JP6580334B2 - Information processing apparatus, program, and information processing method - Google Patents

Description

  The present invention relates to an information processing apparatus, a program, and an information processing method.

A prediction apparatus using an analysis technique such as multivariate analysis is known (see, for example, Patent Documents 1 to 4).
[Prior art documents]
[Patent Literature]
Patent Literature 1 Patent No. 5293393 Patent Literature 2 Patent No. 5018809 Specification Patent Literature 3 Patent No. 3324265 Patent Literature 4 Japanese Patent Laid-Open No. 1994-337852

  It is desired to improve the prediction accuracy of the prediction model.

  In a first aspect of the present invention, an information processing apparatus is provided. The information processing apparatus includes a factor information acquisition unit that acquires at least one record information of one or more types of factors that contribute to a phenomenon to be predicted. The information processing apparatus includes a model determination unit that determines a model used for predicting a phenomenon based on the type of factor from which the factor information acquisition unit has acquired the record information.

  In the information processing apparatus, the model determination unit may determine a model used for predicting a phenomenon based on the type of factor from which the factor information acquisition unit has acquired the actual information and the number of the actual information. The information processing apparatus may further include a model storage unit that stores one or more models. The information processing apparatus may further include a phenomenon information acquisition unit that acquires the history information of the phenomenon. In the information processing apparatus, each of the one or more models includes a phenomenon as an objective variable, one or more explanatory variables corresponding to each of one or more types of factors, and a parameter corresponding to each of the one or more explanatory variables. It may be a mathematical model that expresses the objective variable using.

  In the information processing apparatus, the model determination unit calculates, for each of one or more models stored in the model storage unit, a phenomenon calculated by the model using at least part of the performance information acquired by the factor information acquisition unit A model evaluation unit that evaluates at least one of the prediction accuracy and the calculation time of the model based on at least a part of the predicted value and the actual result value of the phenomenon acquired by the phenomenon information acquisition unit. In the information processing apparatus, the model determination unit may include a model selection unit that selects a model used for predicting a phenomenon based on the evaluation by the model evaluation unit.

  In the information processing apparatus, when the performance information used for evaluating at least one model of one or more models is insufficient, the model evaluation unit uses reference data predetermined for each of one or more types of factors. The prediction accuracy of at least one model may be evaluated. The information processing apparatus may include a prediction unit that calculates a predicted value of a phenomenon based on at least a part of the performance information acquired by the factor information acquisition unit and the model determined by the model determination unit.

  The information processing apparatus may include a position information acquisition unit that acquires position information indicating a position where the phenomenon occurs. The information processing apparatus includes a sample storage unit that stores sample information in which at least one record information of one or more types of factors acquired by the factor information acquisition unit is associated with position information acquired by the position information acquisition unit May be provided. The above information processing apparatus, when the sample information of the phenomenon does not include the actual information of the specific factor, based on the position information of the sample information of the phenomenon and the sample information of the other phenomenon, The information of the specific factor included in the sample information of the phenomenon may be extracted, and a complementing unit may be provided to complement the performance information of the specific factor of the sample information of the phenomenon based on the extracted performance information. In the information processing apparatus, the model evaluation unit includes at least a part of the performance information acquired by the factor information acquisition unit and at least the performance information supplemented by the complementing unit for each of the one or more models stored in the model storage unit. Evaluate at least one of the prediction accuracy and calculation time of the model based on the predicted value of the phenomenon calculated by the model using a part and at least a part of the actual value of the phenomenon acquired by the phenomenon information acquisition unit You can do it.

  Said information processing apparatus may be provided with the time information acquisition part which acquires the time information which shows the time or timing when the phenomenon occurred. The information processing apparatus includes a sample storage unit that stores sample information in which at least one record information of one or more types of factors acquired by the factor information acquisition unit is associated with time information acquired by the time information acquisition unit May be provided. When the above information processing apparatus does not include the actual information of the specific factor in the sample information of the phenomenon, based on the time information of the sample information of the phenomenon and the sample information of the other phenomenon, It includes a complement that extracts the actual information of specific factors included in the sample information of other phenomena that occurred at the timing, and complements the actual information of specific factors in the sample information of the phenomena based on the extracted actual information It's okay. In the information processing apparatus, the model evaluation unit includes at least a part of the performance information acquired by the factor information acquisition unit and at least the performance information supplemented by the complementing unit for each of the one or more models stored in the model storage unit. Evaluate at least one of the prediction accuracy and calculation time of the model based on the predicted value of the phenomenon calculated by the model using a part and at least a part of the actual value of the phenomenon acquired by the phenomenon information acquisition unit You can do it.

  The above information processing apparatus predicts a phenomenon based on at least a part of the result information acquired by the factor information acquisition unit, at least a part of the result information supplemented by the complementing unit, and the model determined by the model determining unit. You may provide the prediction part which calculates a value. Said information processing apparatus may be provided with the schedule information acquisition part which acquires the schedule information regarding the future schedule of a factor. In the information processing apparatus, the prediction unit may calculate a predicted value of the phenomenon based on at least a part of the schedule information acquired by the schedule information acquisition unit. In the above information processing apparatus, the model determination unit is configured to use model parameters based on at least a part of the result information acquired by the factor information acquisition unit and at least a part of the result value of the phenomenon acquired by the phenomenon information acquisition unit. A parameter determining unit for determining

  In the second aspect of the present invention, an information processing method is provided. The information processing method includes a factor information acquisition step of acquiring at least one record information of one or more types of factors that contribute to a phenomenon to be predicted. In the information processing method, the phenomenon information is obtained based on at least one of the number of types of the factor for which the performance information is acquired in the factor information acquisition stage and the number of the performance information for each type of the factor. A model determination stage for executing at least one of a process for determining a model used for prediction and a process for determining a parameter value of a model used for prediction of the phenomenon; The subject of the operation at each stage of the prediction method may be a computer.

  In a third aspect of the present invention, a program for causing a computer to function as the information processing apparatus is provided. A program for causing a computer to execute the information processing method may be provided. A computer-readable medium for storing a program may be provided, which causes the computer to function as the information processing apparatus when the program is executed on the computer. A computer-readable medium for storing the program, which is a computer-readable medium that causes the computer to execute the information processing method when the program is executed on the computer, may be provided.

  It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

An example of the system configuration of prediction system 100 is shown roughly. An example of a system configuration of model decision part 134 is shown roughly. An example of data table 300 is shown roughly. An example of data table 400 is shown roughly.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

  FIG. 1 schematically shows an example of the system configuration of the prediction system 100. The prediction system 100 includes a sensor 102, a user terminal 104, and an information providing server 110. In the present embodiment, the information providing server 110 includes an acquisition unit 122, an information storage unit 124, a complementing unit 126, a model storage unit 132, a model determination unit 134, and a prediction unit 142. Each element of the information providing server 110 may transmit / receive information to / from each other.

  The prediction system 100 and the information providing server 110 may be an example of an information processing device. At least one of the sensor 102, the user terminal 104, and the acquisition unit 122 is an example of at least one of a factor information acquisition unit, a phenomenon information acquisition unit, a position information acquisition unit, a time information acquisition unit, and a schedule information acquisition unit. Good. The information storage unit 124 may be an example of a sample storage unit. The measurement result of the sensor 102 may be an example of performance information regarding at least one of one or more types of factors that contribute to the specific phenomenon corresponding to the performance information of the specific phenomenon.

  In the present embodiment, the sensor 102 and the user terminal 104 and the information providing server 110 transmit and receive information via the communication line 10. The communication line 10 may be a wired communication transmission line, a wireless communication transmission line, or a combination of a wireless communication transmission line and a wired communication transmission line. The communication line 10 may be a mobile communication network such as a mobile phone line network, a wireless packet communication network, the Internet, a dedicated line, or a combination thereof.

  In the present embodiment, the prediction system 100 calculates the predicted value of the phenomenon using the past performance information of the specific phenomenon, the past performance information of the factor contributing to the phenomenon, and the model of the phenomenon. . Examples of the phenomenon model include a mathematical model and a mathematical model. The prediction system 100 uses the past performance information of a specific phenomenon, past performance information of a factor contributing to the phenomenon, future schedule information of the factor, and a model of the phenomenon to predict the phenomenon. A value may be calculated. At least one of the one or more information included in the performance information, the one or more information included in the schedule information, and the predicted value of the phenomenon may be quantitative data such as a numerical value, and whether or not some event has occurred Non-numeric data such as “high” or “low” may be used.

  For example, when the user grows a virus, a microorganism, or an animal or plant, the prediction system 100 predicts the growth status of the virus, microorganism, or animal or plant. As the growth of viruses, microorganisms or animals and plants, (a) cultivation of microorganisms or plants, (b) breeding or aquaculture of animals, (c) culture of viruses, microorganisms, cells, etc. (d) viruses, microorganisms, cells, etc. Examples include production of various substances by culture. Examples of the growth status include (a) the growth status of animals and plants, (b) the growth status of viruses, microorganisms, cells, etc., and (c) the production status of various substances by culturing viruses, microorganisms, cells, etc. Can do. The prediction system 100 may predict when to reach each stage of the growth stage, predicting at least one of yield or production volume, quality, labor or expense required, sales, and changes over time. It's okay.

  The sensor 102 measures at least one of a phenomenon to be predicted by the prediction system 100 and one or more types of factors that contribute to the phenomenon. In one embodiment, the sensor 102 may transmit the measurement result to the information providing server 110. The measurement result may include at least one of an average value, a minimum value, and a maximum value of the measurement values of each factor in a specific period. For example, the average daily temperature, maximum temperature and minimum temperature may be included. The sensor 102 may transmit the measurement result to the information providing server 110 in association with the position information of the measurement location. The sensor 102 may transmit the measurement result to the information providing server 110 in association with information indicating the time or timing when the measurement is performed.

  The measurement result of the sensor 102 may be at least one example of the achievement information of the phenomenon and the achievement information of one or more types of factors that contribute to the phenomenon. The performance information may be time series data. The measurement location may be an example of a position where the phenomenon occurs. The information indicating the time or timing when the measurement is performed may be an example of time information.

  The position information of the measurement location may be information indicating the position coordinates of the measurement location, may be information indicating the geographical range of a specific area including the measurement location, and one or more included in the area It may be information indicating the position coordinates of each position. Examples of the position information include address information and GPS information. For each of the one or more sensors 102, when sensor identification information for uniquely identifying each sensor is associated with information for specifying the position or measurement position of each sensor, the position information is one or more sensors 102. It may be identification information for uniquely identifying each of the above.

  For example, when the target of the prediction by the prediction system 100 is the growth status of a virus, microorganism, or animal or plant, the sensor 102 includes temperature, moisture, pH, electrical conductivity, total carbon, total nitrogen, ammonia nitrogen, It may be a sensor for measuring at least one of nitrate nitrogen, total phosphorus, and the like. Examples of the growing place include soil, a culture medium, a cultivation container, a farm, a water tank, and the like.

  The sensor 102 may be a sensor for measuring meteorological information such as the temperature, humidity, solar radiation amount, sunshine duration, wind speed, rainfall amount, and atmospheric pressure at the breeding place. The sensor 102 may be a sensor for measuring environmental information such as room temperature, water temperature, humidity, illuminance, amount of illumination light, illumination time of the growth place. The sensor 102 may be a sensor for measuring at least one of the supply amount of fertilizer or feed and the spraying amount of chemicals such as agricultural chemicals, antibiotics, and hormone agents.

  The sensor 102 may be an optical sensor such as an image sensor that captures an image of the growing place. Thereby, the information regarding the growth condition of a virus, microorganisms, or animals and plants can be acquired, for example.

  Information on a phenomenon related to the growth status of a virus, a microorganism, or an animal or plant may be an example of performance information on the phenomenon. Phenomena related to the growth of viruses, microorganisms or animals and plants include at least one change in the color, shape, size and quality of the virus or virus strain, microorganism or cell line thereof, or animal or plant cell line, and A growth event can be illustrated. Examples of the growth event include reaching a specific stage of the growth stage, occurrence of physical damage, occurrence of pests and the like. Examples of specific stages of the growth stage may include stages such as budding, parting, young panicle formation, heading, flowering, fallen flower, fruiting, fruit picking, tilting, heading, maturation, and ripeness.

  In this specification, “time” is not limited to date and time, and may be date. Examples of the timing include timing when various events such as a growth event occur, timing when specific work is performed, timing when a specific period such as a breeding period has passed, and the like. Examples of specific work include sowing, transplanting, watering, fertilization, feeding, chemical spraying, harvesting, inoculation or seeding of inoculum, sprout cultivation, leveling cultivation, controlled cultivation, introduction of egg or fry or livestock. Can do. For example, for the purpose of simplifying the input of information, it is assumed that the time or timing at which a phenomenon has occurred is the same as the time or timing at which performance information of one or more types of factors contributing to the phenomenon is acquired. Also good.

  The user terminal 104 may be any device that can send and receive information to and from the information providing server 110, and may be a personal computer, a portable terminal, or the like. Examples of portable terminals include notebook computers or laptop computers, PDAs, tablets, mobile phones, smart phones, wearable terminals, wireless terminals, and the like. The user terminal 104 is used by a user of a service provided by the prediction system 100 or the information providing server 110, for example.

  In the present embodiment, the user terminal 104 has an input unit that receives input from the user. The user terminal 104 transmits information received by the input unit to the information providing server 110. In addition, the user terminal 104 receives information provided by the information providing server 110 and outputs the information from the output unit so that the user can perceive the information. Examples of the output unit of the user terminal 104 include a display device voice output such as a display, a voice output device such as a speaker, and the like.

  The user terminal 104 may receive an input related to the actual result information of the phenomenon from the user, and relates to at least one of position information indicating a position where the phenomenon occurs and time information indicating a time or timing when the phenomenon occurs. Input may be accepted. The user terminal 104 may receive an input regarding at least one performance information of one or more types of factors contributing to the above phenomenon from the user, or may receive an input regarding schedule information regarding a future schedule of the factor.

  For example, the user inputs the measurement result of the sensor 102 of the past or the current day, the weather information of the past or the current day, the information regarding the work content of the past or the current day, etc. as performance information. In addition, the user inputs, for example, information related to the work schedule for each day in the growing period until the optimum harvest time arrives, weather forecast information, pest occurrence prediction information, and the like as schedule information.

  One or more factors that contribute to the phenomenon include the time since the occurrence of a specific growth event, such as the number of days raised, the temperature of the surrounding environment, the humidity of the surrounding environment, the pressure of the surrounding environment, the illuminance of light, Examples of the irradiation amount, the light irradiation time, the wind speed, the water supply amount, the soil characteristics, the culture medium characteristics, the work content, and the timing of performing the work can be exemplified. Examples of the temperature of the surrounding environment include air temperature, water temperature, soil or culture medium temperature. As the humidity of the surrounding environment, humidity in the atmosphere, humidity of soil or culture medium, and the like can be exemplified. Examples of the ambient pressure include atmospheric pressure and water pressure. Examples of the soil characteristics include physical characteristics of the soil, chemical characteristics of the soil, and biological characteristics of the soil. The culture medium characteristics may be similar to the soil characteristics.

  Examples of the physical characteristics of the soil include soil structure, effective soil layer depth, moisture, and site conditions. As a structure of soil, the composition distribution and particle size distribution of the stone or sand which comprise soil, the water permeability of a soil, air permeability, water retention ability, etc. can be illustrated. Examples of the location conditions include the slope of the land containing the soil, the geographical division of the land (for example, mountainous area, urban area, etc.) and the like. Examples of the chemical characteristics of soil include nutrient retention, nutrient supply and supply, soil reaction such as pH, and components contained in soil. Examples of biological characteristics of soil include organic substance degradation rate, buffering / detoxifying action, biological activity and the like.

  Examples of the schedule information include information relating to future breeding periods, information relating to future work schedules, weather forecast information such as weather forecasts, and forecast information on occurrence of pests. Examples of information relating to the future breeding period include future dates, dates, and timings. The work schedule may be information in which the content of a specific work is associated with the scheduled date of execution of the specific work or the timing of scheduled execution. The “future” may mean the future based on the execution of the prediction process by the information providing server 110 or when the user requests the prediction process.

  In the present embodiment, the information providing server 110 receives various types of information from at least one of the sensor 102 and the user terminal 104. The information providing server 110 predicts a phenomenon using information received from at least one of the sensor 102 and the user terminal 104. The information providing server 110 may determine or update a model used for predicting a phenomenon in accordance with how information is collected from at least one of the sensor 102 and the user terminal 104.

  In one embodiment, the information providing server 110 executes a factor information acquisition step of acquiring at least one record information of one or more types of factors that contribute to a phenomenon to be predicted. In addition, the information providing server 110 executes a model determination stage for determining a model used for predicting a phenomenon based on the type of the factor included in the performance information acquired in the factor information acquisition stage.

  The information providing server 110 may be an apparatus that can transmit and receive information to and from at least one of the sensor 102 and the user terminal 104, and may be an information processing apparatus having a general configuration such as a server or a personal computer. . The information providing server 110 may be a virtual server or a cloud system. The information providing server 110 may be realized by a single server or may be realized by a plurality of servers.

  Each unit of the information providing server 110 may be realized by hardware, may be realized by software, or may be realized by hardware and software. Each unit of the information providing server 110 may be realized by starting software or a program that defines the operation of each unit of the information providing server 110 in an information processing apparatus having a general configuration.

  An information processing device used as the information providing server 110 includes a data processing device having a processor such as a CPU, a ROM, a RAM, a communication interface, an input device such as a keyboard, a touch panel, and a microphone, and an output device such as a display device and a speaker. And a storage device such as a memory or an HDD. The data processing device or the storage device may store the above software or program. The software or program is executed by a processor to cause the information processing apparatus to execute an operation defined by the software or program.

  The acquisition unit 122 acquires various types of information from at least one of the sensor 102 and the user terminal 104. For example, the acquisition unit 122 acquires the measurement result of the sensor 102 from the sensor 102. The acquisition unit 122 acquires information input by the user from the user terminal 104. The acquisition unit 122 may acquire position information indicating a position where the phenomenon occurs, or may acquire time information indicating the time or timing when the phenomenon occurs.

  The acquisition unit 122 may store information acquired from at least one of the sensor 102 and the user terminal 104 in the information storage unit 124. For example, the acquisition unit 122 may transmit the schedule information acquired from the user terminal 104 to the prediction unit 142.

  In one embodiment, the acquisition unit 122 acquires performance information on a specific phenomenon related to the growth status of a virus, a microorganism, or an animal or plant from at least one of the sensor 102 and the user terminal 104. The acquisition unit 122 acquires performance information regarding at least one of one or more types of factors that contribute to the specific phenomenon. The acquisition unit 122 may acquire performance information regarding at least two factors that contribute to the specific phenomenon. The acquisition unit 122 associates the result information of a specific phenomenon with the result information regarding at least one of one or more types of factors contributing to the specific phenomenon, and stores the information in the information storage unit 124. For example, the measurement result of the sensor 102 whose measurement target is a specific phenomenon is associated with the measurement result of one or more sensors 102 whose measurement target is a phenomenon corresponding to at least one of the factors contributing to the specific phenomenon. Store.

  In another embodiment, the acquisition unit 122 may acquire performance information of each of a plurality of phenomena related to the growth status of viruses, microorganisms, or animals and plants from at least one of the sensor 102 and the user terminal 104. Moreover, you may acquire the performance information regarding at least 1 of the 1 or more types of factor which contributes to the said phenomenon about each of several phenomenon. For each of a plurality of phenomena, the acquisition unit 122 associates the actual result information of the phenomenon with the actual result information related to at least one of one or more types of factors that contribute to the phenomenon, and stores them in the information storage unit 124. Good.

  The information storage unit 124 stores the information acquired by the acquisition unit 122. The information storage unit 124 associates, for example, at least one result information of one or more types of factors contributing to a specific phenomenon acquired by the acquisition unit 122 and at least one of position information and time information related to the phenomenon. Store. In one embodiment, the information storage unit 124 is sample information in which at least one result information of one or more types of factors contributing to a specific phenomenon is associated with at least one of position information and time information regarding the phenomenon. Is stored. The sample information may be information in which at least the actual result information of the phenomenon acquired by the acquisition unit 122 is associated with the actual result information of at least one of the one or more types of factors contributing to the phenomenon. .

  The complementing unit 126 complements at least one piece of information selected from the group consisting of the historical information of the phenomenon acquired by the acquiring unit 122, at least one historical information of one or more types of factors contributing to the phenomenon, and schedule information. Perform the process. Although a specific method of the complementing process is not particularly limited, in one embodiment, the complementing unit 126 performs an interpolation process using information stored in the information storage unit 124. In another embodiment, the complementing unit 126 performs an interpolation process using weather forecast information, pest occurrence prediction information, information on soil characteristics of a specific region, and the like stored in another information processing apparatus. .

  In one embodiment, the information acquired by the acquisition unit 122 contributes to the specific phenomenon corresponding to the actual phenomenon information of the specific phenomenon, even though the actual event information of the specific phenomenon is included in the information. Performance information about specific factors may not be included. For example, regarding lettuce cultivated in the field A, the information on the rainfall on January 22, 2015 in the field A is included even though the diameter information on January 22, 2015 is included. There may not be. The information on the diameter of the lettuce in the field A on January 22, 2015 is an example of the performance information of the specific phenomenon, and the information on the rainfall on January 22, 2015 in the field A is the specific phenomenon. It is an example of the performance information regarding the specific factor to contribute.

  In this case, the complement unit 126 may supplement the record information related to the specific factor corresponding to the record information of the specific phenomenon based on the record information related to the specific factor contributing to another phenomenon. For example, the complementing unit 126 is based on the rainfall information on January 22, 2015 in the field B, which is associated with the diameter information of the cabbage cultivated in the field B located in the vicinity of the field A. The rainfall information on January 22, 2015 in the field A is supplemented. More specifically, for example, the complementing unit 126 considers the rainfall on January 22, 2015 in the field B as the rainfall on January 22, 2015 in the field A. Note that the information on the diameter of cabbage cultivated in the field B is an example of performance information of other phenomena, and the rainfall information on January 22, 2015 in the field B is a specification that contributes to other phenomena. It may be an example of performance information regarding the factor.

  The complementing unit 126 may supplement the track record information regarding the specific factor corresponding to the track record information of the specific phenomenon based on a plurality of track record information regarding the specific factors contributing to other phenomena. For example, the complementing unit 126 calculates the average value of the rainfall on January 22, 2015 in the field B, the field C, and the field D located in the vicinity of the field A as the rainfall on January 22, 2015 in the field A. I look forward to it. The value obtained by multiplying the rainfall on January 22, 2015 in each field by the weight according to the distance between each field and the field A is regarded as the rainfall on January 22, 2015 in the field A. Also good.

  In one embodiment, if the sample information related to the specific phenomenon does not include the performance information of the specific factor, the complementing unit 126 first refers to the information storage unit 124 to check the sample information of the specific phenomenon and other information. Based on the position information and / or time information of the sample information of the phenomenon, appropriate sample information is extracted from the sample information of one or more other phenomena, and the specific factor included in the extracted sample information is extracted. Results information is extracted as supplementary data.

  Next, the complementing unit 126 supplements the performance information of the specific factor of the sample information regarding the specific phenomenon based on the extracted performance information. The complementing unit 126 may supplement specific performance information of a specific factor by performing statistical processing or interpolation processing on the plurality of extracted performance information. When the actual information of a specific factor is not included in the sample information related to the specific phenomenon, the actual information of the specific factor contributing to the specific phenomenon is not included in the information acquired by the acquisition unit 122 It may be an example.

  In one embodiment, the complementing unit 126 records the results of specific factors included in the sample information of other phenomena that have occurred in nearby positions based on the sample information of specific phenomena and the position information of sample information of other phenomena. Extract information. In another embodiment, the complement unit 126 is included in the sample information of another phenomenon that occurs at the same or similar time or timing based on the sample information of a specific phenomenon and the time information of the sample information of another phenomenon. To extract performance information for specific factors.

  For example, a specific example of the complementing process in the complementing unit 126 will be described by taking as an example the case of predicting the growth situation of the lettuce in the field A (for example, harvest time and harvest amount) using the prediction system 100. In this specific example, the information storage unit 124 includes, for each of a plurality of fields, a data table X including a plurality of records associated with information regarding ID, date, temperature, and work content that uniquely identifies each field, A data table Y including a plurality of records in which ID of each field, position information, information on cultivated varieties, and information on soil characteristics are associated is stored. The data table X and the data table Y may be an example of sample information. The information regarding the cultivar may include information regarding the history of the current and past cultivars.

  According to one embodiment, for example, in the record of the field A of the data table Y, when the information regarding the soil characteristics of the field A is missing, the complement unit 126 first selects the other fields included in the data table Y. Based on the position information included in each record, a record of a field in the vicinity of the field A that includes information on soil characteristics is extracted from the records. When a plurality of records are extracted, the complementing unit 126 further extracts a record of the field in which the lettuce is cultivated from the extracted field records based on information on the cultivar included in each record. May be.

  Next, the complement part 126 determines the information regarding the soil characteristic of the field A based on the information regarding the soil characteristic contained in the extracted record. For example, the complementing unit 126 determines an average value of information regarding the extracted soil characteristics as information regarding the soil characteristics of the field A. The complementing unit 126 may determine the information regarding the soil characteristics of the field A based on the extracted information regarding the soil characteristics and the weight coefficient of each information. For example, the weighting factor is increased for information on fields closer to the position, or the weighting factor is increased for information on fields where the same or similar varieties are cultivated.

  In another embodiment, for example, in the record of the field A of the data table X, when the information regarding the work content of the field A is missing, the complement unit 126 first refers to the data table X and the data table Y. Based on the date included in each record, information on the cultivar and the position information, the record of the other field where the lettuce is cultivated from the record of the other field among the records of the other field Then, a record including information on work contents on the same or similar date is extracted. The complement unit 126 determines the extracted work content as the work content of the field A.

  In another embodiment, for example, in the record of the field A of the data table X, when the temperature information for a specific date is missing, the complement unit 126 refers to, for example, the website of the Japan Meteorological Agency, Acquires temperature information for the date. The complementing unit 126 determines the acquired temperature information as information related to the temperature of the field A.

  In still another embodiment, the complementing unit 126 may supplement missing information using reference data that is determined based on a criterion that is predetermined for each type of one or more factors. The predetermined reference data may be a predetermined value or a predetermined value according to the growing period. The predetermined reference data may be a mathematical model. For example, the predetermined reference data is determined based on a function having the growing period as an independent variable.

  The predetermined reference data may be determined by performing statistical processing or interpolation processing on a plurality of other data of factors that are to be complemented. The statistical processing and the interpolation processing may be a well-known method or an arbitrary method developed in the future. The statistical process may be a process of calculating basic statistics such as an average value, a median value, a mode value, etc. regarding other plural data, and constructs a probability model from other plural data, Processing for calculating target reference data may be used. The interpolation processing may be primary interpolation, secondary interpolation, cubic interpolation, Lagrange interpolation, spline interpolation, or the like.

  For example, when data on the temperature of a specific day is missing, statistical values such as the average value, median value, and mode value of the temperature data of other days are used as the temperature data of the specific day. Is calculated. The plurality of other days may be a plurality of days before the specific day, may be a plurality of days before and after the specific day, and may be a plurality of days after the specific day. May be a day.

  In the present embodiment, a case has been described in which the information acquired by the acquisition unit 122 does not include track record information of a specific factor. However, the processing by the complementing unit 126 is not limited to this embodiment. In another embodiment, even if the information acquired by the acquisition unit 122 does not include the actual result information of the phenomenon or the schedule information, the complementing unit 126 uses the same method to delete the missing data. Can be complemented.

  The model storage unit 132 stores one or more models used in the prediction unit 142. Each of the one or more models uses a phenomenon as an objective variable, and represents the objective variable using one or more explanatory variables corresponding to each of one or more types of factors and parameters corresponding to each of the one or more explanatory variables. It may be a mathematical model. The model storage unit 132 may store a constraint condition indicating a condition for using the model in association with the model.

  Examples of the mathematical model include a univariate analysis model, a multivariate analysis model, a kernel multivariate analysis model, a generalized linear model, a neural network model, a Bayesian network model, and a deep learning model. The mathematical model may be a continuous function or a discontinuous function. The discontinuous function is used when, for example, the occurrence of a growth event such as flowering or heading is used as an objective variable.

  The model determination unit 134 determines a model used for predicting a phenomenon based on the type of factor for which the acquisition unit 122 has acquired the record information. The model determination unit 134 may transmit model identification information for identifying a model determined to be used for prediction to the prediction unit 142.

  In one embodiment, the model determination unit 134 compares the explanatory variable of each model stored in the model storage unit 132 with the factor whose performance information has been acquired by the acquisition unit 122, and is acquired by the acquisition unit 122. Extract one or more models that can be used using the information. Instead of the above extraction method or in addition to the above extraction method, the model determination unit 134 compares the constraint conditions of each model stored in the model storage unit 132 with the information acquired by the acquisition unit 122. Then, one or more models that can be used using the information acquired by the acquisition unit 122 may be extracted.

  The model determination unit 134 determines a model used in the prediction unit 142 from one or more extracted models. In one embodiment, the model determining unit 134 uses the information acquired by the acquiring unit 122 to calculate at least one of prediction accuracy and calculation time for each of the extracted one or more models. The model determination unit 134 determines a model having the best prediction accuracy and / or calculation time as a model used in the prediction unit 142.

  For example, when the information acquired by the acquisition unit 122 includes performance information regarding two types of factors, date and temperature, as factors contributing to the growth of lettuce in the field A, the model determination unit 134 includes the prediction unit 142. In, it is decided to use a univariate analysis model that predicts the harvest time based on the effective integrated temperature method. On the other hand, in the information acquired by the acquisition unit 122, as factors contributing to the growth of lettuce in the field A, two or more such as date, temperature, precipitation, sunshine duration, work content, soil characteristics, cultivar history, etc. When the performance information regarding the factor is included, the model determination unit 134 uses the multivariate analysis model for predicting the harvest time and the yield based on the information in the prediction unit 142 or the kernel multivariate analysis model. To decide.

  In another embodiment, the model determination unit 134 may determine a model used for predicting a phenomenon based on the type of factor from which the acquisition unit 122 has acquired the actual information and the number of the actual information. For example, the model determination unit 134 first extracts one or more models that can be used based on the type of factor for which the acquisition unit 122 has acquired the record information. Next, the model determination unit 134 determines a model to be used in the prediction unit 142 based on the number of pieces of performance information from one or more extracted models.

  For example, the model determining unit 134 is used in the model predicting unit 142 when the information acquired by the acquiring unit 122 includes sample information for the past one month and when sample information for the past three months is included. A different model is determined as the model to be used. The model determination unit 134 may determine to use models having different mathematical formulas, or may determine to use models having the same mathematical formula and different parameters.

  Depending on the phenomenon, a series of processes from the first event to the second event may be periodically repeated. For example, in the field A, a series of processes from lettuce planting to lettuce maturation or harvesting is repeated every year. In the case of multi-period work, the process is repeated several times during the year. In the case of multi-hair cropping, for example, a series of processes from lettuce planting to lettuce maturation or harvesting and a series of processes from planting of Chinese cabbage to maturation or harvesting of Chinese cabbage are alternately repeated. In this case, the growth situation of lettuce and the growth situation of Chinese cabbage may be different phenomena.

  In one embodiment, the model determination unit 134 may execute a model determination process in the middle of a series of processes from a first event to a second event that are periodically repeated. Thereby, the model utilized in the prediction part 142 can be changed in the middle of a series of processes in one cycle. As a result, prediction accuracy can be improved. The timing for executing the model determination process in the model determination unit 134 includes a timing at which a predetermined period or time has elapsed, a timing at which a predetermined number of results information is acquired, a timing at which an instruction from the user is received, and the like. It can be illustrated.

  For example, with respect to a first period included in a series of processes in one cycle, prediction is performed using a plurality of pieces of sample information including a plurality of pieces of actual information regarding phenomena and factors of the first period in other past cycles as learning data. The model used in the unit 142 is determined. The plurality of pieces of sample information may include a plurality of pieces of performance information related to phenomena and factors in other periods in other periods in the past. The performance information regarding the phenomenon and the factor may be performance information regarding the growth information of lettuce in the field A, or may be performance information regarding the growth information of lettuce in a plurality of fields including the field A. Further, at least a part of the performance information regarding the factors in the field A may be performance information regarding the growth information of crops other than the lettuce in the field A.

  After the first period has elapsed, the model determination unit 134 uses a plurality of pieces of sample information including a plurality of pieces of performance information relating to phenomena and factors in the first period in the cycle as learning data, and is used in the prediction unit 142 To decide. The plurality of sample information includes a plurality of performance information related to the phenomenon and factor in the first period in the period, a plurality of performance information related to the phenomenon and factor in the first period in the other past period, and the other period in the past. And at least one of a plurality of pieces of performance information related to the phenomenon and factors of the second period.

  The second period may be a period later than the first period. At least one of the plurality of performance information related to the phenomenon in the first period in the cycle and the plurality of performance information related to the factors in the first period in the cycle is an example of at least a part of the performance information acquired by the acquisition unit 122. It may be.

  The prediction unit 142 calculates a predicted value of the phenomenon based on at least a part of the performance information acquired by the acquisition unit 122 and the model determined by the model determination unit 134. The prediction unit 142 includes at least one of at least a part of the result information acquired by the acquisition unit 122, at least a part of the result information supplemented by the complement unit 126 and at least a part of the schedule information acquired by the acquisition unit 122, and a model. The predicted value of the phenomenon may be calculated based on the model determined by the determination unit 134.

  At least a part of the performance information acquired by the acquisition unit 122 and at least a part of the performance information supplemented by the complementing unit 126 may be performance information after the day when the growth of the virus, microorganism, or animal or plant is started. At least a part of the schedule information acquired by the acquisition unit 122 may be schedule information in an arbitrary future period.

  When the prediction unit 142 calculates the predicted value of the phenomenon using both the result information acquired by the acquisition unit 122 and the result information supplemented by the complement unit 126, a value obtained by multiplying each of the result information by a weighting factor is used as a model. You may enter. In this case, the weighting factor of the performance information acquired by the acquisition unit 122 and the weighting factor of the performance information supplemented by the complementing unit 126 may be different. In another embodiment, in part of the performance information acquired by the acquisition unit 122, information on at least one of one or more factors corresponding to one or more explanatory variables of the model used in the prediction unit 142 is missing. In this case, the prediction unit 142 may calculate a predicted value based on other performance information excluding the performance information.

  FIG. 2 schematically shows an example of the system configuration of the model determination unit 134. In the present embodiment, the model determination unit 134 includes a model evaluation unit 232, a model selection unit 234, and a parameter determination unit 236. Each element of the model determination unit 134 may transmit and receive information to and from each other.

  The model evaluation unit 232 evaluates each of one or more models stored in the model storage unit 132. The model evaluation unit 232 may evaluate each model after adjusting the parameters of each model, or may evaluate each model before adjusting the parameters of each model.

  For example, for each of one or more models stored in the model storage unit 132, the model evaluation unit 232 evaluates at least one of prediction accuracy and calculation time of the model. In one embodiment, the model evaluation unit 232 uses the predicted value of the phenomenon calculated by the model using at least a part of the performance information acquired by the acquisition unit 122 and the actual value of the phenomenon acquired by the acquisition unit 122. Based on at least a part, at least one of prediction accuracy and calculation time of the model may be evaluated.

  For example, the model evaluation unit 232 first extracts sample information used for evaluating each model from one or more pieces of performance information stored in the information storage unit 124. For example, the model evaluation unit 232 extracts a plurality of pieces of sample information based on the acquisition timing of the record information, the position information of the record information, and the like. Note that the extraction condition may be appropriately determined according to a phenomenon to be predicted. Next, for each of one or more models stored in the model storage unit 132, the model evaluation unit 232 can be used for predicting a phenomenon based on the type of factor for which the acquisition unit 122 has acquired the actual information. Judge whether or not.

  For example, the model evaluation unit 232 determines whether or not each of the one or more models can be used for predicting a phenomenon according to the following procedure. First, the model evaluation unit 232 acquires information related to one or more types of explanatory variables included in a model to be evaluated. In one embodiment, the model evaluation unit 232 refers to the information related to the mathematical formula of each model stored in the model storage unit 132 and acquires the type of explanatory variable included in each model. In this case, since the model evaluation unit 232 automatically analyzes each model and each sample information, it is possible to greatly reduce the setting effort by the user. In another embodiment, the model evaluation unit 232 refers to the information on the constraint conditions of each model stored in the model storage unit 132 and acquires information on the types of explanatory variables included in each model.

  Next, the model evaluation unit 232 analyzes performance information included in each of the extracted plurality of sample information, and acquires information on one or more types of factors corresponding to each performance information. The information related to one or more types of factors may be information related to the type of sensor 102 and measurement items, and may be information related to one or more input items included in an input form of various performance information displayed on the input device 104. There may be. Next, the model evaluation unit 232 extracts a plurality of pieces of sample information extracted based on information on one or more types of factors corresponding to each performance information and information on types of explanatory variables of the model to be evaluated. It is determined whether or not each includes performance information regarding a factor corresponding to an explanatory variable of a model to be evaluated. The model evaluation unit 232 includes a correspondence relationship between at least one of the type of the sensor 102 and the measurement item and the explanatory variable, and each input item and explanatory variable included in the input form of various performance information displayed on the input device 104. The above items may be determined based on at least one of the correspondence relationships.

  The model evaluation unit 232, for example, the ratio of the number of pieces of sample information in which performance information related to factors corresponding to the explanatory variables of the model to be evaluated is missing with respect to the number of extracted pieces of sample information (data loss degree and If the model to be evaluated is larger than a predetermined first value, it is determined that the model to be evaluated cannot be used for predicting the phenomenon. In another embodiment, the model evaluation unit 232 uses the performance information supplemented by the complementing unit 126 instead of the missing performance information when the data loss degree is larger than a predetermined first value. You may decide. The model evaluation unit 232 calculates the data deficiency when the performance information supplemented by the complement unit 126 is used, and when the data deficiency is greater than a predetermined first value, the model to be evaluated May not be used to predict the phenomenon.

  In still another embodiment, the model evaluation unit 232 determines that the model to be evaluated cannot be used for predicting a phenomenon when the degree of data loss is greater than a predetermined first value. When the data loss degree is larger than the predetermined second value, the model evaluation unit 232 determines to use the record information complemented by the complementing unit 126 instead of the missing record information. The second value may be smaller than the first value. In these embodiments, if the model evaluation unit 232 determines to use the performance information supplemented by the complementing unit 126, the model evaluation unit 232 may request interpolation processing from the complementing unit 126 to acquire data after the complementing processing. It is also possible to refer to the information storage unit 124 and acquire the data after the complementary processing stored in the information storage unit 124.

  When the model evaluation unit 232 determines that the model to be evaluated is not usable for predicting a phenomenon, the model evaluation unit 232 evaluates that the model is unusable without specifically calculating the prediction accuracy and calculation time of the model. Or give a very low rating to the model. On the other hand, when the model to be evaluated is available for predicting a phenomenon, the model evaluation unit 232 specifically calculates at least one of the prediction accuracy and calculation time of the model, and the prediction accuracy and calculation time of the model. Evaluate at least one of

  For example, the model evaluation unit 232 extracts performance information related to the factor corresponding to the explanatory variable of the model to be evaluated from performance information related to one or more types of factors included in each of the extracted plurality of sample information. To the model. Thereby, the model evaluation unit 232 calculates a predicted value based on the model for the phenomenon to be predicted. Note that when the model evaluation unit 232 determines to use the performance information supplemented by the complementing unit 126, the model evaluation unit 232 may calculate the predicted value based on the model by also using the data after the complementing process. The model evaluation unit 232 may calculate the calculation time required for calculating the predicted value.

  Next, the model evaluation unit 232 refers to the information storage unit 124 and extracts performance information related to the phenomenon corresponding to the performance information related to the factor input to the model. The model evaluation unit 232 compares the predicted value based on the model with the performance information regarding the extracted phenomenon, and calculates the prediction accuracy. The model evaluation unit 232 may evaluate the model by classifying the model to be evaluated into one of a plurality of evaluation categories based on at least one of the prediction accuracy and the calculation time. In addition, the model may be evaluated based on the numerical value of at least one of the calculation times.

  In the present embodiment, the model evaluation unit 232 selects an available model based on the extracted sample information, and then calculates a predicted value for each selected model. However, the processing in the model evaluation unit 232 is not limited to this embodiment. In another embodiment, the model evaluation unit 232 first extracts sample information used for evaluation of each model from one or more pieces of performance information stored in the information storage unit 124.

  Next, the model evaluation unit 232 evaluates each of the one or more models stored in the model storage unit 132 from performance information regarding one or more types of factors included in each of the extracted plurality of sample information. By extracting performance information relating to factors corresponding to the explanatory variables of the target model and inputting it to the model, a predicted value based on the model is calculated. In this case, when the sample information does not include performance information related to the factor corresponding to the explanatory variable of the model, the data may be discarded without being input to the model. Alternatively, instead of the data, it may be determined to use the performance information supplemented by the complementing unit 126.

  Next, the model evaluation unit 232 refers to the information storage unit 124 and extracts performance information related to the phenomenon corresponding to the performance information related to the factor input to the model. The model evaluation unit 232 compares the predicted value based on the model with the performance information regarding the extracted phenomenon, and calculates the prediction accuracy. The model evaluation unit 232 may evaluate the model based on the degree of data loss and at least one of prediction accuracy and calculation time. Specific items of each process in the present embodiment may be the same as each process in the above embodiment.

  As described above, the information storage unit 124 may store the performance information supplemented by the supplementing unit 126. In this case, for each of the one or more models stored in the model storage unit 132, the model is used by using at least a part of the result information acquired by the acquisition unit 122 and at least a part of the result information supplemented by the complement unit 126. Based on the calculated predicted value of the phenomenon and the actual value of the phenomenon acquired by the acquiring unit 122, at least one of the prediction accuracy and the calculation time of the model may be evaluated. For example, when the performance information used for evaluating at least one model of one or more models is insufficient, the model evaluation unit 232 uses the reference data predetermined for each of one or more types of factors, and uses at least one The prediction accuracy of the model may be evaluated.

  The model evaluation unit 232 may give a higher evaluation as the prediction accuracy is higher. Also, the shorter the calculation time, the higher the evaluation may be given. The model evaluation unit 232 may set a weighting factor for each of the prediction accuracy and the calculation time, and may evaluate each model based on a predetermined calculation formula.

  When evaluating the prediction accuracy of each model, the model evaluation unit 232 may divide the evaluation period into a plurality of periods and set a weighting factor for each of the classifications. In this case, the weighting factor may be increased as the prediction accuracy in the period close to the model evaluation date. For example, even if the prediction accuracy in the initial stage of the growth period is low, the prediction accuracy is high in the vicinity of the model evaluation date. High evaluation can be given to the model.

  In one embodiment, the model evaluation unit 232 may execute a model evaluation process in the middle of a series of processes from a first event to a second event that are periodically repeated. Thereby, the model utilized in the prediction part 142 can be changed in the middle of a series of processes in one cycle. As a result, prediction accuracy can be improved. The timing for executing the model determination process in the model determination unit 134 includes a timing at which a predetermined period or time has elapsed, a timing at which a predetermined number of results information is acquired, a timing at which an instruction from the user is received, and the like. It can be illustrated.

  In the present embodiment, the model selection unit 234 selects a model used for predicting a phenomenon in the prediction unit 142 based on the evaluation by the model evaluation unit 232. The model selection unit 234 may select a model based on the resource of the information processing apparatus assigned to the prediction unit 142. For example, when the resource amount allocated to the prediction unit 142 is larger than a predetermined value, a model having a high evaluation regarding prediction accuracy is preferentially selected. On the other hand, when the resource amount allocated to the prediction unit 142 is smaller than a predetermined value, a model having a high evaluation regarding the calculation time is preferentially selected.

  In the present embodiment, the parameter determination unit 236 includes, for each of one or more models stored in the model storage unit 132, at least a part of the performance information acquired by the acquisition unit 122 and the phenomenon acquired by the acquisition unit 122. The parameter of the model is determined based on at least a part of the actual value. For example, the parameter determination unit 236 determines the parameters of each model by using, as learning data, information for a part of the period information of the phenomenon acquired by the acquisition unit 122 and the result information of the factor contributing to the phenomenon. To do.

  The parameter determination unit 236 may determine the parameters of each model before the model evaluation unit 232 evaluates each model. The parameter determination unit 236 may determine parameters only for models that are determined to be usable by the model evaluation unit 232. The parameter determination unit 236 may determine parameters only for the model selected by the model selection unit 234.

  FIG. 3 schematically shows an example of the data table 300. In the present embodiment, the data table 300 stores the farmland ID 310, the date 320, the position information 330, the phenomenon information 340, and the factor information 350 in association with each other. In the present embodiment, the phenomenon information 340 includes information related to the growth condition 342 and information related to the growth event 344. The factor information 350 includes weather information 352, soil information 354, information on the cultivar 356, and information on the work content 358.

  The data table 300 may be an example of information acquired by the acquisition unit 122 and stored in the information storage unit 124. The data table 300 may be an example of sample information. The information related to the growth condition 342 and the information related to the growth event 344 may be an example of performance information on a phenomenon that is a target of prediction by the prediction system 100. Each of the weather information 352, the soil information 354, the information on the cultivar 356, and the information on the work content 358 is at least one record information of one or more types of factors contributing to the phenomenon to be predicted by the prediction system 100. It may be an example. The information regarding the date 320 and the information regarding the growth event 344 may be an example of time information. The growth event 344 may be an example of a timing at which a phenomenon occurs.

  FIG. 4 schematically shows an example of the data table 400. In the present embodiment, the data table 400 stores a model ID 410, a mathematical model 420, and a constraint condition 430 in association with each other. In the present embodiment, the mathematical model 420 includes information regarding the mathematical formula 422 and information regarding the parameter value 424.

  For example, the mathematical model 420 uses a phenomenon as an objective variable, and represents the objective variable using one or more explanatory variables corresponding to each of one or more types of factors and parameters corresponding to each of the one or more explanatory variables. The constraint condition 430 represents a condition in which the model can be used. The condition for using the model may be a condition for calculating a predicted value with higher prediction accuracy than a predetermined value when the model is used. The condition for using the model may be a condition that, when the model is used, a predicted value can be calculated in a shorter time than a predetermined time using a predetermined resource. .

  The data table 400 may be an example of information regarding a model stored in the model storage unit 132. In the data table 400, f is an objective variable, and X, Y, and Z are explanatory variables. Each of a, b, c, and K is a parameter, i may be a positive integer from 1 to j, for example, and j may be the number of breeding days on the predicted date.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. In addition, the matters described in the specific embodiment can be applied to other embodiments within a technically consistent range. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

  The order of execution of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown in the claims, the description, and the drawings is particularly “before” or “prior to”. It should be noted that the output can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the description, and the drawings, even if it is described using “first”, “next”, etc. for convenience, it means that it is essential to carry out in this order. It is not a thing.

  DESCRIPTION OF SYMBOLS 10 Communication line, 100 Prediction system, 102 Sensor, 104 User terminal, 110 Information provision server, 122 Acquisition part, 124 Information storage part, 126 Complement part, 132 Model storage part, 134 Model determination part, 142 Prediction part, 232 Model evaluation Part, 234 model selection part, 236 parameter determination part, 300 data table, 310 farmland ID, 320 date, 330 position information, 340 phenomenon information, 342 growth condition, 344 growth event, 350 factor information, 352 weather information, 354 soil information 356 Cultivars, 358 Work content, 400 Data table, 410 Model ID, 420 Formula model, 422 Formula, 424 Parameter value, 430 Restriction condition

Claims (23)

A factor information acquisition unit that acquires at least one record information of one or more types of factors that contribute to the phenomenon to be predicted;
A model determination unit that determines a mathematical model used for prediction of the phenomenon, based on the type of the factor from which the factor information acquisition unit has acquired the performance information, from among a plurality of mathematical model having the phenomenon as an objective variable When,
With
Each of the plurality of mathematical models uses the phenomenon as an objective variable, and uses one or more explanatory variables corresponding to each of the one or more types of factors and a parameter corresponding to each of the one or more explanatory variables. A mathematical model representing the objective variable,
Information processing device. The model determination unit determines a mathematical model used for prediction of the phenomenon based on the type of the factor from which the factor information acquisition unit has acquired the performance information and the number of the performance information.
The information processing apparatus according to claim 1. Further comprising a phenomenon information acquisition unit to acquire the performance information of the previous Symbol phenomenon,
The model determining unit
For each of the previous SL plurality of mathematical expression models, the predicted value of the phenomenon that is calculated by the mathematical expression model using at least a portion of the record information said factor information acquisition unit has acquired, and the phenomenon information acquisition unit acquires A model evaluation unit that evaluates at least one of prediction accuracy and calculation time of the mathematical model based on at least a part of the actual value of the phenomenon;
A model selection unit that selects a mathematical model used for prediction of the phenomenon based on the evaluation by the model evaluation unit;
Having
The information processing apparatus according to claim 1 or 2. The model determination unit includes, for each of the plurality of mathematical models, at least a part of the result information acquired by the factor information acquisition unit, and at least a part of the result value of the phenomenon acquired by the phenomenon information acquisition unit. A parameter determination unit for determining parameters of the mathematical model based on
The information processing apparatus according to claim 3. The model evaluation unit, if the record information to be used for evaluation of the at least one mathematical model of the plurality of mathematical expression models is insufficient, by using the reference data set in advance for each factor of the one or more, at least Evaluate the prediction accuracy of one mathematical model,
The information processing apparatus according to claim 3 or 4.   The model evaluation unit
  Divide the evaluation period into multiple periods, determine the weighting factor for each category,
  Evaluating the prediction accuracy of each mathematical model based on a predetermined formula using the weighting factor,
  The information processing apparatus according to any one of claims 3 to 5.   The model evaluation unit
  For each of the plurality of mathematical models, a data deficiency degree is calculated, which is a ratio of the number of pieces of performance information in which information about factors corresponding to the explanatory variables of the mathematical model is missing with respect to the number of pieces of performance information used for the evaluation. And
  For a mathematical model whose data deficiency is larger than a predetermined first value, it is evaluated that (i) the mathematical model cannot be used without calculating the prediction accuracy and calculation time of the model. Or (ii) assigning a lower evaluation to the mathematical model than the mathematical model in which the data deficiency is smaller than the first value.
  The information processing apparatus according to any one of claims 3 to 6. A position information acquisition unit that acquires position information indicating a position where the phenomenon occurs;
A sample storage unit for storing sample information in which at least one result information of the one or more types of factors acquired by the factor information acquisition unit is associated with the position information acquired by the position information acquisition unit;
If the sample information of the phenomenon does not include the actual information of the specific factor, the sample of the other phenomenon that occurred in the neighboring position based on the position information of the sample information of the phenomenon and the sample information of the other phenomenon Extracting the actual information of the specific factor included in the information, and based on the extracted actual information, a complement unit that complements the actual information of the specific factor of the sample information of the phenomenon;
Further comprising
The model evaluation unit, for each of the previous SL plurality of mathematical expression models, using at least a portion of (i) and at least a portion of the record information which the complementary part is complemented performance information which the factor information acquisition portion has acquired the predicted value of the phenomenon that is calculated by a mathematical model, and, (ii) based at least in part on the actual value of the phenomenon that the phenomenon information acquiring unit has acquired, at least one of prediction accuracy and computation time of the mathematical model Evaluate,
The information processing apparatus according to any one of claims 3 to 6 . A time information acquisition unit for acquiring time information indicating the time or timing when the phenomenon occurs;
A sample storage unit for storing sample information in which at least one record information of the one or more types of factors acquired by the factor information acquisition unit is associated with the time information acquired by the time information acquisition unit;
If the sample information of the phenomenon does not include the actual information of the specific factor, it occurred at the same or similar time or timing based on the time information of the sample information of the phenomenon and the sample information of another phenomenon Extracting the actual information of the specific factor included in the sample information of the other phenomenon, based on the extracted actual information, a complementing unit that complements the actual information of the specific factor of the sample information of the phenomenon;
Further comprising
The model evaluation unit, for each of the previous SL plurality of mathematical expression models, using at least a portion of (i) and at least a portion of the record information which the complementary part is complemented performance information which the factor information acquisition portion has acquired the predicted value of the phenomenon that is calculated by a mathematical model, and, (ii) based at least in part on the actual value of the phenomenon that the phenomenon information acquiring unit has acquired, at least one of prediction accuracy and computation time of the mathematical model Evaluate,
The information processing apparatus according to any one of claims 3 to 6 . The model evaluation unit
  For each of the plurality of mathematical models, a data deficiency degree is calculated, which is a ratio of the number of pieces of performance information in which information about factors corresponding to the explanatory variables of the mathematical model is missing with respect to the number of pieces of performance information used for the evaluation. And
  For a mathematical model whose degree of data deficiency is greater than a predetermined second value, (i) at least part of the result information acquired by the factor information acquisition unit and the result information supplemented by the complement unit Based on at least a part of the predicted value of the phenomenon calculated by the mathematical model using at least a part and (ii) the actual value of the phenomenon acquired by the phenomenon information acquisition unit Evaluate at least one of accuracy and computation time,
  For a mathematical model whose data deficiency is larger than a predetermined first value, it is evaluated that (i) the mathematical model cannot be used without calculating the prediction accuracy and calculation time of the model. Or (ii) giving a lower evaluation to the mathematical model than the mathematical model in which the data deficiency is smaller than the first value;
The second value is smaller than the first value;
  The information processing apparatus according to claim 8 or 9. Based on at least a part of the result information acquired by the factor information acquisition unit, at least a part of the result information supplemented by the complementing unit, and the mathematical model determined by the model determination unit, the predicted value of the phenomenon is calculated. A prediction unit for calculating,
The information processing apparatus according to any one of claims 8 to 10 . A prediction unit that calculates a predicted value of the phenomenon based on at least a part of the performance information acquired by the factor information acquisition unit and the mathematical model determined by the model determination unit;
Further comprising
The information processing apparatus according to any one of claims 1 to 7 . A schedule information acquisition unit for acquiring schedule information on a future schedule of the factor;
The prediction unit further calculates a predicted value of the phenomenon based on at least a part of the schedule information acquired by the schedule information acquisition unit.
The information processing apparatus according to claim 11 or 12 .   The model determination unit is configured to determine the mathematical model at a timing when a predetermined period or time has elapsed, a timing when a predetermined number of performance information is acquired, or a timing when a user instruction is received. Run the
  The information processing apparatus according to any one of claims 1 to 13. A factor information acquisition stage for acquiring each performance information of one or more types of factors contributing to a phenomenon to be predicted;
The phenomenon from a plurality of mathematical expression models that objective variable, based on the type of the factors involved in the factor information the record information which has been acquired at the acquisition step, determining a mathematical model used for the prediction of the behavior The model decision stage;
Have
Each of the plurality of mathematical models uses the phenomenon as an objective variable, and uses one or more explanatory variables corresponding to each of the one or more types of factors and a parameter corresponding to each of the one or more explanatory variables. A mathematical model representing the objective variable,
Information processing method.   A phenomenon information obtaining step of obtaining the performance information of the phenomenon;
  The model determination step includes:
  For each of the plurality of mathematical formula models, the predicted value of the phenomenon calculated by the mathematical formula model using at least a part of the performance information obtained in the factor information obtaining step, and obtained in the phenomenon information obtaining step. A model evaluation stage for evaluating at least one of prediction accuracy and calculation time of the mathematical model based on at least a part of the actual value of the phenomenon;
  A model selection step of selecting a mathematical model used for prediction of the phenomenon based on the evaluation in the model evaluation step;
  including,
  The information processing method according to claim 15.   The model evaluation stage includes
  Dividing the evaluation period into a plurality of periods and determining a weighting factor for each division;
  Evaluating the prediction accuracy of each mathematical model based on a predetermined formula using the weighting factor;
  including,
  The information processing method according to claim 16.   The model evaluation stage includes
  For each of the plurality of mathematical models, a data deficiency degree is calculated, which is a ratio of the number of pieces of performance information in which information about factors corresponding to the explanatory variables of the mathematical model is missing with respect to the number of pieces of performance information used for the evaluation. And the stage of
  For a mathematical model whose data deficiency is larger than a predetermined first value, it is evaluated that (i) the mathematical model cannot be used without calculating the prediction accuracy and calculation time of the model. Or (ii) assigning a lower evaluation to the mathematical model than the mathematical model in which the data deficiency is smaller than the first value;
  including,
  The information processing method according to claim 16 or claim 17.   A position information acquisition step of acquiring position information indicating a position where the phenomenon occurs;
  A sample storage step for storing sample information in which at least one record information of the one or more types of factors acquired in the factor information acquisition step is associated with the location information acquired in the location information acquisition step; ,
  If the sample information of the phenomenon does not include the actual information of the specific factor, the sample of the other phenomenon that occurred in the neighboring position based on the position information of the sample information of the phenomenon and the sample information of the other phenomenon Extracting the performance information of the specific factor included in the information, and based on the extracted performance information, a complement stage for complementing the performance information of the specific factor of the sample information of the phenomenon,
  Further comprising
  The model evaluation stage uses (i) at least a part of the performance information acquired in the factor information acquisition stage and at least a part of the performance information supplemented in the completion stage for each of the plurality of mathematical formula models. Based on the predicted value of the phenomenon calculated by the mathematical model and (ii) at least part of the actual value of the phenomenon acquired in the phenomenon information acquisition stage, the prediction accuracy and calculation time of the mathematical model Including evaluating at least one of the following:
  The information processing method according to claim 16 or claim 17.   A time information acquisition step of acquiring time information indicating the time or timing when the phenomenon occurs;
  A sample storage step for storing sample information in which at least one record information of the one or more types of factors acquired in the factor information acquisition step is associated with the time information acquired in the time information acquisition step; ,
  If the sample information of the phenomenon does not include the actual information of the specific factor, it occurred at the same or similar time or timing based on the time information of the sample information of the phenomenon and the sample information of another phenomenon Extracting the actual information of the specific factor included in the sample information of the other phenomenon, and based on the extracted actual information, a complement stage for complementing the actual information of the specific factor of the sample information of the phenomenon;
  Further comprising
  The model evaluation stage uses (i) at least a part of the performance information acquired in the factor information acquisition stage and at least a part of the performance information supplemented in the completion stage for each of the plurality of mathematical formula models. Based on the predicted value of the phenomenon calculated by the mathematical model and (ii) at least part of the actual value of the phenomenon acquired in the phenomenon information acquisition stage, the prediction accuracy and calculation time of the mathematical model Including evaluating at least one of the following:
  The information processing method according to claim 16 or claim 17. The model evaluation stage includes
  For each of the plurality of mathematical models, a data deficiency degree is calculated, which is a ratio of the number of pieces of performance information in which information about factors corresponding to the explanatory variables of the mathematical model is missing with respect to the number of pieces of performance information used for the evaluation. And the stage of
  For a mathematical model with a degree of data deficiency larger than a predetermined second value, (i) at least a part of the performance information acquired in the factor information acquisition stage and the results supplemented in the interpolation stage Based on the predicted value of the phenomenon calculated by the mathematical model using at least a part of the information, and (ii) at least a part of the actual value of the phenomenon acquired in the phenomenon information acquisition stage. Evaluating at least one of model prediction accuracy and computation time;
  For a mathematical model whose data deficiency is larger than a predetermined first value, it is evaluated that (i) the mathematical model cannot be used without calculating the prediction accuracy and calculation time of the model. Or (ii) giving the mathematical model a lower evaluation than the mathematical model in which the data deficiency is smaller than the first value;
  Including
The second value is smaller than the first value;
  The information processing method according to claim 19 or 20.   In the model determination step, the mathematical model determination process is performed at a timing when a predetermined period or time elapses, a timing when a predetermined number of performance information is acquired, or a timing when a user instruction is received. Including the step of performing
  The information processing method according to any one of claims 15 to 21. A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 14 .