336results about How to "Resource optimization" patented technology

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

The invention provides an intranet service quality optimization method and system based on deep reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional performance index data, carrying out the preprocessing, constructing an analytic hierarchy model, outputting the performance score of each layer, integrating the performance score of each layer, and outputting an intranet service quality score. Constructing a simulation environment, adjusting hyper-parameters of the adaptive deep reinforcement learning model, continuously learning in an actual intranet environment, determining multi-dimensional rewards, performing multi-target optimization, and generating a multi-target optimization result; and setting a hierarchical performance index threshold, if a hierarchical optimization strategy is triggered, adjusting a network topology structure, adjusting resource allocation and service configuration, adjusting weights of different indexes in the service quality evaluation model, updating a reward function, solving an optimal service decision, generating an optimal service decision sequence, and executing the optimal service decision sequence.

The invention discloses a robot pose estimation method based on laser point cloud and visual SLAM, and relates to the technical field of robot sensing and positioning, and the method comprises the steps: in a data collection and integration link, installing a specific camera and a radar to collect and compress data; a data preprocessing and feature strengthening stage: processing the image and the point cloud to improve the quality and extract the features; a cross-modal network is constructed through feature fusion and accurate matching, and a special matching mode is adopted; space-time calibration and coordinate unification are realized by means of a high-precision circuit; multi-modal fusion and reinforcement learning are applied to loop detection and intelligent optimization; correcting filtering and visualizing uncertainty by combining pose smoothing and intelligent output with a prediction model; according to the method, the pose estimation precision, reliability and environmental adaptability of the robot are effectively improved, resource utilization is optimized, the pose uncertainty is visually evaluated, and the overall performance and the intelligent level of the robot are improved.

The invention relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

The invention relates to the technical field of road maintenance and management, in particular to a pavement internal disease analysis method and system based on artificial intelligence. Five core processes of multi-layer sensor network real-time data acquisition, multi-modal data fusion and deep learning feature extraction, dynamic environment and use mode combined disease prediction and analysis, intelligent prediction and decision support system, and real-time disease condition evaluation and report generation are carried out. Through the technical means, comprehensiveness and timeliness of data acquisition, accuracy of disease identification and prediction, scientificity of a maintenance strategy and optimization of resource allocation are realized; the finally generated disease current situation report provides a detailed disease distribution diagram, disease type analysis and disease severity evaluation, comprehensive, accurate and timely disease information is provided for maintenance personnel, and the efficiency and effect of pavement maintenance and management are remarkably improved.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

The invention discloses a multi-time-sequence three-dimensional visualization expression method for flood routing, and the method comprises the steps: analyzing NetCDF data of a two-dimensional hydrodynamic model, building an element index library and a space library, and storing the indexes of water level, water depth, flow velocity and the like and the space information of an irregular triangular mesh. And generating an association relationship between the grid points and the element indexes, and creating a time sequence cache attribute table and a spatial data set. And reading three-dimensional point space data and element index data, constructing a three-dimensional space grid, and performing range cutting by using a two-dimensional surface data set. And generating a time sequence service conforming to a three-dimensional model tile data format by adopting a thread pool technology. And loading a three-dimensional service and a terrain service at a browser end, carrying out color and height rendering based on the element values, and dynamically displaying the flood process. And a timer is arranged to realize three-dimensional visual playing moment by moment. The method has the advantages that the dynamic display capability of flood routing is effectively improved, and efficient three-dimensional visual rendering and real-time playing are supported.

The invention provides an intelligent exhibition hall control method and system based on Internet of all things. The method belongs to the technical field of the Internet of Things, and comprises the following steps: constructing an intelligent exhibition hall network architecture based on the Internet of Things, and wirelessly connecting to a central control platform to form a network environment of Internet of all things; acquiring user identity information through an identity recognition device, and constructing a user identity database; and analyzing user preferences through an intelligent algorithm according to the user identity information in combination with user historical behavior data. According to the method, the identity information and the historical behavior data of the user are analyzed through an intelligent algorithm, personalized exhibition recommendation and exhibition routes can be provided for the user, the user can find interested exhibits more quickly, and therefore the visiting experience is improved.

The invention relates to bacillus velezensis and application thereof in preparation of a water-soluble microbial agent, and belongs to the technical field of microorganisms. The bacillus velezensis has the effects of producing exopolysaccharides, promoting the formation of soil aggregates, degrading abamectin, hydrolyzing protein, producing indoleacetic acid, promoting the growth of tomato plants and promoting the enzyme activity of tomato rhizosphere soil enzyme. The fertilizer not only has the effects of improving the soil aggregate structure and promoting the growth of tomato plants, but also can accelerate the temperature rise of the abamectin fermentation waste residue compost, prolong the high-temperature period and shorten the compost period, and is beneficial to promoting the harmless treatment and resource utilization of the abamectin fermentation waste residue.

The invention aims to provide an intelligent pavement maintenance scheme design method based on a dynamic knowledge graph, so as to solve the problem that the pavement maintenance scheme design in the prior art depends on manpower and lacks dynamic adjustment capability. According to the method, road basic construction information, road surface detection conditions, road surface disease description and grading and classification information are received, a dynamic knowledge graph is constructed based on input entities and mutual relations thereof, and applicable road surface maintenance scheme templates including maintenance content, a maintenance method, a disposal drawing, project amount estimation and project scheduling are output by utilizing the dynamic knowledge graph. By introducing a dynamic knowledge graph and a large model technology, the pavement condition is automatically analyzed and judged, a maintenance scheme template with high adaptability is generated, and scheme content is intelligently assigned and generated.

The invention discloses a reservoir ecological scheduling method integrating fish spawning and foraging demands, and the method comprises the steps: determining a scheduling target river reach based on the fish inhabiting and breeding habitat feature information and spawning site distribution information of a to-be-researched reservoir region; constructing a hydrodynamic model according to the dispatching target river reach; acquiring first hydrological demand information for scheduling the target fishes to lay eggs in the breeding season and second hydrological demand information for scheduling the target fishes to find food in the non-breeding season; on the basis of a hydrodynamic model, the first hydrological demand information and the second hydrological demand information, determining the ecological flow discharged by the reservoir; determining reservoir water level regulation and control information based on the water level change demand information for scheduling the target fishes to spawn in the breeding season and foraging in the non-breeding season; and performing reservoir ecological scheduling according to the reservoir discharged ecological flow and the reservoir water level regulation and control information. According to the technical scheme, the adverse effect of reservoir dispatching operation on fish oviposition and foraging can be relieved, and fish resource recovery and biodiversity protection are facilitated.

The invention discloses an unmanned aerial vehicle cluster collaborative search method and system based on deep multi-agent reinforcement learning. The method comprises the following steps: defining a tracker unmanned aerial vehicle, a reconnaissance unmanned aerial vehicle and an intelligent target unmanned aerial vehicle; initializing an actor network, a commentator network and an experience playback buffer area for each unmanned aerial vehicle agent; each unmanned aerial vehicle agent selects an action and executes the action through an actor network according to the current state, and after the environment returns a new state and a corresponding reward, the interaction experience is stored in an experience playback buffer area; randomly extracting a batch of experience samples from the experience playback buffer area, and updating a commentator network; calculating gradients using the commentator network, and updating the actor network of each agent according to the gradients; and continuously performing interaction, data collection, network training and target network updating of the intelligent agent until a certain training period or performance standard is reached. According to the invention, the efficiency and safety of the unmanned aerial vehicle cluster for executing the collaborative search task in a complex environment can be improved.

The invention discloses a multi-service scheduling optimization method for infrastructure safety management. The multi-service scheduling optimization method comprises the following steps: intelligently acquiring structured and unstructured data from a plurality of service systems; cleaning, filtering and formatting the collected data, analyzing by using a data fusion technology, and extracting comprehensive information useful for safety and quality management; based on multi-objective optimization, a self-adaptive scheduling algorithm is designed, and resources, tasks and manpower are automatically allocated; the method comprises the following steps: constructing a digital twin model of the infrastructure, performing omnibearing real-time monitoring on the infrastructure through a synchronous virtual and physical system, and simulating risk points and potential problems; an intelligent monitoring feedback loop is used for detecting abnormal behaviors and potential risks; on the basis of real-time monitoring and feedback, the scheduling process is continuously optimized, and the adaptability of a scheduling algorithm is improved by applying machine learning and a prediction model. The method has the advantages that data of multiple service systems can be integrated, and data sharing and fusion analysis are realized.

The invention discloses an ODPS-based customer service data aggregation method and server, and relates to the field of electric digital data processing, and the method comprises the steps: carrying out the field classification of collected source data, and obtaining a plurality of source data sets; loading the source data sets to an ODPS platform, and performing first aggregation processing on the source data in the plurality of source data sets by using the ODPS platform to obtain a plurality of field wide tables; pulling the field wide table to a DRDS platform, and performing distributed data processing on the field wide table by using the DRDS platform to obtain a plurality of pieces of distributed data; determining a target data feature corresponding to a target report in a report request instruction according to the report request instruction sent by the user terminal; and extracting multiple pieces of target data corresponding to the target data features in the multiple pieces of distributed data, and performing second aggregation processing on the target data to generate a target report. By implementing the method, the data processing efficiency can be improved when the multi-azimuth data display of the customer service staff is carried out.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention relates to the technical field of data processing, in particular to an equipment cluster multi-modal edge data intelligent processing method based on multi-agent deep reinforcement learning, and the method comprises the steps: firstly classifying data in an equipment cluster according to the characteristics of the data, and dynamically adjusting the priority of data preprocessing according to the importance and access frequency of the data; secondly, a deep reinforcement learning framework is adopted, each edge computing node serves as an independent intelligent agent, self-learning and optimization are conducted through interaction with the environment, the intelligent agents dynamically select computing resource allocation of the edge nodes, instant rewards are obtained according to the data processing efficiency, and the strategy is continuously optimized; the intelligent agents achieve collaborative learning through the shared experience pool, the data processing efficiency is improved, and the load pressure of the central server is relieved. According to the method, the efficiency and real-time performance of equipment cluster multi-modal data processing are effectively improved, flexible scheduling of edge computing nodes is ensured, and therefore the dynamic change requirement is met.

The invention discloses a construction site inspection method, inspection system and equipment and a nonvolatile storage medium, and relates to the technical field of intelligent construction, and the method specifically comprises the steps: carrying out the inspection planning according to the inspection requirements of a construction site; based on the inspection plan, executing an inspection task, and obtaining inspection data of a target area; and analyzing the inspection data by constructing a deep learning algorithm model, and carrying out risk event classification based on an analysis result. According to the invention, a closed-loop processing mechanism of data in a platform-nest-unmanned aerial vehicle-platform is driven through control of the unmanned aerial vehicle cruise by the intelligent construction transportation and management platform for construction engineering, and a mobile supervision mode for engineering quality and safety is innovated.

The invention provides a service area customer group portraying method and system based on deep clustering. The method comprises the steps that S1, customer group information of a service area is acquired and preprocessed; s2, performing feature processing on the customer group information to obtain feature variables; s3, further extracting feature variables by using the trained model to obtain deep features; s4, performing clustering analysis on the customer group information of the service area, and outputting a clustering result; s5, calculating a contour coefficient to evaluate the clustering quality; optimizing and adjusting the model according to an evaluation result; and S6, according to a clustering result, dividing the customer group information of the service area into multiple types to construct customer group portraits. According to the method, the deep clustering algorithm is introduced into the customer group portrait of the high-speed service area, the limitation of a traditional method is effectively solved, different types of customer groups are identified, and the accurate customer group portrait is constructed.

The invention discloses a large model online reasoning service implementation method based on no-service calculation, and relates to the technical field of model reasoning, and the implementation method comprises the following operations: constructing a large model resource sniffing interface for realizing an API Server mode of sniffing server resources first and then transmitting reasoning data JSON; the model preloading module is constructed and used for rapidly loading a large model by using a model fragmentation technology; a shared memory module is constructed and used for planning and managing GPU and CPU memories and dynamically deducing the utilization rate of the simulated memories; gPU resource sharing is used when model reasoning and concurrent elastic capacity expansion and contraction are realized based on a service-free computing principle. According to the method, the problems of relatively high occupation of online reasoning resources, slow preheating of the resources and the like of a large model are solved, resource waste can be avoided, the utilization rate of the resources is improved, online reasoning service with quick response is realized, and the user experience is remarkably improved.

The utility model discloses a freshwater pond culture tail water treatment system which comprises ecological ditches arranged among culture ponds, and a sedimentation tank, a filter dam I, an aeration tank, a filter dam II and an ecological purification tank which are sequentially connected with one side of each ecological ditch, tail water is primarily purified through the ecological ditch, suspended solids and other substances in the water body are precipitated through the sedimentation tank, the suspended solids in the water are further removed through the first filter dam and the second filter dam, aeration equipment in the aeration tank is used for increasing dissolved oxygen in the water body and accelerating decomposition of organic matter in the water body, and nitrogen and phosphorus nutritive salt in the water body is absorbed through the ecological purification tank. The freshwater pond culture tail water treatment system is simple in structure, reasonable in design, reasonable in layout, high in tail water treatment efficiency and good in treatment effect, the area of the freshwater pond culture tail water treatment system accounts for more than 10% of the total aquaculture area, new technologies and new processes such as physical and biological purification treatment are applied to the whole system, construction of a pond circulation treatment system is accelerated, and the cost is reduced. The aquaculture tail water resource utilization or up-to-standard discharge is promoted, and the healthy and green development of the aquaculture industry is promoted.

The invention relates to the technical field of intelligent chemical engineering, and discloses an intelligent chemical engineering construction safety and quality detection method based on big data, and the method comprises the following steps: 1, installing a monitor and a sensor; step 2, intelligent analysis; step 3, predictive maintenance; step 4, risk assessment; step 5, quality control; step 6, safety management; 7, accident prevention; 8, remote real-time management is carried out; step 9, decision support; step 10, intelligent reporting; and step 11, displaying an interface. According to the intelligent chemical engineering site construction safety and quality detection method and system based on big data, through real-time monitoring and risk assessment, the intelligent chemical engineering site system can improve the safety management level of a construction site and reduce accidents, and the system can predict potential safety risks and take measures to avoid the potential safety risks by utilizing predictive maintenance and accident prevention technologies; and by using big data and an intelligent optimization algorithm, the resource allocation and the construction process are optimized, and the construction efficiency is improved.

The invention provides an intelligent fully-mechanized coal mining electromechanical equipment cooperative control system, and relates to the field of coal mining. The intelligent fully-mechanized coal mine electromechanical equipment cooperative control system comprises a data acquisition module which is used for acquiring operation state data of all electromechanical equipment in a fully-mechanized coal mine in real time; the data processing and analyzing module is used for processing and analyzing the collected data in real time and identifying the running state and potential problems of the equipment; the cooperative control module is used for intelligently adjusting operation parameters of the electromechanical equipment according to an output result of the data processing and analyzing module, so that cooperative work among the equipment is realized; and the safety monitoring module monitors the running state of the equipment in real time, and immediately starts safety protection measures once an abnormal condition is found. The system can detect abnormal conditions in operation of the equipment in real time and give an alarm in time, so that the possibility of accidents is reduced, and when security threats are detected, the system can automatically execute preset emergency response measures.

The invention relates to the technical field of equipment state evaluation, in particular to an evaluation method and system for the health state of offshore operation equipment. The method comprises the following steps: acquiring component data of offshore operation equipment; performing initial life marking on the data of the offshore operation equipment component to generate initial life marking data of the offshore operation equipment component; performing real-time operation data acquisition on the data of the offshore operation equipment component to generate real-time operation data of the offshore operation equipment component; performing component division on the real-time operation data of the components of the offshore operation equipment based on the components of the initial life mark data to generate novel component operation data and old component operation data; performing environment change influence analysis on the operation data of the novel part to generate an external change influence factor of the novel part. According to the invention, through multi-dimensional data acquisition, environment and electrical influence analysis, fatigue damage assessment and intelligent prediction, the accuracy of health state assessment of the offshore operation equipment is improved.

The invention discloses a position deployment and resource allocation method for multi-unmanned aerial vehicle communication. The method comprises the following operation steps: S1, initializing parameters and a model; s2, modeling: modeling for unmanned aerial vehicle position deployment and resource allocation problems; s3, splitting the problem by using a block coordinate descent method, and splitting the mixed integer non-convex problem by using the block coordinate descent method; s4, user association and the three-dimensional position of the unmanned aerial vehicle, wherein the user association and the three-dimensional position of the unmanned aerial vehicle are obtained through an ant lion algorithm; s5, transmitting power of the unmanned aerial vehicle, and optimizing the transmitting power of the unmanned aerial vehicle by using a successive convex approximation technology; and S6, an iterative optimization algorithm is carried out. Under the condition of multi-unmanned aerial vehicle deployment, the transmission rate requirement of the ground users can be met at the same time, the position deployment and resource allocation of the multiple unmanned aerial vehicles are optimized, the transmission rate is maximized, the system throughput can be improved, and the lowest rate requirement of all the ground users can be met.

The invention provides a shared memory-based data interaction method for an embedded virtualization platform, which comprises the following steps of: dividing a shared memory area, including a management information area and a shared data area, initializing the management information area, including setting a magic number, configuring a mutual exclusion access flag and recording use information of the shared data area; acquiring a physical initial address of the shared memory, verifying the availability of the management information area, and ensuring that the management information area is correctly initialized; a proper data transmission mode is selected according to the data size, small data volume can be directly transmitted through an inter-core communication register, and large data volume is transmitted through a shared memory; the Szymanski algorithm is used to realize mutual exclusion access control, and the consistency of data is ensured; after data interaction is finished, if shared memory resources which do not need to be used any more need to be released, use information in the management information area is updated; and other related operation areas or virtual machines are notified according to needs, and state changes of data interaction are notified to the operation areas or the virtual machines.

The invention discloses a power distribution network equipment vulnerability assessment method in an extreme scene and related equipment, and belongs to the technical field of power equipment disaster influence assessment, and the method comprises the steps: constructing a grid water accumulation model, and calculating the water accumulation height of each grid based on the grid water accumulation model and a rainfall center position; calculating the fault probability of the distribution transformer and the line based on the ponding height and the environmental factor; carrying out fault sampling based on the fault probability of the distribution transformer and the line, and determining which components may have faults; and outputting the fault component list and the fault probability. Through ponding dynamic simulation and equipment failure probability calculation, the accuracy of vulnerability evaluation of the power system is improved.

The invention discloses a virtual power plant carbon footprint tracking and optimization method in a carbon market, and the method comprises the steps: S1, proposing to establish a virtual power plant full life cycle carbon emission tracking accounting system, and carrying out the carbon emission tracking of typical resources of a virtual power plant; s2, establishing a low-carbon operation cost and income model of the virtual power plant in the carbon market, and obtaining the cost and income condition of the virtual power plant during typical resource scheduling; and S3, in the carbon market, considering the cost and income conditions of the virtual power plant during typical resource scheduling in the S2, establishing a virtual power plant carbon footprint optimization model, and inputting the typical resource carbon emission data in the S1 to obtain a virtual power plant carbon footprint optimization result. According to the method, the actual carbon emission of the emission main body can be accurately calculated, so that the typical resource carbon emission of the virtual power plant can be accurately tracked, the total emission of the hybrid energy power chain and the hybrid energy carbon footprint can be obtained, and the energy utilization efficiency and the low-carbon level of the virtual power plant can be improved.

The invention provides an intelligent laboratory environment safety monitoring system, which comprises a region division unit, an environment monitoring point, a partition environment data management unit, a data recording alarm unit and a visual display background, and is characterized in that the region division unit firstly performs gridding processing on a laboratory space to form a plurality of laboratory partitions; the environment monitoring points are arranged in the laboratory subareas, monitoring layers are divided in each laboratory subarea, at least two sets of environment monitoring points are arranged in the corresponding monitoring layers in each laboratory subarea, environment data collected by the environment monitoring points are sent to the subarea environment data management unit, and the subarea environment data management unit summarizes the data in all the environment monitoring points and sends the summarized data to the environment monitoring points. And a time data relation curve is drawn according to the environment data of the corresponding monitoring layer in each laboratory partition, and the data recording and alarming unit records the time data relation curve and sets limiting intervals in different time periods.