479 results about "Application module" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention provides a tidal flat evolution prediction method and system based on a hybrid model, and relates to the field of dynamic geomorphology and artificial intelligence cross technology, and the method comprises the steps: constructing a tidal flat erosion and deposition evolution database under different scenes of a target coastal zone; constructing a landform prediction model through an artificial intelligence algorithm based on the database, and taking hydrodynamic characteristic data as input and seabed erosion and deposition variation as output; hybrid simulation is executed based on the physical mechanism model and the landform prediction model, and long-duration evolution prediction is realized by updating terrain boundary conditions through loop iteration; and the configuration service application module is used for receiving user input parameters through the interactive interface and outputting a visual result. According to the method, traditional hydrodynamic force-sediment-landform coupling calculation is decoupled into a physical mechanism and AI coordinated chain process, the calculation complexity is remarkably reduced, offline rapid calculation business of tidal flat evolution is achieved, and real-time technical support is provided for coastal zone planning and disaster prevention decision making.

The invention discloses a cross-modal travel knowledge graph construction system based on a large language model. The cross-modal travel knowledge graph construction system comprises a data source module, a data access module, a change identification module, an incremental updating module, a verification feedback module, a core storage and application module and a technical base module. According to the method, optimization is carried out around dynamic updating accuracy, real-time efficiency and scene adaptability, and four dimensions of hidden conflict resolution, real-time processing efficiency, small and medium main body access and multi-modal deep fusion are improved.

The invention discloses a real-time data receiving and transmitting system and method based on a kernel bypass, and the method comprises the steps: processing the received data when the data receiving and transmitting system receives the data, and achieving the high-level data with complete semantics; updating a write pointer through atomic operation, and writing high-level data into a lockless annular queue in the shared memory module; the upper-layer application module reads the lock-free annular queue in the shared memory; when the data receiving and transmitting system transmits data, upper-layer data to be transmitted is transmitted to the shared memory module, and a write pointer is updated; the upper layer data in the shared memory is read in a polling or interruption mode, a read pointer is updated, the upper layer semantic data is segmented according to the format, a UDP data packet is obtained, the UDP data packet is written into a network card in the polling or interruption mode, and data sending is achieved. Through the data receiving and transmitting system disclosed by the invention, microsecond-level ultra-low delay of data certainty is realized, and efficient decoupling and stable throughput are realized.

The invention discloses a cloud-side collaborative household arrhythmia early warning AI agent, and relates to the technical field of AI agents. The system specifically comprises a data acquisition module, an edge computing module, a cloud computing module, an application module and a safety guarantee module, the data acquisition module acquires electrocardio and related physiological signals in real time through wearable equipment, and the edge computing module performs preprocessing and preliminary anomaly detection on data, quickly responds and screens key data and uploads the key data to a cloud; the cloud computing module carries out deep analysis and personalized model training; the application module converts a cloud result into real-time early warning and health suggestions and pushes the real-time early warning and health suggestions to the user and medical personnel; the security guarantee module runs through the whole process, and ensures the privacy and security of data in acquisition, transmission, storage and application. According to the invention, data processing is scientific and efficient, data can be comprehensively collected and preprocessed, meanwhile, real-time preliminary screening and secure transmission are realized, and the transmission pressure and privacy risk are reduced.

An application module for a control device, wherein the application module is configured to communicate and interact with further components of the control device as part of the control of a machine or system, where the application module is configured to generate an additional functionality for the control device through the use of a user application, where an automation engineering system is provided for establishing the control device and an application engineering system is provided for creating the user application, and where the application module is configured such that testing of the user application occurs or can be performed via an interaction of the application module with the application engineering system.

The invention belongs to the field of Web application technology, XML document processing technology and online editor technology, and discloses an XML semantic editing system based on an aviation knowledge graph. The system comprises a front-end application module, an XML processing engine module, a DTD rule verification module, a visual editing module, a plug-in extension module, a collaborative editing module and an aviation knowledge graph integration module. According to the method, professional and efficient editing of XML documents in the aviation field is achieved through the full-process design of front-end interaction, XML processing, DTD verification, visual editing, semantic assistance and collaborative management, and the Web technology, the XML processing technology, the DTD verification technology, the knowledge graph technology and the collaborative editing technology are integrated, so that the XML documents in the aviation field are edited in a professional and efficient mode. The problems that an existing XML editing tool is difficult to deploy, poor in compliance, low in efficiency and weak in cooperation in the aviation maintenance field are solved, and a specialized and efficient XML semantic editing scheme is provided for the aviation maintenance industry.

The invention provides an electromagnetic transformer state tracking and foresight verification method and system based on digital twinning. The system comprises an on-site lightweight acquisition module, a digital twin construction and calibration module, an intelligent analysis and prediction module and a visual application module, wherein the on-site lightweight acquisition module is responsible for acquiring on-site lightweight state data of a mutual inductor; the digital twin construction and calibration module constructs and dynamically updates a high-fidelity virtual model consistent with a physical entity based on a multi-physical field model and a parameter identification algorithm; the intelligent analysis and prediction module performs all-condition simulation and trend prediction by using the calibrated virtual model; and the visual application module is used for generating a diagnosis report and early warning information and providing decision support for the user. The invention aims to solve the problems of low efficiency and lack of deep diagnosis and prospective prediction capabilities of the existing electromagnetic transformer verification method, and realizes accurate mapping of the internal state and full-working-condition performance simulation and prediction of the electromagnetic transformer verification method.

The invention discloses an oral diagnosis and treatment knowledge graph dynamic construction system based on deep learning, and relates to the cross technical field of artificial intelligence and medical information technology, and the system comprises a data preprocessing and labeling module, an entity recognition stage module, a relationship classification stage module, a graph construction and application module and a local database. A structured annotation file is generated through data cleaning and expert annotation, the adaptability and quality of a data source are guaranteed, the problems that traditional preprocessing is insufficient in specialty and disordered in format are solved, a BERT-LSTM-CRF model is adopted, BERT is used for obtaining depth vector representation, Bi-LSTM is used for capturing long-distance dependency features, CRF is used for optimizing a tag sequence, the problems that a universal model is fuzzy in recognition and low in recall rate are solved, and the method is suitable for large-scale popularization and application. According to the method, semantic coding is carried out on the original sentences embedded with the special symbol marking entity pairs, then the entity pair relation is judged through a full connection layer and a Softmax classifier, accurate recognition of the deep clinical logic relation is achieved, and the problem that only shallow layer correlation is extracted traditionally is solved.

The invention relates to the technical field of natural language processing, and discloses a special disease knowledge spectrogram construction and analysis system, which comprises a federal data acquisition module, a dynamic ontology mapping preprocessing module, a cross-source knowledge collaborative extraction module, a self-adaptive graph construction module, an intelligent dynamic updating module and an interactive analysis application module, the cross-source knowledge collaborative extraction module is used for collaborating multi-source data on the premise of guaranteeing data privacy, the dynamic ontology mapping preprocessing module is used for mapping knowledge representation of the multi-source data to a unified standard, and the cross-source knowledge collaborative extraction module is used for extracting medical entities and semantic relationships based on multi-modal data fusion. According to the method, through the cooperation of the longitudinal federated learning framework and the secure multi-party computing algorithm, the problem of multi-source medical data privacy collaborative computing is solved, and the advantages of safely aggregating multi-mechanism data and providing original data support for map construction on the premise of ensuring data privacy are achieved.

The invention belongs to the technical field of sensor measurement and recognition, and particularly relates to a human body motion recognition system based on LSTM (Long Short Term Memory), which comprises a data acquisition module, a data calculation module and a data application module, and utilizes a neural network model and a wearable electronic sensor to detect a human body walking state. A new long short term memory (LSTM) recurrent neural network model, namely an LSTM-STRM model, is established, the model combines multi-task learning, an attention mechanism and spatio-temporal feature fusion to accurately classify the walking state of the human body, and the walking state is compared with the original LSTM model. The result shows that the LSTM-STRM model can be used for classifying the time sequence data collected by the measuring unit, the states of the five targets can be classified with high precision, and the recognition accuracy is higher than that of an original LSTM model. The method improves the recognition precision, is high in adaptability, is suitable for the fields of medical treatment, human-computer interaction, exercise training and the like, and has a wide application prospect.

The invention relates to the technical field of prefabricated part modeling, in particular to an interaction system for prefabricated part steel bar parameter driving design and a modularization implementation method. Incrementally updating the engine; the model application module is used for acting the change patch sequence on the three-dimensional reinforcement model in a transaction mode and driving display refreshing; the system further comprises a rule base module and an interpretable verification module. The rule base module stores a steel bar arrangement rule and / or a standard verification rule in a constraint declaration form; the interpretable verification module is used for performing compliance verification on the three-dimensional reinforcement model or the derived quantity thereof based on the evidence data field, and outputting a violation item list; the interactive interface comprises parameter panels and a violation interactive area, and the parameter panels are displayed in groups according to component type parameters, geometric parameters, steel bar parameters and specification options and support interactive modification. According to the method, the script dependency is reduced, the dependency graph increment patch is updated in real time, the automatic specification matching and positioning are realized, and the low-cost expansion is governed by the plug-in.

The embodiment of the invention provides a data rendering method, and the method comprises the steps that a rendering application module reports a rendering process to a rendering agent module, obtains a starting parameter in the rendering agent module, and establishes connection with a signaling server according to the starting parameter, and the signaling server is allocated to the rendering application module by a platform module; and the rendering application module establishes connection with the terminal through the signaling server, renders the geographic information system GIS data and sends video data obtained after rendering to the terminal. According to the embodiment of the invention, the problems that the rendering process cannot be monitored during data rendering and the coupling performance of the modules related to data rendering is relatively high in the prior art are solved, so that the effects of monitoring the rendering process of the rendering application module and reducing the coupling performance of the modules related to data rendering are achieved.

The invention relates to a streaming data and mass batch data storage method, which belongs to the technical field of data storage, and comprises the following steps: classifying mine data into streaming data and batch data; according to the method, a mixed storage architecture is constructed, the mixed storage architecture comprises a data acquisition layer, a batch processing layer, a speed layer, a service layer and an application layer, so that streaming data and batch data are processed and stored by adopting different rules, and the method comprises the following steps of: acquiring the streaming data by adopting an acquisition adaptation gateway and a real-time message queue Kafka; collecting the batch data by adopting a relational database DM8 and a timing scheduling Airflow mode; the batch processing layer is used for dividing the data into cold and hot temperature data and performing classified storage on the data; the speed layer is used for processing and storing the real-time streaming data; the service layer is used for carrying out data integration on the temporary stream processing data and the batch processing result data and providing data query interface service; and the application layer is used for setting a business application module and accessing each database to realize data application.

The invention provides a subject intelligent evaluation informatization system based on medical record home page data. Comprising a data integration and treatment module, a data standardization processing module, a core index intelligent calculation engine, a subject competitiveness multi-dimensional evaluation model, a DRG / DIP analysis application module, a medical quality and safety monitoring module, an intelligent report generation and visualization platform and a system management and configuration center. Through full-process automatic and intelligent analysis, multi-source data can be automatically integrated and strict quality control and standardization processing can be carried out, the accuracy and consistency of evaluation data are guaranteed from the source, complex analysis from basic index operation to comprehensive competitive power score ranking can be rapidly and accurately completed, and the method is suitable for large-scale popularization and application. And the result is visually presented through a visual platform, and even a structured analysis report is automatically generated, so that managers are liberated from tedious data processing and can concentrate on decision analysis, and the management efficiency and the response speed are remarkably improved.

The invention, which belongs to the technical field of intelligent parking lot parking space guiding, discloses an intelligent parking lot parking space guiding system comprising a sensing module, a processing module, a transmission module, an application module and an execution module. The sensing module is used for realizing parking space multi-dimensional data acquisition through a multi-dimensional data acquisition mechanism; the processing module is used for cleaning, analyzing, calibrating and judging the data acquired by the sensing module and outputting an accurate parking space state instruction; the transmission module ensures real-time and stable transmission of data; the application module is used for realizing parking space state real-time display, reservation process management and dynamic path guide service; the execution module is used for instructing to execute parking space locking or unlocking operation; through cooperative work of multiple modules and combination of hardware optimization and algorithm innovation, the system significantly improves the parking space occupation state identification precision, guarantees the effectiveness of a reservation function, optimizes the parking experience of a vehicle owner, and improves the parking space utilization rate of a parking lot.

The invention discloses a geographic space entity vectorization method and a geographic space entity vectorization system based on multi-modal fusion and metric learning, which are used for solving the problems that the traditional geographic information representation is single and multi-source data cannot be effectively fused. The method comprises the following steps: firstly, carrying out multi-modal feature extraction on text and coordinate information of a geographic space entity; then, through a neural network model with an independent coding stream and a deep fusion layer, intelligently fusing the multi-modal features and projecting the multi-modal features to a unified embedded vector space; and finally, optimizing the model by adopting a metric learning normal form through a hybrid triple sampling strategy combining semantic and spatial constraints. The invention also provides a system for realizing the method. The system comprises a feature engineering module, a training set generation module, a model training module and a reasoning application module. The system can automatically complete the whole process from data preprocessing, model training to vectorization reasoning. Compared with the prior art, the method has the advantages that the embedded vector with high semantic distinction degree and accurate spatial perception capability can be generated, and the computability and intelligence of geographic spatial data are effectively improved.

The invention discloses an improved SAC-based intelligent arrangement system for a power-cut plan of a power grid. The system comprises an objective function construction module which establishes a multi-objective function of the power-cut plan of the power grid according to power demand data; the constraint condition construction module establishes a multi-dimensional constraint condition according to the safety operation data of the maintenance equipment; the Markov decision process modeling module constructs an intelligent learning framework by using a multi-objective function and a multi-dimensional constraint condition, wherein the intelligent learning framework comprises a state space, an action space and a reward function; the action cutting module is used for dynamically cutting the action space based on constraint conditions; the SAC network construction module learns an optimal strategy through intelligent agent and environment interaction to obtain a trained SAC network; and the SAC network application module deploys the strategy parameters in a power grid dispatching system to generate an intelligent arrangement scheme of the power-cut plan. According to the invention, the problems of low efficiency, difficult coordination, easy conflict and difficult global optimization caused by dependence on artificial experience in traditional power-cut plan arrangement are solved, and the intelligent level of arrangement and the decision-making efficiency are significantly improved.

The invention discloses an automatic design method and system based on BIM software. The method comprises the following steps: initializing an adaptation layer dynamic link library, and establishing a parameter transmission channel with BIM software; preparing BIM design data, registering a basic task in BIM software based on the BIM design data, and establishing a standardized project directory; establishing a parameterized set of tasks which can be automatically modeled; serially arranging the task set into a task list according to design logic, and recording the task list to a project project; and calling the adaptation layer interface through the parameter transmission channel to drive the BIM software or a professional application module in the BIM software to complete automatic modeling. According to the method, the engineering design rule is solidified into the accurate task sequence by calling the BIM software or the interface of the professional application module in the BIM software, automatic execution of the whole process is achieved, the technical problems that manual operation efficiency is low and an existing RPA technology cannot process complex BIM design logic are solved, and design efficiency and quality are remarkably improved.

The invention discloses a personal electronic archive and genealogy management system based on a block chain, and the system comprises an archive generation module which is used for building a personal electronic archive; the genealogy application module is used for receiving a genealogy construction request initiated by a user and forming a candidate block set according to information in a user starting block; the genealogy generation module is used for calculating genetic relationship and a time sequence among the candidate blocks, and generating a topologically balanced religious pedigree diagram according to a calculation result and hash reference information among the candidate blocks; the automatic updating module is used for automatically updating the religious pedigree diagram; the access application module is used for generating an access token containing the permission range and the effective duration according to the access application; and the access evidence storage module is used for completing access and storing an access behavior. According to the method, efficient construction and dynamic management of personal archives and genealogy structures are realized by fusing block chain authentication and an intelligent retrieval modeling algorithm.

The invention relates to the technical field of low-temperature storage tank dome concrete construction management, and particularly discloses a storage tank dome concrete pouring intelligent pressure maintaining system which comprises a sensor fusion acquisition module, a data preprocessing and feature extraction module and a trend prediction module. An adaptive control decision module, an execution and feedback closed loop module and a mobile terminal application module; according to the invention, an end-to-end intelligent closed loop is formed based on trend prediction of LSTM + Transformer and a decision algorithm of MPC + terminal stability punishment. The system not only can accurately pre-judge the pressure dynamic state, but also can adaptively balance the pressure maintaining precision, the energy consumption and the equipment wear, so that the whole-process online self-correction is realized, and the reliability and the construction efficiency of the system are improved; by monitoring current, vibration, temperature and other multi-dimensional states of key equipment such as a fan, an electric control cabinet and a valve, the system can be automatically switched to a standby scheme or a safe shutdown mode under extreme working conditions, it is ensured that basic safety can still be maintained under various emergencies, and construction continuity is guaranteed.

The invention provides a vehicle network interaction AI cooperative scheduling method and system based on multi-source data fusion. According to the method, a'federated learning node + abnormal data processing unit 'collaborative architecture and an AI collaborative scheduling model containing topological modeling, prediction and optimization decision submodules are innovatively constructed, and a unique technical system is formed; fusing vehicle, power grid, environment, user and charging station multi-source heterogeneous data, and generating a vehicle-power grid topological association feature set through isolated forest + GAN preprocessing and transverse federated learning encryption fusion; gAT heterogeneous graph modeling, LSTM-Transform prediction, deep reinforcement learning optimization and online learning dynamic parameter adjustment technologies are integrated, and the data processing efficiency is improved; the data privacy security (the original data cross-domain transmission quantity is equal to 0) is realized, the topological association precision is improved by 40%, and the manual risk and the management cost are reduced; the application module generates a charging and discharging instruction, a load adjusting quantity and an excitation scheme, the scientific management of the whole process of vehicle network interaction is supported, the peak-valley cost is reduced by 15-20%, and the carbon emission is reduced by 10-15%.

The invention provides a banana nutrient deficiency detection method based on a multi-modal enhanced collaborative multi-scale module. The banana nutrient deficiency detection method comprises a data enhancement module which generates new data conforming to real pathological distribution based on a Stable Diffusion technology; the data processing module is used for extracting image multi-resolution features based on a multi-scale feature fusion network; the model optimization module is used for improving the element deficiency differentiation feature capturing capability based on a channel attention mechanism; the model training module dynamically focuses the difficult sample based on a mixed loss function of focus loss and cross entropy, and optimizes model training in a sample imbalance scene; the model application module is used for deploying a model to a WeChat applet based on model lightweight design to realize convenient detection of a mobile terminal; finally, the method is based on an MS-ConvNeXt model constructed by the modules, so that the accuracy of leaf element deficiency detection is greatly improved, and the convenience of field real-time detection is met.

The invention discloses a data interaction management and control system and method for judicial and administrative business resource integration. The system comprises a presentation layer, an application layer, a service layer, a data access layer and an infrastructure layer, wherein the presentation layer is used for providing a visual interface of a judicial administration data collaborative sharing portal and an interactive management and control portal, and supporting data collaborative sharing and management and control operation of a plurality of business scenes; the application layer is used for combining functions of the service layer, realizing a business scenarized application module and recording an operation behavior log in real time; the service layer is used for packaging preset business logic and providing an atomization service interface; the infrastructure layer comprises network resource load balancing, an API gateway, a hardware security device, a computing resource server cluster, a message queue, a log storage, a relational database, a distributed file storage and a block chain platform node. The block chain is utilized to realize on-chain data access, behavior data is prevented from being tampered, traceability and truth restoration are facilitated, and a person in charge is positioned.

A rail train environment sensing system based on ARM platform AI application deployment comprises a data acquisition module, an AI application module, a multi-modal algorithm module and an application display module, the data acquisition module acquires to-be-detected image data and preprocesses the to-be-detected image data through an ARM platform, the preprocessed image is transmitted to an edge computing server, the AI application module is called to recognize a target image, and the application display module displays the target image. The application display module sends a processing result of the multi-modal algorithm module to a display terminal platform; the edge computing platform based on the ARM architecture can support real-time reasoning of a lightweight deep learning model by means of the characteristics of low power consumption and high parallel processing, provides a reliable carrier for on-orbit processing of multi-modal sensing data, and meets the strict requirements of a train operation control system for safety standards.