246results about How to "Reduce potential risk" patented technology

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention discloses a transformer substation patrol robot management method based on a company Internet of Things platform, and the method comprises the steps: collecting multi-modal data in a transformer substation, carrying out the fusion and analysis of the collected multi-modal data, and generating equipment state information based on a deep learning algorithm; according to the equipment state information and the routing inspection task priority, task allocation is performed on the routing inspection robot through a dynamic scheduling algorithm, according to a task allocation result, an optimal routing inspection path for the robot to execute tasks is formulated by using a path planning algorithm, and the path is adjusted through real-time data; constructing an equipment health management model, and predicting an equipment operation state based on the time sequence data and a machine learning algorithm; real-time monitoring is carried out through an Internet of Things platform, and data is stored in a centralized database. According to the invention, the inspection efficiency and accuracy of the transformer substation can be obviously improved, the potential risk caused by equipment failure or inspection omission is reduced, and the operation maintenance mode of the unattended transformer substation is optimized.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention relates to the field of legal service management, and discloses a legal service management system based on a block chain, and the system comprises the following modules: a block chain network, an intelligent contract module, a data storage module, a user interface module, a feedback and monitoring module and a user management module, the non-tampering property and the transparency are ensured; the intelligent contract module automatically executes contract terms and supports intelligent management; the data storage module adopts a hierarchical structure, and on-chain data and off-chain data are stored in a distinguished manner, so that the storage and access efficiency is optimized; and the user interface module provides user-friendly operation experience through a visual tool. By using the block chain technology, all contract execution and compliance audit data are encrypted and stored in a distributed mode, it is ensured that tampering is avoided and whole-course traceability is achieved, and a user and related mechanisms can verify the authenticity and integrity of the contract data at any time.

The invention discloses an environmental pollutant toxicity detection and evaluation method, which comprises the following steps: deploying a multi-parameter sensor array in a target monitoring environment, and obtaining initial monitoring data; performing deep mining and pollutant toxicity feature extraction on the initial monitoring data to generate a pollutant feature data set; forming a customized evaluation model framework according to the target environment monitoring requirement, the preset monitoring parameters and the configuration of the evaluation model; inputting the generated pollutant characteristic data set into a preset customized evaluation model, and performing toxicity evaluation on the environmental pollutants to obtain a preliminary evaluation result; and automatically adjusting parameters of the customized evaluation model based on the obtained preliminary evaluation result by adopting an adaptive learning algorithm to obtain an optimized evaluation model, carrying out toxicity evaluation again to obtain an optimized evaluation result, and the like. The method adapts to monitoring requirements under different environmental conditions, and comprehensively evaluates the toxicity risk of pollutants.

The invention provides a multi-source data fusion high-altitude falling object detection system and a high-altitude falling object detection method. The system comprises an acceleration sensor, a sound sensor, a processor and an alarm module. The acceleration sensor monitors the acceleration change of the target falling object and preliminarily judges an object falling event; when the sound sensor preliminarily judges that an object falling event occurs, a sound signal generated by a target falling object is monitored, and sound source positioning is achieved; the processor receives data of the sensor and verifies an object falling event again through a data fusion algorithm, so that misjudgment is avoided. In addition, the alarm module further generates alarm information including the falling time, the object type and the falling position to give an alarm. The system solves the problem that in the prior art, the accuracy of object falling detection through a single-type sensor is low.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention relates to the technical field of hydraulic engineering management, in particular to a hydraulic engineering management method and system based on digital twinning. The method comprises the following steps of obtaining hydraulic engineering design data, hydraulic engineering operation data and hydraulic engineering environment data, and performing digital twin space modeling on the corresponding hydraulic engineering to obtain a hydraulic engineering digital twin space model; performing operation condition virtual simulation analysis and water flow distribution change analysis on the hydraulic engineering digital twin space model, and performing engineering structure stress load analysis and engineering operation risk assessment to obtain hydraulic engineering simulation operation process risk data under different operation and environment change conditions; and performing risk decision management processing on the corresponding water conservancy project to generate a water conservancy project operation process risk management decision so as to execute corresponding water conservancy project operation risk management adjustment work. According to the invention, the management effect and response capability of the hydraulic engineering can be greatly improved.

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.

The invention belongs to the technical field of water conservancy gates, and discloses a water conservancy project flood prevention gate, method and system.The water conservancy project flood prevention gate comprises two barrier gate cement foundations, an anti-blocking mechanism and a cleaning mechanism are arranged on the two barrier gate cement foundations, and the anti-blocking mechanism comprises door bodies arranged on the sides, close to each other, of the two barrier gate cement foundations; an electric gate is slidably mounted on the inner wall of the front face of the gate body, anti-blocking assemblies are arranged at the tops of the two barrier gate cement foundations correspondingly, the electric gate is driven by a motor to ascend and descend, and automatic reciprocating motion of a cleaning strip on a sliding frame is achieved through meshing transmission of a tooth groove and a gear and the combined design of a connecting rod mechanism; according to the filter screen cleaning device, impurities attached to the filter screen are effectively removed, the filter screen is prevented from being blocked, the cleaning efficiency and the automation degree are greatly improved, frequent manual intervention is not needed, the labor intensity is reduced, meanwhile, potential risks caused by blocking of the filter screen are reduced, and it is guaranteed that water flows smoothly.

The invention relates to the technical field of transistor detection, in particular to a quick test method and system for an MOSFET device. The method comprises the following steps: acquiring and integrating multi-source parallel data of the MOSFET device to obtain real-time I-V characteristic data of the device; performing MOSFET device performance prediction based on the real-time device I-V characteristic data to obtain MOSFET device performance prediction data; screening according to the performance prediction data of the MOSFET device to obtain performance data of a primarily screened qualified device; performing adjustment based on the performance data of the preliminarily screened qualified device to obtain an MOSFET device test scheme; acquiring real-time test data of the MOSFET device, and screening qualified devices to obtain performance data of re-screened qualified devices; and performing device test summarization based on the performance data of the primarily screened qualified device and the performance data of the secondarily screened qualified device to obtain an MOSFET device test report. According to the invention, the test period can be shortened.

The invention discloses a tower crane installation path planning method and system, a terminal and a medium, and the method comprises the following steps: collecting and preprocessing three-dimensional point cloud data and visual information in a tower crane installation area, fusing the point cloud data with image data, and generating a fused three-dimensional point cloud image which comprises distance and color information; a deep learning model is adopted to carry out obstacle detection on the fused three-dimensional point cloud image; an RRT algorithm is adopted, and a safety path is planned according to the obstacle detection result and the requirement of the hoisting task; and controlling the movement of the hoisting equipment according to the obtained safe path, and executing ascending, descending, horizontal moving and rotating actions of a lifting hook according to a path planning result. The method has the advantages that the safety and the efficiency in the hoisting operation of the power transmission tower are improved, and the quality and the reliability of power transmission line construction are improved.

The invention provides a swivel measurement and control system for a swivel bridge. The method comprises the following steps: establishing a multi-parameter monitoring model in a bridge swivel process; the multi-parameter monitoring model is a monitoring model which is based on N times of sampling and comprises p sensor parameters; establishing a control model of swivel measurement and control of the swivel bridge; for the control model, establishing an adaptive adjustment algorithm under a multi-parameter monitoring model; and performing swivel measurement and control on the swivel bridge through a self-adaptive adjustment algorithm. According to the invention, accurate monitoring and control of the bridge swivel process can be realized, the safety and accuracy of swivel operation are ensured, good adaptability and real-time performance are achieved, and the efficiency and quality of bridge swivel engineering can be effectively improved.

The invention belongs to the technical field of industrial automation and machine learning application, and particularly relates to an engine tightening fault type classification method based on an improved random forest, which is characterized by comprising the following specific operation steps: 1) attaching a label to tightening curve data; 2) preprocessing data, removing noise points, and completing extraction of main curve segments; 3) feature extraction; and 4) training the improved random forest model to complete classification. Compared with the prior art, the method has the beneficial effects that the defect data can be classified based on the defect data classification algorithm of the improved random forest, the defect data of the new type can be effectively detected, the data of the existing defect type can be classified, the fault data of the new type can be effectively identified, and the fault detection accuracy is improved. The potential risk is reduced, and remarkable technical and economic application values are realized.

The invention discloses a shield forming tunnel form data processing system and method, and relates to the technical field of tunnel data processing. The shield forming tunnel form data processing method specifically comprises the following steps: S1, collecting and preprocessing data of a plurality of sections in a shield forming tunnel; s2, calculating the offset of the center of each segment; s3, continuously measuring and analyzing a deformation trend; s4, predicting offset change by using a linear regression model; s5, predicting the final offset of the duct piece by using the trained model; the removed abnormal values are recorded and analyzed, so that abnormal factors of segments in the tunnel can be screened, potential problems can be found and solved in time, further deterioration of the problems is prevented, and safety and stability of the tunnel are ensured; the deformation is calculated and the deformation trend is analyzed by using a time sequence, so that the deformation trend can be found in time, a subsequent detection plan can be formulated, the deformation problem can be found and processed in time through continuous monitoring, and the stability and the safety of the tunnel are ensured.

The invention relates to the technical field of AGV navigation communication, in particular to a navigation communication method and system applied to AGV remote control interruption, and the method is applied to local control after an AGV remote control center breaks down to interrupt control. The method comprises the following steps: generating local navigation activation request information when the AGV detects that the communication quality with a remote control center is reduced to a preset quality level, and sending the local navigation activation request information to a local communication node which is pre-loaded into a scene map; the local communication node receives local navigation activation request information, analyzes and extracts key parameters, and obtains an AGV behavior feature set; the AGV behavior feature set comprises an identity authentication number, a destination coordinate and a real-time position coordinate of the AGV; the path conflict is effectively avoided, the collision risk between the AGVs is reduced, and the safety and stability of the system are improved.

The invention relates to the field of machine room temperature control, in particular to a machine room temperature automatic control system and method based on the Internet of Things, and the method comprises the steps: obtaining related data information of a machine room through a deployed sensor in the machine room, dividing the interior of the machine room into different regions, and building a multi-element space; marking each area according to the computer room temperature data information of different areas to obtain an integrated related influence degree weight set of the temperatures of different areas of the computer room; according to the integrated related influence degree weight feature set of the temperatures of the different areas of the machine room and in combination with related data information of the machine room, setting a priority level so as to formulate a machine room temperature regulation and control strategy optimization method; when second-time regulation and control are carried out, a machine room temperature correction coefficient is obtained according to the energy consumption condition of machine room temperature regulation equipment, the first-time regulation and control effect and the temperature change trend; and according to the adjusted temperature of the machine room, the system response time and the related data information in the machine room, establishing a machine room equipment working state monitoring model for regulation and control monitoring.

The invention discloses an alarm flow control method based on consistent Hash of alarm priority and related equipment, the cache size is firstly configured according to the actual physical memory size to ensure that alarm software can process alarm information as efficiently as possible on the premise of not exhausting system resources, then priority arrangement is carried out, and the alarm flow control efficiency is improved. Priority processing of high-priority alarms can ensure that key safety problems are responded in time, potential risks are reduced, setting of an avalanche value provides an early warning mechanism for the system, the system can respond in advance before the number of alarms increases sharply, the system state is automatically adjusted according to real-time changes of the number of alarms, and the system safety is improved. The method ensures that the processing capability can be quickly recovered after the system returns to normal, finally ensures that all nodes can obtain complete alarm information and keep data consistency, improves the stability and reliability of the system under extreme conditions through a series of optimization measures, preferentially processes high-priority alarms, and improves the reliability of the system. And key safety problems can be timely responded and processed.

The invention discloses a personnel positioning method based on a Beidou and LoRa ad hoc network, and relates to the technical field of Beidou and LoRa ad hoc networks. By combining a Beidou positioning module and a LoRa communication module, accurate transmission and error prediction of positioning information in a complex terrain or an urban environment are realized, specifically, the LoRa module forms an ad hoc network through automatic scanning and carries out detailed signal strength and quality data measurement and analysis in a multi-path effect influence area, so that the accuracy and the accuracy of the positioning information are improved. Through deep analysis of the multipath effect, the system can evaluate and predict positioning errors in advance, thereby improving accuracy and reliability of positioning information, effectively coping with delay and interference caused by signal reflection, and ensuring stable communication and accurate positioning in a complex environment.

The invention discloses a method for preparing a porous ceramic filter ball from electrolytic manganese residues, and relates to the technical field of environmental protection and ceramic materials, and the method comprises the following steps: (1) raw material treatment: crushing the electrolytic manganese residues into particles of 2-3 cm, drying by using a dryer, and after drying, carrying out ball milling and sieving on the electrolytic manganese residues by using a planetary ball mill to obtain a raw material; (2) weighing and proportioning raw materials: weighing and proportioning the raw materials in percentage by weight: 50-80% of electrolytic manganese residues, 20-50% of calcined kaolin, 0-10% of an additional cosolvent and 0-10% of a pore-forming agent; (3) mixing the raw materials; and (4) granulating and aging the mixture. According to the method for preparing the porous ceramic filter balls from the electrolytic manganese residues, a brand new path is developed for comprehensive utilization of the electrolytic manganese residues, the path enables the utilization rate of the electrolytic manganese residues to be greatly increased, the path becomes an extremely effective path for consuming the electrolytic manganese residues, the electrolytic manganese residues can be fully utilized due to the characteristic of high utilization rate, and efficient cyclic utilization of resources is achieved.

The invention relates to the technical field of electrical digital data processing, in particular to a logging data intelligent analysis method and system.The method comprises the steps that porosity data in the logging process and corresponding timestamps are collected, and each data point serves as a combination of the porosity and the timestamps; calculating the abrupt change degree of each data point, and measuring the abrupt change characteristic by comparing the porosity change of the current data point and the neighborhood data point of the current data point; calculating linear strength based on the timestamps and the porosity of the data points to reflect the regularity of the data points along with time change; calculating weighted complexity by using the initial complexity, the mutation degree and the linear intensity; and finally, comparing a difference value between the weighted complexity and the initial complexity, and if the difference value exceeds a set threshold value, judging that the data point is an abnormal data point. The problem that the accuracy of a logging data analysis result is not high is solved.

The invention discloses a laboratory safety IOT (Internet of Things) intelligent early warning system, which comprises a laboratory environment early warning system, a laboratory equipment early warning system, a laboratory personnel behavior early warning system, a self-adaptive learning system and an integrated control system, wherein the laboratory environment early warning system, the laboratory equipment early warning system, the laboratory personnel behavior early warning system and the adaptive learning system are connected with the integrated control system through a wireless communication network; the adaptive learning system is connected with the laboratory environment early warning system, the laboratory equipment early warning system and the laboratory personnel behavior early warning system through a wireless communication network; the self-adaptive learning system is used for optimizing early warning models of the subsystems, and the integrated control system is used for efficient integration of the subsystems to ensure data sharing and cooperative work among the subsystems. According to the invention, the safety and management efficiency of the laboratory are effectively improved, and the potential risk is reduced.

The invention discloses a hydraulic support group cloud measurement method based on AR calibration matching. A physical system, a remote cloud data processing system and a VR system are included. In the physical system, AR glasses provide an interactive interface for virtual-real interaction and data display, and an inspector performs manual matching and dragging adjustment through an MRTK toolkit to complete detection. In the remote cloud data processing system, the cloud service computing system carries out data storage and preliminary analysis, the centralized control worker and the GPT decision making system send a mixed decision making result to the AR glasses through secondary data processing to make a detection data supplement request, and finally the field inspection worker completes measurement. In the VR system, a twin hydraulic support group model is constructed and corrected, an overall working space is constructed, and pose reconstruction precision verification is carried out, so that virtual pose real-time measurement of the hydraulic support group is realized.

The invention relates to the field of live-line maintenance equipment, and discloses a self-walking insulating lifting platform capable of adapting to various road sections, comprising mounting adaptation mechanisms which are positioned at four side ends of a fixed frame and are used for adapting to the mounting of a lifting structure; the buffering and damping mechanism is located on the installation adaptation mechanism and matched with the chaining base and the fixing plate to be used for relieving vibration of the lifting platform structure. The concentrated heat absorption mechanism is located on the fixing frame and used for absorbing and relieving heat of the lifting platform driving element. The hydraulic system, the linkage frame, the clamp spring and other structures are reasonably designed, and stable operation of equipment in the operation process is ensured. The technology greatly improves the reliability and durability of the equipment in long-time and high-frequency operation, and the design of the equipment fully considers the safety of emergency power maintenance personnel. Meanwhile, a plurality of independent control systems and buffering and damping mechanisms ensure that potential risks to operators and equipment can be reduced to the maximum extent in the moving and operating process of the equipment.

The utility model relates to the technical field of chemical production, and discloses a hydrogen fluoride reaction furnace gas guide device which comprises a gas inlet pipe, a mounting pipe arranged at the top of the gas inlet pipe, an extension pipe arranged at the top of the mounting pipe, a gas outlet pipe arranged at the top of the extension pipe, and an air supply pipe arranged on one side of the extension pipe and used for supplying air into the extension pipe for cooling, and the water spraying assembly is arranged in the mounting pipe and used for conducting water spraying purification on the gas, the gas is effectively cooled through the water spraying assembly, efficient treatment on the gas is achieved, and the environment-friendly effect and the working efficiency are remarkably improved.

The invention discloses an environment-friendly macromolecular modifier PVC (polyvinyl chloride) composition, and belongs to the technical field of polyvinyl chloride materials. The composition comprises the following components in parts by weight: 100 parts of PVC, 1-50 parts of a polycarbonate toughening modifier and 0-30 parts of an auxiliary agent. The polycarbonate toughening modifier with a certain polymerization degree is used as the modifier of the PVC material, the plasticity, toughness, impact resistance and other properties of PVC can be obviously improved, the polycarbonate toughening modifier is a macromolecular polymer and cannot be separated out in the use process, the safety of the PVC material is improved, and the service life of the PVC material is prolonged.

The invention relates to the technical field of structure safety detection, and discloses a digital twinning-based port wharf structure safety evaluation system and method, and the system comprises a model building module which is configured to build a digital twinning model according to the actual structure of a port wharf; the sensor assembly is arranged in a to-be-monitored area of the port wharf structure; the acquisition module is configured to acquire structure information and environment information; the structure evaluation module is configured to calculate a structure evaluation initial value through the pressure value and the vibration value when the structure is loaded, and adjust the structure evaluation initial value according to the inclination angle influence value to obtain a structure evaluation final value; the environment evaluation module is configured to calculate an environment evaluation initial value through the water level value and adjust the environment evaluation initial value according to the wind power influence value to obtain an environment evaluation final value; and the grade evaluation module is configured to divide the structure safety evaluation grade according to the structure safety evaluation value. According to the invention, the structure safety monitoring and evaluation capability is improved, and the evaluation precision and efficiency are high.

The present invention relates to a multi-ship anchoring scheduling path planning method based on an improved RRT algorithm, comprising the following steps: S1, constructing a multi-ship anchoring scheduling destination selection mechanism; S2, constructing a multi-ship anchoring scheduling evaluation index selection mechanism; S3, calculating the ship scheduling priority according to the multi-ship anchoring scheduling evaluation index; S4, constructing a multi-ship anchoring scheduling collision avoidance decision-making mechanism; S5, calculating the multi-ship anchoring scheduling path planning result. The present invention can be used to deal with the situation where multiple ships need anchoring scheduling at the same time, optimize their berthing sequence and navigation path, thereby minimizing the ship anchoring scheduling waiting time, reducing the navigation distance of all scheduled ships as much as possible, and improving the berth utilization rate; it has important significance in multi-ship scheduling in complex waters, emergency evacuation of ships, and comprehensive management of water transportation hubs.

The invention discloses an intelligent nursing safety management skin management reckoning disc which comprises a chart module of a multilayer concentric circle structure, a calculation model and intelligent analysis algorithm module, a touch operation interface, a data recording and exporting module and an implementation module. The reckoning disc adopts a chart module of a multi-layer concentric circle structure, through different colors and layered display of skin stages, clinical manifestation, nursing measures and prevention strategies, nursing personnel can obtain information visually and conveniently, a skin health score calculation model, a treatment scheme recommendation algorithm, a nursing effect prediction model and a risk early warning algorithm are integrated in the system, and the system is convenient to use. The skin condition of the patient is accurately evaluated based on multiple skin health indexes, and the nursing suggestions are updated in real time, so that the nursing efficiency is improved, and the misjudgment risk is reduced.