58results about How to "Realize accurate quantification" patented technology

The invention provides a power business anti-quantum cryptography migration progressive control method based on risk awareness, and belongs to the technical field of migration control, and the method comprises the steps: collecting key parameters of all to-be-migrated businesses in a power system, carrying out the standardization preprocessing of the collected key parameters, and constructing a business feature matrix; acquiring relevant parameters of quantum attacks in real time, and calculating a real-time quantum attack risk value of each service to be migrated; based on the service feature matrix and the real-time quantum attack risk value, constructing a dynamic migration rhythm adaptation model, and calculating a migration priority, a migration rate and a migration interval of each service to be migrated; and constructing a migration fault-tolerant and recovery model, monitoring the migration process in real time, calculating a fault influence range and recovery cost based on fault information, and executing corresponding adjustment operation. Accurate, dynamic and high-reliability control of anti-quantum password migration of the power business is realized, safe and stable operation of the power business in the migration process is guaranteed, and the migration efficiency and the anti-quantum attack capability are improved.

The invention belongs to the field of soil environment pollution simulation and prediction, and provides a prediction method based on a two-point chemical unbalanced model, which is used for quantitatively evaluating the effect of regulating and controlling the migration behavior of p-nitrophenol in acid red soil by straw stalk biochar. According to the method, a biochar-red soil composite medium system is constructed, a soil column leaching experiment is carried out to obtain penetration curve data, a two-point chemical unbalance model in CXTFIT software is utilized to carry out parameter inversion, and key parameters such as a retardation factor, a two-region distribution coefficient, a mass transfer coefficient and a liquid phase degradation coefficient are accurately identified. Model verification shows that the goodness of fit is high, and a parameter change rule reveals the inherent mechanism of enhancing the pollutant retention capacity, changing the proportion of fast and slow adsorption sites and influencing mass transfer kinetics by the biochar. Based on the verified model, the long-term migration behavior and pollution risk of p-nitrophenol under different environmental conditions can be simulated, and a scientific basis is provided for groundwater pollution control and remediation strategy optimization.

The application belongs to the technical field of fluorescent probe detection, and discloses a bimetallic nanocluster fluorescent probe compound and application thereof in preparation of a product for rapidly detecting acetylene. The fluorescent probe compound is a gold-copper bimetallic nanocluster solution modified by adamantane thiol, which is used as a fluorescent probe for acetylene detection. The solution has a clear and controllable preparation process. Through specific proportioning and reaction conditions of cuprous chloride, adamantane thiol, tetrachloroauric acid, sodium borohydride and other reagents, a probe solution with stable fluorescence characteristics can be prepared. The bimetallic nanocluster fluorescent probe compound is applied in preparation of a product for rapidly detecting acetylene. Through specific combination of fluorescent molecules and acetylene gas to trigger the secondary effect of fluorescence quenching, detection is realized, and other substances cannot trigger quenching. At a wavelength of 700 nm, the linear relationship (R 2 >0.99) between the fluorescence intensity and the acetylene concentration realizes accurate quantification of the acetylene concentration, and the minimum detection limit is 0.026 muL / L.

A method and device for evaluating the influence effect of an academic recommendation algorithm based on a social robot, the method comprising: collecting academic papers, citation relationships and author information through a data interface or a web crawler, and screening target personnel data according to a research field; constructing a virtual social robot and establishing a research interest vector of the virtual social robot based on historical literature and research theme information of the target personnel; presetting multiple literature acquisition strategies for the social robot to form differentiated experimental conditions; controlling the social robot to perform operations such as paper retrieval, access and click recommendation on an academic platform, and recording literature access paths, recommendation results and browsing sequence data in real time; calculating the research theme distribution of the social robot according to a literature set contacted in the experiment, and comparing the research theme distribution with the initial research interest, so as to obtain the change degree of the research direction; and comparing the change results of the research direction of the social robot under different strategies, and quantitatively evaluating the influence degree of the recommendation system on the research direction evolution of the scientific researchers.

The invention relates to the technical field of cultural relic identification and visual large model fusion, in particular to a ceramic cultural relic full-dimensional identification system and method based on multi-modal visual large model and microscopic feature fusion. After high-quality applicable data are screened, other data are calibrated with the optimal mode as the reference, cross-mode deviation is eliminated, and a data foundation is built firmly; the core features are converted into topological nodes, association strength is calculated, feature association edges with guidance are established, a traceable and explainable identification guidance link is generated, and the strong ceramic identification logic requirement is met; the optimal link is screened to be combined with the pre-training model, and the identification precision and the careful property are balanced by depending on a confidence hierarchical judgment mechanism, so that the black box pain point of the traditional model is effectively solved, and the credibility of the identification result is improved.

The invention discloses a hydropower station peak capability evaluation method under an extreme scene. The method comprises the following steps: automatically classifying hydrological scenes of the hydropower station based on a ten-day runoff drought threshold value and a ten-day runoff flood threshold value of the hydropower station in a kurtosis period; based on the hydrological scene classification result of the hydropower station, simulating the operation process of the hydropower station by using a hydropower guarantee supply absorption operation model, and obtaining the peak duration of the hydropower station so as to construct a training set; and constructing a hydropower station peak capability evaluation model based on an extreme gradient lifting method, training the hydropower station peak capability evaluation model by using the training set to obtain a trained hydropower station peak capability evaluation model, and further obtaining a peak capability evaluation result and a corresponding visual analysis result of the hydropower station in a peak summering period. According to the method, the problem that the peak capacity of the hydropower station in extreme weather cannot be quantitatively evaluated by a traditional method is solved, the conversion from passive response to active pre-judgment is realized, and the scientificity and foresight of a power grid supply guarantee decision are improved.

The application discloses a kind of high-wet high-salt environment under transformer cellulose insulating paper aging evaluation method, comprising the following steps: constructing cellulose insulating paper multi-environment molecular dynamics simulation model;Simulation and extraction micro-characteristic parameters;Based on the improved entropy weight-TOPSIS method, the correlation model of micro-characteristic parameters and aging degree is established;Based on BP neural network, the mapping relationship model of field detection index and micro-characteristic parameters is constructed;Transformer cellulose insulating paper aging state evaluation and life prediction.The method of the application is aimed at the special service environment of high-wet high-salt offshore transformer, starting from micro-mechanism, the precise evaluation of insulating paper aging degree is realized through quantitative algorithm, and the evaluation result is fitted to the actual operating state of transformer under marine environment, to provide scientific basis for operation and maintenance and life evaluation of offshore wind power transformer.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

The application belongs to the technical field of tunnel engineering disaster prevention and reduction, and specifically discloses a tunnel / gallery structure toughness calculation method under disaster coupling, which comprises the following steps: determining the disaster source type borne by the target tunnel / gallery structure; constructing a lining index system based on key parameters; constructing a tunnel / gallery-rock-soil refined numerical model, determining a disaster intensity index, and carrying out vulnerability analysis; collecting multiple sets of measured data of the toughness index of the target tunnel / gallery structure at different times; combining weighting based on dynamic game equilibrium and decision test and evaluation laboratory method, and calculating the dynamic weight of each index; according to the multiple sets of measured data and the above toughness index system, multiple composite performance parameters are solved; through comprehensive evaluation of the multiple sets of composite performance parameters, the damage state and toughness strength of the target tunnel / gallery structure are determined, and the comprehensive toughness grade of the structure is determined. The application can realize accurate quantification of the toughness of the tunnel / gallery structure under disaster coupling.

This invention relates to the field of bid document management technology, and in particular to an automatic bid document review method and system based on natural language processing. The method includes: identifying target sections; determining historical performance and financial information; judging whether false performance exists; analyzing the complete sentences containing target keywords; calculating material scores; determining price deviation; and determining the review result. This invention achieves multi-dimensional and comprehensive review of bid documents by intelligently analyzing multiple sections such as qualifications, risks, materials, and pricing. This effectively improves the comprehensiveness and depth of the review. Utilizing natural language processing technology, it can accurately identify and extract key information from bid documents, overcoming the subjectivity and limitations of traditional manual review. Quantitative assessment based on risk level significantly improves the accuracy and relevance of risk identification. By comprehensively weighing material scores and price deviation, it significantly improves review efficiency.

The present application relates to carbon emission monitoring technical field, especially to a kind of integrated energy house's carbon emission monitoring method and system.The method includes the following steps: periodically monitoring real-time supply and return water temperature difference in integrated energy house heating system normal heating state, to assess the loop flow imbalance degree of each heating loop;According to loop flow imbalance degree, respectively account the overheating loss caused by user behavior and the heat source degradation energy consumption caused by system compensation operation, to convert the carbon emission increment data of heating system;Flow regulation processing is carried out to overcurrent loop and undercurrent loop, and integrated collector valve adjustment instruction data is generated.The present application realizes accurate quantification of additional carbon emission caused by uneven flow distribution by monitoring distributed heating pipe network flow in integrated energy house.

The application discloses a laboratory motion line safety early warning method and device, which realizes personnel space-time trajectory reconstruction and accurate identification of abnormal motion line segments by real-time collection of relevant personnel positioning and behavior monitoring data streams, formation of motion line basic data with space-time stamps and identity tags, and combination with standard templates, and provides reliable support for risk assessment. By combining personnel characteristics and basic risk coefficients corresponding to the identity, the personal risk contribution value is calculated and the personal risk field is generated, realizing accurate quantification and differentiated assessment of personnel risk. Superimposing the personal risk field generates a dynamic group risk heat map, which can intuitively present the global risk distribution; building a risk coupling network can predict the conduction effect of high-risk areas, dynamically determine the early warning threshold according to the regional personnel and activity types, effectively improve the early warning accuracy, and reduce false positives and false negatives. In summary, the application realizes accurate control and risk early disposal of the whole process of laboratory motion line safety, and significantly improves the fine and intelligent level of safety management.

This invention provides a multi-sensor fusion-based dual-modal health data acquisition system for equipment operation, comprising: a miniaturized data acquisition terminal, an edge computing module, and a low-power Bluetooth transmission module. The miniaturized data acquisition terminal is fixedly installed on the operating mechanism of the monitored equipment and internally includes an integrated sensor module. The integrated sensor module is used to collect operational behavior modal data and health modal data in real time during equipment operation. The low-power Bluetooth transmission module is used to transmit the operational behavior modal data and health modal data to the edge computing module. The edge computing module is used to perform time alignment and feature extraction on the operational behavior modal data and health modal data, and determine the dynamic correlation between equipment operational behavior parameters and equipment wear parameters based on the time alignment and feature extraction results. This effectively solves the problems of data isolation, delayed analysis, and disconnect between training and maintenance in traditional methods.

This application relates to a method and application for determining the amount of conductive agent in each layer of a multilayer coated electrode. The method includes the following steps: determining the optimal amount of conductive agent in a single-layer coated electrode with m different areal densities, wherein the single-layer coated electrode includes a single active material layer; based on the optimal amount of conductive agent in the single-layer coated electrode with different areal densities, constructing a fitting curve between the optimal amount of conductive agent in the single-layer coated electrode and the areal density of the single-layer coated electrode, obtaining a functional relationship with areal density as the independent variable G and conductive agent amount as the dependent variable B; for each active material layer in the multilayer coated electrode, the sum of the areal densities of the active material layer and all active material layers located on the surface of the active material layer away from the current collector is used as the independent variable G in the functional relationship, and the obtained dependent variable B is used as the amount of conductive agent in the active material layer. This achieves precise quantification of the amount of conductive agent in each layer of the multilayer coated electrode.

The application discloses a mine area closed goaf pollution release early warning method based on water quality and water level monitoring, and belongs to the underground water monitoring field. The method comprises the following steps: collecting water level data and representative water samples of water-filled rock layers, performing water chemical and isotope analysis on the water samples, analyzing the direct contribution water volume of each water-filled rock layer, inputting a data set containing dynamic monitoring parameters and future expected parameters into a pre-trained water level prediction model to obtain a predicted water level, calculating the expected exchange flow between the closed goaf and the water-filled rock layer by using the Darcy seepage formula based on the predicted water level and the goaf shape, and calculating the expected pollutant discharge amount in the prediction period. If the expected pollutant discharge amount exceeds a preset safety threshold, a warning for the corresponding closed goaf is generated, so that the early warning of the pollutant discharge of the closed goaf is realized, and timely and scientific decision-making basis is provided for the active prevention and control and precise management and control of the mine area underground water pollution risk.

The invention relates to a table tennis practice equipment cleaning demand prediction system based on big data analysis, and belongs to the technical field of data analysis. According to the system, equipment use data and environment data are collected in real time through a data collection module, a pollution index is dynamically calculated through a pollution index calculation module, a future cleaning demand time point is predicted based on a long-short-term memory network, and a decision execution module outputs a cleaning signal according to a prediction result. The problem of resource waste caused by fixed-period cleaning is solved, precise quantification of the equipment pollution state and intelligent prediction of the cleaning requirement are achieved, the maintenance efficiency is remarkably improved, and resource consumption is reduced.

The application discloses a battery pack energy state estimation method, device, equipment, storage medium and product, which are used for accurately and reliably estimating the energy state of a battery pack. The method comprises the following steps: acquiring a first operation data sequence of a first battery pack, wherein the first operation data sequence comprises operation data of the first battery pack at multiple time points; inputting the first operation data sequence into a first model to output a first energy state of the first battery pack; wherein the first model is obtained by training based on a second operation data sequence and a second energy state of a second battery pack, the single cell parameter distribution of the second battery pack is similar to the single cell parameter distribution of the first battery pack, and the single cell parameter distribution of the battery pack is obtained by a second model based on an operation data sequence of the battery pack.

This invention relates to a multi-energy flow carbon footprint tracking method based on a carbon memory matrix and conservation constraints, belonging to the technical fields of energy internet, industrial carbon management, and intelligent manufacturing. It constructs a multi-source energy-carbon coupled data foundation to achieve unified data across multiple time scales; establishes an energy storage carbon memory matrix model with time and temperature hierarchies to characterize the dynamic evolution of carbon intensity in the energy storage system; based on this, it constructs a multi-energy flow directed carbon flow graph to achieve unified carbon tracking and allocation for electrical, thermal, and cooling energy flows; introduces carbon flow conservation constraints and a weighted least squares state estimation method to achieve physical consistency verification and self-repair of carbon data; finally, it constructs a mixed-integer linear programming model with explicit embedded carbon memory constraints to achieve optimal multi-energy coordinated scheduling of carbon emissions throughout the entire life cycle. This invention realizes the transformation of carbon footprint from static accounting to dynamic tracking, and from post-event statistics to proactive optimization, significantly improving the reliability of carbon data and the effectiveness of low-carbon scheduling.

The quantitative medicine outlet bottle comprises a bottle body, a bottle cap, a medicine dividing piece and a medicine outlet piece, a bottle opening is formed in one end of the bottle body, the bottle cap is correspondingly connected with the bottle opening in a threaded mode, an isolation piece is arranged in the bottle cap, a plurality of medicine outlet holes are formed in the isolation piece, the medicine outlet holes are matched with pills, and a rotating groove is formed in the side, correspondingly away from the bottle body, in the bottle cap. The medicine distributing part is rotationally arranged in the rotating groove and tightly attached to the medicine outlet holes, so that the number of the medicine outlet holes can be controlled by rotating the medicine distributing part, the medicine outlet part is rotationally arranged in the rotating groove and corresponds to the side, away from the medicine outlet holes, of the medicine distributing part, and a medicine containing groove is formed in the side, corresponding to the partition part, of the medicine outlet part; by means of the linkage design of the medicine distributing piece and the medicine discharging piece, accurate quantification, flexible adjustment and convenient taking of the pills are achieved, the problems that in the prior art, dosage is not accurate, operation is complex, and the pills are prone to being blocked are solved, and the medicine dispensing device has remarkable practical value and market prospects.

The invention discloses a novel energy system safety risk analysis method and device based on carbon-energy coupling, and the method comprises the steps: inputting energy data and a carbon emission factor, and building a carbon-energy coupling dynamic model; generating typical risk scenes such as energy price fluctuation and extreme weather, and reducing to obtain a simplified scene set; then an upper-lower layer coupled risk quantification model is constructed, the upper layer focuses on energy supply cost and carbon emission optimization, and the lower layer focuses on power system operation cost minimization; and finally, solving based on a Monte Carlo method, and outputting a multi-dimensional risk assessment index. According to the method, the problem that multi-field risk interaction is difficult to describe by a traditional method is solved, accurate quantification of the multi-scene risk of the provincial novel energy system is realized, and scientific support is provided for provincial energy safety decision.

This invention discloses a lactation-promoting composition containing a lime flavonoid extract, comprising, by weight, the following raw material components: 10-50 parts lime flavonoid extract, 5-30 parts synergistic active ingredients, and 20-85 parts excipients; wherein the lime flavonoid extract contains ≥80% total flavonoids (calculated as hesperidin), of which hesperidin ≥40%, neohesperidin ≥15%, naringin ≥10%, and hesperidin ≥5%. Using the lime flavonoid extract as the core, it achieves highly efficient lactation promotion through multi-target synergistic effects. In livestock applications, it can increase milk yield in dairy cows by 15%-28% and milk yield in sows by 20%-38%, while significantly improving milk fat, milk protein, and other milk nutritional indicators.

This invention discloses a method for screening and regulating environmentally adaptive minor genes during wheat propagation, comprising the following steps: Step S1: Define the target environment and adaptability phenotype, extract key environmental covariates of the target ecological zone, and construct a digital environmental profile; screen physiological and ecological traits corresponding to environmental stress and measure them using high-throughput phenotyping technology; Step S2: Construct genetic materials and design experiments, using a genetic material system composed of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines, and setting up multi-environment gradient experiments and precise stress control experiments; Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change synergistically with environmental signals; determine gene information strongly correlated with adaptability phenotypes through weighted gene co-expression network analysis; Step S4: Functional verification and mechanism analysis to confirm the environment-specific function of the genes.

This invention discloses a precise deagglomeration and dispersal modification control system for non-metallic powders, belonging to the field of intelligent control technology based on deep learning. Specifically, it includes: a non-metallic powder identification module, a non-metallic powder deagglomeration module, an atomization coating module, and a coordination control module. The non-metallic powder identification module deploys hardware units to collect raw powder data; the non-metallic powder deagglomeration module constructs a calculation model to calculate the energy required for dispersing the non-metallic powder and implements precise deagglomeration; the atomization coating module uses reverse reasoning to charge and directionally adsorb droplets, completing the atomization of the modifier and coating of the newly formed surface in the same space where deagglomeration occurs; the coordination control module, through the construction of a time-window preemptive scheduling control strategy and the embedding of a main control board with a built-in feedforward fuzzy neural network controller, triggers the injection of the modifier during the interval of deagglomeration energy release, achieving precise synchronous control of "deagglomeration equals modification".

The invention relates to a video processing method based on audio and picture synchronization, and the method comprises the steps: screening candidate segments from a video material library according to the content inputted by a user, determining a target segment from the candidate segments based on a deep interaction algorithm and semantic entity correlation analysis, determining the video time length of the target segment, and outputting the video time length of the target segment. Determining the line number of the target segment based on the large language model, calculating a target playing duration according to the line number and a preset dubbing speed, judging whether the target segment has a duration deviation based on the target playing duration and the video duration, and if the target segment has the duration deviation, playing the target segment based on the target playing duration and the video duration. And performing adaptive speed change processing on the target clip to generate a final video clip. According to the method and the device, the problem that the audio and the picture of the video are asynchronous is solved, the speed change algorithm is dynamically optimized based on the deviation degree of the target playing duration and the video duration, and the integrity of the picture and the audio is kept while the audio and picture synchronization is ensured.

The application discloses a kind of walking network three-dimensional accessibility evaluation method, device, equipment, medium and product, the method includes the road facility data of target area in mountain city is preprocessed;According to the road facility data after processing, construct multilayer walking network;According to multilayer walking network and elevation constraint, construct composite impedance function that fuses time cost and physical cost;According to user group parameters and composite impedance function, the minimum cumulative travel cost of starting point to target node is obtained to obtain the optimal path planning result of different user groups;According to optimal path planning result and preset threshold, generate the three-dimensional accessible domain of different user groups.Based on multilayer walking network, composite impedance function and user group parameters, optimal path solving can be realized, and the real travel burden of different people under the constraint of mountainous elevation can be accurately quantified, so as to solve the problem that existing technology cannot adapt to the elevation, real walking burden and three-dimensional accessibility of mountain city.

The application provides a kind of active penetration depth testing device and method of penetration crystalline waterproof material, belongs to penetration testing technical field, the active penetration depth testing device of penetration crystalline waterproof material, comprising: installation cabinet, driving part, water injection equipment, gas pressurizing device, placing piece and sealing cover, the raw material control sample and experimental test sample are placed in corresponding placing groove in the application respectively, the sealing cover is precisely attached with placing piece using driving part, and independent sealing environment of each station is constructed with sealing groove and sealing strip, then water or gas is injected into groove by water injection equipment or gas pressurizing device to implement pressurized penetration, so that the accurate quantification and nondestructive testing of waterproof material penetration depth are realized without destructive cutting sample, which fundamentally avoids irreversible damage to sample caused by traditional testing means.

This invention discloses a routing partitioning method and apparatus for large-scale low-Earth orbit satellite networks, primarily addressing the problems of high link overhead and slow convergence speed in existing satellite networks. The scheme includes: generating a spatial network topology based on constellation size, number of orbital planes, orbital altitude, and orbital inclination; dividing the constellation into multiple routing regions based on the relative positions of the satellites, establishing link state information exchange rules, a routing table storage and update mechanism, and a data packet forwarding mechanism; establishing a quantitative relationship between the number of partitions and link overhead based on the network topology, routing region partitioning results, and link state information exchange rules; establishing a quantitative relationship between the number of partitions and routing convergence time based on the dynamic topology change characteristics of the satellites, routing region partitioning results, and routing table storage and update mechanism; calculating link overhead and routing convergence time under different numbers of partitions, constructing routing performance indicators, and outputting the optimal routing partitioning result. This invention can reduce link overhead and accelerate routing convergence speed, and can be applied to large-scale satellite network communication systems.

This application discloses a deep learning-based intelligent monitoring method and system for surface cracks in bridge structures. First, it performs spatiotemporal alignment and heterogeneous fusion of multi-source sensor data, then utilizes a dual-stream deep neural network to extract the probability distribution features and pixel-level extension direction fields of the cracks in parallel. Based on this, by introducing a directionally regularized potential energy field and a non-maximum suppression mechanism, high-precision skeleton extraction conforming to hydrodynamic characteristics is achieved. Furthermore, this scheme transforms the visual perception space into a graph theory space, constructing a connection cost matrix based on crack breakpoints and their local tangent directions, and using a minimum spanning tree algorithm to intelligently stitch together visually broken but logically connected paths. Finally, by combining depth information to invert the true physical size and hazard level of the cracks, it achieves a leap from simple image segmentation to physical structural integrity assessment, ensuring the topological authenticity and engineering usability of the monitoring results.

This invention relates to the field of deep learning technology, and more particularly to a method and system for post-training channel-mixed precision quantization of neural networks. For each weight channel in each layer of the pre-trained model to be quantized, the invention calculates the initial scaling factor of the weight channel at different bit widths based on the weight range of the weight tensor of that weight channel; optimizes the initial scaling factor of each weight channel in each layer of the pre-trained model to be quantized at different bit widths to obtain the target scaling factor of the weight channel at different bit widths; constructs an optimal bit width allocation integer linear programming problem based on the obtained target scaling factor; obtains the optimal bit width allocated to each weight channel by solving the optimal bit width allocation integer linear programming problem; and quantizes the pre-trained model to be quantized based on the obtained optimal bit width of the channels. This invention effectively improves the processing accuracy of the quantized neural network model for data such as text, images, and audio.

The application discloses a large model data privacy protection method and system based on data cooperation, and relates to the technical field of data privacy protection.The application detects model weights, attention and activation layer privacy isotopes, and calculates memory sensitivity; executes differentiated dynamic masks according to sensitivity classification; allocates exclusive privacy budgets in combination with knowledge gain and sensitivity; uploads decoupled general knowledge components, a server counts privacy residuals to generate a global forgetting template; nodes weaken privacy memory according to the template and implant knowledge with alternative data, and iteration is performed until privacy risks and precision meet standards.The application initiates the concept of privacy isotopes, actively governs implicit memory leakage, and can resist advanced attacks such as model inversion; graded masks and dynamic budgets realize fine balance between privacy and utility, and precision loss is reduced; adaptive weighted aggregation and cooperative forgetting-implantation closed loop inhibit privacy diffusion from the root, communication overhead is greatly reduced, and a full-link self-evolution privacy protection system is formed.