98 results about "Optimal matching" patented technology

The present application relates to the technical field of log analysis, and particularly relates to a kind of audit system operation and maintenance log automatic generation method and equipment.The method parses log stream to distinguish business log entries from unassociated abnormal entries, constructs thread execution sequence and calculates the idle time slice between adjacent business log entries in thread execution sequence;Load the resource constraint mapping table defining hardware resource type and minimum duration;Get the candidate business entries of unassociated abnormal entries, and perform mutual exclusivity and capacity physical verification using the resource constraint mapping table to filter out effective candidate business entries;According to the time difference between unassociated abnormal entries and its effective candidate business entries, filter out the best matching business entries, and then backtrack the business context, inject the exception stack and correct the state to generate structured audit records.The present application realizes non-invasive and high-precision exception attribution based on the rules of runtime resource constraints, and improves the integrity and accuracy of audit data.

This invention provides a method for filtering air quality forecast results based on sliding optimal matching, relating to the fields of environmental air quality forecasting and data science technology. The method includes: acquiring multiple predicted air quality index data obtained from various air quality prediction models; acquiring multiple measured air quality index data at multiple times within a past first preset time period; setting a time window; filtering target air quality prediction models from among the multiple air quality prediction models; and obtaining air quality index forecast data. According to this invention, predicted air quality index data and measured air quality index data can be acquired, and error statistics can be performed based on the time window to filter target air quality prediction models. The sliding optimal matching algorithm enables real-time evaluation and automatic filtering of model performance, overcoming the technical bottlenecks of traditional manual operation and static integration, improving the real-time performance and efficiency of air quality prediction model filtering, and providing high-precision and high-efficiency forecast support.

This invention discloses a method, apparatus, and computer-readable storage medium for optimal matching of multi-dimensional conditions, comprising the following steps: S101: Obtain the conditions for each dimension, perform a one-to-one correspondence judgment on the conditions according to the row order of the data, and record the row numbers of all data that meet the conditions; S102: Summarize the number of data row numbers recorded in step S101; S103: Arrange the summarized number of data row numbers recorded in step S102 in ascending order; S104: Find the conditions based on the data row numbers. If the conditions contain the data row number, match the quantity corresponding to the condition with the data row number, and deduct the quantity of the data in this row to the maximum extent possible, until the quantity of the data in this row is reduced to 0, at which point the data row number stops participating in the calculation. In this invention, the accuracy of the matching results is improved through multi-dimensional condition matching and corresponding calculation.

This application relates to the field of artificial intelligence, and more particularly to a method, apparatus, device, and program product for controlling document editing. The method includes: receiving a document editing request from a large language model agent; if no exact matching sequence is found in the target file for the content to be replaced, calculating the similarity between the content to be replaced and the sequence in the target file, and locating the best matching segment based on the similarity; performing a difference analysis between the content to be replaced and the best matching segment and generating an error diagnosis report including difference information; returning the error diagnosis report to the large language model agent, guiding the agent to correct the document editing request. This method finds the best matching segment for sequences that fail to match precisely and generates an error diagnosis report including difference information, facilitating rapid location of differences, reducing invalid attempts, and improving the agent's repair efficiency.

This invention belongs to the field of power monitoring technology and discloses a brightness balancing and regional smoothing fusion algorithm for power monitoring images. The method includes: image denoising, using mean filtering and median filtering to smooth the image; image registration, performing optimal matching on two or more images obtained from different sensors, at different times, or from different angles; and image region fusion, smoothly fusing images with different angles and brightness contrasts. This method is applicable to panoramic video monitoring systems. Designed specifically for the complex lighting and high electromagnetic interference characteristics of power operation sites, this invention effectively eliminates on-site noise through adaptive preprocessing, and achieves a balance between global brightness uniformity and local detail preservation through layered brightness balancing, solving the problems of incomplete brightness balancing and detail loss in power scenarios encountered by general algorithms.

The present application relates to the field of automatic drawing, in particular to a substation secondary circuit drawing multi-element collaborative identification and topology reconstruction method, the method comprises the following steps: receiving scanning drawing, photo, CAD export image multi-source input, drawing standardization pretreatment; terminal and device symbol are recognized by using target detection, text information is extracted and classified through OCR, and connection line is tracked by using skeleton extraction; confidence data of each element is generated synchronously; based on distance, direction and format constraint, matching score is calculated, terminal and number binding is realized by using optimal matching, and correlation is iteratively corrected by device and terminal attribution, connection line and terminal adsorption rule; the recognition result is converted into graph structure, topology network is constructed, loop type and closure are identified by using depth first search; abnormal elements are automatically marked, quality evaluation report is generated; output data supports drawing incremental update and version traceability, and connects downstream application platform.

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.

This invention discloses an intelligent anti-jamming method based on radar-infrared heterogeneous fusion, comprising: radar and infrared sensors performing local filtering based on their respective measurements to obtain local state estimates; constructing a set space of track mapping relationships between radar and infrared sensors, and selecting the optimal track matching pair based on a minimum cost function; adaptively configuring the fusion weights of the heterogeneous sensors according to the optimal matching result to complete the radar-infrared anti-jamming fusion; deriving variable-dimensional Mahalanobis distance based on the projection of the reliable dimension measurement space to extract spatial statistical features, and constructing a main lobe active deception interference feature dataset in conjunction with the physical law features of the measurement itself; and building a deep learning model based on a long short-term memory network to achieve accurate identification of radar main lobe active deception interference types. This invention effectively suppresses main lobe active deception interference and achieves stable tracking of real targets through the designed distributed heterogeneous fusion criterion.

The application provides a resource coordination and time synchronization method and system for a large-scale computing power cluster, and relates to the technical field of computing power network architecture. First, the computing power nodes are registered and maintained by an authoritative DNS. After receiving a task, the system intelligently selects the optimal target computing power node and plans the data transmission path. To achieve precise coordination, the system dynamically creates a clock synchronization domain according to the task, elects a master clock and plans the optimal clock synchronization path. Finally, the system encapsulates and packages the optimal task data transmission path, the master clock timestamp and the optimal clock synchronization path identifier, and distributes them to the related nodes, thereby realizing efficient resource coordination and high-precision time synchronization. The method not only realizes the optimal matching of tasks and resources, guarantees the efficiency of data transmission, but also guarantees the accuracy and reliability of time synchronization from the physical link, and finally realizes efficient resource coordination and high-precision time synchronization in the computing power cluster.

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 provides a security policy matching method and device, a storage medium and a computing device. When any security policy tree is constructed, a node priority is set for each tree node, which can accurately reflect the highest policy priority of the subordinate sub-tree of each tree node, thereby providing a core basis for pruning low-priority nodes during security policy matching, ensuring the order of dynamic maintenance of the node priority, and improving the security policy matching efficiency. Furthermore, the early stop attribute is set for the security policy of the leaf node in the multi-security policy tree in stages, which realizes the fast marking of the absolute high-priority security policy and the accurate marking of the non-absolute high-priority security policy without intersection, and the combination of the two realizes the accurate setting of the early stop attribute of the security policy. In this way, the large-scale security policy matching efficiency is improved, the optimal matching result is ensured, the computing cost is reduced, and the matching performance of the security policy tree is significantly improved.

This invention proposes an intelligent review method for evidence matching and dynamic weighting in project acceptance. It collects multi-source data, including project application guidelines, change approval forms, acceptance applications, task books, performance evaluation reports, research reports, achievement certificates, financial statements, audit reports, and process logs, to construct acceptance indicator units and evidence units. Attributes such as amount, date, standard number, responsible entity, and report number are obtained through layout parsing, entity recognition, and rule extraction. The optimal matching result between acceptance indicator units and evidence units is determined through lightweight recall, source range filtering, comprehensive matching scoring, and constraint optimization. When one acceptance indicator unit corresponds to multiple evidence units, a group evidence fusion result is generated. Cross-material conflict detection and dynamic weight updates are performed, outputting acceptance conclusions, evidence paths, problem lists, and risk causes. This invention can improve the efficiency of evidence matching and the stability of consistency judgment for multi-source acceptance materials.

The invention relates to the technical field of vehicle scheduling, in particular to an online car-hailing cross-platform transport capacity cooperative scheduling method and system. According to the method, semantic ablation and protocol escape of underlying data can be realized by acquiring parameters such as self-developed interface protocols, data formats and positioning precision of all platforms and building decentralized middleware, and abnormal data can be found and isolated in time by adopting double-link cross check and a multi-stage abnormal data judgment model; the dynamic weight calibration scheduling model is combined, more flexible and accurate transport capacity scheduling is provided based on real-time order data and factors such as positioning accuracy and data transmission stability of each platform, optimal matching between user requirements and optimal platform drivers is realized, and the scheduling efficiency and user experience of the whole system are improved.

This invention belongs to the field of computer storage system architecture technology, and provides a heterogeneous storage acceleration system and method for edge AI computing. The technical solution includes a main processor for initiating storage access requests; a three-tiered heterogeneous storage unit, comprising a dedicated AI model cache, a hybrid storage pool, and a data cache module; and a hardware intelligent scheduler connected to the main processor and the three-tiered heterogeneous storage unit, including: a data feature recognition module for extracting feature vectors from storage access requests and classifying them into AI model, video stream, or system data categories based on preset classification rules, and adding data classification labels; and a storage routing decision module for performing differentiated storage routing decisions based on data type and arbitrating concurrent requests to ensure the highest priority for AI model requests and deterministic bandwidth for video stream requests. Optimal matching of data streams and storage resources is achieved through hardware logic.

The application belongs to the technical field of computers, and provides a PDF document comparison method and system based on intelligent differential merging. The method is as follows: the visual fingerprint sequences of a source PDF document and a target PDF document are calculated; the visual fingerprint sequences of the two PDF documents are compared through a Myers differential algorithm, a difference sequence is generated, all deletion operation sequence regions adjacent to insertion operation sequence regions are identified, and a candidate page pair is constructed; pre-filtering is performed on the candidate page pair, a similarity score is calculated, and based on a threshold judgment and a greedy strategy, the best match is locked, the deletion operation node and the insertion operation node are combined and replaced by a modification operation node; finally, spatial quantization index technology is used to compare internal objects, and the final comparison result is output. The application can not only accurately identify the subtle changes of the PDF document, but also reduce the comparison complexity, improve the comparison efficiency, and meet the real-time comparison needs of users.

This invention discloses an automatic generation and execution method for intelligent closed-loop workflows in industrial events, belonging to the field of automation technology. The method includes receiving and parsing industrial event data, extracting key event elements and generating a structured event tag set, matching the event tag set with a pre-defined standardized workflow template's matching tag set to determine the optimal matching template, generating a specific execution workflow based on the selected template, and configuring parameters for each node. This invention solves the technical problem of achieving accurate matching and automatic generation of execution paths based on structured event tags and standard workflow templates. It realizes intelligent generation of the entire process from raw event data to precise workflow execution paths, effectively improving the accuracy and automation level of event-workflow matching. It has the advantages of more accurate event matching, more flexible process scheduling, and stronger system versatility, and is suitable for closed-loop handling of events from multiple scenarios, devices, and sources.

This invention relates to the field of intelligent operation and maintenance and fault diagnosis technology for photovoltaic power plants, and particularly to a photovoltaic power generation monitoring method and system based on artificial intelligence. The method includes the following steps: parsing the time-of-use power generation data of the target photovoltaic modules, retrieving the total cumulative power generation for the entire day, performing normalization calculations for irradiance fluctuations, and constructing an actual power generation percentage sequence; performing time-domain cross-correlation matching between the actual power generation percentage sequence and a preset historical benchmark percentage sequence to determine the optimal matching time shift, and generating an aligned benchmark sequence based on the optimal matching time shift; calculating the negative distortion component sequence of the actual power generation percentage sequence relative to the aligned benchmark sequence. This invention, by normalizing power generation and using solar trajectory for spatiotemporal mapping, achieves remote inference of the physical location of interference sources and automatic classification of fault characteristics without relying on image equipment, effectively reducing false alarm rates and improving troubleshooting efficiency.

A system and method may identify the best matching entities using artificial intelligence, by for example determining, from a set of features describing a number of entities, a subset of features which when input to a machine learning module allow the machine learning module to match a pair of entities based on the ability of the second entity in the pair to successfully take over responsibilities handled by the first entity in the pair; training a machine learning module using the determined features; and for a first entity described by the determined set of features, using the machine learning module to determine a list of entities matching the first entity, each entity in the list of entities associated with a matching score.

This invention discloses a method and system for clustering distributed adjustable resource nodes in a power system. The method includes: performing global preprocessing based on the global load, topology, and node types of the power system to generate a basic feature matrix containing various load characteristics, a topological region ID defining the topological affiliation of nodes, and a node symbol vector defining the uncertainty direction of nodes; segmenting the topology of the power system based on the topological region ID and constructing a high-dimensional feature matrix within each region; running a first-stage evaluation algorithm based on the high-dimensional feature matrix to determine the upper limit of the number of clusters; and performing a second-stage clustering and evaluation based on the upper limit of the number of clusters to obtain the optimal clustering result. This invention achieves globally optimal matching of the "fluctuation direction" and "fluctuation amplitude" between nodes while ensuring clustering accuracy, providing a novel and refined modeling paradigm for the aggregation analysis of heterogeneous resources in distribution networks.

The application discloses a bronchoscope supply-demand dynamic matching method based on multi-source data fusion, comprising: collecting multi-source heterogeneous data and performing fusion processing to obtain a device feature matrix, a demand feature matrix and an environment feature vector; determining a score calculation strategy according to a comparison result of a historical matching data accumulation amount and a preset threshold, determining a device availability score and a demand urgency score based on the device feature matrix and the demand feature matrix according to the score calculation strategy; constructing a device queue and a demand queue, attaching an initial scene label to each demand and performing identification, and outputting a standardized scene type; outputting an optimal matching scheme through a reinforcement learning intelligent agent based on the device queue, the demand queue, the standardized scene type and the environment feature vector; performing intelligent scheduling based on the optimal matching scheme, updating the queue based on external real-time events, and if both queues are non-empty at the same time, obtaining a new optimal matching scheme, otherwise, keeping a waiting state. The application can improve matching efficiency and accuracy.

This invention discloses a real-time monitoring system and method for power distribution networks based on dynamic perception. The method includes setting weight coefficients for power distribution network equipment and various scheduling operations, and establishing identity profiles for operators; fusing real-time data collected by heterogeneous sensors through a device perception layer; receiving and analyzing the fused data transmitted from the device perception layer through an edge computing layer, and sending the analysis results to a cloud server; receiving the analysis results, and when the real-time monitoring system detects an anomaly or requires scheduling operations, the cloud server performs weighted calculations on multi-dimensional data based on a dynamic model, automatically achieving optimal matching of maintenance and scheduling, thereby improving the intelligence and response speed of operation monitoring decisions, ensuring that the most suitable operator takes the fastest route and carries the appropriate tools to handle anomalies or perform scheduling, and realizing the global optimal allocation of resources such as manpower and vehicles in the spatiotemporal dimension.

This invention belongs to the field of computer vision and non-rigid point cloud registration technology, and specifically to a scale-adaptive non-rigid point cloud registration method based on gradient decoupling. It includes the following steps: S1: Solving for the stiffness matrix and node multimodal eigenvectors; S2: Solving for the optimal matching point index and robust weights; S3: Solving for the scale gradient and deformation gradient; S4: Solving for the updated scaling coefficients. This invention achieves parameterized separation of scale mismatch and local physical deformation by explicitly introducing a continuously differentiable scale factor into the physical finite element equations and combining it with a spatial orthogonal gradient decoupling strategy. This not only solves the problem of numerical convergence instability caused by misjudging "volume expansion" in traditional methods when scale mismatch occurs, but also ensures that the algorithm maintains stable registration accuracy under scale disturbances.

A system and method may identify the best matching entities using artificial intelligence, by for example determining, from a set of features describing a number of entities, a subset of features which when input to a machine learning module allow the machine learning module to match a pair of entities based on the ability of the second entity in the pair to successfully take over responsibilities handled by the first entity in the pair; training a machine learning module using the determined features; and for a first entity described by the determined set of features, using the machine learning module to determine a list of entities matching the first entity, each entity in the list of entities associated with a matching score.

This invention relates to a robot-task allocation method based on the KM algorithm, belonging to the field of robotics. The invention transforms the integer programming problem into a bipartite graph optimal matching problem, solves the maximum matching problem in the bipartite graph using the Hungarian algorithm, and then uses the KM algorithm to solve the optimal matching problem. This invention provides an easy-to-implement, efficient, and effective robot task allocation method. This algorithm is more efficient than other algorithms on dense graphs and can be applied to multi-robot systems performing tasks such as patrol and rescue.

This invention proposes an intelligent matching and allocation method and device for experts and projects. The method includes: constructing a multi-dimensional fusion matching model based on acquired expert and project information. This model calculates a comprehensive matching score between experts and corresponding projects by weighted fusion of sub-scores based on expert professional matching degree, experience fit, workload balance, and geographical distribution balance; selecting a matching algorithm based on the number of experts and projects to be allocated; using a first matching algorithm when the problem size is no greater than a preset threshold; and using a second matching algorithm when the problem size is greater than the preset threshold; and generating the optimal matching scheme between experts and projects based on the comprehensive matching score using the selected matching algorithm. Based on this method, this invention also proposes an intelligent matching and allocation device for experts and projects. This invention achieves more accurate quantitative matching by constructing a comprehensive evaluation model integrating four dimensions: professional expertise, experience, workload, and geographical distribution.

The application provides a special-shaped glass cutting method and system based on edge feature matching, and belongs to the technical field of glass processing. The special-shaped glass cutting method based on edge feature matching comprises the following steps: collecting multi-view images of glass to be cut to generate an original image dataset containing complete edge contour information; performing image enhancement and denoising processing on the original image dataset to extract a clear edge pixel set; constructing an edge feature vector based on the edge pixel set and generating an edge key point descriptor through a scale invariant feature transformation algorithm; matching the edge key point descriptor with a template descriptor in a preset reference feature library to determine an optimal matching template and corresponding local geometric transformation parameters; and performing spatial correction on the optimal matching template according to the local geometric transformation parameters to generate an initial cutting path.

This invention discloses a method for energy community electricity-carbon differential matching based on an improved slime mold algorithm, belonging to the field of smart grids. First, considering the coupling characteristics of energy hubs at the distribution network level, a data-driven method is used to quantitatively analyze the peak-valley coupling potential between different types of energy hubs. Initial differential matching is then performed on energy hubs with different external characteristics. Combining the load characteristics and carbon quota balance of energy hubs, a comprehensive index system that considers both regional structural and functional needs is constructed. Finally, aiming for optimal overall energy community characteristics, a matching strategy based on an improved slime mold optimization algorithm is proposed, introducing an adaptive search mechanism and information sharing strategy to optimize the structure and boundaries of the energy community. This invention simulates the dynamic matching process of energy communities, achieving differential matching that considers both electricity and carbon quotas, efficiently and accurately determining the boundaries and optimal matching schemes of energy communities, providing a new approach to improving the comprehensive benefits of electricity and carbon emission management.

This invention discloses a control method for an ion cyclotron resonance heating system, belonging to the field of radio frequency heating control technology. This invention trains a coupling impedance prediction model using ICRH system operating parameters and plasma parameters, and outputs future coupling impedance prediction results during ICRH system operation to characterize the changing trend of the coupling state between the ICRH system and the plasma. During ICRH system operation, the signals required for the coupling impedance prediction model are acquired in real time to predict the matching state of the ICRH system. When the prediction results indicate a potential mismatch in the future coupling impedance, the impedance matching module in the ICRH system is preemptively adjusted to ensure that the ICRH system is always in an optimal matching state. This solves the technical problems of adjustment lag and transient mismatch inherent in existing technologies.

The application relates to the technical field of office automation, and particularly discloses a keyword-driven intelligent generation method and system for a bidding document and a storage medium. The method is implemented through an automatic program deeply integrated in a word processing software (such as Microsoft Word) and aims at the pain points of low manual operation efficiency, errors and non-uniform formats in the existing bidding document preparation process. The core of the method comprises the following steps: intelligently analyzing a bidding document, extracting structured format features and content keywords of the bidding document; constructing and managing a bidding material knowledge base with metadata labels; based on the extracted keywords, performing multidimensional intelligent retrieval and optimal matching in the knowledge base; automatically filling the matched material content into a new document according to the structure of the bidding document, and applying accurate automatic layout. The application realizes intelligent generation from a bidding document to a standardized bidding document, significantly improves the preparation efficiency and quality, and reduces human errors and compliance risks.

This invention discloses a radar target tracking method based on spatiotemporal clustering and a multi-physics model, belonging to the field of radar target tracking technology. The method includes the following steps: S1, inputting radar point target data; S2, adaptive spatiotemporal clustering to remove noise; S3, establishing a multi-physics model and adaptive threshold weight matching point-by-point and track-by-track; S4, cyclic matching of predicted points; S5, integrating matching scores S1, S2, ..., Sn from the matching score queue; S6, taking the point corresponding to the optimal matching score as the output result. This invention proposes a multi-physics model matching and judgment approach, constructing multiple target motion physical models and combining the target's historical motion state with the current point's adaptability analysis. Even in complex scenarios such as changes in target motion patterns and multiple trajectory intersections, it can accurately identify point ownership, effectively solving the problems of insufficient robustness and poor adaptability of traditional methods, and significantly improving the continuity and accuracy of target tracking in complex scenarios.