2156 results about "Alarm signal" patented technology

The invention discloses a power equipment detection method and device based on data acquisition of an inspection unmanned aerial vehicle, electronic equipment and a storage medium, and the method comprises the steps: obtaining a multi-view image of to-be-detected power equipment, and inputting the multi-view image into an abnormal region detection model, and generating an anomaly existence probability and a suspected anomaly region coordinate of the equipment under the current view angle. When it is detected that the abnormal probability at a certain visual angle exceeds a preset threshold value, a reinspection instruction is sent to the inspection unmanned aerial vehicle in an adjacent area, and image data of the equipment at a new visual angle are fed back. A second anomaly existence probability is calculated by re-inputting the new view angle image to the anomaly detection model. If the probability exceeds a preset threshold value, judging that the power equipment is abnormal, and sending an alarm signal; otherwise, judging that the equipment is normal. According to the invention, the abnormal accurate positioning and intelligent alarm of the power equipment can be realized, and the efficiency and accuracy of equipment inspection can be effectively improved.

The invention belongs to the technical field of image analysis, and discloses an old people health state prediction and alarm method based on AI image analysis, and the method comprises the steps: obtaining a depth image, a color image and a pressure image of an activity environment of old people, carrying out the combined analysis of the depth image and the pressure image, recognizing a terrain semantic region in a monitoring region, and carrying out the prediction of the health state of the old people; and carrying out dynamic segmentation on the gait contour of the old people. And the terrain gait coupling degree is calculated by analyzing the depth change characteristics and the motion period characteristics of the gait sub-regions, so that the abnormal gait sub-regions are screened out. And for the abnormal regions, the terrain gait coupling degree, the dynamic texture features of the color image and the pressure distribution features of the pressure image are fused, and a cross-modal health risk vector is constructed. Based on the vector, the health state of the old people is accurately predicted, and a corresponding alarm signal is generated. According to the invention, precise evaluation and timely early warning of the health state of the old people are realized, and intelligent guarantee is provided for the home safety of the old people.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention discloses a behavior analysis camera monitoring method and system based on deep learning, and the method comprises the following steps: S1, collecting an image sequence and environment parameters of a target object in a scene through a multi-modal sensor, the environment parameters comprising illumination intensity and background noise level; s2, performing priority division on the image sequence by using a dynamic region-of-interest weight calculation module, and generating a region-of-interest mask based on the movement track and posture change of the target object; s4, generating an abnormal behavior index according to an output result of the classifier; and S5, when the comprehensive abnormal index exceeds a preset threshold value, a cross-camera cooperative tracking mechanism is triggered, an alarm signal is generated, and meanwhile, high-risk behavior fragment marks are stored to a priority queue, and accurate perception of human body behaviors in a complex environment is realized through an innovative architecture combining multi-modal data fusion and dynamic weight adjustment.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention relates to the technical field of state detection and fault diagnosis, in particular to an intelligent early warning and fault diagnosis system for a thermal power plant, which comprises a multi-source data acquisition module for acquiring data in real time; the edge computing node is used for performing noise filtering and abnormal value correction on the acquired data; the digital twin modeling unit is used for constructing a dynamic simulation model of the equipment based on a physical model and historical data; the hybrid analysis engine is used for positioning early abnormal detection and fault sources; the visual early warning interface is used for dynamically displaying the health state and the fault probability of the equipment and generating a graded alarm signal; according to the invention, the multi-source data acquisition module acquires equipment multi-dimensional signals in real time, after edge computing node filtering and denoising, a digital twin modeling unit constructs a precise simulation model, a hybrid analysis engine fuses LSTM and a Bayesian algorithm, fault features are deeply mined, data weights are optimized, and the fault detection accuracy is improved. According to the system, the accuracy and timeliness of diagnosis are remarkably improved.

The invention relates to the field of wafer processing, and discloses a wafer grinding and polishing adjustment control system based on a tension sensor, which comprises a processing pressure acquisition module for acquiring the gravity of a heavy object, acquiring the tension of an electric cylinder in real time based on the tension sensor, calculating the processing pressure through a formula, and acquiring grinding state parameters of a wafer in real time; the processing pressure stability analysis module comprises a first analysis unit and a second analysis unit; the first analysis unit is used for calculating the accumulated variable quantity of the processing pressure in a preset monitoring period, comparing the accumulated variable quantity with a change threshold value and sending out an alarm signal; the second analysis unit inputs the grinding state parameters into a grinding state model to analyze a grinding state value, and reversely evaluates the state of the grinding disc; the adjusting control module adjusts the pulling force of the electric cylinder based on the first analysis unit and judges whether the grinding disc is replaced based on the second analysis unit. Accurate monitoring and stability control of the machining pressure are achieved, the state of the grinding disc can be effectively evaluated, and the wafer grinding and polishing quality and efficiency are improved.

The invention is applicable to the technical field of safety monitoring and early warning, and provides a new energy station operation site safety monitoring and early warning method, which comprises the following steps: collecting multi-source heterogeneous data in real time through an edge computing unit deployed in an operation site; analyzing the video stream in real time by using a preset artificial intelligence visual analysis model, identifying personnel violation behaviors, equipment abnormal states and environmental risk factors, performing cross validation in combination with an electronic access control state and a work ticket permission state, and generating a primary alarm signal when it is detected that preset condition information is not matched; risk grade evaluation is carried out based on the primary alarm signal and environmental risk factors, and a dynamic early warning instruction is generated and synchronized to a station level platform; and the station control layer triggers video review of the associated area, a linkage access control system locks the dangerous area, early warning information is pushed to the target terminal, and an emergency processing plan is generated. And the real-time response speed and the active protection capability of a high-risk operation scene are effectively improved.

The invention provides a method and a device for identifying and alarming a safety helmet under a mine and computing equipment, the method comprises the following steps: acquiring a frame image of a real-time video stream from a camera device, the real-time video stream being used for fixedly monitoring a working place under the mine; performing human body detection identification on the frame image to obtain a human body identification area; performing head area positioning on the human body recognition area to obtain a head recognition area; carrying out safety helmet wearing identification detection on the head identification area to obtain a safety helmet wearing identification result; and rechecking the safety helmet wearing identification result based on a semantic segmentation model, and determining whether to output a safety helmet non-wearing alarm signal or not according to a rechecking result. According to the technical scheme of the invention, accurate safety helmet wearing identification can be realized for mines with complex environments and under-mine environments.

The invention relates to the technical field of mining area pollution monitoring, and discloses an atmospheric pollution monitoring system based on a mining area and a monitoring method thereof.The atmospheric pollution monitoring system based on the mining area comprises the steps that firstly, geographic environment data, outdoor environment data and well area environment data are obtained through a network deployment module and stored; the real-time transmission module is used for preprocessing the acquired data and transmitting the preprocessed data to the comprehensive analysis module, and the comprehensive analysis module is used for analyzing the atmospheric pollution index, judging whether to send out pollution early warning or not according to the calculation result of the atmospheric pollution index, and sending out a pollution alarm signal under the condition that the pollution early warning needs to be sent out; the mining area air pollution monitoring system can comprehensively monitor the mining area air pollution in a multi-dimensional manner, accurately identify the main pollution source and the dynamic change of the main pollution source, break through the limitation of traditional fixed site monitoring, flexibly capture the spatial and temporal change of the mining area pollution, accurately identify the pollution source, improve the monitoring accuracy and improve the monitoring efficiency. And an alarm signal is sent timely.

The invention belongs to the technical field of target detection, and particularly relates to a high-speed rail dropper insulator integrity identification method and system based on improved YOLOv9, and the method comprises the following steps: collecting dropper insulator image data with preset definition, and generating a double-category annotation data set; performing negative sample screening on the double-category annotation data set, and dividing a training set, a verification set and a test set based on a preset proportion of 8: 1: 1; constructing an improved detection model based on a YOLOv9 framework; performing iterative training based on the training set, monitoring convergence based on the verification set, and outputting a trained improved detection model; inputting the test set into the trained improved detection model, judging whether the performance reaches the standard or not, and outputting the trained improved detection model; inputting a to-be-detected image in real time and outputting a detection result; and generating a defect alarm signal and a visual report based on the detection result. The method has the advantages of being high in high-speed rail dropper insulator integrity recognition accuracy and high in recognition speed.

The invention discloses a mobile robot control device giving consideration to field navigation patrol and transportation load, and relates to the field of robot control, and the device comprises a sensor data collection module which forms multi-source sensor data; the environment understanding module is used for predicting terrain risks and weather influences based on the feature vectors and outputting environment feature data and risk prediction; the navigation control module is used for navigating a path based on the environment characteristic data and generating a robot motion control instruction; the load management module adjusts parameters of the navigation control module based on the load state; the feedback adjusting module is used for dynamically adjusting control parameters according to the load state and feeding back the control parameters to the navigation control module to optimize and modify a robot motion control instruction; and the intelligent detection module is used for detecting environment abnormity and dangerous case events and outputting an alarm signal. The method has the advantages that accurate prediction of environmental risks is realized through multi-sensor layered feature extraction and attention weighted fusion, and motion parameters are dynamically adjusted in combination with a moment balance model.

The invention relates to the technical field of artificial intelligence and the technical field of equipment fault prediction and detection, and discloses a fault prediction and detection method based on an intelligent model, and the method comprises the steps: obtaining and preprocessing the historical data of a sensor, and generating time series data; pre-training the time sequence data to generate a pre-training model; performing fine tuning on the pre-training model based on the attention mask of position perception to generate a fine-tuned model; acquiring current data of the sensor, and generating prediction data; and analyzing the deviation between the predicted data and the actual data, and triggering an alarm signal when the deviation exceeds a preset threshold value. According to the method, the time series data features are learned through pre-training, multi-segment prediction is performed in combination with an attention mechanism, and the accuracy of fault prediction is improved; the fine-tuned model adapts to different working conditions, and the generalization ability of the model is enhanced; alarm is triggered through deviation analysis, intelligent monitoring of the equipment state is achieved, and real-time performance and reliability are improved.

The invention discloses a method for effectively studying and judging false alarm of a fire alarm system in a transformation power station in a multi-dimensional mode, and the method comprises the steps: collecting and fusing multi-channel sensor data, such as smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and the like, and constructing a scenarized data set in combination with an environment label and an operation and maintenance log; through multi-level feature processing such as normalization, wavelet denoising and dimension reduction, the data quality and discriminability are improved; according to the method, the space-time diagram neural network is used for achieving multi-period environment subarea and typical signal mode division, multi-period fusion confidence judgment is conducted on alarm signals based on the fuzzy set theory and Bayesian reasoning, dynamic judgment of false alarms and real alarms is achieved, and the accuracy and robustness of alarm judgment in the complex environment are effectively improved.

The invention belongs to the technical field of agricultural supervision, and particularly relates to an intelligent agricultural planning decision-making system based on the Internet of Things, which comprises a planting plan decision-making unit, a resource allocation decision-making unit, an irrigation and fertilization monitoring unit, an agricultural planting management and analysis unit and an agricultural management and control terminal, planting plan decision-making information is generated through the planting plan decision-making unit to improve planting benefits, and monitoring analysis of the resource distribution decision-making unit and the pest and disease damage monitoring decision-making unit is beneficial for achieving precise irrigation and fertilization and timely preventing and treating pest and disease damage. The irrigation and fertilization monitoring unit analyzes the nutrient water content suitability abnormal performance of the corresponding area in the detection period, enhances subsequent irrigation and fertilization supervision when an irrigation and fertilization alarm signal is generated, and comprehensively evaluates the planting management performance of the corresponding area when an irrigation and fertilization qualification signal is generated; and subsequent planting supervision is enhanced when the planting management alarm signal is generated, so that the growth of crops in corresponding areas is ensured, and the intelligent level is high.

The invention provides a safety patrol method, a patrol device and patrol equipment, and relates to the technical field of building safety, and the safety patrol method comprises the steps: responding to an operation instruction of an editable interface, configuring a multi-stage process template of building safety patrol, and generating a corresponding safety patrol task; the shooting device collects real-time patrol data of a construction site; identifying violation operation, equipment abnormity and environment risk in the real-time patrol data, and generating a corresponding risk level result; generating a visual alarm signal according to the risk level result, issuing the visual alarm signal to a mobile terminal, and sending the visual alarm signal to a monitoring center when the visual alarm signal is an emergency signal; monitoring response information of the mobile terminal, updating the state of the safety patrol task and recording an operation log of processing a risk level result by patrol personnel; if the mobile terminal does not respond within the preset time, the alarm information is sent to the mobile terminal again, the alarm information is sent to the monitoring center, and the patrol efficiency and the risk handling capacity can be improved.

The invention discloses an alarm information research and judgment noise reduction method. According to the method, multiple pieces of alarm information with internal relation are combined into a structured alarm cluster through deep mining and by utilizing context association information between alarm events, so that redundant information is greatly compressed, and the alarm signal-to-noise ratio is improved. And multi-dimensional and comprehensive evaluation is carried out on the alarm cluster by fusing three mechanisms of information entropy analysis, customization rule evaluation and semantic evaluation of a large model, so that high threat alarms can be identified more accurately. Wherein the information entropy analysis helps to identify potential unknown threats by quantifying the uncertainty of the alarm, and the large model semantic evaluation can deeply analyze the alarm content, so that the accuracy and efficiency of alarm analysis are 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 abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention discloses a control and alarm method and device for multi-parameter grain condition data and a medium, and relates to the technical field of granary management. The method comprises the following steps: acquiring grain depot data and grain pile data through a sensor, and synchronously acquiring warehouse in-out receipt information; the grain depot data and the grain pile data are compared with a historical database, a dynamic threshold value model is established, and the dynamic threshold value model adjusts a parameter deviation range based on climate data and a storage period of the grain depot in the same region; according to the dynamic threshold model, obtaining a corresponding boundary value, generating a 3D distribution diagram of the grain pile, and calculating an interlayer difference; if the grain depot data and the grain pile data reach boundary values or the interlayer difference exceeds a first preset threshold value, an alarm signal is triggered, and the stock quantity is verified by associating the in-out stock receipt information. According to the method, real-time collection of multi-source data and application of the nano biosensor are achieved, and comprehensive and accurate monitoring of internal and external environmental parameters and grain physiological indexes of the grain depot is achieved through the dynamic threshold value model.

The invention discloses a Beidou intelligent safety helmet with an environment monitoring function, which relates to the field of safety helmets and comprises an inspection safety helmet, a signal transmission unit and a control terminal. According to the invention, through monitoring the environmental parameters and the body parameters of the operating personnel, the overall health condition of the personnel is comprehensively evaluated, through the comprehensive and accurate evaluation, potential safety hazards and health risks can be found in time, a scientific basis is provided for taking corresponding measures, early warning is timely and accurately carried out, and the working efficiency is improved. The safety of operators is guaranteed by monitoring the environment where the operators are located in real time, pre-judging dangers and early warning, an alarm signal is automatically sent out when the operators fall down and are impacted, the operators are reminded that the operators are possibly in a dangerous state, meanwhile, the position information of the operators is accurately recorded, and the motion trails of the operators are traced, so that the safety of the operators is guaranteed. And rescue workers can quickly find an accident site.

The invention provides a power system intrusion detection and defense method based on artificial intelligence, and belongs to the technical field of power system intelligent control, and the method comprises the steps: 1, collecting data based on a multi-dimensional data collection layer, and forming an original data flow containing a time-space synchronization mark; 2, dynamically segmenting the original data stream by adopting a self-adaptive sliding window mechanism, automatically adjusting the size of a window according to the arrival frequency of a data packet, and converting the continuous data stream into a final triple sequence; 3, extracting and analyzing the features of the final triple sequence, and determining an equipment behavior mode and sequential logic of power operation; 4, performing deviation analysis on the equipment behavior mode and the normal behavior mode, and generating a primary alarm signal when the deviation degree exceeds a dynamic threshold value; and 5, starting multi-stage interlocking verification based on the primary alarm signal to determine an intrusion type, and matching a preset response rule base according to the intrusion type. Intrusion behaviors in the power system are effectively detected and defended in real time.

The invention relates to the technical field of fishing port safety monitoring, and discloses a fishing port fishing boat berthing safety monitoring system. The system comprises a fishing boat dynamic sensing module which collects dynamic position and attitude data of each fishing boat in a fishing port in real time; the berth state analysis module generates berth occupation state distribution information based on the data; the environmental parameter acquisition module acquires fishing port hydro-meteorological parameters in real time; the safety constraint initialization module outputs an initial safety monitoring constraint including an allowable yaw threshold value and a maximum allowable collision risk value in combination with a berth occupancy state and hydro-meteorological parameters; the parking risk assessment module executes risk quantitative analysis and generates risk level identification data by taking the initial safety monitoring constraint as a condition; a scheduling parameter optimization module optimizes berth resource scheduling according to the risk level identification data to obtain safety scheduling control parameters; and the alarm signal generation module converts an alarm dimension according to the safety scheduling control parameter and the dynamic position data updated in real time, and outputs a multi-stage acoustic alarm signal.

The invention belongs to the technical field of fire safety early warning, and discloses a fire detection alarm method and system. The method comprises the following steps: monitoring the environmental smoke concentration in real time through a smoke detector arranged in a battery compartment to obtain smoke parameters; synchronously monitoring the environment temperature change through a temperature sensing detector to obtain a temperature parameter; comparing the smoke parameter with a preset smoke threshold value, and comparing the temperature parameter with a preset temperature threshold value; determining an alarm signal grade according to a comparison result, and triggering a corresponding alarm signal based on the alarm signal grade; and after the alarm signal is triggered, corresponding alarm measures and corresponding fire extinguishing measures are executed according to the grade of the triggered alarm signal. Through the above mode, composite grading alarm can be carried out, the accuracy of fire alarm is improved, targeted fire extinguishing measures are taken for different alarms, and the fire extinguishing efficiency is also improved.

The invention belongs to the technical field of industrial automation control, and discloses a PLC-based fault diagnosis method and device. The method comprises the following steps: acquiring data of target equipment acquired by a sensor through a data acquisition point; according to a preset conversion method, determining the data as a physical quantity numerical value of a corresponding physical quantity type, and storing the physical quantity numerical value to a circular buffer area with a preset time window length; determining a period according to a preset time length, and determining a plurality of characteristic values of the physical quantity values in the circular buffer area based on the data acquisition points; inputting the plurality of feature values into a pre-trained decision tree fault recognition model, and outputting a fault recognition result of the target equipment, and according to the target fault type label of the fault identification result, triggering an alarm signal, and recording a fault log of the target equipment. Whether the equipment breaks down or not can be identified based on the data during the operation of the equipment, and the fault type can be determined.

The invention belongs to the technical field of fire safety, and particularly relates to a control method of a fire detection alarm. Firstly, a distributed sensor network is deployed to collect temperature and smoke data, self-calibration is carried out, and abnormal data is eliminated. Then, through three-level progressive anomaly screening, namely, first-level preliminary screening monitoring mutation features and second-level feature verification, a space-time matrix is constructed, CNN analysis is used for the space-time matrix, third-level scene adaptation is combined with historical data to calculate the probability, and an anomaly screening signal is output. A visual camera is used for collecting images to generate a visual evidence matrix, a sensor data vector and an abnormal screening signal are combined to calculate a comprehensive confidence coefficient, and an alarm signal is triggered if the comprehensive confidence coefficient is larger than a threshold value; and finally, through time window sliding verification taking one minute as a period, determining an alarm when an alarm signal is greater than or equal to three periods. According to the method, the data accuracy is improved, fire hazards are accurately identified, the false alarm rate is reduced, and the alarm timeliness and accuracy are guaranteed.

The invention provides a wire harness crimping quality intelligent detection and self-adaptive control method and system, and the method comprises the steps: calculating a stress distribution value of a crimping point through the obtained internal metal flow distribution characteristics in combination with a mechanical strength simulation algorithm, and obtaining a mechanical strength evaluation index; if the mechanical strength evaluation index is lower than a preset threshold value, triggering an alarm signal, extracting deviation type data from the alarm signal, and judging a quality judgment result; generating a feedback control instruction according to the judged quality judgment result, transmitting the feedback control instruction to crimping equipment to adjust crimping force parameters, and obtaining optimized crimping process data; updating a real-time monitoring database by adopting the obtained optimized crimping process data, and determining a geometric parameter reference value of a subsequent crimping batch; and matching the determined geometric parameter reference value with a vehicle safety requirement to obtain a final quality verification report.

The invention relates to the technical field of fire-fighting early warning, and provides a fire-fighting gas alarm method. The method comprises the following steps: acquiring response time signal stability and detection precision data through a sensor to obtain a real-time monitoring sequence; calculating a correlation coefficient between response time and signal stability according to the real-time monitoring sequence, and determining a change trend index; inputting the change trend index into a preset regression model to obtain a performance reduction predicted value; if the performance reduction predicted value exceeds a preset threshold value, judging that a fault is a potential fault and generating an alarm signal; after an alarm signal is obtained, a detection precision parameter is adjusted through a threshold mechanism to optimize sensor configuration; recollecting gas concentration and concentration fluctuation data according to the optimized sensor configuration to determine an updated monitoring sequence; the updated monitoring sequence is matched with the abnormal smoke characteristics to judge the fuel gas leakage risk level; and a fault prediction result is obtained by inputting risk levels into a classification model, and a maintenance system is activated to respond, so that high-efficiency early warning is provided for catering kitchen gas monitoring, and safety is ensured.

The invention relates to the technical field of arc fault diagnosis, and provides an energy storage system arc fault diagnosis method and system based on voiceprint detection, and the method comprises the following steps: collecting a voiceprint signal in real time, and carrying out the anti-interference preprocessing, and obtaining a time-frequency domain spectrogram; identifying the time-frequency domain spectrogram based on a multi-branch convolutional neural network and weighting the time-frequency domain spectrogram to obtain a time sequence feature sequence; inputting the time sequence feature sequence into a time sequence convolutional network model, and outputting global context features; performing dynamic weighted pooling on the global context features to obtain key frame feature vectors representing the arc fault; and inputting the key frame feature vector into an arc fault diagnosis classification model to obtain an arc fault type, an arc fault severity grade and sound source positioning information, and generating a corresponding alarm signal and a control instruction based on the arc fault severity grade. According to the invention, the detection sensitivity and the anti-interference capability are improved, and the safety protection level and the fault early warning capability of the energy storage system are improved.

The invention discloses a self-adaptive integrated pump station dynamic optimization control method and system, relates to the technical field of water pump station intelligent control, and solves the problems that a control strategy adopting a preset rule in the prior art is difficult to adapt to the real-time working condition change of a pump station operation environment, and the working efficiency is low. The energy efficiency of the pump station control method is not high; and the maintenance cost is increased. Predicted water inlet flow is generated according to pump station data and environment data; generating a device state coefficient based on the device parameter; generating a working condition state label based on the predicted water inlet flow; based on the equipment state coefficient and the working condition state label, constructing a multi-objective optimization function and generating an alarm signal; obtaining a control instruction based on the multi-objective optimization function; according to the method, related equipment is dynamically controlled based on a control instruction, pump station water inlet data in a period of time in the future is predicted, and a target optimization function is constructed in the directions of energy consumption and cost, so that energy consumption and cost minimization is achieved, and the efficiency of the pump station control method is improved.