1689results about How to "Improve the environment" patented technology

The invention relates to the technical field of autonomous robot decision-making systems, in particular to an autonomous robot decision-making system and method based on multi-modal sensing fusion, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal sensing data of an environment; the data preprocessing module is connected with the multi-modal data acquisition module and is used for preprocessing the multi-modal sensing data; the feature extraction module is connected with the data preprocessing module and used for extracting features from the preprocessed multi-modal sensing data; the dynamic space-time diagram construction module is connected with the feature extraction module and is used for constructing a dynamic space-time diagram based on the features; the multi-modal data fusion module is connected with the dynamic space-time diagram construction module and is used for fusing the multi-modal data in the dynamic space-time diagram; through adaptive time alignment and a cross-modal space attention mechanism, the system can effectively process space-time inconsistency of data of different sensors, and centimeter-level positioning precision is realized.

The invention relates to the technical field of marine biological diversity assessment, and particularly discloses a marine biological diversity geographic information system and an ecological diversity risk assessment method.The marine biological diversity geographic information system comprises a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module and an RDA analysis method screening module; according to the system and the method, marine organism data and water quality data of a sea area for many years are integrated, an interactive visual system is developed, data interconnection and intercommunication are realized, and the sea area can be evaluated through diversity investigation of multiple organisms in the sea area, water environment physical and chemical factor analysis, correlation mining and evaluation model construction. The model can assess the sea water environment condition, the water body environment quality and the biodiversity risk, provides basic data and methods for related research, powerfully promotes ocean monitoring to be converted from manual work to intelligence, promotes biodiversity data integration and ecological environment monitoring in a bay area, and tracks the biological activity and survival state.

The invention discloses a semantic alignment method for infrared image and microwave non-image information fusion. The method comprises the steps that infrared image and microwave non-image information fragments in the same scene are collected; carrying out preprocessing operation on the acquired infrared image and microwave non-image information; extracting spatial and semantic features of the infrared image, and extracting frequency domain features of microwave non-image information; embedding the infrared image features and the microwave non-image features into the same high-dimensional semantic space; calculating the similarity in semantic representation; and carrying out global optimal matching by adopting a Hungary algorithm. Respectively performing wavelet decomposition on the infrared image and the microwave features by adopting a wavelet transform fusion algorithm, and extracting low-frequency and high-frequency coefficients under different resolutions; by constructing a unified semantic representation space and an efficient feature fusion mechanism, the difference between the infrared image and the microwave data in the feature expression form and the semantic level is effectively solved, complementary information of two modes is fully mined, and the target recognition and environment perception ability in a complex environment is improved.

The invention relates to the field of intelligent traffic management, and discloses a big data intelligent real-time processing analysis method and system, and the method comprises the following steps: obtaining structured, semi-structured and unstructured multi-modal traffic data; preprocessing the data and extracting feature vectors; fusing the feature vectors of different modes through a self-attention mechanism to generate a unified fusion feature; performing time sequence modeling on the fusion features by using a long short-term memory network, and predicting traffic flow; based on a traffic flow prediction result, a deep reinforcement learning algorithm is adopted to dynamically optimize the duration and phase of a traffic signal lamp, and meanwhile, an optimal path is provided for a vehicle in combination with a path recommendation algorithm; and applying the optimized signal control and path recommendation to a real-time traffic management system. According to the invention, intelligent fusion of multi-modal data, accurate traffic flow prediction and intelligent traffic signal control and path recommendation can be realized, and the real-time performance and efficiency of traffic management are improved.

The invention relates to the technical field of intelligent lock recognition, in particular to an intelligent lock image feature recognition method and system based on deep learning. The recognition method comprises the following steps: constructing a multi-branch deep neural network model, wherein the multi-branch deep neural network model comprises a backbone network, a feature extraction branch and a feature fusion branch; a framework training model is used to learn identity recognition, attitude estimation and environment perception tasks; and identifying the comprehensive feature representation by using a trained framework training model, and generating a control instruction of the intelligent lock according to an identification result. According to the intelligent lock image feature recognition method based on deep learning provided by the invention, comprehensive analysis of multi-dimensional information such as identity, posture and environment is realized by constructing the multi-branch deep neural network model.

The invention discloses a method for rapidly improving a plough layer soil structure of saline-alkali land, which relates to the technical field of agriculture and comprises the following steps: arranging a drainage foundation of a water-permeable concealed conduit, sampling and measuring soil above the water-permeable concealed conduit, including soil bulk density, water content, pH, EC value, SOC and TN content, calculating SOC reserve, determining the proportion of an improver according to a detection result, and determining the soil structure of a plough layer of the saline-alkali land according to the proportion. Performing deep ploughing and rotary tillage treatment, and uniformly applying a modifier; the method comprises the following steps: carrying out rapid fertilization treatment, applying gypsum, biochar, a high-carbon-based organic fertilizer and fulvic acid, carrying out drip irrigation or flood irrigation, planting green manure crops, covering plant straws, turning and pressing into soil, and integrally carrying out hidden pipe salt elimination and deep ploughing physical improvement, gypsum, biochar and high-carbon-based organic fertilizer chemical improvement, straw returning and green manure matched biological improvement. According to the method, through comprehensive measures, the soil structure is rapidly and effectively improved, fertility is improved, salinity is prevented from rising, healthy growth of crops is promoted, and sustainable utilization of the saline-alkali soil is achieved.

The invention relates to a multi-sensor fusion positioning method suitable for a multi-axle steering vehicle, and the method comprises the following steps: S1, installing a plurality of sensors on a multi-axle all-wheel steering vehicle platform, collecting IMU data, a laser radar real-time scanning point cloud, a GNSS signal, the reading of each wheel angle sensor, and the reading of each wheel speed meter, and carrying out the real-time scanning of the IMU data, the laser radar real-time scanning point cloud, the GNSS signal, the reading of each wheel angle sensor, and the reading of each wheel speed meter; and the timestamps of all the collected signals are aligned. And S2, establishing a kinematic model of a to-be-estimated state, and performing distortion removal on the laser radar point cloud data according to the IMU data to realize motion compensation of the point cloud. And S3, establishing an error state discrete model, a laser radar observation model, a multi-axis steering vehicle kinematics observation model and a GNSS observation model. And S4, considering the influence caused by multi-wheel non-Ackerman steering, and providing a method for adaptively adjusting the observation noise covariance of the multi-axle steering vehicle so as to ensure the correct weighting of chassis observation data. And S5, fusing IMU, GNSS, laser radar, and rotation angle and rotation speed observation information of each wheel of the multi-axis steering vehicle, constructing a sequential iterative error state Kalman filter, and obtaining an optimal positioning estimation result through iterative calculation.

The invention discloses low-shrinkage all-solid waste concrete and a preparation method thereof. The low-shrinkage all-solid waste concrete is prepared from the following raw materials in parts by mass: 14 to 20 parts of solid waste-based cementing material, 25 to 40 parts of high-titanium heavy slag sand, 35 to 50 parts of high-titanium heavy slag gravel, 5.5 to 7.5 parts of water and 0.2 to 0.5 part of water reducing agent, the solid waste-based cementing material comprises the following components in parts by mass: 40-70 parts of water-quenched slag powder, 10-30 parts of carbide slag, 10-20 parts of gypsum, 5-20 parts of fly ash and 0-5 parts of silica fume. By utilizing the internal curing effect of the titanium slag aggregate, continuous water can be provided for concrete hydration, the later-stage dry shrinkage of the concrete is reduced, and the strength of the concrete is improved; the high alkalinity of the carbide slag and the high sulfate content of the gypsum are utilized, and the alkali-sulfate synergistic excitation effect is utilized, so that the activity of the water-quenched slag powder and the like can be fully excited under the condition that cement and a chemical exciting agent are not used, and the strength performance of the concrete is ensured; meanwhile, steel slag is not used as a component material, so that the problem of poor concrete volume stability caused by the steel slag can be avoided.

The invention relates to a catalyst for ethylene oxide ring-opening polymerization and a preparation method thereof, and belongs to the technical field of high polymer material synthesis, and the catalyst is a composite structure based on a silicon dioxide nanoparticle loaded bimetal organic complex; a modified nitrogen-containing polydentate ligand is prepared by adopting a special method, is coordinated with metal ions through covalent bonds and is loaded on silicon dioxide nanoparticles; by utilizing the high dispersity and stability of the silicon dioxide nanoparticles and the synergistic catalytic effect of the bimetal organic complex, the catalytic effect is effectively improved, the polymerization process is accelerated, the polymerization stability is enhanced, the polymerization molecular weight is improved, and the distribution is narrow.

The invention relates to the technical field of crane control, in particular to an intelligent tower crane anti-collision accurate hoisting system based on multi-sensor fusion, which comprises a data acquisition module, a data processing module, a safety early warning module, a control execution module, a data processing module, a data processing module and a control module, an alarm module electrically connected with the safety early warning module and the data processing module and used for receiving the alarm signal sent by the safety early warning module; a hoisting control instruction is generated based on the alarm signal and the real-time three-dimensional position coordinates of the hoisted object sent by the data processing module; according to the method, the lifting control instruction is executed, the lifting arm angle, the rotation angle and the lifting height of the tower crane are adjusted, accurate lifting and anti-collision control are achieved, and the safety, accuracy and efficiency of the tower crane are comprehensively improved through organic combination of advanced technologies such as multi-sensor fusion, an intelligent algorithm and self-adaptive control.

The invention discloses an IMU (Inertial Measurement Unit) and RGB-D (Red, Green and Blue-Depth) fusion-driven real-time three-dimensional modeling method and system for buildings and structures, belongs to the field of three-dimensional reconstruction, and solves the problem that a fixed sampling rate is adopted in the prior art. The method comprises the following steps: acquiring data of an RGB-D camera in real time based on an outdoor scene, wherein the data comprises IMU data acquired by an IMU sensor, a depth image acquired by the camera, a color image and a camera pose; after the sampling rate of the RGB-D camera is dynamically adjusted by a self-adaptive sampling rate adjustment algorithm based on IMU data, the RGB-D camera scans the building structure to obtain data of the RGB-D camera; recognizing and rejecting the dynamic feature points based on the result and a dynamic feature perception pose resolving optimization algorithm to obtain a static feature pose of the RGB-D camera; progressive point cloud replacement is carried out on point clouds corresponding to the depth image and the color image based on static feature poses and point cloud comprehensive precision evaluation of the RGB-D camera, and outdoor scene progressive three-dimensional modeling of real textures is carried out in combination with structural feature constraints. The method is used for real-time three-dimensional modeling of buildings and structures.

The invention belongs to the technical field of sodium ion batteries, and mainly relates to a multi-metal element in-situ doped sodium ferric sulfate positive electrode material prepared from byproduct sulfate, a method thereof and a sodium ion battery. The method comprises the following steps: adding solid waste sodium sulfate, anhydrous sodium sulfate, by-product ferrous sulfate and ferrous sulfate heptahydrate into deionized water, stirring until the mixture is fully dissolved, and filtering and separating insoluble substances to obtain a sodium ferric sulfate precursor solution; adding an organic carbon source and an inorganic carbon source into the precursor solution, fully stirring and mixing, and performing spray drying to obtain a precursor; and sintering the precursor in an inert atmosphere to obtain the multi-metal element in-situ doped sodium ferric sulfate positive electrode material. According to the method, two byproducts sodium sulfate and ferrous sulfate are utilized, and an organic carbon source and an inorganic carbon source are matched for use to obtain a carbon-coated material, so that the cost of a sodium ferric sulfate raw material of a sodium-ion battery is reduced to the greatest extent, and the sodium ferric sulfate positive electrode material with ultralow cost and good electrochemical performance is prepared.

The invention relates to the technical field of biology, and discloses a stem cell exosome anti-hair loss and hair growth complex and a preparation method thereof.A hair growth agent is wrapped and loaded in adipose-derived mesenchymal stem cell exosome to form a drug-loaded stem cell exosome; the medicine-carrying stem cell exosome is loaded in cross-linked hydrogel composed of a gentisic acid-cyclodextrin complex and epsilon-polylysine modified hyaluronic acid to form the stem cell exosome anti-hair loss and hair growth complex, and the stem cell exosome anti-hair loss and hair growth complex is directly smeared and externally applied to the scalp surface and acts on the hair follicle infection part in a targeted mode, so that the hair loss prevention and hair growth promotion effect is achieved. The stem cell exosome anti-hair-loss and hair-growth-promoting compound can inhibit bacterial growth, relieve and treat hair follicle inflammation caused by bacterial infection and improve the scalp environment, so that under the condition that inflammation is effectively controlled, the drug-loaded stem cell exosome in the stem cell exosome anti-hair-loss and hair-growth-promoting compound is stably released, damaged hair follicle cells are repaired, hair regeneration is promoted, the exosome treatment effect is prolonged, and the anti-hair-loss and hair-growth-promoting effect is achieved. The purpose of treating alopecia is achieved.

The invention belongs to the technical field of packaging materials, and particularly relates to a high-barrier antibacterial food packaging film and a preparation method thereof. The high-barrier antibacterial food packaging film comprises a polyethylene antibacterial inner layer, a first nylon / polyethylene blending layer, an ethylene-vinyl alcohol barrier layer, a second nylon / polyethylene blending layer and a polyethylene outer surface layer from inside to outside, wherein the polyethylene antibacterial inner layer is added with polystyrene antibacterial microspheres, and the polyethylene outer layer is added with nano titanium dioxide modified by isocyanate propyl triethoxy silane. The high-barrier antibacterial food packaging film prepared by the invention has excellent barrier property under low-humidity and high-humidity conditions, has good antibacterial property, heat sealing property, high and low temperature resistance stability and mechanical property, and can prolong the shelf life of food.

The invention discloses a treatment process for acid mine wastewater and a combined reactor thereof, and relates to the technical field of water treatment. The method specifically comprises the following steps: oxidizing the mine acid wastewater through an aerobic reactor; adjusting the pH value to 2.8 by using KOH in a first neutralization reactor, and further adjusting the pH value to 4.5 by using KOH in a second neutralization reactor; the wastewater enters a pretreatment reactor, H2S is introduced for treatment, and the treated wastewater continuously enters two anaerobic reactors for reaction. According to the method, an aerobic-anaerobic combined reactor containing iron oxidizing bacteria and sulfate reducing bacteria is adopted for treating the mine acid wastewater, on one hand, AMD can be effectively treated, on the other hand, heavy metal can be selectively precipitated, and Schwertmannite and metal sulfide with utilization value are obtained. The method not only realizes the economic value of effectively utilizing the wastewater, but also realizes the target of environmental treatment, and has better economic value.

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

The invention discloses a method for selectively recycling phosphorus pollutants in low-concentration phosphorus-containing sewage and preparing high-quality blue iron ore and application, and belongs to the technical field of water treatment and recycling thereof. According to the method, the low-concentration phosphorus-containing sewage is adsorbed by a phosphorus specific nano adsorption material HFO-201, and then the material is desorbed by adopting sodium hydroxide and sodium chloride with specific concentrations. The desorbed filtering material and the filtered material can be repeatedly used for adsorbing phosphorus in low-concentration sewage. After the pH value of the filtered desorption solution is adjusted to a certain concentration, FeCl2 with a certain concentration is added, and high-quality blue iron ore (Fe3 (PO4) 2.8 H2O) can be generated by keeping the pH value through sodium hydroxide under an anaerobic condition. The phosphorus product recovered by the process can be used for preparing a high-value phosphate fertilizer and a high-energy-density energy storage material, namely lithium iron phosphate (LiFePO4). The HFO-201 used in the invention can realize low-concentration phosphorus adsorption through an ammonium group combined with a D-201 matrix and loaded HFO nanoparticles. And moreover, after alkali desorption, the damage to the material is very low, and the material can be repeatedly used. And finally, the process of producing the high-quality blue iron ore through phosphorus recovery of the desorption solution is simple and easy to operate. Through XRD, ICP and other detection, it is found that the phosphorus recovery product is determined to be the blue iron ore, and the purity is very high. Through economic analysis and life cycle evaluation, it can be found that the method has remarkable economical efficiency, and the influence on the environment in the production process is small.

The invention relates to a fault prediction method for a mining high-voltage power distribution device. Temperature, current, voltage and gas leakage sensors are installed in the mining high-voltage power distribution device; integrating the multi-dimensional data by using a multi-sensor data fusion algorithm to form a real-time operation portrait of the power distribution device; extracting time sequence characteristics of temperature and voltage key variables by combining an operation rule of the high-voltage power distribution device; capturing abnormal fluctuation of the key characteristic parameters by using a sequence analysis model, and identifying a specific fault type in the high-voltage environment; constructing a feature mode database based on a time sequence, and matching new data with a historical fault mode; applying a feature selection algorithm to extract representative fault prediction features from the sensor data; establishing a fault prediction risk level model according to the hazard degrees of different fault modes; based on the fault risk level predicted in real time, an optimized self-adaptive maintenance scheme is generated, and the maintenance period and strategy are dynamically adjusted in combination with the actual operation state of the equipment.

The invention relates to a multi-mode-based laboratory environment monitoring method and system, and relates to the field of environment monitoring, and the method comprises the steps: obtaining environment information of each animal cabin in a laboratory in a preset historical time period and a monitoring video of each animal cabin, the environment information comprises a temperature change curve, a humidity change curve and an oxygen content change curve, determining an activeness change curve of each animal cabin according to the monitoring video, determining an environment quality change curve of each animal cabin according to the environment information, determining a target cabin of which the activeness reaches a preset activeness threshold according to the activeness change curve, and determining a target cabin of which the activeness reaches a preset activeness threshold according to the target cabin. And judging whether an abnormal cabin exists in the target cabin or not according to the environment quality change curve and the activity change curve, and if yes, controlling a ventilation device to ventilate the abnormal cabin. The method has the effects that the abnormal cabin can be conveniently found in time, and the environment of the abnormal cabin is improved.

The invention relates to the technical field of battery management, in particular to a decommissioned battery active equalization method and online detection device based on a direct current converter and time division multiplexing, and the method comprises the following steps: S1, collecting the health state, capacity and historical use data of a main control chip and a sensor; s2, classifying the battery pack through a preset classification algorithm; s3, dynamically adjusting voltage output; s4, adjusting parameters of the switching network based on the voltage in the S3; s5, short pulse current is applied to the battery pack, and current flowing in the equalization process is controlled; s6, automatically adjusting the working parameters of the DC converter and the configuration of the switching network; and S7, automatically adjusting the parameters in the steps S3 to S6 according to an evaluation result. According to the invention, by implementing the active pulse modulation technology and real-time battery health monitoring, the energy efficiency and safety of the retired battery in secondary utilization are remarkably improved, and meanwhile, the battery maintenance is optimized and the service life of the battery is prolonged through the intelligent management system.

The bacillus pasteurii is preserved in the China Center for Type Culture Collection, and the preservation number of the bacillus pasteurii is CCTCC M 20241864; the urealyticum mutant strain with high urease activity is successfully screened by adopting a normal-pressure and room-temperature plasma technology, and the capability of inducing precipitation of calcium carbonate can be remarkably enhanced, so that the mineralization efficiency of microorganism-induced calcium carbonate deposition and the sandy soil reinforcement effect are improved; in addition, an improved bacterium-mediated microorganism-induced mineralization reaction kinetic model is established, and an improved process and method for reinforcing sandy soil through strains are provided according to the requirement for efficient control of ammonium emission. The research result of the invention has important scientific significance and wide application prospect for improving the practical application effect of the microbial soil reinforcement technology and improving the environmental problem caused by the byproduct waste ammonium solution.

The invention discloses a method for preparing a slow-release medium element fertilizer by using low-quality phosphoric acid acidolysis phosphate tailings, which comprises the following steps: 1) drying phosphate tailings, crushing and sieving to obtain phosphate tailings powder for later use; 2) uniformly stirring low-quality phosphoric acid for later use; and (3) stirring and mixing the phosphate tailing powder in the step (1) and the low-quality phosphoric acid in the step (2) according to the mass ratio of 1: (0.25-1.50), fully reacting, drying and crushing to obtain the phosphate tailing-based slow-release medium element fertilizer. According to the technology provided by the invention, the influence of an acidolysis process on phosphate tailing decomposition is explored, the optimal process condition of the medium element fertilizer is provided under the condition of ensuring the highest conversion rate, the process efficiency and the conversion rate of calcium and magnesium are improved, and multi-target collaborative optimization in the activation process is realized; the medium element product prepared by the invention is rich in medium nutrient elements such as calcium, magnesium and the like required by crops and soil, is lower in preparation cost, is more in line with actual agricultural requirements, and has higher economic value and environmental value.

The invention provides a method and device for calculating pipeline leakage flow based on pressure change, and belongs to the technical field of gas leakage detection. The method for calculating the pipeline leakage flow based on the pressure change comprises the following steps that S1, a pressure simulation model is established, a pipeline is simulated according to the pressure simulation model, and pressure change data, changing along with time, in the pipeline is obtained; s2, the leakage opening area value and the total gas volume value corresponding to the gas meter and the pipeline are obtained; and S3, establishing a leakage rate calculation model, and inputting the gas total volume value, the leakage port area value and the pressure change data into the leakage rate calculation model to calculate and obtain a leakage flow rate value. The pressure simulation model simulates the pipeline to obtain the pressure change data, the gas leakage flow can be accurately calculated by comprehensively considering the pressure change, the leakage opening area and the gas volume, the method can be widely applied to household and industrial gas pipelines, gas waste is reduced, and environmental pollution is reduced.

The invention relates to the field of biological recognition and environmental monitoring, and discloses a chironomidae larva recognition system based on deep learning and image recognition, which comprises an image acquisition module, an automatic sample processing device, an image superposition processing module, a deep learning recognition module and an automatic category classification and statistics module, the image acquisition module acquires multi-focal plane chironomidae larva images through an optical microscope and a digital camera, the automatic sample processing device realizes automatic loading operation of a sample, and the image superposition processing module performs adaptive fusion on the multi-focal plane images to generate a full-focal plane image containing multiple pieces of depth information; the deep learning recognition module is used for extracting fine structure features of chironomidae larvae and automatically recognizing and classifying different types of chironomidae larvae, and the automatic type classification and statistics module is used for performing statistical analysis according to classification results and generating a report of type distribution and time sequence change. The method is suitable for automatic monitoring of water ecological environment.

The invention relates to the technical field of water supply scheduling, in particular to an intelligent scheduling operation method for building water supply, which comprises the following steps of: collecting water flow, water pressure, energy price and water pump operation state, calculating water supply demand and energy cost at each time point, and obtaining real-time water supply demand data; and performing time sequence analysis on the real-time water supply demand data, and extracting trend and periodic characteristics of the water supply demand. According to the method, the water flow, the water pressure, the energy price and the running state of the water pump are collected, the water supply demand and the energy cost at each time point are obtained through calculation, water supply demand prediction is optimized, meanwhile, the periodicity and trend of water supply are determined through time sequence analysis, and it is ensured that decision is based on a solid scientific basis. Besides, the operation parameters of each water pump are optimized, the energy consumption, the water supply capacity, the operation cost and the equipment abrasion degree are comprehensively evaluated, and the energy efficiency is improved and the service life of equipment is prolonged by adjusting the operation parameters.

The invention relates to the field of material science, and discloses a preparation process of a softwood rubber metal composite material, which comprises the following steps: S1, cleaning the surfaces of metal, softwood and rubber; s2, plasma treatment is conducted on the surfaces of the metal, the cork and the rubber; s3, preparing a modified chemical coupling agent, and coating the modified chemical coupling agent on the surfaces of metal, cork and rubber; s4, hot press molding is carried out, and metal, rubber and cork are stacked and subjected to hot press treatment; and S5, cooling treatment is conducted, and the cork rubber metal composite material is obtained. The modified chemical coupling agent is prepared from the following components in percentage by weight: 70 to 80 percent of 3-methyl propyl triethoxy silane; 10 to 20 weight percent of diisocyanate-based silicone oil; 5 to 10 wt% of polyethylene glycol; and 2-5 wt% of dimethyl diallyl ammonium chloride. The interface bonding strength of the softwood rubber metal composite material is remarkably improved, and the overall mechanical performance is enhanced.

The invention relates to the technical field of power grids, in particular to a control method and system for an anti-resonance energy storage converter to be connected to a grid-connected system, a terminal and a medium. FFT Fourier transform is carried out on obtained current voltage and current of a grid-connected point, a complex time domain signal is decomposed into superposition of a plurality of simple sine and cosine functions, and the voltage and current of the grid-connected point are obtained; in a spectrogram, the frequency components and characteristics of the signal, including the amplitude and phase information of each frequency component, including harmonic parameters and characteristic sub-harmonics, can be clearly obtained. In the step S2, negation can be carried out on harmonic waves appearing in the grid-connected system through characteristic subharmonic waves, and deduction effect reduction is carried out on the harmonic waves. In the step S4, when the harmonic parameter still exceeds the preset standard value after the step S2 is carried out, it shows that harmonic oscillation causes a resonance phenomenon, and at the moment, the system impedance is changed by adjusting the virtual impedance of the energy storage converter, so that the harmonic oscillation can be weakened or even eliminated to a great extent.

The invention relates to the technical field of optimization operation, in particular to a virtual power plant low-carbon optimization operation method considering carbon transaction, which comprises the following steps: S1, virtual power plant framework design and modeling: designing an operation transaction framework of a low-carbon virtual power plant, and modeling each unit in the virtual power plant; s2, depicting the uncertainty of wind and light output: depicting the uncertainty of wind and light output through scene generation and reduction; s3, obtaining an income and carbon emission optimization objective: constructing an objective function taking income maximization and carbon emission minimization as objectives; s4, optimizing an output scheme by using a DQN algorithm: optimizing a next-day output scheme by using the DQN algorithm; s5, a deviation punishment mechanism is implemented, wherein when the actual wind and light output deviates from the prediction, the deviation punishment mechanism is implemented; according to the method, the next-day output prediction and scheduling optimization capability of the virtual power plant is improved, and achievement of revenue maximization and carbon emission minimization is ensured.

The invention discloses a biological functional scaffold system driven by dual-mode printing, relates to the technical field of biomedicine, and is used for providing more accurate control on the aspects of porosity, cell attachment and surface modification. By integrating two advanced manufacturing technologies of 3D printing and aerosol spraying, the porosity and surface characteristics of the stent can be accurately controlled in the same printing process, and the cell attachment and proliferation environment is optimized. The cooperative working mechanism greatly improves the biocompatibility and mechanical strength of the biological scaffold, and further effectively promotes the tissue regeneration and repair efficiency. The whole manufacturing process is more efficient and accurate due to introduction of the control unit. The control unit automatically adjusts the printing speed and the spraying pressure based on real-time monitoring feedback, the uniformity and stability of the support are ensured, the error rate in the production process is reduced, and therefore the consistency and reliability of products are improved.

The invention relates to a shrimp feeding behavior prediction and automatic feeding method based on machine vision, which comprises the following steps: S1, identifying and analyzing the behaviors of shrimps: identifying the dynamic and overall behaviors of the steps of the shrimps through feature labeling and model training of the steps of the shrimps, and judging the resting, swimming and foraging behavior states of the shrimps; s2, identifying and analyzing the behavior state of the shrimps through the body labeling features, and combining with identification of residual feed in water to adjust a feeding strategy, reduce waste and reduce water treatment load; s3, the computing power center obtains real-time image frames of the underwater high-definition camera, after computing power analysis is applied, signals are sent to the feeder control center, feedback information is received, and signal linkage is achieved; and S4, video real-time monitoring and breeding data real-time display. According to the method, the training model for accurately identifying the prawn foraging behavior can be realized, the method can adapt to the complex and changeable underwater environment of the circulating water culture pond, the technical robustness is high, accurate identification can be realized, and the intelligent degree is high.