TWI633450B - Used to optimize the network recitation mode of the data mining system and methods - Google Patents

Abstract

A search system and a surveying method for optimizing a network tracking mode, in which a plurality of functional objects in a human-machine interface respond to an operator's operation to generate a sample feature record, and sample the feature record as an input data, and extract these functions. The used frequency of the object is used as the output data. After the data exploration and analysis, an analysis model is generated. Subsequently, when the operator operates the human-machine interface, the corresponding functional object will generate a test feature record, and the test feature record is processed through the analysis model. If the test result is further generated, the test result can be used as a basis for judging the merits of the functional object.

Description

Exploration system and exploration method for optimizing network memorial mode

The invention relates to a exploration system and a exploration method, in particular to a exploration system and a exploration method for optimizing a network memorial mode.

It is always the traditional advantage of the Chinese people to pursue the pursuit of the future. In traditional customs, many acts are used to assist in the pursuit, worship, and hang-up, such as holding hands together, worshiping, burning money, offering sacrifices, or offerings. Flowers, etc. Among them, there are many ways that are less environmentally friendly or waste materials.

The time has come to today, the concept of pursuing the pursuit of the future can never be forgotten, but with the changes and improvement of the environment, science and technology, education, etc., it is necessary to maintain the attitude of cautious pursuit with a newer and better behavior. Currently, it has been buried in some funeral management units or funerals. In the webpage of the industry, a web-based memorial, worship, or hanging mode is established, and the screen is presented for use by the hanger.

However, most of the current web worship modes lack creativity. For example, triggering a button will automatically cause a picture of the worship on the webpage. After investigation, it is found that worshipping people in this mode is completely devastating. Some new creative groups also want to implant some new ideas to enhance the interactivity in worship, but these new ideas are not suitable for the needs of the people, but also a big question.

Accordingly, it is a primary object of the present invention to provide a sourcing system and a sourcing method for optimizing a network tracking mode to solve the above problems.

The object of the present invention is to provide a exploration system and a exploration method for optimizing a network memorial mode. When the test phase or the initial release of an innovative technology worship mode is started, it can be found that the mode is not suitable for a specific region. The use of the population, in order to assist the promotion unit to effectively and quickly carry out various environmental protection, remembrance, worship, and enthusiasm, and thus improve the current tomb behavior.

The invention relates to a exploration system and a exploration method for optimizing a network memorial mode. The exploration system comprises a plurality of recovery function modules, a human machine interface, a storage unit, a exploration analysis module, and a detection module.

Triggering each of the retrieving function modules respectively generates different retrieving functions; the human machine interface is coupled to the reconnaissance function modules and can correspondingly display a plurality of functional objects, responding to the operation of the operator, corresponding to each of the plurality of functional objects The functional object generates at least the same feature record.

The storage unit is coupled to the human machine interface for storing each of the functional objects, respectively storing the sample feature records generated by the human machine interface, and storing the frequency of use of each functional object by the operator.

The exploration and analysis module is coupled to the storage unit, and the sample feature records of the functional objects are taken as input data from the storage unit, and the frequency of use of the functional objects is taken as the output data for data exploration and analysis. Generate an analytical model.

After the operator subsequently operates the human-machine interface, the detection module generates at least one inspection feature record corresponding to the functional object, and the detection module processes the inspection feature record through the analysis model to generate a detection result, wherein the detection result can be detected As a basis for judging the merits of functional objects. The detection module compares the detection result with the preset detection threshold, and can judge the advantages and disadvantages of the functional object.

The sample feature record or the test feature record includes: a brain wave record of the operator when the function object is operated by the operator, a length of time taken by the function object when the operator is operated, and a time when the function object is operated by the operator. The trend of the length of time, or the order in which the functional objects are manipulated by the operator.

The data mining system is related to association analysis, classification analysis, or group analysis.

Therefore, the present invention provides a search system and a search method for optimizing the network memorial mode, and the operator can operate the state of the functional objects in the human-machine interface and analyze the data through the data exploration analysis, and can be in the test phase or at the beginning. When an innovative technology worship model is issued, it can be found that this model is not suitable for a specific population in a specific region, so that the promotion unit can effectively and quickly promote various environmental protection-related pursuits, worships, and enthusiasm, thereby improving the current situation. Tombs, such as promoting environmentally friendly natural burials.

The advantages and spirit of the present invention will be further understood from the following detailed description of the invention.

Please refer to FIG. 1. FIG. 1 is a schematic diagram of a network recollection mode of the present invention. The network recollection mode is applied to the network 14 architecture, and the operator 16 uses the terminal device 10, such as a computer or a mobile phone, to connect to the remote server 12 through the network 14 at the near end, and can start the recovery function module. The group 30, the recollection function module 30 can be disposed in the server 12, can be disposed in the terminal device 10, or partially disposed in the server 12 and disposed in the terminal device 10.

Triggering each of the retrieving function modules 30 respectively generates different recollection functions, and the recollection function module 30 is designed on behalf of the creator or manufacturer for various recollections, hangs, and worship modes on the network 14, such as through the network 14, With a camera facing a virtual image such as a Buddha statue, a tombstone, a tree burial tree, an ashes, etc., a virtual worship screen, a virtual worship screen, for example, augmented reality images to generate paper money, incense, and tribute The effect of a product, a wish, etc., the result of augmenting the reality is, for example, in the real life of a tree burial tree, flying virtual burning paper money into the tree.

Please refer to FIG. 2 for further reference. FIG. 2 is a functional association diagram of the exploration system of the present invention. The present invention relates to a mining system for optimizing a network tracking mode, the exploration system including a tracking function module 30, and a human machine interface 32. At the end of the terminal 10 of FIG. 1, the human interface is used to interact with the operator 16. The human interface 32, such as a mouse, a keyboard, a screen, a touch panel, and the terminal 10, must cooperate with each other. The human interface 32 is coupled to the function module 30 and can display a plurality of functional objects 3202. The functional object 3202 is, for example, an icon (Icon) of the screen. Touching one or several functional objects 3202 triggers the activation of the foregoing. The function module 30 is traced. The operator 16 operates the functional object 3202 through the human interface 32, and corresponding to each of the plurality of functional objects 3202, at least the same feature record 3402 is generated.

The sample feature record 3402 may be, for example, a brain wave record of the operator 16 when the function object 3202 is operated by the operator 16, a time length when the function object 3202 is operated by the operator 16, and a functional object 3202 by the operator 16 The time elapsed during operation is a trend in length, or the order in which the functional objects 3202 are operated by the operator 16.

The brainwave record of the operator 16 represents, for example, the emotional reflection of the operator 16 during operation, and also represents an emotional response to the function of the functional object 3202 and the subsequent recollection function module 30, such as expectation, Bored, excited, etc. The length of time taken by the operator 16 to operate the functional object 3202 can be proportional to the amount of interest of the functional function of the functional object 3202 and the subsequent retrieving function module 30, while the functional object 3202 is operated by the operator 16. The tendency to take time to length may represent an increasing interest in the operator 16, such as an increasing trend, or an operator 16 becoming less and less interested, such as a decreasing trend. In addition, the single function object 3202 may represent a recollection function of the recollection function module 30. It is possible that the plurality of function objects 3202 represent the recollection function of one or more remembrance function modules 30, and the plurality of function objects 3202 are operated differently by the operator 16. The order may also represent the recollection function of the plurality of trace function modules 30. Therefore, the order of operations by the operator 16 may also be a sample feature record 3402.

Further, the exploration system includes a storage unit 34, a exploration analysis module 36, and a detection module 38 in addition to the foregoing recollection function module 30 and the human machine interface 32.

As described above, each of the retrieving function modules 30 is triggered to generate different retrieving functions. The human interface 32 is coupled to the plurality of functional modules 302 and corresponding to the plurality of functional objects 3202. In response to the operation of the operator 16, each of the plurality of functional objects 3202 corresponds to at least one of the functional objects 3202. Feature record 3402.

The storage unit 34 can be, for example, a hard disk, a flash memory, a random access memory, etc., coupled to the human interface 32 for storing the sample features generated by the human interface 32 for each functional object 3202. A record 3402 is recorded and the usage frequency 3404 at which each of the functional items 3202 is operated by the operator 16 is stored. The frequency of use 3404 operated by the operator 16 is the number of times the operator 16 operates the function object 3202 for a period of time. The more the number of times, the operator 16 is interested in the function of retrieving the function object 3202 and the subsequent function module 30.

The probe analysis module 36 is coupled to the storage unit 34. The sample feature record 3402 of the functional object 3202 is taken as the input data from the storage unit 34, and the usage frequency 3404 of the functional object 3202 is taken as the output data. Data mining 3602 analysis is performed to generate an analysis model 3604. The data mining 3602 may be data exploration 3602 for association rule analysis, classification analysis, or cluster analysis.

Subsequently, when the operator 16a subsequently operates the old or new functional object 3202 in the human interface 32, the detection module 38 generates at least one verification feature record 3802 corresponding to the functional object 3202, and the detection module 38 is The test feature record 3802 is processed by the analysis model 3604 to generate a test result 3804, and the test result 3804 can be used as a basis for judging the merits of the function object 3202. Further, in the detection module 38, the detection result 3804 can be compared with a preset detection threshold 3806, that is, the quality of the functional object 3202 can be judged, and if the value is from high to low, the value of the detection result 3804 is large and small. Then, the detection result 3804 exceeds the detection threshold 3806. Conversely, if the value from the superior to the inferior detection result 3804 is small and large, the detection result 3804 is smaller than the detection threshold 3806. Therefore, as long as the operator 16a operates the test, the functional object 3202 and the function of the subsequent tracking function of the functional object 3202 can be known immediately to determine whether to continue to use, slightly improve or cancel the use of the functional object 3202 or remember. Features.

It should be noted that the check feature record 3802 may, for example, still be: the brain wave record of the operator 16 when the function object 3202 is operated by the operator 16, and the length and function of the time when the function object 3202 is operated by the operator 16. The trend of the length of time taken by the operator 3202 by the operator 16 or the order in which the functional objects 3202 are operated by the operator 16. Its characteristics and meanings are as described above and will not be redundant here.

Please refer to FIG. 3. FIG. 3 is an example correspondence diagram of the sample feature record 3402 or the test feature record 3802 of the present invention in time sequence. 3 is a sample feature record 3402 or a check feature record 3802 generated when the operator 16 performs the network tracking mode. The "operator" indicates which operator 16 is performing the tracking operation, and the "function object code" indicates different functions. The code of the object 3202. Both "concentration" and "meditation value" are derived from the brainwave record of the operator 16, and the distribution of the two values is a percentage distribution from 0 to 100. The "time to length" (A) indicates the time elapsed by the operation of the function object 3202 by the operator 16, in minutes/second. "The average value of the time length in the first schedule range" (B) is the average value of the time elapsed by the functional object 3202 during a predetermined schedule, and the average unit of the time length is still minutes/second. The range is, for example, 7 days. The "average of the length of time in the second schedule" (C) is the average of the elapsed time during which the functional object 3202 is operated in another predetermined schedule, and the other schedule is longer than the first schedule. The average unit of the time length is still minutes/second, and the schedule range is, for example, 30 days.

"The trend of the length of time" is the ratio of the "time length" to the "average of the length of the time in the first schedule" (B) / (A), or "the average of the length of the time in the first schedule" The ratio (C)/(B) of the value to the "average of the length of the time in the second schedule". If the ratio relationship of the above example is greater than 1, it indicates that the trend is increasing, and if it is less than 1, it indicates that the trend is decreasing. Further, the "operation sequence of functional items in the batch" indicates the order in which the plurality of functional objects 3202 are operated during the batch operation of the same operator 16 this time, which may represent the preference and behavior pattern of the operator 16.

These sample feature records 3402, which can be used as input data, can be quantified. The frequency of use as the output data 3404 can also be quantified or clarified without any doubt. Therefore, it can be used as good information for data exploration, and the exploration analysis module The sample feature record 3402 of the functional object 3202 is taken as the input data, and the use frequency 3404 of the functional object 3202 is taken as the output data, and the data exploration 3602 is analyzed to generate the analysis model 3604. Subsequently, the inspection feature record 3802 is processed by the detection module 38 through the analysis model 3604 to generate a detection result 3804, which can be used as a basis for judging the merits and demerits of the functional object 3202.

Please refer to FIG. 4. FIG. 4 is a flow chart of the method for exploring the present invention. The present invention is also a method for searching for a network recollection mode. The network recollection mode has a plurality of reconsideration function modules 30, and each of the retrieving function modules 30 respectively generates different recollection functions, and the exploration method includes The following steps:

Step start (S01): the operator 16 can operate through a plurality of functional objects 3202 displayed in the human machine interface 32 corresponding to the function module 30;

Step 1 (S02): corresponding to each of the plurality of functional objects 3202, respectively, generating at least the same feature record 3402;

Step 2 (S03): corresponding to each functional object 3202, respectively storing the sample feature record 3402 generated by the human interface 32, and storing the usage frequency 3404 of each functional object 3202 operated by the operator 16;

Step 3 (S04): taking the sample feature record 3402 of the functional object 3202 as the input data, and taking the use frequency 3404 of the functional object 3202 as the output data;

Step 4 (S05): performing data exploration 3602 analysis to generate an analysis model 3604;

Step 5 (S06): after the operator 16a subsequently operates the human interface 32, the corresponding functional object 3202 generates at least one inspection feature record 3802;

Step 6 (S07): The inspection feature record 3802 is processed by the analysis model 3604 to generate a detection result 3804, wherein the detection result 3804 can be used as a basis for judging the merits of the functional object 3202. The step 6 of the exploration method (S07) may further include the following steps: comparing the detection result 3804 to the preset detection threshold 3806, thereby determining the merits of the functional object 3202.

Therefore, with the present invention, a search system and a search method for optimizing the network memorial mode can be performed in the test phase by the operator 16 operating the state of the functional object 3202 in the human interface 32 and analyzing by the data exploration 3602. Or when the innovative technology worship mode is issued at the beginning, it can be found that this mode is not suitable for the specific population of a specific region, so as to assist the promotion unit to actively and quickly promote environmental protection, remembrance, worship, and enthusiasm. In turn, it can improve the current behavior of tombs, such as promoting environmentally friendly natural burials.

The features and spirit of the present invention will be more apparent from the detailed description of the preferred embodiments. On the contrary, the intention is to cover various modifications and equivalents within the scope of the invention as claimed.

10‧‧‧ Terminal
12‧‧‧Server
14‧‧‧Network
16, 16a‧‧‧ Operator
30‧‧‧Remembrance function module
32‧‧‧Human Machine Interface
3202‧‧‧Functional objects
34‧‧‧ storage unit
3402‧‧‧sample feature record
3404‧‧‧Use frequency
36‧‧‧Exploration and Analysis Module
3602‧‧‧Information exploration
3604‧‧‧analytical model
38‧‧‧Test module
3802‧‧‧ Inspection feature record
3804‧‧‧ Test results
3806‧‧‧Detection threshold

1 is a schematic diagram of a network recollection mode of the present invention; FIG. 2 is a functional association diagram of the exploration system of the present invention; FIG. 3 is an example correspondence of a sample feature record or a test feature record according to a time sequence of the present invention; and FIG. Flow chart of the method.

Claims (8)

A search system for optimizing a network recollection mode, the exploration system comprising: a plurality of reconsideration function modules, each of which is triggered to generate different recollection functions; a human machine interface coupled to the recollection function The module can display a plurality of functional objects corresponding to the operation of an operator, and each of the plurality of functional objects respectively generates at least the same feature record; a storage unit coupled to the human machine interface, For each functional object, respectively storing the sample feature records generated by the human interface, and storing the frequency of use of each functional object by the operator; a prospecting analysis module coupled to the storage unit, The storage unit captures sample feature records of the functional objects as input data, and captures the frequency of use of the functional objects as output data, performs data exploration analysis to generate an analysis model, and a detection module. After the operator subsequently operates the human-machine interface, the detection module generates corresponding functional objects to Wherein a test recording, characterized in that the detection module and inspection of the recording process through the model to generate a detection result, wherein the detection result can be used as a basis to determine whether the object function merits. The prospecting system of claim 1, wherein the sample feature record or the test feature record comprises: an electroencephalogram of the operator when the functional object is operated by the operator, the functional object being operated by the operator The time elapsed is the length, the trend of the length of time the functional object is subjected to operation by the operator, or the order in which the functional objects are operated by the operator. For example, the exploration system described in claim 1 is characterized in that the data exploration system is a correlation rule analysis, a classification analysis, or a cluster analysis. The locating system of claim 1, wherein the detecting module compares the detection result with a preset detection threshold to determine the merits of the functional object. A method for optimizing a network recollection mode, wherein the network recollection mode has a plurality of reconsideration function modules, and each of the retrieving function modules respectively generates different recollection functions, and an operator can pass through a human machine interface. The search method includes the following steps: Step 1: Corresponding to each functional object of the plurality of functional objects, respectively generating at least the same feature record; Step 2: Corresponding Each functional object stores a sample feature record generated by the human interface, and stores the frequency of use of each functional object by the operator; Step 3: capturing sample feature records of the functional objects as input data And taking the frequency of use of the functional objects as the output data; Step 4: performing data exploration analysis to generate an analysis model; Step 5: When the operator subsequently operates the human-machine interface, corresponding to the functional object is generated At least one test feature record; and step six: recording the test feature Through the process model to generate a detection result, wherein the detection result can be used as a basis to determine whether the object function merits. The method of claim 5, wherein the sample feature record or the test feature record comprises: an electroencephalogram of the operator when the functional object is operated by the operator, the functional object being operated by the operator The time elapsed is the ratio of the length ratio, or the length of time the functional object is subjected to operation by the operator, or the order in which the functional objects are operated by the operator. The method of exploration according to claim 5, wherein the data mining system is a correlation rule analysis, a classification analysis, or a cluster analysis. The method of claim 5, wherein the method further comprises the step of: comparing the detection result to a preset detection threshold to determine the merits of the functional object.