2119results about How to "Improve balance" patented technology

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses a video advertisement putting effect intelligent analysis and management system based on big data analysis, and relates to the technical field of data analysis. The system comprises: a data processing part for collecting a multi-dimensional data set after advertisement putting, mapping the multi-dimensional data set to a high-dimensional feature space by using a kernel function to obtain a high-dimensional data set, and performing dimensionality reduction on implicit feature representation by using Laplacian feature mapping to obtain low-dimensional embedded representation; the user behavior prediction model part is used for capturing time dynamic characteristics of user behaviors by adopting a bidirectional long-short-term memory network according to the low-dimensional embedded representation, and predicting future user behaviors; and the putting effect management part is used for defining a multi-target evaluation function and formulating a putting action, so that the putting strategy parameter vector approaches to the target putting strategy parameter vector, and the putting effect of the advertisement is maximized. According to the invention, the advertisement putting effect can be evaluated, the advertisement putting strategy is improved, and the accuracy and intelligence of the advertisement putting effect are improved.

The invention discloses a three-dimensional scene re-coloring optimization method based on Gaussian splashing and deep learning, and the method comprises the following steps: S1, constructing a multi-view data set of a given scene, and generating an initial point cloud; s2, based on the initial point cloud, extracting a representative color set from all input pixels, generating a palette and taking the palette as initial setting; s3, rendering a multi-view result picture by using the palette and a Gaussian splash algorithm, calculating a loss function, and generating an optimized Gaussian radiation field through back propagation optimization parameters; and S4, re-rendering the scene and generating a re-coloring result based on the optimized Gaussian radiation field and the palette color modified by the user. According to the method, the Gaussian splash algorithm and the deep learning technology are combined, efficient re-coloring optimization of the three-dimensional scene is achieved, and the method has the advantages of being visual in editing, accurate in illumination expression and real in rendering.

The invention provides an Internet of Things data processing method and system based on a block chain, and relates to the technical field of block chains, and the method comprises the steps that an Internet of Things device carries out the digital signature of an authentication request through employing a pre-stored private key, and generates a signed authentication request; the smart contract in the block chain network receives the signed authentication request, and the smart contract generates an authentication token including an authentication result, a validity period and an authorization level based on the historical authentication record, the credit score, the current timestamp and the challenge value; the Internet of Things equipment receives the encrypted authentication token and the intelligent contract address; the Internet of Things equipment configures own access authority and function limitation according to the authentication result, the validity period and the authorization level; and the Internet of Things equipment takes the intelligent contract address as an identity credential within the period of validity, and is used for subsequent secure communication and data exchange with other Internet of Things equipment or application programs.

The invention discloses a complex target recognition method based on a deep twin self-attention network, and relates to the technical field of target recognition methods. According to the method, a twin network based on a sliding window multi-head self-attention transformation mechanism is applied, the global modeling capability of a feature extraction network and the acquisition efficiency of long-distance semantic information are enhanced, and the bottom-layer feature extraction capability and the abstract effect of advanced semantic information are improved; the twin network personalized modeling improves the feature segmentation effect; the whole network forms a U-shaped structure, a local symmetric encoder and decoder structure is adopted, global context information is supplemented, and semantic information space association is enhanced; a residual link is added, so that the deep network is trained efficiently, and the recognition precision is enhanced; and adaptive image feature scale input processing is added, and adaptive scale transformation is performed on the input image, so that an accurate end-to-end semantic segmentation result is realized, and multi-scale input is realized to enhance the generalization ability and accuracy of a network model.

The invention provides a large model security alignment method based on dynamic constraint reinforcement learning. The method comprises the following steps: constructing a dialogue preference data set; respectively training an initial reward model and an initial cost model based on the dialogue preference data set to obtain a trained reward model and a trained cost model; generating a first fine tuning strategy and a second fine tuning strategy based on the trained reward model and the trained cost model; and updating the initial large language model according to the first fine tuning strategy and / or the second fine tuning strategy to obtain an updated large language model. According to the method, the reward model and the cost model are respectively trained through the constructed dialogue preference data set, and the first fine tuning strategy and the second fine tuning strategy are generated through the trained reward model and the cost model, so that the large language model is updated, and the balance between the safety and the usefulness of the large language model is improved.

A self-adaptive control technology for rotational inertia and damping coefficient of an optical storage VSG system comprises a grid-connected system overall structure which is provided with an energy storage device, takes photovoltaic as a source end and is controlled by a grid-connected inverter virtual synchronous machine, and the grid-connected system overall structure is formed by electrically connecting a photovoltaic array, the energy storage device, an inverter, a DC / DC (Direct Current / Direct Current), a converter and a filter; the energy storage device is additionally arranged at a direct current bus of the system, the energy storage device and the photovoltaic array form a common direct current bus structure, the inverter is controlled by a virtual synchronous machine, inertial response and frequency adjustment are provided for the optical storage power generation system, and the power shortage between the photovoltaic array and the photovoltaic inverter is made up by adding the energy storage device; and the frequency deviation and the frequency change rate are introduced into VSG parameters, and the inertia and damping parameters of the VSG system are adaptively adjusted, so that the frequency stability of the micro-grid during power sudden change is improved, and the direct-current measurement energy storage power balance is met.

The invention relates to the technical field of building design, and discloses a GAN and GA-based low-carbon building design decision-making method in a hot-summer and warm-winter region, and the method comprises the steps: setting a generator in a GAN model to generate external design parameters and internal design parameters of a building in a hierarchical manner, introducing a reinforcement learning algorithm, and setting an adaptive discriminator, thereby achieving the design of a low-carbon building in a hot-summer and warm-winter region. And the genetic algorithm GA is combined with particle swarm optimization PSO, so that the balance between the energy efficiency and the building material cost can be achieved while the low-carbon emission requirement is met, and the intelligence and dynamic response capability of the building design are fully realized.

The invention discloses a robot optimal path planning method based on an improved angle skid optimization algorithm, and the method comprises the steps: designing a single-target optimization problem under a constraint condition based on the angle skid optimization algorithm, setting a target function as the shortest distance between a starting point and an end point, and determining the constraint condition; a good point set strategy is adopted to replace random node initialization distribution, and an adaptive dynamic weight, a dynamic weight, an adaptive coefficient, a Brownian motion random disturbance parameter and a random walk and adaptive t distribution disturbance strategy of step length adjustment are introduced. A hiding behavior, a blood jetting behavior, an escape behavior, a skin blackening and brightening behavior and a melanocyte stimulating hormone (MSH) rate formula of the algorithm are optimized respectively, and a planned path is optimized through the improved HLOA algorithm. Compared with the prior art, the method has the advantages that the convergence speed, the optimization precision and the global search capability of the angle exid optimization algorithm in robot path planning are improved, and the method is suitable for optimal path planning in a complex environment.

The invention discloses a video anomaly detection method based on causal normality learning. The video anomaly detection method comprises the following steps: acquiring a monitoring video of multiple scenes and cutting the monitoring video into b frames of continuous fragments with a uniform size as training samples; a CRCL model is constructed, the CRCL model comprises an M1 scene encoder, an M3 motion perception feature extractor, a scene depolarization learning module, a causality heuristic normality learning module and an M5 clustering module, the training samples are input into the CRCL model for training, a correlation matrix and loss are calculated, and model parameters are optimized through gradient back propagation; after a preset round is reached, calculating a clustering center of causal representation through an M5 clustering module, and minimizing clustering loss to obtain an optimal cluster; and the rounds of training are repeatedly carried out until the drop-out value of the loss function between two adjacent rounds of iteration in the training meets a set threshold value or reaches the maximum number of iterations, and the trained CRCL model is output. According to the invention, the video anomaly detection performance in a multi-scene environment can be improved.

The invention provides a microbial agent for promoting growth of medicago sativa and application of the microbial agent, and relates to the technical field of agriculture, the microbial agent is prepared from sinorhizobium meliloti MCSWL1, sinorhizobium meliloti MCSD1 and nitrogen-fixing rhizobium sp. MCDC3 according to the mass ratio of 1: 1: 2, and the mass ratio of the sinorhizobium meliloti MCSWL1, the sinorhizobium meliloti MCSD1 to the nitrogen-fixing rhizobium sp. MCDC3 to the nitrogen-fixing rhizobium sp. MCDC3 to the nitrogen-fixing rhizobium sp. MCDC3 is 1: 1: 2. The microbial agent for promoting the growth of the medicago sativa, provided by the invention, can form a close symbiotic relationship with the medicago sativa (particularly grassland No.3), and provides sufficient nitrogen nutrition for the growth of the medicago sativa, so that the soil fertility is effectively improved, and the healthy growth of plants is promoted.

The invention discloses an automatic measuring and adjusting method and system for flow of an intelligent separate injection well, and relates to the technical field of layered flow allocation. The method comprises the steps that the residual oil saturation of each layer in an oil reservoir is calculated based on historical and real-time data of the oil reservoir, and the initial water injection rate is distributed according to the residual oil saturation of different layers; and predicting an oil-water output ratio by using a water drive curve model according to the distributed initial water injection rate, and dynamically adjusting a water injection strategy in combination with actual production data for implementation. According to the method, the seepage rate and the pressure distribution are calculated by combining the physical constraint model and the Darcy law, so that the scientificity of regulation and control of the water injection rate of each layer is improved, and pressure balance and seepage path optimization of each layer in the oil reservoir development process are ensured; the real-time water injection strategy is optimized by dynamically predicting and adjusting the oil-water output ratio through the water drive curve model, the water injection rate is dynamically adjusted through the nonlinear prediction control model, it is ensured that the system can still operate stably in the complex dynamic environment, and the water injection efficiency and the recovery efficiency are remarkably improved.

The invention relates to the technical field of ballast water, in particular to a ship ballast water comprehensive treatment system which comprises a ballast water dynamic adjustment module, a ballast water risk management module, a ballast water safety optimization module and a ballast water ecological monitoring module. According to the invention, by monitoring the key sea condition data and the ship state in real time, the ballast water configuration can be adjusted in real time, and the balance and operation flexibility of the ship can be optimized, so that the ship can be kept stable in a changeable marine environment, the extra fuel consumption caused by improper configuration of the ballast water is obviously reduced, and the service life of the ship is prolonged. Potential risks in ballast water are intelligently assessed through the Bayesian network, the system can early warn the risks in advance and formulate coping strategies, so that the safety of the ship is improved, ecological threats are recognized in time through careful monitoring and analysis of biological and chemical components of the ballast water, the cross-regional spreading risk of pests and pathogens is reduced, and the risk of risk prevention and treatment is reduced. The ecological safety and the biological diversity of the destination water area are effectively protected.

The invention relates to a lattice-based specified server searchable encryption optimization method, device and equipment and a medium, and the method comprises the following steps: a receiver mechanism calculates and determines # imgabs0 # imgabs1 # according to a system public parameter pp, a public key pkr = Ar of the receiver mechanism, a private key skr = Tr of the receiver mechanism and a keyword w 'belongs to {0, 1} * to be searched, and a SamplePre (Ar, Tr, uw', sigma) algorithm is called to generate a trap door ew '; and the cloud server matches keyword information in the ciphertext and the trap door according to the ciphertext Cw = (c1, c2, c3), the trap door ew'and a private key skc = ec of the cloud server, and sends a matching result to a user client corresponding to a receiver mechanism to complete lattice-based specified server searchable encryption optimization. The system is convenient to manage and maintain, the specified server can centrally manage encrypted data and search trap doors, and maintenance and management of the system are facilitated.

The invention provides a radio signal modulation classification method based on a deep integrated network, and the method comprises the steps: S1, constructing an attention CNN module, inputting an IQ signal sample into the attention CNN module, and converting the IQ signal sample into multi-channel feature mapping; s2, constructing a time sequence information extraction module, and extracting a sequence mode from the local features by using the time sequence information extraction module; s3, constructing a visual converter module, and inputting an output result of the time sequence information extraction module into a global feature extractor module to extract global features so as to obtain feature vectors representing modulation categories; and S4, constructing a classifier, mapping the feature vector representing the modulation type from a high-dimensional space to a low-dimensional space representing the signal type, and then performing activation by using softmax to obtain the probability distribution of the modulation type of the signal. The method provided by the invention shows excellent accuracy and expandability under different signal-to-noise ratio levels and modulation modes, and provides favorable support for cognitive radio and other wireless communication systems in practical application.

The invention belongs to the field of artificial intelligence, medical treatment and finance, and relates to a speech synthesis method, which comprises the following steps: performing phoneme conversion and context embedding on a text data set, obtaining a phoneme sequence and a text context vector, extracting reference speech features of a reference audio sample, inputting a phoneme-level potential diffusion model, and generating phoneme-level prior potential features; inputting the phoneme-level prior potential features into a phoneme-level variational self-encoding model to obtain reconstructed speech features; extracting a target voice feature of the target audio sample; performing alignment and up-sampling on the target voice feature and a phoneme sequence to obtain a target audio frame-level feature; and training based on the reconstructed speech features and the target audio frame-level features to obtain a conditional control speech synthesis model, and synthesizing the to-be-converted text speech. The invention further provides a speech synthesis device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the to-be-converted text can be stored in a block chain. The speaker fidelity and the text understandability are improved.

The invention relates to an impact fatigue finite element simulation method considering a scale effect, and belongs to the technical field of material damage failure simulation analysis. According to the invention, a constitutive model suitable for advanced materials and materials prepared by an advanced process is established, static characteristics, dynamic characteristics, scale effects and damage evolution processes of the materials are combined, and impact damage and impact fatigue processes of the advanced materials can be effectively simulated; a non-localization cross-scale constitutive method is adopted, the efficiency requirement of engineering finite element simulation can be met, meanwhile, localization non-uniform modeling is compatible so as to face more accurate scientific research calculation, and good balance is achieved between precision and efficiency; by means of the method, integrated calculation in commercial finite element software can be achieved, impact damage and fatigue simulation considering the material scale effect can be more efficiently conducted, and application of advanced materials in the aerospace field impact and reciprocating impact load environment is promoted.

The invention relates to the field of hybrid energy storage, in particular to a movable hybrid energy storage device which is applied to an energy storage power supply and comprises a movable frame and an energy storage cabinet body, universal wheels with brake pads are arranged at the bottom of the movable frame, and the energy storage cabinet body is provided with an anti-disengaging assembly used for automatically fixing a plug after the plug is connected with a socket in an inserted mode. The anti-disengaging assembly comprises a guiding bin body, clamping pieces arranged on the periphery of the guiding bin body and a triggering piece which is arranged in the guiding bin body and can synchronously push all the clamping pieces inwards, and the guiding bin body is further provided with a triggering driver which is matched with the triggering piece and pushes all the clamping pieces inwards. Through the linkage of the trigger piece and the clamping piece, the trigger driver is automatically activated when the energy storage power supply is turned on, so that the trigger piece is driven to push the clamping piece, the edge of the plug is limited between the clamping piece and the socket, and the power failure risk caused by loosening or falling of the plug is avoided.

The invention discloses an image enhancement method based on high-frequency and low-frequency adaptive fusion. The method comprises the following steps: respectively carrying out contrast enhancement of color channels on original images to obtain intermediate feature maps; calculating pixel mean values of the intermediate feature maps under the color channels; performing color compensation on the intermediate feature map according to the pixel mean value to obtain a color reconstruction image; decomposing the color reconstruction image into high-frequency image features and low-frequency image features; performing normalization processing and gamma correction on the high-frequency image features to obtain a high-frequency corrected image; sharpening the high-frequency correction image to obtain a high-frequency recovery image; carrying out defogging processing on the low-frequency image feature weight to obtain a low-frequency recovery image, and carrying out adaptive fusion on the high-frequency recovery image and the low-frequency recovery image to obtain an output image; rich detail and edge information is provided by the high-frequency recovery image, and optimized background and color information is provided by the low-frequency recovery image, so that the visualization effect of the output image is remarkably improved.

The invention relates to the technical field of soil layer drilling, in particular to an efficient underground water quality detection sampling device which comprises a base, a fixing seat is further arranged on the base, and a device storage structure and a sample storage structure are arranged on the left side and the right side of the fixing seat respectively; a supporting structure is further arranged in the middle of the fixing base and comprises a vertical plate, a transverse plate, an axial motor, a mounting base, a lead screw, a guide rod and a drill rod. The drill rod is connected with the mounting seat through a rotating piece; the drill rod comprises a drill bit, a rod body, a sampling water pump and a sampling pipe; a sampling hole is further formed in the drill bit, and a liquid collecting cavity which is coaxially distributed is formed in the center in the rod body; a first cavity and a second cavity which are distributed from inside to outside are further formed in the rod body and located on the outer side of the liquid collecting cavity, and the sampling hole extends inwards, penetrates through the first cavity and the second cavity and is communicated with the liquid collecting cavity. The device is reasonable in structural design, filtering, collecting, sampling and storing operation of underground water at different heights can be completed on the basis that efficient drilling of underground water soil layers at different depth positions is achieved, and the using effect is good.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention discloses a pipeline combined type single-autorotation intelligent counter-current chromatograph, and relates to the technical field of counter-current chromatographs, the pipeline combined type single-autorotation intelligent counter-current chromatograph comprises a rotating disc, a surrounding separation channel is formed in the rotating disc, a filling block is mounted in the surrounding separation channel, one end of the rotating disc is rotatably mounted through a liquid slip ring, and the other end of the rotating disc is rotatably mounted through a liquid slip ring. The other end of the rotating disc is connected with a main shaft, the main shaft is connected with a driving device, unwinding is conducted in a liquid slip ring mode, the rotating disc can complete unwinding action under single rotation, mixing of two phases in the separation channel can be enhanced through a filling block, and therefore arrangement of a planetary gear train can be abandoned; the main shaft is directly driven by the driving equipment to rotate, and the main shaft drives the rotating disc to rotate to realize two-phase separation and extraction, so that a higher rotating speed can be obtained, and the separation efficiency and effect are improved; and secondly, after the planetary gear train is abandoned, the equipment noise is greatly reduced, and good dynamic balance is achieved.

The invention belongs to the technical field of power battery cooling, and particularly relates to a flow control method and system for a liquid cooling system of an electric vehicle-mounted power battery, and the method comprises the steps: carrying out the parameterization of a geometric model of the liquid cooling system; establishing an equipment resistance model based on the equipment flow resistance characteristics; initializing flow distribution, constructing a dynamic flow distribution model, calculating the flow of each node and judging whether the flow meets a target requirement or not, and if not, readjusting the flow; calculating the resistance of each section, determining the total resistance of the pipeline, and checking the water pump lift; the flow uniformity is judged by calculating the pipeline resistance between the nodes, and if the flow is not uniform, the flow is redistributed or the pipeline design is optimized until the flow is uniform. Through flow control, the problems of non-uniform flow distribution and poor temperature consistency of multiple battery packs in the prior art are solved, the optimal working state of the power battery is ensured, overheating and overlarge temperature difference are avoided, and the operation efficiency, stability, durability and safety are improved.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention discloses a topological optimization method for an optical storage integrated AC / DC system based on an adaptive algorithm, and the method comprises the following steps: obtaining an initial topological structure and operation parameters of the optical storage integrated AC / DC system, including photovoltaic power generation, configuration of an energy storage system, operation state parameters of a microgrid, and external environment parameters; optimizing and adjusting the topological structure, and dynamically generating a plurality of candidate topological structures according to system operation data and environment change which are acquired in real time; carrying out analogue simulation on the candidate topological structure, and evaluating the stability, power distribution and energy utilization efficiency of the system under different operation conditions; performing reliability analysis on the optimized topological structure, and monitoring and adjusting the topological structure of the system in real time; and performing simulation verification on the adjusted topological structure to obtain an optimal topological structure. According to the invention, the system stability, reliability and fault processing capability can be improved.

The invention discloses a multi-agent experience exploration cooperation method based on a curiosity mechanism. The method comprises the following steps: constructing a multi-agent decision framework; a curiosity mechanism suitable for a multi-agent decision-making framework is constructed; constructing an experience exploration component suitable for the multi-agent decision-making architecture; constructing an experience playback pool and a state playback pool; a multi-agent cooperative confrontation environment is adapted; training the obtained deep neural network formed by the multi-agent experience exploration decision architecture based on the curiosity mechanism to obtain a multi-agent experience exploration collaborative strategy model; and in the multi-agent experience exploration collaborative strategy model, solving the multi-agent collaborative confrontation strategy in the current confrontation scene. Through the above mode, the method can balance the exploration and utilization process of a multi-agent algorithm during training, alleviates the influence caused by reward sparsity in a multi-agent confrontation environment, helps the multi-agent to carry out experiential exploration, searches an optimal strategy for confrontation, and improves the learning efficiency at the same time.

The invention relates to the technical field of agricultural engineering, in particular to a reclamation land soilless culture and green manure intercropping culture system which comprises a reclamation land matrix preparation module, a soilless culture module, a green manure planting module and a microorganism bionic regulation and control module. The reclamation land matrix preparation module is used for preparing a matrix for soilless culture of a reclamation land; the soilless culture module comprises a culture bed, a nutrient solution supply unit and an environment control unit; the green manure planting module is provided with a special area for planting green manure crops, and the improvement of a soil structure and the optimization of nutrient circulation are realized by alternately or simultaneously planting the green manure and the crops; and the microbial bionic regulation and control module monitors and regulates and controls a microbial community structure in the matrix of the reclamation land in real time. According to the invention, not only are the ecological balance and stability of microbial communities improved, but also the nutrient absorption and disease resistance of crops are enhanced, and the growth conditions and yield of the crops are optimized.

The invention discloses a carbon metering system based on cloud pipe edge end cooperation. The carbon metering system comprises a carbon metering terminal, a carbon metering safety pipeline, a carbon metering edge cloud and a carbon metering center cloud. The carbon metering edge cloud comprises an enterprise end edge cloud, a government supervision mechanism end edge cloud, a third-party certification mechanism end edge cloud and a financial mechanism edge cloud which are nodes in a block chain distributed account book; the carbon metering edge cloud stores real-time carbon emission source original data in a distributed account book and upwards migrates the real-time carbon emission source original data to the carbon metering center cloud, the carbon metering center cloud takes the original data as a sample to regularly train a carbon metering algorithm model, if the difference of trained model parameters exceeds a preset threshold value, training parameters are updated, and if the difference of the trained model parameters exceeds a preset threshold value, the carbon metering algorithm is updated; and the model and the new parameters are issued to the carbon metering edge cloud, the carbon metering edge cloud timely updates the calculation parameters for accounting, and the calculation result is issued to complete real-time display. According to the scheme, the balance between resource allocation and calculation is optimized, the scheduling is flexible, and the response is fast.

The invention provides a rectus abdominis ultrasonic image enhancement method for postpartum rehabilitation optimization, and relates to the field of image enhancement, a multi-dimensional feature enhancement network proposed by the invention combines a CNN and a Transform to realize efficient image enhancement, an encoder and a decoder are processed by using a CNN module, a Transform module is introduced into a global context feature extraction module, and the image enhancement efficiency is improved. The advantages of CNN in the aspect of local feature modeling and the capability of Transform in global feature extraction are fully utilized, multiple attention mechanisms are captured by designing an adaptive convolution block, and the local feature extraction effect is further enhanced, so that good balance is achieved between the image enhancement performance and the calculation complexity.