193 results about "Multilevel data" patented technology

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The embodiment of the invention provides an unmanned aerial vehicle data anomaly detection method based on multi-modal time sequence modeling, and the method comprises the steps: carrying out the multi-stage data preprocessing of original data obtained in the working process of an unmanned aerial vehicle, and generating a standardized input data flow; extracting time sequence features of the standardized input data stream through continuous LSTM blocks in the multi-level feature extraction network, and generating a shallow time sequence feature set; performing depth time sequence modeling on the shallow time sequence feature set to generate a deep time sequence feature set; performing multi-stage information fusion and mask screening on the shallow time sequence feature set and the deep time sequence feature set to generate a fusion time-space correlation feature set; and generating an attitude parameter prediction result of the unmanned aerial vehicle according to the fusion time-space correlation feature set, and obtaining an anomaly detection decision result of the unmanned aerial vehicle by using a deviation comparison and multi-level anomaly judgment mechanism based on an adaptive threshold obtained based on the attitude parameter prediction result and prediction error statistical distribution. Therefore, the anomaly detection accuracy of the unmanned aerial vehicle data is improved.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention relates to a road collapse risk comprehensive assessment method based on multi-source data fusion, and the method comprises the following steps: collecting and preprocessing multi-source data related to road collapse, the multi-source data related to road collapse comprising remote sensing image data, underground water level monitoring data, geological exploration data and surface deformation monitoring data; spatio-temporal feature fusion and multi-level data fusion are carried out on the preprocessed multi-source data, the spatio-temporal feature fusion comprises spatial registration and time alignment, and the multi-level data fusion comprises low-level data fusion and high-level data fusion; and performing feature extraction on the fused multi-level data, inputting a feature extraction result into a road collapse risk assessment model constructed based on a machine learning model, outputting a road collapse risk occurrence probability, and generating a road collapse risk level according to the road collapse risk occurrence probability. Compared with the prior art, the method can comprehensively and accurately predict and evaluate the risk of road collapse.

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

The invention belongs to the technical field of energy storage resource regulation and control, and particularly relates to a distributed energy storage network resource regulation and control decision-making method, which comprises the following steps of: collecting and processing multi-level data; a two-way interaction mechanism with a user side is established, the electricity demand and the energy storage intention of the user are collected, and a demand response signal is sent to the user to encourage the user to adjust the electricity consumption behavior when the electricity is insufficient or the electricity price fluctuates; and based on the multilevel data and the user demand response result, constructing a dynamic energy routing model of the distributed energy storage network, and predicting future energy demand and supply conditions. According to the method, multi-level data can be comprehensively utilized, the accuracy of energy demand prediction is improved, the flexibility and the intelligent level of a regulation and control strategy are enhanced, a global optimization method is adopted for resource regulation and control, and the robustness and the reliability of the system are improved.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides an enterprise financial comprehensive management system based on an intelligent technology, and relates to the technical field of financial management. The accounting intelligent subsystem is used for acquiring financial data, performing semantic analysis and conversion on the financial data through a preset rule and a multi-stage data mapping mechanism, generating a standardized accounting service instruction and issuing the standardized accounting service instruction to an automatic process engine, thereby automatically generating a financial document meeting an accounting criterion; the financial management subsystem is used for integrally realizing execution of backlog management, compliance guidance manual query and financial data structured storage according to a preset standardized process; and the business service subsystem is used for providing tax-related business service, asset management service, fund management service and operation management service processing and guidance for enterprises through modular design. The invention aims to improve the operation efficiency, reduce the error rate, break information islands, realize effective integration and utilization of resources, and provide powerful support for decision making of enterprises.

The invention is applied to the technical field of data storage, and particularly discloses a multilevel data encryption storage and access control method based on cloud computing, which comprises the following steps of: 1, data classification and multilevel encryption; step 2, constructing a layered secret key; step 3, data access control; and 4, constructing and dynamically updating the data access authority. According to the multi-level data encryption storage and access control method based on cloud computing, encryption grade division is performed on data according to the data confidentiality degree, and algorithm encryption with different intensities is performed on the data according to different encryption grades, so that encryption can be performed for different types of data, and compared with traditional single algorithm encryption, the encryption efficiency is improved. According to the technical scheme, the adaptability of data encryption can be improved, meanwhile, three levels of key structures are arranged to be matched with one another, the main key protects the middle key, the middle key isolates direct association of the main key, the working key and the data, and the exposure risk of the main key is reduced.

The invention relates to a pumped storage and compressed air energy storage coupled scheduling method, which comprises the following steps of: establishing a multi-stage data system based on input data of multiple time scales; constructing a multi-time scale scheduling model; when the load of the pumped storage mixed compressed air energy storage power station is lower than a first preset threshold value, electricity is abandoned preferentially, water pumping and compression are conducted, and when the load is larger than a second preset threshold value, the pumped storage mixed compressed air energy storage power station is controlled to conduct combined energy release. The nonlinear relation between the pumped storage efficiency and the water level and the nonlinear relation between the compressed air energy storage power and the pressure are converted into linear constraints, solving is conducted by dividing the water level interval and the pressure level, and a scheduling strategy is obtained. Therefore, the problems of relatively low energy storage efficiency, insufficient scheduling refinement, relatively high system standby cost and the like caused by the problems that pumped storage water scheduling and CAES energy management are separated, a standby capacity coordination mechanism is lacked and the like in a hybrid system scheduling method in related technologies are solved.

The invention provides a low-power-consumption wireless ad hoc network dynamic configuration method and system based on adaptive routing, and relates to the technical field of network protocols, and the method comprises the steps: determining a data transmission path through obtaining node working state parameters and calculating an energy level value, and setting a multi-level data cache occupation threshold value; calculating a network load distribution state, and carrying out differentiated scheduling on nodes; selecting a low-power-consumption forwarding channel based on node routing adaptability; and monitoring the network state in real time and updating parameters. According to the invention, the network adaptability is improved, the communication power consumption is reduced, the network life cycle is prolonged, and the data transmission efficiency is improved.

The invention relates to the technical field of power equipment safety operation and maintenance, in particular to a power distribution network single-phase grounding positioning method and device based on multi-source information fusion. Heterogeneous sensing nodes deployed in a layered and distributed mode are adopted to synchronously collect steady-state and transient-state electrical quantity data of a power distribution network, and preprocessing is carried out to generate a standardized data set; multi-dimensional feature extraction is carried out; carrying out weighted fusion on the extracted multi-dimensional features based on a multi-feature fusion strategy of a dynamic weight adjustment mechanism and a D-S evidence theory, and calculating a line fault probability; when the calculated line fault probability meets a preset threshold value, the position of a fault point is locked through first-stage line screening, second-stage section division and third-stage accurate positioning in sequence. Therefore, through a multi-stage data fusion and hierarchical positioning algorithm, the fault positioning error is obviously improved from the line level in the prior art to the section level, and the fault positioning problem under the scenes of high-resistance grounding, intermittent faults and a complex power distribution network containing a distributed power supply is effectively solved.

The invention discloses a model multiplexing-based image-text retrieval graph network method, which comprises the following steps of: firstly, constructing a multi-field image-text pair data set from an internet public data source according to a user demand, performing multi-stage data cleaning by utilizing a pre-training model to ensure the data quality, and secondly, multiplexing a large-scale pre-trained multi-modal model as a feature extractor to obtain a multi-domain image-text pair data set; the deep semantic representation of the image text pair is efficiently obtained, and the model training cost is remarkably reduced. Then, a heterogeneous topological structure is designed, image text nodes respectively form same-proton graphs, and cross-modal edges are dynamically generated through learnable attention weights; and finally, by utilizing the constructed image text semantic relation graph, completing context information supplementation of a retrieval target through graph sampling and aggregation, further generating retrieval features, and completing a retrieval task of multi-modal combination. According to the method, the pre-training model features are multiplexed, so that the process is more efficient, and the model convergence speed is greatly improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention relates to the technical field of Internet of Things, intelligent monitoring and sensor fusion, and discloses an intelligent multi-sensor data fusion processing system and method, and the system comprises a data collection preprocessing module, a feature and confidence evaluation module, a core data fusion module, a real-time decision early warning module and a performance feedback optimization module. The system preprocesses data through adaptive Kalman filtering, dynamically calculates the confidence coefficient of each sensor by using error entropy, further performs multi-stage data fusion by using weighted fusion and a D-S evidence theory, and finally outputs accurate state estimation and intelligent decision. Meanwhile, internal parameters are adaptively optimized through a closed-loop feedback mechanism. The system effectively solves the problems of data isolation, low precision, slow response and poor adaptability of a traditional sensor system, remarkably improves the precision, reliability, real-time performance and self-adaptability of Internet of Things monitoring, and is suitable for industrial equipment monitoring, smart cities and other scenes.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention discloses an intelligent prediction method for the stirring power consumption of a reaction kettle, and the method comprises the steps: collecting and calibrating the fluid physical property and process operation parameters in the reaction kettle in real time through a high-precision sensor array and an automatic system, carrying out the multi-stage data cleaning, denoising, normalization and time alignment, generating standardized high-quality data, and carrying out the intelligent prediction of the stirring power consumption of the reaction kettle. Based on physical property mapping, dimension reduction and feature fusion, extracting a low-dimensional fluid physical property descriptor, inputting the low-dimensional fluid physical property descriptor into a deep regression network in combination with process parameters, carrying out stirring power consumption modeling and dynamic prediction, and carrying out dynamic prediction by introducing a self-adaptive physical property embedding mechanism and distribution alignment training. The self-adaption and generalization ability of the model to different and unseen fluid physical property distributions is improved, and the accuracy, the stability and the engineering decision support ability of energy consumption prediction under complex working conditions are remarkably enhanced.

The invention relates to the field of network public opinion data monitoring and analysis, in particular to a clustering analysis-based network public opinion data monitoring and processing method, which comprises the following steps of: establishing multi-stage data characteristics of network public opinion data by utilizing real-time network public opinion data; acquiring a basic classification result of the real-time network public opinion data based on clustering analysis by utilizing the multi-stage data features of the network public opinion data; according to the method, a basic classification result of real-time network public opinion data is obtained, a network public opinion data monitoring processing result is obtained by utilizing a basic classification result of the real-time network public opinion data, historical semantic features and network communication features of real-time data are respectively output into independent comparison data on the basis of clustering analysis, result misjudgment of a single semantic or single source IP is avoided first choice, and then through an iterative optimization process, the real-time network public opinion data monitoring processing result is obtained. According to the scheme, the complete process of implementing the scheme is improved, the monitoring efficiency is improved, the clustering analysis database is gradually expanded according to the scheme implementation time, and the generalization ability of scheme implementation and the processing accuracy of large-batch data are improved.

According to the method, PCIe extended storage equipment is identified by responding to a system starting signal, and initialization and address mapping control logic is executed to construct a first address space addressable by a GPU (Graphics Processing Unit); then, loading model data according to an AI task instruction, and dynamically dividing hot, warm and cold multi-level data popularity levels by monitoring memory access frequency; a migration data block is generated based on the popularity level, a migration data flow is organized and verified through priority scheduling logic, and intelligent data scheduling between extended storage and a GPU video memory is achieved; finally, the resources are released after the task is completed. Therefore, dynamic expansion of the physical video memory capacity is effectively achieved, large-scale AI model localization training is met, dependence on a high-end GPU chip is remarkably reduced, and data safety and system economy are improved.

The invention provides a self-adaptive task scheduling method and device based on a multi-level architecture, and belongs to the field of task scheduling. The method comprises the following steps: predicting an access mode of a to-be-executed task to to-be-accessed data by utilizing a long-short-term memory model, dynamically migrating a storage position of data to be accessed in a multi-stage data storage architecture based on the access mode; determining a computing resource matched with the to-be-executed task from multi-level computing resources according to the task characteristics of the to-be-executed task and the characteristics of multiple computing resources; and based on the migrated to-be-accessed data and the determined computing resources, realizing adaptive scheduling of a plurality of to-be-executed tasks with different priorities through an improved particle swarm optimization algorithm. According to the technical scheme, the efficiency and flexibility of adaptive task scheduling are improved.

The invention discloses a railway sound barrier construction and operation period data information classification method and system, and the method comprises the following steps: S1, constructing a multi-stage data structure system which covers the whole flow information of a sound barrier project; s2, performing standardized classification on the data in the multi-level data structure system; and S3, constructing an association relationship among the hierarchical data in the multi-level data structure system to form a hierarchical data management framework. By constructing a three-level data structure system of projects, work points and components and defining four data types of character strings and numerical arrays, hierarchical management is achieved through unique codes, application characteristics of the sound barrier combination components in the stages of design, construction, operation and maintenance are considered, a classification algorithm of the sound barrier combination components and associated data of the sound barrier combination components is compiled, and the classification efficiency of the sound barrier combination components is improved. A classification method of various components and associated data of the sound barrier is defined, the problems of data fragmentation and poor full-life-cycle applicability of a traditional data structure and classification method are solved, the sound barrier design efficiency and full-life-cycle management capability are improved, and the multi-specialty cooperation efficiency is improved; and the method has important engineering application value.

A method, apparatus, and computer product for scheduling Pauli-terms by selecting an order for the Pauli-terms, comprising: obtaining first and second ordered sets of Pauli-terms; obtaining first and second multitree data structures representing the first and second ordered sets, respectively; and determining whether or not the first and second ordered sets should be concatenated by: generating a third multitree data structure that represents all Pauli-terms of the first and second ordered sets; calculating a difference between a resource utilization score of the third multitree data structure and between resource utilization scores of the first and second multitree data structures; and based on the difference, determining whether or not the first and second ordered sets should be concatenated.

Embodiments of the invention disclose a data display method and apparatus, an electronic device and a storage medium. The method comprises the steps of receiving a first viewing operation of a first entry control in a current display component; according to the first viewing operation, displaying the first entry control and other entry controls except the first entry control in the current display component in a distinguished manner; obtaining sub-data contained in first data in the first entry control; creating a new display component; wherein the new display component is located on the upper layer of the current display component, and at least part of a first entry control in the current display component is not shielded by the new display component; and loading the sub-data contained in the first data to the entry control of the new display component for display. And the data structure of the multi-level data can be clearly displayed.

The invention discloses a method for managing and controlling the autonomous flight path of a low-altitude unmanned aerial vehicle in a transformer substation, and relates to the technical field of power system automatic inspection, and the method comprises the steps: obtaining a real-time space coordinate system constructed by at least three transformer substation positioning devices, and carrying out the fusion operation of the real-time space coordinate system and a pre-established transformer substation three-dimensional coordinate system, obtaining flight coordinate data of the unmanned aerial vehicle; unmanned aerial vehicle flight nodes are generated based on the flight coordinate data, and a connection relation between the flight nodes is determined according to a preset path planning rule; and in the process that the unmanned aerial vehicle flies according to the flight node, acquiring the position data of the unmanned aerial vehicle according to a preset frequency, performing spatial position measurement and correction when the unmanned aerial vehicle arrives at the preset flight node, and filtering abnormal data according to a preset rule. Through multi-point positioning, real-time data updating, position correction, path planning optimization and a multi-stage data filtering mechanism, the inspection efficiency and safety of the unmanned aerial vehicle in the transformer substation are remarkably improved.

The invention relates to a software system based on a self-adaptive decentration architecture and a communication method thereof, and belongs to the technical field of computer software and communication. According to the system, an ad hoc network is adopted as a communication means, a decentralized software architecture is adopted as a core, and the task processing efficiency and the system robustness in a distributed environment are improved. Through a self-adaptive decentralized task allocation mechanism, each node can make a decision autonomously according to a network state, computing power and task requirements, so that dynamic and balanced task scheduling is realized, and the problem of single-point failure is avoided. The system adopts a multi-stage data consistency management strategy to ensure data reliability and consistency in a distributed environment. The method is widely applied to the fields of unmanned systems, emergency communication, intelligent manufacturing and the like, and the software system stability and the collaborative operation capability in a network environment are improved.

The invention discloses a distributed photovoltaic new energy data management method and system based on cloud computing, and belongs to the field of new energy management, and the method comprises the steps: building a photovoltaic topology architecture; the method comprises the following steps: reading an operation record of a distributed photovoltaic system in a preset time zone, mining a multi-stage data topology node, and configuring a data acquisition array; controlling the data acquisition array, and determining photovoltaic data; the photovoltaic data are transmitted to a cloud computing platform, data modular storage and photovoltaic operation and maintenance evaluation are carried out, a photovoltaic analysis result is determined, and a photovoltaic evaluation model is built in the cloud computing platform; determining a photovoltaic system adjustment plan based on the photovoltaic analysis result; and carrying out terminal visualization display on the photovoltaic analysis result and the photovoltaic system adjustment plan, and releasing the photovoltaic system adjustment plan to carry out optimization regulation and control management on the distributed photovoltaic system. The technical problems of low system operation efficiency and complex maintenance caused by high data management and optimization control difficulty and poor real-time performance of a distributed photovoltaic system in the prior art are solved.

The invention belongs to the technical field of big data processing, and discloses an index construction method of mass seismic data, an index database and a query method.The index construction method of the mass seismic data comprises the steps that a data index analysis file for indexing a first keyword is created based on MapReduce, keyword-node distribution is conducted by reading the file, and the data index analysis file is stored in a database; according to the method, data distributed to each Reducer process is ordered in a Map partition stage when index files are created on the basis of a MapReduce parallel programming framework, and finally, each Reducer process outputs the index files in a multi-level data table form to obtain an overall ordered sorting result. The maximum innovation point of the index construction method for the massive seismic data is that two times of MapReduce operation are executed on target data, and all output results are selected not to be concentrated on a main node to be read, sorted and merged in a final stage, so that the problem that merging time is greatly increased along with the increase of the seismic data volume is solved, and the method is suitable for large-scale popularization and application. And the index creating efficiency of the PB-level massive seismic data is effectively improved.