79 results about "Neural network regression" patented technology

The invention discloses a remote automatic quality control and calibration method and system for a pH meter, and the method comprises the following steps: S1, carrying out the initial temperature compensation correction of a pH value, and generating a corrected pH value; s2, environment parameters collected by an environment sensor and the corrected pH value are combined and formatted; s3, cleaning the standardized data set at an edge computing node, and generating an optimized measurement data set; s4, performing confidence evaluation based on a committee query algorithm, and triggering a calibration process for a detected low-confidence sample; s5, generating an enhanced calibration curve by using a double-neural network regression model, and optimizing measurement data in real time by combining particle filtering; s6, displaying the final data in real time through a visual tool, and uploading the final data to a cloud end; and S7, dynamically adjusting the calibration period, the acquisition frequency and the communication strategy of the sensor through the edge computing node. According to the method, double neural network regression, particle filtering, cloud edge cooperation technologies and the like are utilized, and accurate measurement and remote intelligent calibration of the pH meter are achieved.

The invention discloses an electrical load prediction method based on a multivariate meteorological characteristic index and a combinatorial optimization model. The electrical load prediction method comprises the following steps: dynamically selecting meteorological characteristics by adopting an SHAP characteristic selection method; calculating to obtain meteorological characteristic indexes, and dividing the meteorological characteristic indexes into a training set and a test set; constructing a BP neural network regression model and a CNN-GRU network time sequence model; performing parameter optimization on the BP neural network regression model and the CNN-GRU network time sequence model by adopting a Bayesian optimization algorithm; respectively training and predicting the BP neural network regression model through the training set and the test set to obtain a prediction result; respectively training and predicting the CNN-GRU network time sequence model through the training set and the test set to obtain a prediction result; and performing weighted summation on prediction results of the BP neural network regression model and the CNN-GRU network time sequence model to obtain a final electrical load prediction result. According to the method, the overall precision of load prediction can be improved, the robustness is high, and data support can be provided for accurate planning and scheduling of a power grid.

The invention provides a road surface illumination detection system and method based on machine learning and visual identification. The system comprises: an image acquisition module obtains an image to be analyzed by using a machine learning method; the semantic segmentation module performs semantic segmentation on the to-be-analyzed image to obtain a road region image; and the illumination prediction module performs multi-dimensional feature extraction by using a feature extraction model, and inputs the extracted image features into a self-defined multi-layer neural network regression model for training and prediction to obtain the average illumination and illumination uniformity of the road surface. The method comprises the following steps: S1, acquiring a road image in real time; s2, performing semantic segmentation on the to-be-analyzed image to obtain a road region image; and S3, inputting the extracted image features into a self-defined multi-layer neural network regression model for training and prediction to obtain the average illumination and the illumination uniformity of the road surface. According to the method, the existing road monitoring system or image acquisition equipment is utilized, and an advanced machine learning algorithm is combined, so that efficient and accurate road surface illumination detection is realized.

The invention belongs to the technical field of radiation protection, and particularly relates to a neutron shielding material optimization method based on machine learning and chaotic mapping NSGA-III, and the method comprises the steps: 1, determining an optimization target, and building a multi-target neutron composite shielding material attribute optimization model; step 2, building a Monte Carlo model to simulate and calculate shielding doses of the composite shielding materials with different attributes to a neutron source, and obtaining a data set with the material component proportion and thickness as characteristic attributes and the shielding doses as labels; and step 3, optimizing initial parameters of the BP neural network by adopting an improved particle swarm optimization algorithm, improving calculation precision and operation efficiency of the neural network, and training a neural network regression model by taking the data set obtained in the step 2 as a sample to complete establishment of a shielding dose prediction model and the like. An improved particle swarm optimization algorithm for improving Levy flight mechanism strategy optimization is designed to optimize initial parameters of the BP neural network, and the precision and operation efficiency of a neural network shielding dose regression prediction model are improved.

The invention discloses a lithium ion battery preheating method and system in a low-temperature environment, and the method comprises the steps: carrying out the experimental data analysis of a lithium ion battery, and constructing a lithium ion battery RC equivalent circuit model and a lithium ion battery thermal model; simulating the lithium ion battery RC equivalent circuit model and the lithium ion battery thermal model to obtain a lithium ion battery simulation data set; performing optimization processing on the lithium ion battery simulation data set through a neural network regression prediction model and a multi-objective optimization algorithm to obtain optimal preheating parameters of the lithium ion battery; and performing optimal preheating treatment on the lithium ion battery based on the optimal preheating parameter of the lithium ion battery to obtain the preheated lithium ion battery. According to the method, the optimal preheating parameters enabling the battery to reach the maximum available energy can be found under different low-temperature conditions, the preheating energy consumption is reduced, and the overall energy utilization rate is improved. The lithium ion battery preheating method and system in the low-temperature environment can be widely applied to the technical field of lithium ion battery treatment.

The invention provides a grounding grid intelligent operation and maintenance system based on digital twinning and computer equipment. A multi-factor coupled corrosion kinetic equation is generated based on a laboratory grounding grid steel sheet accelerated corrosion test, and the corrosion kinetic equation is used for predicting the instantaneous corrosion rate of a galvanized steel sheet and estimating the mass corrosion loss of the steel sheet within specified time; combining the corrosion kinetic equation with the simulation model to establish a steel sheet corrosion data set, and training a physical information neural network to construct a corrosion prediction physical model; training the corrosion prediction physical model by adopting a neural network regression algorithm; constructing a grounding grid digital twin model by taking the corrosion prediction physical model as a base and combining field environment monitoring data; defining a Markov decision process in the digital twin environment of the grounding grid; and based on the feedback and reward function of the digital twin model of the grounding grid, training by adopting a near-end strategy optimization algorithm to obtain an intelligent decision-making body, and continuously optimizing the decision-making of the intelligent decision-making body through an online fine tuning function.

The invention relates to a processing temperature optimization control method and system applied to a plastic pipe fitting, and the method comprises the steps: M1, collecting the change data information of historical temperatures of different sections of a charging barrel of an extruder in the processing process of the plastic pipe fitting in the extruder, the data information of the temperature of each section of a charging barrel of the extruder, the data information of the rotating speed of a screw rod and the data information of the pressure of a machine head are collected in real time; and M2, based on the change data information of the historical temperatures of the different sections of the extruder charging barrel, predicting the temperature of each section of the extruder charging barrel by adopting a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain predicted data information of the temperature of each section of the extruder charging barrel. According to the invention, the temperature of each section of the extruder cylinder can be accurately controlled and adjusted, so that the energy consumption is reduced, and self-adaptive adjustment can be carried out according to different application scenes in the control process, so that the robustness of the system is improved.

The invention provides an artificial intelligence grading classification-regression combination prediction method for multi-class materials, and belongs to the technical field of material informatics and artificial intelligence. According to the method, a historical sample data set of material chemical composition and crystal structure information is obtained, a band gap value is divided into a plurality of intervals based on an equal width principle, and interval labels are generated. Key features are extracted, an SVM classification model is trained to predict a band gap interval, and then a multi-layer neural network regression model is utilized to predict a band gap value. If the highest confidence coefficient is higher than the threshold value, directly calling the multi-layer neural network regression model of the corresponding interval; and if the value is lower than the threshold value, multi-interval parallel regression and weighted fusion prediction are carried out. According to the artificial intelligence grading classification-regression combination prediction method for the multi-class materials, the precision and robustness of band gap prediction can be effectively improved, particularly, the performance is prominent when high and low band gap samples are distributed imbalanced, and a precise and reliable solution is provided for material band gap prediction.

The invention discloses an electroencephalogram multi-index network standardized modeling method which comprises the following steps: S1, adopting a multi-country electroencephalogram cross-spectrum data set based on a large-scale full life cycle as original electroencephalogram data, calculating a cross-spectrum matrix of the original electroencephalogram data, and preprocessing the original electroencephalogram cross-spectrum matrix; s2, constructing a weighted undirected network by using the preprocessed original electroencephalogram cross spectrum matrix to serve as a brain function network; s3, calculating network features of three dimensions of brain network function integration, function separation and centrality of the brain function network, and establishing an evolution trajectory of the network features; and S4, designing an interpretable lightweight neural network regression model, and reconstructing upper triangular elements of the brain function network to obtain a complete brain function network, namely a normalized brain function network corresponding to the age. The invention further discloses an electroencephalogram multi-index network standardized modeling system. The principle of the brain function network can be reflected more accurately.

The invention provides a small sample AI proxy model construction method based on sensitivity analysis and supplementary sampling, and the method comprises the steps: carrying out the global sensitivity analysis through the automatic simulation process of parameterized parts of wind power equipment in combination with test design, and removing insensitive variables, thereby guaranteeing the accuracy and standardization of a sample, avoiding the invalid consumption of irrelevant variables, and achieving the automatic simulation of the parameterized parts of the wind power equipment. Latin hypercube sampling is adopted based on key variables, a core design area is uniformly covered, the number of initial simulation times is greatly reduced, the cost is controlled, the problem that the generalization ability is poor due to uneven small sample distribution is solved, a deep neural network regression model is trained through a training set, the strong nonlinear fitting ability of the deep neural network regression model adapts to a complex mapping relation, the core law is efficiently learned, and the robustness is high. Based on initial model prediction precision and sensitivity information, samples are accurately supplemented in weak areas, samples are not increased blindly, investment is reduced, prediction blind areas are made up, errors are gradually reduced through iterative training closed-loop optimization, and finally the small sample AI proxy model meeting preset requirements is obtained.

The invention relates to a control method and system for a security patrol car based on autonomous navigation multi-machine cooperation, and the method comprises the steps: U1, enabling a plurality of security patrol cars to carry out a patrol task, collecting the data information of the historical driving track of each patrol car, obtaining the data information of the position and state of each patrol car in real time, and carrying out the data information of the position and state of each patrol car; the method comprises the following steps: constructing a multi-machine cooperative path planning model of a security patrol car, predicting a track point of each patrol car based on an improved BP neural network regression prediction algorithm of a Spearman correlation coefficient to obtain predicted data information of the track point of each patrol car, constructing the multi-machine cooperative path planning model of the security patrol car, and performing synchronous dynamic planning on a driving path of each patrol car to obtain a multi-machine cooperative path planning model of the security patrol car. And data information of the planned driving path of each patrol car is obtained. Inspection security tasks of different vehicles can be shared at the cloud terminal, task arrangement is changed according to execution conditions of the tasks, group operation efficiency of the security patrol car is improved, and practical application value of the security patrol car is improved.

The invention discloses a valve life test method based on multi-modal sensing and deep learning, which comprises the following steps: acquiring high-frequency original data of valve operation through a multi-modal sensing system consisting of a broadband acoustic sensor array, a vibration accelerometer and a high-precision micro-flowmeter; the method comprises the following steps: automatically extracting features sensitive to early degradation from original data by using a deep learning model fused with a one-dimensional convolutional neural network and a Transform encoder; the high-dimensional features are mapped into single health index values through neural network regression to quantify the performance degradation degree; and a historical sequence of the health indexes is learned by using a time sequence convolutional network, and a future evolution trajectory is predicted, so that the residual life of the valve is accurately calculated. Sensitive detection and non-destructive life prediction of early weak faults of the valve are achieved, and the defects that a traditional physical testing method is long in period, high in cost and incapable of early warning are overcome.

The invention discloses an intelligent irrigation system based on oil shale reservoir stress regulation and control, and relates to the technical field of intelligent irrigation, and the system comprises a data acquisition end, a stress analysis end and an irrigation decision end. According to the invention, the first irrigation unit uses an SVM classification model to decide the irrigation opportunity, accurately discriminates the critical state of vegetation water demand based on multi-source features, avoids the blindness of the traditional irrigation decision based on experience or a fixed period, and enables the irrigation start to adapt to the ecological water demand rhythm, and the second irrigation unit calculates the irrigation water amount through neural network regression. The water demand and multi-factor mapping are accurately quantified by utilizing the learning ability of the model for the complex nonlinear relationship, meanwhile, the water amount is dynamically adjusted by calculating the irrigation target value from the water-saving and ecological two-dimensional power generation, the water-saving coefficient and the ideal humidity are adjusted in combination with the stress change rate, and the water-saving effect is improved while vegetation growth is guaranteed through irrigation. The method is adaptive to the stress environment of the oil shale reservoir, and the balance of efficient utilization of water resources and ecological protection is realized.

The invention discloses a solid waste raw material activity prediction method based on oxide composition characteristics, and the method comprises the steps: firstly, constructing a standard data set, obtaining oxide component data, D50 particle size data and corresponding actually measured activity indexes of silicon-aluminum solid wastes such as fly ash, slag, steel slag and coal gangue, and carrying out the normalization and abnormal value processing to form training data; secondly, training an oxide-particle size and activity nonlinear mapping model by adopting a neural network regression model, analyzing and quantifying the contribution degree of each input feature to the activity in combination with SHAP, and ensuring the interpretability of the model; and finally, inputting the oxide of the solid waste to be detected and D50 data into the trained model, and outputting an activity predicted value. According to the method, the solid waste activity can be rapidly predicted within 10 minutes, the prediction precision R2 is larger than or equal to 0.9, the relative error is smaller than or equal to 10%, the detection cost is greatly reduced, the method can be directly used for solid waste purchase front-end pre-judgment, cement-based material formula design and raw material screening, and efficient technical support is provided for solid waste resource utilization.

This invention provides a dual-model defect detection method, training method, and apparatus based on a Bi-LSTM neural network. The dual-model defect detection method includes: acquiring N frames of infrared thermal images during the cooling process; acquiring a pulsed thermal imaging data sequence of each pixel on the test surface of the workpiece as a function of time from the N frames of infrared thermal images; inputting the pulsed thermal imaging data sequence into a trained Bi-LSTM neural network classification model to obtain a binary classification map representing whether the pixel location is a defect or not; inputting the pulsed thermal imaging data sequence into a trained Bi-LSTM neural network regression model to obtain a defect depth prediction map representing the defect depth; and obtaining the defect distribution of the workpiece from the binary classification map and the defect depth prediction map. This invention does not require prior information such as material thermal properties, reference areas, or characteristic times, and is more convenient to apply.

The invention relates to the technical field of additive manufacturing, and discloses a calibration method of a high-precision inherent strain coefficient calibration model, comprising an original inherent strain coefficient calibration model; the heat conduction correction structure changes the heat transfer rate and the heat dissipation path in the printing process; and the topological hollowed-out structure is arranged in the heat conduction correction structure, a local heat conduction path is regulated and controlled through topological form changes, and the heat history in the original inherent strain coefficient calibration model printing process is adjusted. Acquiring the thermal history of the original inherent strain coefficient calibration model in the model in the actual printing process through data acquisition; accumulating the obtained inherent strain coefficients corresponding to the thermal history in a database, supervising and training a deep neural network regression model by using the basic database, calculating a temperature field of a complex model needing stress simulation, applying the corresponding inherent strain coefficients according to the thermal history of each point of the model, and performing stress simulation on the complex model. And the refined calculation of the inherent strain method is realized.

To provide a delay estimation device capable of accurately estimating a delay time of transmission on the basis of text and information about the transmission time.SOLUTION: A delay estimation device comprises a model that inputs text and a transmission time of the text and outputs the delay time of the transmission time. In one embodiment, the model connects a BERT-type word embedding layer being a layer on an input side and a neural network regression layer being a layer on an output side. The BERT-type word embedding layer inputs the text and the transmission time of the text. The neural network regression layer inputs the information output from the BERT-type word embedding layer and outputs the delay time of the transmission time.SELECTED DRAWING: Figure 1

The invention relates to a facial acupoint real-time recognition model construction and recognition method based on facial key points and a neural network regression model. The method comprises the following steps: acquiring a face video containing various head postures and expressions and with mark points attached to target acupoints, and extracting a facial image frame; the face key point detection model is used for extracting two-dimensional coordinates of key points, the target detection model is used for recognizing two-dimensional coordinates of acupuncture points, and the two-dimensional coordinates are combined into a data set after normalization. A data set is divided into a training set and a test set, and the training set is amplified by a principal component analysis method. And training a neural network regression model by using the amplified training set, inputting key point coordinates, outputting acupuncture point coordinates, obtaining an identification model by using a root mean square error of predicted and actual acupuncture point coordinates as a loss function, and evaluating by using a verification set. The framework takes a key point as input and a perceptron as a core, compared with a complex structure of empirical rule fitting or mixed features, the framework is simpler, the prediction precision is higher, and the performance reaches or is superior to the optimal level of the existing end-to-end scheme.

The application provides a SPR connection quality tracing method and system based on pruning light weight, which comprises the following steps: training a multi-output neural network regression model by using standardized SPR curves and standardized process parameters; applying local disturbance to the standardized process parameters, inputting the disturbed samples into the trained multi-output neural network regression model to obtain predicted SPR curves; training a simplified linear model based on the relationship between the disturbed samples and the predicted SPR curves to obtain a regression coefficient matrix and a bias term; training an inverse regression model by using the standardized SPR curves and the process parameters, the regression coefficient matrix and the bias term; and inputting an abnormal SPR curve detected in real time into the inverse regression model to obtain process parameters of the abnormal SPR curve. The application improves the prediction accuracy and reasoning speed of the model, increases the interpretability of the model, and ensures the accuracy and stability of the SPR connection process.

The invention discloses a high-toughness nodular cast iron design method fused with a physical information neural network, and belongs to the technical field of material design and intelligent manufacturing. The method comprises the following steps: firstly, systematically constructing a nodular cast iron data set covering chemical components, microstructures and mechanical properties, and carrying out preprocessing and feature dimension reduction, so as to construct a deep neural network regression model, and taking a physical law of nodular cast iron as a constraint condition; forming a physical information neural network through a composite loss function formed by weighting data loss and physical loss and embedding the composite loss function into a training process; and the trained physical information neural network is used as a fitness predictor of a genetic algorithm, multi-target optimization is carried out on the chemical components, and an optimal parameter combination is reversely deduced. According to the method, deep fusion of data driving and a physical mechanism is achieved, the problems that a traditional trial and error method is long in period and a pure data model is poor in extrapolation are solved, and an innovative solution is provided for rapid and accurate design of high-performance nodular cast iron.

The invention relates to a commercial vehicle energy consumption attribution and optimization method and related equipment based on a neural network and interpretability, and the method comprises the steps: extracting multi-dimensional features and energy consumption values from commercial vehicle operation data, and constructing a training data set; training a neural network regression model by taking the multi-dimensional features as training samples and the energy consumption values as labels to obtain an energy consumption prediction model; performing SHAP interpretability analysis on the energy consumption prediction model to obtain an SHAP influence result; carrying out statistics on the distribution of each feature of the multi-dimensional features in the training data set to obtain a feature distribution reference; comparing the multi-dimensional features of the target commercial vehicle with the feature distribution reference to obtain a single vehicle feature distribution offset result; determining a vehicle optimization index based on the SHAP influence result and the single vehicle feature distribution offset result; and according to the vehicle optimization index and the single vehicle feature distribution offset result, matching energy consumption optimization measures in the knowledge base, and generating a vehicle energy consumption optimization suggestion. According to the method, the transparency, individualization and engineering landing capability of energy consumption analysis are improved.

The present application relates to a fusion method and system based on interactive iteration of sensor data and satellite data. The method obtains sensor data of one or more sensor observation points and satellite data of satellite observation points in a long period, to form a sample data with short period data and long period data in the same long period, to construct a neural network regression model with short period data as input value and long period data as output value, to obtain a prediction model by using the actual obtained long period data as expected output value and adopting a back propagation algorithm to iteratively train the model. The system comprises a satellite remote sensing system, a plurality of ground sensors and a neural network system / platform, and the neural network system / platform is used to implement the method. The present application can construct a prediction model based on fusion of sensor data and satellite data, and can be used for modeling, prediction / analysis of land subsidence and other suitable occasions / purposes.

The present invention discloses an automatic recognition method, device, terminal and storage medium for thyroid ultrasound images. By simultaneously extracting the feature data of the transverse view and the longitudinal view, and using a neural network regression model to determine the respective weights of the transverse view and the longitudinal view according to the feature data, the recognition results of the transverse view and the longitudinal view are weighted and fused to obtain the final recognition result. Compared with the prior art, the feature information extracted by the present invention is richer, and it can comprehensively focus on the ultrasound image recognition results of the two views, with a lower dependence on the clarity of the ultrasound image, and can automatically recognize the lesion area in the ultrasound image.

Embodiments of the present application provide a pronunciation detection method, apparatus, and computer-readable medium based on speech technology and machine learning methods in artificial intelligence. The pronunciation detection method includes: extracting audio frame features from a speech audio to be detected; generating a first posterior probability based on the matching degree between the audio frame features and first speech phonemes in a preset first language, and generating a second posterior probability based on the matching degree between the audio frame features and second speech phonemes in a preset second language; performing neural network regression processing on the first posterior probability and the second posterior probability to generate a probability score that the phonemes in the speech audio correspond to the second language phonemes. The technical solution of the embodiments of the present application can obtain accurate pronunciation detection results, improve the accuracy of pronunciation detection, and the practice efficiency of the pronunciation speaker.

The present invention relates to the technical field of medical training, and particularly to a comprehensive puncture simulation system based on an auto-injector. The system includes a user training puncture simulation operation selection module, a puncture gyroscope deviation calibration module, a puncture training scenario path simulation generation module, and a puncture simulation path visualization module. It is possible to select the puncture scenario to be simulated, configure the puncture needle and operating instruments for the corresponding auto-injector, and simultaneously perform comprehensive puncture simulation operations on the selected puncture scenario to generate the simulation process of the selected puncture scenario; perform puncture gyroscope measurement processing and neural network regression deviation estimation on the simulation process of the selected puncture scenario, and simultaneously perform static deviation calibration calculation and puncture path simulation recognition analysis to generate a puncture simulation training path; perform puncture simulation visualization processing on the puncture simulation training path to execute the corresponding puncture scenario visualization teaching process. The present invention can significantly improve the effectiveness of puncture simulation training.

The present application belongs to the field of oil and gas exploration, and discloses a horizontal well formation pressure artificial intelligence prediction method and system, which comprises: horizontal well logging data sensitive response feature construction; original logging response feature data preprocessing; softening treatment of pressure coefficient; pressure prediction model training based on neural network regression; and horizontal well pressure coefficient prediction. The present application is a cross-border innovation of the existing artificial intelligence method in the field of oil and gas exploration. Through comparative analysis of the measured pressure coefficient and the predicted pressure coefficient, the rationality and obvious advantages of the present application can be proved. As an effective horizontal well pressure coefficient prediction method, the present application significantly improves the performance accuracy of the traditional formation pressure prediction method, makes the formation pressure prediction method widely applicable to different lithology conditions, ensures the performance stability of the formation pressure prediction method in the application of horizontal wells, and accurately solves the 'pain points' of the traditional formation pressure prediction method.

The present invention provides a sleeper health diagnosis method, device and electronic equipment, which belongs to the field of fault diagnosis technology. The method includes: using multiple sensors pre-set on the sleeper to obtain monitoring data of the sleeper to construct a complete monitoring data matrix; performing a random missing operation on the complete monitoring data matrix to obtain a corresponding missing monitoring data matrix; inputting the missing monitoring data matrix into a neural network regression model to output a complete monitoring data estimation matrix; and performing health diagnosis on the sleeper based on the difference between the complete monitoring data estimation matrix and each row of monitoring data of the complete monitoring data matrix. The present invention does not require feature selection, extraction and feature preprocessing, and only uses the correlation between multi-channel monitoring data to diagnose sleeper health. In addition, the method can flexibly diagnose all channel monitoring data at the same time, and has the advantages of flexibility, reliability and high diagnostic efficiency.

The invention provides a probabilistic neural network mineral product prediction method based on spatial awareness, and relates to the technical field of mineral product prediction, and the method comprises the steps: extracting mineral control elements from multi-source geological data as feature data, extracting mineral production area data as label data, and randomly dividing the feature data and the label data into a training set and a test set; constructing a mineral product prediction model based on the neural network, introducing a space kernel function to perform output category weighting on a neural network output layer, outputting a neural network regression coefficient and bias, and introducing Dirichlet distribution to model output; training the model by using the training set, constructing a loss function through negative logarithm likelihood of Dirichlet distribution and KL divergence regularization, and testing the trained model by using the test set; and performing mineral product prediction by using the tested model to obtain a prediction probability, and calculating the uncertainty of each sample. According to the invention, non-stationary prediction and uncertainty measurement and simulation of a predicted target space are realized.

The invention discloses a dynamic load bridge verification coefficient rapid correction method based on monitoring data, and belongs to the bridge structure health monitoring technology. The method comprises the following steps: firstly, extracting dynamic weighing data and bridge measuring point deflection data based on a dynamic weighing system and a deflection monitoring system deployed on a bridge, and analyzing bridge single-vehicle bridge crossing working condition information through the data; then, speed correction is carried out based on vehicle load information, and the speed is used as input of the model, so that a neural network regression model with vehicle weight-vehicle speed as input and single-vehicle bridge deflection variation response as output is constructed; and then, according to the regression model, predicting bridge deflection variation response caused by different vehicle weights when the speed is low, and calculating a dynamic load bridge verification coefficient by adopting a function fitting mode. According to the method, a set of calculation method for the bridge verification coefficient is constructed through monitoring data, the defects that a load test blocks a road and the cost is high are overcome, and rapid calculation of the bridge verification coefficient is achieved.

The present invention relates to a control method and system for a multi-machine collaborative security patrol car based on autonomous navigation. The method comprises: U1. multiple security patrol cars perform patrol tasks, collect data information on the historical driving trajectory of each patrol car, and obtain data information on the position and status of each patrol car in real time, and predict the trajectory points of each patrol car based on an improved BP neural network regression prediction algorithm with a Spearman correlation coefficient to obtain data information on the predicted trajectory points of each patrol car, and construct a multi-machine collaborative path planning model for the security patrol car, perform synchronous dynamic planning on the driving path of each patrol car, and obtain data information on the planned driving path of each patrol car. The present invention not only allows the inspection and security tasks of different vehicles to be shared on a cloud terminal, and changes the task arrangement according to the execution status of the task to expand the group operation efficiency of the security patrol car, but also improves the practical application value of the security patrol car.