WO2017145710A1 - Dispositif de traitement d'informations, procédé de traitement d'informations, et programme - Google Patents

Dispositif de traitement d'informations, procédé de traitement d'informations, et programme Download PDF

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WO2017145710A1
WO2017145710A1 PCT/JP2017/004041 JP2017004041W WO2017145710A1 WO 2017145710 A1 WO2017145710 A1 WO 2017145710A1 JP 2017004041 W JP2017004041 W JP 2017004041W WO 2017145710 A1 WO2017145710 A1 WO 2017145710A1
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variable
livestock
information
information processing
processing apparatus
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PCT/JP2017/004041
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English (en)
Japanese (ja)
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由幸 小林
正範 勝
文一 中村
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ソニー株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present disclosure relates to an information processing apparatus, an information processing method, and a program.
  • an acquisition unit that acquires a plurality of livestock related information related to livestock, a storage unit that stores the livestock related information, and a variable of interest and a factor of the variable of interest based on the stored livestock related information
  • an information processing apparatus including a causal analysis unit that analyzes a relationship with a cause variable.
  • An information processing method comprising: analyzing a relationship with a cause variable.
  • a means for acquiring a plurality of livestock related information relating to livestock, a means for storing the livestock related information, a cause that causes the variable of interest based on the stored livestock related information A program for causing a computer to function as a means for analyzing a relationship with a variable is provided.
  • FIG. 2 is a schematic diagram illustrating an information processing apparatus according to an embodiment of the present disclosure and a configuration around the information processing apparatus.
  • FIG. It is a schematic diagram which shows the sensor used for the kind of sensing part, the sensing information detected by various sensors, the detail of sensing information, and sensing. It is a schematic diagram which shows the example of the information which an input information acquisition part acquires. It is a schematic diagram which shows the data stored in the database. It is a schematic diagram which shows the analysis result by a causal analysis part.
  • FIG. 6 is a schematic diagram showing a directed graph when the user further specifies a variable “meat quality” from the state of FIG. 5.
  • FIG. 10 is a characteristic diagram showing a result of analyzing a relationship between a noticed variable “wholesale price” and a variable “temperature” that is causal of “wholesale price”. It is a schematic diagram which shows the result of the causal analysis part performing the prediction display of the influence with respect to various variables. It is a schematic diagram which shows the directed graph at the time of designating an attention variable to "weight”.
  • FIG. 5 is a schematic diagram showing screens of “predicted performance display”, “cause variable list display”, and “cause variable influence display”. It is a flowchart which shows the process of the causal analysis by a causal analysis part. It is a schematic diagram which shows the mode of the discretization by a causal analysis part. It is a schematic diagram which shows UI for inputting cost.
  • FIG. 10 is a characteristic diagram showing a result of analyzing a relationship between a noticed variable “wholesale price” and a variable “temperature” that is causal of “wholesale price”. It is a schematic diagram which shows the
  • FIG. 5 is a characteristic diagram showing a relationship between a variable “cost” of interest and a variable “employee stress” that causes “cost”. It is a schematic diagram which shows the trade-off graph display of an effect and cost. It is a schematic diagram which shows the example which displayed the card
  • FIG. 10 is a schematic diagram illustrating an example in which an experiment proposal is made for a variable X whose dependency relationship does not appear in the directed graph even though the correlation with the variable of interest is high. It is a flowchart which shows the process of FIG. It is a flowchart which shows the process which estimates the magnitude
  • Example of configuration of information processing apparatus In the field of raising livestock, there is a need to know the cause and effect, such as what can be seen to understand the physical condition of the livestock and what is an index of good quality meat.
  • data of various sensors attached to livestock and various sensors installed in the farmer's environment are accumulated, causal analysis is performed on the data, and the analysis result is presented to the farmer who is a user.
  • the information processing apparatus 1000 includes a sensing information acquisition unit 100, a feature extraction unit 200, a database 300, a causal analysis unit 400, an input information acquisition unit 500, an operation information acquisition unit 600, and a presentation information output unit 700. It is configured.
  • a central processing unit such as a CPU and a program (software) for causing it to function.
  • the sensing information acquisition unit 100 acquires a sensor signal when a sensor (sensing unit 2000) attached to an individual animal, an environment where the animal is placed, a farmer, or the like senses time-series data, and the sensor signal Is stored in the database 300.
  • the sensing unit 2000 includes various sensors.
  • FIG. 2 is a schematic diagram showing the type of the sensing unit 2000, sensing information detected by various sensors, details of the sensing information, and sensors used for sensing.
  • an individual wearing sensor to be attached to a livestock individual an environmental sensor for obtaining environmental information such as a facility for raising livestock, a farm animal breeder, a robot, etc. Includes farmer-mounted sensors.
  • data to be sensed includes data sensed from livestock, data sensed from an environment such as a barn, and data sensed from a person such as a grower.
  • the sensing unit 2000 obtains the sensing information, direct information acquisition, acquisition by wired communication, acquisition by wireless communication, and the like can be mentioned, but the method is not limited.
  • the input information acquisition unit 500 acquires information other than that obtained by sensing according to a user's operation input, and stores it in the database 300.
  • the input information acquisition unit 500 acquires operation input information input by an input unit such as a keyboard or a mouse, for example. Examples of the method by which the input information acquisition unit 500 acquires operation input information include direct information acquisition, wired communication acquisition, and wireless communication acquisition, but the method is not limited.
  • FIG. 3 is a schematic diagram illustrating an example of information acquired by the input information acquisition unit 500.
  • the input information acquisition unit 500 acquires information related to livestock individuals and information related to farmers as shown in FIG. 3 and stores them in the database 300.
  • the feature extraction unit 200 extracts the feature from the information and performs the causal analysis.
  • the information is processed into usable information and stored in the database 300.
  • the feature extraction unit 200 calculates an amount of exercise (consumed calories) based on an individual's movement history and action type, and calculates an average value and a magnitude of change of each sensing information for each month.
  • the causal analysis unit 400 analyzes causality between variables for the data stored in the database 300.
  • the causal analysis unit 400 performs causal analysis periodically or at a timing designated by the user.
  • the analysis result by the causal analysis unit 400 is displayed as a directed graph or the like.
  • the causal analysis unit 400 includes a factor extraction unit 402, a directed graph estimation unit 404, a prediction performance analysis unit 406, an influence degree estimation unit 408, and a Pareto optimal solution calculation unit 410. Processing of each component of the causal analysis unit 400 will be described later.
  • the operation information acquisition unit 600 acquires information for operating the analysis result display by the causal analysis unit 400.
  • the operation information acquisition unit 600 acquires information input from an input unit such as a keyboard or a mouse.
  • the method by which the operation information acquisition unit 600 acquires information includes direct information acquisition, acquisition by wired communication, acquisition by wireless communication, and the like, but the method is not limited.
  • the presentation information output unit 700 outputs information for presenting the analysis result to the display device 3000.
  • FIG. 4 is a schematic diagram showing data stored in the database 300.
  • a cow is illustrated as a livestock, and an example in which various variables associated with a cow individual ID are stored is shown.
  • Variables include variables related to cow breeding and variables related to breeding results.
  • variables related to breeding delivery workers, castration personnel, basic feed, feed composition, exercise amount, sleep time, number of chewing, breeding density, average air volume, average temperature, average humidity, average noise, average brightness, average number of pests.
  • Examples include average breeding time, gender, frequent robot contact, and food consumption.
  • the variables related to the growth result include body weight, carcass weight, carcass yield, loin core, back fat thickness, fat cross, meat color, meat fat color, meat quality, price, and the like.
  • the causal analysis unit 400 analyzes the causality between variables for the data shown in FIG. Specifically, the causal analysis unit 400 performs a causal analysis using, for example, a known max-min Hill-Climbing method, and analyzes the causality between variables.
  • FIG. 5 is a schematic diagram illustrating an example of the analysis result.
  • the presentation information output unit 700 outputs information for presenting the analysis result by the causal analysis unit 400 to the display device 3000, whereby the analysis result illustrated in FIG. 5 is displayed on the display device 3000.
  • FIG. 5 a portion where each variable is connected by a line indicates that there is a cause and effect between the variables.
  • the operation information acquisition unit 600 acquires information for operating the analysis result display by the causal analysis unit 400.
  • FIG. 6 is a schematic diagram (directed graph) showing a case where the user further specifies the variable “meat quality” from the state of FIG. 5.
  • the useful graph estimation unit 404 of the causal analysis unit 400 analyzes the attention variable “meat” and the attention variable “meat” as shown in FIG. 6 based on the analysis result of the designated variable “meat quality” and the causal variable.
  • the presentation information output unit 700 outputs information for presenting the analysis result by the causal analysis unit 400 to the display device 3000, whereby the analysis result illustrated in FIG. 6 is displayed on the display device 3000.
  • the “meat quality” of cattle is due to the large number of contact with the robot, the amount of exercise, the noise, and the amount of food. Therefore, changing these variables changes the meat quality. I understand that. Therefore, in order to improve the “meat quality”, the user can take measures such as changing variables such as exercise amount and meal amount. Therefore, the user can understand the cause that has not been noticed so far in order to improve the meat quality, and can take a new countermeasure as a result.
  • FIG. 21 is a flowchart illustrating processing in which the influence degree estimation unit 408 estimates the magnitude of the influence.
  • step S40 the user designates an attention variable.
  • step S42 the variables that cause the variable of interest are listed.
  • step S44 the variables of interest are sorted in order of influence.
  • step S46 a causal variable is presented.
  • the specified variable and the causal variable are analyzed and displayed as in FIG.
  • the factor (causal) that is an analysis result is presented. Therefore, the user can change the noted variable by changing a variable having a factor (causal).
  • the causal analysis unit 400 the user can optimally change various variables when raising the cow. Since it can be understood which factors have an influence on indicators such as physical condition and meat quality of livestock, if the cause is known, measures can be taken to improve the indicators.
  • FIG. 7 is a characteristic diagram showing the result of analyzing the relationship between the noticed variable “Wholesale Price” and the variable “Temperature” causal of “Wholesale Price”.
  • the presentation information output unit 700 outputs information for presenting the analysis result of FIG. 7 to the display device 3000, whereby the analysis result shown in FIG. 7 is displayed on the display device 3000.
  • the user can recognize that there is a possibility that the “wholesale price” can be increased by setting the temperature to around 20 degrees.
  • FIGS. 6 and 7 may be presented in text. For example, if you increase the number of contact with cattle by 10%, the chance of getting sick will be reduced by 5%, or you may want to reduce the noise.
  • a sentence from the viewpoint of (cow) may be presented. Documenting may be performed by a technique such as a recurrent neural network (RNN). Further, these sentences may be synthesized by speech and uttered.
  • RNN recurrent neural network
  • the presentation of causal information recognizes user behavior and presents it at an appropriate time. For example, at the timing when the user feeds the cow, the wholesale price may increase if the amount is smaller than usual. Alternatively, the information may be presented in a fortune-telling style, such as “Today's lucky food is hay, lucky temperature is 18 degrees”.
  • FIG. 8 is a schematic diagram showing a result of the causal analysis unit 400 performing prediction display of influences on various variables. As shown in FIG. 8, when an action is taken, predictions about how the action affects a plurality of variables can be presented in a list. In the example of FIG. 8, when the food is changed from the current food to food A, the meat quality changes by 5%, the disease probability changes by 3%, and the cost changes by 2%.
  • the farmer may be encouraged to take a sensing action to clarify the result. For example, information such as “There is a possibility that the influence of the color may be understood if the color of the barn is changed” is presented to the user. It is possible to provide a service that reflects user behavior in sensing, accumulates the sensed data, and performs further analysis and presents the data.
  • the information processing device 1000 is connected to a device that actually controls the variables (for example, an air conditioner that controls the room temperature in the barn or a device that mixes food), and the variable that causes the factor is based on the causal analysis result. It is also possible to perform automatic control so that changes directly.
  • a device that actually controls the variables for example, an air conditioner that controls the room temperature in the barn or a device that mixes food
  • the variable that causes the factor is based on the causal analysis result. It is also possible to perform automatic control so that changes directly.
  • the operation information acquisition unit 600 acquires information for operating the display of the analysis result, the display of the analysis result transitions, and a more detailed analysis result can be obtained.
  • the variable of interest is designated as “weight”.
  • “basic feed ID”, “feed formulation ID”, “sleeping time”, and “breeding density” are extracted as variables that cause “weight” by the causal analysis unit 400, and the directed graph shown in FIG. 9 is obtained. It is done.
  • the screen transitions from the state shown in FIG. 9 to each screen of “predicted performance display”, “cause variable list display”, and “cause variable influence display” shown in FIG. be able to.
  • the analysis result of each screen can be obtained by performing the influence analysis of the factor on the result by generalized linear regression.
  • the body weight predicted on the basis of four variables (basic feed ID, feed composition ID, sleep time, breeding density) and “cause weight” (on the horizontal axis of the predicted performance display). "Weight” shown) and the actual weight are shown, and in this example, the correlation degree R is 0.89. Therefore, it can be seen that the weight can be predicted with high accuracy. When the predicted body weight 'matches the actual body weight, the correlation degree R is 1.0. This prediction is performed by the prediction unit 402 of the causal analysis unit 400.
  • FIG. 11 is a flowchart showing a causal analysis process performed by the causal analysis unit 400.
  • step S ⁇ b> 10 a causal analysis target data set is input to the causal analysis unit 400.
  • the data set shown in FIG. 4 is input to the causal analysis unit 400.
  • the continuous value variable is discretized for the input data. Only the discretized variables (variables having specific values such as 0, 1, 2, 3) that can be handled by the Max-min Hill climbing method described above are continuous values (such as 0.0 to 1.0). The variable with a continuous value of () cannot be handled, so discretization is performed. In the present embodiment, the interval between the minimum value and the maximum value is evenly discretized to a predetermined number.
  • FIG. 12 is a schematic diagram showing the state of the discretization process. When the continuous value is discretized in 8 steps (0, 1, 2,..., 7), the minimum value and the maximum value are divided into eight.
  • the DAG (directed graph) is estimated by the Max-min Hill climbing method. Thereby, for each variable of the data set, an estimation result as to which other variable is a factor can be obtained, and a directed graph as shown in FIG. 6 can be obtained.
  • the influence analysis of each factor is performed by generalized linear regression. Thereby, the relationship between the variable and the variable that causes it is obtained, and “prediction performance display”, “list display of the cause variable”, and “effect display of the cause variable” as shown in FIG. Can get a relationship.
  • FIG. 13 and FIG. 14 are schematic diagrams showing an example in which the system can automatically estimate the cost fluctuation due to the fluctuation of the variable for the variable that can be controlled among the variables and the cost that is difficult to calculate directly.
  • FIG. 13 illustrates a UI for inputting a cost
  • information input via the UI is acquired by the operation information acquisition unit 600.
  • FIG. 14 is a characteristic diagram showing the relationship between the variable “cost” of interest and the variable “employee stress” that causes “cost”.
  • a causal analysis result that “cost” decreases as “employee stress” increases is shown.
  • FIG. 15 is a schematic diagram showing a trade-off graph display of effect and cost.
  • the measure plan is presented in a scatter diagram with the vertical axis representing the effect (cow price) and the horizontal axis representing the running cost.
  • the measure plan to be presented is selected and presented as Pareto optimal (better than other solutions in either effect or cost) in two variables of effect and cost.
  • the running cost and the price change when the variable “food” that causes the cost is changed. It can be seen that changing the food to food A for the current situation (NOW) increases the running cost but also the price of the cow. It can also be seen that changing the food to the food B for the current situation reduces the running cost but also the price of the cow.
  • the user can recognize the merits and demerits of changing the food based on the analysis result of FIG. In FIG. 15, the measure plan that is not Pareto optimal is not displayed or the display priority is lowered.
  • FIG. 16 is a schematic diagram showing an example in which the cards C shown in FIG. 15 are displayed as a list.
  • One card describes one measure and the effects and costs obtained thereby. Cards can be sorted and displayed based on cost and effectiveness.
  • FIG. 22 is a flowchart showing a process performed by the Pareto optimal solution calculation unit 410 of the causal analysis unit 400 to perform the presentation of FIG. First, in step S50, the cause variable-attention variable-cost relationship is listed while changing the cause variable.
  • next step S52 the Pareto optimal solutions of the attention variable-cost relationship are listed.
  • next step S54 a measure plan is presented by the trade-off graph shown in FIG. 15 or the display by the card C shown in FIG.
  • next step S56 the user selects and adopts one of the measure plans.
  • FIG. 17 is a schematic diagram showing an example of providing a function capable of comparing and examining how the variable of interest changes in such a case when a combination pattern of variables changed by the user is input.
  • variables shown in bold indicate numerical values input and changed by the user.
  • the underlined variable indicates a variable whose value has changed due to the changed variable.
  • the influence estimation unit 408 of the causal analysis unit 400 estimates changes in other variables based on the numerical values changed by the user shown in bold. The user can recognize a variable whose value has been changed by changing the desired variable as appropriate. It is also possible to create and save a plurality of plans as shown in FIG.
  • FIG. 18 is a flowchart showing a process for obtaining the causal analysis result of FIG.
  • step S20 the user inputs a cause variable after intervention.
  • the variables shown in bold in FIG. 17 are changed.
  • the causal analysis unit estimates the values of other variables that are affected by the changed cause variable. Thereby, the value of the variable underlined in FIG. 17 is estimated.
  • step S24 a list of variable values is displayed.
  • step S26 the changed content is saved as a plan.
  • plan C is stored in the list shown in FIG.
  • a plurality of plans stored in the past are presented. As a result, the plans A and B saved in the past are presented together with the plan C as shown in FIG.
  • FIG. 19 is a schematic diagram showing an example in which an experiment proposal is made for a variable X whose dependency relationship does not appear in the directed graph even though the correlation with the target variable is high.
  • variable B is set as the variable of interest Y, and a high correlation with the variables A, C, and D can be confirmed.
  • variable A is a variable X for which an experiment proposal is made
  • a path of X ⁇ Y exists in the directed graph. Accordingly, no further change proposal (experimental proposal) is made for the variable A in which a path to the target variable Y exists.
  • variable C is a variable X for which an experiment is proposed
  • Y ⁇ X path in the directed graph. Accordingly, no further change proposal (experimental proposal) is made for the variable C in which a path to the target variable Y exists.
  • variable D is the variable X for which an experiment is proposed
  • X the variable X for which an experiment is proposed
  • FIG. 20 is a flowchart showing the processing of FIG. First, in step S30, other variables X having a high correlation with the target variable Y are listed. In the next step S32, it is determined whether or not a path of X ⁇ Y or Y ⁇ X exists in the directed graph. If there is no path, the process proceeds to step S34. In step S34, the acquisition of data with the variable X condition changed is proposed. On the other hand, if a path of X ⁇ Y or Y ⁇ X exists in the directed graph, the process ends without proposing acquisition of data.
  • variable D will automatically be removed from the proposal.
  • the correlation decreases as a result of increasing the data, it becomes clear that there is no cause and effect between the variable D and the variable B. In this case, it becomes out of the proposal when enumerating highly correlated variables. If the correlation remains even if the data is increased, some causality exists between the variable D and the variable B. Therefore, the path of the variable D ⁇ the variable B or the variable B ⁇ the variable D is present in the directed graph at the time of the causal analysis. Will appear. In this case, it is excluded from the proposal when determining whether or not an X ⁇ Y or Y ⁇ X path exists.
  • a plurality of data relating to livestock is accumulated in the database 300, and based on the accumulated data, the relationship between the attention variable and the cause variable that causes the attention variable is analyzed. To be presented to the user. Thereby, the user can understand the cause for improving the attention variable, and can take a new countermeasure as a result.
  • An acquisition unit that acquires a plurality of livestock related information related to livestock, An accumulating unit for accumulating the livestock related information; Based on the accumulated livestock related information, a causal analysis unit that analyzes the relationship between the attention variable and the cause variable that causes the attention variable;
  • An information processing apparatus comprising: (2) The information processing apparatus according to (1), wherein the causal analysis unit includes a directed graph estimation unit that estimates a directed graph indicating a relationship between the attention variable and the cause variable.
  • the information processing apparatus (4) The information processing apparatus according to (3), wherein the influence degree estimation unit estimates a change in the attention variable or another cause variable when an arbitrary cause variable is changed. (5) The information processing apparatus according to any one of (1) to (4), wherein the causal analysis unit includes a prediction unit that predicts the attention variable from the cause variable. (6) The causal analysis unit includes a Pareto optimal solution calculation unit that calculates a Pareto optimal solution for a plurality of patterns in which the variable of interest changes when the cause variable is changed. ). (7) The information processing apparatus according to any one of (1) to (6), wherein the livestock related information is sensing information detected by a sensor attached to a livestock subject to livestock.
  • the information processing apparatus according to any one of (1) to (7), wherein the livestock related information is sensing information in which a sensor detects a living environment of livestock that is a subject of livestock.
  • the information processing apparatus according to any one of (1) to (7), wherein the livestock related information is information input by a livestock farmer.
  • the information processing apparatus according to any one of (1) to (9), further including an operation information acquisition unit that acquires operation information for designating an analysis result by the causal analysis unit.
  • the information processing apparatus according to any one of (1) to (10), further including a presentation information output unit that outputs presentation information for presenting an analysis result by the causal analysis unit.
  • the information processing apparatus according to any one of (1) to (11), further including a feature extraction unit that extracts a feature amount from the livestock-related information and stores the feature amount in the storage unit.
  • the information processing apparatus according to any one of (1) to (12), further prompting acquisition of the livestock related information based on a degree of association between the attention variable and an arbitrary cause variable.
  • An information processing method comprising: (15) Means for obtaining a plurality of livestock related information relating to livestock, Means for accumulating the livestock related information, Means for analyzing the relationship between the attention variable and the causal variable that causes the attention variable based on the accumulated stock raising-related information; As a program to make the computer function as.
  • DESCRIPTION OF SYMBOLS 1000 Information processing apparatus 100 Sensing information acquisition part 200 Feature extraction part 300 Database 400 Causal analysis part 402 Prediction part 404 Useful graph estimation part 406 Predictive performance analysis part 408 Influence degree estimation part 410 Pareto optimal solution calculation part 500 Input information acquisition part 600 Operation Information acquisition unit 700 Presentation information output unit

Abstract

Le problème abordé par l'invention consiste à améliorer des produits de bétail en analysant le facteur causal et en réglant de façon optimale le facteur causal afin d'obtenir des produits souhaités associés à l'élevage. Selon la solution décrite par la présente invention, un dispositif de traitement d'informations comprend : une unité d'acquisition qui acquiert une pluralité d'informations concernant l'élevage qui se rapportent à l'élevage ; une unité d'accumulation qui accumule les informations concernant l'élevage ; et une unité d'analyse de cause et d'effet qui analyse, en fonction des informations concernant l'élevage accumulées, la relation entre une variable d'attention et une variable de cause qui est une cause de la variable d'attention.
PCT/JP2017/004041 2016-02-23 2017-02-03 Dispositif de traitement d'informations, procédé de traitement d'informations, et programme WO2017145710A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019194849A (ja) * 2018-04-30 2019-11-07 富士通株式会社 機械学習システムのための因果関係

Citations (4)

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Publication number Priority date Publication date Assignee Title
JPH078128A (ja) * 1993-06-25 1995-01-13 Natl Fedelation Of Agricult Coop Assoc 家畜・家禽飼養管理システム
JPH11242366A (ja) * 1997-10-14 1999-09-07 Xerox Corp マシンの設定方法
JP2006195916A (ja) * 2005-01-17 2006-07-27 Hitachi Ltd 原単位作成方法，原単位作成装置，プロセス評価方法
WO2014097379A1 (fr) * 2012-12-17 2014-06-26 三菱電機株式会社 Dispositif de support d'analyse de programme et dispositif de commande

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH078128A (ja) * 1993-06-25 1995-01-13 Natl Fedelation Of Agricult Coop Assoc 家畜・家禽飼養管理システム
JPH11242366A (ja) * 1997-10-14 1999-09-07 Xerox Corp マシンの設定方法
JP2006195916A (ja) * 2005-01-17 2006-07-27 Hitachi Ltd 原単位作成方法，原単位作成装置，プロセス評価方法
WO2014097379A1 (fr) * 2012-12-17 2014-06-26 三菱電機株式会社 Dispositif de support d'analyse de programme et dispositif de commande

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019194849A (ja) * 2018-04-30 2019-11-07 富士通株式会社 機械学習システムのための因果関係
JP7275791B2 (ja) 2018-04-30 2023-05-18 富士通株式会社 機械学習システムのための因果関係

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