34 results about "Bipartite graph matching" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the technical field of internet data processing and resource scheduling, and discloses an intelligent order sending method and system for housekeeping service and a storage medium, and the method comprises the steps that a server side receives original order information containing house investigation data; calling a spatial granularity decomposition model to analyze the order into independent operation area sub-tasks, and calculating the lowest skill level demand in combination with the feature vector; attribute vectors such as comprehensive skill levels and real-time coordinates of service personnel are obtained; constructing a dynamic bipartite graph matching model by taking the subtask set and the attributes of the service personnel as input; an optimal mapping relation list is determined by solving the maximum value of an objective function, and a dispatching instruction is sent. The method further comprises an attendance checking judgment step based on a geofence and a closed-loop iteration step of dynamically updating skill levels according to attendance checking and evaluation data. According to the method, refined disassembly and global optimal scheduling of housekeeping tasks can be realized, and the resource allocation efficiency and the service quality are remarkably improved through a dynamic closed-loop mechanism.

The invention discloses a timing sequence statement positioning model training method and device based on proposal selection and anchor point distribution, and relates to the technical field of timing sequence statement positioning. The method comprises the following steps: initializing a fixed number of learnable queries according to a first static anchor point set; on the basis of the first learnable query, according to the unpruned long video and the natural language description text, performing proposal generation through a time sequence statement positioning model; based on the proposal selection module, redundant proposal filtering is carried out by using a non-maximum suppression algorithm; based on an anchor point distribution module, according to the untrimmed long video, the first static anchor point set and the candidate proposal set, performing bipartite graph matching by using a Hungary algorithm; and carrying out loss function weighting calculation according to the unpruned long video and the optimal proposal set, and carrying out parameter optimization on the time sequence statement positioning model to obtain an optimized time sequence statement positioning model. The method is an efficient and accurate time sequence statement positioning model training method combining proposal selection and anchor point distribution.

The invention discloses a traditional Chinese medicinal material defect detection method based on machine vision, relates to the technical field of intelligent detection, and solves the problems that in the prior art, a threshold cannot be migrated, so that an inter-batch judgment standard is unstable, and high-resolution block reasoning causes boundary pseudo defects and instance wrong affiliation. According to the method, a reference area is constructed in a batch domain, a statistical descriptor is extracted, and monotone mapping calibration is performed on defect scores so as to realize cross-batch unified judgment; meanwhile, a seam continuity field is introduced in the image block splicing process, defect connected domains are fused through a cross-window adjacency relation, and accurate attribution of defects and medicinal material pieces is achieved based on bipartite graph matching; according to the method, the judgment stability and the statistical reliability in the traditional Chinese medicinal material defect detection process are remarkably improved.

The invention discloses a multi-source geographic vector data matching method based on topological relevance, which comprises the following steps: collecting multi-source basic geographic vector data to form a preprocessed data set; constructing multi-index data quality evaluation based on local topological features; extracting and classifying spatial topological relation edges; constructing a weighted adjacent matrix of the target map; a neighborhood topology consistency measurement index is calculated, and neighborhood topology consistency quantization and adaptive weight fusion are carried out; establishing a global optimization objective function by taking the confidence coefficient matrix as input, solving a maximum weight bipartite graph matching problem with topology constraints by taking the topology retention rate as a hard constraint condition, and outputting a global optimal matching scheme; and constructing a quantitative evaluation index topology deviation measure based on topology invariant difference, judging whether a matching scheme has a significant topology conflict, and performing hierarchical repair according to the conflict condition. According to the method, the reliability and logic self-consistency of multi-source vector data fusion are improved, and the key problem of topological contradiction in the prior art is solved.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention belongs to the technical field of energy storage system operation and maintenance and data diagnosis, and particularly relates to a highway zero-carbon service area-oriented energy storage data diagnosis method and device, and the method comprises the steps: carrying out the real-time monitoring of an energy storage system asset SN set, channel-asset binding and channel physical feature data, recognizing an abnormality, and triggering self-verification; identifying a natural or artificial anchor point through a charging session and grid-connected power steady-state constraint, determining a time window, extracting channel fingerprint features, matching an asset SN historical feature template according to an operation mode, and calculating a normalized difference degree verification binding relation; when mismatching is bound, an optimal rematching scheme is solved through bipartite graph matching according to physical topology constraints, independent verification is carried out through a station-level power closing auditing model, a diagnosis baseline is reconstructed in a layered mode after verification is passed, and an alarm and diagnosis conclusion is output by adopting a hierarchical gating strategy in combination with multiple states. The alarm accuracy is improved, misdispatch work orders are reduced, the diagnosis conclusion can be traced and audited, and stable support is provided for zero-carbon operation accounting.

The invention discloses a monocular vector high-precision map data association method based on geometric context, and relates to the technical field of map data association. Extracting a map landmark set in a current frame view range from the map through the initial vehicle pose, and projecting a point set to a pixel plane; performing structured modeling on each point set in the detection road sign set and the projection road sign set; constructing a cost matrix for the current estimated vehicle pose; introducing a gating mechanism based on map distance and a self-adaptive gating range based on map depth, and eliminating matching pairs with inconsistent geometric structures; complete data association is obtained through cost matrix calculation, optimal bipartite graph matching is performed on the cost matrix by adopting a Hungary algorithm, and a data association relationship meeting global optimum is obtained. According to the method, feature information of road signs in a vector high-precision map is fully utilized, outlier matching relation pairs are eliminated through self-adaptive gating, finally, data association is conducted through a Hungary algorithm, and matching robustness and accuracy are improved.

The invention provides a super-large-scale MIMO system beam focusing method based on an adaptive time delay-phase structure, and relates to the technical field of radio communication, comprising the following steps: constructing an adaptive time delay-phase structure suitable for a super-large-scale MIMO system, and establishing a near-field beam focusing joint optimization mathematical model; solving an all-digital optimal beam focusing matrix through maximum ratio transmission; a switch matrix optimization problem is converted into a bipartite graph matching problem, and an optimal matching matrix is solved by using a Hungary algorithm; solving a phase shift matrix through a Riemann spectrum conjugate gradient method; optimizing the time delay matrix by using a gradient descent method; optimizing the digital precoding matrix by using a least square method; and giving a base station beam focusing matrix through alternate iteration, and completing beam focusing of the super-large-scale MIMO system. According to the structure provided by the invention, dynamic sharing with the antenna array elements can be realized by utilizing the switching network, so that the adaptive capacity of hybrid precoding to a channel environment is effectively enhanced.

The invention discloses a cutting track planning method and device and a storage medium, and the method comprises the steps: building a bipartite graph model of a workpiece graph set and a threading hole set, and building a weight matrix based on an edge set in the bipartite graph model; based on the weight matrix, performing optimal matching on a workpiece graph node set and a threading hole node set by adopting a bipartite graph matching algorithm, and determining a matching pair between the workpiece graph and the threading hole according to nodes which can be matched with each other; performing type division on the workpiece graph according to the matching pair to obtain a male die and female die workpiece graph set, and combining the cutting starting point position of the workpiece graph to obtain a first corresponding relation between the female die workpiece graph and the cutting starting point position and a second corresponding relation between the male die workpiece graph and the cutting starting point position; and according to the obtained first and second corresponding relations, carrying out cutting track planning on the workpiece graphs in the female die and male die workpiece graph set. Therefore, batch programming of a plurality of male dies and female dies is realized, and the efficiency of generating a machining program is improved.

This invention relates to the fields of data processing and talent matching technology, and in particular to a two-way matching method for university-enterprise talent based on financial capability profiling. The method includes: constructing a corporate financial capability profiling indicator system, including indicators of corporate financial stability and job financial capacity; constructing a talent financial capability profiling indicator system, including indicators of financial knowledge reserves, financial practice skills, financial risk awareness, and financial digitalization capabilities; collecting and preprocessing data from both the corporate and talent sides; calculating a comprehensive evaluation value based on a combined weighting method using an improved analytic hierarchy process (AHP) and entropy weighting method; constructing a corporate job demand vector and a talent supply vector; solving for the optimal matching scheme based on a bipartite graph matching algorithm, with the objective function of maximizing two-way matching satisfaction; and outputting the matching results. By constructing a two-dimensional evaluation system of corporate and talent financial capabilities, and using an improved weighting method and matching algorithm, a precise two-way matching of university and enterprise talent is achieved, improving the scientific nature of the matching.

The application provides a kind of based on adaptive time delay-phase structure's super large scale MIMO system beam focusing method, it is related to radio communication technical field, including the following steps: constructing adaptive time delay-phase structure suitable for super large scale MIMO system, and establishing near-field beam focusing joint optimization mathematical model;Through maximum ratio transmission solving all digital optimal beam focusing matrix;Switch matrix optimization problem is converted into two-part graph matching problem, and optimal matching matrix is solved using hungarian algorithm;Through riemann spectrum conjugate gradient method solving phase shift matrix;Utilize gradient descent method to optimize time delay matrix;Least square method is used to optimize digital precoding matrix;Through alternate iteration, base station beam focusing matrix is given, and the beam focusing of super large scale MIMO system is completed.The structure provided in the application can be realized with switch network and the dynamic sharing of antenna array element, so as to effectively enhance the adaptive ability of hybrid precoding to channel environment.

The application belongs to but is not limited to the technical field of automatic student behavior analysis, and discloses a classroom student behavior recognition method and system based on a Transformer detection and hierarchical soft routing decision. Frame images are intercepted from classroom monitoring videos and input into a pre-trained RT-DETR model; multi-scale features of the images are extracted by a hybrid encoder of the RT-DETR and input into a Transformer decoder, global context information of the images is captured by using a self-attention mechanism, and a set of boundary boxes of N student targets is predicted by using a bipartite graph matching mechanism; each boundary box detected is dynamically expanded and cropped; a pre-trained visual-language large model CLIP is used to construct a hierarchical decision system with a logical tree structure to perform hierarchical feature extraction; and an output result is obtained by reasoning based on a low-threshold parallel activation soft routing decision mechanism; and the application significantly improves the recognition accuracy of fine-grained actions.

The invention relates to the technical field of vehicle task scheduling, in particular to a dynamic vehicle task scheduling method and system based on two-stage differentiation and multiple targets. The method comprises the following steps: acquiring vehicle data and task data; performing time slice static modeling based on the task data; carrying out vehicle classification and attribute modeling based on the vehicle data; according to a modeling result, performing two-stage differentiation and multi-target dynamic vehicle task allocation, including a first stage of sparse bipartite graph matching allocation and a second stage of CMOEA-EG multi-target optimization; performing fairness evaluation and feedback based on a distribution result; and updating and optimizing based on a feedback result. The task execution result is fed back in real time through the dynamic updating module, and multi-round iterative optimization is supported.

The application discloses a photovoltaic array fault detection and positioning method based on bipartite graph matching, comprising the following steps: 1) based on the connection structure of a photovoltaic array, a photovoltaic array bipartite graph is established, and the connection points of components on the same photovoltaic component string constitute an independent node set in a sub-bipartite graph; 2) under the complete matching rule of the bipartite graph, the minimum edge covering rule and the constraint that equal-weight points in the bipartite graph are not connected, edge matching of the bipartite graph is derived, the edge is converted into a voltage sensor, and optimal configuration of the voltage sensor is obtained; 3) the fault voltage threshold of the photovoltaic component under different faults is calculated, the actual measured value of the photovoltaic component voltage collected by the voltage sensor is compared with the fault voltage threshold, and photovoltaic array fault detection and positioning are realized. The application realizes accurate detection and positioning of open-circuit, short-circuit, aging and shading faults of each photovoltaic component by using fewer voltage sensors, and the voltage sensor only needs to be connected between adjacent photovoltaic component strings, and is easy to operate.

This invention discloses a client selection method and system for multi-task federated learning. Addressing the shortcomings of client selection and insufficient consideration of task urgency in multi-task dynamic federated learning scenarios, this invention first constructs a multi-task federated learning system model, defining a utility function that includes learning quality and penalty terms. Second, it establishes fairness constraints and introduces a fairness queue to transform the problem into a queue stability problem. Then, based on Lyapunov optimization theory, it constructs a drift-plus-utility function, transforming a long-term stochastic optimization problem into a deterministic optimization problem for each round of communication by minimizing its upper bound. Finally, it constructs an auxiliary bipartite graph to transform client selection into a minimum-weight bipartite graph matching problem. This invention, by jointly optimizing fairness, learning quality, and task urgency through the Lyapunov framework, transforms long-term constraints into a solvable problem for each round, reducing computational complexity and achieving efficient and fair dynamic client selection.

The invention discloses a coal mine area personnel limit monitoring method, device and equipment, a medium and a product. The method comprises the following steps: acquiring image data of a coal mine area access channel at the current moment in real time, and preprocessing to obtain processed image data; performing background detection and updating processing on the processed image data according to a background model and an image segmentation threshold value at the previous moment to obtain a foreground model and a background model; performing personnel fitting modeling on the foreground model to obtain a spatial position set of all personnel in the foreground model; and according to the spatial position set, a time sequence bipartite graph matching method is adopted to generate a personnel movement track of the coal mine area, and the real-time number of people in the coal mine area is determined so as to carry out area personnel limiting monitoring. The method can accurately position the positions of people in the foreground, avoids repeated counting or missed counting, guarantees the accurate real-time counting of people in the region, and provides reliable data support for personnel limit monitoring.

The application provides a security conflict detection and elimination method for weighted bipartite graph matching. Firstly, the method uses the order-revealing encryption to encrypt the weight data of the bipartite graph, so that the edge server can compare the data size without obtaining the data plaintext. Secondly, the method uses the secret sharing technology to split the weight value into two share weight values, so that any edge server cannot obtain the complete weight value in the conflict elimination process. Finally, the method designs a secure conflict detection and elimination algorithm based on the idea of the greedy algorithm, and iteratively compares the conflicts through the multi-edge server cooperation mode until the optimal allocation result is found. Through the scheme, the complexity of the bipartite graph optimal matching is reduced under the condition of ensuring the data security, and the matching efficiency is greatly improved.

The application provides a method and device for online order matching of online car-hailing based on double-target linear programming, wherein the method constructs a double-target online bipartite graph matching model which can be reused by online car-hailing drivers for real online car-hailing historical order data, and obtains linear programming offline optimal score solutions of driver income and passenger fairness respectively, and sets a parameter a for balancing driver income and passenger fairness, and flexibly balances between driver income and passenger fairness; according to estimation of historical order data, a two-stage matching strategy of detection-submission is completed, patience of passenger request is fully considered, and the method is more practical and applicable; occupied resources are marked as unavailable, and are recycled after being released for a period of time, which conforms to the real scene that resources are put into use again after being occupied for a period of time.

The application discloses a dynamic environment-oriented embodied multi-agent flexible arrangement system. Firstly, the group intelligence layer solves the dynamic environment lag problem through the local semantic observation flow with time dimension and the information time effectiveness decay mechanism, incrementally constructs a global probability semantic topological graph, generates a space probability density field through a graph attention network reasoning, and calculates target prediction coordinates; secondly, the strategy layer constructs a dynamic multi-dimensional utility tensor based on the topological graph, optimizes scheduling by using an incremental bipartite graph matching algorithm with virtual node splitting, and realizes scheduling and fault tolerance coupling by combining with the guardian cascaded monitoring topological; finally, the autonomous layer adopts a double-process asynchronous parallel architecture, executes a main thread to realize task disassembly and real-time execution, and guarantees stable operation of the system by a guardian monitoring thread. The application can realize seamless task takeover when an agent unexpectedly exits or fails, supports dynamic increase or decrease of agents during task execution, and rebalances the load according to real-time state.

The invention discloses a network utility optimization method for a multi-unmanned aerial vehicle cooperative mobile edge computing network, and belongs to the field of wireless communication. The implementation method of the invention comprises the following steps: constructing a multi-UAV collaborative MEC network architecture; respectively constructing a communication and calculation model, a network cost model and a network utility model by combining the unloading selection variable and the calculation selection variable; 2, constructing an optimization problem by taking maximization of the network utility model obtained in the step 1 as a target; the UPTSM algorithm decomposes an original optimization problem into two sub-problems, and the two sub-problems are converted into two bipartite graph matching problems; sequentially solving the two problems by adopting a Hungary algorithm to obtain an unloading selection variable and a calculation selection variable; the VC-WOA limits variables violating the task processing time limit to quickly search legal solutions, so that the network utility is improved to the greatest extent. According to the invention, by optimizing multi-UAV cooperative task scheduling and energy management, the cooperation efficiency can be improved, and fair task distribution is realized, so that the network income is maximized.

The invention discloses an illumination adaptive underwater checkerboard ordered sub-pixel angular point detection method, and relates to the technical field of computer vision and underwater robots. The method comprises the following steps: firstly, carrying out feature enhancement on an input image through an illumination adaptive front end based on a Retinex theory and gradient fusion, and extracting a global illumination embedding vector; and dynamically modulating the characteristics of the backbone network according to an illumination embedding vector by using a condition batch normalization mechanism so as to adapt to different underwater illumination conditions. Secondly, outputting an angular point confidence map, a sub-pixel offset field, a row sequence classification thermodynamic diagram and a column sequence classification thermodynamic diagram in parallel through a multi-head prediction decoder, and introducing angular point auxiliary classification loss in training to strengthen row and column semantic separability; and finally, constructing a global cost matrix fusing angular point confidence and row and column response, solving optimal topological mapping by using a bipartite graph matching algorithm, and directly outputting ordered sub-pixel angular points conforming to a physical topological structure. According to the method, the detection robustness and the positioning precision in an underwater complex scene are remarkably improved, and the problem of disordered angular point sorting is effectively solved.

The invention discloses a novel power equipment intelligent inspection system based on a large model. The novel power equipment intelligent inspection system comprises a multi-modal data module, a degree detection module, a quantitative analysis module and a cross-modal enhancement module, the multi-modal data module is used for collecting multi-dimensional data in a non-contact mode and conducting triggering type fusion and time synchronization on the multi-dimensional data. The degree detection module can utilize priori knowledge of a pre-trained matching large model, obtains limited sample data by generating a descriptive text to guide visual feature matching, identifies multi-dimensional data outliers in a feature space as potential unknown aging candidates by adopting a bipartite graph matching technology, and outputs an insulation aging degree; and the quantitative analysis module calculates a one-way distance value between the insulation aging degree point cloud and the target point cloud. According to the method, the aging degree data which does not appear in the training set can be actively found and marked, the aging degree detection range of the system is fundamentally expanded, and the capability of identifying the abnormality of the equipment in the booster station is improved.

The present application belongs to the technical field of energy storage system operation and maintenance and data diagnosis, and particularly relates to an energy storage data diagnosis method and device for a zero-carbon service area of a highway, which identifies abnormalities and triggers self-checking by real-time monitoring of energy storage system asset SN set, channel-asset binding and channel physical characteristic data; identifies natural or artificial anchor points and determines a time window through charging session and grid-connected power steady-state constraint, extracts channel fingerprint characteristics, matches asset SN historical characteristic templates according to the operation mode, and calculates the normalized difference degree to check the binding relationship; when the binding is mismatched, the optimal rematching scheme is solved through bipartite graph matching according to the physical topology constraint, and is independently verified through a station-level power closed audit model, and after verification, the hierarchical reconstruction diagnosis baseline is verified, the multi-state is combined, a hierarchical gating strategy is adopted to output an alarm and a diagnosis conclusion, and the accuracy of the alarm is improved, the number of misdispatched work orders is reduced, the diagnosis conclusion is traceable and auditable, and stable support is provided for zero-carbon operation accounting.

The invention relates to the technical field of model optimization, in particular to an aggregation Markov model dimension reduction method and device for dynamic system monitoring, and the method comprises the steps: firstly obtaining a probability transfer matrix Q, an observation probability matrix O and an initial distribution vector p of a target model; generating a diagonal matrix based on O, and constructing an intermediate matrix M fusing dynamic and initial information of the model; calculating the maximum matching number E of the bipartite graph constructed by the M, and comparing the maximum matching number E with the model dimension n to judge whether the dimension of the model can be reduced or not; if the dimension can be reduced, for each observation category, constructing a conversion sub-matrix based on a bipartite graph matching result of the sub-matrix blocks, and combining to form a conversion matrix T; and finally, constructing a dimensionality reduction aggregation Markov model with lower dimensionality by utilizing T and a left inverse matrix thereof. The invention provides a determinable automatic dimension reduction mechanism, which can obviously reduce the calculation complexity on the premise of ensuring the function equivalence of the model, and is particularly suitable for edge calculation scenes with limited resources.

The invention discloses an ambiguity fixing method for Beidou satellite multi-frequency adaptive distribution, which belongs to the technical field of satellite navigation and positioning, and comprises the following steps: taking Beidou satellite multi-frequency observation data; constructing a multi-level decision tree, carrying out hierarchical identification on an observation environment based on signal quality parameters and geometric configuration factors, and determining an optimal frequency candidate set; selecting an optimal frequency combination based on a bipartite graph matching algorithm to form a differentiated frequency configuration scheme, and performing individualized frequency allocation for signal characteristics of different satellites; based on the differential frequency configuration scheme, a multi-level ambiguity fixing strategy is adopted to carry out step-by-step fixing, and integer ambiguity is obtained; and the reliability of a fixed result is confirmed through multi-frequency cooperative verification. Through environment adaptive identification and personalized frequency distribution, the problems of poor environment adaptability, frequency configuration solidification and the like of a traditional method are solved, the ambiguity fixing success rate and the positioning precision in a complex environment are improved, and centimeter-level high-precision positioning is realized.

The embodiment of the invention belongs to the technical field of artificial intelligence, and relates to a user clue circulation display method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the sorting operation of network resources of displayable networks according to a preset network priority library, and obtaining a network priority set; calculating an optimal matching scheme of the business opportunity information set and the website priority set according to a bipartite graph matching algorithm to obtain a business opportunity website allocation set; sorting distribution results of the business opportunity website distribution set by priority according to a real-time circulation display algorithm to obtain a user clue display list; and carrying out website circulation display operation on the user clues corresponding to the target mobile phone number according to the user clue display list. The method can be used for clue circulation processing in a financial science and technology business system, realizes accurate matching of business opportunities and websites, fully considers multiple factors such as business capabilities of the websites, customer preferences and importance degrees of the business opportunities, and improves the conversion rate of the business opportunities.

The application provides a tooth recognition and classification method, an electronic device and a storage medium. The tooth recognition and classification method first samples segmented tooth data and pre-processes each sampling point to obtain spatial three-dimensional coordinates of sampling points corresponding to all teeth; then, for each tooth, according to a tooth region where the current tooth is located and the spatial three-dimensional coordinates of the sampling points corresponding to the current tooth, all sampling points belonging to the current tooth are cropped to obtain a cropped block corresponding to the current tooth; next, a preset tooth category query vector and all cropped blocks are input into a trained tooth category recognition model to obtain a preliminary recognition result of tooth categories of all teeth; finally, the preliminary recognition result of the tooth categories is post-processed by a bipartite graph matching method to obtain a final recognition result of tooth categories of all teeth. The application realizes automatic recognition of tooth categories, has high recognition accuracy and good generalization.

The invention discloses an information completion method and device for table identification, and the method comprises the steps: carrying out the identification of data containing a table, and outputting a Token set T containing specified dimension information; in combination with visual features and OCR row block information in the Token set, predicting table boundaries, row and column division and merged cells, and outputting an initial two-dimensional structure grid; corresponding cells distributed to the initial two-dimensional structure grid in the set are screened out of cells which are not distributed or whose distribution confidence is lower than a preset threshold value, and a non-falling grid set is formed; for each non-lattice text in the non-lattice set, generating a candidate cell set; based on a comprehensive scoring function, calculating a matching score of each non-lattice text and each candidate cell in the corresponding candidate cell set; performing bipartite graph matching based on the matching score to maximize a global matching score; and complementing the missing text into the table based on the global matching score.