139 results about "Load analysis" patented technology

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention discloses an offshore wind turbine supporting structure reliability evaluation method based on a reduced-order digital twin model, and the method comprises the steps: obtaining the response data of an offshore wind turbine supporting structure through multi-dimensional load analysis; and finally, setting a limit state function of the offshore wind turbine support structure in an extreme state, and calculating the failure probability of the offshore wind turbine support structure through a first-order reliability method under the condition of combining response data of the offshore wind turbine support structure so as to evaluate the reliability of the offshore wind turbine support structure. According to the method, the problems of insufficient model precision, calculation efficiency bottleneck, reliability evaluation limitation and the like of an existing reliability evaluation method for the offshore wind turbine supporting structure can be solved, and the requirements of offshore wind power engineering for high precision, high efficiency and comprehensiveness of reliability evaluation of the supporting structure can be met.

The invention discloses a machine room energy efficiency optimization simulation system and method based on digital twinning, and relates to the technical field of machine room energy efficiency optimization. The system comprises a model construction and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module and an optimal scheme adaptation and output module. According to the method, related parameters of a machine room are collected to construct a digital twinborn simulation model, different levels of load intervals are divided, a differentiated combination scheme composed of a high-voltage fixed-frequency large cold source unit and a low-voltage variable-frequency small cold source unit is designed for each interval, an energy efficiency optimal scheme is matched through simulation, a correlation database is established, and a result is output. Dynamic adaptation of the cold source equipment and the load interval is achieved, low-load energy efficiency attenuation is avoided, the full-working-condition energy efficiency of a machine room is remarkably improved, and reliable support is provided for optimal configuration of a cold source system.

The invention discloses a self-adaptive scheduling method and system for multi-process production, and the method comprises the steps: generating a production plan according to basic data of a multi-process production task, executing the plan, monitoring a production field data flow in real time, and when an abnormal event is monitored, extracting abnormal event information including an abnormal source, abnormal occurrence time and predicted duration; inputting the abnormal event information into a preset rule engine, positioning an influenced scheduling block set according to a preset influence evaluation rule, marking the influenced scheduling block set as a to-be-rearranged task, and outputting the to-be-rearranged task; local rescheduling optimization is carried out in the affected time-resource range, and candidate rescheduling schemes are generated; and screening out an optimal scheme from the candidate rescheduling schemes, updating and integrating the optimal scheme into the production plan, and automatically updating the associated productivity load analysis data. According to the method, the scheduling block set influenced by abnormity can be accurately positioned, local rearrangement optimization is carried out, and the stability of a production plan is guaranteed on the basis of low computing power consumption.

The invention discloses an electrical load analysis method and system based on multi-source data fusion. The method comprises the steps of obtaining load data and a meteorological data sequence of a target area; carrying out high-fidelity interpolation on the load missing data by adopting a strategy of combining form matching and meteorological verification; adaptively splitting the complete load sequence into a plurality of internally stable load data subsequences based on a preset change rate threshold; calculating a time weighted average value of the meteorological data corresponding to each sub-sequence as target meteorological data; performing two-stage clustering according to the data quantity and the target meteorological data to form a homogeneous load sub-sequence set; a representative sub-sequence with the meteorological closest to the central value is extracted from each set and input into the load analysis model to obtain a reference result; and efficiently deriving the reference result to all other subsequences in the set according to the meteorological difference by using a preset load correction coefficient. According to the method, on the premise of ensuring the analysis precision, the processing efficiency and the result consistency of the mass load data are remarkably improved.

The invention discloses a dynamic edge resource adaptive optimization method in a heterogeneous environment, which relates to the technical field of edge computing, and comprises the following steps: extracting a resource state feature set from a global resource state map through sliding segmentation, and carrying out coding mapping to generate a multi-dimensional resource state vector; according to the multi-dimensional resource state vector, executing interpolation operation, generating a dynamic resource thermodynamic diagram, performing comparative analysis on the dynamic resource thermodynamic diagram and historical edge resource data, and identifying a current high-load area; performing fusion analysis on the current high-load area and the matching relationship between the real-time monitoring task request and the node resource occupation to obtain a conflict task set, inputting the conflict task set into the quantum optimization model, executing quantum annealing calculation, and generating a task-node mapping table; unified modeling of distributed edge resources is realized, space-time relevance among the resources can be dynamically captured, and a high-precision global view is provided for subsequent load analysis and task scheduling.

The invention provides a task scheduling method and device based on dynamic priority and resource awareness, a computer system and a storage medium, and the method comprises the steps: calculating the numerical priority of each task through a pre-constructed dynamic priority evaluation model based on the related factors of each task; acquiring a current resource index of each computing node of the system, and performing load analysis based on the current resource index by using a pre-constructed resource sensing module to obtain a load condition of each computing node; and performing task scheduling by using an improved genetic algorithm and a multi-agent negotiation mechanism based on the numerical value priority of each task and the load condition of each computing node, and generating a task scheduling scheme. According to the scheme, the fundamental transformation of task scheduling from static state and isolation to dynamic state and collaboration is realized, and the comprehensive technical effects of reducing key task delay, improving resource utilization efficiency and guaranteeing stable operation of the system are achieved.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.

The invention discloses a load curve decomposition method and system based on a curvature equipartition rational B-spline basis function, and the method comprises the steps: calculating the curvature of a historical load sequence, generating a continuous curvature function, determining a group of B-spline node sequences meeting the cumulative absolute curvature equipartition condition, and enabling the node distribution to be adaptive to the load fluctuation intensity; a B-spline primary function is generated based on the node sequence, and a rational B-spline primary function is constructed through weight optimization so as to finely describe a continuous time fluctuation mode; a fitting model is established in combination with time period virtual variables, and a load curve is decomposed into two parts, namely a time period characteristic load level representing different time period reference power consumption intensity and a time characteristic load curve representing a continuous change fluctuation form. The defect that a traditional uniform spline is poor in adaptability to a non-uniform load form is overcome, self-adaptive decomposition with definite physical significance is achieved, and a better-quality data basis is provided for load analysis and prediction.

The invention relates to the field of transaction electricity price prediction, in particular to an electricity spot market transaction electricity price prediction method. According to the invention, on the basis of the power capacity data of the power spot market, the historical accidental outage capacity of each unit time period in each historical day is obtained and the periodicity is analyzed, and the total estimated available capacity is determined by combining the environmental parameters of each unit time period of the prediction day and the total load analysis accidental outage capacity of the system. Calculating a spinning reserve rate and obtaining a corresponding initial electricity price in combination with the total system load of each unit time period of the prediction day, constructing a temperature trajectory vector of each unit time period of each historical day according to the sensible temperature of the same month over the years of the prediction day, and counting the electricity price change rates corresponding to different temperature trajectory vectors; according to the sensible temperature variable quantity of each unit time period of the prediction day, all the time periods to be corrected are screened, the initial electricity price is corrected according to the electricity price change rate corresponding to the temperature trajectory vector of each time period to be corrected, and the accuracy of electricity price prediction in a complex environment is improved.

This invention discloses a method for establishing an integrated calculation data model for power distribution networks based on real-time measurement data, relating to the field of power distribution networks. It addresses the problem of existing methods lacking regional specificity in establishing integrated calculation data models for power distribution networks. The method includes steps S1: periodically monitoring load changes at power nodes in a sample power network, obtaining the load calculation complexity corresponding to each power node based on the monitoring results, and obtaining time-period load analysis data; step S2: periodically monitoring frequency changes at power nodes in the sample power network based on the time-period load analysis data, obtaining the frequency control complexity corresponding to each power node based on the monitoring results, and obtaining time-period frequency analysis data; and step S3: matching the calculation data model of the sample power network based on the time-period frequency analysis data and the time-period load analysis data. This invention improves the regional specificity of the integrated calculation data model establishment method for power distribution networks.

The invention relates to the technical field of irradiation transmission line automatic control, and discloses an irradiation transmission line tray dynamic scheduling method and system.The irradiation transmission line tray dynamic scheduling method comprises the steps that an integrated sensor module is installed on each tray, and data are transmitted to a data acquisition server in a wireless communication mode; performing filtering processing and standardization processing on the original sensor data; collecting product information and identifying product characteristics according to the opportunity when the product arrives at the transmission line inlet, and establishing a mapping relation between the product characteristics and irradiation requirements; predicting an equipment operation state and a production load; constructing a multi-objective optimization model by adopting an improved non-dominated sorting genetic algorithm; generating a scheduling strategy by adopting fuzzy logic decision and deep reinforcement learning; a predictive control algorithm is adopted to realize trajectory tracking and dynamic adjustment; according to the invention, through equipment health monitoring, system load analysis, energy consumption monitoring and a fault prediction algorithm, the operation state can be comprehensively evaluated and the future trend can be predicted.

This invention discloses a method, apparatus, equipment, and medium for power system load clustering, relating to the field of power system load analysis technology. First, this invention dynamically extracts load characteristic indicators to take into account the spatiotemporal differences in user electricity consumption behavior, making it more consistent with the periodic nature of load behavior. In weight allocation, the in-class-outside-class variance ratio weighting method is adopted, directly extracting the consistency within each feature and the separability between classes from the preliminary clustering results. This allows the weights to directly serve the clustering objective; that is, through the natural filtering of the in-class-outside-class variance ratio, the weights of interfering features are reduced, improving the ability to identify real electricity consumption patterns. Finally, during clustering, high-density data points are preferentially selected as initial cluster centers. Furthermore, based on the density requirement, the point furthest from the selected center is selected as the new cluster center for iterative clustering to promote class dispersion and ensure cluster stability, ultimately achieving highly accurate load clustering.

The application relates to the technical field of unmanned aerial vehicle structure design and optimization, and discloses a layout optimization method based on an unmanned aerial vehicle landing gear system. The method comprises the following steps: a three-dimensional design model of the landing gear is constructed; real flight attitude data and a load spectrum are imported; stress nephograms and displacement fields under multiple working conditions are dynamically generated to accurately identify a region to be optimized; preset layout rule library is used for iterative adjustment; component layout is optimized until mechanical indexes are met; the optimized model is compared with a historical failure case database; a risk component list with geometric or mechanical similarity is extracted; and local reinforcement or topological reconstruction is carried out according to the risk component list to generate a final scheme. The method fuses dynamic load analysis and historical failure data, improves the accuracy of layout design, and identifies and prevents potential structural failure risks.

This invention discloses a linear guide rail SOMC design method, equipment, medium, and program product, including: (1) constructing a simulation model and SOMC design model with static stiffness as the target and weight and maximum contact stress as constraints based on the structural characteristics and static load analysis of the linear guide rail; (2) constructing a design space, generating an initial population and establishing a database, and obtaining a set of high-influence continuous variables and a set of high-influence discrete variables through SRC and CEA sensitivity analysis methods; (3) generating corresponding candidate sets based on the two sets of variables, and obtaining a complete set of candidate guide rails through individual pairing; (4) establishing a random forest prediction model, screening excellent subsets and selecting the best offspring guide rails; (5) updating the database and prediction model after simulation evaluation, returning to step (3) until the indicators meet the standards, and outputting the optimal parameter values. This invention can effectively balance the SOMC process with stiffness as the optimization target and weight and maximum contact stress as constraints, achieving higher accuracy and better overall performance.

The invention relates to the technical field of goods shelf stability safety early warning, in particular to a goods shelf stability safety early warning system based on multi-sensor fusion, which comprises nodes for monitoring and acquiring state information, recording position time, screening signal changes, registering node labeling structural parts and generating a state change set; semantic identification is carried out to extract change nodes, identify inclined load change, position a connection area and generate instability characteristics, transition judgment is carried out to analyze bearing combination and mark response abnormity to generate an unbalanced combination set, hierarchical balance is carried out to extract staggered nodes, check a stress direction, identify an offset part and generate a track drift record. And early-warning response screening misplaced number record behavior sequence summarizing triggers nodes to generate a stability early-warning list. According to the method, the structure state of the goods shelf is tracked through real-time monitoring and data fusion, signal abnormity and direction changes are identified, stability risks are analyzed and evaluated in combination with inclined loads, track drifting is positioned, the early warning accuracy and response timeliness are improved, and missing detection and false alarm are avoided.

The invention discloses a pavement load analysis method based on discrete elements and a related device, and the method comprises the steps: collecting geometric features and material parameters of an asphalt pavement, and constructing a discrete element model; based on the constructed discrete element model, creating a rigid load plate in the target area, and simulating a tire-road contact surface; a target load is applied through a rigid load plate, displacement field distribution under the action of the load is calculated based on a discrete element model subjected to the load, and settlement response analysis is achieved. The discrete element model of the asphalt pavement is established on a physical scale closer to reality, and interlayer and particle contact conditions are flexibly defined through fine unit division; on the premise of reducing the cost, the transmission path and the distribution characteristic of the dynamic load in an actual pavement structure and the complex internal mechanical response caused by the transmission path and the distribution characteristic can be more accurately captured, and powerful support is provided for comprehensive improvement of scientificity, reliability and precision of pavement engineering design and accurate prediction of pavement diseases.

The invention relates to a power grid power utilization load analysis system based on novel power utilization demand equipment, and the system comprises a load data collection module and a load feature analysis module which are used for carrying out the multi-dimensional feature extraction of collected original power utilization data; the scene mode recognition module is used for recognizing the scene category to which the power consumption behavior of the user belongs through a mode matching algorithm based on the analyzed load characteristics; the impact load evaluation module is used for quantitatively evaluating the impact load intensity and duration possibly caused by multi-device linkage or remote centralized control according to the identified specific scene; and the adaptive prediction engine is used for generating a load prediction curve of a specific time scale in the future. A complete technical chain from data acquisition, feature analysis, scene recognition and impact evaluation to adaptive prediction and scheduling decision is constructed, and the problem of load characteristic complication caused by novel electric equipment is systematically solved.

The invention relates to the technical field of smart tourism management, and discloses a dynamic management system based on smart scenic spot resources. The system establishes a resource real-time state portrait through a state portrait module, and a load analysis module extracts a periodic load waveform of each resource point according to a historical operation log and calculates a current theoretical load phase. The offset calculation module compares a real-time state with a standard state under a theoretical phase to generate a real-time offset vector, and a global offset field is constructed through the global fusion module. And the phase calibration module reversely corrects a theoretical load phase according to the space gradient and intensity distribution of the biased field to obtain a predicted load phase after feedback calibration. And the load prediction module extracts a load prediction value sequence of a plurality of time slices in the future. According to the method, through periodic phase modeling and full-field dynamic calibration, finer time sequence description and more collaborative space prediction of scenic spot resource states are realized, and the perspectiveness and adaptability of management are improved.

The application relates to the technical field of power systems, and discloses an air conditioner load analysis method and system based on data fusion and carbon emission reduction accounting. The method comprises the following steps: acquiring air conditioner related data in different dimensions, and performing fusion processing on all the air conditioner related data to obtain air conditioner load fusion knowledge; a plurality of benchmark scenes are set based on first power consumption behavior characteristics, and the first air conditioner carbon emission of each benchmark scene is determined according to the air conditioner load fusion knowledge; a project scene is set based on second power consumption behavior characteristics, and the second air conditioner carbon emission of the project scene is determined according to the air conditioner load fusion knowledge; the air conditioner carbon emission reduction is obtained based on each first air conditioner carbon emission and second air conditioner carbon emission, and the air conditioner carbon emission reduction and the air conditioner power consumption and charging data obtained by analyzing the air conditioner load fusion knowledge are integrated to form an air conditioner load analysis result. The application deeply couples the air conditioner load data and the carbon emission reduction accounting, and helps the energy system to upgrade to intelligentization.

This invention relates to the field of flood evolution simulation technology, and more particularly to a multi-model fusion flood evolution simulation system, comprising: a model building module; a range determination module for determining the baseline matching setting or direction matching setting for a target viewport based on the linkage viewport fit and the number of linkage viewports; a loading analysis module for determining whether to adjust the loading method of the target viewport's pre-loading range from direct loading to time-domain layered loading based on the network degradation coefficient and viewport feature anomaly degree; a time-domain layering module for determining whether to perform uniform layering based on anomaly evaluation values ​​or clustering layering based on evolution correlation degree in time-domain layered loading based on evolution law coefficients and tile evolution intensity; and an optimization adjustment module for determining whether to perform optimization adjustment based on loading level heterogeneity and multi-viewport loading interference degree. This invention can optimize scene loading strategies to improve the scene loading construction effect of flood evolution digital twins.

The invention discloses a dance posture optimization system based on real-time monitoring, and relates to the field of dance training and sports health. The system comprises the following components: a data acquisition and digital twinborn construction module, a real-time monitoring and load analysis module, a damage early warning and evaluation module, a safety substitution action generation and optimization module and an interactive feedback and collaborative management module. According to the method, the action data of the dancer is monitored in real time, the digital twinborn body and the skeletal muscle dynamics model are combined, the load condition of each part of the body of the dancer can be accurately simulated, and when the load data exceed the biomechanical limit parameters which are set in a personalized mode, the system can immediately give out early warning and point out the damaged part and the damage type; the real-time feedback mechanism helps the dancer to adjust actions in time, potential sport injuries are avoided, and meanwhile, the system can generate a safe alternative action scheme according to the physical condition and the training target of the dancer.

The application discloses a kind of server power consumption adjustment method, electronic equipment and storage medium, especially in the technical field of server operation, comprising: in the case where determining that the server to be processed satisfies power consumption adjustment condition, based on the reverse mapping relationship of the power consumption load association model of the server to be processed, determine the reverse mapping function corresponding to power consumption load association model;Reverse load analysis is carried out on the target power consumption data of the server to be processed using the reverse mapping function, and the target load threshold value matched with the target power consumption data is obtained;Based on target load threshold value, generate the power consumption adjustment strategy of the server to be processed, to adjust the power consumption of the server to be processed according to power consumption adjustment strategy.It can effectively adapt to the dynamic change of server load, accurately capture the complex association between load and power consumption, realize the flexible and accurate adjustment of server power consumption, and guarantee the stable operation of server.

The invention discloses an enterprise energy consumption management method based on data fluctuation load analysis. The method comprises the following steps: collecting multi-dimensional real-time operation data of a power enterprise production system; in a sliding time window, extracting fluctuation characteristics such as standard deviation, kurtosis and spectral entropy from the time sequence data of the key parameters, and converting the fluctuation characteristics into fluctuation characteristic vectors; constructing a data fluctuation load model, and learning a non-linear relationship between fluctuation feature vectors and system energy efficiency and carbon emission indexes; on the basis of a model prediction result, a fluctuation load index (FLBI) is calculated and used for quantitatively evaluating the health bearing capacity of the system for operation fluctuation; and finally, according to the FLBI value and the change trend thereof, proactive sub-health early warning, root cause diagnosis and accurate optimization control suggestions are generated. According to the invention, the information is used as core information for analyzing the dynamic health degree of the system, and the transformation from post-event alarm to pre-event early warning is realized.

The invention discloses an electric power metering load analysis system and method based on intelligent prediction, and relates to the technical field of electric power systems and energy metering, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting electric power parameters and executing preprocessing; the power system diagram module is used for constructing a power system diagram; the feature prediction module is used for generating a prediction feature vector by improving the MTGNN model; the exception identification module is used for calculating an exception score and identifying an exception node; the exception optimization module is used for generating an exception node power parameter optimization report; and the deployment feedback module is used for deploying the abnormal node power parameter optimization report to a power management system. According to the method, the limitation that a static model is low in data processing efficiency and poor in adaptability in traditional electric power metering load analysis is broken through, and an efficient and intelligent solution is provided for operation and management of an intelligent power grid.

The invention belongs to the field of airplane structure load analysis, and particularly relates to a statically indeterminate structure multi-intersection-point load analysis method. The statically indeterminate structure multi-intersection-point load analysis method based on the finite element is provided for the common multi-intersection-point component butt joint problem in an aircraft structure in combination with the structural design scheme design, preliminary design, detailed design and design improvement stages, and accurate loads can be rapidly obtained to be used for structural design in different stages.

The application provides a road surface load analysis method, an electronic device and a medium, and the load analysis method comprises the following steps: determining various transport vehicles of various cargos according to the cargo types, calculating representative axle loads of various axle types of the various transport vehicles, and determining design axle loads; and calculating the cumulative action times of 100KN single-axle design axle loads at critical load positions within a design service life of the road surface; the design axle loads are calculated and determined in combination with different transport vehicle types, total weights of the transport vehicles, axle load coefficients, various axle types and the like, the main vehicle conditions of the road surface can be actually reflected, the accuracy of the road surface load calculation is high, and more accurate design basis can be provided for the road surface design; and according to the load analysis, the double-index traffic load grade grading range of the calculated and determined double-axle design axle loads and 100KN single-axle design axle loads is established, and the problem that the road surface design error is large due to the excessive conversion of the axle loads caused by the serious overload of the transport vehicles can be avoided.

The application discloses a kind of wind turbine blade life intelligent monitoring method and system, method includes steps: extracting blade low-frequency damage model feature, constructs blade fatigue damage database;High-frequency load analysis model is established, and then based on high-frequency load analysis model, blade load is calculated, and blade fatigue load is obtained;Based on blade fatigue damage database and blade fatigue load, blade fatigue life is obtained, and blade fatigue life is evaluated.The application avoids using complex nonlinear fatigue damage model, while ensuring simulation accuracy, simplifies the order of magnitude of calculation;SCADA data and wind resource data are read online, and the blade load is evaluated in real time by high-frequency load analysis model, which improves the accuracy and timeliness of online monitoring of blade cross-section fatigue load.

The present invention relates to the technical field of computers. Disclosed are a data processing method and apparatus, a data storage system, and an electronic device. The method comprises: for any storage controller, performing load demand analysis on an initial data packet to be processed of the storage controller; on the basis of the load analysis result corresponding to each storage controller, performing a first adjustment on the initial data packet to be processed, so that each storage controller obtains an intermediate data packet to be processed; detecting a current data processing capability of each storage controller; and on the basis of the current data processing capability of each storage controller, performing a second adjustment on the intermediate data packet to be processed, so that each storage controller obtains a target data packet to be processed. Two load balancing adjustments are performed on the initial data packet to be processed of each storage controller on the basis of the load demand of data to be processed and the processing capability of a processor itself, respectively, so as to ensure that the data processing load of the storage controllers is balanced, thereby laying a foundation for improving the performance of data storage devices.

The present application belongs to the technical field of tire design, and discloses a mining wide-body vehicle tire crown body contour design method and system, which comprises the following steps: collecting data to construct a working condition spectrum, performing load analysis to obtain an inflated equilibrium state target contour, and forming a crown stress and strain ideal distribution domain; mapping a material rigidity change mapping table, constructing a deformation matrix, and inversely solving to obtain a mold initial contour atlas; dividing the crown body curvature partition, performing crown force analysis, matching evaluation to form a deviation correction parameter set; accurately correcting the contour design scheme, simulating inflation deformation, and adaptively evaluating and selecting to obtain a final crown body contour atlas. The present application combines mechanical analysis with material characteristics, realizes accurate contour control of the tire from design to production, and improves the durability and working condition adaptability of the mining wide-body vehicle tire.