WO2018092274A1 - Control apparatus, control method, and control program - Google Patents

Control apparatus, control method, and control program Download PDF

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Publication number
WO2018092274A1
WO2018092274A1 PCT/JP2016/084296 JP2016084296W WO2018092274A1 WO 2018092274 A1 WO2018092274 A1 WO 2018092274A1 JP 2016084296 W JP2016084296 W JP 2016084296W WO 2018092274 A1 WO2018092274 A1 WO 2018092274A1
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Prior art keywords
observation
value
control
information
control device
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PCT/JP2016/084296
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French (fr)
Japanese (ja)
Inventor
眞男 相見
松木　譲介
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株式会社日立製作所
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Priority to JP2018550970A priority Critical patent/JP6778760B2/en
Priority to PCT/JP2016/084296 priority patent/WO2018092274A1/en
Publication of WO2018092274A1 publication Critical patent/WO2018092274A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Definitions

  • the present invention relates to a control device, a control method, and a control program for controlling a controlled object.
  • Patent Document 1 discloses a sensor interface having several sensor inputs and several client inputs. Some client inputs can receive data requests from some clients, and some data requests are returned without identifying the specific physical sensor to be used when capturing a particular type of data. It includes at least one data request specifying a particular type of data to be performed.
  • the sensor interface also includes a processor that i) determines which sensor data can be used to satisfy some data requirements, and ii) extracts sensor data from several physical sensors. Some of the sensor inputs are configured to receive, and iii) if possible, configured to meet some data requirements using the received sensor data.
  • Patent Document 1 discloses a virtual sensor that abstracts an arbitrary type and number of physical sensors, and a client input device that is an interface with the virtual sensor.
  • Patent Document 1 cannot be applied to a device that does not have a physical sensor, and cannot handle non-sensor data. Since the Internet of Things (IoT) business uses relatively inexpensive devices, not all devices are designed to handle data from specific physical sensors.
  • IoT Internet of Things
  • data from non-sensors in the cloud can be combined with data from surrounding physical sensors, or data from surrounding physical sensors can be analyzed to improve accuracy and output as virtual data You may want to Such data output cannot be realized by the technique of Patent Document 1.
  • An object of the present invention is to provide a control parameter to a control object without depending on a device that detects the control parameter.
  • a control device, a control method, and a control program according to an aspect of the invention disclosed in the present application are a control device, a control method, and a control program that give a control parameter for controlling a control target to the control target,
  • a processor that executes a program; a storage device that stores the program; and a correspondence device that associates an adjustment amount of the control parameter with a value of analysis information that is a specific factor of the adjustment amount; and Communication that communicates with one or more physical sensors that observe observation values of observation information that are specific factors from the observation target, and communicates with a server that stores the values of the analysis information and the observation information at the positions of the one or more physical sensors
  • the processor includes a position of the control device among the one or more physical sensors.
  • a specifying process for specifying the adjustment amount of the control parameter corresponding to the value of the value from the correspondence information, and the adjustment amount specified by the specifying process is compensated based on a calculation result of the calculation process. And, and executes a correction process for outputting the correction result to the control target.
  • control parameter control amount can be given to the control object without depending on the device that detects the control parameter.
  • FIG. 1 is an explanatory diagram illustrating an example of control of a control target by a control parameter using a virtual sensor.
  • FIG. 2 is a block diagram illustrating a hardware configuration example of the control device.
  • FIG. 3 is an explanatory diagram of an example of the contents stored in the address character string storage table.
  • FIG. 4 is an explanatory diagram showing an example of the stored contents of the weather information storage table.
  • FIG. 5 is an explanatory diagram showing an example of the stored contents of the adjusted luminance storage table.
  • FIG. 6 is a block diagram of a functional configuration example of the control device according to the first embodiment.
  • FIG. 7 is a flowchart of a control processing procedure example performed by the control device according to the first embodiment.
  • FIG. 8 is a graph showing a change with time of the first observation value.
  • FIG. 9 is an explanatory diagram showing an example of the stored contents of the observation value storage table.
  • FIG. 10 is a block diagram of a functional configuration example of the control device according to the first embodiment.
  • FIG. 11 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value.
  • FIG. 12 is an explanatory diagram of an example of acquiring the first observation value according to the third embodiment.
  • FIG. 13 is an explanatory diagram of an example of stored contents of the weighting table.
  • FIG. 14 is an explanatory diagram of an example of stored contents of the first observation value acquisition table.
  • FIG. 15 is a block diagram of a functional configuration example of the control device according to the first embodiment.
  • FIG. 16 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value.
  • FIG. 1 is an explanatory diagram illustrating an example of control of a control target by a control parameter using a virtual sensor.
  • the control target is the display device 113 connected to the inside or outside of the control apparatus 101
  • the control parameter is the luminance of the display device 113
  • the external physical sensor is the temperature sensor 102.
  • the control apparatus 101 gives the display device 113 control parameters for controlling the display device 113 that is an example of a control target.
  • the control device 101 adjusts the luminance of the display device 113 without mounting a luminance sensor that detects the luminance around the control device 101. Instead, the control device 101 has a virtual sensor 111.
  • the virtual sensor 111 is a virtual sensor that collects the temperature observed by the temperature sensor 102 in the vicinity of the control device 101 to obtain a statistical value of the temperature, and obtains the temperature and the weather at the position of the control device 101 from the weather forecast site 103. Sensor device.
  • the control apparatus 101 adjusts the brightness
  • the luminance of the display device 113 can be adjusted without mounting a luminance sensor.
  • the control system is a system in which a control device 101, a temperature sensor 102, which is an example of a physical sensor, and a weather forecast site 103, which is an example of a server, are connected to be communicable via a network 104.
  • An example of the network 104 is a sensor network.
  • a sensor network is a network that connects sensor nodes that implement physical sensors, and that uses the ad hoc function of a sensor node and utilizes a relay routing function for sending data from another sensor node to a relay node. .
  • the sensor network has a function of ensuring data arrival at the relay node by autonomously reconstructing another relay route when a failure occurs in the relay communication between the sensor nodes.
  • a wireless communication network using a wireless access point may be used instead of the sensor network.
  • the wireless communication network is a network that transmits and receives TCP / IP packets.
  • the control device 101 controls the control target using data obtained from the virtual sensor 111.
  • the control device 101 may be a fixed device or a moving body.
  • the temperature sensor 102 is included in a communication terminal connected to a network. Further, the temperature sensor 102 may be directly connected to the network 104.
  • the temperature sensor 102 may be a fixed device or a moving body.
  • the server periodically acquires and holds the temperature and weather at a plurality of points.
  • the server is, for example, the weather forecast site 103.
  • the control device 101 acquires the position and temperature of the temperature sensor 102 from the temperature sensor 102 in the vicinity of the control device 101 by the virtual sensor 111.
  • the neighboring temperature sensor 102 is a physical sensor that exists statically or dynamically within an allowable range including the position of the control device 101.
  • the allowable range may be, for example, a range that does not change the temperature and the weather.
  • a physical sensor connected in a communication area of a wireless base station for example, a wifi spot
  • the control device 101 registers an entry 121 including the acquired position and temperature in the internal table 120.
  • the position field value may be the position a of any neighboring physical sensor as long as it is included in the area A.
  • the value of the date / time field may be the control processing time b.
  • the value of the temperature field may be a statistical value of temperature from a plurality of neighboring physical sensors (for example, an average value, a median value, a maximum value, or a minimum value).
  • the control device 101 transmits a request to the weather forecast site 103 by the virtual sensor 111.
  • the request includes the control processing time (for example, the latest acquisition date and time) and the position (latitude and longitude) of the control device 101.
  • the weather forecast site 103 accepts the request, the weather forecast site 103 returns to the virtual sensor 111 the temperature and weather within the allowable range including the position of the control device 101 in the time zone including the control processing time as a response.
  • the virtual sensor 111 registers the entry 122 including the position, time, acquired weather, and acquired temperature of the control device 101 in the internal table 120.
  • the control device 101 uses the virtual sensor 111 to average the temperature of the entry 122 (26.0 degrees in the example) and the temperature of the entry 121 (in the example, 28.0 degrees, 28.0 degrees, and 28.6 degrees).
  • control device 101 uses the virtual sensor 111 to specify the brightness adjustment amount corresponding to the weather “cloudy” in the entry 122 and correct the specified adjustment amount according to the difference D.
  • the control device 101 outputs a correction amount, which is an adjustment amount after correction, to the driver 112 to be controlled by the virtual sensor 111.
  • the driver 112 is software that controls a control target.
  • the control device 101 adjusts the brightness of the control target according to the adjustment amount from the driver 112.
  • the control parameter adjustment amount is given to the control object without depending on the sensor device (luminance sensor) that detects the control parameter.
  • the configuration in which the control device 101 includes the driver 112 and the control target has been described, but the driver 112 and the control target may be provided outside the control device 101.
  • the control apparatus 101 has been described with respect to a configuration that does not include a sensor device (luminance sensor).
  • the control device 101 is configured to use the sensor device and the virtual sensor 111 together, and the virtual sensor 111 is used when the sensor device is not used. It is good also as a structure to use.
  • FIG. 2 is a block diagram illustrating a hardware configuration example of the control device 101.
  • the control apparatus 101 includes a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF 205).
  • the processor 201, the storage device 202, the input device 203, the output device 204, and the communication IF 205 are connected by a bus.
  • the processor 201 controls the control device 101.
  • the storage device 202 serves as a work area for the processor 201.
  • the storage device 202 is a non-temporary or temporary recording medium that stores various programs and data.
  • Examples of the storage device 202 include a ROM (Read Only Memory), a RAM (Random Access Memory), a HDD (Hard Disk Drive), and a flash memory.
  • the input device 203 inputs data. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a GPS receiver, and various sensors.
  • the output device 204 outputs data. Examples of the output device 204 include a display (a display device 113 to be controlled) and a printer.
  • the communication IF 205 is connected to the network 104 and transmits / receives data.
  • the data structure is described in a table format.
  • the data structure is not necessarily represented by a table, and may be represented by a data structure such as a list, a DB, a queue, or the like.
  • FIG. 3 is an explanatory diagram showing an example of the contents stored in the address character string storage table.
  • the address character string storage table 300 includes a website name 301 and an address character string 302 as fields.
  • the website name 301 is a field for storing a character string indicating the name of the website as a value.
  • the website name 301 is a primary key of the address character string storage table 300.
  • the address character string 302 is a field for storing a character string indicating a website address (for example, URL (Uniform Resource Locator)) as a value.
  • the address character string storage table 300 is stored, for example, in the storage device 202 shown in FIG.
  • FIG. 4 is an explanatory diagram showing an example of stored contents of a weather information storage table.
  • the weather information storage table 400 includes a position 401, a time 402, a weather 403, and a temperature 404 as fields. Each field corresponds to each field of the internal table 120 shown in FIG.
  • the position 401 is a field that stores, for example, the latitude and longitude of the observation point as values.
  • the time 402 is a field for storing the observation time as a value.
  • the interval between the values at time 402 is an observation interval. In this example, the observation interval is 1 hour. For example, when the value of the time 402 is “13:00”, the observation interval is 1 hour, so the time zone of the time 402 is “13:00 to 13:59”.
  • the weather 403 is a field that stores weather information indicating an atmospheric condition that affects the surface of the position 401 at time 402 as an analysis value of analysis information that is a specific factor of the control parameter adjustment amount, for example. For example, 15 types of weather information for general citizens determined by the Japan Meteorological Agency such as “clear” and “clear” are stored in the weather 403 as values.
  • the temperature 404 is a field that stores, for example, meteorological information indicating the temperature of the atmosphere at a position 401 at time 402 as an observation value of observation information observed by a physical sensor.
  • the weather information storage table 400 is stored in, for example, the storage device 202 shown in FIG.
  • FIG. 5 is an explanatory diagram showing an example of stored contents of the adjusted luminance storage table.
  • the adjustment luminance storage table 500 is correspondence information in which the adjustment amount of the control parameter is associated with the analysis value of the analysis information that is a specific factor of the adjustment amount.
  • the adjusted brightness storage table 500 includes weather 501 and adjusted brightness 502 as fields.
  • the weather 501 is a field for storing weather information as an analysis value of the analysis information.
  • the adjustment brightness 502 is a field that stores a brightness adjustment amount for the display device 113 that is an example of a control target as a value.
  • the adjusted luminance storage table 500 is stored in, for example, the storage device 202 shown in FIG.
  • FIG. 6 is a block diagram of a functional configuration example of the control device 101 according to the first embodiment.
  • the control device 101 includes an address character string storage table 300, a weather information storage table 400, an adjusted luminance storage table 500, a first acquisition unit 601, a second acquisition unit 602, a calculation unit 603, and a specification unit 604.
  • the first acquisition unit 601 to the control unit 606 are functions realized by causing the processor 201 to execute a program stored in the storage device illustrated in FIG.
  • the first acquisition unit 601 to the correction unit 605 are functions corresponding to the virtual sensor 111 illustrated in FIG. 1, and the control unit 606 is a function corresponding to the driver 112 illustrated in FIG.
  • the first acquisition unit 601 is a function realized by causing the processor 201 to execute the first acquisition process.
  • the first acquisition process includes observation information observed from a specific physical sensor located within an allowable range from the position of the control device 101 among one or more physical sensors at a control processing time when the specific physical sensor controls a control target. This is a process for acquiring the first observation value.
  • the physical sensor is, for example, the temperature sensor 102 outside the control device 101 shown in FIG.
  • the specific physical sensor is, for example, the temperature sensor 102 of the neighboring user shown in FIG.
  • the control processing time is, for example, the current time.
  • the control processing time may be a past time. In this case, the control processing time may be a preset time or a time given from the input device 203.
  • the first observation value of the observation information is an observation value from a specific physical sensor, for example, an observation value of the temperature sensor 102 of a neighboring user, and becomes a calculation source of the temperature of the entry 121 of the internal table 120 in FIG.
  • the processor 201 may calculate a statistical value for a plurality of observation values of the observation information observed by the plurality of specific physical sensors.
  • the statistical value may be, for example, a statistical value of air temperature (for example, an average value, a median value, a maximum value, or a minimum value) from a plurality of neighboring temperature sensors 102.
  • the second acquisition unit 602 is a function realized by causing the processor 201 to execute the second acquisition process.
  • the second acquisition process is a process of acquiring the second observation value of the observation information and the analysis value of the analysis information from the server at a position within the allowable range from the position of the control device 101 and including the control processing time. is there.
  • the server is, for example, the weather forecast site 103.
  • the period including the control processing time is a time zone including the control processing time. For example, when the observation interval is 1 hour and observation is performed at the timing of 0 minute, if the control processing time is 13:27, the period including the control processing time is a time zone of 13:00 to 13:59.
  • the second observation value of the observation information is an observation value from a specific physical sensor, for example, an observation value of the temperature sensor 102 of a neighboring user, and the entry 121 of the internal table 120 in FIG. It becomes the calculation source of temperature.
  • the first observation value and the second observation value are observation values that are different from each other by the neighboring temperature sensor 102 serving as an observation source.
  • the analysis information is the weather in the time zone including the control processing time stored in the weather forecast site 103, and the analysis value is a specifically specified value such as “sunny” or “cloudy”, for example.
  • the calculation unit 603 is a function realized by causing the processor 201 to execute calculation processing.
  • the calculation process calculates a difference D between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process. Specifically, for example, in the calculation process, the processor 201 subtracts the second observation value from the first observation value.
  • the difference D is positive, it indicates that the temperature is higher than the observation of the weather forecast site 103, and when the difference D is negative, it indicates that the temperature is lower than the observation of the weather forecast site 103.
  • the processor 201 may calculate the difference between the above-described statistical value and the second observation value. Specifically, for example, as shown in FIG. 1, when 28.0 degrees, 28.0 degrees, and 28.6 degrees are observed by the three temperature sensors 102 of the neighboring users, respectively, 28.2 degrees is calculated.
  • the specifying unit 604 is a function realized by causing the processor 201 to execute a specific process.
  • the specifying process is a process of specifying the adjustment amount of the control parameter corresponding to the analysis value acquired by the second acquiring process from the correspondence information.
  • the processor 201 specifies the adjusted brightness value corresponding to the weather value acquired by the second acquiring process from the adjusted brightness storage table 500 that is the correspondence information. For example, if the value of the weather 501 is “cloudy”, the value “ ⁇ %” of the adjustment brightness 502 is specified.
  • the correction unit 605 is a function realized by causing the processor 201 to execute specific processing.
  • the correction process is a process of correcting the adjustment amount specified by the specifying process based on the calculation result of the calculation process and outputting the correction result to the control target.
  • the processor 201 multiplies the identified adjustment amount by the difference D that is the calculation result.
  • a preset correction coefficient may be multiplied. This multiplication result becomes the correction result.
  • the processor 201 outputs the correction result to the control target via the control unit 606.
  • the control unit 606 is a function realized by causing the processor 201 to execute control processing.
  • the control process is a process for controlling the control target using the correction result.
  • the processor 201 controls the control target according to the correction result.
  • the processor 201 performs control so that the luminance is increased if the correction result is a positive value. If the correction result is negative, the luminance may be controlled to be low. Further, the processor 201 may perform control so as not to adjust the luminance if the correction result is negative. Further, the processor 201 may perform control so as not to adjust the brightness if the weather information is “sunny” or more such as “sunny” or “sunny” regardless of whether the correction result is positive or negative.
  • the processor 201 is not limited to the luminance adjustment of the display device 113, and may make other specific adjustments of other devices. For example, in the case of brightness adjustment such as camera exposure and lens F-number, the processor 201 may perform control so that the smaller the correction result value, the brighter the brightness.
  • FIG. 7 is a flowchart of an example of a control processing procedure performed by the control device 101 according to the first embodiment.
  • the control device 101 waits for a control instruction (step S701: No). Specifically, for example, when the user of the control apparatus 101 inputs a control instruction, or when a preset time is reached, for example, when an observation value is acquired from the temperature sensor 102, the control instruction Is issued.
  • the control instruction includes the control processing time described above. In the subsequent processing, the control processing time is set to “current time”, which is the timing at which the control instruction is received.
  • the control device 101 acquires the position of the control device 101 at the control processing time by the GPS receiver (step S702).
  • control device 101 acquires the first observation value from the temperature sensor 102 of the neighboring user (step S703). If a plurality of first observation values are acquired, the processor 201 calculates a statistical value of the first observation value. The control device 101 transmits a request including the acquisition position of step S702 to the weather forecast site 103, and as a result, acquires an analysis result from the weather forecast site 103 and stores it in the weather information storage table 400 (step S704).
  • the control apparatus 101 specifies an entry including the acquisition position and the control processing time in step S702 from the weather information storage table 400 (step S705). In this case, the latest entry is specified. Then, the processor 201 calculates a difference D between the latest first observation value acquired in step S703 and the second observation value in the entry specified in step S705 (step S706). In addition, the processor 201 specifies an adjusted luminance value corresponding to the weather value of the specified entry from the adjusted luminance storage table 500 (step S707).
  • the processor 201 corrects the specified adjustment brightness with the difference D, and outputs the correction result to the control unit 606 (step S708). Finally, the processor 201 performs brightness adjustment of the display device 113 with the correction result (step S709). As a result, the control process ends.
  • the control parameter control amount (luminance adjustment amount in this example) can be given to the control object without depending on the device that detects the control parameter. Further, when there are a plurality of first observation values, the accuracy of the actual observation value at the position of the control device 101 is increased by calculating the statistical value. Therefore, the control amount of the control parameter can be optimized.
  • Example 2 is an example in which the accuracy of the first observation value is improved in Example 1.
  • the observation period for acquiring the first observation value may vary depending on the vendor and price even for the same type of sensor. If the observation period is short, there is not much problem, but if the observation period is large, there is a large difference between the time when the first observation value is acquired and the current time, and a correct observation value at the current time cannot be acquired. Therefore, in the second embodiment, the first observation value is stored in time series, and the first observation value at the current time is calculated based on the correlation of the past first time observation values.
  • control device 101 generates a regression line from the first time-series observed values so far, and predicts the first observed value at the timing of the control instruction using the regression line.
  • the first observation value is highly accurate, and more appropriate control can be performed on the control target.
  • FIG. 8 is a graph showing a change with time of the first observed value.
  • a black circle is an actual measurement value of the first observation value.
  • symbol 801 is the newest measured value of a 1st observed value.
  • a white circle is a predicted value 802 of the first observed value predicted from the regression line 800 by correlation from the first observed value in time series.
  • the observation interval of the actually measured values is 50 [ms].
  • FIG. 9 is an explanatory diagram showing an example of the stored contents of the observation value storage table.
  • the observation value storage table 900 is a table for storing time-series first observation values for each temperature sensor 102 of the neighboring user.
  • the observation value storage table 900 has time and observation values as fields.
  • the time is a field for storing the observation time of the first observation value that arrives at each observation interval as a value.
  • the observed value is a field for storing the first observed value observed at the time as a value.
  • FIG. 10 is a block diagram of a functional configuration example of the control device 101 according to the second embodiment.
  • the difference from FIG. 6 is that the observation value storage table 900 is provided, and the first acquisition unit 601 refers to the observation value storage table 900.
  • the processor 201 performs time series based on the correlation of the first series of observed values observed by a specific physical sensor (for example, the temperature sensor 102 of the neighboring user) before the control processing time. The first observation value next to the latest first observation value among the first observation values is calculated.
  • the processor 201 determines, for each specific physical sensor, the first time-series observation value that the specific physical sensor observes before the control processing time. Based on this correlation, the statistical value of the next first observation value may be calculated.
  • FIG. 11 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value.
  • the process of FIG. 11 is a process corresponding to step S703 of FIG.
  • the predicted value of the first observed value is referred to as “interpolated observed value”.
  • the observation interval is called “sleep time”.
  • the control device 101 determines whether or not to end the first observation value acquisition process (step S1101). If not finished (step S1101: No), the control device 101 determines whether or not there is a request for acquiring an interpolation observation value (step S1102).
  • the acquisition request is, for example, a control instruction in step S701.
  • the control apparatus 101 determines whether the sleep time has elapsed (step S1103).
  • step S1103: NO If the sleep time has not elapsed (step S1103: NO), the process returns to step S1101.
  • step S1103: Yes the control device 101 acquires the first observation value from the temperature sensor 102 of the neighboring user and stores it in the observation value storage table 900 together with the current time (step S1104). Then, the control device 101 resets the sleep time and starts time counting again (step S1105), and returns to step S1101.
  • Step S1102 when there is an acquisition request (Step S1102: Yes), the control device 101 acquires the past first observation value in the observation value storage table 900 (Step S1106).
  • the control apparatus 101 calculates a correlation coefficient from the acquired past first observation value, and generates a regression line (step S1107).
  • the control device 101 specifies the interpolation observation value at the current time from the regression line 800 (step S1108), outputs the specified interpolation observation value to the calculation unit 603 (step S1109), and specifies the specified interpolation observation value and the current time.
  • the past interpolation observation values are updated according to the regression line 800 (step S1111), and the process returns to step S1101.
  • step S1101 when the acquisition process of the first observation value is terminated (step S1101: Yes), the process of this flowchart is terminated.
  • the control target can be appropriately adjusted by predicting the first observation value.
  • Example 3 is an example in which the accuracy of the first observation value is improved in Example 1 or Example 2. If there is a temperature sensor 102 with a long observation period among the temperature sensors 102 of neighboring users, a so-called outlier that is significantly different from the observation values of other temperature sensors 102 may be observed.
  • FIG. 12 is an explanatory diagram of an example of obtaining the first observation value according to the third embodiment.
  • the observation period of the temperature sensor 102 with sensor ID S1 is 30 [ms]
  • the observation period of the temperature sensor 102 with sensor ID S2 is 100 [ms]
  • the observation period of the temperature sensor 102 with sensor ID S3 is 10 [Ms].
  • the temperature sensor 102 with the sensor ID: S2 observes the first observation value that is an outlier.
  • the control apparatus 101 of Example 3 calculates
  • FIG. 13 is an explanatory diagram showing an example of the contents stored in the weighting table.
  • the weighting table 1300 has sensor IDs 1301 and weights 1302 as fields.
  • the sensor ID 1301 is a field that stores identification information (sensor ID) that uniquely identifies the temperature sensor 102 as a value.
  • the weight 1302 is a field for storing a weight value corresponding to the observation period of the temperature sensor 102 as a value.
  • the weight value decreases as the observation period increases.
  • the weight value w2 is smaller than other weight values. Thereby, the influence of the 1st observation value of the temperature sensor 102 with a long observation period can be reduced.
  • FIG. 14 is an explanatory diagram showing an example of the stored contents of the first observation value acquisition table.
  • the acquisition table 1400 includes a time 1401, an observed value 1402, and a sensor ID 1403 as fields.
  • the time 1401 is a field for storing the observation time of each temperature sensor 102 as a value.
  • the observed value 1402 is a field for storing the first observed value observed at time 1401 as a value.
  • the sensor ID 1403 is a field that stores identification information (sensor ID) that uniquely identifies the temperature sensor 102 as a value.
  • the first observation values are sequentially acquired from the acquisition table 1400 in units of the oldest four data.
  • the buffering including the first observation value of the sensor ID S2 is BF1 and BF5.
  • the processor 201 refers to the weighting table 1300 and calculates a statistical value.
  • BF1 V0A ⁇ w1 + V0B ⁇ w2 + V0C ⁇ w3 + V1C ⁇ w3
  • BF2 V2C ⁇ w3 + V3C ⁇ w3 + V3A ⁇ w1 + V4C ⁇ w3
  • BF3 V5C ⁇ w3 + V6C ⁇ w3 + V6A ⁇ w1 + V7C ⁇ w3
  • BF4 V8C ⁇ w3 + V9C ⁇ w3 + V9A ⁇ w1 + V10C ⁇ w3
  • BF5 V10B ⁇ w2 + V11C ⁇ w3 + V12C ⁇ w3 + V12A ⁇ w1
  • FIG. 15 is a block diagram of a functional configuration example of the control apparatus 101 according to the first embodiment. 10 differs from FIG. 10 in that it has a weighting table 1300, an observation value storage table 900, the first acquisition unit 601 refers to the observation value storage table 900, has an acquisition table 1400, and has a first acquisition. The unit 601 writes to and reads from the acquisition table 1400.
  • the processor 201 calculates a statistical value of the first observation value of each specific physical sensor based on the length of the observation interval of each specific physical sensor (for example, the temperature sensor 102 of the neighboring user). To do. Specifically, for example, the processor 201 calculates a weighted average value as a statistical value using a smaller weight value as the observation interval is longer.
  • the processor 201 when applied to the second embodiment, in the first acquisition process, performs a specific process based on the correlation of the first time-series observation values observed by a specific physical sensor before the control process time.
  • the next first observation value is calculated for each physical sensor, and the statistical value of the next first observation value calculated for each specific physical sensor is calculated based on the length of the observation interval of each specific physical sensor. .
  • the processor 201 determines from the past series of first observation values. A correlation coefficient is calculated, a regression line 800 is generated, and a predicted value (interpolated observed value) of the first observed value at time tc is obtained. In the first acquisition process, the processor 201 similarly refers to the weighting table 1300 to calculate the weighted average value even when the predicted value is calculated instead of the buffered observation value (actual measurement value). To do.
  • the actual measurement values are buffered as shown in FIG. 14 and used for the weighted average.
  • FIG. 16 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value.
  • the process of FIG. 16 is a process corresponding to step S703 of FIG.
  • the control device 101 sets the current time as the starting time (step S1601).
  • step S1603 No
  • the control apparatus 101 determines whether there is data in the sensor queue (step S1604).
  • the sensor queue is a memory that temporarily holds data from the temperature sensor 102 (observation value, observation time, sensor ID).
  • step S1604 If there is no data in the sensor queue (step S1604: No), the process returns to step S1603. If there is data in the sensor queue (step S1604: Yes), the control apparatus 101 acquires data held in the sensor queue (step S1605), and assigns a weight value corresponding to the sensor ID in the acquired data to the weighting table 1300. (Step S1606). The identified data is written into the acquisition table 1400. Then, the process returns to step S1603. In step S1604, when the time obtained by subtracting the starting time from the current time is larger than the predetermined acquisition interval (step S1603: Yes), the control device 101 resets the weighted average value (step S1607), and the observed value group from the buffering area. Is read out (step S1608).
  • the buffering area is an area where the oldest data group (four in FIG. 14) in the acquisition table 1400 is buffered.
  • step S1609 the control apparatus 101 calculates a weighted average value about the read 1st observation value (step S1609). And the control apparatus 101 acquires the time zone information of the observation value group which calculated the weighted average value (step S1610).
  • the time zone information is the time of the acquisition table 1400 regarding the observation value group. The time to be adopted is determined in advance.
  • the control device 101 outputs the weighted average value and the time zone information as observation value information to the calculation unit 603 (step S1611), and clears the buffering area from which the observation value group has been acquired (step S1612). Then, the process returns to step S1602.
  • step S1602 when the acquisition process of the first observation value is finished (step S1602: Yes), the process of this flowchart is finished.
  • control object can be controlled more appropriately.
  • step S1610 the processor 201 calculates an interpolated observation value and calculates a weighted average value for a sensor that does not have an observation value at the current time. . Thereby, even when the observation value at the current time does not exist, the control target can be appropriately adjusted by predicting the observation value.
  • the present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the scope of the appended claims.
  • the above-described embodiments have been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to those having all the configurations described.
  • a part of the configuration of one embodiment may be replaced with the configuration of another embodiment.
  • each of the above-described configurations, functions, processing units, processing means, and the like may be realized in hardware by designing a part or all of them with, for example, an integrated circuit, and the processor 201 performs each function. It may be realized by software by interpreting and executing the program to be realized.
  • Information such as programs, tables, and files for realizing each function is recorded on a memory, a hard disk, a storage device such as SSD (Solid State Drive), or an IC (Integrated Circuit) card, SD card, DVD (Digital Versatile Disc). It can be stored on a medium.
  • SSD Solid State Drive
  • IC Integrated Circuit
  • SD card Digital Card
  • DVD Digital Versatile Disc
  • control lines and information lines indicate what is considered necessary for the explanation, and do not necessarily indicate all control lines and information lines necessary for mounting. In practice, it can be considered that almost all the components are connected to each other.

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Abstract

This control apparatus has: a storage device that stores correspondence information associating an adjustment amount of a control parameter for controlling an object to be controlled with a value of analysis information which becomes a specifying factor for the adjustment amount; and a communication interface that communicates with a physical sensor for observing, from the object to be observed, an observation value of observation information which becomes a specifying factor for the analysis information, and that also communicates with a server which stores the values of the analysis information and the observation information at the position of the physical sensor. The control apparatus acquires a first observation value of the observation information observed at the time of a control process when a specific physical sensor controls the object to be controlled, from the specific physical sensor which is located within a permissible range from the position of the control apparatus, acquires, from the server, a value of the analysis information and a second observation value of the observation information at a position within the permissible range from the position of the control apparatus during a period including the control process time, calculates the difference between the first observation value and the second observation value, specifies, from the correspondence information, the adjustment value of the control parameter corresponding to the value of the analysis information, corrects the specified adjustment value on the basis of the calculation result, and outputs the correction result to the object to be controlled.

Description

制御装置、制御方法、および制御プログラムControl device, control method, and control program
　本発明は、制御対象を制御する制御装置、制御方法、および制御プログラムに関する。 The present invention relates to a control device, a control method, and a control program for controlling a controlled object.
　センサを用いた装置の開発において、センサ値を取得するためのセンサを入手できないという課題がある。例えば、天気を考慮した輝度センサを開発したいが、天気を計測するセンサは価格が高価であったりするなど、入手が難しい。 In the development of a device using a sensor, there is a problem that a sensor for acquiring a sensor value cannot be obtained. For example, we would like to develop a brightness sensor that takes the weather into account, but it is difficult to obtain a sensor that measures the weather because it is expensive.
　そこで、特許文献１は、いくつかのセンサ入力およびいくつかのクライアント入力を備えたセンサインタフェースを開示する。いくつかのクライアント入力は、いくつかのクライアントからのデータ要求を受信でき、いくつかのデータ要求は、特定の種類のデータを取り込むときに使用されるべき特定の物理センサを識別することなく、戻されるべき特定の種類のデータを指定する少なくとも１つのデータ要求を含む。また、センサインタフェースは、プロセッサを備え、このプロセッサは、ｉ）いくつかのデータ要求を満たすにはどのセンサデータを使用することができるかを決定し、ｉｉ）いくつかの物理センサからセンサデータを受信するようにセンサ入力の中のいくつかを環境設定し、そして、ｉｉｉ）可能であれば、受信されたセンサデータを用いていくつかのデータ要求を満たすように、環境設定されている。 Therefore, Patent Document 1 discloses a sensor interface having several sensor inputs and several client inputs. Some client inputs can receive data requests from some clients, and some data requests are returned without identifying the specific physical sensor to be used when capturing a particular type of data. It includes at least one data request specifying a particular type of data to be performed. The sensor interface also includes a processor that i) determines which sensor data can be used to satisfy some data requirements, and ii) extracts sensor data from several physical sensors. Some of the sensor inputs are configured to receive, and iii) if possible, configured to meet some data requirements using the received sensor data.
　このように、特許文献１は、任意の種類および個数の物理センサを抽象化する仮想センサと、その仮想センサとのインタフェースであるクライアント入力装置を開示する。 As described above, Patent Document 1 discloses a virtual sensor that abstracts an arbitrary type and number of physical sensors, and a client input device that is an interface with the virtual sensor.
国際公開ＷＯ２００７／１１８２４７号International Publication No. WO2007 / 118247
　しかしながら、特許文献１は、物理センサを有しない装置に対しては適用できず、また非センサデータを扱うことができない。Ｉｎｔｅｒｎｅｔ　ｏｆ　Ｔｈｉｎｇｓ（ＩｏＴ）事業では比較的安価な装置を用いるため、すべての装置が特定の物理センサからのデータを扱うようには設計されているとは限らない。 However, Patent Document 1 cannot be applied to a device that does not have a physical sensor, and cannot handle non-sensor data. Since the Internet of Things (IoT) business uses relatively inexpensive devices, not all devices are designed to handle data from specific physical sensors.
　たとえば、クラウド上に存在する非センサによるデータを周囲の物理センサからのデータと組み合わせたり、または、周囲の物理センサからのデータを解析し精度を向上させたりしたうえで、仮想的なデータとして出力したい場合がある。このようなデータ出力を、特許文献１の技術では実現することができない。 For example, data from non-sensors in the cloud can be combined with data from surrounding physical sensors, or data from surrounding physical sensors can be analyzed to improve accuracy and output as virtual data You may want to Such data output cannot be realized by the technique of Patent Document 1.
　本発明は、制御パラメータを検出するデバイスに依存することなく制御パラメータを制御対象に与えることを目的とする。 An object of the present invention is to provide a control parameter to a control object without depending on a device that detects the control parameter.
　本願において開示される発明の一側面となる制御装置、制御方法、および制御プログラムは、制御対象を制御するための制御パラメータを前記制御対象に与える制御装置、制御方法、および制御プログラムであって、プログラムを実行するプロセッサと、前記プログラムを記憶するとともに前記制御パラメータの調節量と前記調節量の特定要因となる分析情報の値とを対応付けた対応情報を記憶する記憶デバイスと、前記分析情報の特定要因となる観測情報の観測値を観測対象から観測する１以上の物理センサと通信するとともに前記１以上の物理センサの位置における前記分析情報および前記観測情報の値を記憶するサーバと通信する通信インタフェースと、を有し、前記プロセッサは、前記１以上の物理センサのうち前記制御装置の位置から許容範囲内に位置する特定の物理センサから、当該特定の物理センサが前記制御対象を制御する制御処理時刻に観測した前記観測情報の第１観測値を取得する第１取得処理と、前記制御装置の位置から前記許容範囲内の位置で、かつ、前記制御処理時刻を含む期間における前記観測情報の第２観測値および前記分析情報の値を、前記サーバから取得する第２取得処理と、前記第１取得処理によって取得された前記第１観測値と前記第２取得処理によって取得された前記第２観測値との差分を算出する算出処理と、前記第２取得処理によって取得された前記分析情報の値に対応する前記制御パラメータの調節量を前記対応情報から特定する特定処理と、前記特定処理によって特定された調節量を前記算出処理による算出結果に基づいて補正して、補正結果を前記制御対象に出力する補正処理と、を実行することを特徴とする。 A control device, a control method, and a control program according to an aspect of the invention disclosed in the present application are a control device, a control method, and a control program that give a control parameter for controlling a control target to the control target, A processor that executes a program; a storage device that stores the program; and a correspondence device that associates an adjustment amount of the control parameter with a value of analysis information that is a specific factor of the adjustment amount; and Communication that communicates with one or more physical sensors that observe observation values of observation information that are specific factors from the observation target, and communicates with a server that stores the values of the analysis information and the observation information at the positions of the one or more physical sensors An interface, and the processor includes a position of the control device among the one or more physical sensors. A first acquisition process for acquiring a first observation value of the observation information observed at a control process time at which the specific physical sensor controls the control object from a specific physical sensor located within an allowable range from the control unit; A second acquisition process for acquiring a second observation value of the observation information and a value of the analysis information in a period including the control processing time from a position of the apparatus within the allowable range; A calculation process for calculating a difference between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process, and the analysis information acquired by the second acquisition process A specifying process for specifying the adjustment amount of the control parameter corresponding to the value of the value from the correspondence information, and the adjustment amount specified by the specifying process is compensated based on a calculation result of the calculation process. And, and executes a correction process for outputting the correction result to the control target.
　本発明の代表的な実施の形態によれば、制御パラメータを検出するデバイスに依存することなく制御パラメータの制御量を制御対象に与えることができる。前述した以外の課題、構成及び効果は、以下の実施例の説明により明らかにされる。 According to the representative embodiment of the present invention, the control parameter control amount can be given to the control object without depending on the device that detects the control parameter. Problems, configurations, and effects other than those described above will become apparent from the description of the following embodiments.
図１は、仮想センサを用いた制御パラメータによる制御対象の制御例を示す説明図である。FIG. 1 is an explanatory diagram illustrating an example of control of a control target by a control parameter using a virtual sensor. 図２は、制御装置のハードウェア構成例を示すブロック図である。FIG. 2 is a block diagram illustrating a hardware configuration example of the control device. 図３は、アドレス文字列格納テーブルの記憶内容例を示す説明図である。FIG. 3 is an explanatory diagram of an example of the contents stored in the address character string storage table. 図４は、天候情報格納テーブルの記憶内容例を示す説明図である。FIG. 4 is an explanatory diagram showing an example of the stored contents of the weather information storage table. 図５は、調節輝度格納テーブルの記憶内容例を示す説明図である。FIG. 5 is an explanatory diagram showing an example of the stored contents of the adjusted luminance storage table. 図６は、実施例１にかかる制御装置の機能的構成例を示すブロック図である。FIG. 6 is a block diagram of a functional configuration example of the control device according to the first embodiment. 図７は、実施例１にかかる制御装置による制御処理手順例を示すフローチャートである。FIG. 7 is a flowchart of a control processing procedure example performed by the control device according to the first embodiment. 図８は、第１観測値の経時的変化を示すグラフである。FIG. 8 is a graph showing a change with time of the first observation value. 図９は、観測値格納テーブルの記憶内容例を示す説明図である。FIG. 9 is an explanatory diagram showing an example of the stored contents of the observation value storage table. 図１０は、実施例１にかかる制御装置の機能的構成例を示すブロック図である。FIG. 10 is a block diagram of a functional configuration example of the control device according to the first embodiment. 図１１は、第１観測値の取得処理手順例を示すフローチャートである。FIG. 11 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value. 図１２は、実施例３にかかる第１観測値の取得例を示す説明図である。FIG. 12 is an explanatory diagram of an example of acquiring the first observation value according to the third embodiment. 図１３は、重み付けテーブルの記憶内容例を示す説明図である。FIG. 13 is an explanatory diagram of an example of stored contents of the weighting table. 図１４は、第１観測値の取得テーブルの記憶内容例を示す説明図である。FIG. 14 is an explanatory diagram of an example of stored contents of the first observation value acquisition table. 図１５は、実施例１にかかる制御装置の機能的構成例を示すブロック図である。FIG. 15 is a block diagram of a functional configuration example of the control device according to the first embodiment. 図１６は、第１観測値の取得処理手順例を示すフローチャートである。FIG. 16 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value.
　＜仮想センサを用いた制御パラメータによる制御例＞
　図１は、仮想センサを用いた制御パラメータによる制御対象の制御例を示す説明図である。ここでは、例として、制御対象を制御装置１０１の内部または外部に接続された表示デバイス１１３とし、制御パラメータを表示デバイス１１３の輝度とし、外部の物理センサを温度センサ１０２とする。制御装置１０１は、制御対象の一例である表示デバイス１１３を制御するための制御パラメータを表示デバイス１１３に与える。制御装置１０１は、たとえば、制御装置１０１周囲の明るさである輝度を検出する輝度センサを実装せずに、表示デバイス１１３の輝度を調整する。そのかわり、制御装置１０１は、仮想センサ１１１を有する。仮想センサ１１１とは、制御装置１０１近隣の温度センサ１０２が観測した気温を収集して気温の統計値を得、制御装置１０１の位置における気温を天気予報サイト１０３から気温と天気を取得する仮想的なセンサデバイスである。 <Example of control using control parameters using a virtual sensor>
FIG. 1 is an explanatory diagram illustrating an example of control of a control target by a control parameter using a virtual sensor. Here, as an example, the control target is the display device 113 connected to the inside or outside of the control apparatus 101, the control parameter is the luminance of the display device 113, and the external physical sensor is the temperature sensor 102. The control apparatus 101 gives the display device 113 control parameters for controlling the display device 113 that is an example of a control target. For example, the control device 101 adjusts the luminance of the display device 113 without mounting a luminance sensor that detects the luminance around the control device 101. Instead, the control device 101 has a virtual sensor 111. The virtual sensor 111 is a virtual sensor that collects the temperature observed by the temperature sensor 102 in the vicinity of the control device 101 to obtain a statistical value of the temperature, and obtains the temperature and the weather at the position of the control device 101 from the weather forecast site 103. Sensor device.
　天気を考慮した輝度センサを開発したいが、天気を輝度センサで計測するのは難しい場合がある。このため、制御装置１０１は、物理的な輝度センサを実装せずに仮想センサ１１１からの情報を用いて、気温の変化に応じて表示デバイス１１３の輝度を調整する。これにより、輝度センサを実装しなくても表示デバイス１１３の輝度を調整することができる。 Develop a brightness sensor that takes the weather into account, but it may be difficult to measure the weather with the brightness sensor. For this reason, the control apparatus 101 adjusts the brightness | luminance of the display device 113 according to the change of temperature using the information from the virtual sensor 111, without mounting a physical brightness | luminance sensor. Thus, the luminance of the display device 113 can be adjusted without mounting a luminance sensor.
　制御システムは、制御装置１０１と、物理センサの一例である温度センサ１０２と、サーバの一例である天気予報サイト１０３と、がネットワーク１０４を介して通信可能に接続されたシステムである。ネットワーク１０４の一例としてセンサネットワークがある。センサネットワークとは、物理センサを実装するセンサノード同士を繋ぐネットワークであり、センサノードのアドホック機能を利用し、他のセンサノードから中継ノードへデータを送るための中継ルーティング機能を活用するネットワークである。センサネットワークは、センサノード間の中継通信に障害が出た場合、別の中継経路を自律的に再構築して中継ノードへのデータ到達を確保する機能がある。また、センサネットワークではなく、無線アクセスポイントを用いた無線通信ネットワークでもよい。無線通信ネットワークは、ＴＣＰ／ＩＰのパケットを送受信するネットワークである。 The control system is a system in which a control device 101, a temperature sensor 102, which is an example of a physical sensor, and a weather forecast site 103, which is an example of a server, are connected to be communicable via a network 104. An example of the network 104 is a sensor network. A sensor network is a network that connects sensor nodes that implement physical sensors, and that uses the ad hoc function of a sensor node and utilizes a relay routing function for sending data from another sensor node to a relay node. . The sensor network has a function of ensuring data arrival at the relay node by autonomously reconstructing another relay route when a failure occurs in the relay communication between the sensor nodes. In addition, a wireless communication network using a wireless access point may be used instead of the sensor network. The wireless communication network is a network that transmits and receives TCP / IP packets.
　制御装置１０１は、仮想センサ１１１から得られるデータを用いて、制御対象を制御する。制御装置１０１は、固定された装置でもよく移動体でもよい。温度センサ１０２は、ネットワークに接続された通信端末に含まれる。また、温度センサ１０２は、直接ネットワーク１０４に接続されてもよい。温度センサ１０２は、固定された装置でもよく移動体でもよい。サーバは、複数地点における気温および天気を定期的に取得して保持する。サーバは、たとえば、天気予報サイト１０３である。 The control device 101 controls the control target using data obtained from the virtual sensor 111. The control device 101 may be a fixed device or a moving body. The temperature sensor 102 is included in a communication terminal connected to a network. Further, the temperature sensor 102 may be directly connected to the network 104. The temperature sensor 102 may be a fixed device or a moving body. The server periodically acquires and holds the temperature and weather at a plurality of points. The server is, for example, the weather forecast site 103.
　（１）制御装置１０１は、仮想センサ１１１により、制御装置１０１の近隣の温度センサ１０２から、温度センサ１０２の位置および気温を取得する。近隣の温度センサ１０２とは、制御装置１０１の位置を含む許容範囲内に静的にまたは動的に存在する物理センサである。許容範囲は、たとえば、気温や天気が変わらない程度の範囲でよい。また、制御装置１０１または温度センサ１０２が移動体である場合、制御装置１０１が接続するネットワーク内の無線基地局（たとえば、ｗｉｆｉスポット）の通信圏内において接続された物理センサが、近隣の物理センサに該当する。制御装置１０１は、内部テーブル１２０に、取得した位置および気温を含むエントリ１２１を登録する。なお、近隣の温度センサ１０２が複数存在する場合、位置フィールドの値は、地域Ａに含まれれば、いずれかの近隣の物理センサの位置ａでよい。また、日付時刻フィールドの値は、制御処理時刻ｂでよい。また、気温フィールドの値は、複数の近隣の物理センサからの気温の統計値（たとえば、平均値、中央値、最大値、最小値）でよい。 (1) The control device 101 acquires the position and temperature of the temperature sensor 102 from the temperature sensor 102 in the vicinity of the control device 101 by the virtual sensor 111. The neighboring temperature sensor 102 is a physical sensor that exists statically or dynamically within an allowable range including the position of the control device 101. The allowable range may be, for example, a range that does not change the temperature and the weather. Further, when the control device 101 or the temperature sensor 102 is a moving body, a physical sensor connected in a communication area of a wireless base station (for example, a wifi spot) in a network to which the control device 101 is connected becomes a nearby physical sensor. Applicable. The control device 101 registers an entry 121 including the acquired position and temperature in the internal table 120. If there are a plurality of neighboring temperature sensors 102, the position field value may be the position a of any neighboring physical sensor as long as it is included in the area A. The value of the date / time field may be the control processing time b. The value of the temperature field may be a statistical value of temperature from a plurality of neighboring physical sensors (for example, an average value, a median value, a maximum value, or a minimum value).
　（２）また、制御装置１０１は、仮想センサ１１１により、天気予報サイト１０３にリクエストを送信する。リクエストには、制御処理時刻（たとえば、最新の取得日時）と制御装置１０１の位置（緯度および経度）とが含まれる。天気予報サイト１０３は、リクエストを受け付けると、その制御処理時刻を包含する時間帯で制御装置１０１の位置を含む許容範囲の気温および天気をレスポンスとして仮想センサ１１１に返す。制御装置１０１は、レスポンスを受信すると、仮想センサ１１１により、内部テーブル１２０に、制御装置１０１の位置、時刻、取得した天気、および取得した気温を含むエントリ１２２を登録する。 (2) Further, the control device 101 transmits a request to the weather forecast site 103 by the virtual sensor 111. The request includes the control processing time (for example, the latest acquisition date and time) and the position (latitude and longitude) of the control device 101. When the weather forecast site 103 accepts the request, the weather forecast site 103 returns to the virtual sensor 111 the temperature and weather within the allowable range including the position of the control device 101 in the time zone including the control processing time as a response. When the control device 101 receives the response, the virtual sensor 111 registers the entry 122 including the position, time, acquired weather, and acquired temperature of the control device 101 in the internal table 120.
　（３）制御装置１０１は、仮想センサ１１１により、エントリ１２２の気温（例では２６．０度）とエントリ１２１の気温（例では、２８．０度、２８．０度、２８．６度の平均値の２８．２度）との差分Ｄを求める。この場合の差分Ｄは、Ｄ＝＋２．２度となる。 (3) The control device 101 uses the virtual sensor 111 to average the temperature of the entry 122 (26.0 degrees in the example) and the temperature of the entry 121 (in the example, 28.0 degrees, 28.0 degrees, and 28.6 degrees). The difference D from the value (28.2 degrees) is obtained. In this case, the difference D is D = + 2.2 degrees.
　（４）そして、制御装置１０１は、仮想センサ１１１により、エントリ１２２の天気「曇り」に対応する輝度の調節量を特定し、特定した調節量を差分Ｄに応じて補正する。 (4) Then, the control device 101 uses the virtual sensor 111 to specify the brightness adjustment amount corresponding to the weather “cloudy” in the entry 122 and correct the specified adjustment amount according to the difference D.
　（５）制御装置１０１は、仮想センサ１１１により、制御対象のドライバ１１２に補正後の調節量である補正量を出力する。ドライバ１１２は、制御対象を制御するソフトウェアである。 (5) The control device 101 outputs a correction amount, which is an adjustment amount after correction, to the driver 112 to be controlled by the virtual sensor 111. The driver 112 is software that controls a control target.
　（６）制御装置１０１は、ドライバ１１２からの調節量により制御対象の輝度を調整する。これにより、制御パラメータを検出するセンサデバイス（輝度センサ）に依存することなく制御パラメータの調節量を制御対象に与える。なお、ここでは、制御装置１０１が、ドライバ１１２および制御対象を有する構成について説明したが、ドライバ１１２および制御対象は、制御装置１０１外に設けられてもよい。また、ここでは、制御装置１０１は、センサデバイス（輝度センサ）を有しない構成について説明したが、当該センサデバイスと仮想センサ１１１を併用する構成とし、当該センサデバイスが使用されない場合に仮想センサ１１１を用いる構成としてもよい。 (6) The control device 101 adjusts the brightness of the control target according to the adjustment amount from the driver 112. Thus, the control parameter adjustment amount is given to the control object without depending on the sensor device (luminance sensor) that detects the control parameter. Here, the configuration in which the control device 101 includes the driver 112 and the control target has been described, but the driver 112 and the control target may be provided outside the control device 101. In addition, here, the control apparatus 101 has been described with respect to a configuration that does not include a sensor device (luminance sensor). However, the control device 101 is configured to use the sensor device and the virtual sensor 111 together, and the virtual sensor 111 is used when the sensor device is not used. It is good also as a structure to use.
　＜ハードウェア構成例＞
　図２は、制御装置１０１のハードウェア構成例を示すブロック図である。制御装置１０１は、プロセッサ２０１と、記憶デバイス２０２と、入力デバイス２０３と、出力デバイス２０４と、通信インターフェース（通信ＩＦ２０５）と、を有する。プロセッサ２０１、記憶デバイス２０２、入力デバイス２０３、出力デバイス２０４、および通信ＩＦ２０５は、バスにより接続される。プロセッサ２０１は、制御装置１０１を制御する。記憶デバイス２０２は、プロセッサ２０１の作業エリアとなる。また、記憶デバイス２０２は、各種プログラムやデータを記憶する非一時的なまたは一時的な記録媒体である。記憶デバイス２０２としては、たとえば、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）、フラッシュメモリがある。入力デバイス２０３は、データを入力する。入力デバイス２０３としては、たとえば、キーボード、マウス、タッチパネル、テンキー、スキャナ、ＧＰＳ受信機、各種センサがある。出力デバイス２０４は、データを出力する。出力デバイス２０４としては、たとえば、ディスプレイ（制御対象の表示デバイス１１３）、プリンタがある。通信ＩＦ２０５は、ネットワーク１０４と接続し、データを送受信する。 <Hardware configuration example>
FIG. 2 is a block diagram illustrating a hardware configuration example of the control device 101. The control apparatus 101 includes a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF 205). The processor 201, the storage device 202, the input device 203, the output device 204, and the communication IF 205 are connected by a bus. The processor 201 controls the control device 101. The storage device 202 serves as a work area for the processor 201. The storage device 202 is a non-temporary or temporary recording medium that stores various programs and data. Examples of the storage device 202 include a ROM (Read Only Memory), a RAM (Random Access Memory), a HDD (Hard Disk Drive), and a flash memory. The input device 203 inputs data. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a GPS receiver, and various sensors. The output device 204 outputs data. Examples of the output device 204 include a display (a display device 113 to be controlled) and a printer. The communication IF 205 is connected to the network 104 and transmits / receives data.
　＜各種テーブルの説明＞
　つぎに、実施例１で用いる各種テーブルの記憶内容例について説明する。なお、実施例１ではテーブル形式でデータ構造を説明するが、必ずしもテーブルによるデータ構造で表現されていなくてもよく、リスト、ＤＢ、キュー等のデータ構造やそれ以外で表現されていてもよい。 <Description of various tables>
Next, examples of stored contents of various tables used in the first embodiment will be described. In the first embodiment, the data structure is described in a table format. However, the data structure is not necessarily represented by a table, and may be represented by a data structure such as a list, a DB, a queue, or the like.
　図３は、アドレス文字列格納テーブルの記憶内容例を示す説明図である。アドレス文字列格納テーブル３００は、フィールドとして、ウェブサイト名３０１と、アドレス文字列３０２と、を有する。ウェブサイト名３０１は、値として、ウェブサイトの名称を示す文字列を格納するフィールドである。ウェブサイト名３０１は、アドレス文字列格納テーブル３００の主キーとなる。アドレス文字列３０２は、値として、ウェブサイトのアドレスを示す文字列（たとえば、ＵＲＬ（Ｕｎｉｆｏｒｍ　Ｒｅｓｏｕｒｃｅ　Ｌｏｃａｔｏｒ））を格納するフィールドである。アドレス文字列格納テーブル３００は、たとえば、図２に示した記憶デバイス２０２に格納される。 FIG. 3 is an explanatory diagram showing an example of the contents stored in the address character string storage table. The address character string storage table 300 includes a website name 301 and an address character string 302 as fields. The website name 301 is a field for storing a character string indicating the name of the website as a value. The website name 301 is a primary key of the address character string storage table 300. The address character string 302 is a field for storing a character string indicating a website address (for example, URL (Uniform Resource Locator)) as a value. The address character string storage table 300 is stored, for example, in the storage device 202 shown in FIG.
　図４は、天候情報格納テーブルの記憶内容例を示す説明図である。天候情報格納テーブル４００は、フィールドとして、位置４０１と、時刻４０２と、天気４０３と、気温４０４と、を有する。各フィールドは、図１に示した内部テーブル１２０の各フィールドに対応する。位置４０１は、値として、たとえば、観測地点の緯度および経度を格納するフィールドである。時刻４０２は、値として、観測時刻を格納するフィールドである。時刻４０２の値の間隔は、観測間隔となる。本例では、観測間隔は１時間である。たとえば、時刻４０２の値が「１３：００」である場合、観測間隔は１時間であるため、当該時刻４０２の時間帯は「１３：００～１３：５９」となる。 FIG. 4 is an explanatory diagram showing an example of stored contents of a weather information storage table. The weather information storage table 400 includes a position 401, a time 402, a weather 403, and a temperature 404 as fields. Each field corresponds to each field of the internal table 120 shown in FIG. The position 401 is a field that stores, for example, the latitude and longitude of the observation point as values. The time 402 is a field for storing the observation time as a value. The interval between the values at time 402 is an observation interval. In this example, the observation interval is 1 hour. For example, when the value of the time 402 is “13:00”, the observation interval is 1 hour, so the time zone of the time 402 is “13:00 to 13:59”.
　天気４０３は、たとえば、制御パラメータの調節量の特定要因となる分析情報の分析値として、時刻４０２における位置４０１の地表に影響をもたらす大気の状態を示す気象情報を格納するフィールドである。たとえば、「快晴」や「晴れ」といった日本の気象庁により定められている一般市民向けの１５種類の気象情報が値として天気４０３に格納される。気温４０４は、たとえば、物理センサで観測される観測情報の観測値として、時刻４０２における位置４０１での大気の温度を示す気象情報を格納するフィールドである。天候情報格納テーブル４００は、たとえば、図２に示した記憶デバイス２０２に格納される。 The weather 403 is a field that stores weather information indicating an atmospheric condition that affects the surface of the position 401 at time 402 as an analysis value of analysis information that is a specific factor of the control parameter adjustment amount, for example. For example, 15 types of weather information for general citizens determined by the Japan Meteorological Agency such as “clear” and “clear” are stored in the weather 403 as values. The temperature 404 is a field that stores, for example, meteorological information indicating the temperature of the atmosphere at a position 401 at time 402 as an observation value of observation information observed by a physical sensor. The weather information storage table 400 is stored in, for example, the storage device 202 shown in FIG.
　図５は、調節輝度格納テーブルの記憶内容例を示す説明図である。調節輝度格納テーブル５００は、制御パラメータの調節量と調節量の特定要因となる分析情報の分析値とを対応付けた対応情報である。調節輝度格納テーブル５００は、フィールドとして、天気５０１と、調節輝度５０２と、を有する。天気５０１は、分析情報の分析値として、気象情報を格納するフィールドである。調節輝度５０２は、値として、制御対象の一例である表示デバイス１１３に対する輝度の調節量を格納するフィールドである。調節輝度格納テーブル５００は、たとえば、図２に示した記憶デバイス２０２に格納される。 FIG. 5 is an explanatory diagram showing an example of stored contents of the adjusted luminance storage table. The adjustment luminance storage table 500 is correspondence information in which the adjustment amount of the control parameter is associated with the analysis value of the analysis information that is a specific factor of the adjustment amount. The adjusted brightness storage table 500 includes weather 501 and adjusted brightness 502 as fields. The weather 501 is a field for storing weather information as an analysis value of the analysis information. The adjustment brightness 502 is a field that stores a brightness adjustment amount for the display device 113 that is an example of a control target as a value. The adjusted luminance storage table 500 is stored in, for example, the storage device 202 shown in FIG.
　＜制御装置１０１の機能的構成例＞
　図６は、実施例１にかかる制御装置１０１の機能的構成例を示すブロック図である。制御装置１０１は、アドレス文字列格納テーブル３００と、天候情報格納テーブル４００と、調節輝度格納テーブル５００と、第１取得部６０１と、第２取得部６０２と、算出部６０３と、特定部６０４と、補正部６０５と、制御部６０６と、を有する。第１取得部６０１～制御部６０６は、具体的には、たとえば、図２に示した記憶デバイスに記憶されたプログラムをプロセッサ２０１に実行させることで実現される機能である。第１取得部６０１～補正部６０５は、図１に示した仮想センサ１１１に対応する機能であり、制御部６０６は、図１に示したドライバ１１２に対応する機能である。 <Example of Functional Configuration of Control Device 101>
FIG. 6 is a block diagram of a functional configuration example of the control device 101 according to the first embodiment. The control device 101 includes an address character string storage table 300, a weather information storage table 400, an adjusted luminance storage table 500, a first acquisition unit 601, a second acquisition unit 602, a calculation unit 603, and a specification unit 604. A correction unit 605 and a control unit 606. Specifically, the first acquisition unit 601 to the control unit 606 are functions realized by causing the processor 201 to execute a program stored in the storage device illustrated in FIG. The first acquisition unit 601 to the correction unit 605 are functions corresponding to the virtual sensor 111 illustrated in FIG. 1, and the control unit 606 is a function corresponding to the driver 112 illustrated in FIG.
　第１取得部６０１は、プロセッサ２０１に第１取得処理を実行させることで実現する機能である。第１取得処理は、１以上の物理センサのうち制御装置１０１の位置から許容範囲内に位置する特定の物理センサから、当該特定の物理センサが制御対象を制御する制御処理時刻に観測した観測情報の第１観測値を取得する処理である。 The first acquisition unit 601 is a function realized by causing the processor 201 to execute the first acquisition process. The first acquisition process includes observation information observed from a specific physical sensor located within an allowable range from the position of the control device 101 among one or more physical sensors at a control processing time when the specific physical sensor controls a control target. This is a process for acquiring the first observation value.
　ここで、物理センサは、たとえば、図１に示した制御装置１０１外の温度センサ１０２である。特定の物理センサは、たとえば、図１に示した近隣ユーザの温度センサ１０２である。制御処理時刻は、たとえば、現在時刻である。制御処理時刻は、過去の時刻でもよい。この場合、制御処理時刻は、あらかじめ設定された時刻でもよく、入力デバイス２０３から与えられる時刻でもよい。観測情報の第１観測値とは、特定の物理センサからの観測値、たとえば、近隣ユーザの温度センサ１０２の観測値であり、図１の内部テーブル１２０のエントリ１２１の気温の算出元となる。 Here, the physical sensor is, for example, the temperature sensor 102 outside the control device 101 shown in FIG. The specific physical sensor is, for example, the temperature sensor 102 of the neighboring user shown in FIG. The control processing time is, for example, the current time. The control processing time may be a past time. In this case, the control processing time may be a preset time or a time given from the input device 203. The first observation value of the observation information is an observation value from a specific physical sensor, for example, an observation value of the temperature sensor 102 of a neighboring user, and becomes a calculation source of the temperature of the entry 121 of the internal table 120 in FIG.
　第１取得処理では、プロセッサ２０１は、特定の物理センサが複数存在する場合、複数の特定の物理センサによって観測された観測情報の複数の観測値について統計値を算出してもよい。統計値は、たとえば、複数の近隣の温度センサ１０２からの気温の統計値（たとえば、平均値、中央値、最大値、最小値）でよい。 In the first acquisition process, when there are a plurality of specific physical sensors, the processor 201 may calculate a statistical value for a plurality of observation values of the observation information observed by the plurality of specific physical sensors. The statistical value may be, for example, a statistical value of air temperature (for example, an average value, a median value, a maximum value, or a minimum value) from a plurality of neighboring temperature sensors 102.
　第２取得部６０２は、プロセッサ２０１に第２取得処理を実行させることで実現する機能である。第２取得処理は、制御装置１０１の位置からその許容範囲内の位置で、かつ、制御処理時刻を含む期間における観測情報の第２観測値および分析情報の分析値を、サーバから取得する処理である。 The second acquisition unit 602 is a function realized by causing the processor 201 to execute the second acquisition process. The second acquisition process is a process of acquiring the second observation value of the observation information and the analysis value of the analysis information from the server at a position within the allowable range from the position of the control device 101 and including the control processing time. is there.
　サーバとは、たとえば、天気予報サイト１０３である。制御処理時刻を含む期間とは、制御処理時刻を含む時間帯である。たとえば、観測間隔が１時間であり、それぞれ０分のタイミングで観測する場合、制御処理時刻が１３：２７だとすると、制御処理時刻を含む期間は、１３：００～１３：５９という時間帯となる。観測情報の第２観測値とは、第１観測値と同様、特定の物理センサからの観測値、たとえば、近隣ユーザの温度センサ１０２の観測値であり、図１の内部テーブル１２０のエントリ１２１の気温の算出元となる。第１観測値と第２観測値は、観測元となる近隣の温度センサ１０２が異なる観測値である。分析情報は、天気予報サイト１０３に格納されている制御処理時刻を含む時間帯での天気であり、分析値は、たとえば、「晴れ」や「曇り」といった具体的に特定された値である。 The server is, for example, the weather forecast site 103. The period including the control processing time is a time zone including the control processing time. For example, when the observation interval is 1 hour and observation is performed at the timing of 0 minute, if the control processing time is 13:27, the period including the control processing time is a time zone of 13:00 to 13:59. Similar to the first observation value, the second observation value of the observation information is an observation value from a specific physical sensor, for example, an observation value of the temperature sensor 102 of a neighboring user, and the entry 121 of the internal table 120 in FIG. It becomes the calculation source of temperature. The first observation value and the second observation value are observation values that are different from each other by the neighboring temperature sensor 102 serving as an observation source. The analysis information is the weather in the time zone including the control processing time stored in the weather forecast site 103, and the analysis value is a specifically specified value such as “sunny” or “cloudy”, for example.
　算出部６０３は、プロセッサ２０１に算出処理を実行させることで実現する機能である。算出処理は、第１取得処理によって取得された第１観測値と第２取得処理によって取得された第２観測値との差分Ｄを算出する。具体的には、たとえば、算出処理では、プロセッサ２０１は、第１観測値から第２観測値を減算する。差分Ｄが正である場合、天気予報サイト１０３の観測よりも気温が高いことを示し、差分Ｄが負である場合、天気予報サイト１０３の観測よりも気温が低いことを示す。 The calculation unit 603 is a function realized by causing the processor 201 to execute calculation processing. The calculation process calculates a difference D between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process. Specifically, for example, in the calculation process, the processor 201 subtracts the second observation value from the first observation value. When the difference D is positive, it indicates that the temperature is higher than the observation of the weather forecast site 103, and when the difference D is negative, it indicates that the temperature is lower than the observation of the weather forecast site 103.
　また、特定の物理センサ、すなわち、近隣ユーザの温度センサ１０２が複数存在する場合、算出処理では、プロセッサ２０１は、上述した統計値と第２観測値との差分を算出してもよい。具体的には、たとえば、図１に示したように、近隣ユーザの３台の温度センサ１０２でそれぞれ、２８．０度，２８．０度，２８．６度が観測された場合、平均値として２８．２度が算出される。 Also, when there are a plurality of specific physical sensors, that is, the temperature sensors 102 of neighboring users, in the calculation process, the processor 201 may calculate the difference between the above-described statistical value and the second observation value. Specifically, for example, as shown in FIG. 1, when 28.0 degrees, 28.0 degrees, and 28.6 degrees are observed by the three temperature sensors 102 of the neighboring users, respectively, 28.2 degrees is calculated.
　特定部６０４は、プロセッサ２０１に特定処理を実行させることで実現する機能である。特定処理は、第２取得処理によって取得された分析値に対応する制御パラメータの調節量を対応情報から特定する処理である。具体的には、たとえば、特定処理では、プロセッサ２０１は、第２取得処理によって取得された天気の値に対応する調節輝度の値を、対応情報である調節輝度格納テーブル５００から特定する。たとえば、天気５０１の値が「曇り」であれば、調節輝度５０２の値「δ％」が特定される。 The specifying unit 604 is a function realized by causing the processor 201 to execute a specific process. The specifying process is a process of specifying the adjustment amount of the control parameter corresponding to the analysis value acquired by the second acquiring process from the correspondence information. Specifically, for example, in the specifying process, the processor 201 specifies the adjusted brightness value corresponding to the weather value acquired by the second acquiring process from the adjusted brightness storage table 500 that is the correspondence information. For example, if the value of the weather 501 is “cloudy”, the value “δ%” of the adjustment brightness 502 is specified.
　補正部６０５は、プロセッサ２０１に特定処理を実行させることで実現する機能である。補正処理は、特定処理によって特定された調節量を算出処理による算出結果に基づいて補正して、補正結果を制御対象に出力する処理である。補正処理では、プロセッサ２０１は、たとえば、特定された調節量と算出結果である差分Ｄとを乗算する。乗算の際、あらかじめ設定された補正係数を乗じてもよい。この乗算結果が補正結果となる。プロセッサ２０１は、補正結果を制御部６０６を介して制御対象に出力する。 The correction unit 605 is a function realized by causing the processor 201 to execute specific processing. The correction process is a process of correcting the adjustment amount specified by the specifying process based on the calculation result of the calculation process and outputting the correction result to the control target. In the correction process, for example, the processor 201 multiplies the identified adjustment amount by the difference D that is the calculation result. At the time of multiplication, a preset correction coefficient may be multiplied. This multiplication result becomes the correction result. The processor 201 outputs the correction result to the control target via the control unit 606.
　制御部６０６は、プロセッサ２０１に制御処理を実行させることで実現する機能である。制御処理は、補正結果を用いて制御対象を制御する処理である。制御処理では、プロセッサ２０１は、補正結果に応じて制御対象を制御する。輝度調整の場合は、プロセッサ２０１は、補正結果が正の値であれば、輝度が高くなるよう制御する。補正結果が負であれば、輝度が低くなるよう制御してもよい。また、プロセッサ２０１は、補正結果が負であれば、輝度調整をしないように制御してもよい。また、プロセッサ２０１は、補正結果の正負にかかわらず、天気が「晴れ」や「快晴」のように「晴れ」以上の気象情報であれば、輝度調整をしないように制御してもよい。 The control unit 606 is a function realized by causing the processor 201 to execute control processing. The control process is a process for controlling the control target using the correction result. In the control process, the processor 201 controls the control target according to the correction result. In the case of luminance adjustment, the processor 201 performs control so that the luminance is increased if the correction result is a positive value. If the correction result is negative, the luminance may be controlled to be low. Further, the processor 201 may perform control so as not to adjust the luminance if the correction result is negative. Further, the processor 201 may perform control so as not to adjust the brightness if the weather information is “sunny” or more such as “sunny” or “sunny” regardless of whether the correction result is positive or negative.
　また、プロセッサ２０１は、表示デバイス１１３の輝度調整に限らず、他のデバイスの他の特定の調整をしてもよい。たとえば、カメラの露出やレンズのＦ値といった明るさ調整の場合、プロセッサ２０１は、補正結果の値が小さいほど明るくなるように制御してもよい。 Further, the processor 201 is not limited to the luminance adjustment of the display device 113, and may make other specific adjustments of other devices. For example, in the case of brightness adjustment such as camera exposure and lens F-number, the processor 201 may perform control so that the smaller the correction result value, the brighter the brightness.
　＜制御処理手順例＞
　図７は、実施例１にかかる制御装置１０１による制御処理手順例を示すフローチャートである。制御装置１０１は、制御指示を待ち受ける（ステップＳ７０１：Ｎｏ）。具体的には、たとえば、制御装置１０１のユーザが制御指示を入力した場合、または、例えば、温度センサ１０２からの観測値の取得タイミングのように、あらかじめ設定された時刻に到達した場合に制御指示が発行される。制御指示には、上述した制御処理時刻が含まれる。なお、以降の処理では、制御処理時刻を、制御指示を受け付けたタイミングである「現在時刻」とする。制御装置１０１は、制御処理時刻における制御装置１０１の位置をＧＰＳ受信機により取得する（ステップＳ７０２）。 <Example of control processing procedure>
FIG. 7 is a flowchart of an example of a control processing procedure performed by the control device 101 according to the first embodiment. The control device 101 waits for a control instruction (step S701: No). Specifically, for example, when the user of the control apparatus 101 inputs a control instruction, or when a preset time is reached, for example, when an observation value is acquired from the temperature sensor 102, the control instruction Is issued. The control instruction includes the control processing time described above. In the subsequent processing, the control processing time is set to “current time”, which is the timing at which the control instruction is received. The control device 101 acquires the position of the control device 101 at the control processing time by the GPS receiver (step S702).
　また、制御装置１０１は、近隣ユーザの温度センサ１０２から第１観測値を取得する（ステップＳ７０３）。第１観測値が複数取得された場合、プロセッサ２０１は第１観測値の統計値を算出する。制御装置１０１は、ステップＳ７０２の取得位置を含むリクエストを天気予報サイト１０３に送信し、その結果、天気予報サイト１０３から解析結果を取得して、天候情報格納テーブル４００に格納する（ステップＳ７０４）。 Further, the control device 101 acquires the first observation value from the temperature sensor 102 of the neighboring user (step S703). If a plurality of first observation values are acquired, the processor 201 calculates a statistical value of the first observation value. The control device 101 transmits a request including the acquisition position of step S702 to the weather forecast site 103, and as a result, acquires an analysis result from the weather forecast site 103 and stores it in the weather information storage table 400 (step S704).
　制御装置１０１は、ステップＳ７０２での取得位置および制御処理時刻を含むエントリを天候情報格納テーブル４００から特定する（ステップＳ７０５）。この場合は、最新のエントリが特定されることになる。そして、プロセッサ２０１は、ステップＳ７０３で取得された最新の第１観測値と、ステップＳ７０５で特定されたエントリ内の第２観測値との差分Ｄを算出する（ステップＳ７０６）。また、プロセッサ２０１は、特定したエントリの天気の値に対応する調節輝度の値を調節輝度格納テーブル５００から特定する（ステップＳ７０７）。 The control apparatus 101 specifies an entry including the acquisition position and the control processing time in step S702 from the weather information storage table 400 (step S705). In this case, the latest entry is specified. Then, the processor 201 calculates a difference D between the latest first observation value acquired in step S703 and the second observation value in the entry specified in step S705 (step S706). In addition, the processor 201 specifies an adjusted luminance value corresponding to the weather value of the specified entry from the adjusted luminance storage table 500 (step S707).
　そして、プロセッサ２０１は、特定した調節輝度を差分Ｄで補正して、補正結果を制御部６０６に出力する（ステップＳ７０８）。最後に、プロセッサ２０１は、補正結果で表示デバイス１１３の輝度調整を実行する（ステップＳ７０９）。これにより、制御処理を終了する。 Then, the processor 201 corrects the specified adjustment brightness with the difference D, and outputs the correction result to the control unit 606 (step S708). Finally, the processor 201 performs brightness adjustment of the display device 113 with the correction result (step S709). As a result, the control process ends.
　このように、実施例１によれば、制御パラメータを検出するデバイスに依存することなく制御パラメータの制御量（本例でいう輝度の調節量）を制御対象に与えることができる。また、第１観測値が複数存在する場合は、その統計値を算出することで、制御装置１０１の位置における実際の観測値の精度が高くなる。したがって、制御パラメータの制御量の適正化を図ることができる。 As described above, according to the first embodiment, the control parameter control amount (luminance adjustment amount in this example) can be given to the control object without depending on the device that detects the control parameter. Further, when there are a plurality of first observation values, the accuracy of the actual observation value at the position of the control device 101 is increased by calculating the statistical value. Therefore, the control amount of the control parameter can be optimized.
　実施例２は、実施例１において、第１観測値の精度を向上させる例である。第１観測値を取得する観測周期は、同じ種類のセンサであってもベンダや価格によって異なることがある。当該観測周期が短い場合はあまり問題にならないが、当該観測周期が大きい場合は、第１観測値を取得した時刻と現在時刻に大きなズレがあり、現在時刻における正しい観測値が取得できない。このため、実施例２では、第１観測値を時系列で保存しておき、過去の時系列な第１観測値の相関により、現在時刻の第１観測値を算出する。すなわち、実施例２にかかる制御装置１０１は、これまでの時系列な第１観測値から相関により回帰直線を生成し、回帰直線を用いて制御指示のタイミングでの第１観測値を予測する。これにより、第１観測値の高精度化が図られ、制御対象に対してより適切な制御をおこなうことができる。 Example 2 is an example in which the accuracy of the first observation value is improved in Example 1. The observation period for acquiring the first observation value may vary depending on the vendor and price even for the same type of sensor. If the observation period is short, there is not much problem, but if the observation period is large, there is a large difference between the time when the first observation value is acquired and the current time, and a correct observation value at the current time cannot be acquired. Therefore, in the second embodiment, the first observation value is stored in time series, and the first observation value at the current time is calculated based on the correlation of the past first time observation values. In other words, the control device 101 according to the second embodiment generates a regression line from the first time-series observed values so far, and predicts the first observed value at the timing of the control instruction using the regression line. As a result, the first observation value is highly accurate, and more appropriate control can be performed on the control target.
　なお、実施例２では、実施例１とは異なる点を中心に説明し、実施例１と共通する部分については説明を省略する。 Note that the second embodiment will be described with a focus on differences from the first embodiment, and the description of the parts common to the first embodiment will be omitted.
　図８は、第１観測値の経時的変化を示すグラフである。黒丸が第１観測値の実測値である。このうち、符号８０１が第１観測値の最新の実測値である。白丸が時系列な第１観測値から相関により回帰直線８００から予測した第１観測値の予測値８０２である。実測値の観測間隔は、例として５０［ｍｓ］とする。第１観測値８０１から次の第１観測値を取得するまでの間に、時刻ｔｃで制御指示を受け付けた場合、制御装置１０１において、第１取得処理では、プロセッサ２０１は、これまでの時系列な第１観測値（実測値のみ）から回帰直線８００を求め、回帰直線８００における時刻ｔｃの第１観測値を予測値８０２として算出する。 FIG. 8 is a graph showing a change with time of the first observed value. A black circle is an actual measurement value of the first observation value. Among these, the code | symbol 801 is the newest measured value of a 1st observed value. A white circle is a predicted value 802 of the first observed value predicted from the regression line 800 by correlation from the first observed value in time series. As an example, the observation interval of the actually measured values is 50 [ms]. When a control instruction is received at time tc during the period from the first observation value 801 until the next first observation value is acquired, in the control device 101, in the first acquisition process, the processor 201 determines the time series so far. The regression line 800 is obtained from the first observed value (only the actually measured value), and the first observed value at time tc in the regression line 800 is calculated as the predicted value 802.
　図９は、観測値格納テーブルの記憶内容例を示す説明図である。観測値格納テーブル９００は、近隣ユーザの温度センサ１０２ごとに時系列な第１観測値を格納するテーブルである。観測値格納テーブル９００は、フィールドとして、時刻と、観測値と、を有する。時刻は、値として観測間隔ごとに到来する第１観測値の観測時刻を格納するフィールドである。観測値は、値として時刻で観測された第１観測値を格納するフィールドである。 FIG. 9 is an explanatory diagram showing an example of the stored contents of the observation value storage table. The observation value storage table 900 is a table for storing time-series first observation values for each temperature sensor 102 of the neighboring user. The observation value storage table 900 has time and observation values as fields. The time is a field for storing the observation time of the first observation value that arrives at each observation interval as a value. The observed value is a field for storing the first observed value observed at the time as a value.
　＜制御装置１０１の機能的構成例＞
　図１０は、実施例２にかかる制御装置１０１の機能的構成例を示すブロック図である。図６との相違は、観測値格納テーブル９００を有し、第１取得部６０１が観測値格納テーブル９００を参照する点である。第１取得処理では、プロセッサ２０１は、特定の物理センサ（たとえば、近隣ユーザの温度センサ１０２）が制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、時系列な第１観測値のうち最新の第１観測値の次の第１観測値を算出する。また、特定の物理センサが複数存在する場合、第１取得処理では、プロセッサ２０１は、特定の物理センサごとに、特定の物理センサが制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、次の第１観測値の統計値を算出してもよい。 <Example of Functional Configuration of Control Device 101>
FIG. 10 is a block diagram of a functional configuration example of the control device 101 according to the second embodiment. The difference from FIG. 6 is that the observation value storage table 900 is provided, and the first acquisition unit 601 refers to the observation value storage table 900. In the first acquisition process, the processor 201 performs time series based on the correlation of the first series of observed values observed by a specific physical sensor (for example, the temperature sensor 102 of the neighboring user) before the control processing time. The first observation value next to the latest first observation value among the first observation values is calculated. In addition, when there are a plurality of specific physical sensors, in the first acquisition process, the processor 201 determines, for each specific physical sensor, the first time-series observation value that the specific physical sensor observes before the control processing time. Based on this correlation, the statistical value of the next first observation value may be calculated.
　＜第１観測値の取得処理手順例＞
　図１１は、第１観測値の取得処理手順例を示すフローチャートである。図１１の処理は、図７のステップＳ７０３に対応する処理である。図１１では、第１観測値の予測値を、「補間観測値」と称す。また、観測間隔を「スリープ時間」と称す。 <Example of first observation value acquisition process>
FIG. 11 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value. The process of FIG. 11 is a process corresponding to step S703 of FIG. In FIG. 11, the predicted value of the first observed value is referred to as “interpolated observed value”. The observation interval is called “sleep time”.
　まず、制御装置１０１は、第１観測値の取得処理を終了するか否かを判断する（ステップＳ１１０１）。終了しない場合（ステップＳ１１０１：Ｎｏ）、制御装置１０１は、補間観測値の取得要求があるか否かを判断する（ステップＳ１１０２）。当該取得要求は、たとえば、ステップＳ７０１の制御指示である。取得要求がない場合（ステップＳ１１０２：Ｎｏ）、制御装置１０１は、スリープ時間が経過したか否かを判断する（ステップＳ１１０３）。 First, the control device 101 determines whether or not to end the first observation value acquisition process (step S1101). If not finished (step S1101: No), the control device 101 determines whether or not there is a request for acquiring an interpolation observation value (step S1102). The acquisition request is, for example, a control instruction in step S701. When there is no acquisition request (step S1102: No), the control apparatus 101 determines whether the sleep time has elapsed (step S1103).
　スリープ時間が経過していない場合（ステップＳ１１０３：Ｎｏ）、ステップＳ１１０１に戻る。スリープ時間が経過した場合（ステップＳ１１０３：Ｙｅｓ）、制御装置１０１は、近隣ユーザの温度センサ１０２から第１観測値を取得して、現在時刻とともに観測値格納テーブル９００に格納する（ステップＳ１１０４）。そして、制御装置１０１は、スリープ時間をリセットして再計時を開始し（ステップＳ１１０５）、ステップＳ１１０１に戻る。 If the sleep time has not elapsed (step S1103: NO), the process returns to step S1101. When the sleep time has elapsed (step S1103: Yes), the control device 101 acquires the first observation value from the temperature sensor 102 of the neighboring user and stores it in the observation value storage table 900 together with the current time (step S1104). Then, the control device 101 resets the sleep time and starts time counting again (step S1105), and returns to step S1101.
　ステップＳ１１０２において、取得要求がある場合（ステップＳ１１０２：Ｙｅｓ）、制御装置１０１は、観測値格納テーブル９００の過去の第１観測値を取得する（ステップＳ１１０６）。制御装置１０１は、取得した過去の第１観測値から相関係数を算出し、回帰直線を生成する（ステップＳ１１０７）。制御装置１０１は、現在時刻の補間観測値を回帰直線８００から特定し（ステップＳ１１０８）、特定した補間観測値を算出部６０３に出力して（ステップＳ１１０９）、特定した補間観測値と現在時刻とを観測値格納テーブル９００に格納し（ステップＳ１１１０）、回帰直線８００にしたがって、過去の補間観測値を更新し（ステップＳ１１１１）、ステップＳ１１０１に戻る。ステップＳ１１０１において、第１観測値の取得処理を終了する場合（ステップＳ１１０１：Ｙｅｓ）、本フローチャートの処理を終了する。 In Step S1102, when there is an acquisition request (Step S1102: Yes), the control device 101 acquires the past first observation value in the observation value storage table 900 (Step S1106). The control apparatus 101 calculates a correlation coefficient from the acquired past first observation value, and generates a regression line (step S1107). The control device 101 specifies the interpolation observation value at the current time from the regression line 800 (step S1108), outputs the specified interpolation observation value to the calculation unit 603 (step S1109), and specifies the specified interpolation observation value and the current time. Are stored in the observation value storage table 900 (step S1110), the past interpolation observation values are updated according to the regression line 800 (step S1111), and the process returns to step S1101. In step S1101, when the acquisition process of the first observation value is terminated (step S1101: Yes), the process of this flowchart is terminated.
　これにより、観測値の取得タイミングにおいて第１観測値が存在しない場合でも、第１観測値を予測することで、制御対象を適切に調整することができる。 Thereby, even when the first observation value does not exist at the observation value acquisition timing, the control target can be appropriately adjusted by predicting the first observation value.
　実施例３は、実施例１または実施例２において、第１観測値の精度を向上させる例である。近隣ユーザの温度センサ１０２の中に、観測周期が大きい温度センサ１０２があると、他の温度センサ１０２の観測値から著しく外れた、いわゆる外れ値を観測する場合がある。 Example 3 is an example in which the accuracy of the first observation value is improved in Example 1 or Example 2. If there is a temperature sensor 102 with a long observation period among the temperature sensors 102 of neighboring users, a so-called outlier that is significantly different from the observation values of other temperature sensors 102 may be observed.
　図１２は、実施例３にかかる第１観測値の取得例を示す説明図である。センサＩＤ：Ｓ１の温度センサ１０２の観測周期は、３０［ｍｓ］、センサＩＤ：Ｓ２の温度センサ１０２の観測周期は、１００［ｍｓ］、センサＩＤ：Ｓ３の温度センサ１０２の観測周期は、１０［ｍｓ］とする。この例では、センサＩＤ：Ｓ２の温度センサ１０２が、外れ値となる第１観測値を観測する。 FIG. 12 is an explanatory diagram of an example of obtaining the first observation value according to the third embodiment. The observation period of the temperature sensor 102 with sensor ID S1 is 30 [ms], the observation period of the temperature sensor 102 with sensor ID S2 is 100 [ms], and the observation period of the temperature sensor 102 with sensor ID S3 is 10 [Ms]. In this example, the temperature sensor 102 with the sensor ID: S2 observes the first observation value that is an outlier.
　このような場合、外れ値の影響でノイズを含んだ統計値が算出されてしまう。したがって、実施例３の制御装置１０１は、取得した複数の第１観測値を、温度センサ１０２ごとの重みで加重平均して、統計値を求める。これにより、外れ値による影響を抑制することができる。 In such a case, a statistical value including noise is calculated due to the influence of an outlier. Therefore, the control apparatus 101 of Example 3 calculates | requires a statistical value by carrying out the weighted average of the some 1st observation value acquired with the weight for every temperature sensor 102. FIG. Thereby, the influence by an outlier can be suppressed.
　図１３は、重み付けテーブルの記憶内容例を示す説明図である。重み付けテーブル１３００は、フィールドとして、センサＩＤ１３０１と、重み１３０２と、を有する。センサＩＤ１３０１は、値として、温度センサ１０２を一意に特定する識別情報（センサＩＤ）を格納するフィールドである。重み１３０２は、値として、温度センサ１０２の観測周期に応じた重み値を格納するフィールドである。観測周期が大きいほど重み値は小さくなる。本例の場合、重み値ｗ２が他の重み値よりも小さい値となる。これにより、観測周期が大きい温度センサ１０２の第１観測値の影響を低減させることができる。 FIG. 13 is an explanatory diagram showing an example of the contents stored in the weighting table. The weighting table 1300 has sensor IDs 1301 and weights 1302 as fields. The sensor ID 1301 is a field that stores identification information (sensor ID) that uniquely identifies the temperature sensor 102 as a value. The weight 1302 is a field for storing a weight value corresponding to the observation period of the temperature sensor 102 as a value. The weight value decreases as the observation period increases. In this example, the weight value w2 is smaller than other weight values. Thereby, the influence of the 1st observation value of the temperature sensor 102 with a long observation period can be reduced.
　図１４は、第１観測値の取得テーブルの記憶内容例を示す説明図である。取得テーブル１４００は、フィールドとして、時刻１４０１と、観測値１４０２と、センサＩＤ１４０３と、を有する。時刻１４０１は、値として、各温度センサ１０２の観測時刻を格納するフィールドである。観測値１４０２は、値として、時刻１４０１で観測された第１観測値を格納するフィールドである。センサＩＤ１４０３は、値として、温度センサ１０２を一意に特定する識別情報（センサＩＤ）を格納するフィールドである。 FIG. 14 is an explanatory diagram showing an example of the stored contents of the first observation value acquisition table. The acquisition table 1400 includes a time 1401, an observed value 1402, and a sensor ID 1403 as fields. The time 1401 is a field for storing the observation time of each temperature sensor 102 as a value. The observed value 1402 is a field for storing the first observed value observed at time 1401 as a value. The sensor ID 1403 is a field that stores identification information (sensor ID) that uniquely identifies the temperature sensor 102 as a value.
　ここで、第１取得処理において、最古の４個のデータ単位で順次第１観測値を取得テーブル１４００から取得するものとする。１回目～５回目のバッファリングＢＦ１～ＢＦ５のうち、センサＩＤ：Ｓ２の第１観測値を含むバッファリングは、ＢＦ１、ＢＦ５である。バッファリングＢＦ１、ＢＦ５において、センサＩＤ：Ｓ２の第１観測値Ｖ０Ｂ，Ｖ１０Ｂが外れ値となると、統計値にノイズが含まれてしまう。このため、第１取得処理において、プロセッサ２０１は、重み付けテーブル１３００を参照して、統計値を算出する。 Here, in the first acquisition process, the first observation values are sequentially acquired from the acquisition table 1400 in units of the oldest four data. Of the first to fifth buffering BF1 to BF5, the buffering including the first observation value of the sensor ID S2 is BF1 and BF5. In the buffering BF1 and BF5, when the first observation values V0B and V10B of the sensor ID: S2 are outliers, noise is included in the statistical values. For this reason, in the first acquisition process, the processor 201 refers to the weighting table 1300 and calculates a statistical value.
　たとえば、バッファリングＢＦ１～ＢＦ５の場合は、以下のようになる。 For example, in the case of buffering BF1 to BF5, it is as follows.
　ＢＦ１の統計値＝Ｖ０Ａ×ｗ１＋Ｖ０Ｂ×ｗ２＋Ｖ０Ｃ×ｗ３＋Ｖ１Ｃ×ｗ３
　ＢＦ２の統計値＝Ｖ２Ｃ×ｗ３＋Ｖ３Ｃ×ｗ３＋Ｖ３Ａ×ｗ１＋Ｖ４Ｃ×ｗ３
　ＢＦ３の統計値＝Ｖ５Ｃ×ｗ３＋Ｖ６Ｃ×ｗ３＋Ｖ６Ａ×ｗ１＋Ｖ７Ｃ×ｗ３
　ＢＦ４の統計値＝Ｖ８Ｃ×ｗ３＋Ｖ９Ｃ×ｗ３＋Ｖ９Ａ×ｗ１＋Ｖ１０Ｃ×ｗ３
　ＢＦ５の統計値＝Ｖ１０Ｂ×ｗ２＋Ｖ１１Ｃ×ｗ３＋Ｖ１２Ｃ×ｗ３＋Ｖ１２Ａ×ｗ１ Statistical value of BF1 = V0A × w1 + V0B × w2 + V0C × w3 + V1C × w3
Statistical value of BF2 = V2C × w3 + V3C × w3 + V3A × w1 + V4C × w3
Statistical value of BF3 = V5C × w3 + V6C × w3 + V6A × w1 + V7C × w3
Statistical value of BF4 = V8C × w3 + V9C × w3 + V9A × w1 + V10C × w3
Statistical value of BF5 = V10B × w2 + V11C × w3 + V12C × w3 + V12A × w1
　＜制御装置１０１の機能的構成例＞
　図１５は、実施例１にかかる制御装置１０１の機能的構成例を示すブロック図である。図１０との相違は、重み付けテーブル１３００を有し、観測値格納テーブル９００を有し、第１取得部６０１が観測値格納テーブル９００を参照する点と、取得テーブル１４００を有し、第１取得部６０１が取得テーブル１４００に書き込んだり、読み出したりする点である。 <Example of Functional Configuration of Control Device 101>
FIG. 15 is a block diagram of a functional configuration example of the control apparatus 101 according to the first embodiment. 10 differs from FIG. 10 in that it has a weighting table 1300, an observation value storage table 900, the first acquisition unit 601 refers to the observation value storage table 900, has an acquisition table 1400, and has a first acquisition. The unit 601 writes to and reads from the acquisition table 1400.
　第１取得処理では、プロセッサ２０１は、各特定の物理センサ（たとえば、近隣ユーザの温度センサ１０２）の観測間隔の長さに基づいて、各特定の物理センサの第１観測値の統計値を算出する。具体的には、たとえば、プロセッサ２０１は、観測間隔が長いほど小さい重み値を用いて、統計値として、加重平均値を算出する。 In the first acquisition process, the processor 201 calculates a statistical value of the first observation value of each specific physical sensor based on the length of the observation interval of each specific physical sensor (for example, the temperature sensor 102 of the neighboring user). To do. Specifically, for example, the processor 201 calculates a weighted average value as a statistical value using a smaller weight value as the observation interval is longer.
　また、実施例２に適用される場合、第１取得処理では、プロセッサ２０１は、特定の物理センサが制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、特定の物理センサごとに次の第１観測値を算出し、各特定の物理センサの観測間隔の長さに基づいて、特定の物理センサごとに算出された次の第１観測値の統計値を算出する。 Further, when applied to the second embodiment, in the first acquisition process, the processor 201 performs a specific process based on the correlation of the first time-series observation values observed by a specific physical sensor before the control process time. The next first observation value is calculated for each physical sensor, and the statistical value of the next first observation value calculated for each specific physical sensor is calculated based on the length of the observation interval of each specific physical sensor. .
　この場合、図８の時刻ｔｃが観測周期の時刻に一致しない温度センサ１０２については、実施例２に示したように、第１取得処理では、プロセッサ２０１は、過去の一連の第１観測値から相関係数を算出し、回帰直線８００を生成して、時刻ｔｃにおける第１観測値の予測値（補間観測値）を得ることになる。バッファリングされた観測値（実測値）ではなく、予測値が算出された場合であっても、第１取得処理では、プロセッサ２０１は、同様に重み付けテーブル１３００を参照して、加重平均値を算出する。なお、図８の時刻ｔｃが観測周期の時刻に一致する温度センサ１０２については、図１４に示したように実測値がバッファリングされ、加重平均に用いられる。 In this case, for the temperature sensor 102 in which the time tc in FIG. 8 does not coincide with the time of the observation cycle, as shown in the second embodiment, in the first acquisition process, the processor 201 determines from the past series of first observation values. A correlation coefficient is calculated, a regression line 800 is generated, and a predicted value (interpolated observed value) of the first observed value at time tc is obtained. In the first acquisition process, the processor 201 similarly refers to the weighting table 1300 to calculate the weighted average value even when the predicted value is calculated instead of the buffered observation value (actual measurement value). To do. For the temperature sensor 102 at which the time tc in FIG. 8 coincides with the time of the observation cycle, the actual measurement values are buffered as shown in FIG. 14 and used for the weighted average.
　＜第１観測値の取得処理手順例＞
　図１６は、第１観測値の取得処理手順例を示すフローチャートである。図１６の処理は、図７のステップＳ７０３に対応する処理である。まず、制御装置１０１は、現在時刻を起点時刻に設定する（ステップＳ１６０１）。つぎに、第１観測値の取得処理を終了するか否かを判断する（ステップＳ１６０２）。終了しない場合（ステップＳ１６０２：Ｎｏ）、制御装置１０１は、現在時刻から起点時刻を減算した時間が所定取得間隔より大きいか否かを判断する（ステップＳ１６０３）。大きくない場合（ステップＳ１６０３：Ｎｏ）、制御装置１０１は、センサキューにデータがあるか否かを判断する（ステップＳ１６０４）。センサキューとは、温度センサ１０２からのデータ（観測値、観測時刻、センサＩＤ）を一時的に保持するメモリである。 <Example of first observation value acquisition process>
FIG. 16 is a flowchart illustrating an example of a processing procedure for acquiring the first observation value. The process of FIG. 16 is a process corresponding to step S703 of FIG. First, the control device 101 sets the current time as the starting time (step S1601). Next, it is determined whether or not to end the first observation value acquisition process (step S1602). If not finished (step S1602: No), the control device 101 determines whether or not the time obtained by subtracting the starting time from the current time is larger than the predetermined acquisition interval (step S1603). When not large (step S1603: No), the control apparatus 101 determines whether there is data in the sensor queue (step S1604). The sensor queue is a memory that temporarily holds data from the temperature sensor 102 (observation value, observation time, sensor ID).
　センサキューにデータがない場合（ステップＳ１６０４：Ｎｏ）、ステップＳ１６０３に戻る。センサキューにデータがある場合（ステップＳ１６０４：Ｙｅｓ）、制御装置１０１は、センサキューに保持されているデータを取得し（ステップＳ１６０５）、取得データ内のセンサＩＤに対応する重み値を重み付けテーブル１３００から特定する（ステップＳ１６０６）。特定されたデータは、取得テーブル１４００に書き込まれる。そして、ステップＳ１６０３に戻る。ステップＳ１６０４において、現在時刻から起点時刻を減算した時間が所定取得間隔より大きい場合（ステップＳ１６０３：Ｙｅｓ）、制御装置１０１は、加重平均値をリセットし（ステップＳ１６０７）、バッファリング領域から観測値群を読み出す（ステップＳ１６０８）。バッファリング領域とは、取得テーブル１４００のうち最古のデータ群（図１４では４個）がバッファされている領域である。 If there is no data in the sensor queue (step S1604: No), the process returns to step S1603. If there is data in the sensor queue (step S1604: Yes), the control apparatus 101 acquires data held in the sensor queue (step S1605), and assigns a weight value corresponding to the sensor ID in the acquired data to the weighting table 1300. (Step S1606). The identified data is written into the acquisition table 1400. Then, the process returns to step S1603. In step S1604, when the time obtained by subtracting the starting time from the current time is larger than the predetermined acquisition interval (step S1603: Yes), the control device 101 resets the weighted average value (step S1607), and the observed value group from the buffering area. Is read out (step S1608). The buffering area is an area where the oldest data group (four in FIG. 14) in the acquisition table 1400 is buffered.
　そして、制御装置１０１は、読み出した第１観測値について加重平均値を算出する（ステップＳ１６０９）。そして、制御装置１０１は、加重平均値を算出した観測値群の時間帯情報を取得する（ステップＳ１６１０）。時間帯情報とは、観測値群に関する取得テーブル１４００の時刻である。どの時刻を採用するかはあらかじめ決めておく。そして、制御装置１０１は、加重平均値と時間帯情報とを観測値情報として算出部６０３に出力し（ステップＳ１６１１）、観測値群を取得したバッファリング領域をクリアする（ステップＳ１６１２）。そして、ステップＳ１６０２に戻る。ステップＳ１６０２において、第１観測値の取得処理を終了する場合（ステップＳ１６０２：Ｙｅｓ）、本フローチャートの処理を終了する。 And the control apparatus 101 calculates a weighted average value about the read 1st observation value (step S1609). And the control apparatus 101 acquires the time zone information of the observation value group which calculated the weighted average value (step S1610). The time zone information is the time of the acquisition table 1400 regarding the observation value group. The time to be adopted is determined in advance. Then, the control device 101 outputs the weighted average value and the time zone information as observation value information to the calculation unit 603 (step S1611), and clears the buffering area from which the observation value group has been acquired (step S1612). Then, the process returns to step S1602. In step S1602, when the acquisition process of the first observation value is finished (step S1602: Yes), the process of this flowchart is finished.
　これにより、外れ値による影響を低減させ、ノイズを抑制した統計値を得ることができる。したがって、制御対象をより適切に制御することができる。 This makes it possible to reduce the influence of outliers and obtain statistical values that suppress noise. Therefore, the control object can be controlled more appropriately.
　また、実施例３が実施例２に適用される場合、ステップＳ１６１０において、プロセッサ２０１は、現在時刻での観測値が存在しないセンサについて、補間観測値を算出し、加重平均値を算出すればよい。これにより、現在時刻での観測値が存在しない場合でも、当該観測値を予測することで、制御対象を適切に調整することができる。 Further, when the third embodiment is applied to the second embodiment, in step S1610, the processor 201 calculates an interpolated observation value and calculates a weighted average value for a sensor that does not have an observation value at the current time. . Thereby, even when the observation value at the current time does not exist, the control target can be appropriately adjusted by predicting the observation value.
　なお、本発明は前述した実施例に限定されるものではなく、添付した特許請求の範囲の趣旨内における様々な変形例及び同等の構成が含まれる。例えば、前述した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに本発明は限定されない。また、ある実施例の構成の一部を他の実施例の構成に置き換えてもよい。また、ある実施例の構成に他の実施例の構成を加えてもよい。また、各実施例の構成の一部について、他の構成の追加、削除、または置換をしてもよい。 The present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the scope of the appended claims. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to those having all the configurations described. A part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Moreover, you may add the structure of another Example to the structure of a certain Example. Moreover, you may add, delete, or replace another structure about a part of structure of each Example.
　また、前述した各構成、機能、処理部、処理手段等は、それらの一部又は全部を、例えば集積回路で設計する等により、ハードウェアで実現してもよく、プロセッサ２０１がそれぞれの機能を実現するプログラムを解釈し実行することにより、ソフトウェアで実現してもよい。 In addition, each of the above-described configurations, functions, processing units, processing means, and the like may be realized in hardware by designing a part or all of them with, for example, an integrated circuit, and the processor 201 performs each function. It may be realized by software by interpreting and executing the program to be realized.
　各機能を実現するプログラム、テーブル、ファイル等の情報は、メモリ、ハードディスク、ＳＳＤ（Ｓｏｌｉｄ　Ｓｔａｔｅ　Ｄｒｉｖｅ）等の記憶装置、又は、ＩＣ（Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）カード、ＳＤカード、ＤＶＤ（Ｄｉｇｉｔａｌ　Ｖｅｒｓａｔｉｌｅ　Ｄｉｓｃ）の記録媒体に格納することができる。 Information such as programs, tables, and files for realizing each function is recorded on a memory, a hard disk, a storage device such as SSD (Solid State Drive), or an IC (Integrated Circuit) card, SD card, DVD (Digital Versatile Disc). It can be stored on a medium.
　また、制御線や情報線は説明上必要と考えられるものを示しており、実装上必要な全ての制御線や情報線を示しているとは限らない。実際には、ほとんど全ての構成が相互に接続されていると考えてよい。 Also, the control lines and information lines indicate what is considered necessary for the explanation, and do not necessarily indicate all control lines and information lines necessary for mounting. In practice, it can be considered that almost all the components are connected to each other.

Claims (9)

  1. 　制御対象を制御するための制御パラメータを前記制御対象に与える制御装置であって、
    　プログラムを実行するプロセッサと、前記プログラムを記憶するとともに前記制御パラメータの調節量と前記調節量の特定要因となる分析情報の値とを対応付けた対応情報を記憶する記憶デバイスと、前記分析情報の特定要因となる観測情報の観測値を観測対象から観測する１以上の物理センサと通信するとともに前記１以上の物理センサの位置における前記分析情報および前記観測情報の値を記憶するサーバと通信する通信インタフェースと、を有し、
    　前記プロセッサは、
    　前記１以上の物理センサのうち前記制御装置の位置から許容範囲内に位置する特定の物理センサから、当該特定の物理センサが前記制御対象を制御する制御処理時刻に観測した前記観測情報の第１観測値を取得する第１取得処理と、
    　前記制御装置の位置から前記許容範囲内の位置で、かつ、前記制御処理時刻を含む期間における前記観測情報の第２観測値および前記分析情報の値を、前記サーバから取得する第２取得処理と、
    　前記第１取得処理によって取得された前記第１観測値と前記第２取得処理によって取得された前記第２観測値との差分を算出する算出処理と、
    　前記第２取得処理によって取得された前記分析情報の値に対応する前記制御パラメータの調節量を前記対応情報から特定する特定処理と、
    　前記特定処理によって特定された調節量を前記算出処理による算出結果に基づいて補正して、補正結果を前記制御対象に出力する補正処理と、
    を実行することを特徴とする制御装置。     A control device that gives a control parameter for controlling a control target to the control target,
    A processor that executes a program; a storage device that stores the program; and a correspondence device that associates an adjustment amount of the control parameter with a value of analysis information that is a specific factor of the adjustment amount; and Communication that communicates with one or more physical sensors that observe observation values of observation information that are specific factors from the observation target, and communicates with a server that stores the values of the analysis information and the observation information at the positions of the one or more physical sensors An interface, and
    The processor is
    Of the one or more physical sensors, the first of the observation information observed from a specific physical sensor located within an allowable range from the position of the control device at a control processing time when the specific physical sensor controls the control target. A first acquisition process for acquiring observation values;
    A second acquisition process for acquiring a second observation value of the observation information and a value of the analysis information from the server at a position within the allowable range from the position of the control device and including the control processing time; ,
    A calculation process for calculating a difference between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process;
    A specifying process for specifying, from the correspondence information, an adjustment amount of the control parameter corresponding to the value of the analysis information acquired by the second acquisition process;
    A correction process for correcting the adjustment amount specified by the specifying process based on a calculation result of the calculation process, and outputting a correction result to the control target;
    The control apparatus characterized by performing.
  2. 　請求項１に記載の制御装置であって、
    　前記第１取得処理では、前記プロセッサは、前記特定の物理センサが複数存在する場合、前記複数の特定の物理センサによって観測された前記観測情報の複数の観測値について統計値を算出し、
    　前記算出処理では、前記プロセッサは、前記統計値と前記第２観測値との差分を算出することを特徴とする制御装置。     The control device according to claim 1,
    In the first acquisition process, when there are a plurality of the specific physical sensors, the processor calculates a statistical value for a plurality of observation values of the observation information observed by the plurality of specific physical sensors,
    In the calculation process, the processor calculates a difference between the statistical value and the second observation value.
  3. 　請求項２に記載の制御装置であって、
    　前記特定の物理センサのうち少なくとも１つの観測間隔は、他の特定の物理センサの観測間隔とは異なっており、
    　前記第１取得処理では、前記プロセッサは、前記各特定の物理センサの前記観測間隔の長さに基づいて、前記各特定の物理センサの第１観測値の統計値を算出することを特徴とする制御装置。     The control device according to claim 2,
    The observation interval of at least one of the specific physical sensors is different from the observation interval of the other specific physical sensors,
    In the first acquisition process, the processor calculates a statistical value of a first observation value of each specific physical sensor based on a length of the observation interval of each specific physical sensor. Control device.
  4. 　請求項１に記載の制御装置であって、
    　前記第１取得処理では、前記プロセッサは、前記特定の物理センサが前記制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、前記時系列な第１観測値のうち最新の第１観測値の次の第１観測値を算出し、
    　前記算出処理では、前記プロセッサは、前記次の第１観測値と前記第２観測値との差分を算出することを特徴とする制御装置。     The control device according to claim 1,
    In the first acquisition process, the processor determines the time series of first observation values based on a correlation of time series first observation values observed by the specific physical sensor before the control processing time. Calculate the first observation after the latest first observation,
    In the calculation process, the processor calculates a difference between the next first observation value and the second observation value.
  5. 　請求項４に記載の制御装置であって、
    　前記第１取得処理では、前記プロセッサは、前記特定の物理センサが複数存在する場合、前記特定の物理センサごとに、前記特定の物理センサが前記制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、前記次の第１観測値の統計値を算出し、
    　前記算出処理では、前記プロセッサは、前記次の第１観測値の統計値と前記第２観測値との差分を算出することを特徴とする制御装置。     The control device according to claim 4,
    In the first acquisition process, when there are a plurality of the specific physical sensors, the processor acquires, for each specific physical sensor, a time-series first time that the specific physical sensor observes before the control processing time. Based on the correlation of one observation value, the statistical value of the next first observation value is calculated,
    In the calculation process, the processor calculates a difference between a statistical value of the next first observation value and the second observation value.
  6. 　請求項５に記載の制御装置であって、
    　前記特定の物理センサのうち少なくとも１つの観測間隔は、他の特定の物理センサの観測間隔とは異なっており、
    　前記第１取得処理では、前記プロセッサは、前記特定の物理センサが前記制御処理時刻よりも前に観測した時系列な第１観測値の相関に基づいて、前記特定の物理センサごとに前記次の第１観測値を算出し、前記各特定の物理センサの前記観測間隔の長さに基づいて、前記特定の物理センサごとに算出された前記次の第１観測値の統計値を算出することを特徴とする制御装置。     The control device according to claim 5,
    The observation interval of at least one of the specific physical sensors is different from the observation interval of the other specific physical sensors,
    In the first acquisition process, the processor performs the following for each specific physical sensor based on a correlation of first time-series observation values observed by the specific physical sensor before the control processing time. Calculating a first observation value, and calculating a statistical value of the next first observation value calculated for each of the specific physical sensors based on a length of the observation interval of each of the specific physical sensors. Control device characterized.
  7. 　請求項１に記載の制御装置であって、
    　前記プロセッサは、
    　前記補正結果を用いて前記制御対象を制御する制御処理を実行することを特徴とする制御装置。     The control device according to claim 1,
    The processor is
    A control apparatus that executes a control process for controlling the control target using the correction result.
  8. 　制御対象を制御するための制御パラメータを前記制御対象に与える制御装置による制御方法であって、
    　前記制御装置は、
    　プログラムを実行するプロセッサと、前記プログラムを記憶するとともに前記制御パラメータの調節量と前記調節量の特定要因となる分析情報の値とを対応付けた対応情報を記憶する記憶デバイスと、前記分析情報の特定要因となる観測情報の観測値を観測対象から観測する１以上の物理センサと通信するとともに前記１以上の物理センサの位置における前記分析情報および前記観測情報の値を記憶するサーバと通信する通信インタフェースと、を有し、
    　前記プロセッサは、
    　前記１以上の物理センサのうち前記制御装置の位置から許容範囲内に位置する特定の物理センサから、当該特定の物理センサが前記制御対象を制御する制御処理時刻に観測した前記観測情報の第１観測値を取得する第１取得処理と、
    　前記制御装置の位置から前記許容範囲内の位置で、かつ、前記制御処理時刻を含む期間における前記観測情報の第２観測値および前記分析情報の値を、前記サーバから取得する第２取得処理と、
    　前記第１取得処理によって取得された前記第１観測値と前記第２取得処理によって取得された前記第２観測値との差分を算出する算出処理と、
    　前記第２取得処理によって取得された前記分析情報の値に対応する前記制御パラメータの調節量を前記対応情報から特定する特定処理と、
    　前記特定処理によって特定された調節量を前記算出処理による算出結果に基づいて補正して、補正結果を前記制御対象に出力する補正処理と、
    を実行することを特徴とする制御方法。     A control method by a control device that gives a control parameter for controlling a control object to the control object,
    The control device includes:
    A processor that executes a program; a storage device that stores the program; and a correspondence device that associates an adjustment amount of the control parameter with a value of analysis information that is a specific factor of the adjustment amount; and Communication that communicates with one or more physical sensors that observe observation values of observation information that are specific factors from the observation target, and communicates with a server that stores the values of the analysis information and the observation information at the positions of the one or more physical sensors An interface, and
    The processor is
    Of the one or more physical sensors, the first of the observation information observed from a specific physical sensor located within an allowable range from the position of the control device at a control processing time when the specific physical sensor controls the control target. A first acquisition process for acquiring observation values;
    A second acquisition process for acquiring a second observation value of the observation information and a value of the analysis information from the server at a position within the allowable range from the position of the control device and including the control processing time; ,
    A calculation process for calculating a difference between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process;
    A specifying process for specifying, from the correspondence information, an adjustment amount of the control parameter corresponding to the value of the analysis information acquired by the second acquisition process;
    A correction process for correcting the adjustment amount specified by the specifying process based on a calculation result of the calculation process, and outputting a correction result to the control target;
    The control method characterized by performing.
  9. 　制御対象を制御するための制御パラメータを前記制御対象に与える制御装置のプロセッサに実行させる制御プログラムであって、
    　前記制御装置は、前記プロセッサと、前記制御プログラムを記憶するとともに前記制御パラメータの調節量と前記調節量の特定要因となる分析情報の値とを対応付けた対応情報を記憶する記憶デバイスと、前記分析情報の特定要因となる観測情報の観測値を観測対象から観測する１以上の物理センサと通信するとともに前記１以上の物理センサの位置における前記分析情報および前記観測情報の値を記憶するサーバと通信する通信インタフェースと、を有し、
    　前記プロセッサに、
    　前記１以上の物理センサのうち前記制御装置の位置から許容範囲内に位置する特定の物理センサから、当該特定の物理センサが前記制御対象を制御する制御処理時刻に観測した前記観測情報の第１観測値を取得する第１取得処理と、
    　前記制御装置の位置から前記許容範囲内の位置で、かつ、前記制御処理時刻を含む期間における前記観測情報の第２観測値および前記分析情報の値を、前記サーバから取得する第２取得処理と、
    　前記第１取得処理によって取得された前記第１観測値と前記第２取得処理によって取得された前記第２観測値との差分を算出する算出処理と、
    　前記第２取得処理によって取得された前記分析情報の値に対応する前記制御パラメータの調節量を前記対応情報から特定する特定処理と、
    　前記特定処理によって特定された調節量を前記算出処理による算出結果に基づいて補正して、補正結果を前記制御対象に出力する補正処理と、
    を実行させることを特徴とする制御プログラム。     A control program for causing a processor of a control device to give a control parameter for controlling a control target to the control target,
    The control device stores the control program, a storage device that stores the control program, and stores correspondence information in which the adjustment amount of the control parameter is associated with the value of analysis information that is a specific factor of the adjustment amount; A server that communicates observation values of observation information, which is a specific factor of analysis information, with one or more physical sensors that observe the observation target, and stores the analysis information and the values of the observation information at the positions of the one or more physical sensors; A communication interface for communicating,
    In the processor,
    Of the one or more physical sensors, the first of the observation information observed from a specific physical sensor located within an allowable range from the position of the control device at a control processing time when the specific physical sensor controls the control target. A first acquisition process for acquiring observation values;
    A second acquisition process for acquiring a second observation value of the observation information and a value of the analysis information from the server at a position within the allowable range from the position of the control device and including the control processing time; ,
    A calculation process for calculating a difference between the first observation value acquired by the first acquisition process and the second observation value acquired by the second acquisition process;
    A specifying process for specifying, from the correspondence information, an adjustment amount of the control parameter corresponding to the value of the analysis information acquired by the second acquisition process;
    A correction process for correcting the adjustment amount specified by the specifying process based on a calculation result of the calculation process, and outputting a correction result to the control target;
    A control program characterized by causing
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JP2009533906A (en) * 2006-04-07 2009-09-17 クゥアルコム・インコーポレイテッド Sensor interface and method and apparatus related to the sensor interface
JP2015103006A (en) * 2013-11-25 2015-06-04 三菱日立パワーシステムズ株式会社 Management apparatus, management system, management method, and program

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009533906A (en) * 2006-04-07 2009-09-17 クゥアルコム・インコーポレイテッド Sensor interface and method and apparatus related to the sensor interface
JP2015103006A (en) * 2013-11-25 2015-06-04 三菱日立パワーシステムズ株式会社 Management apparatus, management system, management method, and program

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