208 results about "White box" patented technology

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention discloses an intelligent power grid dispatching instruction optimization system based on semantic analysis, relates to the technical field of power grid dispatching instruction optimization, realizes the conversion of dispatching instruction generation from black box output to white box reasoning, constructs a new intelligent dispatching normal form with deep integration of knowledge driving and data driving, and improves the intelligent dispatching efficiency. The automation level, decision reliability and operation safety of power grid dispatching are remarkably improved, and the method has outstanding innovativeness and wide engineering application prospects.

The invention provides a client identity authentication security evaluation method and system combined with adversarial sample simulation, and relates to the technical field of information security. According to the invention, end-cloud integrated evaluation is carried out under a white-box and black-box threat model for multi-mode identification objects of human face, voiceprint, fingerprint and behavior characteristics. The method comprises the steps of establishing reference performance through legal samples, generating transferable adversarial input, performing secure injection on a communication layer, calculating a robustness index, performing adaptive optimization, performing differential evaluation on protection effectiveness under protection configuration starting and non-starting, and performing risk grading and reinforcement according to a result. And finally, outputting a reproducible security and compliance conclusion through differential privacy and federal evaluation.

The present disclosure relates to a system and method for automated verification of a battery management system for a battery pack, an energy storage device, etc., and the problem to be solved is to enable automated verification for items that should be determined by modifying internal variable information or verifying results with the internal variable information during white box verification of the internal architecture logic of BMS firmware of an ESS. There is provided a system for automated verification of a BMS, including a device for automated verification configured to create a test case sequence for testing firmware operation items of the BMS, perform a simulation on the BMS, and model operation items of the BMS, a BMS controller that is connected to the device for automated verification and controls the BMS to operate by applying modeled data for the operation items to the BMS, and a BMS internal variable acquirer that acquires internal variable information of the BMS during execution of the simulation.

The invention discloses a blasting vibration and dust control-oriented multi-objective collaborative optimization parameter setting method, which comprises the following steps of: training a neural network model capable of predicting a complete vibration time travel curve and a three-dimensional dust concentration field on the basis of a training sample containing geological conditions, blasting parameters and an actual blasting effect; the model is used for carrying out multi-target optimization on a target function containing four indexes of vibration, dust, cost and regulation compliance through a preset evolutionary algorithm, a Pareto optimal solution set composed of a plurality of non-dominated parameter schemes is obtained, the schemes in the optimal solution set are analyzed through a preset explanation algorithm, and the optimal solution set is obtained. The influence and contribution degree of each blasting parameter on a prediction result are quantified, so that a black box model is converted into an interpretable white box decision; according to the method, the problems that a traditional method is difficult to balance multiple conflict targets and the decision-making process is opaque are solved, and an optimal blasting parameter scheme considering safety, environmental protection and cost effectiveness is provided for blasting operation.

The invention discloses an AI-driven full-scene intelligent test platform which comprises the following steps: performing static scanning on a target code under the triggering of a code submission event through a code static scanning module to generate a scanning result, and then inputting the scanning result into a pre-trained first AI model to obtain code quality evaluation data. And generating a white box test case through a white box case generation module. In addition, a black box test case is generated through a black box case generation module. An automatic script generation module inputs the black box test case into a pre-trained fourth AI model to generate an automatic test script; and finally, running the white box test case and the automatic test script to obtain an original test report. And inputting the original test report into a pre-trained fifth AI model through a result analysis module, and generating defect distribution data and test suggestion data. The full-scene test coverage of the software life cycle is realized, the test of each link of the software is comprehensively and systematically covered, and the test efficiency is improved.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.

The invention relates to the technical field of chips, and provides an interrupt module verification method and device, a medium and a program product, the method comprises the following steps: when it is monitored that an interrupt module generates an external observable behavior, comparing monitored behavior information with a reference value of the external observable behavior, and verifying the correctness of the external observable behavior; performing state calculation on the interrupt request input to the interrupt module, and determining an internal processing state of the interrupt request; an internal register state of the interrupt module is verified based on an internal processing state of the interrupt request. According to the invention, through a mode of combining black box verification of external observable behaviors with white box verification based on an internal processing state, comprehensive, deep and efficient function verification of a complex interrupt module is realized.

The invention relates to a scene self-adaptive white-box prompt injection attack system. The system comprises a self-adaptive context prompt generation module, an enhanced GCG algorithm-based confrontation suffix generation module and a simulator-based intelligent agent test module with a body. The adaptive context prompt generation module constructs specific prompt templates for different scenes, and automatically identifies and encodes unique semantic features of different application scenes. And the adversarial suffix generation module based on the enhanced GCG algorithm calculates the coordinate gradient of each token position, provides direction guidance for subsequent token replacement based on a double-component loss function, and adopts a weighted combination loss function to realize system optimization. The simulator-based intelligent agent test module carries out semantic understanding and intention recognition through a target large language model, converts a natural language into a control instruction, recognizes different types of commands, and maps the analyzed commands to a specific API for calling.

The invention discloses a new energy grid-connected inverter parameter identification method fusing time-frequency domain dynamic characteristics, and the identification process is divided into three stages: a time domain identification stage, a frequency domain identification stage and a time-frequency domain characteristic verification stage. According to the method, a new energy grid-connected inverter impedance characteristic curve database is established by combining time domain and frequency domain operation characteristic curves of a new energy grid-connected inverter'white box model 'of a known control strategy, and the operation characteristic curve in the impedance characteristic curve database is compared with the operation characteristic curve of a new energy grid-connected inverter'black box model'. And the control mode conforming to the operation characteristics of the new energy grid-connected inverter is identified and obtained. Compared with an existing new energy inverter parameter identification method, the new energy inverter parameter identification method has the advantages that the modeling process of the new energy inverter is simplified, and the new energy inverter parameter identification efficiency is greatly improved; and the input of a new control strategy is supported, and a guarantee is provided for the identification range and precision of the impedance characteristic curve database.

The invention discloses a confrontation sample defense method driven by a structural pixel retention strategy, and relates to the field of deep learning security. According to the method, a structural pixel retention strategy is provided, and by combining dual mechanisms of pixel-level input transformation and spatial feature transformation, the recognition performance of an original clean sample is not obviously influenced while the adversarial disturbance is effectively inhibited. According to the method, a fast gradient symbol method (FGSM), a projection gradient descent method (PGD) and Carliniamp can be effectively resisted on a CIFAR-10 data set; wagner (Camp; w) and the like, and the average recognition accuracy on a clean sample is hardly influenced. According to the method, the black box attack and the white box attack can also be resisted on the CIFAR-100 data set and the Mini-Image Net data set; and an adversarial sample subjected to white-box attack can be perceived by human eyes. The method has the advantages of being high in calculation efficiency, easy to integrate to an existing deep learning model and the like, and the adversarial robustness is improved.

The invention, which belongs to the technical field of product appearance inspection, discloses an all-dimensional inspection system for the appearance of an automobile pipeline finished product, and the system comprises four core modules, namely a full-view image acquisition work station, an image processing and interpretation engine, a result feedback and execution mechanism and a data management and tracing interface. Wherein the image acquisition work station realizes multi-angle dead-corner-free image acquisition of a pipeline through cooperative work of a multi-axis positioning clamp and a fixed camera array; the image processing engine adopts a white-box algorithm library based on physical imaging characteristics and combines a graphical configuration interface to realize efficient and reliable defect detection; and the data management interface is used for binding and tracing the whole process of the inspection data, and reversely guiding the quality information to the previous process through the closed-loop feedback module. According to the invention, manual inspection is converted into automatic and intelligent inspection, the detection efficiency and accuracy are obviously improved, the labor cost is reduced, and reliable quality guarantee is provided for automobile pipeline manufacturing.

The invention relates to the technical field of data processing, and provides a business processing method, device and equipment under a new and old system parallel architecture, and the method comprises the steps: receiving a processing request; shunting the processing request to a target system for processing according to shunting configuration; the target system comprises at least one of a new system and an old system; when the new system fails to process the processing request, identifying the failure category of the processing failure according to a white box model; when the white box model fails to identify the failure category, identifying the failure category of the processing failure according to a black box model; and re-distributing the processing request according to the identified failure category decision. According to the embodiment of the invention, the processing burden and invalid processing overhead of the old system can be reduced under the parallel architecture of the new and old systems.

The invention discloses a neural network model fusion method based on robust interpolation and Bayesian optimization, and belongs to the technical field of artificial intelligence and deep learning model safety protection. The method aims at solving the problems that robustness and accuracy of a deep neural network are difficult to consider under white-box adversarial attacks, and an existing method has the defects in the aspects of interpolation parameter selection, Fisher information estimation and iterative optimization. According to the method, efficient fusion of the neural network model is realized through data division and adversarial sample generation, baseline model normal training and adversarial training, interpolation model generation and experience Fisher information matrix calculation, Bayesian optimization dynamic search of optimal interpolation parameters, and return retraining and iterative optimization. According to the method, the confrontation robustness is remarkably improved while the standard accuracy of the model is kept, and the method can be widely applied to safety key scenes such as a face recognition access control system, financial voiceprint authentication and Internet of Things malicious software detection.

The application discloses a secure communication method and device for password algorithm detection, and relates to the technical field of network communication, and the method comprises the steps of determining a target encryption technical scheme through an intelligent grading strategy comprising hardware password device technology and white-box password technology; receiving a negotiation request sent by a client; selecting a target algorithm based on a password algorithm list in the negotiation request, in combination with server support conditions and a preset strategy; performing SSH secure communication with the client based on the target encryption technical scheme, in combination with the target algorithm, and performing password algorithm detection in the SSH secure communication process, so that the security communication cost can be reduced.

A system and method for provisioning confidential data such as unique credentials is described. The technique initializes a whitebox cryptographic software module to a particular PKI client to soft-lock whitebox cryptographic operations to the particular PKI client and uniquely encrypting the credentials with a node-locking key (NLK) derivable from a digital certificate.

The invention discloses a protection method, device and system for a block white-box cryptographic algorithm and a storage medium, and relates to the technical field of information security. The method comprises the following steps: according to a preset insertion bit sequence, inserting a first preset number of pseudo operations into any two associated real cryptographic operations in the execution process of the grouping white-box cryptographic algorithm to obtain an operation set; cyclically executing each sub-operation in the operation set to obtain a plurality of operation results; the sub-operation is real password operation or pseudo operation; according to a preset insertion bit sequence, determining a first operation result and a second operation result of the two real password operations from the plurality of operation results; verifying the first operation result and the second operation result to obtain a target verification result; and if the target verification result indicates that the first operation result is inconsistent with the second operation result, determining that execution processes corresponding to the two real password operations are abnormal, and executing protection operation.

The invention discloses a user password protection method and device based on a white-box algorithm, equipment and a medium, and belongs to the field of data processing. The method comprises the following steps: randomly generating a white box key and a salt value; according to a white-box encryption algorithm, encrypting the salt value by using the white-box key to obtain a salt value ciphertext; and based on the salt value ciphertext, the user password and the salt value, generating a user password ciphertext and a password verification voucher by using a confusion algorithm and a hash algorithm, and storing the user password ciphertext, the password verification voucher and the white-box key, the password verification voucher being used for verifying the to-be-verified password input by the user and the user password. According to the embodiment of the invention, the security of the user password can be improved.

1.The name of the design product: graphical user interface of the near-expiration product management system of electronic equipment. 2.The use of the design product: for interactive operation, display information. 3.The design points of the design product: the content of the graphical user interface. 4.The picture or photo that best indicates the design points: front view. 5.The design points of the design product are the content of the graphical user interface, and the rear view, left view, right view, top view, and bottom view are omitted. 6.The use of the graphical user interface: to display data information related to near-expiration product management in the sales management system and provide interactive operations related to near-expiration product management, including near-expiration product filtering, near-expiration product sorting, and new near-expiration product list related functions. 7.The change state description of the graphical user interface: the front view is the main interface of the near-expiration product management system of the sales cabinet; clicking "new near-expiration product" in the front view jumps to the interface change state diagram. XX in each interface represents the text content, and the gray box and white box areas in each interface are content pictures, not the shape of the content pictures. The gray box and white box areas can be replaced by text or pictures.

The invention provides a Modbus protocol automatic testing method and system suitable for an overspeed protector, and belongs to the technical field of industrial automatic testing. The method comprises the following steps: analyzing a protocol configuration file to generate a parameter list; a Modbus communication connection is established; upper computer software downloading parameters are automatically controlled through a UI, and a bottom register value is read through a protocol in real time for consistency verification; the control signal generation device inputs an analog signal and dynamically collects online data; relevance and interference verification is carried out based on a snapshot difference algorithm and a physical logic formula (such as the relation between the rotating speed and the frequency and the tooth number). The problem that the consistency of UI display and underlying storage in a traditional test is difficult to verify is solved through a black box and white box bidirectional verification mode, meanwhile, unexpected interference is effectively recognized in combination with physical formula verification, and the test efficiency and the coverage degree are greatly improved.

The invention discloses a photovoltaic power generation power prediction method based on a block chain, and relates to the technical field of block chains, and the method comprises a data provider system, a block chain system, a server system and a prediction model system. According to the method, a data provider system and a prediction model system are separated, a local model unit is introduced, it is ensured that original power generation data do not need to leave the local, model training is only carried out locally, the risk of data leakage and abuse is fundamentally eradicated, and meanwhile, by uploading model parameters instead of the original data, the real-time performance of the system is improved. The method achieves the safe utilization of data values, carries out the non-tampering evidence storage of the hash values of key data and results through the introduction of a block chain system, can achieve the comparison and verification of a server through a result verification unit, and achieves the conversion of a prediction result from the black box output to a white box output. And the credibility and the acceptability of the prediction result in severe scenes such as power transaction and power grid dispatching are improved.

The application discloses a kind of power spot market electricity price prediction model establishment method, it is related to electric power technical field.The application is by collecting the time-sharing electricity price of day-ahead market, real-time market;Utilize similar day to fill in missing value, isolated forest algorithm detects abnormal value;ARIMA data model is constructed, and periodicity is captured Price;Reasonable correction is carried out to negative electricity price prediction value, and prediction output is adjusted in combination with price upper limit, and probability prediction form provides prediction interval;Setting prediction error early warning threshold, model is re-estimated every week, and the adaptability of model to market change is maintained;The present application is based on the mature theoretical framework of time series analysis, parameter has clear mathematical explanation, and complete "white box" model, prediction process is explainable, each component can be separated and analyzed, non-stationarity of electricity price sequence is effectively eliminated by difference processing (d parameter), can adapt to the trend change and seasonal fluctuation of electric power market price, especially good at modeling autocorrelation characteristics of electricity price.

The embodiment of the invention discloses a consistency-driven confrontation and defense method. The method comprises the following steps: constructing a feature view network and a structure view network; analyzing node features of a target node by using the feature view network; analyzing the graph structure of the target node by using the structure view network; comparing whether the analysis results of the feature view and the structure view are consistent or not; and if the analysis result between the feature view and the structure view is inconsistent, determining whether the target node is attacked by adopting a structure guidance correction mechanism based on neighbor voting. According to the method, the characteristics that data packet level characteristics in network intrusion detection are easy to be manipulated by an opponent but network topology is essentially kept stable are utilized, and 95.9% of robustness is kept under strong white-box attack through double-view learning, cross-view consistency analysis and structure-guided neighbor voting.

The invention relates to the technical field of audio and video scheduling, and discloses an audio and video scheduling system with AI intelligent analysis, which comprises an audio and video acquisition standardization module, a mathematical modeling intelligent analysis module, an intelligent collaborative scheduling decision module and a closed-loop feedback calibration module. The audio and video acquisition standardization module outputs synchronized audio and video streams through preprocessing; the mathematical modeling intelligent analysis module is used for completing scene judgment, quality deviation calculation and causal derivative index generation based on multi-modal feature quantification and in combination with a white-box causal graph model; the intelligent collaborative scheduling decision module generates an optimal scheduling instruction through causal reasoning, multi-agent collaborative negotiation and global linear programming; and the closed-loop feedback calibration module dynamically optimizes the running state of the system through a triple calibration loop. According to the system, intelligent analysis is taken as a core, full-process collaboration of audio and video processing, analysis, scheduling and calibration is realized, the accuracy and foresight of a scheduling decision are effectively improved, and the audio and video transmission quality and the resource utilization efficiency are guaranteed.

The application provides a bus duct temperature warning method and device, a terminal and a storage medium. The method comprises the following steps: a white box model for representing the corresponding relationship between the bus current and the bus temperature rise is constructed according to the device mechanism modeling method and the thermodynamic principle; an initial gray box model between the bus current and the bus temperature rise is constructed based on the white box model, the initial gray box model is trained based on a machine learning algorithm to obtain a trained target gray box model; the target gray box model is used to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value; the current current value is a current value corresponding to an actual temperature value of the bus duct currently monitored, and a comparison result is obtained by comparing the difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold; and it is determined whether to trigger a warning based on the comparison result. The application generates a dynamic threshold through model prediction, and solves the problem that the abnormal temperature rise of the bus cannot be accurately fed back due to manual configuration of a static threshold.

The invention discloses a short insurance dynamic pricing method based on an AI large model, and the method comprises the steps: S1, constructing a multi-modal risk dynamic perception architecture, and outputting a unified dynamic risk vector with a fixed length; s2, establishing a real-time risk pricing adaptive mechanism based on the unified dynamic risk vector with the fixed length output in S1, and realizing a real-time pricing decision across insurance types; s3, designing an intelligent strategy generation and conflict resolution system, optimizing the real-time pricing decision across insurance types in the S2, and ensuring that the pricing decision achieves the optimal balance between market competitiveness and profit targets on the premise of meeting actuarial constraints and supervision requirements; and S4, constructing a complete decision tracing mechanism by adopting a multi-mode interpretable artificial intelligence technology, and generating an attribution report based on the data output from S1 to S3 so as to realize the conversion of the pricing decision from a black box to a white box. According to the patent technology, through innovative application of an AI large model, a new-generation intelligent pricing solution is provided for short-term insurance services.

The invention relates to the technical field of data processing, and discloses a data protection method and system for content generated by artificial intelligence. The method comprises the following steps: carrying out three-level treatment of sensitive knowledge discovery, grading and desensitization on an original corpus; a dynamic forgetting mechanism is introduced in the model training stage; a bidirectional streaming security gateway is deployed in the reasoning stage; embedding an invisible semantic watermark into the finally generated content; recording information such as watermark hash through the side chain of the block chain; and an edge calculation white box is adopted in a terminal display link. The system comprises a data management subsystem, a model training subsystem, a bidirectional streaming security gateway, a semantic watermark embedding subsystem, a block chain auditing subsystem and an edge white box playing subsystem. According to the technical scheme, full-link privacy protection can be realized, the safety of the reasoning process is improved, the robust traceability is provided, the terminal display compliance is ensured, large-scale deployment is supported, and the operation and maintenance efficiency is improved.

The application belongs to the technical field of broadcast television network, and is a DVB full-signal source fusion intelligent network management system, which comprises a three-dimensional integrated no-reference evaluation system of a protocol layer, a content layer and a device layer, and comprises the following steps: S1: performing multi-dimensional no-reference intelligent perception of multicast signal quality, deeply analyzing the protocol layer and monitoring core indexes, and simultaneously analyzing audio and video content based on a single stream to identify implicit faults; S2: performing DCM device full-life-cycle white-box monitoring, collecting device hardware states and service information through deep fusion of a standard protocol and a management information base, and realizing intelligent correlation analysis of device layer alarms and signal layer alarms; S3: performing intelligent closed-loop linkage control, constructing an alarm strategy engine, and designing a safe switching control process based on a finite state machine; and S4: deploying a distributed micro-service architecture, splitting the system into multiple micro-services and decoupling through a message queue, and realizing dynamic balanced scheduling and fault migration of tasks.

The application provides a detection method and system of a large language model security protection mechanism. The method is executed on a retrieval enhanced generation framework. In a black box access permission, the text of a query question is used as prefix text. In a white box access permission, the text vector obtained by the encoder in the retriever is coded to construct an embedding matrix. The similarity matrix between each text vector is calculated, and each text vector is subjected to clustering operation. The text of the cluster center of each category question set is used as the initial prefix text, and the prefix text is obtained after internal optimization of the category. A jailbreak question is constructed by a preset open source jailbreak question template, and prompt words are added before and after the question. The prefix text and the suffix text are combined into malicious text and added to a knowledge database. The target query question is input into the retriever, and the question answer containing the malicious text is retrieved from the knowledge database. The query question and the malicious text are input into a large language model, and whether the large language model security protection mechanism is effective is determined according to the output result.

The invention belongs to the technical field of artificial intelligence security, and discloses an anti-attack defense method based on a spiking neural network and internal dynamic activation examination. The method aims at solving the problem that tiny countermeasure disturbance is difficult to capture in existing static detection. The method comprises the following steps: presetting a feature probe in a frozen target model; calculating gradient sensitivity and generating a dynamic attention weight; constructing a time sequence input code fusing the dynamic weighted stream and the static reference stream; the static code is converted into a dynamic pulse sequence to perform time sequence dynamics auditing by using the leakage integral distribution characteristic of the pulse neural network; and generating a discrimination score according to the output layer membrane potential to intercept an adversarial sample. Through the adaptive focusing key feature layer and the time sequence amplification effect of the spiking neural network, the method can effectively recognize and hide extremely deep countermeasure traces, and remarkably improves the robustness and safety of a deep learning model under various white box and black box attacks.