39 results about "Impact index" patented technology

The invention discloses a lightweight AI prediction control method for embedded equipment, and particularly relates to the technical field of embedded control, comprising the following steps: acquiring and preprocessing real-time multi-dimensional data to form a unified time sequence structure; identifying whether to enter a high heat accumulation or control response delay risk area based on the static characteristic threshold value and the dynamic trend function, if so, calculating a lag impact index and a heat load accumulation index, and substituting the indexes into a pre-trained artificial intelligence prediction model to output a scheduling risk coefficient, executing a control path dynamic switching operation according to the scheduling risk coefficient and the combined trend of the two indexes; according to the invention, through unifying a multi-dimensional data time sequence structure, preposed identification of high heat accumulation and control response delay risk is realized; a double quantization mechanism of a hysteresis impact index and a thermal load accumulation index is introduced, so that the operation state expressive power is enhanced; and in combination with artificial intelligence prediction output and an index trend, a control path is dynamically switched, and self-adaptive adjustment and structure optimization of a control strategy are realized.

The invention is suitable for the technical field of cost control, and provides a cost control method and system, and the system comprises a data collection and integration module, a geological change detection module, a geological risk prediction module, and a cost influence evaluation module. According to the method, through real-time data acquisition, historical data integration and dynamic prediction, the scientificity and accuracy of construction cost management are remarkably improved. A weighted interpolation method is utilized to predict a geological parameter trend, a historical risk mode and real-time dynamic change are combined, a risk occurrence probability and a construction influence index are scientifically quantified, a high-risk area is determined through priority ranking, resource allocation is optimized, and therefore construction time and economic cost are effectively reduced. By dynamically optimizing the construction scheme and resource allocation, the method realizes the flexibility and high efficiency of the construction process, and avoids delay and waste caused by risk response lag in traditional construction.

The invention relates to the technical field of tunnel engineering safety prevention and control, and provides a gypsum rock tunnel water disaster risk evaluation and prevention and control scheme selection method which comprises the following steps: step 1-3, based on parameters such as rock mass structural integrity, stratum karst development degree, water yield property, groundwater corrosivity, gypsum rock mineral composition, expansibility and corrosion property, establishing a grading evaluation system, dividing corresponding grades and determining influence indexes R1-R8; 4, adopting an AHP method, an entropy weight method and a CRITIC method to perform multi-dimensional weight calculation on the subjectivity, objectivity and conflict influence indexes, correcting abnormal weights in combination with expert experience and verification rules, generating a comprehensive score, dividing risk grades, and matching graded prevention and control measures; and 5, optimizing and adjusting prevention and control measures in real time through a parameter risk level dynamic judgment and construction method matching mechanism. According to the method, multi-source parameter dynamic empowerment can be fused, and a full-life-cycle prevention and control method with accuracy, timeliness and self-adaptability is provided for tunnel engineering.

The invention discloses an impact resistance evaluation method and device for a double-phase structure. The method comprises the following steps: obtaining a performance index set of the double-phase structure; the performance index set comprises a plurality of performance index subsets; each performance index subset comprises acquisition sequence values of a plurality of parameters; utilizing the performance index set to construct an anti-impact index numerical matrix; and carrying out comprehensive evaluation processing on the impact resistance index numerical value matrix to obtain an impact resistance evaluation value. According to the method, the problem that the impact resistance of the material in practical application is difficult to accurately predict due to the fact that existing evaluation methods mostly depend on empirical formulas or qualitative analysis is solved.

The present invention discloses a lightweight AI predictive control method for embedded devices, which specifically relates to the field of embedded control technology and includes the following steps: collecting and preprocessing real-time multidimensional data to form a unified time series structure, identifying whether entering a high heat accumulation or control response delay risk zone based on a static feature threshold and a dynamic trend function, and if entering, calculating a hysteresis impact index and a heat load accumulation index, substituting them into a pre-trained artificial intelligence prediction model to output a scheduling risk coefficient, and performing a dynamic switching operation of the control path based on the scheduling risk coefficient and the joint trend of the two indices; the present invention realizes pre-identification of high heat accumulation and control response delay risks by unifying the multidimensional data time series structure; introduces a dual quantization mechanism of the hysteresis impact index and the heat load accumulation index to enhance the expressiveness of the operating status; combines the artificial intelligence prediction output and the index trend to dynamically switch the control path to realize adaptive adjustment and structural optimization of the control strategy.

This invention discloses a traceable management method for AI model training data, specifically relating to the field of artificial intelligence training data traceability management technology. It addresses the problem in existing AI model training processes where the lack of continuous traceability between original samples, derived samples, training consumption records, and model versions makes it difficult to accurately pinpoint the actual impact range of problematic samples. By continuously associating original sample identifiers, derived sample identifiers, consumption vouchers, model version identifiers, and impact indexes, and tracing derived sample identifiers level by level along the original sample identifier based on the problematic sample trigger request, and locking the associated model version, the actual impact range of problematic samples can be accurately defined. This solves the problem of existing technologies struggling to trace specific problematic samples back to training batches and model versions, leading to inefficient handling.

The invention provides an international trade measure workflow intelligent deploying and early warning method, and relates to the technical field of resource scheduling distribution, and the method comprises the steps: calculating the vector difference of a policy text in a semantic vector space, and introducing an information source authority degree as a regulation factor, so as to precisely quantify the talent policy tightening degree; meanwhile, a principal component analysis method is adopted to perform weighted fusion on the basic emotion score of the public opinion, the logarithmic propagation influence factor and the subject risk weight so as to objectively calculate the exclusion degree of the social public opinion, thereby focusing on public opinions really having systematic threats; and finally, probability coupling is performed on the accurately quantified external environment risk and the core talent loss impact index in the enterprise by using a dynamic Bayesian network, so that penetrating and foresight early warning from a macroscopic environment to a microcosmic core asset risk is realized, a fuzzy trade risk is converted into a manageable and accurate internal safety problem of people, and the safety of the enterprise is improved. And the accuracy and efficiency of decision making are obviously improved.

The invention provides an electric power engineering project progress prediction method, system and device and a storage medium, and relates to the technical field of electric power engineering, and the method comprises the steps: dividing an electric power engineering project into a plurality of engineering sub-projects, and obtaining the construction data of a construction team corresponding to each engineering sub-project, and the associated data between the engineering sub-projects; calculating the first construction efficiency of each construction team according to the construction data; determining a first influence index according to the associated data, and adjusting the first construction efficiency and the second construction efficiency according to the first influence index; obtaining a construction environment of each project sub-project, and determining a second influence index according to the construction environment; and adjusting the second construction efficiency according to the second influence index, generating the target construction efficiency of each construction team, and determining the completion time of the electric power engineering project according to the target construction efficiency. The technical effect of the invention is that the deviation between the prediction result of the project completion time and the actual situation is reduced.

The present application relates to the field of building environment regulation technology, and specifically to an energy-saving regulation method, device and system for building environment, the method comprising: obtaining outdoor wind speed, outdoor temperature, indoor temperature, CO2 concentration and VOCs concentration at each time in each area inside the building; dividing all time into multiple time periods; determining the potential impact coefficient of each time period in each area; determining the regional difference of each time period in each area; determining the short-term impact index of each time period in each area; determining the air conditioning intensity of each area; obtaining the duty cycle of the PWM controller in the air conditioning equipment of each area, and regulating the air of the building environment. The present application can use different regulation strengths for air conditioning in different areas to reduce energy consumption, achieve energy-saving regulation of the building environment, and improve the efficiency of air conditioning in the building environment.

The invention discloses a professional term extraction method and device and a medium. The method comprises the following steps: extracting candidate terms from a corpus document in a specified field; determining a professional influence index of each candidate term; the professional influence indexes comprise at least two of a significance index, a professional confidence coefficient and an importance index; determining a professional score of each candidate term according to the professional influence index; screening out target terms of which the professional scores are greater than a score threshold value; and on the basis of the target terms, constructing a terminology library of the specified field. Therefore, the candidate terms are extracted based on the corpus document of the specified field, and an efficient and accurate automatic extraction mode is provided for constructing a professional term library for different fields. According to the technical scheme of the invention, the candidate terms are subjected to professional scoring by fusing multiple professional influence indexes, so that the professional terms meeting the field requirements are accurately screened out, a high-quality professional vocabulary library in a specified field is constructed, and data support is provided for application scenes such as a speech recognition system and the like.

The present invention is a computer system for creating fractal spider graphs which display data in a nested structure. The present invention includes a data store, a data analytics engines, and a computer platform for data visualization. The present invention analyzes both qualitative and quantitative data to determine a pattern of behavior based on the data. The data is then broken down into key performance indicator (KPI) units using NLP and machine learning. These KPI units are then used as the atomic unit for constructing the fractal spider graphs. The fractal spider graphs of the present invention are hierarchical, nested spider graphs which simultaneously display information and analysis from multiple levels of an organizational hierarchy. Further, the present invention is operable to generate inferences (i.e., decision outputs) based on the data, wherein the inferences are displayed as prioritized action lists, weighted confidence scores, risk-impact indices, and / or narrative recommendations.

The present invention can assist realization of a front loading design in consideration of an environmental impact index.　The present invention comprises: a processing category reception unit that receives processing categories for design targets on a design screen; a bill-of-process (BOP) candidate calculation unit that specifies, for each of the processing categories, a candidate for a processing method which belongs to the processing category, and calculates, as a BOP candidate, a combination of candidates for the processing method which have been specified for all of the design targets; an environmental impact index calculation unit that calculates the environmental impact index of the BOP candidate; and a display information generation unit that displays, on the design screen, the environmental impact index in association with the BOP candidate.

This invention provides a water hammer prediction system and device. The invention relates to the field of pipeline fluid transport safety technology. The water hammer prediction system is configured to: acquire static topology and physical attribute information and multi-dimensional real-time operating condition data of the fluid transport system, and pre-calibrate a dynamic simulation model based on the information and data; when a water hammer triggering event is detected, invoke the pre-calibrated dynamic simulation model to perform an initial prediction to generate an initial prediction result, and issue a pre-control command to the first-level protection device based on the initial prediction result; acquire the measured impact index at the first-level protection device and compare it with the initial prediction result to determine the prediction error; and, based on the prediction error, correct the dynamic simulation model in real time, and drive the corrected dynamic simulation model to perform cascade control of the next-level protection device.

This invention discloses an intelligent monitoring method for water conservancy engineering data based on digital twins, specifically relating to the field of intelligent monitoring technology for water conservancy engineering. The method includes the following steps: acquiring control command data and real-time monitoring data of the actual water conservancy engineering, analyzing their temporal characteristics and trends, detecting whether the control command data is transmitted back and participates in system calculations, and identifying data echo effects; after detection, calculating short-term and long-term impact indices based on the convergence of prediction errors, evaluating the impact of echo effects on the current operating status and future scheduling accuracy, and determining the echo effect level as low, medium, or high based on the degree of impact, and employing corresponding optimization strategies; this invention achieves dynamic evaluation and hierarchical determination of data echo effects, improving the system's early warning and adaptability; employing fuzzy logic reasoning to optimize decision-making, enhancing the intelligence of regulation; and reconstructing models through differentiated strategies and reliable data sources to ensure the operational stability and scheduling accuracy of water conservancy engineering.

This invention proposes a safety control method and system for ultra-high bridge towers based on the time-varying effect of concrete, belonging to the technical field of bridge tower control systems. Addressing the problems in existing technologies such as insufficient prediction accuracy of long-term internal force redistribution caused by concrete shrinkage and creep, and insufficient consideration of the coupling effect between multiple risk factors and bridge tower concrete shrinkage and creep, resulting in low accuracy in predicting the state of bridge towers and a lack of guidance for maintenance control, this invention integrates multi-source heterogeneous data to calculate the impact indices of concrete shrinkage and creep, dynamic ice impact, static ice pressure, concrete freeze-thaw cycles, and ice wedging. A machine learning-based comprehensive risk assessment model is then used to fuse these indices, generating a comprehensive risk report containing risk levels and control recommendations. This invention significantly improves the accuracy of single risk factor assessment, enhances the comprehensive prediction accuracy of complex coupled risks, and strengthens the system's decision-making robustness and maintenance efficiency in complex scenarios with multiple concurrent risks.

The invention discloses a building material life cycle traceability management system and method based on RFID, and relates to the technical field of building material life cycle traceability management, and the method comprises the steps: S10, generating a traceability feature of a building material product; s20, predicting a life influence index of the building material product in a logistics transportation stage based on the searched abnormal transportation data; s30, obtaining a traceability index of each building material product to be used; and S40, carrying out life traceability management on the building material product to be used. According to the method, the traceability index of the to-be-used building material product is calculated, the building material product with the quality problem can be quickly and accurately screened out from numerous products, the quality prevarication problem caused by the storage mode or storage time of the building material product is avoided, and the quality of the building material product can be accurately determined according to the occurrence frequency of various target traceability indexes. And the root cause of the quality problem of each to-be-used building material product is quickly and accurately locked.

The present invention discloses a fire escape evacuation simulation method based on an improved ant colony algorithm. By setting a grid map and environmental parameters such as fire source points, crowd density, smoke diffusion information and other environmental factors, the objective function, constraint conditions, improved heuristic functions, and comprehensive impact index functions, temperature change functions, CO concentration change functions, and visibility change functions are further set. By improving the pheromone content update method, a pheromone update model that conforms to the crowd evacuation process is established, the traditional pheromone update method is improved, the calculation speed is accelerated, and it avoids falling into a local optimal solution. r The setting of needs to comprehensively consider the convergence speed and the global search ability of the algorithm. r The value is adjusted adaptively. It has practical value for the evacuation of personnel in a fire environment.

To provide a technology for reducing the effort involved in trading improvement credits.SOLUTION: An information processing device according to one aspect of the present disclosure acquires the remaining improvement credits held by a target company to enhance the value of an environmental impact indicator. The information processing device then distributes at least a portion of the acquired remaining improvement credit to the accounts of one or more companies that supply one or more containing products. The containing products are identified by traversing a product tree that hierarchically shows the supply relations within the supply chain to which the target company belongs for product groups that include the target company's target product. Each containing product is included as one or more upstream products within the target company's target product.SELECTED DRAWING: Figure 1

The present invention discloses a method and system for simulating and deducing performance boundary bottlenecks of a cloud computing system. The method comprises collecting performance impact index data of a target cloud computing system; extracting a convolution kernel and introducing a convolutional neural network model; selecting a set of performance impact index data x n , input the convolutional neural network model to calculate the performance boundary bottleneck a for the nth time n If a n If convergence is achieved, the gradient of the sum of the previous n performance boundary bottleneck deduction results is derived and the final boundary bottleneck deduction result is calculated. Otherwise, the iteration continues. The present invention can calculate the performance boundary bottlenecks of various cloud computing systems, including physical servers with different hardware combinations, server clusters with different numbers of hardware, server clusters with different numbers of hardware using different application software, and different teams using different application software in server clusters with different numbers of hardware.

The invention discloses an urban system toughness dynamic measurement method and device, equipment, a medium and a product, and relates to the field of urban disaster management and risk assessment, and the method comprises the steps: obtaining toughness index data and disaster environment data of a target city; determining an initial rainstorm impact index according to the disaster environment data; constructing a toughness index system according to the toughness index data and the initial rainstorm impact index; calculating a weighted comprehensive index of each toughness dimension according to each toughness index value under each toughness dimension to obtain a plurality of toughness attribute parameter values; according to the toughness attribute parameter values, a parameterized gamma family recovery function model is adopted to simulate system performance changes after the target city is subjected to disaster impact, and a dynamic performance recovery curve is generated; and calculating a dynamic toughness quantized value and recovery time of the target city based on the dynamic performance recovery curve. According to the method, dynamic simulation of the whole process of'impact-response-recovery 'can be carried out on the city, and the accuracy of city toughness measurement is improved.

The invention discloses an intelligent aviation flow prediction method based on an MLP-LightGBM hybrid model, and relates to the technical field of air transportation management, artificial intelligence and big data analysis. The problems of data isomerism and space-time alignment are solved by innovatively adopting periodic time-triangular transform coding and an aviation cloud influence index model. Then, constructing a'micro-macro 'two-stage prediction architecture: a micro layer processes unstructured meteorological data by using MLP, and predicts single flight delay; and the macroscopic layer aggregates the MLP result and combines the structured features, and the LightGBM performs hour-level flow accurate prediction and correction. According to the method, the three bottlenecks of insufficient critical mutation capture, cross-scale modeling fault and space-time decoupling in the high influence weather in the existing method are effectively solved, the prediction error is reduced by 7.2% compared with the optimal single model, the threshold accuracy is as high as 93.75%, and an advanced and reliable technical support is provided for improvement of airspace operation efficiency and prevention and control of large-area flight delay.

The application discloses an environmental impact prediction system and method thereof, relates to the technical field of environmental impact prediction, and comprises the following steps: in response to a design change event, real-time capture of component information; characteristic parameter mapping processing is performed to obtain calibrated input parameters; the whole life cycle comprehensive environmental impact index is calculated and compared with the initial construction cost of the project; in response to the predicted whole life cycle comprehensive environmental impact index being greater than the compliance baseline, constraint optimization decision processing is performed to generate an optimized design scheme; the predicted whole life cycle comprehensive environmental impact index and the predicted initial construction cost of the project are visually presented; and the optimized design scheme is visually presented on the user interface of a computer-aided design environment. The application pre-positions environmental and cost considerations and integrates them into instantaneous design decisions, solves the problems of disconnection between design and evaluation and feedback lag through real-time prediction and intelligent optimization.

The invention discloses a method for evaluating the ecological adaptability of a traffic network, and relates to the technical field of ecological adaptability evaluation. The invention aims to ensure the balance and representativeness of data in a collection process, by classifying the data to be collected, and then designing a collection ratio for each type of data, and because the collection ratio is calculated based on a series of data-driven methods, specifically in the calculation process of obtaining the collection ratio, a comprehensive score for each type of data is obtained by combining and analyzing an influence index, a correlation index, a comprehensive data quality index and a weight, and then a collection ratio is formed according to the ratio of the comprehensive score to the total score. Therefore, the method has high scientificity and accuracy, and good executability, thereby helping to avoid analysis deviations caused by imbalanced data collection ratios, and further being able to effectively improve the scientificity and accuracy of the final evaluation results to the greatest extent, so as to effectively improve the effect of final decision execution.

The present invention relates to a method and system for identifying potential risk personnel based on graph-data fusion, wherein the method comprises the following steps: collecting view data and business data of risk personnel with criminal records and fusing them to form graph-data fusion data, and creating a fusion information base based on the graph-data fusion data; extracting risk personnel and related personnel in the fusion information base as nodes, constructing a relationship network diagram, and collecting related personnel information to supplement the nodes in the relationship network diagram, and creating a node attribute list and a relationship list according to the fusion information base and the related personnel information; calculating the influence index between two adjacent nodes through the relationship between each node, selecting a candidate node, and constructing an influence relationship network of the candidate node with the selected candidate node as the center, and the node inserted in the influence relationship network of the candidate node is determined by the influence index and a set threshold; confirming whether the candidate node is a potential risk person through the influence relationship network of the candidate node.

A data-driven academic expansion management system for the institutional analysis and control of expansion activities within an educational institution. The system includes: a variety of data collection units configured to capture metadata on cultural and academic expansion activities, including identifiers, category, activity mode, associated department, responsible persons, and temporal attributes; a central processing unit that is operationally connected to a storage unit and a non-volatile database, wherein the storage unit stores program instructions that can be executed by the central processing unit to structure, classify and link the captured metadata according to predefined institutional frameworks; a coordination controller configured to generate digital records in an institutional management repository for each identified activity, with the coordination controller linked to the user authentication parameters of deans, school principals, department heads, and outreach managers; a planning and distribution control unit in communication with the central processing unit, configured to digitally register posters, distribution emails, participant registration forms, multimedia evidence and activity feedback in a unified repository through cloud storage synchronization; a performance evaluation unit connected to the database, configured to calculate Key Performance Indicators (KPIs) that represent management indicators and acceptance criteria for each outreach activity based on activity frequency, inter-institutional collaboration, and participant satisfaction metrics; an impact analysis controller linked to a repository of graduate profile matrices, wherein the controller is configured to link extension activities with corresponding graduate outcome parameters and calculate an impact index (IG) that indicates the competency alignment of graduates; a satisfaction assessment processor configured to aggregate survey data, calculate the satisfaction levels of internal and external beneficiaries, and generate satisfaction reports for academic and cultural activities, which are stored in shared digital folders for at least a predetermined retention period; and a system interface controller configured to display institutional dashboards in real time, representing KPIs, impact levels and satisfaction metrics aligned with the institutional strategy plan, thus enabling automated evaluation, reporting and archiving of expansion activities.

The application discloses a kind of urban system resilience dynamic measurement method, device, equipment, medium and product, it is related to urban disaster management and risk assessment field, the method comprises: obtaining the resilience index data and disaster environment data of target city;According to disaster environment data, determine initial storm impact index;According to resilience index data and initial storm impact index, construct resilience index system;According to each resilience index value under each resilience dimension, the weighted comprehensive index of each resilience dimension is calculated respectively, obtain multiple resilience attribute parameter values;According to each resilience attribute parameter value, using parameterized gamma family recovery function model simulates the system performance change of target city after suffering disaster impact, generates dynamic performance recovery curve;Based on dynamic performance recovery curve, the dynamic resilience quantitative value and recovery time of target city are calculated.The application can carry out dynamic simulation to the whole process of "impact-response-recovery" of city, improve the accuracy of city resilience measurement.

The present invention provides a method and system for determining the vertical dynamic stiffness of a pipe pile, which relates to the technical field of dynamic stiffness determination, and specifically includes: collecting the material properties, geometric properties and soil parameters of the pipe pile; performing a dynamic load test on the pipe pile, obtaining the dynamic response data of the pipe pile, and generating a dynamic response index; combining the material properties and geometric properties of the pipe pile to generate a first influence index; generating a second influence index based on soil parameters; and synthesizing the first influence index, the second influence index and the dynamic response index to generate a calculation expression for the vertical dynamic stiffness of the pipe pile. The present invention improves the design accuracy and enhances the bearing capacity and safety of the pipe pile by collecting and analyzing the material properties, geometric properties and soil parameters of the pipe pile in detail. Secondly, the natural frequency and damping ratio data provided by the dynamic load test enable a deeper understanding of the dynamic characteristics of the pipe pile and reduce the risk of structural damage.

The invention discloses a traffic network ecological adaptability evaluation method, relates to the technical field of ecological adaptability evaluation, and aims to ensure the balance and representativeness of data in a collection process, classify the data to be collected, design a collection proportion for each type of data, and improve the data collection efficiency. The acquisition proportion is calculated based on a series of data driving modes, and specifically, in the calculation process of obtaining the acquisition proportion, the comprehensive score of each type of data is obtained through combined analysis of the influence index, the correlation index, the comprehensive data quality index and the weight; according to the method, the comprehensive score and the total score are obtained, and the acquisition proportion is formed according to the ratio of the comprehensive score to the total score, so that the method has high scientificity and accuracy and also has good performability, analysis deviation caused by imbalance of the data acquisition proportion is avoided, the scientificity and accuracy of a final evaluation result can be effectively improved to the greatest extent, and the method is suitable for popularization and application. And the final decision execution effect is effectively improved.

The application provides a document influence index analysis method, comprising the following steps: S1, obtaining the cited amount, publication year Y and current year Y of N documents n1 ; S2, determining the burst start year Y n2 , burst end year Y n3 and burst duration according to the cited amount; S3, obtaining the burst intensity S n according to the document burst state and the proportion of burst documents; S4, determining the frontier index of the N documents; and S5, determining the importance index of the N documents. Thus, in the application, when judging the frontier and importance of the documents, the various factors of the documents are comprehensively considered, i.e. the current year, the burst intensity, the document sequence, the publication year, the burst start year, the burst end year, the burst duration and the time interval between the burst end year and the current year, so that the judgment of the document frontier and importance is more accurate and more comprehensive.

Embodiments of the invention provide an influence degree prediction method and apparatus, a storage medium and a computing device. The method comprises the steps of obtaining a plurality of synchronous machine influence parameters; based on the plurality of synchronous machine influence parameters, predicting a control influence index of the influence of the fan on the synchronous machine, and generating a control influence index prediction value; and based on the control influence index prediction value, predicting the influence degree and generating a predicted influence degree, thereby quantifying the influence of the fan on the synchronous machine, and obtaining the control influence index prediction value by quantifying the influence of the fan at the next moment on the synchronous machine. The influence of the fan on the synchronous machine at the next moment is determined according to the control influence index predicted value, the predicted influence degree is obtained, the influence degree of the fan on the synchronous machine at the next moment can be accurately predicted, and the accuracy and reliability of predicting the influence of the fan on the synchronous machine are improved; and the fan operation at the next moment can be adjusted based on the predicted influence degree.