293 results about "Sigmoid function" patented technology

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention relates to the technical field of automobile part processing, and discloses an automobile part process parameter real-time optimization method and system based on machine learning, and the method comprises the steps: collecting the temperature gradient, pressure distribution, cutting speed and other multi-source process parameter data, extracting a process feature sequence through a self-attention mechanism and a time convolution network, and carrying out the real-time optimization of the process feature sequence; calculating a process fluctuation coefficient; and generating a process correlation weight through covariance matrix characteristic decomposition and Sigmoid function transformation, dynamically adjusting a reference parameter to generate an optimized parameter, and regulating and controlling equipment operation. The system comprises a multi-source data acquisition module, a process feature extraction module, a fluctuation coefficient calculation module and the like. And a multi-dimensional process parameter space is also constructed to monitor an abnormal state, and a joint optimization model is established to realize collaborative optimization of process and equipment control parameters. The real-time performance and accuracy of technological parameter optimization are improved, machining error accumulation is effectively restrained, the method is suitable for intelligent machining of automobile parts, and the machining quality and efficiency are guaranteed.

The invention discloses a three-dimensional reconstruction method based on a 3DGS self-adaptive dynamic optimization strategy and gradient perception enhancement. The compression performance and the rendering quality of a Reduce 3DGS are further optimized through multi-stage hybrid optimization; firstly, a self-adaptive loss strategy is adopted, the weight between SSIM and L1 loss is dynamically adjusted through a Sigmoid function, structure alignment is emphasized in the early stage, and enhancement detail optimization is focused in the later stage; secondly, sparsity regularization is introduced in the optimization stage, transparency entropy maximization constraint is introduced to be combined with attenuation weight, and generation of redundant primitives is actively inhibited while the redundant primitives are trimmed; finally, gradient sensing detail enhancement is carried out, gradient matching loss is increased, high-frequency texture and edge information are reserved, and detail loss caused by trimming and quantization is made up; experiments prove that the method disclosed by the invention achieves better balance in the aspects of compression ratio and visual quality.

The invention provides a method for efficiently detecting a fish target in a complex underwater environment based on a priori knowledge guide network, which comprises the following steps of: 1, acquiring an underwater image and preprocessing the underwater image; 2, establishing a recovery subnet module, a relation reasoning attention module and a self-adaptive feature fusion module, and performing complex underwater environment fish target detection; the recovery subnet module is used for guiding network learning to remove underwater turbid features through an underwater scattering model, generating a clear image through a water body recovery decoder WR, and reconstructing an underwater turbid image; the relation reasoning attention module is used for constructing a co-occurrence relation graph, performing relation reasoning by using a graph convolution network, and dynamically adjusting attention weight; and the adaptive feature fusion module is used for optimizing feature expression in combination with a Sigmoid function and a channel-by-channel weighting mechanism. The method can effectively cope with the conditions of sudden change of turbidity of a water body or complex background and the like, and can keep higher detection performance under different underwater conditions.

The invention discloses an agricultural irrigation control method and system, and relates to the technical field of irrigation control, and the method comprises the following steps: constructing a crop water demand model based on the fusion of crop physiological sensing data and a data driving model, and outputting a target irrigation flow; calculating a control error between the target irrigation flow and the actually acquired flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, and superposing the output of a dynamic correction model driven by state sensing data to generate a final driving signal; feedback data is collected, a driving signal and the feedback data are subjected to closed-loop comparison, and irrigation control strategy parameters are adaptively optimized through a reinforcement learning controller; according to the method, the crop physiological sensing data and the data driving model are fused, the space and time sequence feature fused water demand model is dynamically constructed, and the problems that agricultural irrigation cannot be accurately controlled and the irrigation strategy is rigid are solved in combination with a static and dynamic composite control mapping mechanism.

The invention discloses an aquaculture disease prediction method based on multi-modal data fusion, and relates to the technical field of aquaculture, and the method mainly comprises the steps: collecting aquaculture data containing structured data and unstructured text data; inputting the structured data into a TabTransform model, carrying out column embedding and modeling of a context relationship between features through a multi-layer Transform encoder, and outputting a structured feature vector; inputting the unstructured text data into a pre-trained BERT encoder, and extracting an output vector marked by the CLS as a text semantic feature; and splicing the structured feature vector and the text semantic feature into a fusion feature, inputting the fusion feature into a full connection layer, and predicting the incidence probability of each target disease through a Sigmoid function. The prediction accuracy is improved, and the problems of single prediction dimension and weak generalization ability in the prior art are effectively solved.

The invention provides a coal mine comprehensive guarantee capability dynamic coupling early warning method and system, and relates to the technical field of coal mine safety production assessment, and the method comprises the steps: constructing a multi-dimensional fusion assessment system comprising a first-level index and a second-level index according to a coal mine comprehensive guarantee demand and an assessment target; collecting corresponding multi-source heterogeneous index data under each dimension in real time; a two-stage dynamic weight distribution mechanism is adopted, an improved entropy weight method and a working condition sensing factor are combined, and the final weight of each index at the current moment is calculated; constructing a four-dimensional feature vector and forming a multi-dimensional tensor, calculating a comprehensive score of each dimension, and generating a coal mine comprehensive support capability index through weighted fusion and Sigmoid function mapping; and performing graded early warning according to the comprehensive support capability index of the coal mine and outputting an early warning signal. According to the method, dynamic coupling evaluation of multi-dimensional risks such as coal mine technology, safety, personnel and system can be realized, comprehensiveness and real-time performance of coal mine safety guarantee are improved, and closed-loop management of risk early warning and resource configuration is effectively supported.

The invention relates to the technical field of distributed micro-grids, and discloses a distributed micro-grid grid-connected protection and power balance intelligent regulation and control method, which comprises the following steps of: firstly, acquiring multi-source operation data of a micro-grid, extracting characteristics by using an intelligent analysis model to obtain an operation characteristic sequence, and calculating a grid correlation coefficient; dimensionality reduction is carried out on correlation matrixes of correlation coefficients of different regional power grids, key feature vectors are extracted, and regulation and control weights of the regional power grids are obtained through Sigmoid function conversion. And dynamically adjusting the reference regulation and control points of the operation feature sequence according to the weights, generating regulation and control parameters and updating regulation and control records. In addition, a multi-dimensional power grid regulation and control space is constructed to monitor abnormity, and a joint optimization model is established to solve an optimal regulation and control scheme. According to the method, the operation state can be accurately analyzed, and the grid-connected protection capability and the power balance regulation and control level of the distributed micro-grid are effectively improved.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention provides a lightweight target detection method and system based on a multi-modal image, and relates to the field of computer vision, and the method comprises the steps: respectively carrying out the convolution, batch normalization and ReLU activation operation of an infrared image and a visible light image to obtain a feature map, then generating a feature weight mask through the convolution operation and a Sigmoid function, and carrying out the recognition of the feature weight mask; performing pixel-by-pixel weighting on the feature map based on the feature weight mask to generate an optimized feature map; extracting illumination perception information through a local illumination perception module so as to perform weighted calculation on the optimized feature map to obtain a weighted feature map, and splicing the visible light weighted feature map and the infrared weighted feature map in the channel dimension to generate a spliced feature map; and performing subsequent feature extraction operation through the residual connection and the parameter-free channel attention module, and inputting the feature extraction operation to the target detection network for target detection so as to output a detection result. According to the method, target detection with higher precision can be realized on the premise of not introducing larger parameter quantity and calculation quantity.

The invention discloses a photovoltaic power station defect detection method based on thermal imaging data, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, collecting the thermal imaging data of a photovoltaic module under different working conditions, and carrying out the preprocessing of the thermal imaging data, and obtaining a thermal imaging data set; s2, based on the thermal imaging data set, dynamically dividing a temperature interval and calculating an adaptive bandwidth, and performing weight distribution and color band center weighted fusion by using a Sigmoid function to generate an adaptive pseudo-color image capable of enhancing local contrast; s3, constructing a double-branch defect detection model based on the original temperature data and the pseudo-color image; and S4, performing defect detection on the real-time thermal imaging data based on the double-branch defect detection model to obtain a defect detection result of the current photovoltaic power station. The method can be effectively applied to large-scale inspection operation and maintenance of the photovoltaic power station.

The invention provides an agent training method and system based on an action mask, and relates to the field of artificial intelligence, and the method comprises the steps: recording decision data of an expert model in a confrontation process as teaching data; based on a strategy and value function collaborative simulation mechanism, performing mapping learning from a state space to an action space on a strategy network of the intelligent agent according to the teaching data so as to endow the intelligent agent with initial intelligence; performing a confrontation test on the intelligent agent, recording a test winning rate, and entering a subsequent reinforcement learning stage under the condition that the winning rate reaches the standard; inputting the test winning rate into a modulation Sigmoid function to generate an action mask; and updating the intelligent agent by using a reinforcement learning method under the action of the action mask by using a near-end strategy optimization algorithm. According to the method, by designing a degradation mechanism of an action mask, an intelligent agent efficiently avoids sampling of illegal actions at the initial stage of training and explores a strategy space more boldly at the later stage, so that the training efficiency and the final decision performance are remarkably improved.

The invention discloses an AI generated image detection method and device and a storage medium, and the method comprises the steps: obtaining a to-be-detected image, and inputting the to-be-detected image to an image encoder of a counterfeit detection model; extracting a depth visual feature vector of the to-be-detected image through the image encoder; performing linear operation on the depth visual feature vector and a preset weight parameter matrix, and superposing a preset offset parameter in the linear operation process to obtain an intermediate scalar value; the middle scalar value serves as input of a Sigmoid function, and a scalar confidence coefficient score is calculated; and judging whether the to-be-detected image is an AI generation image or not according to a comparison result of the scalar confidence score and a dynamic judgment threshold. According to the method, depth features are extracted through cross-modal alignment training, linear mapping efficient reasoning is carried out, the generalization ability of an unknown generation model is remarkably improved, dynamic threshold value self-adaptive adjustment is combined, lightweight deployment is achieved while high precision is guaranteed, and the actual application requirement is met.

The invention relates to a heart disease occupational health risk assessment system based on an RF-MLP model, and belongs to the technical field of health risk prediction. In order to solve the problem that prediction performance is limited due to the fact that an existing method is insufficient in the aspects of feature selection and model optimization, the invention provides a collaborative prediction scheme fusing recursive feature elimination (RFE), a random forest (RF) and a multi-layer perceptron (MLP). According to the technical scheme, the method comprises the steps that a data collecting and preprocessing module cleans and normalizes physiological indexes; the feature selection module calculates feature importance scores through an RFE-RF algorithm to screen key indexes; the random forest optimization module constructs a decision tree cluster; the MLP module is used for realizing nonlinear mapping through ReLU and Sigmoid functions; and the model fusion module integrates the output of the double models to generate final prediction. The system remarkably improves the prediction precision and generalization ability, provides high-robustness support for early warning of heart diseases of occupational people, and promotes disease prevention to be converted from passive response to active intervention.

The invention discloses a wavelet attention shadow removal method based on soft prior guidance, and belongs to the field of image processing. The method comprises the following steps: constructing a multi-scale wavelet attention codec architecture, decomposing an input image into a low-frequency component representing illumination characteristics and a high-frequency component containing texture details by using discrete wavelet transform, and establishing multi-scale frequency domain representation; proposing probabilistic soft prior modeling, adaptively generating a continuous differentiable probability distribution diagram by using a convolutional sub-network and a double-slope Sigmoid function, and guiding differentiated repair of high and low frequency features in a dynamic weight mode; and cross-domain illumination correction based on gray world hypothesis is introduced, spatial domain enhancement is performed on a frequency domain restoration result, illumination consistency of cross-domain processing is ensured, and finally a shadow-free image is output. According to the method, wavelet domain feature optimization is guided through the probabilistic soft mask, high-quality shadow removal is realized, and the robustness and visual quality of shadow removal are remarkably improved while the light weight of the model is kept.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

The invention discloses a robot path planning method for warehouse logistics cluster operation, and belongs to the technical field of path planning. The method comprises the following steps: constructing a two-dimensional model diagram by using a grid method; the ant colony algorithm is improved; an improved ant colony algorithm is adopted to plan an operation path for each logistics robot; a local obstacle avoidance strategy is carried out through a dynamic window algorithm, and whether obstacles exist around is detected; and the collision type is judged, and a corresponding collision avoidance strategy is adopted until all the robots safely and correctly arrive at the preset target point. According to the method, the pheromone concentration is initialized by introducing the Sigmoid function, blind search in the early stage is avoided, the self-adaptive slope parameter is introduced, and the steep degree of the Sigmoid function is dynamically adjusted according to environmental characteristics; meanwhile, the ant colony algorithm and the dynamic window algorithm are fused, parameters are dynamically adjusted according to the number of iterations, the obstacle rejection weight is introduced, the algorithm is prevented from falling into local optimum, and global path planning and local dynamic obstacle avoidance are achieved.

The invention discloses a reentry attack detection system and method based on a time sequence transaction aggregation graph network, and the method comprises the steps: constructing a time sequence multilateral directed transaction graph through a transaction graph generation module, constructing and enhancing the node and edge representation of a block chain transaction graph through a multilayer mechanism in a time sequence transaction aggregation graph network construction module, and the attack detection module carries out weighted fusion on the node features, the time sequence features and the structural features and then inputs the fused features into a logistic regression classifier, a probability value of a reentry attack behavior is calculated through a Sigmoid function, classification judgment is carried out according to a set threshold value, and a reentry attack detection result is obtained. The reentry attack detection method provided by the invention has a real-time monitoring capability, can accurately identify an ongoing reentry attack behavior, and effectively prevents further loss of contract funds through an instant interception mechanism. The method has high adaptability to a continuously evolving block chain transaction environment, and a novel unknown reentry attack mode can be continuously and effectively detected.

The invention provides a quantitative risk assessment method and system for an ice rock collapse-river blockage-dam break flood disaster chain. The method comprises the following steps: acquiring an ice rock collapse risk grade index by adopting a BP neural network improved by an Adam optimizer; if the ice rock collapse occurs, correcting and calculating an ice rock collapse movement path based on a D8 algorithm in combination with a rock-soil friction coefficient, correcting the volume in real time through an erosion-accumulation dynamic balance model, and constructing a river blocking risk grade index in combination with the river entering volume and speed; the stability of the dam body is evaluated through a dynamic catchment area model and a Sigmoid function, and the dam break risk is calculated in combination with the ratio of the volume to the area; and if the dam break disaster does not occur in the barrier dam, integrating the ice rock collapse risk grade, the river blocking risk grade and the dam break risk grade index, and evaluating the comprehensive risk of the disaster chain. According to the method, disaster prediction of each stage is realized through neural network improvement, path optimization analysis and variance tracking change, and comprehensive risks are evaluated through multi-element coupling.

The invention relates to the technical field of electric power systems, and discloses a GFL-GFM parallel system fault ride-through control method, system, device and medium, according to the method, under the condition that a GFL-GFM parallel system breaks down, a magnetic flux coupling current limiter is used for achieving fault ride-through, and when the fault of the GFL-GFM parallel system recovers to be normal, fault ride-through is achieved. When the power angle fluctuation of the GFL system and the power angle fluctuation of the GFM system are both larger than a preset power angle fluctuation threshold value, a phase-locked loop parameter of the GFL system and a control parameter of the GFM system are set based on a Sigmoid function until the power angle fluctuation of the GFL system and the power angle fluctuation of the GFM system are both not larger than the preset power angle fluctuation threshold value, the equivalent inertia and damping characteristics of the GFL system and the GFM system are enhanced, and the damping performance of the GFL system and the GFM system is improved. And the whole process of the GFL-GFM parallel system from a fault transient state to fault recovery is controlled, and the robustness of fault ride-through control of the GFL-GFM parallel system is improved.

The invention relates to the technical field of PWM (Pulse Width Modulation), and discloses a method for optimizing double random PWM, which comprises the following steps: randomizing a carrier frequency fc and a pulse position to obtain double random PWM, performing parameter setting on random distribution of the carrier frequency and the pulse position by adopting a Sigmoid function, improving a genetic algorithm by utilizing a particle swarm algorithm, and optimizing the double random PWM. Optimizing a minimum value fmin of a random number of a carrier frequency or a pulse position, a maximum value fmax of the random number of the carrier frequency or the pulse position and a curve transverse adjustment parameter sigma of random number Sigmoid distribution in the double random PWM by adopting an improved genetic particle swarm algorithm; according to the method, harmonic energy is dispersed from frequency and phase dimensions to a wider range through double random PWM, the switching frequency and the harmonic amplitude at the integral multiple of the switching frequency are reduced, random numbers are distributed in a Sigmoid mode, and the carrier frequency and the pulse position can more easily avoid a sensitive area by means of the nonlinear probability density characteristic that samples are more on the two sides and less in the middle, so that the switching efficiency is improved. And EMI peaks caused by extreme parameters in traditional uniform distribution are reduced.

The invention belongs to the technical field related to deep learning, and discloses a training method and application of a three-dimensional flow field prediction system of an underwater vehicle, and the training method comprises the steps: calculating the mass center of a neighbor point set for each surface grid point of a vehicle model, constructing a covariance matrix between the neighbor point set and the centroid of the neighbor point set, and performing eigenvalue decomposition to obtain a normal vector of the point; calculating a normal vector included angle between the surface grid point and each point in the neighbor point set, taking the obtained maximum included angle as the geometric feature measurement of the point, and converting the geometric feature measurement into a weight through a Sigmoid function, thereby obtaining the sampling probability of each point; randomly extracting surface grid points from the original surface grid of the aircraft based on the obtained sampling probability to obtain a point cloud of a corresponding model; and training a point cloud neural network by using the sampled point cloud data to obtain the three-dimensional flow field prediction system of the underwater vehicle. Based on the method, the prediction precision and the flow field detail recovery capability can be improved while the calculation efficiency is maintained.

The invention discloses a numerical control machine tool thermal error prediction method based on dynamic physical information fusion, and belongs to the technical field of intelligent manufacturing and precision machining. The method creatively introduces a Bayesian dynamic weight adjustment mechanism and a multi-stage joint optimization strategy through hierarchical fusion of a physical mechanism and a data driving model, and specifically comprises the following steps: establishing a lightweight analysis model based on a thermal deformation mechanism; key temperature and displacement data are collected through a thermal characteristic test, and model parameters are fitted; constructing a fusion prediction model based on Gaussian process regression, taking a mechanism model as a mean value function, and combining global and local kernel function combinations to fit the time-varying characteristics of thermal errors; based on the distribution consistency of a KL divergence dynamic evaluation mechanism and data prediction, generating an adaptive weight factor through a Sigmoid function; a double-stage training strategy is adopted, kernel parameters are optimized through pre-training, dynamic weight adjustment is gradually introduced, and collaborative optimization of mechanisms and data is achieved. According to the method, the thermal error prediction precision (the root mean square error is less than or equal to 0.6 mu m) is remarkably improved while the physical interpretability is ensured, the adaptability of the model to multiple working conditions is enhanced through a dynamic weight mechanism, and the method is suitable for real-time monitoring and prediction of thermal deformation of a high-precision numerical control machine tool.

The invention discloses a non-singular fast terminal sliding mode observer based on a neural network phase-locked loop. A Sigmoid function is adopted to design a non-singular fast terminal sliding mode surface in the observer; a stator current error system is defined, an equivalent item of a sliding mode control law is obtained according to an equivalent sliding mode control principle, a switching control law is designed for control of the stator current error system state on a sliding mode surface, and the sliding mode control law in the observer is obtained according to the equivalent item and the switching control law; introducing a rapid tracking differentiator comprising a terminal attractor, wherein the rapid tracking differentiator is used for accurately tracking and filtering the back electromotive force; and a phase-locked loop in the observer is controlled through the BP neural network, and the phase-locked loop is used for extracting an electrical angle and an electrical angular velocity signal from the back electromotive force to obtain a final sliding-mode observer. According to the method, the convergence speed is effectively increased, high-frequency buffeting is reduced, phase delay is compensated, and the dynamic performance is improved.

The invention discloses a device and method for improving synchronous tracking precision of a double-permanent-magnet synchronous motor motion platform servo system, and belongs to the technical field of numerical control. Synchronous control of the double permanent magnet synchronous motors is realized by using the preset performance cross coupling controller; estimating system lumped disturbance through a self-adaptive extended state observer, and inhibiting the system lumped disturbance by adopting a novel sliding mode maker; according to the novel sliding mode controller, an exponential function term and a power function term are added in the reaching law to enable the system state to converge rapidly, when the system state is close to a sliding mode surface, the exponential term in the reaching law is attenuated rapidly, the improved power function term plays a leading role, convergence stagnation caused by premature reduction of the reaching rate is avoided, the adaptability of the reaching rate is enhanced, and the control precision is improved. The arrival time is reduced. A sigmoid function is used for replacing a switching function, continuous control input is achieved, and high-frequency buffeting is weakened. And the high-performance Hall sensor and the incremental encoder are adopted, so that the signal acquisition precision is improved.

The invention provides a method for designing a TPMS (Tire Pressure Monitor System) heat exchanger core body for realizing local resistance reduction design, and belongs to the technical field of heat exchangers. According to the design method for the 3D printing modularized TPMS heat exchanger core body, local resistance reduction design is achieved through a parameterization method, a traditional Gyandroid structure expression serves as the basis, parameterization expression is conducted on an offset parameter C item in the Gyandroid structure expression through a Sigmoid function, continuous change of the relative volume of an inlet area and an outlet area of the core body is achieved, and the local resistance reduction design is achieved. And the purpose of reducing resistance of the core inlet and outlet is achieved. Based on a parameterization method, TPMS curved surface offset with controllable local parameters is realized; the TPMS heat exchanger module is locally adjusted by applying a parameterized curved surface offset scheme, continuous change of the relative volume of single fluid is realized by changing cell elements at an inlet and an outlet of the TPMS heat exchanger module, sudden change of the flow area at the inlet and the outlet of cold fluid and hot fluid of the TPMS heat exchanger module is reduced, and local energy loss is effectively reduced.

The invention relates to the technical field of network acceleration, and discloses an edge-end large language model reasoning acceleration method and accelerator, and the method comprises the following steps: reconstructing a calculation process of a decoding stage, and carrying out the deep fusion of a multi-head attention mechanism and the calculation operation of a feedforward network; the weight and key value data are stored in HBM, and the coefficient and the accumulated attention score are stored in DDR; for linear matrix calculation, a unified matrix calculation unit is used for executing multi-precision matrix operation; for nonlinear function calculation, a mathematical transformation and linear fitting method is adopted, a Softmax function is converted into operation with 2 as the bottom through a bottom conversion formula, and truncation and third-order linear fitting are conducted on a Sigmoid function; and constructing a key value screening algorithm based on the accumulated attention score, dynamically adjusting a key value storage position, maintaining a recent key value cache region and an important key value cache region in a limited cache space, and realizing key value efficient cache in long text reasoning.

The invention discloses a construction method of a water-containing rock damage statistical constitutive model considering compaction deformation, which comprises the following steps: carrying out a uniaxial compression test on a rock sample to obtain an axial stress-axial strain curve and a demarcation point in a compaction stage and a linear elasticity stage in the axial stress-axial strain curve; a sigmoid function is introduced, and the constitutive relation of the water-containing rock in the microfracture compaction stage is constructed; acquiring a damage variable after coupling of rainfall damage and stress damage caused by rock moisture infiltration; based on a damage statistical theory, constructing a constitutive relationship between a linear elasticity stage and a damage softening stage after a microfracture compaction stage of the water-containing rock; and constructing a damage statistical constitutive model of the water-containing rock considering compaction deformation. The constitutive curve corresponding to the constitutive model well reflects the compaction characteristic, the elastic characteristic, the damage softening characteristic and other deformation characteristics of the rock mass, and high prediction precision and practical application value are shown.

The invention relates to the technical field of image data processing, in particular to an MBR water production turbidity detection method and system based on image processing, and the method comprises the steps: obtaining an original image of an MBR water production pipeline, and extracting a water body region by using a mask matrix; calculating the local contrast of the pixel points relative to the neighborhood, constructing a suppression weight based on the noise standard deviation of the image sensor, and weighting the local contrast to obtain scattering response intensity; obtaining the texture disorder degree based on the ratio of the geometric mean value to the arithmetic mean value of the feature values of the structure tensor; the particle confidence is obtained based on the scattering response intensity and the texture disorder degree, the weighted particle confidence is obtained through weighting of a Sigmoid function, and the average value of the particle confidence is calculated to serve as the comprehensive turbidity so as to evaluate the water production state. The scattering response intensity and the texture disorder degree are fused, a weight suppression and soft threshold mechanism is introduced, pipe wall scratches and thermal noise interference are reduced, and the detection accuracy is improved.

The invention provides a hybrid energy storage cooperative control method and apparatus. The method comprises the steps of obtaining an actual value, a rated value and a frequency change rate of a power grid frequency; dynamically adjusting the initial virtual inertia coefficient by adopting an improved Sigmoid function based on the frequency change rate, and generating a dynamic virtual inertia coefficient; dynamically adjusting the initial droop coefficient by using the same function according to the deviation between the actual value and the rated value of the power grid frequency to generate a dynamic droop coefficient; calculating a power reference value of electrochemical energy storage according to the dynamic virtual inertia coefficient and the dynamic droop coefficient; based on the difference value between the power reference value and the power demand of the power grid, determining the number of weight blocks needing to be put into gravity energy storage; and controlling the gravity energy storage unit to output corresponding power according to the determined input quantity of the weight blocks so as to realize the stability of the power grid frequency. The core of the invention lies in determining the input amount of the weight block through the power difference value, thereby regulating and controlling the gravity energy storage output, and guaranteeing the stable power grid frequency.