57results about How to "Realize complementary advantages" patented technology

The invention discloses a private domain user management method and system for multiple touch channels, a medium and a program product, and the method comprises the steps: obtaining the historical behavior data of a user in each touch channel; identifying the behavior relevance of the user among the arriving channels; constructing an inter-channel behavior mapping relation of migration paths of the user among the touch channels based on behavior relevance; according to the behavior mapping relation between the channels, the overlapping ratio of user behaviors between the reaching channels, the difference of conversion effects and the overlapping ratio of time sequences are calculated; based on the overlapping rate, the difference and the coincidence degree, determining the reaching channel complementation degree; grouping and clustering the reaching channels according to the reaching channel complementation degree to obtain a plurality of channel collaboration units; and the optimal time difference of the arriving users in each channel cooperation unit is calculated, and the marketing resources are allocated among the arriving channels based on the optimal time difference, so that the accuracy of private domain operation management is improved.

The invention provides a two-phase flow measurement method based on differential pressure capacitance sensing and data knowledge driving. The two-phase flow measurement method comprises the steps that throat extension design is conducted on a standard Venturi tube; a multi-layer capacitive sensor array is embedded into a long-throat-neck Venturi tube, various transmitters are installed on the tube, and differential pressure, temperature and pressure data of the various transmitters are synchronously acquired by using a multi-channel data acquisition module; a configured capacitive sensor array excitation-measurement module is utilized to synchronously acquire capacitor array data; developing a gas-liquid two-phase flow calculation model based on data knowledge driving, and packaging the gas-liquid two-phase flow calculation model in a two-phase flow calculation module; capacitance, differential pressure, temperature and pressure data are input into a two-phase flow calculation module, and field learning is carried out by utilizing actually measured data, so that gas-liquid two-phase flow on-line real-time calculation is realized. The problem that time and space of data measured by traditional tandem type multiple sensors are inconsistent is solved, and the problem that when a traditional venturi tube is used for measuring two-phase flow, the phase volume fraction is lost is solved.

The invention discloses a short-term load prediction method considering error compensation and modal decomposition. The short-term load prediction method comprises the following steps: S1, acquiring load data and characteristic data of a power system; s2, carrying out the preliminary decomposition of the load data of the power system based on ICEEMDAN, and obtaining an intrinsic mode function; s3, training a first network model TCN-GRU by taking the intrinsic mode function and the feature data as input to realize initial short-term load initial prediction; s4, comparing the preliminary short-term load prediction result with a target load, constructing an error sequence, and reducing the unpredictability of the error sequence by adopting AVMD to obtain a variational mode function; and S5, training the second network model TCN-GRU by taking the variational mode function as input, and correcting the initial short-term load initial prediction result to obtain a final short-term load prediction result. The problems that in the prior art, a single prediction model is insufficient in precision, and the effect of an error sequence on the aspect of improving the load prediction precision is not fully utilized are solved.

The invention provides a traffic prediction method and system, a server and a readable storage medium. The method comprises the following steps: extracting spatial features related to a road network from individual trajectory data collected by a client; the features are processed by using a graph convolutional network and embedded into a global model to capture spatial dependency of a road network. Meanwhile, in combination with time information, spatio-temporal features are generated through a graph convolutional network and a time attention mechanism, and a global model is updated to reflect the latest traffic state. The client constructs a personalized model based on an individual trajectory, adds noise through a differential privacy technology to protect user privacy, and then uploads model parameters to the central server. And the server adopts a federal learning strategy to carry out weighted averaging and aggregation on the model uploaded by the client to generate an updated global model. According to the invention, the driving time prediction accuracy is improved by using federated learning and the graph convolutional network, and meanwhile, the user privacy and the real-time updating of the model are ensured.

The invention discloses an adaptive timestamp coding enhanced complex equipment incomplete state monitoring data time sequence interpolation method, and relates to the technical field of industrial sensing data processing, and the method comprises the steps: carrying out the standardization of obtained online incomplete state monitoring time sequence data, carrying out the normalization processing of online timestamp sequence data, and carrying out the normalization processing of the online timestamp sequence data; then, a timestamp prior feature coding module is used for outputting online timestamp prior coding features, and effective utilization of timestamp information in the monitoring data in the incomplete state in an interpolation task is achieved; using the trained timestamp-local interpolation model joint framework to obtain an online interpolation result of the monitoring data in the incomplete state; according to the scheme, advantage complementation between a locality time sequence interpolation model and a timestamp probabilistic interpolation model is achieved, the accuracy and robustness of complex equipment state monitoring data time sequence interpolation are effectively improved, and the method can be widely applied to a data enhancement link in various complex equipment operation state analysis tasks.

The invention relates to the technical field of concrete materials, and particularly discloses a composite mineral admixture, a preparation method thereof and concrete. The preparation method of the composite mineral admixture comprises the following steps: mixing and modifying dolomite powder, gypsum and calcium hydroxide to obtain pre-modified dolomite powder; then adding hexadecyl trimethyl ammonium chloride and octylphenol polyoxyethylene ether to obtain modified dolomite powder; and adding nickel slag, metakaolin, desulfurized fly ash and a grinding aid, and grinding to obtain the composite mineral admixture. The concrete prepared from the composite mineral admixture is excellent in compressive strength, shrinkage resistance, permeation resistance and the like, and the strength and durability of the concrete can be remarkably improved.

The invention relates to the technical field of oilfield development, and discloses a multi-level sublithology intelligent interpretation model construction method and application thereof, and the method comprises the steps: carrying out the preprocessing of an original logging curve; performing multi-level sub-wavelet decomposition according to the preprocessed original logging curve to obtain a plurality of wavelet components with different frequencies; according to the multiple wavelet components with different frequencies, logging curve reconstruction is carried out, and a first reconstructed logging curve and a second reconstructed logging curve are obtained; according to lithology data of a target rock area, an original logging curve, a first reconstruction logging curve and a second reconstruction logging curve, through an integration algorithm and a training model, learning mapping relations between the original logging curve and a composite sand body, between the first reconstruction logging curve and a single sand body and an argillaceous interlayer, and between the second reconstruction logging curve and a thin reservoir and a physical interlayer; and a multi-level lithology intelligent explanation model is obtained. According to the method, well logging fine interpretation of lithology of different thicknesses can be realized, particularly accurate identification of lithology of centimeter-level thickness can be realized, and the well logging interpretation precision is improved.

The invention belongs to the technical field of generators, and particularly relates to a hybrid nano generator and a power management circuit thereof, comprising a shell, and an electromagnetic power generation unit, a friction power generation unit and a piezoelectric power generation unit which are located in the shell; and a corresponding power management circuit. The interior of the shell is connected with the top end and the bottom end in a penetrating mode through a glass fiber connecting shaft. A friction power generation unit, an electromagnetic power generation unit and a piezoelectric power generation unit are sequentially mounted at the bottom end, opposite to the shell, of the glass fiber connecting shaft; the piezoelectric power generation unit comprises at least four cantilevers and a disc, one end of each cantilever is fixed on the disc, and a mass block is placed on the upper surface of the other end; the center of the disc is connected with a glass fiber connecting shaft, and the inner sides of the upper surfaces of the four cantilevers are covered with PZT materials. According to the hybrid nano generator, vibration energy in the environment can be converted into electric energy, and energy is supplied to wireless sensor network nodes.

The utility model provides a light offshore booster station and offshore hydrogen production and hydrogenation fusion platform. A fusion platform is arranged in an edge area of an offshore wind power plant; the fusion platform is provided with five layers, the first layer to the second layer of the fusion platform are hydrogen production function areas, and the third layer to the fifth layer of the fusion platform are power transformation function areas; a cable shaft is jointly arranged on the hydrogen production functional area and the power transformation functional area of the fusion platform, and a power supply cable, a hydrogen pipeline and a cooling water pipe are arranged in the cable shaft; the power supply cable is connected with the hydrogen production functional area and the power transformation functional area, the power transformation functional area is provided with a main transformer radiator and a main transformer body below the fifth layer of the fusion platform in a staggered manner, and the power transformation functional area is provided with a storage battery chamber on the fourth layer of the fusion platform. The fusion platform integrates the functions of offshore hydrogen production and hydrogenation, can effectively relieve the contradiction between the rapid increase of offshore wind power and the slower construction speed of a power grid without additionally using a sea land, solves the problem of offshore wind power consumption, and improves the utilization rate of wind energy.

The invention discloses a proton exchange membrane fuel cell performance degradation prediction method based on deep learning, and belongs to the technical field of automation. According to the method, dynamic working condition degradation data obtained by a fuel cell system in a period of continuous time is taken as an original input signal, a Pearson's correlation coefficient method is adopted for processing, and then an output voltage variable in the preprocessed data is smoothed by adopting wavelet transform; and decomposing a single batch of input by the trained multi-scale decomposition module, and inputting the long-term modal sequence and the short-term modal sequence of each batch into the trained performance degradation prediction model to obtain a final fuel cell degradation data prediction result. By utilizing the method, long-term modal and short-term modal data characteristics of the proton exchange membrane fuel cell under the dynamic working condition can be deeply researched, and the prediction accuracy of the degradation data is powerfully enhanced.

The invention discloses a few-sample fine-grained classification method fusing FRN and Long-LIP models, and the method comprises the steps: collecting fine-grained image data, and making a data set; building an FRN (Field Register Networking) and Long-LIP network model; training the FRN network model, storing the optimal parameters of the FRN network model in the training process, and naming the file of the FRN network model as best.pth; inputting a preprocessed to-be-detected image into the trained FRN network model, inputting the image into the Long-LIP network model to obtain a prediction result, and carrying out weighted summation on the two prediction results. Compared with the prior art, the method has the advantages that deep reconstruction is carried out by adopting the FRN feature map to replace a traditional method for aggregating categories by using similarity average, and an ECA attention mechanism is introduced into the FRN network, so that the extraction capability of the model on key features is improved, and a finer and more accurate prediction result is generated; fRN and Long-LIP models are fused, and a good effect is achieved on a plurality of small-sample fine-grained classification tasks.

The invention relates to a low-slow small target identification method based on radar and visual data fusion, and the method comprises the steps: S1, setting a radar system for collecting radar data, and a camera system for collecting visual data; s2, synchronizing data acquisition of a radar system and a camera system, and respectively obtaining corresponding radar point cloud data and image data; s3, after radar point cloud data obtained by the radar system and image data of the camera system are preprocessed, data fusion is carried out to obtain fused data; and S4, inputting the fusion data into a pre-trained neural network model, and carrying out detection and classification of low-slow small targets.

The invention provides an IMU (Inertial Measurement Unit)-based multi-sensor fusion positioning method and device and a storage medium. The positioning method comprises the following steps: obtaining an IMU pre-integration result based on a constructed IMU pre-integration model; adding the system positioning result at a moment and the IMU pre-integration result to obtain a system positioning predicted value at the current moment; obtaining original data of the plurality of sensors, and performing preprocessing and synchronization to obtain observation values of the plurality of sensors at the current moment; independently filtering the observation values of the plurality of sensors and the system positioning predicted value at the current moment by using the constructed filtering subsystem so as to obtain updated system positioning predicted values of the plurality of sensors at the current moment; according to the system positioning observation values of the plurality of sensors and the confidence coefficients of the plurality of sensors at the previous moment, updating the confidence coefficients of the plurality of sensors at the current moment; and according to the confidence of the plurality of sensors at the current moment, carrying out weighted summation on the updated system positioning predicted values of the plurality of sensors to obtain a system positioning result at the current moment.

The invention discloses a bio-based high-strength and high-toughness polylactic acid / polyamide 11 alloy material and a preparation method thereof in the field of high polymer materials. The bio-based high-strength and high-toughness polylactic acid / polyamide 11 alloy material comprises the following raw materials in percentage by weight: 60-80% of poly-L-lactic acid, 13-37% of polyamide 11 and 3-7% of a reactive compatilizer. The reactive compatilizer is obtained by reacting amino-terminated polyamide 11 and hydroxyl-terminated poly (dextrolactic acid) with an epoxy chain extender, the size of a polyamide 11 phase can be effectively reduced by adding the reactive compatilizer, polylactic acid is subjected to heterogeneous nucleation on a polyamide 11 crystal, and the spherocrystal size of polylactic acid is remarkably reduced, so that the polylactic acid-poly (dextrolactic acid) composite material is obtained. And the polylactic acid / polyamide 11 alloy material shows excellent ductility and crystallization capability. According to the invention, the spherocrystal size and density of the polylactic acid are regulated and controlled by regulating and controlling the size and phase structure of the polyamide 11, the obtained alloy material overcomes the defect of poor toughness of the polylactic acid, the production process is simple, and the used polylactic acid and polyamide 11 are bio-based materials and meet the requirements of green development.

This invention belongs to the field of soil conditioner technology, and relates to a method for preparing a soil conditioner, the resulting soil conditioner, and its application method. The preparation method includes the following steps: mixing brewing sludge and biogas residue with sorghum straw particles to obtain a mixture; adjusting the carbon-to-nitrogen ratio to 20:1 to 40:1; adding an organic material composting agent; aerobic fermentation at 55℃ for ≥15 days; turning and cooling the pile at temperatures >65℃; and fermenting for 15-30 days after cooling to below 50℃ to obtain the soil conditioner. By utilizing the nitrogen source of the biogas residue to neutralize the high-carbon characteristics of the straw, and further enhancing the synergistic decomposition of the two by the organic material composting agent, this invention promotes rapid aerobic fermentation and stable high-temperature fermentation for over 15 days. This ensures harmlessness while avoiding the high-temperature burning of beneficial bacteria in the composting agent and the disruption of the synergistic decomposition of the biogas residue and straw, achieving complementary advantages between the biogas residue and straw, and producing a loose, breathable, nutrient-balanced, and safely composted soil conditioner.

The invention relates to a residential energy system low-carbon scheduling method considering a hydrogen-terrestrial heat coordination effect. The residential energy system low-carbon scheduling method comprises the following steps: step (1), constructing a basic physical model for representing energy flow and equipment physical characteristics in a residential energy system; (2) converting the basic physical model into a coupling constraint model based on a dynamic coupling constraint between energy supplies of a set type in the residential energy system; step (3), on the basis of the coupling constraint model, according to a set scheduling period, constructing a low-carbon scheduling objective function to obtain a complete optimization model; and step (4), solving the optimization model to obtain an operation strategy of each device in the residential energy system in the set scheduling period.

The invention relates to a bi-crosslinking cyanate ester-epoxy blended adhesive and a preparation method thereof, and relates to the technical field of high polymer materials, the adhesive comprises the following components in parts by weight: 45-85 parts of cyanate ester resin, 5-35 parts of epoxy resin, 0-20 parts of a reactive diluent, 2-6 parts of a bi-crosslinking curing agent and 0-50 parts of a functional filler, the bi-crosslinking curing agent has the following structure: R is alkyl, aliphatic amine or aryl; r'is a primary amino group and / or a secondary amino group, R ''is-C = N and / or-OCN, m is an integer from 1 to 3, and n is an integer from 1 to 2. By adding the double-crosslinking curing agent containing the amino group and the cyano group, the technical problems of high curing temperature, large stress after curing, poor temperature resistance of epoxy resin and the like of the existing cyanate ester resin system are solved, and the curing temperature, the bonding strength and the temperature resistance of the adhesive are balanced. The adhesive can be cured at the temperature lower than 90 DEG C, and has high glass transition temperature, low thermal weight loss and excellent adhesive property.

The application relates to an independent hybrid frequency modulation energy storage power station capacity configuration method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the field of electric power energy storage, and can improve the resource utilization rate of a hybrid energy storage system. The method comprises the following steps: acquiring total power control instructions of a hybrid energy storage system in a target frequency modulation scene and operation characteristic parameters of each energy storage unit; based on the operation characteristic parameters and the total power control instructions, determining first charge and discharge instructions and second charge and discharge instructions of different energy storage units according to a real-time power distribution strategy; based on the first charge and discharge instructions and a life attenuation model, determining a device replacement cycle of an energy type energy storage unit; based on system frequency modulation response and periodic resource consumption, determining a comprehensive performance model in a full life cycle; under the premise of meeting preset operation constraint conditions, taking the comprehensive performance value of the comprehensive performance model as a target, and determining capacity configuration parameters through a Bayesian optimization algorithm.

The invention belongs to the technical field of satellite communication, and relates to a satellite-ground cooperative beam fault recovery method and system, and the method comprises the steps: carrying out the multi-dimensional monitoring of a downlink signal of a satellite through a ground station, and building a health baseline model for each beam; the satellite-borne equipment executes self-inspection, generates a preliminary alarm data packet when monitoring that the parameter of the key part exceeds a preset threshold value, and reports the preliminary alarm data packet to the ground station through the telemetering link; the ground station performs fusion analysis on the health baseline model, the preliminary alarm data packet, other beam data and space environment data to obtain a fault root cause; the fault root causes comprise temporary attenuation, beam pointing offset and permanent performance recession; the ground station generates a recovery strategy based on the fault root cause; a ground station is taken as a core dominant, and rapid and accurate recovery of on-satellite beam faults is realized through cooperative work with a satellite.

The invention discloses a method for simulating regional runoff based on a coupling model VMD-CNN-LSTM-Attention of irrigation water consumption of main crops. The method comprises the following steps: 1, calculating daily reference crop evapotranspiration ET0 according to meteorological station data of a target region; 2, calculating the irrigation water consumption of different crops in different periods by utilizing the crop growth coefficients of the different crops; and 3, taking the main crop irrigation water consumption, rainfall, temperature, evapotranspiration, soil water content and other monthly elements in the target area calculated in the step 2 as driving factors, and inputting the driving factors into the coupling model for monthly runoff prediction. The method has the following beneficial effects that the irrigation water consumption of main crops with high correlation with the runoff in the research area is calculated, and driving factors with higher correlation with the runoff can be found out according to local conditions as input, so that the prediction precision is improved.

The invention provides a degradation-resistant wastewater treatment system, which comprises: a chemical oxidation system, which comprises an acid adjusting tank, an electro-Fenton reaction tank, an alkali adding neutralization tank and a solid-liquid separation tank which are sequentially connected along a water flow direction; and the biochemical system comprises a denitration selection tank, an anaerobic tank, an anoxic tank, at least one aerobic tank and an eMBR membrane tank which are sequentially connected along the water flow direction. Compared with the prior art, the non-degradable wastewater treatment system has the advantages that the denitration selection tank is additionally arranged at the front end of the anaerobic tank, nitrite nitrogen returned by sludge is removed by using a raw water carbon source, the influence on the growth of phosphorus-accumulating bacteria is reduced, the phosphorus removal effect is improved, an anaerobic ammonia oxidation generation environment is created, and the denitrification effect of the system is enhanced.

The invention discloses a full-crosslinking layered fluorine-containing epoxy super-hydrophobic coating and a preparation method thereof, and belongs to the technical field of super-hydrophobic coatings. The coating comprises the following raw material components: epoxy resin, fluorine-containing epoxy acrylic resin, KH560 modified nano silicon dioxide, a curing agent, an accelerant and the like. The preparation method of the coating comprises the following steps: carrying out surface grafting modification on the nano silicon dioxide particles by using a KH560 siloxane coupling agent; the preparation method comprises the following steps: synthesizing fluorine-containing epoxy acrylate low-surface-energy resin by using tridecafluorooctyl methacrylate, glycidyl methacrylate and isobornyl methacrylate; the coating is prepared by adopting a step-by-step spraying method, the primer is epoxy resin, the surface wear-resistant super-hydrophobic layer is fluorine-containing epoxy acrylate low-surface-energy resin blended modified nano silicon dioxide particle filler, and the three components are integrally crosslinked through epoxy groups. According to the method, the adhesive force and durability of the coating can be improved, and the coating has good super-hydrophobic performance and is suitable for various working conditions.

The invention discloses a multi-scale time information fusion proton exchange membrane fuel cell degradation prediction method, and belongs to the technical field of automation. The method comprises the following specific steps: acquiring degradation data as original input data from a fuel cell system according to working condition characteristics of the proton exchange membrane fuel cell, and smoothing by adopting a wavelet denoising method to reduce noise such as peaks in the data; an exponential decomposition module is adopted to decompose the preprocessed data into subsequences of different time scales, and an exponential decomposition sequence set is obtained; and finally, inputting the exponential decomposition sub-sequence set into a trained prediction model to obtain a voltage prediction result of a future time step, and using the voltage prediction result to predict the degradation of the proton exchange membrane fuel cell. According to the method, the data characteristics of the proton exchange membrane fuel cell under the dynamic working condition can be fully mined, and the prediction accuracy of the degradation data is effectively improved.

The present invention discloses a method, device and equipment for evaluating line loss in a substation based on improved fuzzy decision theory, which relates to the field of multi-attribute decision-making in power systems. The method includes collecting corresponding data according to the evaluation principle of substation line loss in power enterprises and constructing a substation line loss evaluation index system for substation line loss management in power enterprises based on multiple dimensions; characterizing the uncertainty variables in the substation line loss evaluation index system based on triangular fuzzy numbers integrating interval factors; calculating the weights of substation line loss evaluation indicators in the substation line loss evaluation index system through an index weight perception algorithm; constructing a substation line loss evaluation model based on improved fuzzy decision theory based on the characterized uncertainty variables and the calculated weights, so as to realize the perception and evaluation of the substation line loss management situation of power enterprises. The present application can comprehensively construct a substation line loss evaluation index system.

The invention provides a medical insurance fraud cross identification method based on fusion of multiple machine learning algorithms, and belongs to the field of machine learning technology and insurance risk management cross technology in information technology. The technical problems that in medical insurance fraud recognition, the recognition precision is not high, characteristics are complex, behaviors are hidden, and distinguishing is difficult are solved. According to the technical scheme, the method comprises the following steps of S1, performing data preprocessing on an original data set D0; s2, finding out feature information with high correlation by using the heat map; s3, constructing a medical insurance fraud detection model based on multiple machine learning algorithms; s4, checking the model; and S5, extracting main medical features influencing fraud discrimination through an algorithm. The method has the advantages that the accuracy and efficiency of medical insurance fraud detection are improved, and a more effective risk management solution is provided for insurance companies.

The application discloses a low-temperature liquid carbon dioxide battery system applied to SOFC power generation, and relates to the technical field of carbon dioxide energy storage.The system comprises a solid oxide fuel cell-gas turbine module, which is used for making the solid oxide fuel cell perform an electrochemical reaction based on air, natural gas and water, generating electric energy, and discharging tail gas; a waste heat recovery module, which is connected with the solid oxide fuel cell-gas turbine module, is used for absorbing waste heat in the tail gas discharged by the solid oxide fuel cell-gas turbine module through two circulating loops and recovering electric energy; and a low-temperature liquid carbon dioxide battery module, which is connected with the waste heat recovery module, is used for storing electric energy and heat energy of the waste heat recovery module and supplying power for electric equipment.The application realizes the complementary advantages among the modules, thereby increasing the operation efficiency of the overall system, meeting the power consumption scene demand of medium and small scale peak load shifting, and realizing power supply stability in a region.

The invention relates to a microseism event point separation method and system based on logging constraint, and the method comprises the steps: analyzing curve energy differentiation characteristics through logging data, and calculating a crack strength curve; extracting the energy level, depth and spatial position of the microseism event, and determining the three-dimensional distribution characteristics of the microseism event; constructing a fracture network form based on the time sequence and the spatial position of the microseism event; then, interactive analysis is carried out on the microseismic event B value and the logging crack strength, and energy compensation is carried out on the microseismic energy level; primary and secondary cracks are distinguished according to the energy conservation principle of fracturing fluid; and evaluating a power law relationship between the number of the microseism events and the magnitude to form systematic statistical analysis. According to the method, by combining logging information and a microseism monitoring technology, the problem of energy attenuation in a traditional method is effectively solved, the reliability and precision of microseism event separation are improved, a scientific basis is provided for fracturing construction, and efficient development of oil and gas resources is promoted.

The present application discloses an energy control method and device for a flexible interconnected distribution network, the method comprising: obtaining real-time output information of new energy units through smart meters, and collecting real-time status of electric-hydrogen hybrid energy storage systems through energy storage information collection systems, determining energy control requirements of the flexible interconnected distribution network based on real-time output information and real-time status, generating energy control instructions for energy control requirements through a comprehensive energy management system, and driving the electric-hydrogen hybrid energy storage system to perform energy control based on the energy control instructions for energy control requirements. It can be seen that the electric-hydrogen hybrid energy storage system in the flexible interconnected distribution network mixes hydrogen energy storage systems and battery energy storage systems, so that the flexible interconnected distribution network can meet different energy control requirements. Battery energy storage systems are mainly used in short-time scale scenarios, and hydrogen energy storage systems are mainly used in long-time scale scenarios, realizing the complementary advantages of hydrogen energy storage systems and battery energy storage systems.

The application discloses a water-rich soft rock foundation pit adjacent tunnel stability control method based on artificial intelligence, which comprises the following steps: prior prediction based on a finite element model, monitoring sensor arrangement, construction and training of a neural network model, scheme safety verification, initial decision based on the neural network model, initial grouting scheme execution, real-time monitoring-based cyclic intelligent decision and online incremental learning; training data is generated by establishing a three-dimensional finite element model containing a water-soil coupling mechanism, a neural network model capable of mapping the complex nonlinear relationship between monitoring parameters and optimal grouting schemes is constructed and trained, dynamic decision is made, and the grouting scheme that has passed safety verification is output and the model is continuously optimized; the method realizes the whole-process closed-loop intelligent control from intelligent prediction, real-time decision, safety control to self-optimization, and significantly improves the guarantee capacity and risk control level of the stability of the adjacent tunnel during foundation pit excavation in the water-rich soft rock complex stratum.

The invention discloses a forward view blind area assisting method, which comprises the following steps of: requesting to start a forward fisheye camera when recognizing that a view blind area appears in front according to the shooting content of the forward camera; the forward fisheye camera obtains a fisheye image of the forward view blind area to form to-be-displayed content; transmitting the to-be-displayed content to a display end of the vehicle for displaying; and after it is detected that the forward view is recovered, requesting to turn off the forward fisheye camera. The invention further discloses a corresponding system and device and a storage medium. By implementing the method and the device, the safety of the driving function can be improved when a forward view blind area appears.