75results about How to "Objective assessment" patented technology

The invention discloses an AI-based linkage response and control system, belongs to the technical field of safety monitoring, and aims at solving the problems of comprehensive and real-time acquisition, accurate analysis, timely early warning and decision making of environmental data so as to realize early warning and quick response of a fire disaster. The system comprises a data acquisition module, an AI analysis module, an AI identification module, an AI decision module and an execution terminal robot. The data acquisition module is used for acquiring environmental data of a monitoring sensor and a monitoring camera in a target scene; the AI analysis module is used for processing and analyzing the environmental data acquired by the monitoring sensor and outputting a sensor abnormal area; the AI identification module is used for analyzing an environment video stream acquired by the monitoring camera in real time and outputting a camera abnormal area; the AI decision module is used for further evaluating the abnormal region and dividing abnormal levels; and the execution terminal robot is used for performing real-time response and control on the abnormal area in the control room according to the abnormal level.

The invention belongs to the technical field of artificial influence weather, and discloses an artificial rain and snow enhancement effect evaluation method based on a layer-by-layer iteration mode, and the method comprises the steps: determining a catalyst diffusion track and an affected region through employing a high-resolution numerical mode, the airplane observation data comprises the background ice crystal number concentration of the affected area and the ice crystal number concentration under the condition that the catalyst is used; reading the ice crystal number concentration of the sowing layer from the observation data, and calculating the correlation between the L0 ice crystal number concentration of the sowing layer and the L1 radar reflectivity factor of the first layer; calculating correlation coefficients of radar reflectivity factors between every two adjacent layers from the sowing layer to the ground layer by layer; a high-resolution numerical mode is adopted to simulate diffusion of a catalyst, the ice crystal number concentration of an affected area in airplane observation data is read and substituted into the relational expression, radar reflectivity factors of all layers are calculated layer by layer in an iterative mode, and the precipitation rate is calculated according to the radar reflectivity factors of all layers; and calculating the rainfall rate under the background ice crystal number concentration, comparing, and quantitatively evaluating the artificial rain and snow increasing effect.

The invention relates to the technical field of logistics management, in particular to a cross-border logistics demand prediction method and system, which can provide accurate and reliable prediction results and decision support for cross-border logistics enterprises. The method comprises the following steps: acquiring a destination cargo demand influence parameter set and a delivery place cargo supply influence parameter set; respectively performing feature analysis on the destination cargo demand influence parameter set and the shipping place cargo supply influence parameter set to obtain a destination cargo demand feature index and a shipping place cargo supply feature index; comparing the destination cargo demand characteristic index with the shipping place cargo supply characteristic index, extracting a minimum characteristic index, and marking the minimum characteristic index as a logistics demand characteristic index; obtaining the real-time cross-border transportation cost and the historical same-period cargo transportation characteristic quantity corresponding to the logistics demand characteristic index; and inputting the logistics demand characteristic index and the real-time cross-border transportation cost into a pre-constructed cross-border logistics demand influence analysis model as input.

The present invention belongs to the field of construction site safety technology, specifically a construction site construction safety assessment system, the system includes: a scene reconstruction design module, a data perception module, a data communication module and a data processing module. The present invention has the beneficial effects of being able to realize real-time, accurate and comprehensive data collection and transmission of construction sites, improving the data quality and timeliness of construction safety assessments; being able to realize clear, intuitive and dynamic scene reconstruction and display of construction sites, improving the visualization and interactivity of construction safety assessments; being able to realize scientific, objective and consistent risk analysis and assessment of construction sites, improving the systematicness and reliability of construction safety assessments; being able to realize efficient, accurate and intelligent design and construction optimization and adjustment of construction sites, improving the optimization and automation of construction safety assessments.

The invention discloses an anti-unmanned aerial vehicle interference system and method based on a radio cracking technology, and relates to the technical field of unmanned aerial vehicles. The anti-unmanned aerial vehicle interference system comprises an environment monitoring management module, a signal interference identification module, an instantaneous interference suppression module, a discontinuous interference management module, a continuous interference confrontation module, an effect evaluation module and a feedback adjustment module. The technical key points are as follows: the system aims at performing efficient and intelligent anti-interference processing on various interferences faced by unmanned aerial vehicle signals through a radio cracking technology to ensure stable flight and control of the unmanned aerial vehicle in a complex electromagnetic environment, the system integrates a plurality of functional modules, different strategies are adopted for instantaneous, intermittent and continuous interferences respectively, and the unmanned aerial vehicle can stably fly and control the unmanned aerial vehicle in a complex electromagnetic environment. Therefore, accurate and rapid anti-interference adjustment is realized. The stability and reliability of the unmanned aerial vehicle in a complex electromagnetic environment are remarkably improved, and a powerful guarantee is provided for safe flight and efficient operation of the unmanned aerial vehicle.

The invention discloses a green credit risk assessment system, which comprises a data acquisition module, a risk assessment module and a result output module, and is characterized in that the data acquisition module is used for collecting information of enterprise environment, enterprise finance and enterprise projects, and the risk assessment module is used for analyzing and assessing the collected data information; and the result output module is used for outputting an evaluation result to a financial institution and an enterprise, and the financial enterprise performs loan approval according to the evaluation result and determines loan pricing and post-loan management. The system can provide an effective green credit risk assessment tool for a financial institution, help the financial institution better manage the risk and promote the development of green finance, and meanwhile, the system can also provide a self-assessment tool for an enterprise to help the enterprise know the green credit risk condition of the enterprise, thereby improving the safety of the enterprise. And the environment management level and the sustainable development capability of an enterprise are improved.

The invention discloses a network security situation prediction method and system, and relates to the technical field of network security situation assessment. The network security situation prediction method comprises the following steps: continuously obtaining network equipment operation data of a plurality of network operation time periods when network equipment operates, and carrying out preprocessing analysis to obtain a network internal stability index and a network external threat index of each network operation time period when the network equipment operates; according to the method and the device, the data of the plurality of network operation time periods during the operation of the network equipment is preprocessed and analyzed, the internal stability and external threat factors of the network are comprehensively evaluated, so that the network security index is generated, and the network security index of each network operation time period can accurately reflect the security state of the current network; according to the method, the change rate of the network security index is analyzed, the security index of the next time period is predicted, the monitoring accuracy of the network security situation can be effectively improved, potential network risks are found in time, and corresponding protection measures are taken.

The invention discloses a skin care product anti-aging effect multi-dimensional comprehensive detection system, which comprises an in-vitro cell model experiment module for detecting indexes such as cell activity, proliferation, migration, apoptosis and antioxidant enzyme activity by simulating natural aging and light aging conditions; the content change of skin structure components such as collagen, elastic fibers and laminin is quantitatively analyzed, the DNA damage repair capacity is reflected by measuring the expression quantity of gammaH2AX, epigenetics research is developed, and the methylation state change of a CpG island is monitored; and the tissue engineering and three-dimensional skin model experiment module is used for performing morphological observation, biochemical index detection and skin physiological parameter evaluation under the condition of simulating a real skin environment by utilizing an artificial skin model. The whole process from the in-vitro cell model to the clinical human body test is covered, and the actual efficiency of the skin care product for delaying or improving the skin aging process can be accurately and objectively evaluated through multi-angle and multi-level data integration analysis.

The invention discloses a large model answer effect evaluation method and system based on a knowledge graph, and relates to the field of large language models. The method comprises the following steps: preprocessing a plurality of questions; carrying out convolution calculation on the preprocessed sentences to obtain keyword nodes; keyword attributes are extracted through the keyword nodes; screening the keyword attributes according to the attention influence score of the keyword attribute of each keyword node; constructing a context graph by using the screened keyword attributes, and classifying keyword nodes through a multi-layer perceptron to obtain multiple groups of keyword head nodes and keyword tail nodes; generating a keyword knowledge graph by using the keyword nodes as entities and the classification results as entity relationships; questions input by the user are answered and answered through the large model; and evaluating the result of the large model. The answering effect of the question answering system can be automatically evaluated, and the evaluation efficiency and accuracy are improved.

The invention provides a man-machine collaborative program repairing method based on a large language model, relates to the technical field of automatic program repairing, and remarkably improves the accuracy and success rate of program repairing. According to the method, a large language model is guided through preliminary analysis of manual teaching, errors in a program can be more accurately positioned and repaired by combining a repairing process of multiple iterations and more information introduced step by step, misjudgment and omission are reduced, and the program repairing efficiency is greatly improved. Compared with a traditional method, blind exploration of a large language model on complex problems is avoided through a man-machine cooperation mode, time and computing resources are saved, and adaptability of program repair is enhanced. According to the method, program defects of various types and complexity can be handled, and simple grammar errors or complex logic errors can be effectively handled through a step-by-step deep repair process.

The invention relates to the related technical field of ship safety and pollution prevention, in particular to an oil spill area measuring and calculating method based on improved edge detection, and the method comprises the steps: obtaining a thermal infrared remote sensing image of an oil film region through the cruise monitoring of an unmanned aerial vehicle, and processing the collected thermal infrared image; the thermal infrared image is measured and calculated by adopting an improved Canny algorithm to obtain the final area of an oil spilling region, the measured and calculated actual area of the oil spilling region is visually displayed, and the improved Canny algorithm effectively eliminates impurity information under the condition of ensuring the completeness of edge information features, so that the accuracy of the oil spilling region is improved, and the accuracy of the oil spilling region is improved. And in the aspect of oil film area edge main information response, the accuracy of edge identification is improved while the identification efficiency is ensured.

The invention is suitable for the technical field of education management, and particularly relates to an education management supervision method and system, and the method comprises the steps: obtaining a teaching video; determining teaching text data based on the teaching video; determining a classroom order score and classroom interaction behavior information based on the teaching video and the teaching text data; determining a classroom type based on the classroom interaction behavior information; determining the classroom participation degree of the student based on the classroom interaction behavior information and the classroom type; and determining education management quality based on the classroom order score and the classroom participation degree. According to the educational management supervision method provided by the embodiment of the invention, the problems that the evaluation dimension of the educational management quality is single, and the evaluation is not accurate and objective enough can be solved.

The invention belongs to the field of medical image mode recognition, provides a sacroiliac joint bone marrow edema prediction method based on deep learning, and aims to accurately predict existence of sacroiliac joint bone marrow edema. The specific process comprises the following steps: firstly, collecting and sorting two groups of distinct magnetic resonance image data: one group is used as a healthy control group without bone marrow edema signs; and the other group clearly shows the pathological characteristics of sacroiliac joint bone marrow edema. Subsequently, we establish a region of interest focused on analysis through a precise image processing technique. And it is ensured that image data in all regions of interest reach consistent standards in the aspects of brightness, contrast and the like. Gaussian random noise is superposed in the standardized region of interest, image interference in a real scene is simulated, and model learning is promoted to be more robust feature representation. A visual and reliable reference basis is provided for clinicians, and more accurate illness state assessment is facilitated.

The invention discloses an active tuberculosis diagnosis method based on proteomics and metabonomics. The method comprises the following steps: collecting a serum sample of a suspected active tuberculosis patient; detecting specific proteins in the serum sample by using a proteomics technology; detecting specific amino acids in the serum sample by using a metabonomics technology; carrying out standardization treatment on the obtained protein, amino acid and bile acid data; establishing a diagnosis model by using a machine learning algorithm; inputting data of a to-be-diagnosed sample into the model, and calculating a probability value of the to-be-diagnosed sample suffering from active tuberculosis; according to a preset threshold value, whether the sample provider suffers from the active tuberculosis or not is judged. By comprehensively analyzing multiple omics data, the sensitivity and specificity of diagnosis are greatly improved; by utilizing a machine learning algorithm, the disease risk can be objectively and quantitatively evaluated, and human misjudgment is reduced; the detection process is simple and fast, the sample demand is small, and clinical popularization and application are facilitated.

The invention provides a Parkinson's disease gait comparative analysis method based on a time sequence characteristic curve, and the method comprises the steps: obtaining video data containing gait information which comprises gait information before and after a user uses a medicine; performing posture recognition on a human body in the video data, and extracting position information of key body parts; calculating gait features according to the extracted key point information, and constructing a time sequence curve according to changes of the features along with time; performing data processing on the extracted gait features by using a digital filter; local frequency and amplitude are integrated into distance measurement by using a frequency amplitude and amplitude enhanced dynamic time warping method to quantify the difference of gait movement; obtaining a curve difference distance; metrics are calculated based on cumulative frequency differences and amplitude differences in the optimal regular path. The method has remarkable advantages in the aspects of accuracy, time sensitivity, robustness, adaptability, comprehensive evaluation ability, quantitative analysis, clinical application potential and the like.

The invention relates to the technical field of urban planning and sustainable development, in particular to a low-carbon toughness evaluation method for urban renewal. Comprising the following steps: data collection and analysis; formulating a scheme; evaluating carbon emission; evaluating the toughness level; constructing a low-carbon toughness index system; comparing and optimizing the scheme; and updating scheme correction. The invention provides a low-carbon toughness evaluation index system for city renewal, through a scientific and reasonable evaluation method, the low-carbon performance in the city renewal process is comprehensively evaluated, and city low-carbon transformation and toughness improvement are promoted; stable operation of the project under the low-carbon emission condition is ensured by evaluating the low-carbon toughness operation condition of the urban update project; by evaluating the low-carbon toughness condition of the city, the city region urgently needing city updating and reconstruction is found, and the city updating plan and implementation scheme are optimized.

The invention discloses an assessment method and system combining a language model and a virtual technology, and the method comprises the following steps: drilling a police and people dialogue process, analyzing the drilling language output of a subject through a first language model, and obtaining a drilling analysis result; obtaining a drill score of the subject based on the drill analysis result; when the drill score of the subject meets a preset score requirement, obtaining an assessment permission; based on the examination permission, combining a virtual technology to construct police and people dialogue scene questions; the subject is assessed based on the police and people dialogue scene questions, and assessment language output is obtained; and analyzing the assessment language output by adopting a second language model to obtain an assessment evaluation report of the subject. According to the method and the system, the language model is combined with the virtual technology, so that the more advanced and efficient assessment method and system are constructed, and the assessment quality and effect are improved.

The present invention discloses a method, device, electronic device and storage medium for determining a product elimination strategy. First, the time series corresponding to the historical sales data of the target product is obtained, and the target product includes the product to be evaluated and the eliminated product; the sales features of each time series in multiple dimensions are extracted; the target product is clustered based on the sales features to obtain multiple clusters; the number of eliminated products in each cluster is determined to obtain the elimination number; the life cycle corresponding to the product to be evaluated in each cluster is determined based on the elimination number; and the elimination strategy of the product to be evaluated is output in combination with the life cycle. The sales features of different dimensions represent the performance of the product in different aspects in the market. Clustering is based on sales features. The product to be evaluated and the eliminated product are clustered and evaluated together. Products with similar features and performances to the eliminated products can be screened out in the product to be evaluated. The product elimination strategy can be automatically output in combination with historical data to reduce the interference of human subjective factors.

The invention discloses a drop-point-based quality evaluation method and device in a tennis match, and relates to the technical field of tennis matches. According to the main technical scheme, the method comprises the steps that multi-modal track data in the tennis ball landing process is collected; the sideline distance and the bottom line distance between the actual drop point position and an effective area corresponding to tennis ball landing and the deviation distance between the actual drop point position and a preset expected drop point position in the effective area are calculated; processing the sideline distance, the bottom line distance, the deviation distance, the linear speed, the rotating speed, the angle of the rotating direction and the first bounce height by using a preset comprehensive scoring function to obtain a tennis ball quality evaluation result; and generating a quality evaluation report corresponding to the tennis ball drop point based on the tennis ball quality evaluation result. The tennis quality evaluation method mainly aims at providing accurate and comprehensive tennis quality evaluation in a tennis match and providing fair and accurate judgment assistance and technical guidance for referees and players.

The present invention discloses a method and system for realizing unified threat management in a heterogeneous network, and relates to the technical field of unified threat management, including calculating the real-time traffic anomaly coefficient and historical threat risk coefficient of each network or device, and calculating the network risk coefficient of each network or device; parsing the header of a data packet entering the unified threat management, identifying abnormal data packets, counting the number of abnormal data packets per unit time of each network or device, and deeply analyzing the content of the abnormal data packets, and counting the number of malicious data packets per unit time of each network or device; calculating the real-time carrying capacity of the unified threat management according to the real-time average traffic and the number of abnormal data packets of all networks or devices, issuing a data communication restriction warning, calculating the communication risk index of each network or device, and limiting the communication of the corresponding network or device. The communication of high-risk networks or devices can be automatically restricted to prevent malicious traffic from spreading in the network.

The invention relates to the technical field of medical care informatics, and discloses a construction method and application of a metabolic disorder degree evaluation model, and the method comprises the steps: learning the facial non-uniformity features, the facial sequence features and the facial symmetry features of a healthy group and a metabolic disorder group through a model based on a facial front infrared image of a target group; a high-precision metabolic disorder degree evaluation model is obtained through training, the evaluation result can be quantitatively output and visually displayed, simple, sensitive and economical metabolic disorder degree evaluation is achieved, medical workers are helped to find patients suffering from chronic diseases in time and formulate proper treatment schemes for the patients, and the treatment efficiency is improved. The assessment process is noninvasive, green, objective and accurate, the psychological burden and economic burden of the patient are effectively relieved, and meanwhile waste of medical resources is prevented.

This application discloses a method and related equipment for evaluating the smoothness of in-vehicle infotainment systems, relating to the field of vehicle testing technology. The method includes: acquiring a recorded video stream of the in-vehicle interaction process of a target vehicle; parsing and processing the recorded video stream to obtain temporal data of the visual features of the in-vehicle infotainment system during the interaction process; determining quantitative information on the smoothness of the target in-vehicle infotainment system based on the temporal data of the visual features; and generating a smoothness score characterizing the smoothness of the in-vehicle infotainment system based on touch response latency, animation frame dropping features, and stuttering features. This application, by parsing and extracting temporal data of the visual features from the recorded video stream of the in-vehicle interaction process, quantifying touch response latency, animation frame dropping features, and stuttering features, and generating a unified smoothness score, can achieve an objective, detailed, and comprehensive evaluation of the smoothness of the in-vehicle infotainment system.

The invention discloses an association rule mining and traceability analysis-based appeal attribution optimization method, and relates to the technical field of power marketing, and the method comprises the following steps: inputting the inconvenience of bill query and the unsmooth payment channel into an optimization module, outputting a comprehensive association degree and a comprehensive influence degree, and carrying out the analysis of the comprehensive association degree and the comprehensive influence degree; and an optimization measure is formulated based on the comprehensive correlation degree and the comprehensive influence degree, the measure is optimized in real time in an electric power marketing system, and then an optimization effect index is output. The method has the advantages that relevance among different appeals can be identified, follow-up analysis results are convenient to be comprehensive and accurate, a complete appeal chain can be formed to connect multiple appeal times in series, potential problem traceability analysis on power marketing is facilitated, artificial experience and judgment are reduced on the basis of a data driving mode, and power marketing efficiency is improved. Therefore, the accuracy is improved, the root cause of the customer appeal can be accurately identified, the corresponding optimization strategy can be formulated, the customer problem can be more effectively solved, and the customer satisfaction and loyalty are further improved.

The embodiment of the present application provides a construction site information verification and processing method and system based on image analysis, which relates to the field of artificial intelligence technology, and can accurately capture the image data of the real construction behavior of the construction site nodes and their derived collaborative behavior, which not only improves the comprehensiveness and accuracy of data collection, but also significantly enhances the real-time and dynamic adaptability of construction site monitoring. By introducing node scene feature analysis, it is possible to accurately identify and quantify monitoring noise errors, and then optimize the original image data, significantly reducing the impact of external interference factors on the monitoring results, ensuring the reliability and accuracy of monitoring data, and greatly improving the quality of monitoring data. Finally, by comparing and analyzing the optimized construction behavior and collaborative behavior image data of the nodes in the test cluster and the reference cluster, it is possible to scientifically and objectively evaluate the effectiveness of different monitoring schemes, providing strong data support and scientific basis for construction site management decisions.

The invention discloses an automobile maintenance teaching system combining virtual simulation and an intelligent game. The system comprises a user terminal, a server, a database, a virtual simulation module and an intelligent game module. When a student selects a learning mode, the server receives a learning instruction of the user terminal, calls corresponding learning data from the database, calls the virtual simulation module to generate an automobile maintenance virtual teaching scene, and performs task teaching through the user terminal; when a student selects a game mode, the server receives a game instruction of the user terminal and calls the intelligent game module to generate a corresponding AI opponent, meanwhile, the virtual simulation module is called to generate an automobile maintenance virtual competition scene, competition type teaching is carried out through the user terminal to obtain a single task score, and total points are issued to the student. The virtual simulation technology and the intelligent game are combined, personalized teaching is achieved by enhancing interactivity, the learning enthusiasm and motivation of students are improved, and the learning condition is comprehensively evaluated.

This invention relates to a system for identifying and assessing valvular motion abnormalities in fetal echocardiography, specifically in the field of echocardiography imaging. The system first robustly extracts the valvular central skeleton from a single frame of ultrasound image, overcoming image noise interference. Then, it constructs a physically constrained curve model to simulate the valvular tissue's mechanical properties and precisely tracks its motion trajectory in the image sequence through spatiotemporal optimization. Finally, the system isolates the overall cardiac motion, quantifies the valve's own opening and closing deformation, interleaflet motion synchronicity, and local tissue strain rate, thereby achieving early, objective, and quantitative accurate assessment of fetal valvular motion dysfunction and assisting in clinical diagnosis.

The application discloses a salt-underground seismic image quality evaluation method and device, equipment and a storage medium. The salt-underground seismic image quality evaluation method comprises the following steps: acquiring a salt-underground seismic image to be evaluated, and uniformly setting at least two quality detection points in the salt-underground seismic image; determining a linear quality score relationship between an expert score result of a quality detection point and an optimal image attribute based on the expert score result of the quality detection point and an attribute value of the optimal image attribute corresponding to the quality detection point; and determining a predicted expert score result of the quality detection point based on the linear quality score relationship, and performing quality grade evaluation on the salt-underground seismic image to be evaluated. Through the technical solution, objective, rapid and accurate evaluation of a large amount of seismic images is realized.

This application provides a learning status assessment method, system, and device based on big data analytics, relating to the field of big data analytics. The method includes: collecting multi-source learning behavior data of students to construct individual knowledge graphs; collecting students' resting physiological signals to construct a physiological signal database; using the individual knowledge graphs as federated nodes and combining them with the physiological signal database to construct a group knowledge graph federation; constructing a teaching progress timeline based on knowledge points and calculating the progress offset between the student's current learning progress and the class's teaching progress; comprehensively evaluating the individual knowledge graphs, the group knowledge graph federation, and the progress offset to obtain a multi-dimensional comprehensive evaluation report; and using a multi-objective optimization algorithm to generate personalized learning plans and push them to students. This solves the technical problems of one-sided, subjective, and inaccurate learning status assessment results caused by a single evaluation dimension and neglect of the dynamic influence of class teaching progress.

The present invention discloses a linkage response and control system based on AI, which belongs to the field of security monitoring technology and aims to solve the comprehensive and real-time collection, accurate analysis, timely warning and decision-making of environmental data to achieve early warning and rapid response of fire. It includes a data acquisition module, an AI analysis module, an AI recognition module, an AI decision module and an execution terminal robot; the data acquisition module is used to collect environmental data of monitoring sensors and surveillance cameras in the target scene; the AI ​​analysis module is used to process and analyze the environmental data obtained by the monitoring sensor, and output the sensor abnormal area; the AI ​​recognition module is used to perform real-time analysis on the environmental video stream obtained by the surveillance camera, and output the camera abnormal area; the AI ​​decision module is used to further evaluate the abnormal area and divide the abnormal level; the execution terminal robot is used to respond and control the abnormal area in the control room in real time according to the abnormal level.

The application discloses a measurement method of a brain load positive effect threshold based on a beta high-frequency band time specificity, and is characterized in that a plurality of test tasks of different brain load grades are set from low to high, and original electroencephalogram data of a person to be measured under different brain load grades are collected; the original electroencephalogram data are preprocessed, average band power index values of the person to be measured in a beta high-frequency band under different brain load grades are calculated according to the preprocessed electroencephalogram data; and the minimum time segment serial number of a statistical significant difference between double conditions of different brain load grades is calculated t i and a time segment serial number difference between adjacent brain load grades Adjt i , obtaining Adjt i a brain load grade corresponding to the minimum time i , i *is the brain load positive effect grade threshold of the person to be measured; and the method has the advantages that the beta high-frequency band time specificity change in electroencephalogram signal data is analyzed, and the brain load positive effect threshold of the person to be measured is objectively and accurately evaluated.