183 results about "Park management" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention relates to the field of digital twinning technology and intelligent energy management, and discloses an industrial park dynamic resource regulation and control method based on digital twinning, which comprises the following steps of: constructing a digital twinning platform: acquiring operation states and environmental data of various devices in a park in real time through an Internet of Things sensor network, establishing a digital twin model of each device, and updating the virtual state of the device in real time through data transmission; real-time data of the devices are input into the platform, the state of each device is dynamically updated, synchronization of the digital twin model and the physical device is ensured, and accurate simulation of the current state of the device is provided. According to the invention, by integrating the park management system and the closed-loop control system, real-time collaborative scheduling of park equipment and energy management is realized, energy distribution is optimized, and balanced utilization of various energies is ensured; meanwhile, real-time feedback and error correction are realized through a closed-loop control mechanism.

The invention discloses a smart park safety protection system and method based on smart video analysis and multi-mode integration, and relates to the technical field of smart park management. The system comprises a camera, an environment microphone and an access control terminal which are deployed in a park, an edge computing node is used for extracting structured video, audio and access control features, a central server realizes multi-modal space-time alignment through a self-calibration module, a multi-modal fusion module constructs a joint representation vector based on a low-rank tensor algorithm, and the joint representation vector is used for realizing multi-modal space-time alignment. And the security decision module identifies an abnormal event and triggers a response strategy in combination with a classification and anomaly detection model. According to the invention, multi-mode cooperative identification, high-robustness behavior analysis and intelligent response linkage are realized, and the real-time performance, accuracy and intelligent level of a park security system are improved.

The invention discloses a smart park digital twinborn management platform and a virtual-real interaction method, and belongs to the technical field of park management. Structural states, equipment behaviors, environment disturbance and user activity data of entity objects are hierarchically collected according to frequency, and a plurality of digital twinborn models are synchronously constructed based on a scene labeling mechanism; the platform can generate a control strategy and mark a prediction channel according to the model evolution trajectory, then acquire entity response and perform graph structure comparison with a prediction path, identify response deviation and generate a deviation graph; the platform further constructs an error learning weight according to the deviation map, drives a twin model to perform progressive optimization adjustment of confidence parameters, and can trigger structure-level network evolution to realize dynamic enhancement of a model structure and prediction capability; compared with the prior art, the invention provides a smart park digital twinning implementation scheme supporting virtual and real closed loops, self-learning evolution and structure reconstruction.

The invention relates to the technical field of smart park management, in particular to an analysis monitoring method and system based on a smart park operation state, and the method comprises the following steps: obtaining park equipment operation data parameters and associated environment parameters, analyzing the equipment operation difference, recognizing the equipment operation state, and predicting the state change trend. And calculating an abnormal event propagation path and rate, determining a response priority, constructing early warning information, and obtaining a smart park monitoring record. According to the method, the accuracy of park equipment state identification is improved by combining the fluctuation analysis of the equipment operation data and the environmental parameters, early warning of abnormal states is realized by combining the time sequence correlation analysis and equipment state prediction, the influence of equipment faults on park operation is reduced, and the accuracy of park equipment state identification is improved through the topological structure and propagation probability calculation. The method achieves the prediction of an abnormal event propagation path, enhances the accuracy and timeliness of emergency response, improves the real-time monitoring and fault prediction capability, and guarantees the efficient operation and safety of a park.

The invention discloses an event early warning efficient management method and system based on park management, and relates to the technical field of safety management, and the method comprises the steps: constructing a hexagonal cellular unit layout, generating an equipment registry and a network topology structure, and forming a structured data set through data alignment and sliding window verification; the edge server loads an initial weight, calculates a dynamic weight matrix based on the LSTM network, and generates a high-order feature vector set in combination with the graph neural network; inputting the high-order feature vector set into a space-time prediction model to generate an environment evolution trend prediction value, fusing network topology and BIM model parameters to calculate a dynamic risk index, and triggering graded early warning; the command center analyzes the early warning signal to generate a fusion visual interface, historical cases are matched through federal learning, a resource allocation scheme is optimized, and an optimal disposal strategy is output through Monte Carlo simulation. By dynamically adjusting the weight of the sensor, the weight of the infrared sensor in the high-temperature-sensitive area is increased, and the abnormality capturing efficiency is improved.

The invention discloses an intelligent park management system based on cloud computing and Internet of Things, and relates to the technical field of park management systems, and the system comprises a dynamic digital twinborn body module which comprises a heterogeneous data fusion layer which integrates a BIM model, LiDAR point cloud, an unmanned aerial vehicle inspection image and distributed loT sensor data, and eliminating timestamp deviation of multi-source data by adopting a space-time alignment algorithm, and generating a millimeter-level precision three-dimensional park twin model. Through the construction of the high-precision digital twinborn model, the panorama and equipment operation state of the park can be accurately presented in real time, the timestamp deviation and spatial registration error of multi-source data are eliminated, the real-time performance and accuracy of the data are improved, a solid data basis is provided for subsequent intelligent simulation and early warning decision, and the response and monitoring capabilities of the system are improved.

The invention discloses a park management method based on digital twinning, particularly relates to the field of park operation, and is used for solving the problem that a multi-source event is difficult to stably chain under the conditions of compensation delay and evidence gap and causes misalignment of disposal triggering. Comprising the following steps of: registering an event in a pool to generate an event number and writing the event number into a platform arrival moment; screening and mapping an anchor point event to a park object identifier and a process stage identifier to generate a management token; checking a stage sequence and an evidence item requirement set according to an event chain rule table in event chain construction and generating a conflict point list; the delay portrait library generates a credible mark when a source is generated and forms a reverse sequence violation spectrum; the digital twin object relation graph forms an influence domain span mark; the closed table is returned to obtain a disposal convergence guarantee level; the cost-sensitive decision tree outputs a disposal template identifier, and the disposal template table generates a management action instruction and a trigger voucher and records an instruction acceptance identifier; and returning, comparing and freezing the exception management token, and updating the event chain rule table and the delay portrait library.

The invention belongs to the technical field of pollution reduction and carbon reduction management, and particularly relates to a pollution reduction and carbon reduction platform which comprises a data acquisition module, a data analysis and prediction module, a data transmission optimization module, a business display module, three major application modules, a heterogeneous data fusion and security sharing module, a management decision support module, a public service module and a transformation and promotion module. According to the method, a mixed mode of edge calculation, double-queue synchronization and opportunistic synchronization is introduced, the idea of a time delay tolerant network is innovatively combined, accurate synchronization and transmission solutions for different real-time requirements are provided, and data guarantee is provided for efficient response and accurate decision of a park management system.

The invention provides a smart park operation and maintenance management method and system based on CIM and a medium, and belongs to the technical field of smart park management. The method comprises the following steps: constructing a CIM digital twinborn model based on a park building group, a geographic position, Internet of Things equipment and business data, and synchronously extracting equipment state and energy consumption information; analyzing the state data by using the equipment fault prediction model and generating a repair work order to realize predictive maintenance; performing gridding processing on the energy consumption data, generating a thermodynamic diagram and identifying an abnormal energy consumption area; and positioning a high-energy-consumption grid by combining an energy consumption analysis model, and generating a comprehensive operation and maintenance scheme of equipment regulation and control, energy scheduling and system optimization through matching of an energy management strategy library. According to the invention, the digital twinning technology and the CIM platform are fused, so that full-life-cycle management and dynamic energy consumption optimization of the equipment are realized, the operation and maintenance efficiency is remarkably improved, the failure rate and energy waste are reduced, and both economy and environmental protection are achieved.

The invention relates to the technical field of resource scheduling, in particular to a smart park resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous information network representing the current state of a park; performing feature extraction on the heterogeneous information network by adopting a graph attention convolutional network, and generating a state embedding vector fused with high-order neighborhood information; inputting the state embedding vector into a scheduling strategy model, and predicting the expected variation of the candidate scheduling action to future park energy consumption, security index and traffic efficiency; according to the state embedding vector and an external management instruction, determining an optimization target of a current scheduling period from a preset operation normal form, and allocating a dynamic weight; and calculating a comprehensive utility score of each candidate scheduling action, and issuing and executing the candidate scheduling action with the highest score as a final scheduling instruction. According to the invention, the refinement level and the overall operation benefit of park management are improved.

The invention relates to the technical field of smart park integrated management, and discloses a smart park comprehensive management and control system and method based on multi-modal data analysis, and the method comprises the steps: obtaining a park multi-dimensional real-time data flow, and extracting a current situation feature vector and a personnel distribution thermodynamic diagram; analyzing the historical emergency event data sequence by using a space-time convolutional neural network, and outputting an event type identification vector; and generating a dynamic evacuation path planning and resource scheduling scheme based on a graph optimization algorithm, constructing a minimum cost flow optimization problem taking the weighted sum of time cost and waiting time as a target function, and solving to obtain an optimal people flow allocation scheme. The problem that emergency response cannot adapt to dynamic environment changes is solved. Experiential knowledge cannot be effectively inherited and utilized. And the emergency response system is slow in response and improper in resource allocation.

The invention discloses an artificial intelligence and Internet of Things smart park integrated management method and system, and belongs to the technical field of smart park management, and the method comprises the following steps: S1, collecting multi-source operation data of Internet of Things smart park equipment in real time, and generating a multi-source data stream; s2, generating a multi-dimensional feature set based on the multi-source data stream; s3, analyzing a behavior mode of an administrator of the Internet of Things smart park based on the historical operation record, and generating an administrator behavior portrait and an event priority evaluation result; s4, dynamically constructing a visual management interface according to an event priority evaluation result, and generating a one-key operation instruction set; and S5, collecting operation response data of the administrator on the visual management interface, and updating the administrator behavior portrait. The system comprises a data acquisition module, a feature analysis module, a priority evaluation module, a generation module and a feedback module. According to the method, the complex state of the park is comprehensively captured through the steps S1 to S5, and the problem of information fragmentation is solved.

The invention relates to the field of park management, and particularly discloses a smart park management system and method based on the Internet of Things, and the method comprises the steps: firstly obtaining park air quality parameter data collected by a plurality of sensors and park noise parameter data, collected by a sound sensor, at a plurality of preset time points, and then employing a deep learning technology to carry out the recognition of the park air quality parameter data and the park noise parameter data; and feature extraction and correlation analysis are carried out on the two parameters, and finally, whether to send out park environment detection early warning is judged through a classifier, so that the air pollution problem is found in time, related personnel are helped to quickly respond, and the health of park residents is protected.

The invention relates to the technical field of park management, in particular to a smart park energy efficiency management system and method based on an IBMS. The method comprises the following steps: acquiring real-time park data; based on a preset energy efficiency prediction model, analyzing the real-time park data, and predicting energy efficiency change; analyzing the energy efficiency change based on the real-time park data, and determining the credibility of each energy efficiency change; and determining an energy efficiency management scheme according to the credibility. Energy distribution is optimized, and energy waste is reduced. The credibility evaluation model is helpful for identifying the reliability of the prediction result, and ensures that the prediction result is not trust blindly. By analyzing the equipment health degree index and the fault reason, the credibility of each energy efficiency change is more accurately judged, so that wrong scheduling caused by inaccurate prediction is avoided. And according to the credibility of the prediction result, a corresponding energy efficiency management scheme is generated, so that the accuracy and effectiveness of energy scheduling are ensured, and energy waste and equipment failure are avoided.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention belongs to the technical field of lighting facilities, and relates to a park management controllable lighting street lamp. The device comprises a lamp pole, a fixing block and a pressing plate; the fixing block is installed at the top of the lamp pole, a lampshade is installed at the lower end of the fixing block, and an illumination assembly is installed in the fixing block; a transparent plate is installed on the outer side of the lampshade, a trap lamp is arranged between the lampshade and the transparent plate, and the pressing plate is arranged on the fixing block in a sleeving mode in an up-down moving mode. A scraping rod is arranged on the outer side of the transparent plate in a sliding manner; a driving assembly for driving the scraping rod to move is arranged on the fixed block; an opening is formed in the pressing plate; the scraping rod supports the pressing plate so that an insect trapping space can be formed between the pressing plate and the transparent plate. And the transparent plate is cleaned, so that the light transmission of the transparent plate is ensured, and the insect trapping efficiency of the insect trapping lamp is ensured. And the pressing plate is pushed to rotate through the scraping rod, then the connecting base rotates, the light-transmitting plate is wiped and cleaned through the cleaning plate, and the lighting effect is guaranteed advantageously.

The invention discloses a cloud-edge collaborative multi-modal reasoning method and system for park governance, and relates to the related field of multi-modal intelligent reasoning, and the method comprises the steps: collecting and processing local multi-modal sensing data through a plurality of edge nodes, carrying out the single-modal reasoning based on a lightweight AI model, and generating a plurality of pieces of edge event description information; based on a dynamic triggering mechanism, evaluating the multiple pieces of edge event description information to determine whether an event processing strategy judges whether to trigger a collaborative reasoning request; for the marked edge node triggering the request, based on an attention mechanism, performing fusion reasoning on the multi-modal feature information uploaded from the marked edge node to generate a joint reasoning result; and the cloud generates a governance instruction according to the joint reasoning result and issues the governance instruction to the marked edge node to drive and execute a park governance action. The technical problem that the real-time performance and the reliability of existing multi-mode reasoning oriented to park management are insufficient is solved, and the technical effect of improving the real-time performance and the reliability of park management is achieved.

The invention relates to a park green land service level optimization evaluation method and device, a terminal and a medium, and the method comprises the steps: coding environment monitoring data and tourist positioning data into a unified space-time grid through a multi-source data fusion technology, and constructing a dynamic supply and demand prediction model; based on collaborative analysis of vegetation irrigation capability and tourist gathering intensity, quantifying a time-space conflict degree between a resource strategy and peak activities; a dynamic weight distribution mechanism is adopted to optimize collaborative parameters of an irrigation period and a diversion path, and an adjustment scheme considering both resource efficiency and tourist experience is generated; and finally, realizing closed-loop optimization of the park service level through multi-dimensional index fusion evaluation. According to the method, the problems of resource supply and tourist demand separation, response lag, experience decision deviation and the like in a traditional management mode are effectively solved, the irrigation conflict rate is remarkably reduced, the tourist satisfaction degree is improved, and a decision basis is provided for intelligent park management.

The invention belongs to the technical field of smart park management, and discloses an Internet of Things data fusion method for comprehensive management of a city-level smart park. The method comprises the following steps: constructing an odor spatial distribution model corresponding to each park, and carrying out chemical component analysis to obtain park odor characteristics of each park; comparing the park odor characteristics with a predefined normal odor baseline, and dynamically identifying abnormal odor characteristics corresponding to each park; determining a potential risk event corresponding to each park based on the abnormal odor characteristics; based on the odor spatial distribution model and the abnormal odor characteristics, reversely positioning the position of an abnormal source, and generating an odor source positioning map corresponding to each park; according to the potential risk event and the smell source positioning map, a targeted emergency response strategy is automatically generated and executed; according to the invention, a closed-loop management system from monitoring and early warning to emergency response can be formed, and the environmental risk prevention and control capability and the emergency response speed of the park are improved.

The application discloses a data management platform for smart park construction and belongs to the technical field of smart park management, and specifically comprises the following steps: collecting resource demand data and resource supply data of each enterprise in the park, performing demand feature extraction operation on the collected resource demand data to generate a structured demand description, performing supply feature extraction operation on the resource supply data to generate a structured supply description, calculating the matching degree between demand and supply based on the structured demand description and the structured supply description and generating an initial matching scheme, performing constraint condition checking operation on the initial matching scheme according to the park operation strategy to identify matching pairs that violate the constraint conditions, performing scheme adjustment operation on the matching pairs that violate the constraint conditions to generate an optimized matching scheme, and finally scheduling the park resources to complete resource data sharing between enterprises; and the application realizes intelligent management and efficient sharing of resource data in the park, and improves the overall resource utilization efficiency of the park.

The invention relates to the technical field of intelligent Internet of Things platforms, in particular to a smart park management platform for the Internet of Things and an intelligent algorithm, and the system comprises a terminal state sensing module, a response path combination module, a control path sorting module, a communication window adjustment module and a distribution execution regulation and control module. According to the method, equipment response records with associated numbers are extracted, a control set with a parallel relationship in a scheduling period is constructed, a control chain structure with a sequential triggering relationship is formed through connection in combination with the consistency of signal emission and a target response sequence in an instruction track, and paths are divided according to control sources, a number sequence and target types; and the initial time sequence and the response interval characteristics are matched to carry out channel grouping, so that the coordination relationship among multiple chains is enhanced, the hierarchical structure of path organization is enhanced, and the coherent operation capability of a control process in a multi-device scheduling scene, the instruction passing efficiency and the distributed regulation and control capability of communication execution are improved.

The invention relates to the technical field of park facility management, and provides a smart park management system based on big data and the Internet of Things technology, and the system comprises a digital twinborn modeling unit which is used for collecting park space data and constructing a dynamically updated park digital twinborn model; the environment monitoring units are distributed at preset position points of the park, monitor environment parameters in real time and upload the environment parameters to the cloud; the intelligent patrol unit moves according to a preset route and collects environment data and image information of the patrol route; and the data analysis unit is used for predicting environmental parameters of the patrol path based on the digital twinborn model and the environmental monitoring data. Through an innovative segmented prediction-deviation coupling algorithm, the system realizes the breakthrough progress of reducing the calibration deviation rate of the sensor. A risk coefficient Ri model of multi-parameter collaborative analysis is introduced, so that the false alarm rate of environment anomaly recognition is greatly reduced, and the common invalid overhaul problem in a traditional monitoring system is effectively avoided.

The invention relates to the technical field of park management, discloses a smart park management system, and aims to solve the problems of information islands, low management efficiency, resource waste, data security and the like in traditional park management. The system integrates user access, data management, intelligent model construction, information integration, request analysis, intelligent decision support, facility control and block chain verification and recording. Multi-type requests are received through a user access platform, a block chain data management center ensures secure storage of data, an intelligent model construction module realizes construction and training of a park management model, a park information integration module integrates various information of a park, and a user request analysis module analyzes user requests. The intelligent decision support module generates a management decision suggestion, the facility control module adjusts the facility state, and the block chain verification and recording module guarantees the authenticity and traceability of the data. According to the invention, comprehensive, intelligent and efficient management of the park is realized, and the overall operation level of the park is improved.

The invention provides a safety management and control method and system applied to an emergency material storage park, and relates to the technical field of emergency material storage management.The safety management and control method comprises the steps that firstly, a safety state link which is based on multi-source monitoring information, comprises material storage nodes, environment nodes and active nodes and forms a net-shaped structure through associated edges is constructed; tracking the evolutionary process, and recording the state information of each node and the associated edge along with time change to generate link evolutionary data; then, a management and control action set of a corresponding relation is formed based on link evolution data adaptation and node states; the management and control action set is sent to the park management and control system to drive an execution device to execute, and feedback data after execution is collected; and finally, calibrating the evolution trend of the safety state link according to the feedback data. Therefore, the safety management and control level of the emergency material storage park can be comprehensively improved.

The invention relates to the technical field of park management, in particular to an intelligent chemical park risk management and control system and method. The method comprises the following steps: acquiring an entrance monitoring image; analyzing the entering monitoring image, and determining the type of a vehicle to be entered; determining a vehicle danger coefficient according to the type of the to-be-entered vehicle; obtaining a vehicle reservation condition, and determining a lane queuing condition according to the vehicle reservation condition and the entrance monitoring image; and determining lane characteristics according to the danger coefficient and the lane queuing condition, and determining a lane queuing suggestion according to the lane characteristics. A safety priority basis is provided for lane distribution, and the probability of safety accidents caused by retention of high-risk vehicles is reduced from the source. The defect of queuing extension caused by non-reserved vehicles is eliminated, a complex scene is quantified through double data sources, and the queuing evaluation accuracy is improved. The balance between safety and efficiency is realized, the problem of lane feature staticization is eliminated, and the vehicle is prevented from entering a high-risk lane by mistake.

The invention provides a smart park digital management method and system, and belongs to the technical field of park management, and the method specifically comprises the steps: determining an illumination control scheme of a target region based on an abnormal duration interval and illumination brightness thresholds of different positions in the target region, when it is determined that the lighting control reliability of the target area meets the requirement according to the number of the lighting control schemes in the target area and the idle data of the street lamps in the different lighting control schemes, the street lamps in the lighting control schemes serve as target control street lamps; and according to the influence condition on the illumination control scheme of the street lamps in the target area when the target control street lamp breaks down, whether the target control street lamp needs to be subjected to access processing of the remote fault diagnosis module on the park control platform is determined, and the reliability of road illumination processing of the smart park is improved.

The invention discloses a smart park management platform based on park informatization construction, which belongs to the field of park management and comprises a data management module, a simulation modeling module, a scheduling evaluation module, an access control module, a block evidence storage module, an intelligent service module, a user interaction module, a closed-loop optimization module and a system management module. According to the method, accurate prediction of park resource requirements and dynamic calibration of the digital twinborn model can be realized, the park operation efficiency and the resource utilization rate are improved, and compared with traditional static authority management, internal threat risks are remarkably reduced, and a transparent basis is provided for security audit and responsibility definition.

The present invention relates to the technical field of three-dimensional visualization for park management, and specifically discloses a three-dimensional visualization-based intelligent park management platform, which includes an adaptive sensor calibration module that dynamically calculates the accuracy coefficient of sensor data to ensure the accuracy of data acquisition; a multi-dimensional data fusion and three-dimensional modeling module that calculates the adaptation coefficient by integrating multi-source data to evaluate the system's response ability to environmental changes; an intelligent image data registration module that optimizes the matching accuracy coefficient to improve the spatial docking accuracy between image data and the three-dimensional model; a real-time error monitoring and feedback optimization module that uses machine learning algorithms to correct the error between the sensor and the three-dimensional model, dynamically calculates the error correction coefficient to ensure data consistency; and a comprehensive performance evaluation module that obtains the comprehensive performance coefficient by normalizing and calculating the sensor data accuracy coefficient, adaptation coefficient, matching accuracy coefficient, and error correction coefficient, and optimizes the system operation strategy in real time.