172 results about "Graph traversal" patented technology

The invention discloses an engineering safety progress intelligent monitoring method based on multi-source data collaboration, which relates to the technical field of intelligent engineering monitoring, and comprises the following steps of: mapping multi-source engineering monitoring data into nodes and edges of a graph in real time by utilizing an incremental graph updating algorithm, generating a dynamic knowledge graph, and generating a dynamic mapping result; performing graph traversal on the dynamic knowledge graph through an association subgraph extraction algorithm, extracting a security event matrix and a project progress matrix, calculating an SPI index value by using a dynamic weighted fusion algorithm, synchronously performing multi-threshold interval grading on the SPI index value, generating an SPI early warning level, performing state coding on the SPI early warning level, and generating a comprehensive feature vector; according to the method, data of different types can be effectively integrated and a basis is provided for formulating a targeted engineering safety progress solution through an incremental graph updating algorithm and a Bayesian causal graph model, and node probability distribution characteristics are extracted by using a forward propagation layer. The method is advantaged in that the incremental graph updating algorithm and the Bayesian causal graph model are utilized to effectively integrate data of different types and provide a basis for formulating a targeted engineering safety progress solution.

A system for affinity-based user acquisition is disclosed. An affinity analysis agent analyzes affinity patterns from existing customers and automatically identifies potential referrals without accessing message content. A relationship mapping agent calculates relationship strength between customers and non-customers based on communication frequency, interaction duration, timing patterns, and automatically finds the high-probability network candidates by scoring these relationships. An outreach and engagement agent creates personalized referral offers using relationship information and delivers them through optimal communication channels at ideal times. When someone accepts a referral offer, a billing integration AI agent may be initiated for automated account creation and service activation through identity verification, service selection, and account activation. Each new individual becomes part of the network. The AI communication agents analyze affinity patterns associated with the new individuals for discovering additional compatible individuals through graph traversal algorithms and centrality analysis.

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 provides a power transmission line mountain fire disaster risk assessment method and system based on space-time event driving, and the method comprises the steps: obtaining mountain fire monitoring data of a to-be-assessed region, and generating a space-time event according to the mountain fire monitoring data; according to an event association rule, the space-time event is associated with a power transmission line tower through a directional edge, and then a power transmission line mountain fire disaster hidden danger map is constructed; performing similarity matching on the time-space events generated in real time and the time-space events in the power transmission line mountain fire disaster hidden danger map, and searching the corresponding time-space events in the power transmission line mountain fire disaster hidden danger map by adopting a map traversal algorithm; and for the searched towers influenced by the space-time events, quantitatively calculating the risk score of each tower based on the data values of the real-time space-time events, and dividing the risk grades according to the risk scores. According to the method, the association precision of power transmission line risk assessment and the precision and efficiency of power transmission line forest fire prevention and control can be improved.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention discloses a method for realizing a logic breakpoint debugging function of an industrial configuration control system, which relates to the technical field of industrial automation control, and comprises the following steps of: constructing a fault prediction model, performing multi-scale time sequence modeling and spatial correlation analysis on spatial-temporal characteristic tensors, and generating a high-probability fault pile point set; constructing an equipment logic association graph, performing ontology reasoning to obtain an association diagnosis characteristic graph of high-risk logic nodes, and generating a directional monitoring instruction set in combination with the high-probability fault pile point set; and performing space-time scene reconstruction and root cause path analysis on the high-confidence breakpoint trigger signal and the five-dimensional section snapshot to generate an interactive fault diagnosis report. According to the method, the equipment-logic association graph is constructed, graph traversal and ontology reasoning are carried out, the dynamic association relationship between equipment physical data and control logic execution data is modeled into a weighted directed graph structure, node risk values are quantized by utilizing a graph diffusion algorithm, and visual tracking of a fault propagation path is realized.

The invention relates to the field of resource association analysis, and discloses a knowledge graph-based archive decision support and association analysis method, which comprises the following steps of: firstly, acquiring structured and unstructured archive data; preprocessing is carried out through data cleaning, duplicate removal and format conversion, semantic disambiguation is carried out on historical fuzzy words in unstructured data, and entity normalization processing is carried out on multi-category names of the same character; secondly, based on the preprocessed structured data, entities, relationships and attributes are extracted, and entity association evidences are supplemented through a cross-modal alignment technology; performing consistency verification, and storing the verified entity relationship network into a graph database to form an archive knowledge graph; then, in combination with a graph traversal and time sequence analysis algorithm, a hidden association path is mined, and trend prediction is carried out; and finally, generating a decision evidence chain according to an association mining result. And establishing a knowledge graph dynamic updating mechanism, and automatically reconstructing the entity association network when a file or a historical research result is newly added.

The invention relates to the technical field of ship manufacturing, and discloses a ship block assembly man-hour matching method based on multi-modal process feature recognition. Multi-modal process data including structured process parameters, unstructured process documents and the like are collected, each modal process feature vector is obtained through feature extraction, and then a fusion feature vector is generated through a multi-modal feature fusion algorithm. And constructing a man-hour matching knowledge base according to historical assembly order data, and determining an assembly process sequence and a man-hour allocation scheme by using a dynamic matching algorithm. According to the method, a process set can be optimized through a graph traversal algorithm, real-time sensor data are analyzed to deal with abnormal fluctuation, and process compliance is verified by means of a process rule knowledge graph. According to the method, multi-modal data can be effectively fused, the man-hour matching precision is improved, the assembly process is optimized, the ship block assembly efficiency and quality are improved, and the cost is reduced.

The invention relates to the field of power networks, and provides an intelligent generation method for power equipment accident recovery. The method comprises the following steps: converting key semantic elements into a structured query instruction; acquiring operation state data of the power grid equipment from a power grid monitoring system in real time, and dynamically updating the operation state data to a pre-constructed power knowledge graph; on the basis of a structured query instruction, performing graph traversal and reasoning on the updated electric power knowledge graph, and searching all reachable electric paths from an available power supply point to a target recovery object; carrying out feasibility evaluation on the reachable electrical path in combination with an operation constraint condition, and generating a candidate recovery path; performing multi-objective optimization sorting on the candidate recovery paths to generate an operation instruction sequence; and converting the operation instruction sequence into at least one form of output scheme. According to the invention, automatic generation and optimization from the natural language instruction to the intelligent recovery scheme are realized.

The invention relates to the technical field of network security, and discloses a security vulnerability AI positioning method and system based on log files. The method comprises the following steps: firstly, analyzing user feedback describing open source component security vulnerabilities to extract fault key entity information, screening related log entries based on the fault key entity information, and constructing a vulnerability propagation graph; identifying a vulnerability triggering initial point set by using a graph traversal algorithm and a graph neural network, calling a component vulnerability case library to deduce a vulnerability utilization propagation chain and calculating a matching degree with a known open source component vulnerability utilization chain feature, and obtaining a maximum probability vulnerability utilization propagation chain and an associated vulnerability triggering initial point set; and generating a root cause positioning report, and updating the knowledge base or model training data according to a user verification result. According to the method, the initial vulnerability point and the complete vulnerability propagation chain of the security vulnerability of the open source component can be accurately and efficiently positioned, and the positioning accuracy and timeliness are improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention relates to the technical field of electrical equipment maintenance, in particular to an electrical equipment maintenance plan intelligent decision-making system based on spatial-temporal characteristics and an application method. Comprising a space-time knowledge graph construction unit; the spatial-temporal feature extraction unit is used for extracting equipment spatial distribution features, load time sequence features and maintenance resource time constraint features from the power grid operation data, constructing a multi-dimensional feature vector, and realizing feature extraction based on a graph traversal algorithm and an LSTM neural network; an intelligent decision-making unit; a main distribution network space-time cooperation unit; and a space-time risk assessment unit. According to the invention, the splicing of the main network and the distribution network CIM model is realized through the main and distribution network space-time cooperation unit, the full-voltage class fusion topological graph is generated, and the influence range of main network maintenance on the power supply path of the distribution network can be automatically identified based on a cross-voltage class topology tracking method; the mechanism effectively reduces the problem of repeated power failure or forwarding path conflict caused by insufficient coordination of the main network and the distribution network.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention discloses a collection, analysis and utilization linkage method for the full life cycle of vulnerability intelligence, which comprises the following steps: respectively acquiring original vulnerability intelligence of a plurality of heterogeneous data sources, and carrying out standardization processing on the original vulnerability intelligence to obtain vulnerability description information; constructing an asset knowledge graph, wherein the asset knowledge graph is used for representing IT asset objects in the enterprise and dependency and deployment relationships among the IT asset objects; performing graph traversal query on the vulnerability description information based on an asset knowledge graph, and identifying an affected asset list affected by vulnerabilities; and according to a preset response strategy, generating a safety response instruction from the affected asset list and sending the safety response instruction to the safety protection system. According to the method, the asset knowledge graph is constructed and the multi-source vulnerability information is subjected to standardization and mapping correlation analysis, so that the accurate and rapid identification of the affected assets of the power system and the automatic response strategy generation are realized, and the efficiency and the accuracy of the vulnerability management of the key information infrastructure of the power grid are effectively improved.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention relates to the technical field of railway four-power operation and maintenance, provides a railway four-power operation and maintenance knowledge intelligent question-answering method and system based on a large language model, and solves the problems of low logic accuracy and poor field applicability of an operation and maintenance question-answering result. The method comprises the following steps: acquiring a question and answer request containing a device identifier, position data and a question text; performing syntactic analysis on the problem text to obtain a target equipment entity and an operation intention, positioning graph nodes in the railway four-electric interlocking relation knowledge graph according to the equipment identifier, performing graph traversal, obtaining associated equipment entities, an electrical connection relation and interlocking logic information, and combining the associated equipment entities, the electrical connection relation and the interlocking logic information into a regulation constraint condition set; performing vector retrieval based on the equipment identifier and the position data to obtain an operation and maintenance knowledge fragment; and inputting the regulation constraint condition set and the operation and maintenance knowledge fragment into the large language model, and carrying out logic constraint on the generation process according to the regulation constraint condition set to generate an intelligent question and answer result text. The logic accuracy and field applicability of the operation and maintenance question and answer result are improved.

The invention discloses a vacuum furnace equipment fault intelligent diagnosis method based on AI, and belongs to the technical field of equipment fault diagnosis. According to the method, fault diagnosis data of the vacuum furnace are obtained through a sensor, fault diagnosis features are obtained through preprocessing and feature engineering processing, a vacuum furnace AI equipment fault diagnosis model is constructed accordingly, and the fault diagnosis features are processed through the vacuum furnace AI equipment fault diagnosis model to obtain a fault type. And performing graph traversal search recommendation on the fault types through a fault knowledge graph constructed based on historical maintenance records to obtain fault causes corresponding to the fault types. According to the method, AI and knowledge graph technologies are fused, full-process intelligent diagnosis from data acquisition to fault reason positioning is realized, a scientific basis is provided for vacuum furnace equipment maintenance, the problems of low efficiency and poor accuracy of a traditional diagnosis method are solved, and the intelligent level and reliability of equipment fault diagnosis are improved.

The embodiment of the invention discloses a coal mine multi-source geological data dynamic diagnosis system and method based on a knowledge graph, and the system comprises a data collection module which is used for collecting and preprocessing multi-source heterogeneous data, and obtaining a standardized database; the data fusion module is used for determining a geological knowledge map, performing multi-source data knowledge extraction on the standardized database, performing knowledge fusion and conflict resolution, obtaining a knowledge triple and updating the geological knowledge map in real time; the intelligent diagnosis and decision module is used for identifying abnormal events in the standardized database, and performing graph traversal and evidence fusion based on the geological knowledge graph to obtain a diagnosis conclusion; a geological situation is adapted to obtain a diagnosis model, and an optimized diagnosis model is obtained through an incremental learning mechanism; and the decision application module is used for performing three-dimensional visual presentation on the diagnosis conclusion and the geological knowledge map, generating a diagnosis report, updating the geological knowledge map based on the feedback data flow and optimizing the diagnosis model. According to the invention, the accuracy and real-time performance of dynamic diagnosis can be improved.

The invention discloses a multi-dimensional index calculation method based on a graph database. The method comprises the following steps: defining an index graph storage model based on the graph database; registering a parameterized calculation task corresponding to the index in a built-in calculation framework of a graph database; when a user query request arrives, submitting a query transaction through a long connection channel established by driving the graph database, and directly accessing local storage data by a calculation framework to complete real-time calculation and return a result; when the original data in the graph database is changed, a database trigger captures a change event and pulls up the calculation task in the same process; user query triggering and data updating triggering share the same set of calculation task and graph traversal logic, and storage and calculation integrated real-time multi-dimensional index calculation and updating are achieved. The multi-dimensional index calculation method based on the graph database provided by the invention only depends on the graph database and its own calculation framework, simplifies a data processing link, and improves the index output efficiency.

The invention relates to a transformer substation switch state monitoring system based on centralized monitoring, and relates to the technical field of power system automation, and the system comprises a data collection module which is used for integrating multi-source heterogeneous data; the state judgment module outputs the unique real-time comprehensive state of each switch according to the communication state, the alarm condition function and the multi-stage judgment logic of the original remote signaling; the outage diagnosis module is used for classifying events when the switch trips and automatically screening event sequence records (SOE) to position core diagnosis information; the influence domain pre-judgment module is used for determining all potential influenced switch sets through a graph traversal algorithm on the basis of a power grid topological structure when accident tripping occurs; and an interface generation module. According to the invention, through automatic integration and visual linkage of the operation state, the fault reason and the influence range of the switch, the monitoring intuition, the diagnosis accuracy and the emergency disposal decision-making efficiency are significantly improved.

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 application discloses a crop gene function research scheme planning method based on an experimental reasoning chain and belongs to the technical field of scientific research experiment intelligent planning. In view of the problems of lack of experimental reasoning chain modeling, insufficient search and recommendation capacity and low experimental scheme planning precision in the prior art, the application constructs a gene research knowledge graph by extracting research hypothesis-experimental operation-result interpretation triplets from historical documents, receives user experimental information, generates single-step experimental recommendations based on semantic vector matching and graph traversal, simultaneously generates a multi-round experimental scheme set through multi-scenario search, standardization processing and multi-dimensional score sorting, realizes the automatic planning of the crop gene function research experimental scheme, and improves the research efficiency and scientificity.

The invention discloses a crop gene function research scheme planning method based on an experimental reasoning chain, and belongs to the technical field of scientific research experiment intelligent planning. Aiming at the problems of lack of experimental reasoning chain modeling, insufficient retrieval and recommendation ability and low experimental scheme planning precision in the prior art, the method comprises the following steps: extracting a research hypothesis-experimental operation-result interpretation triple from historical literatures to construct a gene research knowledge graph; user experiment information is received, single-step experiment recommendation is generated based on semantic vector matching and graph traversal, meanwhile, a multi-round experiment scheme set is generated through multi-situation retrieval, standardized processing and multi-dimensional scoring sorting, automatic planning of crop gene function research experiment schemes is achieved, and research efficiency and scientificity are improved.

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 a multi-source property right data verification method, and relates to the technical field of data processing. The method comprises the following steps: acquiring property right data from different data sources, and converting the property right data into a unified field according to semantics of the property right data and a preset mapping rule; after the knowledge graph is constructed based on the uniform field, the business rule vectors are coded into node attributes of the knowledge graph; and traversing nodes and edges in the knowledge graph through a preset graph traversal algorithm, and verifying the property right data according to the business rule vector to obtain a verification result. According to the method, the association relationship between the knowledge graph integration entities is constructed, and the service rule is encoded into the knowledge graph for verification, so that verification is not limited to single node data any more, cross-node verification can be performed in combination with the association information, and the verification accuracy is improved.

The invention relates to the technical field of maritime data processing, and discloses an intelligent photoelectric system in a complex maritime environment and a data processing method thereof. According to the method, real-time marine environment data including illumination intensity, sea wave dynamics and target object positions are collected, noise is removed through a preprocessing module, and a clean environment data set is generated; based on the data set, the identification and motion track characteristics of the target ship are extracted, high-priority and low-priority monitoring sub-areas are divided, uncovered areas are identified as background monitoring sub-areas, and a to-be-analyzed area group is formed; calculating risk difference indexes of adjacent sub-regions, and constructing a risk influence network diagram by taking the sub-regions as nodes and the indexes as edges; solving the critical path by applying a graph traversal algorithm, and deducing a data acquisition sequence; and controlling the photoelectric sensor unit of each sub-region to execute a marine environment monitoring task. The method can adapt to a complex environment, and the pertinence and orderliness of monitoring are improved.

The present invention discloses a multi-document question-answering retrieval method combining a generative language model and a semantic document graph. The method further improves and optimizes the construction and traversal methods of the document knowledge graph, designs the construction of the graph based on a bert-like model, and designs the KGP3 algorithm to optimize the retrieval traversal method. During the graph traversal process, combined with the main question and the obtained nodes, sub-questions related to the information of the next required node are generated, the most suitable document node is accurately selected from the neighbor nodes, and the relevance of the node to the initial question is judged. If it is not relevant, it is not included in the subsequent retrieval list. This method is not only efficient but also traceable and interpretable, and at the same time abstracts and generalizes the overall process of the model selecting the next-hop node during graph traversal. In order to reduce costs, the small encoder-decoder T5 model is also improved, and a contrastive learning mechanism is introduced into the model to further improve the retrieval performance of the small model.

The invention discloses a Verilog vulnerability detection method generated based on a graph traversal rule, and the method comprises the steps: firstly receiving a Verilog source code as a target detection code, generating and fusing three code structures, and forming an attribute graph VeriPG; secondly, acquiring natural language description information of a target vulnerability, and extracting a potential vulnerability judgment condition by utilizing a judgment condition LLMs; and then generating a vulnerability detection rule of the target vulnerability based on the VeriPG and a vulnerability judgment condition drive detection rule LLMs, and verifying the generated vulnerability detection rule. And finally, designing and deploying a rule executor, and executing graph traversal on the verified vulnerability detection rule to complete Verilog vulnerability detection. According to the method, the relationship between the control logic and the data can be comprehensively described, and the Verilog security vulnerability detection method with high accuracy, high automation and high adaptability is constructed.

In one aspect, a method for modeling graphs on distributed storage, comprising: receiving graph data including vertices, edges, and properties; mapping each vertex to a vertex record in a distributed storage system; mapping each edge to an edge record in the distributed storage system; mapping each property to a property record in the distributed storage system; determining, for each vertex, whether a number of incident edges exceeds a maximum record size threshold; storing pointers to incident edge records directly within vertex records when the number of incident edges does not exceed the maximum record size threshold; utilizing an adjacency index to lookup incident edge records when the number of incident edges exceeds the maximum record size threshold; and executing graph traversal operations that navigate from vertex to vertex by accessing the appropriate edge records through either the stored pointers or the adjacency index.

The invention provides a digital main line processing method based on an industrial data atlas, a medium and a system, and belongs to the technical field of the industrial data atlas. Unified access of cross-system data is realized by constructing a multi-source data acquisition adapter, cleaning and standardization processing are performed on heterogeneous data, an integrated data set is constructed, and the data collection efficiency is improved. Data entities are abstracted as nodes, cross-system association relationships are abstracted as edges to construct an industrial data graph, a graph index optimization strategy and a hierarchical storage architecture are adopted to improve cross-system query performance, a graph adaptive evolution model is utilized to realize dynamic adjustment of a graph structure, and a cross-system deep association analysis function is realized through a graph traversal algorithm. And an access control and data security mechanism is established to complete system protection, so that the technical problem that the industrial data processing system is difficult to realize cross-system deep correlation analysis is solved.