1175 results about "Byte" patented technology

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention is suitable for the technical field of file management, and provides a file analysis method and system and a vehicle, the file analysis method and system are applied to a file analysis system, the file analysis system runs at a browser end, and the file analysis system comprises the steps that binary header information of a to-be-analyzed file is analyzed according to the byte position, obtained in advance, of the binary header information in the to-be-analyzed file, and the binary header information of the to-be-analyzed file is obtained; binary index position information of the to-be-analyzed file is obtained; according to the binary index position information, reading and analyzing binary index data of the to-be-analyzed file to obtain index data, determining a target data block meeting a preset requirement according to basic information of each data block, and reading the target data block; and analyzing the data in the read target data block to obtain formatted target data. According to the invention, the problem that the cost of cloud data storage and data analysis is increased due to the fact that a large amount of file data is analyzed and stored at the cloud in the prior art can be solved.

The invention discloses a micro burst traffic detection method, network element equipment, a computer readable storage medium and a computer program product, and relates to the technical field of communication, the method is applied to the network element equipment, the network element equipment is provided with a microsecond-level timer, and the method comprises the following steps: counting the number of forwarding message bytes of each sub-time window in a sliding time window; performing mean value calculation on the forwarding message byte numbers of all the sub-time windows in the sliding time window to obtain a sub-window byte number mean value of the sliding time window; determining a sub-window byte number threshold value of the sliding time window based on the sub-window byte number mean value; and determining the state of the sub-time window of which the forwarding message byte number is greater than the byte number threshold value of the sub-window as a flow micro-burst state. According to the invention, the size of the micro-burst flow and the corresponding time delay can be accurately detected in a fine-grained manner, and the detection precision of the micro-burst flow is effectively improved.

A neural-symbolic hybrid system for generating binary document formats directly from natural language input comprises a binary-aware hierarchical tokenizer operating across four levels (binary bytes, structural elements, semantic content, and concepts), a constraint satisfaction engine with 64 parallel processing cores for enforcing structural integrity and mathematical consistency, format-specific processors for Excel, PowerPoint, PDF, and CAD documents, and a formal verification system generating mathematical proofs of correctness. The system includes custom AI Document Generation Processor (AIDGP) silicon spanning 600 mm2 with specialized cores providing 500 TOPS processing power. Performance characteristics include 99.7% structural accuracy, 100% format compliance, 15.3 second average generation time for complex documents, and distributed capacity of 1,000,000 documents per hour. The system eliminates intermediate conversion steps while maintaining semantic preservation through hardware-accelerated constraint satisfaction and formal verification engines ensuring structural integrity, format compliance, and security through AES-256 encryption and automated regulatory compliance across 25+ international standards.

The invention relates to an encrypted traffic classification method fusing space, time sequence and frequency spectrum features, and belongs to the field of encrypted traffic classification and deep learning. The method comprises the following steps: analyzing a network original flow Pcap packet, segmenting the packet into different sessions according to a quintuple, and preprocessing each session: extracting first n data packets, and then extracting first m bytes and t time sequence data from each packet; utilizing byte data of the session to train a spatial feature module, and extracting spatial features of the session; training a time sequence feature module by using the time sequence data of the session, and extracting time sequence features of the session; the byte data of the session are converted into frequency spectrum information through fast Fourier transform, a frequency spectrum feature module is trained, and frequency spectrum features of the session are extracted; and fusing the extracted global spatial features, time sequence features and frequency spectrum features, and training by using multi-modal joint feature representation to realize fine classification of encrypted traffic. According to the invention, the precision and robustness of traffic classification can be improved.

The invention discloses a key value storage system indexing method oriented to an NVM-NVMe SSD (Non-Volatile Memory-Non-Volatile Memory Express Solid State Disk) hybrid architecture, which comprises the following steps of: constructing a heterogeneous storage architecture taking cold and hot data perception as a core driving mechanism, coordinating and managing two types of storage media, namely a non-volatile memory NVM and a solid state disk NVMe SSD, and realizing efficient identification, layered writing and dynamic migration of cold and hot data. According to the method, the characteristics of low delay, durability and byte addressing of the NVM are utilized, the frequency and I / O overhead of Flush and Compaction operations are reduced in a data write-in path, meanwhile, the problem of mixed storage of cold and hot data is avoided, and therefore the overall performance and storage efficiency of a system are remarkably improved. In addition, by introducing an asynchronous migration module, the method can dynamically adapt to the change of data popularity along with time evolution, and effectively support high performance and high availability of the key value system in long-term operation.

The invention discloses a model performance test method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The theoretical maximum lexical throughput of a target large language model is calculated based on the video memory bandwidth of a graphics processor, the model parameter quantity, the byte number corresponding to the quantization precision and the video memory bandwidth utilization rate; meanwhile, the benchmark performance throughput is obtained, a theoretical corresponding first concurrency number is calculated in combination with the theoretical maximum lexical unit throughput and the concurrency competition loss coefficient, then the model test is executed based on the first concurrency number to obtain the actual maximum lexical unit throughput and a corresponding second concurrency number, and a model performance test result is generated. The problems that in the prior art, due to the fact that manual testing is conducted depending on manual intervention, a continuous approaching attempt mode is adopted, a reasonable test starting point is not deduced in combination with hardware core bottlenecks and key parameters, evaluation is time-consuming and labor-consuming, the result is prone to being affected by artificial factors, and accuracy and consistency are poor can be solved.

The invention provides an intelligent solitary policy processing method based on a neural network and machine learning, and relates to the field of data processing. According to the method, strategy byte codes and execution contexts are generated by extracting business rules, supervision calibers and field dependency relationships of the single insurance policy, discovery, repair and arbitration of field abnormity are achieved in combination with a dynamic data governance model, an account-falling confirmation receipt is output through a voting game and a compensation transaction, and a replayable trace and a rare mode index are generated. And a high-confidence label and an error correction prior increment are obtained in combination with triggering verification, and finally, full-link evidence is solidified through continuous distillation, chain type evidence storage and an auditable snapshot library, so that a traceable record of the single insurance policy is formed. By implementing the technical scheme provided by the invention, full-link data processing can be conveniently carried out on the single insurance policy, so that the processing efficiency is effectively improved.

The invention discloses a secondary power modulus-based post-error-correction-free quantum lattice signature method, which comprises a counter, and comprises the following steps of: S1, sampling a random seed, and expanding the seed through a hash function to obtain a byte stream element I, a byte stream element II and a byte stream element III; s2, calculating and generating a public key and a private key based on the throttling element I, the byte stream element II and the byte stream element III; s3, assigning initial values to the counter and the signature, and calculating and generating a hash value I and a hash value II based on the private key and the signature message; s4, performing signature loop calculation based on the hash value I, the hash value II, the private key and the initial value of the counter to generate a legal signature final value, and returning the legal signature final value; s5, a signature verification variable is generated through calculation based on the public key and the signature final value, if the signature verification variable meets a verification condition, signature verification is passed, otherwise verification is not passed, the post-quantum lattice signature calculation efficiency, bandwidth optimization and stability are greatly improved, and a more efficient solution is provided for practical application.

The invention discloses an atypical sampling bit width data transmission method and device, equipment and a medium. Comprising the steps of obtaining a target transmission protocol and constructing instances according to target transmission protocol requirements; calculating the number of temporary frame bytes according to the instance, and when the number of temporary frame bytes is not an integer, determining that the current scene is atypical sampling bit width transmission; and determining a transmission frame number based on the temporary frame byte number, generating aggregation reconstruction data according to the transmission frame number, and performing data transmission based on the aggregation reconstruction data. By constructing an instance, protocol rules and parameters which need to be followed by data transmission can be determined. An atypical sampling bit width transmission scene is determined by calculating the number of temporary frame bytes, and a scene basis is provided for subsequently adopting an aggregation reconstruction scheme in a targeted manner. By determining the number of transmission frames, the atypical sampling bit width data can meet protocol requirements after being aggregated and reconstructed, redundant bits do not need to be supplemented or the sampling rate does not need to be reduced, protocol compatible transmission of the atypical data is directly achieved, and meanwhile the effective bandwidth utilization rate and the data transmission efficiency are guaranteed.

The invention discloses an encrypted traffic detection method based on space-time fusion and small sample self-supervised learning, and the method comprises the steps: 1, multi-dimensional encrypted traffic feature extraction: extracting discriminative time sequence features and spatial header byte features from original encrypted traffic data; step 2, constructing and training a space-time fusion detection model, constructing an efficient space-time fusion detection model, and performing deep fusion and classification on the time sequence features and the space head byte features extracted in the step 1; and step 3, realizing a small sample self-supervised learning mechanism. According to the method, a space-time fusion detection model specially designed for encrypted traffic is constructed, and parallel processing and deep fusion are carried out on a time sequence and a space head byte feature. The fusion of the time-space dual features enables the model to understand the time sequence features and the structural features at the same time, significantly improves the detection precision and robustness, and can effectively cope with various encryption protocols and traffic confusion technologies.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The invention relates to the field of buried facility monitoring, and discloses a piezoelectric cantilever vibration sensing and super capacitor self-powered energy closed-loop operation method and system, and the method comprises the steps: building a star array composed of a master pile and a slave pile, and configuring a TDMA time slot and an uplink message length; the slave pile collects a vibration signal through a piezoelectric cantilever, the vibration signal is rectified by a piezoelectric rectification energy taking module and then charges a super capacitor, and the voltage state of the two ends of the super capacitor is obtained; when the voltage state reaches a preset upper threshold, the boosting module supplies power to the processing circuit and the transmitting circuit of the slave pile, and the processing circuit generates a four-byte feature code; the slave piles send four-byte feature codes in a magnetic induction link according to TDMA time slots, the master pile receives and aggregates the four-byte feature codes sent by the multiple slave piles, completes positioning and fusion according to the arrival time difference, and returns the four-byte feature codes to the platform through LoRa and 4G, and the platform completes storage and output according to the four-byte feature codes and a positioning result. According to the invention, end-side cloud connection and replicable deployment can be realized, and low-frequency vibration acquisition and buried communication are adapted.

Embodiments of the present application provide a method and device for compiling program code. The method for compiling program code may include: obtaining source code; and performing a compilation operation to obtain a first compilation result of the source code, where a first code segment in the source code is compiled into machine code in the first compilation result, a second code segment in the source code is compiled into bytecode in the first compilation result, and the first compilation result is configured to be deployed to a target environment. The proposed technical solutions may be conducive to ensuring performance at low storage resource utilization.

The invention relates to the technical field of data storage, and discloses a numerical data variable-length coding storage method, device, equipment and medium, which comprises the following steps: converting target numerical data into fixed-length binary representation according to definition precision, executing filling removal interception processing on an integer part, executing digital-level packet compression coding on a decimal part, and performing variable-length coding on the target numerical data; and generating a variable-length binary sequence containing length information and symbol information, and writing the sequence into a storage medium to realize compact storage of numerical data. According to the method, redundant filling bytes are eliminated from an integer part, compressed encoding is performed on a decimal part according to effective digits, and unified processing of sign bits is combined, so that a storage space dynamically changes along with actual numerical digits on the premise of keeping numerical precision and analyzability, space waste of fixed-length numerical types is reduced, and the method is suitable for large-scale popularization and application. Meanwhile, the precision error of the floating-point number is avoided, and the storage efficiency and the data reliability are improved.

The invention relates to a byte code function retrieval method and device of an Ethereum virtual machine and computer equipment, and the method comprises the following steps: processing all byte codes in a byte code library of the Ethereum virtual machine to obtain all byte code functions; calculating a first similarity between the to-be-retrieved natural language query content and all byte code functions based on a pre-trained byte code retriever; inputting the natural language query content into a preset large model to generate a code language function corresponding to the intelligent contract function; calculating a second similarity between the code language function and all byte code functions based on a pre-trained specific retriever; and retrieving a target byte code function corresponding to the natural language query content according to the first similarity and the second similarity. Through the method and the device, the problem of low retrieval accuracy in related technologies is solved, the accurate target byte code function is retrieved on the basis of the first similarity and the second similarity, and then the retrieval recall rate and the retrieval accuracy are enhanced.

The invention discloses a contract vulnerability detection method and system based on cross-granularity feature fusion and meta-learning, and relates to the technical field of block chain security, the method comprises the following steps: obtaining a source code and a compiled byte code of a contract to be detected; source code semantic features are extracted from the source code, operation code execution features are extracted from the byte code, and graph structure features are extracted after a program dependency graph is constructed from the source code; performing cross-granularity attention interaction to generate cross-granularity fusion features; performing type-aware feature modulation on the cross-granularity fusion feature and a vulnerability type identifier of the current detection task, training a classifier by adopting a meta-learning strategy, and outputting a vulnerability detection result; and in response to the vulnerability detection result that the vulnerability exists, positioning the vulnerability code position based on the graph-source code attention weight matrix generated in the cross-granularity attention interaction process, thereby solving the problem of poor generalization ability in a rare vulnerability scene with scarce samples.

The invention relates to an IPSEC (Internet Protocol Security) key exchange method resistant to quantum computing. According to the invention, based on a lattice cryptographic algorithm and improved SM4-256 symmetric encryption, secure communication between a master mode and a fast mode is realized; in the main mode, an initiator sends an IKE first message through a UDP (User Datagram Protocol), negotiates SA parameters with a responder and exchanges a lattice password certificate; the two parties encapsulate a temporary 32-byte secret key by using the public key of the opposite party, generate a 512-bit random number through SM4-256 encryption, and sign and transmit the 512-bit random number to realize secure random number exchange and certificate verification; and the two parties calculate a first session key based on a PRF (Pseudo Random Function), and encrypt an exchange data HASH value to complete main mode key consistency confirmation. And after entering the fast mode, taking the main mode session key as an SM4-256 symmetric key to continue communication, sending an SA message carrying a 512-bit random number by the two parties, calculating to obtain a second session key, and establishing an ESP tunnel. According to the method, the security of IPSEC under the threat of quantum computing is improved through the lattice password.

The invention discloses an AI service intelligent fault tolerance and degradation method, device and equipment based on a Java bytecode enhancement technology, a medium and a product, and relates to the technical field of AI service integration. According to the method, in a class loading period of Java application starting, a loaded class is scanned to identify target methods marked with declarative fault-tolerant annotations, a bytecode operation framework is utilized to enhance the target methods so as to implant a unified fault-tolerant logic interception entry, and then in a running period, when the target methods are called, the target methods are subjected to fault-tolerant annotation processing. And triggering a fault-tolerant interceptor through the interception entrance to analyze the annotation to obtain a fault-tolerant strategy, allocating an independent fault-tolerant component instance to the fault-tolerant interceptor based on the unique identifier of the method, constructing and executing an enhanced call chain containing a fusing check and retry mechanism, and finally, if the execution of the call chain fails, executing the fault-tolerant interceptor. And if yes, a preset degradation method in the fault-tolerant strategy is automatically executed, so that complete non-intrusive decoupling of AI service fault-tolerant management and service logic can be realized, and the reliability of the system is remarkably improved.

Embodiments of the present disclosure enable users to efficiently verify digital data produced by queried databases, even when that data is differentially-private (e.g., satisfying the conditions of differential privacy in order to protect sensitive or private data). In addition to the query result, a database computer can provide the client with a non-interactive zero-knowledge proof (NIZK), data that the client can use to verify the digital data contained in the query result, without revealing any private data to the client. Various innovations, including vectorized proofs, enable the database computer to generate proofs that require less data (e.g., when measured in bytes) than most NIZK proof systems. Consequently, these proofs can be transmitted and verified more quickly and efficiently. Embodiments of the present disclosure can make use of partially or homomorphic commitments and efficient vector proof techniques to achieve these performance improvements.

The invention provides a Gaussian data tree index construction method and device supporting incremental updating and a rendering method.The Gaussian data tree index construction method comprises the steps that firstly, normalization processing and integer format conversion are conducted on original Gaussian data, spatial index key values are generated based on coordinates of an integer format, and the spatial index key values serve as coded values corresponding to the original Gaussian data; segmenting the original Gaussian data into a plurality of data blocks according to the coded values; then constructing a local octree; then, generating a global octree; a storage offset is distributed to each node of the global octree, and a byte range corresponding to each node is determined based on the storage offset; each data block stores the original Gaussian data according to the corresponding storage offset and the sequence of the coded values. The subsequent query efficiency can be improved by storing the data blocks according to the coded value sequence, meanwhile, the memory mapping technology is adopted to process the oversized node data so as to reduce IO overhead, and the storage layout and the output mode effectively improve the data writing efficiency and the subsequent reading performance.

The embodiment of the invention relates to the technical field of computers, in particular to an R-BTB table item retrieval method and system, computer equipment and a storage medium. The R-BTB table item retrieval method comprises the following steps: searching a branch prediction R-BTB table according to an obtained to-be-retrieved address value to obtain a current table item where the to-be-retrieved address value is located; determining a next retrieval address value according to the head address of the current table item and the byte length of the current instruction block of the current table item; taking the next retrieval address value as a to-be-retrieved address value, re-searching from the branch prediction R-BTB table to obtain a next table item where the next retrieval address value is located, and re-determining a new retrieval address value according to the head address of the next table item and the byte length of the next instruction block of the next table item until a set number of table items are retrieved; and according to all the obtained retrieval address values, performing parallel searching in the corresponding table entries to obtain a target instruction corresponding to each retrieval address value. The R-BTB table item retrieval method provided by the invention is higher in efficiency.

The invention belongs to the field of network traffic classification, and discloses a malicious traffic classification method and system based on lightweight time sequence coding and expert routing.The method comprises the steps that session aggregation and burst segmentation are conducted on original traffic, fixed-length byte sequences of headers and loads are intercepted respectively, and dual-channel embedded representation is formed; carrying out local time sequence modeling by adopting MinLSTM, and introducing a sparsely activated expert route into a Transform feed-forward layer so as to fuse global features; in the pre-training stage, parameter initialization is realized through mask byte modeling, and in the fine tuning stage, a classification head is optimized based on annotation data; and finally, splicing header and load features in a classification stage, and inputting the header and load features into a global attention encoder after prepolymerization compression to finish classification. According to the method, small sample and cross-scene robustness of malicious traffic detection can be improved, attention complexity and resource consumption are remarkably reduced, and the method is suitable for resource limited scenes.

A cloud-based service assesses sequences of bits / bytes in natural language using a large byte model representing a large language model trained using a byte vocabulary expansion. The byte vocabulary expansion allows the large language model's textual vocabulary to also include byte-related information associated with different sequences of bits / bytes (e.g., 1's and 0's). The large byte model may thus be given a binary input, and optionally a textual instruction, and the large byte model generates simple natural language descriptions explaining / describing binary input.

The invention provides a monitoring method and system for a high-frequency transaction scene based on an eBPF. The monitoring method comprises the steps that a pre-compiled eBPF byte code program is loaded into a Linux kernel; an eBPF probe is deployed at an event trigger point of each layer of the network protocol stack; establishing a data storage and processing mechanism in a kernel space by using an eBPF Map, acquiring real-time data through a probe, and performing preliminary analysis; further processing is carried out based on the analysis result, and if abnormity is detected, an alarm is given and information is recorded. The technical problem that microsecond-level continuous monitoring and accurate acquisition of abnormal events cannot be realized in high-frequency transactions is solved.

The invention relates to a kernel hot patch symbol table generation method and device, equipment, a storage medium and a program product. The method comprises the following steps: in a kernel hot patch compiling stage, collecting a compiling log through a pipeline, storing the compiling log as a log file, outputting a hot patch module, and extracting and storing a modified kernel module name at the same time; reading a segment for storing patch function information in the hot patch module, acquiring the byte length of the segment by means of a binary file analysis tool, checking whether the byte length is an integral multiple of 72 or not, if not, terminating the process, if so, scanning the data of the segment line by line, extracting kernel symbol position information and a function address according to a remainder rule of modulo 9 by a line number, and storing the kernel symbol position information and the function address in the hot patch module; querying a function name matched with the function address, comparing the function name with a to-be-patched function name in a log, screening a matching item, and forming a scanning result; and writing a result into a file, and generating a kernel hot patch symbol table. By adopting the method, whether the current hot patch is suitable for loading or not can be conveniently detected in advance during operation and maintenance deployment, and the corresponding preposed environment is prepared.

The invention discloses a universal serial communication framing and de-framing method for external parameter configuration of an inertial sensor, which comprises the following steps of: dynamically defining a data frame structure by loading an external editable parameter file, including frame header byte content and position, various standard and non-standard data type codes of a data segment, a verification mode of a verification segment and frame tail parameters; in the framing process, the sensor data is converted into byte arrays according to a selected protocol, and complete data frames are assembled and output; and verifying the completeness of the frame header and the frame tail based on the same parameter file in the frame decoding process, executing verification calculation and analyzing the content of the data segment. According to the method, universal adaptation of different serial port communication protocols is realized, the problem of poor reusability of a traditional hard coding mode is solved, the communication processing efficiency and flexibility of the inertial sensing equipment are remarkably improved, and the method is particularly suitable for multi-protocol scenes such as fiber-optic gyroscopes and inertial measurement units.

The invention relates to a coding and decoding method and a dual-mode coding and decoding method based on a Hash-fractal image chain, and the method comprises the steps: segmenting a text into blocks according to a set number of characters when the length of the text is greater than a threshold value, and generating a Hash value chain for each block through SHA-256; intercepting the first 16 bytes of the hash value of each block to generate a hippeastrum set image, then generating a binary fractal image, embedding metadata dot matrixes into four corners of the fractal image, and storing the metadata dot matrixes in sequence; traversing the target folder, obtaining all PNG images, and sorting the PNG images according to file name indexes; for each image, extracting metadata areas in sequence and decoding the metadata areas, firstly verifying fractal dimension errors of the image to ensure that a carrier is complete, then checking the consistency of each prevhashframe and a previous nexhashframe, positioning a chained tampering position, recalculating a CRC8 checksum, comparing with a storage value and marking a damaged data block; and finally, splicing effective data according to a block position sequence, and outputting a complete text and an abnormal mark. According to the method, the unlimited-capacity ordered associated storage breakthrough can be realized, and the tamper-resistant capability is improved.

The invention belongs to the field of end-cloud collaborative computing and lightweight virtualization, and provides an end-cloud collaborative lightweight application distribution and isolated operation method and system, which adopts a WASM byte code as a unique format for application distribution, compiles an application into a WASM module at a cloud and then distributes the WASM module to an end side device; the end side equipment combines read-only configuration data of a host with a temporary file system Tmpfs of a memory by utilizing OverlayFS on a file level, and maps the data into a virtual file system in a WASM sandbox; on the symbol level, a dynamic WASI bridge is introduced, according to the requirement of an import section in a WASM module, the native capacity of a host system is injected into WASM operation in a host function mode in real time, and the heterogeneous hardware characteristics of end-side equipment are adapted. The method is suitable for an end-side environment with an immutable operating system as a base, and is compatible with a cloud containerization deployment scene.

The invention provides a kernel bypass-based non-sharing architecture key value cache acceleration method and system, and the method comprises the steps: splitting a complete key value cache processing logic into a plurality of eBPF subprograms, compiling the plurality of eBPF subprograms into bytecode files, loading the bytecode files to a kernel, and mounting the bytecode files to an XDP hook point of a specified network interface; eBPF mapping is created and initialized; the method comprises the following steps: receiving a data packet at a network card driving layer through an XDP hook point, after screening a target data packet conforming to a preset protocol type and a port, analyzing the target data packet to extract a request command type and a key, calculating a hash value of the key, and temporarily storing the request command type and the hash value in a head reserved field of the target data packet; redirecting the target data packet to the target CPU core in combination with eBPF mapping, the request command type and the hash value; and the target CPU core executes data operation on the target data packet to generate a response message, and sends or forwards the response message from the receiving network card to the target network card. According to the method, high-performance cache processing in the kernel is realized.