99results about How to "Improve system performance" patented technology

The invention discloses an automatic driving test and evaluation method and device based on a large language model, and solves the problems that the pertinence of a test scene is insufficient, the result understanding depends on manpower, the evaluation interpretation is weak and the like in a traditional method. By means of semantic analysis, logical reasoning and domain knowledge support of a large language model and in combination with an automatic driving test domain knowledge graph, generation of a structured test portrait of a tested system, construction of a test demand list, generation of a semantic test scene description file, interpretable test result attribution, multi-dimensional comprehensive evaluation and report output are completed in sequence. And a test portrait is updated through report feedback to form a closed loop. The method is suitable for function verification and performance evaluation of an automatic driving simulation test, can also be used for post-processing analysis of real vehicle test data, realizes automation of a whole test process and interpretability of an evaluation result, and improves test precision and efficiency.

The application relates to the field of crop seeding technology, and particularly discloses a wheat-corn interplanting self-adaptive seeding width regulation method based on crop and soil parameter identification, which comprises the following steps: normalizing soil body penetration resistance data to generate soil body penetration resistance score values and dividing seeding condition grades; jointly analyzing wheat grain moisture content data and soil slope data to construct a slope moisture content correlation matrix and perform dimension expansion to obtain interference characteristic factors; inputting wheat row spacing data, the seeding condition grades and the interference characteristic factors into a decision rule library to generate seeding width values and seeding density values; converting the seeding width values into channel boundary coordinate instructions of seeding machines, and driving the seeding machines to adjust the operation width of corn seeding according to the channel boundary coordinate instructions; and synchronously regulating the single irrigation amount of wheat medium-shallow buried drip irrigation. The application can improve the seeding precision and resource utilization efficiency of wheat-corn interplanting operation.

The invention relates to the related technical field of safety monitoring and early warning, in particular to a subway fire safety monitoring and early warning method and system under multi-data fusion, and the method comprises the steps: constructing a risk evolution graph through a first fire characteristic index and a second fire characteristic index; according to the method, initial fire source heat release rates under different combustible load densities are taken as core correlation characteristics, a fire influence range is subjected to multi-scale evaluation, and a dynamic early warning threshold adjustment strategy is used for graded warning, so that the problem of lack of systematic risk modeling taking a fire development stage as a core is solved; according to the method, the technical problems of poor potential risk area pre-judgment and chain failure influence analysis capability and difficulty in meeting subway fire prevention and control requirements in the prior art are solved, multi-dimensional monitoring data are integrated, a risk evolution graph taking a fire development stage as a node and heat release rate and smoke concentration time sequence coupling as a constraint is constructed, a fire spreading track is dynamically tracked, and the fire prevention and control requirements are met. And the reliability and systematicness of subway fire prevention and control are comprehensively enhanced.

The application discloses a multi-value programming method of a memristor for improving device retention characteristics. The method divides the target conductance of the programming of the memristor in a low conductance range or a high conductance range based on a certain conductance threshold, and adopts a programming scheme with different initial conductance states, programming pulse directions and denoising pulse directions for different ranges. The multi-value programming method of the application is based on the physical process of the growth and rupture of a conductive filament in the resistance change process of the memristor, so that the state of the memristor after the programming is as stable as possible, and the memristor device has better multi-value retention characteristics. Compared with the existing multi-value programming scheme of the memristor, the application can improve the retention characteristics of the data storage after the multi-value programming of the device without greatly increasing the programming time and power consumption of the device, so that the device can be better used in the data storage field or the in-memory computing field, thereby improving the reliability and system performance of the storage and calculation of the memristor.

The application discloses a kind of man-machine collaborative multi-modal data intelligent labeling and quality checking method, it is related to man-machine collaborative technical field.The method first obtains at least containing two modalities of original data of image, text, audio, generates multi-modal joint data sample after pre-processing and modal alignment;Input pre-trained multi-modal AI pre-labeling model generates pre-labeling result with confidence;Through cross-modal consistency check calculation consistency score, sample is divided into high, medium, low confidence three categories;High confidence sample is directly adopted and enters the sampling pool, and medium and low confidence sample is based on labeling personnel ability portrait and AI model dynamic capability evaluation and carries out intelligent task distribution.The application effectively solves the problem that efficiency and quality are difficult to balance in multi-modal data labeling, significantly improves labeling accuracy and automation rate, reduces labeling cost, and can meet the construction demand of large-scale high-quality multi-modal data set.

This invention belongs to the field of computer vision and image processing technology, and particularly relates to a query key-guided asymmetric feature enhancement system and method. The system includes: Q-ASFE modules embedded in the backbone network, specifically in the link where features are transmitted from the C3K2 module to the CBS module. The P2 and P3 layer feature maps output by the backbone network are processed by the neck network to output three feature fusion maps at different scales. These three feature fusion maps are then processed by a detection head to obtain the final detection result. This invention achieves efficient enhancement of image features through the organic integration of spatial detail awareness and global semantic guidance.

This application discloses a method, system, electronic device, and storage medium for creating virtual machine instances, relating to the field of cloud computing technology. The method includes: receiving a resource scheduling request from a console, wherein the resource scheduling request requests a target scheduling system to prepare resources for a virtual machine instance to be created for a target scenario; preparing resources for the virtual machine instance based on the resource scheduling request; in response to the completion of resource preparation, obtaining target physical resources to be used by the virtual machine instance from a target physical resource pool, wherein the target physical resource pool is a shared physical resource pool used when creating different virtual machine instances for different application scenarios; and creating the virtual machine instance using the target physical resources. This application solves the technical problems of high creation cost and low resource utilization in related technologies when creating virtual machine instances.

The invention discloses a layered fine-grained image forgery detection method and a layered fine-grained image forgery detection system. And the authenticity of the input image is preliminarily judged through the authenticity judgment module. Constructing a multi-branch pixel-level counterfeit positioning network, and obtaining multi-scale feature representation from low resolution to high resolution through a feature extraction backbone network; a high-resolution forged mask is obtained through pixel-level positioning network prediction; and the decision network constructs a coarse-to-fine reasoning path according to the multi-level label system, sequentially completes forgery property judgment, forgery type judgment, method-level traceability and platform-level traceability, and outputs a conditional probability of a corresponding-level label as a traceability result. The method has a unified multi-level label system, the systematicness and the accuracy of detection are remarkably improved, the source and the position of counterfeiting are visually presented through mask positioning and hierarchical classification, and the interpretability is high.

The application relates to a tropical cyclone disaster loss assessment method and system, and relates to the technical field of natural disaster risk assessment. The method is executed by a computer device and comprises the following steps: acquiring historical cyclone paths, wind field parameters and social and economic statistical data; performing uniform spatial resolution and coordinate reference alignment processing on population and economic spatial distribution data; calculating a hazard intensity index P; determining a cyclone influence range, extracting population exposure PE and economic exposure G; selecting a population and economic vulnerability index set, constructing a population vulnerability index PV and an economic vulnerability index EV; adopting Poisson regression to construct a death assessment model with P, PE and PV as independent variables; and adopting gamma regression to construct an economic loss assessment model with P, G and EV as independent variables. The application realizes double-dimensional and comparable collaborative attribution analysis of historical tropical cyclone event economic loss and population death, and outputs the relative contribution degrees of hazard, exposure and vulnerability to the two types of losses.

This invention relates to the field of cable insulation monitoring and fault diagnosis technology, specifically disclosing a multi-parameter dynamic monitoring and fault prediction system for cable insulation and its implementation method. The system includes: a data acquisition module that collects insulation resistance and load current parameters and performs spatiotemporal registration to output a load dataset; an interference analysis module that filters insulation analysis segments and determines latent damage accumulation through energy accumulation analysis; an attenuation analysis module that calculates the attenuation reversibility value through coupled analysis of impact intensity and recovery efficiency, determining whether an attenuation warning signal is triggered; and a cluster intervention module that, after triggering the warning, divides the globally weakened impact protection segments into spatial clusters, constructs a reversible prediction model by combining reversible parameters and impact parameters, and outputs the attenuation conversion time. The method is used to achieve multi-parameter dynamic monitoring and fault prediction of cable insulation. This invention improves the accuracy of prediction through multi-parameter coupling and global cluster analysis, providing a basis for cable operation and maintenance.

This invention provides a privacy-preserving intelligent agent cross-domain knowledge hybrid retrieval method and system, relating to the fields of artificial intelligence and data security technology. The method includes: responding to a query request initiated by a cloud-based intelligent agent, parsing the privacy tags embedded in the request workflow definition; and concurrently executing the following two operations through an asynchronous task scheduler: sending the public subquery to a cloud-based knowledge retrieval service, and routing the private subquery to a privacy computing agent client deployed in the target local environment; the privacy computing agent client executing the private subquery in a local private knowledge base; and at a cloud aggregation point, aggregating the decrypted ciphertext knowledge fragment set and the public knowledge fragment set into unified knowledge data, and returning it to the intelligent agent. This invention achieves efficient, secure, and integrated retrieval and utilization of distributed heterogeneous knowledge bases by a cloud-based intelligent agent while ensuring that the original privacy data does not leave the local security boundary.

The application relates to the technical field of medical information, and discloses a knowledge graph driving intelligent analysis system based on the medical field, which comprises a knowledge graph construction module, which is used for constructing a medical knowledge graph containing subjects, events, object entities and information accessibility relationships; a behavior pattern learning module, which is used for learning a parameterized behavior decision model for the subject entity; a dynamic deduction module, which is used for deducing market changes after injecting events according to the behavior decision model and the information accessibility relationship; and a strategy generation module, which is used for taking the dynamic deduction process as a fitness function to reversely generate a market strategy. The method comprises the following steps: constructing the medical field knowledge graph; learning the behavior decision model of the subject entity based on the graph; dynamically deducing the market changes in a virtual environment; and reversely solving and generating the market strategy. The application can perform forward-looking dynamic deduction on the market and automatically generate an optimized strategy, thereby improving the scientificity and timeliness of decision-making.

The application discloses a user behavior analysis method, which can deeply understand the operation mode and behavior characteristics of users by modeling the user behavior sequence using an RNN model, thereby optimizing the user interaction experience; the ARIMA model is used to model the page performance data, which can accurately capture the time sequence characteristics of the page performance, identify potential performance bottlenecks and change trends; the RNN model is introduced into the ARIMA model, the page performance data is taken as an input of the sequence model, and the user behavior data is analyzed together, so that comprehensive analysis of the user behavior and the page performance can be realized, and the prediction accuracy and practicability of the model are improved; the trained model parameters are used to predict the optimization scheme and optimization cost of a future time point, so that the possible performance problems and optimization requirements in the future can be predicted, the optimization scheme can be prepared and implemented in advance, and potential performance bottlenecks can be avoided.

The application provides a kind of vehicle-mounted detection equipment data processing method and device applied to fault detection, by the time sequence scene correlation modeling of original operation data collected by vehicle-mounted detection equipment, generate time sequence scene correlation mapping body and abnormal starting time sequence marker cluster, and based on both, construct fault correlation semantic topology, generate fault semantic correlation graph and scene-semantic correlation mapping table, then carry out fault mode qualitative clustering, generate fault mode classification label set and fault-scene correlation binding cluster, then carry out fault root cause topology reasoning, construct the logical correlation topology chain of fault explicit semantic representation and potential cause, generate fault root cause reasoning topology chain and fault propagation topology path set, based on both, carry out fault risk trend topology deduction, generate fault risk early warning semantic package and risk propagation trend topology graph.The application can improve the accuracy and system of vehicle fault detection as a whole, reduce analysis bias.

The invention discloses an unmanned aerial vehicle assisted holographic digital-energy integrated transmission performance evaluation method. The method comprises the following steps: constructing an unmanned aerial vehicle assisted holographic IDET system model; determining a data transmission and energy transmission model under the limited code length mechanism; a holographic IDET beamforming design scheme assisted by the unmanned aerial vehicle is provided; analyzing a closed expression of an end point of the Pareto optimal curve; and designing an optimization algorithm to obtain a Pareto optimal boundary, and giving rate-energy domains of the DU and the EU. Aiming at the problem that a traditional fixed base station is difficult to meet the requirements of wide coverage, high energy efficiency and reliable transmission under the limited code length at the same time, the performance is remarkably improved by introducing unmanned aerial vehicle flexible deployment, holographic surface near-field focusing capability and a reliability analysis mechanism under the limited code length; and a rate-energy domain is obtained according to the Pareto optimal boundary curve.

The application is named reservoir scale intelligent optimization demonstration system and method based on multi-target risk coupling, and belongs to the technical field of intelligent decision-making of water conservancy and hydropower engineering. The technical problem to be solved is that the traditional reservoir scale demonstration relies on static scenarios, cannot quantize the multi-target conflict coupling relationship, does not systematically consider multiple uncertainties and risks, has low optimization efficiency and insufficient scheme robustness. The technical solution points are that the application sets six modules of hydrological risk modeling, multi-target coupling analysis, scale optimization decision, uncertainty propagation simulation, comprehensive resilience evaluation and intelligent demonstration integration, generates dynamic hydrological risk indexes by fusing climate scenarios and historical data, quantizes the multi-target coupling relationship, optimizes the reservoir scale parameters by combining reinforcement learning and risk constraint, and automatically generates the optimal reservoir scale demonstration scheme with high robustness through uncertainty simulation and resilience evaluation.

The application discloses an interactive form design method and system based on a language large model, and belongs to the technical field of human-computer interaction and form automatic generation. In view of the problems that a traditional form design tool is tedious to operate, highly depends on manual experience, and an existing method based on a large model has high training cost, weak interaction ability and difficulty in real-time collaboration, the following scheme is used for improvement: defining a JSON Schema specification of a form and constructing a visual form designer, integrating a language large model interaction entrance in an interface, dynamically assembling a request containing user instructions, a JSON Schema specification and existing form data according to whether the form is empty, and submitting the request to the language large model; receiving a response in a streaming manner, and real-time analyzing and progressively updating a form designer preview interface, so that a user can directly generate or modify a form through natural language instructions, and multi-round interaction and manual subsequent adjustment are supported. The method is mainly used for reducing a form design threshold, improving design efficiency and intelligent collaboration level.

The invention belongs to the technical field of functional microorganism application, and particularly relates to a novel lipid-lowering peptide derived from a probiotic thallus and application thereof, the novel lipid-lowering peptide is a crushed substance derived from a probiotic lactobacillus plantarum thallus, and the molecular weight of the novel lipid-lowering peptide is less than 3kDa. And two key polypeptides APGFGDR (AP7) and TESGAFGR (TE8) are identified from the components, and can specifically inhibit the activities of pancreatic lipase and pancreatic cholesterol esterase. The extracted thallus peptide can be efficiently combined with cholate to inhibit the formation of cholesterol micelles, and has a remarkable inhibiting effect on pancreatic lipase and pancreatic cholesterol esterase. Animal experiments show that the mycopeptide can effectively improve lipid metabolism disorder induced by high fat diet, inhibit weight gain, reduce serum and liver lipid levels and relieve fatty degeneration and inflammation of the liver. The lipid-lowering peptide disclosed by the invention is high in safety and multi-target in action, and can be used for preparing functional products for preventing or treating metabolic diseases such as obesity, hyperlipidemia and the like.

The invention relates to the technical field of intelligent medical treatment, in particular to a hospital-oriented intelligent scoring performance assessment method, system and device and a medium, and the method comprises the steps: obtaining the performance data of a plurality of hospitals, and extracting a standardized performance indicator vector; unsupervised clustering analysis is carried out on the standardized performance indicator vectors, multiple performance levels are divided, and benchmark groups are determined based on the performance levels; on the basis of the performance data of the benchmark group, key performance indicators and critical threshold values thereof are identified, and score weights are deduced through reverse regression analysis; constructing a scoring formula based on the key performance indicators, the critical threshold values and the scoring weights; and based on the performance data, calculating the comprehensive performance score of each hospital by using a scoring formula. According to the method, objective and adaptive scoring based on data driving can be realized, and scientificity and efficiency of hospital performance assessment are remarkably improved.

The invention provides a conference room water cup water replenishing strategy optimization method and system based on reinforcement learning, and relates to the technical field of intelligent services, and the method comprises the steps: obtaining the liquid remaining amount feature and the spatial position feature of a container in a target space, and constructing a state representation vector to represent the incidence relation between the two features; predicting the liquid consumption trend of each container in the target time window through the time sequence analysis model to obtain a demand prediction result; inputting the state representation vector and the demand prediction result into a reinforcement learning strategy network, and based on the multi-container liquid residual amount distribution state and the prediction consumption trend, generating a decision sequence containing a supplement priority and a supplement opportunity by optimizing a long-term accumulation reward function; and dynamically adjusting the supplementary execution sequence of each container according to the supplementary priority in combination with the available supplementary resource constraint, and generating and executing a supplementary execution scheme. According to the invention, intelligent optimization of the water replenishing strategy is realized through reinforcement learning, and the resource utilization efficiency and the service quality are improved.

The application relates to a linear sweep frequency laser generating device, which comprises a semiconductor laser, a driving circuit, a laser beam splitter, a laser frequency discriminator unit, a radio frequency signal generating unit and a laser frequency shift unit; the semiconductor laser is controlled by the driving circuit to generate sweep frequency laser, and is divided into two beams by the laser beam splitter and enters the laser frequency discriminator unit and the laser frequency shift unit respectively; the laser frequency discriminator unit extracts a nonlinear sweep frequency component of the sweep frequency laser and generates a control signal to control the radio frequency signal generating unit; the radio frequency signal generating unit generates a radio frequency signal with a frequency opposite to that of the nonlinear sweep frequency component according to the control signal to drive the laser frequency shift unit; the laser frequency shift unit modulates the frequency of the radio frequency signal on the input light frequency to offset the nonlinear sweep frequency component of the sweep frequency laser and output linear sweep frequency laser. Compared with the prior art, the application has the advantages of high sweep frequency rate, large sweep frequency range and linear sweep frequency laser.

This invention relates to the field of intelligent emission reduction technology, and discloses an intelligent generation method and system for emission reduction optimization strategies based on artificial intelligence. The method monitors actual carbon emission data and actual energy use data of a target area within a preset time period; based on the actual energy use data, corresponding predicted carbon emission data is obtained through a preset carbon emission prediction model; the difference index between the actual carbon emission data and the predicted carbon emission data is calculated to determine whether the target area meets preset carbon emission anomaly warning conditions; if so, the abnormal carbon emission factors of the target area are analyzed based on the difference index and the actual energy use data; and an emission reduction optimization strategy for the target area, including carbon emission optimization strategies and energy use optimization strategies, is generated based on the abnormal carbon emission factors, the actual carbon emission data, and the predicted carbon emission data. Therefore, implementing this invention can improve the timeliness and adaptability of the emission reduction optimization strategy generation while ensuring the accuracy of the generated strategy.

This invention proposes a method and apparatus for robustness evaluation of face recognition algorithms, relating to the field of computer data processing technology. The method includes: acquiring face image samples and preprocessing them to obtain an original face image test set; for each face image in the original face image test set, injecting various preset pixel-level damage information and various preset semantic-level change information of different interference types, and setting different preset severity levels to generate a multidimensional out-of-distribution test set; loading the face recognition model to be evaluated, performing a recognition task based on the original face image test set, and obtaining a clean accuracy rate; performing recognition tasks using the face recognition model to be evaluated based on the multidimensional out-of-distribution test set, obtaining recognition accuracy rates under different interference scenarios; determining the relative damage error and relative semantic change error, analyzing the weaknesses of the face recognition model under different interference scenarios, and generating a robustness evaluation report.

The invention relates to the technical field of health assessment, in particular to a sleep quality and emotion regulation ability assessment system based on traditional Chinese medicine constitution identification, which comprises the steps of S1, traditional Chinese medicine constitution basic parameter acquisition, S2, constitution corresponding symptom parameter library calling and association degree initial calculation, S3, symptom expression association multi-stage verification, S4, symptom and sleep basic parameter association operation, S5, emotion regulation ability assessment and S6, emotion regulation ability assessment. And S5, performing comprehensive correlation evaluation on the symptom and emotion parameters. According to the method, the preliminary pre-judgment is generated through matching of the traditional Chinese medicine constitution typing parameters and the tongue vein posture parameters, the overlapping degree is calculated in combination with the corresponding symptom library, the symptom collection priority is generated, and the data collection efficiency is improved. And then a traditional Chinese medicine constitution nine-score method associated threshold matrix is called to check symptom information, and invalid data interference is reduced. And finally, the correlation degree is calculated by fusing symptom, sleep and emotion parameters, so that closed-loop evaluation of physique-sleep-emotion deep fusion is formed, the systematicness and accuracy of evaluation are improved, and targeted support is provided for health management.

Embodiments of this application provide a virtual machine management method, a related system, and a storage medium. The method includes: A virtual machine manager receives a system call instruction from a virtual machine application, where the system call instruction carries a call number, and the system call instruction is a privileged instruction; the virtual machine manager obtains a first context save area CSA based on lower context of a virtual machine and a free CSA, and processes a second CSA based on the call number, to obtain a processed second CSA, where the second CSA is determined before the first CSA, and the first CSA is adjacent to the processed second CSA; and the virtual machine manager jumps, based on a return address of the first CSA, to a system call service processing address of a virtual machine kernel, so as to trigger the virtual machine kernel to perform system call processing. In embodiments of this application, system operation efficiency is improved, and system performance is improved.

This invention discloses a method and device for diagnosing the health status of flow batteries based on particle swarm optimization (PSO), aiming to solve the problems of difficult modeling and poor real-time performance of traditional physical model-driven methods, and the unscientific feature selection and insufficient prediction stability of existing data-driven methods. The system is data-driven at its core, collecting multi-dimensional data such as voltage, current, and capacity of the flow battery during operation through sensors. After preprocessing, LASSO and grey relational analysis are used to jointly screen the optimal subset of health features. An extreme learning machine (ELM) is built as the main model for SOH prediction, using the ReLU activation function to improve nonlinear expression capabilities, and introducing a particle swarm optimization algorithm to optimize the weights and biases of the ELM, improving the instability caused by random initialization.

The invention discloses an endangered plant phoebe hainanensis identification and systematic monitoring method based on multi-source data fusion, and belongs to the technical field of endangered plant protection. According to the method, a potential distribution area is delineated through a species distribution model, individual accurate identification and filing are achieved through a multi-branch feature fusion model, multi-scale growth and microenvironment data are collected through Internet of Things nodes, and finally a'discovery-identification-monitoring-protection 'whole-process closed loop is formed through a unified data management platform. According to the method, phenological feature extraction, transfer learning modeling, man-machine collaborative verification and low-power-consumption Internet of Things monitoring technologies are adopted, and the problems that a traditional method is low in efficiency, insufficient in recognition precision and fragmented in monitoring are solved. According to the method, efficient discovery, accurate identification and long-term dynamic monitoring of the wild population of the phoebe hainanensis are achieved, scientific support is provided for protection decision making, and the method is convenient to operate and high in practicability and has protection value and application prospects.

This invention discloses a nutritional composition and preparation method for improving joint health in senior pets. The nutritional composition comprises: N-acetylglucosamine, chondroitin sulfate, methanesulfonylmethane, unsaponifiable avocado and soybean extract, hyaluronic acid, curcumin microcapsule powder, and high-concentration fish oil. This invention uses a lipid / polysaccharide bilayer microcapsule structure to encapsulate curcumin and fish oil, and prepares the product through low-temperature homogenization, spray drying, and granulation processes. The composition of this invention works synergistically through multiple pathways, simultaneously exerting effects on three key levels: anti-inflammation, lubrication, and cartilage repair. It can significantly inhibit joint inflammation, improve joint lubrication, and promote cartilage repair. This invention solves the problems of existing pet joint products, such as single pathways of action, low stability and bioavailability of lipid-soluble active ingredients, and lack of overall formulation optimization tailored to the characteristics of aging pets. It is suitable for daily joint health maintenance or adjunctive treatment in middle-aged and senior dogs and cats.

The invention belongs to the technical field of computers and artificial intelligence, particularly relates to an auxiliary teaching material generation method and system based on an artificial intelligence technology, and aims to solve the problems that existing teaching materials are low in production efficiency, insufficient in individuation degree and difficult to generate visual materials. The method comprises the steps of obtaining and analyzing a teaching material generation request to determine a teaching range requirement and a core knowledge point; according to the requirements and the core knowledge points, aggregating related contents from a plurality of resource libraries, and extracting and generating a hierarchical knowledge point structure by using an artificial intelligence large model; and generating at least one teaching material including an interactive animation, a knowledge card or a mind map based on the knowledge point structure. According to the invention, the teaching materials can be automatically, intelligently and individually generated, the lesson preparation efficiency is remarkably improved, the expression form of the teaching resources is enriched, and the teaching effect is improved.

This invention relates to the field of material mechanical property testing technology, specifically to a method and system for determining the strength grade of planted concrete using recycled aggregate. The method includes the following steps: quantifying the multi-level loading response characteristics and deformation compatibility of planted concrete; identifying damage sequence and dividing waveband propagation paths; statistically analyzing aggregate frequency to assess weight and analyze hierarchical strength; screening associated measurement paths through key nodes; calculating extension values ​​and prioritizing them; and obtaining a concrete strength propagation aggregation information table. In this invention, by extracting response data from the loading stage, multi-level stress states and damage trajectories are identified, achieving accurate characterization of deformation compatibility and strength evolution, improving damage evolution identification capabilities, associating recycled aggregate types with damage stages, enhancing the ability to assess the impact on structural performance, identifying key damage nodes and triggering relationships, optimizing strength measurement paths, and solving the problems of insufficient measurement accuracy and incomplete understanding of structural response under complex working conditions.