Population status determination system and model generation system

Abstract

The present invention suitably determines the state of a population.　A population state determination system 10 comprises: an acquisition unit 11 that acquires population information indicative of a population in a time series, for an area in which the state of the population is to be determined; a model calculation unit 12 that inputs the population information acquired by the acquisition unit 11 into an encoder-decoder model which is stored in advance and which compresses and restores input data, that performs calculation, and that obtains an output from said encoder-decoder model; and a determination unit 13 that compares the population information acquired by the acquisition unit 11 with the output obtained by the model calculation unit 12 and that determines the state of the population in the area.

Description

Technical Field

[0001] The present invention relates to a population state determination system for determining the population state of an area and a model generation system for generating an encoder-decoder model.

Background Art

[0002] Conventionally, a technique for estimating the population for each area and time zone using data from mobile terminals such as mobile phones has been proposed (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Using the above-estimated population information, it is possible to detect an area where the population is abnormal, such as more than usual. As a result, it is possible to detect sudden events or discover places where many people are staying during a disaster.

[0005] As a method for detecting abnormal population, there is one based on a statistical method using the mean and variance of the population in an area and time zone. With this method, it is possible to detect an abnormality in a certain area and time zone. However, this method does not consider population trends and cannot necessarily detect abnormalities with high accuracy.

[0006] One embodiment of the present invention has been made in view of the above, and an object thereof is to provide a population state determination system capable of appropriately determining the population state and a model generation system related to population determination.

Means for Solving the Problems

[0007] To achieve the above object, a population state determination system according to an embodiment of the present invention includes an acquisition unit that acquires population information indicating the time-series population of an area to be determined for the population state, and inputs the population information acquired by the acquisition unit into an encoder-decoder model stored in advance that compresses and restores input data to perform calculations and obtains an output from the encoder-decoder model. A model calculation unit, and a determination unit that compares the population information acquired by the acquisition unit with the output obtained by the model calculation unit to determine the population state of the area. , the acquisition unit acquires type information indicating the type of the area to be determined for the population status, and the model calculation unit performs calculations using an encoder-decoder model based on the type information acquired by the acquisition unit. .

[0008] According to the population state determination system according to an embodiment of the present invention, it is possible to determine the population state considering the time-series population of the area. In addition, the input to the encoder-decoder model and the output are compared to make a determination. Therefore, according to the population state determination system according to an embodiment of the present invention, the population state can be appropriately determined.

[0009] Further, a model generation system according to an embodiment of the present invention includes a learning acquisition unit that acquires learning population information indicating time-series population, which is used for generating an encoder-decoder model that compresses and restores input data, and performs machine learning based on the learning population information acquired by the learning acquisition unit to generate an encoder-decoder model that inputs information indicating time-series population. And a model generation unit. , the learning acquisition unit acquires learning type information indicating the type of the area related to the learning population information, and the model generation unit generates an encoder-decoder model based on the learning type information acquired by the learning acquisition unit. .

[0010] According to the model generation system according to an embodiment of the present invention, it is possible to generate an encoder-decoder model used in the population state determination system.

Effects of the Invention

[0011] According to an embodiment of the present invention, the population state can be appropriately determined.

Brief Description of the Drawings

[0012]

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Embodiments for Carrying Out the Invention

[0013] Hereinafter, embodiments of a population state determination system and a model generation system according to the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same reference numerals are given to the same elements, and redundant descriptions are omitted.

[0014] FIG. 1 shows a computer 1 which is a population state determination system 10 and a model generation system 20 according to the present embodiment. The population state determination system 10 is a system (device) that determines (estimates) the state of the population in a geographical area. The area to be determined is, for example, an area of 500 m square divided by regions. As the area, a standard regional mesh or a half regional mesh may be used. Further, as the area, an administrative division such as a municipality or a prefecture, or a preset land use division may be used. In the following description, the area will be described as a mesh. Note that the area to be determined does not have to be the above, and can be any geographical area.

[0015] The determination by the population state determination system 10 is made based on population information indicating the time series population of the area to be determined. For example, for the determination, population information indicating the population every hour on a daily basis as described later is used. The determination is, for example, a determination as to whether the population in the area to be determined is in an abnormal state different from the normal time. That is, the determination is to detect an abnormality in the population change in the area to be determined. An abnormal state in which the population is different from the normal time is, for example, a state in which the population change is excessively different from the normal population change. By the above determination, for example, a sudden event can be detected, or a place where many people are staying during a disaster can be found. Note that the determination by the population state determination system 10 may be a determination of the degree of abnormality instead of a determination as to whether it is an abnormal state. Alternatively, the determination by the population state determination system 10 may be other than the above as long as it is a determination of the state of the population in the area.

[0016] As described later, the determination by the population state determination system 10 is made by performing an operation on the population information using an encoder-decoder model which is a learned model generated by machine learning. The encoder-decoder model is a model that compresses and restores input data. The model generation system 20 generates an encoder-decoder model used for the determination by the population state determination system 10.

[0017] As the computer 1 of the population state determination system 10 and the model generation system 20 according to the present embodiment, a conventional computer can be used. Also, the computer 1 may be a computer system including a plurality of computers.

[0018] Subsequently, the functions of the population state determination system 10 and the model generation system 20 according to the present embodiment will be described. First, the functions of the model generation system 20 will be described, and then the functions of the population state determination system 10 will be described. As shown in FIG. 1, the model generation system 20 includes a learning acquisition unit 21 and a model generation unit 22.

[0019] The learning acquisition unit 21 is a functional unit that acquires learning population information indicating a time-series population used for generating an encoder-decoder model. The learning acquisition unit 21 may acquire learning type information indicating the type of area related to the learning population information. The learning acquisition unit 21 may perform clustering using the learning population information to acquire the learning type information. The learning acquisition unit 21 acquires each piece of information as follows.

[0020] Each piece of learning population information is information in the same format as the population information used for determining the state of the population. For example, the population information is information indicating the population of an area every hour (0:00, 1:00,..., 23:00) of a day. A part of the graph G1 of an example of the population information is shown in FIG. 2. When such population information is used, the population state determination system 10 determines the state of the population of the area to be determined on that day. Note that the entire time period (one day in the above example), the time interval (every hour in the above example), and the format of the population information to be determined are not necessarily the above.

[0021] To generate the encoder-decoder model, a large number of population information for training is used. The large number of population information for training usually includes population information for training related to multiple areas. The acquisition unit 21 for training acquires, for example, the data shown in FIG. 3(a). The data shown in FIG. 3(a) is information in which a mesh code (information in the "meshcode" column), information indicating time (information in the "timestamp" column), and information indicating population (information in the "population" column) are associated. The mesh code is information such as a character string that identifies the mesh which is an area, and is set in advance for each area. The information indicating time is, for example, information indicating the year, month, day, and the time within a day. The information indicating population indicates the population in the area and at the time indicated by the corresponding mesh code and the information indicating time.

[0022] The data related to the population shown in FIG. 3(a) is generated as spatial statistical information from, for example, information indicating the position of a mobile phone and information registered about the subscribers of the mobile phone by an existing method. Also, the data related to the population shown in FIG. 3(a) may be generated by any method other than the above. The acquisition unit 21 for training acquires the data related to the population shown in FIG. 3(a) that is stored in advance in the database of the computer 1 or other devices.

[0023] The acquisition unit 21 for training formats the acquired data into data for each mesh code and for each hour (0:00, 1:00,..., 23:00) in units of one day as shown in FIG. 3(b), that is, data on the population change per day in units of area. This data on the population change corresponds to the population information for training. The acquisition unit 21 for training acquires a sufficient number of data on population change for generating the encoder-decoder model by machine learning. The data on population change may or may not include data on the area that is the object of determination of the population state. Note that the acquisition unit 21 for training may acquire information indicating the population in a time series other than the above as the population information for training.

[0024] The learning acquisition unit 21 may acquire learning type information indicating the type of area related to population trend data. The type of area is a type that can affect population trends in the area. For example, the type of area is an urban type such as "office street" and "residential street".

[0025] For example, the learning acquisition unit 21 acquires the learning type information stored in advance in the database of the computer 1 or other devices. FIG. 3(c) shows an example of data that is the learning type information stored in advance. The data shown in FIG. 3(c) is information in which a mesh code (information in the "meshcode" column), information indicating an urban type (information in the "urban type" column), and a type code (information in the "type code" column) are associated. The information indicating the urban type is information indicating the meaning of the type of area indicated by the corresponding mesh code. The information indicating the urban type is set in advance for each area. Note that since the information indicating the urban type may not be used in the processing in the model generation system 20, it may not be acquired.

[0026] The type code is information (a flag indicating an area) that specifies the type of area indicated by the corresponding mesh code, and is set in advance for each area. The type code is a numerical value that can be used in machine learning. The type code has the same numerical value for the same urban type and different numerical values for different urban types. The learning acquisition unit 21 acquires the type code corresponding to the mesh code of the area related to the population trend data as the learning type information.

[0027] Instead of acquiring the learning type information stored in advance, the learning acquisition unit 21 may perform clustering using the learning population information to acquire the learning type information. The learning acquisition unit 21 performs clustering using the data of the daily population trend in the above area unit. For example, as follows, the learning acquisition unit 21 performs area clustering using the data of the daily population trend in the above area unit. By performing such clustering, areas with similar population trends can be divided into clusters.

[0028] The learning acquisition unit 21 calculates the average population for each time period in terms of areas, and generates population trend data for one area. For example, the learning acquisition unit 21 calculates the average for each time period of the population trend data for each day in a preset period (for example, the period from one month before the current time to the current time) for each area, and generates population trend data for each area. The learning acquisition unit 21 clusters the population trend data to perform area clustering. The clustering itself may be performed by a conventional method (for example, the k-means method).

[0029] Alternatively, the learning acquisition unit 21 may cluster population trend data that may include multiple population trend data for one area. The learning acquisition unit 21 sets, for each area, the cluster containing the most population trend data as the cluster for that area.

[0030] The learning acquisition unit 21 assigns different type codes (cluster numbers) to each cluster. The learning acquisition unit 21 sets the type code of the cluster to which the area belongs as the learning type information related to that area. The learning acquisition unit 21 causes the computer 1 to store the association between the mesh code and the type code for each area so that it can also be used in the population state determination system 10. Note that when clustering is performed and learning type information is acquired, there is no information indicating the urban type.

[0031] The learning acquisition unit 21 outputs the acquired learning population information to the model generation unit 22. Also, in the mode of acquiring learning type information, the learning acquisition unit 21 also outputs the acquired learning type information to the model generation unit 22.

[0032] The model generation unit 22 is a functional unit that performs machine learning based on the population information for learning acquired by the acquisition unit 21 for learning, and generates an encoder-decoder model that inputs information indicating the population over time. The model generation unit 22 may generate an encoder-decoder model based on the type information for learning acquired by the acquisition unit 21 for learning. The model generation unit 22 may generate an encoder-decoder model that also inputs type information indicating the type of area. The model generation unit 22 may generate a plurality of encoder-decoder models corresponding to the type information indicating the type of area.

[0033] The encoder-decoder model generated by the model generation unit 22 will be described. An example of the encoder-decoder model is shown in FIG. 4. The encoder-decoder model is composed of a neural network, and is a learned model that is trained to input population information indicating the population over time in an area, perform dimensionality reduction, and then output the original population information. As the encoder-decoder model, an autoencoder (Geoffrey Hinton and Salakhutdinov Ruslan, “Reducing the dimensionality of data with neural network.” Science, pp. 504-507, 2006), or a Transformer (Ashish Vaswani et al., “Attention Is All You Need.” Advances in neural information processing system 2017), etc. can be used. In the input layer of the encoder-decoder model, neurons corresponding to the number of elements (dimensionality) of the population information are provided. When the population information is information (numerical values) indicating the population of an area every hour of a day (0:00, 1:00, …, 23:00), 24 neurons (vectors) for inputting the numerical values of the population of the area every hour are provided in the input layer of the encoder-decoder model. In the output layer of the encoder-decoder model, the same number of neurons (vectors) as the neurons in the input layer, corresponding to each neuron in the input layer, are provided.

[0034] The configuration of the encoder-decoder model itself may be the same as that of a conventional encoder-decoder model. As shown in FIG. 4, between the input layer and the output layer, a hidden layer provided with a plurality of neurons (vectors) is provided. Each neuron in the input layer and each neuron in the hidden layer are connected with weights w used for calculation set. Also, each neuron in the hidden layer and each neuron in the output are connected with weights w used for calculation set. The number of neurons provided in the hidden layer is less than the number of neurons in the input layer and the output layer. Thus, dimensional compression is performed in the hidden layer.

[0035] The model generation unit 22 generates an encoder-decoder model as follows. First, an example of a mode in which the learning type information is not used will be described, and then an example of a mode in which the learning type information is used will be described.

[0036] The model generation unit 22 inputs the population transition data, which is the learning population information, from the learning acquisition unit 21. As shown in FIG. 4, the model generation unit 22 performs machine learning using the population transition data as the input value and the output value (correct answer) of the encoder-decoder model to generate an encoder-decoder model. The above machine learning for generating the encoder-decoder model itself can be performed in the same manner as the conventional machine learning method. The above is an example when the learning type information is not used.

[0037] Subsequently, an example of a mode in which the learning type information is used will be described. The model generation unit 22 inputs the type code, which is the learning type information, together with the population transition data from the learning acquisition unit 21. In this case, the model generation unit 22 generates an encoder-decoder model that also inputs the type code. An example of this encoder-decoder model is shown in FIG. 5. This encoder-decoder model is provided with neurons corresponding to the type code in the input layer and the output layer in addition to the encoder-decoder model shown in FIG. 4.

[0038] As shown in FIG. 6(a), the model generation unit 22 associates the area and the daily population change data with the type code of the area. This association is performed using the mesh code as a key. As shown in FIG. 5, the model generation unit 22 uses the associated population change data and type code (data D1 shown in FIG. 6(a)) as input values to the encoder-decoder model and performs machine learning with the output value (correct answer) of the encoder-decoder model to generate the encoder-decoder model.

[0039] Further, the model generation unit 22 may generate a plurality of encoder-decoder models according to the type code. For example, the model generation unit 22 may generate an encoder-decoder model for each type code. The model generation unit 22 uses the area of the same type code and the daily population change data as shown in FIG. 6(b) for generating one encoder-decoder model. That is, the model generation unit 22 filters the population change data for each type code and uses the filtered population change data for generating the encoder-decoder model.

[0040] In this case, the model generation unit 22 may generate an encoder-decoder model (encoder-decoder model that does not take the type code as input) that inputs only the population change data as shown in FIG. 4. The model generation unit 22 uses the population change data as an input value to the encoder-decoder model and performs machine learning with the output value (correct answer) of the encoder-decoder model to generate the encoder-decoder model.

[0041] Alternatively, the model generation unit 22 may generate an encoder-decoder model that inputs not only the population trend data as shown in FIG. 5 but also a type code. In this case, the model generation unit 22 uses the population trend data and the type code (data D2 shown in FIG. 6(b)) associated with each other as input values to the encoder-decoder model and performs machine learning using the output value (correct answer) of the encoder-decoder model to generate the encoder-decoder model. The model generation unit 22 performs machine learning as described above for each type code to generate an encoder-decoder model for each type code.

[0042] The model generation unit 22 outputs the generated encoder-decoder model to the population state determination system 10. When the model generation unit 22 generates an encoder-decoder model for each type code, the model generation unit 22 also outputs the corresponding type code for each encoder-decoder model to the population state determination system 10. The above is the function of the model generation system 20 according to the present embodiment.

[0043] Next, the function of the population state determination system 10 will be described. As shown in FIG. 1, the population state determination system 10 includes an acquisition unit 11, a model calculation unit 12, and a determination unit 13.

[0044] The acquisition unit 11 is a functional unit that acquires population information indicating the time-series population of the area to be determined for the population state. The acquisition unit 11 acquires the above-described population information for the area and time zone to be determined. The acquisition unit 11 may acquire type information indicating the type of the area to be determined for the population state.

[0045] For example, the acquisition unit 11 receives a designation of an area and a time zone (date) to be determined from the user of the population state determination system 10, and acquires the population information related to the designated area and time zone in the same manner as the acquisition of the learning population information by the above-described learning acquisition unit 21.

[0046] The acquisition unit 11 may be configured to acquire type information indicating the type of the area to be determined for the population status. For the area related to the population information to be acquired, the acquisition unit 11 acquires the same type information as the type information for learning acquired by the learning acquisition unit 21. For example, when the learning acquisition unit 21 acquires the pre-stored type information for learning shown in FIG. 3(c) described above, the acquisition unit 11 acquires the type code corresponding to the mesh code of the area related to the population information from the same information as the type information.

[0047] Also, when clustering is performed by the learning acquisition unit 21, the acquisition unit 11 acquires, as the type information, the type code corresponding to the mesh code of the area related to the population information from the information on the association between the mesh code and the type code stored in the computer 1 as a result of the clustering.

[0048] The acquisition unit 11 outputs the acquired population information to the model calculation unit 12 and the determination unit 13. Also, when the acquisition unit 11 acquires the type information, the acquired type information is also output to the model calculation unit 12.

[0049] The model calculation unit 12 is a functional unit that inputs the population information acquired by the acquisition unit 11 into a pre-stored encoder-decoder model for calculation and obtains the output from the encoder-decoder model. The model calculation unit 12 may perform calculation using the encoder-decoder model based on the type information acquired by the acquisition unit 11. The model calculation unit 12 may also input the type information into the encoder-decoder model and obtain the output from the encoder-decoder model. The model calculation unit 12 may select the encoder-decoder model to be used in the calculation from a plurality of pre-stored encoder-decoder models based on the type information and perform the calculation using the selected encoder-decoder model.

[0050] The model calculation unit 12 inputs and stores the encoder-decoder model generated by the model generation system 20. The model calculation unit 12 inputs the population information from the acquisition unit 11.

[0051] The model calculation unit 12 performs a calculation using the weights w of the encoder-decoder model on the population information as an input value to the encoder-decoder model, and obtains an output value from the encoder-decoder model. The output value from the encoder-decoder model is restored data of the population transition data, which is the population information, and is information in the same format as the population information. A graph G2 showing an example of the output value when the population information shown by the graph G1 shown in FIG. 2 is used as the input value is shown.

[0052] In the mode in which the type information is used, the model calculation unit 12 inputs the type information from the acquisition unit 11 and performs the following processing. In this case, for example, as described above, an encoder-decoder model that also inputs type information is generated by the model generation system 20. The model calculation unit 12 performs a calculation using the weights w of the encoder-decoder model on the population information and the type information as input values to the encoder-decoder model, and obtains an output value from the encoder-decoder model.

[0053] Also, in this case, for example, as described above, a plurality of encoder-decoder models corresponding to type codes are generated by the model generation system 20. The model calculation unit 12 selects an encoder-decoder model corresponding to the type code, which is the input type information, from the plurality of encoder-decoder models. The model calculation unit 12 uses the selected encoder-decoder model to obtain an output value from the encoder-decoder model in the same manner as above.

[0054] The model calculation unit 12 outputs the obtained output value from the encoder-decoder model to the determination unit 13. Note that the output value output to the determination unit 13 may be only the part corresponding to the population information.

[0055] The determination unit 13 is a functional unit that compares the population information acquired by the acquisition unit 11 with the output obtained by the model calculation unit 12 to determine the state of the population in the area. The determination by the determination unit 13 is, for example, a determination as to whether the population in the area to be determined is in an abnormal state different from the normal time as described above. However, any determination other than the above may be made as long as it can be made by comparing the population information input to the encoder-decoder model with the output from the encoder-decoder model. The determination unit 13 determines the state of the population in the area as follows.

[0056] The determination unit 13 inputs population information from the acquisition unit 11. The determination unit 13 inputs the output value corresponding to the above population information from the model calculation unit 12. The determination unit 13 compares the input to the encoder-decoder model (data on population transition, for example, the graph G1 in FIG. 2) with the output from the encoder-decoder model (restored data of the population transition data, for example, the graph G2 in FIG. 2), and calculates an error as the degree of abnormality. For example, the determination unit 13 calculates the absolute value of the difference between the input and the output for each time zone every hour, and takes the sum of all time zones as the error.

[0057] The determination unit 13 compares the calculated error with a preset threshold value. If the error is equal to or greater than the threshold value, the determination unit 13 determines that the population in the area to be determined is in an abnormal state. In this case, it is presumed that an event different from the normal time, such as an event, has occurred in the area to be determined. If the error is less than the threshold value, the determination unit 13 determines that the population in the area to be determined is not in an abnormal state.

[0058] The above determination utilizes the fact that when the encoder-decoder model is generated by performing machine learning only with normal data, abnormal data cannot be restored well when input to the encoder-decoder model. Therefore, the normal time related to the determination is based on the learning population information used when generating the encoder-decoder model in the model generation system 20.

[0059] The determination unit 13 outputs information indicating the determination result. For example, the determination unit 13 may display the determination result on a display device provided in the computer 1 so that the user can refer to the determination result. Alternatively, the determination unit 13 may transmit information indicating the determination result to another device. Further, the determination unit 13 may output information indicating the determination result to an output destination other than the above by a method other than the above. The above is the function of the population state determination system 10 according to the present embodiment.

[0060] Subsequently, the process executed by the computer 1 according to the present embodiment (the operation method performed by the computer 1) will be described using the flowcharts of FIGS. 7 and 8. First, the process executed by the model generation system 20 will be described using the flowchart of FIG. 7.

[0061] In this process, first, learning population information is acquired by the learning acquisition unit 21 (S01). Subsequently, learning type information is acquired by the learning acquisition unit 21 (S02). Note that in an aspect where the learning type information is not used, the acquisition of the learning type information (S02) may not be performed. Subsequently, machine learning is performed based on the learning population information by the model generation unit 22 to generate an encoder-decoder model (S03). Also, in an aspect where the learning type information is used, an encoder-decoder model is generated based on the learning type information. The generated encoder-decoder model is output to the population state determination system 10 and stored by the model calculation unit 12. The above is the process executed by the model generation system 20 according to the present embodiment.

[0062] Next, the process executed by the population status determination system 10 will be described using the flowchart of FIG. 8. In this process, first, the acquisition unit 11 acquires population information and type information (S11). Note that in a mode where type information is not used, the acquisition of type information may not be performed. Subsequently, the model calculation unit 12 inputs the population information into the encoder-decoder model and performs calculations to obtain an output from the encoder-decoder model (S12). Also, in a mode where type information is used, calculations using the encoder-decoder model are performed based on the type information.

[0063] Subsequently, the determination unit 13 compares the input to the encoder-decoder model with the output from the encoder-decoder model (S13). Subsequently, the determination unit 13 determines the population status of the area based on the above comparison (S14). Subsequently, the determination unit 13 outputs information indicating the determination result (S15). The above is the process executed by the population status determination system 10 according to the present embodiment.

[0064] According to the population status determination system 10 according to the present embodiment, since time-series population information is used, it is possible to determine the population status considering the time-series population of the area. Also, the input to the encoder-decoder model and the output are compared to make a determination. Therefore, according to the population status determination system 10 according to the present embodiment, the population status can be appropriately determined with high accuracy.

[0065] Also, type information may be used as in the above-described embodiment. By using type information, it is possible to appropriately determine the population status according to the characteristics of the area. For example, it is possible to perform a determination considering the functional characteristics of the city such as an office street or a residential street. As a result, compared with a determination according to average population fluctuations without considering type information, a more accurate and appropriate determination can be made.

[0066] As for how to use the type information, as described above, it may be used as an input to the encoder-decoder model. Also, it may be possible to select an encoder-decoder model used for calculation based on the type information. According to such a configuration, the type information can be used reliably and appropriately, and the determination can be made reliably and appropriately. However, the type information may be used by methods other than the above. Also, the type information does not necessarily have to be used.

[0067] According to the model generation system 20 according to the present embodiment, an encoder-decoder model used in the population state determination system 10 can be generated. Also, when generating the encoder-decoder model, learning type information corresponding to the type information may be used. Also, the learning type information may be obtained by performing clustering using the learning population information as described above. According to this configuration, even when the type information is not associated with the area in advance, it is possible to generate an encoder-decoder model based on the type of the area and perform determination using the encoder-decoder model.

[0068] In the present embodiment, the computer 1 is assumed to include the population state determination system 10 and the model generation system 20. However, the population state determination system 10 and the model generation system 20 may be implemented independently of each other.

[0069] Note that the block diagram used in the description of the above embodiment shows blocks of functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Also, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically combined device, or two or more physically or logically separated devices may be directly or indirectly (for example, using wired, wireless, etc.) connected and realized using these multiple devices. The functional block may be realized by combining software with the above one device or the above multiple devices.

[0070] Functions include, but are not limited to, judgment, decision-making, determination, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), assigning, etc. For example, a functional block (component) that enables transmission is referred to as a transmitting unit or a transmitter. In any case, as described above, the implementation method is not particularly limited.

[0071] For example, the computer 1 in one embodiment of the present disclosure may function as a computer that performs the information processing of the present disclosure. FIG. 9 is a diagram showing an example of the hardware configuration of the computer 1 according to one embodiment of the present disclosure. The above-described computer 1 may physically be configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0072] In the following description, the term "device" can be read as a circuit, device, unit, etc. The hardware configuration of the computer 1 may be configured to include one or more of each device shown in the figure, or may be configured without including some devices.

[0073] Each function in the computer 1 is realized by causing the processor 1001 to perform operations by loading a predetermined software (program) onto hardware such as the processor 1001 and the memory 1002, and controlling communication by the communication device 1004, or controlling at least one of reading and writing data in the memory 1002 and the storage 1003.

[0074] The processor 1001 controls the entire computer by operating, for example, an operating system. The processor 1001 may be constituted by a central processing unit (CPU: Central Processing Unit) including an interface with peripheral devices, a control device, an arithmetic device, registers, and the like. For example, each function in the computer 1 described above may be realized by the processor 1001.

[0075] Also, the processor 1001 reads a program (program code), software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. As the program, a program for causing a computer to execute at least a part of the operations described in the above embodiments is used. For example, each function in the computer 1 may be stored in the memory 1002 and realized by a control program operating in the processor 1001. Although it has been described that the above various processes are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. Note that the program may be transmitted from a network via a telecommunication line.

[0076] The memory 1002 is a computer-readable recording medium and may be constituted by at least one of, for example, a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), and the like. The memory 1002 may be referred to as a register, a cache, a main memory (main storage device), or the like. The memory 1002 can store a program (program code), a software module, etc. executable for carrying out information processing according to an embodiment of the present disclosure.

[0077] Storage 1003 is a computer-readable recording medium and may be composed of, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital versatile disc, a Blu-ray (registered trademark) disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may be referred to as an auxiliary storage device. The storage medium included in computer 1 may be, for example, a database, a server, or other appropriate media including at least one of memory 1002 and storage 1003.

[0078] Communication device 1004 is hardware (a transceiver device) for performing communication between computers via at least one of a wired network and a wireless network and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc.

[0079] Input device 1005 is an input device for receiving external input (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.). Output device 1006 is an output device for performing output to the outside (e.g., a display, a speaker, an LED lamp, etc.). Note that input device 1005 and output device 1006 may have an integrated configuration (e.g., a touch panel).

[0080] Also, each device such as processor 1001 and memory 1002 is connected by a bus 1007 for communicating information. Bus 1007 may be configured using a single bus or may be configured using different buses for each device.

[0081] Further, the computer 1 may include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be implemented by the hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0082] The processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in the present disclosure may be rearranged as long as there is no contradiction. For example, for the methods described in the present disclosure, the elements of various steps are presented using an exemplary order and are not limited to the specific order presented.

[0083] The input / output information, etc. may be stored in a specific location (e.g., memory) or may be managed using a management table. The input / output information, etc. may be overwritten, updated, or appended. The output information, etc. may be deleted. The input information, etc. may be transmitted to other devices.

[0084] The determination may be made based on a value represented by 1 bit (0 or 1), a boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0085] Each aspect / embodiment described in the present disclosure may be used alone, in combination, or switched and used during execution. Also, the notification of predetermined information (e.g., notification of "being X") is not limited to being explicitly performed and may be performed implicitly (e.g., by not performing the notification of the predetermined information).

[0086] As described above in detail, it is obvious to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modifications and variations without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is for illustrative purposes only and does not have any limiting meaning for the present disclosure.

[0087] Software should be broadly construed to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether called by the name of software, firmware, middleware, microcode, hardware description language, or other names.

[0088] Also, software, instructions, information, etc. may be transmitted and received via a transmission medium. For example, when software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cables, fiber optic cables, twisted pairs, digital subscriber lines (DSLs)) and wireless technologies (such as infrared rays, microwaves), at least one of these wired technologies and wireless technologies is included within the definition of the transmission medium.

[0089] The terms "system" and "network" used in the present disclosure are used interchangeably.

[0090] Also, the information, parameters, etc. described in the present disclosure may be represented using absolute values, relative values from a predetermined value, or corresponding other information.

[0091] The terms "determining" and "deciding" as used in this disclosure may encompass a wide variety of operations. "Determining" and "deciding" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up (e.g., searching in a table, database, or another data structure), ascertaining, and considering something as having been "determined" or "decided". "Determining" and "deciding" may also include receiving (e.g., receiving information), transmitting (e.g., transmitting information), inputting, outputting, accessing (e.g., accessing data in memory), and considering something as having been "determined" or "decided". "Determining" and "deciding" may further include resolving, selecting, choosing, establishing, comparing, etc., and considering something as having been "determined" or "decided". That is, "determining" and "deciding" may include considering something as having determined or decided some action. Also, "determining (deciding)" may be read as "assuming", "expecting", "considering", etc.

[0092] The terms "connected" and "coupled," or any variations thereof, mean any direct or indirect connection or coupling between two or more elements and can include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements can be physical, logical, or a combination thereof. For example, "connected" may be read as "accessed." As used in this disclosure, two elements can be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as, by way of some non-limiting and non-exhaustive examples, electromagnetic energy having wavelengths in the radio frequency region, microwave region, and optical (both visible and invisible) region.

[0093] As used in this disclosure, the recitation "based on" does not mean "based only on" unless otherwise specified. In other words, the recitation "based on" means both "based only on" and "based at least on."

[0094] Any reference to an element using designations such as "first," "second," etc. used in this disclosure does not generally limit the quantity or order of those elements. These designations can be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not mean that only two elements can be employed or that the first element must precede the second element in some form.

[0095] In this disclosure, when the terms "include," "including," and variations thereof are used, these terms are intended to be inclusive in the same manner as the term "comprising." Further, the term "or" used in this disclosure is not intended to be exclusive.

[0096] In the present disclosure, for example, when articles are added by translation like a, an, and the in English, the present disclosure may include that the nouns following these articles are in the plural form.

[0097] In the present disclosure, the term "A and B are different" may mean that "A and B are different from each other". Note that the term may also mean that "A and B are each different from C". Terms such as "separate" and "coupled" may also be interpreted in the same way as "different".

Description of Reference Numerals

[0098] 1... computer, 10... population state determination system, 11... acquisition unit, 12... model calculation unit, 13... determination unit, 20... model generation system, 21... learning acquisition unit, 22... model generation unit, 1001... processor, 1002... memory, 1003... storage, 1004... communication device, 1005... input device, 1006... output device, 1007... bus.

Claims

1. An acquisition unit that acquires population information indicating the time-series population of an area to be determined for the population state; A model calculation unit that inputs the population information acquired by the acquisition unit into a pre-stored encoder-decoder model that compresses and restores input data to perform calculations and obtains an output from the encoder-decoder model; A determination unit that compares the population information acquired by the acquisition unit with the output obtained by the model calculation unit to determine the population state of the area; Comprising: The acquisition unit acquires type information indicating the type of the area to be determined for the population state; The model calculation unit performs calculations using the encoder-decoder model based on the type information acquired by the acquisition unit, a population state determination system.

2. The population state determination system according to claim 1, wherein the model calculation unit also inputs the type information into the encoder-decoder model to obtain an output from the encoder-decoder model.

3. The population state determination system according to claim 1, wherein the model calculation unit selects an encoder-decoder model to be used in the calculation from a plurality of pre-stored encoder-decoder models based on the type information and performs calculations using the selected encoder-decoder model.

4. A learning acquisition unit that acquires learning population information indicating the time-series population used for generating an encoder-decoder model that compresses and restores input data; A model generation unit that performs machine learning based on the learning population information acquired by the learning acquisition unit and generates an encoder-decoder model that inputs information indicating the time-series population; Comprising: The learning acquisition unit acquires learning type information indicating the type of the area related to the learning population information; The model generation unit generates the encoder-decoder model based on the learning type information acquired by the learning acquisition unit, a model generation system.

5. The model generation system according to claim 4, wherein the model generation unit generates an encoder-decoder model that also inputs type information indicating the type of the area.

6. The model generation system according to claim 4, wherein the model generation unit generates a plurality of encoder-decoder models corresponding to the type information indicating the type of the area.

7. The model generation system according to claim 4, wherein the learning acquisition unit performs clustering using the learning population information to acquire learning type information.

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