88 results about "Identifier scheme" patented technology

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

The invention provides a method for identifying the characteristics of a metal foreign body in a GIS, and the method comprises the steps: building an electric field simulation model, and setting the metal foreign body; a multi-modal sensor is adopted to collect signals generated by partial discharge of the metal foreign matter so as to obtain a multi-modal atlas database; performing multi-domain feature extraction on the multi-modal atlas database, and generating a lightweight fingerprint feature vector through feature dimension reduction; the lightweight fingerprint feature vector sequentially passes through all steps of a combinatorial algorithm to solve identification items of the metal foreign matter; and selecting a globally optimal group of identification items to be combined into a metal foreign matter feature identification scheme. According to the invention, fine analysis and processing are carried out on partial discharge signals through a signal processing and pattern recognition algorithm, so that the detection and classification precision of metal foreign matters can be effectively improved; real-time monitoring and quick response under complex working conditions can be realized, a quantitative basis can be provided for fault early warning and equipment maintenance, and the operation reliability and safety of GIS equipment are further enhanced.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The invention relates to the technical field of construction management, and discloses a construction progress recognition method and system based on image recognition. The method comprises the steps that real-time image data of a construction scene are obtained, a construction progress feature monitoring database mapped with the real-time image data is established, and database monitoring indexes comprise image definition, feature matching degree and the like; configuring a directional recognition target, including key node recognition, construction stage division and the like, and then determining a target classification identifier; key identification parameters are selected through the main identification target and the auxiliary identification target, directional identification target influence analysis is carried out, parameter directional identification is executed after parameter identification constraints are established, and construction progress identification is completed. When parameter directional recognition is executed, a time efficiency and accuracy evaluation function is also established, and a balance adaptation function is constructed to screen recognition schemes. The system comprises a data acquisition module, a target configuration module and the like and can implement the method. Through multi-dimensional analysis and optimization, efficient and accurate recognition of the construction progress is achieved, and the construction progress management requirement is met.

The invention discloses a multiphase flow real-time flow pattern recognition method based on updip pipe pressure and a lightweight deep neural network model, adopts updip pipe pressure data, designs a training strategy, proposes a YI-Net network model, belongs to the field of deep learning, and is used for flow pattern recognition. The method comprises the steps of data preprocessing, model reasoning, loss calculation, back propagation optimization of model parameters and the like. In the data preprocessing process of the training stage, the generalization ability of the model is improved through a data enhancement training strategy, such as Gaussian noise addition, amplitude scaling, overall time translation and the like. The YI-Net model comprises two parts of a backbone network (backbone) and a head (head), and various one-dimensional calculation operations such as one-dimensional convolution and the like are adopted. And the backbone is used for extracting the characteristics of the flow pattern sample. And the head is a Classify layer and is used for determining the category of the backbone output characteristics. The flow pattern recognition scheme provided by the invention has the characteristics of good real-time performance and small parameter quantity, and provides a solution for the field of flow pattern recognition.

The invention discloses an electric power system wiring detection method based on block parallel processing, and relates to the field of current detection, and the method comprises the steps: collecting real-time operation data through a sensor disposed at a key node, and constructing a basic data set after Kalman filtering denoising and normalization processing; establishing a sub-block identification system based on topological structure blocks, and extracting electrical and topological feature vectors; a master-slave parallel computing architecture is adopted, a master node allocates tasks according to priorities, and an improved random forest model is utilized to perform sub-block level anomaly preliminary screening; cross-sub-block collaborative verification is realized by verifying the boundary interconnection switch state, the branch current and the bus voltage; and finally, constructing a global wiring state map to perform difference analysis, accurately positioning abnormal equipment and types, and verifying a result through high-precision retest. The method has the advantages that the efficiency is improved through scientific partitioning and distributed parallel computing, the accuracy is guaranteed in combination with multi-dimensional detection, boundary verification and a retest mechanism, wiring abnormity is efficiently positioned, and reliable support is provided.

The invention discloses a self-adaptive link control method for a multi-dimensional identification system, and belongs to the technical field of link control. The method comprises the following steps: embedding an environment sensing module in a multi-dimensional identification system to obtain network state information; introducing a reinforcement learning algorithm to carry out adaptive coding; the self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); a self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); and establishing a link layer frame format of the multi-dimensional identification system to realize communication link management. According to the invention, through combination of a multi-dimensional identification system and an environment perception technology, a network state and channel characteristics can be perceived in real time, and a link control strategy is dynamically adjusted; the data transmission efficiency can be dynamically optimized according to the network state and the channel condition by introducing the adaptive coding modulation and adaptive waveform technology; the data transmission time delay is obviously reduced, and the data packet disorder and packet loss rate are reduced, so that the user experience is improved.

The invention discloses an open source environment software hidden vulnerability patch identification method, which is based on a multi-stage architecture and collaborative relationship modeling, and provides a new patch group identification scheme: firstly, obtaining candidate code submission from an open source warehouse, extracting correlation characteristics of vulnerabilities and candidate code submission, including rule-based characteristics and semantic characteristics, calculating a correlation score and screening high-correlation submission; pairwise pairing the high-correlation submissions, and fusing multi-dimensional features to predict a cooperative relationship between the submissions; and finally, constructing an undirected graph based on the correlation score, dividing a maximum connected sub-graph, fusing the features in the group through maximum pooling, calculating the correlation with the vulnerability, and outputting an optimal patch group. Meanwhile, an existing patch identification technology based on sorting learning is combined, an enhancement method based on a submission cooperative relation is provided, ranking logic submitted by candidate codes is updated through internal association between code submission and by means of correlation between group vectors and vulnerabilities, and the identification precision in a multi-patch scene is improved.

The invention provides a smart financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: firstly generating a key pair and a digital ID according to a user certificate, and constructing a safety identity identification system; and obtaining user financial data through the digital ID, obtaining a credit value by combining a credit calculation intelligent contract, and screening selectable financial products from a product library. And then the data is stored in a distributed manner according to business departments to form a multi-node computing architecture. And each node adopts a K-means + + algorithm to perform initial clustering and calculate model parameters, and optimal model parameters are determined through target function evaluation and neighbor node parameter migration. And calculating the posterior probability of the user data point based on the optimal parameter, determining the category of the user data point, and outputting a clustering result. And finally, generating a grouping label according to a clustering result, and screening and recommending financial products from the selectable products. According to the method, through multi-technology fusion, accurate grouping and recommendation are realized while the data security is guaranteed, and the recommendation efficiency and reliability are improved.

The invention discloses a five-axis machine tool geometric error identification method based on single-axis and multi-axis measurement, and belongs to the field of numerical control machine tool precision detection. Error kinematics and multi-axis motion models are established based on a multi-body system theory, and error identification models of a linear axis and a rotating axis are respectively constructed. On the aspect of a linear axis, an identification method based on measurement of a positioning error and a straightness error is provided, two measurement / identification schemes of single-axis motion and two-axis linkage are designed, and 18 linear axis geometric error parameters related to the position and 3 linear axis geometric error parameters irrelevant to the position are obtained through calculation. In the aspect of a rotating shaft, 12 rotating shaft geometric error parameters related to the position and 4 rotating shaft geometric error parameters irrelevant to the position are obtained on the basis of an existing linear shaft result based on a measurement and identification scheme of a double-ball-rod instrument in three directions and six positions. According to the method, positioning and straightness error detection are completed in combination with auxiliary equipment such as a laser interferometer and a double-ball-rod instrument, and the method has good universality.

The embodiment of the invention discloses a target recognition method, device and equipment, a medium and a computer program product. The method comprises the steps of obtaining continuous multi-frame point cloud data corresponding to each target object in a target space; dividing the point cloud data corresponding to the target range according to the center of the point cloud data of each target object in each frame; based on the point cloud features corresponding to the divided point cloud data, generating target features corresponding to each target object in each frame; and performing target identification processing on each target object based on the target features to obtain a target identification result. According to the target identification scheme provided by the invention, the point cloud is associated with the target object, so that each target has exclusive point cloud information, identification is carried out according to the point cloud information of each target, the effect of tracking and identifying each target in the space is realized, multiple targets in the space are accurately identified, and the accuracy of target identification is improved.

The application provides an AI-driven software project full life cycle intelligent management method and system, relates to the technical field of software project management, and comprises the following steps: realizing demand structural analysis by fusing NLP and a knowledge graph, automatically supplementing implicit demand and compliance requirements; completing task automatic disassembly, work hour estimation and resource dynamic scheduling based on reinforcement learning; identifying risks in real time by fusing multiple source data and generating a treatment scheme; completing online verification according to an optimal path and realizing data closed loop self-optimization; the system comprises five modules, namely, demand analysis, task management, risk control, online verification and data storage, and breaks data islands by means of a unified data platform, a distributed message queue and a globally unique identification system. The application effectively solves the problems of demand deviation, progress lag, risk lag and data island, significantly improves demand accuracy, management efficiency, risk response speed and delivery quality, and is suitable for intelligent management of software project full life cycle in multiple fields.

The invention provides a dynamic identification generation and updating method for a data cross-border service, a switch and equipment, which realize breakthrough in the aspects of structural design, state management, scheduling strategy, propagation mode and the like of a data identification mechanism. A complete dynamic identification system supporting multi-service isolation, state sensing updating, non-intrusive propagation and boundary analysis auditing is formed, the method is particularly suitable for cross-domain and cross-border data application scenes with high requirements for supervision intensity, data identification precision and real-time performance, and the safety of data propagation is guaranteed. On a critical path of data cross-border transmission, identifiability of service identities, updatability of state information, controllability of identification behaviors and traceability of propagation paths are realized, and capabilities of cross-domain data streams in aspects of compliance supervision, behavior auditing, abnormity traceability and the like are remarkably improved; the method is suitable for data security management requirements in multi-service complex scenes such as government affair cloud, financial science and technology, industrial internet and the like.

The invention discloses a nuclear power intelligent labeling platform construction method based on semantic understanding, and relates to the technical field of data processing, and the method comprises the steps: determining a classification recognition scheme of a nuclear power file format; performing manual annotation to form annotated sample data; establishing an annotation task identification model, forming an annotation task decomposition mechanism, and forming an annotation precision setting mechanism; forming an annotation implementation mechanism, and integrating the task recognition model, an annotation task decomposition mechanism and an annotation precision setting mechanism into a nuclear power intelligent annotation platform; after iterative training, completing the construction of the nuclear power intelligent labeling platform; and after format recognition, the nuclear power intelligent labeling platform labels contents of different formats respectively. By forming a classification identification scheme, an annotation task identification model, an annotation task decomposition mechanism, an annotation precision setting mechanism and an annotation implementation mechanism, annotation is determined by using different modes based on text identification of different formats, and different annotations are performed according to different annotation requirements.

The invention discloses a multi-industrial equipment-oriented ELM fault identification method fusing feature attention and manual LU decomposition, and belongs to the technical field of industrial equipment fault diagnosis. The core of the method is that a feature attention mechanism and a manual LU decomposition module are fused: firstly, the feature contribution degree is quantified through a channel attention mechanism, key fault features are automatically focused, weak correlation feature interference is inhibited, and the quality of a hidden layer output matrix is improved; secondly, the manual LU decomposition module realizes autonomous controllability and numerical stability of matrix inverse operation through row principal component selection and regularization optimization, and is separated from the dependence of a third-party math library. According to the method, the accuracy of equipment fault identification is remarkably improved, the omission ratio and the false alarm rate are effectively reduced, memory limitation of edge equipment is overcome, and an efficient and reliable real-time identification scheme is provided for bearing abrasion and gearbox abnormity of a wind power variable pitch system and faults of other industrial equipment such as a machine tool and an industrial motor.

The invention relates to the technical field of high-altitude platform station operation and maintenance management, and provides a multi-load airship platform OMC system equipment identification and management method, which comprises the following steps of: distributing an equipment identification number for each managed equipment on an airship platform, pre-defining a unified management model for each equipment type, and adopting a layered micro-service distributed system architecture, an equipment management function is realized through cooperation among layers; and in the equipment access adaptation layer, a JavaScript-based plug-and-play plug-in framework is adopted, and a JS script corresponding to an equipment type is dynamically loaded and executed, so that protocol adaptation and format conversion of a management command and reported data are realized. The core problem of unified management of heterogeneous equipment in a multi-load airship platform is solved by introducing a hierarchical equipment identification system, a model-driven unified management framework, a micro-service architecture and a JS plug-in adaptation technology.

The invention discloses a power transformer parallel operation multi-modal data optimization storage method, which relates to the technical field of power data storage, and comprises the following steps: constructing an association identification system based on power transformer parallel operation characteristics; establishing an event-triggered time sequence document collaborative storage model which comprises three layers of data models; scene event driven synchronization is realized based on power grid event triggering, and a parallel operation characteristic parameter out-of-limit event is monitored as a synchronous triggering condition; when an event is triggered, scene identification change information in the event is analyzed through an Flink engine, the time sequence layer and the document layer are linked to execute dynamic storage strategy recombination, and cross-library associated corresponding data is injected. According to the invention, cross-device data association can be realized, traceable linkage of high-frequency measurement and scene parameters of all transformers in a parallel operation group is realized, and meanwhile, the data storage efficiency is improved through an event-driven synchronization mechanism.

The invention discloses an aquatic organism resource identification and traceability method and system based on image identification and deep learning, and is applied to the technical field of aquatic organism resource traceability. The method comprises the following steps: obtaining an image of an aquatic organism resource, sequentially carrying out ROI region extraction, attitude standardization and key point coordinate extraction and identification, establishing a data file of the whole life cycle of the aquatic organism resource, and storing traceability information to a cloud platform; and assigning a unique identifier to each batch of aquatic organism resources and associating the unique identifier with the traceability information, reading all the traceability information of the corresponding batch of aquatic organism resources through the identification identifier, performing identification through the shot image, and determining whether the identification information is consistent with the traceability information recorded in the traceability information. According to the method, the overall calculation amount is greatly reduced through a staged identification scheme, so that real-time processing in the aquatic organism resource transaction process is easier to realize, and the traceability information is ensured to be real and reliable in combination with a data verification technology.

The invention discloses a large language model intellectual property protection method and device based on domain-specific prejudice feature fingerprints, and belongs to the cross technical field of artificial intelligence safety and intellectual property protection. The method specifically comprises the steps that a three-dimensional orthogonal prejudice data set is constructed, and the data set depicts model prejudice features from the three dimensions of the field, the prejudice type and the response mode; screening the three-dimensional orthogonal prejudice data set based on a two-stage screening mechanism so as to extract core sample data with intra-family high stability and inter-family high discrimination; performing domain-specific prejudice feature fingerprint extraction based on the core sample data to obtain a standardized reference fingerprint; and performing ownership judgment on the to-be-tested model by adopting a confrontation verification mechanism based on the standardized reference fingerprint. According to the method, a quantifiable and high-robustness ownership identification scheme can be provided for the closed-source large language model, and the method has important application value for solving the safety problems of infringement and embezzlement of intellectual property of the large language model and the like.

The invention discloses an XGBoost algorithm-based direct-current source load-containing low-voltage system selective protection method. The method comprises the following steps of: extracting a fifth-scale detail component dd5 of a current signal in real time; comparing the value of the fifth scale detail component with a preset threshold, and only uploading threshold exceeding data; constructing a feature vector based on the uploaded data, and inputting the feature vector into an XGBoost model to locate an alternating current side fault; and the direct current side directly triggers the circuit breaker to open through local threshold comparison. An alternating current system fault position identification scheme based on an XGBoost algorithm is combined with a direct current system in-situ protection scheme based on short circuit fault early detection to form a low-voltage alternating current and direct current system selective protection method. The method is beneficial to improving the rapidity, sensitivity and selectivity of low-voltage AC / DC system protection.

The invention relates to a scientific research data modeling method and device, electronic equipment and a storage medium. The method comprises the following steps: uniformly describing a data structure of multi-modal scientific research data according to metadata by adopting a scientific research data modeling algorithm based on a DO-I RP protocol under a digital object architecture, and establishing a digital object class; a unique digital object identifier of the whole network is allocated to each multi-mode scientific research data resource through the established identification system; defining a digital object model by adopting metadata of a digital object class, establishing a relation type of a digital object, and generating a scientific research data model; and analyzing and accessing the scientific research data model through the scientific research data model operation support platform. According to the method, data line granularity identification is carried out on the scientific research data through data description and identification, and high-granularity safety control and quality management of the scientific research data can be supported. The scientific research data model is analyzed and stored through the scientific research data model operation supporting platform, and unified data packaging is achieved.

The invention provides a load scheduling processing method and system for a zero-carbon park, and belongs to the technical field of zero-carbon parks, and the method specifically comprises the steps: taking interaction types under different weather types, adjustment matching conditions of a user load in different time periods, and interval time period data of an internet surfing time period as a basis; the method comprises the following steps: determining an update identification scheme of a load scheduling strategy under a weather type when a settled enterprise in a park changes, determining an update processing scheme of the load scheduling strategy under different weather types based on the update identification scheme and a coincidence deviation condition of load data of a newly settled enterprise and an internet surfing time period, and updating the load scheduling strategy under the load scheduling strategy under the different weather types based on the update processing scheme and the coincidence deviation condition of the load data and the internet surfing time period of the newly settled enterprise. And the carbon emission of the park is reduced.

The application belongs to the technical field of image processing, and particularly relates to a crack image segmentation method and system. The robustness of a model to noise is improved by introducing Gaussian noise into image data for enhancement processing. An encoder composed of three layers of progressive stacked residual units is used to extract crack features, and a DropBlock regularization module layer is used to reduce feature redundancy and enhance feature diversity. The image space resolution of the crack features is restored by a decoder symmetrical to the encoder, and the fine structure of the crack boundary is restored. Finally, a dense conditional random field is used to optimize the crack probability distribution map, and the final crack segmentation map is output. The application effectively alleviates the foreground-background class imbalance problem, reduces false positives and false negatives, improves the segmentation performance of the model, and is particularly suitable for deployment on edge devices such as unmanned aerial vehicles and mobile phones, providing a high-precision, low-complexity and strong-robustness crack identification scheme for infrastructure health monitoring.

The present application provides a defense method, device, and processing equipment for intranet segment detection, which is used to provide a more refined identification scheme for intranet segment detection, thereby achieving an effective and stable defense effect and ensuring network security. The defense method for intranet segment detection provided by the present application includes: obtaining target network traffic with a traffic direction of a preset intranet segment from different network traffic; for the target network traffic, based on the three dimensions of the number of session packets, the load length, and the number of target segments, analyzing whether the target network traffic has intranet segment detection behavior; if so, responding to the target network traffic.

The invention belongs to the technical field of information, and particularly relates to a method and a device for analyzing data resources based on IPv6 (Internet Protocol Version 6) address identification. The method comprises the following steps: identifying a data resource by using an IPv6 address to form a data resource identifier based on the IPv6 address; and analyzing the data resource identifier based on the IPv6 address by using a DNS (Domain Name Server) system, and querying a target data resource according to an analysis result. According to the method, the massive advantage of the IPv6 address is fully exerted, data resource identification is realized based on the IPv6 address, and the reliability, the safety and the stability are higher than those of a newly established identification system; addressing analysis is achieved based on an existing network architecture and a DNS system, the existing network architecture does not need to be transformed on a large scale, the method has the advantage of being capable of being popularized and deployed on a large scale, and the problem that data resources cannot be accessed due to the fact that IP addresses are not changed in time due to the fact that geographic positions of data are frequently replaced can be solved.

The application discloses a kind of holographic fire management systems and methods, belong to computer and wisdom fire-fighting technical field.The method includes: obtaining fire-fighting facilities three-dimensional point cloud data and establishing equipment unique identification system;Collect equipment operating parameters;Real-time data correlation mapping is constructed, and sensor data is associated with three-dimensional digital model by message queue middleware and edge computing unit;Three-dimensional model and operating parameter are superimposed and displayed in actual equipment position by holographic projection or augmented reality module;Response user's gesture recognition and voice instruction bimodal interaction;Based on equipment logical correlation rule, the linkage relationship between water system, electrical system and control system is exhibited in different visual coding in three-dimensional holographic model.The application also discloses a kind of holographic fire management systems.The application solves the problem that space position and state data are separated in traditional fire management, and linkage logic is difficult to intuitively present, improves the visual management and operation and maintenance efficiency of fire-fighting system.

The invention discloses an identification analysis and conversion system and method based on GS1 and Ecode, and relates to the field of industrial internet identification analysis, and the identification analysis and conversion system based on GS1 and Ecode mainly comprises a registration management subsystem, a GS1 identification analysis subsystem, an Ecode identification analysis subsystem, a data support service subsystem and a platform docking service subsystem. According to the identification analysis and conversion system and method based on the GS1 and the Ecode, the GS1 and the Ecode can be integrated into a novel identification system of the industrial internet.

The present invention discloses a circulation data supervision method based on data interaction, which belongs to the field of data supervision technology and specifically comprises: establishing a dynamic identification system in the process of data circulation, wherein each data unit generates a unique dynamic identifier when initiating an interaction, and the dynamic identifier is irreversibly chain-updated with each data interaction behavior; setting an environmental perception node in the circulation path, collecting the network topology state, device physical position offset, and transmission medium electromagnetic characteristic parameters when the data interaction occurs in real time, and generating an environmental fingerprint data packet; asynchronously binding the dynamic identifier with the environmental fingerprint data packet to form a supervision index with time-space correlation; when the data unit circulates across domains, reconstructing a three-dimensional trajectory model of the data circulation path by reverse parsing the environmental fingerprint data packet in the supervision index; the present invention improves the efficiency and accuracy of data circulation supervision.

The present invention designs a method for traffic identification of open source proxy software based on feature optimization. The method constructs a cloud-based large-scale traffic collection and processing platform to generate a complete high-quality data set. According to machine learning theory and feature optimization ideas, an efficient identification scheme with three independent tasks is established, and the scheme is applied to complete flow identification. The cloud-based large-scale traffic collection platform mainly includes the construction of open source proxy services, the writing of user behavior simulation scripts, the collection and storage of server-side traffic, and the preprocessing of traffic data. The traffic identification scheme based on feature optimization defines three classification tasks on the one hand to achieve supervision from different angles and granularities, and on the other hand proposes a two-stage feature selection algorithm (TT) based on two dimensions to construct the optimal feature subsets under different classifiers for different identification tasks, which meets the requirements of rapid on-site detection while ensuring classification performance.

This invention discloses a method and system for automated generation and optimization of database table structures. The method specifically includes: receiving the user's backend project source code; extracting a semantic information network graph related to data persistence through static code scanning; inputting the semantic information network graph into an intelligent parsing engine to analyze code logic relationships and obtain index recommendation strategies and potential table structure design flaws; based on the index recommendation strategies and potential table structure design flaws, identifying sensitive fields and recommending encryption or de-identification schemes to obtain a first table structure; performing automated stress testing on the first table structure and adjusting the index strategy and partitioning scheme based on the test results; and optimizing the first table structure according to the adjusted index strategy and partitioning scheme to obtain a second table structure. This invention achieves intelligent generation and optimization of database table structures, improving development efficiency, enhancing maintainability, improving user experience, and ensuring data security.