168 results about "Quantitative assessment" patented technology

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention discloses a high geological disaster chain risk quantitative assessment method adapted to a high-cold and high-altitude environment, and relates to the technical field of geological disaster monitoring and prevention and control, and the method comprises the following steps: S100, building an irradiation and reflection combined observation layer, collecting the dynamic intensity of direct light and reflected light, generating an exposure time sequence fingerprint baseline, and obtaining an exposure time sequence fingerprint baseline; and determining a critical threshold value of the direct light intensity according to the base line. The method comprises the following steps: constructing an irradiation and reflection combined observation layer to extract light intensity characteristics, and generating an exposure time sequence fingerprint baseline; a trigger point boundary is extracted by adopting coherent phase decomposition and anti-fact playback, and a continuous track is reconstructed; the overexposure risk is predicted based on the time-frequency score, and spectrum gating and polarization rotation are linked to regulate and control the exposure rhythm; and reverse phase traction and phase conjugate intervention are implemented in a steady-state window, so that boundary information is maintained and the risk identification precision is improved.

The invention belongs to the technical field of power system risk assessment, and particularly relates to a new energy station broadband oscillation risk redisk assessment method based on multivariate data, which comprises the following steps: performing data acquisition and data preprocessing on a new energy station-power grid coupling system to obtain a historical data set; extracting oscillation characteristic indexes to form index vectors; carrying out oscillation frequency source positioning analysis, calculating a time-frequency spectrum energy function to obtain an oscillation energy spectrum, carrying out main frequency energy track extraction, and introducing a frequency-power cross analysis mechanism to realize accurate identification of an oscillation source device; constructing a source-network coupled transfer function, performing stability margin calculation, and judging the stable state of the coupled system; and based on the index vector and the stability margin, carrying out oscillation risk grade division, and combining a source positioning result to generate an oscillation redisk map. According to the invention, panoramic recovery and risk quantitative evaluation of the broadband oscillation problem of the new energy field station can be realized.

The invention discloses a building outer wall falling risk detection method and system based on deep learning, and the method comprises the steps: obtaining an original surface image, extracting vertical face geometric features, constructing a homography matrix based on vanishing points, carrying out the geometric correction, and reconstructing an orthographic projection image; performing enhancement processing on the orthographic projection image, extracting an effective detection area and intercepting a detection image; inputting the detection image into a deep learning segmentation model into which edge weight constraint is introduced, and performing semantic segmentation, binaryzation and optimization to obtain a falling region; a mapping relation is established according to the actual physical size of the building outer wall, and pixel information of the falling area is converted into multi-dimensional parameters such as the actual physical area, the elevation, the width and the height; and combining the defect intensity input quantification risk assessment model to calculate a comprehensive risk index, determining a risk level and outputting a result. Automatic identification and quantitative evaluation can be realized, distortion and background interference are eliminated, accurate conversion from pixels to a multi-dimensional physical space is realized, and a scientific basis is provided for safety investigation.

The invention discloses an intelligent pulmonary tuberculosis recognition method and system based on image data, and relates to the technical field of image recognition, and the method comprises the steps: collecting and preprocessing conventional inspection data from different medical devices, and forming a standardized data set; extracting features from the standardized data set by using a deep learning model, and positioning a suspected focus area; based on the positioned suspected focus area, constructing a personalized risk assessment model, and generating a risk score; performing focus quantitative analysis based on the risk score, and generating a diagnosis report; the diagnosis report is applied to clinical practice, and a personalized diagnosis and treatment plan is made; according to the method, the personalized risk assessment model is constructed based on the positioned suspected focus area, the risk score is generated, and quantitative assessment of the health condition of the patient is realized.

The invention discloses a quantitative evaluation system based on a contract document dynamic risk map model, and relates to the field of contract document evaluation. The system comprises a data acquisition and preprocessing module, a dynamic risk map construction module and a quantitative evaluation module. And the data acquisition and preprocessing module is used for performing character recognition on the scanned contract PDF through an optical character recognition (OCR) engine. According to the method, the risk map can dynamically evolve along with changes of internal and external environments by defining the risk entities capable of being updated in real time and the incidence relation and combining a triple updating mechanism of time triggering, event triggering and rule triggering, and compared with a traditional static rule engine, the mechanism can respond to law and regulation updating, industry policy change and enterprise business adjustment in real time, and the efficiency is improved. And the problem that the risk assessment model lags behind the actual risk change is solved.

The invention discloses an AI-driven transport layer security protocol adaptive optimization system, which relates to the technical field of traffic data access and comprises a data access module, a state sensing module, a risk assessment module, an optimization transmission module and an adaptive encryption module. The data access module is used for capturing an original data flow from a network and a host in real time; the state sensing module is used for receiving the original data stream and setting a feature engineering state index; the risk assessment module is used for quantitatively assessing the safety score of the current state according to the current traffic environment state; the self-adaptive encryption module is used for constructing a password strategy library and generating an encryption configuration strategy according to a current traffic state; and the optimization transmission module is used for constructing a parameter intelligent optimization model and controlling algorithm interaction to generate performance scheduling optimization actions.

The invention relates to the technical field of faults of the Internet of Things, in particular to a work order closed-loop management method and system for automatically handling faults of fire-fighting Internet of Things equipment. The method comprises the following steps: obtaining the equipment type, the located area code and the linkage fire-fighting equipment number, and calculating a fault risk index; the partial derivatives of the weight coefficients are solved according to the objective function, the gradient is formed according to the partial derivatives, and the weight coefficients are updated according to the partial derivatives in the reverse direction of the gradient through the principle of the gradient descent method. And the updated weight coefficient is combined with the maximum accommodation number of people corresponding to the region, the maximum linkage number of single equipment, the equipment type weight corresponding to the equipment type, the real-time number of people corresponding to the region and the linkage fire-fighting equipment number to recalculate the fault risk index, and after the weight is dynamically adjusted, influence factors are comprehensively considered, so that the fault risk index is obtained. More reasonable risk assessment is provided for each fault, risk misjudgment caused by unreasonable weight is avoided, and the accuracy of quantitative assessment of fault influence is improved.

The invention discloses an intelligent construction site real-time monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting video data and three-dimensional coordinate data of construction site workers, obtaining posture features and position features, inputting the posture features into a posture recognition model based on a long-short-term memory network, and obtaining a posture recognition result; and outputting the instantaneous attitude category probability of the worker, inputting the position features into a regional risk assessment model based on a support vector machine, outputting the risk level of the position where the worker is located, performing weighted summation on the instantaneous attitude category probability and the quantitative value of the risk level, outputting a comprehensive risk score, and generating an early warning instruction. And marking the position of the corresponding worker on the three-dimensional electronic map of the construction site, and calling the corresponding real-time video data. According to the method, quantitative risk assessment is carried out by combining the attitude features and the position features, accurate judgment of the risk of the worker and quantitative assessment of the scene danger level are realized, and the early warning accuracy and the system reliability are remarkably improved.

The invention discloses a high-resolution standardized microarray compound eye lens module and a light field regulation and control system, particularly relates to the technical field of light field regulation and control, and realizes high-precision standardized definition of key parameters of a microlens unit and optical axis micro-angle deviation calibration through a microlens array standardized module. A standardized micro-lens array is used for completing efficient acquisition of multi-view and multi-focus high-resolution light field data of a target three-dimensional scene, and non-consistent temperature control focus drift information and visual axis offset accumulation out-of-control information between micro-arrays are synchronously captured in real time; a light field reconstruction imbalance risk assessment model is cooperatively constructed based on a non-uniform temperature control focus drift coefficient and a visual axis offset accumulation out-of-control coefficient between microarrays, a light field reconstruction imbalance risk assessment index is output in real time, quantitative assessment and dynamic early warning of the imbalance risk degree in the light field reconstruction process are achieved, and an intelligent decision basis is provided for reconstruction regulation and control.

The invention provides a cognitive decision security conversion and risk regulation and control method for an intelligent autonomous unmanned system, belongs to the technical field of artificial intelligence, and aims to solve the problems of low confidence and inaccurate risk assessment result caused by the fact that the decision process of a current machine learning system is completely not externally controlled and cannot be objectively understood. The method comprises the following steps: S1, building a super real-time parallel simulation platform; s2, constructing a decision risk quantitative evaluation model; and S3, designing a real-time accurate risk regulation and control mechanism. According to the method, super-real-time parallel simulation software is adopted for large-scale comparison tests, the unmanned aerial vehicles successfully complete tasks or safely return voyage and evacuate through no less than secondary tests, and it is proved that the cognitive decision safe conversion and risk regulation and control method for the machine learning system has excellent performance and is suitable for popularization and application. And the safety of task execution of the unmanned aerial vehicle in the simulated battlefield is greatly improved.

This invention discloses a method, equipment, medium, and product for quantitatively assessing the risk of erosion damage to pressure equipment, relating to the safety of pressure equipment in the petrochemical and chemical industries. The method includes: determining the damage form of the equipment; calculating a local / uniform thinning factor based on the damage form; calculating an equipment correction coefficient based on the local / uniform thinning factor, a general condition factor, a mechanical factor, and a process factor; calculating the probability of erosion corrosion failure based on the equipment correction coefficient, a management system evaluation coefficient, the average failure probability of similar equipment, and an impact coefficient for defects exceeding standards; calculating the leakage consequence area; determining the consequences of erosion corrosion failure based on the leakage consequence area; and establishing a two-dimensional risk matrix based on the probability of erosion corrosion failure and the consequences of erosion corrosion failure. The two-dimensional risk matrix is ​​used for real-time quantitative assessment of erosion risk. This invention can effectively guide the prevention and control of erosion corrosion risks in the petrochemical and chemical industries.

The invention discloses an electronic equipment commissioning and testing quality problem management system and method based on an algorithm model, and the system comprises a quality basic data maintenance module which is used for building a quality problem classification algorithm model through historical quality problem data, classifying problems, and giving quality problem types; the quality problem maintenance module is used for automatically retrieving and recommending similar historical problems and solving paths thereof according to fault features corresponding to the current quality problem; the quality data analysis application module is used for carrying out quantitative evaluation and trend prediction on potential risks in batch production; the expert experience knowledge base module is used for recording and carding the extracted historical quality problems and solving measures thereof to form an expert experience knowledge base, and storing solutions of the quality problems subjected to cross-department processing in the expert experience knowledge base when new quality problems occur every time; the method has the advantage that the management efficiency and effect of the quality problem are improved.

The invention discloses a bridge two-dimensional over-limit partition monitoring and early warning and safety risk control method, device, equipment, medium and product, and relates to the field of bridge safety risk control, and the method comprises the steps: carrying out the bi-two-dimensional safety evaluation according to a finite element simulation value of a to-be-evaluated bridge structure state and a real bridge monitoring value, determining the technical condition and the evaluation grade of the to-be-evaluated bridge; determining a plurality of over-limit vehicle load grades based on bridge technical conditions and evaluation grades thereof; determining a load range corresponding to each over-limit vehicle load grade according to the vehicle critical load in the normal use limit state and the bearing capacity limit state; based on this, determining a load upper limit corresponding to the current structure state; and according to the determined load upper limit, determining a current over-limit vehicle load grade from the plurality of over-limit vehicle load grades so as to determine a current early warning grade and a current safety risk control measure, and carrying out early warning and safety risk control on the to-be-evaluated bridge. According to the invention, quantitative evaluation of the bridge safety risk is realized.

The invention relates to a cable defect monitoring and risk grading method based on multi-modal depth fusion, and the method comprises the steps: obtaining visual modal data, carrying out the geometric correction and adaptive illumination enhancement processing, and obtaining the time sequence modal data of a sensor, and carrying out the standardization processing; image defects are extracted from the preprocessed visual modal data, the image defects are quantified into geometric parameters, and geometric feature vectors are formed by the geometric parameters; extracting a depth feature vector representing the deep state of the equipment from the modal data; splicing and fusing the depth feature vectors of different modes, and outputting a final fused feature vector; and splicing the final fusion feature vector and the geometric feature vector, and inputting into a multi-task decision network based on a multi-layer perceptron to obtain a cable health state and a quantitative evaluation result. Compared with the prior art, the method has the advantages that the early fault feature recognition degree is enhanced, associated information is extracted through multi-modal fusion, and health state assessment and risk grading are quantified.

The invention relates to the technical field of power grid emergency management, and discloses a transformer risk assessment and emergency equipment scheduling system, which comprises a data access module used for accessing original transformer multi-source data from a power grid resource business middle station and a data middle station through a preset data interface, the material management platform accesses the original emergency equipment internet-of-things data; and the data management module is used for receiving the original transformer multi-source data and the original emergency equipment internet-of-things data. A data collection and treatment technology is adopted, standardized treatment is performed based on multi-source heterogeneous data, and the treated data constructs a risk quantitative evaluation model, so that the technical effect of comprehensively and objectively quantitatively analyzing the short-circuit risk of the transformer is achieved. The defect that a final risk assessment result cannot comprehensively and objectively reveal the health condition of the transformer, and the risk early warning accuracy is affected is overcome.

The invention discloses a dynamic risk assessment method and system before network security insurance protection based on a PIPDR2 model, and the method comprises the steps: building and determining the PIPDR2 model, and enabling the PIPDR2 model to comprise a PIPDR2 risk assessment factor layer and a risk assessment index layer; a security policy factor module, a risk identification factor module, a security defense factor module, a security detection factor module, a security response factor module and a security recovery factor module are configured in the PIPDR2 risk assessment factor layer; in the risk evaluation index layer, corresponding risk evaluation indexes are configured for each evaluation factor module in the PIPDR2 risk evaluation factor layer based on network security level protection evaluation data; on the basis, an analytic hierarchy process (AHP) is adopted to carry out risk quantitative evaluation. According to the scheme, risk quantification, accurate premium pricing and continuous optimization are realized by integrating the equal insurance evaluation data and the AHP, and the collaborative efficiency of insurance products and safety protection is improved.

The invention discloses a noise-to-human health risk assessment and management system, relates to the technical field of noise health risk management, and aims to solve the technical problem that existing noise health risk assessment cannot be dynamically associated with quantitative assessment. The construction module is used for constructing a three-dimensional evaluation model taking the noise exposure level as the X axis, the human body physiological index fluctuation degree as the Y axis and the health hazard index as the Z axis according to the noise exposure level and the human body physiological index data set; the noise exposure level acquisition module is responsible for acquiring noise exposure data; and the health hazard index acquisition module is responsible for acquiring Z values corresponding to health hazard indexes, and inputting the acquired noise exposure data and the health hazard indexes as X-axis and Z-axis coordinates into the three-dimensional evaluation model respectively. The method has the advantages of realizing dynamic association quantitative evaluation of the health risk and improving the accuracy of the evaluation result.

The invention discloses a pumped storage power station high-fall cable tunnel fire risk assessment method and system, relates to the technical field of fire science and safety, and aims to solve the problem that an existing cable tunnel fire risk assessment method is not suitable for a high-fall scene and lacks consequence quantification capability. Comprising the following steps: identifying a hazard source through multi-mode investigation and in combination with a high-fall cable tunnel structure and the like; a fire accident tree is constructed based on the method, and the occurrence possibility of a top event and the importance of various factors are determined through qualitative and quantitative analysis; a fire consequence severity grade standard is formulated, fire parameters are obtained through CFD simulation, and the fire consequence severity is determined from the two angles of transverse spreading and longitudinal spreading; adopting a risk matrix method to complete risk quantitative evaluation; and according to the accident tree index and the evaluation result, proposing prevention and control suggestions from multiple aspects. According to the invention, accurate quantitative evaluation of the cable tunnel fire risk in the high-fall scene is realized, and a scientific basis is provided for risk prevention and control.

The present invention relates to the technical field of wellbore risk assessment, and in particular to a method for quantitatively assessing the wellbore integrity risk of a carbon dioxide injection well, comprising: constructing a hierarchical model for injection well integrity assessment; determining the weight coefficient of each risk indicator; evaluating each risk indicator separately; calculating the total risk level score and determining the risk level. The present invention can comprehensively consider a variety of risk factors and achieve a detailed assessment of wellbore integrity through the determination of a hierarchical model and weight coefficients. The acquisition of real-time data and parameter correction ensure the accuracy and timeliness of the evaluation results. In addition, the method can identify high-risk areas in advance, achieve early warning and timely governance, and effectively solve the problem of low evaluation accuracy caused by the high subjectivity of the integrity analysis and evaluation method in the prior art, which makes it impossible to conduct a more objective quantitative evaluation of the wellbore integrity risk.

The present application belongs to the technical field of power system risk assessment, and particularly relates to a new energy station wide frequency oscillation risk review assessment method based on multi-element data, steps of which comprise: for the coupling system of a new energy station-grid, data collection and data preprocessing are performed to obtain a historical data set; oscillation characteristic indexes are extracted to form an index vector; oscillation frequency source positioning analysis is performed, time-frequency spectrum energy functions are calculated, oscillation energy spectrum is obtained, main frequency energy trajectory extraction is performed, and a frequency-power cross analysis mechanism is introduced to realize accurate identification of the oscillation source device; a source-grid coupling transfer function is constructed, stability margin calculation is performed, and the stability state of the coupling system is determined; based on the index vector and the stability margin, oscillation risk level division is performed, and in combination with the source positioning result, an oscillation review spectrum is generated. The present application can realize panoramic review and risk quantitative assessment of the new energy station wide frequency oscillation problem.

This invention provides a method for calculating the risk level of online individuals based on multi-source data fusion and dynamic perception, relating to the fields of cyberspace security and social governance. By integrating online and offline behavioral data, this invention extracts behavioral frequency, violation records, trajectory features, and emotional characteristics to construct a basic risk level. This risk level is then dynamically corrected by incorporating time decay, event sensitivity, emotional polarization, and platform amplification effects, outputting the risk level of online individuals. This achieves a quantitative assessment of individual online individual risk, providing decision support for public opinion command and online risk early warning.

This application relates to a monitoring strategy adjustment method and apparatus based on quantitative risk assessment. The method involves detecting risks in a target scenario based on real-time monitoring data; obtaining quantitative parameters based on detected risk events; calculating the comprehensive risk value of a security event based on these quantitative parameters; the quantitative parameters include the prior probability of a security event occurring, the probability of the risk event occurring when the security event occurs, and the probability of historical risk events occurring; calculating monitoring strategy parameters based on the comprehensive risk value; and controlling monitoring equipment within the target scenario based on these monitoring strategy parameters. This approach solves the problems of single and inflexible monitoring strategies, enabling quantitative assessment of detected risk events, analysis of the comprehensive risk of security events, and dynamic strategy adjustment based on the quantitative assessment results, thereby improving the effectiveness of the monitoring system in preventing security problems.

This invention discloses a distributed situational awareness and safety decision-making method for manned and unmanned aerial vehicles (UAVs) in collaborative airspace. It dynamically aggregates the spatial topology and relative motion features of the local neighborhood of the aircraft using an EdgeConv graph neural network to generate a collaborative situational representation that incorporates the intentions of both manned and unmanned aircraft. A DRNN-LSTM hybrid model is used to learn temporal situational sequences, enabling a forward-looking quantitative assessment of future conflict risks. Manned aircraft commands are intrinsically embedded as conditional variables in the perception and prediction processes, making the safety-first principle an inherent constraint of the algorithm. Based on the risk assessment results and the constrained situational representation, a safety-first collaborative control command is generated through a distributed policy network, forming an autonomous closed loop of "perception-prediction-decision-execution" after execution. This invention achieves deep fusion of spatiotemporal situational awareness and risk foresight in complex airspace, internalizing manned aircraft safety as the core logic, and effectively improving the safety, adaptability, and mission efficiency of manned and unmanned aircraft collaborative operations.

This invention provides a method and system for predicting atrial fibrillation (AF) load based on intermittent PPG signals, belonging to the field of data processing technology. The method includes: training a first neural network using a first PPG signal with known AF results to obtain an AF probability prediction model; using the AF probability prediction model to predict the AF probability of a second PPG signal within a target time period and mapping it to an AF risk value; determining the AF probability at any given time based on the risk values ​​of two adjacent second PPG signals, integrating to obtain the total AF time, and thus obtaining the AF load; training a second neural network using historical AF loads from multiple time periods to obtain an AF load prediction model, used to predict the AF load for multiple future time periods. This invention achieves quantitative assessment and future trend prediction of AF load from discrete PPG signals to continuous AF load.

The invention relates to the technical field of unmanned aerial vehicle automatic inspection, provides an unmanned aerial vehicle take-off and landing platform rapid deployment and path planning method for a building curtain wall, constructs a gradient risk field model fusing geometric structures, environmental risks, visual perception and task regulations, and realizes unified quantitative evaluation of operation environmental risks. And based on the risk field, generating a global safe energy-saving path on the curved surface of the curtain wall through mixed search and path smoothing optimization guided by a risk navigation potential function. Through real-time updating of a local risk field and model prediction control, autonomous obstacle avoidance and smooth return flight of the unmanned aerial vehicle to sudden obstacles are realized. Meanwhile, a dynamic scheduling mechanism considering multi-target cost is introduced, and collaborative optimization of multiple unmanned aerial vehicles and take-off and landing platform resources is achieved. According to the method, full-link closed loop from environment modeling, global planning, real-time control to system scheduling is realized, and the safety, continuity and overall operation efficiency of automatic routing inspection of the super high-rise building curtain wall are improved.

This invention relates to the field of bid document management technology, and in particular to an automatic bid document review method and system based on natural language processing. The method includes: identifying target sections; determining historical performance and financial information; judging whether false performance exists; analyzing the complete sentences containing target keywords; calculating material scores; determining price deviation; and determining the review result. This invention achieves multi-dimensional and comprehensive review of bid documents by intelligently analyzing multiple sections such as qualifications, risks, materials, and pricing. This effectively improves the comprehensiveness and depth of the review. Utilizing natural language processing technology, it can accurately identify and extract key information from bid documents, overcoming the subjectivity and limitations of traditional manual review. Quantitative assessment based on risk level significantly improves the accuracy and relevance of risk identification. By comprehensively weighing material scores and price deviation, it significantly improves review efficiency.

This invention relates to the field of biomass pellet fuel transportation technology, and discloses a method and system for optimizing biomass pellet fuel transportation routes. By introducing mechanisms such as real-time data perception, quality risk quantitative assessment, dynamic task rescheduling, and multi-objective optimization, it can effectively cope with uncertainties in the transportation process, ensure the transportation quality and timeliness of biomass pellet fuel, significantly improve logistics efficiency, and reduce operational risks.

The invention discloses a large-scale low-altitude aircraft deviation early warning method and system, and the method comprises the steps: firstly integrating an aircraft airborne sensor and external environment data through a multi-source heterogeneous data fusion technology, and achieving the high-precision estimation of a flight state; the future three-dimensional trajectory of the aircraft is predicted based on deep learning; performing quantitative evaluation and grading by integrating multiple risk factors such as deviation distance, airspace density, meteorological conditions, airspace properties and the like; and finally, issuing a corresponding early warning or control instruction to the aircraft, a ground controller or a supervision department through an air-ground cooperative network according to the risk level. According to the invention, the dynamic deviation monitoring and early warning problem of a large-scale and high-density aircraft group in low-altitude traffic is solved, the conversion from passive lag response to active prediction and early warning is realized, and the safety, management and control precision and airspace operation efficiency of low-altitude flight are improved.

The invention relates to an AI-assisted lumbar intervertebral disc resection visual navigation device with a postoperative care monitoring function, and belongs to the technical field of medical equipment.The AI-assisted lumbar intervertebral disc resection visual navigation device comprises an AI assisting module, a visual navigation module, a postoperative care monitoring module and a data processing and transmission module, and the AI assisting module is internally provided with an image recognition model and a risk prediction model; the system is used for performing lesion recognition and positioning on image data of a patient before an operation, performing operation risk assessment in combination with multi-source information of the patient, and overlapping and displaying the operation risk in a three-dimensional model to realize real-time navigation in the operation, the AI auxiliary module realizes accurate lesion positioning and risk quantitative assessment, and the visual navigation module provides real-time guidance with the precision smaller than or equal to 0.5 mm. The postoperative nursing monitoring module achieves all-around real-time monitoring of physiological parameters, wound states and rehabilitation training, and the abnormity early warning subunit discovers the risks in time and treats suggestions in a grading mode to improve nursing pertinence.