142 results about "Pruning algorithm" patented technology

The invention discloses a building electromechanical BIM model information rapid retrieval method and system, and the method comprises the steps: generating composite retrieval parameters fusing semantic keywords and three-dimensional coordinate constraints according to a multi-mode retrieval instruction inputted by a user; on the basis of the composite retrieval parameters, constructing a dynamic search space by utilizing a hierarchical graph convolutional network, and generating a candidate model index structure of multi-dimensional feature coding; inputting the candidate model index into a multi-objective optimization engine, performing real-time optimization on a search path by adopting a dynamic pruning algorithm driven by reinforcement learning, and outputting a candidate model set of which the confidence coefficient is higher than a preset confidence threshold after pruning; and on the basis of the candidate model set, associated equipment nodes are expanded through a knowledge graph embedding and complementing technology, and an enhanced retrieval result set containing the hidden associated equipment is generated. By utilizing the embodiment of the invention, efficient, multi-dimensional and multi-modal information accurate positioning and quick retrieval can be realized in a large-scale complex BIM model.

The invention discloses an enterprise scientific and technological achievement adaptation method based on big data accurate retrieval and query, and relates to the technical field of big data processing and knowledge maps, and the method comprises the steps: constructing an enterprise technical field knowledge map based on a standardized multi-modal feature matrix, receiving incremental technical data in real time by adopting an Apache Flink streaming processing framework, and carrying out the real-time retrieval and query of the enterprise technical field knowledge map. Dynamically updating a graph node relationship, removing expired nodes through a pruning algorithm, and outputting a dynamic knowledge graph with a timestamp; and receiving a query request of a user, extracting a query vector by utilizing the cross-modal embedding model, retrieving a Top-N candidate node list through a dynamic knowledge graph, calculating a comprehensive similarity score in combination with a graph relation weight, and outputting a sorted technical achievement list. Multi-source heterogeneous data is mapped to a unified semantic space through a cross-modal embedding model, and the intelligent level and the actual application effect of scientific and technological achievement adaptation are comprehensively improved in combination with construction and updating of a dynamic knowledge graph.

The invention belongs to the technical field of artificial intelligence information security, particularly discloses a neural network privacy reasoning method and device based on winograd sparse convolution and differentiable activation function approximation, and aims at solving the problems that existing neural network privacy reasoning is high in calculation time delay and large in communication cost. And in combination with a multi-granularity weight pruning algorithm, the sparsity of a linear convolutional layer is maximized, and the communication cost required by the convolutional layer with dense multiplication is reduced. Meanwhile, collaborative optimization is carried out on a nonlinear activation function, a new activation function sensitivity and importance quantitative evaluation algorithm is designed, and an adaptive learnable polynomial function is introduced, so that the fitting precision of the activation function is improved, and the polynomial replacement loss is reduced; and the powerful prediction performance of the neural network model is reserved by using a multi-stage knowledge distillation method, and the calculation and communication efficiency of privacy protection neural network reasoning is effectively improved on the premise of not influencing the prediction precision.

The invention relates to the technical field of model pruning algorithms, in particular to an unmanned aerial vehicle target detection model lightweight method based on a pruning algorithm, and the method comprises the steps: initializing a training model; sparse training; pruning the model; finely adjusting the model; according to the method, pruning optimization of parameters and structures is carried out on the STUAV-YOLO model, redundant and unimportant connections and weights in a neural network are removed, so that the operation efficiency of the model is improved, the model is more suitable for being deployed on unmanned aerial vehicle equipment with limited resources, and real-time and accurate high-altitude target detection tasks are achieved.

The invention discloses an AI-based intelligent park source, network, load, storage and charging integrated scheduling method, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting park equipment data in real time, carrying out the preprocessing, and generating a standardized time series data matrix; time sequence information is used for screening and extracting data features of the standardized time sequence data matrix, a hybrid neural network frame is used for constructing a source network load storage and charging optimization model, and an embedded vector is obtained; optimizing the preliminary scheduling strategy by using a game theory, and generating a collaborative scheduling instruction set; converting the collaborative scheduling instruction set into an equipment control instruction by adopting protocol conversion, and executing the equipment control instruction; dynamic threshold value filtering is used for monitoring the equipment operation state, the safety boundary and the economical efficiency threshold value in real time, and online learning is used for optimizing an equipment control instruction. According to the invention, by using the near-end strategy to optimize the cutting algorithm and the Nash equilibrium game, the economical efficiency of the scheduling strategy is improved.

The invention discloses a lightweight power transmission line insulator defect detection algorithm based on SGA and SE-F. Firstly, a brand new attention mechanism SGA is introduced on the basis of a YOLOv5 network, and the saliency of a defect target to be detected is enhanced, so that the problem that small insulator defect features are difficult to extract is solved; secondly, a same-scale weighted fusion module SE-f is provided and used for replacing a Concat module in the feature fusion network, and the fusion capability of the network on features of low-level position information and high-level semantic information is improved; thirdly, carrying out sparse training and channel pruning on the improved YOLOv5, and further optimizing a model structure; the knowledge distillation guide model is used for knowledge learning, and the detection precision is improved. The beneficial effects of the invention are that the method firstly introduces an attention mechanism, constructs a novel feature fusion mode to improve an initial model, then achieves the light weight of the model in combination with a channel pruning algorithm, and finally constructs a knowledge distillation model to improve the detection precision of the pruned model.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention provides a visual algorithm model development and reasoning method and device for urban governance. The method comprises the steps of defining services and problems; collecting related data according to the business and the problem, and labeling the collected related data through a data labeling platform to obtain a data set; training a visual algorithm model based on a YOLO framework by using the data set, performing pruning, algorithm chip adaptation, model evaluation and optimization iteration on the trained visual algorithm model, and deploying the visual algorithm model into a production environment; and inputting image data to be processed, preprocessing the image data to be processed by using the visual reasoning engine, inputting the preprocessed image data into the visual algorithm model for feature extraction, performing reasoning and decision making according to the extracted features, and outputting a final reasoning prediction result. The method has the advantages that the visual algorithm model can be improved in the aspects of model generalization ability, deployment complexity, model labeling efficiency, real-time performance, prediction accuracy and the like.

The invention particularly discloses a model compression method based on a quantized large language model, and relates to the technical field of large language model compression. The method comprises the following steps: S1, performing quantization processing on a pre-trained large language model to obtain a quantized weight matrix, and calculating a pruning optimization objective function after quantization; s2, designing a post-quantization pruning measurement index based on the quantized weight matrix, and combining the pruning measurement index with an absolute value of the quantized weight and a pruning measurement regularization result of the original weight; and S3, based on a pruning measurement index, carrying out importance sorting on the quantized weight, generating a dynamic binary pruning mask, and minimizing quantization and pruning errors through a block optimization algorithm. The method adopts a block quantization post-pruning algorithm, minimizes quantization and pruning errors, and maximizes the model compression ratio on the premise of ensuring the model performance so as to meet the requirements of actual application on the model.

The invention discloses a model optimization system and method based on deep learning, the system comprises a model square module, a my model module, a model fine tuning module and a model optimization module, and the method comprises the following steps: S1, selecting an initial model; s2, applying an optimization algorithm; s21, a pruning algorithm is applied, and weights or neurons to be removed are determined; s22, a quantification algorithm is applied, and weight and activation are converted into low-precision representation from floating-point numbers; s23, knowledge distillation: selecting a teacher model, defining a student model, and training the student model by using the output of the teacher model as a guidance signal; and S3, selectively performing fine adjustment. Based on a leading edge deep learning method, the open source technology is fully utilized for improvement, and the target of low-cost deployment is achieved under the condition that performance is not lost basically; the model size is reduced, storage is saved, the method is particularly important for mobile equipment or an embedded system, and meanwhile the transmission speed and the deployment speed can be increased.

The invention relates to the technical field of satellite transmission service or radio transmission service of a space station or an airborne station, in particular to a narrow-band interference elimination method based on spread spectrum communication applied to satellite transmission service, which comprises the following steps: S1, performing FFT (Fast Fourier Transform) operation on a digital baseband signal, converting the digital baseband signal into a frequency domain signal, determining a judgment threshold according to the background noise power, and judging a component greater than the threshold as an interference signal; s2, based on a sequential return-to-zero algorithm or a sequential pruning algorithm, frequency points with interference signal components in each frequency domain under the AWGN condition are cut off; s3, using windowed FFT operation, and popularizing the application under the AWGN condition in the S2 to the application under the narrowband interference condition; and S4, reducing the frequency domain signal after the interference signal component frequency points are removed into a time domain signal by adopting IFFT (Inverse Fast Fourier Transform) operation. The method has a good application prospect in the fields of manufacturing of space-based satellite systems and other spacecrafts, manufacturing of satellite antennas and other communication system equipment, satellite transmission services and the like.

The invention discloses a hierarchical database system tuning method and system based on a large language model, and relates to the technical field of database system tuning. The method comprises the steps that expert knowledge in the database field is processed based on a large language model, a cue word integration algorithm is used for converting the knowledge into structured knowledge capable of being used by a computer so as to optimize a search space, and a coarse granularity-fine granularity Bayesian optimization algorithm is used for determining optimal configuration, so that configuration optimization of a system level is completed; a parameter screening algorithm, a space pruning algorithm and a context Bayesian optimization algorithm are utilized, and an optimal execution plan is generated by selecting an appropriate optimization prompt for each query in the complete workload, so that configuration optimization of query levels is completed. According to the method, the search space is optimized by utilizing expert knowledge, the optimized space is explored by utilizing a Bayesian optimization algorithm, multi-level optimization is performed on the database system from a system configuration level and a query level, and the optimization efficiency and the system performance are improved.

The invention relates to the technical field of electric power ice melting, in particular to an electric power ice melting intelligent control method and system.The method comprises the steps that when it is detected that the current external environment meets the grounding knife switch opening condition according to meteorological parameters of a target line of an electric power ice melting system, a grounding opening instruction is generated; based on the grounding opening instruction and the line equipment state parameters, determining an optimal opening time point of the phase selection disconnecting link; an opening time sequence rule is generated according to the optimal opening time point, and a phase selection disconnecting link is controlled to execute opening operation according to the opening time sequence rule; determining a priority coefficient of the short circuit closing instruction; in the process of controlling the short-circuit disconnecting link to execute the closing operation according to the priority coefficient, if the ground wire and the wire are successfully connected in series, verifying the actual ice melting operation sequence to obtain an ice melting operation verification result; and based on the verification result of the ice melting operation, generating an ice melting operation optimization sequence by using a decision tree pruning algorithm. According to the invention, intelligent and accurate control of the electric power ice melting process is realized, and the influence of current oscillation on the system is effectively reduced.

The invention provides a system-level fault analyzing and tracing method and system based on multi-layer causal diagram extraction, and belongs to the technical field of fault diagnosis. Using a multi-level convolutional neural network to convert the time sequence monitoring data features into a feature matrix; by introducing a hierarchical adjacency pruning algorithm and an elastic network regularization constraint, sparse modeling of a multi-level causal matrix is realized, and a causal matrix graph, namely a prediction contribution matrix graph, is obtained; according to a proposed score quantization algorithm, direct propagation and indirect propagation effects are comprehensively considered, prediction information provided by each variable is quantified, a reason score is provided, and a fault reason variable is determined. According to the method, multi-dimensional feature information of the system-level fault can be compared, all useful information is fully utilized, the contribution degree of the system variable fault is accurately evaluated, and the high-level fault reason detection rate is obtained.

Disclosed in the present invention are a method for constructing a channel fingerprint twin, and a system. In the present invention, a coarse-grained channel fingerprint and a fine-grained channel fingerprint are respectively regarded as a physical object and a digital twin object, and image super-resolution technology is used to construct a relationship between the coarse-grained channel fingerprint and the fine-grained channel fingerprint. In the present invention, on the basis of variational inference and a re-parameterization theory, an evidence lower bound of a fine-grained channel fingerprint twin likelihood is derived to serve as an objective function, and the coarse-grained channel fingerprint is introduced as side information to design a conditional generative diffusion model for generating the fine-grained channel fingerprint, wherein the conditional generative diffusion model can be deployed in a core computing center of a channel fingerprint twin. In addition, in the present invention, a one-shot pruning algorithm and multi-objective knowledge distillation technology are further introduced to acquire a lightweight conditional generative diffusion model. The method for constructing a channel fingerprint twin provided in the present invention not only ensures the reconstruction accuracy, but also has relatively strong scalability and generalization capability in wireless communication scenarios with different fine-grained channel fingerprints.

The invention discloses a channel fingerprint twinning construction method and a channel fingerprint twinning construction system. Coarse-grained channel fingerprints and fine-grained channel fingerprints are regarded as physical objects and digital twinborn objects, and the relation between the physical objects and the digital twinborn objects is constructed by using an image super-resolution technology. According to the method, an evidence lower bound of fine-grained channel fingerprint twinborn likelihood is derived according to a variational reasoning and re-parameterization theory to serve as a target function, coarse-grained channel fingerprints are introduced to serve as side information to design a conditional generation type diffusion model for generating the fine-grained channel fingerprints, and the conditional generation type diffusion model can be deployed in a core computing center of channel fingerprint twinborn. In addition, the invention further introduces a one-time pruning algorithm and a multi-objective knowledge distillation technology to obtain a lightweight condition generation type diffusion model. The channel fingerprint twinning construction method provided by the invention not only ensures the reconstruction precision, but also has relatively strong expandability and generalization ability in a wireless communication scene with channel fingerprints with different fine granularities.

The invention discloses a hybrid expert model structured pruning and acceleration method and system based on micro expert sorting, belongs to the technical field of large language models, and solves the problems that an existing MoE pruning method cannot consider the requirements of fine-grained pruning, reasoning acceleration and structural analysis generalization at the same time, coarse-grained expert-level pruning damages the performance of a model, and the reliability of the model is poor. And fine-grained compression lacks speed increase and lacks a unified micro-analysis method. The method comprises the following steps: splitting each expert network in a hybrid expert model into a plurality of micro experts, and modeling the plurality of micro experts, so that the micro experts in different expert networks have comparability; sorting all the micro experts according to the energy indexes of the micro experts by adopting a micro expert sorting algorithm; and processing the sorted micro-experts by adopting a pruning algorithm, selecting a core micro-expert for reservation, and directly deleting the rest of the micro-experts. The method is suitable for application scenes such as edge calculation and multi-task learning.

The invention provides a user consumption behavior multi-dimensional portrait analysis method and system based on a neural network, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user historical consumption behavior data, and constructing a basic feature vector; key time sequence features are determined through a sub-sequence dynamic pruning algorithm and entropy value weighted mapping; extracting sequence features by adopting a bidirectional long-short-term memory network; constructing a multi-task adversarial feature extraction network to obtain scene invariant features; performing feature fusion to obtain multi-dimensional combined features; constructing a feature index tree to calculate user similarity; and hierarchical clustering is carried out to obtain a consumption behavior portrait. According to the invention, high-precision user portraits are realized, and scene adaptability and calculation efficiency are improved.

The invention provides a comprehensive solution for a medical ultrasonic image diagnosis task, which comprises two innovative parts, namely a CAPT parameter efficient fine tuning method based on a convolution adapter and embedded prompt tuning and a model lightweight method based on evolution pruning. Aiming at double challenges of scarcity of data and difficulty in model adaptation in the field of medical ultrasonic images, on one hand, the CAPT method introduces a convolution module into a traditional adapter to enhance the fine-grained feature extraction capability by fusing adapter tuning and prompt tuning mechanisms, and dynamically generates a prompt vector embedded with attention calculation by using adapter learning context information; the adaptability and the small sample generalization ability of the Transform model in an ultrasonic image classification task are obviously improved; on the other hand, aiming at a model edge deployment requirement, designing an evolution pruning algorithm, taking DenseNet-121 as a basic framework, inheriting parent strong neuron connection by adopting an asexual reproduction mechanism, and through a channel importance evaluation method for coupling a BN layer scaling factor and a weight L1 norm and in combination with a sparse evolution strategy guided by environmental constraint, constructing a model edge deployment model. And the classification precision is maintained, and meanwhile, the model parameters and the calculated amount are greatly compressed. The two technologies respectively break through the technical bottlenecks of medical image diagnosis from two dimensions of parameter efficient fine tuning and model structured pruning, the former improves the adaptability of the model field through feature extraction optimization, and the latter balances network lightweight and performance stability by means of an evolutionary algorithm. And an innovative technical path is provided for efficient deployment of an ultrasonic image auxiliary diagnosis system in a data limited scene and an edge computing environment.

The invention relates to the field of environment-friendly packaging boxes, and discloses a multi-modal data fusion environment-friendly packaging box intelligent design auxiliary system which comprises a packaging full-life-cycle heterogeneous atlas database module, a design constraint parameter analysis module, a topological variation index engine module, a compliance and multi-objective optimization module and a parameterization scheme generation module. According to the method, a time dimension is introduced through a topological variation index engine, instantaneous stress in a folding process is calculated in combination with a nonlinear viscoelastic model, physical evolution of a structure is simulated in a virtual design stage, and the fracture risk is predicted; meanwhile, multi-objective optimization is carried out by utilizing a Hash mask mechanism based on laws and regulations and a pruning algorithm, and a compliance design scheme containing production process parameters is output, so that the problems of lack of physical simulation and low compliance verification efficiency in environmental protection material design are solved, and the physical feasibility and the production yield of the design scheme are improved.

The invention discloses an alkaline electrolytic cell time-varying operation domain calculation method and system based on a dynamic model, and belongs to the field of power system operation regulation and control. The dynamic model-based alkaline electrolytic cell time-varying operation domain calculation method comprises the following steps: establishing an alkaline electrolytic cell operation domain model according to an electrochemical steady-state model, a thermal dynamic model and an oxygen-in-hydrogen dynamic model of an alkaline electrolytic cell; converting the operation domain model of the alkaline electrolytic cell into an optimization model; traversing and solving the optimization model under different initial temperatures and hydrogen concentrations in oxygen to obtain an operation domain; an electrochemical steady state model, a thermodynamic dynamic model and a hydrogen-in-oxygen dynamic model are introduced, so that a comprehensive time-varying operation domain model is established; therefore, the operation domain can reflect the time-varying characteristics of the electrolytic cell more accurately, a reliable boundary is provided for optimization and regulation of the system under different environmental conditions, and the production efficiency and the safety are improved; and a polyhedron pruning algorithm and a geometric constraint decoupling algorithm are adopted, so that the solving efficiency of the operation domain is remarkably improved.

The invention relates to the technical field of model lightweight, and particularly discloses a deep neural network model optimization method based on hierarchical reinforcement learning and multi-agent collaborative distillation, and the method comprises the steps: building a structured pruning searcher based on an ABC algorithm, constructing a pruning combination reduction strategy dynamic artificial bee colony pruning algorithm, and carrying out the optimization of a deep neural network model. Performing fitness evaluation to guide a search process, and outputting an optimal pruning network structure under resource constraint; establishing a staged distillation architecture and a multi-dimensional hierarchical loss function, and realizing smooth and progressive knowledge transmission between the teacher model and the assistant model; a fine-grained quantization scheme based on parameter classification is designed, differential bit widths are configured for weights, batch normalization parameters and activation output respectively, a quantization perception training loss function fusing a hardware delay look-up table model is constructed, hardware perception joint fine tuning of the network weights and quantization parameters is achieved, and the quantization precision of the network weights and the quantization parameters is improved. Therefore, the effect of remarkably improving the model compression efficiency on the premise of keeping the precision is achieved.

The invention provides a real-time incremental RAG method and system based on a dual-tense knowledge graph, and belongs to the technical field of artificial intelligence and knowledge graphs. The method comprises the following steps: through dual-tense decoupling modeling of event effective time and system input time, constructing a versioned knowledge graph and establishing a tense index; efficient retrieval is carried out by adopting a time sequence pruning algorithm based on indexes, and dynamic expansion of a graph pattern is realized through a declarative compiler; realizing abstract-free high-precision result sorting by utilizing a multi-modal concurrent retrieval and attention fusion mechanism; incremental updating of the knowledge graph is realized through streaming data processing and a distributed engine; and closed-loop optimization is formed according to user feedback. The system effectively solves the problems that a traditional RAG scheme is high in data updating delay, tense information is lost, retrieval efficiency is low and expansibility is poor, tense query accuracy, retrieval efficiency and system throughput are remarkably improved, and meanwhile operation and maintenance complexity is reduced.

The invention discloses an abnormal data detection method based on improved differential privacy and clustering algorithm optimization, and the method comprises the following steps: S1, obtaining a multi-dimensional to-be-processed data set, and carrying out the standardization processing of each parameter of a data point; s2, calculating Euclidean distance between data points based on an OPTICS clustering algorithm, adding noise to distance information by using a differential privacy algorithm, and optimizing privacy budget parameters in combination with an alpha-beta pruning algorithm; s3, core objects are judged, a core object set is constructed, and data points connected in density are classified into preliminary clustering clusters through a density analysis algorithm; s4, redistributing sparse data points in combination with a recommendation domain mechanism, and classifying the sparse points into the most adjacent high-density clustering cluster; and S5, carrying out classification by using an improved random forest algorithm, and balancing the problem of non-uniform data distribution through a weighted sampling mechanism. Through the differential privacy protection mechanism, the clustering optimization algorithm and the improved random forest classification model, efficient and accurate abnormal data detection giving consideration to privacy protection is realized.

The invention provides a personnel behavior detection method based on an LHNFNet network model. A personnel behavior image is detected by using the LHNFNet network model which is optimally constructed; in an optimized and constructed LHNFNet network model, a C2f-Star module fused with star operation calculation is designed in a backbone network layer, input features of different subspaces are combined through element multiplication, a large number of interaction items are created, the feature space dimension is remarkably improved, calculation in a compact low-dimensional feature space is achieved, implicit high-dimensional features are benefited, and the feature extraction capacity is enhanced; a lightweight detection head of a parameter sharing convolutional layer structure is also designed, and a continuous channel at the front section or the rear end part is taken for calculation, so that the parameter quantity and the memory access quantity generated by the detection head during calculation are reduced; meanwhile, the hierarchical structure of the LHNFNet network model is constrained and identified by applying a channel pruning algorithm, unimportant channels are automatically pruned, the model volume is compressed, and the detection performance is improved, so that the model meets the deployment requirements on edge equipment with limited performance resources.

The invention provides a method for evaluating the overall combat effectiveness of a combat system network. According to the method, network combat effectiveness evaluation is carried out based on subnetwork combat effectiveness and target importance. The method comprises three parts of node and connecting edge modeling, combat link searching and efficiency calculation, and node importance evaluation based on a Topsis entropy weight method. In a combat link search process, a pruning algorithm is adopted to improve search efficiency, and the overall combat ability is evaluated by comprehensively considering the importance of a combat link and an attack target. According to the method, a new thought is provided for combat system network performance evaluation, and the accuracy and efficiency of combat performance evaluation can be effectively improved.

The invention relates to the technical field of purchase demand prediction, and discloses a part purchase demand prediction method and system based on machine learning, and the method comprises the steps: obtaining modular product basic data, and generating a BOM graph structure; calculating prior probability distribution through a Bayesian inference algorithm; executing an adaptive probability pruning algorithm to solve the problem of combinatorial explosion; quantizing uncertainty by using a Bayesian deep learning network; calculating a differentiated safety inventory coefficient based on the value-at-risk model; executing an importance sampling algorithm to carry out Monte Carlo simulation on high-risk low-frequency configuration, and verifying a demand coverage rate in an extreme scene; an incremental learning mechanism is utilized to update probability distribution and a pruning threshold according to the new order data, and a self-adaptive optimization demand prediction result is output; according to the method, the inventory cost is remarkably reduced, the stockout risk is reduced, and the balance between the calculation efficiency and the prediction accuracy is realized.

The invention discloses a cross-border logistics service quotation system and a real-time dynamic quotation adjustment method thereof, and the method comprises the steps: dynamically obtaining multi-source heterogeneous data, and constructing a door-to-door service chain graph through a knowledge graph technology; business rules are compiled into a calculation unit capable of being subjected to hot updating through a dynamic rule engine, and automatic compliance auditing is carried out; generating a candidate logistics path combination based on a service chain graph and the dynamic rule, and filtering by using a path pruning algorithm; and carrying out parallel real-time calculation on the effective path by utilizing a stream-oriented calculation framework to obtain a plurality of quotation schemes, and carrying out multi-dimensional visual output on the quotation schemes. Through technology fusion, the beneficial effects of remarkably improving the quotation precision, greatly increasing the calculation efficiency, enhancing the system flexibility, reducing the user decision cost and the like are realized.

The invention discloses a micro-service instance redundant backup deployment method oriented to an edge computing environment. The method comprises the following steps of: acquiring redundant backup deployment of a micro-service instance and selection of a main instance by adopting deep reinforcement learning T-DRL based on Transform; for businesses which do not meet the service level agreement (SLA), the actual business reliability is improved through micro-service instance switching priority degradation; and for the service meeting the service level agreement (SLA), the micro-service instance deployment cost is reduced through a pruning algorithm. According to the method, under the condition that the service level agreement (SLA) is guaranteed, the deployment cost of the micro-service instance is effectively reduced, the number of active edge nodes is minimized, and resource consumption in an edge computing environment is effectively reduced.

The invention relates to the technical field of computer vision, in particular to an improved YOLOv8n lightweight inland ship detection algorithm. According to the technical scheme, the method comprises the steps of designing a lightweight detection head EFSDH and replacing an original decoupling detection head, introducing a neck network lightweight method slip-check, designing a global channel adaptive amplitude pruning algorithm GCAMBP for channel pruning, and designing a space-channel correlation knowledge distillation method SCCKD for knowledge distillation. According to the light-weight YOLO model for inland ship detection, the detection performance of the model is successfully improved, meanwhile, light weight is achieved, and the problems of real-time performance and accuracy in a computing resource limited environment are solved.