349 results about "Match algorithm" patented technology

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

The invention relates to the technical field of software architecture design, and discloses a software architecture design method based on intelligent bionic implementation of a large model group. The method comprises the steps of obtaining software design requirement input; performing multi-level semantic analysis on software design requirement input to generate an initial component set and context environment description; based on the context environment description, candidate component knowledge related to the initial component set is retrieved from a distributed knowledge base; performing consistency verification on the candidate component knowledge and the initial component set by utilizing a dynamic inference engine to generate a verified component map; according to the verified component atlas, candidate software architectures are generated through an architecture template matching algorithm; and performing iterative adjustment on the candidate software architecture by adopting an optimization strategy, and outputting a target software architecture design. Through collaborative analysis and intelligent reasoning of the large model group, automation and intelligence of software architecture design are achieved, and the efficiency and quality of software design are improved.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention provides a reverse osmosis membrane cleaning system which is characterized in that a variable-frequency water pump control system is adopted to adjust water flow momentum according to cleaning parameters output by a matching algorithm, differential impact strength control of different areas on the surface of a membrane is realized through a flow adjusting valve group, and a stress distribution mode adaptive to pollution distribution is obtained; mechanical stress data of the membrane surface in the cleaning process are collected in real time through a membrane material stress monitoring sensor, if the stress value exceeds a membrane material safety threshold value, a stress protection mechanism is triggered, and the impact strength of a corresponding area is automatically reduced; establishing an adaptive parameter optimization algorithm according to pressure feedback data and a pollutant stripping effect evaluation result in the cleaning process, and calculating a parameter adjustment scheme of the next cleaning period through a tradeoff function of the cleaning effect and mechanical damage; a membrane performance degradation prediction model is established through accumulation of historical cleaning data, the remaining service life of a membrane material is predicted according to accumulated mechanical stress and cleaning frequency data, and a quantitative judgment basis of the membrane replacement opportunity is obtained.

The invention relates to the field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention discloses a clinical decision interaction method and system based on structured evidence reasoning, and relates to the technical field of computer technology and medical informatization. The method obtains a user input query; the method comprises the following steps: performing mixed entity identification from a query input by a user based on an entity identification model and a rule matching algorithm to obtain a candidate entity set, and performing conflict resolution and entity standardization processing on the candidate entity set to obtain a core entity; performing multi-granularity intention recognition based on confidence calculation between the classification systems of the core entity and the medical exclusive intention to obtain a target intention; performing screening query in a pre-constructed structured knowledge graph according to the core entity and the target intention to obtain a corresponding structured knowledge fragment; generating a cue word based on the structured knowledge fragment, user input query, instruction constraint and multiple verification; and inputting the cue word into the large language model, and outputting an answer. According to the invention, more accurate and more reliable dialogue service can be provided for the medical field.

The embodiment of the invention provides an intelligent monitoring method and system based on multi-source heterogeneous data fusion. The intelligent monitoring method based on multi-source heterogeneous data fusion comprises the steps that production data sets in different production scenes are collected and stored in a classified mode; mining association features of equipment operation behavior records and material circulation track information through semantic association analysis, and extracting influence features of production environment perception data in combination with environmental factor mapping to obtain multi-source qualitative features; generating a node relation chain according to the business process association relation, and configuring a matching algorithm of qualitative scene features; screening feature subsets conforming to association rules, and combining and associating the feature subsets to obtain a scene feature combination; creating a scene exception reasoning model by using the scene feature combination and performing exception feature evolution analysis to obtain an exception reasoning result; and the abnormal propagation path is traced, the root cause node is positioned, and the abnormal monitoring label is generated, so that the accuracy of abnormal root cause positioning and the scene adaptability are improved.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention provides a milling cutter wear volume in-situ calculation method and system based on three-dimensional reconstruction, and relates to the technical field of machine tool cutter state detection, and the method comprises the steps: obtaining a high-quality multi-view image, employing an improved SLIC superpixel segmentation algorithm to extract region characterization parameters of a wear region superpixel block based on the high-quality multi-view image, and calculating the wear volume of a milling cutter according to the region characterization parameters. Inputting the region characterization parameters into a multi-scale fusion feature extraction network, and extracting a two-dimensional binarization matrix of the wear region; wear region feature points of the two-dimensional binarization matrix are extracted through a fusion feature detection algorithm, and a high-precision sparse point cloud of a wear region is generated through optimization by adopting a dual-stage feature matching algorithm; reconstructing the high-precision sparse point cloud by adopting an improved PMVS dense optimization algorithm to obtain a high-precision wear area point cloud; and based on the high-precision wear area point cloud, carrying out precise quantitative calculation on the wear area by utilizing a double-model fusion algorithm, and outputting to obtain a wear volume value. According to the invention, in-place accurate measurement and calculation of the wear volume of the milling cutter are realized.

The invention discloses an e-commerce data cleaning method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the data information of various e-commerce customer data sources, recognizing the data type and the connection configuration of the e-commerce customer data sources, and converting the various data sources into target data according to a standardized interface and the connection configuration; determining a cleaning strategy based on the target data evaluation result, the data context and the historical cleaning effect through a reinforcement learning mechanism, and cleaning the target data based on the cleaning strategy; business meanings of fields in the cleaned data and the initial e-commerce user data are determined through semantic analysis, and a mapping relation between the initial e-commerce user data and the cleaned data is established according to the business meanings and a pattern matching algorithm; and converting the format of the cleaned data into a format meeting the data processing requirement of the e-commerce data processing system through the mapping relationship, so that the e-commerce data processing system correspondingly processes the cleaned data. And intelligent cleaning of the data is realized.

The invention provides an SM9-based identity-based searchable public key encryption method and an SM9-based identity-based searchable public key encryption system. The system comprises a key generation center, a cloud storage server and a file sending and receiving party. A secret key generation center generates system public and private keys by using an SM9 algorithm, and generates a private key for a user in combination with identity information of the user. The file sender uses the user identity of the receiver and the file keyword to generate a keyword index and uploads the keyword index to the cloud server; when a receiver retrieves the file, the auxiliary parameters in the index are firstly obtained from the cloud server, then a keyword trap door is generated through a private key of the receiver, and the keyword trap door is uploaded to the cloud server; and the server compares the index with the trap door through a matching algorithm, and returns a corresponding ciphertext file to a receiver for decryption if matching succeeds. The secret key generation method is consistent with an SM9 algorithm, the application field of SM9 is expanded, and the safe and efficient searchable public key encryption method which does not need certificate management and can effectively resist internal keyword guessing attacks is achieved.

The invention discloses an anti-leakage encryption system and method based on power grid data, and relates to the technical field of power grid data security encryption, and the method comprises the steps: dividing encryption levels according to power grid data sensitivity levels, and matching algorithms; during service operation, generating a dynamic key seed based on a real-time scene, and binding the dynamic key seed with data transfer node information to generate an initial dynamic key; user permission change is monitored, and the secret key is automatically updated according to the newest role and node information when conditions are met; encrypting each level of data by adopting a corresponding algorithm and a dynamic key to form a ciphertext; and performing permission verification during access, and decrypting the ciphertext by using the corresponding key and algorithm according to the authorized decryption hierarchy. According to the method, the real-time linkage of encryption protection, the data flow state and the user permission change is realized, so that a security mechanism can dynamically adapt to the service scene change, and the active defense capability of power grid data leakage prevention and the immediate effectiveness of permission control are enhanced.

The invention belongs to the technical field of lattice tower structure health detection, and relates to a lattice tower rod piece missing intelligent detection method based on multi-view three-dimensional reconstruction. According to the method, an adaptive feature matching algorithm fusing geometric features and texture descriptors is provided, camera pose estimation is optimized by constructing a global depth consistency scoring mechanism, and high-precision sparse point cloud reconstruction is realized; designing a depth map optimization strategy based on energy minimization, and effectively overcoming the interference of complex illumination and a low-texture environment on three-dimensional reconstruction in combination with density threshold filtering and geometric structure feature analysis; an ordered / disordered point cloud adaptive noise reduction method is innovatively provided, and noise robustness processing is realized through gradient constraint and covariance matrix eigenvalue analysis; and finally, an improved neighborhood radius adaptive registration algorithm is adopted, and a rod piece missing region is accurately identified through a significance scoring mechanism. The method remarkably improves the automation level and reliability of rod piece missing detection, and can be widely applied to structural health detection of the lattice tower.

The invention belongs to the technical field of computer vision and intelligent analysis, and particularly relates to a video image analysis system and an analysis method thereof.The method includes the steps that firstly, through the multi-modal processing thought of scene partition compensation, dynamic noise distinguishing and detail enhancement and self-adaptive interference coping, high-quality images are output for follow-up analysis; the target identification basic precision is guaranteed from the source; then fusing the appearance, motion and semantic multi-dimensional features of the target to realize accurate recognition, establishing a unique target ID in combination with a prediction matching algorithm, and matching with a shielding trajectory prediction and recovery mechanism to ensure that the target is continuously and stably tracked in a shielding and rapid moving scene and avoid interruption of a tracking link; on the basis, the method can adaptively cope with complex environments such as backlighting and rainy days, the image quality is improved through targeted preprocessing, a reliable foundation is laid for target recognition, and the problem of low recognition accuracy in complex scenes is effectively solved.

The invention discloses an agricultural machinery operation scheduling method, device and equipment based on Beidou positioning, and a storage medium, and relates to the technical field of Beidou intelligent agricultural machinery scheduling, and the method comprises the steps: calculating the matching degree score of each agricultural machinery for each operation task based on an agricultural machinery operation capability dynamic evaluation model, and obtaining a matching degree score result; judging whether a score difference value of each agricultural machine for the same task in the matching degree score result is smaller than a preset confidence threshold value or not, and obtaining a judgment result; when the judgment result is that the score difference value is smaller than a preset confidence threshold value, a multi-machine collaborative conflict resolution mechanism is started to execute a bilateral matching algorithm on each agricultural machine and each operation task, and a target scheduling scheme is obtained; and generating an operation path according to the target scheduling scheme, sending the operation path to the agricultural machine positioning terminal to perform agricultural machine operation, and through conflict resolution and dynamic path correction, significantly reducing the empty driving range and improving the operation response speed.

The invention relates to the field of three-dimensional vision, in particular to a label-free three-dimensional point cloud segmentation method based on a visual large model, which comprises the following steps: acquiring three-dimensional point cloud data, and extracting a boundary point set according to the three-dimensional point cloud data in combination with curvature and normal vector to construct a target function; solving the objective function by using an optimization algorithm, selecting an optimal projection visual angle combination, and extracting two-dimensional features of all two-dimensional projection images and three-dimensional features of three-dimensional point cloud data by using a visual large model; performing consistency matching on the two-dimensional features and the three-dimensional features through a matching algorithm to obtain a plurality of matching results; acquiring a definition weight factor and a coverage rate weight factor to calculate an importance weight; fusing the two-dimensional features according to the weight factors to obtain overall fused two-dimensional features; and inversely mapping the integrally fused two-dimensional features to a three-dimensional space to realize three-dimensional point cloud unmarked segmentation. The three-dimensional point cloud segmentation method has the effect of realizing three-dimensional point cloud automatic segmentation without labels.

The invention relates to the technical field of forest tree seed breeding, in particular to a seed vigor rapid detection method oriented to a forest tree seed breeding process. The technical problem that in the prior art, it is difficult to effectively distinguish signal changes introduced by physical property fluctuation and substantive spectral differences generated by biochemical deterioration in seeds, and the working efficiency and the detection accuracy are seriously affected is solved. The method comprises the following steps: determining reference parameters for correcting diffuse reflection spectrums of seeds based on spectral data of a plurality of seeds collected at the initial stage of material change; determining a first difference value based on the internal substance characteristic spectrum, a preset reference spectrum characteristic and a preset graph theory matching algorithm; performing compensation calculation on the first difference value based on the thickness characterization parameter, the reference parameter and a preset nonlinear attenuation model to determine a second difference value; and sorting the target seeds based on the second difference value and a preset sorting threshold value. The method is used for forest tree seed breeding scenes.

The invention discloses an operation state identification method and device of a transformer, a medium and equipment. According to the method, the real-time operation data, the historical data and the physical attribute parameters of the converter transformer are acquired, and the data are combined with the preset multi-physical-field high-fidelity simulation model to carry out multi-physical-field solution, so that a plurality of predicted values are obtained. Meanwhile, a time sequence neural network model based on an LSTM-Transform network obtained by training real-time and historical data is utilized to predict a temperature residual value. And dynamically adjusting the fusion weight of the first temperature prediction value and the actual monitoring value by comparing the residual error of the first temperature prediction value and the actual monitoring value of the simulation model, and generating a mixed temperature prediction value. And finally, the mixed temperature prediction value is input into a knowledge layer decision-making system, the system uses a preset threshold value judgment and similarity matching algorithm to output an operation state identification result of the transformer, and the problem that the operation state of the transformer cannot be accurately and efficiently identified in the prior art is effectively solved.

The invention discloses a real-time traffic flow abnormal event tracing method and system based on edge calculation, and the method comprises the steps: obtaining traffic flow data of edge nodes, analyzing the dynamic impedance of a road segment according to the traffic flow data, calculating the path selection probability of each path through employing an abnormal event wave proportion, extracting a traffic flow abnormal road segment, and carrying out the tracing of the traffic flow abnormal event. And performing spatial overlapping analysis on the abnormal road sections according to the path selection probability, matching the traffic event announcement root cause and the meteorological early warning root cause based on the association strength of each abnormal road section, generating an optimal matching root cause through a maximum weight bisection matching algorithm, and tracing the upstream traffic flow by using the optimal matching root cause and the path selection probability. According to the method, dynamic impedance analysis, SIR propagation simulation and Shapley value are utilized to quantify the contribution degree of the root cause to the abnormal road section, full-link traceability of the root cause, the path and the upstream node is formed, relatively complete data support is provided for traffic abnormity diffusion caused by traffic abnormity and meteorological abnormity, and the practicability and decision value of a traceability result are improved.

The invention discloses a vehicle control compensation method and device based on a cross-system coupling relation, and relates to the technical field of vehicles. The method comprises the steps that in the vehicle running process, multi-source running data related to a steering system, a driving system, a braking system and vehicle body postures are collected in real time; inputting the multi-source operation data into the reference health model to obtain a predicted behavior signal of the vehicle in a health state; comparing the predicted behavior signal with a corresponding actually collected behavior signal, calculating a coupling deviation between the predicted behavior signal and the behavior signal, and extracting a spatial-temporal characteristic of the coupling deviation; performing trend analysis on the coupling deviation based on the spatial-temporal characteristics of the coupling deviation, and identifying a recessive progressive degradation mode of the vehicle in combination with a preset threshold and a mode matching algorithm; and when the degradation trend is recognized, a control instruction is generated to perform control compensation on the vehicle. According to the invention, the problem of low timeliness and accuracy of a vehicle control compensation mode in the prior art is solved.

The invention discloses a visual anti-occlusion tracking method based on AIS and video fusion. The method comprises the following steps: synchronously obtaining AIS data and corresponding video data; preprocessing the AIS data, predicting latitude and longitude coordinates of the AIS data, projecting the latitude and longitude coordinates to a video pixel plane, and generating a ship AIS track; meanwhile, identifying a ship bounding box in a video frame by using a video detection algorithm, and tracking by using a three-stage association anti-occlusion tracking algorithm; the similarity between the AIS trajectory and the video trajectory is calculated through a dynamic time warping algorithm, and the correlation matching of the AIS trajectory and the video trajectory is realized by using a Hungary matching algorithm, so that a stable and anti-shielding ship tracking trajectory fusing AIS and video information is obtained. According to the invention, the problem that pure visual tracking is easy to fail under the shielding condition is effectively solved.

The application discloses a lossless compression storage method and system based on heterogeneous data in a Xinhua environment, first extracts the file type and structure feature mapping relationship by scanning the target file, classifies and groups, and then carries out preliminary lossless compression, further adopts a sliding window matching and sequence matching algorithm to optimize the compressed data for the redundant mode, then generates a metadata structure containing a check code and compression parameters, encapsulates into a data packet supporting cross-platform transmission, finally transmits to the target storage device through a secure write protocol, and verifies the data consistency and recovery path feasibility by using the check code. The application significantly improves the data compression efficiency, ensures the compatibility of cross-platform transmission and the integrity of storage data, and provides efficient and reliable technical support for complex electronic file processing.

The embodiment of the application discloses a self-defined encryption protocol clustering method and device, the method comprises the following steps: obtaining a target encryption protocol to be identified, and extracting traffic data and metadata of the target encryption protocol; constructing a label matrix and a value matrix based on the traffic data; and fusing the label matrix and the value matrix by using a multi-mode matching algorithm to obtain a clustering label. In this way, the self-defined encryption protocol clustering method provided by the application combines double-matrix combination with a pattern matching algorithm to realize clustering of self-defined encryption protocols. The method solves the problem that mixed self-defined protocols cannot be quickly and accurately clustered, and provides support for subsequent protocol analysis by quickly and accurately realizing clustering of self-defined protocols.

The invention provides an image generation method and device based on live-action fusion, equipment and a medium. The method comprises the following steps: S10, performing semantic element extraction on a style graph, and generating an element mask, a style description vector and a style depth graph; s20, carrying out structural feature preprocessing on the product graph to generate a contour line draft graph, a product depth graph and a scene description vector; s30, performing illumination parameter calculation on the style graph, the element mask, the style description vector, the style depth graph, the contour line draft graph, the product depth graph and the scene description vector by adopting a dynamic light matching algorithm to generate an illumination parameter set; and S40, inputting the contour line draft image and the product depth image into a control network of an image generation model, and inputting the element mask, the style description vector and the illumination parameter set of the style image into a cross-modal encoder to generate a final fusion image.

The invention relates to the technical field of crossing of computer vision, computer graphics and digital fashion, and discloses a three-dimensional digital clothing stylization method, which comprises the following steps of: firstly, constructing a double-encoder color mapping network to realize color alignment of a clothing image and a style image; the problem that training data is difficult to obtain is solved through a self-supervision strategy; then extracting depth features of the multi-view garment image and the style image by using a pre-trained VGG network, and embedding the style features into a garment feature space by means of an edge enhancement nearest neighbor feature matching algorithm; secondly, introducing an attention-guided edge enhancement feature extraction network, extracting edge features, and performing alignment optimization; finally, a stylized three-dimensional digital clothing image which has high style fidelity and clear structure details and supports rendering at any viewing angle is generated, the problems of color distortion, fuzzy details, inconsistent styles and the like in the prior art are effectively solved, and the method is suitable for the fields of virtual fitting, digital fashion design and the like.

The invention provides a clearing price interval prediction method based on an LSTM network and a similar point matching algorithm, and the method comprises the following steps: carrying out the optimization of an independent variable feature set, taking a real-time clearing price as a target variable based on the historical data of an electricity market at an interval of 15 minutes, constructing a point prediction model based on the LSTM network, and carrying out the calculation of the point prediction model. On the basis of optimization of an independent variable feature set, a long-short-term memory neural network is adopted to construct a real-time clearing price point prediction model, high-precision point prediction, similar point matching and statistical feature extraction are realized, feature vectors of to-be-predicted time points are constructed to be used for matching similar time points in a historical data set, and a real-time clearing price point prediction model is obtained. And constructing upper and lower limits of the prediction interval. A real-time clearing price point prediction model is constructed based on an LSTM network, and an upper limit and a lower limit of a real-time clearing price prediction interval are formed in combination with a similar point matching algorithm and an interval feature statistical analysis method, so that uncertainty in an electricity market is quantified better, and more decision bases are provided for market participants.

The invention discloses an intelligent tightening data acquisition method based on a part identification code, and relates to the technical field of data acquisition. According to the method, a tightening result is obtained by scanning a part identification code at a sub-packaging station, a sub-packaging tightening data packet is generated, and a sub-packaging timestamp is recorded; and the same part identification code and the VIN code of the whole vehicle are scanned again at the main line assembly station, compliance verification is carried out on the part identification code based on the BOM list corresponding to the VIN code, only the compliance part is allowed to enter the main line data cache region, and the assembly timestamp is recorded. And through a time window matching algorithm, a matching interval is constructed according to the average transfer time length and the standard deviation of the part types between sub-packaging and assembling, sub-packaging and assembling data are automatically matched, and data verification is executed. And after matching succeeds, binding the subpackage tightening data with the VIN code of the whole vehicle, if a multi-version record exists, executing version control and generating a version chain, and finally uploading the tightening associated data of the whole vehicle to a tightening server.

The invention relates to the technical field of wireless communication networks, and discloses an air-ground three-dimensional networking data access optimization method, system, device and medium, and the method comprises the steps: obtaining the operation state data of an unmanned aerial vehicle in a target area and the to-be-transmitted data of a ground terminal in real time, so as to carry out the data value-transmission cost basic evaluation, and obtaining a core evaluation index; introducing a space-time value compensation mechanism to carry out time dimension and space dimension supplementary evaluation on the to-be-transmitted data, and determining a data access utility value of the to-be-transmitted data admitted by the unmanned aerial vehicle based on two evaluation results; a distributed bilateral matching algorithm is adopted, and admission matching of the unmanned aerial vehicle and the ground terminal is carried out based on the data access utility value; according to the method, data key information can be effectively extracted, invalid energy consumption of unmanned aerial vehicle data admission is reduced, the data admission requirement of a three-break geographic space data unbalanced distribution scene during peacetime and wartime combination is met, and the method has outstanding engineering practical value and innovativeness.

The invention discloses a loose coupling unified task arrangement method supporting multi-dimensional expansion, which comprises the following steps of: firstly, defining a transmission agreement of task arrangement: constructing a unified intermediate representation structure for describing an arrangement process, the intermediate representation structure being used for recording a process topological structure, a node type and parameter configuration; secondly, on the basis of the intermediate representation structure, an executable directed acyclic graph is automatically constructed by adopting a child node matching construction algorithm based on queues and backtracking; and thirdly, executing an arrangement process based on the constructed directed acyclic graph: starting from all write nodes, recursively calling a data processing method of a father node until reaching a read node, and executing a complete data processing link in a bottom-up manner. Through a unified intermediate representation structure and a queue backtracking matching algorithm, automatic topology construction and dependency management of a complex process are realized, flexible expansion of node types is supported, development complexity is reduced, and adaptability, efficiency and maintainability of a data management system are improved.

The invention discloses a three-dimensional target reconstruction and instance association method based on a multi-view image. The method comprises the following steps: firstly, acquiring initial three-dimensional representation and camera parameters of a scene through a three-dimensional reconstruction model; secondly, carrying out two-dimensional target detection on the plurality of input images; the method comprises the core steps that cross-view instance association is carried out on a detected target, a six-dimensional feature vector containing a three-dimensional position and a camera observation direction is constructed, clustering is carried out by adopting a greedy matching algorithm with constraints, and the algorithm ensures that one instance only corresponds to one detection frame in a single image; finally, the model can be selectively optimized through instance segmentation and secondary reconstruction, and a three-dimensional bounding box is generated for each instance. According to the method, the problem that instances are wrongly merged in a traditional clustering method is solved, the accuracy and robustness of multi-view target association are remarkably improved, and a high-quality independent three-dimensional target model can be generated.