12125 results about "Data transport" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention discloses a photovoltaic module fault diagnosis system and method based on deep learning, which significantly improves the accuracy and comprehensiveness of fault diagnosis through multi-modal data fusion and advanced deep learning technology, and improves the system operation efficiency through the application of an adaptive sampling strategy and edge calculation. The system can simultaneously execute fault classification, severity evaluation and life prediction through a multi-task learning framework, the diagnosis depth and breadth are enhanced, a predictive maintenance decision-making mechanism based on reinforcement learning optimizes a maintenance strategy, the service life of equipment is prolonged, the operation and maintenance cost is reduced, and the maintenance efficiency is improved. The distributed learning framework ensures data privacy and continuous optimization of the model, improves the overall power generation efficiency and economic benefits of the photovoltaic power station, and provides powerful support for large-scale application of renewable energy sources.

The disclosed embodiments include computer-implemented systems and processes that train adaptively and deployment of coupled machine-learning and explainability processes within distributed computing environments. By way of example, an apparatus may receive first interaction data associated with a first temporal interval from a computing system. Based on an application of a first and a second trained artificial-intelligence process to an input dataset that includes at least a subset of the first interaction data, the apparatus may generate output data indicative of a predicted likelihood of an occurrence of a target event during a second temporal interval, and may generate explainability data that characterizes the predicted likelihood. The apparatus may also transmit portions the output and explainability data to the computing system, and the computing system may modify an operation of an executed application program in accordance with at least one the output or explainability data.

The disclosed embodiments include centralized, computer-implemented processes for feature generation and management within web-based computing environments. By way of example, an apparatus may transmit first data characterizing a plurality of features to a device, the first data causes the device to present interface elements associated with the features within a digital interface. The apparatus may receive second data that identifies at least a subset of the features from the device, and based on the second data, the apparatus may generate, for each of the subset of the features, elements of executable code associated with a calculation of a corresponding feature value. The apparatus may transmit third data that includes the elements of executable code to the device, which may present the elements of executable code within one or more additional portions of the digital interface.

A vehicle dash cam may be configured to execute one or more neural networks (and / or other artificial intelligence), such as based on input from one or more of the cameras and / or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and / or to a safety manager.

The invention discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

An improved system and method of selectively transmitting asset data from one or more sensors associated with the vehicle to a backend server, which is configured to analyze the asset data and, if necessary for further analysis of the asset data (e.g., to determine whether a safety event has occurred) and / or to provide actionable data for review by a safety analyst, requests further asset data from a vehicle device.

The invention provides an unmanned aerial vehicle intelligent routing inspection method for routing inspection of energy facilities, and the method comprises the steps: carrying a high-definition camera and a thermal sensor through an unmanned aerial vehicle, carrying out the routing inspection of the energy facilities, collecting a visual image and thermal imaging data in real time, and transmitting the data to a server side. And the server performs processing, analysis, risk classification and anomaly detection on the data by using various recognition training and deep learning models. The unmanned aerial vehicle acquires three-dimensional information by using a simultaneous positioning and map construction technology, constructs a three-dimensional map, and plans a routing inspection path by using an RRT algorithm, thereby realizing multiple functions. Potential risks are identified by using a deep learning technology, and early warning and automatic obstacle avoidance are realized. The edge calculation unit processes the data in real time and transmits the data to the ground control center, and further analyzes and predicts the risk trend. The system integrates a plurality of advanced modules, realizes efficient, intelligent and automatic inspection by using deep learning, edge computing and high-speed data transmission technologies, reduces cost and risks, and provides powerful support for safety management of energy infrastructures.

The invention provides an intelligent workshop scheduling optimization system, method and device based on artificial intelligence and a storage medium. The system comprises a data acquisition module, a data fusion and preprocessing module, an intelligent scheduling model training module, a real-time scheduling decision module and an execution monitoring module. The data acquisition module collects production equipment operation parameters, material circulation information, production order details and other data, and transmits the data to the data fusion and preprocessing module for cleaning, feature extraction and labeling. And the intelligent scheduling model training module constructs an optimization model by adopting a deep reinforcement learning algorithm based on the annotation data. And the real-time scheduling decision module receives real-time data, inputs a model to generate a scheduling instruction, and performs risk assessment and adjustment. And the execution monitoring module controls workshop production according to the instruction, collects feedback data to form closed-loop control, and realizes production dynamic optimization. The method can improve the production efficiency, reduce the cost, and enhance the robustness and adaptability of the scheduling scheme.

The invention provides a method and system for monitoring the running state of a photovoltaic power station in real time, and relates to the technical field of photovoltaic power station monitoring, and the method comprises the steps: integrating a multi-mode sensor array in a photovoltaic module junction box, and collecting the sensor data of the photovoltaic power station in real time; performing localization preprocessing on the sensor data through an edge computing node, and transmitting different priority data to a cloud based on a dynamic hybrid communication protocol; sensor data are fused at the cloud, a four-dimensional digital twinborn model is constructed, and time-space continuous meteorological prediction, component aging and dust retention evolution data are fused; recognizing the fault mode of the photovoltaic power station, and dynamically optimizing the string topological structure of the photovoltaic power station in combination with the real-time operation state of the photovoltaic power station, the equipment health data and the fault mode recognition result; by integrating the distributed sensor, the edge calculation module and the intelligent alarm mechanism, the problems of delay, insufficient intelligent analysis and response lag in the prior art can be effectively solved.

The invention discloses an online detection system and method based on ink-jet printing, and relates to the technical field of ink-jet printing, and the method comprises the steps: collecting the operation state data of ink-jet printing in real time, and transmitting the data to a deep learning model; predicting the track, drop point and shape change of the ink droplet through a deep learning model in combination with a fluid dynamics principle, generating a prediction result, and providing positioning information of an ink jet area; inputting the multispectral data into a convolutional neural network model, identifying color deviation and defect types in combination with a prediction result, and generating corresponding correction parameters; the correction parameters are transmitted to ink jet control equipment, and the jet parameters of the nozzle are adjusted in real time; according to the invention, the information of the boundary, the central point, the area, the coverage rate and the offset of the ink jet area is generated in real time, and a high-precision positioning basis is provided for subsequent multispectral detection and defect correction, so that efficient and accurate online detection and ink jet quality control are realized.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

The present invention discloses a method and system for detecting and tracing medicinal and edible ingredients based on blockchain, which relates to the technical field of food safety traceability, including a detection module, a data acquisition module, a blockchain storage module, a smart contract module, a credit evaluation module and a user interaction module, wherein the detection module generates structured data and transmits it to the data acquisition module, the data acquisition module is connected to the blockchain storage module through a database communication protocol, the blockchain storage module is connected to the smart contract module through a rule base communication protocol, and the credit evaluation module is connected to the smart contract module and the user interaction module through a two-way communication interface. The present invention realizes decentralized data storage and exception handling functions by designing a blockchain storage module and a smart contract module, eliminates data tampering caused by manual intervention, improves system automation and credibility, and improves food safety guarantee efficiency.

The invention belongs to the field of parallel computing architecture optimization, and relates to a matrix multiplication acceleration method and system based on dynamic computing resource mapping, and the method comprises the steps: constructing a dynamic topology model driven by tensor dimension features, and generating a thread block distribution mode according to matrix parameters and GPU hardware information; constructing a multi-dimensional resource scheduling strategy library, dynamically selecting an optimal thread block distribution strategy from the multi-dimensional resource scheduling strategy library, and generating a binding relationship between the thread blocks and the data blocks; calculating collaborative access logic of thread blocks and storage hierarchies based on block parameters and dynamic mapping function optimization; distributed calculation is carried out, calculation and data transmission are parallelized through pipelining and a double-buffering mechanism, and result aggregation across calculation units is completed synchronously through atomic operation and a barrier. According to the method, discontinuous memory access conflicts can be effectively reduced, the execution efficiency of the calculation instruction and the utilization rate of the cache space are improved, the parallel calculation process of accelerating and optimizing the general matrix multiplication is realized, and the data processing efficiency is improved.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention relates to the technical field of power transformer fault diagnosis, and discloses a transformer fault diagnosis method based on knowledge distillation and collaborative incremental learning. The method comprises the following steps: firstly, training a cloud high-performance model by using historical fault data of a transformer, lightening a large model obtained by cloud training by using a knowledge distillation mode, and deploying the large model at an edge side; secondly, constructing a cloud edge collaboration framework, performing on-site processing and identification on each piece of monitoring data on the edge side, and only sending an identification result to a cloud end to relieve the data transmission pressure; and finally, establishing a collaborative incremental learning strategy, and when the models are misaligned along with the increase of new samples, updating the cloud and edge side models by adopting a collaborative incremental learning mode based on knowledge distillation, thereby realizing synchronous updating of the cloud and edge side models, ensuring that the models can continuously and effectively learn and adapt to the new samples in an edge computing environment, and improving the accuracy of the new samples. And the fault diagnosis accuracy is improved.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention discloses a cloud edge cooperation method for AI intelligent Internet of Things equipment data processing, and relates to the technical field of data processing, and the method comprises the steps: S1, intelligent data collection, S2, edge side AI preprocessing, S3, edge-cloud end cooperation reasoning, S4, intelligent data transmission, S5, cloud end AI big data analysis, S6, real-time feedback and self-optimization, S7, adaptive resource scheduling, and S8, full-link visualization. Through AI-driven dynamic sampling and multi-modal data fusion, the efficiency and precision of data acquisition are optimized, redundancy or omission caused by fixed sampling is avoided, meanwhile, the transmission load is reduced, layered task dynamic unloading and intelligent transmission protocol optimization are achieved, the flexibility and stability of cloud edge collaboration are improved, and the cloud edge collaboration efficiency is improved. Manual intervention is reduced through a real-time feedback and self-optimization mechanism, the autonomy of the system is enhanced, and the interpretability and fault diagnosis capability of the system are remarkably improved through a visual panel and a causal reasoning model.

The invention discloses an intelligent advertisement putting processing method and system based on user data, and relates to the technical field of data processing, and the method comprises the steps: putting an advertisement in an advertisement pool based on an edge device, collecting the video information during the advertisement playing period through the edge device, analyzing the stored video information, extracting the user feature information, and storing the user feature information in the advertisement pool; the user feature information is integrated into user feature vectors, the user feature vectors stored in the edge devices are uploaded to the cloud platform, the cloud platform analyzes the user feature vectors uploaded by the edge devices to generate user portrait information, and the user portrait information is displayed on the basis of the user portrait information generated by the cloud platform. And putting corresponding advertisements to the user groups with similar user portrait information, collecting feedback data, and dynamically optimizing the advertisements put by the edge devices and the user groups based on the feedback data. The method has the advantages that the data transmission delay and the cloud computing pressure are reduced through the cloud edge collaboration method.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

Systems and methods for generating a response to a visual information query can include obtaining input data, processing the input data with one or more machine-learned models, transmitting data to one or more data processing tools, and generating a response based on the input data and the one or more outputs of the one or more data processing tools. The one or more machine-learned models may iteratively process data to generate planning data that may include application programming interface calls to the one or more data processing tools. The one or more machine-learned models may be utilized for planning, reasoning, and response generation when obtaining and generating a response to a visual information query.

The invention discloses a personalized recommendation system based on semantic analysis, and relates to the technical field of personalized recommendation systems.The personalized recommendation system comprises a data acquisition module, a dynamic situational semantic modeling module and a recommendation engine module, the data acquisition module transmits acquired user behavior data and situational data to the dynamic situational semantic modeling module, and the dynamic situational semantic modeling module transmits the acquired user behavior data and situational data to the recommendation engine module; the dynamic situational semantic modeling module extracts sequence features of user behavior data through a behavior context analyzer, generates context embedding through a situational semantic embedding network, completes cross-modal semantic alignment of multi-modal data through a multi-modal fusion layer, and generates multi-level semantic embedding features. According to the method, the dynamic situational semantic modeling module is designed, the semantic alignment function of the situation and the behavior is achieved, the problem that a traditional recommendation system is insufficient in situation data utilization is solved, the adaptability of the recommendation result to the dynamic situation is enhanced, and the method is suitable for dynamically changing user demand scenes.

The present disclosure relates to cyber-attack mitigation, information theoretic security in-motion, at-rest and storage intrusion mitigation with exclusive-path, content routing across an overlay network. Overlay network built upon Data Transport Controllers (DTC), SG nodes and SV nodes. System modules are deployed across various geo locations in a Wide Area Network, operating at the control of Universal Security Controller (USC). USC extracts system, security and storage activity telemetry data from Secure Vaults, Storage Gateways and Data Transport Controllers to orchestrate Autonomous Security Operations. SG nodes create SP fragments and store in SV nodes upon a data input operation. USC initiates an Exclusive-path content forwarding from the first SG node, across the DTC overlay through an exclusive list of DTC nodes and move it to second SG node. USC keeps the security control of SG nodes, DTC nodes and SV nodes and realize multi-vectored storage security at-rest and in-motion. SG nodes are connected to SV nodes which are micro-segmented, data vaults with restricted network reachability.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.