JP2023057476A - Administrative problem search program - Google Patents

Abstract

To provide an administrative problem search program which searches for administrative problems in examining a policy.SOLUTION: A policy proposal system 1 with a policy problem search program implemented thereon to search for administrative problems in examining the policy includes: an information acquisition unit 9 which acquires external environment information related to an external environment in a new search period; and a search apparatus 2 which searches for administrative problems, using three or more stages of degrees of association between administrative problems and reference external environment information generated by classifying external environments in advance, on the basis of, reference external environment information according to the external environment information acquired in the information acquisition unit 9, while prioritizing the administrative problems with higher degrees of association.SELECTED DRAWING: Figure 1

Description

The present invention relates to an administrative problem search program for searching administrative problems in examining policies.

When formulating various policies in national politics or municipalities, various political, economic, technological, social data, and administrative data such as population and industry are collected, analyzed, and issues are extracted, and future Consider the necessary policies after considering the scenario.

With the advent of an era in which IoT devices such as edge cameras will be installed everywhere in the future, even in proposing this policy, we will use the data obtained from these IoT devices to create better policies. It is conceivable that there will be cases where proposals can be made.

However, in the past, the current situation is that no technology has yet been devised that utilizes data obtained from such IoT devices and connects them to policy proposals.

Therefore, the present invention has been devised in view of the above-mentioned problems, and its purpose is to utilize data obtained from IoT devices to propose administrative issues necessary for policy proposals. It is to provide a problem search program and a policy proposal program that proposes the policy itself.

In an administrative problem search program for searching administrative problems in considering a policy according to the present invention, an information acquisition step of acquiring external environment information related to the external environment at the time of a new search, and categorizing the external environment in advance. Using the external environment information for reference and the degree of relevance of three or more levels with administrative issues, based on the external environment information for reference according to the external environment information acquired in the information acquisition step, the degree of relevance is increased and a search step of searching for administrative issues with high priority given to the computer.

Even without special skills or experience, it is possible to propose future scenarios necessary for policy proposals by utilizing data obtained from IoT devices without relying on human labor.

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

A policy proposal program to which the present invention is applied will be described in detail below with reference to the drawings.

FIG. 1 is a block diagram showing the overall configuration of a policy proposal system 1 in which a policy proposal program to which the present invention is applied is implemented. The policy proposal system 1 includes an information acquisition unit 9 , a search device 2 connected to the information acquisition unit 9 , and a database 3 connected to the search device 2 .

The information acquisition unit 9 is a device for a person using this system to input various commands and information. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Moreover, the information acquisition part 9 may be integrated with the searching device 2 which will be described later. The information acquisition unit 9 outputs the detected information to the searching device 2 . Further, the information acquisition unit 9 may be configured by means for specifying position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be configured with a sensor for Also, the body sensor may acquire biological data not only of humans but also of animals. The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

In addition, the information acquisition unit 9 is dispatched to respond to plumbing troubles, and the sales data for each region recorded in the database of the contractor who solves the problem by actually performing the work, the number of dispatches for each region, etc. means for acquiring dispatch frequency data calculated according to the above.

The database 3 accumulates various information necessary for making policy proposals. As the information necessary for making policy proposals, the market information of each reference information equipment data described later is accumulated in relation to future scenarios necessary for policy proposals as output data and policies to be proposed.

That is, in the database 3, one or more of such reference information equipment data market information, future scenarios necessary for policy proposals, and policies to be proposed are linked to each other and stored.

The search device 2 is composed of an electronic device such as a personal computer (PC), for example, but in addition to the PC, it is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc. It may be one that is made into. A user can obtain a search solution by this search device 2 .

FIG. 2 shows a specific configuration example of the search device 2. As shown in FIG. This searching device 2 performs wired or wireless communication with a control unit 24 for controlling the entire searching device 2 and an operation unit 25 for inputting various control commands via operation buttons, keyboards, etc. A communication unit 26 for searching, a determination unit 27 for making various judgments, and a storage unit 28, represented by a hard disk or the like, for storing a program for performing a search to be executed are connected to the internal bus 21, respectively. . Further, the internal bus 21 is connected with a display unit 23 as a monitor for actually displaying information.

The controller 24 is a so-called central control unit for controlling each component implemented in the search device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the determination unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The determination unit 27 determines the search solution. The determination unit 27 reads various information stored in the storage unit 28 and various information stored in the database 3 as necessary information in executing the determination operation. This determination unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

The display unit 23 is composed of a graphic controller that creates a display image based on control by the control unit 24 . The display unit 23 is implemented by, for example, a liquid crystal display (LCD).

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the policy proposal system 1 configured as described above will be described.

In the policy proposal system 1, for example, as shown in FIG. 3, it is assumed that three or more degrees of association are set in advance between reference equipment data and future scenarios required for policy proposals. The reference facility data is data consisting of one or more of electric power, electricity, voltage, vibration, sound, gas, and water acquired from each facility of a building structure or public transportation system. I say here. Architectural structures include all kinds of structures such as houses, condominiums, buildings, public facilities (schools and government offices), hospitals, shops, game facilities, stadiums, ballparks, and amusement facilities. Each installation of such building structure includes all electrical installations, gas installations and water installations. Public transportation is assumed to be a train, a bus, etc., but it is not a train or a bus, but a station or a building or structure connected thereto. Public transportation also includes roads. Facilities applied to these public transportation systems include electrical equipment, gas equipment, water equipment applied to stations, etc., as well as street lights installed on roads and electronic bulletin boards installed on expressways. Includes equipment, gas equipment, and water equipment. The water supply facility here includes not only water supply but also sewerage.

Reference equipment data includes any data obtained by directly or indirectly attaching sensors or measuring instruments to such equipment. It also includes data detected from various IoT devices. For example, power, electricity, voltage, vibration, sound, light, radio waves (hereinafter collectively referred to as physical data) supplied to the above-mentioned various facilities, water supply, sewage flow rate, gas flow rate, etc. equipment data. The reference facility data includes physical data for operating the facility, supply amount data for supplying electricity, gas, and water, and operation data consisting of one or more of the light intensity and wavelength of the light emitted through the lighting facility. By detecting these operation data, it is possible to grasp whether the current state of the cultivation environment is normal or whether some kind of abnormality has occurred. Also, by detecting these operation data, it is possible to estimate whether there is a possibility that an abnormality will occur in the cultivation environment in the near future, or whether the cultivation environment will remain normal.

These reference facility data can be managed in any chronological units such as yearly, monthly, weekly, daily, hourly, and minutely. In addition, all the reference information described below, including this reference facility data, may be data for each region that proposes a policy. If it is a policy, it may consist of data for each prefecture, and if it is a municipal level policy, it may be composed of data for each municipality.

Future scenarios include an increase in foreign immigrants, an increase in tourism, a further evolution of overconcentration in and around Tokyo, an increase in electricity consumption, etc. It is categorized. This future scenario may indicate how often each categorized scenario may occur.

In the example of FIG. 3, it is assumed that the input data are reference facility data P01, P02, and P03. Such reference facility data P01, P02, P03 as input data are linked to the future scenario as output.

The reference facility data P01, P02, and P03 are associated with each other through three or more degrees of association with the future scenarios A to D as output solutions. This future scenario is shown, for example, A is an increase in foreign immigrants, B is a further evolution of unipolar concentration in Tokyo and its surroundings, etc., but it is not limited to this, and it is more detailed. Scenarios may be classified into The facility data for reference is arranged on the left side through this degree of association, and each future scenario is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance to which future scenario the facility data for reference arranged on the left side is highly relevant. In other words, the degree of association is an index indicating which future scenario each reference equipment data is likely to be associated with, and is used to select the most probable future scenario for each reference equipment data. It indicates the accuracy of In the example of FIG. 3, w13 to w19 are shown as association degrees. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of mutual relationship between each combination as an intermediate node and the future scenario as an output. , conversely, the closer to 1 point, the lower the degree of correlation between each combination as an intermediate node and the future scenario as an output.

The search device 2 acquires three or more degrees of association w13 to w19 shown in FIG. 3 in advance. In other words, the search device 2 accumulates past data sets to determine which of the reference equipment data and the future scenario in that case has been adopted and evaluated in determining the actual search solution, and analyzes these. , to create the degree of association shown in FIG.

For example, in the past, it is assumed that the reference equipment data is indicated by a change transition indicated by a line graph. In the case of such reference facility data shown by a certain line graph, it is assumed that A is actually the most common future scenario in that area. By collecting and analyzing such data sets, the degree of association with reference facility data in each region will be strengthened.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference equipment data P01, analysis is performed from past sales and various data of future scenarios. In the case of facility data P01, if there are many cases of future scenario A, the degree of association leading to the evaluation of this future scenario is set higher, and if there are many cases of future scenario B, this future scenario is evaluated. Set a higher degree of association leading to For example, in the example of reference facility data for facility data P01, future scenario A and future scenario C are linked. is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In such a case, as shown in FIG. 4, reference equipment data for each region is input as input data, a future scenario is output as output data, and at least one hidden layer exists between the input node and the output node. may be provided and machine-learned. Either or both of the input nodes and the hidden layer nodes are set with the above-mentioned degree of relevance, which serves as a weighting for each node, based on which the output is selected. Then, when the degree of association exceeds a certain threshold, the output may be selected.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through data sets of previous reference equipment data for each region and future scenarios, the above-mentioned learned data is used to actually discriminate new future scenarios from now on. It will be used to explore future scenarios. These data sets may be input as learning data by acquiring past facility data, examining what scenarios have developed in the stage after acquiring the facility data. Further, it may be possible to manually determine to which of the previously typified classifications the future scenario corresponds, or to automatically acquire text data of the scenario to determine which typified classification the future scenario corresponds to. and may be automatically categorized using text mining techniques.

When searching for a new future scenario, input of equipment data is accepted.

Next, this input equipment data is collated with the reference equipment data. In such a case, reference is made to the degree of association shown in FIG. 3 (Table 1), which has been acquired in advance. For example, if the newly acquired facility data is the same as or similar to P02, future scenario B is associated with w15 and future scenario C is associated with w16. In such a case, the future scenario B with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the future scenario C, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

By the way, collation of equipment data and reference equipment data depends on whether or not the average value is within ±10% if these data are represented by the average value for a certain period. You may make it discriminate|determine whether it is the same and similar. Moreover, if the facility data is shown by a time-series transition graph, it may be determined based on the similarity of the trend.

In this way, it is possible to search for the most suitable future scenario from the newly acquired facility data and display it to the user. By looking at the results of this search, it is possible to determine in advance what kind of future scenario the region may have in the future. By proposing future scenarios, it is possible to refer to them when formulating new policies.

The example of FIG. 5 is an example in which the degree of association is formed between combinations of reference equipment data and reference market information. Reference market information is the probability that a resident of a building structure actually requested dispatch based on a plumbing problem to a contractor, and the contractor was unable to accept the request and declined. . This market information is represented by the number of times of refusal against the number of dispatch requests. The number of dispatch requests, the number of refusals, and regions are managed on the database 3 by the contractor. Market information can be obtained by reading from the database 3 the number of refusals against the number of dispatch requests for the area for which the market information is actually desired.

In future scenarios, in addition to sales in the region, if there are too many dispatch requests, there will be many cases of refusal, so the market information will also be relied on. Therefore, the future scenario can be determined with higher accuracy by combining the reference market information with the learning data in addition to the reference facility data. Therefore, in addition to reference equipment data, reference market information is combined to form the above-described degree of association.

In the example of FIG. 5, it is assumed that the input data are reference facility data P01-P03 and reference market information P14-17. An intermediate node shown in FIG. 5 is a combination of reference market information and reference facility data as such input data. Each intermediate node is also connected to an output. In this output, a future scenario is displayed as an output solution.

Each combination (intermediate node) of reference equipment data and reference market information is associated with the future scenario as the output solution through three or more degrees of association. The facility data for reference and the market information for reference are arranged on the left side through this degree of association, and the future scenario is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance to the future scenario for the reference equipment data and the reference market information arranged on the left side. In other words, this degree of association is an index that indicates to what future scenario each reference facility data and reference market information are likely to be linked. It indicates the accuracy in selecting a probable future scenario. Therefore, the optimum future scenario will be searched for by combining these reference facility data and reference market information.

In the example of FIG. 5, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires in advance three or more degrees of association w13 to w22 shown in FIG. In other words, the search device 2 accumulates past data as to which of the reference facility data, the reference market information, and the future scenario in that case is suitable for determining the actual search solution, By analyzing these, the degree of association shown in FIG. 5 is created.

Reference market information is various information about market conditions. The market conditions referred to here may target any range, such as a single company, the entire industry including the company, the entire Japan, or the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of various brands, crude oil, futures, precious metals, bitcoins, and other price movements. This reference market condition information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, MACD, moving average line, etc. may be attached. In addition, this market information may include the fundamental indicators of the company of each brand. Return on equity) and other indicators may be included. Information such as charts, Bollinger bands, MACD, moving averages, etc. showing price movements between currencies may also be attached to exchange rates. The reference market condition information may categorize the market potential itself. In other words, the reference market information P14 to P17 in FIG. 5 may be information classified by type. For example, the information may be classified by whether the stock price growth rate is 0% or more per year. In addition, they may be categorized according to patterns (for example, patterns such as whether the growth rate of the stock price increases rapidly or gradually).

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference equipment data P01 and the reference market information P16, the future scenario is analyzed from the past data. If there are many cases of future scenario A, the degree of connection leading to this future scenario A is set higher, and if there are many cases of future scenario B and few cases of future scenario A, it leads to future scenario B. The degree of association is set high, and the degree of association leading to future scenario A is set low. For example, in the example of the intermediate node 61a, the outputs of future scenario A and future scenario B are linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that combines the reference market information P14 with the reference facility data P01. is w16. The node 61c is a node of a combination of the reference market information P15 and P17 with respect to the reference equipment data P02, and the degree of relevance of the future scenario B is w17 and the degree of relevance of the future scenario D is w18.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually determining a future scenario. In such a case, the region for which the future scenario is to be determined is similarly entered. Then, the facility data and market information arranged for each region in the database 3 are acquired.

Based on the newly acquired equipment data and market information, the optimum future scenario is searched for. In such a case, reference is made to the degrees of association shown in FIG. 5 (Table 1) that have been acquired in advance. For example, when the newly acquired facility data is the same as or similar to P02 and the market information is the same or similar to P17, the node 61d is associated via the degree of association. This node 61d is associated with the future scenario C with w19 and the future scenario D with the degree of association w20. In such a case, the future scenario C with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the future scenario D, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

Table 2 below shows examples of degrees of association w1 to w12 extending from the input.

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

According to the present invention, in addition to the above-mentioned reference facility data, a combination of reference population projection data instead of the above-mentioned reference market condition information, and three or more levels of association with the future scenario for the combination, You may make it search for a solution based on.

This reference population projection data, which is added as an explanatory variable instead of reference market information, shows the population projection for the region, and includes a population pyramid (a diagram showing the population distribution by age group and gender) and its time series. This data may also include population trends, numbers of people moving in, numbers of people moving out, numbers of families moving in, numbers of families moving out of the area, and occupational classifications for each population. Future scenarios are influenced by such population projections in addition to facility data. As the population of elderly people increases, the number of cases in which it is not possible to deal with clogged toilets, etc., increases, and the number of occasions for dispatch requests may increase. In addition, the more positive the number of in-migrants minus the number of out-migrants, the more the population is increasing, and it is conceivable that the future scenario will be higher accordingly. Such reference population projection data is managed in the database 3 for each region.

Since such population projections also affect the future scenario, it is possible to improve the discrimination accuracy by combining with reference equipment data and discriminating the future scenario through the degree of association. At the time of solution search, by actually inputting the area for which the future scenario is to be determined, facility data and population estimation data for that area are obtained. Search for optimal future scenarios based on newly acquired facility data and population projection data. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-described reference facility data, a combination of reference geographical information instead of the above-described reference market information, and three or more levels of association with the future scenario for the combination. You may make it search for a solution based on.

This reference geographical information, which is added as an explanatory variable instead of the reference market information, indicates all the geographical information in the area, such as the existence and position of rivers, seas, distances, areas, and how many meters above sea level. , information on contour lines, information on roads, relative positional relationship of building structures with respect to rivers, and the like. Such reference geographical information is managed in the database 3 for each region.

Such geographical information also influences future scenarios and policy proposals. If you are close to a river, the possibility of water-related troubles may increase accordingly. Accuracy can be improved. At the time of searching for a solution, facility data and geographical information in the region for which the future scenario is to be determined are actually obtained. Search for optimal future scenarios based on newly acquired facility data and geographical information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-mentioned reference equipment data, a combination of reference external environment information instead of the above-mentioned reference market information, and three or more levels of association with the future scenario for the combination. You may make it search for a solution based on.

This external environmental information for reference added as an explanatory variable instead of the market information for reference is all information relating to the external environment. The external environment information here refers to economic data (GDP, employment statistics, industrial production index, capital investment, labor force survey, consumer price index, Bank of Japan Tankan, etc.), household data (household consumption survey, household data, weekly average working hours, statistical data on savings, statistical data on annual income, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, etc.) volume data, wind direction data, humidity data, etc.). External environmental information may include information on any external environment such as politics, economy, society, technological evolution, epidemics, trends, epidemics, natural disasters, etc., in addition to those that reflect part or all of these data good. The reference external environment information may be defined by text information, or may be defined by a pattern (for example, whether the GDP growth rate is rapid or gradual). patterns). Also, the reference external environment information and environment information may be indicated by the number of tourists in a specific area, the prefectural gross product, and the like.

At the time of searching for a solution, the facility data in the region for which the future scenario is to be actually determined and the external environment information at the time of the search are acquired. Search for the optimal future scenario based on newly acquired facility data and external environment information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-mentioned reference facility data, a combination of reference policy information instead of the above-mentioned reference market information, and a future scenario for the combination based on three or more levels of association It is also possible to search for a solution using

This reference policy information, which is added as an explanatory variable instead of the reference market condition information, is information on policies that have already been implemented in the region. This policy information for reference and policy information may be composed after the text data of the articles and ordinances themselves are obtained and text mined, or may be composed of statistical data related to the policy being implemented. However, it may be displayed in the classification of policies categorized in advance.

Since such policy information also influences the next policy proposal and future scenario, it is possible to improve the discrimination accuracy by combining this with reference equipment data and discriminating the future scenario through the degree of association. At the time of searching for a solution, facility data and policy information in the region for which the future scenario is to be determined are actually acquired. Search for optimal future scenarios based on newly acquired facility data and policy information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-described reference facility data, a combination of reference congestion degree information instead of the above-described reference market information, and three or more levels of association with the future scenario for the combination. You may make it search for a solution based on.

This reference congestion degree information and congestion degree information, which are added as explanatory variables instead of reference market condition information, are information about the congestion degree at one or more points in the area where the policy is proposed. That is, the reference congestion degree information and the congestion degree information are information indicating how congested passers-by (traffic vehicles) and people staying in the area are in each area.

FIG. 6 is a diagram for explaining this reference congestion degree information. Each area is a concept including both outdoors and indoors. The number of people is different for each area, the area with the fewest people is L3, and the area with the most people is L4. They are L4, L2, L1, and L3 in descending order of the number of people, and the degree of congestion is also in that order. The reference congestion degree is information indicating such a congestion degree. That is, this reference congestion degree may be indicated by the number of people per unit area.

In order to obtain such reference congestion degree data, it may be obtained from location information transmitted from a device possessed by each person. The device here is an electronic device owned by each person, such as a mobile phone, a smart phone, or a wearable terminal, and each device is composed of a device capable of acquiring position information. As a method of acquiring position information, for example, GPS or the like may be used. In this way, the location information of each person can be acquired by collecting the location information acquired by the device possessed by each person. By collecting such position information from each person in each of the areas L1 to L4, the above-described degree of congestion can be obtained. By sequentially acquiring such position information of each person in chronological order, the degree of congestion can be sequentially obtained in chronological order. It is possible to update.

In order to obtain such reference congestion degree data, it may be obtained from image information obtained by capturing pedestrian traffic conditions from a camera installed in each area. A camera captures an image that can capture the situation of each area. Passers-by are extracted from the obtained image through image analysis technology, and the number of passers-by is counted. This extraction of passers-by may use deep learning technology as necessary, automatically discriminate based on the feature amount of the analysis image, and convert it into data. By executing such image analysis for each of the areas L1 to L4, the number of people in each of the areas L1 to L4 can be detected, and the degree of congestion described above can be obtained. By sequentially obtaining image information of pedestrian traffic conditions from cameras installed in each area in a time-series manner, it is possible to determine the degree of congestion in a time-series manner. The congestion degree information for reference can also be sequentially updated to the latest information in real time.

In order to obtain such reference congestion degree data, it may be obtained from voice information obtained by recording voices on the street with a microphone installed in each area. Record audio in each area with a microphone. Estimate the number of passers-by using speech recognition technology. A simple model may be used in which it is estimated that the louder the sound detected by the microphone, the more passers-by. By executing such voice recognition for each of the areas L1 to L4, the number of people in each of the areas L1 to L4 can be detected, and the congestion degree described above can be obtained. By sequentially acquiring the traffic conditions of pedestrians from the microphones installed in each area in chronological order, the degree of congestion can be obtained in chronological order. can be sequentially updated to the latest in real time.

The method of obtaining the congestion degree information is also the same as that for the reference congestion degree information described above.

At the time of searching for a solution, the facility data in the region for which the future scenario is to be determined and the degree of congestion information regarding the degree of congestion at the time of the proposal are actually acquired. Search for optimal future scenarios based on newly acquired facility data and policy information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the reference equipment data described above, a combination of the reference attribute information instead of the reference market information described above and the future scenario for the combination based on three or more levels of association It is also possible to search for a solution using

This attribute information for reference, which is added as an explanatory variable instead of the market information for reference, includes the age, health status, annual income, date of birth, and place of birth of people who live, work, or pass through the area. , family environment, and if the child is in school, the school fees and expected future school fees, and whether the individual is currently looking for a job or working. Regarding the state of health, whether the individual is in perfect health, or whether or not he or she has any congenital disability and the degree of the disability, and whether or not the individual has acquired a disability after birth and the degree of the disability. Also, whether or not you suffered from any disease after birth, whether or not the disease continues and its degree, allergy status, inflammation status, injury status, chronic disease status, medications you are taking, etc. , including any information indicating health status. The health condition included in this reference attribute information may be derived from medical data such as heart rate, pulse rate, blood data, electrocardiogram data, and X-ray images. Any of these may be obtained through a report from each family, a submitted document, a doctor's certificate, or the like. When this reference attribute information is applied to the reference information P34 to P36 described above, for example, the reference information P34 is age 46, hometown: Shizuoka, annual income: 00,000 yen, works at 0x company, home environment: Family of four, wife, eldest son, eldest daughter, eldest son in high school, eldest daughter in junior high school, health condition: had surgery for gastric ulcer in the past, reference information P35 is age 38, hometown: Tokyo, annual income 00,000 Yen, work at 〇 company, family environment: wife and eldest daughter, eldest daughter in elementary school, health condition: good, etc.

Attribute information for reference, attribute information is all information about corporations existing in the area. The attributes here include the type of business, technical field, history, history, capital, scale, number of employees, years of establishment, etc., but are not limited to these, and include corporate culture, employee morale, It also includes the number of hires.

Such reference attribute information and attribute information may be obtained from databases and statistical data managed by each region and country from public information, but from surveillance cameras installed on the road It is also possible to determine the age, gender, etc. of a person appearing in a captured image by analyzing the image, and to acquire it as attribute information for reference and attribute information.

At the time of searching for a solution, facility data and attribute information in the region for which the future scenario is to be determined are actually acquired. Search for the optimal future scenario based on newly acquired equipment data and attribute information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-mentioned reference equipment data, a combination of reference satellite image information instead of the above-mentioned reference market information, and three or more levels of association with the future scenario for the combination. You may make it search for a solution based on.

This reference satellite image information and satellite image information, which are added as explanatory variables in place of the reference market condition information, are composed of satellite images taken from artificial satellites so as to include the area. The satellite image information for reference and the satellite image information may be anything other than satellite images as long as they are composed of data obtained by radiating electromagnetic waves from a satellite and acquiring the reflection characteristics of the electromagnetic waves.

Such reference satellite image information and satellite image information also affect the next policy proposals and future scenarios. can be improved. At the time of searching for a solution, facility data and satellite image information in the region for which the future scenario is to be determined are actually acquired. Search for the optimal future scenario based on newly acquired facility data and satellite image information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-mentioned reference facility data, a combination of reference weather information instead of the above-mentioned reference market information, and a future scenario for the combination are based on three or more levels of association. It is also possible to search for a solution using

This reference weather information and weather information, which are added as explanatory variables instead of reference market information, include information such as weather (clear, cloudy, rain), disasters (typhoon, heavy rain, etc.), temperature, humidity, etc. at the time of shooting. show. In addition to this, it may consist of all kinds of data related to wind direction, wind speed, thunderstorms, typhoons, droughts, and the like. For obtaining the reference weather information and weather information, the weather at that point in time may be taken in from the data of the Meteorological Agency, or the weather ascertained by oneself may be input.

At the time of searching for a solution, facility data and weather information in the region for which the future scenario is to be determined are actually acquired. Search for the optimal future scenario based on newly acquired facility data and weather information. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

In addition, according to the present invention, in addition to the above-mentioned reference equipment data, a combination of reference boarding and alighting information instead of the above-mentioned reference market information, and a future scenario for the combination based on three or more degrees of association It is also possible to search for a solution using

This reference boarding/alighting information and boarding/alighting information, which are added as explanatory variables in place of the reference market condition information, are information relating to the number of passengers getting on and off public transportation (trains, buses, etc.) in the area. For the number of getting on and off, in addition to the data of the number of passengers held by public transportation, the number of passengers who actually board the train and the number of passengers who exit the train may be counted by image analysis of the camera installed on the platform of the station. .

At the time of searching for a solution, the equipment data and boarding/alighting information in the future scenario discrimination target area are actually acquired. Based on the newly acquired facility data and boarding/disembarking information, search for the optimal future scenario. In such a case, a future scenario is searched for based on the method described above with reference to the degree of association acquired in advance.

Further, as an alternative to the reference information described above, reference time information related to the time when the reference facility data was acquired may be used. The reference time information here is composed of all data indicating the time, such as the month, the week, the day, the season, etc., as the time when the reference equipment data was acquired.

In such a case, a combination having over-reference facility data and reference time information relating to the acquisition time of the reference facility data and three or more degrees of association with the future scenario are acquired in advance. Then, based on the facility data and reference time information corresponding to the acquired time information, a future scenario is searched based on the degree of association described above.

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those represented by two levels, that is, whether or not they are associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily identify and search future scenarios without special skills or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

In addition to the facility data for reference, the degree of association described above includes reference market information, reference population estimate data, reference geographical information, reference external environment information, reference policy information, reference congestion information, reference Although the case where it is composed of a combination of attribute information, reference satellite image information, reference weather information, reference boarding/alighting information, reference time information, etc. has been described as an example, it is not limited to this. do not have. In other words, in addition to reference facility data, the degree of association includes reference market information, reference population projection data, reference geographical information, reference external environment information, reference policy information, reference congestion information, reference attribute information, It may be composed of a combination of two or more of reference satellite image information, reference weather information, reference boarding/alighting information, and reference time information. In addition, the degree of association is determined by reference facility data, reference market condition information, reference population estimate data, reference geographical information, reference external environment information, reference policy information, reference congestion information, reference In addition to any one or more of the attribute information, the reference satellite image information, the reference weather information, the reference boarding/alighting information, and the reference time information, other factors may be added to this combination to form the degree of association.

In either case, data is input in accordance with the reference information of the degree of association, and the future scenario is obtained using the degree of association.

Further, according to the present invention, as shown in FIG. 7, the future scenario is determined based on the degree of relevance of a combination of two or more types of information, ie, information for reference U and information for reference V. FIG. This reference information U is reference facility data, and reference information V is reference market information, reference population estimate data, reference geographical information, reference external environment information, reference policy information, and reference congestion information. , attribute information for reference, satellite image information for reference, weather information for reference, boarding/alighting information for reference, time information for reference, and the like.

At this time, the output obtained for the reference information U may be used as input data as it is, and may be associated with the output (future scenario) through the intermediate node 61 of the combination with the reference information V. FIG. For example, for reference information U (reference equipment data), after generating an output solution as shown in FIG. An output (future scenario) may be searched.

Moreover, according to the present invention, it is characterized in that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value of, for example, 0 to 100% in addition to the 10 stages described above, but is not limited to this, and can be described by a numerical value of 3 or more stages. may be configured.

By discriminating the most probable future scenario based on the degree of association represented by numerical values of three or more levels, under the situation where there are multiple candidates for the possibility of the search solution, It is also possible to search and display. If it is possible to display to the user in descending order of degree of association in this way, it is also possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting a threshold value by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

Furthermore, in the present invention, the degree of association described above may be updated. This update may reflect information provided via a public communication network such as the Internet, for example. In addition, when each reference information including reference equipment data is acquired, and knowledge, information, and data related to the future scenario for these are acquired, the association degree is increased or decreased according to these.

In such a case, collect examples of whether or not the reference equipment data and other reference information actually met, and the results of determining the degree of danger and symptoms, and calculate the degree of association according to the number of cases. Raise or lower. At this time, the above-mentioned equipment data, market information, population estimate data, geographical information, external environment information, policy information, congestion information, attribute information, satellite image information, weather information, boarding and alighting information, and time information are acquired and determined. , the update may be performed based on these.

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

Also, the process of initially building a trained model and the above-described updating may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of loading and learning datasets of input and output data, learning is performed by loading information corresponding to the input data, and from there self-forming the degree of association related to the output data. You can let it run.

Second Embodiment A second embodiment will be described below. In executing the second embodiment, the future scenario proposal system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are used in the same manner. Description of each of these configurations is omitted below by citing the description of the first embodiment.

In the second embodiment, reference external environment information and future scenario datasets are learned.

In the example of FIG. 8, it is assumed that the input data are reference external environment information P01, P02, and P03 in each area. Such reference external environmental information P01, P02, P03 as input data are linked to the future scenario as output.

The reference external environment information P01, P02, and P03 are related to the future scenarios A to D as the output solutions through three or more degrees of correlation. The external environment information for reference is arranged on the left side through this degree of association, and each future scenario is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance to which future scenario the reference external environment information arranged on the left side is highly relevant. In other words, the degree of association is an index indicating which future scenario each reference external environment information is likely to be associated with, and the most probable future scenario is selected for each reference external environment information. It shows the accuracy of the above.

Also, this degree of association may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through the data set of the previous reference external environment information for each region and the future scenario, in order to actually discriminate the new future scenario from now on, the above-mentioned learned data will be used to explore future scenarios. These data sets may be created by reading from a database managed by a vendor. Since the solution search method is the same as that of the above-described first embodiment, the description thereof will be omitted.

The present invention is not limited to the above-described embodiment. For example, as shown in FIG. 9, it is also possible to use three or more levels of degree of association between the reference information serving as the keynote and the future scenario. In such a case, the solution search is performed based on three or more degrees of association between the reference information corresponding to the newly acquired information and the future scenario. The basic reference information includes all of the reference information described above (reference facility information, reference market information, reference population projection data, reference geographical information, reference external environment information, reference policy information, reference congestion information for reference, attribute information for reference, satellite image information for reference, weather information for reference, boarding/alighting information for reference, time information for reference, etc.) can be applied.

In these cases, similarly, when information corresponding to reference information used as learning data is input, solution search is performed based on the above-described method.

The search solution obtained through the degree of association may be further modified or weighted based on other reference information.

The other reference information referred to here corresponds to any reference information other than the basic reference information when any of the reference information described above is used as the basic reference information.

For example, it is assumed that in certain reference boarding/alighting information F as one of the other reference information, the future scenario B was often discriminated in the past. When boarding/alighting information corresponding to such reference boarding/alighting information F is newly acquired, the search solution B as the future scenario is weighted higher, in other words, it is connected to the search solution B of the future scenario. It is set in advance so as to perform processing to

For example, the other reference information G is an analysis result that suggests a search solution C as a future scenario, and the reference information F is an analysis result that suggests a search solution D as a future scenario. Assume that there is After the setting with the reference information in this way, if the actually obtained information is the same as or similar to the reference information G, the weighting of the future scenario C is increased. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weight of the future scenario D is performed. In other words, the degree of association itself leading to the future scenario may be controlled based on the reference information FH. Alternatively, after determining the future scenario based only on the degree of association described above, the obtained search solution may be modified based on the reference information FH. In the latter case, how the future scenario as the search solution is to be modified based on the reference information F to H depends on what is designed on the system side each time.

The reference information is not limited to any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the future scenario suggested by the reference information, the higher the discriminant type as the search solution obtained via the degree of association may be corrected.

Similarly, as shown in FIG. 10, when forming the degree of relevance with a future scenario for a combination of reference information that is the keynote and other reference information, the reference information that is the keynote is Any reference information (reference facility information, reference market information, reference population projection data, reference geographical information, reference external environment information, reference policy information, reference congestion information, reference attribute information, reference Satellite image information, weather information for reference, boarding/alighting information for reference, time information for reference, etc.) are also applicable. Other reference information includes any reference information other than the underlying reference information.

At this time, if the basic reference information is reference external environment information, other reference information includes any other reference information.

In such a case as well, the future scenario can be estimated by searching for solutions in the same way. At this time, as shown in FIG. 9 described above, the future scenario is corrected by further using other reference information (reference information F, G, H, etc.) for the search solution obtained through the degree of association. can be

At this time, the degree of association may be learned by combining not only one piece of other reference information but also two or more pieces of information.

Further, as shown in FIG. 11, the degree of association may be formed between only the reference information that is the keynote and the future scenario. This basic reference information is any reference information in the first and second embodiments (reference facility information, reference market information, reference population projection data, reference geographic information, reference external environment information, policy information for reference, congestion information for reference, attribute information for reference, satellite image information for reference, weather information for reference, boarding/alighting information for reference, time information for reference, etc.) are also applicable. Description of the solution search method in FIG. 11 will be omitted by citing the description of FIG.

In addition, in the first embodiment and the second embodiment, the case where the future scenario is searched as the search solution has been described as an example, but the search solution is not limited to this. For example, a policy may be tied to each future scenario in advance. In such a case, policy p is associated with future scenario A, policy q is associated with future scenario B, and policy r is associated with future scenario C, and stored in advance as a database. The policy here includes all genres such as economy and finance, decentralization reform/regional revitalization, regulatory reform, science and technology/innovation, intellectual property, health/medical care, space, and disaster prevention. For example, from a rough level, such as “to reduce the cost of higher education to XX yen”, “to limit the application of drones to an altitude of XX m”, and “to focus on research in the field of XX”, each policy may be attached with necessary conditions for defining in detail. Then, as a result of the solution search described above, the policy linked to the future scenario obtained as the search solution is read out from the database and output. For example, when future scenario B is obtained as a search solution, policy q linked to future scenario B may be read out from the database and output.

In addition, in the first and second embodiments, instead of searching future scenarios as search solutions, policies may be searched as shown in FIG. In such a case, a policy may be associated with the reference information as an alternative to the future scenario for learning. In such a case, all the future scenarios mentioned above can be implemented by replacing them with policies.

Similarly, as shown in FIG. 13, instead of searching future scenarios as search solutions, administrative problems may be searched. In such a case, administrative issues may be associated with the reference information as a substitute for the future scenario for learning. The administrative issues mentioned here include, for example, “raising citizens’ awareness toward a gender-equal society,” “creating a bustle with citizens visiting the city,” “promoting the utilization of vacant houses,” We will provide guidance, training, and support so that they can grow up healthy and actively participate in society,” and “promote citizen activities to protect and pass down valuable cultural properties.” issues are included. In addition to defining the task by such a simple text sentence, conditions necessary for defining the task in more detail may be attached. In such a case, all the future scenarios mentioned above can be embodied by replacing them with administrative issues.

In addition, each policy may be associated with each administrative issue. In such a case, policy p is associated with administrative issue A, policy q is associated with administrative issue B, and policy r is associated with administrative issue C, and stored in a database in advance. It is Then, as a result of the above-described solution search, the policy linked to the administrative problem obtained as the search solution is read out from the database and output. For example, when administrative issue B is obtained as a search solution, policy q linked to administrative issue B may be read out from the database and output.

1 Policy proposal system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Discrimination unit 28 Storage unit 61 Node

Claims (10)

In the administrative problem search program that explores administrative problems in considering policies,
an information acquisition step of acquiring external environment information regarding the external environment at the time of the new search;
Based on the external environment information for reference that corresponds to the external environment information acquired in the information acquisition step above, using the external environment information for reference that categorizes the external environment in advance and the degree of association with administrative issues at three levels or more and a search step of searching for administrative problems by prioritizing those with a higher degree of association. In the above information acquisition step, market information on market conditions at the time of the new search is acquired,
In the search step, a combination of the external environment information for reference and the market condition information for reference in which market conditions are categorized in advance, and the degrees of association with administrative issues at three or more levels are used, and in the information acquisition step, It is recommended to search for administrative issues for which a higher degree of correlation is set between a combination of reference external environment information corresponding to acquired external environment information and reference market condition information corresponding to market condition information. The administrative problem search program according to claim 1. In the information acquisition step, facility data consisting of any one or more of electric power, electricity, voltage, vibration, sound, gas, and water acquired from each facility of building structures or public transportation facilities in the area where the policy is proposed is acquired. death,
In the search step, the reference external environment information and the reference facility consisting of any one or more of electric power, electricity, voltage, vibration, sound, gas, and water previously acquired from each facility of the building structure or public transportation system data and three or more degrees of association with administrative issues, external environment information for reference according to the external environment information acquired in the above information acquisition step, and reference external environment information according to facility data 2. The administrative problem search program according to claim 1, wherein a search is made for an administrative problem for which a higher degree of association is set with a combination having facility data. In the above information acquisition step, market information on market conditions at the time of the new search is acquired,
2. The administrative problem search program according to claim 1, wherein, in said search step, priority is given to those with a higher degree of association, and further, administrative problems are searched for based on said market information. In the information acquisition step, facility data consisting of any one or more of electric power, electricity, voltage, vibration, sound, gas, and water acquired from each facility of building structures or public transportation facilities in the area where the policy is proposed is acquired. death,
2. The administrative problem searching program according to claim 1, wherein, in said searching step, priority is given to those having a higher degree of association, and administrative problems are searched based on said facility data. In the information acquisition step, acquire policy information on policies in force in the area where the policy is proposed or in other areas;
2. The administrative problem searching program according to claim 1, wherein, in said searching step, priority is given to those having a higher degree of association, and administrative problems are further searched for based on said policy information. The information acquisition step acquires congestion degree information regarding the degree of congestion at one or more points in the area where the policy is proposed,
2. The administrative problem searching program according to claim 1, wherein, in said searching step, priority is given to those having a higher degree of association, and administrative problems are searched based on said congestion degree information. The information acquisition step acquires satellite image information captured from an artificial satellite so as to include the area where the policy is proposed,
2. The administrative problem search program according to claim 1, wherein, in said search step, priority is given to those with a higher degree of association, and administrative problems are searched based on said satellite image information. The administrative problem searching program according to any one of claims 1 to 8, wherein in the searching step, a policy linked to the administrative problem is proposed. 10. The administrative problem search program according to any one of claims 1 to 9, wherein the degree of association is composed of nodes of a neural network in artificial intelligence.

Images