JP2007004638A - Information circulation predicting device, information circulation predicting method and information circulation prediction system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an information circulation predicting device, information circulation predicting method, and information circulation prediction system, which predict the transition of a data circulation rate beforehand. <P>SOLUTION: The information circulation predicting device is provided with: an input means 200 for inputing a parameter which predicts a circulation rate in a prescribed group of transfer information and is related to prescribed information; an information circulation rate calculating means 201 for calculating the circulation rate of the transfer information on the basis of a parameter by which a portion of elements of a prescribed function to be calculated is input and calculating the circulation rate of the transfer information on the basis of the prescribed function by which the portion of elements are calculated and of the input parameter; and an output means 202 for outputing the calculated circulation rate. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an information spread prediction device, an information spread prediction method, and an information spread prediction system that predict a transition of spread of things and information.

One way to model changes in the population of various species is the logistic equation. The logistic equation is a mathematical model that explains the saturation phenomenon of the population, published by the Dutch mathematical biologist Verhuls. This model is a phenomenon model in which it gradually increases in the initial stage, then increases rapidly, and finally becomes saturated. At present, this model is applied to long-term forecasts such as changes in mobile phone penetration and license ownership. Note that a description regarding the logistic equation is disclosed in Non-Patent Document 1 below.
http://mathworld.wolfram.com/LogisticEquation.html

  On the other hand, when distributing information and data (hereinafter, also simply referred to as data) to network information devices such as in-vehicle information devices and mobile terminals, all network information devices can be made in a short time depending on the importance (urgency) of the data to be transmitted. May need to be distributed to. At this time, we want to predict the data penetration rate in order to know the data transmission time in advance. However, there is no short-term prediction for predicting the transition of the data penetration rate when data is transmitted to network information devices such as in-vehicle information devices and portable terminals. This is because the conventional prediction is adapted to some obtained data, and therefore, when the data fluctuation is large as in the short-term prediction, an appropriate prediction cannot be made.

  An object of the present invention is to solve the above-described problem, and to provide an information diffusion prediction device, an information diffusion prediction method, and an information diffusion prediction system that can predict a transition of a data diffusion rate in advance. And

  In order to achieve the above object, according to the present invention, an input means for inputting a parameter related to predetermined information that is a parameter for predicting a diffusion rate in a predetermined group of transmission information, and the diffusion of the transmission information A part of a predetermined function for calculating a rate is calculated based on the input parameter, and the transfer function is calculated based on the predetermined function for which the part is calculated and the input parameter. There is provided an information diffusion prediction apparatus comprising an information transmission diffusion rate calculating unit for calculating the diffusion rate and an output unit for outputting the calculated diffusion rate. With this configuration, the transition of the data (information) penetration rate can be predicted in advance.

  Further, in the information spread prediction device of the present invention, when a parameter related to specific information is input when the parameter is input, the information transmission spread calculation means includes the parameter related to the specific information and the specific information. It is a preferable aspect of the present invention to calculate the penetration rate based on a parameter obtained from a parameter database based on a part of parameters of the input parameters other than the parameter relating to and on the input parameters. With this configuration, it is possible to predict the transition of the diffusion rate of data (information) in advance by inputting the minimum necessary parameters.

  In addition, according to the present invention, a step of inputting parameters related to predetermined information for predicting a diffusion rate in a predetermined group of transmission information, and a predetermined function for calculating the diffusion rate of the transmission information Calculating a part of the transmission information based on the input parameter, and calculating the penetration rate of the transmission information based on the predetermined function for which the part of the element is calculated and the input parameter And a method of outputting the calculated diffusion rate is provided. With this configuration, the transition of the data (information) penetration rate can be predicted in advance.

  In the information spread prediction method of the present invention, when a parameter related to specific information is input when the parameter is input, in the step of calculating the spread rate, the parameter related to the specific information and the specific It is a preferable aspect of the present invention to calculate the penetration rate based on a parameter obtained from a parameter database based on a part of parameters of an input parameter other than a parameter related to information on the information and an input parameter . With this configuration, it is possible to predict the transition of the diffusion rate of data (information) in advance by inputting the minimum necessary parameters.

  Further, according to the present invention, an information diffusion prediction device capable of predicting a diffusion rate in a predetermined group of transmission information inputs a part of elements of a predetermined function for calculating the diffusion rate of the transmission information. Calculated based on the predetermined parameters related to the predetermined information, and calculated the prevalence rate of the transmission information based on the predetermined function for which the partial element is calculated and the input parameters, and the calculated An information diffusion prediction system is provided that outputs a diffusion rate and a terminal device connected to the information diffusion prediction device via the network acquires the diffusion rate calculated by the information diffusion prediction device via the network. The With this configuration, the transition of the data (information) penetration rate can be predicted in advance.

  In the information spread prediction system of the present invention, when the information spread prediction device receives a parameter related to specific information when the parameter is input, the information spread prediction apparatus relates to the parameter related to the specific information and the specific information. Calculating the penetration rate based on the parameters obtained from the parameter database based on some parameters of the input parameters other than the parameters, and the input parameters, and outputting the calculated penetration rate, This is a preferred embodiment of the present invention. With this configuration, it is possible to predict the transition of the diffusion rate of data (information) in advance by inputting the minimum necessary parameters.

  The information diffusion prediction device, the information diffusion prediction method, and the information diffusion prediction system of the present invention have the above-described configuration, and can predict the transition of the data diffusion rate in advance.

  Hereinafter, an information spread prediction device, an information spread prediction method, and an information spread prediction system according to embodiments of the present invention will be described with reference to FIGS. FIG. 1 is a configuration diagram showing a configuration of an information diffusion prediction system according to an embodiment of the present invention. FIG. 2 is a block diagram showing the configuration of the information spread prediction apparatus according to the embodiment of the present invention. FIG. 3A is a diagram showing an information transmission diffusion rate represented by the screen output unit of the information diffusion prediction device according to the exemplary embodiment of the present invention. FIG. 3B is a diagram showing an information transmission diffusion rate represented by the file output unit of the information diffusion prediction device according to the exemplary embodiment of the present invention. FIG. 4 is a diagram showing a screen image displayed on the display means by the screen output unit of the information spread prediction apparatus according to the embodiment of the present invention. FIG. 5 is a flowchart showing a flow from the calculation of the information transmission diffusion rate to the output of the result in the information diffusion prediction apparatus according to the embodiment of the present invention. FIG. 6 is a flowchart showing a parameter check flow in the process of calculating the information transmission diffusion rate in the information diffusion prediction apparatus according to the embodiment of the present invention.

  First, the configuration of the information diffusion prediction system according to the embodiment of the present invention will be described with reference to FIG. As shown in FIG. 1, the information spread prediction system 100 includes an information spread prediction apparatus 101 and terminals 103 a and 103 b connected to the information spread prediction apparatus 101 via a network 102. The information diffusion prediction device 101 is a device that predicts the transition of the diffusion rate of data and information. Details of the information spread prediction apparatus 101 will be described later. The terminals 103a and 103b can acquire the result output by the information diffusion prediction device 101 via the network 102. Note that the number of terminals is not limited to two. The network 102 here refers to the Internet, for example.

  Next, the configuration of the information spread prediction apparatus according to the embodiment of the present invention will be described with reference to FIG. As shown in FIG. 2, the information spread prediction apparatus 101 includes a parameter input unit (corresponding to the above-described input unit) 200, an information spread rate calculating unit (corresponding to the above-described information transmission spread rate calculating unit) 201, and a result output unit ( 202, a communication unit 203, a screen output unit 204, a file output unit 205, and a basic parameter database 206. The parameter input unit 200 is used to input parameters used when calculating an information transmission diffusion rate (corresponding to the above-described diffusion rate) of data transmitted by the information diffusion rate calculation unit 201 described later. Here, the input parameters include, for example, information on the total number of network information devices connected to a predetermined network, data dissemination (transmission) method information, network information device type information, network This includes information on the presence density of information devices, information on the operation rate of network information devices, information on communication error rates in data dissemination (transmission) methods, information on initial values of information transmission dissemination rates, and information on processing termination conditions.

  Here, the network information device connected to the network refers to a device connected to a predetermined network that spreads certain data (for example, map data, weather information, news information, advertisements, update programs, etc.). These include in-vehicle information devices, portable terminals, PDAs (Personal Digital Assistance), portable music playback devices, PCs (Personal Computers), cameras, printers, televisions, videos, and the like mounted on vehicles such as navigation systems. Further, a data dissemination (transmission) method (hereinafter also referred to as an information transmission method) includes a communication unit and a transmission unit.

  The communication means includes wide-area network communication, narrow-area network communication, vehicle-to-vehicle communication, the Internet (fixed and wireless), and composite communication of these. The transmission means is broadcast, unicast, multicast, other transmission means, or the like. For example, a network information device A having data transmits data to other network information devices B and C, and the network information device B to which the data is transmitted further transmits the data to the network information device C. The same data transmitted from is transmitted.

  The type of network information device refers to a network information device to which data is transmitted, and includes an in-vehicle information device, a portable terminal, a PDA, a portable music player, a PC, mounted on a vehicle such as the car navigation system described above. Say camera, printer, TV, video, etc. Further, the existence density of network information devices refers to the ratio of network information devices. That is, it indicates how many network information devices exist within a predetermined network range for transmitting data. Further, the process termination condition refers to a condition such as until a certain time, until a certain diffusion rate, until all spread. The information transmission penetration rate is calculated by inputting all the parameters described above.

  On the other hand, even if not all the parameters described above are input, the information transmission method information, the network information device type information, the initial value of the information transmission penetration rate, the processing end condition information, and the assumed area information The information transmission rate can also be calculated by inputting the information. Here, the assumed area refers to, for example, an area in which certain data is spread. For example, when the entire area of Tokyo is targeted, “Tokyo” is designated. By specifying “Tokyo” and specifying the type of network information device, for example, “mobile terminal”, the total number of mobile terminals existing in Tokyo can be known, and the density of the mobile terminals can also be known. In addition, information on the operating rate of the mobile terminal and information on the communication error rate described above can also be found. Therefore, the information transmission penetration rate can be calculated without inputting parameters related to the information. Information such as the total number of portable terminals used at this time is stored in the basic parameter database 206. The basic parameter database 206 is a constituent element of the information diffusion prediction apparatus 101, but may be configured independently of the information diffusion prediction apparatus 101.

  The information diffusion rate calculation unit 201 calculates the information transmission diffusion rate of transmitted data based on the input parameters and a predetermined function for calculating the diffusion rate of transmitted data. Here, the predetermined function will be described. The predetermined function here is an information transmission spread prediction function (logistic equation). The function is represented by the following formula (1).

  Here, x (t) is an information transmission penetration rate at time t, N is the total number of network information devices, C is a constant (information transmission penetration rate at time t = 0), r is an information transmission increase rate, and ρ is a proportional constant. , P is the information transfer probability. r is calculated based on the information such as the information transmission method, the existence density of network information devices, the operation rate of network information devices, the communication error rate of the information transmission method, and the like. r may be calculated by the information penetration rate calculation unit 201 described above, or may be calculated by a calculation unit (not shown). Details of the calculation of r will be described later. Also, ρ is calculated based on information such as the information transmission method described above and the presence density of network information devices. Further, p is calculated based on information such as the above-described density of network information devices, the operation rate of the network information devices, and the communication error rate of the information transmission method.

The aforementioned r is calculated by the information transmission increase rate evaluation function f. Here, the existence density of the above-mentioned network information device is d (units / m ² ), the daily operation rate of the network information device is a (%), and the communication error rate that is the probability of one communication error. When e (%), r is represented by the following formula (2).

For example, when d is 1 (car / m ² ), a is 50 (%), and e is 1 (%), r can also be obtained by the following calculation. It is 1 * 0.5 * 0.01 = 0.005. That is, r is 0.005. In this case, r is obtained by d × a × e, but can be obtained by another equation. In this case, the communication error rate 0.01 is a numerical value that assumes only cellular phone communication. However, when other network information devices are mixed, the communication error rate in the case of mixing is considered. There is a need.

Hereinafter, calculation of a communication error rate when network information devices are mixed will be described. The communication error rate e is calculated by the evaluation function f ′ and is expressed by the following equation (3). Note that e _i indicates the communication error rate of each communication means.

Here, the total number of the network information device N, a communication error rate e _i when the number n _z of the network information device a communication error rate using the communication means z of e _z is calculated by the following formula (4) The

  For example, out of a total of 100 network information devices, 50 network information devices using mobile phone communication with a communication error rate of 0.01, and networks using a wireless LAN with a communication error rate of 0.03 The communication error rate e when the number of information devices is 30 and the number of network information devices that use vehicle-to-vehicle (P2P) communication with a communication error rate of 0.05 is 20 can be obtained by the following equation (5). it can. According to equation (5), the communication error rate e is 0.024. Note that the equation for calculating the communication error rate is an example, and it can be calculated by other equations.

  The result output unit 202 outputs the information transmission penetration rate calculated by the information penetration rate calculation unit 201. The screen output unit 204 displays the information transmission penetration rate output from the result output unit 202 on a display unit such as a display. For example, the information transmission penetration rate is graphed as shown in FIG. 3A. The file output unit 205 displays the information transmission diffusion rate output from the result output unit 202 on a display unit such as a display, and displays the information in a format as shown in FIG. 3B, for example. The display format is, for example, text, CSV (Comma Separated Value), XML (eXtensible Markup Language), plot format, or the like.

  When displayed on the display means by the screen output unit 204, for example, a screen image as shown in FIG. 4 is obtained. The same format is used when displayed on the display means by the file output unit 205, but when displayed by the file output unit 205, the graph display area is displayed instead of the one shown in FIG. 3B. The communication unit 203 communicates with the external terminals 103a and 103b and the like via the network 102, and transmits information such as the output information transmission penetration rate to the terminals 103a and 103b.

  Next, the flow from the calculation of the information transmission penetration rate to the output of the result in the information diffusion prediction apparatus according to the embodiment of the present invention will be described with reference to FIG. First, parameters are input via the parameter input unit 200 (step S501). Next, it is checked whether or not parameters have been appropriately input (step S502). Details of the processing in step S502 will be described later. When an appropriate parameter is input, an information transmission diffusion rate is calculated (step S503). In addition, before the information transmission penetration rate is calculated, r, which is a component of the function shown in Expression (1) used when calculating the information transmission penetration rate, is calculated based on the parameter.

  Then, it is determined whether or not a preset end condition is satisfied (step S504). If the end condition is satisfied, the information transmission diffusion rate is output (step S505). Then, a display (such as the display shown in FIG. 4) based on the information transmission diffusion rate output by the screen output unit 204 or the file output unit 205 is displayed on the display means (step S506). On the other hand, when the end condition is not satisfied, the time is returned by one step, the process of step S503 is performed, and the process is performed until the end condition is satisfied.

  Here, the contents of the parameter check in step S502 will be described with reference to the flowchart of FIG. First, it is checked whether or not information on the assumed area has been input (step S601). If the information of the assumed area is input, it is checked whether information of the information transmission method is input (step S602). If information on the information transmission method has been input, it is checked whether information on the type of network information device has been input (step S603). If the network information device type information has been input, it is checked whether or not the initial value of the information transmission penetration rate and the information on the end condition of the processing have been input (step S604).

  If the initial value of the information transmission diffusion rate and the information about the process end condition are input, the process proceeds to step S503 in FIG. 5 described above. In Steps S602 to S604, if the information to be input is not input, a message indicating that the information is not input is output to the display means or the like (Step S605), the parameter is input, and the process returns to the corresponding step. Judge again. Regarding other necessary information to be input (information on the total number of network information devices, information on the existence density of network information devices, information on the operation rate of network information devices, information on communication error rates, etc.), as described above, It can be acquired from the basic parameter database 206 based on the input information of the assumed area and information of the information transmission method.

  On the other hand, if no information on the assumed area is input in step S601, it is first checked whether information on the total number of network information devices has been input (step S606). If information on the total number of network information devices has been input, it is checked whether information on the information transmission method has been input (step S607). If information on the information transmission method has been input, it is checked whether information on the type of the network information device has been input (step S608). If information on the type of network information device is input, it is checked whether information on the presence density of the network information device is input (step S609).

  When the information on the existence density of the network information device is input, it is checked whether the information on the operation rate of the network information device is input (step S610). When the information on the operation rate of the network information device is input, it is checked whether the information on the communication error rate is input (step S611). If the communication error rate information has been input, it is checked whether or not the initial value of the information transmission diffusion rate and the information on the process end condition have been input (step S612). If the initial value of the information transmission diffusion rate and the information about the process end condition are input, the process proceeds to step S503 in FIG. 5 described above.

  If information to be input is not input in steps S606 to S612, a message indicating that the information has not been input is output to a display means or the like (step S605), parameters are input, and the process returns to the corresponding step. Judge again. Note that the order of checking parameter input items (input information) is not limited to the order described above. Further, when information on the assumed area is input and information in steps S606 to S612 is input, information on the total number of network information devices obtained from the basic parameter database 206, information on presence density of network information devices, network information Information on the operation rate of the device and information on the communication error rate overlap with those information input via the parameter input unit 200. In this case, information obtained from the basic parameter database 206 may be used preferentially, or input information may be used preferentially.

  Since the information diffusion prediction device, the information diffusion prediction method, and the information diffusion prediction system according to the present invention can predict the transition of the diffusion rate of data in advance, the information diffusion that predicts the transition of the diffusion of data and information This is useful for prediction devices, information diffusion prediction methods, information diffusion prediction systems, and the like.

It is a lineblock diagram showing the composition of the information spread prediction system concerning an embodiment of the invention. It is a block diagram which shows the structure of the information spread prediction apparatus which concerns on embodiment of this invention. It is a figure which shows the information transmission penetration rate represented by the screen output part of the information spread prediction apparatus which concerns on embodiment of this invention. It is a figure which shows the information transmission penetration rate represented by the file output part of the information spread prediction apparatus which concerns on embodiment of this invention. It is a figure which shows the screen image displayed on the display means by the screen output part of the information spread prediction apparatus which concerns on embodiment of this invention. It is a flowchart which shows the flow from the calculation of the information transmission penetration rate to the output of a result in the information spread prediction apparatus which concerns on embodiment of this invention. It is a flowchart which shows the flow of the parameter check of the calculation process of the information transmission penetration rate in the information penetration prediction apparatus which concerns on embodiment of this invention.

Explanation of symbols

100 Information diffusion prediction system 101 Information diffusion prediction device 102 Network (Internet)
103a, 103b terminal 200 parameter input unit (input means)
201 Information diffusion rate calculation unit (information transmission rate calculation means)
202 Result output unit (output means)
203 Communication Unit 204 Screen Output Unit 205 File Output Unit 206 Basic Parameter Database

Claims (6)

An input means for inputting parameters relating to the predetermined information that are parameters for predicting the penetration rate of the transmission information in the predetermined group;
A part of a predetermined function for calculating the diffusion rate of the transmission information is calculated based on the input parameter, and the predetermined function for which the part of the element is calculated and the input parameter are calculated. Information transmission penetration rate calculating means for calculating the penetration rate of the transmission information based on;
Output means for outputting the calculated penetration rate;
An information dissemination prediction device.   When a parameter related to specific information is input when the parameter is input, the information transmission and spread calculation unit may include a part of the input parameter other than the parameter related to the specific information and the parameter related to the specific information. The information diffusion prediction apparatus according to claim 1, wherein the diffusion rate is calculated based on a parameter obtained from a parameter database based on the parameter and an input parameter. A parameter for predicting a penetration rate in a predetermined group of transmission information, which is related to the predetermined information;
A part of a predetermined function for calculating the diffusion rate of the transmission information is calculated based on the input parameter, and the predetermined function for which the part of the element is calculated and the input parameter are calculated. Calculating the penetration rate of the transmission information based on:
Outputting the calculated penetration rate;
Information dissemination prediction method possessed.   When a parameter related to specific information is input when the parameter is input, in the step of calculating the penetration rate, a parameter other than the parameter related to the specific information and the parameter related to the specific information is input. The information diffusion prediction method according to claim 3, wherein the diffusion rate is calculated based on a parameter obtained from a parameter database based on some parameters and an input parameter. An information diffusion prediction device capable of predicting a diffusion rate of a transmission information in a predetermined group uses some elements of a predetermined function for calculating the diffusion rate of the transmission information as parameters relating to the input predetermined information And calculating the diffusion rate of the transmission information based on the predetermined function and the input parameters calculated for the partial elements, and outputting the calculated diffusion rate,
An information diffusion prediction system in which a terminal device connected to the information diffusion prediction device via the network acquires the diffusion rate calculated by the information diffusion prediction device via the network. In the information dissemination prediction device, when a parameter related to specific information is input when the parameter is input, a part of the input parameter other than the parameter related to the specific information and the parameter related to the specific information 6. The information diffusion prediction system according to claim 5, wherein the diffusion rate is calculated based on a parameter obtained from a parameter database based on the parameter and the input parameter, and the calculated diffusion rate is output.

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