Prediction device, prediction method, and storage medium
A forecasting device and forecasting model technology, applied in forecasting, image data processing, calculation models, etc., can solve problems such as inability to properly estimate the state of public order, and achieve the effect of improving processing efficiency
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no. 1 approach
[0042] figure 2 It is a diagram showing an example of the configuration of the prediction device 100 according to the first embodiment. The prediction device 100 includes, for example, an acquisition unit 110 , a derivation unit 120 , and a storage unit 150 . Each part of the acquisition unit 110 and the derivation unit 120 is realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Part or all of these constituent elements can be realized by hardware (circuit department; including circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing Unit), etc. Realization can also be realized through the cooperation of software and hardware. The program can be saved in advance in a storage device such as HDD (Hard Disk Drive) or flash memory (a storage device with a non-transitory storage medium), or it can be stored in a...
no. 2 approach
[0060] Hereinafter, a second embodiment will be described. In the first embodiment, the object state evaluation table 154 specifying the penalty for the state of a specific object is preset by a certain method, but in the second embodiment, the object state evaluation table 154 is generated by machine learning .
[0061] Figure 11 It is a figure which shows an example of the structure of 100 A of prediction apparatuses of 2nd Embodiment. Compared with the prediction device 100 of the first embodiment, the prediction device 100A further includes a penalty learning unit 130A. The object state evaluation table 154A is generated by the penalty learning unit 130A.
[0062] For example, the penalty learning unit 130A sequentially selects one image from a plurality of images (preferably, the acquisition date is sufficiently earlier than the current one), and assigns 1 when it matches the state of the specific object and assigns 1 if it does not match the selected image. case ass...
no. 3 approach
[0069] Hereinafter, a third embodiment will be described. In the first and second embodiments, the public table information 152 is used as input data when deriving the security index, but in the third embodiment, the public table information 152 is exclusively used as teacher data for machine learning.
[0070] Figure 13It is a figure which shows an example of the structure of the prediction apparatus 100B of 3rd Embodiment. Compared with the prediction device 100 of the first embodiment, the prediction device 100B further includes a prediction model learning unit 130B. The prediction model 155B is generated by the prediction model learning unit 130B.
[0071] The prediction unit 123B of the third embodiment derives a security index indicating the future security state of the target block based on the total penalty calculated by the image analysis unit 122 . For example, assuming that the current year is 2018, the prediction unit 123 derives the public security index in th...
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