970results about How to "Achieving processing power" patented technology

The invention provides an enhanced document generation and retrieval method based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the following steps: receiving heterogeneous document input, and carrying out dynamic segmentation on the heterogeneous document; constructing a Graph-RAG model, wherein the Graph-RAG model comprises a knowledge graph-document index extraction module and a design document generation module; constructing and updating a knowledge graph based on document segmentation through a knowledge graph-document index extraction module; extracting feature vectors of the document segments by using a feature extraction model, establishing a mapping relationship between the document segments and entities in the knowledge graph through a matching strategy, and constructing a retrieval index; and receiving a query sequence of a user, retrieving the related entities and document segments through the retrieval index, and generating a target design document in combination with a preset template. According to the method, accurate retrieval and high-quality document generation based on semantic understanding are realized, and the technical problems of insufficient semantic understanding, low knowledge association degree, poor generation quality and the like in a traditional document processing method can be solved.

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

The invention discloses an expressway service area sewage treatment system based on the Internet of Things, and relates to the technical field of sewage treatment.The expressway service area sewage treatment system is provided with a multi-modal sensor and an edge computing unit, real-time monitoring and data processing of sewage flow and water quality parameters are achieved, the water quality change trend and the parameter change rate are rapidly recognized, and the sewage treatment efficiency is improved. The sensing capacity of the system to load changes is remarkably improved, the system conducts real-time adjustment according to the water quality changes, fluctuation of the treatment effect and waste of energy consumption are avoided, an LSTM model is adopted for conducting dynamic prediction on water quality parameters, in combination with real-time monitoring data, the aeration strategy is optimized through intelligent self-adaptive adjustment and closed-loop fine control, and the aeration efficiency is improved. The LSTM model can predict the future sewage load and ammonia nitrogen concentration change, the aeration intensity and time are adjusted in advance, the fluctuation of the ammonia nitrogen concentration is quickly responded through PID control and closed-loop control of a feedback mechanism, and the ammonia nitrogen removal efficiency and the aeration accuracy are improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an inspection unmanned aerial vehicle flight control management method, which comprises the following steps: step 1, an unmanned aerial vehicle obtains environment three-dimensional point cloud data through a laser radar, captures terrain visual information through an RGB camera and a depth camera, obtains real-time position and attitude information of the unmanned aerial vehicle through a GPS and an inertial measurement unit, and obtains the real-time position and attitude information of the unmanned aerial vehicle; and fusing the collected data to construct a spatial grid map, and taking the grid map as input data of path planning. A high-precision space grid map is generated through three-dimensional environment perception and modeling in combination with an unmanned aerial vehicle multi-sensor fusion technology, and a path is updated in real time by using dynamic environment data, and an air route is planned and optimized, so that path dynamic adjustment and energy consumption minimization of the unmanned aerial vehicle in a complex dynamic environment are realized; and the effects of improving the task execution efficiency, reducing the flight energy consumption and improving the flight safety are achieved.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention discloses a multi-modal information fusion and dynamic updating group behavior analysis method. The method comprises the steps of information integration and dynamic updating, multi-modal information obtaining and information preprocessing, feature extraction, feature representation conversion, alignment and fusion, and multi-modal information real-time processing and dynamic updating through a stream processing framework. A self-learning mechanism and a self-optimization mechanism are introduced; group portrait generation: extracting population statistical characteristics, geographic position distribution, behavior and emotional psychological characteristics, social relationships, social media and economic characteristics from the multi-modal data to generate a group portrait; analyzing group behaviors; and outputting visualization, and outputting group behavior prediction results in a visualized manner, including a behavior trend graph, a group clustering graph, an association rule graph and an abnormal behavior detection graph. According to the method, multi-modal data from various sources and forms are effectively integrated and analyzed, self-learning and self-optimization mechanisms are introduced, and finally group behavior analysis is performed.

The invention relates to the technical field of water quality and water level monitoring, and discloses a water quality and water level monitoring method for a water conservancy project, which comprises the following steps: S1, arranging a multi-parameter water quality and water level monitoring sensor at a key position of the water conservancy project; s2, performing real-time processing on the data acquired by the multiple sensors based on a data fusion algorithm, wherein the data fusion algorithm is based on a Kalman filtering model; s3, acquiring external data through an environment sensor, and dynamically adjusting a water level and a water quality monitoring threshold value based on environment change; s4, predicting the water level and the water quality by using a deep learning algorithm based on LSTM (long short-term memory network); and S5, if the monitoring data exceed the dynamic threshold value, automatically triggering an early warning mechanism. By arranging the multi-parameter water quality sensor and the water level sensor at the key positions of the water conservancy project, the water quality and the water level can be comprehensively monitored, the coverage range and the precision of the monitoring system are greatly improved, and the complex monitoring requirements of multi-source pollution can be effectively met.

The invention discloses a battery thermal runaway detection system based on an early warning function, and relates to the technical field of battery thermal runaway, and the system comprises a supervision center which is used for carrying out the real-time supervision of the data information of a plurality of batteries, carrying out the early warning of the time data of the battery thermal runaway, and facilitating the timely issuing of warning information and notification announcements, and a monitoring device which is used for monitoring the data information of a plurality of batteries. The monitoring device is used for collecting thermal runaway parameters of each detection node of a plurality of batteries, the thermal runaway parameters comprise battery voltage, temperature, current and pressure, and the protection device is used for carrying out delayed protection operation on the monitoring device. The battery thermal runaway detection system based on the early warning function establishes a thermal runaway early warning model by fusing multi-parameter data of heat, force, electricity and the like and historical data of a thermal runaway phenomenon and by utilizing various learning algorithms, monitors battery parameters and sets simple judgment standards and threshold values such as a non-working state current threshold value and the like, so that the battery thermal runaway early warning model is established. Early detection of battery thermal runaway is realized, and monitoring and early warning effects are achieved.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention relates to the technical field of fire early warning, in particular to an electrical fire early warning method, device and equipment based on leakage current temperature detection. The method comprises the following steps: acquiring information data of electrical equipment; performing electrical equipment power line position positioning on the electrical equipment information data to generate electrical equipment power line position information data; performing leakage current sensor data acquisition on the electrical equipment power line position information data to obtain standard electrical equipment leakage current monitoring data; and performing maximum fluctuation curve segment confirmation on the leakage current monitoring data of the standard electrical equipment to obtain a maximum fluctuation curve of the leakage current of the electrical equipment. According to the invention, by introducing leakage current fluctuation mode identification, accurate power line positioning, intelligent anomaly detection and heat map analysis, the accuracy, response speed and positioning capability of the electrical fire early warning system are improved.

The invention discloses an audio stream adaptive decryption method, system and device and a storage medium. The method comprises the following steps: acquiring an MPEG-DASH list file and analyzing the MPEG-DASH list file; the standard DRM interface is initialized; performing feature extraction on the identified audio segmentation information in the audio segmentation mode, and dynamically adjusting a segmentation request and processing logic according to the extracted features to perform adaptive segmentation downloading and preprocessing; extracting the encryption parameter to identify an encryption mark of the target audio stream platform, and selecting a corresponding decryption algorithm according to the encryption mark to decrypt the current audio segment; and performing audio frame reconstruction on the decrypted data to form an updated audio stream, caching the updated audio stream to a preset cache region based on a self-adaptive buffer strategy, and outputting the final audio stream to ensure the continuity of the final audio stream and realize efficient parallel processing of decryption and playing, so that the decryption efficiency is improved, and the user experience is improved. And particularly, in a high-concurrency scene, the user experience is improved.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention relates to the technical field of micro-grid distributed control, in particular to a micro-grid cluster distributed control method and system based on information interconnection. The method comprises the following steps: identifying a micro-grid cluster in a region, and extracting a plurality of micro-grid nodes; obtaining a plurality of microgrid operation state parameters; performing filtering standardization processing on the plurality of micro-grid operation state parameters, and constructing a micro-grid time sequence load change curve; discrete wavelet transform is carried out on the micro-grid time sequence load change curve, load fluctuation frequency trend mining is carried out, and micro-grid load fluctuation frequency trend characteristics are generated; carrying out data stream encapsulation on the load fluctuation frequency trend characteristics of the micro-grid, and constructing a cloud environment distributed computing framework; carrying out dynamic load demand calculation on the plurality of micro-grid nodes based on a cloud environment distributed calculation framework so as to obtain multi-period load demand prediction data of each micro-grid; according to the invention, high-efficiency microgrid cluster control with high energy utilization rate is realized.

The invention provides a method and a system for intelligently generating a complete dispatch report for financial unfavorable assets, and relates to the technical field of data investigation and arrangement, comprising Sp1, complete dispatch demand acquisition, Sp2, intelligent reasoning planning, Sp3, data acquisition and processing, Sp4, public opinion retrieval and analysis, Sp5, legal information extraction and verification, Sp6, data integration and report generation, and Sp7, report modification and output, and the method comprises the steps of Sp1, complete dispatch demand acquisition, Sp2, intelligent reasoning planning, Sp3, data acquisition and processing, Sp4, public opinion retrieval and analysis, Sp5, legal information extraction and verification, Sp6, data integration and report generation, and Sp7, report modification and output. By integrating a large model, an online retrieval tool, a data table tool and a key point extraction tool, automation and intellectualization of a complete dispatching process are realized. The system can automatically acquire, process and integrate data, greatly improves the efficiency of data acquisition, analysis and report generation, and reduces the probability of manual intervention and errors.

The invention relates to the technical field of intelligent data processing and artificial intelligence in the power industry, and provides an adaptive graph database multi-modal power knowledge question-answering method and system.The method comprises the steps that for a data query subtask in a semantic nested tree, intelligent error correction and expansion are conducted on natural language query, and after an expanded query statement is obtained, the data query subtask in the semantic nested tree is obtained; extracting a data result from the power industry database; for the knowledge query sub-tasks in the semantic nested tree, matching data results with nodes in the graph knowledge base, and obtaining knowledge results through a large language model based on feature representation of the matched nodes and the data results; based on the knowledge result and the data result, a natural language answer is obtained through a generation function of a large language model; and cross-channel cooperative processing and comprehensive answering are realized.

The invention relates to the technical field of substation control, in particular to a substation control system based on cloud control and a local area network. Comprising a substation data acquisition module, a data analysis module, a cloud cooperative control module, an edge calculation response module and a safety guarantee module. The substation data acquisition module acquires multi-dimensional data through a local area network, and the data analysis module identifies abnormal and fault modes by adopting a dynamic multivariate autoregression tree algorithm. The cloud cooperative control module performs global load scheduling by using a sparse distributed optimization algorithm, and the edge calculation response module realizes rapid local response control based on a Markov random field model. And the safety guarantee module is combined with deep learning and a multi-factor identity authentication technology to ensure data transmission safety. According to the invention, the automatic control level and the overall operation efficiency of the transformer substation are improved, and efficient, stable and safe intelligent power management is realized.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention discloses an environment adaptive meta-universe scene perception system and method, and belongs to the technical field of data transmission, and the method specifically comprises the steps: collecting and preprocessing multi-dimensional environment and sensory data in a meta-universe scene in real time through a sensor array, and constructing a full sensory environment data set; radar scanning and depth camera technologies are utilized to accurately obtain three-dimensional information of a physical environment, and a super-reality three-dimensional model is established to simulate a complex ecological system and environment characteristics of the physical world; in combination with real-time full-sensory environment data, an intelligent algorithm is adopted to dynamically adjust meta-universe scene elements, a user behavior prediction technology is introduced, scene layout and an interaction mode are intelligently adjusted according to user history and current states, and user experience is improved; user feedback is collected through VR immersive interaction, a scene perception method is evaluated, and iterative optimization is implemented according to an evaluation result so as to continuously improve the sense of reality and interactivity of the meta-universe scene; and efficient construction and intelligent management of the meta universe scene are realized.

The invention belongs to the field of artificial intelligence, and particularly relates to a multi-modal knowledge matching system which comprises a construction module, an image processing module, a matching module and an output module to realize seamless connection of an image and a text knowledge base. According to the method, the user is allowed to use the picture to perform knowledge retrieval, the user experience is improved, the picture is conveniently used to obtain required information, text information can be accurately extracted from the picture and matched with the text knowledge base, the matching accuracy and integrity are improved, the user can directly use the picture to perform knowledge retrieval, and the user experience is improved. And the system automatically identifies characters in the picture and matches the characters with the knowledge base, so that intelligent processing and utilization of image information are realized, and a more intelligent, convenient and efficient information retrieval and question-answering system is realized.

The invention provides a software demand analysis method which can be applied to the field of artificial intelligence. The method comprises the steps that demand information of target software is collected in a multi-modal mode, and initial data of m modals are obtained; wherein m is a positive integer greater than 1; based on the initial data of the m modalities, performing multi-modal demand analysis by using a first large language model to obtain information data of the m modalities; performing information fusion on the information data of the m modes to obtain software demand data; and performing demand optimization processing on the software demand data to obtain a software demand document corresponding to the target software. According to the software demand analysis method and device, intelligent demand analysis and automatic and intelligent processing of the target software are achieved, manual intervention is reduced, the processing efficiency is improved, and the invention further provides a software demand analysis device and device, a storage medium and a program product.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator set with a vibration reduction function and a construction method thereof.According to the wind turbine generator set with the vibration reduction function, a vibration reduction structure is additionally arranged, and the two elastic ends, arranged in the axial direction, of an elastic buffering piece are connected with the outer wall of a tower drum and the inner wall of a sleeve correspondingly; the elastic buffering piece can effectively absorb and relieve vibration of the sleeve, vibration impact borne by the tower drum is reduced, the overall stability of the wind turbine generator is improved, and safe and stable movement of the wind turbine generator is guaranteed. Compared with a conventional scheme that the diameter or the wall thickness of the tower drum is increased, the damping structure is simple in structure, convenient to install and maintain and low in cost.

The invention discloses an engineering investigation risk assessment method based on AI, and relates to the technical field of engineering investigation risk assessment, and the method comprises the following steps: data collection: collecting data related to engineering investigation, including geological data and environmental data; the geological data collection method comprises the following steps: firstly, determining a prospecting purpose and required information, determining the boundary of a prospecting area so as to determine a prospecting target and range, and collecting existing geological reports, maps and research paper data in the prospecting area; according to the method, the engineering investigation risk is evaluated by adopting an artificial intelligence technology, and a large amount of geological and environmental data are deeply analyzed by utilizing a machine learning algorithm, so that the defect that a traditional method excessively depends on subjective experience judgment is overcome, and the accuracy and objectivity of risk evaluation are improved; through the automatic feature selection and data preprocessing process, it is ensured that the evaluation result is more accurate, and interference of human factors is reduced.

The invention provides a network risk quantification method and apparatus, a computer device and a storage medium, and belongs to the technical field of network security, and the method specifically comprises the steps of obtaining information of a plurality of risk data items of a service party; evaluating the risk data items, and determining corresponding risk values; respectively substituting the risk values into a risk rate function to obtain a single-factor risk model corresponding to each risk data item; adjusting the single-factor risk model based on the information of the industry where the customer is located and the attack type, and constructing a double-factor evaluation model for the customer; and inputting the evaluation time node into the two-factor evaluation model to obtain a risk quantification result for the customer. Through the processing scheme provided by the invention, the consequence caused by the security event is comprehensively evaluated and analyzed in combination with the influence of personnel management / consciousness on the network security event from the perspective of insurance, intelligent analysis and processing of data are realized, and the reliability and comprehensiveness of network security analysis are improved.

The embodiment of the invention provides a legal affair information processing method and system fusing a knowledge graph and a large language model, and belongs to the technical field of knowledge graphs. The method comprises the steps that existing information related to the legal field is collected, and data preprocessing is conducted on the existing information; entity recognition and relation extraction are carried out based on the preprocessed existing information, and a knowledge graph is constructed based on the relation between the entities; collecting a legal affair information processing instruction of a user, and performing user intention recognition based on the legal affair information processing instruction; and based on the identification intention and the legal affair information processing instruction, generating result prediction information through the knowledge graph, and pushing the result prediction information to a user side. According to the scheme, deep analysis and intelligent processing of legal problems can be achieved, the accuracy of information retrieval and the reliability of legal suggestions are improved, and therefore decision making of legal practitioners and legal problem solving of users are better supported.

The invention discloses a 3D multi-target tracking method, a 3D multi-target tracking product, a 3D multi-target tracking medium and a 3D multi-target tracking system, and the method comprises the steps: S1, obtaining original point cloud data of a vehicle, and carrying out the preprocessing of the original point cloud data, and obtaining the preprocessed point cloud data; s2, extracting features in the preprocessed point cloud data to obtain a target detection result and generate a thermodynamic diagram; s3, performing motion compensation on the spatial position and orientation of the target by associating the thermodynamic diagrams of the multiple frames of point clouds; and S4, carrying out confidence evaluation on the corrected target in the thermodynamic diagram, and adjusting a detection tracking strategy according to an evaluation result. According to the invention, the tracking precision and stability can be obviously improved.

The invention discloses a treatment device and process based on zero discharge of high-concentration coating wastewater, wastewater passes through an adjusting tank and then enters a pretreatment module through a pipeline, the coating wastewater passes through a Fenton tank body, a coagulation tank body and a primary sedimentation tank of the pretreatment module in sequence through the pipeline to be treated, and the coating wastewater is continuously conveyed to a biochemical treatment module; the biochemical treatment module is used for carrying out anaerobic environment treatment through an anaerobic granular sludge bulking bed and then carrying out aerobic environment treatment through an aerobic aeration tank; and introducing the wastewater into a secondary sedimentation tank to separate the formed sludge from water, entering a biochemical tank, carrying out ultrafiltration and reverse osmosis treatment through a deep treatment module, recycling the treated wastewater to a production line, and recycling the generated condensate water and RO fresh water. The adopted wastewater treatment device is combined and optimized by various advanced technologies, so that the treatment efficiency and the water quality are improved, efficient recovery and reutilization of resources are also realized, and the system has good economic benefits and environmental benefits and is suitable for the requirements of modern coating wastewater treatment.

The invention provides a sea surface pollution intelligent monitoring system based on multi-source data, and the system comprises a comprehensive analysis module, an information topology module, a sea surface monitoring module, and the sea surface monitoring module is used for obtaining sea surface pollution monitoring data. The data transmission module realizes interactive communication between the sea surface monitoring module and the information topology module, and the interactive communication comprises transmitting sea surface pollution monitoring data to the comprehensive analysis module; the comprehensive analysis module and the information topological optimization module are used for analyzing and early warning multi-source data, integrating, cleaning and analyzing the data, identifying and correcting errors, abnormities or noise in the data, ensuring the accuracy and credibility of the data, mining data information by using deep learning, then performing intelligent decision distribution, and finally performing data processing. And the monitoring, early warning and emergency response capabilities of sea surface pollution events are improved. Through the above modules, omnibearing, three-dimensional and real-time monitoring and early warning of offshore sudden pollution can be realized.

The invention discloses a communication machine room environment monitoring system and method based on edge computing, and the system comprises a data collection device, an edge gateway end, and a machine room monitoring terminal device, and the edge gateway end comprises a communication machine room map construction module, a risk assessment module, and a grading processing module. The computer room monitoring terminal is used for receiving the environment data and the equipment operation data information transmitted by the data acquisition equipment through the data communication link, comprehensively processing and analyzing the environment data and the equipment operation data to generate a data analysis result, and displaying the environment state and the equipment operation state of the computer room through the computer room monitoring terminal. According to the invention, by combining the construction of the communication machine room map and the risk assessment technology, the safety and efficiency of communication machine room monitoring are remarkably improved, abnormal conditions can be found in time and response can be made, stable operation of the communication machine room is ensured, and the reliability and safety of a communication network are enhanced.

The invention discloses an earthquake monitoring linkage triggering device, and the device comprises an earthquake intensity instrument which is used for collecting an earthquake signal and outputting earthquake intensity; the sensor is used for collecting seismic activity data; the data acquisition host is used for processing the seismic intensity and the seismic activity data and transmitting the seismic intensity and the seismic activity data to the upper computer; and the upper computer is used for receiving, analyzing and displaying the seismic data. According to the invention, high-performance modular hardware is utilized, efficient and flexible software is combined to complete various testing work, signals of acquisition equipment are displayed and stored through the upper computer, seismic wave acquisition information is fused, data analysis and judgment are carried out, and earthquake monitoring can be comprehensively and automatically realized.