177 results about "Big data management" patented technology

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

The invention relates to the technical field of intelligent agriculture, in particular to a multi-source heterogeneous big data governance method and device.The multi-source heterogeneous big data governance method comprises the following steps that S1, multi-source heterogeneous data collection and semantic alignment are conducted, hidden semantic conflicts are eliminated; s2, performing dynamic data source adaptation and quality monitoring; s3, performing cross-domain permission penetration control; s4, building an industrial model driven by federal learning; and S5, double-chain block chain evidence storage and traceability are carried out. The method is used for disambiguating dialect term conflict, federated learning cross-domain privacy modeling and double-chain block chain evidence storage traceability through a dynamic semantic alignment neural network, and systematically solves the problems of dialect semantic segmentation, data islands and sensitive information leakage caused by small scattered distribution of peasant households in the field of traditional Chinese medicinal material planting. And a trust closed loop of government-enterprise-peasant household three-party cooperative treatment is constructed.

The invention discloses an energy big data right confirmation method and system based on a hierarchical hash tree and dynamic authorization, and belongs to the field of energy big data management and information security, and the method comprises the steps: obtaining and standardizing original data in an energy scene, and dividing the original data into a data block set according to equipment and time; constructing a hierarchical hash tree and generating root hash; writing the root hash and the associated metadata into the block chain to realize right confirmation and evidence storage; executing proxy re-encryption according to the access token, and converting the ciphertext into a decryptable format; verifying the data consistency through the Hash path and the root Hash, and completing the access; and recording the access behavior and distributing transaction earnings by the smart contract based on the contribution degree. Hash calculation and local path updating of a data block level are supported through a layered Hash tree structure, so that when large-scale energy data is frequently updated, a new root Hash value can be quickly generated and right confirmation updating can be completed only by carrying out local Hash recalculation on a changed path.

The invention relates to the technical field of big data management, in particular to a platform saturation management method and system based on multi-objective optimization, and the method comprises the steps: collecting platform, task, resource and path data in real time through a resource coupling modeling module; a resource conflict strength value and a path conflict strength value are calculated based on a warehouse operation resource task queue and a key passage path occupation state, a dynamic resource coupling weight matrix integrating three-dimensional conflicts is constructed, and the problem of conflict data splitting is solved; the resource mismatch response module is used for triggering a degradation task priority instruction and iteratively outputting a platform allocation scheme set of which the resource conflict intensity value reaches the standard when the resource conflict intensity value exceeds a preset threshold value by taking the platform occupation state as a physical basis and combining with an operation timeliness requirement; and the secondary congestion suppression module performs weighting on the path conflict intensity value by multiplexing the matrix, screens the lowest congestion risk final case in the scheme set, blocks a secondary congestion spreading chain, and realizes collaborative optimization of resource utilization and path smoothness.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The invention relates to the technical field of big data management and artificial intelligence application, in particular to an automatic data quality inspection system and method based on a big model and data flow orchestration, and the method comprises the following steps: system architecture construction: employing a three-layer architecture design which comprises a data access layer, an intelligent quality inspection layer and a flow orchestration layer; the method has the beneficial effects that the full-link automatic quality inspection of structured and unstructured data is realized by integrating the intelligent analysis capability of a large language model (LLM) and the data processing pipeline of a low-code process arrangement tool. The system combines a rule engine and large model dynamic reasoning, supports customized quality inspection rule generation, abnormal data intelligent identification and quality inspection process adaptive optimization, improves the automation level and complex scene adaptability of data quality detection, and is suitable for data governance scenes of multiple industries such as government, finance, medical treatment, e-commerce and the like.

The invention relates to the technical field of big data management, in particular to a collaborative optimization decision-making system for electric power-environment coupling big data, and the system comprises a drainage basin coupling module which is used for fusing multi-source ecological data obtained in real time with a preset mechanism model, and constructing a drainage basin water-energy-grain-ecological coupling model; the hierarchical decision-making module comprises a coordination layer agent representing a top layer strategy target and execution layer agents representing different benefit parties; the decision attribution analysis module is used for analyzing the collaborative scheduling strategy and acquiring a key driving factor causing a preset scheduling behavior; the anti-fact deduction optimization module is used for defining an anti-fact scheduling scene based on the key driving factors, calling a watershed water-energy-grain-ecology coupling model to perform rapid deduction, quantitatively comparing the comprehensive influence of the anti-fact scheduling scene and the collaborative scheduling strategy on water, energy, grain and ecology under a preset time scale, and performing collaborative scheduling on the water, the energy, the grain and the ecology. And optimizing the collaborative scheduling strategy.

The invention relates to the technical field of remote sensing agricultural data management, and discloses a remote sensing agricultural big data management system based on a block chain. According to the system, image data is subjected to radiation, atmosphere and geometric correction preprocessing to generate a standardized remote sensing image; the feature extraction module performs multi-scale segmentation on the crop feature region, identifies the crop feature region, extracts texture, spectrum and shape feature values, and fuses the feature values to form a multi-dimensional feature vector; the block chain storage module performs hash operation on the vector to generate a feature fingerprint, constructs a new block in combination with a timestamp and a preorder block hash value, and forms a non-tampering agricultural data chain after verification of a consensus mechanism; a path analysis module traverses the block sequence, extracts a storage path and screens a high-relevance feature path set; and the intelligent classification module extracts feature tags, and generates an agricultural data classification structure through a multi-dimensional matching algorithm. The system realizes standard processing, safe storage and intelligent application of remote sensing agricultural data, and meets the requirements of agricultural modernization development.

The invention relates to the technical field of big data management, in particular to a data tracing method based on big data mining, which comprises the following steps of: acquiring an unstructured operation log, analyzing the unstructured operation log into a structured event record, and associating to generate a heterogeneous operation session set; performing access mode association analysis on the session set to extract a read-write path mode, and combining association strength and a time decay factor to determine a dependency weight and construct a blood relationship map; eliminating a loop of the graph to generate a directed acyclic graph, constructing a spanning tree serving as a retrieval trunk and a node index coding interval, and mapping a cross-branch associated edge to a bitmap index to generate a probability skeleton tree index; and positioning a trunk path by using the interval, calling a bitmap to perform multi-path verification, and outputting a traceability path and confidence. A probability consanguinity map and a probability skeleton tree index are constructed through mining logs, and data consanguinity reconstruction and large-scale node low-delay retrieval in the heterogeneous black box environment are achieved.

The invention discloses a total-factor integrated large Token data management system, and the system comprises an identity credential collection unit which is used for obtaining an identity label and associated business data from a plurality of social subjects; the identifier abstraction unit is used for mapping the identity identifier into a standardized Token primary key according to a main body type and structuring the associated business data into a corresponding digital asset package; the total element aggregation unit is used for aggregating and packaging the digital asset package into a single verifiable large Token data structure, and the large Token comprises a Token primary key and a dynamic trust level; the dynamic trust engine is used for calculating and updating the trust level of each main body in real time and binding the trust level to the corresponding large Token; and the intelligent service execution unit is used for responding to an external business instruction and calling the large Token to realize intelligent service.

The invention relates to the technical field of intelligent regulation and control and energy conservation of hospital environment air quality, in particular to a big data management and intelligent evaluation system for the hospital environment air quality. The system comprises a multi-modal data acquisition module, a data processing module, a dynamic transmission modeling module, a dynamic Bayesian network risk modeling module and a prospective risk hedging and energy consumption optimization module. The system calculates a dynamic air transmission coefficient by collecting environment and people flow data, and constructs a dynamic Bayesian network model to calculate a cross-region propagation risk probability; the method is characterized in that when the risk probability exceeds a threshold value, a system actively solves a multi-objective optimization problem with minimization of energy consumption as an objective, and an optimal HVAC control instruction is generated; according to the method, the conversion from lagging evaluation to prospective risk hedging is realized, and the risk can be actively identified and regulated before the pollution exceeds the standard.

The invention relates to the technical field of big data management, in particular to a big model metadata authority management method and system based on an RBAC-ABAC fusion model, and the method comprises metadata annotation, data product release, big model and user access, semantic perception, dynamic authority adjustment, row-level authority control and authority life cycle management. The method has the beneficial effects that by fusing the advantages of the RBAC model and the ABAC model, the defects of a single authority management model are overcome, the efficient, dynamic and fine-grained authority management of the large model metadata is realized, the security of the large model metadata is improved, and the authority management requirement of the large model for accessing the dynamic business data is met.

The invention discloses a medical big data management system and method based on cloud edge collaboration, and relates to the technical field of medical data management. Efficient data management is achieved through cooperation of multiple modules, and the heterogeneous data fusion module collects multi-source data in a unified mode and constructs a knowledge graph; the privacy protection calculation module guarantees data security by using homomorphic encryption and differential privacy; the edge intelligent analysis module deploys a lightweight model to realize real-time analysis; the cloud edge collaborative decision-making module dynamically allocates tasks according to the data and the equipment state; in addition, data quality evaluation, knowledge graph updating and other mechanisms are included, and the data reliability and timeliness are improved. Efficient and safe management of medical data is realized, the data processing efficiency is greatly improved, and the cloud cost and energy consumption are reduced; the data privacy is guaranteed, and the leakage risk is almost zero; the model diagnosis accuracy is improved, and clinical decision making is assisted; the diagnosis time is shortened, the error rate is reduced, and the medical service quality is remarkably optimized.

The invention relates to the technical field of data management, in particular to a supply chain-based manufacturing industry big data management system, which comprises an event attribution module, a data integration module, an inventory trend module, a batch judgment module and a parameter tracing module. According to the method, automatic association analysis of multi-dimensional parameters such as process, logistics, inventory and environment is realized through time sequence synchronous acquisition of multi-source data and dynamic linkage of business events, and accurate circulation of whole-process data is promoted by adopting an event-level feature recognition and parameter screening mechanism; furthermore, positioning and traceability of an abnormal fluctuation process are supported in a process traceability and trend discrimination mode, information interconnection and parameter relation chain establishment of each manufacturing link are enhanced, synchronous processing and hierarchical clustering of multi-parameter heterogeneous data are realized, and the real-time decision support capability and traceability precision of the data are improved; and the response speed of an abnormal process and inventory fluctuation in a manufacturing scene and the intelligent level of data application are effectively enhanced.

The invention provides a method for detecting the furnace temperature uniformity of a roller hearth type heat treatment furnace. The method is applied to a roller hearth type heat treatment furnace system comprising a furnace body, a heating device, a temperature sensor array and a medical service big data management system. The method comprises the steps that a temperature sensor array containing 14 measuring points is arranged at a specific position of a furnace body, and a furnace temperature data recording black box is fixed to the lower right corner of the furnace body; controlling the heating device to heat to a target process temperature of 920 DEG C and keeping the temperature constant for 30 minutes; the temperature data of the whole furnace area is collected every five minutes; and calculating a reference temperature and an absolute deviation value set of each measurement point through a medical service big data system, and evaluating the temperature field uniformity of the furnace body. And when the deviation exceeds a threshold value, the system generates a correction parameter group containing a heating zone power compensation coefficient and a refractory material density gradient value, and executes power redistribution or thermal insulation layer enhancement operation. High-precision detection and dynamic optimization of the temperature field uniformity of the roller hearth type heat treatment furnace can be realized, and the stability of a heat treatment process and the consistency of product quality are improved.

The invention relates to the technical field of financial industry big data management, in particular to a financial knowledge graph updating and compliance verification method based on multi-source heterogeneous data, and the method comprises the steps: building a global sensing network for the financial field, collecting multi-source heterogeneous financial data in real time, and carrying out the normalization processing; performing named entity recognition and relation extraction on the normalized financial data by using a pre-trained large language model, and constructing a financial knowledge graph; when a new supervision rule is released, detecting whether there is a conflict between the new supervision rule and the old supervision rule, and if there is a conflict, carrying out lossless dynamic updating on the financial knowledge graph; and when a financial service request is received, taking a service main body and a service type corresponding to the current financial service request as anchor points, extracting a local sub-graph from the financial knowledge graph, reasoning the local sub-graph by using the graph neural network, and finally outputting an interpretable compliance audit report.

The invention relates to the technical field of computers, discloses a full-life-cycle big data management method and system for a meteorological engineering project, and aims to solve the problems of data islands, different standards, processing splitting, decision lag and the like in the prior art. The method comprises the following steps: collecting multi-source heterogeneous original data; performing standardization processing to form a data set with unified semantics and space-time reference; classifying and storing according to the full life cycle stage of the project, and constructing a project entity association index; calling a stage adaptation model based on the data set to execute prediction, diagnosis or optimization; generating visual early warning and operation and maintenance suggestions; and pushing a decision instruction to realize closed-loop management. Through adoption of the technical scheme, unified convergence, efficient retrieval, intelligent analysis and closed-loop decision of meteorological engineering project data can be realized, and data availability, early warning timeliness and management automation level are remarkably improved.

This invention discloses a big data-based method for maximizing efficiency management in a hardware processing workshop, relating to the field of big data management technology. The method includes: acquiring historical video data of hardware workpiece processing tasks; quantifying the skill levels of processing personnel in the hardware processing workshop; clustering and classifying the processing personnel according to their skill levels; initializing the time distribution of hardware workpiece processing actions under each skill level; constructing a hardware workpiece processing action-effective / ineffective time identification model; marking the types of ineffective time-consuming tasks for each worker's skill level; performing causal tracing for these ineffective time-consuming task types; constructing a causal list of ineffective time-consuming tasks; and generating a sequence of causal improvement tasks for ineffective time-consuming tasks. The beneficial effects of this invention are: improving the accuracy of ineffective time analysis and improvement, and maximizing the efficiency of the hardware processing workshop.

The application relates to the technical field of financial big data management, and discloses a financial big data management system based on a time sequence neural network, wherein the system comprises the following steps: each jurisdictional edge node generates a dynamic transaction directed graph based on a real-time transaction event stream, and extracts a local graph topology difference sequence containing a boundary node in-out degree change vector; a lightweight deep separable causal convolution encoder encodes the sequence, generates a local context-aware node representation through attention-weighted fusion; a boundary embedding vector is uploaded to a coordination node after being anonymized, cross-jurisdiction embedding space progressive unification is realized based on a federal-level contrast learning loss and Fisher information matrix weighted aggregation; asynchronous federal synchronization is triggered based on distribution drift detection; cross-jurisdiction candidate link logical splicing and distributed verification are completed through density clustering and cosine similarity matching; and finally, a multilayer perceptron classification network outputs a risk level and generates a cross-jurisdiction money laundering risk report.

The invention relates to the technical field of big data management and database optimization, in particular to an index tuning method and system for medical multi-modal data mixed query, and the method comprises the following steps: accessing structured and unstructured medical data into a data lake; performing expansion processing on a nested structure in the structured data according to field types, extracting semantic feature vectors from unstructured data, packaging the unstructured data into a standardized JSON format, performing data fusion, and loading the fused data to a PostgreSQL database; analyzing a mixed query SQL statement submitted by a user, and constructing high-dimensional mixed feature representation; carrying out clustering compression on the query request set by adopting a K-Medoids clustering algorithm to generate a representative query set; performing virtual index simulation evaluation on the structure field by utilizing HypoPG, creating a real index for the vector field in combination with a pgvector plug-in, and collecting a query performance index; and based on IBST multiplexing historical evaluation data, in combination with a structure change prediction model based on a Transform architecture, outputting an optimal index combination. According to the method, the query performance and the index recommendation efficiency are remarkably improved.

The application discloses a kind of intelligent scheduling and distribution method of protein complex detection task in cloud platform, it is related to big data management technical field;By constructing task feature model;Real-time sequencing is carried out to task using dynamic priority evaluation function containing vulnerability index, and dynamic scheduling sequence is generated;Using improved space-time graph convolutional neural network to construct resource matching prediction model, and generating resource-task matching matrix;Using improved adaptive genetic algorithm, with minimizing completion time and maximizing equipment utilization as target, under the constraint of resource-task matching matrix, the optimal allocation strategy and execution instruction set are generated;Through abnormal hierarchical judgment and dynamic adjustment resource allocation, and generate exception traceability feedback report;Realize biological characteristic perception and physical resource state prediction deep coupling, improve the survival rate of easy-degradable sample and the utilization efficiency of experimental equipment, guarantee the intelligentization and high robustness of cloud platform detection whole process.

The invention discloses a data visualization big data management platform comprising a data acquisition module used for acquiring heterogeneous data and constructing a standardized input set; the preprocessing module is used for extracting a trend component and a periodic component and constructing a structural relation graph; the frequency domain modeling module is used for inputting the trend component and the periodic component into an improved FEDform model to generate a frequency domain feature and a response mode; the structure representation module is used for generating structure feature representation; the fusion generation module is used for fusing the frequency domain features, the response mode and the structural features to generate a visual parameter set; the display configuration module is used for completing component layout and interaction logic setting; the graphic rendering module is used for executing rendering and interaction at the terminal; and the fault-tolerant switching module is used for switching to an edge node local mode when communication or power supply is abnormal and calling the energy storage device to guarantee the operation of the key component. According to the invention, intelligent visualization and stable scheduling of complex data are realized.

The utility model relates to the technical field of big data management devices, in particular to a management device for emergency management of big data, which comprises a database management hard disk and a pop-up device used for popping up the database management hard disk when equipment is overloaded during reading and writing. The pop-up device comprises a protective shell and pop-up assemblies, an installation fastening abutting assembly and an overload protection loosening bouncing assembly which are located in the protective shell, pop-up clamping grooves are symmetrically formed in the outer walls, close to the two sides of the read-write end, of the database management hard disk, and the pop-up assemblies are located on the inner walls, at the pop-up clamping grooves, of the protective shell; the top of one side of the protective shell is provided with a placement groove for placing the database management hard disk, and the end part of one side, close to the placement groove, of the protective shell is provided with a displacement groove for read-write insertion of the database management hard disk; through cooperative work of mechanical and electronic components, effective protection of the database management hard disk in the emergency management big data device is achieved, and especially in the aspect of preventing short circuit damage caused by equipment overload.

The application belongs to the technical field of big data management, and discloses a big data management system and method based on a layered label system, which comprises a data access preprocessing unit, a label modeling and layered configuration unit, a label analysis and data binding unit, a label-driven data management unit, and a label conflict detection and dynamic updating unit. The label-driven data management unit is triggered by the label binding result, the management rules are loaded through a dynamic rule engine, and the data is cleaned, classified, and safely protected by using a multi-dimensional verification strategy and an encryption control mechanism. The label conflict detection and dynamic updating unit monitors the label system in real time, adjusts and repairs conflicts by means of path traversal and version management technology, and ensures the stability of the label system. The two units jointly automatically process abnormal data, maintain the label system, and guarantee data quality and safety.

The invention discloses a neurosurgery patient data management method, and relates to the technical field of big data management, and the method comprises the steps: defining a special disease library body and a data extraction rule, and distributing the special disease library body and the data extraction rule to all medical branch centers; performing regularized extraction on medical data in the local medical data source to generate special disease feature data, and performing scanning comparison on medical record texts in the local medical data source through a large language model to generate clinical feature suggestions; traceability auditing information is added to all special disease feature data to form a special disease data pool, and clinical feature suggestions are incorporated into local ontology evolution suggestions; and encrypting and transmitting the local ontology evolution suggestions to a coordination center, integrating all the local ontology evolution suggestions by the coordination center, and generating a global ontology updating instruction through a federated collaborative evolution mechanism. According to the method, the large language model is deployed in the local medical branch center, and semantic comparison is performed in combination with the special disease library ontology, so that supplementary recognition of clinical information which is not regularly extracted and covered in the medical record text is realized.

This invention discloses a big data-based safety design management method for chemical projects, relating to the field of big data technology in chemical safety. The method includes: collecting multi-source heterogeneous data and preprocessing it to form a project baseline dataset; collecting design change data and performing differential comparison with a safety knowledge graph to identify differential objects; starting from the differential objects, performing dependency propagation analysis along the safety dependencies in the safety knowledge graph to output affected objects and risk propagation chains; determining the review order of affected objects based on the risk propagation chains, sequentially extracting the specifications to be reviewed that match the affected objects from the specification knowledge base, determining whether the affected objects meet the specifications to be reviewed, and outputting the specification review conclusion. This invention realizes dynamic and precise management and automated compliance review of chemical project safety design, shifting from experience-driven to big data-driven management.

The application discloses a big data management system based on pet classification labels, and relates to the technical field of big data management.The system comprises a content data collection module, a pet classification label module, a user portrait module, a content recommendation module, a geographic position service module, an interactive statistical module and a data storage module.The pet classification label module identifies pet breeds through image recognition technology, and generates a three-level classification label system comprising pet category labels, pet breed labels and content theme labels.The content recommendation module realizes accurate recommendation by combining pet classification labels and user interest labels.The interactive statistical module calculates a comprehensive heat value by using a weighting method.The application realizes fine classification management and accurate recommendation and distribution of pet content.

The invention discloses a sewage treatment equipment operation intelligent monitoring system based on big data management, and relates to the technical field of big data. The method comprises the following steps: acquiring an Internet of Things sensor acquisition network of target sewage through big data, acquiring sewage characteristics, acquiring a sewage characteristic putting data pool through the sewage characteristics, and acquiring a sewage putting data pool of the Internet of Things sensor acquisition network through the sewage characteristic putting data pool; a preprocessing mechanism is set, and the sewage data of the sewage data pool is preprocessed through the preprocessing mechanism to obtain a to-be-put sewage data pool; the unit sewage abnormal coefficient of the to-be-put sewage data pool is obtained, and then abnormal sewage water quality data is obtained through the unit sewage abnormal coefficient; the monitoring module is used for carrying out real-time monitoring treatment on the abnormal sewage treatment data; and the operation monitoring accuracy of the sewage treatment equipment is improved.

The invention relates to the technical field of data management, in particular to a land space planning geographic information big data management method and system. According to the geographic data dimension suspected abnormal degree of the coordinate points and the neighborhood coordinate points under the same dimension; according to the ranking characteristics of the geographic data of the coordinate points in the neighborhood and the geographic data of the coordinate points and the neighborhood coordinate points, obtaining a single-dimensional abnormal confidence coefficient; clustering is carried out according to geographic data of all dimensions of the coordinate points and the neighborhood coordinate points, and cluster characteristic values are obtained according to point clusters where the coordinate points are located and distance characteristics between the point clusters where the coordinate points are located and other point clusters; obtaining a dimension correlation feature value according to the change difference feature of the geographic data of the coordinate points between the dimensions; obtaining a multi-dimensional abnormal confidence coefficient according to the cluster characteristic value and the dimension correlation characteristic value; and obtaining the abnormal degree of the coordinate point under any dimension according to the comprehensive abnormal confidence coefficient and the dimension suspected abnormal degree, and detecting the geographic data, thereby improving the accuracy of territorial space planning.