JP2012181789A - In-home probability calculating system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a system for calculating an in-home probability using information of a power consumption.SOLUTION: An electric power company server 1 calculates information indicating a state being at home using information of a power consumption detected by an automatic meter reading system. The information of the power consumption is converted into an "in-home probability" in a specified future time based on past power consumption information after adding behavioral characteristics of each household. Preferably, the "in-home probability" may be provided to a service user, and the service user can schedule a user's house visiting plan using the "in-home probability" as a criterion. Furthermore, a degree of certainty can be improved by adding data up to the given time thereto before the specified future time, recalculating the "in-home probability", and performs a second notification.

Description

  The present invention relates to a technique for determining the status of staying in a house or the like. In particular, the present invention relates to information processing technology for notifying the home status for home delivery services. At this time, information on the amount of power used is used.

  A home delivery company, a door-to-door sales company, a security company, and the like desire to grasp the user's home status in order to provide the service. For this reason, for example, when a delivery company delivers a delivery item to a user, the user can visit the user's home and deliver the delivery item when the user is at home. Then, the delivery company carries out the re-delivery by taking the delivery item home and calling the delivery company to the delivery company.

As a technique for improving the efficiency of business by knowing whether the customer is at home or not at that time, Patent Document 1 is cited. Patent Document 1 discloses a technique for determining presence / absence based on a probability that a business operator automatically calls a user and calls the user.
Further, Patent Document 2 is cited as a means for determining whether or not a resident is at home based on the amount of power used, and a data processing method thereof.

JP-A-5-136888 JP 2009-217666 A

  However, in Patent Document 1, no consideration is given to a case where the user cannot answer a call even when at home, or a case where the user does not own the landline telephone, and it is determined that the user is at home although he / she is at home. I can't. Further, in Patent Document 2, since the purpose is achieved if the fact that electricity is used even if it is in a minute amount, the purpose is reached. Therefore, the use of electricity by the reservation function when absent is not taken into consideration, and it is determined that the electricity is correctly absent. I can't.

  Therefore, in the present invention, information indicating the home status is calculated using the information on the power consumption. In the present invention, the power usage information is processed into a “home probability” at a specified future time after taking into account the behavioral characteristics of each home based on the past usage information. More preferably, it is provided to the service user, and the service user can make a user home visit schedule based on the “home probability” as a criterion. Furthermore, before the designated future time is taken into account, the “at home probability” is recalculated taking into account the data up to that point in time, and notification is made again, thereby further improving the accuracy.

  According to the present invention, it is possible to reduce the number of times of take-out at the time of delivery, and it is possible to reduce the labor and re-delivery cost of the delivery company. In addition, the user can eliminate or reduce the re-delivery procedure.

System utilization image diagram (conceptual diagram) in one embodiment of the present invention Configuration diagram of home probability notification system in one embodiment of the present invention Whole home probability notification process flowchart in one embodiment of the present invention The flowchart in the at-home probability calculation process in one embodiment of the present invention The figure which shows the table used for the at-home probability calculation process in one Embodiment of this invention. The figure which shows the idea of the at-home probability calculation formula in one Embodiment of this invention.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
FIG. 1 shows a usage image of this system. As a precondition for this embodiment, an automatic meter reading system is introduced at the user's home, and the power company acquires the power usage every 30 minutes and maintains a load profile that stores the power consumption characteristics of the user. Yes. For example, a user can conventionally set a home delivery company and a date and time on the WEB when purchasing a product at an online shop or the like. In this system, the user selects whether to provide home probability information to the home delivery company. This home probability information provision permission data is transmitted to the server of the electric power company. The electric power company calculates the home probability based on the user's load profile. The delivery company inquires of the power company system about the probability of the user being at home. When the user has permission, the power company provides the home delivery probability information to the delivery company. The home delivery company selects a date and time when the user's home probability is high, and performs delivery.

FIG. 2 shows a configuration diagram of the home probability notification system in the present embodiment.
An automatic meter-reading system 2 is installed at the user's home, and the user's power usage is measured every 30 minutes, and the result is transmitted via the network 8 and stored in the power usage DB 12 of the power company. . The automatic meter reading system 2 is composed of a communication infrastructure and a power meter, and the power meter ID and installation location information are stored in the power meter DB 21. The notification permission information DB stores information such as the meter ID and which courier service the user is permitted to notify. For the user who has given the notification permission, the home probability calculation unit 15 creates a home probability table and calculates the home probability (the details of this process will be described later). When there is a request from the delivery company, the home probability notification process returns the home probability of the designated user date and time to the delivery company. The notification setting unit 17 determines whether or not to reply to the home delivery company. The notification permission determination process of the notification setting unit confirms whether or not the delivery service provider is permitted to notify in the notification permission information DB. The home delivery company authentication information DB registers a home delivery company that can be trusted in advance, and only the registered home delivery company can use the service. The use authentication unit 16 performs home delivery company authentication processing. When the delivery is completed, the notification permission is canceled by the notification excluding process of the notification setting unit 17. Each of the above parts is realized as a part or all of a so-called program. That is, each process is performed by a CPU (not shown) of the power company server 1 according to these programs. Details of these processes will be described later using a flowchart and the like.

  The delivery company system (for example, the B delivery company system 5) includes a delivery information DB 51, a delivery processing unit, a usage authentication unit 52, and a delivery staff authentication information DB 54. The delivery staff can browse and confirm the delivery destination by using the portable terminal by the delivery information inquiry processing of the delivery processing unit 52 (that is, access information stored in the server 52 via the communication network). To do). The delivery staff authentication process of the usage authentication unit 53 performs authentication so that a third party other than the delivery staff cannot view. The delivery staff authentication information DB stores delivery staff IDs and passwords. Note that the processing of each part of the server is realized by processing with a CPU according to a program, as with the power company server 1.

FIG. 3 is a flowchart of the at-home probability calculation process according to the present embodiment.
The terminal device (terminal A3) used by the user registers the use of the at-home probability information notification service online for each item to be delivered according to the input from the user, and registers it as notification permission information in the notification permission information DB of the power company (S1-3). On the other hand, the power company server 1 creates and saves a load profile from the power usage data (stored in the power usage 21) transmitted at 30-minute intervals from the automatic meter reading system 2 at the user's house. Although this road profile is shown in FIG. 6B, it is a graph (excluding home and absence), and is information indicating the amount of power for each time (S3). Next, the power company server 1 determines the maximum value and the minimum value of power consumption per unit time from the past load profile 365 days in the server of the power company for the user registered to use the at-home probability information notification service. It is acquired and set as a variable for home probability calculation (S4). This variable corresponds to “MAX” and “MIN” in the calculation formula 1 in FIG. As the maximum value and the minimum value, a so-called maximum value or minimum value is more preferable.

  Then, the electric power company server 1 creates a user home probability table in the delivery service time zone of the delivery company (that is, calculates the home probability), and calculates and stores the home probability at regular time intervals (S5). This storage may be stored in the power usage DB 11 or the power meter DB in correspondence with the corresponding ID, or a separate home probability table (not shown) is provided to identify the user such as the ID or address. Stored in association with the information to be processed. Here, the user to be registered may be a group (organization) such as a family or a workplace in addition to an individual unit. Details of the home probability calculation are described in the flow A of FIG. This content will be described later together with FIG.

  The delivery company server 5 inquires of the power company about the home probability by designating the address of the user who is the delivery destination of the delivery item and the desired delivery date and time (S6). In this case, the person in charge of the B delivery company usually inputs an inquiry request when trying to deliver or when making a delivery plan, and transmits information for inquiry accordingly. Here, information for inquiring including information specifying the user is transmitted. The information for specifying the user may be the address and telephone number shown in FIG. 5, or the same ID may be used by the power company and the delivery company. In this case, this ID is described in the delivery slip. Further, a meter ID may be used as this ID. Further, the information for inquiring may include one or more desired delivery times, and the home probability of these times may be returned.

  In response to this, the electric power company server 1 reads out the home probability of the user at the relevant date and time from the home probability table and returns it to the delivery company (S5). For S5, the home probability may be calculated in advance as described above and stored in a table, or the home probability of the desired delivery time required for each inquiry in S6 may be calculated and returned. Also good. In the latter case, the power company server 1 may not store the home probability. Furthermore, the home probability calculation unit 15 may be provided in the delivery company server 5 and calculated by the server 5. In this case, the electric power company server 1 transmits the load profile to the server 5.

  Next, the courier company determines whether delivery is to be performed at the date and time based on the answered home probability. Repeat the inquiry. Although a series of processing on the electric power company side is automatically performed, the process such as inquiry and determination on the home delivery company side is not limited to whether it is automatic or manual. That is, in the present embodiment, the information processing apparatus executes, but a person may confirm using a telephone or the information processing apparatus. Hereinafter, processing when this confirmation is performed by the apparatus (home delivery company server 5), that is, processing of S7 to S10 will be described. In addition, this demonstrates the case where the electric power company server 1 calculates and stores the at-home probability. These processes are executed by the delivery processing unit 52 (program). The delivery company server 5 receives the at-home probability transmitted in response to S6 (S6). Next, it is determined whether the received at-home probability is higher than a predetermined value (S8). This predetermined value may be a constant value or a value determined for each user. As a result, if NO, the process ends or returns to S6. If YES, the process proceeds to S9.

  If S8 is YES, the desired delivery date and time are determined as the scheduled delivery date and time (delivery date and time). Then, this is stored in the delivery information DB 51. In this case, as shown in FIG. 5, a delivery ID is generated, the address in S6 is stored in the delivery address, and other information is recorded in the corresponding location. Alternatively, “delivery address” may be searched using the address in S6 as a key and stored in the corresponding location.

  Here, the delivery date / time is stored, but when the time from the storage (and calculation) to the delivery date / time is empty, the closer the delivery date / time is, the higher the accuracy is. Therefore, recalculation may be performed as follows.

  It is determined whether or not the scheduled delivery date is O hours before the current time (that is, a predetermined time) (S10). In the case of NO, the step is repeated (wait until ○ time = return). In the case of YES, recalculation is requested to the electric power company server 1 (S11). This transmits request information including the above-mentioned ID.

Then, the power company server 1 determines the matching rate between the user-specific daily load curve pattern and the pattern up to the inquiry point on the day of the scheduled delivery date, reviews / updates the home probability, An answer is transmitted to the server 5 (S12). Note that the determination by the usage authentication unit 16 and the notification setting unit 17 in the power company server 1 may also be made in the case of an inquiry from the probability re-inquiry process before the scheduled time. Also, the calculation of the relevance ratio here is realized by the flow B of FIG. The contents will be described later.
Thereafter, the delivery company server 5 determines whether delivery is possible based on the latest home probability (S13, 14). As a result, when a predetermined criterion is satisfied, such as being greater than a predetermined precision, if “at-home probability is high” is determined in S14, information is output so that the delivery company performs delivery (S15). This may be output to terminals B6 and C7 used by the delivery staff.

  According to this output, when delivery is delivered to the user or when the delivery time limit determined by the delivery company is exceeded, these events are detected or input, and delivery completion setting processing is performed at the server 5 of the delivery company (S16, 17). In addition, the power company server 1 is accessed from the delivery completion setting process, the notification disapproval setting process is performed, and the user's authority to acquire at-home probability information is deleted. On the other hand, when the user is absent and the delivery is not received within the storage period, the delivery company accepts information from the terminal B or C (6.7) that the delivery has been brought back to the office, The inquiry about the home probability to the electric power company server 1 is executed again.

  FIG. 5 is a table used for the home probability calculation process. The concept of the home probability calculation formula will be described with reference to the table of FIG. 5 and FIG. Further, the processing described here follows the flows A and B shown in FIG.

  FIG. 6A shows an example of a load profile of a user in a certain time zone. The horizontal axis is time, and the vertical axis is power consumption. A meter reading at 30 minute intervals is assumed. As shown in this figure, since it is assumed that home appliances such as lighting and an air conditioner are used at home, the amount of power used increases, and generally the amount of power used decreases when going out. However, just because the amount of power used is small, it does not always go out. When the climate is calm, such as in spring and autumn, the frequency of use of air conditioners will decrease. Therefore, the possibility of not going out is defined as “at-home probability (%)”.

  FIG. 6B is an example of a daily load profile of the user. In general, people perform cyclic actions, so when you look at the road profile shown in the figure, it is assumed that the power consumption is low and the flat area is standby power, going out or sleeping. Is done. However, when a computer, not a human being, judges, it is necessary to define whether the user is at home or absent from some threshold. It should be noted that the value of the standby power varies depending on the home and varies depending on the replacement of home appliances.

  FIG. 6-3 is an example for explaining the concept of the at-home probability calculation formula, and represents a meter reading value for a certain 30 minutes assuming meter reading at intervals of 30 minutes. MAX and MIN values can be defined from the user's past load profile. When the meter reading value for 30 minutes is X, the closer to X, the higher the home probability, and the closer to MIN, the lower the home probability. This home probability is not proportional to the value of X, and it can be assumed that the probability is high as a quadratic curve. In other words, just because the power consumption is half of MAX, the at-home probability should be larger than half.

  FIG. 6-4 shows an at-home probability calculation formula, and the processing contents will be described below with reference to FIGS. This process is executed by the at-home probability calculation unit 15. MAX is the maximum amount of power used by a certain user, and MIN represents the standby power of the user. The contents studied in FIGS. 5-1 to 5-3 are combined, and the at-home probability is defined as the square of X-MIN divided by MAX-MIN. Here, as described above, the maximum value and the minimum value are more preferable.

  For example, if the day of the week that the courier company wants to deliver to the user is Monday, then the probability of being home on Monday of the previous week, the probability of being home on Monday of the previous week, and the same day of the week before that, Go back about the past year, add home probabilities, divide by the number, define the final home probabilities, and respond to the courier's request.

DESCRIPTION OF SYMBOLS 1 ... Electric power company server, 2 ... User home automatic meter-reading system, 3 ... Terminal A (used by the user) 4, 4, 5 ... Delivery agent server, 6, 7 ... (Used by delivery staff) Terminals B, C

Claims (5)

In the home probability calculation system that calculates the home probability indicating the human home situation,
An automatic meter-reading device that is installed at the location of the person and detects the amount of power used at the location;
A power company device connected to the automatic meter reading device and a network,
Means for receiving the power usage;
Means for specifying the maximum and minimum values of the power consumption;
The calculation request for the home probability is received and includes a calculation request, and a local maximum value and a local minimum value related to the time are specified, and the home probability is calculated based on the specified content. An at-home probability calculation system comprising: an electric power company device having means for calculating. In the at-home probability calculation system according to claim 1,
The means for calculating the at-home probability predicts the power usage amount at the time from the specified maximum value and minimum value, and based on the predicted power usage amount, the maximum value and minimum value A home probability calculation system for calculating a home probability so that the home probability is high in a quadratic curve in relation to values. In the at-home probability calculation system according to claim 1 or 2,
Further comprising a courier device connected to the network;
The home power calculation system, wherein the power company device transmits the home probability to the home delivery device. In the at-home probability calculation system according to any one of claims 1 to 3,
The home probability calculation system, wherein the calculating means recalculates the home probability at a predetermined timing after the home probability is calculated. In the at-home probability calculation system according to claim 4,
The home probability calculation system according to claim 1, wherein the calculating means recalculates a preset behavior situation regarding the human home using time-series data.

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