228 results about "Decision making analysis" patented technology

The invention belongs to the field of network security, and particularly discloses a network defense agent system based on a large language model. Through the design of the sensing layer, the decision analysis layer and the action execution layer, comprehensive protection of network threats is realized. The sensing layer is responsible for collecting original information from multiple channels and converting the original information into standardized data; the decision analysis layer performs modeling and threat reasoning on attack behaviors, evaluates a risk level and predicts subsequent actions; and the action execution layer specifically executes defense operation according to the defense strategy scheme output by the decision analysis layer. In addition, the application also constructs a data set oriented to attack and defense confrontation, records a complete attack sequence, defense response and effect evaluation thereof, and provides a reliable basis for continuous learning of defense agents. Experimental results show that the framework provided by the invention is superior to the traditional method in the aspects of attack detection accuracy, attack chain identification and defense strategy generation, and has stronger adaptability and real-time response capability.

The invention belongs to the technical field of electric energy dispatching, and particularly relates to a virtual power plant electric energy dispatching scheme optimization method based on intelligent decision analysis, which comprises the following steps: acquiring multi-source related parameter data; constructing a cascade prediction model based on the equipment basic data and the external environment data to obtain prediction results of all levels; constructing a risk assessment model based on prediction results of all levels in combination with equipment basic data and external environment data, generating a risk map in combination with a network vulnerability analysis method, and forming a schedulable resource pool; market risk data are acquired, a market risk comprehensive score is obtained, and a corresponding safety alternative scheme is triggered; and constructing a decision tree, determining a decision path in combination with the risk map, and generating a final optimization strategy. According to the method, through multi-dimensional means such as cascade prediction, risk quantitative evaluation, resource optimization screening and market risk response, high precision, high adaptability and intelligentization of virtual power plant electric energy scheduling are realized.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention is suitable for the field of atomic layer deposition, and discloses an atomic layer deposition process management method and system based on digital twinning, and the method comprises the steps: building a digital twinning model of an atomic layer deposition production system based on a multi-scale physical information neural network model; acquiring real-time reaction chamber process parameters and real-time state variables, and inputting the real-time reaction chamber process parameters and the real-time state variables into the digital twin model to obtain physical field distribution in the reaction chamber and a predicted growth state of a film on the surface of the substrate; based on the physical field distribution in the reaction cavity and the substrate surface film prediction growth state, decision analysis is carried out through a pre-trained reinforcement learning agent, and correction parameters used for optimizing the film growth quality are obtained; and generating a correction instruction based on the correction parameter, and updating the process parameter of the reaction cavity based on the correction instruction, so that the film on the surface of the substrate in the reaction cavity reaches the target film thickness and uniformity, and thus real-time intelligent closed-loop control of perception-prediction-decision-execution can be formed.

The invention relates to the technical field of data processing, and discloses a digital intelligent water affair global data operation decision analysis comprehensive service system. The system comprises an acquisition module for acquiring standardized processing data; the construction module generates a water plant three-dimensional model and virtual representation; the storage module executes hierarchical storage to obtain quality enhancement data; the identification module constructs an index system to diagnose the operation state; the evaluation module uses a Bayesian network to check asset cost; and the generation module provides decision support and an early warning scheme. According to the invention, by constructing the global data operation decision analysis comprehensive service method, the data barrier is broken, the whole-process data integration, analysis and intelligent decision support of the water affair system from the source to the terminal are realized, and the operation efficiency and management level of the water affair system are improved.

The invention discloses an electric power AI security detection model optimization method and system fusing attribution quantification and adversarial correction. The optimization method comprises the following steps: step 1, carrying out structured semantic representation on heterogeneous security alarms of an electric power network; 2, performing model decision logic analysis based on hybrid attribution quantization; step 3, automatically diagnosing decision prejudice based on domain knowledge masks; step 4, constructing an adversarial sample generated based on an anti-fact text; and 5, performing closed-loop fine adjustment and optimization on the attribution regularization model. According to the method, the interpretable ability of large model decision analysis, the root cause positioning ability of misinformation and the autonomous repair optimization ability are improved, the transparency and credibility of model decision are improved, the model misinformation caused by environmental influence is reduced, and the efficiency of model autonomous correction is improved.

The invention discloses a distributed collaborative decision-making system based on multi-modal data driving and an implementation method thereof, and relates to the technical field of group intelligence and distributed decision-making, and the system comprises a user end interaction module which provides a multi-modal interaction and decision-making scheme visual interface; the distributed node management module comprises a main node and an edge node, and the main node manages node registration, state monitoring and task distribution; the information fusion and preprocessing module is used for processing multi-source heterogeneous data; the decision analysis module is used for carrying out clustering analysis on the opinions and generating candidate schemes in combination with domain knowledge; the domain knowledge graph module is used for constructing a domain entity relationship network; the consensus mechanism and credit evaluation module determines multiple rounds of interaction rules, calculates a user credit value and influences an opinion weight; and the decision result output and feedback module is used for collecting user feedback for system optimization. According to the method, the stability, the response speed and the load balancing capacity are improved, the multi-source information processing and opinion aggregation quality is optimized, and efficient and reliable support is provided for distributed collaborative decision making.

The invention discloses an enterprise operation data analysis method and system based on a knowledge graph, and relates to the technical field of data processing, and the method comprises the steps: collecting an enterprise multi-source heterogeneous operation data set, and carrying out the standardization processing and multi-dimensional entity extraction; performing knowledge graph modeling and dynamic updating optimization based on the enterprise operation multi-dimensional entity set, and constructing an enterprise operation knowledge graph; constructing an operation data analysis task list and performing prediction driving on the enterprise operation knowledge graph; and obtaining enterprise operation period data, performing risk prediction reasoning and operation decision analysis based on the operation data analysis task engine, determining a target enterprise operation decision scheme, and performing operation decision control through the target enterprise operation decision scheme. The technical problems of enterprise operation data islanding, weak knowledge analysis and reasoning ability and decision lag in the prior art are solved, and the technical effects of improving operation data analysis and reasoning intelligence and providing prospective risk prediction and accurate operation decision support are achieved.

The invention provides a cooperative control method and system based on a port automatic driving mixed driving scene, and relates to the technical field of port traffic management, and the method comprises the steps: firstly obtaining a dynamic interaction data set containing information of multiple aspects such as an automatic driving vehicle and manual driving equipment in a port operation region; and then carrying out association feature extraction on the dynamic interaction data set, generating dynamic association representation data containing traffic participant association relationships and other features, executing collaborative decision analysis based on the dynamic association representation data, and determining a right-of-way priority sequence and a collaborative path planning scheme. And a cooperative control instruction set including speed cooperative parameters and the like is generated according to a cooperative decision analysis result, and finally the cooperative control instruction set is distributed to a corresponding control system, so that dynamic cooperative driving control in a port mixed driving scene is realized, and the port operation efficiency and safety are improved.

Disclosed is a procurement optimization approach that integrates real-time reinforcement learning and blockchain-based compliance checks for dynamic, transparent decision-making. Initial procurement specifications and weightings are automatically generated using AI analysis of historical procurement data, followed by supplier evaluations using multiple criteria decision analysis with buyer-defined weightings, while a reinforcement learning module continuously refines these weightings based on observed procurement outcomes or feedback. Each updated weighting is automatically verified against predefined procurement rules encoded on a distributed ledger, ensuring that no unauthorized criteria change bypass regulatory thresholds. If found compliant, the system immutably stores the refined weighting and resulting supplier evaluation as a ledger transaction, creating an unalterable audit trail. Optional modules handle anomaly detection, predictive risk analytics, duplication detection across departments, automated tender generation, quantum-resistant cryptography, and ERP integration. This modular design addresses persistent procurement challenges: static weighting, delayed or incomplete compliance checks, and limited traceability. By unifying adaptive AI logic with an immutable ledger, the disclosed system offers robust, real-time improvements in cost efficiency and governance.

The invention relates to the technical field of power system operation and control, in particular to a risk quantification and optimization decision method and system for power grid operation management. Comprising the following steps: S1, collecting historical operation data, real-time sensing data and power grid static topology data of a power distribution network to construct a basic data set for management decision analysis; s2, calculating a conventional risk probability, and calculating a comprehensive physical vulnerability index; s3, determining a coupling risk index of each key element in combination with the conventional risk probability and the comprehensive physical vulnerability index; s4, in response to the condition that the coupling risk index of any element exceeds an early warning threshold value, identifying the element as a potential initial fault point; s5, deducing a chain failure path based on the potential initial fault point; and S6, aiming at the chain failure path, generating an optimal strategy recommendation for supporting an operator to carry out active defense decision making. According to the method, the one-sidedness of a single-dimension evaluation method is overcome, and the comprehensiveness and accuracy of risk quantitative evaluation are remarkably improved.

The invention discloses a marketing decision analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining the marketing data of a plurality of channels, carrying out the preprocessing of the marketing data, and carrying out the classification of the preprocessed marketing data according to the data type; s2, obtaining a marketing scene demand, obtaining a corresponding analysis model according to the marketing scene demand, and generating a marketing decision-making scene through the intelligent decision-making model based on the analysis model; s3, acquiring user behavior data, then collecting behavior feedback of the user on the marketing decision scene, merging user behavior characteristics into user portrait data, and analyzing the user portrait data; and S4, extracting feature information in the marketing decision scene, constructing a marketing effect evaluation index according to the extracted feature information, and predicting a marketing effect suitable for the marketing decision scene from the historical marketing data based on the marketing effect evaluation index. According to the method, the problem of data islands when the multi-source heterogeneous marketing data is processed by a traditional method is solved.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The embodiment of the invention relates to the technical field of large model data analysis, and discloses a large model evolution multi-agent investment decision matrix system. The investment Transform module is used for acquiring financial data and performing feature extraction and feature fusion on the financial data in combination with a context learning enhancement function and a financial domain knowledge enhancement function; the interpretability analysis module is used for acquiring the feature representation in the investment Transform module and carrying out feature relation mapping and interpretability analysis; the multi-agent decision-making module is used for carrying out collaborative decision-making through multiple agents; the optimization module is used for providing interpretation optimization for the interpretability analysis module and providing strategy optimization for the multi-agent decision-making module; and the knowledge base module is used for providing an investment template for the investment Transformer module, providing causal relationship knowledge for the interpretability analysis module and providing rule constraints for the multi-agent decision-making module. The technical problem of investment decision analysis can be at least solved.

The invention provides an intelligent marketing decision analysis method and system for a real-time competitive product strategy, and the method comprises the steps: collecting the marketing content of a competitive product published on a social media platform, extracting an account number, an arriving person portrait feature, a content structure label, a platform identifier and publishing time, and constructing a standardized strategy behavior vector; performing time sequence alignment on the strategy behavior vectors and our marketing indexes, and identifying competing product strategy behavior subsets which have significant influence on the our indexes; based on the subset, generating a structured response strategy containing a recommended person type, a content structure label, a delivery platform and a time period; and performing multi-dimensional matching on the marketing resource library and the content template library, and outputting an executable marketing resource combination and scheduling instruction. According to the method, a closed loop from competitive product behavior perception and causal influence identification to response strategy automatic generation and landing execution is realized, and the real-time performance, the accuracy and the automation level of marketing response are improved.

The invention relates to the field of data decision analysis, in particular to a unit internal control management decision method and system based on multi-modal data analysis. The method comprises the following steps: collecting heterogeneous original data streams of a plurality of heterogeneous data sources; performing space-time dimension entropy change differential analysis and multi-dimensional entropy change characteristic mining, performing multi-level semantic analysis on the heterogeneous original data stream, performing layer-by-layer abnormal mode semantic deconstruction, and constructing a precise semantic feature portrait of each abnormal mode; performing multi-target coordination request analysis and dynamic target priority evaluation on the cognitive entropy change reference characteristic spectrum to obtain a coordination request priority; and carrying out maximum decision capacity calculation on the cognitive entropy change reference feature map, and carrying out dynamic optimal decision boundary calculation based on the coordination request priority and the accurate semantic feature portrait so as to generate a dynamic tradeoff constraint range. Through multi-target collaborative decision analysis, the decision target conflict is smoothed, and the decision demand execution efficiency of the user is improved.

The invention discloses a model training method and device, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining a first training sample; the first training sample comprises a service target and a service state feature; processing the first training sample through a to-be-trained first large language model to obtain a plurality of first sample responses; obtaining each first reward value corresponding to each first sample response; each first reward value is used for representing the effectiveness of realizing the business target based on each first sample response; and updating parameters of the first large language model according to each first reward value, and executing the step of obtaining the first training sample again until a first preset training stop condition is reached, thereby obtaining a trained first large language model. By adopting the model training method, the training effect of the model can be effectively improved, so that a reliable decision analysis service is provided for a user.

The invention provides a water chamber mining area underground water resource allocation and decision analysis method, and relates to the technical field of mining area water resource management and intelligent decision, and the method comprises the steps: obtaining the perception data of a set region in a mine; constructing a steady-state seepage flow field of the mine goaf; constructing a water resource bearing capacity evaluation model; constructing water demand models of different areas of the mine; obtaining allocation demand distribution vectors and allocation demand sequences of different areas of the mine according to the water demand model; determining a priority allocation vector of each mine area by combining the allocation priorities of different areas of the mine; constructing a multi-objective optimization model, and calculating the multi-objective optimization model based on a pre-selected optimization algorithm to obtain an optimal water resource allocation strategy; and based on the optimal water resource allocation strategy, the water storage and drainage equipment is controlled to act, and the models or parameters are corrected according to execution result data. According to the scheme, dynamic correction and real-time response of the allocation strategy are realized, and accurate supply and scheduling optimization of underground water resources in the water-chamber-filled mining area are ensured.

The invention discloses a rail transit operation and maintenance decision analysis method based on a big data processing technology, and the method comprises the steps: collecting multi-source data through the deployment of a bionic cilium sensor array and metasurface noise reduction, mining fault features through a chaotic neural network, and carrying out the early warning; constructing a dynamic heterogeneous knowledge graph, and realizing rapid reasoning by using a bionic vision mechanism; an operation and maintenance decision considering multiple targets is generated based on ecological game optimization, and digital twinborn verification is passed; the method further comprises the steps of cross-dimension data fusion traceability, edge intelligent collaboration, knowledge dynamic updating and the like, and intelligentization and high efficiency of rail transit operation and maintenance are achieved. According to the method, fault early warning is advanced to 96 hours, the operation and maintenance cost is reduced, the energy consumption is reduced, the knowledge updating accuracy is high, and the reliability, economy and safety of rail transit operation and maintenance are improved.

The invention relates to the technical field of artificial intelligence and digital operation decision, in particular to an AI-based operation data decision analysis method and system, and the method comprises the steps: constructing a constraint-guided operation digital twinborn model; sensing an operation data flow in real time; when the operation data stream is received, generating state update; executing multi-agent decision deduction; instantiating the SKU into an independent agent; the independent agent performs concurrent game playing and optimization according to the reward function in the service logic simulator so as to generate an optimal strategy; executing the optimal strategy, and translating the optimal strategy into an action instruction; calculating a residual error between a real world result and the constraint probabilistic model; reversely calibrating the constraint probabilistic model by using the residual error to form a self-adaptive closed loop; according to the method, the calculation bottleneck of a traditional centralized optimization algorithm is avoided, so that the optimal operation strategy is quickly generated in a complex scene related to tens of thousands of inventory units.

The invention relates to an AI production decision-making method and system based on industrial big data, and the method comprises the following steps: collecting real-time sensor data of an industrial production field, and obtaining industrial production data; detecting whether abnormal parameters exist in the industrial production data or not by setting a parameter threshold range, and if yes, performing production link positioning on the industrial production field based on the abnormal parameters to obtain abnormal production links; performing influence analysis on the industrial production field based on the abnormal production link to obtain an influence analysis result; and carrying out production decision analysis on the industrial production field based on an AI model and the influence analysis result, and generating a production decision suggestion. The root cause of the abnormal generation and the propagation path and the influence range of the abnormal generation in the whole production process are difficult to explain.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

The invention discloses an ocean disaster comprehensive decision-making system based on reinforcement learning, and relates to the technical field of ocean disaster analysis, the ocean disaster comprehensive decision-making system comprises an ocean disaster decision-making platform, the ocean disaster decision-making platform is in communication connection with an ocean environment sensing module, a disaster decision-making analysis module, a disaster risk early warning response module and a feedback optimization module, the modules are in electric signal connection; the marine environment sensing module is used for collecting and integrating marine environment data of a target sea area in real time. According to the method, the historical marine environment data and the real-time marine environment data are learned and analyzed through the reinforcement learning model, the early-stage features and evolution trends of marine disasters can be accurately recognized, the occurrence time, intensity and influence range of the disasters can be accurately predicted in advance, and then high-precision early warning information is quickly generated in combination with historical similar scenes, so that the early warning efficiency is improved. And more time is won for precaution preparation for coastal areas, and disaster loss is effectively reduced.

The invention relates to the technical field of logistics intelligent decision, and discloses a logistics intelligent decision method and system based on an AI model, and the method comprises the steps: obtaining full-link logistics data, carrying out the hierarchical desensitization fusion of the full-link logistics data, and obtaining a target logistics data graph; extracting logistics space-time features of the target logistics data graph, and performing feature learning on a preset decision expert model according to the logistics space-time features to obtain a plurality of decision sub-models; performing multi-scene simulation state analysis on the full-link logistics data by using the decision sub-model, and performing model optimization on the decision sub-model according to a result of the multi-scene simulation state analysis to obtain a target logistics decision model; carrying out logistics trajectory modeling on the full-link logistics data by utilizing the target logistics decision model to obtain a target decision trajectory; and performing decision analysis on the full-link logistics data according to the target decision trajectory to obtain multiple pieces of logistics decision information. According to the invention, intelligent logistics decision-making can be realized and the accuracy of logistics decision-making can be improved.

The invention relates to the technical field of conference information processing and management decision analysis, and discloses a method for realizing automatic clustering of conference summary by adopting a topic model, which comprises the following steps of: respectively acquiring an acoustic confidence sequence and an original topic entropy sequence by analyzing conference audio signals and corresponding transcripted texts in parallel; according to the method, the original topic entropy sequence is subjected to compulsory weighted correction by using the acoustic confidence coefficient, a decision anchor point is identified in the obtained reliable entropy sequence, and reverse association clustering is performed on the text by taking the decision anchor point as a core, so that the limitation that only content induction can be performed in the prior art is avoided; by recognizing the decision anchor points in the entropy sequence corrected by the acoustic confidence coefficient, automatic reconstruction of the decision forming process is achieved, the quality of decision consensus can be further discriminated, and an objective logic link is provided for management tracing and risk auditing.

The invention discloses a production scheduling decision method and system for a regional power grid ring main unit in a data space, and relates to the technical field of production scheduling plan decision analysis, and the method comprises the steps: taking a power grid side correlation parameter as a core input, constructing a prediction demand and a delivery deadline, and enabling the production scheduling to face a real demand of a power grid instead of a single order signal; a nonlinear coupling function is designed, an equipment reliability risk index and a supply chain state jointly act on priority calculation, a high-risk task is highlighted in a mean deviation mode, and a supply chain limited task is inhibited through index attenuation, so that dynamic balance between risk response and performability is realized; a standard deviation-driven capacity adjustment algorithm is adopted, so that the capacity distribution is inclined when the priority difference is large so as to guarantee a key task, and balance is kept when the difference is small so as to stabilize production; and generating a production scheduling plan capable of landing in combination with process resource constraints. Therefore, the production scheduling decision result not only meets the reliability requirement of the power grid side, but also meets the supply chain and production execution conditions.

The invention discloses a full-chain food safety monitoring method and system based on multi-modal data fusion and an intelligent algorithm. The method comprises the steps of S1, sensing layer data acquisition; s2, edge calculation data processing; s3, block chain evidence storage; s4, intelligent decision analysis is carried out; and S5, an application layer responds, so that a closed-loop system of'full-chain data real-time acquisition-dynamic credible evidence storage-cross-modal intelligent analysis' is successfully constructed, and the problems of'data islands, fragile evidence storage and lagged decision 'of traditional monitoring are solved.

The invention discloses a power distribution room operation and maintenance robot based on a semantic analysis model. The power distribution room operation and maintenance robot comprises a mobile platform, a data acquisition module, a semantic analysis module, a decision analysis module and a communication module, the mobile platform is used for robot inspection; the data acquisition module is used for the robot to obtain equipment state detection parameters of a target power distribution room; the communication module is used for communicating with a remote terminal; the system is characterized in that the communication module is also used for obtaining a log record of a target power distribution room, and extracting quantified object data based on the semantic analysis module; and the decision analysis module carries out analysis. According to the invention, the tiny deterioration trend of the equipment state can be captured in advance, and the inspection frequency is dynamically adjusted, thereby reducing the accident risk.

The application provides an enterprise informatization management integrated platform based on big data, which is constructed based on a Hadoop distributed computing framework, integrates MapReduce parallel processing, Spark real-time computing, HDFS distributed storage and other technologies, realizes data parallel processing through a swarm intelligence algorithm and a pheromone transmission mechanism, carries out heterogeneous data mapping by using a hierarchical self-encoding network, realizes resource dynamic scheduling in combination with a biological cell division mechanism and an entropy value balance mechanism, optimizes storage management by using adaptive grid partitioning and data temperature layering technology, and constructs multi-scale feature fusion and knowledge reasoning based on Spark MLlib to support decision analysis. The platform overcomes problems in traditional enterprise informatization management, such as low data processing efficiency, limited system expansion and difficult data sharing, and provides reliable technical support for enterprise digital transformation.