1291 results about "Client-side" patented technology

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The embodiment of the invention provides an information generation method and device, electronic equipment and a storage medium. The information generation method comprises the following steps: in response to an obtained question and user information, calling a first agent to determine a target intention based on the question and the user information; a second agent is called to determine a to-be-executed task corresponding to the target intention, the second agent is called to execute the to-be-executed task to generate target information, the target information serves as reply information of the target intention to be sent to the client side, and the first agent and the second agent are responsible for intention confirmation and task planning respectively. According to the technical scheme, the intelligent agent has the learning ability and can make corresponding actions based on the change of the environment, so that the target intention can be determined according to the problem of the user and the experience of similar problems appearing before, the to-be-executed task is further determined, the efficiency of processing the user problem is finally improved, and the user experience is improved.

The invention discloses a hotel recommendation method and device based on user behavior analysis, and a server side method comprises the steps: receiving a hotel reservation request sent by a client side, wherein the hotel reservation request carries a destination and a target user identifier; obtaining a target travel standard corresponding to the target user identifier; screening hotel data which is within a preset range around the destination and meets a target travel-on-business standard, and generating an alternative hotel set; acquiring historical user behavior data corresponding to the target user identifier before the current moment; based on the user behavior data, fitting a factor change curve graph of a plurality of preset hotel screening factors of a target user to which the target user identifier belongs in a preset period; inputting the factor change curve graph into a pre-trained personal preference prediction model, and outputting a preference keyword sequence; and screening hotels meeting the preference keyword sequence from the alternative hotel set and recommending the hotels to the client. Therefore, by adoption of the hotel recommendation method and apparatus, the recommended hotel can meet the expectation of the user, so that the hotel recommendation accuracy is improved.

The invention discloses a post-quantum TLCP protocol design method, device, equipment and medium, and relates to the field of information security, the post-quantum TLCP protocol design method is applied to a client, and the post-quantum TLCP protocol design method comprises the following steps: constructing client handshake information based on an information security technology transport layer cryptographic protocol, and sending the client handshake information to a server to receive returned server handshake information and establish connection; receiving a server-side digital certificate constructed based on the cryptographic algorithm encrypted public key information and the post-quantum key encapsulation algorithm public key information of the server-side and server-side key exchange information constructed based on the post-quantum key encapsulation algorithm and the cryptographic algorithm; and performing identity authentication based on the post-quantum digital signature algorithm public key information and the national cryptographic algorithm signature public key information to construct a pre-master key for data transmission. An active public key cryptographic algorithm and functions are reserved in an information security technology transport layer cryptographic protocol, and a post-quantum public key cryptographic algorithm is put into the protocol, so that the quantum security is realized while the active public key cryptographic security is realized.

The invention discloses a dynamic weight aggregation federated learning method and system for credit risk assessment, and the method comprises the steps: firstly, calculating the cosine similarity of each client model and the last gradient of a global model, so as to assess the consistency of model parameters; secondly, the distribution difference is quantified by calculating the data statistic difference between each client data and the global data, so that the diversity of the data is evaluated; further, a contribution degree comprehensively considering model parameter similarity and data distribution difference is obtained by combining a hyper-parameter with cosine similarity and data distribution difference; and finally, dynamically adjusting the weight of each client in global model updating according to the contribution degree of each client, so that the model pays more attention to the client with higher data quality, thereby improving the overall model performance. The federated learning method is especially suitable for processing non-independent identically distributed data, optimizes the generalization ability of the model, and is suitable for credit risk assessment in the financial field.

The invention relates to the technical field of new energy vehicle charging, and provides a new energy vehicle charging data interaction method and a charging pile operation management system. The method comprises the steps that a target charging pile responds to a charging control request sent by a BMS, attribute information of the charging control request is acquired, and a verification strategy is determined based on the attribute information; when it is determined that the verification strategy is local verification, verifying the charging control request based on historical charging data to determine whether the charging control request has an attack risk; when the verification strategy is determined to be far-end verification, extracting a first charging parameter in the charging control request, and constructing a first parameter verification request; the target client processes the first parameter verification request to generate a first verification result; and the target charging pile judges whether the charging control request has an attack risk or not according to the first verification result, so that identification of a network attack behavior is realized, and the communication security between the target charging pile and the BMS is improved.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention discloses a privacy protection-oriented personalized federal recommendation system and method, and relates to the field of federal recommendation systems. According to the method, an article preference embedding vector is constructed for each client, and a global article embedding orthogonal mapping matrix and a double-layer perceptron used for generating an article embedding mixed factor matrix are trained, so that the global article embedding matrix and the article preference embedding vector of a user are fused; a mixed article embedding matrix which is more personalized and finer in granularity is constructed for the client, and the mixed article embedding matrix is input into a personalized recommendation prediction perceptron constructed for the client to obtain a recommendation result, so that the final recommendation effect is improved; when the client side uploads data to the central server, local differential privacy encryption is carried out; according to the technical scheme, negative sampling is carried out on each client side, the positive samples and the negative samples are mixed and then jointly used for training, the training effect is improved, meanwhile, the server cannot distinguish the historical interaction situation between the client sides and the articles according to the update data uploaded by the client sides, and user privacy is protected.

The invention discloses a continuous verification method for the forgotten right of a federal reduction learning client, and the method comprises the steps: embedding an EndoraMark mark in the data of a forgotten client, tracking the performance of a global model in a plurality of training rounds, and guaranteeing that whether the data of the federal reduction learning client is successfully forgotten or not can be accurately verified. According to the method, the forgetting effect is evaluated by adopting the backdoor attack, and the potential privacy leakage problem is avoided. According to the method, an improved scaling strategy and an imprint enhancement data enhancement method are introduced, the durability of the private imprint in the federated learning model is enhanced, the influence of model benign updating on imprint dilution is reduced, and verification of the forgetting right of the client side still has high effectiveness in long-term model training.

The invention discloses a federated learning method based on quantum transfer learning, relates to the cross technical field of quantum computing and federated learning, and aims to solve the problem of performance bottleneck of traditional federated learning under non-independent identically distributed data. The local model of each client is fused with a classic convolutional layer and a quantum convolutional layer; during local training, firstly, classic features are extracted by a classic convolutional layer, and then the classic features are coded and input into a quantum convolutional layer to generate quantum features; and the two features are spliced and then classified, and model parameters are uploaded. And the server side adopts a FedAvg algorithm to aggregate parameters so as to update the global model. According to the method, a transfer learning mechanism is introduced to optimize the initialization of the quantum layer, and an experimental result shows that the classification performance and robustness of the method are remarkably superior to those of a traditional federated CNN model on a non-IID image data set, and the method is particularly suitable for a privacy protection cooperative computing scene.

The invention discloses a method and device for collaborative reasoning of multiple heterogeneous models, and relates to the technical field of artificial intelligence reasoning prediction.The method comprises the steps that a reasoning request and a cue word submitted by a client side are received; according to the context information of the reasoning request and a preset condition, selecting a plurality of matched heterogeneous models and performing Token prediction at the same time; and selecting an optimal prediction result from the prediction results of all the heterogeneous models, splicing the optimal prediction result and the cue word, if reasoning of the splicing result is not completed, submitting the splicing result as a new cue word to a plurality of matched heterogeneous models, and continuing iterative reasoning until reasoning is completed. According to the method, the overall reasoning performance and efficiency are improved, and the task execution capability of the model in a specific field is enhanced.

The invention relates to the technical field of industrial Internet of Things and wireless sensor networks, in particular to a large-scale industrial fault diagnosis system and method based on Bluetooth MESH, and efficient monitoring and fault early warning of industrial equipment are realized through low-power-consumption sensor nodes, edge computing, a Bluetooth Mesh network and a clustered Mesh tree architecture. The system comprises a client, a server, a gateway, a cluster head and a cluster node, the cluster node comprises a sensor node and a relay node, a lightweight neural network model is built in the sensor node, and edge reasoning and fault detection can be realized locally. Stable communication and real-time cooperation of large-scale nodes are ensured through a decentralized architecture of Bluetooth Mesh, establishment of a dynamic cluster head chain and optimization of an RSSI threshold value. And a low-power-consumption mechanism combining event driving and fixed polling is adopted, so that the energy consumption of the system is remarkably reduced. In-cluster communication loads are reduced through a clustering architecture, and the communication efficiency and the anti-interference capability of the system are improved in combination with channel separation of Mesh and BLE protocols.

The invention discloses a virtualization hardware verification method and verification system based on a multi-layer architecture, the multi-layer architecture sequentially comprises a first module, an exchange module and a second module, the first module is a client side, and the second module is a simulation side; the verification method comprises the following steps: starting and simulating virtual hardware equipment in a first module by adopting a virtualization platform, and generating hardware behavior data; transmitting the hardware behavior data to the exchange module; the switching module receives the hardware behavior data from the first module and processes the hardware behavior data to obtain target data meeting the requirement of the second module; the processing is to convert the language format of the hardware behavior data generated by the first module into a target language format adaptive to a second module based on a hardware event flow mechanism; the target data is transmitted to the second module, and the second module receives the target data and executes simulation of hardware behaviors based on the target data.

The invention relates to the technical field of dialogue information analysis, and discloses an emotion recognition method and system based on a federal multimodal global diffusion model, a terminal and a storage medium, and the method comprises the steps: capturing a dialogue dependency relationship through a dialogue graph network, and carrying out the forced semantic alignment through a semantic condition network; consistency of the recovery modality and the available modality in context, speaker identity and semantics is ensured, and the recovery module and the classifier module are periodically frozen to facilitate federal collaborative optimization. According to the method, federal learning and generative mode recovery are combined, so that the problem of unpredictable mode missing of the client in a multi-mode dialogue emotion recognition scene is solved, and the emotion recognition accuracy is improved.

The invention provides a communication segmentation learning system and method for adaptive channel compression, and a medium, and relates to the technical field of segmentation learning and communication compression. The system comprises a plurality of clients, a server side and a channel compression device arranged between the clients and the server side, the channel compression device comprises a channel sensitivity modeling module, a rate distortion adaptive compression module and a cross-client fair coordination module. In a channel sensitivity modeling module, channel importance is dynamically evaluated through intermediate layer activation value fusion; differentiated quantization and compression strategies are designed on the basis of sensitivity scores in a rate-distortion self-adaptive compression module, so that key information is reserved while communication overhead is reduced; and meanwhile, the cross-client fair coordination module realizes balanced distribution of communication resources among multiple clients through a fairness regularization and dual optimization mechanism, so that the influence of excessive compression on global convergence is avoided. According to the method, the communication efficiency and the training stability of segmentation learning in a complex heterogeneous environment are remarkably improved.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention discloses a geographic data processing system based on a cryptographic technology, and relates to the technical field of data security, the system comprises a server side and a client side, the server side comprises hardware equipment burnt with a binary unit and a binary unit message authentication code; the binary unit is generated by jointly compiling a source code of a space coordinate conversion processing algorithm and a source code of at least one cryptographic algorithm, and the binary unit comprises a space coordinate conversion interface and an encryption interface; the client comprises an identity verification interface, an integrity verification interface, a decryption interface, a space coordinate-based conversion interface, an encryption interface, an identity authentication interface, an integrity verification interface and a decryption interface. The server side and the client side jointly complete safety protection on input and output geographic data based on the cryptographic technology; the geographic data is subjected to space coordinate conversion and encryption processing at the server side, and a processing result is returned to the client side for decryption processing. According to the invention, the security of the geographic processing process data is improved.

The invention discloses a WebSocket-based instant message task assigning system. The system comprises two parts, i.e., a server and clients, wherein the server is deployed on an application server and configured with ports for providing external services, and the server is then started; the clients comprises an APP client and a PC client; and information data transmission among the server, the APP client and the PC client is achieved based on the WebSocket protocol, that is, the server provides the instant messaging service, and the clients achieve acquisition of data from the server. Compared with the prior art, the system provided by the invention has the advantages that WebSocket-based instant message task assigning is achieved, and information can be synchronized between the information acquisition clients, so that high timeliness, effectiveness and safety of the information are ensured.

The invention discloses an unmanned aerial vehicle group real-time image recognition method and system based on asynchronous federated learning, and the method comprises the steps: carrying out the local recognition model training of each unmanned aerial vehicle, and uploading a trained local model to a central server; calculating a corresponding Shapley value, and introducing an old degree attenuation factor to adjust the weight to obtain an adjusted Shapley value; updating the global model through weighted aggregation based on the adjusted Shapril value; and after the global model is updated, the unmanned aerial vehicle with the highest utility is selected by adopting a confidence upper limit algorithm to participate in subsequent federated learning, and client optimization scheduling in a dynamic environment is realized. The asynchronous federated learning mechanism based on the Shapley value can effectively measure the contribution of each unmanned aerial vehicle to the global model, reduces the influence of outdated updating by introducing an attenuation factor, improves the accuracy of the global model in a dynamic environment, enables the model to better adapt to the complex environment and data change of the unmanned aerial vehicle group, and improves the robustness of the unmanned aerial vehicle group. Therefore, the accuracy of image recognition is improved.

The disclosure relates to systems and methods for automated performance testing in a containerized environment. For example, a system may invoke one or more PTSs (PTSs) that each manage a respective type of performance test, such as a load test, a stress test, and a soak test. Each PTS may instantiate one or more dynamic clusters in a container to execute performance test scripts that execute operations to conduct the performance test. The PTS may estimate a testing capacity of each dynamic cluster so that the load during testing does not exceed those limits. The dynamic clusters may perform client-side load balancing that approximates real-world load profiles to more accurately simulate real-world loads based on user behaviors that would occur throughout a test session. The system may use logging and metrics from a diverse set of services that monitor target systems during the automated performance test.

The invention discloses a plating line material continuous production abnormity early warning protection method and related equipment, and relates to the plating line material related field, and the method comprises the steps: collecting the audio information of a plating line material production line, and employing a pre-constructed sound mode sample database to achieve the precise recognition of abnormal noise, abnormal vibration is judged by analyzing the difference between vibration data and a standard vibration mode, the dynamic vibration state of equipment is monitored, automatic extraction and representation of audio features and vibration features are adopted, the accuracy of state detection is improved, operation risk assessment is carried out, risks generated by misoperation are avoided, stable operation of the system is guaranteed, and the reliability of the system is improved. And remote monitoring of the equipment is realized through a client instruction, so that the accuracy of continuous production monitoring of the electroplating line material can be improved.

The invention provides a visual vehicle control method and device based on a vehicle model and a storage medium. The method comprises the following steps: generating a vehicle model image of a vehicle model according to a controllable function list; cutting a controllable component in the car model image, and determining coordinate information of the controllable component; drawing the car model image and the controllable component on the interface of the client according to the interface size of the client and the coordinate information of the controllable component; binding the vehicle control state of the controllable component with the picture processing logic; in response to an interactive operation of a user on the vehicle model image, converting parameters collected by the interactive operation into a vehicle control instruction; and a vehicle control instruction is sent to the vehicle, the loading dynamic effect is displayed in the controllable component area in the vehicle model image interacted by the user, a control result returned by the vehicle is received, and the display state of the controllable component in the vehicle model image is updated according to the control result. The vehicle control operation is more visual and comprehensive, the operation is easy, and the user experience and the operation efficiency are improved.

The invention discloses a drug molecule generation method capable of explaining a federal large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, through a data feature extraction and model initialization step, extracting multi-modal features from distributed data, constructing and initializing a drug molecule generation model, and obtaining a drug molecule generation model; s2, performing parameter optimization on a federal learning client through the large model, including local training fine tuning based on a distributed client, and integrating fine-tuned parameters into a global model of a server to improve model performance and data utilization efficiency, S3, based on the optimized model, generating drug molecular structure information including a molecular description sequence and a molecular graph; according to the drug molecule generation method capable of explaining the federal large model, on the basis of guaranteeing data privacy, the generation capacity and diversity of new molecules are remarkably improved, meanwhile, the transparency and credibility of the model are enhanced through causal analysis and an attention mechanism, the performance and robustness of the model are improved, and the method has wide application prospects.

The invention discloses a short video automatic generation method and device based on a new media content creation model, and a server side method comprises the steps: receiving a short video generation request sent by a client side, wherein the short video generation request carries a video production description text and a user identifier; inputting the video production description text into a pre-fine-tuned large language model, and outputting video production scheme information; searching temporary video materials and system materials uploaded by the user from a BI system through the user identifier; under the condition that the temporary video material is not empty, inputting the video production scheme information, the temporary video material and the system material into a video generation model; or under the condition that the temporary video material is empty, inputting the video production scheme information and the system material into the video generation model; and outputting the target short video, and sending the target short video to the client. Therefore, by adopting the embodiment of the invention, the generated short video can show rich layering sense and emotion tension, so that the quality of the short video is improved.

The invention provides a federated learning posterior inference method and system based on mutual information regularization, and the method is applied to a client, and comprises the steps: receiving an initialized global model or an updated global model; initializing a local model according to the initialized global model or the updated global model, and determining the initialized local model and prior probability distribution of the local model; carrying out model training on the initialized local model by adopting local data, and determining a loss function of the local model; according to the loss function of the local model, performing first updating processing on the local model based on back propagation; performing second updating processing on the local model according to Gaussian noise sampled from preset normal distribution; determining a local model change amount; and sending the local model change quantity to a server side. Through the method and the device, mutual information regularization of the global model and the data is introduced, deviation in posterior estimation is relieved, and generalization ability of the global model is improved.

The present application relates to the field of federated learning and privacy protection, and particularly relates to a direction-aware local differential privacy federated learning method. The present application effectively solves the problem of serious decline in model performance caused by the destruction of gradient direction information by traditional differential privacy methods by adopting a direction-aware noise selection strategy based on an exponential mechanism locally on the client side. Specifically, the client first performs L2 norm clipping on the gradient to control the sensitivity, then generates K candidate noise vectors from a Gaussian distribution, evaluates the direction consistency of each candidate noise and the true gradient through cosine similarity, and selects the noise closest to the gradient direction in a probabilistic manner using the exponential mechanism, and finally adds the selected noise to the gradient to complete the perturbation. The method provides a strict epsilon-local differential privacy mathematical proof for the entire noise selection process, reduces the calculation time by about 80% compared to existing direction-aware methods, and achieves a triple balance of privacy protection, model utility and computational efficiency.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis model construction method based on distributed causal discovery and federated learning. According to the method, a covariance tensor containing statistical association information of an observation variable and an agent variable is obtained, a global causal graph is obtained by combining a federal causal discovery method introducing the agent variable, then a converted causal intensity matrix is embedded into a graph convolutional neural network, and finally a personalized and global diagnosis model is trained by using a FedAvg framework fused with a Dito algorithm. According to the method, distributed heterogeneous data can be effectively processed while data privacy is protected, model interpretability is improved by mining a causal relationship between variables, global generalization and client personality requirements are considered, the accuracy and generalization ability of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The method is especially suitable for fault diagnosis of industrial bearings and other scenes needing dispersed sensitive data processing.

The invention discloses a fault reason analysis method and device for an IT operation and maintenance scene. A server side method comprises the steps that a question request sent by a client side is received; the question request carries a to-be-analyzed operation and maintenance question; converting the problem into high-dimensional vector representation as a target vector; calculating the similarity between the target vector and a vectorized object of each entity stored in a preset dynamic vector knowledge base, and taking the entity of which the similarity is greater than a preset similarity threshold as a target entity; querying a target vectorization object and a target field attribute corresponding to each target entity from a preset dynamic knowledge graph; according to the target vectorization object and the target field attribute, constructing a plurality of prompt words used for guiding a large language model to carry out fault reason analysis; and inputting the plurality of prompt words into a pre-fine-tuned large language model, outputting a fault reason corresponding to the operation and maintenance problem, and sending the fault reason to the client. Therefore, by adopting the method and the device, the fault reason of the system can be quickly positioned, so that the stability of the system is improved.

The invention discloses an intelligent beef cattle breeding monitoring management system based on whole industry chain data. The system comprises a data acquisition module which is used for deploying sensors and data acquisition terminals in each link of a beef cattle whole industry chain for data acquisition; the data transmission and integration module is used for transmitting data acquired in each link to a cloud data center for processing by using a wireless communication technology; the intelligent analysis module is used for mining the integrated data and adjusting breeding processes such as seed selection and matching, a nutrition formula and a management strategy; the monitoring and early warning module is used for setting a key index threshold value, and when the key index exceeds the threshold value, the system automatically sends out early warning information to a mobile phone terminal or a computer client of a culture worker; and the breeding decision support module is used for providing guidance for the breeding direction according to long-term accumulated data and analysis results and screening out good beef cattle individuals and families thereof. The system has the advantages of high breeding efficiency, high beef cattle variety improvement speed and the like.

The invention discloses a temperature control method of a board card, a program product, electronic equipment and a storage medium, and relates to the technical field of big data algorithms, and the method comprises the steps: obtaining the current temperature of the board card collected by a plurality of temperature sensors in a working state to determine the actual temperature of the board card, and calculating the difference value between the current temperature and a preset temperature; and based on a comparison result of the difference value and a preset safety threshold value, generating a target adjusting impedance of a heating circuit of the board card or a target wind speed of a fan so as to adjust a variable resistor of the heating circuit or the wind speed of the fan, so that the actual temperature is in a preset safety interval. The technical problems that due to the fact that one path of sensor of board card design is abnormal, the whole board card works abnormally, user service interruption is caused, board card cost and maintenance cost are increased, and manpower and material resources are wasted are solved, and the purposes of dynamically adjusting board card impedance, heating circuit power and fan rotating speed according to the environment temperature to ensure normal work of the board card and improving the service life of the board card are achieved. And the technical effect that the service interruption of the client is avoided is ensured.