282results about How to "Respond quickly" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention provides a configuration method and system for an overall electrical scheme of a machine room, and relates to the technical field of electrical planning, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out feature extraction and semantic mapping, constructing a high-dimensional semantic association network, carrying out semantic enhancement, semantic modeling and logic association, extracting an abnormal mode, and obtaining a machine room operation state portrait; determining a machine room energy consumption rule and high-energy-consumption equipment, carrying out composite characteristic analysis to obtain a machine room electrical load curve, carrying out hierarchical progressive prediction in combination with exogenous variables, constructing a target function, solving to obtain optimal configuration parameters, and generating a power distribution system design scheme; a self-learning controller is added in a power distribution unit, the electrical load of a machine room is optimized, a power distribution equipment degradation mechanism model is constructed, service life prediction is performed, a power distribution equipment service life management strategy is output, equipment running abnormally is identified, abnormal reasons are judged by referring to historical abnormal data, and an abnormal report is generated and sent to equipment maintenance personnel.

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The invention provides an intelligent access method and system for small accessories of an unmanned aerial vehicle, and relates to the technical field of intelligent access. Generating an action feature vector through an action semantic understanding model; identifying key action nodes and generating a basic action sequence; constructing an action semantic mapping model; performing mapping analysis based on the execution equipment capability; generating an equipment control instruction; collecting action execution state data; establishing an execution evaluation model; generating a compensation instruction and updating the control instruction; and intelligent access operation is completed. According to the method, intelligent storage and taking of the unmanned aerial vehicle accessories are achieved, and the operation efficiency and precision are improved.

The embodiment of the invention discloses a chip register description document generation method and device, a storage medium and terminal equipment, and relates to the field of document processing. The method comprises the following steps: analyzing a register attribute table file of a chip, and determining each functional module included in the chip and a register included in each functional module according to an analysis result; querying an associated register description template document according to the model of the chip; extracting field values corresponding to fields in the register list display area and the register description display area from the register attribute table file, and filling the read field values into a register description template document to generate a register description document of the chip, the problems that in the prior art, compiling of the chip register description document is low in efficiency, prone to errors and poor in timeliness are effectively solved, and a more efficient, accurate and reliable method is provided for compiling and managing the chip document.

The invention discloses a user intelligent analysis method and device based on multi-source data and electronic equipment, and relates to the field of user analysis, and the user intelligent analysis method comprises the following steps: 1, deploying an image collection part and an information release part, and registering authorized interaction point data; 2, obtaining the attention state of a certain user in the image acquisition area for the released information, performing quantitative processing, and obtaining the attention coefficient of the user for the released information; 3, calling historical interaction data of the user from the database, analyzing past consumption behaviors of the user, preferences of watching and reading the released information and interaction behaviors of browsing interaction points, and obtaining historical behavior coefficients; the real-time data collected by the fusion image, the user historical behavior data and the group preference data are obtained for analysis and recommendation, the recommendation content better meeting the instant demand of the user is generated, the comprehensiveness and accuracy of recommendation are improved in a multi-source data fusion mode, and the recommendation result is more personalized and intelligent.

The invention relates to the technical field of data processing, in particular to an abnormal behavior detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining historical operation data, and carrying out the preprocessing and division processing, and obtaining first and second preprocessed historical data; performing clustering analysis on the first preprocessed historical data by adopting a clustering algorithm, training based on a clustering analysis result to obtain a normal behavior baseline model, and inputting the second preprocessed historical data into the normal behavior baseline model to obtain normal behavior baseline information; training by adopting the normal behavior baseline information to obtain an abnormal behavior recognition model; real-time operation data is acquired and preprocessed, the preprocessed real-time operation data is input into the abnormal behavior recognition model to obtain an abnormal recognition result, and whether a response mechanism is triggered or not is judged according to the abnormal recognition result; according to the method disclosed by the invention, the abnormal behavior can be quickly identified, and the corresponding automatic response measure can be executed, so that the potential threats can be quickly detected and coped with.

The invention discloses an industrial control ring network monitoring system and a monitoring method thereof. The system comprises a data acquisition module which is used for acquiring an industrial control ring network data packet in an industrial control network; the data processing module is used for carrying out data cleaning and data arrangement on the collected data and carrying out protocol matching on a data packet so as to identify an industrial protocol in the data packet; the data identification module is used for carrying out protocol matching on data in the network data packets and identifying communication data of each network packet; through organic cooperation of a plurality of units, the system realizes omnibearing monitoring, analysis and dynamic adjustment of the network, so that the system architecture provides a real-time, intelligent and self-adaptive network security solution, and compared with a traditional intrusion detection system, the system pays more attention to real-time optimization of network performance, and the network security is improved. Under the condition that the bandwidth occupancy rate is relatively high, unnecessary interference to other servers is reduced, so that the problems of lengthy and tedious protocol identification are solved.

The invention discloses a gas leakage point leakage detection system and method based on an e-dog, and belongs to the technical field of gas leakage detection. Through the autonomous navigation capability of the mobile platform module, the system can automatically avoid obstacles in a complex environment and inspect along a predetermined path, optimizes the inspection path in real time after finding a gas leakage point, improves the inspection efficiency, guarantees the high-density inspection of a key area, and the rapid movement and precise positioning capability of an e-dog, and improves the inspection efficiency. The gas leakage detection is quicker and more efficient, the risk that personnel enter a dangerous environment is reduced, the system has the functions of autonomous navigation, obstacle avoidance, remote control and the like, the intelligent level of detection is improved, the system is suitable for detection requirements of various complex environments and different types of gases, and through cooperative work of the gas detection module and the data acquisition and processing module, the detection efficiency is improved. The gas concentration data can be accurately acquired, the position of the leakage point can be judged, and the leakage point can be quickly and accurately positioned based on the combination of the GPS and the gas concentration data.

The invention provides an anomaly detection and response method and system based on intelligent perception driving, and is particularly applied to cloud service information security monitoring. The method comprises the following steps: acquiring multi-type data in real time and fusing the multi-type data through Kalman filtering and weighted average; constructing a self-encoding network to carry out feature extraction and dimension reduction, detecting abnormity through a reconstruction error and setting a dynamic threshold value; a reinforcement learning optimization algorithm is introduced, a dynamic response strategy is designed, and a reward mechanism is defined, so that the response effect is improved; designing an automatic response process, including alarm, isolation and repair measures; migrating the optimized self-encoding network knowledge to a new scene by adopting a transfer learning technology; a cloud-edge collaborative architecture is constructed, and data are analyzed in a centralized manner and synchronized in real time. According to the method, the real-time performance and accuracy of information security monitoring are effectively improved, potential threats can be quickly responded, the security risk is reduced, and the method is suitable for various information security monitoring scenes.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

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 security monitoring, in particular to an intelligent all-in-one machine operation security monitoring method and system, and the method comprises the following steps: obtaining an operating system log and an application program log, integrating the logs into a comprehensive data stream, judging the security risk level of the comprehensive data stream, obtaining a primary risk assessment result, and obtaining a secondary risk assessment result; and adjusting intrusion detection sensitivity and an access control list of the firewall according to the primary risk assessment result to obtain dynamic security policy configuration. In the invention, the dynamic security policy configuration enables the security system to automatically adjust according to real-time analysis, such as firewall setting and access control, so as to quickly cope with a changing threat environment. The strategy not only improves the adaptability of defense measures, but also optimizes the use efficiency of system resources. Furthermore, behaviors which do not conform to a normal mode can be identified and predicted through analysis of the comprehensive data stream, and the prediction capability of potential threats is enhanced.

The invention discloses a landslide geological disaster early warning pushing method and system, and relates to the technical field of emergency response, and the method comprises the steps: introducing a time dimension to generate an early warning pushing coefficient Awarning based on a landslide influence range Aimpact and a comprehensive risk score Rreg, and carrying out the dynamic adjustment through analyzing the change of a risk along with the time, thereby achieving the early warning pushing of a landslide geological disaster. And comparing with a preset first push threshold value F1 and a preset second push threshold value F2, and timely transmitting the risk condition of the landslide influence area. When the early warning pushing coefficient Awarning exceeds a first pushing threshold value F1, an emergency early warning mechanism is triggered immediately, first landslide information is pushed, and related personnel are informed to take necessary safety measures; and when the early warning push coefficient Awarning is between the first push threshold F1 and the second push threshold F2, second landslide information is pushed, residents are reminded to make precaution preparations, and the dynamic evaluation mechanism ensures that the early warning information can quickly and accurately reflect risk changes.

The invention relates to the technical field of digital video devices, in particular to a digital video device control method and device. The method comprises the following steps: acquiring digital monitoring data, performing real-time scene acquisition and dynamic region optimization according to the data, and generating optimized dynamic region data; video frames are read based on the dynamic region optimization data, and abnormal behavior analysis and behavior intention evaluation are carried out; calculating the complexity of monitoring behaviors by combining video frame data and behavior intention data, and evaluating resource consumption increase; the processing bottleneck is evaluated according to the resource consumption growth data, and the processing capability attenuation analysis is carried out, so that the monitoring device is adjusted, and the system performance is optimized; according to the method, the resource allocation and processing capability of the video device is dynamically adjusted through real-time analysis and processing of monitoring data. According to the invention, the digital video device is adjusted, so that the prediction identification of the digital video device is more accurate and efficient.

The invention discloses a real-time detection method suitable for subsynchronous oscillation of a wind power plant. The method comprises the following steps: firstly, continuously acquiring grid-connected line current by adopting a sliding time window, then performing noise reduction processing on acquired data by using a singular spectrum analysis method, then adaptively determining a VMD modal number according to a grouping effective order solved in an SSA process, and performing VMD decomposition on a signal after noise reduction to obtain an effective modal; and then applying a symmetric differential energy operator to the related mode to solve instantaneous frequency and amplitude, and further solving to obtain system oscillation parameters. And finally, performing a simulation test based on the grid-connected data of the direct-driven wind power plant. The subsynchronous oscillation real-time detection method has high time-frequency measurement precision and good dynamic performance, can accurately and continuously reflect the system operation state change process, and has certain guiding significance for system stability evaluation, oscillation analysis and suppression research and disturbance source positioning research.

The invention relates to the technical field of intelligent manufacturing, and discloses a product production process scheduling method and system based on intelligent manufacturing, and the method comprises the steps: 1, data collection and analysis; 2, establishing a production process model; 3, applying an intelligent scheduling algorithm; 4, a real-time monitoring and feedback mechanism; and 5, continuously optimizing and learning. According to the invention, in a real-time monitoring and feedback mechanism, the scheme combines historical data to dynamically correct real-time production data, and performs anomaly detection. By comparing the trend of the current production data with the trend of the historical data in real time, the system can identify and correct data abnormity in time, and it is ensured that each link in the production process is smoothly carried out according to a predetermined plan. The mechanism has the advantages that the accuracy of production data can be ensured by quickly detecting and correcting the deviation, the production delay caused by data errors or lagging is avoided, and the production efficiency and the scheduling precision are improved.

The invention discloses a four-flapping-wing aircraft and an attitude control system thereof, and relates to the technical field of micro aircrafts, and the four-flapping-wing aircraft comprises a fixed rack, a guide rod, a slide block mechanism, a pitching frame, a rolling frame, a pulley and a chord driving system. The fixed rack is provided with four cantilevers, a sliding block mechanism is installed on a guide rod, and the pitching frame and the rolling frame control pitching and rolling actions through steering engines respectively. The four pulleys are connected to the pitching frame, and each pulley is hinged to one wing. The string driving system comprises a guide wheel, a string and a crank, one end of the string is connected with the slider mechanism, and the other end bypasses the guide wheel and is connected with the pulley to realize wing flapping. Through the deflectable spars and fine steering engine control, efficient aerodynamic performance, enhanced thrust output and excellent flight flexibility and adaptability are achieved, meanwhile, due to the design of the crank sliding blocks, structural stress is relieved, the service life is prolonged, the aircraft adapts to multiple flight modes, and the environment adaptability and task execution efficiency of the aircraft are improved.

The invention relates to the technical field of computers, and discloses a computer security protection management system which comprises a firewall module, an intrusion detection and defense module, an anti-virus and anti-malicious software module, a user identity and access management module, a security information and event management module, a vulnerability management module and a data encryption module. And the firewall module controls access of network traffic. An advanced machine learning and behavior analysis algorithm is adopted, false alarm and missing alarm are effectively reduced, high accuracy of threat detection is ensured, novel and unknown attacks can be rapidly recognized, the overall protection capability can be improved, in the aspect of system performance optimization, efficient flow analysis and threat response are achieved through a distributed architecture and a hardware acceleration technology, and the system performance is optimized. The system load is reduced, the scanning and monitoring strategy and the efficient encryption algorithm are optimized, the occupation of system resources is remarkably reduced, and the response speed and stability of the system are improved.

The invention discloses a visual characterization method for carbon emission of a sewage treatment plant, belongs to the field of carbon emission monitoring, and solves the problem that the fluctuation and trend of carbon emission data cannot be analyzed by an existing method. The method comprises the following steps: checking carbon emission intensity; preprocessing the carbon emission intensity data; defining a color mapping function according to the numerical range of the carbon emission, and mapping the numerical value to a color space; based on the color mapping function, creating a color bar for representing a corresponding relationship between the data value and the color; according to the color mapping function and the color bar, converting the carbon emission data at each time point into a corresponding color, and drawing the color on the heat map; and optimizing and outputting the image. According to the method, the calorific value map technology is introduced, so that the visualization effect of carbon emission intensity data is remarkably improved, the change of the carbon emission intensity in a time sequence is visually displayed, complex data becomes clear at a glance, and the peak value and the fluctuation trend of carbon emission can be quickly captured.

The present invention discloses a method and system for monitoring fall prevention safety of aerial work, the method includes obtaining physiological sign data and operation behavior data of aerial work personnel, use status data of fall prevention devices and environmental data; determining human risk index, equipment risk index and environmental risk index; judging whether there is a situation where the risk value among human risk index, equipment risk index and environmental risk index is greater than a first preset threshold value; if so, triggering an alarm prompt and generating a first operation guide to push to the aerial work personnel; if not, determining the fall risk value according to the human risk index, equipment risk index and environmental risk index, and when it is greater than a second preset threshold value, triggering an alarm prompt and generating a second operation guide to push to the aerial work personnel. The present invention takes into account more comprehensive risk factors, so that the fall risk value can be accurately predicted, and different operation guides are provided for different risks, thereby improving the energy efficiency of aerial work safety monitoring.

The invention belongs to the technical field of geological disasters, and discloses an urban slope stability characteristic analysis method and system based on a block gliding risk, and the method comprises the steps: calculating the stability characteristic of each station based on a plane gliding method through measuring the slope size, soil and vegetation characteristics and other data; the relationship between the stability coefficient and each sensitive factor is analyzed, and the research result shows that the slope stability coefficient of each sampling point is basically more than 1.0, and the landslide risk is extremely low; the soil cohesive force has the greatest contribution in maintaining the stability of the slope and is most sensitive to the stability coefficient of the slope; the soil water content can reduce the slope stability coefficient from the aspects of increasing the self weight of the soil body, reducing the soil cohesive force and the like; the soil root system density can improve the overall stability of the slope from the aspects of enhancing the shear strength of soil, reducing soil erosion and the like. Soil is reinforced through vegetation concrete and other technologies, meanwhile, the slope vegetation coverage rate is increased, and regular monitoring of the midsole risky slope state is a good method for reducing the landslide risk of the midsole risky slope.

The invention belongs to the technical field of network security, and discloses an enterprise network security self-checking method and system, and the method comprises the steps: collecting network flow data, equipment parameter data, user behavior data and security situation awareness data; preprocessing the collected network traffic data, equipment parameter data and user behavior data to obtain a network traffic feature data set, an equipment feature data set, a user behavior feature data set and a security situation awareness feature data set; fusing the network traffic feature data set, the equipment feature data set, the user behavior feature data set and the security situation awareness feature data set to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a network security prediction model, and predicting through the network security prediction model to obtain an enterprise network security state; the possibility of occurrence of network security events is reduced, response delay caused by human intervention is reduced through an automatic process, and the detection efficiency is greatly improved.

The invention discloses an underground gas-liquid separator performance optimization method and system based on an artificial neural network, and relates to the technical field of underground gas-liquid separator performance optimizing.The underground gas-liquid separator performance optimization method comprises the following steps that when an underground gas-liquid mixture is introduced into a gas-liquid separator, flow information and pressure information are obtained at an inlet of the separator in real time; the flow information and the pressure information at an inlet when the gas enters the gas-liquid separator are preliminarily analyzed, and the fluctuation trend of the flow and the pressure is recognized. According to the method, the flow and pressure information is obtained in real time, the deep learning prediction model is combined, the abnormal fluctuation of underground gas-liquid flow is recognized, and the highly dynamic nonlinear change environment and the normal environment are distinguished through the gas-liquid flow environment change coefficient. In a normal environment, the artificial neural network optimizes energy consumption; and during violent fluctuation, fuzzy logic control adjusts parameters of the separator in real time, so that quick response is ensured, energy consumption and equipment loss are reduced, system stability is improved, and the service life of equipment is prolonged.

The invention relates to the technical field of vehicle driving intelligent control based on the Internet of Vehicles, and discloses a vehicle driving intelligent control method based on the Internet of Vehicles, which is characterized in that obtaining of starting place and destination information of a user is the basis of a navigation system, it is ensured that an optimal path from the current position of the user to the destination can be accurately calculated, and the system generates a plurality of paths; distance, time, road conditions and the number of traffic lights are comprehensively considered, diversified choices are provided, the traffic lights serve as segmentation points to divide routes, the system carries out refined path analysis, the driving strategy of each road section is adjusted in a personalized mode, the overall driving efficiency is improved, lanes of each road section are distinguished in detail, the system can accurately plan and adjust the routes, and the driving efficiency is improved. A high-precision map and real-time vehicle information provide data support for dynamic adjustment of a driving strategy, and by analyzing road and vehicle data, the system can recognize dangerous points in advance and optimize a driving path to help a user to make an optimal choice.

The invention provides a factory monitoring method and system based on digital twinning, belongs to the field of intelligent control, and solves the problems that video monitoring in a factory has a monitoring dead angle and also has a large amount of non-key information, and the monitoring efficiency is poor. If undifferentiated analysis is carried out on video data in real time, a large number of computing resources are consumed on meaningless data, and in addition, the problem that personnel video information and a digital twinborn model are difficult to fuse in video monitoring is solved. The method comprises the following steps: acquiring a manual control signal of operation equipment in a factory; based on the manual control signal, determining manual operation information currently implemented by an operator for the operation equipment; and deducing the current operation action of the operator in the digital twin model through the manual operation information so as to simulate the three-dimensional simulation dynamic effect of the operator corresponding to the operation action.

The invention discloses a control method and device of a photovoltaic power generation system, electronic equipment and a storage medium, and relates to the technical field of data processing, and the main technical scheme comprises the following steps: obtaining an external access request; wherein at least one access target exists in one external access request; and carrying out identity verification on the external access request, and forwarding the external access request to an access target corresponding to the external access request after the verification is successful. And through remote monitoring and verification of an external access request, it is ensured that only the request passing identity verification can be processed and forwarded to a corresponding target. The remote control capability not only improves the safety of the system, but also enhances the flexibility and response speed. The administrator can update the security policy in real time and quickly deal with new threats or abnormal behaviors without physically contacting the server. In addition, remote management reduces the demand for on-site maintenance, reduces the operation and maintenance cost, and allows more efficient troubleshooting and problem solving.

The invention provides an attack path restoration method and equipment, which can be applied to the technical field of computers. The method comprises the following steps: monitoring the running state of an application program in each server by using an RASP agent installed on each server, and collecting key data of the application program in the running process; when an attack event is detected, alarm information is generated according to the key data, and a target server having an association relationship with the attack event in the servers is determined according to the alarm information; generating a server attack path according to the interaction relationship among the plurality of target servers; and generating an attack path corresponding to the attack event according to the server attack path and each component in the target server. According to the method, the attack path of the attack event can be quickly and accurately restored.

The invention relates to a risk behavior detection method and system based on the DLP technology, and the method comprises the following steps: building a user risk behavior monitoring model based on the DLP technology, and the user risk behavior monitoring model is used for collecting and monitoring user behavior data and IM communication software communication data; establishing a user risk behavior identification model through a machine learning technology according to user historical behavior data collected by the user risk behavior monitoring model, and monitoring daily operation behaviors of the user in real time; according to IM communication software communication data collected by the user risk behavior monitoring model, whether risk content exists in the IM communication software communication data or not is recognized through the NLP technology; and according to an output result of the user risk behavior identification model and risk content identified by the NLP technology, constructing a comprehensive risk assessment model, when a risk value output by the risk assessment model is higher than a preset value, giving a risk alarm, and automatically recording related data to provide evidence for subsequent investigation and auditing.

The invention relates to the technical field of greening management, in particular to an urban greening state monitoring method and system based on digital mirror image information, and the method comprises the steps: collecting the real-time live-action data of a designated city, combining the real-time operation rule of the city, and generating a virtual city update mirror image through a digital twinborn technology; extracting digital mirror image information in a specified time period from the virtual city updated mirror image to obtain greening state mirror image data, and generating a virtual city greening state by analyzing the mirror image data; further detecting the greening state of the virtual city, generating greening state early warning information, and feeding back the greening state early warning information to the real city to determine a monitoring area and a monitoring index; and performing live-action monitoring on the designated area of the real city, collecting and analyzing monitoring data to obtain an actual monitoring result of the alarm area, and synchronizing the actual monitoring result to the virtual city update mirror image. Dynamic monitoring and bidirectional feedback of the greening state are realized, and technical support is provided for fine management and scientific decision-making of urban greening.