191 results about "Cloud architecture" patented technology

The invention provides a satellite resource dynamic allocation method under a cloud-native hybrid cloud architecture, and the method comprises the steps: packaging satellite load resources and cloud resources through a containerization technology, so as to form a unified virtual resource pool; constructing a deep learning model, predicting resource demands of each beam coverage area in a future time period based on satellite task historical data and real-time demands, and generating a resource matching scheme; a reinforcement learning algorithm is adopted to dynamically adjust the beam radius, channel allocation and time slot scheduling so as to match the real-time load change of cloud resources; according to the method, a satellite and cloud digital twin model is constructed, a scheduling strategy is simulated and predicted based on a resource matching scheme through the satellite and cloud digital twin model, a resource state is mirrored in real time, and a strategy library is updated according to simulation feedback data of the satellite and cloud digital twin model, so that multi-satellite task automatic recombination is realized. The invention further provides a satellite resource dynamic allocation system under the cloud and native hybrid cloud architecture and a computer storage medium. According to the method, three-dimensional cooperation of dynamic resource pooling, intelligent prediction and elastic scheduling is realized, and the problem that satellite task response time, resource utilization rate and safety cannot be achieved at the same time can be effectively solved.

The invention relates to the technical field of cloud computing, in particular to a resource arrangement and automatic execution method and system based on a hybrid cloud architecture. The resource arrangement and automatic execution method based on the hybrid cloud architecture comprises the steps of pulling or receiving monitoring data, performing streaming and batch cleaning, and generating input required by training and reasoning in a feature processing pipeline; the model is deployed, a prediction result is sent to a decision module in real time, and the decision module triggers automatic execution to carry out capacity expansion and shrinkage on resources; and finally, comparing an execution effect with a prediction result to realize continuous optimization. According to the resource orchestration and automatic execution method and system based on the hybrid cloud architecture, cross-cloud resource dependence analysis and task-driven orchestration execution are realized, strategy-driven dynamic deployment and elastic capacity expansion and contraction are also realized, intelligent execution optimization and exception handling can be performed in combination with artificial intelligence or a rule engine, and the resource orchestration and automatic execution method and system based on the hybrid cloud architecture are high in practicability. The resource utilization rate and the operation and maintenance efficiency of the hybrid cloud environment are improved, and the risk of manual operation is reduced.

The invention relates to a multi-modal data-driven rail transit construction potential safety hazard identification method and system, and solves the problem that the accuracy and real-time performance of potential hazard identification are insufficient due to the fact that multi-source data cannot realize deep collaboration, and the method comprises the following steps: obtaining multi-modal data containing a BIM + GIS model, an unmanned aerial vehicle image stream and Internet of Things data; according to an edge end-cloud architecture, by taking BIM + GIS coordinates as a reference, the edge end associates and binds data and cleans and transmits the data to the cloud, and the cloud corrects and generates a standardized data set; and the comprehensive risk index is calculated by using an entropy weight method, and a report is generated and pushed to a construction management terminal. The method has the following effects: the multi-modal data barrier is broken, the integrated accurate identification of the hidden hidden danger of the rail transit construction is realized, and the accuracy and efficiency of the construction safety management and control are fundamentally improved.

The invention provides an environment supervision method and system based on an edge cloud architecture. The method comprises an edge end device and a cloud platform, wherein the edge end device is arranged on an environment supervision site; the execution steps of the side end device comprise: collecting a pollution source multi-modal data stream; according to the pollution source multi-mode data flow, triggering an alarm through a grading abnormity identification model; in response to the alarm, generating an evidence chain with a timestamp; extracting a monitoring deviation feature D, a behavior anomaly feature B and a semantic feature S according to the evidence chain; compressing the monitoring deviation feature D, the behavior anomaly feature B and the semantic feature S to generate a feature packet Z; and uploading the feature packet Z to a cloud platform. The cloud data matching performance can be effectively improved, and the false alarm rate is reduced.

The invention provides a chip design and verification system and method.The system comprises a tool chain integration and automation module, the tool chain integration and automation module is provided with a tool containerization unit and a multi-physics field collaborative optimization unit, and the tool containerization unit is used for containerizing and packaging an EDA tool into an independent functional unit; each EDA tool container comprises a complete runtime environment, a dependency library and a license; the multi-physics field collaborative optimization unit is used for acquiring design parameters of the chip and evaluating the performance of the chip design scheme; the system further comprises a chip particle heterogeneous integration module which is used for carrying out system modeling and layout planning on the chip according to the received chip design file and carrying out multi-physics field simulation on a chip model. The system further comprises a security and credibility verification module which is used for carrying out vulnerability detection and security analysis on the chip design file in a credible environment in the chip design process. The invention further provides a working method of the system. According to the invention, the problem of isolation of a chip design tool chain can be avoided.

The invention provides an edge diagnosis and maintenance system and method for industrial equipment. The system comprises a data acquisition module, a cloud collaboration module and at least one edge calculation module, the data acquisition module is used for acquiring multi-mode real-time operation data of industrial equipment; the cloud collaboration module is used for performing incremental training on the initial diagnosis model and the initial knowledge graph based on the multi-modal real-time operation data to obtain a target diagnosis model and a target knowledge graph, and issuing the target diagnosis model and the target knowledge graph to the edge calculation module; and the edge calculation module is used for determining whether the industrial equipment has a fault based on the multi-modal real-time operation data and the target diagnosis model, and if so, determining a fault position and a maintenance suggestion of the industrial equipment based on the multi-modal real-time operation data and the target knowledge graph. According to the invention, by setting the edge-cloud architecture, the accuracy and real-time performance of fault diagnosis and maintenance are improved.

The invention provides a deep reinforcement learning-based dependency task unloading method and device under an end-side cloud architecture. The method comprises the following steps of: obtaining a to-be-executed application program and an end-side cloud architecture internet of things for executing the application program; extracting a dependency relationship between the sub-tasks to obtain a directed acyclic graph, determining the priority of each sub-task, and generating a queue containing all ready tasks; collecting computing resources of the terminal equipment, the edge server and the cloud server to form system information in the time slot; obtaining task information of a current to-be-executed sub-task; fusing the system information and the task information, and constructing a state space; and based on the state space, evaluating the performance index of each unloading strategy by using a dominant action comment model so as to generate a task unloading decision. Compared with other unloading algorithms, the technical scheme provided by the invention has better convergence and stability in different scenes, and has lower cost.

The invention discloses an intelligent power plant management system construction method, and relates to the technical field of intelligent power plant construction. The method comprises the following steps: constructing a data integration platform based on a cloud architecture, and uniformly storing and managing data of a power plant in each stage from planning, design, construction, operation and maintenance to decommissioning; deploying a sensor network, and collecting equipment data information in real time; the collected data information is deeply mined and analyzed, an equipment fault diagnosis model and an operation state prediction model are established, and the health condition of the equipment is monitored in real time; a unified management and scheduling platform of intelligent applications is constructed, and centralized management and cooperative scheduling are carried out on various intelligent applications of a power plant. By constructing a data integration platform based on a cloud architecture, unified storage and management of data in the whole life cycle of the power plant are realized. A solid foundation is provided for subsequent data analysis and intelligent application, and the timeliness and accuracy of data acquisition are improved.

The invention discloses a collaborative secret state task matching method based on edge calculation in mobile crowdsourcing. The method comprises the steps of system initialization and key generation, requester-cloud platform registration authentication, worker-edge server registration authentication, login authentication and attribute submission, requester task issuing, cloud platform task delegation, secret state matching, worker task content decryption, worker answer submission, answer forwarding and decryption. According to the invention, cooperative authentication of workers and requesters and precise task matching based on attributes are realized under an edge-cloud architecture. A fuzzy extractor and an authentication encryption method with associated data are introduced, so that low-cost user local authentication and registration and authentication of a joining system are realized, the security is improved, and the calculation cost is reduced. Through function hidden inner product encryption and a Paillier cryptographic algorithm, task-worker matching and task answer submission are realized in a ciphertext state, and the requirements of a mobile crowdsourcing application scene with high safety, high privacy and dispersed cost are met.

The invention discloses an immersive learning method based on a wearable brain-computer interface and three-dimensional virtual simulation linkage. The system collects EEG, heart rate, galvanic skin, respiration and other multi-mode signals in real time, after band-pass filtering, ICA, wavelet noise reduction and feature extraction, attention indexes and cognitive loads are output within 200 ms by means of a TCN-GNN fusion model, a virtual scene is driven to highlight key knowledge points, step-by-step guidance or difficulty dynamic adjustment, and a learner keeps cardiac flow. The system further optimizes and designs interaction trajectory multi-modal fusion, generates a concentration curve, a cognitive load heat map and personalized tutoring suggestions, and pushes the concentration curve, the cognitive load heat map and the personalized tutoring suggestions to a teacher / parent end in real time through an end-edge-cloud architecture to realize objective quantification of cognitive states and precision of teaching feedback.

The invention provides an unmanned aerial vehicle photovoltaic real-time inspection system and method based on an end-edge-cloud architecture, and belongs to the field of photovoltaic power station operation and maintenance. Comprising an end device which directly interacts with an environment or a user and collects data and comprises an unmanned aerial vehicle end, a flight controller, a pan-tilt camera end and a mobile phone end; the edge computing platform is realized by adopting an embedded platform carried above the unmanned aerial vehicle end, and is used for reading end equipment data, executing fuzzy judgment based on a visible light image and two-stage defect detection based on an infrared image, and uploading an image detection result to the cloud server; and the cloud server is responsible for data storage and message pushing, receiving the image result uploaded by the embedded platform and pushing the image result to the mobile phone terminal. According to the invention, an end-edge-cloud collaborative architecture facing a power station is provided, the diagnosis and decision delay is greatly shortened, the rapid identification and positioning of the defects of the photovoltaic equipment are realized, and the high-precision identification of the defects is realized by adopting a two-stage detection algorithm based on target identification and defect classification.

The invention provides a remote sensing data processing task thermal replacement method based on a cloud architecture. The method comprises the following steps: constructing a comprehensive state model of nodes; carrying out structure standardization processing on the received original remote sensing data so as to carry out unified management on data sources; sensing the comprehensive state of each node in the data source and the node cluster in real time; extracting a graph structure of a current to-be-scheduled task, automatically selecting a most suitable scheduling algorithm according to the graph structure, and generating a task execution topological graph; performing task distribution and execution according to the task execution topological graph; sensing the comprehensive state of each node in real time in the execution process of the task, and determining a resource elastic scaling strategy of the node; fault detection is conducted on the nodes and the task execution environment, after it is judged that faults occur, the operator operation state is rebuilt in other available nodes based on operator-level local snapshots, tasks are recovered through hot loading, and persistent storage and output of result data are completed.

The invention relates to the technical field of computers, and discloses a document collaboration platform based on cloud computing. Real-time semantic complementation, automatic typesetting optimization and content abstract generation are provided through the intelligent editing assistant module, the manual editing burden is remarkably reduced, and the efficiency is improved; a rule engine of an automatic operation module is combined to realize automatic management of document versions, intelligent resolution of conflicts and standardized review, and the problems of communication cost and conflicts in multi-person cooperation are thoroughly solved; the problem of information overload is solved by relying on the deep semantic processing capability, and accurate positioning of core content is realized; and meanwhile, based on the elastic expansion capability of the distributed cloud architecture, the stability and high availability of multi-terminal real-time cooperation are ensured. Finally, a document processing closed loop integrating intelligent assistance, an automatic process and efficient cooperation is formed, and the bottlenecks of a traditional platform in the aspects of editing efficiency, cooperation fluency, information processing and the automation level are comprehensively broken through.

The invention discloses a distributed audio and video collaborative production system based on a cloud architecture, and relates to the technical field of audio and video processing and cloud computing, the distributed audio and video collaborative production system comprises a cloud service platform, a distributed storage module, an audio and video processing node and a client, the cloud service platform is based on a micro-service architecture, containerization and Kubernetes management are used, tasks are accurately distributed by means of a resource adaptation factor formula, and the cloud service platform is used for storing the tasks. The distributed storage module adopts a Ceph system, guarantees data reliability through a data robustness formula, carries out hierarchical storage according to data value density, distributes audio and video processing nodes in multiple places, is matched with professional hardware, processes audios and videos through a high-speed network connection platform, and has a visual interface at a client. The system has obvious advantages, the cloud platform accurately distributes tasks to improve efficiency, the storage module guarantees data reliability and hierarchical storage to reduce cost, the processing node processes audio and video with high performance, the client side is convenient to operate, the real-time cooperation and intelligent auditing module guarantees collaboration and content compliance, an interface is opened to expand an application scene, and the system competitiveness is enhanced.

The invention relates to the technical field of cloud architecture distributed data processing. The cloud architecture distributed data processing method comprises the following steps: verifying identity data based on multiple identity authentication, if the identity verification is passed, allocating data access authority through a role-based access control mechanism, establishing a distributed data center based on a cloud architecture, and sending the distributed data center to the cloud architecture. Receiving real-time monitoring data from a plurality of terminals, storing the real-time monitoring data to the distributed data center based on the cloud architecture, and after the real-time monitoring data is stored to the distributed data center based on the cloud architecture, sending the real-time monitoring data to the server; network traffic in a distributed data center based on a cloud architecture is monitored through an intelligent intrusion detection mechanism driven by artificial intelligence to obtain user access request data, and dynamic security assessment is performed on an access request of a data access user by using a zero-trust architecture. The method has the effects of realizing dynamic access control and efficient data protection.

The invention relates to the field of Internet of Vehicles information security, and provides a multi-source heterogeneous detection model based on federated learning for potential safety hazards of vehicle and road cloud architecture information. A federated learning mechanism is utilized, different communities are fused, and high-precision and low-delay diversified attack detection is realized aiming at detection models of different attack characteristics. According to the technical scheme, the method comprises the following steps: dynamically dividing a local model by using multiple graph clustering algorithms, and performing community classification according to feature similarity; selecting a community initial for guiding optimization in the same community by means of a dual-objective function model; a weighted average or gradient optimization aggregation community model is adopted to improve the accuracy and convergence speed of the model; heterogeneous knowledge fusion among models is realized through mapping and transfer learning technologies, and a reward and punishment mechanism is designed to dynamically adjust the contribution degree of the models. The invention aims to construct a complex attack detection model with higher detection speed, higher precision and less resource consumption through dynamic community division and multi-objective optimization to form an integrated safety guarantee system of a distributed architecture of the Internet of Vehicles.

The invention relates to a mountain photovoltaic automatic laying method and system based on BIM and GIS, and the method comprises the steps: obtaining the engineering parameters of a to-be-laid photovoltaic project based on the GIS, and inputting the engineering parameters into a built-in photovoltaic module database of a preset cloud architecture system to obtain a photovoltaic module matched with the to-be-laid photovoltaic project and the corresponding photovoltaic module parameters; calculating the capacity of photovoltaic panels in the photovoltaic modules and the inverter based on the parameters of the photovoltaic modules, and taking the capacity as the boundary of the photovoltaic layout area; calculating layout parameters of the boundary of the photovoltaic layout area by utilizing a GIS (Geographic Information System); based on the layout parameter of each grid unit and a preset screening condition, screening out an area capable of being laid on the GIS, and establishing a BIM model of the area capable of being laid; based on the layout parameters of the grid units in the layout area, a shadow analysis mathematical model is adopted to calculate the spacing between the photovoltaic panels before and after layout; generating a photovoltaic panel layout array based on the front-back spacing of the photovoltaic panels; and inputting the photovoltaic panel layout array into the BIM model of the layout area to complete automatic layout of the photovoltaic panels.

A cloud architect guides cloud architecture design and deployment for users of all skill levels. Generative artificial intelligence (AI) interprets user specifications to provide architectural diagrams for a wide variety of application scenarios. A cloud architect generates search results responsive to a request for a cloud architecture. The search results indicate at least one example cloud architectural diagram. The cloud architect generates a request to a large language model (LLM) for a recommended cloud architecture based on the user request and the search results. The cloud architect receives a response generated by the LLM indicating at least one recommended cloud architecture. The cloud architect provides an interactive LLM response. The cloud architect enables selection of a recommended cloud architectural diagram, e.g., for deployment, manual editing, or dialog leading to automated customization. The cloud architect deploys a workload to a cloud using cloud resources determined based on the selected diagram.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.

The collaborative scheduling system of the end-side cloud architecture comprises a cloud subsystem, a plurality of side subsystems and a plurality of terminal systems, the side subsystems are associated lower-level systems of the cloud subsystem, and the terminal systems are associated lower-level systems of the side subsystems; the cloud subsystem is used for responding to a task request, determining a task scheduling process, generating task scheduling information and sending the task scheduling information to the side subsystems or the terminal subsystems through an encrypted transmission channel for re-scheduling or execution, and an initiator of the task request is any one of the cloud subsystem, the plurality of side subsystems and the terminal systems; the side subsystem is used for receiving a task request and generating task scheduling information, or reporting the task request to the cloud subsystem so as to receive the task scheduling information, and then sending the task scheduling information to the side subsystem through an encryption transmission channel for execution, so that dynamic optimization and efficient utilization of resources of the whole network are realized; and a more efficient and safer collaborative system is constructed.

The invention relates to the technical field of key distribution, in particular to a multi-cloud architecture-oriented dynamic key fragmentation storage management method and system, which comprises the following steps of: dividing a manufacturing process of the manufacturing industry into a plurality of time sequence stages corresponding to different data access authority levels and security requirements, and constructing corresponding quantum state evolution operators; driving the key state to evolve among different time sequence stages through a quantum state evolution operator according to the progress of the manufacturing process, and adjusting the access authority of the key; quantum entanglement connection is established between supply chain nodes, the trust degree between the nodes is measured and quantized through the association of the quantum entanglement state, and the distribution weight of the key fragments is adjusted based on the change of the trust degree; collecting constraint parameters of production takt, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; and when it is detected that the constraint condition changes, recalculating the key fragment distribution scheme based on the quantum state constraint condition and the credibility, and synchronously distributing the key fragment distribution scheme to each supply chain node.

A computing services environment may include application gateways receiving application-layer request messages from various sources. The computing services environment may also include an autonomous agent platform configured to instantiate and execute an autonomous agent to evaluate network traffic associated with a portion of the computing services environment. The computing services environment may also include an orchestration engine configured to determine one or more mitigation policies corresponding with one or more of the application gateways based on identification of the application-layer distributed denial of service attack by the autonomous agent. The computing services environment may also include application-layer web application firewalls corresponding to the plurality of application gateways and implementing the one or more mitigation policies to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

The invention provides a method and a system for improving scalability of a micro-service, a medium and a processor, focuses on the problems of idle and waste of resources, blocked business collaboration and the like faced by a micro-service architecture, and provides the method for improving scalability of the micro-service. Micro-services are deployed on different cloud platforms according to business types to construct a multi-cloud architecture, and the deployment is optimized by using a quantization formula; performing hierarchical design on the micro-service and elastically storing data; deploying a Sidecar agent to form a service grid to manage communication; the system is monitored in an omnibearing manner, and micro-service instances are automatically expanded The method effectively improves the resource utilization rate, reduces the cost, and enhances the system performance and stability. Meanwhile, the invention further relates to a system applying the method, a computer readable storage medium and a processor, and an effective scheme is provided for solving the problem of micro-service scalability in the field of software development.

The invention discloses a BIM-based building structure detection method, and relates to the field of building structures, and the method comprises the following steps: S1, geometric detection: S11, detecting whether an artificially extracted building model and a live-action model are tightly nested or not, and detecting whether the geometric shape, size and position of a building meet design requirements or not; s12, constructing an analysis system; s13, based on the analysis system constructed in the step S12, carrying out automatic analysis on the average distance and error between each plane of the model and the original live-action model; s14, based on the analysis system constructed in the step S12, performing contrastive analysis on the three-dimensional live-action model one by one; s15, carrying out quality inspection statistical evaluation on the real scene model; s16, constructing a cloud architecture batch processing system; according to the BIM-based building structure detection method, the accuracy, the automation degree, the comprehensiveness and the efficiency of the BIM-based building structure detection method can be remarkably improved, and therefore powerful support and optimization means are provided for the design, construction and management stages of the building industry.

The invention provides a unified account management method and device under a multi-cloud architecture, and the method comprises the steps: constructing a globally unified account management system at a multi-cloud management platform layer, carrying out the butt joint with a plurality of heterogeneous cloud platforms through a standardized interface, building the dynamic bidirectional mapping of a cloud management layer account and a local account through an account mapping engine, and carrying out the dynamic bidirectional mapping of a cloud management layer account and a local account. A unified permission strategy is converted into a specific format of each platform through a permission converter, and atomicity of account life cycle operation is ensured through a distributed transaction coordinator. The method is also integrated with a unified authentication gateway, supports single sign-on, and realizes centralized identity authentication and access control of a plurality of cloud platforms. The method has the beneficial effects that account unified management and resource scheduling in a multi-cloud environment are realized, the resource visualization coverage rate is improved, the operation and maintenance complexity is reduced, and the management efficiency is improved.

The invention discloses a full-link monitoring and operation and maintenance management method for a point mall supply chain platform under a multi-cloud architecture, which can monitor full-link nodes of the supply chain platform in real time and provide a basis for optimizing resource configuration and improving system performance. The monitoring data is analyzed and processed through the star large model, and when the monitoring data of the system is found to be abnormal, an alarm notice can be sent out in time to remind operation and maintenance personnel to carry out fault processing, so that problems during use of a user are avoided. When it is monitored that some resources are low in long-term utilization rate and zombie applications may occur, an alarm notice is sent to remind operation and maintenance personnel to optimize resource allocation, and cost is saved. Stable operation of the point shopping mall supply chain platform is guaranteed, and user experience is improved. The stable platform operation and the timely fault processing can improve the system performance, optimize the operation cost, enhance the credibility and loyalty of the user to the platform, and promote the improvement of the activeness and retention rate of the user. According to the full-link monitoring and fault processing method under the multi-cloud architecture, unified management and monitoring of a plurality of cloud platforms can be realized, so that the management process is simplified, the management cost is reduced, and the flexibility and expandability of the whole system are improved.

The invention discloses an early cancer screening model based on a cloud architecture and a construction method, and particularly relates to the technical field of medical artificial intelligence. The method comprises the following steps: firstly, collecting and normalizing heterogeneous medical image and biomarker data of a plurality of geographic areas, and constructing a unified data set; then extracting image features and marker features by adopting multi-task learning, fusing equipment and region attributes, establishing a joint representation vector, and training a preliminary screening model; after the model is deployed to a cloud end, target area data is adapted through online transfer learning, and an area model is generated; further extracting an activation heat map, identifying a weak signal high response area in combination with sensitivity analysis, comparing with manual annotation, constructing a local weight adjustment strategy based on errors, optimizing the model, and outputting a final model; the method improves the recognition capability of early lesions under the condition of low signal-to-noise ratio, is suitable for remote and basic screening scenes, and has a high practical value.

The invention discloses an ultra-low delay smart home cooperation system and method based on an edge computing gateway, the system comprises smart home equipment, the edge computing gateway and a cloud server, and the edge computing gateway comprises a local storage module, a data processing module, a communication module and an equipment cooperation control module. Four core units including a local storage module, a data processing module, a communication module and an equipment cooperative control module are integrated through an edge computing gateway to form an acquisition-processing-control localized closed loop, and real-time data processing, equipment state monitoring and cooperative decision are sunk to an edge layer. The method breaks through the hundred millisecond delay bottleneck of the traditional cloud architecture, realizes millisecond response speed, and in addition, only uploads desensitized abstract data to the cloud by only performing necessary processing on the original data of the equipment at the edge gateway, thereby avoiding the risk of user privacy disclosure.

The invention relates to the technical field of construction safety management, in particular to a cloud platform-based construction industry operation condition risk evaluation system, which integrates cloud computing, Internet of Things and AI (artificial intelligence) technologies, and constructs a whole-process safety management system. Risk indexes are intelligently calculated in combination with an improved LEC-M model, four levels of risk levels are divided, and parameter customization is supported; multi-mode early warning is automatically triggered based on a risk threshold value, and rectification closed-loop management is realized through dynamic recheck; cloud architecture deployment is adopted, multi-terminal access and data interaction are supported, and data security is guaranteed through HTTPS encryption and an AES-256 technology; and the supervision docking module realizes real-time sharing of risk data, provides risk map visualization, historical data mining and AI auxiliary analysis, and supports risk trend prediction and intelligent recommendation of rectification schemes. According to the system, the accuracy and efficiency of building safety management are improved, and the intelligence from risk identification to supervision decision making is realized.

The invention discloses a multi-channel GNSS (Global Navigation Satellite System) navigation signal simulation system based on a cloud architecture, which adopts a distributed system architecture based on the cloud architecture to realize parallel testing of different spacecrafts, simulation of the different spacecrafts at any moment and random access of user tracks. A modular general signal simulation module and unified interface parameters are designed, the frequency point reconfigurable function is achieved, hardware is not changed, and the system is suitable for compatible simulation and testing of any constellation and any frequency point combination of a user terminal. The test equipment configuration number is reduced, the joint test requirements of parallel modular flight, butt joint and the like of multi-model spacecrafts are met, and the development cost is reduced.