224 results about "Depth-first search" patented technology

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention discloses a method for constructing a digital twinborn model of a full life cycle of an automobile part, which relates to the technical field of digital twinborn, and comprises the following steps: inputting a life cycle data set into an initial life digital twinborn model, carrying out time domain local feature fusion by a feature extraction layer, capturing a degradation trend by a time sequence analysis layer, and constructing a digital twinborn model. Extracting an actual detection value in the life cycle data set; calculating a deviation value between the life prediction value and the actual detection value of the automobile part; carrying out sequential relation mapping on the deviation value to generate a dynamic causal atlas; carrying out graph traversal on the dynamic causal atlas to obtain key degradation path nodes; the dynamic causal reasoning is used for carrying out correlation analysis on the key degradation path nodes, degradation characteristic parameters are generated, the depth-first search and analytic hierarchy process are adopted, dynamic causal correlation precise modeling of the life digital twin model is achieved, and the robustness of the life digital twin model under the actual complex working condition is remarkably enhanced.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention relates to a real-time power grid topology analysis method based on a graph neural network, and the method comprises the steps: firstly, carrying out the electrical topology analysis at a substation level through constructing an optimized power system physical connection model, carrying out the electrical topology analysis in combination with a label propagation algorithm (LPA), precisely recognizing a calculation region, optimizing the division of the calculation region through employing the graph neural network (GNN), and enhancing the topology adaptability, mistaken division is reduced; and the robustness of equipment state change is improved. Besides, according to the method, the real-time performance, the accuracy and the intelligent level of power grid topology analysis are further improved by predicting the influence of the state change of the circuit breaker and the disconnecting switch on the power grid topology and training a GNN model and reinforcement learning (RL) optimization region division strategy through historical data. And finally, bus-branch connection is optimized through depth-first search (DFS), the topology integrity is ensured, and the efficiency of electrical island analysis, power supply area division and fault recovery is remarkably improved. The real-time performance, the accuracy and the intelligent level of power grid topology analysis are effectively improved.

The invention belongs to the field of data processing, and particularly discloses a method and system for automatically generating a document according to a template and structured data, and the method comprises the steps: processing an archive template file according to a document object model analysis method, extracting the position, the type and the associated tag of a placeholder, and obtaining a placeholder metadata set; according to the placeholder metadata set, traversing a document hierarchical nesting relationship by adopting a depth-first search algorithm to obtain a hierarchical placeholder mapping table; obtaining the structured data of the document repair record through an external data source, judging whether the structured data field corresponds to the tag in the mapping table according to the hierarchical placeholder mapping table, and if so, generating a matching pair list from the field to the placeholder to obtain a preliminary matching data set; the objective of the invention is to solve the problem of a business scene in which complex nested data and file template placeholders cannot be accurately matched in a document repair record in the prior art.

The invention discloses a decentralized identity management method based on a trusted execution environment, and the method comprises the steps: generating, isolating and storing a user decentralized identity in the trusted execution environment of equipment, carrying out the Hash signature of a decentralized identity document, and recording an uplink; the block chain consensus node verifies the decentralized document uplink transaction through a Byzantine fault-tolerant consensus mechanism and achieves consistency, and writes the metadata of the decentralized identity into the block; encrypting and storing the voucher in a trusted execution environment of the equipment by adopting a Pearson commitment, and supporting voucher Hash uplink and commitment uplink; and introducing a multilevel access structure based on a binary tree, and verifying the and / or logic combination voucher by adopting a depth-first search algorithm. According to the method, decentralized autonomy of the user identity and hardware security isolation are supported, and the reliability and privacy protection capability of user identity management are improved.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

The invention discloses a graph theory-based industrial internet identification system full-process tracing method, which realizes full-process tracing in a supply chain by introducing identifier fields into metadata and constructing a directed graph structure. The identifier fields include fields with similar representations for representing associations of the product upstream and downstream of the supply chain. The system queries across enterprises through a graph traversal algorithm (such as depth-first search (DFS) or breadth-first search (BFS)), and tracks the full life cycle of a product. The method supports attribute pointing code management, is used for tracing multi-attribute information (such as allocation weight, time and the like) of a product, solves cross-enterprise and cross-system coding differences through an alias mechanism, and simplifies a tracing process. The method has a flexible traceability path setting capability, can monitor the product circulation process in real time, gives out an early warning when an abnormality is found, and improves the transparency and safety of a supply chain.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention belongs to the technical field of data mining and analysis processing, and particularly discloses a real-time data stream processing method and system based on distributed computing, and the method comprises the steps: obtaining real-time and historical data streams of a server program window, employing a time sequence synchronization mode to rapidly position and start a window matching path and historical abnormal performance, and obtaining a real-time data stream of the server program window; the high real-time requirement is met; historical abnormal event statistics and real-time data flow similarity analysis are combined to dynamically construct a distributed tracking link, a depth-first search algorithm is used to predict a risk event, and an abnormal probability and a deviation degree are accurately quantified; different optimization strategies are configured according to different performance parameters, a multi-level defense mechanism is formed, and the self-adaptive capacity of the system is improved. According to the method, the interaction track and the performance parameter set are comprehensively analyzed, the abnormal performance link relation is marked, abnormal event correlation analysis is refined, the maintainability and expansibility of the system are enhanced, and the data processing efficiency is remarkably improved.

The invention provides an invoice closed-loop transaction risk assessment method based on a graph neural network, and relates to the technical field of artificial intelligence and machine learning, and the method comprises the steps: constructing a graph model (an invoice is a node, the upstream and downstream relation of an enterprise is a directed edge, and the amount of money, a timestamp and enterprise information are recorded); automatically learning node features by using a graph neural network and predicting a risk probability; identifying a closed-loop path through depth-first search based on the node representation vector, and extracting a hierarchical depth, a total amount and an average risk probability; and finally, a comprehensive risk value is calculated through a weighting formula (combining the hierarchy depth, the sum of money and the risk probability), and multi-dimensional closed-loop transaction risk assessment is realized.

The invention relates to a method for identifying a picture block and a picture label in an architectural drawing, a storage medium and electronic equipment. The method comprises the following steps: acquiring coordinate information of end points of all straight line segments detected from the architectural drawing; constructing a two-dimensional line segment tree by utilizing the coordinate information of the end points of all the straight line segments; performing traversal search on the two-dimensional line segment tree by adopting a depth-first search algorithm to obtain a plurality of target straight line segments related to an outer frame of a drawing frame area in the architectural drawing in all straight line segments, and determining the position of the drawing frame area based on the plurality of target straight line segments; based on the position of the drawing frame area, the drawing label area in the architectural drawing is recognized from the drawing frame area, and compared with an existing manual drawing frame marking method, the workload can be reduced, and the recognition efficiency is improved.

The invention provides a node clustering method based on communication network connectivity analysis, and belongs to the technical field of network analysis. The method comprises the following steps: constructing a graph model according to input nodes and a connection relationship thereof, wherein the construction of a graph supports a single network or multi-network relationship; traversing each node in the graph by using a depth-first search algorithm, recording an access path, and classifying the nodes with a communication relationship into the same communication component; and outputting a set of all connected components. According to the method, when the graph is constructed, topology analysis of various network conditions in the undirected graph is supported by dynamically judging the nodes and the neighbor relation of the nodes. The method has the advantages of low calculation complexity and high expansibility, is suitable for the fields of network communication, equipment connectivity check, complex command and control system information transmission modeling and the like, and provides an efficient and accurate solution for connectivity analysis.

The invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, and relates to the technical field of data processing, and the method comprises the following steps: receiving a client request, obtaining a data generation template, constructing a data dependency graph to analyze a field dependency relationship, and determining a field generation sequence by adopting a recursive generation strategy and depth-first search; data validity verification is executed, a data generation thread pool is created by adopting an adaptive load balancing mechanism based on the data dependency graph, and asynchronous processing is realized through double buffer queues, so that the data generation efficiency is improved, the data logic integrity is ensured, and the system resource consumption is reduced.

The invention provides a networking scene-oriented multipath service optimization method and system, and relates to the technical field of network scheduling optimization, and the method comprises the steps: obtaining topological structure information and service demand information of a communication network in a networking scene, and employing a depth-first search algorithm in combination with a pruning mechanism to generate a candidate path set; on the basis of a marginal value contribution degree theory, calculating a transmission value factor and a path complementary factor of each candidate path in the candidate path set, calculating marginal value contribution degrees of different candidate path combinations according to the two factors, and taking the path combination with the highest marginal value contribution degree as an optimal path combination; and aiming at each path in the optimal path combination, establishing a multi-objective optimization model, and obtaining a final scheduling scheme of the service flow among the paths in the optimal path combination. According to the invention, the problems of lack of differentiated path selection mechanism, poor fault tolerance of single-path transmission, insufficient resource coordination capability and the like in multi-service transmission in the prior art can be solved.

The invention belongs to the technical field of digital image processing, and particularly relates to a regional hole filling method based on run-length coding. According to the method, the candidate background area is directly obtained by negation of the original area and construction of the extended rectangular area, the complexity of boundary processing in traditional hole filling is simplified, the area is segmented by utilizing run length coding, and then dual adjacency relation judgment is combined, so that the accuracy of hole filling is improved. The connectivity among the codes in the background area can be identified more accurately, so that the accuracy of extraction of the connected area is improved, the mark numbers of the connected run-length codes are recorded in a mask mode, so that the run-length codes do not need to be additionally reordered when the background area is subsequently summarized, the computing resources are saved, the algorithm complexity is reduced, and the efficiency is improved. The integrity of the candidate background area is guaranteed through the extended rectangles, accurate extraction of the background area is guaranteed through depth-first search, the method can adapt to filling of original area holes of various shapes and sizes, and good robustness is achieved.

The invention relates to the technical field of electronic design automation, and discloses a standard cell layout legalization method and system based on a graph theory constraint graph, and the method comprises the steps: building a bidirectional directed constraint graph based on an existing layout and a design rule, and building a directed edge between adjacent graph edges according to the design rule; traversing the constraint graph, detecting whether a directed edge which does not meet a constraint condition exists, and if so, identifying the directed edge as a constraint violation edge; for two end points of each constraint violation edge, searching a key path based on a depth-first search algorithm, and determining a maximum movable distance in combination with a greedy strategy; moving the endpoint to a legal position according to the critical path and the maximum movable distance, and updating the position of a related node in the constraint graph; and mapping the position changes of the nodes in the constraint graph back to the corresponding graph edges in the layout, and outputting the legalized layout, so that efficient and automatic repair of design rule violation is realized through the method, and the layout compliance rate and the area optimization effect are remarkably improved.

The invention discloses an energy internet network topology optimization method and system based on a multi-objective evolutionary algorithm, and belongs to the technical field of energy internet, and the method comprises the steps of geometrically driving evolutionary operation, preferentially connecting adjacent nodes, strengthening a triangular structure, and optimizing a link layout in combination with an energy transmission efficiency weight. An NSGA-III algorithm is improved, a cosine similarity punishment mechanism and a dynamic weight strategy are introduced, and algebraic connectivity and an R index are balanced; a dynamic repair mechanism quickly complements isolated sub-graphs through local evolution and depth-first search, and the response time is shortened; a learning prediction topology adjustment strategy is reinforced, and a sliding window multiplexing historical solution is combined, so that the convergence speed under wind and light fluctuation is improved. According to the method, the problems of target singleness, uneven Pareto solution set distribution and dynamic response lag in the prior art can be solved, algebraic connectivity and node balance are improved, and the method can be widely applied to dynamic energy internet optimization scenes containing wind and light energy.

The invention discloses a graph theory analysis-based power grid project supplier equity relationship intelligent identification method. The method comprises the following steps of S1, collecting supplier relationship data; s2, stock equity structure features and transaction features of suppliers in the standardized data set are extracted; s3, constructing a supplier equity relation graph, traversing the supplier equity relation graph by adopting a depth-first search algorithm and a breadth-first search algorithm, and calculating a control path length and an equity control weight; s4, optimizing the stock right control relation identification of the suppliers by using an improved Grapporter network, and predicting the stock right control relation strength between the suppliers; s5, calculating the risk scores of the suppliers, setting a threshold value according to the risk scores, and classifying the risk levels of the suppliers; and S6, an incremental learning method is adopted to optimize the Grapher network. According to the method, graph theory analysis and Grapher network improvement are combined, the supplier stock equity control relation is intelligently identified, the risk is dynamically evaluated, and the method has the advantages of being accurate in identification, high in adaptability and high in real-time performance.

The invention relates to the technical field of computer aided design, in particular to a method, a system and equipment for automatically identifying a typical structure of a complex product based on case library matching and a medium. According to the method, a predefined typical structure instance library is constructed, part shape information and space neighbor characteristics among parts are comprehensively considered, part matching is carried out in a product three-dimensional model by taking typical structure instances as targets, structures with similar shapes and structures are searched through depth-first search, and finally, typical structure identification is realized. The problem that the part connection relation in a complex product is difficult to obtain is effectively avoided.

The invention discloses a novel power distribution network real-time topology tracing method and system, and the method comprises the steps: carrying out the comprehensive collection of the voltage, current and power of a key node based on a power distribution network SCADA system, and constructing a historical measurement data set containing a topological structure label; designing and training a Transform deep learning model suitable for the characteristics of the power distribution network; the feature space of the Transform model is optimized on the basis of the maximum margin principle; constructing a topological graph model of the power distribution network, carrying out weight assignment, and rapidly tracing and positioning a problem region when a fault or an abnormal condition occurs by using an improved depth-first search algorithm; and constructing a multi-scene topology traceability collaborative decision-making system, and generating a visual traceability report including a fault area topology structure, an influence range and a key equipment state. According to the method, the problems of incomplete data collection, limited learning ability of an identification algorithm, lack of an efficient local tracing mechanism and the like in novel power distribution network topology tracing are solved, and the fault processing efficiency and the operation reliability of the power distribution network are improved.

The invention provides a distributed robust topology control method and system for cluster unmanned aerial vehicles, and the method comprises the steps: carrying out the construction of a midpoint coverage circle with a node as a center for each node of a cluster unmanned aerial vehicle network, and judging potential key nodes; acquiring local topology through k times of neighbor information exchange for potential key nodes, generating a simplified block tree model based on depth-first search, traversing connected components by taking the simplified block tree model as a root node to form block point sets, and judging that the root node is a key node when the number of the block point sets to which the root node belongs is greater than 1; and constructing an enhanced edge candidate set which is arranged according to a cost ascending order by the determined key nodes, selecting the edge with the minimum cost to communicate different blocks, sending a moving instruction to controlled nodes in k hops to establish a communication link, and finally enabling the network topology to meet vertex 2-communication. According to the method, the key nodes in the network are detected, and the key nodes are eliminated in a node moving mode, so that the stable and reliable unmanned aerial vehicle cluster network topology is constructed at low cost.

The invention relates to the technical field of detection and identification of small targets in images in the power industry, and provides a city knowledge correctness evaluation method and system based on multi-level abnormal signals. The method comprises the steps that firstly, entity subgraphs are generated based on depth-first search, the association strength of entity pairs is calculated through the resource transfer amount, and entity-level credibility evaluation is constructed; secondly, innovatively providing a hierarchical path modeling method, dynamically allocating path weights in combination with an adaptive attention mechanism, and realizing credibility evaluation of a relationship level; and finally, the consistency of the entity type and the graph structure is introduced into an evaluation system as a core abnormal signal for the first time, semantic consistency is quantified through a relation hypergraph and a type hypergraph, and credibility evaluation of a type-graph structure level is generated. By integrating the entity association strength, the path credibility and the type-structure credibility, the model can generate the final credibility of the triple, and reliable theoretical and method support is provided for correctness evaluation of the knowledge graph.

The invention discloses a layout parameterization hierarchical parallel mesh generation method based on Net. The method comprises the following steps: inputting a layout design file, analyzing hierarchical structure identification ports, generating a port set P and creating an accessed port set V (initial V =); a graph G = (VG, EG) is constructed, VG comprises key vertexes or geometric centers of ports and conductor fragments, an electrically independent Net is generated through traversal of a breadth-first search algorithm or a depth-first search algorithm, and iteration is performed until V is completely equal to P; configuring independent subdivision parameters such as a grid size and a grid algorithm for each Net; net is used as an independent task, grids are divided through parallel computing frameworks such as multithreading, and a complete grid file for subsequent simulation is generated after merging. According to the method, Net automatic identification is realized through graph theory analysis; different Net precision requirements are adapted by differential subdivision parameters, so that waste of computing resources is avoided; through parallel processing, the time consumption of layout grid division is shortened to the time consumption of processing the maximum Net, and the efficiency and the flexibility of simulation pre-processing are remarkably improved.

The invention provides a distribution network dynamic partition power recovery optimization method considering uncertainty resource support under a typhoon extreme disaster, and belongs to the technical field of distribution network partition power recovery. A representative fault scene is generated by adopting a Latin hypercube sampling and spectral clustering method, the output characteristics and the damage degree of distributed power supplies such as photovoltaic wind power energy storage and the like under a typhoon condition are evaluated, and a centralizable power recovery region and an incentralizable power recovery region are dynamically divided by applying a depth-first search algorithm. A multi-target optimization model of fault reconstruction and island power recovery is constructed for different areas, a dynamic feedback adjustment mechanism based on a load recovery rate is established, and multi-period dynamic power recovery scheduling is implemented to realize multi-source coordinated dynamic power supply recovery in the whole process of typhoon disasters. The technical problem that the power restoration method of the power distribution network under typhoon disasters lacks dynamic self-adaptive ability and a multi-source coordinated scheduling mechanism is solved.

The invention discloses a transformer substation work ticket knowledge graph construction method based on a large language model, and belongs to the field of energy power and artificial intelligence. The method comprises the following steps: based on standardized electrical drawing annotation data, generating an independent knowledge graph through data recombination preprocessing, rough knowledge graph construction and attribute supplementary reasoning; all the independent knowledge maps are divided into a framework layer map and a detail layer map according to voltage grades, and full map fusion is completed by adopting a hierarchical fusion strategy; carrying out breakpoint detection by using an isolated point detection algorithm based on degree centrality, carrying out connectivity analysis through depth-first search, and re-fusing multiple non-connected sub-graphs; and screening abnormal nodes based on an electrical connection rule, inputting the abnormal nodes into a large language model to automatically supplement Agents to generate correction suggestions, and completing map optimization. Through cooperation of the large model Agent and multiple algorithms, the electrical data structuring efficiency and accuracy are improved, and knowledge network support is provided for automatic operation and fault diagnosis of the transformer substation.

The invention discloses a network fault prediction method, and relates to the technical field of fault prediction. The method comprises the following steps: constructing a cross-level semantic topology model; screening a key link by using a node importance algorithm and extracting a structural fingerprint vector; calculating the deviation degree between the current link and the structure fingerprint vector, and judging that the structure is abnormal if the deviation degree is smaller than a threshold value; constructing an anomaly propagation rule base according to historical faults and operation and maintenance data; when a plurality of structure anomalies appear at the same time, the conflict resolution algorithm determines an anomaly dominant factor; potential signals are collected according to the abnormal dominant factors, weights are given according to a rule base, and weighting is carried out to obtain comprehensive potential signals; based on this, a fault propagation path is constructed, and a path weight is calculated by using a Pearch ranking algorithm; a fault chain is rehearsed through a depth-first search graph traversal algorithm; and verifying the rehearsal chain by using a preset test statistic circumstantial evidence to obtain a fault prediction result. According to the invention, on the basis of the fault prediction result, cross-level network fault prediction in network propagation is realized.

The invention, which relates to the technical field of interactive script generation, discloses a script-centered interaction-driven scene generation system comprising the following modules: a script management module, a script arrangement module, a dependency propagation module, a thesaurus interaction cooperation module and an execution output module. Creating a basic data structure of the script, distributing script attributes, and managing script elements, constraints and logics; identifying scene nodes and events, constructing three-layer dependency and weighting, and generating a reverse causal chain through recursive dependency and depth-first search; by maintaining a dependency table, event-driven updating and dirty node marking for each node, efficient dependency propagation and low-delay updating are realized through a priority queue and differential generation; dividing three levels to cooperatively realize dynamic script generation and interaction, and realizing script control based on gating residual connection, a reverse causal chain and a time attention mechanism; and constructing an interactive execution layer, integrating a real-time feedback mechanism, automatically generating a script, and outputting and displaying an executable format.

The invention discloses a distribution network auxiliary decision-making method and device based on artificial intelligence, and relates to the technical field of power distribution networks. Performing signal decomposition on current corresponding to each network element in the target distribution network topological graph, then converting the current into an initial spatial-temporal characteristic graph, then substituting the initial spatial-temporal characteristic graph into a preset network for fault detection, and if the fault detection is that a fault exists, performing depth-first search by taking a fault position as a starting point, and generating a target fault influence area; and for each network element in the target fault influence area, determining the final fault probability of the network element, and performing scheme decision according to the final fault probability of each network element. Tiny fault signals can be more accurately captured through signal decomposition and modal component extraction, then an initial modal component is converted into a spatio-temporal feature map, state changes of the power distribution network before and after a fault are better reflected, the fault spreading range is accurately predicted in combination with a fault influence area, and the fault prediction accuracy is improved. And the fault processing efficiency in the case of a power grid topology propagation fault is improved.

The invention discloses a tunnel lining crack parameter quantification and safety evaluation method based on a digital image, and provides a method for accurately calculating key quantification parameters such as crack length, maximum width, fractal dimension and type and the like based on a principal component analysis (PCA) method and a depth-first search (DFS) algorithm for a crack image detection result. Accurate quantification of crack characteristic parameters is realized; according to the method, a tunnel lining crack health evaluation index is established, the tunnel lining crack health evaluation index TCHI is established, the tunnel lining crack health evaluation index TCHI is established, the tunnel lining crack health evaluation index TCHI is comprehensive, objective and easy to calculate, and therefore systematic evaluation of the overall safety degree of a tunnel is achieved. The method is wide in application range and high in adaptability, and can provide technical support for realizing automation of tunnel disease recognition and detection by using a computer vision technology in the future.