161results about How to "Timely warning" patented technology

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention discloses a management system for real-time monitoring and early warning of mine coal and gas outburst parameters, and belongs to the field of safety production, and the management system specifically comprises the steps: collecting gas and ground stress data in a mine in real time through a gas sensor and a microseismic sensor, and carrying out the real-time monitoring and early warning of the mine coal and gas outburst parameters through a time sequence analysis and spatial data mining technology and a multi-scale coupling strategy; identifying the change rule and potential correlation of the crustal stress and the gas parameters; a comprehensive outburst analysis and prediction model is established, the coal and gas outburst risk level is evaluated, and a three-level early warning mechanism and an emergency plan are designed; the method can be flexibly adjusted according to actual conditions of a mine, coal and gas outburst accidents are effectively prevented and controlled, and the safety of the mine is improved.

The invention relates to the technical field of hydrological data analysis, in particular to a dynamic hydrological coupling data analysis method and system for a complex watershed. The method comprises the following steps: acquiring historical meteorological data, historical hydrological data and historical remote sensing data, and performing hydrological cycle simulation analysis to generate a rainfall-evaporation-permeation-runoff hydrological cycle simulation model; performing drainage basin gridding processing on the rainfall-evaporation-permeation-runoff hydrological cycle simulation model to generate a hydrological cycle gridding model; obtaining actually measured meteorological data and actually measured hydrological data, and carrying out space-time simulation division and hydrological space-time correlation analysis to obtain a hydrological change space-time correlation correlation relationship among the sub-partitions of each drainage basin; and performing drainage basin space-time coupling early warning processing on the hydrological cycle gridding model to generate a hydrological change space-time coupling early warning response result corresponding to the complex drainage basin. The method can accurately analyze and predict the hydrological change and the mutual coupling relation thereof in the complex watershed.

The invention discloses a power distribution network overhead line risk refined early warning method and system. The method comprises the following steps: collecting meteorological time sequence data and power distribution network topology and line parameter data; and preprocessing the data, and constructing a prediction data set. And establishing a fault probability prediction model based on the meteorological data and the line parameters. Calculating the fault probability by using the mathematical model, correcting the model, training in combination with static topology and dynamic meteorological data, and constructing a fault probability network model. And analyzing the importance of the nodes and lines of the power distribution network, and evaluating the load loss proportion of the nodes. And carrying out normalization processing on the importance indexes. And comprehensively calculating the predicted fault probability, the node loss load and the importance index to obtain a branch risk parameter. Fine early warning is carried out according to a set early warning threshold value, and dynamic adjustment is carried out to adapt to the actual operation condition. The method has the advantages that comprehensive risk assessment is provided, and the accuracy of fault prediction and the timeliness of risk early warning are improved.

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention belongs to the technical field of power grid main equipment diagnosis, and provides a power grid main equipment fault diagnosis method and system based on voiceprint cloud edge collaboration, which adopts a new cloud edge collaboration method and divides the analysis of voiceprint monitoring data into two stages. According to the method, the exception detection model is deployed on the edge side, exception analysis of mass voiceprint data is achieved, concurrent requests of a large number of concurrent signals at the cloud side are avoided, and the timeliness of voiceprint monitoring and analysis is improved. In the cloud, a multi-modal defect identification method fusing voiceprint, visible light, infrared and other multi-source data is deployed, secondary classification of sound abnormal signals is realized, and the reliability of equipment operation, maintenance and management is improved.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention relates to the technical field of coal mine dynamic disaster monitoring and early warning, and discloses a mine multi-dimensional fusion intelligent monitoring and early warning system and method. The system is a whole time-space integrated with twelve modules including an original data acquisition module, a mining condition analysis module, a data transmission module, data early-stage preprocessing, expert analysis and demonstration, optimal parameter delineation, basic theory calculation, numerical simulation analysis, laboratory verification, machine learning and neural network optimization, coupling model construction and field application detection. The invention discloses a multi-scale and multi-parameter coupling monitoring and early warning model and system. According to the mine multi-dimensional fusion intelligent monitoring and early warning system and method provided by the invention, a multi-level means is utilized, the limitation of a traditional single scheme or the subjectivity of single human interference is avoided, and the purpose of effectively preventing mine dynamic disasters can be achieved to a great extent; the method is of great significance to monitoring and early warning of coal rock gas hybrid dynamic disasters and safety production of mines.

The embodiment of the invention discloses a battery early warning method and device, a platform and a storage medium. The battery early warning method comprises the steps of obtaining battery working condition data of a to-be-detected battery in a preset time period; performing feature extraction on the battery working condition data to obtain trend change features of the to-be-detected battery; comparing the trend change characteristic with a preset rule to obtain a comparison result, the preset rule being the trend change of the battery in a normal state; and carrying out early warning on the to-be-detected battery according to a comparison result. The trend change characteristics of the to-be-detected battery can be monitored in real time, so that whether the to-be-detected battery is abnormal or not is determined according to the trend change characteristics, early warning can be performed in time before the to-be-detected battery has a fault, and occurrence of battery faults is effectively avoided.

The invention discloses a severe convective weather identification method and system based on a neural network, and relates to the technical field of meteorological monitoring. The method comprises the following specific steps: acquiring detection data including multi-source observation data and high-resolution numerical forecasting data; performing data fusion and data preprocessing on the detection data to obtain first data; performing manual labeling on the first data to establish a training label to construct a sample data set; building a convolutional neural network model, and inputting the training set into the model for training to obtain a severe convective weather intelligent identification model; evaluating the severe convective weather intelligent identification model by using the test set to obtain a final severe convective weather intelligent identification model; and inputting real-time acquired real-time observation data into the final severe convection weather intelligent identification model for severe convection weather identification. According to the method, severe convective weather identification is carried out based on the deep neural network technology, automatic and quantitative prediction can be carried out, and the reliability and timeliness of severe convective weather identification are improved.

The invention relates to an intelligent water meter warning method and system, a terminal and a storage medium, and relates to the technical field of water meters, and the method comprises the following steps: obtaining a water volume real-time detection value on a water meter and a water pressure detection value collected by a water pressure detection device preset on the water meter; calculating a difference value between the water pressure detection value and a preset water pressure reference value and taking the difference value as a water pressure deviation value; analyzing and determining a water pressure reference deviation interval according to the water volume real-time detection value; judging whether the water pressure deviation value falls into a water pressure reference deviation interval or not; if yes, continuing to obtain a water volume real-time detection value and a water pressure detection value; if not, water leakage early warning information is analyzed and determined according to the water pressure deviation value and the water pressure reference deviation interval, and the water leakage early warning information is output to the terminal held by the manager. The water leakage early warning device has the effect of conveniently and timely carrying out early warning on the water leakage condition.

The invention, which relates to the technical field of the underground diaphragm wall, discloses a comprehensive pipe gallery underground diaphragm wall deformation risk early warning system comprising a data acquisition module, a data analysis module, a risk early warning module, an external environment monitoring module and a real-time monitoring module. The data analysis module is used for analyzing the continuous wall data collected by the data acquisition module, and the data analysis module comprises a deformation prediction unit, a soil stress detection unit and a deformation comprehensive evaluation unit. The soil stress detection unit is combined with the soil pressure and the internal stress of the wall body to perform quantitative analysis on the mechanical property of the underground diaphragm wall, so that the interaction between the soil and the underground structure can be quantified, finally, the deformation comprehensive evaluation unit is used for performing comprehensive evaluation, and different risk levels are set for the result. The early warning effect is achieved, the deformation risk early warning quality is improved, and potential safety hazards are reduced.

The invention belongs to the field of photovoltaic power generation, relates to a fault diagnosis technology, and aims to solve the problem that the reliability of detection results of all photovoltaic arrays cannot be ensured in the prior art, in particular to an intelligent fault diagnosis system for a photovoltaic power generation module based on a sensor technology. Comprising a data statistics module, a fault monitoring module, a fault diagnosis module and a troubleshooting module. The data statistics module is used for carrying out statistics and analysis on power generation parameters of the photovoltaic array: marking the photovoltaic array as a statistical object, generating an analysis period and obtaining the power generation parameters of the statistical object in the analysis period at the end moment of the analysis period; the power generation data of the photovoltaic array in different illumination environments can be collected in the normal state of the photovoltaic array, the power generation parameters are formed, the power generation parameters serve as the running state judgment standard of the photovoltaic array, and when fault monitoring is carried out, the fault monitoring accuracy is improved. And fault monitoring is carried out by taking the power generation parameters of the same photovoltaic array in the similar illumination environment as a reference.

The invention provides a blast risk early warning system and a flame arrester for petrochemical engineering, and relates to the technical field of risk early warning, the system comprises an in-pipe state prediction module used for generating a preset in-pipe distributed state network; the external state prediction module is used for generating a preset external distributed state network; the real-time monitoring module is used for generating an in-pipe real-time state network and an external real-time state network; the fire blast risk point location identification module is used for generating a first risk point location; the fire-retardant simulation module is used for generating a first fire-retardant failure index and a second fire-retardant failure index; and the failure early warning module is used for generating early warning information. The technical problems that in the prior art, due to the poor monitoring effect and lack of real-time performance evaluation of the flame arrester, the failure accident of the flame arrester cannot be predicted in time, and the fire blast risk early warning accuracy is poor can be solved, and the technical effects that the early warning accuracy is improved, and fire blast accidents are prevented from being expanded are achieved.

The invention relates to the technical field of gas detection, in particular to a gas detection method and system for a dangerous workplace. The method comprises the following steps: acquiring gas concentration and position information of each detection point in real time; dividing the whole detection area into a plurality of grids, and calculating the concentration density of each grid by adopting a kernel density analysis method based on the gas concentration and position information of each detection point; based on the concentration densities of all grids, constructing a gas concentration plane distribution diagram of the whole detection area; based on the gas concentration plane distribution diagram, manual inspection is carried out on the gas concentration overrun position, and the reliability and accuracy of gas detection in the working environment where gas diffusion is complex or uneven are guaranteed.

The invention relates to the field of intelligent detection, and particularly discloses an intelligent detection system for a safe electrical device, which adopts a neural network model based on deep learning to process real-time operation state parameters of monitored electrical equipment and various state parameters marked as fault states. According to the method, the time domain correlation characteristics of the equipment state multi-modal parameters of the electrical equipment in the real-time operation state and various fault states are extracted respectively, and the real-time state parameter characteristics and the fault state parameter characteristics of the electrical equipment are further inquired, matched and analyzed, so that whether the state of the monitored electrical equipment is abnormal or not is intelligently identified.

The invention discloses an elevator accident prediction method and device, a medium and a product, and relates to the field of elevator accident prediction.The method comprises the steps that working parameters of a to-be-detected elevator are obtained; the working parameters comprise an operation parameter, a management parameter and a riding parameter; inputting the working parameters into an elevator accident prediction model to obtain a fault occurrence probability corresponding to the to-be-detected elevator; the elevator accident prediction model is a deep learning network; when the fault occurrence probability exceeds a preset threshold value, the elevator accident type is determined according to the fault occurrence probability; the elevator accident type serves as a target variable, and each working parameter serves as a characteristic variable; respectively calculating standardized mutual information values of the target variable and each characteristic variable, and determining a maximum standardized mutual information value; and determining a to-be-optimized fault combination according to the maximum standardized mutual information value. According to the invention, the elevator accident can be accurately predicted.

The invention relates to a hydrogen transport flexible pipeline and a hydrogen leakage detection method, the hydrogen transport flexible pipeline comprises a lining layer, an airtight layer, a fiber reinforced layer and an outer protective layer, the lining layer, the airtight layer, the fiber reinforced layer and the outer protective layer are all cylinder structures, the lining layer is made of a non-metal thermoplastic resin material, and the outer protective layer is made of a non-metal thermoplastic resin material. The airtight layer is composed of a graphene / aluminum oxide hydrogen-resistant coating and a metal aluminum layer, the fiber-reinforced layer is made of a non-metal high-strength fiber composite material, the outer protective layer is made of a non-metal thermoplastic resin material, and the fiber-reinforced layer comprises a fiber-reinforced inner layer and a fiber-reinforced outer layer; an airtight layer is wound on the outer side of the lining layer, a fiber reinforced inner layer is wound on the outer side of the airtight layer in the forward direction, a fiber reinforced outer layer is wound on the outer side of the fiber reinforced inner layer in the reverse direction, and an outer protection layer is arranged on the outer side of the fiber reinforced outer layer; a monitoring element is arranged in the outer protection layer and used for monitoring the hydrogen concentration change or the resistance change of the outer protection layer in real time, and the hydrogen protection device can be widely applied to the technical field of new energy development.

The invention discloses a high-voltage switch cabinet fault monitoring system and method, which are used for solving the technical problem of non-ideal monitoring effect caused by the traditional high-voltage switch cabinet fault monitoring technology. The system comprises a monitoring module, an identification module, an analysis module, a comparison module and an early warning module which are connected in sequence. The monitoring module comprises a plurality of high-precision temperature sensors, the high-precision temperature sensors are arranged in one-to-one correspondence with monitoring points in the high-voltage switch cabinet, and the monitoring points in the high-voltage switch cabinet are identified through the identification module to obtain a space element monitoring area; a comparison module carries out overheat point monitoring on the space element monitoring area according to the target dynamic temperature threshold value and the real-time temperature data, outputs the number of overheat points corresponding to the space element monitoring area, and carries out early warning according to a comparison result of a preset number range and the number of overheat points corresponding to the space element monitoring area through an early warning module. And generating a high-voltage switch cabinet alarm result.

The invention provides a drug inventory dynamic monitoring method and system based on edge calculation. The drug inventory dynamic monitoring method comprises the steps that drug parameters and environment data of all intelligent drug cabinets and warehouse areas and biological characteristics of operators are collected; determining a behavior log of the operator according to the operation action and the medicine change data, and generating a digital signature of the operator through the biological characteristics; constructing lightweight block chain nodes of the intelligent medicine cabinets and the warehouse areas, and packaging and chaining behavior logs and digital signatures through inter-node consensus of all the block chain nodes to generate an operation voucher; according to the average compliance rate and the operation voucher, carrying out trust evaluation on the compliance condition during operation to obtain a trust score; and comparing the trust score with a trust threshold, and if the trust score is lower than the trust threshold, locking the high-risk drug operation authority of the operator. By adopting the scheme of the invention, real-time credible evidence storage and dynamic evaluation can be carried out through behaviors of an operator, so that timely interception and early warning of high-risk medicine operation are realized.

An industrial wastewater treatment monitoring method and system based on LSTM, relating to the technical field of industrial wastewater treatment, setting water quality monitoring points in the current industrial wastewater treatment process, obtaining water quality data of each water quality monitoring point; analyzing historical water quality data of each water quality monitoring point in several monitoring cycles to obtain key water quality monitoring indicators; building a water quality prediction model based on a long short-term memory network LSTM, obtaining the estimated numerical time series of key water quality monitoring indicators of each water quality monitoring point in the current monitoring cycle; real-time monitoring of the prediction results of the water quality prediction model, retraining the water quality prediction model according to the prediction results, and performing overall early warning real-time monitoring at the same time, obtaining control item measures for the next monitoring cycle according to the overall early warning real-time monitoring results, and realizing accurate monitoring and early warning of wastewater treatment effects.

The invention is suitable for the technical field of disaster monitoring, and provides a natural disaster monitoring method and device, a storage medium and electronic equipment, and the natural disaster monitoring method provided by the invention comprises the steps: responding to a natural disaster event flow acquired by a dynamic visual sensor, and carrying out the reconstruction based on the natural disaster event flow to obtain a natural disaster image; recognizing the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm, and obtaining the occurrence position and spreading characteristics of the natural disaster; recognizing the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; and under the condition that the disaster grade meets the early warning condition, generating early warning prompt information based on the occurrence position, the spreading characteristic, the disaster type and the disaster grade, and outputting the early warning prompt information.

The invention discloses a GA-CNN-based online monitoring data analysis method, system, equipment, medium and program for dissolved gas in oil of an oil-immersed transformer, and belongs to the technical field of transformer detection. The method comprises the following steps: preprocessing historical fault data and normal data of a transformer to be analyzed to obtain a sample data set; and inputting the sample data set into a pre-trained CNN transformer fault diagnosis model, and performing analysis to obtain an analysis result. According to the method, different types of transformers can be identified, and the identification accuracy of overheating and discharge defects of the oil-immersed transformer is effectively improved.

The invention discloses a sensor damage detection method and system, and belongs to the technical field of damage detection. The system comprises a data acquisition module, an anomaly analysis module, a damage detection module and an intelligent early warning module. The data acquisition module is used for acquiring a running log and acquiring vibration acceleration in three-axis directions through a sensor module mounted on mechanical equipment; the anomaly analysis module analyzes parameter records of the sensor modules in the running log, calculates influence coefficients among the sensor modules, analyzes whether influence relations exist or not, establishes a relation network and screens out suspicious sensor modules; the damage detection module calculates an abnormal index through the vibration acceleration of the suspicious sensor module, judges whether to enter an alarm state or not according to the change of the abnormal index, and carries out rapid dredging and abnormal alarm on data in the sensor module in the alarm state; the intelligent early warning module displays the data information uploaded by the alarm device in real time through a large visual screen, and notifies a manager of the occurrence of an abnormal event.

The invention discloses an enterprise safety production joint control information management system, which relates to the technical field of production joint control information management and comprises a collection module, a data processing module, a safety management module and a response module. And selectable. According to the invention, dynamic assessment of the safety risk is realized, intelligent early warning is carried out according to the change trend and severity of the risk, the accuracy and timeliness of early warning are improved, quantitative analysis of the degree of dispersion of the safety risk is realized, a scientific basis is provided for emergency response, rapid response and intelligent scheduling are realized, the accident processing efficiency is improved, and the safety risk is protected. According to the method and the system, the real-time monitoring of the wind speed and the flow velocity data is realized, the quantitative evaluation of the emergency response capability is realized, accurate guidance is provided for the formulation and implementation of emergency measures, the accuracy and effectiveness of emergency processing are improved, dynamic early warning and accurate positioning of a risk source can be realized by using the real-time monitored wind speed and flow velocity data, and the accuracy and timeliness of early warning are improved.

The invention relates to the field of ships, and provides a navigation obstruction observation system and method applied to a ship and electronic equipment, and the system comprises a shipborne system and a shore-based system; the shipborne system comprises: an information acquisition unit for acquiring environmental information of a ship; the navigation obstruction identification unit is used for identifying the environment information based on a navigation obstruction identification model and obtaining an identification result, and the navigation obstruction identification model is obtained through pre-training of a shore-based system; the early warning unit is used for carrying out early warning based on the identification result; and the shore-based system is used for acquiring the environmental information of the ship and training and updating the navigation obstruction identification model based on the environmental information. The method and the device are used for solving the defects that the navigation obstacles on the sea surface cannot be accurately recognized by a navigation obstacle recognition method in the related technology, and the safety of ship navigation cannot be guaranteed, the navigation obstacles on the sea surface can be recognized based on a recognition model obtained through pre-training, and the safety of ship navigation can be effectively improved.

The utility model discloses a pressure sensing composite current collector, a pole piece and a battery. The pressure sensing composite current collector comprises a pressure sensing layer, packaging layers and a metal layer, the pressure sensing layer comprises a pressure sensitive material layer and a lead, and the packaging layers are arranged on the two sides of the pressure sensitive material layer and wrap the pressure sensitive material layer. The pressure sensitive material layer is arranged on the packaging layer, the metal layer is arranged on one side, far away from the pressure sensitive material layer, of the packaging layer, the packaging layer is coated with the metal layer, one end of the lead is connected with the pressure sensitive material layer, and the other end of the lead penetrates through the packaging layer and the metal layer. According to the pressure sensing composite current collector, a sensing device is implanted in a lossless manner, the process complexity is reduced, the service life of a battery is prolonged, meanwhile, the charging and discharging operation states of the battery can be effectively monitored, abnormity can be effectively monitored, early warning can be performed in time, and the safety of the battery is ensured.

The invention discloses a low-energy-consumption heading machine driver position compressed air type respiratory protection system and method. The system comprises a compressed air type respiratory protection part and a heading machine energy recovery part. The compressed air type breathing protection part comprises a compressed air type breathing mask and a compressed air filtering device; the heading machine energy recovery part comprises a piston mechanism, an air storage mechanism, an electric energy storage mechanism, a magnetic induction loop, a heat storage mechanism, a heat conduction switch and an electric control thermal phase change mechanism. By means of the compressed air type breathing protection part, purified air treated by the compressed air filtering device can be supplied to a driver of the heading machine through the compressed air type breathing mask, and the breathing safety of the driver of the heading machine in a complex environment is guaranteed; heat energy generated in the tunneling process of the tunneling machine can be recycled through the energy recycling part of the tunneling machine and converted into electric energy to supply power to the respiratory protection system, and the influence of dust in the working environment on a driver of the tunneling machine can be reduced to the maximum extent on the premise that low energy consumption is achieved.

The invention provides a safety device and method for a constructor to approach live equipment, and relates to the technical field of power construction. The device comprises a wearing device, a sensing module is arranged on the surface of the wearing device and used for detecting the distance between a constructor and electrified equipment, and meanwhile a lighting assembly and a sound-light alarm device are installed on the device. The induction module comprises a charging device and a plurality of distance sensors, and the charging device is further provided with a signal receiving device and a monitoring assembly. The wearing device is a helmet body, and a vibration motor is arranged in the helmet body. The distance sensor is of an ultrasonic wave type, can be charged and is installed on the charging base in a magnetic attraction mode. And the monitoring assembly comprises a rotatable camera, so that a construction site can be conveniently and comprehensively observed. When the distance between the constructor and the electrified equipment is smaller than the preset safety distance, the vibration motor vibrates, and the sound-light alarm device is started. An external worker can receive a monitoring picture through a remote terminal, judges the cause of a problem according to the picture, and remotely controls a constructor through remote communication to deal with an emergency.

The invention relates to the technical field of municipal engineering construction safety, and particularly discloses a municipal engineering construction safety control method and system. According to the method, the corresponding potential safety hazard level of the construction site is obtained by comprehensively analyzing the site environment potential hazard, the construction equipment potential hazard and the surrounding environment potential hazard corresponding to the municipal engineering construction site, and corresponding early warning is carried out, so that the defects of construction safety early warning analysis results in the prior art are overcome, and the construction safety early warning analysis efficiency is improved. The construction potential safety hazard analysis result is more comprehensive, reliable and referential; and on the other hand, the response timeliness of potential safety hazards on a construction site is improved to a certain extent, meanwhile, the occurrence rate of construction accidents is reduced to the maximum extent, and reliable guarantee is provided for smooth construction of municipal engineering.