48 results about "Local matching" patented technology

The invention discloses a business process multi-dimensional logic verification method and system based on a dynamic knowledge graph, and relates to the technical field of artificial intelligence and financial risk control. The method aims at solving the technical problems that a traditional rule engine cannot process unstructured data, and a large generative model has logic illusion and privacy risks. The method comprises the following steps: constructing a rule knowledge graph containing parameterized logic constraints (such as time sequence, mutual exclusion and numerical accumulation); converting the unstructured business operation record into a feature vector by using a locally deployed semantic model; performing global sequence alignment optimization by constructing a global semantic consistency objective function, mapping operation records to a theoretical decision path derived from a graph, and eliminating local matching ambiguity; and finally, on the basis of a mapping result, calling deterministic parameters in map edge attributes to execute strict mathematical logic verification (including complex workday calculation, mutual exclusiveness check and the like). According to the method, a'nerve-symbol 'dual architecture is adopted, accurate understanding of fuzzy semantics is achieved, logic verification certainty and data privacy security are guaranteed, and the method has a hidden process discovery capability.

The invention discloses a video key frame extraction method fused with self-supervised deep learning, and the method comprises the following steps: carrying out the standardized sampling of an input video according to a fixed interval, extracting a SuperPoint local key point and a Video MAE global semantic feature, and generating a dense descriptor and a semantic vector; constructing multi-dimensional change indexes such as local matching degree and global similarity; constructing a soft distribution matrix and a matching point set based on the fusion features; dynamically judging the key frame through a self-adaptive multi-threshold rule; and outputting the key frame set. The method fuses local and global spatio-temporal information, has robust feature extraction and key frame discrimination capabilities under a weak supervision condition, and can effectively improve the efficiency and precision of video compression, abstract and event detection.

The invention relates to an environment sensing method based on target detection and stereo matching. According to the environment perception method based on target detection and stereo matching, targets in an environment image can be recognized through model training, and category information and position information of each target can be recognized, so that a target detection model can recognize the category information and the position information of the targets in the environment image in real time; through a local matching strategy of a target area delineated by focusing on the position information of the target, huge calculation overhead caused by full-image matching is avoided; pixel-level parallax information in the two-dimensional image is accurately converted into a physical depth distance in a three-dimensional space in combination with calibration parameters of a binocular camera in an environment sensing system; and finally outputting an environment perception image capable of displaying a detailed perception result of each detected target through information fusion. According to the environment sensing method, the efficiency and robustness of the whole sensing process are remarkably improved while the accuracy and integrity of environment sensing are ensured.

The invention relates to the technical field of three-dimensional modeling, in particular to an augmented reality-oriented three-dimensional scene reconstruction method and system, and is used for solving the technical problem that an iterative nearest point algorithm cannot meet high real-time performance and high-precision reconstruction requirements required by augmented reality at the same time. The method comprises the following steps: acquiring a pose transformation relationship between multiple frames of point cloud data and adjacent frames of point cloud data; extracting a plurality of feature points in each frame of point cloud data, and determining a feature descriptor of each feature point; according to the feature descriptors of the feature points, calculating a matching weight between feature point pairs in two adjacent frames of point cloud data, and calculating a local matching error based on the matching weight and a pose transformation relationship; performing global matching optimization based on the local matching error of the plurality of feature point pairs and the distribution density of each feature point in the respective point cloud frame, and determining an optimal matching point pair set which minimizes the global matching error; and according to the optimal matching point pair set, fusing the multi-frame point cloud data, and reconstructing a three-dimensional scene model.

The invention relates to the technical field of strain measurement, in particular to a three-dimensional full-field strain measurement method and system based on multi-view collaboration, and the method comprises the steps: constructing a sparse topology skeleton model of a measured target under a global coordinate system, and building a bidirectional projection mapping relation between the global situation and each local situation; the method comprises the following steps: synchronously acquiring a time sequence texture feature image of a measured target, calculating a luminosity geometric coupling confidence tensor of each pixel point and extracting an initial texture feature field under each view angle; utilizing a skeleton model to guide the initial texture feature field to perform limited local matching, and resolving to obtain a local independent displacement field and residual energy distribution of each view angle; performing multi-view weighted fusion based on a bidirectional projection mapping relation, and reconstructing a global three-dimensional displacement field; and performing differential calculation on the global three-dimensional displacement field to obtain a deformation gradient tensor and generate three-dimensional full-field strain distribution. According to the method, gradient mutation caused by view field splicing errors can be effectively inhibited, and physically real and full-field continuous three-dimensional strain distribution is obtained.

The invention discloses a sewer pipe network defect detection method based on multi-label zero sample learning. The method comprises the following steps: generating defect description corresponding to each pipeline defect category through a large language model; performing feature extraction and domain adaptation on the pipeline inner wall image and the defect description by using a representation guide module to obtain global and local image features and defect detailed description text features; synthesizing global and local image features of the image, respectively calculating global and local matching scores of the global and local image features and detailed description text features, and fusing to obtain a defect initial prediction score; constructing a semantic relationship adjacency matrix for displaying the relationship between different defect categories; and correcting the initial prediction score by using the semantic relationship adjacency matrix between the categories to obtain a final prediction score of each defect category. According to the method provided by the invention, knowledge migration from known defects to unknown defects is realized by constructing a guidance-fusion-correction network, and the difficulty in identifying types of unseen defects is effectively solved.

This invention proposes a cross-coordinate system road network data fusion method based on hierarchical matching and non-rigid registration, belonging to the field of road network technology, and solves the problems of computational complexity and poor versatility of existing methods. The method includes: acquiring and preprocessing benchmark and target road network data; estimating global transformation relationships by calibrating feature points, and initially aligning the benchmark road network to the target coordinate system to obtain a global region; constructing a fixed image of the target road network and a moving image of the benchmark road network within the global region; using algorithms such as B-spline free deformation that can model local nonlinear deformations to perform non-rigid registration on the fixed and moving images, optimizing the cost function to obtain pixel-level deformation fields; geometrically correcting the benchmark road network based on the deformation field, transferring the target road network attribute information, and summarizing and outputting a fused dataset. This method improves local matching accuracy and versatility through a hierarchical strategy and pixel-level registration, overcoming the influence of differences in road segmentation.

The invention discloses a fire-fighting monitoring room personnel on-duty monitoring method and system, a medium and a product, and relates to the field of image recognition. The method comprises the following steps: in response to detection that a to-be-monitored object completely enters a preset entrance area, extracting a visual feature vector in an unshielded image frame as an initial reference feature; when the to-be-monitored object moves to the on-duty monitoring area, deducting a shielding part based on an overlapping relationship between the real-time detection frame and the environment shielding mask, and extracting an exposed area; and calculating the local matching degree of the exposed area and the initial reference feature to determine the on-duty state of the personnel, and generating an updated reference feature when the conditions of the spatial overlapping degree and the texture similarity are met. According to the method, the complete features obtained at the entrance are used as the reference, and local comparison is carried out in combination with the exposed area, so that the problem of misjudgment alarm caused by the fact that an existing general target detection model cannot recognize an incomplete human body target in a static facility shielding environment is effectively solved, and the accuracy of on-duty monitoring is improved.

This invention discloses a heterogeneous robot collaborative scheduling method for multiple modules of a space station. It employs a hierarchical distributed collaborative architecture based on a semi-Markov decision process. Physical constraints are transformed into filtering conditions through a pre-defined physical constraint sub-mask that includes area access, resource capacity, deadline reachability, and skill matching. A task residual update mechanism mitigates the waiting and overhead issues caused by explicit synchronization negotiation under communication constraints. A task commitment mechanism is introduced, dynamically updating the task residual based on the confidence level of the preceding robot's commitment to the target task. Subsequent robots then fill in, replace, or re-match based on the updated task residual. When local matching enters an oscillating state or local matching convergence stalls, a skill gap tension vector reflecting the gap status of each skill dimension is generated and fed back to the upper layer. The upper layer determines the cause based on the gap type and degree of different skill dimensions and takes appropriate measures for collaborative scheduling.

The application discloses a kind of based on ecological environmental protection supervision big language model address resolution and geographic coding acquisition method and system.For the fuzzy, non-standard and positioning difficult problems of environmental supervision letter and visit complaint address description, the method first uses big language model to parse the original text into structured address, and cooperates multi-level fault tolerance mechanism to ensure the reliability of parsing result.In the matching stage, the system adopts local priority strategy, and preferentially searches the environmental special position database containing key polluting units and other information;If the initial matching is not hit, the self-defined part-of-speech tag segmentation technology is used to accurately extract the core environmental entities such as pollution outlet and water source protection area for secondary matching.To ensure accurate positioning, the system verifies by semantic similarity and geographical distance, to prevent errors caused by the same name in different places.When local matching fails, the system automatically accesses the network query, and uses big language model to simplify the redundant description and retry, forming a closed-loop error correction mechanism.The application significantly improves the accuracy and efficiency of pollution source positioning, protects data privacy, and provides strong technical support for environmental law enforcement personnel to achieve accurate positioning, rapid verification and scientific division of responsibility area.

The application relates to a multi-sensor-based repositioning method, device, equipment and storage medium. The method comprises the following steps: acquiring environment images collected by respective sensors; performing global matching on the respective environment images and images in a prior environment image library to obtain respective robot poses before updating corresponding to the respective sensors; performing local matching on the respective environment images and respective surrounding environment images corresponding to the respective robot poses before updating in the prior environment image library to obtain score values of respective robot poses after updating; and determining a repositioning result of a current robot pose according to the score values of the respective robot poses after updating. By performing global matching and local matching on the environment images collected by different types of sensors and the images in the prior environment image library, the repositioning result of the current robot pose can be ensured to have accuracy and reliability.

The embodiment of the invention relates to the technical field of federated learning and information security, in particular to a federated multi-target scheduling method and system for industrial control crowdsourcing testing, which is characterized in that sensitive data such as ability, experience and the like of a tester are only reserved at a local terminal by constructing a framework combining local feature coding and federated model training, so that the test efficiency is improved, and the test efficiency is improved. Outward transmission is not carried out; only the model update quantity after the privacy enhancement processing such as gradient cutting and differential privacy noise injection is uploaded to the federated matching learning server, and only the model update quantity after the privacy enhancement processing is uploaded to the federated learning server, so that the leakage risk of concentrated data storage is avoided from the source, the requirements of privacy regulations such as GDPR and the like are completely met, and the reliability of the system is improved. Compared with traditional privacy technologies such as homomorphic encryption, the method has the advantages that the calculation and communication overhead is greatly reduced, the design of local matching degree calculation is matched, the task matching response speed is greatly increased, and the real-time requirement of industrial control system testing is met.

The invention relates to a laser equipment work order automatic identification method and system based on computer vision, and relates to the technical field of laser equipment work order visual identification, and the method comprises the steps: carrying out the model matching of a workpiece image based on a standard workpiece image library, and obtaining a workpiece calibration model; positioning identifier optimization is carried out based on the workpiece calibration model, and a recommended positioning image is obtained; the recommended positioning image is locally matched with the standard workpiece image of the calibration model, and the relative coordinates of the marking position and the recommended positioning image are obtained; performing local matching on the recommended positioning image and the workpiece image to obtain a selected marking position; and according to the recommended positioning image, the relative coordinates, the selected marking position and the workpiece image, laser equipment work order configuration is executed. The problems that when traditional laser equipment is used for marking, marking positions and marking sizes need to be manually set in a user-defined mode according to different workpieces, so that manual intervention is much, the intelligent degree is low, and the processing efficiency is low are solved.

The invention discloses a construction method and device for a mobile robot map and a storage medium, and belongs to the technical field of positioning and mapping. The construction method comprises the following steps: acquiring a current measurement point cloud and a current prediction pose of the mobile robot at a current moment; performing normal distribution transformation matching on the current measurement point cloud and a point cloud corresponding to the current prediction pose on a pre-constructed global voxel map to obtain a point cloud global matching residual error; performing iterative nearest point matching on the current measurement point cloud and a point cloud corresponding to a current prediction pose on a pre-constructed local dense map to obtain a current observation pose and a point cloud local matching residual error; determining a current actual pose according to the current observation pose, the current prediction pose and the point cloud local matching residual error, and obtaining a current actual point cloud matched with the current actual pose; and constructing a mobile robot map according to the current actual pose and the current actual point cloud. The method is used for improving the construction precision of the mobile robot map.

The invention relates to the technical field of semiconductor packaging, and provides a high-precision positioning method for multilayer leads. The method comprises the following steps: based on three-dimensional point cloud data and a two-dimensional image of a chip assembly after wire bonding is completed, carrying out noise reduction processing on the three-dimensional point cloud data; dividing the ROI by taking a connecting line between each pair of welding spots as a central axis, and dividing sub-ROIs in the ROI along the central axis direction; clustering the point clouds in the sub-ROIs according to a preset neighborhood size, and performing inter-class and intra-class noise reduction processing on clustering fragments according to lead features; a local matching score is constructed based on the height feature and the angle feature between the last lead segment of the lead combination and the lead segment to be matched, matching with too large difference is eliminated, and a strategy for searching the matched lead segment under the shielding condition is designed; obtaining an optimal combination by combining the local matching score and the global score; and fitting a target lead by using a Bezier curve. According to the invention, the multi-layer lead can be quickly and accurately positioned, and the robustness to noise is improved.

The present application relates to the field of pipeline data processing, and particularly relates to a pipeline multi-attribute fusion screening method for a pipeline database. The present application acquires a target pipeline and a set of to-be-matched pipelines, determines a set of candidate pipelines associated with the spatial position of the target pipeline based on spatial indexing; normalizes the target pipeline and the candidate pipelines, constructs a pipeline trend development sequence based on pipeline connectivity, and divides the pipeline trend development sequence into a plurality of along-line segmentation units; analyzes the basic matching features of the along-line segmentation units, determines local matching differences, and sets matching state labels for the candidate pipelines based on the local matching differences; determines suspected deviation pipe sections according to the matching state labels, and performs adjacent and continuous re-checking on the suspected deviation pipe sections, or directly determines whether the candidate pipelines are the same pipelines corresponding to the target pipeline according to the overall matching result. The present application can reduce unnecessary overall matching calculation, and improve the pipeline screening efficiency and accuracy.

The application discloses a sewer network defect detection method based on multi-label zero-shot learning, and the method comprises the following steps: generating a defect description corresponding to each pipeline defect category through a large language model; using a representation guide module to perform feature extraction and field adaptation on a pipeline inner wall image and the defect description, so as to obtain global and local image features and defect detailed description text features; comprehensively considering the global and local image features of the image, respectively calculating global and local matching scores of the image and the detailed description text features, and fusing the global and local matching scores to obtain an initial defect prediction score; constructing a semantic relation adjacency matrix for displaying the relation between different defect categories; and using the semantic relation adjacency matrix between the categories to correct the initial prediction score, so as to obtain a final prediction score of each defect category. The method disclosed by the application realizes knowledge transfer from known defects to unknown defects by constructing a guide-fusion-correction network, and effectively solves the problem of recognizing unknown defect types.

The invention discloses a scene identification method and system based on vision-point cloud multi-modal fusion, and the method comprises the steps: collecting a visual image and laser radar point cloud data in a target scene, carrying out the preprocessing, and extracting corresponding visual image features and point cloud features; generating a global feature, a visual modal local feature and a point cloud modal local feature in the target scene; performing global retrieval in a preset scene database by utilizing the global features to obtain a plurality of candidate positions and corresponding global matching scores; performing local matching and geometric verification on the candidate positions based on the visual modal local features and the point cloud modal local features to obtain a local matching score of each candidate position; and reordering the candidate positions according to the global matching score and the local matching score, and outputting an optimal scene recognition result. The method solves the problems of poor feature alignment, insufficient modal fusion, weak complex scene adaptability and data redundancy in the existing multi-modal method.

The invention provides a vamp track positioning and deviation rectifying method, and relates to the technical field of shoe production. According to the vamp track positioning and deviation rectifying method, the local deformation and the size difference of each vamp can be adapted, the specially-made upper and bottom bonding line track of each vamp can be accurately generated, and core technical support is provided for automation of the procedures of vamp roughing, treating agent applying, glue spraying and the like; a local deviation correction method of point-by-point local matching is adopted, so that the local deviation between a vamp template and a sample can be calibrated in a targeted manner, the positioning precision of an upper and sole bonding line track is effectively improved, and the consistency and stability of automatic processing are guaranteed; on the basis of completing local deviation correction, the generated trajectory is smoothed, and the trajectory points are projected to the vamp point cloud, so that the problem of overlarge trajectory fluctuation is avoided, and the smoothness and the accuracy of the execution process of the automatic equipment are further improved.

This invention discloses a smart home linkage method, device, computer equipment, and storage medium. The method includes: a first device controlling the transmission of a trigger signal corresponding to a detected event signal; a second device determining whether to execute a linkage task based on the received trigger signal and preset linkage rules; wherein the first and second devices communicate on a preset frequency channel. By performing local matching in real-time through pre-stored rules and directly communicating between devices via a designated frequency channel, fully offline localized device linkage is achieved. This is unaffected by home network and internet status, remaining usable even in offline environments, significantly improving linkage reliability. Furthermore, linkage latency can be reduced from seconds to milliseconds, resulting in a more seamless and smooth user experience. Deployment is also simplified, and device status and user behavior data only need to be transmitted point-to-point between devices, eliminating the risk of cloud-based privacy leaks.

The invention provides a heterogeneous text matrix local matching correlation degree calculation method based on a sliding window, and belongs to the technical field of text information processing. The method aims at solving the problem that in the prior art, when texts with different lengths are processed, the correlation degree calculation precision is reduced due to the fact that vector supplementing or truncation is adopted. The method comprises the steps of obtaining a first text and a second text, and converting the first text and the second text into a first text representation and a second text representation which are different in length; determining a relatively long text representation and a relatively short text representation according to the length of the text representation, and defining a sliding window of which the size is the same as the length of the relatively short text representation; on the long text representation, moving a sliding window according to a preset step length to obtain a plurality of local representations; for each local representation, calculating a local correlation degree value between the local representation and the short text representation; and finally, selecting a representative value from all the calculated local correlation degree values according to a preset rule, and taking the representative value as a final correlation degree. According to the method, the text representation does not need to be supplemented or cut off, so that noise introduction or information loss is fundamentally avoided, and the accuracy of correlation degree calculation between heterogeneous texts is remarkably improved.

The application discloses a repetitive building element retrieval method based on axial movement and local matching, and specifically comprises the following steps: carrying out preliminary segmentation and optimization on a building facade scene; randomly selecting a building element sample under user driving; determining the X axis of the building facade and other two axes by solving a plane equation; transforming the building element sample to the left lower corner origin of the building facade scene by using an OBB bounding box; extracting key points of the element to be retrieved by using an internal shape descriptor; constructing a local matching constraint by combining FPFH and the OBB bounding box; finally, finding the nearest point of the key points of each element to be retrieved, comparing the difference between the descriptors by using the Euclidean distance, and completing the retrieval of the repetitive building element. The application can directly retrieve the repetitive building element in the building facade scene, and solves the problems of the fuzziness of the retrieval object and the low recall rate in the previous retrieval methods.

The invention provides a cross-coordinate system road network data fusion method based on hierarchical matching and non-rigid registration, belongs to the technical field of road networks, and solves the problems that an existing method is complex in calculation and poor in universality. The method comprises the following steps: acquiring reference and target road network data and preprocessing; estimating a global transformation relation by calibrating feature points, and preliminarily aligning the reference road network to a target coordinate system to obtain an overall area; constructing a fixed image of the target road network and a moving image of the reference road network in the whole area; carrying out non-rigid registration on the fixed image and the moving image by adopting a modeling local nonlinear deformation algorithm such as free deformation of a B spline, and optimizing a cost function to obtain a pixel-level deformation field; and performing geometric correction on the reference road network based on the deformation field, transmitting attribute information of the target road network, and summarizing and outputting a fusion data set. According to the method, through a layering strategy and pixel-level registration, the local matching precision and universality are improved, and the influence of road segment segmentation differences is overcome.

The application discloses a handcart interconnection method based on WiFi Beacon frame identity recognition, relates to the wireless communication and vehicle interconnection technical field, and the method comprises the following steps: a vehicle-mounted device carries encrypted owner identity information, a protocol type list and connection configuration parameters in a Beacon frame through a Vendor Specific field; a mobile terminal performs passive scanning under the condition that a WiFi connection is not established, analyzes the extended information, and completes identity decryption and local matching; when the matching succeeds, a target interconnection protocol is selected in combination with device capacity and user preference, a WiFi connection is established based on the configuration parameters, a corresponding protocol stack is initialized, and a handshake is completed, so that a data transmission channel is established. The application does not need to participate in Bluetooth, realizes quick automatic connection, improves connection efficiency and stability, and simultaneously considers safety and expansibility.

This application provides a method for constructing point cloud correspondences. The method includes: extracting feature points from the original point cloud and the target point cloud by fusing local and global feature values ​​to form an original point set and a target point set; constructing sparse correspondences between the feature points in the original point set and the feature points in the target point set; filtering the sparse correspondences through rigid registration using the sparse correspondences to obtain feature point pairs with correspondences between the original point set and the target point set; performing local matching of nearest neighbor points on the point cloud for the feature point pairs with correspondences; obtaining the correspondences of all points between the original point cloud and the target point cloud; and forming a point cloud correspondence. This application solves the problem that traditional methods can only construct correspondences between point clouds with small deformations and have low computational efficiency.

The application discloses a feedforward Gaussian splatting method based on feature matching and adaptive densification of multi-view scene reconstruction, a device, equipment and a storage medium, and relates to the technical field of computer vision and three-dimensional reconstruction. The method comprises the following steps: acquiring multi-view image set information and camera parameter information; determining corresponding resolution feature map information based on the image set information and the camera parameter information; performing global and local matching on image points at different resolutions based on the resolution feature map information, and determining matching prior information and matching confidence map information; inputting a predefined Gaussian splatting model and a Gaussian densification model based on the resolution feature map information, the matching prior information and the matching confidence map information, and predicting corresponding Gaussian attributes and Gaussian densification attributes, and determining Gaussian attribute information and Gaussian densification attribute information; and reconstructing to-be-densified anchor points in a scene space corresponding to the Gaussian attribute information based on the Gaussian densification attribute information, and determining a scene space reconstruction result.

The invention discloses an intelligent call center customer seat distribution method and system. The method comprises the steps of ordered set establishment, dynamic situation field matching, efficient sorting and storage, batch distribution and dynamic point updating. The invention belongs to the technical field of data processing, and particularly relates to an intelligent call center customer seat distribution method and system.According to the scheme, a dynamic situation field matching algorithm is adopted, consideration of multiple dimensions is quantified into initial matching potential energy in a unified mode, and the system automatically finds an optimal solution with the lowest potential energy; intelligent trade-off and unified decision-making among multiple targets are realized; a mixed index structure is adopted, a data structure guarantee is provided for rapid decision making under high concurrency, through batch distribution, a complex global matching problem is converted into a local matching problem capable of being subjected to parallel calculation, and an approximate optimal solution is rapidly output; a dynamic increment based on a cognitive load theory is adopted, and a convolution integral is used for finely simulating an instantaneous consumption and recovery process of a high-complexity call on seat cognitive resources.

The keyword matching method, device and electronic equipment across language environments provided by the embodiments of the present application relate to the technical field of information retrieval. First, a source language keyword used for matching a target language text is obtained; then, the source language keyword is processed by word segmentation, and the source language keyword is classified into a short keyword string or a long keyword string; then, when the source language keyword is a short keyword string, keyword cross-language matching is performed through semantic expansion to optimize the missing report problem of the source language keyword exact matching; when the language keyword is a long keyword string, the source language keyword cross-language matching is performed based on a semantic-level fuzzy matching technology, the overall matching degree of the keyword is calculated in combination with the semantic matching value and the overall relevance of the matching segment to the target text, so as to comprehensively consider the overall matching degree and the local matching degree of the source language keyword. The above scheme can adopt different matching strategies based on the classification of the source language keyword, and ensure the accuracy of the matching result.

The application discloses a garment image retrieval method and device based on local feature block matching, equipment and storage medium, the method comprises: global feature extraction is carried out to the query image, and the initial image in the database is matched based on the global feature, and the first image sequence containing multiple candidate images is obtained by screening, the local feature block in the image pair composed of the query image and each candidate image is mined through the reordering network, the reordering similarity score is obtained, the first image sequence is reordered based on the reordering similarity score, and the target garment image matched with the query image is determined based on the second image sequence reordered; since the application pays attention to the dependence relationship between the internal features of the image and the local matching information between the image pairs, the perception ability of the local difference in the garment image is enhanced, the inter-class false matching is reduced, the local feature retrieval ability of the occluded image is improved, and accurate and effective retrieval in a large-scale garment image dataset is realized.

The application provides a two-layer matching-based online car-hailing aggregation platform order dispatching method, which is used for realizing efficient driver and passenger matching in a multi-sub-platform collaborative environment. The method comprises the following steps: an aggregation platform integrates passenger order information in an order dispatching period, and broadcasts each order to a sub-platform set preferred by the passenger; each sub-platform performs local matching based on its own vehicle fleet, with the aim of maximizing utility, and returns a candidate vehicle set; the aggregation platform combines candidate results of each sub-platform, constructs a global candidate vehicle set for each passenger, and selects a vehicle with the highest utility from the global candidate vehicle set to complete final assignment. Under the premise of ensuring information isolation and operation compliance, the application realizes efficient integration of cross-platform online car-hailing resources by combining sub-platform local matching and aggregation platform global coordination, and provides reliable technical support for operation management of the online car-hailing aggregation platform.