4911 results about "Server-side" patented technology

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

The invention relates to the technical field of server upgrading, and discloses a server firmware remote upgrading method and system, and the method comprises the steps: a management node obtains a device identifier list and target firmware version information of a to-be-upgraded server, and uses a distributed task scheduling algorithm to divide tasks; transmitting an upgrading instruction and a data packet through an encryption channel, and generating an incremental packet by using a differential upgrading technology; data transmission is guaranteed through heartbeat detection and breakpoint continuous transmission; after pre-verification of the server-side security sandbox, upgrading and isolating tasks in an idle period; verifying by adopting a gray release strategy, and automatically rolling back if the verification fails; and storing an upgrade state and a log by means of a block chain network. The system comprises corresponding function modules. According to the method, the upgrading efficiency, safety and reliability are improved, the influence on services is reduced, data cannot be tampered and can be traced, the problems existing in a traditional upgrading mode are effectively solved, and the method is suitable for various server firmware remote upgrading scenes.

The invention provides an unmanned aerial vehicle intelligent routing inspection method for routing inspection of energy facilities, and the method comprises the steps: carrying a high-definition camera and a thermal sensor through an unmanned aerial vehicle, carrying out the routing inspection of the energy facilities, collecting a visual image and thermal imaging data in real time, and transmitting the data to a server side. And the server performs processing, analysis, risk classification and anomaly detection on the data by using various recognition training and deep learning models. The unmanned aerial vehicle acquires three-dimensional information by using a simultaneous positioning and map construction technology, constructs a three-dimensional map, and plans a routing inspection path by using an RRT algorithm, thereby realizing multiple functions. Potential risks are identified by using a deep learning technology, and early warning and automatic obstacle avoidance are realized. The edge calculation unit processes the data in real time and transmits the data to the ground control center, and further analyzes and predicts the risk trend. The system integrates a plurality of advanced modules, realizes efficient, intelligent and automatic inspection by using deep learning, edge computing and high-speed data transmission technologies, reduces cost and risks, and provides powerful support for safety management of energy infrastructures.

The invention discloses a lightweight dynamic security authentication method and system based on PUF (Physical Unclonable Function), and the method comprises the steps: S1, a server side collects to-be-registered information, generates a random challenge and auxiliary information, receives a response corresponding to the challenge generated by an IOT (Internet of Things) device side through a PUF structure, and sends the response to the server side; storing the pseudonym and the private key of the registered user and the CRP pair of the user equipment; s2, when the server side receives the authentication request, the server side initializes the authentication request, generates a challenge of the next round of authentication by using the challenge of the current authentication in the initialization process, and sends the challenge of the next round of authentication to the Internet of Things equipment side; the server side verifies the identity of the device after receiving the data packet sent by the Internet of Things device side; and S3, updating a next round of pseudonym at the server side, generating a next round of challenge and CRP pair by the server side, and sending the challenge and CRP pair to the Internet of Things equipment side. The method has the advantages of being simple in implementation method, small in resource overhead, high in flexibility, capable of dealing with various security threats and the like.

The invention relates to a model fine tuning method, a text processing method, a medium, equipment and a program product. The method comprises the following steps: acquiring a vocabulary and an embedded layer parameter of a large language model from a server; respectively carrying out confusion processing on the word list and the embedded layer parameter to obtain a target word list and a target embedded layer parameter; sending the target embedded layer parameter to a server, so that the server updates the embedded layer parameter of the large language model into the target embedded layer parameter to obtain a new large language model; performing word segmentation and index conversion operation on the text sample by utilizing the target word list to obtain a first lexical element index corresponding to the text sample; and sending the first lemma index to a server, so that the server performs fine adjustment on the new large language model based on the first lemma index. Thus, the server side can be prevented from obtaining the corresponding relation between the embedded vector and the lexical element, and therefore the model effect can be better guaranteed through fine adjustment operation on the premise that data of a server side user is effectively protected.

The invention discloses a mass concrete intelligent temperature monitoring system and method, and the system comprises a high-precision temperature sensor, a data collection node which is connected with the high-precision temperature sensor and is responsible for carrying out the primary processing and integration of temperature data collected by the sensor, and a data collection terminal which serves as a data convergence center of the whole monitoring system and is used for collecting the temperature data. The intelligent temperature monitoring system comprises a plurality of data acquisition nodes which are connected in a wired or wireless communication mode, a data monitoring platform which operates at a server end and is a core control and management unit of the whole intelligent temperature monitoring system, and a circulating water cooling control system which is composed of a cooling water pump, a cooling tower, a cooling water pipe network, a temperature control main station and the like. The cooling water pipe network is embedded in the concrete in advance to form a closed water circulation loop; the temperature control master station receives a cooling control instruction sent by the data monitoring platform, and adjusts the rotating speed of a cooling water pump and the working state of a cooling tower according to the instruction, so that the flow and temperature of cooling water in a cooling water pipe network are accurately controlled; through flowing of circulating water in the concrete, redundant heat in the concrete is taken away, effective regulation and control over the temperature in the concrete are achieved, key indexes such as the highest temperature and the temperature gradient of the concrete are controlled within a reasonable range all the time, and concrete cracks caused by temperature stress are avoided.

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.

In some examples, a proxy system establishes a client-side secure network tunnel between the proxy system and a client device, and establishes a plurality of server-side secure connections between the proxy system and respective servers of a trust network. The proxy system load balances a plurality of connections of the client device in the client-side secure network tunnel across the servers of the trust network through respective server-side secure connections of the plurality of server-side secure connections.

The invention relates to the technical field of neuromuscular electrical stimulation, and provides a multi-modal feedback self-adaptive adjustment neuromuscular regulation and control treatment system, which comprises wearable working equipment for outputting pulse electric signals to adjust nerves and muscles to form movement or adjustment feeling, electromyographic signals, motion data and user feeling input are collected to conduct feedback adjustment on output parameters of the output pulse electric signals; the handheld control equipment is used for being connected with the wearable working equipment and controlling the output working mode and the working state of the wearable working equipment; and the server-side equipment is used for managing user information and preference of the handheld control equipment, setting function authority of the handheld control equipment, acquiring data acquired by the wearable working equipment, and realizing self-adaptive output of output parameters of the pulse electric signals of the wearable working equipment based on a self-adaptive reinforcement learning algorithm. Adaptive optimization of millisecond-level dynamic electrical stimulation parameters is realized through a multi-modal feedback data fusion technology in combination with a layered reinforcement learning architecture.

The invention provides a front-end performance optimization method based on cache management, and the method comprises the steps: S1, collecting and processing user behavior data, and obtaining target data; s2, analyzing a user behavior based on the target data, constructing a user behavior model, and predicting an access demand of a user by using a prediction algorithm to obtain a prediction model; s3, generating a caching strategy based on the user behavior model and the prediction model; and S4, storing resources on the client, the server and the content distribution network node based on the caching strategy. The cache hit rate is improved; the caching strategy is dynamically adjusted according to the actual use condition of the user, and the user requirement is more accurately met; the cache preloading mechanism can shorten the response time of the user request, can adapt to different user behaviors, and improves the user experience. The system and the electronic equipment provided by the invention have corresponding advantages.

The invention discloses a personalized federal confrontation cross-modal hashing method based on double prototypes, which mainly comprises the following steps of: clustering local class-level prototypes uploaded by a client at a server side to obtain a clustering prototype, averaging to obtain an unbiased prototype, and according to gradient change, obtaining an unbiased prototype; generating personalized hierarchical weights for the client network using the super network; the client constructs an adversarial learning process by using approximate hash codes and clustering prototypes of the two modal networks, keeps the consistency between an unbiased prototype and a local class-level prototype, and further embeds the unbiased prototype into a training process of a client model; after local training is completed, all network parameters and local class-level prototypes of the client side are uploaded to the server; and after the last communication round is completed, generating a to-be-retrieved hash code based on the final client parameter, and providing an original data retrieval result according to the original data position information corresponding to the hash code with the minimum distance.

To provide a method, an apparatus, a system, an architecture, and an interface for a wireless transmit / receive unit (WTRU) to discover available edge network (EN) management (ENM) servers associated with either an edge network (EN) service and an EN application.SOLUTION: A method may include sending a first message to a core network entity including information indicating a request for discovery of an ENM server, receiving a second message from the core network entity responding to the request for discovery of an ENM server, the second message includes ENM server information indicating (1) any number of available ENM servers and (2) respective available ENM server endpoint information, and performs communication with one or more of the available ENM servers.SELECTED DRAWING: Figure 2

The invention discloses a federal forgetting learning method based on small sample knowledge distillation. The method comprises the following steps: initializing a global model by adopting a server and broadcasting the global model to each client; each client carries out local training on the received global model by using local data, and after the local training is completed, the global model is updated and sent back to the server; the server stores and aggregates the global models sent by all the clients, is used for updating the local global model of the server, and repeats the federal learning process from S1 to S3; a target client makes a forgetting request at a certain moment, federal learning is stopped, and at the moment, a server side uses a forgetting module to generate a forgetting model and takes the forgetting model as a student model; the server side finely adjusts and compares the language image pre-training model according to local small sample data, so that the language image pre-training model adapts to downstream data and serves as a teacher model; the server uses local small sample data to carry out recovery training through a knowledge distillation method, so that catastrophic forgetting caused by client data revocation is avoided, and the performance of a forgetting model is improved.

The invention provides a streaming communication method and device based on long connection and computer equipment, and the method comprises the steps: building SSE long connection between a server and a large model when the server is started; a WebSocket long connection is established between the client side and the server side; the client sends a user dialogue text with the large model input by a user to the server through WebSocket long connection; the server receives a user dialogue text of the client, processes the user dialogue text, generates a corresponding data request, and sends the data request to the large model through SSE long connection; the large model receives the data request, processes the data request, and transmits model output information back to the server through SSE long connection; and the server receives the model output information, processes the model output information, generates a corresponding response message, and sends the response message to the client through the WebSocket long connection. The method has the beneficial effect that the communication efficiency and real-time performance between the client and the large model are improved.

The invention relates to the technical field of Web front-end development, and discloses a front-end intelligent menu caching and dynamic authority control method. The method comprises the following steps: firstly, checking whether menu cache data in a local storage of a browser exists and is valid, if so, directly using the cache data, otherwise, obtaining latest menu data from a server and caching according to a preset strategy; the menu data change state of the server side is monitored in a timed polling or WebSocket mode, and the local cache is updated in time when the data change; acquiring role permission information of the current user, matching the role permission information with the menu data, and dynamically generating an accessible menu tree; when routing jump is carried out, the type of a target address is intelligently judged, permission verification is carried out on an internal routing address, and it is ensured that a user can only access pages within the authorization range of the user. According to the application, the server request frequency is reduced, the response speed of the front-end application is improved, and meanwhile, the real-time performance of the menu data and the access security are ensured.

The invention provides a digital intelligence pharmacist medication guidance system based on artificial intelligence, and relates to the technical field of medical treatment, the system comprises a user interaction module, a server and an answer generation module, the user interaction module is used for receiving and initiating a question proposed by a user to the server; the server side is used for packaging questions proposed by a user and submitting the questions to the answer generation module; and the answer generation module is used for receiving the question from the server side, retrieving information associated with the question in a pre-constructed medicine specification knowledge base by using a retrieval enhancement generation (RAG) method, integrating the retrieved information by using a pre-trained language model to generate an answer for the question, and finally, sending the answer to the server side. According to the system and the method, a more convenient, accurate and intelligent medicine information query and medication guidance mode is provided, the quality and the safety of medical services are improved, and the use experience of the user is greatly improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a federal collaborative optimization method, device and equipment and a medium. Each participant terminal executes instruction tuning training and value alignment training based on the initial global model, generates model parameters and uploads the model parameters, and the server side aggregates the received model parameters, generates an iterative global model and repeats the training process until a preset performance index is reached. According to the method, instruction tuning and value alignment training are carried out in parallel at the terminal side of the participant, and the optimization result is fused at the server side, so that the adaptability and training efficiency of the model to multi-source data are improved, the problems that in the prior art, the optimization target is single, and aggregation strategies are lack of collaboration are solved, and the practicability and generalization ability of the model are enhanced.

A method includes receiving, at a server from a user device, a user prompt segment to a large language model (LLM), obtaining an additional prompt segment from a prompt data source, identifying a electronic address in the prompt segment, replacing the electronic address with a placeholder to generate a updated prompt segment, generating a LLM prompt comprising the updated prompt segment and the user prompt segment, and sending the LLM prompt to the LLM. The method further includes receiving a response to the LLM prompt from the LLM, the response comprising the placeholder, replacing the placeholder with the electronic address to generate an updated response, and sending the updated response to the user device.

The invention provides a handshake protocol method and system based on an SM2 algorithm and an SM4 algorithm, and relates to the field of encrypted transmission, and the method comprises the steps: obtaining first request information in a session process; sending a first SM2 public key generated based on an SM2 algorithm and first client random number ciphertext information to the server; decrypting the SM4 ciphertext information based on a first SM2 private key generated by an SM2 algorithm to obtain an SM4 target key; processing the random number ciphertext information of the second client and the random number ciphertext information of the first server; sending the second server random number target ciphertext information to the server; and processing the first request information to receive the session return ciphertext information from the server and decrypt to determine the session return plaintext information. The handshake encryption between the client side and the server side is completed based on the SM2 algorithm and the SM4 cryptographic algorithm, the SM2 algorithm and the SM4 algorithm are used for replacing an RSA algorithm, an ECDHE algorithm and a DES algorithm on which an SSL protocol depends, and autonomous controllability of data security and information security is achieved.

The invention discloses a user-noninductive cold and hot sensing remote memory exchange system, which belongs to the field of computer storage, and is characterized in that cluster nodes are divided into extensible remote memory service nodes and computing client nodes according to roles; the remote memory node comprises a remote memory exchange server and a memory resource registration module; the computing client node comprises a FrontSwap-based remote memory exchange client, and a remote memory exchange server side is registered as a memory resource which is non-inductive to a user; furthermore, popularity statistics based on a popularity histogram is realized in a kernel mode; the memory exchange behavior is guided through the dynamic decision-making module based on online reinforcement learning, the memory management efficiency and the system performance are remarkably improved, and efficient and non-inductive memory expansion service is provided for user application.

Systems and methods are presented herein for requesting a version of media content from a server that includes haptic feedback rending criteria compatible with the haptics capabilities of a client device. At a server, a request is received for a media asset for interaction on a haptic enabled client device, wherein the media asset comprises haptic feedback rendering criteria. Based on the request, haptic feedback capabilities of the haptic enabled client device associated with the request is determined. The haptic feedback capabilities of the haptic enabled client device are compared to the haptic feedback rendering criteria of one or more versions of the media asset available via the server. In response to the comparing, a version of the media asset comprising haptic feedback rendering criteria compatible with the haptic feedback capabilities of the haptic enabled client device is transmitted from the server to the haptic enabled client device.

The invention discloses an NB-IOT technology-based firmware remote upgrading method and an electric energy meter. The upgrading method mainly comprises the following steps of S1, an upgrading preparation stage, wherein the upgrading preparation stage comprises server end preparation and electric energy meter end preparation; s2, a data transmission stage: firmware data transmission, a breakpoint resume mechanism and fragment verification are included; s3, a firmware updating stage which comprises data analysis and storage, preparation before updating and firmware writing and starting; s4, upgrading verification and feedback, including function verification and feedback information sending; and S5, security measures including identity authentication, data encryption and digital signature. An NB-IOT module is arranged in the electric energy meter, and data transmission between the electric energy meter and the server is achieved through the NB-IOT communication technology. According to the remote firmware upgrading method based on the NB-IOT technology and the electric energy meter provided by the invention, reliable remote upgrading of the firmware of the intelligent electric energy meter is realized by adopting an NB-IOT communication mode and combining a fragmented data transmission mechanism, a firmware verification mechanism, a breakpoint resume mechanism and an upgrading feedback mechanism.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

The invention provides a personalized federal learning method and system for a shared model, and the method comprises the steps: enabling a server to store and initialize the shared model, a global model and a global class prototype of a client, and enabling the client to initialize a local learning weight vector; the server sends sharing models of other clients to one client, and the client sets a local sharing model as a global model and trains the global model, and uploads the sharing model and a local class prototype to the server; and the server calculates and obtains a global class prototype and a global model according to all the received shared models and local class prototypes. According to the method, the knowledge sharing problem in federal learning is solved, and the performance of a personalized model is improved; the problem of offset in local model training of the client is solved, and the obtained personalized prototype contains more abundant global information than the personalized prototype; while state dependence and communication overhead are reduced, effective fusion of global knowledge and local characteristics is realized so as to improve robustness and adaptability of the model in a complex scene.

The invention relates to a large model data protection method and device based on a trusted environment, equipment and a medium. In the scheme, after a server side obtains original large model data, the original large model data needs to be encrypted and signed, and then the original large model data and an encrypted information table are issued to a target application side; when the target application end calls the large model data, performing exception check according to a credible strategy; if the check is passed, performing signature verification on each encrypted data slice; if the signature verification succeeds, decrypting the encrypted slice data by using the encrypted information table to obtain original large model data; wherein the server side and the target application side are both in a trusted environment. It can be seen that the trusted environment and the key system are combined, and a dual protection mechanism for large model data is achieved; moreover, before the large model data is called, the operation environment of the target application end needs to be subjected to exception check, so that an attacker is prevented from acquiring or tampering the sensitive data, and all-around data protection is provided for the large model data.

The invention relates to the technical field of medical rehabilitation training, in particular to an intelligent hand training method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a training mode instruction selected by a user; real-time biomechanical data of the affected side hand are collected through a multi-source sensor array, the real-time biomechanical data comprise finger joint angles, electromyographic signal intensity and a touch pressure distribution matrix, and when the affected side hand is in a mirror image training mode, the six-degree-of-freedom motion trail of the uninjured side hand is synchronously collected; calling a corresponding algorithm model for processing based on the training mode, and generating a control instruction set containing pneumatic pressure parameters, electrical stimulation waveforms and virtual interaction instructions; the control instruction set is sent to a server side, a mechanical glove arranged on the hand of the affected side in a sleeving mode executes corresponding pneumatic-electric combined driving operation, and multi-sense feedback is synchronously presented. According to the application, the composite requirements of patients in different rehabilitation stages on training mode diversity, control accuracy and interactive immersion can be met.

Disclosed in the present invention are a large language model federated fine-tuning method and apparatus based on gradient compression. The method comprises the following steps: constructing, on the basis of a gradient tensor generated during fine tuning of a large language model, a raw data set having a time series relationship, performing inference by means of an autoencoder to obtain a reconstructed gradient data set, and constructing a reconstruction loss function to optimize the autoencoder; and initializing a base model of the large language model as a global model at a server end, the server end updating the global model to a client, using a pre-trained encoder to obtain a compressed gradient at the client, and at the server end, using a pre-trained decoder to decode and aggregate the compressed gradient, and then updating the global model. The present invention can improve the fine-tuning efficiency of the large language model and reduce computing resource requirements while ensuring data privacy protection, and is suitable for application scenarios such as communication optimization improvement and privacy protection enhancement in the process of scientific computing-oriented large model fine-tuning and training.

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 mobile communication method based on anti-quantum and national cryptographic algorithm mixing and a mobile terminal. The method comprises the steps that the mobile terminal generates a first mixed verification public key and a first mixed signature private key; obtaining a mixed signature certificate; the mobile terminal obtains the mixed ciphertext and the first symmetric key, and the mobile terminal signs the mixed ciphertext to obtain a mixed signature; the validity of the hybrid signature certificate is verified by the server side by using a second hybrid verification public key, the validity of the hybrid signature is verified by a first hybrid verification public key built in the hybrid signature certificate, and the hybrid signature is decrypted by the server side by using a second hybrid decryption private key to obtain a first symmetric key; and the mobile terminal and the server side perform encryption communication by using the first symmetric key. Compared with the prior art, the mobile communication method based on the anti-quantum and national cryptographic algorithm mixture provided by the invention ensures the secure communication between the client and the server, and can maintain the confidentiality and integrity of data even under the threat of quantum computing.

The embodiment of the invention discloses an offline intelligent wearable device NFC service processing scheme. Comprising the following steps: controlling an NFC card reader on the offline intelligent wearable equipment to be in a closed state; receiving an NFC service triggering instruction; entering a specified service application, and automatically switching the NFC card reader from a closed state to an open state through the specified service application; the target information is read from the target NFC machine tool through the NFC card reader in the open state, and the user information of the offline intelligent wearable device is written back to the target NFC machine tool, so that the target NFC machine tool interacts with the server side of the specified service application according to the user information and executes the corresponding target service; according to the target information, short-distance wireless communication connection with the target NFC machine tool is established, an execution result of the target service returned by the target NFC machine tool is received, and the maximum communication distance corresponding to the short-distance wireless communication connection is larger than the NFC communication distance of the target NFC machine tool.