1843 results about "Computational model" patented technology

The invention relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

A multi-physics computation method and system for digital twin online simulation, relating to the technical field of physics simulation. A multi-physics coupling simulation computation model of a simulated object is established, and the multi-physics coupling simulation computation model is simplified, and the order of a temperature field simulation model is reduced, thereby greatly reducing the amount of computation, and improving computational efficiency; a low-precision dataset is obtained by means of temperature field analysis, so that the data size required for a model is reduced by means of low-precision data, thus reducing modeling costs; and a basic data-driven model is constructed by means of the low-precision dataset and a sample space corresponding to the low-precision dataset, so that a temperature field distribution result can be rapidly outputted, further improving computational efficiency and reducing computational errors.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention relates to the technical field of logistics park management, and particularly discloses a logistics park intelligent early warning and dynamic evaluation method, and the method comprises the steps: carrying out the time-space alignment of vehicle trajectory data, cargo pressure distribution data and environment data collected by a heterogeneous sensor network, and inputting the data into a dynamic threshold adjustment module to generate a self-adaptive warning threshold; the dynamic threshold adjustment module establishes a dynamic calculation model containing the equipment utilization rate and the vehicle density based on the matching relationship between the historical operation mode and the real-time operation state; based on a multi-dimensional evaluation system constructed based on a self-adaptive warning threshold, generating a comprehensive evaluation index through collaborative analysis of three groups of indexes including transportation efficiency, safety risk and resource utilization; and when the comprehensive evaluation index deviates from the preset range, triggering a grading early warning mechanism associated with the deviation degree. According to the invention, dynamic evaluation and intelligent early warning are realized, and real-time monitoring and intelligent evaluation regulation and control of the operation state of the logistics park are realized through space-time correlation analysis and an adaptive decision-making mechanism of multi-source heterogeneous data.

The invention discloses a target multi-attribute identification method based on feature decoupling and cross-task collaboration, and belongs to the technical field of computers of specific calculation models, and the method comprises the following steps: firstly, extracting the initial features of each task through a lightweight backbone network, carrying out feature decoupling in a subspace, and according to the cross-task feature similarity, carrying out feature extraction; according to the target multi-attribute identification method based on feature decoupling and cross-task collaboration, an orthogonal constraint weight is dynamically adjusted, then mutual information confrontation minimization is adopted to further suppress statistical dependence between tasks, task residual errors are injected in a cross-task feature aggregation stage, differentiation enhancement is achieved, and finally unified joint feature representation is formed. According to the method, subspace statistical independence is realized, independence and necessary collaborative information are considered, a stable basis is provided for subsequent fusion, statistical dependence between tasks is further suppressed through mutual information confrontation minimization, complementation information is reserved while independence is ensured, and feature discrimination and robustness are improved.

The invention provides a deep rock mass creep-seepage coupled near-field dynamics simulation method and system, and relates to the technical field of geotechnical engineering.The method comprises the steps that a near-field dynamics numerical calculation model is established based on geological survey data; based on the Burgers creep constitutive model, a near-field dynamic creep-seepage coupling basic motion equation is formed; and carrying out rock mass creep process simulation considering the seepage effect by utilizing the near-field dynamics numerical calculation model and the creep-seepage coupling basic motion equation, and carrying out iterative solution on stress, strain, displacement, water pressure and damage of each time step in the simulation process until iteration stop conditions are met, so as to obtain the rock mass creep process. And finally, obtaining distribution characteristics of a rock mass displacement field, a damage field and a seepage field and a creep characteristic curve, and realizing near-field dynamic simulation of a rock mass creep-seepage coupling process. According to the method, the technical blank of near-field dynamics in the field is filled, and the long-term stability of the engineering rock mass and the disaster evolution process are accurately reproduced and predicted.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a control method and system for an intelligent calculation center comprehensive energy supply device, and the method comprises the steps: collecting the operation data of energy supply equipment and an environment sensor in real time through an SCADA system, and carrying out the preprocessing; based on the collected data and simulation data generated by a simulation environment, performing offline mixed training on the reinforcement learning model to generate an energy scheduling strategy; inputting a real-time state into the trained reinforcement learning model to generate a preliminary scheduling instruction, performing security verification and interpretability analysis by using a large language model, and optimizing a strategy; and fusing the preliminary scheduling instruction with the suggestion of the large language model, generating a final scheduling command through the energy router control unit, and issuing the final scheduling command to the energy supply equipment for execution. The problems that in the prior art, black box decision making and simulation are not accurate, and experience is difficult to solidify are solved, and the purposes of decision making transparency, simulation high fidelity and experience structuring are achieved.

The application provides a large-span bridge substructure vehicle-fire wind force real-time hybrid test platform and implementation method, the platform comprises a physical loading and transmission system, a T-shaped wind-fire coupling simulation system, a modularized prying integrated system, an intelligent control and calculation system and a multi-dimensional high-precision monitoring system. The method realizes real-time pre-test of the boundary displacement / force of the test piece through the construction of a numerical substructure fast calculation model based on LSTM and converts the boundary displacement / force into a loading instruction to drive the through core jack to realize thermal force coupling interactive loading; the T-shaped wind-fire coupling simulation system reproduces the real vehicle fire scene under the influence of the environmental wind. The test platform supports independent loading of any component of the cable system, can execute open space self-defined fire test and closed space standard fire test, and accurately analyzes the cable failure mode, critical temperature threshold and damage evolution law under fire through the multi-dimensional high-precision monitoring system, thereby providing a high-fidelity test platform and analysis method for fire resistance design of large-span bridges.

The invention discloses a semiconductor processing defect detection method based on artificial intelligence, and relates to the technical field of semiconductor processing defect detection. The method comprises the following steps: acquiring wafer position coordinates, an optical image and process equipment dynamic parameter data; generating a microdefect feature matrix by using a multi-scale Gaussian filtering model and an asymmetric convolution algorithm; converting the process parameters into a position-associated process parameter matrix through space-time mapping and a correlation matrix algorithm; performing radial pooling and multi-expansion-rate cavity convolution algorithms on the fused feature tensor, and extracting global and local feature vectors; generating a defect probability distribution diagram by adopting a polar coordinate dynamic weighted fusion algorithm; and outputting the defect coordinates and the type identifier through an adaptive threshold algorithm, and generating a process equipment control signal according to predefined logic. Through an innovative multi-source data fusion calculation model, accurate defect identification and process closed-loop control are realized, and the detection precision and efficiency are improved.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention relates to a food reserved sample quality monitoring method based on machine learning, and the method specifically comprises the following steps: deploying a sensor array in a food reserved sample environment, collecting reserved sample multi-dimensional physicochemical characteristic data in real time, constructing a training set through historical normal and degraded batch data, and marking a state; the method comprises the following steps: constructing a machine learning model for food reserved sample quality monitoring, inputting sample data in a training set into the model, sequentially passing through a dynamic distribution alignment module, a physical constraint confrontation enhancement module and a multi-scale residual space-time network, and performing dynamic attention mechanism enhancement by utilizing physical constraint and environment modulation. And finally, outputting a quality category probability by the multi-modal classifier. Then calculating model loss, and carrying out iterative training on the model to obtain a trained model; and deploying the trained model at a reserved sample monitoring terminal, inputting newly collected monitoring data, and predicting the quality state of the food reserved sample. According to the invention, high-efficiency and real-time quality monitoring on the quality of the reserved food sample can be realized.

The invention relates to an internet of vehicles edge computing task unloading and resource scheduling optimization method, which comprises the following steps: constructing an internet of vehicles edge computing system model fusing a task jump mechanism and dual-priority scheduling, the system model comprising a road test unit deployment model, a communication model, a computing model and a task jump model; establishing a joint optimization function aiming at minimizing the total time delay and the total energy consumption of the system; decomposing a joint optimization problem into three sub-problems of unloading decision, computing resource allocation and communication strategy selection; a deep reinforcement learning algorithm is adopted to intelligently sense a road environment state, an optimal communication priority strategy combination is dynamically selected, and a task unloading decision and computing resource allocation are collaboratively optimized; and in each scheduling time slot, the edge server allocates communication bandwidth and computing resources to the vehicle tasks according to the selected strategy, and triggers a task preemption and cross-server jump mechanism, thereby realizing maximization of system throughput and minimization of task processing time delay.

Embodiments of the present application provide a flexible resource partitioning method and system based on power flow distribution. The method comprises: using a flexible resource aggregation model partitioning method to perform topological aggregation on flexible resources to obtain aggregation units; using the aggregation units as the basic unit for aggregating the adjustable capacity of the flexible resources, using an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and performing aggregation calculation on a power adjustment range that can be provided by all the flexible resources within an aggregation area for power dispatch; and solving the aggregated resource adjustable capacity calculation model considering the network constraints, so that feasible upper and lower power bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed among the flexible resources, thereby implementing flexible resource partition aggregation in continuous time periods.

The invention discloses a federal learning security training method and system based on differential privacy, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize data privacy protection and model precision balance in cross-mechanism model training. Comprising the following steps: constructing a distributed structure comprising a plurality of clients and a server; each client constructs a noise variance calculation model by taking the gradient feature, the privacy budget coefficient, the data sensitivity level coefficient and the training stage coefficient as calculation parameters after each round of local training is finished, and Gaussian noise is calculated according to the noise variance; verifying the noise variance of each round, and adjusting the calculation parameters of the noise variance based on the comparison result of the accumulated budget consumption and the global privacy budget; dynamically allocating the data sensitivity level of the local training data and the budget allocation proportion of the client; and the server performs secure aggregation through a secret sharing algorithm, and distributes the updated global model parameters to each client.

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention provides an electric power overhaul key safe release control method based on a safety measure conflict algorithm, and the method comprises the steps: collecting safety measure contents, carrying out the structural analysis, and constructing a safety measure key pair incidence matrix; detecting the relevance between the safety measure associated with the current application key and the executed or to-be-executed work ticket or operation ticket in the system, and identifying a potential conflict risk according to a preset rule base; performing conflict analysis on the identified conflict risk through a dynamic calculation model to obtain a conflict degree score; and performing key releasing or locking operation according to the conflict degree score. Through automatic conflict detection, the risk of misoperation caused by safety measure conflicts is reduced by more than 90%, and fatal hidden dangers such as overlapping isolation of safety measure operations are eliminated; the maintenance efficiency is improved, a manual checking process is replaced, and rapid key allocation during multi-task parallel maintenance is supported; full-digital tracing of safety measure execution, key use and conflict processing is achieved, and the five-prevention management requirement of the power industry is met.

The invention relates to the field of intelligent information processing, in particular to a carbon border adjustment mechanism intelligent auxiliary customs declaration method and system based on natural language processing, and the method comprises the steps: carrying out the real-time translation and key information extraction of CBAM related rule files and unstructured documents; a dynamic CBAM knowledge graph is constructed based on related rule texts, rule updating is monitored in real time through a text classification and event extraction technology, and calculation rules and declaration logic are dynamically adjusted; performing standardization processing on supplier data in different formats, integrating an industry emission factor library, automatically matching a calculation model according to a product type, generating a carbon emission result meeting a CBAM requirement, and mapping the data to a corresponding position of a CBAM declaration form; based on historical declaration data and related rule texts, analysis and compliance risk prediction are carried out by using a natural language processing technology, so that the declaration data are ensured to accord with latest rules, and violation risks caused by rule changes are avoided.

The invention belongs to the technical field of nuclear reactor physical calculation, and particularly discloses a multi-group steady-state neutron transport coarse mesh node global efficient solving method and system, which effectively fuse and exert the advantages of a coarse mesh node method and a Newton-Kralov method. A general coarse mesh segment transport calculation model suitable for various polyhedral segments is constructed, high-precision and high-efficiency transport scanning calculation is realized under sparse segments, the number of variables is greatly reduced, and the calculation scale of single transport scanning is reduced; a Newton-Kralov subspace global solution framework is established, a global solution variable is selected based on a variable coupling relationship of a coarse network nodal method, a linear equation corresponding to a Newton step length is solved by using a Kralov subspace method, and the Newton step length is used for updating the global solution variable to realize rapid and efficient convergence. On the premise of ensuring the calculation precision, the calculation efficiency is remarkably improved, and the calculation scale is reduced, so that the method is particularly suitable for the problem of large-scale and high-dominance-ratio neutron transportation.

The invention provides a finite difference-based rapid calculation method and system for an electromagnetic regulation and control roller shape and a rolling condition optimization method and system, and belongs to the field of rolling control. The roller shape rapid calculation method comprises the steps that size parameters, an electromagnetic rod loading form, a regulation mode, regulation parameters and rolling working condition environment parameters of an electromagnetic regulation roller to be regulated are obtained, a geometric model of the electromagnetic regulation roller is established, and the electromagnetic rod loading form, the regulation mode, the regulation parameters and the rolling working condition environment parameters are set in the geometric model; grid division is conducted on the geometric model of the roller, the finite difference theory is introduced, an electromagnetic regulation and control roller shape rapid calculation model is constructed, and temperature field distribution is rapidly solved and calculated through an LU decomposition method; and the roller shape is calculated according to the electromagnetic regulation and control roller shape rapid calculation model and the temperature field distribution. According to the method, online dynamic regulation and control of the electromagnetic regulation and control roller shape under parameter optimization is achieved, and the real-time performance and accuracy of the electromagnetic regulation and control roller shape are improved.

The invention discloses an intelligent man-machine interaction system and method based on multi-modal features, and relates to the field of man-machine interaction and artificial intelligence. The problems of low interaction efficiency and frequent errors caused by single intention understanding, feedback lagging and lack of continuous learning ability of a traditional interaction system are solved. According to the method, multi-modal data are collected, and uniform feature representation is generated through feature extraction and time sequence alignment fusion; generating a candidate intention list by using a lightweight encoder and an inference network based on the features; a final execution intention is determined by dynamically generating a multi-modal information probe and analyzing micro-feedback characteristics of a user in combination with iterative screening of a consensus degree calculation model; converting the determined intention into an equipment control instruction and executing the equipment control instruction; a reinforcement learning environment is constructed, a reasoning network and consensus degree model parameters are continuously optimized based on interaction process data and execution results, and accurate understanding of a multi-modal intention, self-adaptive optimization of an interaction process and continuous improvement of system performance are realized.

The invention provides an analysis method and device for flow, efficiency and water consumption rate of a hydraulic power plant generator set. A distributed flow measurement unit based on a domestic PLC controller and a domestic operating system is constructed, and each unit is independently configured with the flow measurement unit to realize decentralization of data acquisition and processing; active power, guide vane opening, water head difference, instantaneous flow and volute inlet pressure data are collected in real time through a distributed flow measurement unit, and a unit efficiency and water consumption rate calculation model is dynamically fitted based on the data; sending an output result of the calculation model to a monitoring system through an anti-interference communication network, wherein the communication network adopts layered wiring and a double-shielded twisted pair to reduce the signal bit error rate; and the IMC integrated management and control platform is used for remote monitoring and diagnosis of the flow measurement unit, and seamless connection with a smart power plant data medium station is realized. The system reliability and the maintenance flexibility are improved, and the autonomous controllable requirements of domestic substitution and intelligent power plants are comprehensively met.

According to the large-span steel structure refined construction method based on the simulation model, through whole-process refined simulation and optimization, the construction level of a large-span steel structure is remarkably improved, the construction scheme is determined based on a multi-objective optimization algorithm, accurate design of parameters such as hoisting, supporting and monitoring is achieved, a temperature field model is combined with actual measurement correction, and the construction efficiency is improved. The integration of optimization closure temperature, node semi-rigidity and initial defects enables simulation errors, support stability evaluation and optimization guarantee of safety coefficients, most construction risks can be predicted in advance on the whole, the field adjustment time is shortened, the formal construction period is shortened, the construction efficiency and safety are greatly improved, the obvious engineering value and economic benefits are achieved, and the method is suitable for popularization and application. The problem that an existing large-span steel structure construction method lacks a calculation model for predicting the risk in advance and shortening the construction period by combining multi-aspect data is solved.

The invention relates to a community unmanned delivery control method and device, equipment and a medium. The method comprises the following steps: predicting a delivery demand thermodynamic diagram of a first preset duration in the future based on a delivery demand prediction model; based on the distribution demand thermodynamic diagram, obtaining the space utilization rate of each express cabinet in the to-be-distributed community and to-be-distributed parcel data every second preset duration, and determining a reward function value according to the space utilization rate of each express cabinet and the to-be-distributed parcel data by adopting a preset dynamic resource scheduling model, according to the reward function value, determining the number of a target express cabinet allocated to the to-be-allocated parcel and the size specification of a corresponding grid opening of the to-be-allocated parcel; and performing global path planning according to the number of the target express cabinet allocated to the to-be-allocated parcel by adopting a distribution path planning algorithm, and generating a bidding value to match the optimal execution unmanned aerial vehicle during task allocation according to a preset bidding value calculation model based on an improved contract network protocol. The resource utilization rate can be increased, the energy consumption can be reduced, and the operation cost can be reduced.

Multiple molecular designs may be generated by computations. One or more attribute calculation models may be applied to determine a plurality of attributes for each molecular design. Each attribute calculation model may be trained to approximate a probability distribution of possible values for the corresponding attribute. A cumulative distribution function indicator corresponding to an expected multivariate rank for each molecular design may be determined based on an output of the attribute calculation model. The multivariate rank of a molecular design may quantify the probability that none of its attributes may be improved without degrading at least one other attribute. One or more molecular designs may be selected as candidates for wet laboratory assessment based on the cumulative distribution function indicator for each molecular design. The molecular design selected for wet laboratory assessment may exhibit incrementally better properties than designs in previous design iterations.

The invention relates to the technical field of intelligent gate valve control, in particular to an intelligent induction type automatic adjusting gate valve system.An analysis module of a calibration unit in the intelligent induction type automatic adjusting gate valve system recognizes signal distortion by comparing historical working condition databases, extracts the viscosity change trend and deduces the impurity accumulation rate; a dynamic compensation factor is generated by an offset calculation model subjected to multi-source transfer learning training, the signal amplitude and phase are corrected by a compensation module through dual-channel closed-loop correction, and compensation logic is optimized by combining distortion residual detection and model online incremental learning; a PID controller of the control unit adopts a parameter self-tuning strategy, dynamically adjusts parameters according to the viscosity acceleration, the impurity accumulation rate and the synchronous error, generates a gate valve opening instruction to drive an execution mechanism, counteracts signal distortion caused by viscosity change and impurity accumulation, improves the adjusting precision and guarantees stable and efficient fluid conveying.

The invention relates to a weak supervision instance segmentation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a training image set and an image-level label, and generating a pseudo-semantic segmentation label; extracting multi-scale image features in the training image set, performing prompt contrast learning based on the multi-scale image features and the pseudo-semantic segmentation labels, and generating contrast loss and a pixel-text matching graph; splicing the multi-scale image features and the pixel-text matching graph to generate enhanced features, and training an instance positioning module based on the enhanced features to generate an instance center point and positioning loss; generating a semantic mapping graph through the segmentation network, and calculating self-matching loss of prompt guidance; calculating the total loss of the model based on the obtained loss, and performing iterative training on the instance segmentation model based on the total loss of the model to generate a target instance segmentation model; and generating a target instance segmentation result based on the to-be-processed image and the category text through the target instance segmentation model. According to the invention, the precision of instance segmentation is improved.

This application discloses a method, computer equipment, and readable storage medium for generating composite equivalent conicity of wheelsets, belonging to the field of rail transit operation and maintenance. The method includes the following steps: acquiring relevant data on the equivalent conicity of the wheelsets of a target railway vehicle; determining calculation parameters for the equivalent conicity of the wheelsets based on the relevant data; and generating the composite equivalent conicity of the wheelsets by calling a target calculation model based on the calculation parameters. The target calculation model is constructed based on a target calculation formula, which is as follows: where represents the composite equivalent conicity of the wheelsets, represents the nominal equivalent conicity of the wheelsets, and represents the nonlinear factor. The nonlinear factor characterizes the degree of distortion of the target curve, and the nominal equivalent conicity of the wheelsets is the equivalent conicity of the wheelsets at a lateral displacement of 3 mm on the target curve. The target curve characterizes the relationship between the equivalent conicity of the wheelsets and the lateral displacement of the wheelsets. This method can reduce safety hazards in rail transit and improve the service life of wheels and rails.

The invention relates to the field of direct drinking water of a fusion calculation model, and discloses a double-membrane mixing direct drinking water system and an operation method.The method comprises the steps that online data of a water supply end and a water quality monitoring end are collected for cleaning and time alignment, and a basic working condition data set is formed; the method comprises the following steps: establishing an operation characteristic model comprising water demand prediction, raw water quality prediction and membrane flux characteristic sub-models, calculating double-membrane target water yield and water quality indexes based on the model to generate a control parameter set, evaluating membrane pollution to generate an online cleaning task queue, performing joint simulation to generate a pressure stability control instruction set, and dynamically correcting the model. The conversion from passive response to active pre-judgment of the double-membrane mixing direct drinking water system is realized, and the operation stability, the water quality guarantee capability and the water-saving and energy-saving effects are improved.