962 results about "Tree based" patented technology

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention relates to an optimization method and device for large-scale graph data visual rendering and a storage medium, and the method comprises the steps: obtaining to-be-rendered graph data, constructing an R-tree spatial index according to the to-be-rendered graph data, and constructing a self-adaptive fragmentation model based on an R-tree data structure; monitoring interaction data of a user in real time, performing viewport prediction according to the interaction data, and determining a to-be-loaded sub-graph according to a prediction result and the adaptive fragmentation model; the method comprises the following steps: acquiring system data of client equipment, analyzing the performance of the client equipment according to the system data, and determining a rendering strategy according to a performance analysis result; and performing off-screen rendering on the to-be-loaded sub-graph according to the rendering strategy, and displaying an off-screen rendering result on the client device after detecting that the user completes the corresponding operation. According to the method, the rendering granularity is dynamically adjusted through the adaptive fragmentation model, and the rendering quality and performance consumption are balanced; the user interaction intention is pre-judged through a viewport prediction algorithm, and window smooth switching is achieved in combination with off-screen rendering.

The invention relates to the technical field of power systems, in particular to a power transmission line tree obstacle hidden danger detection method and system considering the wind deflection influence and a medium, and the method comprises the steps: carrying out the tree growth prediction according to an original multi-source data set, and obtaining a tree growth prediction result; performing three-dimensional windage yaw effect analysis based on the tree growth prediction result and the original multi-source data set to obtain a tree windage yaw quantification result; respectively calculating the risk time of the tree in the vertical direction, the horizontal direction and the inclined direction according to the tree windage yaw quantification result; evaluating a comprehensive risk index between the tree and the power transmission line according to the risk time to obtain a tree barrier hidden danger degree; and taking minimization of the total pruning amount and maximization of the safety margin as optimization objectives, generating a tree pruning strategy based on the hazard degree of the tree obstacle hidden danger, and pruning the risk trees of the power transmission line according to the tree pruning strategy. According to the method, tree growth prediction and windage yaw effect quantification are carried out by fusing multi-source data, and the power transmission line fault risk caused by tree obstacles is effectively reduced.

The invention discloses a policy service recommendation method and system based on a large language model, and relates to the field of policy information, and the method comprises the steps: analyzing a policy file, extracting policy terms with a numbering structure, building a unique identifier, and forming a term node set; identifying explicit or implicit reference behaviors in terms, constructing a reference path, and generating a reference directed graph; constructing a clause mapping tree based on the target clauses; mapping tree nodes are extracted to form structured prompt information, and the structured prompt information is embedded into large language model input; generating a policy report paragraph containing clause reference content; and outputting the structured policy text with clause reference chain closure and recommending the structured policy text to the user. According to the method, the clear reference context combined with the user information can be provided for the large language model, the large language model is guided to generate a text with a reasonable structure and complete terms for recommendation, and the problem of inaccurate generation when an existing model processes a complex reference structure is solved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention provides a social network hierarchical visualization method and system based on a structure entropy and a coding tree, and the method comprises the steps: obtaining graph data, building the coding tree based on the graph data with the minimized structure entropy, and enabling the coding tree to be used for capturing a hierarchical community structure of a graph; constructing a minimum entropy priority queue, adaptively selecting non-leaf nodes for division by adopting the minimum entropy priority queue, and distributing a current structure entropy value as a weight for a cross-community edge; force-oriented layout based on structure entropy and layout entropy is used, attraction is adjusted based on edge weight, standard repulsive force is kept, and a force model is established; solving a layout entropy maximization target for the force model to calculate a layout result; and for the calculated layout result, outputting and rendering a layout result with a clear community separation effect as a final visualization result.

The invention relates to the technical field of data processing, in particular to an efficient transmission method for electric energy meter acquired data, which comprises the following steps: acquiring multi-dimensional feature data of an electric energy meter at each moment, and obtaining multi-dimensional real-time feature data at the current moment and a multi-dimensional historical feature data sequence within a preset historical time period; using an isolated forest algorithm to randomly select sample points to form at least one sample subset, for any sample subset, obtaining the abnormal degree of each sample point in any sample subset under each feature, obtaining a segmentation condition under each feature according to the abnormal degree, and generating an isolated tree; obtaining a normal weight of each path in the isolated tree according to fluctuation features of sample points in a sample subset corresponding to the isolated tree and differences between the sample points and the multi-dimensional real-time feature data; and the final score is obtained by using the normal weight of each path in each isolated tree, and the adjustment frequency is obtained according to the final score, so that the interference of non-system abnormal data such as noise on frequency adjustment is effectively reduced.

Techniques are described herein that are capable of performing AI-based entity maliciousness analysis using embedding and sampling. A representative sample of data associated with an entity is selected by comparing embeddings that represent the data. A potentially anomalous data point is identified in at least a portion of the data based on a proximity of a node, which corresponds to the potentially anomalous data point, in a tree to a root node of the tree. A statistically anomalous data point is identified in representative sample data points, which define the representative sample, as a result of the statistically anomalous data point indicating an unexpected occurrence of an event. An AI model is triggered to determine whether the entity exhibits malicious behavior by providing an AI prompt, including the representative sample and a description of the potentially anomalous data point and the statistically anomalous data point, to the AI model.

The invention provides a human resource management interaction method, system and device and a medium, and relates to the technical field of human resource management.The method comprises the steps that firstly, organizational structure management, electronic contract signing and employee management functions are unified in a single interface through full-process integrated operation, the time cost of multi-module switching is reduced, one-stop processing is achieved, and the management efficiency is improved; the processing efficiency of the human resource business is improved; secondly, realizing dynamic organization architecture management based on a tree diagram structure, allowing real-time adjustment of a department and post hierarchical relationship, generating a version snapshot with a timestamp, ensuring real-time performance and traceability of data, and avoiding errors caused by a traditional static architecture; furthermore, the electronic contract voucher is directly associated with the employee archive, so that the consistency of contract data, the organization structure and the employee state is ensured, and the data security is enhanced; and finally, related configuration is executed by using the current organization structure version snapshot and the electronic contract certificate, and real-time linkage of organization structure change and employee adjustment is realized.

The invention relates to the field of CIMs, and discloses a method and system for automatically degrading a CIM6-level three-dimensional model to CIM5 based on a semantic rule engine, and the method comprises the steps: obtaining CIM6-level three-dimensional model data, and constructing a scene hierarchical tree based on the three-dimensional model data; identifying the category, the attribute, the service label and the importance constant of each object in the scene hierarchical tree; calculating geometric complexity, semantic importance and topological dependency of each object, and calculating a comprehensive weight; matching the interval of the comprehensive weight and executing a corresponding processing operation based on the matched interval; performing grid merging and texture baking operation on the reserved object; performing grid instantiation on the proxy object after proxy replacement; collision holes in the model are detected through an octree in combination with a GJK algorithm to complete topological consistency verification, a lightweight CIM5-level model data packet is generated, and a difference report is output. According to the method, the consistency of the key semantic information and the topology is kept while the data volume of the CIM6 model is reduced.

The invention discloses a data tracing method and device based on a runtime snapshot. Comprising the steps of generating a runtime memory snapshot file based on report traceability information; analyzing the runtime memory snapshot file, extracting all keys in the first mapping relation to construct a header character set, and querying N metadata identifiers corresponding to N header characters according to the first mapping relation; accessing a report data result storage area based on the N metadata identifiers to obtain a data value list, and rendering to generate a visual report snapshot; for each metadata identifier, extracting the metadata identifier of each metadata which directly depends on and indirectly depends on the metadata identifier from the task detail information associated with the metadata identifier, and obtaining a single metadata tree based on the hierarchical dependency relationship among the metadata; rendering and generating N metadata tree snapshots based on the N metadata trees; and displaying the metadata circulation process corresponding to the abnormal data value by skipping to display the metadata tree snapshot of the metadata identifier corresponding to the report data column where the abnormal data value is located.

The invention discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention relates to a retrieval enhancement generation method and system based on tree atlas and large model guiding traceability, which is applied to the field of artificial intelligence and comprises the following steps: acquiring a problem description input by a user, and positioning an initial problem node according to the problem description; performing bidirectional path traversal in a pre-constructed tree-shaped knowledge graph based on the initial problem node, and performing screening to obtain high-probability paths of which the generation probability is ranked in the first several positions; calculating the semantic similarity between the high-probability path and the question description, comparing the semantic similarity with a first preset threshold value, and generating a traceability path of which the semantic similarity value is greater than the first preset threshold value; and generating an analysis report by the tracing path according to a preset logic framework. The method has the technical effects that the redundant links are quickly screened, the calculation overhead is reduced, and the system efficiency and the accuracy of the generated result are improved.

The invention discloses an agent training data set construction method and system combined with cross-modal learning, and relates to the technical field of agent training, and the method comprises the steps: obtaining multi-modal intelligence data from a multi-source database, extracting feature information, and projecting the feature information to a preset semantic space to obtain cross-modal alignment features; constructing an agent function call syntax tree based on cross-modal alignment features, analyzing the syntax tree into an instruction sequence, constructing an analysis reasoning chain, and generating a sample label and an interaction track; constructing a task dependency graph by using the sample labels and the interaction tracks, decomposing the task dependency graph into a plurality of parallel decision branches, and dynamically adjusting a processing strategy to form a training data set; mapping the training data set to an agent target space, optimizing and analyzing an inference chain through a training feedback channel, and outputting a standardized sample library; and finally inputting to-be-analyzed data into the trained agent, and generating an intelligence analysis report according to the analysis reasoning chain.

The invention discloses an intelligent design task decomposition method based on deep learning, and relates to the technical field of intelligent design, and the method comprises the steps: carrying out the semantic analysis of task text data, and obtaining a semantic representation; constructing a dynamic cognitive orchestration map based on semantic representation, generating a multi-granularity sub-task candidate set based on the dynamic cognitive orchestration map, and updating the dynamic cognitive orchestration map; based on the updated dynamic cognitive layout map and the sub-task candidate set, planning an optimal sub-task disassembling path sequence through a sequence decision model; and outputting a structured sub-task tree based on the optimal sub-task disassembly path sequence. According to the method, high-precision and high-efficiency task decomposition can be realized in a complex task scene, the defects of a traditional method in the aspects of granularity inconsistency, logic conflicts, task omission and the like are overcome, and the method has relatively high practicability and popularization value.

The invention provides an environmental radiation risk assessment system and method based on data analysis, and the system and method are applied to the technical field of environmental radiation assessment. The method comprises the steps: obtaining the site selection of a communication base station, collecting the tree group data and sidewalk data in the coverage range of the site selection of the communication base station, collecting the data of the communication base station, analyzing the growth speed of the tree group based on the tree group data and the communication base station data, analyzing the relieving condition of the electromagnetic radiation of the tree group to the communication base station based on the growth speed of the tree group, and analyzing the electromagnetic radiation influence of the communication base station to pedestrians based on the relieving condition of the electromagnetic radiation of the tree group to the communication base station and the sidewalk data. The optimal communication base station site selection is analyzed based on the electromagnetic radiation influence of the communication base station on pedestrians, the attenuation effect on the electromagnetic radiation of the communication base station is analyzed by predicting the tree growth vigor, the radiation influence on group exposure is quantified, the optimal communication base station site selection is screened out, the site selection accuracy is improved, and the site selection efficiency is improved. And harm caused by electromagnetic radiation of the communication base station is reduced.

An ancient and famous tree protection and management method comprises the following steps: 1, establishing an ancient and famous tree health degree evaluation model based on existing ancient and famous tree health degree evaluation related standards; 2, acquiring state information of ancient and famous trees from multiple angles; 3, analyzing the information obtained in the step 2; 4, determining the grading condition of each monitoring index for protecting the ancient and famous trees; 5, calculating the health degree of the ancient and famous trees according to the contents in the step 4 and the step 3; 6, based on the health degree calculation result in the step 5, evaluating the health level of the ancient and famous tree, and describing the health condition characteristics; 7, establishing a targeted old and famous tree maintenance scheme according to the health evaluation information of the old and famous trees in the step 6; and 8, implementing maintenance of the ancient and famous trees established in the step 7, and performing task closed-loop management. The health degree of the ancient and famous trees is comprehensively and objectively evaluated, the health condition characteristics of the ancient and famous trees are completely described, the corresponding maintenance scheme is established according to the health degree, and full coverage of the maintenance requirements of the ancient and famous trees is achieved.

The invention provides a mechanical arm shaft hole assembling method and system based on an AER-DDPG algorithm, and belongs to the field of robot automatic assembling. According to the current pose of the mechanical arm and the pose of the workpiece, a path planning strategy based on tree search is adopted to generate a joint track, and the mechanical arm is controlled to grab the workpiece; a deep reinforcement learning model is constructed based on an AER-DDPG algorithm, a state space is input to generate an action instruction, and a mechanical arm is controlled to move a workpiece to a mounting position to complete assembly; in the deep reinforcement learning model, the double-value network adopts two value networks, and target Q value suppression over-estimation is calculated by taking a minimum value; an adaptive experience playback mechanism divides an experience pool into a successful experience pool and a failed experience pool, and dynamically adjusts the sampling proportion of the two types of experience based on time difference. According to the method, the minimum value of the two-value network is taken to suppress Q value over-estimation, so that the training stability is improved; based on time difference dynamic adjustment success and failure experience sampling, the mechanical arm shaft hole assembling efficiency and reliability are improved.

According to the forest and fruit tree body profiling flapping fruit harvester based on tree form recognition, the flapping action of the flapping strips is achieved through rotation of the crankshaft and push-pull of the piston rod of the air cylinder, different portions of a tree crown can be effectively beaten, missed flapping of the top of the tree crown and the pole portion of the symmetry axis can be avoided, the net picking rate is uniform, and the automation degree is high. The device comprises a chassis vehicle body and a tree-like flapping device, the tree-like flapping device comprises a base arranged on the chassis vehicle body, at least two bearing seats are arranged on the base, a crankshaft with a plurality of crank throws is connected to the bearing seats, a driving device for driving the crankshaft to rotate relative to the bearing seats is arranged on the base, and the lower end of each flapping strip is hinged to one crank throw of the crankshaft. Each sleeve is arranged on the corresponding beating strip in a sliding mode, a plurality of air cylinders arranged in the axial direction of the crankshaft are arranged on the base, and the end of a piston rod of each air cylinder is hinged to the corresponding sleeve.

The invention discloses a large language model reasoning acceleration method and device based on two-stage speculative decoding and a storage medium, and the method comprises the steps: constructing and initializing a Trie tree, and inserting a historical corpus, and phrase sequences in a document library or a code library into the Trie tree one by one; in the reasoning process, longest prefix matching is carried out based on a Trie tree, and a candidate draft sequence is generated by adopting branch backtracking and recursive search; performing confidence evaluation on the candidate draft sequence, calculating a joint confidence score of the sequence through probability multiplication and a Top-K screening mechanism, and judging whether the joint confidence score reaches a confidence threshold; if the accumulated confidence of the candidate sequence reaches a threshold value, skipping a small model generation stage, and directly entering large model verification; otherwise, entering a small model draft completion stage; and the final large model takes the replaced and updated draft sequence as final output. According to the method, adaptive acceleration of the decoding process can be realized, and the long text reasoning delay of the large language model is remarkably reduced while the generation quality is ensured.

A three-stage pipeline is used to create a data structure for efficiently producing grounded recommendations that guarantee that the recommended items are part of a set, D. In the first stage, each item in D is converted into a vector representation. In the second stage, a hierarchical clustering method is used to build a tree based on the vector representations. Each item is a leaf node of the tree. Each non-leaf node represents a group of items or a group of groups of items, and so on. In the third stage, an LLM is used to generate text that encapsulates the information of the group (or groups) of items represented by each node. The generated tree is recursively traversed to generate recommendations.

A system can be used to generate interactive data visualizations and textual content. The system receives a layout of a content item. The content item can be a data visualization dashboard or an article. The system can generate a content structure tree based on the layout. The content structure tree represents hierarchical and semantic relationships between sections of the content item. The system can receive input adding content elements, such as data visualizations or textual paragraphs, to sections within the content item. The system can identify text roles for different sections based on the content structure tree and the content elements. The system can generate text suggestions for the content item based on the text roles. The system can also generate text content for a text suggestion using a large language model, display the generated text content within the content item, and iteratively refine the content item.

The invention discloses a verifiable retrieval method and device, equipment, a medium and a product, and relates to the technical field of block chains. Obtaining a query request; performing data positioning from a retrieval algorithm tree based on the query request to obtain at least one target leaf node and a data index set corresponding to the target leaf node; generating a target node proof and a path proof based on the retrieval algorithm tree, the target leaf node and the data index set; and verifying the target node proof and the path proof based on the retrieval algorithm tree. By the adoption of the technical scheme, under-chain retrieval and verification are carried out through the retrieval algorithm tree, on-chain and off-chain cooperation of query verification is achieved, it is ensured that the certification size is small, verification calculation complexity is lowered, on-chain storage overhead is reduced, and the problems that at present, request query certification is large in size and low in verification efficiency are solved.

A predictive read threshold calibration method is provided in which a dedicated hardware module is used for read threshold calibration. The hardware module uses a tree-based inferencing model to generate trees that produce respective inference results based on a plurality of read thresholds. An inferred read threshold is obtained by summing the inference results of the trees, and the memory is read using the inferred read threshold. Other embodiments are provided.

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

The invention relates to a method, a device, equipment and a product for updating source codes. The method includes generating a page structure tree based on source code, page structure nodes in the page structure tree corresponding to code elements in the source code, and the page structure nodes including tracking information for tracking positions of the code elements in the source code. The method further includes obtaining adjustments by the user to user interface elements corresponding to the page structure nodes through the user interface. In addition, the method includes updating a code element in the source code based on the adjustment and the trace information in the page structure node.

The invention provides a tree wind-induced response evaluation and analysis method, and relates to the technical field of disaster prevention and reduction engineering, and the method comprises the steps: carrying out the construction of a geometric model for a target tree; on the basis of the geometric model, CFD analog simulation is carried out, and wind pressure time history data and crown load data of CFD grid points of the branches under different wind field conditions are obtained; finite element mesh generation is carried out on the geometric model, a finite element model is constructed, and mesh unit types in the model comprise hexahedron units, tetrahedron units and wedge-shaped units; the branch wind pressure time history data and the crown load data are mapped to the surface of a finite element model, structural finite element analysis is carried out, and cloud chart data of stress and displacement of the target tree changing along with time under different wind field conditions are obtained; and carrying out wind-induced response analysis and wind resistance evaluation on the target tree based on the cloud picture data and the wind-induced damage criterion of the tree. According to the technical scheme, a scientific basis can be provided for understanding the response behavior of the target tree under the strong wind condition.