Abstract: The present invention discloses an adaptive personalized federated learning method supporting heterogeneous model, based on the use of models with different structures by various participants supporting federated learning, learning the dynamic weight used for model ensemble and introducing optimization objectives for model integration in the process of training model parameters, realizing highly accurate personalized federated learning with heterogeneous and self adaptive data, the participants are enabled to benefit from federated learning in scenes with heterogeneous data at different levels. The adaptive personalized federated learning method of the present invention does not need to introduce new hyper parameters, and can be conveniently deployed in the existing federated learning system; comparing with the traditional personalized federated learning method, the present invention has stronger adaptability.

Abstract: The present invention discloses a server load prediction method based on deep learning, collecting the trend change of server load long series, and utilizing server load periodic information to establish a deep neural network prediction model to optimize peak load prediction. The present invention provides a method for improving the accuracy of neural network prediction by combining periodic information, long-term trend information, and short-term time series information, and demonstrates superiority over traditional methods in the peak load section. The method of the present invention can effectively improve prediction accuracy, provide more accurate scheduling and evacuation decision-making basis for cloud service providers, thereby reducing the redundant equipment required to ensure high reliability, reducing the operating costs of cloud service providers, and reducing the rental expenses of cloud service tenants.

Abstract: Disclosed in the present invention is a confidence-aware service pattern optimization method, wherein the service pattern optimization method comprises the following steps: (1) inputting an original pattern Pa to be optimized; (2) initializing a candidate list PaList of the original pattern Pa; (3) initializing a temperature T; (4) initializing confidence C; (5) initializing a maximum number of iterations IterMax; (6) initializing a termination threshold Th; (7) circularly searching a target pattern Pa* according to pattern optimization indexes, wherein the number of circulations is IterMax; (8) reducing the temperature T; (9) if the pattern Pa* obtained at the end of the cycle in step (7) remains consistent for consecutive Th times, obtaining the Pa* as an optimized target pattern; otherwise, jumping to step (7).

Abstract: A method and a system for automatically detecting nitrogen content, an electronic device and a medium are provided and relate to the field of nitrogen content detection. The method includes: selecting an optimal nitrogen content prediction model from a prediction model base according to a variety and a growth stage of a target crop to obtain a target model; detecting, based on a spectral image of the target crop, a nitrogen content of the target crop by using the target model to obtain a predicted value of the nitrogen content. The optimal nitrogen content prediction model is the model with best performance among a first nitrogen content prediction model constructed based on a hyperspectral vegetation index, a second nitrogen content prediction model constructed based on a sensitive band, and a third nitrogen content prediction model constructed based on a machine learning method.

Abstract: A neural model storage system and method for an operating system of a brain-inspired computer are provided. The method includes: storing a neural model on three computing nodes, selecting the computing nodes by dynamically calculating a weight according to the number of idle cores of the first computing node, the number of failures of the first computing node, and failure time of the first computing node in each failure thereof, reading the neural model in the same computing node or cross-computing node, recovering from failures of non-master nodes, recovering from failures of the master node, and recovering from a whole machine restart or failure.

Abstract: A method and a device of intra-chip routing of neural tasks for an operating system of a brain-inspired computer are provided. The method includes determining an area defined by target cores, and determining target cores in a row furthest from an edge routing area; determining whether the target cores need to be configured with relay routing; searching nearest edge routing cores in the edge routing area for all the target cores in the area defined by the target cores; configuring the target cores in a far-to-near principle; and searching relay routing cores and the nearest edge routing cores by a shortest path manner and a maximum step length of a single routing manner.

Abstract: Disclosed in the present invention is a spontaneous edge application deployment and pricing method based on an incentive mechanism. The method comprises the following steps: building an edge end application oriented spontaneous deployment system architecture; then proposing an incentive mechanism aiming at spontaneous edge application deployment and prizing; solving the spontaneous edge application deployment and prizing problem based on a backward induction method, thereby obtaining an optimal deployment solution of an edge server and an optimal prizing strategy of an application provider.

Abstract: The present invention discloses a fair task offloading and migration method for edge service networks, taking the Pareto optimality of the utility function of all user tasks executed by the edge system as the optimization objective, this approach not only takes into account the constraints of edge network resources, but also ensures the maximization of the utility function of all user tasks in the system, it proposes a new quantitative measurement index for improving the task utility quality under multi-user competition.

Abstract: The present invention discloses a fair task offloading and migration method for edge service networks, taking the Pareto optimality of the utility function of all user tasks executed by the edge system as the optimization objective, this approach not only takes into account the constraints of edge network resources, but also ensures the maximization of the utility function of all user tasks in the system, it proposes a new quantitative measurement index for improving the task utility quality under multi-user competition.

Abstract: The present invention discloses a semantic segmentation method for cross-satellite remote sensing images based on unsupervised bidirectional domain adaptation and fusion. The method includes training of bidirectional source-target domain image translation models, selection of bidirectional generators in the image translation models, bidirectional translation of source-target domain images, training of source and target domain semantic segmentation models, and generation and fusion of source and target domain segmentation probabilities.

Abstract: Disclosed in the present invention is a method for dynamic evolution of a composite service, which belongs to the field of service composition in software engineering, and comprises a composite service dynamic evolution process, a distributed service evolution mechanism and a multi-layer process evolution mechanism. The present invention can dynamically adjust the composite service according to a real-time state of an atomic service under a condition of dynamic change of the service quality of the atomic service, select different candidate services, and avoid falling into a local optimal solution. At the same time, the present invention can dynamically optimize a service process according to the selection of the atomic service under the condition of loose coupling and low constraints, while taking into account the evolution of a single service and the co-evolution of services.

Abstract: Disclosed is a service quality prediction method in a service network environment. The method includes the steps of (1) constructing a service uncertainty quality model of a user through service quality log information generated when the user invokes the same or different services multiple times; (2) tapping a set of similar users of a target user based on the service uncertainty quality model of the user; and (3) improving a matrix factorization algorithm based on similarity information of the set of similar users of the target user to achieve accurate prediction of service quality. The service quality prediction method of the present invention can provide a basis for decision-making, perform targeted service optimization based on the service quality information, and provide more accurate service recommendation.

Abstract: The present disclosure provides a method and system for determining an optimal flight height of an unmanned aerial vehicle (UAV), an electronic device, and a medium. The method includes: obtaining a multi-band crop spectral image of an experimental area in a preset scenario; performing data processing on the multi-band crop spectral image, to obtain multi-spectral orthographies of a plurality of sample plots; calculating a ground resolution of the multi-spectral orthography of each sample plot, simulating and determining a multi-band crop spectral image of each sample plot at a different flight height by a nearest neighbor interpolation method based on the ground resolution of the multi-spectral orthography of each sample plot; and for the multi-band crop spectral image of each sample plot at the different flight height, determining an optimal flight height corresponding to each sample plot by a hypothetical test method.

Abstract: Disclosed is a service quality prediction method in a service network environment. The method includes the steps of (1) constructing a service uncertainty quality model of a user through service quality log information generated when the user invokes the same or different services multiple times; (2) tapping a set of similar users of a target user based on the service uncertainty quality model of the user; and (3) improving a matrix factorization algorithm based on similarity information of the set of similar users of the target user to achieve accurate prediction of service quality. The service quality prediction method of the present invention can provide a basis for decision-making, perform targeted service optimization based on the service quality information, and provide more accurate service recommendation.

Abstract: The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.

Abstract: The present invention discloses a service caching method for a cross-border service network, wherein the method includes: a cache space of a service switch node is divided into a resident area, a change area, a pre-reclaimed area and a maintenance index area; among them, a cache hit frequency is: a resident area>a change area>a pre-reclaimed area, and the maintenance index area is used for separate storage services call path. when a service call is generated, a cache content in the cache space is replaced according to a cache value of a missed cache or a hit cache; a service router and service switch nodes in the corresponding area jointly form a hierarchical cache mode. When the cache space of any node in the service switch node is insufficient, the service switch nodes in the same area perform collaborative cache and store them in other cache space of the service switch node through indexing.

Abstract: The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.

Abstract: The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.

Abstract: The present invention discloses a service caching method for a cross-border service network, wherein the method includes: a cache space of a service switch node is divided into a resident area, a change area, a pre-reclaimed area and a maintenance index area; among them, a cache hit frequency is: a resident area>a change area>a pre-reclaimed area, and the maintenance index area is used for separate storage services call path. when a service call is generated, a cache content in the cache space is replaced according to a cache value of a missed cache or a hit cache; a service router and service switch nodes in the corresponding area jointly form a hierarchical cache mode. When the cache space of any node in the service switch node is insufficient, the service switch nodes in the same area perform collaborative cache and store them in other cache space of the service switch node through indexing.

Abstract: A method and system of graph feature extraction and graph classification based on adjacency matrix is provided. The invention first concentrates the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix which reduces the non-connection information elements in advance. Then the subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Further, it uses a stacked convolutional neural network to extract a larger subgraph structure. On one hand, it greatly reduces the amount of computation and complexity, getting rid of the limitations caused by computational complexity and window size. On the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves speed and accuracy of graph classification.