894 results about "Hash table" patented technology

The invention discloses an android container rendering optimization method based on cross-domain hard real-time Fen synchronization, which comprises the following steps: by taking a swan-mong system as a host and an android system as a container, creating a hash table, a synchronous thread and a shared memory corresponding to a GPU core when the host is started, acquiring VSync cycle registration callback, transmitting shared memory FD to the container, and finishing shared memory mapping and alignment by the container. Registering a GPU queue to complete callback; when the Android application is started, a container obtains a queue and a physical address of a rendering buffer area, creates a Fen and binds the Fen to the queue, after the queue is submitted, metadata is written into a shared memory to inform a host, after the host receives the metadata, nodes are created and stored in a hash table, and an overtime timer is registered; after the GPU completes the command queue, the container calls back an update state and a verification value to notify the host, and after the host is verified to be valid, the corresponding hash table is updated, the timer is reset, and asynchronous screen loading is triggered; and the host executes buffer area synthesis and submission of the display equipment to finish on-screen, so that the stability of the rendering frame rate is improved, and the reliability of cross-domain synchronization is ensured.

The invention discloses a mixed collision detection method in a virtual operation simulation system. The method comprises the following steps: 1) constructing a surgical instrument and soft tissue model; 2) dividing a virtual space into grids with the same size, indexing an object model, accelerating the construction and updating of a spatial information hash table by utilizing GPU multithreading, mapping the grids occupied by all primitive bounding boxes into the hash table, and determining objects in the same space according to the spatial hash table; 3) before object intersection solving, space reduction is carried out, and hash table data is optimized; 4) constructing a hierarchical bounding box for an object which may collide, and performing intersection detection; if it is detected that a certain node or child nodes of the node intersect, the next step is executed, and otherwise, detection is pushed out; and 5) accurately judging whether the basic triangular units contained in the nodes in the step 4) are intersected or not, and completing collision detection. According to the method, the problem of missed detection and wrong detection in the existing discrete collision detection algorithm is effectively solved, and the detection algorithm has excellent accuracy and calculation efficiency.

The invention provides a timestamp-based data change intelligent deduplication and message simplification method and system, and the method comprises the steps: capturing a change event of a database in real time, and generating a change record containing a timestamp; based on a preset timestamp comparison algorithm, performing timestamp analysis and comparison on the multiple change records of the same service entity; the change records are grouped according to the service keys, the latest change records are stored and dynamically updated through a hash table, the latest timestamp data under the same service key are reserved, and intelligent duplicate removal of redundant data is achieved; when the data volume exceeds a preset threshold value, a data fragmentation strategy is triggered, batch data is uniformly segmented into a plurality of subtasks, multi-thread parallel processing of fragmented data is achieved by dynamically allocating thread resources, and processing results are summarized; and carrying out merging processing on the deduplicated changed data, constructing a standardized message body, selecting immediate sending or delayed queue distribution according to a message priority, and controlling a network transmission load through a message batching mechanism.

The invention discloses an intelligent charging and discharging management method and system for a lithium battery, and the method comprises the steps: collecting multi-dimensional parameter data of the lithium battery, and generating a data set with a timestamp; and analyzing the data features based on the battery type classification model, and determining a battery type identifier. And utilizing the recurrent neural network to model the relevance between the capacity attenuation and the health state, and predicting the residual capacity and the health score. If the capacity or health score is lower than the threshold value, extracting the environment temperature and the load demand to generate a temperature-load feature vector; and matching the candidate strategy set from the pre-established index database through the hash table. And distributing weights according to health scores and load demands, sorting strategy efficiency and life influences by adopting a linear regression model, and screening an optimal strategy. And if the strategy calculation complexity exceeds the equipment capability, iteratively optimizing parameters by utilizing a genetic algorithm, simplifying a strategy instruction, generating a charging current, a voltage curve and a discharging rate control instruction, and executing the charging current, the voltage curve and the discharging rate control instruction in real time by a battery management system. The method prolongs the service life of the battery and improves the charge-discharge efficiency.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention belongs to the technical field of water surface target identification and tracking, and particularly relates to a tracking system and a tracking method for a water surface target under an incomplete information condition, which can be used for solving the problems of incomplete target information and tracking loss caused by wave shielding of an unmanned ship in a marine environment. The multi-modal feature extraction and fusion module is used for improving a convolutional neural network to extract visual features, constructing motion feature vectors in combination with multi-sensor motion information, and performing weighted fusion to obtain multi-modal features; the dynamic target feature library management module stores feature vectors and timestamps through a hash table-linked list structure, and maintains aging by means of a sliding window; when the target is reproduced, the re-identification algorithm module based on space-time joint matching integrates the feature similarity, the position distance and the time interval to determine the ID; the tracking parameter correction module uses Kalman filtering and combines a motion model and an observation value to correct parameters. The method improves the accuracy and stability of target tracking, and has application prospects in the fields of marine rescue, marine monitoring and the like.

Data structures and methods are described to provide a unique extendable group identifier for efficient aggregation query processing using multiple grouping keys. A method comprises retrieving a database query comprising an aggregate function of a selected column from a database table, grouped by a plurality of columns. The method further comprises maintaining a plurality of hash tables that map column values to grouping keys and identify a maximum grouping key. The method further comprises updating a plurality of bitmasks that define per-column grouping key bit positions, capable of storing the maximum grouping key, within a combined index value. The method further comprises allocating a result array sized according to the plurality of bitmasks. The method further comprises using the plurality of bitmasks and the plurality of hash tables to determine the combined index value to apply the aggregate function on the selected column for each row in the database table.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention discloses a semantic-based text content index automatic identification method, which relates to the technical field of information retrieval, and comprises the following steps: initializing sparse projection and LSH signature, performing iterative optimization by using a Lagrange duality form and gradient update, adjusting hash digits, obtaining a fragment index through a k-d tree, and constructing an inverted index. According to the method, compression is performed through Delta coding, an index map is constructed based on Jaccard similarity, compression is performed through WebGraph, CSNMF is used in combination with Z-Laplacian regularization, a low-rank basis matrix and a low-rank coding matrix are generated, a compressed inverted index is reconstructed after iterative optimization, and reconstructed inverted index entries are generated. According to the method, through multi-resolution hash table initialization, joint feature optimization, local adaptive quantization and low-rank index reconstruction, the semantic expression ability and the compression effect of an index structure are improved, the index precision and efficiency are improved, and intelligent identification of index content is achieved.

The invention discloses a data deduplication method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: under the condition that data to be subjected to duplicate removal is obtained, inserting the data to be subjected to duplicate removal into a first hash table for performing hash duplicate removal until the data in the first hash table reaches a target number of rows, and outputting the data in the first target number of rows in the first hash table; under the condition that the first hash table is not full, continuing to insert the data to be subjected to duplicate removal into the first hash table for performing hash duplicate removal until the first hash table is full; under the condition that the first hash table is full, repeated detection is carried out on the data to be subjected to duplicate removal and existing data in the first hash table, and the data to be subjected to duplicate removal, which are judged to be non-repeated, are subjected to disk brushing; and under the condition that the processing of the data to be subjected to duplicate removal is completed, performing duplicate removal processing on all the data in the disk, outputting the data, and outputting the data except the data in the previous target number of rows in the first hash table. According to the scheme, the previous target number of rows of data obtained through duplicate removal can be returned in advance, and the user experience can be improved.

The invention belongs to the technical field of information security, and particularly relates to a super-threshold data set intersection method and system for security information processing. The method comprises the following steps: S1, carrying out casual key value pair storage OKVS encoding and decoding between each participant and other participants; s2, each participant constructs a hash table according to a decoding result and elements of the participant; and S3, sending the constructed hash table to a specified party, summarizing information in the hash tables of all participants by the specified party, and finally obtaining a super-threshold intersection result by using a secret sharing scheme. The method has the characteristics that the hardware and operation and maintenance cost can be reduced, the data security is enhanced, and the calculation efficiency is improved, so that the industrial control requirement of short-time response is met.

The invention provides a cache cleaning method and device based on large model reasoning. The method comprises the following steps: monitoring the cache capacity of a hash table cache pool; when the current cache capacity of the hash table cache pool is greater than or equal to a preset capacity threshold value, obtaining access information corresponding to one or more cache entries, and screening a first to-be-cleaned cache entry through a preset cache cleaning algorithm according to the access information; and / or obtaining importance information corresponding to one or more cache entries, and screening a second to-be-cleaned cache entry meeting an importance cleaning rule according to the importance information; and according to the first to-be-cleaned cache entry and / or the second to-be-cleaned cache entry, cleaning the cache in the hash table cache pool, and updating the cache capacity of the hash table cache pool. According to the method and the device, the cleaned cache items are identified and determined according to the access information and / or the context importance information of the cache items, so that the cache redundancy and the accumulation of invalid data are reduced, and the cache management efficiency is improved.

The invention belongs to the technical field of laser scanning, and discloses a multi-station foundation laser scanning point cloud registration method and system suitable for a forest environment. Firstly, an original three-dimensional point cloud is converted into a two-dimensional distance image, a trunk is extracted based on continuity constraint, and the position of a trunk root point is searched; secondly, constructing a triangular descriptor, describing the spatial relationship among the trunks in scanning, and storing the spatial relationship in a hash table; and searching the corresponding triangular pairs by using the search operation of the hash table, calculating the similarity between scans, and extracting three-dimensional transformation parameters. And taking a to-be-solved scanning station pose as a node, taking relative transformation as a factor constraint, and obtaining a scanning station pose parameter with optimal overall consistency through factor graph optimization. And finally, by taking the conversion parameter after the factor graph optimization as an initial value, optimizing and adjusting the conversion parameter by using an ICP method, deriving the pose of each scanning station in the unified coordinate system through a depth-first strategy, converting the local point cloud into the unified coordinate system, and realizing global point cloud registration.

The invention provides a multi-dimensional Hash acceleration-based grid ray intersection method, which comprises the following steps of: acquiring original model data, and carrying out standardized preprocessing on the original model data to obtain standardized scene data; self-adaptive space grid division is carried out based on the preprocessed standardized scene data, space coordinates of three-dimensional grid units are mapped to a hash table to construct a one-dimensional index of a space grid, and triangular patches of the scene data are associated with the space grid; performing direction parameter decomposition on rays to be intersected, discretizing direction vectors of the rays, and constructing a ray direction composite hash key; and executing mixed intersection calculation in a mode of combining multi-level cache query construction, direction pruning and invalid intersection filtering and space hash traversal intersection, and determining intersection point information of the ray and the geometry. According to the method, the space grids and the ray direction data are converted into the hash values to be stored, the occupied memory is small, and the ray intersection efficiency is improved.

Examples described herein relate to key-value lookups. A first index is calculated based on a first hash function and a received key. A first hash table including entries associated with a first hash calculation on a key is accessed. A collision hint table is accessed to identify any other hash table to search. If the collision hint table indicates any other table to search, the first hash table and the any other hash table are searched to identify an entry associated with an index that matches the first index.

The invention relates to the technical field of data recognition, and discloses an abnormal data recognition method, device and equipment and a storage medium, and the method comprises the steps: extracting data distribution characteristics in each time window in a high-dimensional time series data stream, and carrying out the recognition of abnormal data based on the data distribution characteristics and an adjusted random projection matrix of the high-dimensional time series data stream; the method comprises the steps of generating dimension-reduced low-dimensional data, configuring local sensitive hash resolution parameters according to local volatility of the low-dimensional data, constructing a multi-resolution hash table, and identifying abnormal data points according to in-bucket data density of the multi-resolution hash table. According to the method, the dimension reduction process is adjusted by extracting the data distribution characteristics, the calculation complexity is reduced while key information is reserved, the multi-resolution hash table is constructed based on the local volatility of the dimension reduction data, adaptive analysis of different data areas is achieved, abnormal data points are identified according to the density of the data in the barrel, and the accuracy of the data in the barrel is improved. The dimension problem and the distribution change problem can be effectively solved, and the processing efficiency is improved while the detection precision is ensured.

The invention discloses an astronomical cross identification method based on spherical trapezoidal division, and the method comprises the steps: S1, taking a unit spherical surface as a celestial sphere, dividing the celestial sphere into a plurality of strips parallel to an equatorial line, and determining the right ascension span of each strip for the transverse segmentation of the strip; s2, dividing each strip into a same spherical isosceles trapezoid with two waists perpendicular to the equator, and taking the same spherical isosceles trapezoid as a sky area; s3, determining an index of each sky area; s4, dividing a star catalogue A and a star catalogue B, and creating a zipper type hash table based on the row number of the star catalogue B; s5, mapping each celestial body in the star catalogue B into a linked list in a hash table in a multi-thread parallel mode, and inserting celestial body data into the linked list by means of a two-stage insertion strategy; s6, mapping each celestial body in the star catalogue A into a linked list in a group of hash tables in a multi-thread parallel mode, and traversing to obtain celestial body IDs of which the spherical distance is smaller than the error radius; the method not only can completely avoid the problem of result omission, but also can greatly improve the astronomical cross identification calculation performance.

The invention relates to a message rule searching method. The method comprises the following steps: searching a first candidate quintuple rule in a preliminary screening hash table according to address field data of a target message; the storage position of each quintuple rule in the preliminary screening hash table is determined according to the hash value of the address field data in each quintuple rule; searching a second candidate quintuple rule in the collaborative screening hash table according to a quintuple combination template matched with the address field data in the collaborative screening hash table and the quintuple field data of the target message; the storage position of each quintuple rule in the collaborative screening hash table is determined according to the hash value of each quintuple rule; and determining the first candidate quintuple rule and the second candidate quintuple rule as the quintuple rule of the target message. By adopting the method, the rule searching process of the message can be simplified, and the processing performance of the message can be improved.

The invention provides a method, a device and equipment for realizing a high-concurrency extensible hash table (hash table) in a multi-thread environment and a medium. The implementation content of the method comprises two parts: 1) realizing high concurrency by a hash table fine-grained lock: realizing by adding the fine-grained lock to the hash table, locking each bucket of a bucket array of the hash table, and improving the concurrency of accessing the hash table in a multi-thread environment; 2) expandability of the hash table double-bucket array structure: two bucket arrays are used, the original bucket array is used for storing hash table elements in a non-expansion state, the expansion bucket array is used for storing newly added hash table elements and elements migrated from the original bucket array in an expansion state, the expansion bucket array is used as the original bucket array in an expansion state after migration is completed, and the expansion bucket array is used as the original bucket array; according to the method, the problem of realizing the high-concurrency extensible hash table in the multi-thread environment is solved, the high-concurrency access of the hash table in the multi-thread environment is ensured, the expandability of the hash table in the multi-thread environment is also ensured, the high concurrency and the expandability are carried out in parallel without conflict, and the hash table can adapt to the change of the data scale.

The invention relates to a method for realizing NAT on a core network UPF network element, and the method comprises the steps: when a user terminal establishes a 5G session, the UPF pre-allocates a public network IPv4 address and a continuous source port set for the session, the number of ports is dynamically adjusted according to a user package, and resources are bound to a session data structure; receiving a downlink packet of which a destination address is a public network IP based on a private network-to-public network mapping relation, positioning a session level hash table through a flow label direct index mechanism, adding a GTP-U packet header after reverse NAT conversion, and forwarding the packet to a mobile phone through service processing; on the basis of a stream label direct index mechanism, each session exclusively occupies an IP port pool and a hash table through a session-level resource isolation architecture, and is used for avoiding global lock competition in combination with multi-core CPU parallel processing; based on a DPI function switching strategy, an NAT mapping table is synchronized through a UPF cluster session mirroring mechanism for node fault switching zero packet loss.

According to the kernel indirect control flow transfer dynamic verification method based on the eBPF, an eBPF attachment point of an indirect call instruction is positioned, a verification program is dynamically attached, a source address and a target address are captured to generate a compound key, and the legality of a control flow is verified in a pre-constructed BPF hash table; and meanwhile, three indirect addressing modes of a differential strategy adaptive register, an immediate operand and a memory are adopted. Based on an eBPF technology, an EV program can be dynamically loaded into a running kernel as required during running, and the characteristic of dynamic expansion of a modern kernel can be perfectly adapted; all the capabilities are constructed on a universal software mechanism of a Linux kernel and do not depend on any hardware extension; while powerful security defense is provided, extremely low overhead during operation is realized. Benefited from an innovative hybrid verification strategy and an efficient CFG storage scheme, control flow hijacking attacks in the real world can be effectively defended with extremely low performance loss.

The invention belongs to the technical field of embedded system information security, and discloses an embedded program integrity multi-granularity verification system and method based on hardware on chip. According to the method, the program integrity verification granularity is set to be an instruction level, a basic block level, a function level and a mixing level in a hardware and software mode, user operation is facilitated, and user selection is enriched; meanwhile, the storage and retrieval efficiency of the reference tag is improved based on a hash table algorithm and a stack structure; in addition, on the basis of the characteristics of a sponge structure hash function, a hash value is calculated by adopting multiple channels, so that the difficulty of destroying codes by external attacks is increased, and the time delay of hash calculation is effectively reduced; in addition, a dual verification mechanism based on a sampling value and a random number is adopted, the space occupied by reference tag storage can be reduced, errors can be found as early as possible, the inherent hysteresis of program integrity verification is relieved, and the method is suitable for safety protection and program integrity verification of an embedded system.

The invention relates to the technical field of inventory order auditing management, in particular to a big data-based non-correspondence auditing system and method.The big data-based non-correspondence auditing system comprises a data preprocessing unit, a data preliminary matching unit and a non-correspondence auditing unit. The data preliminary matching unit receives and processes data by using a message queue, partitions, de-duplicates and sorts the data, puts the data into a to-be-queued queue after preliminary matching by using a hash table structure, the non-corresponding auditing unit extracts features from historical data, establishes a dynamic threshold model through kernel density estimation, weights and fuses a threshold according to a data traffic load, and finally obtains a data stream; the deep learning model processes multi-dimensional features to output a normal delay probability to discriminate fuzzy data, and finally, a postponing area is set to review the fuzzy data according to a dynamic period to determine a final type, so that efficient data processing and accurate auditing are realized, data consistency is greatly improved, and enterprise operation decision scientificity is powerfully guaranteed.

The embodiment of the invention discloses a data-space-oriented private domain data federal retrieval method and system, relates to the technical field of computers, and can realize effective and accurate query of dispersed private domain data in a data space on the premise of meeting the requirement of a data owner for locally controlling and managing the private domain data of the data owner. According to the method, the local retrieval capability of a federated private domain data source is called, so that effective retrieval of dispersed private domain data in a data space is realized on the premise that a centralized index does not need to be established; on the basis of the probability locality sensitive hash table, the target data source with the stored private domain data conforming to the query intention of the user can be selected in a fuzzy mode on the premise that the original data in the private domain data source does not need to be known, and the retrieval request is sent to the target data source, so that the leakage risk of the original data in the private domain data source can be reduced; and accurate query of dispersed private domain data in the data space is realized.

The invention relates to a function operation duration detection and stack automatic collection method and device based on an eBPF technology, and aims to solve the problems that a traditional tool cannot accurately monitor function operation timeout, cross-state stack collection lags and the like. The method comprises the steps that an eBPF program is injected into an objective function (a kernel mode passes through kprobe, and a user mode passes through uprobe) entry, and a thread PID / TID and an entry timestamp are recorded into an eBPF hash table; registering a periodic perf event to trigger timed detection, calculating a function operation time length and comparing the function operation time length with a threshold value; when overtime triggering is carried out, a kernel mode stack and a user mode stack are synchronously collected through bpfgetstackid, and after an event is packaged, the kernel mode stack and the user mode stack are reported to the user mode through bpfperfeedback; and analyzing and displaying the stack in the user mode. The scheme has the advantages of non-return dependency detection (covering a blocking scene), cross-state tracking (kernel / user state function), low-overhead event driving (adapting to a production environment), cross-state stack association (accurately positioning a jamming root), low intrusion, high real-time performance and the like, and the system performance analysis efficiency is effectively improved.

The invention relates to the technical field of data processing, in particular to a memory data management system, a resource data storage method and a resource data retrieval method.The memory data management system comprises a first memory storage module, a second memory storage module and a third memory storage module, the second memory storage module is configured to construct a linked list type hash table by utilizing a dynamic memory allocation method; the first counting module is configured to extract the number of elements in the static memory data; and the second counting module is configured to extract the number of dynamic memory structural bodies allocated by the dynamic memory. The method aims at achieving efficient utilization of memory resources and rapid storage and retrieval of resource data.

The invention provides a high-speed mode matching device and method based on an FPGA, and the device comprises an extraction module which is used for carrying out the multi-dimensional feature extraction of a rule set; the clustering module is used for generating rule categories; the Bloom filtering module is used for screening the input data; the Hash matching module is used for verifying whether the data flow is matched with a known mode or not; the probability verification module is used for calculating and verifying a hash matching passing probability; the regular matching module is used for matching complex modes; the feedback module is used for dynamically adjusting Bloom filter parameters, hash table distribution and probability formula coefficients; and the matching result reporting module is used for reporting the matching information. According to the method, the balance of high efficiency, accuracy and expandability can be realized in a multi-mode matching task, the resource utilization efficiency of an FPGA (Field Programmable Gate Array) is improved, and the matching requirement when a mode matching rule set is relatively large is met.

The invention discloses an impossible difference-intermediate encounter attack password analysis method, which comprises the following steps of: obtaining an impossible difference partition device of a certain block cipher, respectively expanding forwards and backwards for several rounds according to the difference characteristics of the head and the tail of the impossible difference partition device, and recording corresponding parameters; selecting a plaintext to attack, encrypting to obtain a corresponding ciphertext, and calculating data complexity required by the attack according to corresponding parameters; a key involved in the forward extension part is guessed, partial encryption is performed on the plaintext, pairing is performed by using data after partial encryption, and ciphertext data pairs corresponding to data pairs satisfying forward differential propagation are stored in a hash table; a key related to the backward expansion part is guessed, partial decryption is carried out on the ciphertext, pairing is carried out by using the partially decrypted data, online matching is carried out on a ciphertext pair corresponding to a data pair satisfying backward differential propagation and a ciphertext data pair in a hash table, and a wrong key is eliminated; and carrying out exhaustion search on the remaining candidate keys to find a correct key.

The invention discloses a simulation method and system for improving the simulation efficiency of a PIN diode, and belongs to the technical field of semiconductor device simulation, and the method comprises the steps: firstly, employing a hash table data structure to optimize the preprocessing process of a semiconductor physical model time domain numerical method, and remarkably improving the parameter preprocessing efficiency; carrying out bandwidth compression optimization on the sparse matrix in combination with an inverse Cuthill-Mckee matrix reordering algorithm; and secondly, constructing a parallel iteration solver architecture to realize efficient distributed solution of a large nonlinear matrix equation. Through cooperative application of the method, the overall calculation efficiency of a PIN diode numerical simulation program is improved by more than three times compared with that of traditional commercial simulation software COMSOL on the premise that the calculation precision is kept, the industrial problem that the simulation timeliness of a semiconductor device is limited is effectively solved, and an innovative technical path is provided for high-precision real-time simulation. The method has important application value in the fields of power device design, integrated circuit simulation and the like.

In the present application, a shared memory is created by means of an mmap function provided by an operating system, wherein a mapping relationship is established between the shared memory and a corresponding specific file among disk files; when data is written into the shared memory, the operating system synchronizes data in the shared memory to the disk files in real time, wherein the shared memory can be used to realize a cached data writing process and a data query process with respect to the disk files; and when cached data exceeds the capacity of the shared memory, an LRU eviction algorithm automatically deletes old data and then writes new data, wherein the shared memory is divided into a lock management area, a data area, a hash bucket area, an algorithm management area and a transaction management area, the lock management area is used for controlling the modification of hash table critical section data to ensure that only one process can modify the critical section data at the same moment, a shared lock is added by means of the lock management area during data writing and reading, and the transaction management area is used for ensuring whether modification is executed, and is responsible for, when a process exits abnormally, cleaning up unreleased locks and rolling back intermediate state transactions.