; Obtain resource allocation ranking indicators ,Based on this sorting index, the system will prioritize the scheduling of data blocks, ,preferentially allocate computing resources to data blocks with high task priority and less ,resources, and optimize the utilization of computing resources in the first-level cache, ,to ensure the optimal data access efficiency.

[0036] See also Figure 5 , the authentication module includes: The identity parameter analysis submodule analyzes user authentication parameters based on the cache scheduling optimization plan. By comparing the password information structure, it screens authentication elements with consistent fields, determines the integrity and correctness of each authentication element, and obtains the identity matching structure characteristics. First, the user authentication parameters are analyzed. These parameters typically include the user's username, password, fingerprint information, and facial recognition. During the specific execution process, the authentication fields are extracted from the recorded user input data. For example, assuming the password entered by the user is "password123", the password field is first extracted and compared with the password structure stored in the database. By comparing the password information structure, the authentication elements that are consistent with the stored password field are screened out. For example, if the stored password is the encrypted "password123", the information will be decrypted and the decrypted result will be compared with the password entered by the user. If the fields are consistent, the integrity and correctness of the authentication element will be further determined. The password length and format will be checked to see if they meet the preset requirements, such as whether they contain numbers, letters, special characters, etc., to ensure that the authentication element meets the security policy requirements. For example, assuming that the password should contain at least 8 characters and include numbers, whether this requirement is met will be checked during the verification process. By judging and screening each authentication element, the identity matching structure feature is obtained, indicating whether the user identity information is consistent with the system record, and confirming the integrity and validity of the authentication element.

[0037] The device environment comparison submodule compares the identity matching structural features with the device identification information, analyzes the operating system, network connection and geographic location parameters, selects the devices and environmental factors that are consistent with the cache scheduling records, determines the information matching situation, and obtains the device environment consistency characteristics; First, the identity matching structure feature is compared with the device identification information. Device identification information includes the device's unique identifier (such as device ID), operating system type, network connection status, and geographic location. By obtaining the device's IP address, operating system version, login time, and other information, combined with the device identification data, a comparison is performed. For example, if the user's device IP address is 192.168.1.10, the operating system is Windows 10, and the geographic location is Shanghai, this information is compared with the device environment data in the cache scheduling record to determine whether the device matches the user's authentication information. If the device information is consistent with the device in the identity authentication information and they log in under the same network connection environment, the system continues to determine whether there is an anomaly. If there is a mismatch, such as when the user logs in from a different geographic location (such as Beijing), this anomaly is identified and the relevant information is recorded. By analyzing the operating system, network connection, and geographic location, the system selects devices and environmental factors that are consistent with the cache scheduling record, and then determines the information match. Finally, the device environment consistency feature is obtained, ensuring that the user's device and environmental information are completely consistent with the records in the system, thereby providing support for further identity authentication and encryption and decryption operations.

[0038] The permission confirmation submodule analyzes the cache task status and access credential accuracy based on the device environment consistency characteristics, using the formula: ; Determine whether to continue to perform the encryption and decryption task and obtain the identity verification result, where: Indicates the access authorization status identifier. Indicates identity matching structural features, Indicates the device environment consistency characteristics, Indicates the accuracy of access credentials, Indicates the cache task cycle, Indicates the The discriminant of the access credential, Indicates the The cache comparison coefficient of the access credential, Indicates the Login delay sequence for each access credential, Indicates the number of access credentials.

[0039] The authentication confirmation result refers to the status determination result on whether the access operation is authorized after identity parameter analysis, device environment comparison, and permission confirmation. It reflects the authorization judgment made by the system for the encryption or decryption task initiated by the user after the comprehensive identity verification of the current user and device; Extract the task status from the cache task to determine whether the task is in a pending state. Then obtain the access credential information, check the timestamp, credential ID and integrity of the credential. By analyzing and comparing the accuracy of each credential and the matching degree of the device environment consistency characteristics, decide whether to continue the encryption and decryption task. If there is: identity matching structure characteristics , device environment consistency characteristics , access credentials accuracy , cache task cycle , number of access credentials , the discriminant items and cache comparison coefficients of the access credentials are: Item 1 , , login delay ; Item 2 , , login delay ; Item 3 , , login delay ; Substitute the new value into the formula to calculate: ; Calculate the absolute value part: ; ; Calculate the square root part: ; ; Final result: ; Scale the result to [0, 1] and normalize it: ; This result indicates that the access authorization status indicator It represents the system's authorization decision on the user to perform encryption and decryption tasks. Specifically, the value reflects the comprehensive evaluation results of the identity matching structure characteristics, device environment consistency characteristics, access credential accuracy and task status. According to the various parameters in the formula, the calculated result provides a basis for whether to continue to perform the encryption and decryption task by combining the identity characteristics, device information and the matching degree of the access credential. The value It is derived from an analysis of the user's identity authentication, device environment matching, and credential accuracy. If the value exceeds the preset security standard (for example, set to 0.5), it indicates that the identity and device verification have passed, and the credential is accurate enough, and the task can continue to be executed; if the value is lower than the security standard, it indicates that there is a security risk and the task is rejected.

[0040] See also Figure 6 , the controller monitoring module includes: Based on the authentication confirmation results, the status acquisition submodule analyzes the SSD controller's current I / O request frequency, operating voltage, current, storage channel occupancy, and task queue length. It compares these status parameters with the system's normal operating standards to determine whether the controller is in a sustainable operating state and obtains the operating status range. Collect the status parameters of the SSD controller, including I / O request frequency, operating voltage, current, storage channel occupancy, and task queue length. The parameters will monitor the working status of the SSD controller in real time. First, obtain the controller's I / O request frequency, that is, the number of input and output requests processed per second. Assuming that the controller processes 3000 requests in one minute, its I / O request frequency is 3000 / 60=50 requests / second. Then obtain the operating voltage and current values. The voltage varies between 3.3V and 5V, and the current usually fluctuates within the range of hundreds of milliamperes. The controller's storage channel occupancy is also collected to understand the usage frequency and resource consumption of each channel. For example, If the controller has four channels and the bandwidth of three channels is close to saturation under the current load, this information will be recorded for subsequent adjustments. Finally, the task queue length is monitored to indicate the number of tasks to be processed. If the task queue length is very long, it means that the system is currently heavily loaded. By comparing the collected status parameters with the normal operating standards of the system, it is determined whether the controller is in a sustainable working state. For example, if the current exceeds the maximum safe value or the I / O request frequency is much higher than the normal level, the controller state is marked as unstable. Otherwise, the controller is considered to be in normal working state. Finally, by comparing the parameters, the operating state range of the controller is obtained to determine whether it is suitable for continuing to perform encryption and decryption tasks.

[0041] The access pattern analysis submodule filters data blocks associated with encryption and decryption tasks based on the operating status interval, analyzes the total number of accesses to the data blocks, the channel switching frequency, and the access distribution within time, determines the criticality of changes in the access density of the data blocks, and obtains the access density distribution group; Based on the previously obtained operating status interval, filter out the data blocks associated with the encryption and decryption tasks. Data blocks are usually related to the target files or data of the encryption and decryption operations. Analyze the access pattern of the data blocks and count the total number of accesses to each data block within a certain period of time. Suppose that in the past 24 hours, data block A was accessed 1000 times, while data block B was only accessed 50 times. This means that data block A is accessed more frequently. Analyze the channel switching frequency of the data blocks, that is, the number of times data is transferred between different storage channels. If a data block frequently switches between multiple channels, this indicates that the access pattern of the data block is not correct. To ensure stability, we need to pay attention to the processing of such data blocks, record the access distribution of each data block, that is, the access density on the timeline, and analyze the regularity of its access. For example, the access density of data block A is higher on weekdays and lower on weekends, while data block B maintains a relatively uniform access density throughout the week. By analyzing the parameters, we can determine which data blocks have more critical access density changes, and then mark the data blocks as high-priority data blocks that need to be processed first. We will obtain access density distribution groups, which contain the access density changes and access patterns of different data blocks over time, and use them for subsequent resource allocation and task scheduling.

[0042] The resource configuration adjustment submodule adjusts the controller's cache allocation and task scheduling order according to the access density distribution group, analyzes the current resource consumption status, optimizes processor thread allocation and data channel utilization, and obtains the controller state adjustment result; According to the access frequency and density analysis results of the data blocks, the controller's cache allocation is adjusted. For example, if data block A is a data block with high frequency access and large density variation, it will be allocated to the first-level cache for fast access. Other data blocks with low frequency access will be allocated to the second or third-level cache. Then the current resource consumption status is analyzed and the utilization of processor threads and storage channels is evaluated. If it is found that the load of the processor thread is too high, some tasks will be migrated from the high-load thread to the low-load thread to ensure the load balance of each thread, thereby avoiding task delays caused by overloading of some threads. The utilization of the storage channel will also be dynamically adjusted to ensure that data blocks with high frequency access use channels with larger bandwidth first, thereby reducing data access delays. By optimizing the processor thread allocation and the utilization of the storage channel, the optimized controller state adjustment result can be obtained to ensure that encryption and decryption tasks can be executed efficiently and securely.

[0043] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A collaborative processing system for solid-state hard drive data encryption and fast decryption, characterized in that: The system comprises: The key management module removes noise interference based on user identity characteristic parameters and SSD controller status, performs XOR and shift operations, creates a unique random seed, generates a key, and periodically inserts perturbation data to obtain a dynamic key generation result; The task scheduling and allocation module analyzes the task priority and data block security level based on the dynamic key generation result, selects key tasks and allocates dedicated channels and buffers, allocates common resources to other tasks, dynamically adjusts the encryption and decryption task paths, and obtains the optimized execution path configuration; The cache optimization module monitors access patterns and analyzes data block access frequencies based on the execution path optimization configuration, schedules high-frequency data blocks to the first-level cache and prioritizes computing resources, and schedules low-frequency data blocks to the second-level or third-level cache, thereby obtaining a cache scheduling optimization plan. The identity authentication module compares the user identity authentication parameters, device and environment information based on the cache scheduling optimization plan, identifies access rights, determines whether to continue to perform the encryption and decryption tasks, and obtains an identity authentication confirmation result.

2. The collaborative processing system for solid-state hard drive data encryption and fast decryption according to claim 1, characterized in that: The dynamic key generation result includes seed parameters, diversified key sequence, and disturbance factor; the execution path optimization configuration includes task allocation structure, priority index, and security level identification; the cache scheduling optimization plan includes high-frequency data mapping, cache hierarchy structure, and resource allocation parameters; the identity authentication confirmation result includes access authorization status, authentication credentials, and environment verification information.

3. The collaborative processing system for solid-state hard drive data encryption and rapid decryption according to claim 1, characterized in that: The key management module includes: The identity feature extraction submodule is based on user identity feature parameters and SSD controller status, combined with their respective feature sequences. By detecting abnormal changes and regular interference in the data stream, it calculates the correlation distribution between identity and device data, optimizes the noise processing process, and obtains the identity controller mapping group; The disturbance factor construction submodule optimizes the embedding order of disturbance data based on the identity controller mapping group, adjusts the XOR and shift operation structures, filters the interference fragments in the signal stream, and determines the abnormal distribution area in the data stream to obtain the disturbance coupling path set; The key sequence generation submodule analyzes the distribution state of each disturbance sequence according to the disturbance coupling path set, selects data segments with related features, adjusts the data sequence in each key generation path, optimizes the interaction rules between multi-channel data, and obtains dynamic key generation results.

4. The collaborative processing system for solid-state hard drive data encryption and rapid decryption according to claim 1, characterized in that: The task scheduling and allocation module includes: The priority sorting submodule optimizes the arrangement of each task based on the dynamic key generation result, combined with the concurrent identification, time sequence and resource occupancy of the task, determines the criticality of each task in the resource distribution, and obtains the task criticality priority sequence; The security level screening submodule analyzes the security sensitivity factors, historical access anomalies and behavioral characteristics of the data blocks associated with each task based on the task criticality priority sequence, identifies tasks that are critical in both security and priority, and obtains a high security criticality task group; Based on the high-security critical task group, the execution path adjustment submodule analyzes the channel call frequency, cache allocation frequency and concurrent interference of each task, obtains the channel resource conflict level parameters, optimizes the allocation path of each task in the channel and buffer, and obtains the execution path optimization configuration.

5. The collaborative processing system for solid-state hard drive data encryption and rapid decryption according to claim 1, characterized in that: The cache optimization module includes: The access frequency identification submodule analyzes the continuous access records of the data blocks based on the execution path optimization configuration, calculates the proportion of the number of accesses of each data block in the statistical period, identifies the high-frequency access segments and the common segments, and obtains the access frequency labels; The cache hierarchical scheduling submodule calls the access frequency label, determines the access label of each type of data block, optimizes the distribution of each data block in the cache hierarchy, assigns high-frequency access labels to the first-level cache area, and hierarchically assigns the remaining data blocks to the second-level or third-level cache area to obtain a cache mapping distribution structure; The resource priority configuration submodule compares the data block allocation tag with the task priority, security level and cache occupancy according to the cache mapping distribution structure, obtains the resource configuration sorting index, optimizes the computing resources of the first-level cache area data block, and obtains the cache scheduling optimization plan.

6. The collaborative processing system for solid-state hard drive data encryption and rapid decryption according to claim 1, characterized in that: The identity authentication module includes: The identity parameter analysis submodule analyzes the user identity authentication parameters based on the cache scheduling optimization plan, compares the password information structure, screens the authentication elements with consistent fields, determines the integrity and correctness of each authentication element, and obtains the identity matching structure characteristics; The device environment comparison submodule compares the identity matching structure characteristics with the device identification information, analyzes the operating system, network connection and geographic location parameters, selects the device and environmental factors that are consistent with the cache scheduling record, determines the information matching situation, and obtains the device environment consistency characteristics; The permission confirmation judgment submodule analyzes the cache task status and access credential accuracy according to the device environment consistency characteristics, determines whether to continue to execute the encryption and decryption task, and obtains the identity authentication confirmation result.

7. The collaborative processing system for solid-state hard drive data encryption and rapid decryption according to claim 1, characterized in that: The system further comprises: The controller monitoring module analyzes the SSD controller I / O load and working status in real time based on the identity verification result, monitors the data block access frequency, dynamically adjusts the controller resource configuration, and obtains the controller status adjustment result; The controller state adjustment result includes I / O load parameters, resource allocation items, and working mode configuration.

8. The collaborative processing system for solid-state hard disk data encryption and rapid decryption according to claim 7, characterized in that: The controller monitoring module includes: The status acquisition submodule analyzes the current I / O request frequency, operating voltage, current, storage channel occupancy, and task queue length of the SSD controller based on the authentication confirmation result, compares the status parameters with the normal operating standards of the system, determines whether the controller is in a sustainable working state, and obtains the operating state range; The access pattern analysis submodule screens data blocks associated with encryption and decryption tasks based on the operating state interval, analyzes the total number of accesses to the data blocks, the channel switching frequency, and the access distribution within time, determines the criticality of changes in the access density of the data blocks, and obtains an access density distribution group; The resource configuration adjustment submodule adjusts the controller's cache allocation and task scheduling sequence according to the access density distribution group, analyzes the current resource consumption status, optimizes processor thread allocation and data channel utilization, and obtains a controller state adjustment result.

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