230 results about "Technology fusion" patented technology

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

The invention discloses a multi-unmanned aerial vehicle formation efficient cooperative control method, and belongs to the field of unmanned aerial vehicles. According to the method, through cooperation of the generator and the discriminator, a multi-level reward function is designed and dynamically adjusted by using diversified scenes and fault model training strategies in the digital twin model, so that complex tasks and scenes can be better coped with, and the cooperation efficiency is comprehensively improved. When a complex search rescue task is executed, search areas and tasks can be distributed more reasonably, and the rescue efficiency is improved. The state of the unmanned aerial vehicle is simulated and predicted by means of digital twin dynamics, errors are corrected, a real-time closed-loop optimization mechanism can rapidly re-plan a path when flight is abnormal, and the difference between virtual data and reality data is reduced through a domain self-adaptive module. When a new obstacle is encountered, the technical scheme of the invention can re-plan a safe path, and at the same time, through collaborative planning and obstacle avoidance of multi-technology fusion, the safety and reliability of unmanned aerial vehicle formation collaborative flight are improved.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention provides a charger fault diagnosis method based on a knowledge graph, which realizes efficient and self-adaptive fault diagnosis and maintenance through multi-technology fusion, constructs a structured knowledge base, integrates charger parts, fault modes and detection methods, stores entities and causal relationships and detection association by adopting a graph database, extracts information through a BERT model, and realizes fault diagnosis and maintenance based on the knowledge graph. Marking causal strength and dynamically adjusting the causal strength, converting spoken description of a user into structured data, matching a fault path in a knowledge graph, dynamically adjusting a weight, analyzing time sequence data, matching a time sequence mode in the knowledge graph in combination with dynamic time warping, generating a candidate path, optimizing a diagnosis path weight, and designing a multi-target reward function; a visual report is generated, the fault probability in the future seven days is predicted, maintenance suggestions are generated in combination with risk levels, knowledge maps and models are continuously optimized through user feedback, diagnosis accuracy and maintenance efficiency are improved, and manual intervention requirements are reduced.

The invention discloses a multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method and system. The multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method comprises the steps of obtaining freight basic elements, constraint conditions and real-time environment information, and assigning a matched number of unmanned transfer vehicles and unmanned aerial vehicle sets; generating an initial optimal path; and the local path of the initial optimal path is adjusted in real time, the unmanned transfer vehicle and the unmanned aerial vehicle set advance to the unloading destination according to the global optimal path, and multi-vehicle cooperative cargo unloading is completed according to the conflict-free motion trail. A multi-machine cooperation technology is adopted, advanced technologies such as unmanned driving, the Internet of Things and an artificial intelligence algorithm are fused, the unmanned transfer trolley, the unmanned aerial vehicle and the intelligent gantry crane can work efficiently and cooperatively, various operation scenes such as indoor and outdoor switching, place layout difference and extreme weather can be dealt with, cargoes can be rapidly and accurately received, unloaded and transferred, and the working efficiency is improved. And the efficiency of multimodal transport is greatly improved.

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The invention provides a port operation optimization method based on big data and air rail transportation technology fusion, and belongs to the technical field of intelligent ports, and the method comprises the steps: obtaining port geographic data, setting a four-layer intelligent system, and collecting basic data based on the four-layer intelligent system; performing multi-modal data fusion processing on the basic data to obtain multi-modal fusion data, performing big data analysis based on the multi-modal fusion data, and constructing an intelligent decision model according to a big data analysis result; the dynamic scheduling of the air rail transport system is guided based on the output of the intelligent decision model, the dynamic scheduling is executed, the execution result is evaluated according to the real-time basic information obtained by the four-layer intelligent system, and then the port operation link is optimized according to the evaluation result; and designing a continuous learning mechanism based on an optimization result, and carrying out comprehensive optimization on the intelligent decision-making system and a port transportation link. And the port competitiveness is enhanced, and the data security and the autonomous controllable level are improved.

The invention discloses a storage intelligent monitoring and checking integrated system and method based on multi-technology fusion, and particularly relates to the technical field of storage monitoring, the system obtains environmental parameters and cargo data in real time through a multi-source data acquisition module, carries out cargo identification and segmentation by using YOLOv7 and Mask R-CNN, and carries out storage monitoring and checking on the cargo. A three-dimensional model is constructed in combination with a Poisson surface reconstruction algorithm to calculate volume weight, an environment anomaly analysis module generates an early warning signal through dynamic threshold and influence level evaluation, and an intelligent decision module fuses data to realize quantity anomaly alarm and multi-level response and outputs a three-dimensional visual inventory report. According to the method, the problems of multiple blind areas, low checking efficiency, slow abnormal response and the like of traditional storage monitoring are solved, full coverage of environment monitoring, high accuracy of goods identification, low error of volume calculation and intelligence of abnormal handling are realized, and the automation and digitization levels of storage management are remarkably improved.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention relates to the technical field of engineering cost management, in particular to an artificial intelligence-based cost whole-process management method and system, and the method comprises the steps: collecting environment data, market data and project data, and carrying out the feature extraction; calculating the weight of each modal data through a self-attention mechanism, and obtaining a fused feature vector; the knowledge graph, the graph convolutional network and TransE are combined to generate a knowledge graph feature vector, a self-adaptive cost prediction model is constructed, and a prediction cost is output and implemented; fusing a graph attention network, a structure causal model and an improved fuzzy comprehensive evaluation method, constructing a risk assessment model, outputting a risk grade score, and automatically generating and adjusting an optimal risk coping strategy; constructing a low-code intelligent collaboration platform; and based on an evaluation index result, optimizing model parameters and platform configuration by using a genetic algorithm. According to the invention, the risk factors are accurately analyzed through the risk assessment model based on fusion of multiple technologies, and the assessment accuracy is improved.

The invention relates to a power transmission line distributed fault diagnosis system and diagnosis method suitable for an electric power internet of things. The system comprises a data center station and a monitoring terminal deployed on a power transmission line. The plurality of monitoring terminals are respectively in wireless communication connection with the data center station; through technology fusion and multi-dimensional data utilization, compared with the prior art, the accuracy, the real-time performance and the reliability of fault detection, the operation and maintenance efficiency and the cost effectiveness are remarkably improved. The fault diagnosis method comprises interval positioning, accurate positioning and result output. The monitoring terminal is used as a demarcation point to divide the line into a plurality of sections, and after a fault occurs, the pre-processing module can determine the section where the fault point is located according to the direction and size of fault current or traveling wave current and reduce the fault point finding range, so that the fault point finding efficiency is improved, and typical fault reasons of the power transmission line are identified according to the waveform of the traveling wave current. And the reason of the circuit fault can be quickly determined.

The invention discloses an IT + OT full-link state detection and early warning method, and belongs to the field of system monitoring and intelligent operation and maintenance integrating information technology and operation technology. The method comprises the following steps: acquiring IT and OT system specific parameters and converting the parameters into a unified data stream; performing alignment quantization on the data stream to generate a two-dimensional event stream; constructing an IT and OT system causal atlas and extracting associated features; the dynamic pruning graph generates a monitoring node and issues an instruction; verifying the feedback data of the OT device to generate a progressive early warning; and adjusting an IT resource strategy according to the early warning and generating an OT instruction correction parameter. According to the method, multi-source data fusion, dynamic causal atlas analysis, cross-domain verification and progressive early warning technologies are adopted, the full-link state of the IT / OT fusion system can be sensed in real time, risk and fault evolution can be identified, early warning and response can be carried out, the monitoring comprehensiveness, early warning accuracy and response speed of the fusion system are remarkably improved, and the real-time performance of the fusion system is improved. And the stability and safety of system operation are enhanced.

The invention relates to the technical field of intelligent equipment, in particular to an intelligent interactive method and system for pet emotion pacifying based on the Internet of Things, and the method comprises the steps: collecting the audio data, physiological data, environment data and owner feedback data of a pet, and carrying out the data cleaning of the collected data; performing feature extraction; combining a decision tree model, a recurrent neural network, a variant of the recurrent neural network, a Transform model and a graph neural network, constructing a hierarchical fusion model, introducing a transfer learning and reinforcement learning mechanism, designing a dynamic model fusion weight adjustment strategy, and judging pet emotion according to extracted features; different pacifying measures are adopted according to the types of the abnormal emotions of the pet judged by the hierarchical fusion model; and establishing a multi-index evaluation system. According to the invention, through the hierarchical fusion model of technology fusion, the pet emotion recognition precision and speed are improved.

The invention relates to the technical field of intelligent control systems, and discloses a multifunctional embedded intelligent cleaning control system which comprises a control execution layer, an internet-of-things transmission layer and a decision analysis layer. The control execution layer comprises an intelligent variable-frequency scraping device, a wind-water sweeping device and the like and executes sweeping and dust removal operation; the Internet of Things transmission layer realizes data interconnection and transmission through an edge computing node, a protocol conversion gateway and the like; and the decision analysis layer adopts a time sequence data analysis and convolutional neural network algorithm, dynamically optimizes the cleaning operation model, constructs a multi-dimensional linkage cleaning control strategy model, and realizes cleaning intensity adjustment, air and water parameter cooperative control and dust removal efficiency dynamic optimization. According to the system, through multi-technology fusion and collaborative optimization, the intelligent, precise and efficient level of sweeping operation is improved, the system is suitable for a conveying belt sweeping scene, the sweeping efficiency can be improved, equipment loss and dust emission can be reduced, and remarkable economic benefits and environmental protection benefits are achieved.

The invention discloses a server heat dissipation control system and method, a storage medium and a program product, and relates to the technical field of hardware heat dissipation, the server heat dissipation control system comprises a data acquisition layer, a resource management layer, a data reasoning layer and a decision execution layer, the current sparseness of a neural network model can be calculated through multi-modal data and system load data of a server, and the current sparseness of the neural network model is calculated; the method comprises the following steps: firstly, calculating the current sparseness of a neural network model, then calculating a weight matrix of the neural network model through the current sparseness, reasoning the multi-modal data by using the neural network model and the weight matrix, finally, generating a control signal based on a reasoning result, controlling a server to execute a heat dissipation action, and realizing high-performance heat dissipation control through dynamic sparse learning multi-modal fusion technology fusion. The technical problem that the requirements of control precision and real-time performance cannot be considered at the same time due to the fact that conflicts exist between model resource occupation and real-time performance during heat dissipation of the server in the related technology is solved, and the technical effects of optimizing sparse calculation and solving conflicts between multi-mode resources and embedded resources are achieved.

The invention belongs to the field of data collection, and provides an additive manufacturing industry information collection method and system based on artificial intelligence, and the method comprises the steps: obtaining original data related to the additive manufacturing industry from a multi-source heterogeneous data channel; the original data is preprocessed; performing representation learning on entities in the map to obtain an analogy score relationship among the entities for reasoning a potential technical evolution direction; fusing the generated new terms and the inferred potential technical evolution direction with originally extracted industry knowledge entities and relationships, and constructing a dynamic semantic knowledge graph containing dominant entity relationships and new concept inference edges; periodic updating and reasoning calculation are conducted on the dynamic semantic knowledge graph, and structured industry intelligence information is output and comprises new terms, high-potential technology paths, cross-domain technology fusion trends and concept evolution trajectories. The method can adapt to additive manufacturing industry information collection with rapid information change.

The invention discloses a power grid inspection method, system and equipment. The method comprises the following steps: acquiring multi-dimensional data of power equipment by constructing a multi-source heterogeneous sensor array; according to the obtained multi-dimensional data of the power equipment, the to-be-inspected power monitoring points are connected in series to generate an initial inspection line; calculating and optimizing line configuration based on the electric power key data identifier and the inspection duration; and dynamically adjusting the inspection path through the reinforcement learning model, and generating an intelligent inspection decision scheme fusing the equipment health degree. According to the method, the power grid inspection efficiency is optimized through technology fusion, the detection precision is improved, and deep cooperation is formed between inspection path planning and task scheduling.

The invention provides an emergency decision-making auxiliary system based on a knowledge graph. Comprising a data acquisition and processing module, a knowledge graph construction and reasoning module, an information retrieval and generation module, a multi-level reasoning and thinking chain prompting module, a multi-agent cooperation and game optimization module, an incremental learning and dynamic knowledge updating module and a block chain trust guarantee module. The emergency decision-making efficiency, accuracy, cooperation capability and safety are broken through, dynamic and complex emergency scenes can be effectively dealt with, and efficient, reliable and intelligent technical support is provided for decision makers.

The invention is suitable for the technical field of construction safety, and provides a construction machinery collision safety protection method and system, and the method comprises the steps: constructing a distributed communication network, sharing the state information of the pose, speed and the like of a machine in real time, and dynamically managing the network topology; the binocular camera, the millimeter wave radar and the IMU are fused to perceive the environment, and the operation path of the adjacent machine in the next five seconds is predicted; a cooperative control strategy of mechanical arm kinematics constraint and sensor view field coverage is established, the track is optimized, and the pointing direction of the sensor is adjusted; and executing three-level collision risk protection, and triggering early warning, potential field correction and emergency shutdown according to the collision time TTC. According to the scheme, full-process intelligentization from information interaction to risk protection is achieved through multi-technology fusion, the safety and cooperation efficiency of the construction machinery under complex working conditions are improved, and the method is suitable for multi-machinery operation scenes in the fields of buildings, traffic and the like.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention discloses a cloud edge cooperative fault diagnosis method for a gas generator set, and the method comprises the following steps: S1, obtaining fault criteria and symptoms of a gas turbine based on domain knowledge, carrying out the fault diagnosis analysis of the gas turbine based on technology fusion according to multiple criteria and symptoms, and building a logic rule database of different faults; s2, establishing a gas turbine modular simulation model, and performing energy efficiency diagnosis and optimization operation on the simulation model based on the logic rule database; s3, analyzing a fault influence mechanism of the optimized simulation model, and evaluating the service life of the core component of the gas turbine based on the fault influence mechanism of the simulation model; and S4, performing overall health assessment on the gas turbine based on the service life assessment result of each core component. According to the method, the effect of accurately mapping the relationship between the equipment health state and the working condition parameters can be achieved, so that the effect of comprehensively and dynamically evaluating the health level of the gas generator set is achieved, and the effect of efficiently and accurately diagnosing the fault of the gas turbine is achieved.

The invention discloses a monitoring management method and system for multi-dimensional construction, and relates to the field of construction monitoring management, and the method comprises the following operation steps: S1, top layer design and system planning; s2, data acquisition and preprocessing; s3, performing data fusion and AI depth analysis; s4, grading an intelligent early warning and response mechanism; s5, construction dynamic optimization and resource scheduling; and S6, quality full-period traceability and management and control are carried out to ensure that the quality of each process is controlled, and problems can be traced and prevented. According to the monitoring management method and system for multi-dimensional construction, an intelligent closed-loop method and system covering construction total factors and whole processes are constructed through innovative technology fusion, and normal form transformation from passive risk coping to active prevention and dynamic optimization can be achieved; the construction safety, quality, efficiency, sustainability and team cooperation level can be remarkably improved, the government supervision compliance rate can be met, and the risk of penalty due to shutdown is avoided.

A method and system for certification of actor model messages stored in event stores utilizing traditional data storage combined with blockchain smart contracts is disclosed. The method and system do not rely solely on blockchain storage mechanisms, which are plagued with high latency, low throughput, and variable cryptocurrency transaction costs (particularly in the case of “public” blockchain networks). Instead, the method and system hybridizes traditional cloud-based storage methods with blockchain smart contract technology, enabling the storage, certification, and re-verification of high-volume data (i.e., large files). This hybridization may eliminate the need for cryptocurrency exchanges, in particular when leveraging a private blockchain network free of cryptocurrencies, which further promotes functionality at low, fixed cost.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention relates to a respiratory dust concentration online monitoring device and method for a coal mine, and belongs to the technical field of respiratory dust concentration monitoring. The device comprises a virtual impact separation module, a light scattering detection module, a flow control module and a data processing module. The virtual impact separation module is used for separating respirable dust and non-respirable dust; the respirable dust enters the light scattering detection module, the respirable dust is irradiated by a laser beam which is incident in the direction perpendicular to the flowing direction of the respirable dust, a scattered light signal obtained after dust scattering is transmitted to the data processing module, and the concentration of the respirable dust is calculated in real time; the flow control module is used for controlling the rotating speed of the sucking pump in real time. Through multi-technology fusion and intelligent algorithm compensation, the accuracy and environmental adaptability of respiratory dust concentration measurement are improved, the problems that a traditional separation device is prone to blockage and needs frequent maintenance are solved, and long-term continuous separation monitoring of respiratory dust can be achieved.

The invention discloses an engineering construction whole process supervision method and system based on multi-technology fusion, and relates to the technical field of engineering supervision, and the supervision steps are as follows: S1, collecting construction site data through a mobile terminal multi-source channel, carrying out the space-time alignment and association fusion of data preprocessing, and carrying out the panoramic visual monitoring of a construction site; abnormal events and potential risks are identified through a preset rule and a machine learning algorithm; and S2, analyzing the engineering data by using a natural language processing technology. According to the invention, on the aspect of data and risk management and control, panoramic visual monitoring is realized through multi-source data fusion, and risks are accurately identified through an intelligent algorithm; the NLP technology analyzes the engineering data, automatically generates the check list, ensures compliance, realizes conversion from passive supervision to active prevention and from artificial experience to data driving, has remarkable effects in the aspects of improving management efficiency, reducing risks, saving cost and enhancing transparency, and is suitable for various large complex engineering scenes.

The invention discloses an intelligent medical inquiry and medical record generation method and system based on multi-technology fusion, and the system comprises a specialized dynamic pre-inquiry module which is used for sequentially executing the analysis of a patient chief complaint, the matching of a dynamic template, the intelligent inquiry and the completion and verification of a knowledge graph according to a chief complaint text inputted by a patient, and obtaining the comprehensive information of the patient; the dual-drive medical risk control module is used for performing dominant and recessive risk identification based on a rule engine and a large model in combination with an RAG knowledge base according to the comprehensive information of the patient, and realizing a collaborative early warning closed loop; the voice structuring and medical record generation module is used for executing double-track voice separation and transcription, specialized structuring processing and multi-source data fusion and EMR template matching in the dialogue process of the patient and the doctor to generate a structured electronic medical record; and the system integration and performance optimization module is used for adapting to a hospital HIS / EMR system through a standardized interface and executing data preloading and cache optimization to guarantee the response performance of the system.

The invention provides a rapid data acquisition method based on fusion of intelligent number asking and an industrial connection technology, and the method comprises the steps: obtaining industrial protocols of a plurality of devices and configuration parameters corresponding to the devices, and generating an acquisition model of each device; generating a structural fingerprint for each acquisition model; performing matching degree analysis on the plurality of acquisition models through multi-modal similarity calculation on the basis of the structure fingerprint, screening the plurality of acquisition models according to a matching degree analysis result to perform uniqueness management, and performing association mapping on the equipment and the corresponding acquisition model to obtain a de-duplicated acquisition model; edge gateway configuration accurate issuing of the collection model after duplicate removal is realized through a two-way transmission channel, and consistency is guaranteed by combining real-time structure fingerprint return and matching degree analysis. Through the AI-driven process arrangement and semantic understanding technology, the modeling efficiency, the information consistency and the issuing capability of the data acquisition system are remarkably improved.

The invention discloses an Internet of Things and AI energized smart energy multi-energy complementary heating control system, which relates to the technical field of smart energy heating control, and comprises an atomic layer deposition sensing module for realizing ultra-sensitive measurement of temperature and humidity; edge nodes are calculated through photons, and the data processing energy efficiency is improved; the shape memory alloy-piezoelectric composite execution module is used for accurate and rapid adjustment; the multi-scale heat coordination module is used for strengthening heat transmission; the magnetofluid sealing and regulating module is used for dynamically controlling the flow; the terahertz metasurface communication module guarantees high-speed communication, and the system optimizes energy complementation and intelligent regulation and control through multi-technology fusion. According to the invention, the sensing precision, the data processing speed and the execution precision are improved, the heat transmission efficiency is enhanced, the flow dynamic regulation and control and high-speed reliable communication are realized, the room temperature standard reaching rate is improved and the energy consumption is reduced through digital twin optimization control, and an efficient low-carbon scheme is provided for smart energy heating.

The invention discloses a multi-technology fusion network security health assessment method and system, and the method comprises the steps: generating an activity index of an IP address; performing port detection on the IP address of which the activeness index is greater than a first threshold value through a configurable port scanning mode to obtain open port information; sending a service detection data packet to the open port; extracting a service type and a version number according to response information of the service detection data packet to obtain a service change frequency; calculating an asset dynamic score based on the activity index of the IP address and the service change frequency; dividing the IP addresses into stable assets and temporary assets according to the asset dynamic scores; a low-frequency depth detection mode is adopted for the stable assets, a high-frequency rapid detection mode is adopted for the temporary assets, and a detection result is obtained; inputting the detection result and the asset dynamic score into a risk assessment function for calculation to obtain a risk security value of each IP address; and generating an assessment report according to the security risk value. The method and the device are used for improving the accuracy of enterprise network security assessment.