212 results about "Graph algorithms" patented technology

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

The invention belongs to the field of multi-modal data processing, and particularly relates to a multi-modal fusion analysis method and system for test data, and the method achieves the intelligent analysis of real-time multi-dimensional demand data through constructing a bidirectional multi-modal constraint generation model and fusing multi-modal reasoning generation, forward logic constraint and reverse causal constraint sub-models. Wherein the forward logic constraint sub-model constructs a forward constraint hierarchical reasoning association node network through entity-relation extraction and a graph algorithm based on a test standard criterion and a historical constraint sequence, and generates a forward logic constraint set in combination with a depth index algorithm; the reverse causal constraint sub-model constructs a combined conflict value through modal reasoning accuracy, performs conflict tracing and adjustment in combination with a cross-modal conflict threshold, and generates a reverse causal constraint set; the two constraint sets are fused through a multi-modal reasoning generation sub-model, a target generation demand text meeting the confidence coefficient requirement is finally generated, and intelligent analysis of clinical test data is achieved.

The invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

The present invention provides a method for evaluating the effects of communication confrontation based on a knowledge graph, which relates to the field of communication confrontation. The present invention first constructs a knowledge graph of the communication network, converts nodes, communication relationships, and interference relationships into a graph structure, and represents the connection and interference between nodes through the edges in the graph. Then, the node connection relationship is obtained through graph query, the impact of interference and communication quality is calculated, and the shortest path between nodes is calculated using a graph algorithm to achieve an evaluation of the communication effect. The present invention aims to provide a more comprehensive and accurate evaluation of the effects of communication confrontation from the perspective of complex communication networking by analyzing the connection relationship and information transmission path between communication nodes. Through this method, the effect of communication confrontation can be evaluated from the global perspective of the communication network, thereby improving the comprehensiveness and accuracy of the evaluation.

The invention relates to the technical field of water supply network monitoring, and discloses a water supply network leakage monitoring and positioning system and method based on big data analysis, and the method comprises the steps: obtaining continuous audio stream data and network topological graph data collected by a distributed sensor, and calculating background baseline parameters; performing multi-scale wavelet transform decomposition on the current audio frame, calculating an energy entropy feature and generating a single-frame abnormal score; applying a cumulative sum control chart algorithm to output a cumulative abnormal index; generating an enhanced anomaly score through a graph attention network; and performing leakage judgment and positioning based on the abnormal scores of the multiple sensors. The technical problem that weak leakage signal detection sensitivity is low is solved.

The invention relates to the technical field of derivative variable calculation, and discloses a derivative variable calculation method and system based on a graph algorithm, and the method comprises the steps: analyzing derivative variable definition and calculation logic through employing a variable SQL syntax tree analysis technology, taking an analyzed variable as a node, determining a variable dependency relationship as an edge according to a processing task, and carrying out the calculation of the derivative variable; constructing a directed acyclic graph and storing the directed acyclic graph in a graph database; the nodes store variable metadata, execution priorities and calculation information, and store calculation task metadata and calculation information; according to node priorities, finding out high-priority variable nodes, querying all in-degree edges of the high-priority variable nodes, forming one or more subtrees, forming high-priority execution subtrees, performing topological sorting on the execution subtrees, and performing execution by an execution engine; the method aims to solve the problems of low calculation efficiency and insufficient utilization of calculation resources in the prior art.

According to the knowledge base-oriented interactive causal inference support method and system provided by the invention, a user causal analysis intention is precisely limited through interactive dialogue, a multi-dimensional intention is precisely captured, a fuzzy causal problem is converted into a computable analysis parameter, and ambiguity is remarkably reduced; multiple strategies such as an explicit causal declaration mode, time sequence correlation, statistics of significant correlation and comparative difference analysis are integrated to collect causal evidence, the four strategies are executed in parallel, multiple evidence sources of causal inference are covered, and limitation of a single method is avoided; and the query efficiency is optimized in combination with a special index and a graph algorithm, and finally evidence is presented in a structured and interactive mode and confidence is clearly distinguished, so that the response efficiency is greatly improved. The system solves the problems of fuzzy intention, single evidence and low efficiency in traditional causal inference, is suitable for the fields of industrial fault analysis, medical diagnosis, social phenomenon research and the like, and provides scientific and transparent causal hypothesis generation support for users.

The invention relates to the technical field of data processing, in particular to a vibration compaction forming energy transfer efficiency evaluation method, which comprises the following steps: firstly, integrating multi-source data, and generating a semantic enhancement data set through a self-adaptive semantic annotation algorithm; then, node relation extraction is carried out based on the semantic enhancement data set, and a knowledge graph model is constructed and dynamically maintained by using a first graph algorithm so as to structurally represent nodes and relations in the compaction process; based on the knowledge graph model, a parameter influence result is evaluated by a second graph algorithm, and a risk prediction result is generated in combination with a time sequence prediction algorithm; then, a reward function is defined according to a parameter influence result and a risk prediction result, and an optimal vibration compaction parameter combination is determined; and finally, generating a parameter recommendation report and collecting user feedback to iteratively optimize the knowledge graph. According to the method, the accuracy of vibration compaction parameter selection can be improved, and the energy transmission efficiency and the engineering quality are optimized.

The invention relates to a knowledge graph construction and retrieval optimization method based on water affair data, and the method comprises the steps: taking the unified knowledge expression of a water affair object and an event thereof as a target, determining a water affair business field and business range, and constructing a water affair knowledge graph conceptual model covering a static knowledge ontology and a dynamic event ontology; performing knowledge extraction on the obtained multi-source heterogeneous water affair data, converting the data into a unified triple form, and constructing a data layer of a water affair knowledge graph; and on the basis of the data layer, node, path and region query is performed on the knowledge graph by adopting a graph algorithm, and semantic enhancement retrieval and reasoning are performed in combination with a large language model. By constructing the water affair knowledge graph and introducing knowledge extraction and retrieval enhancement capability of a large language model, dynamic modeling and semantic reasoning of water affair objects, events and relationships thereof are realized, high-risk area identification, emergency path planning and emergency early warning are supported, and the knowledge utilization rate and intelligent decision level in the water affair field are improved.

The invention belongs to the technical field of industrial robot control, and provides an error compensation method and system for an industrial robot, and the method comprises the steps: collecting a composite sensing signal and a pre-tightening force of the industrial robot, and constructing a contribution degree matrix; an error transfer link is extracted, a space-time correlation function is constructed based on the link, and a pretightening force attenuation dominant area is determined through a dynamic cloud atlas algorithm; gradient change features and attenuation trend coefficients of the attenuation dominant region are extracted, a to-be-compensated region is screened, and the saturation compensation risk of the execution mechanism is judged; and if the saturation risk exists, screening pretightening force sensitive joints to construct a compensation distribution model, outputting a segmented compensation demand quantity, and dynamically calibrating the segmented compensation quantity based on a real-time compensation result. The system comprises a contribution analysis module, an attenuation positioning module, a saturation analysis module and a compensation calibration module. The method is beneficial to positioning an error source dominated by pretightening force attenuation, and is beneficial to improving compensation precision and stability.

The invention discloses a power grid regulation capability evaluation method, system and equipment based on a large industrial load, and a medium, and belongs to the technical field of power management, and the method comprises the steps: collecting power data, carrying out the preprocessing, and generating a load multi-source data set; constructing a load prediction model, extracting time sequence features in the load multi-source data set, and dynamically fusing sequences in the time sequence features to generate a load prediction value; nodes, edges and attributes are defined, a load prediction value is evaluated, and a risk knowledge graph is generated; through a traditional graph algorithm, node importance scores are calculated and normalized, load risk scores are generated, risk threshold values are set, and load risk levels are marked; setting an adjustment rule, and adjusting the load prediction value; based on the load risk level and the time-of-use electricity price in the load multi-source data set, enterprise compensation cost and upstream and downstream economic losses are calculated, and an economic influence quantification table is generated. According to the method, dynamic coupling quantification and positioning of the power grid regulation risk are realized by constructing the risk knowledge graph.

The invention relates to the technical field of water demand prediction, in particular to a crop water demand prediction method and system based on the agricultural Internet of Things, and is applied to accurate moisture management in complex soil structures such as terraces. And in combination with the dynamic weight of interlayer moisture migration, the moisture absorption matrix is updated in real time. Through time sequence sliding and cosine similarity calculation, a moisture retention time matrix is dynamically corrected, and accurate distribution of soil moisture is ensured. A graph theory algorithm is introduced to construct a directed graph model of a water flow path, an interference matrix is generated in combination with binary masks, matching of water absorption and water retention time is optimized, and the irrigation efficiency and the water resource utilization rate are improved. The irrigation strategy can be dynamically adjusted according to the moisture state between the soil layers, moisture waste is avoided, and the accuracy of crop growth moisture supply is improved.

The invention discloses a multi-dimensional graph series-parallel and visualization method for telecommunication network fraud cases based on a graph database. According to the method, intelligent series-parallel analysis across time-space cases is realized by constructing a knowledge graph containing various entities such as cases, personnel, accounts and equipment and deeply fusing a hard association matching algorithm and a soft similarity graph algorithm. The system has the functions of white list filtering, time sequence behavior analysis, NLP information extraction and the like, and can effectively identify criminal gangs, track fund flow directions and mine crime modes. And finally, dynamic visual display is carried out through a plurality of interactive views such as a force-oriented graph, a time axis and a mulberry-based graph, and clue intelligent pushing and report automatic generation are supported. According to the method, the case association mining depth, the serial-parallel analysis efficiency and the interpretability of a complex network are remarkably improved, and an efficient technical support is provided for the public security organization to fight against telecommunication network fraud.

The invention provides a multi-feature factor matching and interrupt processing method for storm tracking. The method comprises the following steps: calculating storm features required by tracking; carrying out discretization processing and coding on the storm features, and constructing a sparse matrix; constructing an auto-encoder model, performing data reconstruction on the sparse matrix based on the auto-encoder model, and calculating main features of the single storm; on the basis of the main features, calculating the feature vector distance between the previous and later time secondary storms, and constructing a feature space distance matrix; a bipartite graph algorithm is adopted, the incidence relation of the previous and later time-order storms is obtained based on the feature space distance matrix, and a storm matching result is constructed; identifying an interrupted storm based on a storm matching result and carrying out secondary matching; and constructing a storm trajectory based on a secondary matching result and removing a repeated storm trajectory. According to the method, the movement track of the storm can be accurately tracked from formation to extinction of the storm, reliable data support is provided for meteorological monitoring and forecasting, and the timeliness and accuracy of severe convective weather early warning are improved.

The invention belongs to the field of robot positioning and mapping algorithms, and provides a visual SLAM method and device for point-line feature fusion and a medium, and the method comprises the following steps: preprocessing an image, and obtaining point features and line features; matching the point features and the line features to obtain matched point features and matched line features; obtaining the initial pose of the camera through the matched point features and the matched line features, and constructing map points; constructing a three-dimensional map straight line through the matched line features; and optimizing the three-dimensional map points and the three-dimensional map straight lines. Complementary advantages are formed through extraction, matching, mapping and optimization processes of tight coupling point features and line features, and the fundamental problem of insufficient features in a weak texture environment is effectively solved.

The embodiment of the invention provides an authority control method and device based on fine granularity, and the method comprises the steps: carrying out the node relation extraction of a mapping database, obtaining a role node and a product node, and introducing a user node and a context node; the method comprises the following steps: constructing a hyperedge structure, performing hyperedge linking on role nodes, product nodes, user nodes and service context nodes, determining a corresponding hyperedge relationship, performing dynamic weight endowing on the hyperedge relationship according to a time decay factor and a context risk coefficient, determining a dynamic graph structure, and constructing a directed acyclic graph based on the role inheritance relationship. Fusing the dynamic graph structure and the directed acyclic graph according to a graph algorithm, and determining a corresponding product permission layer graph database; the method comprises the steps of receiving a user operation instruction, performing context-aware query based on the operation instruction according to a graph traversal algorithm, determining a user permission, and performing permission control on a user according to the user permission. The method can improve the efficiency and the refinement degree of permission allocation.

The invention discloses an interpretable multi-hop reasoning method for enhancing LLM based on KGE, and the method is called as' KELIN '. And embedding a KGE enhanced large language model LLM in the KELIN by adopting a knowledge graph, and reasoning by taking the user question as input to obtain an answer. The KELIN efficiently screens a multi-hop reasoning path and dynamically quantifies the confidence of the path in a knowledge graph KG through path retrieval and confidence quantification guided by KGE, and a verifiable basis is provided for subsequent reasoning. On the basis, the LLM reasoning enhancement strategy performs traceable fine adjustment on the model by using a high-confidence path and scoring information, so that the knowledge boundary is expanded and the reasoning transparency is improved. The KELIN is integrated into an end-to-end KG question and answer system, and the performance is evaluated on mainstream references such as WebQSP and CWQ. The result shows that the KELIN is superior to a mainstream baseline method based on a graph algorithm and an LLM agent in reasoning interpretability, reasoning time consumption and scale adaptability, and has high credibility evaluation capability.

The invention discloses a road structure performance catastrophe early warning method and system. The method comprises the following steps: S1, synchronously acquiring data by using a three-dimensional ground penetrating radar and a deflectometer; s2, introducing a dielectric constant correction signal, quantifying a volume defect, and taking the volume defect as a physical constraint inversion mechanical parameter; s3, fusing the multi-dimensional parameters to construct a damage potential index, and predicting the residual life by using a graph space-time neural network; and S4, on-line mutation detection is carried out based on a Bayesian and cumulative sum control chart algorithm, and the weight of the model is updated through a treatment feedback closed loop. According to the method, physical-data dual-drive modeling is adopted, the problems of one-sided detection means, poor model interpretability and parameter solidification are solved, and accurate evaluation of road performance degradation and continuous evolution of the model are achieved.

The present invention discloses a mine disaster tracing method based on a knowledge graph, applied in coal mine safety. First, a mine disaster-related knowledge graph is built, involving three main steps: constructing a conceptual model, extracting entities from relational databases and geological maps, and establishing entity relationships based on location and process logic. Next, characteristic indexes and transmission rules for entity objects are set, categorizing entities into four types: discrete reporting, continuous monitoring, geological structure, and geological continuity. When an early warning occurs, disaster tracing is performed using the entity transmission rules and a graph traversal algorithm, identifying direct and root causes. This invention offers systematic and timely disaster tracing by utilizing a specialized knowledge graph and graph algorithms.

The invention discloses an automobile maintenance engine fault dynamic diagnosis system and method. The system comprises a data acquisition module, a data preprocessing module, a dynamic monitoring and recording module, a sound signal extraction algorithm module, a wavelet transform algorithm module, a neural network module and a fault diagnosis and prediction module. The method comprises the following steps: step 1, data acquisition; step 2, data preprocessing; step 3, real-time monitoring and recording; step 4, sound signal extraction and analysis; step 5, fault feature extraction; step 6, constructing and training a neural network; step 7, fault diagnosis and prediction; the dynamic monitoring function is designed, the vibration frequency, surface abnormity and working condition parameters of the automobile engine can be comprehensively monitored, real-time alarming is carried out on abnormal conditions, potential risks existing in the engine can be found in time, powerful support is provided for fault prevention and processing, and the working efficiency is improved. And a fast cyclic modulation spectrum peak graph algorithm is designed through a multi-stage decomposition method.

The invention belongs to the technical field of data processing, and particularly relates to a performance prediction method and system based on aluminum product element proportions and technological parameters, and the method comprises the steps: obtaining and preprocessing sequences of historical element proportions, technological parameters and the like of a target aluminum product, and constructing a technological proportion node network in combination with a graph algorithm; generating a ratio-performance forward prediction function and a performance-ratio backward reasoning function by using a multiple regression and factor analysis algorithm; according to the real-time performance requirement, an initial parameter sequence is obtained through a parameter disassembly matching model and a node network, prediction performance and a secondary parameter sequence are obtained through forward and reverse function operation, forward and reverse cycle adjustment is carried out by means of reinforcement learning and a simulation algorithm based on parameter and performance deviation until the deviation meets a threshold value, and the prediction performance is obtained. Outputting an optimal element ratio and a process parameter sequence; according to the method, accurate prediction of the aluminum product performance and intelligent optimization of the process parameters are realized, and the production efficiency and the product quality are improved.

The invention relates to an unsupervised extraction type abstract generation method based on word weight fusion and document comparison, which comprises the following steps: acquiring a set consisting of documents, and embedding sentences in the documents by adopting a sentence encoder to obtain vector representation of the sentences; evaluating the importance of the words in the sentences by adopting a word weight dynamic evaluation method; evaluating the importance of the words in the sentences by adopting a word weight static evaluation method; the evaluation results of the two methods are fused; extracting importance of sentences by adopting a graph algorithm based on degree centrality to obtain significance scores of the sentences in each document, and screening a plurality of sentences with the highest scores as candidate sentences to obtain a candidate sentence matrix; calculating the cosine similarity between each candidate sentence and other candidate sentences to construct a positive example and a counter example for comparative learning; a cosine annealing strategy is adopted to dynamically adjust the temperature of comparative learning. Compared with the prior art, the unsupervised extraction type text abstract performance is effectively improved.

An example method collects first data comprising first surface points within an environment by a sensor associated with a processing system. The method further determines an estimated position of the processing system by analyzing the first data using a simultaneous localization and mapping algorithm and identifies a first set of surface features from the first data. The method further collects second data comprising second surface points within the environment by a three-dimensional (3D) coordinate measuring device associated with the processing system and identifies a second set of surface features from the second data. The method further matches the first set of surface features to the second set of surface features and refines the estimated position of the processing system to generate a refined position of the processing system. The method further displays an augmented reality representation of the second data based at least in part on the refined position.

The invention belongs to the technical field of information, specifically, multiple sensor data are fused through multi-source information fusion and a deep learning algorithm, a specific fusion network and an optimization algorithm are applied, recognition errors and positioning deviation are greatly reduced, grabbing errors are reduced, and the cargo damage rate is remarkably reduced. For dynamic carrying path planning, a traditional preset map algorithm is easy to collide and is long in delay; a real-time map is constructed through reinforcement learning and a real-time environment perception algorithm, training is carried out according to a reward function, the collision frequency is sharply reduced, the carrying time is shortened, and energy consumption is reduced. When the storage space is utilized, the traditional random storage efficiency is low; the space optimization and intelligent storage strategy algorithm utilizes a genetic algorithm for modeling, the storage space utilization rate is improved, the goods searching time is shortened, and the damage rate is reduced.

The invention discloses a communication network operation and maintenance fault tracking method and system. The method comprises the following steps: constructing a communication network operation and maintenance knowledge graph; optimizing the knowledge graph by using a graph algorithm and a communication domain rule; utilizing a PCST algorithm to generate an optimization sub-graph of which the relevancy with the fault query is greater than a first threshold value, and converting the optimization sub-graph into a first natural language text; performing integrity judgment on the first natural language text by utilizing a large language model; when the first natural language text is judged to be incomplete, entities and relationships which do not appear in the optimized sub-graph are extracted from the first natural language text to serve as supplementary query words; iteratively generating a supplementary sub-graph on the optimized knowledge graph based on the supplementary query word to obtain a complete natural language text; performing fault reasoning on the complete natural language text by using a large language model to obtain a reasoning answer; and constructing a four-layer reverse tracing link according to the reasoning answer, the first natural language text and the second natural language text. And the communication network fault tracking precision is improved.

The invention discloses a multi-unmanned-vehicle neural topology exploration method of an unmeasured map, is applied to the field of multi-unmanned-vehicle collaborative exploration, and aims at solving the problem of measurement map dependence in an existing multi-unmanned-vehicle collaborative exploration algorithm. In the mapping stage, environment data are collected through a panoramic RGB-D sensor, pure topological representation is directly constructed from visual features and depth information by applying a topological mapping algorithm based on path points, and dependence on a traditional measurement map is not needed; in the decision-making stage, firstly, a topological map structure is analyzed through a global decision-making module based on a graph neural network (GNN), and an optimal navigation target is intelligently determined; then, planning a path on the topological graph by adopting an A * algorithm and generating a local navigation target; and finally, the abstract target is converted into a specific execution action by the local decision module. In the execution stage, a self-adaptive escape mechanism is integrated, and the state of the unmanned vehicle is monitored in real time and a behavior strategy is intelligently adjusted.

This invention relates to a multi-agent orchestration and execution method supporting complex cyclic graph structures, comprising: S1: receiving workflow configuration and constructing a directed graph data structure; S2: performing a depth-first search traversal on the directed graph data structure, detecting and outputting strongly connected components in the directed graph data structure; S3: abstracting strongly connected components containing multiple nodes into super nodes, and keeping single-node strongly connected components as ordinary nodes, thereby constructing a directed acyclic graph containing super nodes; S4: performing topological sorting on the directed acyclic graph to generate a hierarchical execution plan containing multiple execution layers; S5: scheduling execution items according to the hierarchical execution plan, and when an execution item is the super node, starting a loop executor for iterative execution. This method, by constructing a multi-layered technical system, integrates graph algorithm innovation, unified context management, abstraction layer design, metadata-driven techniques, etc., to achieve automated orchestration and execution of multi-agent workflows supporting cyclic graph structures.

This invention relates to the field of orchard navigation technology, specifically a 3D method for orchard operations based on a grid map. The method includes: constructing an orchard point cloud map using a mapping algorithm; filtering the acquired 3D point cloud map and constructing a triangular grid map of the orchard, embedding the orchard's GPS information into the triangular grid map; based on the triangular grid map with GPS information, performing ICP registration between the point cloud data scanned by the LiDAR and the points on the triangular grid map using the RMCL 3D positioning algorithm, outputting the 3D pose of the operation robot in the orchard; updating the 3D pose of the operation robot in the orchard using GNSS information; planning the navigation path of the operation robot using grid navigation based on the GPS information of the target point; and controlling the operation robot to dynamically avoid obstacles and navigate to the target point. This invention enables precise positioning and autonomous navigation of the operation robot in hilly orchards, allowing it to complete tasks such as orchard inspection, plant protection, harvesting, and transportation.

The embodiment of the invention provides a hierarchical routing data auditing method and device, and the method comprises the steps: constructing a directed graph according to a port directed connection relation in routing data, judging the connectivity of a route and the integrity of a topological structure through employing a graph theory connection component, and when a channel route passes a connectivity test and an auditing result is normal, judging whether the channel route is normal or not. Different integrity auditing rules are selected in combination with service scene characteristics, routing missing judgment is carried out, whether channel routing is complete on a service networking structure or not is judged, and connectivity and integrity of the channel routing are rapidly and accurately detected through auditing rule checking of dividing connected components by using a graph algorithm and adding data dimensions. In the checking process, the checking process does not depend on segment group sequence attributes in the routing data, and it is avoided that checking cannot be carried out due to the data quality of a shallow layer.