1301results about How to "Reduce incidence" patented technology

The invention discloses an intensive care unit patient early warning method based on clinical data, and relates to the technical field of medical data processing. Comprising the following steps: collecting patient data in real time, and performing multi-modal data fusion; performing real-time analysis on the breathing waveform, extracting morphological characteristics in the waveform, and identifying subtle abnormal changes in the respiratory system; based on historical data and real-time data of the patient, constructing a time sequence model, and predicting whether the patient has a potential risk; and dynamically adjusting an early warning threshold value and a strategy based on the change trend of the historical data and the real-time data. According to the invention, the level of risk management of the patient in the intensive care unit is greatly improved, precise identification of subtle abnormity of the patient is ensured, and effective intervention can be carried out before a problem occurs.

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses a nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention discloses a data acquisition and processing method based on an SF6 pressure gauge of a transformer substation, and relates to the technical field of data processing, and the method comprises the steps: collecting the environment data of the transformer substation in real time through an SF6 pressure sensor, a temperature sensor and a humidity sensor; inputting the substation environment data into a multi-sensor fusion algorithm, performing temperature compensation and humidity correction, and generating a corrected multi-dimensional data set; inputting the extracted key features into an artificial intelligence model for fault prediction, and outputting a fault early warning signal; and according to the fault early warning signal, an alarm mechanism is triggered and operation and maintenance personnel are notified, and an actual fault processing result is fed back to the central processing platform. Through a multi-sensor fusion technology and a self-adaptive filtering algorithm, the problem that SF6 pressure data in the prior art is easily influenced by environment temperature and humidity is effectively solved, and the pressure monitoring precision is remarkably improved.

The invention discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention discloses an intelligent power distribution room safety monitoring management system, which comprises the following steps: checking states of a sensor, a gateway and a camera, and collecting environment data through the sensor; preprocessing the collected data, identifying a smoke area by using a YOLOv5 model, and starting a linkage safety mechanism according to abnormal environmental parameters; performing personnel detection by using a YOLOv5s model, capturing and analyzing a behavior track through a SimAM mechanism, and realizing multi-target tracking and abnormal behavior detection; grading the abnormal events based on a rule classifier, and triggering equipment linkage control; and storing the data in an NoSQL database, analyzing a trend to predict potential dangers, and optimizing a YOLOv5 model through historical data. The system can monitor the environment and personnel safety conditions in the power distribution room in real time, has efficient response capability and a trend prediction function, and can be widely applied to the field of safety monitoring of intelligent power grids and industrial environments.

The invention discloses a method for predicting abnormal indexes after thoracic surgery, and relates to the technical field of medical treatment, and the main scheme is as follows: obtaining historical data related to thoracic surgery and preoperative examination, basic information, intraoperative and postoperative recovery data of a patient, and respectively calculating a preoperative comprehensive value, a basic information evaluation value, an intraoperative risk index and a postoperative recovery index; further obtaining a predicted postoperative infection index and a predicted cardiopulmonary function index; calculating a postoperative infection index threshold value and a cardiopulmonary function index threshold value according to the historical data; by comparing a prediction index with a threshold value, triggering corresponding early warning and formulating postoperative management and intervention strategies of different levels, the postoperative rehabilitation quality and safety of a thoracic surgery patient are improved, a comprehensive method is provided for postoperative management of the thoracic surgery patient, risks can be found in time, targeted measures can be taken, and the postoperative rehabilitation safety of the thoracic surgery patient is improved. The operation success rate and the patient life quality are improved.

The invention discloses an automatic monitoring method and system for metal plate laser cutting operation, and relates to the technical field of laser cutting monitoring methods. The invention provides an automatic detection method for metal plate laser cutting operation. The automatic detection method comprises the following operation steps that the cutting length error rate and the laser fluctuation rate of a metal plate within a preset time period are obtained; evaluating an abnormal score according to the cutting length error rate and the laser fluctuation rate; if the abnormal score is greater than a preset threshold value, acquiring image information of the metal plate; evaluating a cutting quality score according to the image information; if the cutting quality score is greater than the target score, outputting an abnormal score misjudgment result, and tracing a misjudgment reason; and if the cutting quality score is smaller than the target score, obtaining a cutting defect type, and adjusting a cutting strategy.

The invention discloses a platform vehicle abnormal behavior intelligent analysis method based on deep learning. Accurate early warning is realized through multi-dimensional data acquisition and deep learning model construction. Firstly, platform passenger flow, road conditions, video monitoring and vehicle operation information are integrated, and a comprehensive data basis is provided for anomaly analysis. After the data is preprocessed, a graph neural network and an attention mechanism are utilized to perform preliminary fusion, and an AGCN adaptive graph convolutional network and a DTW dynamic time warping algorithm are introduced to optimize a fusion result, so that time sequence data are effectively aligned, and the feature extraction accuracy is improved. And then, a deep learning model is constructed to automatically identify abnormal behavior modes of the platform vehicles, such as illegal parking and passenger congestion. Early warning information is issued in real time, potential risks can be timely handled, and safe and efficient operation of a public transportation system is guaranteed. According to the scheme, the identification and early warning capability of the abnormal behavior of the station vehicle is enhanced, and powerful support is provided for bus safety management.

The invention discloses an intelligent safety early warning method and system for excavation equipment operation based on environmental perception, and relates to the technical field of intelligent early warning, and the method comprises the steps: carrying out the initial detection of an operation environment based on an environment monitoring module, and carrying out the adjustment of equipment parameters according to an initial environment state, a matching target early warning mode and a reference target early warning mode, monitoring the working environment according to the adjusted environment monitoring module, obtaining real-time environment data, and performing safety risk identification in combination with a static safety boundary and a dynamic safety boundary; and according to the risk identification result, generating a safety early warning instruction, and performing path dynamic optimization of the mining equipment. The technical problems that in the prior art, a traditional excavation equipment operation early warning method is poor in risk sensing and early warning capacity and high in safety accident rate in a complex environment are solved, and the technical effects that the all-weather risk sensing and early warning capacity of excavation equipment in the complex environment is improved, and the safety accident rate is reduced are achieved.

The invention relates to the technical field of environment automatic control, in particular to an environment regulation and control method and system for automatic control of a breeding house, and the method comprises the following steps: setting the operation parameters of a temperature and humidity controller and lighting equipment in the breeding house, adjusting the equipment to an initial state, checking that the parameters meet the standard during starting, and generating a baseline environment configuration. According to the invention, through environment monitoring and dynamic adjustment strategies, accurate and automatic breeding house environment control, management efficiency and animal welfare are improved, operation parameters are set and verified to be in a standard range during starting, errors are reduced, an optimal environment is maintained, key parameters such as temperature and humidity are continuously tracked, future changes are reflected and predicted in time, and the system is suitable for large-scale popularization and application. The control strategy is adjusted in advance, potential hazards to animals are avoided, equipment is adjusted according to the change trend, resource use is optimized, energy consumption is reduced, the environment needed by the animals is guaranteed, diseases are reduced, higher flexibility and accuracy are achieved for coping with sudden changes, and breeding efficiency and animal welfare improvement are guaranteed.

The invention provides an image-based PICC (Peripherally Inserted Central Catheter) intravenous therapy information monitoring system and method, relates to the field of intelligent image analysis, and is used for judging whether a puncture part of a patient has an infection sign or not by performing time sequence analysis on high-definition images of the puncture part of the patient at different time points in a target monitoring time period. Therefore, the accuracy and timeliness of infection monitoring can be improved, and infection signs can be found in an early stage, so that measures can be taken in time, and the incidence rate and severity of infection are reduced.

The invention relates to the field of circuit breakers, in particular to a primary and secondary fusion pole-mounted circuit breaker which comprises a primary and secondary fusion circuit breaker body, the primary and secondary fusion circuit breaker body comprises a fixing frame, a bottom bin is installed on the rear portion of the fixing frame, bottom columns are arranged in the bottom bin, and bottom grooves are formed in the bottoms of the bottom columns; one side in the bottom groove is in meshed connection with a tooth column, the tooth column is fixedly connected to the middle of the periphery of a rotating shaft, the rotating shaft is rotationally connected to the middle in the bottom bin, the two ends of the periphery of the rotating shaft are fixedly connected with connecting gears through pneumatic expansion heads, and a first branching part and a second branching part are installed on the two portions in the bottom bin correspondingly; through mutual energy storage of the first tapping part and the second tapping part, automatic energy storage work can be achieved, compared with traditional equipment, use of a reset motor is omitted, line arrangement is reduced, meanwhile, electric field interference to the circuit breaker when the reset motor works is avoided, the occurrence rate of abnormity is reduced, and circuit safety is facilitated.

The invention provides a cargo quality monitoring analysis method and system based on image recognition. The method comprises the following steps: acquiring a real-time video stream of a to-be-recognized cargo; constructing a multi-dimensional quality standard atlas database related to the to-be-monitored product; matching the real-time video stream with an atlas in a multi-dimensional quality standard atlas library through an adaptive comparison algorithm to determine whether an area matched with a preset quality defect exists in the real-time video stream, and generating a matching result; if it is determined that the area matched with the preset quality defect exists in the real-time video stream, a response strategy for the defect type corresponding to the area is automatically triggered; and dynamically updating the multi-dimensional quality standard atlas database so as to optimize the monitoring precision of the cargo quality. According to the technical scheme provided by the invention, the cargo quality monitoring effect is improved.

The invention discloses a process for improving surface defects of a quartz substrate of a photomask IC (integrated circuit) chip, relates to the technical field of quartz substrates, and aims to solve the problem that the surface defects of the quartz substrate cannot be effectively improved in the process from material selection to manufacturing to later quality control in the prior art. Through three methods of visual inspection, automatic optical detection and laser scanning detection, multi-dimensional detection from macroscopic to microscopic and from the surface to the structure is realized, the accuracy and comprehensiveness of defect identification are ensured, and by setting a clear defect classification standard and a maximum defect range, the defect identification accuracy is improved. Defects possibly affecting product performance can be found and treated in time, the surface laser repairing method is suitable for shallow layer defects, the internal ion implantation repairing method can effectively treat deep layer defects, the flexibility and adaptability of the technology are improved through diversified repairing strategies, and unnecessary material waste and machining cost are avoided through accurate repairing.

The invention discloses a capacitive battery active equalization circuit and a method thereof. The capacitive battery active equalization circuit comprises a plurality of single batteries, a power MOS (Metal Oxide Semiconductor) tube circuit, an energy storage circuit and a control loop, the control loop comprises an ADC module, a PID module, a constant current controller and a power MOS tube driving module. The corresponding end of the ADC module is electrically connected with the corresponding ends of the plurality of single batteries and the PID module; the corresponding end of the PID module is connected with the corresponding end of the power MOS tube driving module through the constant current controller; and the corresponding end of the power MOS tube driving module is electrically connected with the corresponding end of the power MOS tube circuit. Compared with the scheme in the current market, the device has the advantages of low cost, small size, easiness in integration, simplicity in control, high safety and the like, and can store the energy of the battery with high voltage to the capacitor and carry the energy stored on the capacitor to the battery with low voltage, so that the aim of balancing the electric quantity of the battery is fulfilled.

The invention relates to the field of electric power engineering construction safety management, and particularly discloses an electric power engineering construction safety management system based on multiple sensors. By monitoring the relative deviation distance and direction of the gravity center point, the transfer speed and direction of the gravity center point, the inclination angle and direction of the body and the number and the supporting area of supporting framework joint points corresponding to various postures of the overhead working personnel in the electric power engineering construction process, the falling risk of the overhead working personnel is recognized; monitoring the distance between each easily-impacted part of the body of the aerial working personnel and an object adjacent to the part and the activity coefficient of the object adjacent to each easily-impacted part in the electric power engineering construction process, and identifying the collision risk of the aerial working personnel; the distance between high-altitude operation personnel and all surrounding electrified bodies in the electric power engineering construction process is monitored, the electric shock risk of the high-altitude operation personnel is recognized, recognition and evaluation of various safety risks of the high-altitude operation personnel in the electric power engineering construction process are achieved, and personnel safety is guaranteed.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

The invention discloses a spinal surgery postoperative patient rehabilitation intelligent management system and method, and relates to the technical field of spinal surgery postoperative rehabilitation management, and the system can carry out real-time analysis on implant displacement characteristics, and can carry out early warning on potential deviation of an implant in advance. The first analysis module can calculate the risk index Fx of the implant, so that timely correction or adjustment is promoted, the risk of implant deviation is reduced, and the occurrence rate of postoperative complications is reduced. And the second analysis module is specially used for extracting wear characteristics of the implant, constructing an implant wear index Ms, and helping a doctor to intervene in time by monitoring the wear condition of the implant in real time so as to avoid dysfunction or risk caused by wear of the implant. And the third analysis module calculates a comprehensive fusion index ZRh by extracting bone grafting area change characteristics, bone mineral density and rehabilitation bone healing information, so that a doctor can know bone mineral density change and rehabilitation conditions of a patient, a treatment scheme is adjusted, and a rehabilitation effect is improved.

The invention discloses an intelligent prediction system and method for gestational diabetes mellitus, and relates to the technical field of medical artificial intelligence, and the system comprises a collection module which is used for collecting gestational index data, carrying out the preprocessing of the index data, and extracting the features of the preprocessed data; the evaluation module is used for performing synchronism and rhythm analysis according to the extracted features, establishing a physiological cycle correlation model and evaluating the synchronism and disorder degree of the physiological rhythm; and the prediction module is used for constructing a risk prediction model according to the analysis result of the physiological cycle correlation model and predicting the physiological state risk level. According to the invention, the early recognition rate of gestational diabetes mellitus is obviously improved, the disease incidence and complication risk are reduced, the consumption of medical resources is reduced, and the method has important clinical application value and social benefit. And parameter optimization and model adjustment can be performed according to different crowd characteristics, so that powerful support is provided for personalized medical treatment and accurate prevention.

The invention provides a voice keyword detection method, a storage medium and electronic equipment, and relates to the technical field of voice signal processing, and the method comprises the steps: determining a phoneme scoring path and a background sound path corresponding to an acoustic feature sequence of a to-be-detected target voice; identifying a noise type corresponding to each noise background sound; constructing an initial first linear grammar finite state converter according to the phoneme scoring path, and configuring a corresponding wildcard arc for the first linear grammar finite state converter by using each extracted noise type to obtain a second linear grammar finite state converter based on noise perception; creating a composite weighted finite state converter through a composite operation; and decoding the composite weighted finite state converter to predict whether the target keyword exists in the target voice. Therefore, the keyword detection system can sense different noise environments, the sensitive degree of the system to noise is dynamically adjusted, and the keyword detection accuracy in the high-noise environment is guaranteed.

The invention relates to the technical field of new energy automobile driving safety, in particular to a new energy automobile safety evaluation system based on big data, and the system comprises a data collection module which is used for collecting the operation data, performance data, operation environment data and loss data of a new energy automobile battery; the risk analysis module is used for analyzing the operation risk of the battery; the abnormity monitoring module is used for optimizing the analysis process of the battery operation risk; the loss analysis module is used for analyzing the loss state of the battery and analyzing the abnormal index of the battery; the aging analysis module is used for analyzing the aging state of the battery according to the battery internal resistance and the battery capacity collected in the monitoring period; and the safety evaluation module is used for evaluating the safety of the battery and adjusting the evaluation process of the safety of the battery. According to the invention, the accuracy of new energy automobile battery safety assessment is improved.

The invention relates to the technical field of safety engineering, in particular to an intelligent high-altitude and fire operation safety management system based on risk assessment, which comprises a safety helmet module, a data acquisition and transmission module, an edge calculation and processing module, a risk assessment and decision module, an emergency response module and a data analysis and optimization module. The intelligent safety helmet worn by each operator is integrated with a flame sensor and a height sensor, and flame information in an operation environment and height changes of the operators can be collected in real time. Through the low-power-consumption wireless communication technology, the collected data is transmitted to the edge computing device in real time, and the edge computing device calculates the comprehensive risk index through multi-sensor data fusion. And when the risk index exceeds a preset threshold value, the system automatically triggers alarm, sends early warning information to operators and a management platform, and takes emergency measures, so that the accident rate in high-altitude operation and fire operation can be effectively reduced, and the safety management level of the operation environment is improved.

The embodiment of the invention discloses an intelligent medical material transfer method and system based on an operating room transfer robot, and relates to the technical field of robots. The method comprises the following steps: carrying out environment perception processing on a surgical transfer robot to obtain an environment map, and carrying out path generation processing to obtain a transfer path; obstacle avoidance processing and decision processing are carried out on the motion state of the operation transfer robot, and a moving track is obtained; carrying out identity verification processing on the operation transfer robot arriving at the operating room to obtain a verification result, and carrying out docking processing on the operation transfer robot passing the verification to obtain a stopping state; the method comprises the following steps: releasing materials in an operation transfer robot to obtain a material array, and monitoring the release treatment to obtain a confirmation signal; and carrying out recovery processing and verification processing on the pollution materials in the used material array to obtain an inventory result. Through the technical scheme disclosed by the invention, the efficiency and accuracy of medical material transfer of the operation transfer robot are improved.

The invention relates to the technical field of power grid risk prediction, and discloses a power grid risk monitoring method and system based on meteorological early warning, and the method comprises the steps: dividing a power grid monitoring region into a plurality of monitoring regions; defining a power grid risk fault type, obtaining a risk type set of different monitoring region types, and calculating a risk assessment index and a region influence index; generating a risk meteorological feature set, and calculating a meteorological influence index of the risk meteorological feature set on the risk type; and constructing a risk prediction model, performing risk prediction, and issuing meteorological risk early warning information. Through the risk monitoring method of the system, from data collection, risk assessment and meteorological influence analysis to risk prediction and early warning, the level of power grid risk management is comprehensively improved, the risk is monitored and predicted in real time, early warning information is issued in time, the response speed and efficiency for responding to the risk are improved, the occurrence probability and the influence degree of risk events are reduced, and the risk management efficiency is improved. The economic loss is reduced.

The invention relates to the technical field of safety protection, and provides a high-place operation safety belt failure early warning device and method.The real-time state of a safety rope can be evaluated through cooperation of a monitoring main machine and a monitoring auxiliary machine, and the real-time state of the safety rope is divided into a normal state, a low-hanging high-use state, a virtual-hanging state and a straightening state; the real-time state of the safety rope is sent to the handheld monitoring terminal and the supervision terminal through the monitoring host by using the Lora communication technology, so that safety supervision personnel can quickly master the safety condition of a construction site, timely take necessary safety measures and timely find potential safety hazards; by monitoring and feeding back the real-time state of the safety rope in real time, the occurrence rate of falling accidents in high-altitude operation is remarkably reduced, and the safety of constructors is improved; the handheld monitoring terminal has the function of monitoring the plurality of monitoring hosts at the same time, so that the safety supervision efficiency is enhanced, and the safety personnel can better manage the safety states of the plurality of construction personnel.