39 results about "Node splitting" patented technology

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention provides a distributed computing method and system and related equipment. The method comprises the steps that a management node issues a fusion operator to a computing node; the fusion operator comprises two matrixes and an instruction for realizing matrix multiplication operation of the two matrixes; a plurality of matrix calculation units in the calculation node respectively execute part of the two matrix multiplication operations according to the fusion operator to obtain part of calculation results of the matrix multiplication operations; and executing a communication task to synchronize the partial calculation result. A management node only needs to send a fusion operator to a calculation node, the calculation node can complete calculation of a matrix multiplication according to the fusion operator, and the management node does not need to split the matrix multiplication operation into a plurality of subtasks for calculation and communication and then send the subtasks to the calculation node in sequence. The phenomenon that computing node resources are idle due to management node splitting and task issuing can be avoided, and the utilization rate of the resources and the efficiency of the distributed computing system are improved.

This solution discloses a new method for identifying RNA pseudouridine sites. This method proposes to use a variety of feature representation techniques to extract sequence features, and then uses the SVM-RFE method for feature selection to compress the feature space and optimize the feature subset. The best feature set after feature selection is input into the kernel method KeMRF based on polynomial random forest to identify pseudouridine sites in the sequence. As a newly proposed classification method, compared with the traditional random forest, KeMRF not only optimizes the discriminant criterion for node splitting, but also combines with an easy-to-interpret kernel method, making the classification performance more superior. This method reduces the training time of the model, improves the classification performance of the model, and further enhances the accuracy of identifying pseudouridine sites.

This invention discloses a high-precision indoor personnel positioning method based on machine learning, comprising the following steps: collecting samples, labeling locations, and generating a dataset; extracting inertial features and clustering them to obtain behavioral pattern labels; constructing a signal topology map based on wireless data; constructing extreme random trees for each pattern subset, dynamically selecting splitting features and thresholds to generate an environment-adaptive tree; introducing node splitting into the topology map with topological proximity constraints, and performing local weighted regression combining topology and environmental weights to obtain each extreme random tree model; acquiring real-time data, obtaining pattern probabilities from the clustering model, selecting the corresponding tree model for traversal, and obtaining the predicted location for each pattern; fusing the predicted location output results with probabilities as weights, and updating the topology map, environmental statistics, and tree splitting thresholds. This invention achieves high-precision positioning in dynamic and complex indoor environments, improving positioning robustness and environmental adaptability.

The invention provides a test case generation method and device, equipment, a storage medium and a program product, and relates to the technical field of financial science and technology or the technical field of software testing. The method comprises the following steps: determining a splitting condition of nodes of an adaptive decision tree according to a test entropy and a failure rate gradient of a test object; performing node splitting on the adaptive decision tree represented by the test object based on the splitting condition to generate a test path, the test path being a path from a root node to a leaf node in the adaptive decision tree after node splitting; and generating a test case set according to the test path. The method provided by the invention can adapt to real-time updating of the test object and generate the test case with higher test efficiency.

This invention discloses an approximate nearest neighbor connection optimization method for encrypted databases based on PM# trees, belonging to the field of approximate nearest neighbor connection optimization technology for encrypted databases. It solves the problems of low retrieval efficiency and insecure retrieval processes in existing technologies. The method includes: projecting a high-dimensional dataset into a low-dimensional space using hash projection to obtain a low-dimensional dataset; encrypting the high-dimensional dataset to obtain an encrypted high-dimensional dataset; deleting redundant nodes generated during node splitting in the PM# tree to obtain a PM# tree, and using the PM# tree to build an index on the high-dimensional dataset to obtain an index file; encrypting the index file to obtain an encrypted index; and performing an approximate nearest neighbor connection query on the high-dimensional dataset based on the encrypted index, the low-dimensional dataset, and the encrypted high-dimensional dataset to obtain the query results. This achieves encryption of the high-dimensional dataset while reducing computational load, thus accelerating retrieval speed and improving retrieval quality.

This invention provides a pipeline network segmentation method based on a node explosion algorithm, comprising: constructing an original pipeline network topology; node explosion: breaking the original pipeline network topology at designated segmentation nodes and deleting the designated segmentation nodes to obtain multiple subgraph topologies; node interpolation: in each subgraph topology, constructing edges between the designated segmentation nodes and their neighboring nodes, and re-inserting the designated segmentation nodes into each subgraph topology; and correcting the flow direction of all subgraph topologies after node interpolation. This invention solves the technical problem that the traditional DFS traversal segmentation method suffers from differences in segmentation results due to the different root node input order, and improves the efficiency of topology segmentation and the accuracy of simulation calculations.

The invention discloses a steel frame node identification method and system based on a data center, and belongs to the technical field of computer aided design, the steel frame node identification method based on the data center comprises the following steps: S1, intelligent classification and standardization processing of steel structure node members; s2, setting the connection mode and the connection mode of the steel structure node components; s3, the connection relation between the steel structure node components is calculated; s4, calculating a 3D node constructed by the steel structural member; s5, calculation of steel structure component splitting 2D nodes; and S6, calculating, matching and identifying the characteristic value of the basic node of the steel structural member. The method is used for realizing automatic and high-precision identification and classification of various nodes in the steel structure model, the technical bottlenecks of high human error rate, long modeling period and difficulty in adapting to complex node types in the traditional node identification process are solved, and the efficiency and quality of steel structure node design and construction are improved.

The application relates to the field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, which comprises the following steps: acquiring a jump section direction vector and classifying label nodes with consistent directions, splitting a path lacking a mapping relationship to form an interrupted section, identifying nodes with continuous directions as bridge section entrances, rearranging path sections to generate a sliding connection sequence, and combining semantic fields to sort behavior trajectories to generate a focused analysis set; in the application, the jump section direction is classified according to node order and channel number, the direction aggregation boundary is determined, the semantic field is combined with the path position to identify an interruption, the positioning accuracy of the node sequence structure and the rationality of the paragraph division are improved, the bridge section connection is based on direction consistency screening, the path continuation logic is maintained, the behavior trajectory and the aggregation number are sorted according to the sequence and the level, the behavior aggregation sequence of the semantic chain is constructed, and the coherence of path identification in the channel aggregation area and the stability of node attribution are enhanced.

The application provides a decision tree recommendation method and system based on random sampling, which deduces the conduction path of faults among devices from the device physical topology association relationship in the device production detection scene, determines the sequential association order of each conduction node and the conduction trigger condition between nodes, groups and collects real detection data and virtual detection data according to the conduction node correspondence relationship to obtain a sampling pool, determines the decision tree branch splitting priority based on the upstream and downstream association relationship of the fault conduction path, obtains a decision tree, generates a step-by-step detection action sequence corresponding to the fault conduction path according to the reasoning path, corresponds each reasoning branch to a detection operation link to obtain a step-by-step detection action sequence, obtains an execution feedback result, updates the node splitting weight of the decision tree, adjusts the sample screening condition of the sampling pool, and obtains the sampling pool to optimize the decision tree. The application can realize the linkage adaptation of the decision tree structure and the data source, and continuously improve the accuracy of fault detection.

The application discloses an online incremental learning method and system based on adaptive B+ tree index, relates to the technical field of artificial intelligence, and comprises the following steps: constructing an extended adaptive B+ tree index structure, inserting a new sample and calculating a track disturbance value; combining the disturbance value and a node state to generate a disturbance score, and adding the new sample and associated node samples to an incremental learning scheduling queue when a condition is met; obtaining an affected node and updating the structure code of the affected node when a node splitting, merging or reconstructing event occurs; monitoring sample label track changes, adjusting a label confidence marker when a threshold is exceeded, and limiting the participation frequency according to a screening mechanism; and performing a forgetting compression operation on a leaf node with low access frequency and small label fluctuation degree. Through the construction of the adaptive B+ tree index, the disturbance-driven scheduling, the structure change response and the forgetting compression strategy, the sample management is refined, the learning update is efficient, and the structure maintenance is controllable, so that the stability and resource utilization efficiency of the online incremental learning are improved.

The invention relates to the technical field of abnormal aggregation, in particular to an abnormal data aggregation method for extra-high voltage equipment, which comprises the following steps: performing node splitting and classification through a random forest, and combining depth and sampling proportion setting and cross division training and verification sets, so that risk classification has hierarchical precision; the method comprises the following steps: establishing a dynamic risk index sequence, enhancing the reliability of a result under a constraint condition, carrying out interval mapping and extreme threshold comparison on the risk result to form the dynamic risk index sequence, introducing a graph neural network to carry out neighbor sampling and feature aggregation on the association strength between indexes, and realizing the conversion from single-point anomaly to a coupling mode between multiple indexes. According to the method, the causal relationship and the propagation path between different types of anomalies can be disclosed, a continuous abnormal chain structure is formed through multi-hop path extraction and sequence combination, scattered abnormal information is converted into chain expression with evolutionary logic, and the capacity of recognizing potential faults of a complex system in advance and guaranteeing overall operation safety is improved.

Embodiments of the present specification provide a method and device for training a risk control model and predicting a risk category. The method for training the risk control model comprises: obtaining each first training sample, the first training sample comprising a first feature value corresponding to an attribute feature of a business object and a category label indicating whether the business object has a business risk; determining a second feature value corresponding to a scenario feature, adding the second feature value to the corresponding first training sample to form a second training sample; constructing a first decision tree through node splitting based on each second training sample, the process of splitting for a current node comprising: splitting according to a splitting purity of any splitting condition in a plurality of candidate splitting conditions of the current node; regarding the scenario feature as a category feature during the splitting process; and determining a risk control model for classifying business objects based on the first decision tree. The complexity of the model system can be simplified, and the model has better performance.

The invention discloses a tunnel structure long-term stability evaluation method based on intelligent detection and monitoring data, and relates to the technical field of tunnel engineering monitoring and evaluation, and the method comprises the steps: S1, extracting tunnel monitoring data from a preset database, and carrying out the preliminary classification of user roles, processing the classified data set by adopting a branch condition setting and feature selection mechanism to obtain a mapped user demand set; s2, according to the mapped user demand set, analyzing demand difference by adopting a node splitting rule, if the demand difference points to a manager, extracting a security level quantitative index, and judging preliminary deconstruction information through information gain calculation; according to the tunnel structure long-term stability evaluation method based on intelligent detection and monitoring data, the accuracy of information expression and the efficiency of report generation are improved, a closed-loop information chain from original data to a final report is established, and the reliability and scientificity of tunnel structure long-term stability evaluation are effectively guaranteed.

The invention discloses an unstructured grid adaptive node splitting method for complex form three-dimensional modeling, which relates to the technical field of three-dimensional modeling and grid generation, and comprises the following steps: constructing an initial unstructured grid, and adopting a Delaunay triangulation generation unit to ensure that a covered modeling area has no overlapping gap; key features such as sharp corners and edges are extracted, geometric parameters are calculated, and feature priorities are divided; establishing a splitting judgment system based on a multi-dimensional index, and marking a to-be-split unit; determining split node coordinates, splitting according to unit types, and updating a topological relation; optimizing the grid quality through a smoothing algorithm, and deleting invalid units; and calculating the modeling precision and the grid quality, and if the convergence standard is not reached, repeating the process until the standard is reached. According to the method, complex morphological characteristics are accurately captured, a splitting strategy is adaptively adjusted, and node coordinates are highly fitted with boundaries; error accumulation is effectively controlled, numerical simulation and visualization requirements are met, and reliable support is provided for complex modeling in multiple fields.

This invention relates to the field of unmanned aerial vehicle (UAV) path planning technology, and more particularly to a distributed UAV local replanning and real-time obstacle avoidance method and system. The method includes real-time obstacle detection; triggering local trajectory replanning when the expanded obstacle region overlaps with the UAV's global guidance trajectory in time; independently generating locally replanned trajectories based on an improved time elastic band algorithm and broadcasting them to a decision set for distributed conflict detection; constructing a constraint tree for each conflicting UAV in the conflict set using a high-level node splitting method based on enhanced conflict search, according to a fixed time window synchronization mechanism; calculating the total cost of each child node in the constraint tree using the same cost function and calculation rules; selecting the optimal child node based on the total cost of each child node; and repeatedly selecting the optimal child node to obtain a conflict-free executable trajectory. This invention solves the deadlock problem caused by short-range UAV replanning trajectory conflicts in dense obstacle environments.

This invention discloses an embedded microkernel iterative generation and graph rewriting method and system based on physical constraint feedback. The method establishes a reverse feedback loop from a deterministic backend to the LLM (Limited Language Management Module): First, the deterministic backend is used as a "physical verifier" to tentatively synthesize the initial task graph; when physical violations such as register overflow or volume exceeding limits are detected, a physical diagnostic report containing bottleneck node information is generated; then, the diagnostic report is converted into natural language prompts using feedback encoding technology; finally, the LLM is driven to execute graph rewriting strategies such as node splitting and input serialization to correct the task graph topology. This invention achieves a closed-loop iteration of "physical problem, semantic solution." Experiments show that this method can increase the generation success rate of complex embedded tasks on extremely constrained hardware from 32% to 94%, significantly enhancing the robustness and adaptability of the system.

Provided in the present application are a distributed computing method and system, and a related device. The method comprises: a management node issuing a fused operator to a compute node, wherein the fused operator comprises two matrices and an instruction for implementing a matrix multiplication operation of the two matrices; on the basis of the fused operator, a plurality of matrix computation units in the compute node separately executing a partial operation in the multiplication operation of the two matrices, so as to obtain a partial computation result of the matrix multiplication operation; and then executing a communication task to synchronize the partial computation result. The management node only needs to send one fused operator to the compute node, and the compute node can complete computation of one matrix multiplication on the basis of the fused operator, without the need for the management node to split one matrix multiplication operation into a plurality of sub-tasks for computation and communication, and then sequentially send the sub-tasks to the compute node. Thus, idle resources of the compute node caused by the management node splitting and issuing tasks can be avoided, thereby improving the resource utilization and the efficiency of the distributed computing system.

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

This application proposes a polar code rate-sequence node decoding method based on a minimum combination set, comprising: offline construction of minimum combination sets for SPC nodes and R1 nodes; obtaining the received sequence of the polar code to be decoded, and calculating the LLR of the internal source nodes based on the obtained LLR of the SR1 nodes; calculating the corresponding maximum likelihood estimated codewords based on the LLR of the source nodes, and using the codewords to apply parity check constraints to the root node, splitting the root node into multiple SPC nodes; obtaining the flipped combination set of each split SPC node, and selecting candidate flipped combinations based on the path metric increment; performing a union operation on the candidate flipped combinations to construct a new set, and selecting new candidate flipped combinations from the new set based on the minimum combination set of the R1 nodes; flipping the new candidate flipped combinations to obtain candidate paths and corresponding path metric values ​​as the decoding results of the SR1 nodes. This invention, employing the above scheme, significantly reduces the decoding latency and complexity of the SR1 nodes.

The invention relates to the technical field of data processing, in particular to a cloud-based sluice monitoring and control system, which comprises a water level state sensing module, an index structure self-adaptive module, a node splitting threshold coefficient, a node splitting threshold coefficient calculation module, a node splitting threshold coefficient calculation module, a node splitting threshold coefficient calculation module and a node splitting threshold coefficient calculation module, the causal lag calculation module generates a water flow propagation lag time amount by using an upstream discharge amount and a Manning formula, and the spatial-temporal index construction module generates a logic index time key, sets a B + tree node capacity upper limit according to a splitting coefficient, and writes data into an index structure. According to the method, fluctuation characteristics are quantized, index splitting coefficients are dynamically matched according to variances, the stability of a high-frequency data writing structure is optimized, and a logic index time key is generated in combination with the inversion flow velocity of the upstream discharge amount so as to correct physical acquisition time; and the index construction efficiency and the query response speed of the database during high concurrency are improved on the basis of ensuring the causal alignment of the time series data.

The present disclosure provides a microservice splitting suggestion method, device, computer readable storage medium and electronic equipment, and relates to the field of cloud services. The microservice splitting suggestion method comprises: constructing an initial microservice architecture graph based on communication traffic information between microservices, the initial microservice architecture graph comprising microservice nodes and links connecting the microservice nodes; determining a microservice node in the initial microservice architecture graph whose stress score satisfies a preset node splitting condition as a suggested splitting node; determining the initial microservice architecture graph after splitting the suggested splitting node as an intermediate microservice architecture graph, cutting the intermediate microservice architecture graph into one or more target microservice architecture graphs based on the weights of the links in the intermediate microservice architecture graph, and determining a suggested merging node in the microservice node according to the merging score of each subgraph in the target microservice architecture graph. The present disclosure improves the microservice splitting efficiency.

The invention provides an image processing method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of image processing. The method comprises the following steps: performing visual feature extraction on a to-be-processed image to obtain a fusion feature vector; the fusion feature vector is input into a pre-constructed decision tree model to obtain an image enhancement parameter set, the decision tree model comprises a node splitting threshold, and the node splitting threshold is dynamically adjusted according to historical operation data of a user; and processing the to-be-processed image according to the image enhancement parameter set. According to the method, the problems that a traditional image needs manual parameter adjustment processing and cannot be self-adapted according to user habits are solved, the enhanced parameters automatically fit user preferences by analyzing historical operation data of the user and adjusting the node splitting threshold value of the decision tree in real time, and the image processing efficiency is remarkably improved.

The application discloses a kind of based on mercury commitment key query dynamic index and device, can directly query all data objects of frequent keyword across block, and provide verification vector VO verification query result. When using mercury commitment to generate soft commitment, no specific information is bound, and malicious user cannot obtain additional information. Specifically, the structure and size of the dynamic index tree are predetermined, the number of related data objects is estimated using the historical frequency of keywords, and the leaf node position is reserved. Group the keywords and their data objects together, and the groups of the same keyword are adjacent, ensuring that the similarity of the keyword set under the node is maximized. Use historical data to initialize the tree structure, and update the original data with new data objects to avoid node splitting and merging. When a new data object is inserted, the vector commitment and membership proof of other nodes in the tree do not need to be updated, reducing the update overhead.

The application relates to a capacity retention rate estimation model construction method, device and computer equipment. The method comprises the following steps: obtaining a plurality of feature data sets according to the charge-discharge data of a plurality of sample batteries; each feature data set comprises a plurality of sample features; training an initial random forest model according to the plurality of feature data sets to construct a target prediction model for predicting the capacity retention rate of any target battery; the loss of the initial random forest model in the training process is determined according to the feature weights of the sample features. The feature weights are introduced into the loss of node splitting, the sensitivity of the model to important features is improved, the efficiency of training the initial random forest model is improved, and the accuracy of the target prediction model is improved; and the initial random forest model is trained according to the plurality of feature data sets, the nonlinear relationship between the sample features is fully utilized, and the prediction accuracy of the target prediction model for the later capacity retention rate of the battery is improved.

The invention discloses a cable fault detection method based on distributed nodes, relates to the field of power faults, solves the problem that the existing cable fault detection method is poor in detection effect, and comprises the following steps: S1, obtaining a target area topological graph, and carrying out modularity gain analysis on the distributed nodes in the target area topological graph to obtain a target area topological graph; s2, according to the distributed node subnet division data, carrying out node connection analysis on the node topology subnets according to the distributed node subnet division data, and carrying out node connection analysis on the node topology subnets according to the distributed node subnet division data, s3, according to the node subnet division data, respectively carrying out cable fault detection on the first type of topology subnet and the second type of topology subnet, and carrying out cable fault detection on the second type of topology subnet and the first type of topology subnet. The accuracy and pertinence of the cable fault detection method are improved.

The invention discloses a numerical simulation method for rapidly calculating a fault dislocation discontinuous deformation field, and relates to the field of tectonic geology and seismology, and the method comprises the following steps: constructing a physical coding finite element network SN-PEFEN fused with a daghet node technology; fault dislocation deformation field forward modeling simulation is carried out based on the SN-PEFEN; and fault dislocation deformation field inversion simulation is carried out based on the SN-PEFEN, and numerical simulation of a fault dislocation discontinuous deformation field is realized. According to the method, the problem of essential conflict between the continuous field prediction characteristic based on the continuous microhypothesis and the inherent displacement discontinuous characteristic of the fault dislocation field in the existing method is solved.

The invention discloses a query method and system based on a trajectory index structure, and the method comprises the steps: S1, constructing a probabilistic maneuvering R tree: S11, receiving a to-be-inserted dynamic object, and modeling the motion uncertainty of the to-be-inserted dynamic object into a PMBR which is composed of a plurality of maneuvering unit groups; s12, inserting an object into a leaf node which enables the internal motion confusion degree of the node to be increased to the minimum, if the inserted node overflows, executing a node splitting method based on a future interaction score, and selecting a division scheme which enables the interaction possibility of the two split nodes in a future time window to be minimum; s13, inserting an object at a leaf node; s14, splitting the nodes; and S15, updating the PMBR of the ancestor node upwards. And S2, receiving a query request, and returning a query result. According to the method, the pruning efficiency of predictive query is remarkably improved. A flexible motion model is applied, and a pruning strategy and accurate calculation are combined, so that the query performance is greatly improved while the accuracy is ensured.

The invention provides a decision tree recommendation method and system based on random sampling, and the method comprises the steps: deducing a conduction path of a fault between equipment from an equipment physical topology association relationship in an equipment production detection scene, determining a sequential association sequence of each conduction node and a conduction triggering condition between the nodes, and carrying out the fault diagnosis of the fault; grouping and collecting the real detection data and the virtual detection data according to a conduction node corresponding relationship to obtain a sampling pool, determining a decision tree branch splitting priority based on an upstream and downstream association relationship of a fault conduction path to obtain a decision tree, generating a step-by-step detection action sequence corresponding to the fault conduction path according to a reasoning path, and determining the fault conduction path according to the step-by-step detection action sequence. Each reasoning branch corresponds to a detection operation link, a step-by-step detection action sequence is obtained, an execution feedback result is obtained, the node splitting weight of the decision tree is updated, the sample screening condition of the sampling pool is adjusted, and the sampling pool is obtained to optimize the decision tree. According to the method, linkage adaptation of the decision tree structure and the data source can be realized, and the accuracy of fault detection is continuously enhanced.

The invention belongs to the technical field of artificial intelligence and intelligent medical treatment, and aims to solve the problems that existing medical decision tree construction depends on manpower, time and labor are consumed, an existing automatic method is difficult to analyze a complex flow chart structure, and rule expression is not standard. The invention provides a medical decision tree automatic construction method based on flow chart recognition. The method comprises two stages: firstly, converting a medical flow chart image into a structured Dot script end to end by using a fine-tuning multi-mode large model; and then through semantic enhancement technologies such as node classification, condition judgment result rewriting and composite node splitting, node representation and a tree structure are optimized, and a standardized medical decision tree is generated. Based on the decision tree, interactive differential diagnosis is further realized, and the inquiry process is dynamically guided through decision path retrieval and diagnosis evidence integrity evaluation. According to the method, the medical decision tree with complete logic and high interpretability can be constructed, and the efficiency and accuracy of clinical auxiliary decision making are remarkably improved.