Information processing device, information processing method, information processing program, and meter remote reading system

Abstract

It is possible to accurately calculate the amount of usage of the measured object without changing the meter reading date. [Solution] The information processing device includes a determination unit that determines whether or not it was possible to receive the amount of usage of a target object from a meter installed at a demand location, and an estimation unit that, if it is determined that the amount of usage of the target object could not be received, estimates the amount of usage of the target object on days when the amount of usage of the target object could not be received, based on the usage pattern for each day of the week at the demand location obtained from past data representing past usage of the demand location.

Description

[Technical Field]

[0001] The disclosed technologies relate to information processing devices, information processing methods, information processing programs, and remote meter reading systems. [Background technology]

[0002] Traditionally, if meter reading information could not be collected on the meter reading date, which is the basis for billing, for any reason such as communication failure, the meter reader would have to read the meter directly in order to bill the customer.

[0003] In contrast, Patent Document 1 discloses a technique for generating billing information even when meter reading information cannot be obtained, in which, if past meter reading information was used in the generation of the previous billing information, past meter reading information is used as the starting point for the current billing information. More specifically, if remote meter reading fails, the reading value from the day before the meter reading date is adopted as the reading value for the day the meter reading failed. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Patent No. 6996954 [Overview of the project] [Problems that the invention aims to solve]

[0005] The technology described in Patent Document 1 above has the advantage of being able to generate billing information even when meter reading information cannot be obtained during remote meter reading.

[0006] However, the technology described in Patent Document 1 above requires the meter reading date to be the day before, and the reading value on the day before the meter reading date is considered to be the reading value on the day the meter reading failed, which means the meter reading date is changed, and there is room for improvement in this regard.

[0007] The disclosed technology aims to provide an information processing device, an information processing method, an information processing program, and a remote meter reading system that can accurately calculate the usage amount of the measured object without changing the meter reading date. [Means for solving the problem]

[0008] The information processing device according to the first embodiment includes a determination unit that determines whether or not it was possible to receive the amount of usage of a target to be measured from a meter installed at a demand location, and an estimation unit that, if it is determined that the amount of usage of the target to be measured could not be received, estimates the amount of usage of the target to be measured on days when the amount of usage of the target to be measured could not be received, based on the usage pattern for each day of the week at the demand location obtained from past data representing past usage of the demand location.

[0009] The information processing device according to the second embodiment, in the information processing device according to the first embodiment, estimates the amount of usage of the target to be measured on days when the amount of usage of the target to be measured could not be received, based on the usage pattern of the demand location for each day of the week and the temperature on days when the amount of usage of the target to be measured could not be received.

[0010] The information processing device according to the third embodiment, in the information processing device according to the second embodiment, derives a correlation between the day of the week, temperature, and usage amount based on usage amounts over several past weeks, and estimates the usage amount of the measurement target on days when the usage amount of the measurement target could not be received, based on the derived correlation.

[0011] The information processing device according to the fourth embodiment is an information processing device according to the third embodiment in which the estimation unit derives the multiple regression equation as the correlation by performing multiple regression analysis based on usage amounts over several past weeks.

[0012] The information processing device according to the fifth embodiment, in the information processing device according to the first embodiment, the estimation unit calculates a representative value of the usage amount for each day of the week based on the usage amount over several past weeks, and uses the calculated representative value as the usage amount of the measurement target for days on which the usage amount of the measurement target could not be received.

[0013] The information processing device according to the sixth embodiment is an information processing device according to the fifth embodiment in which the representative value is one of the mean, median, or weighted mean.

[0014] The information processing device according to the seventh embodiment is an information processing device according to any one of the first to sixth embodiments, wherein the estimation unit determines whether or not a user at the demand location is using the measurement target based on the amount of use of other resources other than the measurement target, and corrects the amount of use of the measurement target based on the determination result.

[0015] The information processing device according to the eighth embodiment is an information processing device according to any one of the first to seventh embodiments, in which the estimation unit calculates the meter reading value on the meter reading date based on the most recently received usage amount of the measured object and the usage amount of the measured object on days when the usage amount of the measured object could not be received, from the past data relating to past usage amounts.

[0016] The information processing method according to the ninth aspect includes a computer determining whether it was able to receive the amount of usage of a target object from a meter installed at a demand location, and if it is determined that the amount of usage of the target object could not be received, the computer performs a process that includes estimating the amount of usage of the target object on days when the amount of usage of the target object could not be received, based on the daily usage pattern of the demand location obtained from past data representing past usage of the demand location.

[0017] The information processing program according to the tenth embodiment causes a computer to determine whether or not it was able to receive the amount of usage of a target object from a meter installed at a demand location, and if it is determined that the amount of usage of the target object could not be received, to execute a process that includes estimating the amount of usage of the target object on days when the amount of usage of the target object could not be received, based on the daily usage pattern of the demand location obtained from past data representing past usage of the demand location.

[0018] A remote meter reading system according to an 11th aspect includes a meter provided at a demand site, and an information processing apparatus according to any one of the first to eighth aspects, which is connected to the meter via a network. [Effects of the Invention]

[0019] According to the disclosed technology, it is possible to accurately calculate the usage amount of a measurement target without changing the meter reading date. [Brief Description of Drawings]

[0020] [Figure 1] It is a configuration diagram of the remote meter reading system. [Figure 2] It is a block diagram showing a hardware configuration of a center apparatus. [Figure 3] It is a block diagram showing a functional configuration of a center apparatus. [Figure 4] It is a diagram showing an example of usage amount management data. [Figure 5] It is a flowchart of information processing executed by a center apparatus. [Mode for Carrying Out the Invention]

[0021] Hereinafter, the present embodiment will be described with reference to the drawings. In each drawing, identical or equivalent components are assigned the same reference signs. In addition, dimensional ratios in the drawings are exaggerated for convenience of explanation, and may differ from actual ratios. Furthermore, the present disclosure is not limited in any way to the following embodiments, and can be implemented with appropriate modifications within the scope of the object of the present disclosure.

[0022] The system configuration of a remote meter reading system 100 according to the present embodiment is shown in FIG. 1. As shown in FIG. 1, the remote meter reading system 100 is configured to include a plurality of meters 110, a plurality of sensor nodes 112, a plurality of repeaters 114, and a center apparatus 116.

[0023] The meter (smart meter) 110 is, for example, installed at each demand location and is a device that automatically reads the amount of gas or electricity used when a gas company supplies gas to a demand location, or when an electricity company supplies electricity to a demand location. In this embodiment, a gas meter 110 provided by a gas company is given as an example, but it can also be applied to electricity.

[0024] The meter 110 includes a shut-off valve, a pressure sensor, a flow sensor, a display unit, and a calculation unit. The shut-off valve includes a valve that controls the valve's opening and controls the flow rate of the flammable gas flowing through the gas passage. Therefore, the shut-off valve can shut off the flow of flammable gas by completely closing the valve.

[0025] The pressure sensor is located downstream of the shut-off valve and detects the pressure of the flammable gas. The flow sensor consists of an ultrasonic transducer and a propagation velocity output unit. The ultrasonic transducer is positioned at predetermined locations on the upstream and downstream sides of the gas flow path, downstream of the shut-off valve and upstream of the pressure sensor, and functions as a transmitter and receiver of ultrasonic waves, for example, sound waves of 20 kHz or higher.

[0026] The propagation velocity derivation unit detects the propagation time of ultrasonic waves propagating between ultrasonic transducers via flammable gas and derives the flow rate of the flammable gas based on the propagation time. The display unit consists of a liquid crystal display or the like and is used to notify the cumulative value of the supply amount (usage amount) of flammable gas and abnormalities such as flammable gas leakage. The calculation unit manages and controls the entire meter 110 using a semiconductor integrated circuit including a central processing unit (CPU), a PROM containing programs, RAM as a work area, etc.

[0027] The sensor node 112 is installed in a one-to-one correspondence with each meter 110 and transmits and receives information used by at least the meter 110.

[0028] The repeater (gateway device) 114 is installed in association with one of the multiple sensor nodes 112, establishes wired communication with the associated sensor node 112, and establishes wireless communication with the center device 116 via the base station 118. The repeater 114 then establishes wireless communication with one or more surrounding sensor nodes 112 through the associated sensor node 112.

[0029] The central device 116 is configured as an information processing device such as a computer and is a device belonging to the administrator side, such as a gas company or an electric power company, and collects information from one or more relay devices 114 or transmits information to one or more relay devices 114.

[0030] Figure 2 is a block diagram showing the hardware configuration of the center device 116 according to this embodiment. As shown in Figure 2, the center device 116 includes a controller 11. The controller 11 is composed of a device including a general-purpose computer.

[0031] As shown in Figure 2, the controller 11 comprises a CPU (Central Processing Unit) 11A, a ROM (Read Only Memory) 11B, a RAM (Random Access Memory) 11C, and an input / output interface (I / O) 11D. The CPU 11A, ROM 11B, RAM 11C, and I / O 11D are connected to each other via a bus 11E. The bus 11E includes a control bus, an address bus, and a data bus.

[0032] Furthermore, the communication unit 12 and the storage unit 13 are connected to the I / O 11D.

[0033] The communication unit 12 is an interface for data communication with the meter 110 via the base station 118, the repeater 114, and the sensor node 112.

[0034] The storage unit 13 is composed of a non-volatile external storage device such as a hard disk. As shown in Figure 2, the storage unit 13 stores information processing programs 14, load survey data 15, usage management data 16, and historical data 17, etc.

[0035] CPU11A is an example of a computer. Here, "computer" refers to a processor in a broad sense, including general-purpose processors (e.g., CPUs) or specialized processors (e.g., GPUs: Graphics Processing Units, ASICs: Application Specific Integrated Circuits, FPGAs: Field Programmable Gate Arrays, programmable logic devices, etc.).

[0036] The information processing program 14 may also be stored in the storage unit 13 by being stored on a non-volatile, non-transitory recording medium, or by being distributed via a network and appropriately installed on the center device 116.

[0037] Examples of non-volatile, non-transitional recording media include CD-ROMs (Compact Disc Read Only Memory), magneto-optical disks, HDDs (Hard Disk Drives), DVD-ROMs (Digital Versatile Disc Read Only Memory), flash memory, and memory cards.

[0038] Figure 3 is a block diagram showing the functional configuration of the center device 116. As shown in Figure 3, the center device 116 functionally comprises a determination unit 20 and an estimation unit 21.

[0039] The CPU 11A of the central device 116 reads and executes the information processing program 14 stored in the memory unit 13, thereby functioning as the various functional units shown in Figure 3.

[0040] The determination unit 20 determines whether or not it was able to receive the amount of the measurement target from the meter 110 installed at the demand location. In this embodiment, the case where the measurement target is gas will be described. That is, the case where the amount of the geodetic target is gas usage will be described.

[0041] The central device 116 receives daily load survey data 15 from meters 110 at each demand location and stores it in the storage unit 13. Here, load survey data is time-series data of gas usage measured at regular time intervals, such as every hour or every 30 minutes. Based on this load survey data, daily gas usage can be calculated.

[0042] Figure 4 shows an example of usage management data 16. As shown in Figure 4, usage management data 16 is data that shows the correspondence between the date, whether load survey data was received or not, whether the guideline value was obtained or not, the guideline value, and the estimated usage. Here, the guideline value is the cumulative amount of gas used. Furthermore, here we will explain the case where the meter reading date is N days. The meter reading date is the day on which the guideline value is determined at a predetermined interval, for example, once a month, in order to bill the consumer for gas charges.

[0043] In the example in Figure 4, the meter reading date N was not the date for which remote load survey data 15 was received, and therefore the meter reading value could not be obtained for M days prior to that date. In other words, the load survey data 15 was received and the meter reading value could be obtained up to (NM), but the load survey data 15 could not be received from (N-M+1) to N, and therefore the meter reading value could not be obtained.

[0044] Therefore, if the estimation unit 21 determines that it cannot receive the gas usage amount to be measured, it estimates the usage amount for the day on which it could not receive the gas usage amount to be measured, based on the usage pattern for each day of the week at the demand location, which is obtained from past data 17 representing past usage amounts at the demand location.

[0045] Specifically, the estimation unit 21 calculates the guideline value for the meter reading date based on, for example, the most recently received gas usage data and the gas usage data for days when gas usage data could not be received, from the past data 17 regarding past gas usage.

[0046] In the example in Figure 4, since the remote load survey data 15 was not received on day N, the meter reading day, the guide value X was obtained from the load survey data 15 of day (NM), which is the most recent day when the load survey data 15 could be obtained, going back from day N. N-M This calculates the amount of gas used on (NM) days from the load survey data 15 on (NM) days. N-M Calculate the (NM-1) day guideline value X N-M-1 (NM) Day's gas usage W N-M By adding this, the guide value X for (NM) days is obtained. N-M Calculate.

[0047] The estimation unit 21 then estimates the gas usage for M days for which load survey data 15 could not be received. In the example in Figure 4, the gas usage for M days from (N-M+1) to N is Y1~Y. M We estimate this.

[0048] Gas usage Y1~Y for M days when load survey data 15 could not be received. M For example, we can estimate it as follows:

[0049] For example, the estimation unit 21 obtains usage patterns for each day of the week from historical data 17 representing past gas usage at demand locations, and estimates the amount of gas used on days when the load survey data 15 failed to be received, based on the obtained usage patterns for each day of the week.

[0050] The usage patterns for each day of the week refer to the correlation between gas usage and the day of the week. For example, for a particular location, gas usage may be low on Mondays, Wednesdays, and Fridays due to people going to work, and high on Tuesdays, Thursdays, Saturdays, and Sundays because people are at home. These are patterns specific to that location.

[0051] The estimating unit 21 obtains the usage pattern for each day of the week from the past data 17, and obtains the gas usage amounts Y1 to Y for the day when reception of the load survey data 15 failed M estimates the For example, based on data from a predetermined period in which reception of past load survey data 15 succeeded, the gas usage amounts Y1 to Y for the day when reception of load survey data 15 failed M may be estimated. For example, if the day on which reception of load survey data 15 failed is a Tuesday, the gas usage amount on the Tuesday of the previous week when reception of load survey data 15 succeeded is adopted; and if the day on which reception of load survey data 15 failed is a Thursday, the gas usage amount on the Thursday of the previous week when reception of load survey data 15 succeeded is adopted.

[0052] Next, the estimating unit 21 calculates the guideline value X for N days according to the following formula N is calculated.

[0053] X N =X N-M +(Y1+Y2+···+Y M-1 +Y M ) ···(1)

[0054] Note that a representative value of the usage amount for each day of the week may be calculated based on gas usage amounts over a plurality of weeks in which reception of past load survey data 15 succeeded, and the calculated representative value may be used as the gas usage amount for the day when reception of load survey data 15 failed. Examples of the representative value include an average value, a median value, and a weighted average value. In the case of a weighted average value, for example, a larger weight may be assigned to a date closer to the day when reception of load survey data 15 failed.

[0055] Note that there is a correlation between gas usage amount and air temperature. Accordingly, the estimating unit 21 may estimate the usage amount of the measurement target on the day when the usage amount of the measurement target gas could not be received, based on the usage pattern for each day of the week at the demand location and the air temperature on the day when the usage amount of the measurement target gas could not be received.

[0056] For example, the estimation unit 21 may estimate the amount of gas used on days when it failed to receive the load survey data 15, based on the usage patterns for each day of the week, and then calculate the amount of gas used on those days by performing a calculation, such as multiplication, using a coefficient corresponding to the temperature on those days when it failed to receive the load survey data 15. Since it is thought that the amount of gas used to heat water for bathing tends to increase as the temperature decreases, for example, the coefficient corresponding to the temperature may be set to a larger value as the temperature decreases.

[0057] Furthermore, the estimation unit 21 may derive a correlation between the day of the week, temperature, and gas usage based on usage data from multiple past weeks, and estimate the gas usage on days when the load survey data 15 could not be received based on the derived correlation. For example, the estimation unit 21 may derive a correlation between the day of the week, temperature, and gas usage from multiple weeks of past data 17, and estimate the gas usage on days when the load survey data 15 could not be received. Specifically, for example, multiple regression analysis may be performed based on the gas usage data from multiple weeks when the load survey data 15 was successfully received, a multiple regression equation may be derived to calculate gas usage using the day of the week and temperature as parameters, and the gas usage on days when the load survey data 15 could not be received may be estimated using the derived multiple regression equation.

[0058] Furthermore, if the estimation unit 21 can obtain usage amounts for other resources besides gas, such as electricity and water, it may determine whether the consumer at the demand location is using gas based on the obtained usage amounts for the other resources, and correct the gas usage amount based on the determination result.

[0059] Specifically, the estimation unit 21 obtains at least one of the electricity usage and water usage data for days when it failed to receive the load survey data 15, and determines whether gas is being used or not. If it is determined that gas is being used, the amount of gas used is expected to be high. Therefore, if it is determined that gas is being used on a day when it failed to receive the load survey data 15, the calculated amount of gas used is corrected by multiplying it by a predetermined positive coefficient or the like. This makes it possible to obtain the amount of gas used on days when the load survey data 15 could not be received with good accuracy.

[0060] Figure 5 is a flowchart showing the flow of information processing performed by the central device 116. The CPU 11A reads the information processing program 14 from the memory unit 13, loads it into the RAM 11C, and executes it, thereby performing the information processing shown in Figure 5. Note that the processing shown in Figure 5 is performed daily.

[0061] In step S100, the CPU 11A determines whether or not it was able to receive the load survey data 15 for the day on which this process is executed. If it was able to receive the load survey data 15 for the day, it proceeds to step S101; otherwise, it proceeds to step S102.

[0062] In step S101, the CPU 11A calculates the amount of gas used for the day based on the load survey data 15 received in step S100 and stores it in the storage unit 13.

[0063] In step S102, the CPU 11A estimates the amount of gas used for the day using the method described above and stores it in the memory unit 13.

[0064] In step S103, the CPU 11A calculates the current day's guideline value and stores it in the memory unit 13. That is, the current day's guideline value is calculated by adding the gas usage amount calculated in step S101 or estimated in step S102 to the previous day's guideline value and stored in the memory unit 13.

[0065] In step S104, the CPU 11A determines whether it is a meter reading day or not. If it is a meter reading day, the process proceeds to step S105. If it is not a meter reading day, the process returns to step S100 and repeats the same process as above.

[0066] In step S105, CPU11A determines the guideline value for the day as the guideline value for the month.

[0067] In this embodiment, it is determined whether or not gas usage data can be received from the meter 110 installed at the demand location. If it is determined that gas usage data cannot be received, the gas usage for the day on which the data could not be received is estimated based on the usage pattern for each day of the week at the demand location obtained from past data 17, and a guideline value is calculated. This allows for accurate meter reading without changing the meter reading date.

[0068] The configuration of the meter remote reading system 100 and the center device 116 described in the above embodiment is an example, and may be modified as needed without departing from the main purpose.

[0069] Furthermore, the program processing flow described in the above embodiment is just one example, and unnecessary steps may be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0070] Furthermore, in the above embodiment, each process that the CPU reads and executes the software (program) may be executed by various processors other than the CPU. Examples of processors in this case include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacturing, and dedicated electrical circuits such as ASICs (Application Specific Integrated Circuits) which have a circuit configuration specifically designed to execute a particular process.

[0071] Furthermore, the operation of the processor in the above embodiment may not be performed by a single processor, but may be performed by multiple processors located in physically separate locations working together. Also, the order of the processor operations is not limited to the order described in the above embodiment, but may be changed as appropriate.

[0072] Furthermore, although the above embodiment describes a configuration in which the information processing program is pre-stored (installed) in ROM, the invention is not limited to this. The program may be provided in the form of a recording medium such as a CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), and USB (Universal Serial Bus) memory. Alternatively, the program may be provided in the form of a download from an external device via a network. This disclosure is also applicable to programs and program products. [Explanation of symbols]

[0073] 11 Controllers 12 Communications Department 13 Storage section 14. Information Processing Programs 15 Load Survey Data 16. Usage Management Data 17. Past data 20 Judgment section 21 Estimation part 100-meter remote meter reading system 110 meters 112 Sensor Nodes 114 Repeater 116 Centering device 118 Base Station

Claims

1. A determination unit that determines whether or not it was possible to receive the amount of usage to be measured from a meter installed at the demand location, If it is determined that the usage amount of the target to be measured cannot be received, an estimation unit estimates the usage amount of the target to be measured on days when the usage amount of the target to be measured could not be received, based on the usage pattern of the demand location for each day of the week, obtained from past data representing the past usage amount of the demand location. Equipped with, The estimation unit determines whether a user at the demand location is using the target of measurement based on the amount of other resources used, and corrects the amount of the target of measurement based on the determination result. Information processing device.

2. A determination unit that determines whether or not it was possible to receive the amount of usage to be measured from a meter installed at the demand location, If it is determined that the usage amount of the target to be measured cannot be received, an estimation unit estimates the usage amount of the target to be measured on days when the usage amount of the target to be measured could not be received, based on the usage pattern of the demand location for each day of the week, obtained from past data representing the past usage amount of the demand location. Equipped with, The estimation unit calculates the guideline value on the meter reading date based on the most recently received data on past usage and the usage of the target object on days when data on the target object could not be received. Information processing device.

3. The estimation unit estimates the usage amount of the target object on days when the usage amount of the target object could not be received, based on the usage pattern of the demand location for each day of the week and the temperature on days when the usage amount of the target object could not be received. The information processing apparatus according to claim 1 or claim 2.

4. The estimation unit derives a correlation between the day of the week, temperature, and usage amount based on usage amounts over several past weeks, and estimates the usage amount of the target object on days when usage amounts for the target object could not be received, based on the derived correlation. The information processing apparatus according to claim 3.

5. The estimation unit derives the multiple regression equation as the correlation by performing a multiple regression analysis based on usage amounts over several past weeks. The information processing apparatus according to claim 4.

6. The estimation unit calculates a representative value for usage per day of the week based on usage over several past weeks, and uses the calculated representative value as the usage of the target object for days on which the usage data for the target object could not be received. The information processing apparatus according to claim 1 or claim 2.

7. The aforementioned representative value is one of the mean, median, or weighted mean. The information processing apparatus according to claim 6.

8. Computers Determine whether or not the amount of the measured substance used was received from the meter installed at the point of demand. If it is determined that the usage data for the item to be measured cannot be received, the usage data for the item to be measured on the day when it could not be received is estimated based on the usage patterns for each day of the week at the demand location, which are obtained from past data representing past usage at the demand location. An information processing method that performs a process including the following: The estimation involves determining whether a user at the demand location is using the measured object based on the usage of other resources besides the measured object, and correcting the usage of the measured object based on the determination result. Information processing methods.

9. Computers Determine whether or not the amount of the measured substance used was received from the meter installed at the point of demand. If it is determined that the usage data for the item to be measured cannot be received, the usage data for the item to be measured on the day when it could not be received is estimated based on the usage patterns for each day of the week at the demand location, which are obtained from past data representing past usage at the demand location. An information processing method that performs a process including the following: The aforementioned estimation calculates the guideline value on the meter reading date based on the most recently received data on past usage and the usage data for the measured subject on days when the usage data for the measured subject could not be received. Information processing methods.

10. On the computer, Determine whether or not the amount of the measured substance used was received from the meter installed at the point of demand. If it is determined that the usage data for the item to be measured cannot be received, the usage data for the item to be measured on the day when it could not be received is estimated based on the usage patterns for each day of the week at the demand location, which are obtained from past data representing past usage at the demand location. An information processing program that causes a process to be performed that includes the following: The estimation involves determining whether a user at the demand location is using the measured object based on the usage of other resources besides the measured object, and correcting the usage of the measured object based on the determination result. Information processing program.

11. On the computer, Determine whether or not the amount of the measured substance used was received from the meter installed at the point of demand. If it is determined that the usage data for the item to be measured cannot be received, the usage data for the item to be measured on the day when it could not be received is estimated based on the usage patterns for each day of the week at the demand location, which are obtained from past data representing past usage at the demand location. An information processing program that causes a process to be performed that includes the following: The aforementioned estimation calculates the guideline value on the meter reading date based on the most recently received data on past usage and the usage data for the measured subject on days when the usage data for the measured subject could not be received. Information processing program.

12. A meter installed at the point of demand, The information processing device according to claim 1 or claim 2, connected to the meter via a network, A remote meter reading system including this.

Images