2721 results about "Business data" patented technology

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention belongs to the technical field of supply chain risk early warning, and particularly discloses a server supply chain risk conduction early warning method based on a fusion graph neural network, and the method comprises the steps: obtaining historical time sequence data and real-time business data of each entity in a server supply chain, constructing a dynamic heterogeneous supply chain graph, and carrying out the early warning of the risk conduction of the server supply chain; the method comprises the following steps: identifying a causal association relationship of different risk types among nodes, quantifying a corresponding time lag parameter, dynamically generating at least one risk element path for each risk type, and fusing real-time business data and the risk element paths by taking a graph as a structure basis to obtain a risk element path; through neighborhood state aggregation along a meta path and multi-scale time sequence feature extraction, a dynamic risk score of each node is output, the score is compared with an adaptive early warning threshold to generate a graded early warning signal, and an integrated risk early warning report is generated and output in combination with risk conduction path information. Accurate prediction and early warning of the supply chain risk spatio-temporal dynamic conduction process can be realized.

The invention relates to the technical field of project intelligent management and control, and discloses a multi-agent power grid project intelligent monitoring, management and control and evaluation system and method, and the system comprises a mixed data resource library, a multi-agent system, an index calculation module and a process control module. The mixed data resource library stores structured business data and unstructured documents; the multi-agent system comprises an information extraction agent, an index design agent, a code generation and treatment agent and the like, works cooperatively, and converts a high-level natural language management and control rule into an executable code; the index calculation module is responsible for executing codes according to a scheduling strategy and calculating quantitative indexes; and the process control module automatically identifies risks and executes management and control operations such as process locking according to the index result and a preset threshold value. According to the invention, the complex business logic can be automated and coded, the accuracy and timeliness of risk identification are improved, and refined and prospective intelligent management and control of the power grid project are realized.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention belongs to the technical field of intelligent electric power internet of things, and particularly relates to an unmanned aerial vehicle heterogeneous terminal self-adaptive security access method for electric power edge computing, which comprises the following specific steps: terminal registration and credible anchoring: binding a hardware-level identity, collecting a multi-mode fingerprint and constructing a security behavior portrait; self-adaptive access authentication is carried out; dynamic behavior monitoring and baseline adjustment: establishing an identity authentication and management system, synchronously carrying out multi-dimensional real-time monitoring, carrying out linkage treatment on abnormal behaviors, and adopting a baseline evolution mechanism to avoid misjudgment; the multi-dimensional real-time monitoring comprises terminal service trend monitoring and flow situation analysis; and performing safe closed loop and cooperative response. According to the invention, unified risk detection of the unmanned aerial vehicle can be realized, real-time monitoring of vulnerabilities, access authority and operation states of the unmanned aerial vehicle is carried out, a dynamic behavior monitoring model is constructed in combination with unmanned aerial vehicle terminal information, business data, environment data and the like, and real-time monitoring and early warning of the unmanned aerial vehicle terminal are realized.

The invention discloses a water conservancy intelligent question-answering system and method based on knowledge enhancement and data driving, and aims at water conservancy business structured and unstructured data query, through loop optimization driven by positive and negative examples, precise classification of secondary intentions of water conservancy problems is realized, knowledge questions and answers, data query and professional water conservancy subclass questions and answers can be distinguished, and the system and the method can be applied to water conservancy business. And the semantic analysis efficiency is improved. Aiming at the problem of low query accuracy of retrieval enhancement generation in the field of water conservancy, a differential water conservancy knowledge base oriented to professional books, industrial standards and laws and regulations is constructed, local and networking information is processed through a multi-source knowledge fusion and conflict resolution mechanism, and the accuracy, interpretability and traceability of question and answer content are improved. Aiming at the problems of complex operation and low semantic query accuracy in query of massive water conservancy business data and monitoring data, multi-layer constraint Text-to-SQL conversion is performed based on water conservancy business knowledge, high-precision semantic query and automatic visual output of water conservancy structured data are realized, and query efficiency is improved.

The invention discloses an interactive data insight system based on a large language model. The interactive data insight system comprises an intention recognition and classification module, an entity extraction module, an entity and business term alignment module, an SQL generation module and a structured query verification module. The key technical problems that an existing natural language data analysis system is insufficient in semantic understanding, inaccurate in entity alignment, weak in multi-round dialogue support, unstable in SQL generation, lack of safety and the like can be solved, semantic understanding and natural language interaction of complex business data are achieved, the data analysis threshold is greatly reduced, and the data analysis efficiency is improved. And the query efficiency and the result controllability are improved.

The embodiment of the invention discloses a report generation method and device, computer equipment and a readable storage medium, and the method comprises the steps: displaying a graphical user interface to prompt a report requester to input report demand information; report demand information is obtained, semantic analysis is carried out on the report demand information through a large language model LLM to obtain report generation indication information, and the report generation indication information at least comprises a data range, a data type, a data statistical parameter and a report display type; searching a database through the LLM to obtain a plurality of business data matched with the data range and the data type, and obtaining business indexes of the plurality of business data based on the data statistical parameters through the LLM; and determining report basic parameters based on the report display type through the LLM, and outputting a target report based on the report basic parameters, the multiple pieces of business data and the business indexes. By adopting the method, the technical threshold of data analysis processing can be reduced, the report generation efficiency is improved, and the method is simple, efficient and high in applicability.

The invention relates to a BI intelligent question answering system and method based on business rule retrieval and AI workflow, and belongs to the field of intelligent data processing. The system comprises a user interaction module, a knowledge base module and an AI workflow module. The method comprises the following steps: (1) business rule retrieval: taking a BI analysis problem written by a user interaction module as a target, retrieving business data table information from a knowledge base module, judging a business data table of the user problem in combination with a big language model, obtaining business indexes contained in the table according to the business table, retrieving BI business information related to the user problem, and performing business rule retrieval; and (2) AI workflow execution: taking a BI analysis problem as input, gradually scheduling workflow nodes of business rule retrieval, cue word construction, large language model interaction and database interaction, realizing an accurate Text-to-SQL function and a data visualization function, and further realizing a question-answer ability of converting the BI analysis problem input by a user into a BI analysis result.

The invention relates to an auxiliary verbal skill self-adaptive generation method fusing a knowledge graph and reinforcement learning, and belongs to the technical field of artificial intelligence and service compliance management crossing. The method comprises the following steps: collecting service data, extracting entities, attributes and relationships among the entities through natural language processing, constructing a knowledge graph marked with compliance attributes, and establishing an updating mechanism; determining a verbal skill generation main body according to an actual service scene, and integrating a conversation context, a client real-time state and a business target construction environment state; the verbal skill generation subject retrieves associated business knowledge according to the current environment state, and after the seat selects adaptive verbal skill interaction, verbal skill generation strategy parameters are updated through deep reinforcement learning; and constructing a multi-dimensional effect evaluation method based on the business data, feeding back an evaluation result to an iteration link, and optimizing reward calculation and knowledge graph association logic. Dynamic self-adaption of verbal skills in multiple service scenes is realized, and customer demand solving efficiency and business conversion quality are improved.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention relates to a distributed data unified management and intelligent scheduling method based on data braiding, and the method comprises the steps: collecting the real-time state data of a node, and generating a node capability portrait; constructing a dynamic incidence matrix of the metadata and the node capability portraits; establishing a multi-dimensional scheduling evaluation model, inputting a business data dependency relationship, performance data in the node capability portrait and a node future load predicted by the LSTM model, and outputting an initial scheduling scheme; a static threshold value and a dynamic prediction threshold value are preset to serve as scheduling optimization triggering conditions, the initial scheme is optimized through a reinforcement learning algorithm, and an optimized scheduling decision is obtained; and sending a scheduling instruction containing a priority identifier to a corresponding node, collecting feedback data such as response delay and an error rate in real time, updating the association strength of the dynamic association matrix according to the feedback data, and optimizing the parameters of the multi-dimensional scheduling evaluation model. Unified management of distributed data is achieved, scheduling intelligence and accuracy are improved, node state changes can be dynamically adapted, and data processing efficiency and reliability are effectively guaranteed.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The invention provides a question answering method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a question text; performing semantic understanding on the question text through a large language model to obtain a structured data query instruction; retrieving in a database based on the data query instruction to obtain target data related to the question text, the data included in the database being obtained by integrating first business data obtained from at least one business system; and obtaining a target answer of the question text through the large language model according to the question text and the target data related to the question text.

The invention relates to the technical field of data intelligent traceability, and discloses a whole-process business data intelligent traceability method and system. The method comprises the following steps: collecting first data of a preset business link and creating a semantic tag to form a semantic tag data set; the semantic tag data set is stored in a Merkle tree structure, and a service block chain is constructed; extracting second data of each business link from the business block chain, and generating a fusion data set based on the second data; and establishing a relation graph according to the fused data set, after receiving a traceability query request, starting to search a traceability path from a starting service point in the relation graph, and outputting a traceability result, thereby ensuring the reliability of the traceability result, and solving the problems of serious data island, missing association relationship and low traceability precision of the traditional traceability technology.

The invention discloses an intelligent business decision support system based on multi-modal data and a knowledge graph, and relates to the technical field of data analysis. In order to solve the problems of weak data integration capability and poor model adaptability in existing business analysis, the adopted scheme comprises a multi-modal data processing module used for collecting multi-modal data and constructing a knowledge graph after preprocessing and feature extraction; the model training and optimizing module is used for taking a large-scale pre-training language model as a core, acquiring external knowledge from the atlas in combination with a retrieval enhancement generation technology, performing parameter fine tuning, optimizing a training process and generating an optimized model at the same time; the intelligent decision generation module is used for generating a preliminary suggestion based on the optimization model and generating a report through logic deviation evaluation and correction; the dynamic feedback module is used for constructing evaluation system quantitative model performance by taking the report as input and combining real-time business data; and meanwhile, user feedback is collected to form an incremental sample dynamic optimization model. The accuracy of business decision making can be improved.

The invention relates to the technical field of digital villages, in particular to a digital village construction business data analysis method and system, and the method comprises the following steps: obtaining a field detail comparison mapping role and a label, analyzing the field difference and relation to obtain a field reference set, recognizing the field sequence and relation to obtain a field path sequence column, and obtaining a field path sequence column; and extracting a time position matching stage transaction field processing fragment set, reading a position scanning number to obtain a behavior field structure list, and tracking a field line number extraction text to obtain a business data analysis field set. According to the method, semantic association is established by matching field text content, an attribution level is constructed, a circulation path is restored by relying on field positions, continuous fragments are divided by fusing time intervals and stage identifiers, a parallel advancing track is extracted according to a calling range and serial number arrangement, and context content is extracted by tracking field data lines. A data structure view capable of supporting cross-stage, multi-role and multi-context analysis is constructed, and the coverage range and organization ability of field behavior extraction are enhanced.

The invention relates to the technical field of medicine supply chain management, in particular to a multi-dimensional data-based medicine market demand prediction method and system, and the method comprises the steps: collecting and preprocessing multi-source business data; constructing a dynamic causal graph of the tensor network to obtain causal embedding, aligning the submerged space by adopting a Gragov-Warisstein and Sroudinger bridge, and constraining a trajectory by a path Warisstein regularization; a demand quantile curve is generated in the conditional diffusion model with state space memory, and a feasible region and coverage rate guarantee is realized by combining augmented Lagrange and conformal prediction; and inputting the quantile and the inventory state into the reinforcement learning strategy network, and performing closed-loop updating in the digital twin supply chain. According to the method, the robustness, the interpretability and the strategy stability in an extreme scene are improved.

The invention provides an intelligent resource decision-making method based on industrial chain collaborative optimization, and relates to the technical field of industrial internet management, and the method comprises the steps: integrating resource data, business data, external environment and other multi-source heterogeneous data from all participants, combining a causal conduction rule and an operation constraint rule, and carrying out the collaborative optimization of an industrial chain. According to the method, a dynamic digital model capable of simulating a real industrial chain operation mechanism is generated, concurrent extreme conditions of multiple risks in reality are simulated through a composite interference scene for deduction, and a corresponding multi-dimensional toughness index and a visual toughness profile are generated based on a simulation result; precise positioning of key fragile nodes and fragile conduction paths is realized, deduction of different types of composite interference scenes can simulate influences of various complex factors appearing in an actual industrial chain, so that corresponding resource allocation strategies are generated, a coping basis and a precaution basis are provided for appearing of the actual interference scenes, and the resource allocation efficiency is improved. And the coping capability of the industrial chain network to the emergency scene interference is improved.

The invention discloses a time-frequency double-domain isolation time sequence anomaly detection method based on Mamba-self-attention, which can be applied to the fields of industrial manufacturing, medical equipment and the like and can detect anomaly by quantifying time-frequency difference. The method comprises the steps of extracting a multivariable time sequence sample from business data, and obtaining multivariable data through reversible instance normalization; the input time domain representation module is used for independently inputting a Mama network according to a natural time sequence and an inversion time sequence, capturing forward and reverse time features and fusing the forward and reverse time features into time features; a frequency domain representation module is input, seasonal variables are extracted, frequency features are extracted in combination with discrete cosine transform and an attention mechanism, and the frequency features are reconstructed to a time domain through inverse discrete cosine; the time and frequency characteristics are input into a time-frequency difference module, and the inconsistency is quantified through Kullback-Leible (KL) divergence so as to compare and learn a similarity loss function training model; and generating an anomaly score and setting a hyper-parameter to judge anomaly. The method is based on a bidirectional Mama and self-attention time-frequency double-domain isolation architecture, mode specificity discrimination features masked by traditional fusion are reserved, the consistency of normal mode domains is high, the correlation of abnormal performance is collapsed, and the detection accuracy and reliability are improved.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention discloses a row-level data permission authorization method and device, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: registering basic data into a preset database; configuring a corresponding field management user with a field management authority for the preset service field; creating or updating a record in a user data permission table based on the user permission application to determine a permission relationship between the user identifier and the entity data; registering a service data table and a mapping relation between an entity column and an entity type in the service data table in a preset service field based on a user instruction of a field management user; and dynamically generating a permission filtering condition based on the query SQL and the user identifier so as to generate an SQL statement with permission filtering, and querying corresponding service data from the service data table based on the mapping relationship so as to generate a corresponding query result based on the service data. Therefore, the efficiency and flexibility of line-level data permission authorization can be improved on the premise of ensuring data security.

The embodiment of the invention provides a model training method of a risk prediction model and a risk prediction method and device, and the method comprises the steps: determining the business data of a target business, constructing a knowledge logic diagram according to the business data, and enabling the knowledge logic diagram to comprise at least one knowledge path; inputting the service data into the initial prediction model, and determining a candidate path node set corresponding to the service data through the initial prediction model; according to at least one knowledge path in the knowledge logic diagram, calculating reward information corresponding to the candidate path node set, and selecting a target path node in the candidate path node set based on the reward information; and generating a prediction path set corresponding to the service data based on the target path node, and performing model training on the initial prediction model according to the prediction path set to obtain a risk prediction model, the risk prediction model being used for predicting risk information of the associated target service. The model is trained through high-quality training data, and the risk prediction capability of the model is improved.

The invention provides an aero-engine test whole-process business management method and system, and belongs to the technical field of whole-process business management, and the method comprises the steps: extracting information in a test item information document and a tester information document, and determining structured data; on the basis of the structured data, forming an initial test plan, performing test personnel allocation optimization on the initial test plan, and determining a target test plan; generating a target test card on the basis of the target test plan, and continuously updating the target test card by adopting an editable block chain according to the data in the test process until the test is finished; and after the test is finished, the operation authority of the target test card is removed, and non-tampered and decentralized aero-engine test whole-process business data is formed. The information utilization efficiency of each link is effectively improved, the distribution rationality of testers is improved, and the safe storage of test data is ensured.

The invention discloses a hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion, particularly relates to the technical field of performance assessment, and comprises a data acquisition module, a multi-source data fusion module, a dynamic accounting module, an assessment and evaluation module and a result output module. The data acquisition module is used for acquiring hospital multi-dimensional medical service original data; and the multi-source data fusion module is in communication connection with the data acquisition module, and the multi-source data fusion module is used for cleaning and standardizing the acquired multi-dimensional medical service original data. According to the method, after the multi-source heterogeneous medical business original data is cleaned and standardized, feature extraction and fusion are carried out, data of different sources and formats can be precisely processed, standardized performance data of a unified format can be output, the problem that the data are too many and miscellaneous and are difficult to cover multi-dimensional key indexes is effectively solved, and the accuracy is higher during accounting and assessment.