91results about How to "Real-time" patented technology

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention discloses a construction method of a ship underwater radiation noise online prediction model. The method comprises the following steps: collecting ship body vibration signals and far-field radiation noise signals as a sample database of a radiation noise prediction model; on the basis of a differential evolution feature optimization algorithm, two dimensions of a vibration sensor and a 1 / 3 octave frequency band of prediction model input features are optimized, and an optimal feature subset is obtained to serve as input of a prediction model; establishing an integrated learning prediction model, taking a ridge regression model as a base learning device, and determining optimal parameters of each base learning device by adopting grid search and cross validation to obtain n different base learning devices; and finally, taking a mean value of prediction results of the base learners as a final prediction result of the radiation noise prediction model, and realizing online prediction of the ship radiation noise. According to the method, the utilization rate of existing test data can be improved, the degree of dependence of a prediction model on specific distribution type data is reduced, the sensitivity of the model to small changes of data distribution is relieved, and the model robustness and prediction precision are improved.

The invention discloses a road risk simulation avoidance system and method based on multi-mode Internet of Things perception, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: collecting the road condition information and road section characteristic parameters of a current road section in real time; screening out a plurality of monitoring vehicles; extracting historical driving behavior data and road condition characteristics of the plurality of monitored vehicles; constructing a historical driving behavior model, and constructing a scene model; extracting historical traffic road condition information, and extracting static feature data through the scene model; constructing a spatial feature matrix, and constructing a spatial-temporal feature matrix; performing feature weighted fusion on the spatial feature matrix and the spatial-temporal feature matrix to obtain a multi-model fusion evaluation result, and calculating a vehicle collision probability; and a vehicle owner is prompted to avoid danger and adjust steering, and an auxiliary steering avoiding function is started through the intelligent danger avoiding module. The problems that a traditional monitoring function cannot achieve risk modeling early warning and risk avoidance and lacks a multi-vehicle collision real-time prediction function are solved.

The invention discloses an atmosphere display method and system based on text data information analysis of video streaming, and the method comprises the following steps: emotional atmosphere configuration: configuring the display effects of emotions and corresponding atmosphere devices, and building a mapping relation between the emotions and the corresponding atmosphere expression effects through the configuration; acquiring and playing video stream data in real time; extracting and analyzing subtitles or bullet screen texts from the video stream data; preprocessing the extracted subtitles or bullet screen texts; performing emotional emotion analysis on the preprocessed subtitles or bullet screen texts, wherein an identified emotional emotion classification result comprises two numerical values, namely an emotional category and an emotional weight; emotion and atmosphere conversion is carried out, and an emotion analysis result is converted into expression data capable of being recognized by atmosphere display; and according to the emotion and atmosphere conversion result, obtaining emotion expression data corresponding to the emotion analysis result of the bullet screen content, and driving an atmosphere device to perform corresponding display.

The invention discloses a low-altitude flight capacity sensing method and system based on operation identification, and the method comprises the following steps: obtaining the data information of a predicted flight path aircraft, and carrying out the discretization of an airspace; performing airspace flight corridor division processing based on an airspace discretization processing result and predicted flight path aircraft data information; based on the airspace flight corridor division processing result and the airspace opening and closing time, the waiting duration of the aircraft is determined, and a passable airspace flight corridor is judged; determining an affected range of the aircraft and selecting an airspace flight corridor based on the passable airspace flight corridor determination processing result and the predicted flight path aircraft data information; the real-time middle and long distance perception of the aircraft to the aircraft running around is realized, the operation safety problem is solved, the flight amount can be adjusted in advance from a larger range, and the operation risk caused by too high local flight density can be avoided.

The application discloses a wind turbine load rapid evaluation method for wind farm collaborative regulation, and relates to the technical field of wind turbine safety regulation. The specific implementation scheme is as follows: standard pulse instructions are applied to the wind turbine to extract vibration characteristic parameters and load oscillation initial amplitude; based on the rate of the candidate regulation instruction and the environmental wind speed, a transient excitation intensity function is set and effective excitation equivalent is calculated; according to the ratio of the effective excitation equivalent and the reference excitation equivalent, the relative impact intensity and the load oscillation amplitude are determined; the envelope function is constructed by combining the vibration characteristic parameters of the wind turbine, and the earliest safety execution time required for attenuation to the safety threshold is solved; the executability of the regulation instruction is determined by comparing with the maximum waiting time set by the rated blade bending moment load, and the load evaluation is completed; the collaborative controller can dynamically and accurately arrange the instruction sequence, and the overall safety of the wind turbine is improved.

The application discloses a research report generation method based on natural language processing and relates to the technical field of intelligent document analysis and generation, which comprises the following steps: version storage of source documents and generation of timestamp snapshots; paragraph-level and sentence-level segmentation of the timestamp snapshots and extraction of evidence segments carrying original text positioning indexes; core claim extraction of the evidence segments to generate conclusion units containing claim texts and confidence; establishment of the association relationship between the conclusion units and the evidence segments; generation of chapter texts according to the associated conclusion units and calculation of evidence segment coverage; and structural adjustment and regeneration of the texts when the evidence segment coverage is lower than a preset threshold. The method realizes evidence traceability, multi-type semantic association and automatic optimization based on coverage feedback.

The embodiment of the invention provides a semiconductor wafer Mask ultra-high-cleanliness mute intelligent storage warehouse, and relates to the technical field of semiconductor manufacturing auxiliary equipment, the semiconductor wafer Mask ultra-high-cleanliness mute intelligent storage warehouse comprises a stacking station main body, a storage box body and a sliding guide mechanism, a storage cavity used for containing the storage box body is formed in the stacking station main body, and a high-efficiency air filter is arranged at the top of the storage cavity; the inner sides of the rims are in interference fit with flanges on the two sides of the I-shaped sliding rail, the magnitude of interference gradually changes in the length direction of the sliding rail, the storage box is of a double-layer hollow structure, a miniature constant-temperature and constant-humidity unit is installed on the inner wall of the box, and an intelligent monitoring module is further arranged on the stacking station body. And the intelligent monitoring module comprises a dust sensor arranged in the storage box body and a central controller arranged on the stacking station main body. The sliding mechanism has the effects of sliding stability, ultrahigh cleanliness and low noise.

A navigation light adjustment method and system are disclosed, relating to the field of navigation light technology. The method involves acquiring vessel information and visibility for all vessels within a predetermined water area; determining the vessel density within the predetermined water area based on the vessel information; dividing the predetermined water area into multiple adjustment zones based on the vessel density; calculating the comprehensive navigation condition value for each vessel within each adjustment zone based on the visibility and vessel density; obtaining the navigation routes for each vessel within each adjustment zone; and adjusting the parameters of the navigation lights for each vessel within each adjustment zone based on the navigation routes and the comprehensive navigation condition value. Implementing the technical solution of this application allows for analysis of the surrounding navigation environment and timely adjustment of navigation lights, thereby reducing the probability of vessel collisions.

This invention discloses a comprehensive management method for transparent inspection of power grid materials based on the Internet of Things (IoT), belonging to the field of power material management technology. The method includes the following steps: deploying a three-axis vector probe array at the material entry channel to collect multi-point spatiotemporal signals and calculate the field strength distribution, generating an electromagnetic disturbance spatiotemporal matrix, and outputting a radio frequency identification (RFID) tag resonance drift risk matrix. This invention models electromagnetic disturbance sources using a three-axis vector probe array, combines resonance registration fingerprinting and frequency band compensation to achieve dynamic correction of RFID frequency drift, and integrates phase-encoded backscattering and strain acoustic features to ensure identification consistency. By utilizing optical time reference synchronization and phase-programmable metasurfaces to reconstruct the near-field environment, an adaptively adjustable identification space is constructed, improving the identification stability and traceability reliability of power grid materials under high-disturbance environments, thus breaking through the bottlenecks of traditional identification technologies.

This invention relates to an intelligent concrete vibratory compaction device with multi-source sensing and adaptive feedback capabilities. It includes a compaction execution module, an intelligent decision-making and control module, a multi-source sensing module, an intelligent reminder and early warning module, an energy and power management module, and a data storage and communication module. The multi-source sensing module is integrated into key parts of the vibratory compactor to collect multi-source physical signals in real time. The intelligent decision-making and control module has a built-in signal fusion processing algorithm and equipment status recognition model, which performs real-time fusion analysis on the collected signals and identifies the working status, generating adaptive control commands based on the recognition results. The energy and power management module dynamically adjusts the output power according to the commands. The data storage and communication module encrypts and stores operation data and supports exchange with a remote management platform. This invention, through multi-source sensing and intelligent decision-making, achieves real-time monitoring, status judgment, automatic prompts, and data recording of compaction operations, improving the reliability, traceability, and management efficiency of construction quality.

The application provides a data acquisition device, method and industrial computer based on switch technology, the data acquisition device comprises a clock module, a sensor interface module, a network switch and an upper computer; the sensor interface module comprises a plurality of sensor interfaces, which are used for connecting digital signal sensors or analog signal sensors; the clock module comprises a clock generator and a clock board, the clock generator is used for generating a digital clock signal, and the plurality of sensor interfaces are connected to the clock generator through network cables respectively; the clock generator is electrically connected to the clock board, the network switch is a serial server, the upper computer is a user computer, and the network switch is used for establishing interactive communication. The application realizes a multi-channel, multi-speed, high-integration and high-subsystem interchangeability data acquisition system based on common network protocols and synchronous clock hardware.

The invention relates to an industrial internet data compensation method, which comprises the following steps of: monitoring industrial internet data, performing real-time data anomaly detection by utilizing a machine learning algorithm, and processing at a data source in combination with an edge computing technology, so that delay and bandwidth consumption are reduced; an algorithm for adaptively adjusting a compensation strategy according to data characteristics and historical trends is developed, the compensation accuracy and efficiency are improved, multi-dimensional integrity verification is carried out on data in combination with methods of checksum, hash functions and digital signatures, and more comprehensive safety guarantee is provided; according to the data compensation method provided by the invention, the overall change trend of real data and the accuracy of a single data point can be accurately restored, enough precision is provided, and meanwhile, the method can be applied to a plurality of industrial internet scenes and shows good performance.

This invention provides a real-time adaptive 3D measurement system and method for objects based on microstructured light. The system includes: multiple optical measurement units arranged in an equidistant circular array along the circumference on a ring mounting frame and moving synchronously with a lifting and adjusting mechanism; multiple measurement posture acquisition units, a reference posture monitoring unit, a posture compensation unit, a control unit, a data fusion module, and a quality analysis module. According to this invention, the multiple optical measurement units arranged in a circular array enable multi-view synchronous measurement of the workpiece, avoiding the time consumption and reference conversion errors of traditional indexing measurement methods. The real-time posture monitoring and dynamic compensation mechanism effectively suppresses measurement errors caused by factors such as mechanical vibration and temperature drift, reducing posture-related measurement errors. The system achieves automation, real-time measurement, and high precision in 3D measurement, improving measurement efficiency and accuracy.

The invention discloses a hot-rolled plate structure toughness prediction method and system combined with an impact energy change curve, and belongs to the technical field of metal material structure performance evaluation.The method comprises the steps that an impact test is conducted on a hot-rolled plate sample, and an impact force-displacement curve and an energy absorption-time curve are collected in real time; preprocessing the impact force-displacement curve and the energy absorption-time curve, and extracting characteristic parameters; acquiring hardness distribution data of the hot-rolled plate sample, acquiring hardness gradient data by using the hardness distribution data, and taking the hardness gradient data as a correction factor of the curve characteristics; constructing a structure toughness prediction model based on the characteristic parameters and the correction factors, and predicting the toughness grade and the structure state of the hot-rolled plate; and outputting the toughness grade and the structure state of the hot-rolled plate. According to the method, the limitation that traditional metallographic detection consumes too long time and an impact test only provides a single value is solved, double evaluation results can be obtained within 30 seconds after the impact test, and the method is suitable for rapid online quality monitoring and process adjustment of hot rolling production.

The invention discloses a multi-node data fusion sliding carrier phase smoothing pseudo-range measurement method, and belongs to the technical field of navigation system pseudo-range measurement. The method comprises the following steps that: a node A simultaneously performs microwave bidirectional comparison data transmission with each peripheral node in a space division multiplexing manner, and obtains a bidirectional comparison carrier phase smoothing pseudo-range measurement value of each link in a window sliding manner; and the node A completes data fusion of each pseudo-range measurement value by interpolating a weighted filter coefficient to obtain a carrier phase smoothing pseudo-range measurement result after multi-node data fusion. According to the invention, the idea of fusion of communication transmission and navigation ranging is combined, based on a conduction integration system design and a capability of realizing conduction integration by multiplexing a frame structure leader sequence, a multi-node data fusion sliding carrier phase smoothing pseudo-code ranging method is adopted, and multi-node sliding carrier phase smoothing pseudo-range measurement values are fused. Advantages of respective algorithms can be exerted, algorithm insufficiency is avoided, and ranging and time synchronization precision is improved.

The application provides a coal mining face equipment pose real-time calculation method, system, device and medium. At present, there is a lack of a measurement calculation method which can guarantee measurement accuracy, automatic real-time data calculation and cover the realization of the measurement of the coal mining face equipment group in the industry. To solve the above problems, the method of the application performs absolute pose real-time calculation and automatic processing on the coal mining machine, the scraper conveyor and the support during the mining of the coal mining face, has real-time performance, first measures the initial coordinate position and attitude of the inertial navigation device of the coal mining machine, measures and corrects the real-time position and attitude data of the inertial navigation device, so that the data is more accurate, finally obtains the absolute pose of the coal mining face equipment group, and finally outputs the absolute pose parameters of the coal mining face equipment group, thereby providing conditions for realizing the automation and intelligent operation of the coal mining face equipment group.

The invention discloses an intelligent air switch control monitoring method based on lora communication, and relates to the technical field of air switch control monitoring, and the method comprises the following steps: carrying out the continuous collection of a voltage sampling sequence and a current sampling sequence of an air switch, generating a variable-amplitude density curve, a reversal interval period value and a waveform pole delay track, forming a continuous feature sequence, and carrying out the inversion of the interval period value and the waveform pole delay track; whether the sampling signal has regular distortion under the influence of the electromagnetic interference is judged; and under the condition that the sampling signal has regular distortion, extracting a distortion confirmation point set from the continuous feature sequence, and calculating a feature deviation factor, a trend residual vector and a stable window convergence degree based on each distortion confirmation point to form an abnormal form mapping group. According to the invention, the problem that the structural distortion of the sampled data under electromagnetic interference is difficult to identify is solved, distortion perception and state closed-loop regulation and control based on the feature sequence are realized, and the monitoring accuracy and the system safety are improved.

The application discloses a kind of multi-obstacle prediction navigation obstacle avoidance methods based on single-line laser radar, first, the obstacle point cloud data detected by single-line laser radar is collected, the data is filtered and classified, and the obstacle point cloud array is obtained;Shape fitting is carried out on the classified obstacle point cloud array to realize the detection of obstacles;Data correlation is carried out on the obstacles detected in the adjacent two frames of obstacle point cloud arrays, the current motion state of each obstacle is obtained, and its historical trajectory is recorded to realize the tracking of obstacles;The trajectories of multiple obstacles in the future are predicted, the results of global path planner are read and segmented into multiple local paths;In each local path, map-free end-to-end strategy navigation is realized;The method of the application is applied to floor cleaning robots, service robots and AGVs and other mobile robots, improves the safety of robot obstacle avoidance, and has the characteristics of real-time and low cost.

The invention discloses a real-time auditing method for privacy protection of data cross-border flow. The method comprises the following steps: initializing an auditing mechanism, and generating public and private key pairs of domestic enterprises and overseas enterprises; calculating an exit data encryption key and an entry data encryption key; the domestic enterprise encrypts the outbound data through the outbound data encryption key to generate an outbound data ciphertext, and the review institution performs secret state review on the outbound data ciphertext; the overseas enterprise cooperates with the review institution to decrypt the review result, and obtains a partial decryption result of the review institution on the outbound data ciphertext when the review result is passed, and the partial decryption result and partial decryption of the overseas enterprise on the outbound data ciphertext are aggregated to obtain outbound data; the overseas enterprise encrypts the entry data through the entry data encryption key to generate an entry data ciphertext, and the review institution performs secret state review on the entry ciphertext; and the domestic enterprise cooperates with the review institution to decrypt the review result, and obtains a partial decryption result of the review institution on the entry data ciphertext when the review result is passed, and the partial decryption result and partial decryption of the domestic enterprise on the entry data ciphertext are aggregated to obtain entry data.

This application provides a method, apparatus, device, and storage medium for detecting abnormal battery charging and discharging. The method includes: performing pressure calibration on the battery's clamping state to obtain calibrated clamping state parameters; correcting the original battery data collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data; performing differential calculation between the reference characteristic data and real-time detection data during the charging and discharging test to determine dynamic offset parameters; and aligning the dynamic offset parameters with a standard charging and discharging curve to determine abnormal battery state parameters. By performing pressure calibration on the battery clamping state to obtain calibration parameters and correcting the original data to generate reference characteristic data, and then performing differential calculation and database comparison with real-time detection data, data deviations caused by poor clamp contact can be effectively eliminated, thereby accurately identifying abnormal battery states and improving the reliability of charging and discharging test data and the real-time nature of anomaly determination.

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The application relates to a power distribution network harmonic index data evaluation, transmission method and system, the method comprising: after obtaining harmonic index data, directly judging whether a harmonic abnormal event occurs in real time according to the harmonic index data, so that the uploaded harmonic abnormal event has real-time performance. The harmonic index data is fitted to obtain fitting coefficients and is uploaded, so that a superior terminal such as a harmonic analysis platform can restore the harmonic index data at any time according to the fitting coefficients. Meanwhile, the application also screens the to-be-transmitted data in the harmonic index data, and only uploads the to-be-transmitted data, thereby reducing the data transmission amount and enabling the superior terminal to verify the restored harmonic index data through the to-be-transmitted data.

This invention discloses a method for optimizing river-sea intermodal transport routes, belonging to the field of intelligent logistics and supply chain optimization technology. It solves the problem that existing technologies fail to consider the special loading and unloading requirements of steel cargo, leading to practical difficulties in implementing the proposed solutions. The method includes: constructing a river-sea intermodal transport semantic network graph incorporating port loading and unloading attributes; establishing an optimization model with the goal of minimizing total cost and time, and with the steel loading and unloading sequence as the core constraint, ensuring that vessel loading adheres to the "last unloading, first loading" principle; employing an improved genetic algorithm for solution, which uses a two-layer encoding method combining path layer and loading / unloading sequence layer to deeply integrate loading and unloading operation plans into the route optimization process; and finally outputting the optimal transport route and a corresponding refined loading and unloading operation plan. This invention significantly improves the feasibility of the optimization results in actual operations by quantifying realistic factors such as loading and unloading equipment capacity and time windows, effectively avoiding problems such as vessel congestion and operational conflicts.

This invention relates to an intelligent scheduling method and system for new energy logistics fleets that coordinates dynamic task allocation and energy replenishment. The invention constructs a multi-dimensional disturbance vector by collecting parameters such as the time window offset, spatial dispersion, load mutation rate, and emergency level transition of new tasks. Within a sliding time window, Shannon entropy is used to quantitatively characterize the uncertainty of the task flow, distinguishing between high-disturbance and steady-state regions. The invention adaptively adjusts the energy constraint set based on the task uncertainty state, dynamically relaxing the lower energy limit in high-entropy regions and compensating for the impact of battery aging and degradation in low-entropy regions. It achieves globally optimal vehicle-task-energy ternary matching under the constraints of path continuity and task uniqueness using mixed-integer linear programming, and realizes online adaptive correction of the entropy judgment threshold based on actual execution feedback. This invention improves the responsiveness and energy adaptability to complex dynamic scenarios, effectively reduces the impact of uncertainty, and optimizes operational efficiency.

This application discloses a method, apparatus, device, and medium for detecting loitering events. In the embodiments of this application, it is first determined whether the target object has moved based on the first detection box in the first image and the second detection box in the second image. After determining that the target object has moved, it is further determined whether the target object is moving inside or outside the detection area, thereby judging whether the target object is loitering. This effectively improves the accuracy of loitering event detection and reduces the false alarm rate. Furthermore, this application can determine whether the target object is loitering based on whether it moves back and forth inside or outside the detection area, thus compensating for the blind spots of existing loitering detection algorithms. Specifically, it compensates for the problem that loitering detection algorithms based on dwell time fail when the target object loiteres at the edge of the detection area. Moreover, the technical solution protected by this application has reliability, real-time performance, and generalizability, meeting the characteristics of dependability.

The present invention discloses a combined navigation method based on deep learning and improved Kalman filtering, which belongs to the field of artificial intelligence combined with combined navigation technology, and is used for combined navigation data processing, including using GPS / INS combined navigation when GPS signal is available, using the angular velocity and acceleration output by the inertial measurement unit IMU to calculate and estimate the attitude, position and speed of the object, and then fusing with GPS data to obtain the final combined navigation result; when GPS signal is unavailable, using a neural network model to predict the pseudo position and pseudo speed when GPS signal is unavailable and inputting an adaptive square root volume Kalman filter for data fusion, and obtaining the predicted position and speed when GPS signal is unavailable. The present invention improves the prediction accuracy of the neural network model, optimizes the robustness of the filtering algorithm, and has real-time performance; reduces the impact of noise on the system, and realizes the data fusion and high-precision solution of GPS and INS.

The invention relates to the technical field of electric power engineering construction, in particular to a disassembly and assembly construction planning method and system for an angle steel iron tower, electronic equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data in an angle iron tower construction scene and carrying out standardized preprocessing to obtain structured construction data; inputting the structured construction data into a field fine-tuning large model to execute a construction demand analysis operation, and generating a task decomposition list; performing resource scheduling and progress planning processing on the task decomposition list, and constructing a dynamic construction plan; and the linkage construction knowledge graph performs risk identification processing on the dynamic construction planning plan, and performs deviation analysis processing on the dynamic construction planning plan based on the current construction data to generate an adjustment suggestion. Through the mode, the technical problem that the accuracy and the real-time performance of a planning scheme are difficult to consider in the practical application of angle steel tower construction in the prior art is solved, and the scientificity, the reliability and the overall engineering benefit of construction planning are improved.

This application provides a method and apparatus for linking supplier profiles with procurement activities. The method includes acquiring multi-source heterogeneous data related to a target supplier from multiple data sources. Upon detecting a change in any data point within this heterogeneous data set, the method triggers a real-time recalculation of the target supplier's health score to obtain a real-time health score. This real-time health score is then used to determine and execute the control strategy for the target supplier within the procurement transaction system, thereby achieving dynamic control over procurement activities involving the target supplier. This method enables real-time supplier status awareness, constructs a hierarchical and categorized differentiated control system, effectively solves the problems of limited control methods and delayed risk response, and significantly reduces compliance and financial risks.

The invention discloses a new energy access risk assessment method and device, electronic equipment and a storage medium. The method comprises the steps of S1, collecting multi-source heterogeneous data in real time; s2, through multi-source heterogeneous data fusion, calculating a new energy multi-station short-circuit ratio in real time; s3, extracting and constructing engineering practical risk feature vectors, wherein the engineering practical risk feature vectors comprise transient overvoltage features and corresponding network structure risk features; s4, constructing a nonlinear dynamic mapping model between the new energy multi-station short-circuit ratio and the transient overvoltage key features by using a deep learning model; s5, according to the new energy multi-station short-circuit ratio calculated according to the data collected in real time, risk assessment and grading are carried out through a nonlinear dynamic mapping model; and S6, when the risk level reaches an early warning threshold value or the transient overvoltage predicted value exceeds a limit value, the system automatically triggers early warning and generates a decision suggestion. According to the method, the efficiency, the accuracy and the engineering applicability of risk assessment are remarkably improved, and the automation level and the intelligence level are improved.