566 results about "Data collaboration" patented technology

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

The invention discloses an engineering safety progress intelligent monitoring method based on multi-source data collaboration, which relates to the technical field of intelligent engineering monitoring, and comprises the following steps of: mapping multi-source engineering monitoring data into nodes and edges of a graph in real time by utilizing an incremental graph updating algorithm, generating a dynamic knowledge graph, and generating a dynamic mapping result; performing graph traversal on the dynamic knowledge graph through an association subgraph extraction algorithm, extracting a security event matrix and a project progress matrix, calculating an SPI index value by using a dynamic weighted fusion algorithm, synchronously performing multi-threshold interval grading on the SPI index value, generating an SPI early warning level, performing state coding on the SPI early warning level, and generating a comprehensive feature vector; according to the method, data of different types can be effectively integrated and a basis is provided for formulating a targeted engineering safety progress solution through an incremental graph updating algorithm and a Bayesian causal graph model, and node probability distribution characteristics are extracted by using a forward propagation layer. The method is advantaged in that the incremental graph updating algorithm and the Bayesian causal graph model are utilized to effectively integrate data of different types and provide a basis for formulating a targeted engineering safety progress solution.

The invention relates to the technical field of data processing, and discloses a risk prediction method and system based on dynamic aggregation and privacy protection. The method comprises the steps that multi-source heterogeneous data are collected and subjected to standardization processing; constructing a local risk association graph containing nodes, edges and topological attributes; inputting the map into a federated map neural network, and performing calculation through a local difference privacy mechanism to obtain fusion risk representation; and performing multi-level feature extraction and grade division on the risk representation to generate a comprehensive risk score and an early warning signal. Dynamic aggregation of multi-source heterogeneous data is achieved, data collaboration among multiple mechanisms is achieved while data privacy is protected, the accuracy and interpretability of risk prediction are improved through multi-level feature extraction, cross-mechanism risk knowledge fusion can be conducted under the condition that original data are not shared, and the risk prediction efficiency is improved. And the real-time performance and the accuracy of a risk prediction result can be ensured.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

The invention provides a cardio-pulmonary resuscitation training system based on multi-modal artificial intelligence in combination with a virtual reality technology, and relates to the technical field of virtual reality, and the system comprises a multi-modal sensing module which is used for collecting posture data, mechanical data and environment data; the multi-source data collaboration module is used for generating an operation quality evaluation parameter set and establishing a mapping relation between the operation posture and the pressing efficiency; the virtual scene construction module is used for generating a virtual first-aid environment according to a preset first-aid scene template and the operation quality evaluation parameter set; the decision module is used for generating a feedback control signal; and the interactive closed-loop execution module is used for inputting the feedback control signal into the virtual first-aid environment and updating the operation quality evaluation parameter set according to the operation data after feedback response. According to the invention, standardized, intelligent and scene-diversified cardiopulmonary resuscitation skill training can be realized, and the technical problems of inaccurate posture evaluation, non-personalized feedback, single scene, high resource dependence and the like in traditional training are solved.

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

The invention discloses a multi-mode city security video abnormal behavior real-time detection system and method, and belongs to the technical field of artificial intelligence and city security, and the system comprises an edge node module which is used for collecting a local video stream and sensor data, and executing local model training and real-time abnormal detection; the central coordination server module is used for federal model parameter aggregation, global model distribution and dynamic resource coordination; the multi-modal data fusion module is used for realizing cross-modal feature fusion through space-time alignment and an attention mechanism; the dynamic federation strategy module is used for calculating a load and a network state according to an edge node and adaptively adjusting a model parameter aggregation frequency; and the alarm linkage module triggers a local sound-light alarm when an abnormal behavior is detected, and encrypts and transmits event information to a command center. According to the method, the problems of privacy leakage, bandwidth pressure and multi-modal data collaboration in the prior art can be solved, and on the premise that data privacy is guaranteed, calculation overhead is minimized, and heterogeneous equipment is adapted.

The invention belongs to the technical field of smart city construction and intelligent construction management, and particularly relates to an intelligent scheduling and cooperation system and method for the whole life cycle of city reconstruction. The system comprises an edge sensing layer, a digital twinning network module, a space-time brain module, a block chain collaborative trust layer and an XR collaborative interaction layer. The system collects multi-source data of a construction site in real time through a 5G edge device, realizes fusion modeling of BIM, GIS and TIN models by using a graph convolutional neural network, and performs multi-target scheduling optimization in combination with meta reinforcement learning and a dynamic graph neural network; and meanwhile, construction task verification and fund payment linkage is realized through a block chain smart contract, and immersive cooperation and visual acceptance are provided in cooperation with an AR / VR platform. According to the method, the problems of model splitting, scheduling response lagging, low data collaboration efficiency, insufficient credibility of the performance process and the like in the urban reconstruction project are solved, and the intelligence, transparency and automation level of the construction process is remarkably improved.

The invention discloses a forest grass wet carbon sink metering method and system, and relates to the field of carbon sink metering, and the method comprises the following steps: 1, collecting and processing multi-source data, 2, fusing the data, aligning the multi-source data, adopting a geographic registration and time synchronization technology, and unifying a spatial resolution and a timestamp, and 3, extracting features. 4, constructing a carbon sink dynamic model; and 5, carrying out carbon sink visualization and decision support. According to the forest and grass wet carbon sink metering method and system, multi-source data collaboration is achieved, optical, radar and laser radar data are fused, the defect that traditional carbon sink metering depends on ground sample plot survey or single remote sensing data is overcome, carbon sink contributions of forests, grass and wet lands are accurately distinguished, the overall spatial resolution is higher, cloud and mist interference is avoided, the single data source error is solved, and the measurement accuracy is improved. Through multi-source data fusion and dynamic model optimization, high-precision measurement and real-time monitoring of forest grass wet carbon sink are realized.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention relates to a BIM-based building structure intelligent management method. The method comprises the following steps: step 1, dynamic monitoring factor mining and multi-modal sensing: collecting meteorological hydrology, geological conditions, microenvironment and natural disaster historical data; 2, neural operator-driven virtual-real fusion dynamic analysis: converting the format of the collected data, mapping the data into a BIM model, integrating a virtual-real synchronization engine, and pre-training a Fourier neural operator through real-time displacement and strain data reverse calibration model boundary conditions in a construction stage, and establishing a direct mapping relationship between an external load and a structural response; 3, multi-modal data collaboration and hierarchical permission interaction: forming a multi-dimensional feature matrix through association of timestamps and space coordinates; 4, multi-modal graph convolutional network damage identification: learning dynamic relevance between nodes through an adaptive graph convolutional layer, and extracting damage sensitive features; and step 5, dynamic life early warning of digital twinning drive: predicting the remaining service period of the structure.

The invention relates to the technical field of railway engineering, and provides a cantilever hoop attitude detection method and system based on visual inspection, and the method comprises the steps: calculating the dynamic shrinkage rate and stress distribution gradient of a metal material at a preset low temperature, and generating a deformation compensation coefficient; acquiring surface geometric deformation data of the cantilever hoop at a preset low temperature and non-uniform distribution data of a surface temperature field; and obtaining corrected deformation data, obtaining aligned data based on the corrected deformation data and the non-uniform distribution data, and performing low-temperature deformation field reconstruction on the aligned data to obtain a low-temperature deformation field reconstruction result so as to extract an overlapping region of a surface deformation path and a temperature abnormal boundary of the cantilever hoop. According to the surface deformation path and the geometric center offset of the overlapping region, calculating a posture offset parameter of the cantilever hoop, and generating a low-temperature deformation early warning signal; according to the method, the problem of insufficient detection precision caused by data collaboration loss is solved, and high precision, high robustness and scene adaptability of high-iron cantilever hoop low-temperature deformation detection are realized.

The invention discloses an enamel product and an intelligent spraying system thereof, and belongs to the technical field of enamel product spraying, and the enamel product comprises a high-precision visual guidance module, an environment intelligent control module and an auxiliary function module, the intelligent environment control module is used for monitoring and dynamically regulating and controlling parameters such as temperature and humidity and dust concentration of a spraying environment in real time, ensuring stable coating quality and reducing energy consumption, and the auxiliary function module is used for realizing accurate spraying track planning and quality control through process connection, safety guarantee and data collaboration. And high efficiency and reliability of the spraying process are guaranteed. On the basis that spraying of the enamel product is achieved, the quality of the whole process is improved, and comprehensive benefits can be optimized.

The invention relates to a network security vulnerability intelligent detection method based on artificial intelligence and big data, and the method comprises the steps: carrying out the dynamic time synchronization processing of multi-modal network security data, and generating a multi-source data flow with aligned time sequences through cross-modal correlation analysis; extracting cross-modal features from the data stream, and performing semantic fusion on the cross-modal features in combination with a vulnerability knowledge graph to generate a multi-dimensional feature vector; training the multi-dimensional feature vector through a hybrid model to obtain a vulnerability detection model, detecting real-time network behavior data by using the model, and outputting a vulnerability probability and an abnormal risk score; and performing automatic vulnerability verification according to the vulnerability probability and the abnormal risk score to obtain a verification result, and updating the vulnerability detection model according to the result. The system can effectively improve the accuracy, timeliness and stability of network security vulnerability detection and reduce the false report and missing report rate through multi-modal data collaboration, hybrid model dual detection and closed-loop optimization mechanisms.

The invention discloses a data collaborative directory management method and system, and relates to the technical field of government affair informatization, and the method comprises the steps: generating a directory snapshot containing a global hash value; calculating a directory node hash value of each edge directory node based on the directory snapshot identifier; eliminating clock drift interference through time sequence alignment and dynamic tolerance filtering; inputting the Hash difference time sequence into an isolated forest model to judge a substantial change node; based on the difference entry number and the historical calling weight, combining a dual-threshold rule and an online dichotomy model to hierarchically synchronize requirements; according to a grading result, matching an incremental push mode or a full pull mode, and constructing a synchronous transaction context containing an exponential backoff retry mechanism; synchronous operation is executed through the two-stage state model, and compensation rollback is triggered when the synchronous operation fails; and calculating a health index of the substantially changed node, dynamically selecting a self-healing action and optimizing system parameters. The problem of misjudgment caused by time sequence drift is effectively solved, the synchronization efficiency is improved, and the consistency of directory versions is guaranteed.

The invention discloses an advertisement putting strategy optimization method and system based on preference data collaboration, and relates to the technical field of advertisement putting, and the method comprises the steps: collecting the internal user data of a local advertisement putting platform, obtaining the external user data of a third-party platform through a safety data interface, carrying out the integration and preprocessing of the data, and obtaining an advertisement putting strategy; extracting explicit preference data and implicit preference data; a user-advertisement scoring matrix is constructed based on the preference data, and user preference vectors are generated after decomposition; acquiring copywriting text, image and category information of an advertisement to be put, constructing an advertisement feature expression model and generating an advertisement feature vector; calculating a matching degree score based on the user preference vector and the advertisement characteristic vector, and dynamically optimizing an advertisement putting strategy according to the score; according to the method, through comprehensive utilization of explicit and implicit preferences of the user, more accurate user interest modeling and advertisement matching are realized, and the advertisement putting effect and putting efficiency are effectively improved.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention belongs to the technical field of intelligent information, and particularly discloses a government affair intelligent safety protection and efficient cooperation system which comprises core modules of intelligent safety protection, cross-department cooperative office and the like. By means of deep learning multi-mode detection, block chain credibility verification, reinforcement learning process optimization and other algorithms, security threat accurate identification, data security sharing and efficient operation of a business process are realized. And meanwhile, embodiment schemes such as federated learning and differential privacy cross-regional data collaboration, space-time attention emergency resource scheduling and the like are integrated, so that security threats can be effectively resisted, and the cross-department collaboration efficiency is improved. Through intelligent decision support and data value mining, powerful support is provided for government affair decision, safety and stability of a government affair system are guaranteed comprehensively, and government affair service quality and management efficiency are remarkably improved.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

The invention relates to the technical field of data processing, in particular to a river comprehensive management and control data collaborative optimization method and system based on the Internet of Things. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data, an analysis demand report and a river state evaluation report acquired by an Internet of Things sensor, preprocessing to obtain multi-source preprocessed data, an analysis demand vector and a river state evaluation vector, and inputting the multi-source preprocessed data, the analysis demand vector and the river state evaluation vector into a dynamic multi-source heterogeneous data fusion module based on an attention mechanism to obtain dynamic fusion data; then, inputting the dynamic fusion data into a cross-domain associated riverway state collaborative prediction module to obtain a collaborative prediction result of the riverway state; and finally, inputting the dynamic fusion data and the collaborative prediction result into a multi-target collaborative optimization module, dynamically adjusting the target weight and the decision priority according to the module input, solving by using a multi-target optimization algorithm, obtaining an optimization decision parameter, and performing optimization according to the optimization decision parameter. The method can effectively improve the management decision level of the river data.

The invention relates to the technical field of grain safety management, and particularly discloses a grain safety traceability system and method, and a distributed data collection module obtains data in real time through an Internet of Things sensor network. And the multi-source data fusion engine performs standardization processing on the heterogeneous data by adopting a space-time alignment algorithm to generate a traceability data packet. And the block chain evidence storage node adopts fragmentation storage, and the traceability data packet is stored in a multi-chain parallel structure in a distributed manner. And the risk early warning analysis platform constructs a quality safety evolution model based on deep reinforcement learning, establishes an automatic triggering mechanism, and automatically sends a risk early warning data packet with a block chain evidence to the supervision platform when it is detected that grain is abnormal. According to the method, standardized processing, space-time consistency calibration and cross-link data collaborative analysis of heterogeneous data are realized by utilizing a multi-source data fusion engine; and the risk early warning analysis platform sends a risk early warning data packet to the supervision platform in time by using an automatic triggering mechanism, so that a powerful means is provided for grain safety risk prevention and control.

The invention relates to the technical field of coal mine transportation system state monitoring, in particular to a cloud side-end collaborative coal mine transportation system state monitoring method and a cloud side-end collaborative coal mine transportation system state monitoring model. In order to solve the problems of difficult multi-modal data fusion, poor dynamic response and insufficient model generalization of a coal mine transportation system, the invention provides a new cloud side-end collaborative coal mine transportation system state monitoring method. Comprising the following steps of multi-modal data acquisition, multi-modal data synchronous transmission, edge computing node data compression, lightweight sensitive feature extraction and transmission, federal learning cross-mine data collaboration, cloud teacher large model construction, knowledge distillation driven student model construction and diagnosis early warning. According to the method, knowledge distillation and federal learning are fused, so that real-time monitoring, accurate diagnosis and efficient maintenance of the coal mine transportation system are realized.

The invention discloses a multi-dimensional data collaborative analysis early warning method based on intelligent operation and maintenance of a power transmission network, and relates to the technical field of power transmission network early warning, and the method comprises the steps: collecting target data of target equipment, and obtaining sudden change data of a power load in a system; performing correlation analysis on the instant characteristics of the electrical quantity and the dynamic change of the power grid structure in combination with the sudden change data of the power load to obtain correlation factors capable of predicting a fault; according to the power transmission network intelligent operation and maintenance early warning method, the problem that fault early warning is inaccurate and not timely due to insufficient data collaboration and early warning response lag in power transmission network operation and maintenance is solved, and the effect of improving the early warning precision and response speed of power transmission network intelligent operation and maintenance is achieved.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

The invention discloses a BIM-based construction engineering visual management method and system, and relates to construction engineering management, and the method comprises the steps: collecting a data set containing geometric information, attribute information and associated information; dividing the data set into a plurality of hierarchical subsets according to the hierarchical relationship of the building engineering BIM model; an improved DBSCAN clustering algorithm is adopted to perform clustering analysis on each hierarchy subset; classifying and archiving the data set according to the data cluster, and generating a semantic association data collaboration array A according to a classifying and archiving result; carrying out dimension reduction processing on the semantically associated data collaboration array A; constructing a spatial index of the dimension reduction data array by adopting a density-based spatial index method; and dividing the building engineering BIM model into a plurality of partitions by using a distributed computing framework, and performing distributed matching on the partitions and the spatial index to obtain a data matching point set. For the problem that the BIM-based building engineering high-dimensional data clustering precision is low in the prior art, the analysis precision is improved.

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

The invention discloses an emergency rescue data privacy protection and collaborative analysis system based on federated learning, and belongs to the technical field of emergency rescue data privacy protection and collaborative analysis. A dynamic trust evaluation module; the federated learning privacy protection engine module is used for realizing cross-domain data collaborative training by adopting a secure multi-party computing model; and the block chain intelligent contract module is used for deploying a data access control strategy and a credible auditing rule. According to the method, a global trusted environment is established through an end-side cloud three-level trust chain, the reliability of equipment is quantified through dynamic trust evaluation, data collaboration of privacy protection is realized through federated learning, an auditing strategy is automatically executed through an intelligent contract, an efficient encryption technology for guaranteeing safety and effectively reducing resource consumption is adopted, and the urban emergency data safety problem is solved.

The invention discloses a multi-time-scale coordination control device and method for a novel electric power system, and relates to the technical field of electric power automation. Aiming at the problems of difficulty in multi-source data collaboration, complexity in control strategy optimization and insufficient scene adaptability in a novel power system, the device realizes flexible deployment and mobile operation through a high-strength aluminum alloy frame integrated modular functional assembly; the environment monitoring module is used for high-frequency collection of multi-parameter environment data, and equipment operation safety is guaranteed; a plurality of communication protocols are supported by means of a multi-time scale control module, and distributed energy equipment is seamlessly connected; based on three core units of a data processing center module, a scene simulation unit and a cooperative control center module, second-to-hour-level electric parameter data generation, complex scene simulation verification and intelligent generation, execution and optimization of a control strategy are realized; and finally, under the complex working condition of high-proportion new energy access, the control precision, the operation stability and the comprehensive benefits of the power system are remarkably improved.

The embodiment of the invention provides a vehicle driving intelligent control method and device. Multi-dimensional data collection is achieved through an edge box, an intelligent camera and environment detection equipment. A regional linkage network is dynamically divided based on a vehicle driving track, and a traffic flow prediction and risk assessment model is trained at an edge end. The cloud platform integrates the data of the plurality of analysis and identification units, triggers an area linkage mechanism according to a road passing strategy and a risk assessment value, and generates accurate early warning information including real-time road conditions, suggested detouring routes and safe vehicle speeds. According to the method, the defects of the traditional technology in the aspects of scene perception, data collaboration, early warning decision and the like are effectively overcome, and the intelligent level and the service quality of the auxiliary driving system are remarkably improved.