Communication type SPU generation method and device
Through word segmentation processing and Bayesian classification technology, combined with preset mapping relationship tables, communication SPUs are automatically generated, which solves the problems of low efficiency of manual design and inconsistent specifications, and achieves efficient and high-quality SPU generation.
Patent Information
- Application Number
- CN202411391154.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-30
- Publication Date
- 2025-02-14
AI Technical Summary
Manually designing communication SPUs with low efficiency and inconsistent design specifications, resulting in large design workload, high professionalism, long delivery cycle, and is not conducive to product maintenance and management.
By obtaining the target network communication capability description and service requirements, performing word segmentation processing and Bayesian classification, determining the network communication capability category, and finding matching SPU categories from the preset mapping relationship table, adjusting parameters to generate target communication SPUs.
It realizes automated network communication capability classification and SPU generation, improves design efficiency, ensures the generated SPU quality, and solves the problems of low efficiency and inconsistent specifications of manual design configuration.
Smart Images
Figure CN119442013A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of communication technology, and in particular to a method and device for generating a communication-type SPU. Background Art
[0002] In the systems of communication operators, communication SPUs are usually manually designed and configured based on the professional knowledge and experience of business personnel. This process involves selecting one or more network communication capabilities and combining them according to different business scenarios and rules to generate service products that meet specific needs. However, the manual design and configuration of communication SPUs has significant shortcomings, including huge design workload, high professional requirements, and long delivery cycles. At the same time, due to the lack of unified design specifications, the design process is inefficient and labor costs are high, which is also not conducive to long-term product maintenance and management.
[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention
[0004] The embodiments of the present application provide a method and device for generating a communication SPU, so as to at least solve the technical problems of low efficiency and inconsistent design specifications when manually designing and configuring a communication SPU.
[0005] According to one aspect of an embodiment of the present application, a method for generating a communication class SPU is provided, comprising: obtaining a target network communication capability description and a target business requirement for a target communication class SPU to be generated; performing word segmentation processing on the target network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a target network communication capability category corresponding to the target network communication capability description; searching a first network communication capability category matching the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; if the first network communication capability category is found, determining the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table, adjusting parameters in the first communication class SPU according to the target business requirement, and obtaining the target communication class SPU; if the first network communication capability category is not found, determining the latest updated second communication class SPU in the mapping relationship table, adjusting parameters in the second communication class SPU according to the target business requirement, and obtaining the target communication class SPU.
[0006] Optionally, after obtaining the target communication class SPU, the method also includes: performing word segmentation processing on the target communication class SPU, and performing Bayesian classification on the obtained word segmentation set to obtain a target SPU category corresponding to the target communication class SPU; updating the mapping relationship between the target network communication capability category described by the target network communication capability and the target SPU category of the target communication class SPU into the mapping relationship table.
[0007] Optionally, the process of constructing the mapping relationship table includes: obtaining multiple historical network communication capability descriptions and historical communication class SPUs; for each historical network communication capability description, performing word segmentation processing on the historical network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description; for each historical communication class SPU, performing word segmentation processing on the historical communication class SPU, and performing Bayesian classification on the obtained word segmentation set to obtain a second SPU category corresponding to the historical communication class SPU; calculating the similarity between the second network communication capability category of each historical network communication capability description and the second SPU category of each historical communication class SPU, and recording in the mapping relationship table the second network communication capability category of each group of historical network communication capability descriptions and the second SPU category of the historical communication class SPU whose similarity is greater than a first preset threshold.
[0008] Optionally, the historical network communication capability description is segmented, and the obtained segmented word set is subjected to Bayesian classification to obtain a second network communication capability category corresponding to the historical network communication capability description, including: using a preset segmentation tool and / or a custom segmentation dictionary to segment the historical network communication capability description to obtain a first segmentation set; performing noise reduction processing on the first segmentation set to obtain a second segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation and preset special characters, and removing modifying segmentations; for the second segmentation set, For each word segmentation, the word frequency and inverse document frequency corresponding to the word segmentation are calculated using the word frequency-inverse document frequency TF-IDF algorithm, and the weight factor corresponding to the word segmentation is determined according to the length of the word segmentation, and the product of the word frequency, inverse document frequency and weight factor is used as the importance score of the word segmentation; the word segmentations in the second word segmentation set are sorted from large to small according to the importance score, and a preset number of word segmentations with the top sorting are determined to form the third word segmentation set; the naive Bayes classification algorithm is used to analyze the word segmentations in the third word segmentation set to obtain the second network communication capability category corresponding to the historical network communication capability description.
[0009] Optionally, the historical communication SPU is segmented, and the obtained segmentation set is Bayesian classified to obtain a second SPU category corresponding to the historical communication SPU, including: using a preset segmentation tool and / or a custom segmentation dictionary to segment the historical communication SPU to obtain a fourth segmentation set; performing noise reduction processing on the fourth segmentation set to obtain a fifth segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation and preset special characters, and removing modifying segmentations; for each segmentation in the fifth segmentation set, using the TF-IDF algorithm to calculate the word frequency and inverse document frequency corresponding to the segmentation, and determining the weight factor corresponding to the segmentation according to the length of the segmentation, and taking the product of the word frequency, inverse document frequency and weight factor as the importance score of the segmentation; sorting the segmentations in the fifth segmentation set from large to small according to the importance score, and determining a preset number of segmentations with the top sorting to form a sixth segmentation set; using the naive Bayes classification algorithm to analyze the segmentations in the sixth segmentation set to obtain the second SPU category corresponding to the historical communication SPU.
[0010] Optionally, searching for a first network communication capability category that matches the target network communication capability category from a preset mapping relationship table includes: calculating the similarity between the target network communication capability category and each network communication capability category in the mapping relationship table; and determining that a network communication capability category in the mapping relationship table whose similarity to the target network communication capability category is greater than a second preset threshold is the first network communication capability category.
[0011] Optionally, after obtaining the target communication class SPU, the method further includes: configuring a target communication class SKU for sales management according to the target communication class SPU.
[0012] According to another aspect of an embodiment of the present application, a device for generating a communication class SPU is also provided, comprising: an acquisition module, used to acquire a target network communication capability description and a target business requirement for a target communication class SPU to be generated; a classification module, used to perform word segmentation processing on the target network communication capability description, and perform Bayesian classification on the obtained word segmentation set to obtain a target network communication capability category corresponding to the target network communication capability description; a search module, used to search a first network communication capability category matching the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; a first generation module, used to determine the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table when the first network communication capability category is found, and adjust the parameters in the first communication class SPU according to the target business requirement to obtain the target communication class SPU; a second generation module, used to determine the latest updated second communication class SPU in the mapping relationship table when the first network communication capability category is not found, and adjust the parameters in the second communication class SPU according to the target business requirement to obtain the target communication class SPU.
[0013] According to another aspect of an embodiment of the present application, a computer program product is further provided, the computer program product comprising: a computer program, wherein when the computer program is executed by a processor, the above-mentioned method for generating a communication class SPU is implemented.
[0014] According to another aspect of an embodiment of the present application, an electronic device is further provided, the electronic device comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned method for generating a communication class SPU through the computer program.
[0015] In an embodiment of the present application, a target network communication capability description and a target business requirement for a target communication class SPU to be generated are obtained; the target network communication capability description is segmented, and the obtained segmented set is Bayesian classified to obtain a target network communication capability category corresponding to the target network communication capability description; a first network communication capability category matching the target network communication capability category is searched from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; if the first network communication capability category is found, the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table is determined, and the parameters in the first communication class SPU are adjusted according to the target business requirement to obtain the target communication class SPU; if the first network communication capability category is not found, the latest updated second communication class SPU in the mapping relationship table is determined, and the parameters in the second communication class SPU are adjusted according to the target business requirement to obtain the target communication class SPU. In this way, word segmentation and Bayesian classification technology are used to automatically classify network communication capability descriptions, and a preset mapping table is used to map network communication capability categories to corresponding communication SPU categories, enabling fast matching and selection. The quality of the generated communication SPU is ensured through precise demand matching and parameter adjustment, thereby solving the technical problems of low efficiency and inconsistent design specifications when manually designing and configuring communication SPUs. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0017] Figure 1 It is a flowchart of an optional method for generating a communication class SPU according to an embodiment of the present application;
[0018] Figure 2 is a schematic structural diagram of an optional communication-type SPU generation device according to an embodiment of the present application;
[0019] Figure 3 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.
[0021] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0022] In order to better understand the embodiments of the present application, some nouns or terms that appear in the description of the embodiments of the present application are first translated and explained as follows:
[0023] TF-IDF (Term Frequency-Inverse Document Frequency): A statistical method widely used in information retrieval and text mining to evaluate the importance of a word to a document or a document in a group of documents.
[0024] SKU (Stock Keeping Unit): refers to the code or number used by retailers or manufacturers to uniquely identify each different product in their inventory. SKU usually includes product type, size, color, manufacturer and other information that helps to accurately identify individual products. SKU refers to each sellable item in the inventory, and it is the smallest sellable unit.
[0025] SPU (Standard Product Unit): A unique set of attributes that are usually used to identify a product. These attributes are sufficient to distinguish a product from other variants. It consists of a series of SKUs that share the same core features. SPU usually contains basic information about the product, such as brand, model, configuration, etc., to define the basic characteristics of the product. In a product line, different colors, sizes or other variants can belong to the same SPU.
[0026] Example 1
[0027] According to an embodiment of the present application, a method for generating a communication class SPU is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0028] Figure 1 is a flow chart of a method for generating a communication type SPU according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:
[0029] Step S102, obtaining a target network communication capability description and a target service requirement for a target communication class SPU to be generated;
[0030] Step S104, performing word segmentation processing on the target network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain the target network communication capability category corresponding to the target network communication capability description;
[0031] Step S106, searching a preset mapping relationship table for a first network communication capability category that matches the target network communication capability category, wherein the mapping relationship table stores mapping relationships between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs;
[0032] Step S108, if the first network communication capability category is found, determine the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table, adjust the parameters in the first communication class SPU according to the target service requirements, and obtain the target communication class SPU;
[0033] Step S110: If the first network communication capability category is not found, determine the latest updated second communication class SPU in the mapping relationship table, adjust the parameters in the second communication class SPU according to the target service requirements, and obtain the target communication class SPU.
[0034] The following describes each step of the method for generating a communication class SPU in conjunction with a specific implementation process.
[0035] When generating a new communication class SPU, firstly, a target network communication capability description and a target service requirement for the target communication class SPU to be generated are obtained.
[0036] Since the communication capability description is usually given in natural language, which includes a large amount of unstructured text information, the target network communication capability description can be segmented, and then the obtained segmented word set can be subjected to Bayesian classification to obtain the target network communication capability category corresponding to the target network communication capability description.
[0037] In order to ensure that the generated communication class SPU can accurately reflect the market demand and can efficiently serve the target customer group, the first network communication capability category matching the target network communication capability category can be directly searched from the preset mapping relationship table. For the mapping relationship table, the following steps are used to construct it:
[0038] S1, obtain multiple historical network communication capability descriptions and historical communication class SPUs;
[0039] S2, for each historical network communication capability description, performing word segmentation processing on the historical network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description;
[0040] Specifically, a preset word segmentation tool and / or a custom word segmentation dictionary is used to perform word segmentation processing on the historical network communication capability description to obtain a first word segmentation set; the first word segmentation set is subjected to noise reduction processing to obtain a second word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; for each word segmentation in the second word segmentation set, a word frequency-inverse document frequency TF-IDF algorithm is used to calculate the word frequency and inverse document frequency corresponding to the word segmentation, and a weight factor corresponding to the word segmentation is determined according to the length of the word segmentation, and the product of the word frequency, inverse document frequency and weight factor is used as the importance score of the word segmentation; the word segmentations in the second word segmentation set are sorted from large to small according to the importance score, and a preset number of word segmentations with the highest sorting order are determined to form a third word segmentation set; the word segmentations in the third word segmentation set are analyzed using a naive Bayes classification algorithm to obtain a second network communication capability category corresponding to the historical network communication capability description.
[0041] S3, for each historical communication SPU, perform word segmentation processing on the historical communication SPU, and perform Bayesian classification on the obtained word segmentation set to obtain a second SPU category corresponding to the historical communication SPU;
[0042] Specifically, a preset word segmentation tool and / or a custom word segmentation dictionary is used to perform word segmentation processing on the historical communication SPU to obtain a fourth word segmentation set; the fourth word segmentation set is then subjected to noise reduction processing to obtain a fifth word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; for each word segmentation in the fifth word segmentation set, the TF-IDF algorithm is used to calculate the word frequency and inverse document frequency corresponding to the word segmentation, and the weight factor corresponding to the word segmentation is determined according to the length of the word segmentation, and the product of the word frequency, inverse document frequency and weight factor is used as the importance score of the word segmentation; the word segmentations in the fifth word segmentation set are sorted from large to small according to the importance score, and a preset number of word segmentations with the highest sorting order are determined to form a sixth word segmentation set; the naive Bayes classification algorithm is used to analyze the word segmentations in the sixth word segmentation set to obtain a second SPU category corresponding to the historical communication SPU.
[0043] S4, calculate the similarity between the second network communication capability category of each historical network communication capability description and the second SPU category of each historical communication class SPU, and record in a mapping relationship table the second network communication capability category of each group of historical network communication capability descriptions and the second SPU category of the historical communication class SPU whose similarity is greater than a first preset threshold.
[0044] For example, the second network communication capability category A = {traffic}, the network communication capability includes mobile traffic, broadband traffic, etc., the second SPU category a = {traffic}, SPU includes mobile domestic traffic, mobile international traffic, broadband monthly traffic, etc. When the similarity exceeds the first preset threshold (such as 70%), the second network communication capability category A corresponds to the second SPU category a.
[0045] After the mapping relationship table is constructed according to the above steps, the preset mapping relationship table is used to search for the first network communication capability category that matches the above target network communication capability category. Specifically: the similarity between the target network communication capability category and each network communication capability category in the mapping relationship table is calculated; and then the network communication capability category in the mapping relationship table whose similarity with the target network communication capability category is greater than a second preset threshold is determined to be the first network communication capability category.
[0046] If the first network communication capability category is found, the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table is determined, and the parameters in the first communication class SPU are adjusted according to the target business requirements to obtain the target communication class SPU; if the first network communication capability category is not found, the latest updated second communication class SPU in the mapping relationship table is determined, and the parameters in the second communication class SPU are adjusted according to the target business requirements to obtain the target communication class SPU.
[0047] For example, if the target network communication capability category is 'wireless Internet traffic', if the first network communication capability category B is found, it can correspond to the first SPU category of 'traffic', and the latest updated first communication category SPU is the mobile domestic traffic SPU. After adjusting individual parameters, the target communication category SPU is obtained.
[0048] Optionally, after obtaining the target communication class SPU, a target communication class SKU for sales management is configured according to the target communication class SPU.
[0049] As an optional implementation, after obtaining the target communication class SPU, the target communication class SPU can also be segmented, and the obtained segmented word set can be Bayesian classified to obtain the target SPU category corresponding to the target communication class SPU; and then the mapping relationship between the target network communication capability category described by the target network communication capability and the target SPU category of the target communication class SPU is updated to the mapping relationship table. For example, if the mobile phone domestic traffic SPU is the latest added communication class SPU, the mobile phone domestic traffic SPU is used as the initial value of the SPU intelligently generated by the wireless Internet traffic.
[0050] In an embodiment of the present application, a target network communication capability description and a target business requirement for a target communication class SPU to be generated are obtained; the target network communication capability description is segmented, and the obtained segmented set is Bayesian classified to obtain a target network communication capability category corresponding to the target network communication capability description; a first network communication capability category matching the target network communication capability category is searched from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; if the first network communication capability category is found, the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table is determined, and the parameters in the first communication class SPU are adjusted according to the target business requirement to obtain the target communication class SPU; if the first network communication capability category is not found, the latest updated second communication class SPU in the mapping relationship table is determined, and the parameters in the second communication class SPU are adjusted according to the target business requirement to obtain the target communication class SPU. In this way, word segmentation and Bayesian classification technology are used to automatically classify network communication capability descriptions, and a preset mapping table is used to map network communication capability categories to corresponding communication SPU categories, enabling fast matching and selection. The quality of the generated communication SPU is ensured through precise demand matching and parameter adjustment, thereby solving the technical problems of low efficiency and inconsistent design specifications when manually designing and configuring communication SPUs.
[0051] Example 2
[0052] According to an embodiment of the present application, a communication class SPU generation device for implementing the communication class SPU generation method in embodiment 1 is also provided. Figure 2 As shown, the communication class SPU generation device at least includes: an acquisition module 21, a classification module 22, a search module 23, a first generation module 24, and a second generation module 25, wherein:
[0053] An acquisition module 21 is used to acquire a target network communication capability description and a target service requirement for a target communication class SPU to be generated;
[0054] The classification module 22 is used to perform word segmentation processing on the target network communication capability description, and perform Bayesian classification on the obtained word segmentation set to obtain the target network communication capability category corresponding to the target network communication capability description;
[0055] A search module 23 is used to search a first network communication capability category that matches the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores mapping relationships between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs;
[0056] The first generating module 24 is used to determine the latest updated first communication class SPU corresponding to the first SPU class corresponding to the first network communication capability class in the mapping relationship table when the first network communication capability class is found, and adjust the parameters in the first communication class SPU according to the target service requirements to obtain the target communication class SPU;
[0057] The second generating module 25 is used to determine the latest updated second communication class SPU in the mapping relationship table when the first network communication capability category is not found, and adjust the parameters in the second communication class SPU according to the target service requirements to obtain the target communication class SPU.
[0058] The functions of each module of the communication type SPU generation device are described below in conjunction with a specific implementation process.
[0059] When generating a new communication class SPU, the acquisition module first acquires a target network communication capability description and a target service requirement for the target communication class SPU to be generated.
[0060] Since the communication capability description is usually given in natural language, which includes a large amount of unstructured text information, the classification module can perform word segmentation on the target network communication capability description, and then perform Bayesian classification on the obtained word segmentation set to obtain the target network communication capability category corresponding to the target network communication capability description.
[0061] In order to ensure that the generated communication class SPU can accurately reflect the market demand and can efficiently serve the target customer group, the search module can directly search for the first network communication capability category that matches the target network communication capability category from the preset mapping relationship table. For the mapping relationship table, the following steps are used to construct it:
[0062] S1, obtain multiple historical network communication capability descriptions and historical communication class SPUs;
[0063] S2, for each historical network communication capability description, performing word segmentation processing on the historical network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description;
[0064] Specifically, a preset word segmentation tool and / or a custom word segmentation dictionary is used to perform word segmentation processing on the historical network communication capability description to obtain a first word segmentation set; the first word segmentation set is subjected to noise reduction processing to obtain a second word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; for each word segmentation in the second word segmentation set, a word frequency-inverse document frequency TF-IDF algorithm is used to calculate the word frequency and inverse document frequency corresponding to the word segmentation, and a weight factor corresponding to the word segmentation is determined according to the length of the word segmentation, and the product of the word frequency, inverse document frequency and weight factor is used as the importance score of the word segmentation; the word segmentations in the second word segmentation set are sorted from large to small according to the importance score, and a preset number of word segmentations with the highest sorting order are determined to form a third word segmentation set; the word segmentations in the third word segmentation set are analyzed using a naive Bayes classification algorithm to obtain a second network communication capability category corresponding to the historical network communication capability description.
[0065] S3, for each historical communication SPU, perform word segmentation processing on the historical communication SPU, and perform Bayesian classification on the obtained word segmentation set to obtain a second SPU category corresponding to the historical communication SPU;
[0066] Specifically, a preset word segmentation tool and / or a custom word segmentation dictionary is used to perform word segmentation processing on the historical communication SPU to obtain a fourth word segmentation set; the fourth word segmentation set is then subjected to noise reduction processing to obtain a fifth word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; for each word segmentation in the fifth word segmentation set, the TF-IDF algorithm is used to calculate the word frequency and inverse document frequency corresponding to the word segmentation, and the weight factor corresponding to the word segmentation is determined according to the length of the word segmentation, and the product of the word frequency, inverse document frequency and weight factor is used as the importance score of the word segmentation; the word segmentations in the fifth word segmentation set are sorted from large to small according to the importance score, and a preset number of word segmentations with the highest sorting order are determined to form a sixth word segmentation set; the naive Bayes classification algorithm is used to analyze the word segmentations in the sixth word segmentation set to obtain a second SPU category corresponding to the historical communication SPU.
[0067] S4, calculate the similarity between the second network communication capability category of each historical network communication capability description and the second SPU category of each historical communication class SPU, and record in a mapping relationship table the second network communication capability category of each group of historical network communication capability descriptions and the second SPU category of the historical communication class SPU whose similarity is greater than a first preset threshold.
[0068] After the mapping relationship table is constructed according to the above steps, the search module uses the preset mapping relationship table to search for the first network communication capability category that matches the above target network communication capability category. Specifically: the similarity between the target network communication capability category and each network communication capability category in the mapping relationship table is calculated; and then the network communication capability category in the mapping relationship table whose similarity with the target network communication capability category is greater than a second preset threshold is determined as the first network communication capability category.
[0069] If the first network communication capability category is found, the first generation module determines the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table, adjusts the parameters in the first communication class SPU according to the target service requirements, and obtains the target communication class SPU.
[0070] If the first network communication capability category is not found, the second generation module determines the most recently updated second communication class SPU in the mapping relationship table, adjusts parameters in the second communication class SPU according to the target service requirements, and obtains the target communication class SPU.
[0071] Optionally, after obtaining the target communication class SPU, a target communication class SKU for sales management is configured according to the target communication class SPU.
[0072] As an optional implementation, after obtaining the target communication class SPU, the target communication class SPU can also be segmented, and the obtained segmentation set can be Bayesian classified to obtain the target SPU category corresponding to the target communication class SPU; then the mapping relationship between the target network communication capability category described by the target network communication capability and the target SPU category of the target communication class SPU is updated in the mapping relationship table.
[0073] It should be noted that the modules in the device for generating the communication class SPU in the embodiment of the present application correspond one-to-one to the implementation steps of the method for generating the communication class SPU in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be repeated here.
[0074] Example 3
[0075] According to an embodiment of the present application, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method for generating a communication class SPU in Embodiment 1 is implemented.
[0076] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the method for generating a communication class SPU in Example 1 by running the computer program.
[0077] According to an embodiment of the present application, a processor is further provided, which is used to run a computer program, wherein the method for generating a communication class SPU in Example 1 is executed when the computer program is run.
[0078] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the method for generating a communication class SPU in Example 1 through the computer program.
[0079] Specifically, when the computer program is running, the following steps are executed: obtaining a target network communication capability description and a target business requirement for a target communication class SPU to be generated; performing word segmentation processing on the target network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a target network communication capability category corresponding to the target network communication capability description; searching for a first network communication capability category matching the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; if the first network communication capability category is found, determining the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table, adjusting the parameters in the first communication class SPU according to the target business requirement, and obtaining the target communication class SPU; if the first network communication capability category is not found, determining the latest updated second communication class SPU in the mapping relationship table, adjusting the parameters in the second communication class SPU according to the target business requirement, and obtaining the target communication class SPU.
[0080] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 FIG. 1 shows a hardware structure block diagram of an electronic device for implementing a method for generating a communication class SPU. Figure 3 As shown, the electronic device 30 may include one or more (302a, 302b, ..., 302n are used to illustrate) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown.
[0081] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 30. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).
[0082] The memory 304 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for generating the communication class SPU in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, to implement the vulnerability detection method of the above-mentioned application. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the electronic device 30 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0083] The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.
[0084] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .
[0085] The serial numbers of the above embodiments are only for description and do not represent the advantages or disadvantages of the embodiments.
[0086] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.
[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.
[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.
[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or optical disk, etc., which can store program code.
[0091] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
Claims
1. A method for generating a communication SPU, characterized in that: include: Obtain a target network communication capability description and target business requirements for a target communication standard product unit SPU to be generated; Performing word segmentation processing on the target network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a target network communication capability category corresponding to the target network communication capability description; Searching for a first network communication capability category that matches the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores mapping relationships between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; If the first network communication capability category is found, determine the latest updated first communication class SPU corresponding to the first SPU category corresponding to the first network communication capability category in the mapping relationship table, and adjust the parameters in the first communication class SPU according to the target service requirements to obtain the target communication class SPU; If the first network communication capability category is not found, determine the second communication class SPU that is most recently updated in the mapping relationship table, adjust the parameters in the second communication class SPU according to the target service requirements, and obtain the target communication class SPU.
2. The method according to claim 1, characterized in that After obtaining the target communication class SPU, the method further includes: Performing word segmentation processing on the target communication class SPU, and performing Bayesian classification on the obtained word segmentation set to obtain a target SPU category corresponding to the target communication class SPU; The mapping relationship between the target network communication capability category described by the target network communication capability and the target SPU category of the target communication class SPU is updated into the mapping relationship table.
3. The method according to claim 1, characterized in that The construction process of the mapping relationship table includes: Obtain multiple historical network communication capability descriptions and historical communication class SPUs; For each historical network communication capability description, perform word segmentation processing on the historical network communication capability description, and perform Bayesian classification on the obtained word segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description; For each historical communication class SPU, perform word segmentation processing on the historical communication class SPU, and perform Bayesian classification on the obtained word segmentation set to obtain a second SPU category corresponding to the historical communication class SPU; Calculate the similarity between the second network communication capability category of each historical network communication capability description and the second SPU category of each historical communication class SPU, and record in the mapping relationship table the second network communication capability category of each group of historical network communication capability descriptions and the second SPU category of the historical communication class SPU whose similarity is greater than a first preset threshold.
4. The method according to claim 3, characterized in that Performing word segmentation processing on the historical network communication capability description, and performing Bayesian classification on the obtained word segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description, including: Using a preset word segmentation tool and / or a custom word segmentation dictionary to perform word segmentation processing on the historical network communication capability description to obtain a first word segmentation set; Performing noise reduction processing on the first word segmentation set to obtain a second word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; For each word segment in the second word segment set, the word frequency and inverse document frequency corresponding to the word segment are calculated using the word frequency-inverse document frequency TF-IDF algorithm, and the weight factor corresponding to the word segment is determined according to the length of the word segment, and the product of the word frequency, the inverse document frequency and the weight factor is used as the importance score of the word segment; Sort the individual segmented words in the second segmented word set from large to small according to the importance score, and determine a preset number of segmented words with top rankings to form a third segmented word set; A naive Bayes classification algorithm is used to analyze each segmentation in the third segmentation set to obtain a second network communication capability category corresponding to the historical network communication capability description.
5. The method according to claim 3, characterized in that: Performing word segmentation processing on the historical communication class SPU, and performing Bayesian classification on the obtained word segmentation set to obtain a second SPU category corresponding to the historical communication class SPU, including: Using a preset word segmentation tool and / or a custom word segmentation dictionary to perform word segmentation processing on the historical communication SPU to obtain a fourth word segmentation set; Performing noise reduction processing on the fourth word segmentation set to obtain a fifth word segmentation set, wherein the noise reduction processing includes at least one of the following processing methods: removing stop words, removing punctuation marks and preset special characters, and removing modifying word segmentations; For each word in the fifth word segmentation set, the word frequency and inverse document frequency corresponding to the word are calculated using the TF-IDF algorithm, and a weight factor corresponding to the word is determined according to the length of the word segment, and the product of the word frequency, the inverse document frequency and the weight factor is used as the importance score of the word segment; Sorting the individual participles in the fifth participle set from large to small according to the importance score, and determining a preset number of participles with top rankings to form a sixth participle set; The naive Bayes classification algorithm is used to analyze each segmentation in the sixth segmentation set to obtain a second SPU category corresponding to the historical communication class SPU.
6. The method according to claim 1, characterized in that Searching a preset mapping relationship table for a first network communication capability category that matches the target network communication capability category includes: Calculating the similarity between the target network communication capability category and each network communication capability category in the mapping relationship table; Determine a network communication capability category in the mapping relationship table whose similarity with the target network communication capability category is greater than a second preset threshold as the first network communication capability category.
7. The method according to claim 1, characterized in that After obtaining the target communication class SPU, the method further includes: A target communication class inventory management unit SKU for sales management is configured according to the target communication class SPU.
8. A communication SPU generation device, characterized in that: include: An acquisition module, used to acquire a target network communication capability description and target service requirements for a target communication class SPU to be generated; A classification module, configured to perform word segmentation processing on the target network communication capability description, and perform Bayesian classification on the obtained word segmentation set to obtain a target network communication capability category corresponding to the target network communication capability description; A search module, used to search for a first network communication capability category that matches the target network communication capability category from a preset mapping relationship table, wherein the mapping relationship table stores a mapping relationship between network communication capability categories of multiple network communication capability descriptions and SPU categories of multiple communication class SPUs; A first generating module is used to determine the latest updated first communication class SPU corresponding to the first SPU class corresponding to the first network communication capability class in the mapping relationship table when the first network communication capability class is found, and adjust the parameters in the first communication class SPU according to the target service requirements to obtain the target communication class SPU; The second generation module is used to determine the most recently updated second communication class SPU in the mapping relationship table when the first network communication capability category is not found, and adjust the parameters in the second communication class SPU according to the target service requirements to obtain the target communication class SPU.
9. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method for generating a communication class SPU as claimed in any one of claims 1 to 7 is implemented.
10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the method for generating a communication class SPU according to any one of claims 1 to 7 through the computer program.