4720 results about "Modular design" patented technology

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention discloses an improved convolution neural network model for leukocyte calculation, and relates to the field of artificial intelligence and medical image processing. Aiming at the problems of inaccurate feature extraction, strong background noise interference and insufficient multi-scale feature fusion in a traditional leukocyte calculation method, the model realizes fine processing and feature enhancement of a medical image through a multi-level modular design; the image preprocessing module is used for completing white blood cell positioning and image adaptive division and enhancement; multi-scale edge, texture and gray features are fused by means of an image feature extraction module, and a composite feature vector is constructed; through multi-scale convolution, a double-layer attention mechanism and convolution layer calculation of the improved convolution neural network model module, precise focusing of white blood cell contours and cell nucleus features, background noise suppression and calculation efficiency optimization are realized; and finally, a calculation result is output through a visual terminal, so that the accuracy and robustness of leukocyte counting are remarkably improved, and efficient technical support is provided for medical microscopic image analysis.

The invention belongs to the technical field of target classification, and particularly relates to a three-dimensional point cloud data analysis method and system based on artificial intelligence. Comprising the steps of data acquisition, data preprocessing, model construction, model training, model prediction and the like. The integrity and precision of point cloud data are effectively improved by collecting original point cloud and performing intelligent denoising and complementation operation; constructing a depth model by adopting an input layer, a region proposal layer and a classification and regression layer, and carrying out accurate classification and three-dimensional bounding box prediction on a target; wherein the region proposal layer is combined with a seed point feature extraction and voting mechanism to generate a candidate region, a classification branch outputs a category probability, and a regression branch predicts bounding box parameters. The system adopts a modular design, has the advantages of strong noise suppression, high complementation precision, high target detection accuracy, multi-scene applicability and the like, and is particularly suitable for efficient automatic identification of a complex three-dimensional structure.

The invention discloses a text knowledge automatic extraction and mapping method based on deep learning, which optimizes a semantic understanding algorithm by combining a deep learning model, enhances the perception ability of the model for the internal structure and external relationship of a text, realizes comprehensive extraction and detailed relationship analysis of equipment fault knowledge, and utilizes a knowledge mapping technology to realize automatic extraction and mapping of equipment fault knowledge. According to the method, an automatic graph construction process is implemented to improve the query efficiency and analysis capability of the graph, and the method comprises the following steps: S1, performing structured processing on an original document, segmenting the original document into independent case units, and extracting key information related to an equipment fault; s2, key entities in the document and mutual relations of the key entities are recognized and marked; and S3, based on the extracted entities and relationships, constructing and updating a knowledge graph of the fault case. The system has high automation, intellectualization, expandability and modular design, can be easily integrated into an existing industrial informatization system, and assists an enterprise in realizing intellectualized transformation and upgrading.

The invention discloses a global nerve drawing method and system for a multivariate mixed representation scene, and belongs to the technical field of computer graphics, and the method comprises a scene expression module which is used for loading multivariate mixed scene data; the geometric module is used for generating a multi-channel geometric buffer based on the multivariate mixed scene data; the illumination module comprises at least one neural network sub-module and is used for carrying out illumination calculation based on multi-channel geometric buffering; the scheduling and synchronizing module is used for coordinating cross-module data streams through a rendering graph structure and executing resource merging and task scheduling between the geometric module and the illumination module based on a unified request structure; and the merging output module is used for fusing neural network illumination output and traditional rendering output to generate a final frame image so as to complete global neural rendering. According to the invention, multi-element scene data is integrated through modular design, efficient collaboration of traditional and neural rendering is realized by using a unified interface and a scheduling structure, and the image quality and the system flexibility are improved.

The invention relates to a nuclear power enclosed bus intelligent monitoring model based on big data. The model specifically comprises a plurality of technical links such as data acquisition, cleaning preprocessing, feature extraction and selection, modeling and algorithm design, health condition evaluation and monitoring and the like. The intelligent monitoring module designed by the invention realizes comprehensive, real-time and intelligent monitoring of the operation state of the enclosed bus through integrating a plurality of links such as data acquisition, cleaning, feature extraction, modeling prediction and the like; real-time monitoring and early warning functions are realized by combining an Internet of Things technology and a cloud computing platform; a user is helped to quickly know the equipment state and make a scientific and reasonable operation and maintenance decision through display and decision support functions; potential faults are found in time through comprehensive monitoring and intelligent evaluation of the operation state of the enclosed bus, and effective measures are taken for processing, so that the safety and reliability of equipment are enhanced, and long-term stable operation of a nuclear power station is guaranteed; the intelligent monitoring model adopts a modular design and is easy to adapt to different monitoring requirements and scenes.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention relates to the technical field of garment processes, in particular to a garment process modular design method and system based on adjustable parameters. The method comprises the following steps: acquiring a basic process parameter set; defining a process association relationship according to the basic process parameter set to obtain a process association diagram; performing meta-model construction according to the process association diagram to obtain a process meta-model; obtaining a target style parameter set; mapping parameters in the target style parameter set into the process meta-model, and performing process parameter adjustment to obtain an adjusted process parameter set; performing process flow optimization according to the adjusted process parameter set to obtain a parameterized process model; performing process flow analysis on the parameterized process model to obtain a process flow diagram; and performing module boundary division on the process flow diagram to obtain a module boundary. According to the method, the defects of the existing method in the aspects of efficiency and flexibility are overcome, and a more intelligent, efficient and flexible process design system is realized.

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

According to the intelligent grotto excavation stability analysis system based on the BIM, through deep fusion of the BIM model and multi-source monitoring data, whole-process dynamic control over the grotto excavation stability is achieved. The system adopts a lightweight model processing technology to remarkably improve the calculation efficiency, and an intelligent inverse algorithm effectively solves the subjectivity problem of traditional parameter determination. A three-dimensional numerical simulation and real-time monitoring data interactive verification mechanism greatly improves the reliability of an analysis result, and a dynamic risk assessment system realizes the accuracy of risk early warning through multi-index fusion analysis. A final support scheme optimization module combines engineering experience with an intelligent algorithm, safety is ensured, economical efficiency is taken into account, and an intelligent solution capable of being self-adaptive to engineering condition changes is formed. According to the whole system, seamless connection of all links is achieved through modular design, and full-chain technical support from data to decision making is provided for safety construction of underground engineering.

The invention is suitable for the field of data management, and provides a front-end AI dialogue data management system and method, and the system comprises an asynchronous data flow management module; a state management module; a streaming data control module; a Markdown rendering engine is adopted; the beneficial effects of the invention are that the asynchronous data flow management module realizes high-efficiency synchronization of front-end and rear-end data, significantly reduces the data processing delay, and the state management module ensures the consistency of a user interface and rear-end data, optimizes the user experience, and improves the user experience. The built-in advanced Markdown rendering engine supports various self-defined grammars, so that the output content of the AI can be flexibly subjected to personalized rendering, modular design is adopted, customization and expansion can be performed according to service requirements, and the method is suitable for different application scenes.

The invention discloses a network communication dynamic optimization method based on multi-module collaboration, and relates to the technical field of network communication, a dual-mode communication module is deployed at each network node, and the network communication dynamic optimization method comprises the following steps: each network node broadcasts own existence information and power line channel characteristics through an HPLC (High Performance Liquid Chromatography) channel of the dual-mode communication module; each network node scans surrounding wireless networks through an HRF channel of the dual-mode communication module and reports own wireless channel quality information to the gateway; the global state sensing module collects all information and constructs a global network view containing physical topology and a channel quality map. A dual-mode cooperation mechanism, a machine learning prediction model and a dynamic decision strategy can adapt to complex dynamic environments such as power line noise fluctuation and wireless interference change, and communication parameters can be autonomously optimized without manual intervention; and meanwhile, the modular design is convenient to expand to a multi-mode communication scene, and has a wide application prospect.

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 discloses a multi-module-based analog simulation calculation method, and relates to the technical field of analog simulation calculation, and the method comprises the following steps: S1, system demand analysis and module division; s2, establishing a module model and setting parameters; s3, constructing a parallel computing environment; s4, module parallel computing task allocation; s5, data exchange and synchronization; s6, summarizing results; and S7, feedback optimization. According to the analog simulation calculation method based on the multiple modules, through modular design, a complex system is decomposed into the multiple relatively simple modules, each module can independently conduct simulation and calculation, and the maintainability and expandability of the system are improved; data and information exchange and cooperative work can be carried out among different modules through interfaces, so that comprehensive simulation and optimization of the whole system are realized; in addition, parallel computing resources are fully utilized, parallel computing is carried out on the multiple modules, the computing time is greatly shortened, and the computing efficiency is improved.

The invention discloses a credit loan fraud data identification method and system based on artificial intelligence and a graph algorithm, and relates to the technical field of credit loan risk control. User information and behavior data are collected from multiple sources, a relation network graph is constructed after cleaning, dense communities are identified, cross-community intermediary nodes are positioned, community portraits are generated, behavior patterns are quantified, and similar groups are clustered. According to the method, a complex fraud mode is accurately recognized, hidden association is mined through a graph algorithm, the recognition accuracy is improved in combination with community portraits and behavior clustering, the dynamic adaptability is high, real-time feedback and incremental learning ensure that novel fraud is rapidly coped with, and the method is suitable for popularization and application. A high-interpretability report meets compliance requirements, user trust is improved, large-scale data is efficiently processed, modular design adapts to different credit and loan scenes, cost is reduced through automatic decision, the omission ratio is reduced, fraud loss is directly reduced, and the rate of return on investment is high.

The invention discloses a network management racing item question answering system based on a knowledge graph, and belongs to the technical field of knowledge graph teaching. In the system, the association relationship among massive network equipment, protocol configuration and fault cases is stored in a knowledge graph structuralization mode, and natural language processing and graph database retrieval technologies are combined; core knowledge points of complex problems can be quickly positioned, and strong scene adaptability and expansion potential are shown through modular design and a dynamic cooperation mechanism. The knowledge graph management module supports incremental updating and cleaning fusion of multi-source heterogeneous data, can integrate newest competition cases and technical specifications in time, and guarantees the frontier and authority of a knowledge base; and the interaction optimization module automatically identifies a knowledge blind area according to the score of the user on the answer and triggers graph completion. The self-feedback and self-iteration mechanism not only reduces the manual maintenance cost, but also provides a technical basis for cross-domain knowledge migration.

The invention discloses a digital human real-time interaction system and an AI voice dialogue chip circuit system thereof, and the system employs a modular design, and comprises the following modules: a BaseReal module which is responsible for audio frame management, TTS service initialization, video recording and customized audio and video cycle management; the media stream management module has an audio stream processing function and is responsible for real-time transmission of audio and video streams; the speech recognition module is responsible for speech feature extraction and text conversion; the text-to-voice module is used for converting the collected audio data into a text; the lip shape synchronization module is used for realizing real-time lip shape synchronization animation generation through lip shape synchronization algorithm optimization and inheritance of a basic class; and the large model module comprises an LLM large model based on mass data fine tuning, an api interface or a workflow agent. The method has the effects of voice recognition, text-to-voice conversion, lip shape synchronization, real-time audio and video interaction, real-time translation and the like.

The invention relates to the technical field of industrial detection, and discloses a multi-modal data fusion industrial defect intelligent detection system, which comprises a multi-modal data acquisition module which comprises a high-resolution visible light camera, an infrared thermal imager and an ultrasonic sensor and is used for acquiring product images and data information from different dimensions, the devices are reasonably arranged on a production line to realize comprehensive acquisition; the preprocessing module is used for denoising and normalizing the collected data; the limitation of single-mode detection is overcome through the multi-mode data acquisition module, and omnibearing detection is realized; an advanced fusion strategy is adopted, and feature interference and insufficient information interaction are avoided; the environment self-adaptive adjustment module enables the system to stably work in a complex environment; the parallel computing module improves the data processing efficiency and meets the real-time detection requirement; the encryption module ensures data security, the modular design facilitates maintenance and upgrading, and the data backup and recovery module ensures production and data integrity.

The invention discloses a high-performance satellite platform based on a real-time operating system and a wireless bus and a working method of the high-performance satellite platform, and belongs to satellite communication technologies, future deep space exploration and low-orbit satellite constellations. On the basis of an original structure of a satellite platform, a real-time operating system is effectively combined with a wireless bus system, and the real-time operating system comprises a kernel module, a communication module, an equipment driving module, a file system module, an application interface module, a safety module and a fault management module. The wireless bus system comprises a physical layer, a data link layer, a network layer, a transmission layer and an application layer. According to the invention, efficient and reliable communication and task execution capabilities are provided for a satellite platform, the requirements of real-time performance and reliability of a satellite in a complex environment are effectively met through modular design and tight work of the system, and meanwhile, complex lines and interfaces brought by a wired system are avoided.

The invention discloses a drilling-grouting integrated underwater robot and aims to meet the requirement for precise operation in a complex underwater environment. The robot integrates the fin antenna, the hydraulic telescopic crawler belt, the hydrodynamic force wings, the bionic mechanical arm and the integrated drilling and injecting system, and has the multi-terrain adaptation, efficient navigation and autonomous operation capacity; through modular design and a high-strength pressure-resistant structure, the robot can complete tasks such as pipeline repair, resource exploration and infrastructure construction in a deep sea high-pressure environment; the core technology comprises a multi-mode propulsion system, intelligent sensing and navigation, AI optimization operation and dual-mode operation switching. Typical application scenes include submarine pipeline damage repair, deep sea mining area reinforcement sampling, cross-sea bridge pile foundation grouting and the like. The limitation that a traditional underwater robot is single in function and high in energy consumption is broken through, and the safety and efficiency of complex underwater engineering are remarkably improved.

The invention relates to the technical field of artificial intelligence and edge computing crossing technologies, in particular to an edge deployment system and method of a distributed visual language model, a storage medium and a processor. The invention provides an edge deployment system and method for a distributed visual language model, a storage medium and a processor. The edge deployment system comprises a distributed topological structure with a front end and a rear end working cooperatively. The front end is an edge device end, and the rear end is a reasoning device end; the visual coding module is deployed at an edge device end, and the language reasoning module is deployed at a reasoning device end; and the visual coding module and the language reasoning module carry out feature transmission through a dynamic security protocol. The visual coding module and the multi-modal reasoning module are separately deployed in different hardware devices, edge end efficient feature extraction and back end joint reasoning are achieved through modular design, and the calculation load of the edge devices is reduced while privacy security is guaranteed.

The invention discloses a railway tunnel full-life-cycle model test system and test method considering train vibration, and relates to the technical field of railway tunnel engineering. The model test system adopts modular design and comprises a box frame module, a constant-pressure loading module, an excitation system module, a seepage system module and a monitoring system module. The test method comprises the steps of physical similar model laying, constant-pressure loading control, tunnel excavation and supporting, vibration loading and data monitoring analysis. By accurately simulating an initial stress field and train vibration loads in the construction and operation stages of the tunnel, dynamic monitoring of stress strain, deformation and damage processes of the tunnel surrounding rock and the lining structure in the whole life cycle is achieved. The device has the advantages of compact structure, modular design, high loading precision and stability, real simulation and the like, and a scientific basis can be provided for the fields of performance evaluation, structure optimization, reinforcement design and the like in the whole life cycle of the railway tunnel.

The invention discloses a modular power supply charging abnormity intelligent diagnosis method based on edge calculation, and the method achieves the function separation and fault isolation through the modular design of a power supply system. Data such as voltage, current, power and temperature are collected in real time, preliminary data processing is carried out through edge computing equipment, and invalid data transmission is reduced. And analyzing the data on the edge side by using an isolated forest algorithm, establishing an anomaly detection model, and when the detected data exceeds a preset threshold, determining that the data is abnormal. The system has real-time monitoring and alarming functions and can respond to and record abnormal events in time. By collecting abnormal events and processing strategies thereof, an algorithm model is continuously optimized, and the diagnosis accuracy is improved. A feedback mechanism is established, system parameters are adjusted according to diagnosis results, closed-loop management is formed, and the system is more suitable for the actual operation environment. According to the method, the intelligent level and the operation safety of the power supply charging system are effectively improved.

The invention discloses an outdoor workstation air quality monitoring and intelligent ventilation system and method, and belongs to the technical field of environment monitoring and intelligent ventilation control. The system is composed of an environment sensing module, an intelligent control unit and a ventilation execution device. And the environment sensing module realizes pollutant concentration field reconstruction through a multi-dimensional data fusion algorithm. The intelligent control unit constructs a dynamic ventilation model based on an improved fuzzy PID algorithm, and generates an optimal ventilation strategy in combination with meteorological prediction data and workstation structure parameters. And the ventilation execution device adopts a modular design to realize directional air supply and pollutant blocking. The method comprises four steps of real-time environment data acquisition, dynamic risk assessment, ventilation parameter optimization and execution mechanism linkage control, and remote intelligent regulation and control are realized through cooperation of edge computing and a cloud platform. According to the scheme, a passive response mode of a traditional ventilation system is broken through, and the technical advantages of accurate pollutant tracing, dynamic optimization of ventilation efficiency and intelligent adjustment of equipment energy consumption are achieved.

According to the high-speed serial communication system protocol adaptation layer circuit oriented to interconnection between the core particles and the working method, the circuit serves as a bridging layer between an AXI-S bus and a SerDes physical coding sub-layer, and the problems of reliability and flow control in high-speed data transmission between the core particles are solved. The circuit adopts a modular design idea and comprises a transmitter and a receiver. In the aspect of data packet design, a first packet special processing mechanism is adopted, and an event serial number and target address information are only transmitted in a first packet, so that the bit utilization rate is improved. In the flow control design, a credit-based flow control mechanism is adopted, and the data transmission reliability is guaranteed through a special flow control packet. In addition, the system has a point-to-point retransmission function, data packets which fail in transmission are indexed and retransmitted, and successful data transmission is guaranteed within the configured retransmission times. According to the invention, an efficient and reliable protocol adaptation layer circuit solution is provided for high-speed interconnection requirements in core particle design.

The invention discloses an automobile tire defect automatic detection device and system, and belongs to the field of automobile tire processing, through highly integrated modular design, the automobile tire defect automatic detection device comprises a multifunctional image recognition module, a depth measurement and 3D imaging module, an internal structure and air tightness detection module and an analysis and decision module, comprehensive detection of the surface, the geometric structure, the internal structure and the air tightness of the tire is realized, and efficient, accurate and intelligent tire defect detection is realized through cooperative work of each module and equipment.

The invention discloses an intelligent power conversion cabinet control system and a power conversion cabinet, and relates to the technical field of power conversion cabinet control, and the system comprises a user interaction module, a touch screen and voice recognition convenient operation, and multilingual interaction; the battery management module is used for accurately monitoring parameters, quickly early warning abnormity and intelligently regulating and controlling charging and discharging; the battery replacement control module is high in identity verification speed and stable, accurate and fast in battery replacement operation; the data storage and analysis module is used for mass data storage and deep mining; the communication module is used for realizing dual-mode stable transmission and encrypted cache; the cabinet body comprises a plurality of battery cabins, the protection is comprehensive, the heat dissipation is intelligent, the modular design is beneficial to expansion, and the high-speed bus is strongly connected. The system is convenient in interaction and efficient in battery replacement. The battery management is accurate, the service life is prolonged, the data-driven operation is optimized, the communication is safe and stable, the remote monitoring is convenient, the cabinet body design is reasonable, the protection and heat dissipation are good, the modularization is flexible, the battery replacement service experience and operation benefits are powerfully improved, and the development of the intelligent battery replacement field is promoted.

The invention discloses an electrical element magnetic core loss prediction method based on a soft gating hybrid expert model, and relates to an electrical element magnetic core loss prediction method, which adaptively adjusts the expert weight, so that the model is more flexible and has strong adaptability when facing different types of data. Diversified expert model combinations can complement respective advantages, so that the overall robustness and generalization ability are improved, and the risk of overfitting is reduced; in addition, the modular design enables the model to be easy to expand and maintain, and the interpretability of the model can be enhanced by analyzing the output weight of the gating network; furthermore, automatic parameter adjustment and end-to-end training are carried out through an intelligent optimization algorithm, so that the performance of the model is further improved; and finally, the soft gating hybrid expert model can effectively process non-linear and complex relationships, the powerful non-linear modeling capability of the MLP is utilized to capture a complex mode in the data, and the calculation efficiency is improved through parallel calculation and dynamic resource allocation. The advantages enable the soft gating hybrid expert model to show higher accuracy and reliability when processing complex tasks such as magnetic core loss prediction and the like.

The invention relates to the technical field of solar thermal power generation and energy storage, in particular to a multi-temperature-zone coupled staged fused salt heat storage system and a control method thereof.The system comprises a multi-stage heat storage module, a multi-stage heat storage module and a multi-stage heat storage module, the multi-stage heat storage module comprises a high-temperature storage tank, a medium-temperature storage tank, a low-temperature storage tank and a buffer storage tank, and the storage tanks are dynamically partitioned through adjustable partition plates; the self-adaptive heat exchange network comprises a plate heat exchanger and a double-pipe heat exchanger which are combined in series-parallel connection, the plate heat exchanger is used for a high-temperature section, the double-pipe heat exchanger is used for a medium-low-temperature section, and an electric three-way valve is arranged between the heat exchangers to automatically switch connection modes according to the temperature of a heat source; and the intelligent control module integrates a temperature gradient sensor and a fused salt flow regulating valve, and optimizes the heat storage priority and the heat release rate in real time. The heat storage density is increased by 20%-30%, and the system circulation efficiency reaches 85% or above; multi-heat-source input and multi-grade heat energy output are supported, and complex working conditions are adapted; and through the modular design, the construction cost is reduced by 30%, and the service life is prolonged to 25 years.