74 results about "Data efficiency" patented technology

The invention discloses a drug combination patent intelligent identification method and device based on a large language model, and the method comprises the steps: obtaining a standard data set built based on drug combination labeling information of patent data, carrying out the data expansion of the standard data set, and generating an initial training set; a parameter efficient fine tuning technology is adopted, the initial training set is utilized to train the base large language model, and an initial recognition model is generated; screening and identifying difficult samples in the unlabeled patent data by using the initial identification model, and adding the difficult samples into the initial training set to form an enhanced training set; performing iterative training on the initial recognition model by using the enhanced training set and adopting a multi-task learning framework in combination with a comparative learning mechanism to obtain a drug combination patent recognition model; the to-be-recognized patent data is input to the drug combination patent recognition model, and a drug combination information recognition result is obtained.The accuracy, data efficiency and model interpretability of drug combination patent recognition can be remarkably improved, and the computing resource requirement and the manual review cost are reduced.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control are improved.

The invention provides an autonomous operation obstacle avoidance method and system for a low-altitude aircraft, and the method is executed by the low-altitude aircraft, and comprises the following steps: S1, data acquisition: collecting data through a multi-source heterogeneous sensor group carried by the low-altitude aircraft, airspace control information, real-time meteorological data and collaborative sensing data of other aircrafts sent by the ground sensing base station are received through the air-ground communication interface; preprocessing the collected original data, wherein the preprocessing comprises data format standardization, abnormal value elimination and time synchronization; and S2, data fusion processing: carrying out credibility evaluation on the preprocessed multi-source heterogeneous sensing data and collaborative sensing data. According to the invention, through three-dimensional credibility evaluation and an air-ground data compensation mechanism, the perception precision is greatly optimized, the perception data efficiency is improved in complex environments such as low illumination, strong electromagnetic interference, rainstorm and the like, and obstacle avoidance decision errors caused by data distortion of a single sensor are avoided.

The invention discloses a system and method for improving data configuration efficiency of a test machine, and relates to the technical field of board card testing, the system comprises an FPGA PCIE module, a DMA address data analysis module and a data transmission module, a general data frame is divided into an independent 32b address field and an independent 32b data field through the arranged DMA address data analysis module, then bit [31] and 0 of an 32b address are analyzed to represent write operation, and the data transmission module is connected with the FPGA PCIE module. 1 represents read operation, an identifier is independently extracted and embedded into a subsequent instruction packet, then a bit [27: 0] of a 32b address is extracted, a target daughter card number is determined according to a preset address mapping table, and data is packaged after determination, so that data distribution of different address fields is realized, the data transmission module receives the data and then sends the data to each post-stage daughter card module in parallel, and data read-write is realized. Therefore, the read-write speed of the test machine is greatly increased, and the read-write efficiency is further improved.

The invention discloses a report generation method based on a large model, electronic equipment and a computer program product, and relates to the technical field of artificial intelligence, the report generation method comprises the following steps: identifying a logic structure, a placeholder and a task dependency relationship in a preset report template, and initializing a context state carrier based on the logic structure, the placeholder and the task dependency relationship; obtaining a report generation requirement, converting the report generation requirement into SQL query data, and generating a cue word based on the SQL query data; and inputting the cue word into a preset generation model to obtain filling content, updating the filling content to the context state carrier, and filling placeholders of the report template based on the context state carrier to obtain a final report. According to the method, the defect of low efficiency of manual data arrangement is overcome, and the problem that a simple template filling tool cannot process a complex logical relationship is solved, so that the report generation efficiency is improved.

The invention discloses a construction site worker behavior intelligent identification and early warning system, which collects multi-directional video streams in real time through a front-end video collection module, guarantees timely data updating, and facilitates real-time monitoring of worker behaviors. The early warning and display module displays a field picture, a worker skeleton diagram, a behavior state and duration time in real time, early warning is immediately carried out when abnormity occurs, an instant feedback mechanism of the early warning and display module enables safety management personnel to rapidly know the abnormity and take measures, accidents caused by information lag are avoided, and the defect that the real-time early warning capability is insufficient is overcome; the behavior recognition module adopts an EnhanceST-GCN model, can better capture a space-time relation of skeleton key points, accurately recognize a complex behavior mode, improve recognition precision and solve the problem of low recognition precision, and meanwhile, the model is high in video data processing efficiency, rapid in analysis, feature extraction and classification, reduces unnecessary calculation steps and meets the real-time monitoring requirement of a construction site.

The invention belongs to the technical field of intelligent damper modeling, and discloses a magnetorheological damper modeling method based on a physical information neural network, comprising the following steps: initializing the physical information neural network; introducing physical constraints into the physical information neural network to obtain a magneto-rheological damper prediction model; obtaining experimental data of the magneto-rheological damper, and jointly training the magneto-rheological damper prediction model through the experimental data and a loss function; and obtaining model parameters of the trained magnetorheological damper prediction model, and updating the model parameters by using an adaptive optimization algorithm. In conclusion, the physical information neural network serves as a basic framework, physical constraints such as the electromagnetic equation, the heat conduction equation and the yield stress equation are introduced, the physical consistency, data efficiency optimization and online self-adaptive capacity of the model are improved, and then the method is flexibly suitable for high-precision dynamic control over the magnetorheological damper.

The invention relates to the technical field of block chains, in particular to a block chain tourism data privacy protection method and system, and the method comprises the steps: constructing a distributed cache layer which is disposed between a block chain node and a user terminal, and is used for dynamically capturing the newly-added tourism data of a user, and taking a preset time window or a data size threshold value as a unit; and aggregating the newly added tourism data into a data block group. According to the method, the distributed cache layer is constructed between the block chain node and the user terminal, the newly-added tourism data of the user is aggregated into the data block group according to the preset time window or the data volume threshold value, the situation that a complete privacy protection process is independently executed on each piece of newly-added data is avoided, and repeated operation and verification operation are reduced; double-layer encryption is adopted for a data block group, so that the basic data efficiency is guaranteed through lightweight symmetric encryption, and the group-level metadata security is enhanced through asymmetric encryption; and the fragmented parallel verification is started and the lock-free operation is realized by means of the message queue, so that the verification time is shortened.

This invention provides a technique for microservice scheduling and improving data read / write efficiency based on runtime state analysis. Microservice systems have become one of the fundamental technology platforms for building large-scale, complex applications. By encapsulating modular information processing units in a lightweight virtual machine manner, high fault tolerance is achieved. Various application functions are orchestrated through service mesh to realize stable large-scale, complex application systems. Microservices often need to access multiple data sources or data lake systems, and the location of data has a critical impact on data read / write efficiency. For example, in a distributed storage system, having the execution node of a microservice coincide with or be close to the data storage node will greatly improve data processing efficiency. Addressing the efficiency problem of microservices in data-intensive scenarios in large-scale applications, this invention proposes a technique based on analyzing the runtime state of data read / write operations to determine the optimal location for microservice scheduling and execution. By shortening the data path, data I / O efficiency is effectively improved, enhancing the performance of building large-scale, data-intensive application systems based on microservices.

The application provides a data query method and device, equipment and a storage medium. It can be used in the field of big data technology. The method comprises the following steps: receiving a data query command sent by a control device; the data query command comprises target query information; the target query information reflects target query data; determining a corresponding index type according to the target query information; the index type is a Hash index or a B+ tree index; in response to the index type being a Hash index, directly querying position information corresponding to the target query data from preset mapping information according to the target query information to obtain target position information; the preset mapping information comprises preset data information and position information corresponding thereto; in response to the index type being a B+ tree index, querying position information corresponding to the target query data according to the target query information and a B+ tree index strategy to obtain target position information. The application can reduce the number of scans, speed up the response and improve the efficiency of querying target query data.

The invention discloses a semantic guidance multi-label image classification method based on hierarchical prototype injection. Input multi-label images are classified by adopting a classification model. The classification model is a hierarchical prototype visual converter (HP-ViT) model. According to the method, label co-occurrence statistics is analyzed offline, and a hierarchical semantic community which can reflect high-order semantic dependence and is from coarse to fine is mined; secondly, instantiating the semantic concepts into a group of learnable hierarchical prototypes, and performing hierarchical injection at different depths of a Transform encoder by adopting a coarse-to-fine strategy; and finally, the prototypes are supervised through an auxiliary classification task corresponding to each level under a multi-task learning normal form, so that dynamic and structured semantic guidance of the backbone network feature extraction process is realized. The method shows potential in the aspects of improving classification performance, data efficiency and model interpretability, and provides a new technical path for solving a complex multi-label classification problem.

The application provides a method, device and related product for adjusting a database. The method comprises: in response to a plurality of data usage request instructions continuously initiated by an application end, identifying a plurality of access behavior information corresponding to the plurality of data usage request instructions. When a plurality of single response times in the plurality of access behavior information continuously increase, determining a preset adjustment rule according to the plurality of access behavior information. Adjusting the database according to the determined preset adjustment rule. In this way, the continuously increasing plurality of single response times in the plurality of access behavior information indicates that the database processing data efficiency is declining. When the database processing data efficiency is declining, the preset adjustment rule is determined through the plurality of access behavior information, and the database is automatically adjusted according to the preset adjustment rule, thereby improving the data processing efficiency of the database. No manual intervention is required, and the adjustment process is not limited by the application scenario.

The invention discloses a low-power-consumption quick start awakening method for an acquisition terminal. According to the scheme, the acquisition terminal comprises a main control module, an auxiliary control module and a power supply switching circuit, and a rechargeable low-leakage-current battery capacitor is adopted as a standby power supply; during normal power-on, the terminal is powered by the main power module, the main control module works in a high-performance mode, the clock module and the liquid crystal screen module are switched to the main control module through the multi-channel analog switch, and the auxiliary control module performs voltage sampling of the main power supply and the standby power supply and charging control of the standby power supply. The main control module and the auxiliary control module are simultaneously communicated with the metering chip to read data; when the power-off key is awakened, the multi-channel analog switch is switched to the auxiliary control module, and only a few modules such as the auxiliary control module work. Through cooperation of the main power module, the standby power module, the power supply switching circuit and the like, the problems of long key wakeup starting time, high power consumption and low data reading efficiency are solved, and rapid key wakeup starting, low power consumption and improvement of the data reading efficiency of the acquisition terminal are realized.

The invention discloses an information management net value mail cleaning and analyzing method and system based on a large model, and the method comprises the steps: obtaining a mail of a user mailbox, and extracting and storing the metadata of the mail; performing multiple identification on the mails based on a large model, confirming net value mails, and excluding non-net value mails; aiming at net value mail contents in different formats, adopting a corresponding extraction mode and carrying out data verification; standardizing the extracted net value data according to a preset term mapping table and a standardized data dictionary; performing automatic analysis based on the net value data after standardization processing; and carrying out visual display on the analysis result. According to the method, automatic identification and acquisition of net value mails are realized, net value data in mails in different formats can be extracted and automatically analyzed, and analysis results such as income trend and risk assessment are generated in real time. The method solves the problems that in the prior art, the efficiency of obtaining and analyzing the net value data in the asset management mail is low, errors are prone to occurring, format adaptation is difficult, and a dynamic analysis result is difficult to generate in time.

The application discloses a kind of quick conversion calculation method of aircraft center of gravity data, comprising the following steps: step one, according to the geometric relationship diagram of center of gravity conversion, the geometric relationship between the height h of aircraft center of gravity coordinate, the height of center of gravity when landing gear is fully extended, the distance a between the projection of aircraft center of gravity coordinate to ground point and front wheel contact point and the distance b between rear wheel contact point is established;Step two, the geometric relationship is packaged into Excel software;Step three, input the front wheel coordinate parameters and rear wheel coordinate parameters of the model that needs to be calculated, and then input the center of gravity coordinate, and the corresponding h, a, b value can be automatically converted;The application is aimed at the drawbacks of drawing method, combined with commonly used Excel software can automatically process a large number of formulas, realize the calculation of aircraft h, a, b value in each state, data processing speed is fast, solve the problem of low efficiency and easy to make mistake of traditional drawing method manual reading data.

The invention provides a zero sample anomaly detection method based on bimodal prompt enhancement, and the method comprises the following steps: S1, carrying out the input coding and multi-modal feature extraction: carrying out the prompt coding of an input image and a text into feature vectors, and achieving the preliminary alignment of the features of the image and the text; s2, dynamic vision-text prompt interaction: adopting a PIM module to realize layer-by-layer dynamic guidance of text prompt on visual features through a learnable projection matrix; s3, constructing a hierarchical abnormal prototype memory library: constructing a multi-granularity abnormal prototype library by adopting a PMB module, and enhancing fine-granularity representation through micro-texture and macro-component prototypes; s4, an uncertainty perception anomaly scoring mechanism; calculating an abnormal score by combining multi-granularity prototype matching and the uncertainty weight; and S5, anomaly detection and output: integrating the text-visual similarity and the PMB score to complete anomaly detection. According to the method, the modal semantic deviation is solved, the fine granularity detection precision is improved, and the data efficiency is high.

This invention relates to the field of automotive communication technology, providing a method and system for converting and managing automotive communication database formats. It aims to solve the problems of low efficiency, error-proneness, and difficulty in collaboration associated with traditional manual processing of multi-source heterogeneous communication data. This method automatically parses source data in multiple formats such as DBC and Excel, converting them into a pre-defined intermediate data model. It then automatically generates ARXML files conforming to the AUTOSAR standard and finally uploads the files to a cloud database, associating them with project information to achieve centralized storage, version management, and collaborative work. Through collaboration between local and cloud platforms, multiple verifications and version management can also be achieved. This invention efficiently and accurately converts scattered and heterogeneous communication data into industry-standard files through an automated process, and leverages the cloud to achieve centralized file management and project-based collaboration, effectively improving the efficiency, consistency, and traceability of in-vehicle communication data processing.

The application provides a point cloud instance segmentation method and system based on synthetic data assistance, and the application generates an indoor 3D scene containing rich objects by using a 3D object model data set and a 3D scene model data. On this basis, the camera trajectory during scanning of the scene point cloud is counted, the same scanning process is simulated in the synthetic scene according to the trajectory to obtain labeled synthetic point cloud data. In order to learn semantic rich feature representation from the synthetic point cloud data, the application performs supervised pre-training on the synthetic data, and takes the semantic segmentation task as a proxy task for feature learning. In the pre-training process, the application introduces unlabeled real scene data and gives the data pseudo labels to guide the migration process from synthetic data to real data. When the learned feature representation is used for the real point cloud instance segmentation task, the rich semantic knowledge and object shape knowledge contained in the pre-training model can be used to realize data efficiency of the real data.

The invention provides a construction method of a multi-source heterogeneous equipment collaborative energy scheduling model, a medium and a system, and belongs to the technical field of energy scheduling. A fault propagation prediction model based on a heterogeneous graph structure and an attention mechanism is constructed, and the uncertainty is quantitatively predicted by using a Bayesian neural network; solving the collaborative scheduling mathematical model by adopting a self-adaptive robust optimization method to obtain an initial scheduling scheme, dynamically adjusting charging and discharging power according to a comprehensive aging index, guiding active learning through a sampling priority value to carry out dense sampling on high-uncertainty nodes, and carrying out incremental training on the model; and adjusting the power limitation of the energy storage unit and the control strategy of the photovoltaic inverter according to the node fault probability distribution, thereby solving the technical problem of insufficient reliability of the scheduling scheme caused by the lack of an artificial intelligence model training mechanism with high data efficiency in the fault propagation scene of the multi-source heterogeneous equipment of the micro-grid system.

The invention relates to the technical field of network security, in particular to a heterogeneous social graph detection method and system fusing neighborhood perception and adaptive gating, a heterogeneous social graph is constructed, nodes represent users, and edges represent various social relations among the users; coding the node features to generate a low-dimensional dense initial feature vector; the multi-layer neighborhood perception graph neural network NANNN takes R-GCN as a backbone network to carry out node representation learning, explicitly models heterogeneity of different types of social relations, and introduces an adaptive gating module into each layer of the R-GCN; and based on the final node representation, predicting the probability that the node belongs to a robot or a human through a classifier, and optimizing all model parameters through end-to-end training. According to the method, through a triple perception adaptive gating aggregation mechanism, the remarkable effects of higher precision, lower parameter quantity and higher data efficiency are achieved in a social robot detection task.

The invention discloses a stateless robot motion control method and system based on vision, and belongs to the field of robot intelligent control, and the method comprises the steps: collecting multi-view visual observation data through a top main camera and double-wide-angle wrist cameras installed above and below an end effector; inputting the visual observation data into a pre-trained strategy network, wherein the strategy network outputs a relative pose increment of an end effector; and the current pose of the end effector is updated according to the relative pose increment, and the robot is controlled to execute corresponding actions. The strategy network is trained based on a stateless imitation learning framework, only visual observation data and corresponding expert action labels are used in the training process, and state information is sensed without depending on a robot body; according to the method, pure-vision-driven end-to-end control is realized, and the method has strong space generalization ability, high data efficiency and excellent cross-platform adaptability, and is suitable for complex operation tasks such as industrial manufacturing and service robots.

The invention discloses a track-following card data intelligent identification and entry method, and solves the problems of low efficiency and high error rate of traditional manual entry of enterprise track-following card data. According to the scheme, the method comprises the following steps: pre-establishing identification templates of track-following cards in various formats, and uploading track-following card image scanning header two-dimensional codes to match the identification templates; cutting a corresponding field region according to coordinate information defined in the template; identifying and extracting field contents in the table, decoding the right two-dimensional code of each process, and forming a complete process record by the right two-dimensional code and the field data of the row; each process record is indexed according to the line number or the unique process ID in the two-dimensional code; and the system arranges the structured identification data and transmits the structured identification data to an enterprise internal ERP, MES or quality system. According to the scheme, the data processing efficiency is improved, data traceability is realized, template management and recognition model continuous optimization are supported, an automatic closed-loop process from paper information to system data is constructed, and the method is an important basic tool for promoting enterprise intelligent manufacturing and informatization construction.

This invention relates to the field of coal mine sequestration technology and discloses a method for sequestering carbon dioxide underground in closed coal mines. The method includes: constructing a causal structure of a knowledge base for the evolution of multiphase geological processes in coal mines to form a multi-scale knowledge logic framework; interpreting historical sequestration projects in coal mines using multiple modalities and encapsulating the interpretation results as medium-term prediction capabilities; extrapolating the future state of the medium-term prediction capabilities output under multiple scenarios based on the multi-scale knowledge logic framework to obtain a long-term sequestration efficiency trajectory; performing multi-objective Pareto front analysis on the long-term sequestration efficiency trajectory and compiling the analysis results into an optimized sequestration scheme; applying the optimized sequestration scheme and using dual-track data efficiency to verify the real-time monitoring data and the long-term sequestration efficiency trajectory to form a decision-making source deviation representation; analyzing the decision-making source deviation representation through deviation attribution and encoding the analysis results into a multi-scale knowledge logic framework and medium-term prediction capability collaborative reconstruction instruction. This invention can improve the efficiency of carbon dioxide sequestration in closed coal mines.

The application provides a disease association named entity recognition method based on an AI large model, comprising the following steps: constructing a high-quality medical corpus, wherein the high-quality medical corpus comprises electronic medical record text data and standard medical language data corresponding to the electronic medical record text data; extracting and coding features of the electronic medical record text data and the standard medical language data corresponding thereto through a large model respectively; fine-tuning the large model through a minimization multi-modal alignment loss function according to the electronic medical record coding features and the standard medical language coding features corresponding thereto; performing unsupervised entity extraction on authoritative medical literature through the fine-tuned large model, and screening a dynamic updated entity library through rule filtering and confidence sorting threshold screening; performing initial named entity recognition on a target electronic medical record, performing multi-dimensional semantic matching on the initial entity recognition result and the entity library, and taking the matching result as a final named entity recognition result. The application has the advantages of improving data efficiency, enhancing medical field knowledge adaptability and the like.

The invention discloses a lithium ion battery health state estimation method based on physical information Transform. The method comprises the following steps: acquiring voltage and current time sequence data of a lithium ion battery under a pulse charging condition; feature parameters are extracted from the time series data, and a training data set containing the feature parameters and corresponding health state labels is constructed; and constructing a physical information neural network model based on a Transform architecture, inputting the training data set into the physical information neural network model for training, processing pulse charging data of the lithium ion battery to be tested by using the trained physical information neural network model, and outputting a battery health state estimation result. According to the method, a framework which is high in principle and can be popularized is established, and an important contribution is made for constructing a battery health prediction model which is higher in interpretability, better in data efficiency and higher in physical credibility.

The application relates to the technical field of data storage, and discloses a data reading method, a controller, computer equipment and a storage medium, which are applied to a first controller. The method comprises the following steps: receiving a reading request sent from a host, wherein the reading request carries a stripe identifier; determining whether a target stripe corresponding to the stripe identifier belongs to the first controller according to the stripe identifier; if not, obtaining initialization metadata corresponding to a target controller to which the target stripe belongs from a shared memory, wherein the initialization metadata is used for indicating whether the target stripe is completed initialization; in the case that the initialization metadata indicates that the target stripe is completed initialization, reading target data from at least one disk corresponding to the target stripe, and returning the target data to the host. The application can solve the problems that the data reading efficiency from a storage system is low and the accuracy is poor in a global deduplication scene, the global deduplication efficiency is low and the accuracy is poor, and the performance and reliability of the whole storage system are affected.

The invention relates to the technical field of data processing, in particular to a database reading method based on a SpringBatch framework, which comprises the following steps of: independently extracting a query statement through a redefined subclass of an AbstraactPagingItemReader class, driving query through a dynamic operation parameter set, and obtaining the last data of each paging query as an initial condition of the next paging query, so that the next paging query is completed, and the next paging query is completed. The problems that in the prior art, under large-data-volume paging query, a traditional MyBaatipingItemReader class using SpringBatch needs to traverse and discard first N pieces of data, the data reading efficiency is low, the memory of a database server is likely to overflow, and the read-write load of a disk of the database server is suddenly increased are solved.

The semi-supervised microseismic first arrival intelligent picking method combined with SimMatch and improved TransUGA, Step1, obtaining the real labeled signal in microseismic data, generating forward simulation signals under different main frequencies and different speeds; Step2, using the real signal and the generated simulation signal to make the original data set, inputting the made original data set into the constructed UGATIT generative adversarial network, generating pseudo signal samples through the mutual game of the generator and the discriminator, and screening out the generated pseudo real signal samples; Step3, taking the original labeled data and the unlabeled samples generated by the UGATIT generative adversarial network as a new data set, and semi-supervised training the improved TransUNet network; Step4, obtaining the trained TransUNet network and high confidence pseudo label; Step5, inputting the microseismic data to be tested into the trained TransUNet network to pick up the first arrival signal. Effectively solve the problems of low efficiency of manual labeling data, poor first arrival signal picking accuracy under low signal-to-noise ratio, and difficulty in learning signal features from small sample data.

The application discloses a kind of target behavior anomaly early warning processing system and method, it is related to target behavior analysis processing technical field;First, original situation track data is carried out data cleaning;Then the target abnormal behavior characteristic data model is obtained by target abnormal behavior characteristic data;Then, based on target abnormal behavior characteristic data, the inference detection of target abnormal behavior is carried out using the analysis method of multiple analysis dimensions in target behavior anomaly analysis model;Finally, when target behavior anomaly analysis model detects target abnormal behavior, early warning prompt is carried out through behavior anomaly early warning processing module;While processing system design and integration are carried out by the way of service arrangement;The application can maximize the data efficiency and algorithm efficiency, detect abnormal behavior from different dimensions, solve the accurate and detailed needs of complex and diverse target behavior anomaly early warning, enhance the safety early warning capability of sensitive area and target, effectively prevent the occurrence of security threat events.

The application provides a multi-controller write data method and device, electronic equipment and a storage medium, wherein the method comprises: receiving a write data request, determining to-be-written data corresponding to the write data request and a target data stripe in a RAID to which the to-be-written data is to be written; accessing metadata in an associated memory to determine a target value of a bit corresponding to the target data stripe in the metadata; determining whether the target data stripe is occupied based on the target value, and determining whether to execute a write data operation corresponding to the to-be-written data. Through memory consistency interconnection between multiple storage controllers, memory resource sharing between the storage controllers is realized, the multiple storage controllers are directly associated together through the interconnection memory, and series connection at a physical layer is realized. Through a mode in which the multiple storage controllers jointly operate the same piece of metadata in the interconnection memory, a stripe lock mechanism is abolished, write IO processing delay is reduced, and write data efficiency is improved.