34 results about "Probabilistic neural network" patented technology

This invention discloses a photovoltaic system islanding detection method and system based on Santlet transform and Ridglet probabilistic neural network. The method includes: collecting historical voltage data of the photovoltaic inverter, preprocessing it, and then sequentially acquiring the data to be analyzed using a sliding window; performing time-frequency analysis using Santlet continuous wavelet transform to obtain the corresponding time-frequency spectrum; extracting multi-dimensional time-frequency feature vectors and inputting them into a trained Ridglet probabilistic neural network classification model to obtain the corresponding probability values; if the probability value is greater than a probability threshold, it is determined that the photovoltaic system has experienced islanding, and a trip signal is generated and sent to the grid-connected circuit breaker to disconnect it. This invention significantly improves the accuracy, speed, and reliability of islanding detection, effectively reduces the detection blind zone, and has good adaptability to complex power grid environments.

The invention relates to the technical field of fault diagnosis, in particular to a rapier loom fault diagnosis system. According to the system, fault tree analysis based on whale algorithm optimization and a probabilistic neural network are fused; the fault diagnosis method comprises the following steps: step 1, establishing a rapier loom fault tree diagnosis model; step 2, optimizing parameters of the fault tree model by using a whale algorithm; step 3, constructing a sample data set and carrying out normalization processing; and step 4, carrying out probabilistic neural network training and fault diagnosis. According to the diagnosis model based on fusion optimization of the probabilistic neural network and the fault tree algorithm, on the basis of a rapier loom fault tree diagnosis model, real-time newly-added state monitoring data of a monitoring system is introduced; and converting the data set into a sample feature vector, performing normalization processing, importing the sample feature vector into a probabilistic neural network algorithm model for training, and calculating and outputting a fault symptom probability, thereby realizing real-time, complete and rapid fault identification and positioning of the rapier loom fault diagnosis system.

The invention belongs to the field of oil and gas field exploration and development, and discloses a numerical reservoir model optimization and history fitting method and related device.The method comprises the steps that firstly, geological exploration data are utilized, a spatial clustering algorithm is adopted to automatically determine the local updating range of a structural model, and structural layers and stratums are optimized through spatial interpolation; outputting an optimized construction model and uncertainty measurement; based on the model and logging lithofacies attribute data, adaptively identifying a lithofacies updating key area and variation function parameters by applying a machine learning algorithm, and performing lithofacies simulation to output an optimized lithofacies model; combining the porosity, permeability and saturation attribute data, adaptively determining an attribute optimization range and variation function parameters by adopting a probabilistic neural network, and outputting an optimized attribute model through phase control attribute modeling; a three-dimensional oil-water two-phase black oil seepage model is constructed, a global optimization algorithm is adopted to automatically adjust parameters based on a target function, simulation data is calculated until convergence, optimal parameters are output to complete historical fitting, and efficient and reliable intelligent support is provided for complex oil reservoir development.

This invention discloses a method and system for transformer partial discharge pattern recognition based on an optimized probabilistic neural network. The method first acquires the transformer partial discharge signal; then, it establishes a two-dimensional spectrum of the partial discharge phase distribution pattern based on the partial discharge signal and extracts discharge statistical features from this spectrum; finally, it inputs the discharge statistical features into the optimized probabilistic neural network for pattern recognition to obtain the partial discharge pattern. The optimized probabilistic neural network uses a pollination algorithm to optimize the smoothing factor, and the switching probability in the pollination algorithm is a nonlinear function that decreases with the number of iterations. This invention can more accurately and efficiently classify and identify different partial discharge patterns of transformers, providing a data foundation for transformer fault diagnosis and resolution.

This invention discloses a bolt loosening location method and system based on environmental vibration and multi-scale PNN. The location method includes the following steps: S1: Establishing a refined finite element simulation model of the transmission tower and dividing it into multi-scale hierarchical substructures; S2: Simulating bolt loosening conditions in the finite element simulation model and extracting operational modal analysis results; S3: Calculating the frequency change ratio and modal compliance residual matrix norm, and constructing a multi-source damage-sensitive feature vector after weighted fusion; S4: Constructing and training a probabilistic neural network with an adaptive smoothing factor adjustment mechanism; S5: Acquiring on-site environmental vibration data and identifying modal parameters; S6: Extracting and calculating the measured data of the tower using the random subspace identification method, and inputting it into the trained probabilistic neural network to obtain the location results of the main bolt loosening area. This method achieves accurate bolt loosening location through scientific substructure division and multi-source fusion strategies, providing a reference for the safe operation and maintenance of tower bolts.

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

The application discloses an acceleration-based personalized virtual reality black insertion method for the disabled, which comprises the following steps: step 1, constructing an acceleration-based and personalized black insertion duration dataset based on the types of the disabled users; wherein the acceleration is divided into T types according to the size, and the disabled users have N types; step 2, obtaining the acceleration of the user, and calculating the time domain features and the frequency domain features of the acceleration; step 3, constructing a probability neural network based on the time domain and frequency domain features of the acceleration, inputting the time domain features and the frequency domain features of the acceleration into the probability neural network, and classifying the acceleration; and step 4, judging the type of the disabled user, combining the acceleration classification obtained in step 3, and obtaining the personalized black insertion duration of the user through the personalized black insertion duration dataset. The probability neural network based on the time domain and frequency domain features of the acceleration can well reflect the differences between different accelerations, reduce the influence of abnormal values on the network accuracy, and improve the differences between different features.

The application provides an insulator contamination grade evaluation method based on an infrared image, and comprises the following steps: S1, acquiring an infrared image of a sample insulator by using an infrared imaging device, wherein the image is acquired under different temperatures, different humidities and different contamination grades; S2, preprocessing the infrared image; S3, extracting a temperature characteristic parameter from the preprocessed image; S4, constructing a probabilistic neural network, inputting the temperature characteristic parameter, the environmental temperature and the environmental humidity into the probabilistic neural network as input features, and training the probabilistic neural network; S4, acquiring an infrared image of a to-be-tested insulator in real time, inputting the temperature characteristic parameter of the to-be-tested insulator and the environmental temperature and the environmental humidity of the to-be-tested insulator into the trained probabilistic neural network after processing through steps S2 and S3, and outputting a contamination grade of the to-be-tested insulator.

The application discloses a kind of monitoring method and equipment for reducing the pressure of high-pressure pipeline of drilling and production ship, and relates to high-pressure pipeline technical field, the method comprises: obtaining the real-time performance data of high-pressure pipeline of drilling and production ship, the real-time performance data includes high-pressure pipeline pressure data, motor rotating speed data of high-pressure mud pump, drill pipe motion state data and top drive motion state data;The real-time performance data is preprocessed;The high-pressure pipeline pressure data after preprocessing is input into the high-pressure mud pipeline probabilistic neural network agent model trained in advance to predict the safety condition level;According to the safety condition level predicted, control signal is generated to control the high-pressure pipeline of drilling and production ship.The application can realize closed-loop control of control process, with high accuracy and strong pertinence.

The invention discloses a rotating machine fault feature optimization extraction method based on variational mode extraction and comprehensive detection indexes, and the method comprises the following steps: S1, decomposing an input vibration signal through employing a variational mode extraction method, and calculating a corresponding sample entropy value; s2, constructing a fitness function of a particle swarm optimization algorithm by using the comprehensive detection index; s3, parameters of the variational modal extraction method are optimized in combination with the comprehensive detection index and a particle swarm optimization algorithm; s4, using the variational mode under the optimal parameter to extract and decompose the input vibration signal; s5, calculating the sample entropy of each modal component to obtain a fault feature set; and S6, sending the fault feature set into a probabilistic neural network for fault classification. According to the method, a solution is provided for parameter selection of variational mode extraction, so that the optimal fault feature of the rotating machine is obtained, the signal decomposition efficiency and the fault classification precision are improved, and intelligentization of fault diagnosis of the rotating machine is promoted.

A flexible power distribution network voltage drop identification method based on a reinforcement learning theory comprises the following steps: carrying out dimension reduction processing on original data through principal component analysis, and inputting an optimized probabilistic neural network-learning vector quantization model to carry out voltage drop disturbance source classification; and voltage sag identification caused by three common single disturbance sources and two composite disturbance sources can be realized. The invention provides a principal component analysis-probabilistic neural network-learning vector quantization voltage sag source classification identification model based on learning algorithm optimization, which is based on analog voltage sag data, performs dimension reduction on the data by using principal component analysis, extracts data features based on a deep learning method probabilistic neural network, and performs classification identification on the voltage sag source based on the deep learning method probabilistic neural network. And classifying voltage drop types by utilizing learning vector quantization. The model can effectively identify the voltage drop type. Compared with other models which only use the probabilistic neural network, the principal component analysis-probabilistic neural network and the probabilistic neural network-learning vector quantization, the method has the best recognition precision on the voltage drop source, and has very good performance on small and medium-sized sample data sets.

The application discloses a high-voltage bushing state diagnosis method and system, relates to the technical field of high-voltage bushing state diagnosis, and aims to solve the technical problem of low accuracy of the existing diagnosis method in high-voltage bushing state diagnosis.The high-voltage bushing state diagnosis method comprises the following steps: obtaining a visible light image and a thermal image of a high-voltage bushing; performing feature fusion on the visible light image and the thermal image to obtain a fused image; extracting feature data in the fused image by using a scale-invariant feature transform; inputting the feature data into a probability neural network optimized by using a salp swarm algorithm; and outputting a high-voltage bushing state diagnosis result represented by the fused image by the probability neural network.The diagnosis result comprises normal, mechanical failure and electrical failure.The technical scheme of the application is used for providing a high-voltage bushing state diagnosis method and system.

The invention discloses a short-term photovoltaic power prediction method and system based on data mechanism combined driving. The method comprises the following steps: acquiring the geographic latitude, the solar declination angle and the solar hour angle of a photovoltaic power station to be predicted; calculating a solar elevation angle according to the geographic latitude, the solar declination angle and the solar hour angle, and determining the projection direction of the sun in the horizontal plane to obtain a solar azimuth angle; calculating the ideal irradiance of a photovoltaic panel plane based on the solar elevation angle and the solar azimuth angle in combination with the photovoltaic panel arrangement parameters of the photovoltaic power station to be predicted; introducing ideal irradiance of a photovoltaic panel plane to establish a photovoltaic power prediction probability neural network PNN model; and constructing a photovoltaic power prediction neural network PVFNN model by combining a long short-term memory network LSTM on the PNN model, and realizing short-term photovoltaic power prediction by using the PVFNN model. According to the method, the irradiance model in ideal weather is organically embedded into the neural network, learning in a data driving process is constrained, and a predication result with interpretability can be obtained.

The invention discloses a transformer winding anti-short circuit capability evaluation method and device, and belongs to the technical field of transformer operation states, and the method comprises the steps: carrying out the accelerated aging test of a winding component of a transformer, and obtaining a parameter set reflecting the mechanical performance; establishing an electromagnetic-structure coupling model of the target transformer, and calculating electromagnetic force distribution data acting on each part of the winding under a special short-circuit working condition based on an analytical method and a finite element method; a probabilistic neural network algorithm is adopted, the parameter set and the electromagnetic force distribution data serve as input, and a cumulative effect model of the transformer winding under multiple short circuit impacts is trained and established; and inputting actual operation history or preset short-circuit working condition information into the cumulative effect model, and predicting and evaluating the anti-short-circuit capability of the transformer. The transformer winding cumulative effect model and the winding state under the special working condition are evaluated, and safe and reliable operation of the transformer is guaranteed.

The invention discloses a hyperspectral remote sensing image super-resolution reconstruction method based on a self-attention total probability neural network, and the method comprises the steps: obtaining a hyperspectral image under the same sensor, carrying out the resolution reduction preprocessing of the hyperspectral image, and obtaining a low-resolution hyperspectral image; the method comprises the following steps of: inputting a low-resolution hyperspectral image into a pre-constructed and trained super-resolution network reconstruction network to obtain a reconstructed high-resolution image, and training the network: carrying out coarse extraction on the low-resolution hyperspectral image, obtaining a coarse spatial spectrum feature map, carrying out feature enhancement by utilizing a global self-attention mechanism, and obtaining a reconstructed high-resolution image; the method comprises the following steps: acquiring a fine spatial-spectral feature map, setting a discrete intensity interval, calculating the discrete intensity probability of the fine spatial-spectral feature map, combining the discrete intensity probability with low-resolution position prior, acquiring a total probability feature under high resolution, and reconstructing a high-resolution image according to the total probability feature. According to the invention, high-efficiency super-resolution reconstruction of the hyperspectral image is realized.

The application relates to a robot reinforcement learning control method, system, device and storage medium. The method comprises the following steps: obtaining real interaction data of a robot control task, constructing an environment dynamics model based on a probability neural network set by using the real interaction data, and capturing the uncertainty of a real environment and generating virtual data by using the environment dynamics model; combining the virtual data with the real interaction data to generate mixed training samples, training a parameterized policy network and a value network by using the mixed training samples; performing model-based reinforcement learning on the policy network and the value network, introducing a dynamic relative entropy regularization mechanism in the policy optimization process of the reinforcement learning, and generating an optimal policy of the robot control task. By introducing the dynamic relative entropy regularization constraint mechanism in the model-based policy optimization framework, the cumulative influence of the environment model prediction error is inhibited, and the training instability problem caused by the model bias is effectively alleviated.

The application discloses a power distribution system harmonic detection method and device based on a neural network, and belongs to the technical field of power quality disturbance detection. The method comprises the following steps: in step S1, voltage time domain signals are respectively acquired at R monitoring points of the power distribution system; in step S2, a neural network model outputs power quality disturbance event information according to the voltage time domain signals; in step S3, if the power quality disturbance event type is the power harmonic event, a current time domain signal is acquired by a power signal detection instrument; if it is other events, the power quality disturbance event information is displayed; in step S4, a probabilistic neural network outputs a harmonic component recognition result according to the current time domain signal and displays the result. The application can effectively reduce the complexity of power quality disturbance event judgment, and improve the accuracy of power harmonic detection of the power distribution system.

The present application belongs to the field of unconventional and new energy technology, and discloses a shale oil soluble hydrocarbon prediction method, which comprises the following steps: S1, analyzing and obtaining total organic carbon content sensitive well logging curve, and combining with measured geochemical data to establish a calculation model of soluble hydrocarbon and total organic carbon content sensitive well logging curve; S2, extracting well logging data in a production area, and importing the calculation model to calculate soluble hydrocarbon single well data; S3, establishing a data set of seismic attributes and soluble hydrocarbon single well data, taking soluble hydrocarbon as target data, optimizing sensitive seismic attributes through intersection of target data and seismic attributes, applying a probability neural network method to machine learning to establish a mapping relationship between target data and sensitive seismic attributes, and then calculating a soluble hydrocarbon target probability body on a three-dimensional seismic body to obtain a planar prediction result of soluble hydrocarbon. The present application realizes effective prediction of soluble hydrocarbon by using geochemical data, well logging data and seismic data, and provides accurate data support for shale oil sweet spot optimization.

The arc fault detection method based on the improved grey wolf algorithm probability neural network comprises the following steps: acquiring current signal data sets of different load combinations under normal operation and arc fault of household power lines, preprocessing the data sets, setting control factors of the grey wolf algorithm to optimize position parameters of grey wolves in the wolf pack based on the improved grey wolf optimization algorithm, and building a probability neural network model by using the parameter optimization result; acquiring real-time current signal data, inputting the preprocessed real-time current signal data into the probability neural network model to obtain a classification result of fault diagnosis. The control factors of the grey wolf algorithm are improved, and dynamic adaptive step length weight and leading weight are set, so that the convergence speed and optimization result of the algorithm are greatly improved. The randomness of initial parameter selection is avoided by using the optimization result as a smoothing factor parameter of the alternating current arc fault detection model, and the accuracy and detection efficiency of the arc detection model are greatly improved.

A power plant boiler fault diagnosis method belongs to the technical field of fault diagnosis. The method solves the problem that an existing power plant boiler diagnosis method is prone to falling into local optimum, and consequently the accuracy of fault diagnosis is low. The method comprises the steps of collecting historical operation data of a power plant boiler, and obtaining fault feature data and corresponding fault category data; further obtaining fault identification data; constructing a probabilistic neural network fault diagnosis model, and training the probabilistic neural network fault diagnosis model by using the fault identification data and the fault category; adopting an alpha evolutionary algorithm improved by Sine chaotic mapping to optimize smoothing factors of the trained probabilistic neural network fault diagnosis model; substituting the optimal smoothing factor into the trained probabilistic neural network fault diagnosis model to obtain a final fault diagnosis model; and collecting real-time operation data of the boiler, performing fault feature extraction, and inputting the extracted fault features into the final fault diagnosis model to obtain a final fault diagnosis result. The method is mainly used for power plant boiler fault diagnosis.

The application discloses a breast cancer candidate drug classification prediction method based on AFNN and ADE, and comprises the following steps: performing feature selection on known experimental data by using a random forest RF algorithm, and selecting molecular descriptors with influence; constructing a compound biological activity pIC 50 prediction model based on AFNN; performing parameter optimization on the compound biological activity pIC 50 prediction model based on AFNN by using an adaptive gradient descent algorithm; establishing an ADME / T property classification model based on a probability neural network; and processing a constraint optimization problem by using an adaptive differential evolution algorithm ADE to find the optimal value corresponding to the compound molecular descriptor. The application can find the optimal value corresponding to the compound molecular descriptor, so that the compound has better antagonistic effect on ER alpha activity and has ADME / T properties.

Embodiments of the present disclosure disclose a method and a system for training a neural network for generating universal adversarial perturbations. The method includes collecting a plurality of data samples. Each of the plurality of data samples is identified by a label from a finite set of labels. The method includes training a probabilistic neural network for transforming the plurality of data samples into a corresponding plurality of perturbed data samples having a bounded probability of deviation from the plurality of data samples by maximizing a conditional entropy of the finite set of labels of the plurality of data samples conditioned on the plurality of perturbed data samples. The conditional entropy is unknown. The probabilistic neural network is trained based on an iterative estimation of a gradient of the unknown conditional entropy of labels. The method further includes generating the universal adversarial perturbations based on the trained probabilistic neural network.

The present application relates to the field of photoelectric detection and integrated circuits, and particularly relates to a photon probabilistic neural network chip based on a SPAD array, which comprises a SPAD sensor unit, an analog accumulation and activation unit and a weight regulation unit. The SPAD sensor unit responds to incident photons and outputs avalanche current pulses containing signal pulses and dark count noise pulses. The analog accumulation and activation unit performs analog domain charge accumulation integration on the avalanche current pulses, and outputs pulses to perform nonlinear activation when the threshold is reached. The weight regulation unit independently adjusts the over-bias and dead-time of the SPAD pixel unit based on pre-configured parameters or output feedback. The present application also comprises a method. The present application uses intrinsic dark counts of the device as a probability bias, and completes intrinsic convolution calculation of photons in the analog domain, thereby avoiding data throughput bottlenecks and power consumption overheads caused by traditional digital quantization. The present application also adopts physical weight mapping, thereby simplifying the circuit structure, and introduces a hierarchical double refractory period, thereby improving the calculation robustness.

The application discloses a DC filter capacitor open-circuit fault positioning method and device, wherein the method comprises the following steps: S1, collecting original current data of a DC filter capacitor in a normal state and an open-circuit fault state; S2, extracting an effective value from the original current data, and performing normalization and principal component analysis processing to extract a characteristic vector capable of reflecting the operating state of the DC filter capacitor; and S3, inputting a part of the characteristic vector as a test sample into a DC filter capacitor open-circuit fault positioning model for classification, thereby realizing open-circuit fault positioning of the DC filter capacitor, wherein the DC filter capacitor open-circuit fault positioning model is obtained by inputting another part of the characteristic vector as a training sample into a probabilistic neural network for training.

This invention discloses a multi-domain small-scale fracture identification method based on probabilistic neural networks. By performing edge detection on the "three instantaneous" attributes of seismic records, it fully mines fracture information from attributes such as amplitude, frequency, and phase. Then, probabilistic neural network technology is used for multi-domain small-scale fracture optimization and detection, ultimately achieving accurate identification of small-scale fractures. This innovation improves the speed and quality of small-scale fracture identification, enhances the accuracy and reliability of micro-fracture prediction, provides important evidence for the integration of geological and seismic data, and lays a solid foundation for subsequent comprehensive seismic interpretation, dynamic and static characteristic analysis of oil and gas reservoirs, and adjustment of oil and gas reservoir development plans.

This invention discloses an aquaculture water quality prediction method based on time-difference data fusion and COS-SSA-FBPNN. The method involves collecting aquaculture water quality parameters, establishing a dataset, and performing preprocessing. Time-difference sequence characteristic coefficients are fused into the water environment prediction. Density-based leader position updates and adaptive cosine inertia weight-based follower position updates are introduced to realize the COS-SSA algorithm. Feedback processing is introduced into a probabilistic neural network, and COS-SSA is used to optimize the smoothing parameters, resulting in an aquaculture water quality prediction model based on COS-SSA-FBPNN. The time-difference sequence characteristic coefficients are optimized using COS-SSA, and then COS-SSA-FBPNN is used to fuse the optimized coefficients with other aquaculture water quality parameters to complete the aquaculture water quality prediction. Compared with existing technologies, the model proposed in this invention has the highest prediction accuracy, and the COS-SSA-FBPNN prediction model exhibits excellent performance in predicting aquaculture water quality.

The invention discloses a turbulent heat flux calculation method and system based on a probabilistic neural network, and the method comprises the steps: data collection and preprocessing: taking a plurality of variables at the same moment as model input variables, and taking sensible heat flux or latent heat flux as an output variable true value; adopting a probabilistic neural network to respectively establish an sensible heat flux parameterized model and a latent heat flux parameterized model; the probabilistic neural network adopts a full-connection neural network architecture, and a mean value network and a variance network dual-branch parallel network structure are used for respectively calculating the mean value and the variance of the heat flux; each branch network comprises an input layer, two hidden layers and an output layer, and has the same structure but independent parameters; in the first stage of the parameterized model, only mean value network branches are trained, and in the second stage, mean value and variance network branches are optimized at the same time; and finally, evaluating the model. According to the method, the mean value and the variance of the heat flux can be estimated at the same time, and reasonable uncertainty quantification is provided while the heat flux is accurately estimated under the given input condition.

The invention provides a twin self-calibration photoelectric probability bit circuit unit and a preparation and use method thereof, the circuit unit comprises a self-calibration photoelectric differential module, a voltage comparator and a reference voltage source, the self-calibration photoelectric differential module is a pair of photoelectric indium gallium zinc oxide (IGZO) thin film transistors which are tightly coupled in space; the device is patterned through an ultraviolet lithography process and a radio frequency magnetron sputtering process at the same time, atomic-scale matching of a reference tube and a photosensitive tube in geometric dimension, film thickness and interface state density is ensured, differences only exist in illumination conditions, common-mode interferences such as aging caused by temperature drift and bias stress are offset by effectively utilizing a difference principle, and the performance of the device is improved. The physical characteristic of highly consistent aging trend is utilized to construct a synchronous drifting series voltage division network, and high-precision and high-stability in-situ photoelectric probability calculation is realized; probability bits are generated through dual modulation of grid voltage and light intensity, and the method is suitable for Bayesian reasoning, restricted Boltzmann machines and other probabilistic neural network hardware.

The invention relates to a traffic fusion data acquisition and processing method and a data acquisition system. The method comprises the following steps: acquiring multi-dimensional multi-path signal data acquired by an intelligent lamp post integration system; carrying out data preprocessing on the collected original data; for the preprocessed data, an orthogonal rational wavelet group filter bank is matched with a sensor array and a network to carry out multipath separation on multipath signal data to form multi-dimensional wavelet filter array data; and performing data fusion on the separated multi-path signal data, and performing optimization in the range of the whole network by adopting a wavelet probabilistic neural network model to obtain a numerical value closest to a real value. Compared with the prior art, the method has the advantages that the problem of multi-path separation is solved, multi-dimensional multi-path signals are fused and optimized in the range of the whole network, data accuracy is improved, and urban managers are effectively helped to detect traffic conditions and deal with possible problems.