1193 results about "Alarm message" patented technology

The invention discloses a bulk cargo supply chain data transmission method and system based on an intelligent transportation box, and relates to the technical field of logistics data transmission, and the method comprises the steps: collecting multi-modal sensing data in real time through a multi-source data collection interface of the intelligent transportation box; the intelligent transportation box edge calculation module performs cleaning, compression aggregation and key event intelligent identification on the data; a transmission strategy is adaptively selected according to the data type and the network state, a key event alarm message is immediately sent, data are locally cached when the network is interrupted, and subsequent transmission is recovered; transmitting the data to a cloud by adopting a secure communication protocol; the cloud receives management data and provides remote configuration, alarm notification and data visualization functions, the system comprises an intelligent transportation box terminal unit and a cloud service platform unit, data transmission reliability and efficiency are improved, bandwidth and cloud cost are reduced, cargo transportation safety is guaranteed, and the system is suitable for bulk cargo cross-regional transportation scenes.

The invention discloses a sound acquisition and processing system based on cooperation of multiple microphone arrays. The system comprises a sound acquisition module, a multi-channel signal preprocessing module, a sound signal feature extraction module, an abnormal sound recognition module, a sound source positioning module and an alarm module. A multi-channel mixed data signal is collected through a circularly-arranged multi-microphone array formed by a plurality of microphones, after echo cancellation, wave beam domain noise reduction and multi-sound-source separation, a single-sound-source feature vector is extracted, according to the single-sound-source feature vector, abnormal sound including explosion, screaming or glass breakage is recognized through a BiLSTM and an attention mechanism model, and the abnormal sound is recognized through an attention mechanism model. And the GCC-PHAT and MDS-MUSIC algorithms are combined to position abnormal sound production, and alarm information is generated. According to the invention, accurate identification, positioning and alarm of the abnormal sound can be realized, and the real-time performance, the accuracy and the multi-target processing capability of abnormal sound monitoring in a complex environment can be obviously improved.

The invention discloses a communication switching method for automatic adaptation of a multi-protocol alarm terminal, which comprises the following steps of: realizing intelligent dynamic decision-making of multi-protocol communication switching based on a double-layer adaptive depth strategy and a grey wolf group search mechanism by intelligently sensing a terminal running state and a network environment, so that network fluctuation can be responded in time, and the communication switching efficiency is improved. The protocol switching process is accurately controlled, timely delivery of alarm information is effectively guaranteed, the energy consumption of the terminal is reduced, and the stability of a communication link is improved, so that the automatic adaptation capability and the overall operation efficiency of the multi-protocol alarm terminal under a complex network condition are comprehensively improved.

The invention relates to an alarm information processing system and method for a technological process, and the system comprises a data collection module which is used for collecting the real-time parameter data of each equipment node in the technological process; the knowledge base module is used for storing a risk knowledge graph of the technological process, and the risk knowledge graph comprises a plurality of accident chains representing abnormal propagation paths and alarm signals among equipment nodes; the risk diagnosis module is used for positioning a potential accident chain from a risk knowledge graph according to the received alarm signal and the real-time parameter data; and the alarm deduction module is used for deducing the potential influence of the alarm signal on the technological process according to the potential accident chain. According to the method and the device, the potential influence possibly caused by the alarm event can be automatically deduced, the severity and the influence range of the accident consequence can be effectively pre-judged, a scientific basis is provided for emergency decision making, and the problem of how to improve the safety of the technological process in industrial production in related technologies is at least solved.

The invention relates to an office intelligent control method and device based on multi-source data fusion and electronic equipment. The method comprises the steps that station use state data, environment illumination intensity data, personnel movement data and current lamp state data are acquired; based on the station use state data, the station use state is judged, and a corresponding illumination control strategy is generated; according to the illumination control strategy, the lamp brightness of the corresponding station is controlled; controlling to turn off the lamp in response to judging that no person is at the target station; calculating and outputting a target brightness value based on the environment illumination intensity data in response to the judgment that the current station is provided with the person; in the presence of a person, receiving a manual adjustment instruction sent by a user for the target brightness value, and performing adaptive optimization on the brightness calculation logic of the target brightness value; and in response to the lamp failure, controlling to lighten the lamps near the failed lamp and generating maintenance alarm information. According to the invention, the problems of high misjudgment rate, single control strategy, waste of energy consumption and poor user experience are solved.

The invention provides a bullying recognition notification method based on equipment cloud collaboration and AI keywords, and relates to the field of campus security and artificial intelligence, and the method comprises the steps: an equipment end collects environment audio in real time, detects bullying keywords in real time through a local AI model, and triggers data uploading; the cloud receives the audio and associates the audio with the scene video, performs secondary identification by using a multi-modal fusion model, and generates a behavior emergency index UI and a scene risk coefficient SRF; calculating a comprehensive risk value based on the UI and the SRF, and judging a bullying event in combination with a dynamic threshold value; and sending alarm information to a guardian, a teacher or a security platform according to the comprehensive risk value grade. According to the method, the recognition accuracy is remarkably improved through equipment cloud cooperative processing and multi-modal fusion analysis; a dynamically quantized UI / SRF evaluation system is introduced to realize scientific risk grading; an anti-disassembly mechanism, a local cache, breakpoint resume and an edge computing disaster recovery strategy are built in to guarantee the robustness of the system; the problems of inaccurate identification, risk quantification deficiency, response stiffness and easy equipment failure are solved.

The invention discloses a turnout state monitoring and alarming method based on multi-dimensional time sequence curve correlation analysis, relates to the technical field of railway facility monitoring, fundamentally solves the problem that a traditional supervised learning algorithm cannot be applied due to extremely sparse fault samples, can complete training of a core model without any fault data, and improves the training efficiency. The qualitative leap from numerical alarm to behavior mode alarm is realized. According to the method, by analyzing the correlation behavior mode of the current and the displacement, early hidden faults which cannot be found only by single data, such as switch rail jitter caused by normal current but slow action and current fluctuation can be accurately identified, and the sensitivity and the early warning capability of fault detection are remarkably improved. And finally, as the model deeply understands the influence of normal working condition changes such as temperature and load on curve relevance, the model can effectively distinguish normal working condition fluctuation and abnormal fault symptoms, so that the false alarm rate in the prior art is greatly reduced, and the alarm information is more credible.

The invention discloses an ultrasonic weak scattering enhancement detection system and method for micro leakage of a liquid pipeline, and relates to the technical field of industrial pipeline state monitoring and nondestructive testing. By performing enhancement processing on a bubble weak scattering signal generated by micro-leakage in the pipeline and inverting bubble parameters, online monitoring, graded early warning and positioning analysis of micro-leakage of the liquid pipeline are realized. The system comprises a clamping type transducer assembly and a control processing unit which are electrically connected, the transducer assembly is mounted on the outer wall of the pipeline; the control processing unit is configured to collect a baseline reference signal and construct a background model; transmitting a coding excitation signal, and driving an ultrasonic transducer assembly to transmit ultrasonic waves; synchronously acquiring ultrasonic echoes or transmission signals; carrying out preprocessing and weak scattering enhancement processing on the original sampling signal; inverting a bubble parameter and a gas content parameter; carrying out leakage judgment, and outputting micro leakage monitoring quantity and graded alarm information; the invention further discloses a method based on the system.

The invention relates to the technical field of operation and maintenance, and discloses an equipment fault processing method and system. The method comprises the following steps: acquiring alarm messages of a plurality of devices in real time, identifying associated alarm messages caused by the same fault according to an alarm association rule, and compressing the associated alarm messages and an existing fault group to obtain new alarm messages; determining the work order type and the circulation process of the new work order according to the alarm type of the newly sent alarm message, and carrying out order dispatching priority configuration on each process link based on the alarm level of the newly sent alarm message and the pre-configured authority information of each process link; and calling a corresponding work order template based on the work order type, configuring the work order template to generate a new work order, monitoring the full life cycle of the new work order, and synchronously updating the processing state of the new work order. According to the method, the fault processing efficiency of the machine room intelligent connection equipment is effectively improved, resource configuration is optimized through real-time monitoring and data analysis, and the method can meet the fault troubleshooting requirements of equipment of different scales and types.

The invention discloses a petroleum drilling machine equipment intelligent alarm method based on video real-time display, and belongs to the technical field of petroleum exploitation equipment monitoring, and the method comprises the steps: obtaining the operation video of a key part of petroleum drilling machine equipment, and transmitting the operation video of the key part to a monitoring center; in the monitoring center, preprocessing the operation video of the key part to eliminate the influence of illumination disturbance, and carrying out key part identification and behavior analysis on the preprocessed operation video of the key part by using an equipment part detection and behavior analysis algorithm based on deep learning to obtain a detection result of the key part; and based on the detection result of the key part, the display alarm and the sound-light alarm of the abnormal type and the occurrence position are triggered in real time, and the alarm information is sent to the handheld terminal of the driller. According to the method, intelligent fault detection and alarm are realized, manual intervention is reduced, the comprehensiveness of equipment monitoring is improved, and the accuracy and timeliness of alarm are improved.

The present application provides a traffic accident recognition method and device based on a multi-modal large language model. The method comprises: acquiring multi-modal data in a driving state of a target vehicle, wherein the multi-modal data comprises video data, audio data, and sensor data; inputting the multi-modal data into an accident recognition model obtained by training a multi-modal large language model for multi-modal information mining, and determining, on the basis of the mined multi-modal information, whether a traffic accident has occurred to the target vehicle; and if the traffic accident has occurred to the target vehicle, sending alarm information. According to the method, effective capturing of global information in the driving process of a target vehicle can be realized by means of an accident recognition model obtained by training a multi-modal large language model, so that recognition of traffic accidents in different scenes is enhanced, and the recognition accuracy of traffic accidents in different scenes is improved.

The invention discloses a traffic incident detector system based on a domestic intelligent DSP and a detection method. The system comprises a video acquisition unit, a data exchange and storage unit, an intelligent processing core and a communication interaction unit. The intelligent processing core adopts a heterogeneous SoC chip integrating a multi-core DSP, an NPU and an FFT hardware acceleration module, the FFT module is responsible for video frame frequency domain enhancement, the NPU runs a deep learning model to carry out target detection, and the multi-core DSP executes multi-target parallel tracking and event judgment. And the data exchange and storage unit supports direct carrying of data among the modules through a unified physical address space and a DMA mechanism. The method comprises the following steps: carrying out NPU target detection on a video subjected to environment self-adaptive preprocessing; a multi-core DSP is used for tracking a target in parallel and judging an event; and finally, alarm information is generated and reported. Through heterogeneous hardware division and software and hardware cooperation, high-real-time and high-precision edge analysis is realized, and the detection reliability is improved.

The invention discloses an intelligent road system for vehicle-road cooperation safety alarm, and relates to the technical field of vehicle-road cooperation alarm, and the system comprises a communication evaluation module which inputs an optimal communication path into a communication mapping model, a space matching layer carries out the fusion of road geometry and communication topology, a state mapping layer marks the signal intensity and spike node states, and a state analysis module which carries out the analysis of the optimal communication path; generating a communication quality thermodynamic diagram; the risk decision module is used for evaluating the risk level of each region based on the communication quality thermodynamic diagram and outputting priority transmission parameters; and the information transmission module starts sound-light alarm according to the priority transmission parameters, obtains an execution state report, and transmits the execution state report back to the command center. According to the method, the communication mapping model is constructed and the priority transmission parameters are dynamically generated, so that the accuracy of communication state evaluation is improved, and efficient cooperative processing and intelligent priority distribution of multi-source alarm information are realized.

The invention discloses a classification and grading alarm system based on multi-source data, relates to the technical field of industrial safety and workplace monitoring, and can solve the typical problems that in the prior art, alarm logic is rigid and single, data sources are static and isolated, an intelligent alarm verification mechanism is lacked, and intelligent analysis and management processes are separated. Through multi-source sensing equipment, an edge-cloud cooperative computing architecture, a multi-source event triggering engine, an innovative classification and grading alarm engine and an AI large model rechecking mechanism, event automatic handling measure execution and alarm information distribution are realized through an automatic linkage and notification subsystem; and finally, event closed-loop is completed by the event handling and closed-loop platform, so that efficient, accurate and automatic monitoring, alarming and closed-loop management of industrial and various working place security events are realized.

The invention discloses a communication network fault diagnosis method and related equipment based on multi-modal vector fusion, and the method comprises the steps: carrying out the vector structuring processing of historical heterogeneous telecommunication operation and maintenance data through a multi-modal vectorization engine, and obtaining a historical multi-modal vector set; performing vector fusion processing on the historical multi-modal vector set through a vector fusion center to obtain a historical multi-modal fusion vector; an alarm message queue in a telecommunication network is monitored through a diagnosis decision module, and if an unknown composite fault is detected from the alarm message queue, cosine similarity calculation is carried out on a current fault fusion vector corresponding to the unknown composite fault and a historical multi-mode fusion vector in a hierarchical storage module; and outputting a network fault diagnosis decision result. According to the method, the accuracy and comprehensiveness of network fault diagnosis can be improved, efficient data storage, retrieval and diagnosis decision are realized, the operation efficiency and diagnosis capability of the system are improved, and the method can be widely applied to the technical field of communication network operation and maintenance.

The invention relates to the field of intelligent operation and maintenance, and provides a communication interruption fault monitoring and analyzing method for an interlocking logic part and a control and display machine, which comprises the following steps: acquiring first maintenance data of an interlocking system and an automatic train monitoring system, and performing data analysis on the first maintenance data to obtain a communication state of the interlocking logic part and the control and display machine; according to the communication state, communication interruption between the interlocking logic part and the control display machine is determined, and communication interruption alarm information is generated; and querying second maintenance data of the interlocking subsystem and the train automatic monitoring subsystem according to the communication interruption alarm information, analyzing the second maintenance data, and generating a fault analysis result. According to the invention, the problems of low efficiency and high misjudgment rate caused by dependence on manual troubleshooting of communication interruption faults in the prior art are solved, and automatic fault monitoring and intelligent analysis of the interlocking logic part and the control display machine are realized.

The invention provides a content distribution network traffic alarm method and device and electronic equipment, and aims to solve the problem of insufficient accuracy of abnormal traffic detection. The method comprises the following steps: acquiring a content delivery network (CDN) real-time traffic log, wherein the CDN real-time traffic log is generated by a CDN server; analyzing a plurality of dimension labels from the CDN real-time traffic log; for each label combination determined by the plurality of dimension labels, calculating the CDN real-time traffic log according to the label combination to obtain the peak traffic of the label combination; obtaining a CDN historical flow log, and calculating the baseline flow of the label combination according to the CDN historical flow log; and generating an alarm message of the CDN real-time traffic log according to the peak traffic of the label combination and the baseline traffic of the label combination, wherein the alarm message comprises the label combination and the peak traffic of the label combination.

The invention provides a vehicle-mounted gateway message real-time anomaly detection method, system and device and a vehicle, and relates to the technical field of intelligent driving, and the method comprises the steps: firstly, collecting and preprocessing the message data of a vehicle-mounted gateway, and guaranteeing the data structuring for subsequent analysis; next, stream processing and timestamp alignment are carried out by adopting an adaptive window adjustment strategy to effectively cope with network flow fluctuation, and an ordered real-time data stream is generated; a plurality of key message features including delay, packet loss rate, content consistency and the like are extracted from the data, intelligent anomaly detection is performed by using a deep learning model in combination with a dynamic threshold value, and an abnormal message is accurately identified; and finally, based on a detection result, alarm information is sent through a message queue, and a data mining technology is used for automatically analyzing an abnormal reason, so that root cause positioning and report generation are realized, the safety and the stability of a vehicle-mounted network are effectively improved, and powerful technical support is provided for reliable operation of the vehicle.

The invention discloses a server hardware abnormity alarm method, system and device, and relates to the technical field of server hardware abnormity alarm, and the method comprises the steps: obtaining the characteristic parameters of an abnormal state mode, carrying out the risk grade division of a detected abnormal state according to an abnormity severity grading standard, and obtaining an abnormal state; a comprehensive anomaly scoring result is obtained through multi-dimensional feature weight calculation; triggering an alarm mechanism of a corresponding level according to a comprehensive anomaly score result, if an anomaly score exceeds an emergency threshold, immediately generating high-priority alarm information, and performing alarm information aggregation processing to obtain a complete hardware anomaly diagnosis report; an alarm information deduplication algorithm is adopted to carry out merging processing on repeated alarms of the same hardware component, and the stability and persistence characteristics of an abnormal state are judged through alarm frequency statistics and abnormal duration analysis in a time window.

The invention discloses a detection method and system for judging the eccentricity of an SF6 circuit breaker contact based on dynamic resistance, and relates to the technical field of high-voltage electrical equipment detection, and the method comprises the steps: obtaining an eccentricity dynamic resistance oscillogram and a standard dynamic resistance oscillogram of a to-be-detected eccentric SF6 circuit breaker through employing an SF6 circuit breaker contact eccentricity detection device; obtaining key index data according to the eccentric dynamic resistance oscillogram and the standard dynamic resistance oscillogram, wherein the key index data comprises a fluctuation index, a misalignment index, an initial value over-standard rate and a peak value over-standard rate; and generating alarm information according to the key index data, and starting maintenance operation in combination with different alarm information. The determined key index data is compared with a standard requirement value, and then whether the detected SF6 circuit breaker needs to be overhauled due to the eccentric problem of the movable and static contacts is judged, so that the potential quality hazard caused by the eccentric problem of the movable and static contacts is eliminated, and the unnecessary overhaul workload of the SF6 circuit breaker can be greatly reduced.

The utility model discloses a conveyor belt deviation detecting and correcting device, which relates to the technical field of belt conveyors, is used for being matched with a carrier roller component and a conveyor belt arranged on the carrier roller component, and comprises a correcting device and a detecting device, the deviation rectifying device comprises vertical rollers symmetrically arranged on the two sides of the conveying belt, the vertical rollers are slidably and elastically connected with the placement frame, guide limiting grooves matched with the side edges of the conveying belt are formed in the working faces of the vertical rollers, and the guide limiting grooves and the conveying belt are in elastic dynamic contact fit, so that the vertical rollers can push the conveying belt back to the initial center line position; and the detection device is arranged on the placement frame and is configured to be capable of detecting the value of the lateral pressure borne by the vertical roller in real time so as to obtain the deviation working condition of the conveying belt, and alarm information is sent out to remind workers in time.

The invention belongs to the technical field of equipment early warning, and particularly relates to a multi-source information fusion real-time monitoring and data analysis method and system, and the method comprises the steps: achieving the automatic recognition and data collection, remote assistance and support, and intelligent path planning and navigation functions in the inspection process of a chemical device, so as to standardize the inspection process; the inspection efficiency and the result accuracy are improved; establishing a chemical device image recognition model, collecting and preprocessing chemical device image data, performing model training by using a convolutional neural network deep learning model, and deploying the model to actual equipment; an abnormal feature extraction algorithm is developed, the operation state of the equipment is monitored in real time in combination with an intelligent early warning and processing system, early warning is quickly given out, and corresponding processing measures are taken; by adopting the anomaly detection and classification technology, an anomaly detection algorithm is realized, an efficient alarm information transmission mechanism and an intelligent alarm information processing system are developed, and an alarm information feedback and closed-loop management mechanism is established, so that the accuracy and timeliness of anomaly detection are improved.

The invention provides a smoke sensing module alarm method, device and equipment and a storage medium, and the method comprises the steps: obtaining the smoke concentrations of a smoke sensing module measured at a plurality of consecutive moments in a current environment, carrying out the mean processing of the plurality of smoke concentrations, and determining the to-be-determined smoke concentration; determining a target zero-point smoke concentration matched with the current environment according to the undetermined smoke concentration and a preset zero-point smoke concentration; the preset zero-point smoke concentration is a target zero-point smoke concentration matched with a previous environment of the current environment; and determining whether the smoke sensing module generates an alarm message in the current environment or not according to the target zero smoke concentration matched with the current environment and the smoke concentration currently measured by the smoke sensing module. By adopting the technical scheme of the invention, the alarm accuracy of the smoke sensing module in any scene can be improved.

The invention discloses a water supply and drainage quality anomaly detection method and system, and relates to the technical field of water supply and drainage quality detection. The method comprises the steps of periodically obtaining multiple water quality parameter values according to preset time; aiming at the numerical value obtained each time, carrying out the following operations: respectively reconstructing each water quality parameter value to obtain a water quality parameter reconstruction value corresponding to each water quality parameter value; calculating a current total reconstruction difference value of the water quality according to each water quality parameter value and the corresponding water quality parameter reconstruction value, and calculating a current abnormal accumulated value of the water quality according to the current total reconstruction difference value and a preset time attenuation factor; and if the continuous abnormal threshold value is reached, generating and sending alarm information that the water quality has hidden abnormity. According to the water supply and drainage water quality anomaly detection method and system provided by the invention, the water quality parameters are reconstructed, the reconstruction difference is calculated, and the abnormal value is accumulated, so that the recessive water quality anomaly caused by the tiny relevance change of a plurality of water quality parameters is effectively identified, and the sensitivity and accuracy of water quality detection are improved.

Embodiments of the invention disclose an object abnormal behavior prompting method and apparatus, a wearable device and a medium. The method comprises the steps of collecting and obtaining device motion change data of the wearable device based on a first frequency; when it is detected that the equipment movement change data meet the risk action detection condition, environment perception data are obtained, and the equipment movement change data and object physiological change data of the equipment wearing object are collected based on a second frequency; if any abnormal behavior detection condition is satisfied, determining that the equipment wearing object has an abnormal behavior state; and performing cooperative detection on the multi-modal sensor data, determining abnormal level information corresponding to the abnormal behavior state, and prompting a corresponding target user based on a target prompting mode corresponding to the abnormal level information, the problem of response lag caused by the fact that a traditional wearable device depends on manual alarm information triggering and single-detection-dimension anomaly detection is effectively solved, and the accuracy of object risk behavior judgment and the timeliness of emergency response are improved.

The utility model relates to an electric control cabinet intelligent safety alarm device, which comprises an electric control cabinet main body, an ultrasonic ranging module, an alarm module and a communication module, and is characterized in that the ultrasonic ranging module measures the time difference of transmitting and receiving ultrasonic waves to determine the actual distance between an obstacle and the electric control cabinet main body, so that non-contact ranging is realized; the timeliness of alarm triggering is ensured, and accurate measurement can be carried out even in a severe environment full of dust, smog or corrosive substances and the like; the multi-mode alarm module and the ultrasonic ranging module are combined for use, multi-mode alarm is carried out when the ultrasonic ranging module triggers the alarm function, a multi-level verification and confirmation mechanism is realized, the possibility of false alarm is reduced, diversified alarm signals are provided, and the alarm can be carried out even in a noisy or poor-sight environment. Normal receiving of alarm information can also be ensured; by means of the communication module, the intelligent safety alarm device of the electric control cabinet has remote monitoring and management functions, and the quick response capability is remarkably enhanced.

A public toilet alarm and response system comprises a cleaning administrator mobile terminal, a system administrator mobile terminal, an alarm terminal, an alarm switch and a toilet cubicle detection module. Each toilet cubicle of each public toilet is internally provided with an alarm switch which is used for sending an alarm signal after being passively triggered or actively triggered, and a toilet cubicle detection module which is used for enabling the corresponding alarm switch to be actively triggered when the toilet cubicle is used overtime; the non-toilet-cubicle public area in each public toilet is provided with an alarm end which displays the corresponding toilet cubicle after receiving the alarm signal and uploads the alarm information to the cloud server; the cloud server receives alarm information uploaded by the alarm end, forwards the alarm information to the system administrator mobile end and the cleaning administrator mobile end managing the toilet cubicle, and stores alarm records. The system is high in response real-time performance, low in power consumption, high in reliability and convenient for system optimization based on big data, and the manual inspection burden of a cleaning administrator is reduced.

The invention relates to a limited space operation safety monitoring method based on multi-sign parameter intelligent judgment, which comprises the following steps of: training a single-class support vector machine model by utilizing historical data, and establishing a normal physiological state multi-dimensional reference model under each operation content; the method comprises the following steps: acquiring current operation content in a limited space, determining a vital sign parameter set needing to be monitored, and collecting real-time vital sign data of an operator through a user terminal; calculating a comprehensive abnormal score AS of the real-time vital sign data, and when the comprehensive abnormal score AS exceeds a dynamic threshold value delta, marking the data as comprehensive abnormal data; marking the vital sign parameter with the maximum contribution degree absolute value in the abnormal parameters as a key vital sign; and sending alarm information to a monitoring terminal, wherein the alarm information comprises the identifier of the key vital sign. By comprehensively analyzing the real-time vital signs and comparing the real-time vital signs with the dynamic threshold value, the accuracy of abnormal person recognition is effectively improved.

The invention discloses an anti-prying alarm information processing method and system for an intelligent lock, relates to the technical field of information processing, and constructs a behavior sequence capable of completely representing a continuous interaction process of a user by integrating original data streams from multiple sensors of the intelligent lock and performing unified coding and time sequence windowing processing on the original data streams. On the basis, an intention recognition model is introduced, and the behavior sequence is deeply analyzed to deduce the potential malicious degree of the behavior sequence. Furthermore, the initial malicious probability is dynamically calibrated by fusing external situation information such as time, user authorization state and the like, so that a more accurate and context aware comprehensive risk score is generated. And finally, intelligent grading response decision making is carried out based on the score. Therefore, the problem of false alarm caused by similar physical characteristics of benign abnormal operation and hostile attack can be avoided, so that the accuracy and reliability of the security and protection system are remarkably improved.

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.