185results about How to "Privacy protection" patented technology

The invention discloses a block chain-based battery full life cycle data tracing method and system, and the method comprises the steps: receiving verification request information sent by a battery pack manufacturer, calling corresponding parameter data from a storage server according to the verification request information, and carrying out the processing of the corresponding parameter data to generate a zero-knowledge proof; submitting the zero-knowledge proof, the encrypted data packet and verification request information to a data verification contract for verification, calling an authority management contract to modify the manufacturer access authority after verification is passed, collecting key operation through an audit tracking contract, performing uplink to generate a log record, and finally, according to the log record in the block chain network, performing data verification on the block chain network. The safe and traceable query of the full-life-cycle information of the battery cell is realized, the data privacy is ensured, and the credibility and high efficiency of the full-life-cycle information tracing process are ensured.

The invention relates to the technical field of data instruction optimization, and discloses an instruction optimization method and system based on privacy protection, a terminal and a storage medium, and the method comprises the steps: obtaining information of a plurality of devices through an environment perception layer, taking each device as a node, taking other devices as neighbor nodes, and calculating truncation Q values of all neighbor nodes, according to the method, the global gradient in the strategy network corresponding to each device is replaced, so that the state and action of the device are optimized, the privacy of device information is ensured by dual network isolation during optimization, and finally closed-loop optimization is performed on the strategy parameters at the current moment. According to the method, the measured data and the environment network are transmitted in an isolated manner through dual network isolation, the data security is improved, the global gradient is replaced by the truncated Q value, the complexity and the cost of calculating the global gradient are reduced, the privacy of the data is protected, and the data accuracy is improved.

The application relates to the technical field of building engineering quality detection, and particularly discloses a concealed engineering quality intelligent detection method fusing a cloud data deep learning model. The method first acquires multi-modal heterogeneous data through automatic equipment and performs strict time-space alignment; then extracts lightweight features by using an edge computing gateway and uploads the features to the cloud; in the cloud, a multi-modal cross-feature fusion network large model is used to output a detection result in combination with a cross-attention mechanism; finally, based on digital twinning technology, defects are mapped to a three-dimensional virtual model, and a rule engine is used for comprehensive health degree scoring and intelligent early warning. Through edge-cloud collaborative architecture and multi-modal data deep fusion, the application improves the precision and efficiency of concealed engineering defect recognition, realizes transparent and digital management and tamper-proof tracing of the whole life cycle of concealed parts, and effectively reduces construction quality risks.

The invention relates to the technical field of data management, and discloses a medical data secure transmission method and a big data digital signature system, and the method comprises a hierarchical processing step, a privacy enhancement step, a key negotiation step, a segmented transmission step and an audit tracking step. The system comprises a signature preprocessing module, a hierarchical signature module, a privacy purification module, a verification module and a tracing module. According to the method, a risk association map is constructed through hierarchical processing, and accurate identification of data association risks is realized; privacy and availability are balanced through differentiated privacy enhancement, authorization controllability is enhanced in combination with block chain key management, large file transmission integrity and non-repudiation are guaranteed, and whole-course audit is controllable. According to the system, the relevance between the signature and the privacy feature is improved through module design, the integrity and relevance of the signature chain are guaranteed, privacy protection and verifiability are compatible, efficient verification is achieved, the dispute traceability is provided, and pediatric medical data safety is guaranteed in cooperation with the method.

The invention discloses an electric vehicle distributed charging optimization method and system based on a carbon emission sensitive dynamic electricity price, and the method comprises the steps: setting an initial iteration strategy based on a controllable electric vehicle load model, and averagely distributing the needed charging amount to all charging time periods; defining the size of an algorithm convergence criterion according to the required convergence precision and the number of the electric vehicles; constructing a dynamic electricity price containing coupling of a load item and a carbon emission item, and calculating an EVn cost function according to the dynamic electricity price; starting a kth round of iteration, traversing all EVn in each round of iteration, and counting k from 0; other electric vehicle charging power vectors except the EVn are calculated; after each iteration, checking whether a convergence criterion is met or not; if yes, the obtained load curve is an approximate solution of Nash equilibrium, and if not, the charging strategy is updated repeatedly; and after the load curve under the dynamic electricity price mechanism is obtained, the load curve under the time-sharing static electricity price with the same electricity price level is obtained through calculation. The method is suitable for electric vehicle group charging management in a community scene.

This invention discloses a driver state monitoring and safety early warning method based on millimeter-wave radar, comprising: acquiring driver physiological micro-motion signals and driver macro-behavioral signals respectively through millimeter-wave radar to obtain driving environment information in real time; denoising the driver physiological micro-motion signals and extracting features to obtain driver physiological feature signals; constructing a time-frequency spectrum using driver macro-behavioral signals; determining the driver physiological arousal index and driver emotional state risk value based on the physiological feature signals; obtaining the driver dynamic behavior risk value based on the time-frequency spectrum; calculating an environmental complexity index based on driving environment information; constructing a driver state risk field model based on the driver physiological arousal index, driver emotional state risk value, and driver dynamic behavior risk value; calculating the driver state risk value using the driver state risk field model and environmental complexity index; and providing graded early warning based on the driver state risk value.

The application discloses a kind of distributed photovoltaic in-situ consumption power distribution network hierarchical collaborative operation control method, it is related to electrical engineering technical field, including for the power consumption problem of a large number of distributed photovoltaic and flexible resource in power distribution area, based on day-ahead-intra-day centralized optimization strategy and hierarchical-zoning coordinated control, flexible resource and distributed photovoltaic in substation area are globally scheduled, and the optimization result is applied to real-time control stage, from top to bottom for the distribution of optimized scheduling instruction is allocated, the optimal operation of in-situ consumption of distributed photovoltaic in area is realized.Therefore, using the above-mentioned distributed photovoltaic in-situ consumption power distribution network hierarchical collaborative operation control method, new type power system under complex operation mode can be accurately analyzed, the operation scheme of flexible resource and distributed power supply is optimized by collaborative optimization, the photovoltaic consumption capacity of power distribution network is improved, and the operation economy of new type power system is improved.

This invention discloses a method and system for managing chat logs based on a storage network, relating to the field of log management technology. The method includes the following steps: S1, constructing an interaction matrix; S2, constructing an association matrix; S3, calculating participation; S4, outputting summary text; and S5, assessing risk coefficients. This invention effectively divides different chat logs by constructing an interaction matrix and marks chat logs within the same conversation segment. After the initial construction of the interaction matrix, to further conserve storage resources, the position values ​​in the matrix are optimized: if the logical correlation between two chat logs is weak, the value at the corresponding matrix position is reset to zero, thus avoiding redundant storage; while for strongly correlated log pairs, their position value is retained as 1. This design quantifies the connection strength between chat logs, thereby reflecting the closeness of the relationship between users.

This invention discloses a fall detection method, apparatus, device, and medium. The method includes: determining target noise point detection information corresponding to the target radar detection information of a target person based on personnel radar detection information detected by millimeter-wave radar and noise point detection information detected by an acoustic detection device; determining the detection mode of the target person based on the target radar detection information, and determining target detection information from the target radar detection information and target noise point detection information based on the detection mode; wherein the detection mode includes an obstruction mode, a stationary mode, and a motion mode; and detecting the fall state of the target person based on the target detection information. This invention uses a combination of millimeter-wave radar and acoustic detection for fall detection, enabling continuous fall state detection even when the target person is obstructed by objects indoors, improving the accuracy and efficiency of fall detection; and since both millimeter waves and acoustic waves are invisible waves, user privacy can be effectively protected.

The present application belongs to the technical field of electrocardiosignal data processing, and discloses an electrocardio rhythm feature recognition data processing method, system, medium and program product, which comprises double verification of heartbeat quantity and ventricular premature beat proportion on acquired electrocardio feature data; generation of an instantaneous heart rate sequence, dynamic determination of a total number of bins and a heart rate step, mapping to obtain a symbol array and constructing a word sequence; calculation of Shannon information entropy and P wave disappearance probability; determination of a Shannon information entropy threshold and a P wave disappearance probability threshold, combined feature joint classification matching of the Shannon information entropy and the P wave disappearance probability, and output of a final electrocardio rhythm feature classification label result. The present application solves the problems of insufficient anti-interference ability and dependence on long-range signals in electrocardio data processing of portable devices, and realizes accurate identification of target irregular electrocardio rhythm features in complex environments without long-range signals on the basis of retaining the portability of the devices, thereby better adapting to instant data screening scenes of various terminal devices.

The application relates to the technical field of data monitoring and discloses an FDIA detection method, system, terminal and storage medium based on federal clustering average, the method comprising the following steps: constructing all standardized samples of a client and clustering; constructing a weighted average gradient of the client according to local model parameters and clustering clusters to iteratively optimize the local model; inputting the local model optimization parameters into a central server to globally average to obtain global parameters, inputting the global parameters into the client to optimize the local model; and using the optimized local model to identify false data to obtain attack data vectors and target measurement vectors in a false data injection attack. The application considers the Non-IID distribution property of distributed measurement, protects data privacy through a hierarchical architecture integrating a client, a subserver and a central server, is scalable, and improves the detection efficiency of false injection attack data.

The embodiment of the invention provides a service processing method, electronic equipment, a storage medium and a program product, and relates to the technical field of federated learning, and the method comprises the steps: obtaining historical features and real-time features of a target user in response to a service processing request initiated by the target user; inputting the historical features and the real-time features of the target user into a locally-deployed portrait reasoning model, and performing risk portraying on the target user by using the locally-deployed portrait reasoning model to obtain a risk portrait label value of the target user; performing homomorphic encryption on the risk portrait label value of the target user to obtain an encrypted portrait label value; performing risk assessment on the service processing request by using a service decision engine based on the encrypted portrait label value and the encrypted label threshold to obtain a risk assessment decision result; and executing the business processing request based on the risk assessment decision result. According to the method, the accurate user portrait can be obtained in real time on the premise of protecting the privacy of the user, and the business processing risk is reduced.

The utility model discloses a fireproof door structure, including the door body and being located on the door body observation window, be equipped with the liquid injection cavity in the observation window, be provided with non - transparent liquid in the liquid injection cavity, the liquid injection cavity is connected with the flow pipe, be equipped with the valve pipe on the flow pipe, be provided with the valve rod transversely in the valve pipe, be equipped with the flow hole on the valve rod, be equipped with the electromagnetic switch on the door body, the electromagnetic switch is connected with the fireproof system electricity, the utility model discloses through in non - transparent liquid, liquid injection cavity, electromagnetic valve and fireproof system linkage control, realized the intelligent switching of observation window state. Under the daily state, fill non - transparent liquid in the liquid injection cavity, effectively guarantee the privacy and safety of the space behind the door, when the fire breaks out, the fire control center triggers the electromagnetic switch action, opens the flow channel, empties the liquid, makes the observation window restore transparent, and the personnel in the door observes the external condition, makes the correct judgement, greatly promotes the safety guarantee of personnel in the fire.

This invention provides a privacy protection method and system for preventing misbehavior in mini-programs based on deep learning. It utilizes the collaborative efforts of deep learning and program analysis techniques to detect inconsistencies between the expected and actual behavior of mini-programs. This invention provides a novel mini-program analysis technique that associates UI information of widgets with corresponding program behaviors and determines the actual behavior of widgets based on program analysis, thereby enabling the construction of large-scale, high-quality training datasets. Based on the results of program analysis, deep learning techniques are used to learn an icon-behavior model from unstructured datasets to infer the expected behavior of widgets. This invention uses a defined three-layer standard model to detect differences between actual and expected behavior by calculating outlier scores. If any inconsistency is found, it indicates the presence of a misbehaving mini-program. The risk level of the inappropriate behavior can then be estimated, and countermeasures can be proactively implemented.

The application discloses a mechanical and electrical equipment and electrical equipment remote AI monitoring system based on a management platform, and relates to the technical field of equipment monitoring.The application comprises a data server, a display unit and a control unit, the control unit comprises an edge computing layer, a data acquisition layer and an AI analysis engine, the AI analysis engine comprises a deep learning module, a photovoltaic optimization module, an agent cluster and a fault tracing module, the output end of the data acquisition layer is connected with the input end of the edge computing layer, the port of the edge computing layer is in bidirectional communication with the port of the data server, and the output end of the edge computing layer is connected with the input end of the deep learning module and the photovoltaic optimization module respectively.The application adopts multiple agent learning technologies, can realize sharing of model parameter updating among multiple parks, protects local data privacy at the same time, constructs an industry expert rule library, cross-verify with deep learning results, and avoids misjudgment caused by the black box model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying labels for a data set without disclosing the data set to any individual computing system. A method can include receiving, by a first computing system in a multi-party computation (MPC) system, a query including a first share and a second share of a given user profile. The second share is encrypted with a key that prevents the first computing system from accessing the second share. The second share is transmitted to a second computing system in the MPC system. The first computing system and the second computing system generate a machine learning model and identify respective first and second labels. The first computing system receives the second label from the second computing system as a response. The first computing system responds to the query with a response including the first label and the second label.

According to the heterogeneous resource-oriented virtual power plant hierarchical clustering-cooperative game aggregation and trusted interaction system provided by the invention, refined and structured representation of a strong heterogeneous resource set is realized by constructing a dynamic digital twinborn archive and adaptive clustering of resources, and a hybrid prediction model fusing physical rules and data driving is adopted, so that the reliability of the system is improved, and the reliability of the system is improved. Physical rationality of behavior prediction and quantitative capture of uncertainty are ensured; through a hierarchical distributed optimization architecture of a master-slave-alliance collaborative game, a global complex optimization problem is efficiently decoupled, and real-time collaborative optimal scheduling under large-scale resource access is realized while privacy and autonomy of a resource main body are protected; through an online rolling credibility regulation capability interval evaluation mechanism, a diversified capability vector with a confidence level is output, so that the virtual power plant becomes a reliable transaction object which is transparent to a power grid and has a risk known, and the interaction credibility and credit rating of the virtual power plant in the power market are fundamentally enhanced.

The present application relates to a kind of low-carbon park integrated energy system multi-agent collaborative carbon responsibility allocation method, steps include: the multi-source data of park is collected, with the minimum integrated energy system total operating cost as goal to carry out multi-energy flow optimization scheduling;With the dynamic correction model of two-way carbon flow considering the carbon flow transfer caused by energy storage charging and discharging process, dynamic carbon flow distribution matrix is constructed;Using load carbon responsibility decoupling allocation model, total load is decoupled into multiple sub-load categories, and different carbon responsibility allocation coefficients are given for each type of sub-load;With each interest party in park as the participant of game, Nash bargaining as collaborative optimization framework, maximum all participant utility gain as objective function, Shapley value as the basis for carbon responsibility allocation, multi-agent collaborative optimization is carried out, and the carbon responsibility allocation scheme of each subject is obtained in combination with user carbon responsibility allocation coefficient.Compared with prior art, the present application ensures the fairness, efficiency and operability of carbon responsibility allocation scheme.

A collaborative optimization method and system for energy, transportation, and logistics based on alternating iterations includes: calculating logistics routes and time-series charging and discharging costs based on node electricity prices as the objective function of the upper-level model; optimizing the charging and discharging power of each vehicle in each time period, the battery level at the end of each time period, the route planning of each vehicle, and the arrival time at the node; summing the charging and discharging power of each vehicle at each node in the corresponding time period to obtain the charging load of the node; constructing a middle-level model to correct the charging load of each node; obtaining the power balance relationship between the corrected charging load of each node and the generator load as a constraint, constructing a lower-level model with minimizing the total power generation cost as the objective function, optimizing the node electricity price, and passing the electricity price to the upper-level model; and conducting collaborative iterations of the upper-level model, middle-level model, and lower-level model using the alternating direction multiplier method. This invention guides logistics vehicles to charge during periods of high new energy generation, achieving peak shaving and valley filling and the absorption of abandoned electricity at the system level.

This utility model discloses a privacy bed curtain device, relating to the field of medical supplies technology. It includes a support frame, with a first storage box and a second storage box fixedly mounted on the side and back of the support frame, respectively. A bed curtain assembly is installed inside both the first and second storage boxes. The bed curtain assembly is rotatably connected to a first support shaft on the inner wall of the first storage box, and a first extension frame is fixedly mounted on the surface of the first support shaft. This utility model, by setting the first and second storage boxes on the surface of the support frame, allows the bed curtain assembly to be stored away during normal use. In use, a drive motor can simultaneously extend the first and second extension frames to a horizontal angle, thus opening the bed curtain. Supports can be installed at all four corners of the bed simultaneously, thereby enclosing the bed with the curtain, improving patient privacy. Furthermore, the bed curtain device can move with the bed, effectively protecting patient privacy and reducing medical disputes.

The utility model discloses a kind of delivery clothes, it is related to obstetrical nursing technical field, including: coat and lower skirt, coat and lower skirt are separately set, coat includes front piece and back piece, front piece has front piece connecting edge along with the two sides of human body torso width direction, back piece has back piece connecting edge along with the two sides of human body torso width direction, two front piece connecting edges are respectively with two back piece connecting edges detachably connected, when front piece connecting edge and two back piece connecting edges are not connected, front piece and back piece can be opened towards the collar of coat, the side of back piece away from front piece is connected with first connecting structure and second connecting structure, first connecting structure is closer to the bottom of coat compared to second connecting structure, when back piece is opened towards the collar of coat, first connecting structure and second connecting structure can be detachably connected to fix back piece so that back piece keeps open state. Coat and lower skirt are not connected between each other, can better protect user privacy, convenient to use.

The utility model discloses a kind of intelligent locks of reducing signal attenuation, including front lock body, fingerprint module, antenna, code disc, locking module and PCB board, the opening is opened in the front lock body, the PCB board is set in the opening and is fixed with the front lock body, the first mounting hole is opened in the code disc, the fingerprint module is at least partially set in the first mounting hole, and clearance is formed between the first mounting hole, the antenna is installed in the clearance, the intelligent lock of reducing signal attenuation by installing antenna on fingerprint module, rather than installing in front lock body, so that signal can be received or sent by antenna through opening, to avoid signal in transmission process be interfered by the front lock body shell made of metal, to effectively improve the use efficiency of antenna, lengthen the distance of remote operation.

The invention discloses a portable real-time text recognition and translation method and system based on a lightweight Transform, and belongs to the technical field of computer vision and natural language processing. The system comprises an image preprocessing module, a text recognition module, a text translation module, a result presentation module, a model optimization engine and a system control module. A lightweight Transform architecture is adopted, and the calculation complexity is reduced through a layered mixed attention mechanism, a shared weight design, dynamic depth adjustment and a compression feedforward network. The text recognition module adopts a CNN and Transform hybrid architecture to realize detection and recognition, and the text translation module shares partial encoder weight and supports incremental decoding. The system integrates compression technologies such as knowledge distillation, quantification and pruning, realizes end-side off-line real-time operation in combination with hardware perception optimization, supports multi-language image-text translation, and has the characteristics of high precision, low delay and low power consumption.

This invention provides a method for de-identifying fields on the RWA compliance audit log chain, comprising the following steps: preprocessing configuration; determination of fields to be de-identified; template adaptation and value generation; dual-mode storage; compliance verification and closed-loop optimization. The purpose of this invention is to provide unified and fixed de-identification templates for two core sensitive fields in the RWA on-chain audit log: "personal ID number" and "corporate tax number," ensuring privacy compliance while maintaining log traceability, and without affecting the normal display and use of other non-sensitive fields.

This invention discloses an emotion analysis and feedback device based on visual and audio recognition in the field of psychological instruments and equipment technology. The device includes: a mobile base; a functional panel, vertically fixed to the top of the mobile base; a positioning ring, vertically suspended to the side of the functional panel, with an internal opening larger than the frontal contour of the human body, used for centering the user and restricting their sitting posture; a connecting arm, one end connected to the lower end of the functional panel and the other end connected to the positioning ring, used to support the positioning ring in a suspended state; and a flexible covering assembly located above the connecting arm, its surface forming a sloped structure facing the functional panel, used to guide the user's gaze to focus on the functional panel. This device features a hollow, large-sized positioning ring, which can center and align the user's torso and correct their sitting posture, avoiding blind spots caused by posture deviation. Combined with multi-directionally deployed visual and audio acquisition components, it significantly improves the accuracy of emotion analysis.

The invention relates to a novel quantum secret sharing method based on a Bell state. The method comprises the following steps that: firstly, a secret distributor sends a sequence of mixed Bell states and decoy particles to participants; secondly, the participants encode secret shares of the participants into corresponding Pauli operators, and the Pauli operators are applied to the received particle sequences; and finally, the participants recover the shared secret information through cooperation. And shared secret information can be obtained by executing Bell-based measurement by a secret distributor. Compared with other quantum secret sharing methods, the method has the advantages that the secret distributor does not grasp the secret share of the participant, even if the secret distributor grasp the shared secret information, the secret share of the participant cannot be deduced, and the privacy and benefits of the participant can be effectively protected. On the basis of meeting safety, a Bell state is used as a quantum carrier, and compared with an entangled state of n particles, the Bell state is easier to prepare; and meanwhile, various eavesdropping attacks can be effectively resisted. Compared with a traditional method, the method is more practical and higher in efficiency.

This invention provides a training method, localization method, and system for an intelligent agent localization model based on dialogue and vision collaboration. The training method is applied to a federated learning system and includes: a server initializing a global model and distributing it to a client; the client receiving model parameters, training the model using local dialogue commands and visual data, generating multimodal representations, and then inputting them into two structurally identical but parameter-independent localization branches for parallel processing, outputting localization representations and prediction results, and executing navigation decisions based on the localization results; constructing a total loss function that includes a loss term constraining the consistency of the bi-branch representations and constraining the consistency of navigation state changes, and updating the local model; the client evaluating reliability based on representation consistency and navigation stability, uploading the model update and reliability to the server, and the server using this data to weighted aggregate and update the global model, iterating until convergence. This invention can suppress noisy dialogue interference and improve localization accuracy, navigation stability, and model generalization ability in federated learning scenarios.

The application discloses a data processing method for a trusted data space, comprising: analyzing a data collaboration request by a trusted scheduling gateway, determining a temporary access token and local task data of an edge device, a data provider for collaborative processing of a multi-party collaborative task, a dynamic access route of the data provider, and a target data dimension required to be provided by the data provider; checking the access legality of the edge device based on the temporary access token and the local task data of the edge device; if the checking is passed, requesting the data provider to feed back target task data corresponding to the target data dimension based on the dynamic access route; desensitizing the target task data based on a data desensitization rule corresponding to the data provider to obtain desensitized task data; and sending the desensitized task data and the local task data of the edge device to a task execution party, so that the task execution party processes the multi-party collaborative task. The application guarantees data privacy and access security, and realizes efficient and trusted collaboration of multi-party data.

The embodiment of the application discloses a kind of multi-party collaborative automatic driving traffic responsibility determination method and system, wherein the method comprises: real-time acquisition of the original data of each data source;Each original data is calculated by hash digest;After the accident, the relevant original data of the data source involved in the accident within the accident time period is obtained, the actual hash digest is calculated and compared with the hash digest for integrity check;Identity verification is carried out according to identity certificate;According to the ciphertext accident characteristics of the relevant original data, a space-time consistency graph is constructed, the deviation data of each data source and the abnormal data source mark are calculated;According to the equipment state data, the identity verification result, the integrity check result and the deviation data, the trusted weight of the corresponding data source is calculated;According to the ciphertext accident characteristics and the trusted weight, the responsibility proportion of each responsible party is obtained;According to the responsibility proportion, the trusted weight, the space-time consistency graph and the abnormal data source mark, a responsibility determination report is generated and is chained to store evidence. The responsibility determination efficiency and the reliability are improved.

The invention relates to an AI-assisted lumbar intervertebral disc resection visual navigation device with a postoperative care monitoring function, and belongs to the technical field of medical equipment.The AI-assisted lumbar intervertebral disc resection visual navigation device comprises an AI assisting module, a visual navigation module, a postoperative care monitoring module and a data processing and transmission module, and the AI assisting module is internally provided with an image recognition model and a risk prediction model; the system is used for performing lesion recognition and positioning on image data of a patient before an operation, performing operation risk assessment in combination with multi-source information of the patient, and overlapping and displaying the operation risk in a three-dimensional model to realize real-time navigation in the operation, the AI auxiliary module realizes accurate lesion positioning and risk quantitative assessment, and the visual navigation module provides real-time guidance with the precision smaller than or equal to 0.5 mm. The postoperative nursing monitoring module achieves all-around real-time monitoring of physiological parameters, wound states and rehabilitation training, and the abnormity early warning subunit discovers the risks in time and treats suggestions in a grading mode to improve nursing pertinence.