TWI557665B - Target Business Area Strength Evaluation System and Method - Google Patents

Description

Target business circle intensity assessment system and method

The present invention relates to a decision support system and method, and more particularly to a target business circle strength assessment system and method for integrating and evaluating commercial data and providing provider information to users for reference.

In the prior art, the industry's assessment of the business circle relied on various commercial types of reports. Or, collect the necessary information through a variety of sources, such as: field surveys of commissioners, past experience, various historical documents or verbal messages of relevant personnel, and integrate them into a data chart of demand. Finally, through empirical rules or cost trials, etc., to obtain estimated business circle information.

However, the data charts collected or integrated by such data do not provide intuitive assessment information to users or operators. Secondly, the industry needs to obtain a relatively clear evaluation result through a large amount of data collection, comparison and data certification. Thirdly, depending on the source of the information, there will be problems with different data formats. The information obtained cannot be accessed by users in an intuitive manner. Fourth, the judgment and analysis of data is a human experience. If it is not a long-term training, it may also cause problems that cannot be passed down. Fifth, even with all kinds of commercial materials, the evaluation results may be biased by the industry's choice of data, resulting in incorrect evaluation results, resulting in relatively low convenience and applicability. doubt.

In order to solve the above problems, the present invention discloses a target business circle strength evaluation system and method for evaluating the suitability information of a business circle through existing commercial materials.

The target business circle strength evaluation system disclosed in the present invention comprises a storage unit, a data analysis unit, a data construction unit and an evaluation unit.

The storage unit stores a business district data. Data analysis unit analysis and link provider A number of commercial materials are summarized, and a number of business districts are presented for information. The data construction unit constructs a market consumption scale weight, a traffic convenience weight, a consumption scale weighting data and a traffic convenience proportion according to the business circle-oriented data. The evaluation unit generates a business circle intensity assessment data corresponding to the business circle data based on the above-mentioned consumption scale, traffic weighting weight and specific gravity data.

The method for evaluating the target business circle intensity disclosed by the present invention is to analyze a plurality of commercial materials of a business circle data by a data analysis unit, and to link the commercial materials to each other, and to summarize a plurality of business districts facing the data; The unit constructs a market consumption scale weight, a traffic convenience weight, a consumption scale weighting data and a traffic convenience proportion according to the business circle-oriented data; and, by an evaluation unit, according to the market consumption scale weight, traffic convenience weight, The data on the proportion of consumption scale and the proportion of traffic convenience, and the intensity assessment data of a business district corresponding to the business circle data.

The system and method disclosed by the present invention have the following characteristics:

(1) Simplify the complexity of the business circle assessment. In the absence of a large amount of data collection, comparison and data support, users or operators can obtain more clear and intuitive assessment data, which is more convenient and applicable.

(2) The data will be organized and integrated for easy access to users.

(3) The data is provided with reference data and is provided with objective and logical conditions. It also avoids the problem of insufficient analysis and the inability to obtain more accurate information.

(4) The assessment data is generated through various information-oriented materials in the business district. Therefore, the assessment results are less biased by the data selection of the operators. Therefore, the assessment information obtained is also fair.

100‧‧‧ Target Business Circle Strength Assessment System

110‧‧‧ storage unit

120‧‧‧Data analysis unit

130‧‧‧Data Construction Unit

140‧‧‧Evaluation unit

301‧‧ ‧ business district oriented information

302‧‧‧Second level assessment indicator data

303‧‧‧ Level 3 assessment indicator data

401‧‧‧Business Circle Data Layer

402‧‧‧Business Circle Assessment Layer

S110~S130‧‧‧Steps

FIG. 1 is a schematic diagram showing the architecture of a target business circle strength evaluation system according to the present embodiment.

FIG. 2 is a schematic flow chart of a target business circle strength evaluation method according to the present embodiment.

FIG. 3 is a schematic diagram of a data-oriented index structure of a business circle according to an embodiment of the present invention.

FIG. 4 is a schematic diagram showing the visualization of the business circle data according to an embodiment of the present invention.

FIG. 5 is a schematic diagram showing the visualization of the business circle evaluation according to an embodiment of the present invention.

The embodiments of the present invention are described in detail below with reference to the drawings.

Please refer to FIG. 1 to illustrate the architecture of the target business circle strength evaluation system of the present embodiment, and FIG. 2 is a schematic flow chart of the target business circle strength evaluation method according to the present embodiment. The system is suitable for being constructed independently in a single computing device, or constructed in a multi-computing device, each computing device is responsible for different functions, and each computing device cooperates with each other to perform a cooperative operation.

The target business circle strength assessment system 100 includes at least one storage unit 110, a data analysis unit 120, a data construction unit 130, and an evaluation unit 140. The system can be connected to one or more terminal devices, and the connection mode can be wired or wireless. The terminal device can be a computer or a portable electronic device, such as a smart phone, a tablet computer, or a notebook computer. Any one of the functions of computing, screen, and device connection (such as a network) is applicable, and is not limited. .

The storage unit 110 is configured to store more than one business circle data, and each business circle data includes more than one commercial data. Commercial data and commercial data can be a common data link type. Alternatively, the data structure of the business circle data and the commercial data may be at least one of a hierarchical structure and a hierarchical tree, or both may be used according to requirements.

The target business circle strength evaluation method implemented by the system includes at least the following steps: parsing a plurality of commercial materials of a business circle data by a data analysis unit 120, and linking the commercial materials to each other, and summarizing the plurality of business circles Information 301 (step S110).

The data parsing unit 120 is configured to parse the commercial materials included in each business circle information, and each commercial material establishes an associated link with each other. For example, the data parsing unit 120 can be configured to parse at least one of a vector value and a grid value of the commercial data in a certain data space, thereby establishing a correspondence relationship between the commercial materials in the data space. Alternatively, the data parsing unit 120 may perform data link according to various industrial data categories, materials, related industries, potentials, current conditions, developments, or various data categories or data attributes of policies, humanities, and transportation. This is the link between the business data. For example, the data parsing unit 120 has a graph conversion engine for parsing at least one of a vector value and a grid value of the commercial data in a certain data space, thereby establishing a correspondence relationship between the commercial materials in the data space, that is, An associative link between business materials.

After that, the data parsing unit 120 will compare the results according to the association link. Commercial materials are summarized to obtain a plurality of business district-oriented materials 301. The generalized rule may be planned by the data analysis unit 120 according to at least one of the industrial field, the data category and the data attribute included in the business circle data, or a similar planning manner. Alternatively, the user or the operator enters the rules of induction through the operating interface of the system. In this example, the business circle-oriented data 301 summarized by the data parsing unit 120 includes a potential-oriented data, a current-oriented data-oriented, and a competitive-oriented data.

Please refer to FIG. 3 simultaneously to show the data-oriented index of the business circle according to the embodiment of the present invention. Schematic diagram of the architecture. Each business district-oriented data 301 includes a plurality of sub-level evaluation indicators. If the business circle oriented data 301 is the first layer, according to the type of the data 301 of different business circles, there will be a second layer of evaluation index data 302 and a third layer evaluation index data 303 of different numbers.

The data parsing unit 120 calculates the weight of each second layer evaluation index data 302 through the fuzzy level analysis method. First, the data parsing unit 120 evaluates the relative importance of each of the second layer evaluation index data 302, which is an evaluation method using a pairwise pair comparison. If there are n second-level evaluation indicator data 302, it must be carried out Comparison of times. Thereafter, the data parsing unit 120 maps the important indices of the second-level evaluation index data 302 by the nine-level evaluation scale recommended by Saaty (1990).

After that, the data parsing unit 120 expresses these important indexes as triangular fuzzy numbers, and then uses the triangular fuzzy numbers to establish a fuzzy positive inversion matrix. . Where L _ij is the value of the triangular fuzzy number of the respondent's relative importance comparison of item i to item j; μ _ij is the median value of the triangular fuzzy number of the respondent's relative importance degree of item i to item j R _ij is the value of the triangular fuzzy number for which the respondent compares the relative importance of item i to item j.

Then, the data analysis unit 120 calculates the strategic bias of the entire business circle data by using the geometric mean and the fuzzy positive reciprocal matrix, that is, the influence of each evaluation index data on the business circle data, and numerically Presented. After that, the data parsing unit 120 performs a consistency verification operation on the fuzzy positive reversal matrix. The data parsing unit 120 first calculates the eigenvectors and eigenvalues of the fuzzy positive reciprocal matrix (the inventor further adds the calculation method), and then uses the eigenvector and the eigenvalue to calculate the consistency index (Consistemcy Index, CI): CI = (λ max - n) / (n - 1) , and further calculate the Consistency Ratio (CR): CR = CI . / RI .

Among them, the closer the CR value is to 0, the better, at least less than 0.1. The RI (Random Index) is called the random index and will increase as the matrix order n increases. As shown in Table 1 below, the random index table proposed by Saaty (1977):

When the data parsing unit 120 determines that any C.R. is not less than 0.1, the relative importance of each of the second layer evaluation index data 302 needs to be re-evaluated.

Next, the data parsing unit 120 calculates the blur weights of the second layer evaluation index data 302 for the fuzzy positive recursion matrix through the geometric mean. The data parsing unit 120 further defuzzifies the fuzzy weights of the second layer evaluation index data 302 by using the centroid method, and converts the fuzzy weights into clear weights. The weight of the second layer evaluation index data 302 is always 1.

Similarly, the data parsing unit 120 calculates the weight of the third layer evaluation index data 303 according to the above manner, and then compares each business circle to the data 301 and the second layer evaluation index data 302 and the third layer evaluation index data included therein. 303 is used for serial calculation to obtain the weight of each business circle facing the data 301.

A data construction unit 130 constructs a market consumption scale weight, a traffic convenience weight, a consumption scale weight data, and a traffic convenience weight data according to the business area oriented data 301 (step S120).

Among them, the proportion of consumption scale is based on the daily average passenger flow on a specific date in one or more specific stores within the business circle, and further estimates the total daily average passenger volume of the business circle, and through the calculation of specific commodity-oriented tests. The method (such as using the PERCENTRANK.EXC function in EXCEL) converts the total daily average passengers into percentiles, which is used as the proportion of consumption scale:

among them, For the percentage of consumption scale of the j city i business circle, N _j : j the total number of business districts where the consumption scale of the city is not repeated, A _ij : j The consumption scale in the city is larger than the number of business circles in the i business circle.

The traffic convenience ratio data is based on the number of one or more specific transportation points within the business circle, or further calculated on the date of the specific date, further estimating the total number of passengers in the business circle, and through specific The method of calculating the vehicle-to-test (such as using the PERCENTRANK.INC function in EXCEL) converts the total number of passengers per day into a percentile, which serves as a means of transportation convenience:

: j City i business district traffic convenience percentile, N _j : j city transportation point does not repeat the total number of business districts, A _ij : j city transportation points than the number of business districts.

As shown in Figure 3, the weight of market consumption and the weight of traffic convenience are covered in the current situation-oriented data. The data construction unit 130 may be used directly from the data analysis unit 120, or may be related to each other through the data related to the market consumption scale and the convenience of transportation to construct the weight of the market consumption scale and the traffic convenience weight.

However, the data construction unit 130 may construct a visual business circle information based on the previous data association and the various types of weight values, and the business information of each business circle data. The method of construction, for example, constructs the relevance of the commercial data and the data of each business circle and its weight value into a spatial data, that is, according to the vector values and grid values of the commercial data in the data space, the business is The data forms spatial data, and the spatial data is constructed as a visual business circle information by means of a layer and a map. The visualized business circle information can be a combination of a picture or a picture and a text. FIG. 4 is a schematic diagram showing the visualization of the business circle data according to the embodiment of the present invention. The data construction unit 130 maps the business circle data to the map of the regional information, and changes the corresponding geographical location and data according to each commercial data. , the corresponding business circle data layer 401 is planned on the map.

An evaluation unit 140 generates a business circle strength evaluation data corresponding to the business circle data according to the market consumption scale weight, the traffic convenience weight, the consumption scale weight data, and the traffic convenience weight data (step S130).

After obtaining the data and the weights, the evaluation unit 140 can generate a business circle intensity assessment data corresponding to the business circle data:

among them, :j The proportion of consumption scale of the city i business district, : j City i business district traffic convenience ratio data, W ^C : market consumption scale weight, W ^T : traffic convenience weight.

Thereby, the evaluation unit 140 is the suitability score of the provider circle, or further provides a decision suggestion corresponding to the suitability score.

In some embodiments, the user can use the input interface of the system to enter a business domain profile. The data analysis unit 120 performs data association analysis on the business domain data and each commercial data to find a plurality of domain-oriented materials associated with the business domain data, and then summarizes the business-oriented data 301 according to the domain bias data.

The reason why the business domain information is specified is that the business areas to which the various industries belong are not necessarily the same as the biased product items. Moreover, the environmental conditions of each business district are also different, and the commercial factors to be considered are also different. Furthermore, by appropriately selecting the business domain data, it is helpful to analyze the development of existing or new stores and the evaluation of suitable business circles. It can also analyze the layout and power situation of the competition objects and ours. Can grasp more practical development conditions. In some embodiments, different facet parameters can be intercepted according to the needs of various enterprises, for customized development analysis and enterprise application, and also for accurately grasping the changing factors and trends of the international market.

For example, the business domain data is in the retail sector, and the domain bias refers to the commercial materials associated with the retail sector. The data analysis unit 120 will take out the related commercial materials, and then summarize the business-oriented data 301. Therefore, the business circle oriented to the data 301 is highly correlated with the retail industry. The business circle strength assessment data generated by the evaluation unit 140 is also an assessment for the retail industry. Moreover, the data parsing unit 120 can exclude commercial materials with low relevance to the retail industry to enhance the credibility and focus of the evaluation in the retail industry.

On the other hand, the data parsing unit 120 may also perform data association analysis on the business domain data and each evaluation index data after establishing the business-oriented data, so as to find a plurality of index-biased materials associated with the business domain data, For subsequent evaluation by the system industry.

Therefore, in combination with business data considerations, business district strength assessment data can be integrated into the following representations:

Among them, i: business circle, j: evaluation indicator data, d: business circle oriented information.

FIG. 5 is a schematic diagram showing a schematic diagram of the business circle evaluation layer 402 of the present embodiment on a map. Each business circle of the data construction unit is mapped to the map of the region information, and according to the corresponding evaluation data of the business circle on the map, the corresponding business circle evaluation layer 402 is planned on the map.

Therefore, combined with the previously mentioned, the target business circle intensity assessment system and evaluation method disclosed in this case is the combination of the current business situation, the business circle potential and the actual commercial competition situation, and the business circle intensity by the evaluation rule of the business circle strength. Estimate to provide the industry's most intuitive business district assessment information. In this way, not only can the correct business circle information be effectively provided, but also the auxiliary information of the evaluation of the strength of the provider circle. Second, the designation of business domain data is used to enable different business-oriented analysis to suit different corporate structures and to provide customized decision recommendations and operational assessments. Together with the visualization of business district information and business circle assessment data. The visualized business circle data helps the industry to quickly grasp the overall state of the business circle and contribute to the establishment of decision-making. What is more, it helps the operator to simulate the execution of the decision, and according to the execution status of the decision and the change of the business circle. The situation, decide whether to adjust the way the decision is implemented.

In the above, it is merely described that the present invention is an implementation or an embodiment of the technical means for solving the problem, and is not intended to limit the scope of the invention. That is, the equivalent changes and modifications made in accordance with the scope of the patent application of the present invention or the scope of the invention are covered by the scope of the invention.

S110~S130‧‧‧Steps

Claims (10)

A target business circle strength evaluation system includes: a storage unit storing a business circle data; a data analysis unit connecting the storage unit, parsing a plurality of commercial materials of the business circle data, and associating the commercial materials Sexual links, summarizing a plurality of business districts for data; a data construction unit, connecting the data analysis unit and the storage unit, constructing a market consumption scale weight, a traffic convenience weight, according to the data area of the business circle The consumption scale weighting data and a traffic convenience ratio data; and an evaluation unit connecting the data construction unit, according to the market consumption scale weight, the traffic convenience weight, the consumption scale weight data and the traffic convenience proportion data, corresponding to the corresponding A business district intensity assessment data of the business district data; wherein the consumption scale weighting data is based on the daily average passenger flow of a particular store or a specific store on a specific date within the business circle, and further estimating the daily average passenger volume of the business circle Total amount, and through the calculation of specific commodity to the test, the total daily average passengers are converted into Quantile, take this as a proportion of consumption scale data: , For the percentage of consumption scale of the j city i business circle, N _j :j the total number of business districts where the urban consumption scale is not repeated, A _ij :j the consumption scale of the city is larger than the number of business districts of the i business circle; and the traffic convenience ratio data is based on Within the business circle, the number of one or more specific transportation points, or further calculated as the daily passenger capacity on a specific date, further estimates the total number of passengers in the business circle, and through the specific vehicle sex test Inferred, the total number of passengers per day is converted into a percentile, which is used as a traffic convenience ratio: , : j City i business district traffic convenience percentile, N _j : j city transportation point does not repeat the total number of business districts, A _ij : j city transportation points than the number of business districts. According to the target business circle intensity evaluation system described in claim 1 of the patent application scope, each of the business circle-oriented materials includes a plurality of evaluation index data, and the data analysis unit calculates each of the evaluation indexes through a fuzzy level analysis method. The weight of the data, and the data of each of the business districts and the data of the evaluation indicators included in the business circle are calculated in tandem to obtain the data-oriented weight of each of the business circles, and the data construction unit further uses the evaluation index data and The right reconstruction reconstructs the weight of the market consumption scale, the traffic convenience weight, the proportion of the consumption scale and the proportion of the transportation convenience. According to the target business circle intensity evaluation system described in item 2 of the patent application scope, the data analysis unit divides the important indexes of the evaluation index data through the nine-level evaluation scale to establish a fuzzy positive and negative according to the important indexes. The value matrix is calculated according to the fuzzy positive reciprocal matrix, and the fuzzy weights of each of the evaluation index data are calculated, and then the weighting method is used to convert each of the fuzzy weights into weights of the evaluation index data. According to the target business circle intensity evaluation system described in claim 1, wherein the data analysis unit performs external data input on one of the business domain data and each of the commercial materials to find out the data with the business domain. A number of related fields are biased towards the information, and then based on the biased data in these areas, the business districts are oriented to the information. According to the target business circle intensity evaluation system described in Item 1 of the patent application scope, each of the business district-oriented materials includes a plurality of evaluation index data, and the data analysis unit firstly inputs one of the business domain data and each of the evaluations according to external input. index The data is analyzed for data correlation to find out the multiple indicators with the information in the business domain, and then the weight of each index is calculated by a fuzzy level analysis method, and each of the business circles is oriented. The data is calculated in tandem with the data of the indicators included in the index to obtain the data-oriented weight of each of the business circles, and the data construction unit reconstructs the weight of the market consumption scale, the traffic convenience weight, and the consumption. The scale of the proportion of data and the proportion of the traffic convenience. A method for evaluating the strength of a target business circle, comprising: parsing a plurality of commercial materials of a business circle data by a data analysis unit, and linking the commercial materials to each other, and summarizing a plurality of business districts facing the data; The construction unit constructs a market consumption scale weight, a traffic convenience weight, a consumption scale weight data and a traffic convenience proportion data according to the business circle-oriented data; and an evaluation unit according to the market consumption scale weight, the transportation convenience The sex weight, the proportion of the consumption scale and the proportion of the traffic convenience ratio, generate a business district intensity assessment data corresponding to the business circle data; wherein the consumption scale weighting data is based on one or more specific stores within the business circle. The daily average passenger flow on a specific date, further estimating the total daily average passenger volume of the business circle, and converting the total daily average passenger volume into a percentile by a specific commodity-to-test calculation method, thereby serving as the consumption scale Specific gravity data: , For the percentage of consumption scale of the j city i business circle, N _j :j the total number of business districts where the urban consumption scale is not repeated, A _ij :j the consumption scale of the city is larger than the number of business districts of the i business circle; and the traffic convenience ratio data is based on Within the business circle, the number of one or more specific transportation points, or further calculated as the daily passenger volume on a specific date, further estimating the total number of passengers in the business circle, and passing the specific vehicle sex test Inferred, the total number of passengers per day is converted into a percentile, which is used as a traffic convenience ratio: , : j City i business district traffic convenience percentile, N _j : j city transportation point does not repeat the total number of business districts, A _ij : j city transportation points than the number of business districts. According to the target business circle intensity evaluation method described in Item 6 of the patent application scope, each of the business circle-oriented materials includes a plurality of evaluation index data, and the data analysis unit calculates each of the evaluation indexes through a fuzzy level analysis method. The weight of the data, and the data of each of the business districts and the data of the evaluation indicators included in the business circle are calculated in tandem to obtain the data-oriented weight of each of the business circles, and the data construction unit further uses the evaluation index data and The right reconstruction reconstructs the weight of the market consumption scale, the traffic convenience weight, the proportion of the consumption scale and the proportion of the transportation convenience. According to the target business circle intensity evaluation method described in Item 7 of the patent application scope, the data analysis unit divides the important indexes of the evaluation index data through the nine-level evaluation scale to establish a fuzzy positive and negative according to the important indexes. The value matrix is calculated according to the fuzzy positive reciprocal matrix, and the fuzzy weights of each of the evaluation index data are calculated, and then the weighting method is used to convert each of the fuzzy weights into weights of the evaluation index data. According to the target business circle intensity evaluation method described in claim 6 of the patent application scope, the data analysis unit performs external data input of one of the business domain materials and each of the commercial materials to find a data related to the business domain. Related The multiple fields of the association are biased towards the information, and then based on the biased data in these areas, the business districts are oriented to the information. According to the target business circle intensity evaluation method described in item 6 of the patent application scope, each of the business district-oriented materials includes a plurality of evaluation index data, and the data analysis unit firstly inputs one of the business domain materials and each of the evaluations according to external input. The indicator data is analyzed for data relevance to find out the multiple indicators with the information related to the business domain, and then calculate the weight of each index by a fuzzy level analysis method, and each of the business circles The data-oriented and the index-oriented data included in the index are calculated in tandem to obtain the weight-oriented weight of each of the business circles, and the data construction unit reconstructs the weight of the market consumption scale and the traffic by using the evaluation index data and the rights thereof. Convenience weight, the proportion of the consumption scale and the proportion of the traffic convenience.