City aggregation event prediction and positioning method and device
A positioning method and urban technology, applied in forecasting, neural learning methods, computer components, etc., can solve problems such as long-term prediction and positioning of key areas
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Embodiment 1
[0056] The invention utilizes a new deep neural network structure to predict and locate city aggregation time in advance, and converts aggregation event prediction into a classification and regression problem. Specifically, given a series of observation time series Xt, output the prediction sequence Yt indicating whether the aggregation event occurs, the probability of the immediate occurrence of the aggregation event Pt, and the possible location Lt of the aggregation event.
[0057] In order to achieve this goal, the present invention is mainly divided into three parts: feature extraction, probability prediction, and class activation mapping. The relationship between the three is: first, feature extraction is performed on the input data set, and the extracted features are simultaneously input to the probability prediction and class activity mapping parts.
[0058] According to an embodiment of the present invention, see figure 1 , providing a method for predicting and locat...
Embodiment 2
[0099] According to another embodiment of the present invention, see Figure 4 , providing an urban aggregation event prediction and positioning device, including:
[0100] The picture generating unit 200 is used to convert the actual trajectory of the city vehicle into a multi-frame picture at preset time intervals for a period of time;
[0101] The feature extraction unit 202 is used to perform feature extraction using a deep neural network on multi-frame pictures, and extract the timing features and spatial features of multi-frame pictures;
[0102] The probability prediction unit 204 is used to perform probability prediction by using the temporal features and spatial features of the multi-frame pictures, and calculate the probability of urban aggregation events;
[0103] The class activation mapping unit 206 is configured to perform class activation mapping on the temporal features and spatial features of the multi-frame pictures, and determine the location where the urba...
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