160 results about "Alarm response" patented technology

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention provides a railway line monitoring alarm method and system, and relates to the technical field of computers, and the method comprises the steps: continuously collecting a dynamic monitoring data set comprising a track structure vibration waveform sequence, a track surface optical image sequence and an environment meteorological parameter sequence through a plurality of sensor nodes of a target railway line; carrying out multi-modal feature fusion processing on the dynamic monitoring data set, generating a comprehensive state evaluation matrix containing a vibration energy distribution vector and the like, carrying out risk alarm grade classification on the comprehensive state evaluation matrix by utilizing a preset track safety evaluation model, and outputting an abnormal event type code and an abnormal region position code; then, a multi-stage linkage control instruction set containing a train emergency braking triggering instruction and the like is generated based on the multi-stage linkage control instruction set, the multi-stage linkage control instruction set is synchronously transmitted to a central dispatching system, the central dispatching system is driven to execute rail blocking, train speed regulation and control and equipment maintenance operation according to instruction priorities, and comprehensive and intelligent monitoring and efficient alarm response to railway lines are achieved.

The invention relates to the technical field of motion detection, in particular to an old people falling early warning system based on motion trail analysis, which comprises a gravity center identification module, a trend identification module, an inertia pushing module, a phase judgment module and a grade response module. According to the method, the vertical coordinates of the key points of the waist and the two feet in the image frame are acquired, and the height difference change is continuously tracked, so that the potential falling initial state can be identified from the static posture, the vector included angles among the three groups of bone points of the hip knee, the knee ankle and the shoulder pelvis are subjected to sequence analysis, and whether synchronous disintegration occurs in the coordination action is identified; the identified trend sequence is mapped according to the risk level, a differential response mechanism is applied, a high-density corresponding relation is constructed between the behavior trend and the alarm level, graded response and time sequence triggering of the falling risk are achieved, the accurate identification capability of the falling precursor action is enhanced, the sensitivity and discrimination of the alarm response are also improved, and the safety of the falling risk is improved. And misjudgment and missed judgment are effectively avoided.

The invention discloses an energy-saving and consumption-reducing method for a reverse osmosis membrane system, which belongs to the technical field of water treatment and comprises the following steps: step 1, acquiring various parameter data of the reverse osmosis membrane system in real time; 2, establishing an energy consumption prediction mathematical model of the reverse osmosis membrane system; 3, inputting the real-time parameter data into the energy consumption prediction mathematical model, and judging whether the operation power of the high-pressure pump is reasonable or not according to an output result; 4, when the operation power is not reasonable, the operation power of the high-pressure pump is adjusted in real time; and 5, establishing a membrane pollution concentration early warning model, and carrying out alarm response. According to the method, comprehensive analysis can be carried out in real time according to various parameters of inflow water quality to accurately predict the predicted power, so that whether the set operation power of the high-pressure pump is reasonable or not is judged, the operation power of the high-pressure pump can be dynamically adjusted, the output of energy consumption can be reduced as much as possible under the condition that treatment is qualified, and the operation efficiency of the high-pressure pump is improved. And the cost is saved.

The invention relates to the technical field of abnormal alarm, in particular to an intelligent alarm method and system for the abnormal state of a battery, and the method comprises the following steps: obtaining a current period log, calculating a residual vector, analyzing the change trend of a residual track included angle, extracting an abnormal tendency period, and judging whether temperature rise abnormity exists or not through the combination of a load grade and temperature rise deviation. And judging whether microcirculation characteristics are met or not, correcting an alarm level, and triggering multi-source state abnormity alarm. According to the method, the evolution trend of the battery state can be dynamically tracked and the formation process of the abnormal tendency can be accurately captured by constructing the state residual feature vector and combining with multi-cycle data analysis, and the false alarm rate is reduced and the real-time monitoring capability of the battery abnormal state is improved through refined load and temperature rise response judgment; by detecting the trend change, the temperature response offset and the microcirculation phenomenon in the periodic change of the battery, the alarm response can be adjusted in real time under different load conditions, and the accuracy and the reliability of an alarm mechanism are ensured.

The invention relates to the technical field of charging station fire safety management, and discloses an intelligent charging station fire early warning alarm system and method, and the system comprises an intelligent sensing unit, a data transmission network, a data processing and analysis module, and an early warning information issuing module. The alarm system comprises an alarm triggering unit, an alarm information processing unit, an alarm information issuing unit and a linkage control unit. According to the intelligent charging station fire-fighting early warning and alarming method, through an efficient data processing algorithm, an accurate fire identification technology and a flexible early warning and alarming strategy, the early warning accuracy and the alarming timeliness are improved, through simulation experiments and actual case verification, excellent performance is shown, the early warning accuracy is high, the alarming response speed is high, and the intelligent charging station fire-fighting early warning and alarming method is suitable for popularization and application. Moreover, the system is high in stability, integrates an advanced intelligent sensing unit, a data processing and analysis module, an early warning and alarm mechanism and a linkage control module, and achieves the real-time monitoring and intelligent analysis of the environment of the charging station.

The invention discloses a secondary water supply system fault early warning method and system based on data analysis, relates to the technical field of fault early warning, and aims to solve the problems of difficult troubleshooting and low repair efficiency caused by single alarm response and multiple abnormal problems of a water supply system. The method comprises the following steps: analyzing abnormal conditions in a water supply program, screening out abnormal parts, classifying and dividing equipment in a water supply system, determining repair time and repair cost required when the divided equipment is abnormal, dividing the equipment by integrating the repair time and the repair cost, and performing repair difficulty assessment on each divided equipment by using a superior sequence diagram method. Carrying out equipment classification on the screened abnormal parts to determine an abnormal equipment combination, grading the abnormal conditions by using a hierarchical classification method according to the repair difficulty of the equipment combination, making different alarm responses by using the grades of the abnormal conditions, and recording response behaviors; and storing the abnormal equipment combination information and the abnormal condition level of the corresponding equipment combination.

The invention relates to the technical field of power electronics, in particular to a direct-current charging pile insulation monitoring method and system, and the method comprises the following steps: monitoring the voltage deviation between a branch and a bus in real time, adjusting a measurement compensation parameter, correcting a branch insulation resistance measurement value, recognizing leakage current drift, positioning a leakage branch, triggering relay control, and sending alarm information. And generating a charging pile monitoring record. According to the method, the branch voltage deviation trend and the voltage compensation parameters are combined, the branch voltage measurement consistency is improved, the insulation resistance fluctuation is utilized to compare and correct the observation error, the measurement precision is optimized, the current deviation compensation in the no-load state is adopted to eliminate the zero drift interference of the sensor, and the measurement accuracy is improved. A current jump amplitude and a voltage direction are combined to identify an electric leakage branch, positioning accuracy is enhanced, a relay control and event information linkage mode is used, alarm response and equipment state records form a closed loop, and real-time performance and integrity of system monitoring are improved.

The invention relates to the technical field of sensor measurement, in particular to an intelligent water area virtual electronic fence based on acousto-optic-electric fusion. The system specifically comprises a water area sensor fusion network, a water area interface fluctuation feature extraction unit, an intrusion probability prediction unit and a self-adaptive alarm response unit. The water area sensor fusion network is used for capturing a sound wave signal, an optical signal and an electromagnetic signal in a target water area, and performing dynamic attenuation correction according to physical characteristics of water to obtain a fusion sensing result; the water area interface fluctuation feature extraction unit is used for extracting fluctuation energy spectrum density related to water disturbance at each frequency; the intrusion probability prediction unit is used for calculating the current anomaly metric and predicting the intrusion probability of each position in the target water area; and the adaptive alarm response unit is used for calculating the current alarm intensity of each position and executing an early warning strategy. According to the invention, high-probability intrusion threats can be rapidly identified and amplified.

The invention discloses an intelligent alarm research and judgment and safety operation system combined system and method, and the system comprises an alarm classification device which is used for receiving alarm information and original logs from a safety information and event management system, and classifying alarms into a class A, a class B and a class C; the small model research and judgment module is used for processing the A-type alarms and calculating a false alarm probability through local endogenous information matching and a naive Bayes algorithm; the large model research and judgment module is used for processing the B-type alarms and generating a research and judgment result based on the extracted alarm vulnerability information and a preset question template; the manual research and judgment module is used for processing the C-type alarms and alarms which cannot pass through small model research and judgment or large model research and judgment; and the alarm memory is used for storing research and judgment results and mark information of all alarms. According to the method, the workload of a security analyst is reduced, the alarm average response time is prolonged, and the number of alarms processed manually is reduced on the premise that the missing report rate is not obviously increased.

The invention belongs to the technical field of online quality inspection, and relates to an operation and maintenance management and running state monitoring method of an online quality inspection system, which comprises the following steps: deploying sensors and monitoring equipment at key nodes of the quality inspection system, and collecting equipment running state data, quality inspection data and key parameters of specific algorithm running; and carrying out calibration, verification and encrypted transmission on the acquired data, and setting a synchronization period to synchronize the data to a central server. By constructing an accurate equipment state evaluation and fault diagnosis model, the accuracy of fault early warning and positioning is improved, and powerful support is provided for rapid fault repair; maintenance arrangement is carried out in combination with a production plan, the influence of maintenance on production is reduced to the greatest extent, a system performance optimization module improves the load balancing capacity and the alarm response efficiency of the system, the stability and reliability of the online quality inspection system are enhanced, high-efficiency proceeding of quality inspection work is guaranteed, the production efficiency and the product quality of enterprises are improved, and the economic benefits of enterprises are improved. And the market competitiveness of enterprises is enhanced.

The invention relates to the technical field of electrical fire identification, in particular to an electrical fire intelligent identification method and system based on a visual image, and the system comprises a visual image fire identification platform, a fire information base, an infrared image analysis unit, a region identification unit, a feature matching unit, a dynamic management and control unit and an alarm response unit. According to the method, regional fire monitoring feedback analysis is performed on the infrared feature image preliminarily, so that the suspicious risk condition of each monitoring point in the target electrical equipment can be known, and data support is provided for subsequent analysis; whether the target electrical equipment has a fire or not is analyzed from the two aspects of infrared feature image processing and monitoring video image feature extraction, and through the mode of combining the two fire analysis methods, the fire recognition error rate is reduced, the target electrical equipment with a disaster is analyzed deeply, and the fire recognition efficiency is improved. Reasonable management and control can be carried out according to the dynamic change trend of the fire, and the fire management efficiency can be improved.

The invention relates to the technical field of geological disaster alarm, and discloses a geological disaster remote sound-light alarm and data acquisition system and method, which realize intelligent judgment of early warning level through an early warning rule and a trend model on the basis of supporting acquisition of multi-source geological and environmental data, and realize remote sound-light alarm and data acquisition of geological disasters by introducing a regional linkage mechanism. And the linkage coordination capability of early warning accuracy and alarm response is effectively improved. Besides, according to factors such as monitoring point risk levels, geographic topology and key facility distribution, response priorities of acousto-optic devices in an area are dynamically allocated, multi-point multi-level differential alarm control is realized, post-disaster traceability analysis is realized by using alarm state feedback operation, and the risk level of the monitoring point is greatly improved. And the alarm reliability and the emergency response efficiency in a complex geological scene are obviously improved.

The invention discloses a leaky coaxial cable alarm trend analysis system based on multi-modal storage. The system comprises the following steps: S1, confirming a system core target; s2, a multi-mode storage core support; s3, analyzing the alarm trend of the leaky coaxial cable; and S4, outputting a trend analysis result. On the fault early warning level, a 50-meter geofence constructed by a space-time alignment engine and a GPS clock synchronization mechanism are combined with a dual-channel degradation detection model, the false alarm rate is reduced by more than 40%, and subway tunnel false alarm in a typical scene is reduced from 15 times per day to 9 times per day. In an operation and maintenance decision-making level, a fault root cause probability matrix is fused with a multi-source evidence chain to realize three-minute quick positioning, so that the response efficiency of a railway external damage accident is improved, and in a system efficiency level, a cold and hot data layering strategy and radio frequency parameter piecewise linear compression cooperate to reduce the storage requirement, original data is compressed, and the system efficiency is improved. And the edge side lightweight reasoning module realizes 200ms-level real-time detection through a pruning model and I NT8 quantization, and the alarm response delay is reduced.

The invention provides an intelligent smoke and gas dual-detection alarm device based on multi-mode sensing fusion, and the device comprises a housing which is internally provided with a cavity-divided physical isolation structure which comprises a smoke detection cavity and a gas detection cavity; a smoke detection module; a fuel gas detection module; the main control module is electrically connected with the smoke detection module and the fuel gas detection module and is used for receiving and processing data acquired by the smoke detection module and the fuel gas detection module; the intelligent algorithm module is arranged in the main control module, and the intelligent algorithm module is used for executing multi-modal sensing data fusion analysis and dynamic threshold calibration; the alarm response module is electrically connected with the main control module, and the alarm response module is used for executing graded alarm response according to the analysis result of the intelligent algorithm module; a communication module; and a power supply module. According to the invention, through a cavity-divided physical isolation structure, a multi-sensor fusion technology and an intelligent algorithm system, high-precision detection of smoke and fuel gas is realized, and the false alarm rate is reduced.

The invention provides a water immersion and electric leakage monitoring method, device, system and equipment for power equipment, and relates to the technical field of power equipment safety monitoring. The method comprises the following steps: acquiring electrical monitoring data of a grounding wire of power equipment and water level monitoring data of an area where the power equipment is located; judging whether an electric leakage event occurs or not according to the electrical monitoring data; water immersion event judgment is conducted according to the water level monitoring data, when the water level monitoring data exceed a preset water level threshold value, visual review operation is triggered, and whether a water immersion event occurs or not is judged based on the result of the visual review operation; and determining different alarm levels according to a combination condition between the judgment result of the water immersion event and the judgment result of the electric leakage event, and executing alarm responses corresponding to the alarm levels. According to the method, the electrical monitoring data is obtained in a non-contact manner, and a dual verification mechanism of'water level threshold triggering + visual rechecking 'is established, so that the water immersion and electric leakage states of the electrical equipment can be quickly and accurately identified.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention discloses an alarm method and an alarm system for intelligent video monitoring, and relates to the technical field of computer vision. The method comprises the following steps: acquiring a real-time video stream and a target detection model corresponding to a monitoring area, and detecting the real-time video stream based on the target detection model to obtain a moving target included in the real-time video stream; extracting motion data of the moving target, tracking the moving target in real time according to the motion data of the moving target, and mapping the moving target to a 3D panoramic monitoring picture corresponding to the monitoring area and displaying the moving target; and acquiring a preset alarm rule, tracking and detecting the real-time action of the moving target according to the preset alarm rule, and if the real-time action of the moving target is detected to meet the preset alarm rule, triggering a linkage alarm response corresponding to the preset alarm rule. The panoramic view is generated by fusing the multi-camera data, and the early warning function of the system is realized by combining the multi-dimensional analysis of the target type, the speed and the track, so that the monitoring effectiveness is greatly improved.

The invention discloses a flat gate valve performance detection device, and belongs to the technical field of gate valve detection devices, the flat gate valve performance detection device comprises a detection table, the top of the detection table is provided with a caliber detection mechanism, and the bottom of the detection table is provided with an air tightness auxiliary detection mechanism; according to the invention, through the arranged aperture detection mechanism, the multi-station alternate conveying detection of the flat gate valve body is realized, the detection efficiency and the automation level are significantly improved, and through a mechanical feedback structure formed by the extrusion plate, the reset spring, the push type trigger switch and other components, the detection precision can be greatly improved on the premise of no complex sensor. Structural elasticity changes are matched with displacement triggering, structural abnormity of the gate valve to be detected is accurately judged, sound-light alarm is given out, dependence of a detection system on circuit stability is reduced, meanwhile, the anti-interference capacity and the capacity of adapting to severe industrial environments of the device are enhanced, alarm response is fast, structural configuration is stable, and batched flat valve body rotary detection is facilitated.

The invention relates to an AI analysis alarm platform and method for video monitoring, and relates to the related technical field of video analysis alarm, and the platform comprises a priori threshold determination module which is used for constructing a negative space based on absolute normal data as a preposed priori threshold according to a region operation and maintenance rule for a target monitoring region; the abnormal alarm management module is used for accessing monitoring stream data, triggering a preposed prior threshold, executing data entropy change judgment by taking a negative space as a baseline, and performing abnormal alarm management; comprising a direct alarm response sub-module; a directional alarm response sub-module; wherein the preposed prior threshold and depth discriminator is an embedded plug-in of the AI analysis alarm platform. The technical problem that in the prior art, abnormal recognition model training depends on positive samples and annotation data and is difficult to adapt to dynamic complex scenes, so that video analysis alarm accuracy and timeliness are poor is solved, and the technical effect of improving video monitoring analysis alarm accuracy and timeliness is achieved.

The invention relates to the technical field of nursing informatization, and particularly discloses an alarm system and method for intelligent nursing management. The system comprises a data acquisition module, a threshold judgment module, an intelligent analysis module, an alarm push module and a record archiving module. The data acquisition module is used for acquiring multiple physiological parameter data of a patient in real time; the threshold judgment module judges whether to trigger a primary alarm or not according to a set standard value and tolerance; the intelligent analysis module calculates an alarm comprehensive risk index in combination with the primary alarm result, the historical health state of the patient, the nursing grade and the medication condition; the alarm pushing module synchronously pushes the alarm information to the nurse terminal, the doctor workstation and the electronic medical record system according to the risk index level; and the recording and archiving module performs whole-process recording and automatic archiving on the alarm response process.

The invention relates to a nuclear power plant fire alarm response method and device based on a three-dimensional engine and electronic equipment. The method comprises the following steps: acquiring field environment information of a nuclear power plant; constructing an initial three-dimensional model by adopting a three-dimensional engine method according to the field environment information; integrating field fire-fighting equipment information into the initial three-dimensional model to obtain a composite three-dimensional model; judging whether a fire occurs or not according to the real-time fire-fighting monitoring information; and if yes, generating a visual guidance strategy according to the real-time environment information and the real-time fire-fighting equipment information of the nuclear power plant site. Real-time visual monitoring of the internal structure of the nuclear power plant is achieved, the efficiency and accuracy of fire response are remarkably improved, multiple parameters in the nuclear power plant are monitored in real time through integration of fire fighting equipment information, once parameter abnormity is detected, the system can automatically start an emergency plan, a fire scene is simulated through a domestic three-dimensional engine, and the fire response efficiency and accuracy of the nuclear power plant are improved. And a visual evacuation path and a fire extinguishing scheme are provided for field personnel.

The invention provides a passive perception radar AI weak target detection method and system, and the method comprises the steps: obtaining a multi-dimensional electromagnetic signal data set of a target monitoring region, carrying out the signal feature extraction of the multi-dimensional electromagnetic signal data set, and obtaining the time-frequency distribution features and signal mode features corresponding to the multi-dimensional electromagnetic signal data set; based on a pre-trained weak target identification model, carrying out joint analysis processing on the time-frequency distribution characteristics and the signal mode characteristics, and generating weak target confidence parameters of potential weak target reflection signals; generating a weak target detection result of the target monitoring area according to a comparison result of the weak target confidence coefficient parameter and a preset confidence coefficient threshold value; and transmitting the weak target detection result to the target terminal device to trigger an alarm response operation. According to the method, the problem of characteristic loss caused by signal preprocessing and target detection module separation in a traditional method is solved, and the real-time performance of weak target detection and the system robustness are remarkably improved.

The embodiment of the invention provides an intelligent alarm method and device for tumble monitoring, and the method achieves the effective protection of the privacy of a user through the innovative design of a low-position visual angle collection mechanism, the setting of the installation height of a camera not exceeding the knee joint, and the starting of collection only during the abnormal motion. A detection mechanism based on a human skeleton model is constructed, and an accurate fall judgment strategy is established by combining multi-part vertical distance analysis and motion state parameters. An intelligent alarm mechanism is introduced, and quick response of an emergency contact person is realized through key video packaging and emergency channel establishment. According to the method, while the privacy of the user is protected, the defects of the traditional technology in the aspects of data acquisition, tumble detection, alarm response and the like are effectively overcome, and the practicability and reliability of tumble monitoring are remarkably improved.

The invention discloses a fire-fighting alarm receiving and processing digital management system, and the system obtains real-time environment parameter data through employing a distributed sensor, carries out the data preprocessing of the real-time environment parameter data, and obtains target environment parameter data. Inputting the target environment parameter data into a preset early warning identification model for identification to obtain an accident state type; analyzing the accident state type by using a path planning algorithm to obtain fire-fighting vehicle planning information; analyzing the accident state type by using a fire-fighting resource allocation model to obtain firefighter allocation information; and according to the fire-fighting vehicle planning information and the firefighter distribution information, unified scheduling of vehicles and persons is carried out, and real-time scheduling adjustment is carried out according to real-time conditions. The optimal path can be planned for the fire-fighting vehicle in a short time, resources are reasonably allocated, the police time is shortened, and the alarm response speed is increased.

The invention provides an aluminum processing deep well casting production line AI video violation snapshot monitoring method, which is characterized in that an intelligent safety production early warning model is constructed based on a computer vision deep learning technology, embedded equipment is used as a data acquisition terminal, and the action of the data acquisition terminal is associated with the operation of a console of an aluminum processing deep well casting production line. When the console starts to execute a dangerous process, the data acquisition terminal is triggered, the data acquisition terminal carries out continuous data acquisition on a monitoring picture of the front-end monitoring equipment and sends the acquired data to the AI monitoring video server at the rear end, and an AI system of the AI monitoring video server carries out data analysis through an intelligent safety production early warning model. AI identification is carried out by an AI analysis module, and alarm response is carried out according to an analysis result; according to the invention, the operation behavior of the casting production field operation personnel can be monitored in real time, the unsafe behavior of the personnel is judged through AI visual analysis, an early warning is given to remind the personnel to correct the unsafe behavior, and the occurrence of safety accidents is avoided.

The invention relates to a motor abnormal sound intelligent identification method based on time-frequency characteristics and hearing adaptation, and belongs to the technical field of motor fault detection. The method comprises the steps that motor operation sound signals are collected in real time through a multi-azimuth sound signal collection array, and signal preprocessing and dynamic framing processing are carried out; a time-frequency spectrum is generated through short-time sliding window time-frequency conversion, a normal operation sound auditory baseline is established in combination with an auditory perception adaptive dynamic weighting technology, and hierarchical weighting processing is performed on the time-frequency spectrum to generate fused time-frequency weighted features; a time-frequency weighted feature enhanced residual network model is constructed, and abnormal feature extraction and recognition are realized through a cross-dimension fusion mechanism, a fault feature transfer learning mechanism and a fault feature attention mechanism; and a working condition dynamic adaptive reasoning mechanism and a hierarchical linkage alarm system are adopted to complete state recognition and alarm response. The method effectively improves the accuracy and real-time performance of motor abnormal sound recognition, and is suitable for motor state monitoring in a complex industrial environment.

The invention provides a tunnel safety monitoring and early warning management method and system, and relates to the technical field of tunnel safety, and the method comprises the steps: carrying out the multi-mode data collection of a tunnel environment through a plurality of intelligent monitoring terminals connected to an RS485 bus, and carrying out the environment abnormality alarm triggering analysis; when any intelligent monitoring terminal detects an alarm event, the alarm event is broadcasted to other terminals through an RS485 bus, and other terminals receiving the alarm event autonomously analyze a diffusion probability map, calculate an emergency evacuation route and control an escape indicating device to display an evacuation direction; communication authority differentiation scheduling based on the RS485 bus is executed, and alarm event situation awareness based on multi-modal data dynamic updating is completed. The technical problems that in the prior art, tunnel safety monitoring and early warning management often adopts a centralized control mode, information transmission and processing are lagged, the alarm response time is long, and effective emergency disposal cannot be carried out in time are solved.

The invention discloses a multi-source data fire-fighting facility risk level assessment and early warning decision-making method, and particularly relates to the technical field of intelligent fire-fighting alarm devices and public safety, and the method comprises the steps: S1, collecting the state of a fire-fighting facility and environment dynamic data in real time, S2, calculating the static health degree index of the facility through an uncertainty reasoning model and a topological relation, and S3, calculating the risk level of the fire-fighting facility according to the static health degree index. The method comprises the steps of S1, generating an environment dynamic risk degree index through an adaptive evaluation model, S4, fusing the two indexes through a nonlinear coupling function to generate a comprehensive risk level and a visual map, and S5, matching, verifying and deducing a recommendation decision scheme with a utility score based on risk information. And a grading early warning signal is generated so as to drive a corresponding alarm device to execute early warning. According to the method, the defects of static facility assessment and dynamic environment risk separation are overcome, accurate risk quantification and prospective early warning are realized, and the intelligent level and execution reliability of alarm response are remarkably improved through an alarm decision closed loop and a self-optimization mechanism.

The invention discloses an alarm information processing method and device, computer equipment and a computer storage medium. The alarm information processing method comprises the steps of receiving various alarm information generated by a monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue; performing structured extraction and semantic understanding on the received alarm information to obtain alarm content of the alarm information; key information in the alarm content is extracted, and the classification and the emergency degree of the alarm information are determined based on the key information; adjusting a voice parameter according to the emergency degree, and generating an alarm audio file corresponding to the language parameter based on the classification and the alarm content through a pre-constructed multi-language synthesis system; and obtaining a receiving object and contact information of the alarm information, calling the receiving object according to the contact information, and pushing the audio file to the receiving object. Alarm classification and emergency degree evaluation are optimized, the alarm response efficiency is improved, and the accessibility of alarm notification is improved.