Product competitiveness evaluation method, device and computer-readable storage medium

Abstract

The present invention provides a product competitiveness evaluation method, device, and computer-readable storage medium. The method comprises: obtaining product scoring indicators, the scoring indicators including core indicators, activity indicators, and influence indicators; standardizing each of the scoring indicators to obtain standardized scoring indicators; constructing a dual-attribute judgment matrix for each of the scoring indicators, and obtaining a normalized weight for each of the scoring indicators based on the dual-attribute judgment matrix; constructing a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights, and obtaining a product competitiveness score based on the product competitiveness evaluation model. The method, device, and computer-readable storage medium can address the problem that existing product competitiveness evaluation methods are typically based on product market analysis and have a coarse granularity.

Description

Technical Field

[0001] The present invention relates to the technical field of product evaluation, and in particular to a product competitiveness evaluation method, device, and computer-readable storage medium. Background Art

[0002] With the rapid development of e-commerce, the number and variety of e-commerce products is increasing, and merchants often consume a lot of manpower, material resources, and time when managing these products. In the era of big data, analyzing the competitiveness of e-commerce products requires automated, fast, and effective methods to improve work efficiency and reduce labor costs. By analyzing the competitiveness of e-commerce products, we can help implement automatic product listing and delisting, product ranking, and prioritize the display of popular products, helping to maximize store efficiency.

[0003] However, existing methods for evaluating the competitiveness of e-commerce products are usually based on product market analysis, which has a coarse granularity and is difficult to meet the real customer feedback for detailed products. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the existing technology and provide a product competitiveness evaluation method, device and computer-readable storage medium to solve the problem that the existing product competitiveness evaluation method is usually based on the analysis of the product market and has a coarse granularity.

[0005] In a first aspect, the present invention provides a method for evaluating product competitiveness, comprising:

[0006] Obtain product scoring indicators, including core indicators, activity indicators, and influence indicators;

[0007] Performing standardization on each of the scoring indicators to obtain a standardized scoring indicator;

[0008] Constructing a dual-attribute judgment matrix for each of the scoring indicators respectively, and obtaining a normalized weight for each of the scoring indicators according to the dual-attribute judgment matrix;

[0009] A product competitiveness evaluation model is constructed based on the standardized scoring indicators and normalized weights, and a product competitiveness score is obtained based on the product competitiveness evaluation model.

[0010] Preferably, the obtaining of product scoring indicators specifically includes:

[0011] Obtain core product metrics, including clicks, add-to-cart, favorites, and average browsing time for all users.

[0012] Obtain activity indicators for the product, including the most recent visit time, number of valid reviews, and visit frequency;

[0013] Obtain product influence indicators, including browsing conversion rate, purchase conversion rate, and order conversion rate.

[0014] Preferably, the step of performing standardization on each of the scoring indicators to obtain the standardized scoring indicators specifically includes:

[0015] Each of the core indicators, activity indicators and influence indicators is standardized according to a pre-set standardization interval to obtain the standardized scoring indicators, wherein each of the standardized scoring indicator values ​​falls within the standardization interval.

[0016] Preferably, the step of constructing a dual-attribute judgment matrix for each of the scoring indicators specifically includes:

[0017] Construct a core indicator dual-attribute judgment matrix based on the importance of all core indicators compared with each other;

[0018] Constructing a dual-attribute judgment matrix of activity indicators based on the importance of all activity indicators compared with each other; and

[0019] The influence index dual-attribute judgment matrix is ​​constructed based on the importance of all influence indicators compared with each other.

[0020] Preferably, obtaining the normalized weight of each scoring indicator according to the dual-attribute judgment matrix specifically includes:

[0021] The core indicator dual-attribute judgment matrix, the activity indicator dual-attribute judgment matrix, and the influence indicator dual-attribute judgment matrix are processed using the preset arithmetic mean method, geometric mean method, and eigenvalue method, respectively, to obtain the single-layer attribute weight of each core indicator, activity indicator, and influence indicator;

[0022] Calculating the average value of the single-layer attribute weights of each of the core indicators, the activity indicators, and the influence indicators;

[0023] The normalized weight of each scoring indicator is calculated according to the following formula:

[0024]

[0025] Among them, W i represents the average value of the single-layer attribute weight of the i-th scoring indicator, Represents the sum of the average values ​​of the single-layer attribute weights of all scoring indicators.

[0026] Preferably, the constructing of a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights specifically includes:

[0027] The product competitiveness evaluation model is constructed according to the following formula:

[0028]

[0029] Among them, T i represents the scoring index after the i-th normalization process, Z i Represents the normalized weight of the i-th scoring indicator.

[0030] In a second aspect, the present invention provides a product competitiveness evaluation device, comprising:

[0031] A scoring index acquisition module is used to obtain the product's scoring index, which includes core indicators, activity indicators, and influence indicators;

[0032] A standardization processing module, connected to the scoring indicator acquisition module, is used to perform standardization processing on each of the scoring indicators to obtain a standardized scoring indicator;

[0033] A normalized weight module, connected to the scoring indicator acquisition module, is used to construct a dual-attribute judgment matrix for each scoring indicator and obtain a normalized weight for each scoring indicator according to the dual-attribute judgment matrix;

[0034] The competitiveness evaluation module is connected to the standardization processing module and the normalization weight module, and is used to construct a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights, and to obtain the product competitiveness score based on the product competitiveness evaluation model.

[0035] Preferably, the scoring indicator acquisition module includes:

[0036] A core indicator acquisition unit is used to obtain core indicators of the product, including the number of clicks, add-to-cart, favorites, and average browsing time of all users;

[0037] An activity index acquisition unit, configured to acquire activity indicators of a product, wherein the activity indicators include the most recent visit time, the number of valid reviews, and the visit frequency;

[0038] The influence index acquisition unit is used to obtain the influence index of the product, and the influence index includes browsing conversion rate, purchase conversion rate and order conversion rate.

[0039] In a third aspect, the present invention provides a product competitiveness evaluation device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the product competitiveness evaluation method described in the first aspect.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the product competitiveness evaluation method described in the first aspect is implemented.

[0041] The product competitiveness evaluation method, device and computer-readable storage medium provided by the present invention obtain the scoring indicators of the product, wherein the scoring indicators include core indicators, activity indicators and influence indicators, and then standardize each of the scoring indicators to obtain the standardized scoring indicators, and then respectively construct a dual-attribute judgment matrix for each of the scoring indicators, and obtain the normalized weight of each of the scoring indicators according to the dual-attribute judgment matrix, and finally construct a product competitiveness evaluation model according to the standardized scoring indicators and the normalized weights, and obtain the competitiveness score of the product according to the product competitiveness evaluation model. Since the scoring indicators of the present invention include core indicators, activity indicators and influence indicators, the product competitiveness evaluation model constructed by these scoring indicators can reflect the revenue contribution, customer-bringing ability and influence level of the product, and realize the automation and differentiation of product competitiveness evaluation, thereby solving the problem that the existing product competitiveness evaluation method is usually based on the analysis of the product market and has a coarse granularity. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 : is a flow chart of a product competitiveness evaluation method according to Example 1 of the present invention;

[0043] Figure 2 : A schematic structural diagram of a product competitiveness evaluation device according to Example 2 of the present invention;

[0044] Figure 3 : This is a structural diagram of a product competitiveness evaluation device according to Example 3 of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0046] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0047] It is understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments may be combined with each other.

[0048] It can be understood that, for the convenience of description, the drawings of the present invention only show parts related to the present invention, while parts unrelated to the present invention are not shown in the drawings.

[0049] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0050] It will be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0051] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented using a hardware-based system that implements the specified functions, or may be implemented using a combination of hardware and computer instructions.

[0052] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0053] Example 1:

[0054] This embodiment provides a method for evaluating product competitiveness. Figure 1 As shown, the method includes:

[0055] Step S102: Obtain product scoring indicators, which include core indicators, activity indicators, and influence indicators.

[0056] Optionally, obtain product rating indicators, which may include:

[0057] Obtain core product metrics, including clicks, add-to-cart, favorites, and average browsing time for all users.

[0058] Get product activity indicators, including the most recent visit time, number of valid reviews, and visit frequency;

[0059] Obtain product influence indicators, including browsing conversion rate, purchase conversion rate, and order conversion rate.

[0060] In this embodiment, the number of clicks, add-to-carts, and favorites for a product can be the total number of clicks, add-to-carts, and favorites since the product was launched, or the number of clicks, add-to-carts, and favorites for a specific time period. The most recent visit time is the interval between the last visit to the product and the current time. The number of valid reviews is the number of users who actively reviewed the product. The visit frequency is the number of visits to the product per day.

[0061] In this embodiment, to obtain the product's influence index, we first extract the number of visitors to the product's corresponding website, the number of people who browsed the product, the number of people who entered the purchase process, and the number of orders. Then, we calculate each influence index according to the following formula:

[0062] Browsing conversion rate = number of people browsing the product / number of website visitors

[0063] Purchase conversion rate = number of people entering the purchase process / number of people browsing products

[0064] Order conversion rate = number of orders / number of people entering the purchase process

[0065] Step S104: performing standardization processing on each scoring indicator to obtain a standardized scoring indicator;

[0066] Specifically, each core indicator, activity indicator and influence indicator is standardized according to a pre-set standardization interval to obtain a standardized scoring indicator, wherein each standardized scoring indicator value falls within the standardization interval.

[0067] In this embodiment, the pre-set normalized interval may be [0, 100], and a maximum value and a minimum value may be set in advance for each scoring indicator. The actual indicator value is then scaled proportionally according to the maximum and minimum values ​​so that it falls within the normalized interval of [0, 100]. This means that the maximum value of clicks, add-to-carts, favorites, average browsing time, most recent visit time, number of valid reviews, visit frequency, browsing conversion rate, purchase conversion rate, and order conversion rate is 100, and the minimum value is 1.

[0068] Step S106: constructing a dual-attribute judgment matrix for each scoring indicator respectively, and obtaining a normalized weight value for each scoring indicator according to the dual-attribute judgment matrix.

[0069] Optionally, a dual-attribute judgment matrix is ​​constructed for each scoring indicator, specifically including:

[0070] Construct a core indicator dual-attribute judgment matrix based on the importance of all core indicators compared with each other;

[0071] Constructing a dual-attribute judgment matrix of activity indicators based on the importance of all activity indicators compared with each other; and

[0072] The influence index dual-attribute judgment matrix is ​​constructed based on the importance of all influence indicators compared with each other.

[0073] In this embodiment, the values ​​in each dual-attribute judgment matrix represent the relative importance of two scoring indicators:

[0074]

[0075] Specifically, the core indicator dual-attribute judgment matrix constructed based on the importance of all core indicators compared with each other can be shown as follows:

[0076]

[0077]

[0078] Specifically, the activity index dual-attribute judgment matrix constructed based on the importance of all activity indicators compared with each other can be shown as follows:

[0079] Activity indicators Last access time Number of valid evaluations Frequency of visits Last access time 1 2 1 / 2 Number of valid evaluations 1 / 2 1 1 / 2 Frequency of visits 2 2 1

[0080] Specifically, the influence index dual-attribute judgment matrix constructed based on the importance of all influence indicators compared with each other can be shown as follows:

[0081] Impact indicators View conversion rate Purchase conversion rate Order conversion rate View conversion rate 1 1 / 2 1 Purchase conversion rate 2 1 2 Order conversion rate 1 1 / 2 1

[0082] Step S108: constructing a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights, and obtaining a product competitiveness score based on the product competitiveness evaluation model.

[0083] Optionally, obtaining a normalized weight of each scoring indicator according to the dual-attribute judgment matrix may specifically include:

[0084] The core indicator dual-attribute judgment matrix, activity indicator dual-attribute judgment matrix, and influence indicator dual-attribute judgment matrix are processed using the pre-set arithmetic mean method, geometric mean method, and eigenvalue method respectively to obtain the single-layer attribute weights of each core indicator, activity indicator, and influence indicator;

[0085] Calculate the average value of the single-layer attribute weights of each core indicator, activity indicator, and influence indicator;

[0086] The normalized weight of each scoring indicator is calculated according to the following formula:

[0087]

[0088] Among them, W irepresents the average value of the single-layer attribute weight of the i-th scoring indicator, Represents the sum of the average values ​​of the single-layer attribute weights of all scoring indicators.

[0089] In this embodiment, for each dual-attribute judgment matrix, the steps of processing using the arithmetic mean method include:

[0090] (1) Sum the double-attribute judgment matrix by column to obtain a new matrix a_sum;

[0091] (2) Divide each value in the dual-attribute judgment matrix by the column sum to obtain a new matrix b;

[0092] (3) Calculate the row sum of the new matrix b to obtain the new matrix b_sum;

[0093] (4) Divide each value in b_sum by the total to obtain the weight.

[0094] In this embodiment, for each dual-attribute judgment matrix, the steps of processing using the geometric mean method include:

[0095] (1) Multiply the double-attribute judgment matrix row by row to obtain a new column vector x_plus;

[0096] (2) Raise each element of x_plus to the power of n to obtain a new column vector y_plus;

[0097] (3) Divide each value in y_plus by the total to obtain the weight.

[0098] In this embodiment, for each dual-attribute judgment matrix, the steps of processing using the eigenvalue method include:

[0099] (1) Use the eig function in MATLAB to find the maximum eigenvalue and eigenvector of the dual-attribute judgment matrix;

[0100] (2) Divide each value in the obtained eigenvector m by the sum to obtain the weight.

[0101] In a specific embodiment, after processing using the arithmetic mean method, the geometric mean method, and the eigenvalue method, the single-layer attribute weight of each core indicator is obtained as follows:

[0102] Core indicators Arithmetic mean method Geometric mean method Eigenvalue method Clicks 0.108 0.1020 0.1039 Collection 0.3168 0.3192 0.3187 Add-on quantity 0.3418 0.3532 0.3435 Average browsing time 0.2324 0.2257 0.2337

[0103] In a specific embodiment, after processing using the arithmetic mean method, the geometric mean method, and the eigenvalue method, the single-layer attribute weight of each activity indicator is obtained as follows:

[0104]

[0105]

[0106] In a specific embodiment, after processing using the arithmetic mean method, the geometric mean method, and the eigenvalue method, the single-layer attribute weight of each influence indicator is obtained as follows:

[0107] Impact indicators Arithmetic mean method Geometric mean method Eigenvalue method View conversion rate 0.25 0.25 0.25 Purchase conversion rate 0.5 0.5 0.5 Order conversion rate 0.25 0.25 0.25

[0108] In this embodiment, by calculating the average value of the weights of each scoring indicator obtained by using the above three methods, all attribute weights can be normalized to obtain the normalized weight value of each scoring indicator.

[0109] In a specific embodiment, the obtained normalized weight of each scoring indicator may be as follows:

[0110] Scoring indicators Normalized weights Clicks 0.0349 Collection 0.1061 Add-on quantity 0.1154 Average browsing time 0.0769 Last access time 0.1036 Number of valid evaluations 0.0653 Frequency of visits 0.1645 View conversion rate 0.0833 Purchase conversion rate 0.1667 Order conversion rate 0.0833

[0111] Optionally, a product competitiveness evaluation model is constructed based on the standardized scoring indicators and normalized weights, which may specifically include:

[0112] Construct a product competitiveness evaluation model based on the following formula:

[0113]

[0114] Among them, T i represents the scoring index after the i-th normalization process, Z i Represents the normalized weight of the i-th scoring indicator.

[0115] In this embodiment, the standardized scoring indicators and normalized weights are input into the product competitiveness evaluation model to obtain the product competitiveness score. The higher the score, the higher the product's revenue contribution, customer-bringing ability, and influence level compared to other similar products of the same period.

[0116] The product competitiveness evaluation method provided by the embodiment of the present invention obtains the scoring indicators of the product, wherein the scoring indicators include core indicators, activity indicators and influence indicators, and then standardizes each of the scoring indicators to obtain the standardized scoring indicators, and then constructs a dual-attribute judgment matrix for each of the scoring indicators, and obtains the normalized weight of each of the scoring indicators based on the dual-attribute judgment matrix, and finally constructs a product competitiveness evaluation model based on the standardized scoring indicators and the normalized weights, and obtains the competitiveness score of the product based on the product competitiveness evaluation model. Since the scoring indicators of the present invention include core indicators, activity indicators and influence indicators, the product competitiveness evaluation model constructed by these scoring indicators can reflect the revenue contribution, customer-bringing ability and influence level of the product, and realize the automation and differentiation of product competitiveness evaluation, thereby solving the problem that the existing product competitiveness evaluation method is usually based on the analysis of the product market and has a coarse granularity.

[0117] Example 2:

[0118] like Figure 2 As shown, this embodiment provides a product competitiveness evaluation device, including:

[0119] Scoring index acquisition module 12 is used to obtain product scoring indicators, including core indicators, activity indicators and influence indicators;

[0120] The standardization processing module 14 is connected to the scoring index acquisition module 12 and is used to perform standardization processing on each scoring index to obtain a standardized scoring index;

[0121] The normalized weight module 16 is connected to the scoring index acquisition module 12 and is used to construct a dual-attribute judgment matrix for each scoring index and obtain a normalized weight for each scoring index based on the dual-attribute judgment matrix;

[0122] The competitiveness evaluation module 18 is connected to the standardization processing module 14 and the normalization weight module 16, and is used to build a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights, and to obtain the product competitiveness score based on the product competitiveness evaluation model.

[0123] Optionally, the scoring indicator acquisition module 12 includes:

[0124] The core indicator acquisition unit is used to obtain the core indicators of the product, including the number of clicks, add-to-cart, favorites, and the average browsing time of all users;

[0125] Activity index acquisition unit, used to obtain activity indicators of products, including the most recent visit time, number of valid reviews and visit frequency;

[0126] The influence indicator acquisition unit is used to obtain the influence indicators of the product, which include browsing conversion rate, purchase conversion rate and order conversion rate.

[0127] Optionally, the standardization processing module 14 is specifically used to standardize each core indicator, activity indicator and influence indicator according to a pre-set standardization interval to obtain a standardized scoring indicator, wherein each standardized scoring indicator value falls within the standardization interval.

[0128] Optionally, the normalization weight module 16 specifically includes:

[0129] The first normalization unit is used to construct a core indicator dual-attribute judgment matrix based on the importance of all core indicators compared with each other;

[0130] The second normalization unit is used to construct an activity index dual-attribute judgment matrix based on the importance of all activity indexes compared with each other;

[0131] The third normalization unit is used to construct an influence indicator dual-attribute judgment matrix based on the importance of all influence indicators compared with each other;

[0132] The single-layer attribute weight acquisition unit is used to process the core indicator dual-attribute judgment matrix, the activity indicator dual-attribute judgment matrix, and the influence indicator dual-attribute judgment matrix using the pre-set arithmetic mean method, geometric mean method, and eigenvalue method, respectively, to obtain the single-layer attribute weight of each core indicator, activity indicator, and influence indicator;

[0133] The average value calculation unit is used to calculate the average value of the single-layer attribute weight of each core indicator, activity indicator, and influence indicator;

[0134] The normalized weight acquisition unit is used to calculate the normalized weight of each scoring indicator according to the following formula:

[0135]

[0136] Among them, W i represents the average value of the single-layer attribute weight of the i-th scoring indicator, Represents the sum of the average values ​​of the single-layer attribute weights of all scoring indicators.

[0137] Optionally, the competitiveness evaluation module 18 is specifically configured to construct a product competitiveness evaluation model according to the following formula, and obtain a product competitiveness score according to the product competitiveness evaluation model:

[0138]

[0139] Among them, T i represents the scoring index after the i-th normalization process, Z i Represents the normalized weight of the i-th scoring indicator.

[0140] Example 3:

[0141] refer to Figure 3 This embodiment provides a product competitiveness evaluation device, including a memory 22 and a processor 24, wherein the memory 22 stores a computer program, and the processor 24 is configured to run the computer program to execute the product competitiveness evaluation method in Example 1.

[0142] The memory 22 is connected to the processor 24 . The memory 22 may be a flash memory, a read-only memory, or other memory. The processor 24 may be a central processing unit or a single-chip microcomputer.

[0143] Example 4:

[0144] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the product competitiveness evaluation method in the above-mentioned embodiment 1 is implemented.

[0145] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0146] The product competitiveness evaluation device and computer-readable storage medium provided in Examples 2 to 4 obtain scoring indicators of the product, wherein the scoring indicators include core indicators, activity indicators and influence indicators, and then perform standardization on each of the scoring indicators to obtain the standardized scoring indicators. Then, a dual-attribute judgment matrix is ​​constructed for each of the scoring indicators, and the normalized weight of each of the scoring indicators is obtained according to the dual-attribute judgment matrix. Finally, a product competitiveness evaluation model is constructed according to the standardized scoring indicators and the normalized weights, and the competitiveness score of the product is obtained according to the product competitiveness evaluation model. Since the scoring indicators of the present invention include core indicators, activity indicators and influence indicators, the product competitiveness evaluation model constructed by these scoring indicators can reflect the revenue contribution, customer-bringing ability and influence level of the product, and realize the automation and differentiation of product competitiveness evaluation, thereby solving the problem that the existing product competitiveness evaluation method is usually based on the analysis of the product market and has a coarse granularity.

[0147] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating product competitiveness, characterized in that: include: Obtain product rating indicators, which include core indicators, activity indicators, and influence indicators. Core indicators include the number of clicks, add-to-cart, favorites, and average browsing time of all users. Activity indicators include the most recent visit time, number of valid reviews, and visit frequency. Influence indicators include browsing conversion rate, purchase conversion rate, and order conversion rate. Each of the core indicators, activity indicators, and influence indicators is standardized according to a pre-set standardization interval to obtain the standardized scoring indicators; Constructing a dual-attribute judgment matrix for each scoring indicator, and obtaining a normalized weight for each scoring indicator based on the dual-attribute judgment matrix, wherein the normalized weight is obtained by calculating the average of the single-layer attribute weights of each core indicator, each activity indicator, and each influence indicator after processing each dual-attribute judgment matrix using the arithmetic mean method, the geometric mean method, and the eigenvalue method; A product competitiveness evaluation model is constructed based on the standardized scoring indicators and normalized weights, and a product competitiveness score is obtained based on the product competitiveness evaluation model.

2. The product competitiveness evaluation method according to claim 1, characterized in that: The scoring indicators of the product are obtained, specifically including: Obtain core product indicators; Get product activity indicators; Get product impact metrics.

3. The product competitiveness evaluation method according to claim 2, characterized in that: Each of the standardized scoring index values ​​falls within the standardized interval.

4. The product competitiveness evaluation method according to claim 2, characterized in that: The dual-attribute judgment matrix of each scoring indicator is constructed separately, specifically including: Construct a core indicator dual-attribute judgment matrix based on the importance of all core indicators compared with each other; Constructing a dual-attribute judgment matrix of activity indicators based on the importance of all activity indicators compared with each other; and The influence indicator dual-attribute judgment matrix is ​​constructed based on the importance of all influence indicators compared with each other.

5. The product competitiveness evaluation method according to claim 4, characterized in that: Obtaining the normalized weight of each scoring indicator according to the dual-attribute judgment matrix specifically includes: The core indicator dual-attribute judgment matrix, the activity indicator dual-attribute judgment matrix, and the influence indicator dual-attribute judgment matrix are processed using the preset arithmetic mean method, geometric mean method, and eigenvalue method, respectively, to obtain the single-layer attribute weight of each core indicator, activity indicator, and influence indicator; Calculating the average value of the single-layer attribute weights of each of the core indicators, the activity indicators, and the influence indicators; The normalized weight of each scoring indicator is calculated according to the following formula: Among them, W i represents the average value of the single-layer attribute weight of the i-th scoring indicator, Represents the sum of the average values ​​of the single-layer attribute weights of all scoring indicators.

6. The product competitiveness evaluation method according to claim 1, characterized in that: The step of constructing a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights specifically includes: The product competitiveness evaluation model is constructed according to the following formula: Among them, T i represents the scoring index after the i-th normalization process, Z i Represents the normalized weight of the i-th scoring indicator.

7. A product competitiveness evaluation device, characterized in that: include: A scoring index acquisition module is used to obtain product scoring indicators, which include core indicators, activity indicators, and influence indicators. The core indicators include the number of clicks, add-to-cart, and favorites of the product, as well as the average browsing time of all users. The activity indicators include the most recent visit time, the number of valid reviews, and the visit frequency. The influence indicators include the browsing conversion rate, the purchase conversion rate, and the order conversion rate. A standardization processing module, connected to the scoring indicator acquisition module, is used to standardize each of the core indicators, activity indicators, and influence indicators according to a pre-set standardization interval to obtain the standardized scoring indicators; a normalized weight module connected to the scoring indicator acquisition module, for constructing a dual-attribute judgment matrix for each scoring indicator, and obtaining a normalized weight for each scoring indicator based on the dual-attribute judgment matrix, wherein the normalized weight is obtained by calculating the average value of the single-layer attribute weights of each core indicator, each activity indicator, and each influence indicator after processing each dual-attribute judgment matrix using the arithmetic mean method, the geometric mean method, and the eigenvalue method; The competitiveness evaluation module is connected to the standardization processing module and the normalization weight module, and is used to construct a product competitiveness evaluation model based on the standardized scoring indicators and normalized weights, and to obtain the product competitiveness score based on the product competitiveness evaluation model.

8. The product competitiveness evaluation device according to claim 7, characterized in that: The scoring indicator acquisition module includes: Core indicator acquisition unit, used to obtain the core indicators of the product; An activity index acquisition unit, used to obtain the activity index of a product; The influence index acquisition unit is used to obtain the influence index of the product.

9. A product competitiveness evaluation device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the product competitiveness evaluation method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the product competitiveness evaluation method according to any one of claims 1 to 6.

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