Video data evaluation method, electronic equipment, storage medium and product

Abstract

The invention discloses a video data evaluation method, electronic equipment, a storage medium and a product, relates to the technical field of marketing analysis, and is applied to a video data evaluation system, the video data evaluation system is associated with each video platform, and each video platform comprises each enterprise account of a to-be-evaluated enterprise. Comprising the following steps: for any enterprise account of a to-be-evaluated enterprise, extracting video features of a target dimension from video platform data under the enterprise account based on a preset operation diagnosis standard, and generating a business capability score of the enterprise account based on the video features; classifying each enterprise account of the to-be-evaluated enterprise based on a preset organization account standard to obtain an enterprise account set of different organization levels; and for any enterprise account set, generating a comprehensive service capability score of the enterprise account set based on the service capability score of each enterprise account in the enterprise account set. The technical problem of lack of a global management view angle is solved.

Description

Technical Field

[0001] The present application relates to the technical field of marketing analysis, and in particular to a video data evaluation method, system, electronic device, storage medium and computer program product. Background Art

[0002] Nowadays, the real estate industry generally uses video marketing to promote its own projects by publishing real estate videos, live broadcasts and other content through video marketing platforms. However, with the increase in platform accounts, the existing SaaS tools for account management only provide basic data dashboards and single-account data analysis. They cannot be dynamically bound to the corporate organizational structure to horizontally compare the operational performance of different organizations (such as regional branches, project teams), and cannot generate cross-dimensional comprehensive rankings and cross-account aggregation. Therefore, current companies lack a global management perspective in account management on video marketing platforms. Summary of the invention

[0003] The main purpose of this application is to provide a video data evaluation method, system, electronic device, storage medium and computer program product, aiming to solve the technical problem of lack of global management perspective.

[0004] To achieve the above purpose, the present application proposes a video data evaluation method, which is applied to a video data evaluation system. The video data evaluation system is associated with each video marketing platform, and the video marketing platform includes each enterprise account. The video data evaluation method includes: For any enterprise account of the enterprise to be evaluated, extracting video features of target dimensions from the video platform data under the enterprise account based on preset operation diagnosis standards, and generating a business capability score of the enterprise account based on the video features; Classifying each enterprise account of the enterprise to be evaluated based on a preset organizational account standard to obtain enterprise account sets at different organizational levels; For any enterprise account set, a comprehensive business capability score of the enterprise account set is generated based on the business capability score of each enterprise account in the enterprise account set.

[0005] In one embodiment, the target dimension includes an expressiveness dimension, the business capability score includes an expressiveness score, and the step of extracting video features of the target dimension from the video platform data under the enterprise account based on a preset operation diagnosis standard and generating the business capability score of the enterprise account based on the video features includes: Extracting video features under the expressiveness dimension from the video platform data of the enterprise account, wherein the video features under the expressiveness dimension include video cover data, homepage introduction data, homepage display data, and personal information data of the enterprise account; Detecting the proportion of videos with video titles set in the videos under the enterprise account based on the video cover data to obtain a cover title score of the enterprise account; Detecting the standardization of the profile information of the enterprise account based on the homepage profile data to obtain a homepage profile score of the enterprise account; Detecting the activation status of the homepage page permission setting of the enterprise account and the setting status of the pinned video based on the homepage display data to obtain the homepage display score of the enterprise account; Detecting the personal information standardization of the account name and employee portrait of the enterprise account based on the personal information data to obtain a personal information score of the enterprise account; The expressiveness score of the corporate account is obtained by combining the cover title score, the homepage introduction score, the homepage display score, and the personal information score.

[0006] In one embodiment, the target dimension includes an execution dimension, the business capability score includes an execution score, and the step of evaluating the video platform data of the enterprise account based on a preset operation diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the execution dimension from the video platform data of the enterprise account, wherein the video features under the execution dimension include the first live broadcast data and work data of the enterprise account; Based on the first live broadcast data, detecting whether the average live broadcast duration of the enterprise account has reached the standard and whether the number of live broadcasts has reached the standard, obtaining a first live broadcast score of the enterprise account; Based on the work data, detecting whether the number of new works of the enterprise account has reached the standard obtains the cover title score of the enterprise account; The execution score of the enterprise account is obtained by combining the first live broadcast score and the cover title score.

[0007] In one embodiment, the target dimension includes an operational capability dimension, the business capability score includes an operational capability score, and the step of evaluating the video platform data of the enterprise account based on a preset operational diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the operational power dimension from the video platform data of the enterprise account, wherein the video features under the operational power dimension include the second live broadcast data and work playback data of the enterprise account; Based on the second live broadcast data, the average live broadcast viewing time, the average number of live broadcast viewers per game, the live broadcast exposure entry rate, and the number of new live broadcast fans of the enterprise account are detected to obtain a second live broadcast score of the enterprise account; Detecting the newly added playback volume of the works of the enterprise account based on the play data of the works to obtain the play score of the works of the enterprise account; The operational capability score of the enterprise account is obtained by combining the second live broadcast score and the work playback score.

[0008] In one embodiment, the target dimension includes a customer acquisition capability dimension, the business capability score includes a customer acquisition capability score, and the step of evaluating the video platform data of the enterprise account based on a preset operational diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the customer acquisition power dimension from the video platform data of the enterprise account, wherein the video features under the customer acquisition power dimension include background private message data, live broadcast customer acquisition data, and call retention data of the enterprise account; Detecting the private message retention rate of the enterprise account based on the background private message data to obtain the background private message score of the enterprise account; Based on the live broadcast customer acquisition data, the average number of customers acquired in the live broadcast of the enterprise account is detected to obtain the live broadcast customer acquisition score of the enterprise account; Based on the call data, the number of new calls sent by private messages to the enterprise account and the total number of new calls received are detected to obtain a call score; The customer acquisition score of the enterprise account is obtained by combining the background private message score, the live broadcast customer acquisition score and the call retention score.

[0009] In one embodiment, the target dimension includes a conversion power dimension, the business capability score includes a conversion power score, and the step of evaluating the video platform data of the enterprise account based on a preset operation diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the conversion power dimension from the video platform data of the enterprise account, wherein the video features under the conversion power dimension include subscription information data and visitor data of the enterprise account; Based on the subscription information data, the number of new subscriptions in the enterprise account during the period is detected to obtain a new subscription score; Based on the visitor data, the number of new visitors to the enterprise account during the period is detected to obtain a new visit score; The conversion power score of the enterprise account is obtained by combining the newly added subscription score and the newly added visit score.

[0010] In one embodiment, after the step of generating the comprehensive business capability score of the enterprise account set, the step further includes: Compare the comprehensive business capability score with the industry median, and obtain the score decline dimension corresponding to the score below the industry median; Determine the feature compliance status of each video feature in the score reduction dimension under the enterprise account set, and generate a diagnostic suggestion for the score reduction dimension based on the feature compliance status and a preset corpus.

[0011] In addition, to achieve the above purpose, the present application also proposes a video data evaluation system, wherein the video data evaluation system is associated with each video platform, wherein the video platform includes each enterprise account of the enterprise to be evaluated, and the video data evaluation system includes: A scoring generation module, for extracting, for any enterprise account of the enterprise to be evaluated, video features of a target dimension from the video platform data under the enterprise account based on a preset operation diagnosis standard, and generating a business capability score of the enterprise account based on the video features; An account classification module, used to classify the enterprise accounts of the enterprise to be evaluated based on a preset organizational account standard to obtain enterprise account sets at different organizational levels; The comprehensive scoring module is used to generate a comprehensive business capability score of any enterprise account set based on the business capability score of each enterprise account in the enterprise account set.

[0012] In addition, to achieve the above objectives, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the video data evaluation method described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the video data evaluation method described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the video data evaluation method described above are implemented.

[0015] The present application provides a video data evaluation method, which is applied to a video data evaluation system. The video data evaluation system is associated with each video platform. The video platform includes each enterprise account of the enterprise to be evaluated. The video data evaluation method includes: for any enterprise account of the enterprise to be evaluated, extracting video features of a target dimension from the video platform data under the enterprise account based on a preset operation diagnosis standard, and generating a business capability score of the enterprise account based on the video features; classifying each enterprise account of the enterprise to be evaluated based on a preset organizational account standard to obtain a set of enterprise accounts at different organizational levels; for any enterprise account set, generating a comprehensive business capability score of the enterprise account set based on the business capability score of each enterprise account under the enterprise account set.

[0016] This application can quantify the performance of corporate accounts in video marketing by extracting the video features of the target dimension and generating business capability scores, providing a basis for subsequent comparison and analysis. Based on the preset organizational account standards, the corporate accounts of the enterprise to be evaluated can be classified, and the corporate accounts can be divided according to the organizational structure to achieve horizontal comparison of the operational efficiency of different organizations. By generating a comprehensive business capability score for the corporate account set, the overall performance of corporate accounts at different organizational levels in video marketing can be quantified, providing the enterprise with a global management perspective, which helps the enterprise to better understand the operational efficiency of each organizational level, and thus formulate more effective marketing strategies. Compared with related solutions that can only see the performance of a single account, but cannot grasp the marketing effects of different levels or classified accounts as a whole, this application provides a comprehensive assessment from a single account to different organizational levels, enabling enterprises to review and manage their corporate accounts from a global perspective, making it easier to identify accounts with outstanding performance or areas that need improvement, and provide strong support for formulating and adjusting marketing strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flowchart of the first embodiment of the video data evaluation method of the present application is provided; Figure 2 A five-dimensional capability diagram of the video data evaluation method provided for this application; Figure 3Account level distribution for the video data evaluation method provided for this application; Figure 4 A flowchart of the second embodiment of the video data evaluation method of the present application is provided; Figure 5 The overall flow chart of the video data evaluation method provided in this application; Figure 6 This is a schematic diagram of the module structure of the video data evaluation device according to an embodiment of the present application; Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the video data evaluation method in the embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The embodiment of the present application is applied to a video data evaluation system, which is associated with each video platform. The video platform includes each enterprise account of the enterprise to be evaluated. The main solution is: for any enterprise account of the enterprise to be evaluated, the video features of the target dimension are extracted from the video platform data under the enterprise account based on the preset operation diagnosis standard, and the business capability score of the enterprise account is generated based on the video features; the enterprise accounts of the enterprise to be evaluated are classified based on the preset organizational account standard to obtain enterprise account sets at different organizational levels; for any enterprise account set, the comprehensive business capability score of the enterprise account set is generated based on the business capability score of each enterprise account under the enterprise account set.

[0024] In this embodiment, for the convenience of description, the video data evaluation system is used as the execution subject for explanation below.

[0025] The current real estate industry has significant shortcomings in the management of corporate accounts in digital marketing. First, there is a lack of a global perspective, and data cannot be aggregated across accounts, making it difficult to compare the operational efficiency of different organizations, resulting in low management efficiency. Secondly, the data display has not been optimized for the characteristics of the real estate industry, and there is a lack of vertical analysis models, making it difficult to accurately evaluate customer acquisition efficiency and conversion effects, affecting the formulation of operational strategies. Furthermore, AI review technology is limited to content compliance testing, has not been adapted to real estate homepage decoration standards, and cannot automatically evaluate homepage quality.

[0026] This application provides a solution by building an intelligent data analysis platform, deeply integrating with the corporate organizational structure, and constructing a vertical analysis model based on the characteristics of the real estate industry to provide data support for the formulation of operational strategies. At the same time, it upgrades AI review technology to adapt it to the real estate homepage decoration standards, enabling real estate companies to operate accounts efficiently and improve the overall operational level.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a video data evaluation system, etc. The following takes the video data evaluation system as an example to illustrate this embodiment and the following embodiments.

[0028] Based on this, the present application embodiment provides a video data evaluation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the video data evaluation method of the present application.

[0029] In this embodiment, the video data evaluation system is applied, the video data evaluation system is associated with each video platform, the video platform includes each enterprise account of the enterprise to be evaluated, and the video data evaluation method includes steps S01 to S03: Step S01: for any enterprise account of the enterprise to be evaluated, extract the video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard, and generate the business capability score of the enterprise account based on the video features; It should be noted that for each corporate account of the enterprise to be evaluated, its video platform data on the video platform within the authorized scope will be collected. The video platform refers to an online marketing platform that provides functions such as video publishing and live streaming. A corporate account refers to an account registered on the video platform and used to promote corporate projects or products, such as personal employee accounts, city blue v accounts, project accounts, etc. Video platform data include but are not limited to the number of video views, likes, comments, shares, user interaction rate, video publishing frequency, video duration, etc. The system screens and analyzes these video platform data based on the preset operational diagnostic standards to obtain video features, a set of scoring standards and weights pre-set by the system administrator or enterprise based on industry standards, market trends, corporate strategies and other factors. It stipulates which specific performances or indicators will be considered under different target dimensions, and how to give corresponding scores based on these performances or indicators. For each target dimension, the system will further refine specific scoring items and scoring standards based on the preset operational scoring rules to determine the satisfaction of video features under the target dimension. For example, the score of a single rule = total score of a single item × completion rate (actual completion of a single account / target value). The target dimension can include five dimensions: expressiveness, execution, operation, customer acquisition and conversion (the actual dimension may be adjusted according to business needs). Scoring is performed based on the satisfaction of video features under the target dimension to obtain the business capability score of the enterprise account. This score reflects the overall performance of the enterprise account in video marketing, including content creation capabilities, user appeal, brand influence, etc.

[0030] In addition, it should be noted that users (usually marketing managers or data analysts of enterprises) will configure the assessment cycle. By selecting the assessment cycle, users can view the marketing data and marketing effect display in different cycles. At the same time, the assessment cycle can be customized by the user, or the data of each sub-cycle can be selected in the historical assessment cycle. At the same time, users will also enter the operation diagnosis standard, which is a standard for multi-dimensional evaluation of the performance of enterprise accounts in video marketing. After completing the cycle setting and rule configuration, the account diagnosis task will be published to RocketMQ for execution. RocketMQ is a message middleware used to handle the sending and receiving of large-scale messages, and can be used to trigger the account diagnosis task.

[0031] In addition, it should be noted that users will also input the five-force scoring rules. The five-force scoring rules are summary rules for multiple target dimensions set by users. When obtaining the scores of each target dimension, they can be summarized through the five-force scoring rules. For example, the score of a single corporate account in a certain dimension (such as expressiveness) = rule 1 score + rule 2 score + rule 3 score + rule 4 score…, the business capability score of a single corporate account = expressiveness + execution + operation + customer acquisition + conversion.

[0032] In addition, it should be noted that the operational diagnosis standards and organizational account standards can be determined by the user in the preset rule table, or can be composed of rule text entered by the user. The system parses the rule text entered by the user and converts it into a code program for execution. At the same time, it can also be obtained by parsing the rule text entered by the user through the AI ​​large language model.

[0033] It is understandable that in the operation diagnosis standard, it is understandable that since the existing SaaS tools can only provide basic data dashboards and single account data analysis, step S01 introduces preset operation diagnosis standards to deeply explore the valuable information in the video data and convert it into a quantifiable business capability score, thereby providing enterprises with a more comprehensive and in-depth account operation analysis.

[0034] Step S02, classifying the enterprise accounts of the enterprise to be evaluated based on the preset organizational account standard to obtain enterprise account sets of different organizational levels; It should be noted that the video data evaluation system classifies the various enterprise accounts of the enterprise to be evaluated according to the preset organizational account standards, so as to carry out more detailed and targeted analysis in the subsequent steps. The preset organizational account standards include the internal organizational structure, department divisions, product lines, market areas, etc. of the enterprise. The order of organizational levels under the organizational account standards is enterprise → region → project → individual, where region refers to the geographical distribution of the business, such as the Central China region, the East China region, etc., and project refers to the real estate projects in the region, such as a community, an office building, etc. Individual refers to the employees responsible for the project. By applying these standards, the system can divide enterprise accounts into different organizational levels. Each organizational level contains a series of enterprise accounts with similar characteristics or responsibilities, that is, a set of enterprise accounts, which enables the system to manage and analyze these accounts more centrally.

[0035] In addition, it should be noted that the organizational-level operational diagnostic standards for the project / region / enterprise dimensions in the organizational account standards can be: the score in a certain dimension = the average score of all employee personal accounts within the assessment scope * the weight coefficient corresponding to the employee personal account + the average score of all city blue v accounts within the assessment scope * the weight coefficient corresponding to the city blue v account + the average score of all project accounts within the assessment scope * the weight coefficient corresponding to the project account +..., where the average score is the average score of the account obtained by adding up the scores of the enterprise account set under a certain dimension and dividing it by the number of accounts.

[0036] It is understandable that, since existing SaaS tools cannot be dynamically bound to the enterprise organizational structure, step S02 introduces a preset organizational account standard to associate the enterprise account with the enterprise's organizational structure, thereby achieving horizontal comparison and analysis of accounts at different organizational levels.

[0037] Step S03: for any enterprise account set, a comprehensive business capability score of the enterprise account set is generated based on the business capability score of each enterprise account in the enterprise account set.

[0038] It should be noted that for each corporate account set, the business capability scores of all the corporate accounts under it are collected, and these scores are integrated into a comprehensive business capability score using statistical methods or aggregation algorithms (such as mean, median, weighted average, etc.). This score reflects the overall strength and performance of the organization level in video marketing.

[0039] In addition, it should be noted that the system divides each enterprise account into different levels according to the business capability score of each enterprise account under the enterprise account set, usually according to the total score, such as Grade A (excellent), Grade B (good), Grade C (average), Grade D (poor), Grade E (serious), etc., and counts the number and proportion of accounts at each level to obtain an account level distribution chart. Through the account level distribution chart, users can intuitively see the proportion of high-quality accounts (such as Grade A and Grade B accounts) and the proportion of low-quality accounts (such as Grade C, Grade D and Grade E accounts) at the corresponding organizational level, and understand the account performance at each organizational level.

[0040] For example, the account level classification standard may be as shown in Table 1: Table 1

[0041] For example, if the score of a corporate account is 60 points, the corresponding account level is C.

[0042] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 2 , Figure 2 It provides a schematic diagram of the five-dimensional capability diagram. Module A is the five-dimensional capability diagram module, in which performance, operation, customer acquisition, conversion and execution are the five dimensions of the operation diagnosis standard. Z1 is the five-dimensional capability diagram under the enterprise account indicator, and Z2 is the five-dimensional capability diagram under the industry median indicator, which can reflect the marketing effect of the industry median dimension, thereby reflecting the gap between the enterprise to be evaluated and the industry average level. z1, z2, z3, z4, and z5 are the corresponding scores of the enterprise account under each target dimension, and z'1, z'2, z'3, z'4, and z'5 are the corresponding scores of the industry median under each dimension. Module B shows the evaluation results of the marketing effect, including the total score, month-on-month increase / decrease, the overall judgment on the marketing effect, and the specific evaluation content of the five target dimensions (expressiveness, execution, operation, customer acquisition and conversion).

[0043] In addition, it should be noted that after generating the comprehensive business capability score for the corporate account set, the system will generate a five-dimensional capability map based on the scores of each target dimension included in the comprehensive business capability score. The five-dimensional capability map can reflect the capability distribution of the corporate account set under the corresponding organizational level in each target dimension. Users can intuitively see which target dimensions have outstanding capabilities and which target dimensions have deficiencies and make improvements, and understand the marketing effects of each organizational level.

[0044] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 3 , Figure 3 It provides a schematic diagram of the account level distribution chart. Module C is a fan-shaped chart of the account level distribution, which contains accounts of each level and their proportions. The total number of accounts is shown in the middle of the distribution chart (96 in this example), indicating the number of all accounts at this level. Module B shows the evaluation results of the marketing effect. Figure 2 The content is consistent with that in the previous section and will not be repeated here.

[0045] It is understandable that since existing SaaS tools are unable to generate cross-dimensional comprehensive rankings and cross-account aggregations, step S03 introduces the concept of comprehensive business capability scoring to provide companies with more intuitive and comprehensive account operation effectiveness evaluation results, helping companies to better understand their overall strength in video marketing.

[0046] In a feasible implementation, in step S01, the target dimension includes an expressiveness dimension, the business capability score includes an expressiveness score, and the steps of extracting video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard, and generating the business capability score of the enterprise account based on the video features include steps A01 to A06: Step A01, extracting video features under the dimension of expressiveness from the video platform data of the enterprise account, where the video features under the dimension of expressiveness include video cover data, homepage introduction data, homepage display data, and personal information data of the enterprise account; It should be noted that the system selects video features related to the expressiveness dimension from the video platform data, including the video cover data, homepage introduction data, homepage display data and personal information data of the corporate account. Among them, the expressiveness dimension is a dimension that measures the ability of a corporate account to display its own image and attract user attention on the video platform. The expressiveness score is based on the video features under the expressiveness dimension and is calculated through a series of algorithms. It is used to quantify the expressiveness score of the corporate account on the video platform.

[0047] Step A02, based on the video cover data, the proportion of videos with video titles set in the videos under the enterprise account is detected to obtain the cover title score of the enterprise account; It should be noted that video cover data refers to the cover image and related information of each video published by the corporate account. The video cover data is subjected to image preprocessing, such as denoising and contrast enhancement, and converted into a format suitable for OCR processing (such as PNG, TIFF, etc.). Personalized OCR software is used to perform text recognition on the preprocessed video cover data. The personalized OCR service performs personalized resolution compression optimization to improve recognition performance. The text information in the video cover data, including possible video titles, is identified according to preset naming rules and common formats of video titles. The number of videos with video titles set is counted, and the number of videos with titles set is divided by the total number of videos detected to obtain the percentage of videos with video titles set. The calculated percentage of videos is compared with the preset percentage threshold (for example, 70%) to obtain the cover title score of the corporate account. The cover title score reflects the score of the corporate account's expressiveness in video title setting.

[0048] In addition, it should be noted that when performing video title recognition, the system will combine the machine learning model to review the video cover data to avoid identifying text that is not a video title. The machine learning model analyzes a large number of annotated video screenshots to achieve the effect of distinguishing between video title text and non-video title text.

[0049] Step A03, detecting the standardization of the profile information of the enterprise account based on the homepage profile data to obtain the homepage profile score of the enterprise account; It should be noted that the homepage profile data refers to the brief introduction of the homepage of the corporate account on the video platform. According to the personalized GPT (the personalized GPT is connected to multiple GPTs, such as Deepseek, GLM, Qwen, etc., and can integrate the advantages of multiple large language models), the profile information in the homepage profile data is identified and extracted, and compared with the preset standard elements. The standardization of the corporate account profile information is statistically calculated through the preset standard elements that are met, and the homepage profile score is obtained. The homepage profile score reflects the score of the corporate account's expressiveness in setting the homepage profile information.

[0050] For example, the preset specification elements may include: 1. Working experience and time; 2. Successful house selling cases: sold xx houses; 3. Real estate experience: person in charge of a listed real estate company / xx real estate company; 4. Real estate core area: clearly identify the city / region / sector where the house is sold; 5. Core content: clarify the specific products - new houses / second-hand houses / rental houses / office buildings / shops; When at least two of the above conditions are met, the profile information of the corporate account is judged to be highly standardized, and the homepage profile score is full marks.

[0051] Step A04, based on the homepage display data, detecting the activation status of the homepage page permission setting of the enterprise account and the setting status of the top video to obtain the homepage display score of the enterprise account; It should be noted that the homepage display data covers the page layout and permission settings of the homepage. The system will check the page permission settings of the corporate account homepage, such as whether it is set to retain only the works and merchant pages, whether the like list is closed, etc. If the settings are met, the homepage display score will be deducted. The score is set by the user and will not be repeated later. At the same time, it will also check whether a pinned video is set and whether the content of the pinned video is related to the business of the corporate account. If the settings are met, the homepage display score will be added. Based on the comprehensive situation of this information, the final homepage display score is given. The homepage display score is a score that reflects the expressiveness of the corporate account in homepage display.

[0052] Step A05, detecting the personal information standardization of the account name and employee portrait of the enterprise account based on the personal information data to obtain the personal information score of the enterprise account; It should be noted that personal information data includes basic information such as the name of the corporate account and employee portraits. The system checks whether the account name of the corporate account is clear and accurate and whether it is related to the corporate business, such as whether it contains the company + region / city / project name. If so, additional points will be added to the personal information score. At the same time, the system will also use facial recognition to check whether the employee portrait is a portrait and whether it is a clear frontal photo. If so, additional points will be added to the personal information score, and the personal information score will be finally obtained. The personal information score is a score that reflects the expressiveness of the corporate account in terms of personal information settings.

[0053] Step A06, comprehensively analyzing the cover title score, home page introduction score, home page display score, and personal information score to obtain the expressiveness score of the corporate account.

[0054] It should be noted that the cover title score, homepage introduction score, homepage display score and personal information score obtained in steps A01 to A03 are comprehensively calculated (for example, added together, weighted calculation, etc.) to obtain the final expressiveness score of the corporate account. This score will comprehensively reflect the ability of the corporate account to display its own image on the video platform and attract user attention.

[0055] In addition, it should be noted that when evaluating and scoring each indicator (such as cover title score, homepage introduction score, homepage display score, and personal information score), users can choose to turn on or off the rule items under each target dimension to set operational diagnosis standards that meet personal needs. At the same time, it also supports adding new rule items to the operational diagnosis standards and setting corresponding scores to improve the flexibility and autonomy of rule setting. I will not go into details later.

[0056] In this implementation, by evaluating the performance of the corporate account in setting the cover title and quantifying the standardization issues in setting the video title, the standardization of the title setting is improved, which helps to improve the click-through rate and user attention of the video; by evaluating the standardization of the profile information, the effectiveness and attractiveness of the profile information are ensured, the attractiveness of the homepage profile and the user retention rate are improved, which helps to enhance users' understanding and trust in the company; by evaluating the rationality of its homepage display, the homepage display effect is optimized, and the enthusiasm of users to visit and interact is improved; by evaluating the standardization of personal information, the consistency and professionalism of personal information are ensured, the professionalism and credibility of the corporate account are improved, which helps to enhance users' trust and recognition of the company; through comprehensive score calculation, the performance of the account in terms of cover title, homepage profile, homepage display and personal information can be fully reflected, providing a global management perspective, which helps companies to fully understand their performance on the video platform and formulate targeted improvement strategies accordingly.

[0057] In a feasible implementation, in step S02, the target dimension includes an execution dimension, the business capability score includes an execution score, and the step of extracting video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard, and generating the business capability score of the enterprise account based on the video features also includes steps A11 to A14: Step A11, extracting video features under the execution dimension from the video platform data of the enterprise account, where the video features under the execution dimension include the first live broadcast data and work data of the enterprise account; It should be noted that the system selects video features related to the execution dimension from the video platform data of the corporate account, including the live broadcast data and work data of the corporate account. The execution dimension is a key indicator to measure the operational efficiency and activity of the corporate account on the video platform, and directly reflects the execution ability of the corporate account and the degree of utilization of the platform. The first live broadcast data includes the duration, frequency, and audience interaction of the live broadcast, reflecting the investment and effect of the corporate account in live broadcasting. The work data includes the quantity, quality, and release frequency of the works, reflecting the ability of the corporate account in content creation and release.

[0058] Step A12, based on the first live broadcast data, detecting whether the average live broadcast duration of the enterprise account has reached the standard and whether the number of live broadcasts has reached the standard, and obtaining a first live broadcast score of the enterprise account; It should be noted that the average live broadcast duration refers to whether the average duration of each live broadcast of the corporate account reaches or exceeds the preset duration standard. For example, if the preset standard is at least 1 hour per live broadcast, the system will determine whether the corporate account meets the standard based on the first live broadcast data. The number of live broadcasts that meet the standard refers to whether the number of live broadcasts of the corporate account within a period of time (such as a week or a month) reaches or exceeds the preset number of sessions (for example, 20 sessions), reflecting the activity and continuity of the corporate account in live broadcasting. Based on the first live broadcast data, the system analyzes whether the average live broadcast duration and number of live broadcasts of the corporate account meet the preset standards, and obtains the first live broadcast score. The first live broadcast score is a comprehensive score based on the average live broadcast duration and the number of live broadcasts, and is used to evaluate the execution of the corporate account in live broadcasting.

[0059] Step A13, based on the work data, detecting whether the number of new works of the enterprise account has reached the standard, and obtaining the cover title score of the enterprise account; It should be noted that the number of new works reaching the standard refers to whether the number of new works added by the corporate account within a preset time period (such as a week or a month) reaches or exceeds the preset number standard (for example, 10), and a cover title score is given based on this. The cover title score is a comprehensive score based on the number of new works reaching the standard, reflecting the corporate account's activity and efficiency in content creation.

[0060] For example, the maximum score for the number of new works is 12 points, and the requirement is to publish 20 works. If the current account has only published 10 works, then the score of the account in this rule is: 12*10 / 20 = 6 points. Similarly, other rules are evaluated and calculated in a similar way.

[0061] Step A14, combining the first live broadcast score and the cover title score to obtain the execution score of the enterprise account.

[0062] It should be noted that the system calculates the execution score of the corporate account based on the first live broadcast score and the cover title score. The execution score is used to comprehensively evaluate the performance of the corporate account in the execution dimension, reflecting the overall execution ability and efficiency of the corporate account in live broadcast and content creation.

[0063] In this implementation, by quantitatively evaluating the effectiveness and frequency of the live broadcast activities of the corporate account, the arbitrariness of subjective judgment is avoided, making the scoring results more objective and fair. First, the live broadcast score can intuitively reflect the execution strength of the corporate account in live broadcasting, which helps the company identify its own advantages and disadvantages in live broadcasting strategy, thereby adjusting the strategy and improving the live broadcast effect; by quantitatively evaluating the content creation ability and the attractiveness of the work of the corporate account, the execution of the corporate account in content creation is measured. The cover title score can reflect the investment and effect of the corporate account in content creation, which helps the company optimize the content strategy and increase the click-through rate and viewing rate of the work; the execution score comprehensively reflects the overall execution level of the corporate account in both live broadcasting activities and content creation, so that the company can clearly understand its overall execution performance on the video marketing platform, which helps the company to formulate more scientific and reasonable marketing strategies and improve the efficiency and effectiveness of account management.

[0064] In a feasible implementation, in step S02, the target dimension includes an operational capability dimension, the business capability score includes an operational capability score, and the step of extracting video features of the target dimension from the video platform data under the enterprise account based on a preset operational diagnosis standard, and generating a business capability score of the enterprise account based on the video features also includes steps A21 to A24: Step A21, extracting video features under the operational capability dimension from the video platform data of the enterprise account, where the video features under the operational capability dimension include the second live broadcast data and work playback data of the enterprise account; It should be noted that video features directly related to the operational power dimension are screened out from the video platform data of the corporate account, including the second live broadcast data and work playback data of the corporate account. The operational power dimension is a set of indicators that measure the operational performance of the corporate account, reflecting the operational efficiency and effectiveness of the corporate account. The second live broadcast data records the performance of the live broadcast activities of the corporate account, including but not limited to viewing time, number of viewers, etc. The work playback data reflects the playback status of the video works released by the corporate account.

[0065] Step A22, based on the second live broadcast data, detecting whether the average live broadcast viewing time, the average number of live broadcast viewers per game, the live broadcast exposure entry rate, and the number of new live broadcast fans of the enterprise account meet the standards to obtain a second live broadcast score of the enterprise account; It should be noted that the second live broadcast score of the enterprise account is calculated based on the live broadcast data by detecting the compliance of the average live broadcast viewing time, the average number of live broadcast viewers per session, the live broadcast exposure entry rate, and the number of new live broadcast fans. Among them, the average live broadcast viewing time is a measure of the average interest of the audience in the live broadcast content. The longer the time, the more attractive the content; the average number of live broadcast viewers per session reflects the audience size of the live broadcast activity. The more people there are, the more attractive the live broadcast is; the live broadcast exposure entry rate indicates the proportion of viewers who click to enter after the live broadcast is exposed; the number of new live broadcast fans is the number of new fans attracted by the live broadcast activity, which can evaluate the conversion effect of the live broadcast. The second live broadcast score is a score based on the compliance of each indicator, which is used to quantify the operational effect of the live broadcast activity.

[0066] Step A23, based on the work playback data, detecting the newly added playback volume of the work of the enterprise account to obtain the work playback score of the enterprise account; It should be noted that the work playback score of the corporate account is calculated based on the work playback data by detecting the key indicator of new work playback volume. The new work playback volume refers to the number of new playbacks of video works released by the corporate account within a certain period of time, which can measure the content dissemination effect. The work playback score is a score calculated based on the new work playback volume, which is used to quantify the attractiveness and dissemination effect of video content.

[0067] Step A24, combining the second live broadcast score and the work playback score to obtain the operational efficiency score of the enterprise account.

[0068] It should be noted that the operational strength score of the corporate account is obtained by combining the second live broadcast score and the work playback score through algorithms or weight distribution. The operational strength score reflects the overall performance of the corporate account in the operational strength dimension.

[0069] In this implementation, the complexity of live broadcast data evaluation is solved by multi-dimensionally analyzing the average live broadcast viewing time, the average number of live broadcast viewers per game, the live broadcast exposure entry rate, and the number of new live broadcast fans. The accuracy and comprehensiveness of the scoring are ensured. At the same time, the second live broadcast score can intuitively reflect the operational effect of the corporate account in live broadcasting, help the company identify the advantages and disadvantages in live broadcasting, and provide a basis for optimizing the live broadcast strategy; by analyzing the new playback volume of the works of the corporate account, the playback effect of the work can be accurately evaluated. At the same time, the work playback score can intuitively reflect the operational effect of the corporate account in the release of the work, help the company understand the popularity of the work, and provide a basis for optimizing the content strategy; the operational effectiveness of the corporate account on the video platform is comprehensively reflected through the operational strength score, providing a global management perspective for the company, which not only helps the company identify the advantages and disadvantages in operation, but also provides data support for formulating and optimizing marketing strategies.

[0070] In a feasible implementation, in step S02, the target dimension includes a customer acquisition dimension, the business capability score includes a customer acquisition score, and the step of extracting video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard, and generating the business capability score of the enterprise account based on the video features also includes steps A31 to A35: Step A31, extracting video features under the dimension of customer acquisition power from the video platform data of the enterprise account, where the video features under the dimension of customer acquisition power include the background private message data, live broadcast customer acquisition data, and call retention data of the enterprise account; It should be noted that the video features under the customer acquisition power dimension, background private message data, live broadcast customer acquisition data, and call retention data are extracted from the video platform data of the corporate account. Among them, the customer acquisition power dimension is a dimension that measures the ability of the corporate account to attract new customers, including the interaction between the account and potential customers, the customer acquisition effect of live broadcast activities, and customer call retention behavior; the background private message data records the private message exchanges between the corporate account and potential customers in the background of the video platform, including the number of private messages, reply rate, etc.; the live broadcast customer acquisition data reflects the quantity and quality of new customers attracted by the corporate account through live broadcast activities; the call retention data records the contact information left by customers after watching videos or live broadcasts, which is an important indicator of potential customer conversion.

[0071] Step A32, detecting the private message retention rate of the enterprise account based on the background private message data to obtain the background private message score of the enterprise account; It should be noted that the private message retention rate refers to the proportion of potential customers who leave their contact information after the corporate account replies to the private message, which measures the effectiveness and conversion rate of the account's interaction with potential customers; the backend private message score refers to the score of the account's performance in private message interaction based on the private message retention rate. The higher the score, the better the account performs in attracting potential customers to leave their contact information.

[0072] Step A33, based on the live broadcast customer acquisition data, detecting whether the average number of customers acquired in the live broadcast of the enterprise account meets the standard, and obtaining the live broadcast customer acquisition score of the enterprise account; It should be noted that the average number of customers acquired during a live broadcast refers to the average number of new customers attracted by the corporate account in each live broadcast activity, reflecting the customer acquisition efficiency and attractiveness of the account's live broadcast activities; the live broadcast customer acquisition score is based on whether the average number of customers acquired during a live broadcast meets the preset standards, and scores the account's performance in live broadcast customer acquisition. The better the standard is met, the higher the score.

[0073] Step A34, based on the call data, detecting the number of new calls sent by private messages from the enterprise account and the total number of new calls that meet the standards to obtain a call score; It should be noted that the number of new calls left by private messages refers to the number of new contact information left by customers after private message interaction, which measures the account's ability to convert potential customers into actual contacts in private message interaction; the total number of new calls left refers to the total number of new contact information left by an account through all channels (including private messages, live broadcasts, etc.) within a preset time period; the call score is based on the number of new calls left by private messages and the total number of new calls left, and scores the account's performance in call retention. The better the standard is met, the higher the score.

[0074] Step A35, comprehensively consider the backend private message score, live broadcast customer acquisition score and call retention score to obtain the customer acquisition score of the enterprise account.

[0075] It should be noted that the customer acquisition score is a quantitative assessment of the account's overall performance in the customer acquisition dimension. By comprehensively evaluating the scores of backend private messages, live broadcast acquisition, and call retention, we can have a more comprehensive understanding of the account's ability and efficiency in attracting new customers. This helps corporate accounts identify their own strengths and weaknesses in customer acquisition, thereby formulating more targeted marketing strategies.

[0076] In this embodiment, by calculating the private message retention rate and generating a background private message score, the enterprise can more accurately evaluate the effectiveness of private message marketing, thereby optimizing the private message content and sending strategy; by calculating the average number of customers acquired per live broadcast and generating a live broadcast customer acquisition score, the enterprise can more objectively evaluate the effectiveness of live broadcast marketing, thereby optimizing the live broadcast content and form; by calculating the number of new calls-after-retention and the total number of new calls-after-retention by private messages and generating a call-after-retention score, the enterprise can more accurately evaluate the effectiveness of call-after-retention marketing, thereby optimizing the call-after-retention guidance strategy and subsequent follow-up process; by comprehensively calculating the customer acquisition power score, the enterprise can have a more comprehensive understanding of its own customer acquisition performance on the video marketing platform, thereby formulating more comprehensive marketing strategies and optimization plans.

[0077] In a feasible implementation, in step S02, the target dimension includes a conversion power dimension, the business capability score includes a conversion power score, and the step of extracting video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard, and generating the business capability score of the enterprise account based on the video features also includes steps A41 to A44: Step A41, extracting video features under the dimension of conversion power from the video platform data of the enterprise account, where the video features under the dimension of conversion power include subscription information data and visitor data of the enterprise account; It should be noted that the data directly related to the conversion power dimension is screened out from the video platform data, including the subscription information data and visitor data of the corporate account. Among them, the conversion power dimension refers to the ability of the corporate account to attract viewers through video content and prompt them to take further actions (such as purchases, registrations, etc.), the subscription information data reflects the purchase behavior generated by the audience after watching the video, such as purchasing products and services, etc., and the visitor data records the number and behavior of users who visit the corporate account video page, such as viewing time, number of clicks, etc.

[0078] Step A42, based on the subscription information data, detecting whether the number of new subscriptions in the enterprise account during the period has reached the standard and obtaining a new subscription score; It should be noted that based on the preset target standard (the target number of new subscriptions within a certain period of time, for example 1), the actual performance of the corporate account in terms of the number of new subscriptions within the period is automatically detected, and the new subscription score of the corporate account is generated by comparing the actual number of new subscriptions with the target standard. This score reflects the ability of the corporate account in attracting viewers and converting them into actual buyers.

[0079] Step A43, based on the visitor data, the number of new visitors in the enterprise account during the period is detected to obtain a new visit score; It should be noted that based on the preset visitor target (monthly new visitor target, for example 8), the actual performance of the corporate account in terms of new visitors during the period is detected and evaluated to generate a new visit score, which reflects the corporate account's ability to attract new users.

[0080] Step A44, the conversion score of the enterprise account is obtained by combining the newly added subscription score and the newly added visit score.

[0081] It should be noted that the new subscription score and the new visit score generated in steps A42 and A43 are combined to obtain the conversion score of the corporate account. This score is a comprehensive reflection of the conversion dimension, reflecting the overall ability of the corporate account in attracting and converting viewers into actual customers.

[0082] In this implementation, by calculating the new subscription score, the enterprise can more accurately understand its ability in subscription conversion, thereby optimizing the subscription strategy and improving the subscription conversion rate; by calculating the new visit score, the enterprise can more accurately understand its ability in attracting new visitors, thereby optimizing the visitor attraction strategy and improving the number and quality of visitors; by comprehensively calculating the conversion power score, the enterprise can more comprehensively understand its conversion ability on the video marketing platform, thereby formulating more comprehensive conversion strategies and optimization plans. At the same time, it also solves the problem of the enterprise's lack of a global management perspective in account management, enabling the enterprise to more systematically improve conversion efficiency and visitor attractiveness.

[0083] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 In step S03, after the step of generating the comprehensive business capability score of the enterprise account set, steps S11 to S12 are also included: Step S11, comparing the comprehensive business capability score with the industry median, and obtaining the score reduction dimension corresponding to the score lower than the industry median; It should be noted that the system extracts the scoring data of all corporate accounts at a specific organizational level in each target dimension (expressiveness, execution, operation, customer acquisition, and conversion) from the database. For each target dimension, the median of all corporate account scores is calculated. The median refers to the value in the middle position of a set of data, which can better reflect the central trend of the data and is not easily affected by extreme values. The calculated median will be used as the industry average for the corresponding target dimension of the organizational level, representing the average performance level of corporate accounts in this organizational level in this dimension, providing a benchmark for subsequent comparison and analysis. By comparing the comprehensive business capability score of each corporate account with this industry median, it can be determined in which target dimensions the corporate account is below the industry average, that is, the dimension where the score drops.

[0084] In addition, it should be noted that the system's comparison of industry medians is a horizontal comparison. It will also compare the comprehensive business capability scores of the current cycle with those of the previous cycle (i.e., the previous evaluation cycle), and analyze the changing trends of the corporate account set in each target dimension (expressiveness, execution, operation, customer acquisition, and conversion), and identify which dimensions are improving and which dimensions are declining, i.e., a vertical comparison.

[0085] In addition, it should be noted that after the system obtains the industry median, it will mark and display it in the generated five-dimensional capability diagram, such as Figure 2 As shown, Z2 is the five-dimensional capability diagram under the industry median indicator. The system will output the five-dimensional capability diagram marked with the industry average to the user. The user can intuitively see the difference between the performance of the corporate account in each evaluation dimension and the industry average, so as to compare with the actual score of the corporate account, making it easier to identify strengths and weaknesses.

[0086] Step S12, determining the feature compliance status of each video feature in the score reduction dimension under the enterprise account set, and generating diagnostic suggestions for the score reduction dimension based on the feature compliance status and a preset corpus.

[0087] It should be noted that the system checks the video features involved in each score drop dimension and evaluates whether these video features have reached the preset standards or thresholds, that is, the feature compliance status. Based on the evaluation results of these feature compliance status, the system further refers to the preset corpus, which contains recommendation templates or strategies for different score drop dimensions and feature compliance status. The system selects or combines appropriate suggestions from the corpus to generate specific diagnostic suggestions for the corporate account. The diagnostic suggestions are designed to help companies identify the specific reasons for the score drop and provide corresponding improvement measures. The diagnostic suggestions can provide specific implementation steps, expected results and timetables so that companies can better implement and improve.

[0088] In addition, it should be noted that when generating diagnostic suggestions, diagnostic suggestions can also be generated through a preset diagnostic model. The preset diagnostic model is based on historical marketing data and solution training. At the same time, for diagnostic plans newly added or modified by users, the preset diagnostic model will learn to improve the effectiveness of improvement measures. In addition, after giving diagnostic suggestions, the system will track and evaluate the effects achieved after implementing the diagnostic suggestions, so that the company can understand the implementation of the improvement measures, and feed back the tracked evaluation data to the preset diagnostic model to improve the feasibility of suggestion generation.

[0089] In this implementation, by comparing the comprehensive business capability score with the industry median, the company can clearly understand its own level of competitiveness in the industry, thereby identifying areas that need improvement. By analyzing in detail the compliance of video features in the score reduction dimension and combining the suggestions in the preset corpus, the system can provide the company with practical optimization solutions.

[0090] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 5 , Figure 5 An overall flow chart is provided. In the configuration process stage, the user configures the evaluation cycle and evaluation rules. The evaluation rules include operation scoring rules, operation diagnosis standards, and organization account standards. After completing the configuration, the diagnosis process is entered, scheduled tasks are set, and scheduled tasks (mainly account diagnosis tasks) are published to RocketMQ (message middleware, used to implement asynchronous communication, service decoupling, traffic peak shaving and other functions). When tasks are triggered, the scores can be calculated according to the operation scoring rules through personalized OCR, face recognition, personalized GPT, AI data collection and other methods. After calculating the score of a single account, the account distribution of each level (project, region, enterprise) under the organization account standard is calculated to obtain the account level distribution. At the same time, the account score can also be converted upward (for example, to the regional level and enterprise level), and the scores of each level and each dimension under the organization account standard are calculated, and then multi-dimensional aggregation is performed to obtain the five-force model score, so as to generate diagnostic suggestions based on the corpus.

[0091] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the video data evaluation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0092] The present application also provides a video data evaluation system, which is associated with each video platform. The video platform includes each enterprise account of the enterprise to be evaluated. Please refer to Figure 6 , the video data evaluation system includes: The scoring generating module 10 is used to extract the video features of the target dimension from the video platform data under the enterprise account for any enterprise account to be evaluated based on the preset operation diagnosis standard, and generate the business capability score of the enterprise account based on the video features; An account classification module 20 is used to classify the enterprise accounts of the enterprise to be evaluated based on a preset organizational account standard to obtain enterprise account sets at different organizational levels; The comprehensive scoring module 30 generates a comprehensive business capability score of any enterprise account set based on the business capability score of each enterprise account in the enterprise account set.

[0093] Optionally, the target dimension includes an expressiveness dimension, the business capability score includes an expressiveness score, and the score generating module 10 is further used to: Extracting video features under the dimension of expressiveness from the video platform data of the enterprise account, where the video features under the dimension of expressiveness include the video cover data, homepage introduction data, homepage display data, and personal information data of the enterprise account; Based on the video cover data, the proportion of videos with video titles set in the videos under the enterprise account is detected to obtain the cover title score of the enterprise account; Based on the homepage profile data, the standardization of the profile information of the enterprise account is detected to obtain the homepage profile score of the enterprise account; Based on the homepage display data, the activation status of the homepage page permission settings of the enterprise account and the setting status of the pinned video are detected to obtain the homepage display score of the enterprise account; Based on the personal information data, the personal information standardization of the account name and employee portrait of the enterprise account is detected to obtain the personal information score of the enterprise account; The expressiveness score of the corporate account is obtained by combining the cover title score, homepage introduction score, homepage display score, and personal information score.

[0094] Optionally, the target dimension includes an execution dimension, the business capability score includes an execution score, and the score generating module 10 is further used to: Extracting video features under the execution dimension from the video platform data of the enterprise account, where the video features under the execution dimension include the first live broadcast data and work data of the enterprise account; Based on the First Live data, the average live broadcast duration of the enterprise account and the number of live broadcasts that meet the standard are detected to obtain the First Live score of the enterprise account; Based on the work data, the number of new works of the enterprise account is detected to obtain the cover title score of the enterprise account; The execution score of the corporate account is obtained by combining the first live broadcast score and the cover title score.

[0095] Optionally, the target dimension includes an operational capability dimension, the business capability score includes an operational capability score, and the score generating module 10 is further used to: Extract video features under the operational power dimension from the video platform data of the enterprise account, where the video features under the operational power dimension include the second live broadcast data and work playback data of the enterprise account; Based on the second live broadcast data, the average live broadcast viewing time, average number of live broadcast viewers per game, live broadcast exposure entry rate, and number of new live broadcast fans of the enterprise account are detected to obtain the second live broadcast score of the enterprise account; Based on the work playback data, the newly added playback volume of the works of the enterprise account is detected to obtain the work playback score of the enterprise account; The operational efficiency score of the corporate account is obtained by combining the second live broadcast score and the work playback score.

[0096] Optionally, the target dimension includes a customer acquisition capability dimension, the business capability score includes a customer acquisition capability score, and the score generation module 10 is further used to: Extract video features under the dimension of customer acquisition power from the video platform data of the enterprise account. The video features under the dimension of customer acquisition power include the background private message data, live broadcast customer acquisition data, and call retention data of the enterprise account; Based on the background private message data, the private message retention rate of the enterprise account is detected to obtain the background private message score of the enterprise account; Based on the live broadcast customer acquisition data, the average number of customers acquired by the corporate account during live broadcasts is tested to obtain the live broadcast customer acquisition score of the corporate account; Based on the call-back data, the number of new calls from private messages sent by enterprise accounts and the total number of new calls received are detected to obtain a call-back score; The customer acquisition score of the corporate account is obtained by combining the backend private message score, live broadcast customer acquisition score and call retention score.

[0097] Optionally, the target dimension includes a conversion power dimension, the business capability score includes a conversion power score, and the score generating module 10 is further used to: Extracting video features under the dimension of conversion power from the video platform data of the enterprise account. The video features under the dimension of conversion power include the subscription information data and visitor data of the enterprise account. Based on the subscription information data, the number of new subscriptions in the enterprise account during the period is checked to obtain the new subscription score; Based on visitor data, the number of new visitors to the enterprise account during the period is detected to obtain the new visit score; The conversion score of the corporate account is obtained by combining the new subscription score and the new visit score.

[0098] Optionally, the video data evaluation device further includes a suggestion generation module 40, and the suggestion generation module 40 is used to: Compare the comprehensive business capability score with the industry median, and obtain the score decline dimension corresponding to the score below the industry median; Determine the feature compliance of each video feature in the score reduction dimension under the enterprise account set, and generate diagnostic suggestions for the score reduction dimension based on the feature compliance and the preset corpus.

[0099] The video data evaluation device provided by the present application adopts the video data evaluation method in the above embodiment, which can solve the technical problem of lack of global management perspective. Compared with the prior art, the beneficial effects of the video data evaluation device provided by the present application are the same as the beneficial effects of the video data evaluation method provided by the above embodiment, and other technical features in the video data evaluation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0100] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the video data evaluation method in the above-mentioned embodiment 1.

[0101] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0102] like Figure 7 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 to a random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0103] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0104] The electronic device provided by the present application adopts the video data evaluation method in the above embodiment to solve the technical problem of lack of global management perspective. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the video data evaluation method provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0105] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0107] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the video data evaluation method in the above-mentioned embodiment.

[0108] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0109] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0110] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the video data evaluation device is applied to a video data evaluation system. The video data evaluation system is associated with each video platform. The video platform includes each enterprise account of the enterprise to be evaluated. For any enterprise account of the enterprise to be evaluated, the video features of the target dimension can be extracted from the video platform data under the enterprise account based on the preset operation diagnosis standard, and a business capability score of the enterprise account can be generated based on the video features; the enterprise accounts of the enterprise to be evaluated are classified based on the preset organizational account standard to obtain a set of enterprise accounts at different organizational levels; for any set of enterprise accounts, a comprehensive business capability score of the enterprise account set is generated based on the business capability score of each enterprise account under the enterprise account set.

[0111] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0114] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned video data evaluation method, and can solve the technical problem of lack of global management perspective. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the video data evaluation method provided in the above-mentioned embodiment, and will not be repeated here.

[0115] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned video data evaluation method when executed by a processor.

[0116] The computer program product provided by this application can solve the technical problem of lack of global management perspective. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the video data evaluation method provided by the above embodiment, which will not be repeated here.

[0117] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A video data evaluation method, characterized in that: Applied to a video data evaluation system, the video data evaluation system is associated with each video platform, the video platform includes each enterprise account of the enterprise to be evaluated, and the video data evaluation method includes: For any enterprise account of the enterprise to be evaluated, extracting video features of target dimensions from the video platform data under the enterprise account based on preset operation diagnosis standards, and generating a business capability score of the enterprise account based on the video features; Classifying each enterprise account of the enterprise to be evaluated based on a preset organizational account standard to obtain enterprise account sets at different organizational levels; For any enterprise account set, a comprehensive business capability score of the enterprise account set is generated based on the business capability score of each enterprise account in the enterprise account set.

2. The video data evaluation method according to claim 1, characterized in that: The target dimension includes an expressiveness dimension, the business capability score includes an expressiveness score, and the steps of extracting video features of the target dimension from the video platform data under the enterprise account based on the preset operation diagnosis standard and generating the business capability score of the enterprise account based on the video features include: Extracting video features under the expressiveness dimension from the video platform data of the enterprise account, wherein the video features under the expressiveness dimension include video cover data, homepage introduction data, homepage display data, and personal information data of the enterprise account; Detecting the proportion of videos with video titles set in the videos under the enterprise account based on the video cover data to obtain a cover title score of the enterprise account; Detecting the standardization of the profile information of the enterprise account based on the homepage profile data to obtain a homepage profile score of the enterprise account; Detecting the activation status of the homepage page permission setting of the enterprise account and the setting status of the pinned video based on the homepage display data to obtain the homepage display score of the enterprise account; Detecting the personal information standardization of the account name and employee portrait of the enterprise account based on the personal information data to obtain a personal information score of the enterprise account; The expressiveness score of the corporate account is obtained by combining the cover title score, the homepage introduction score, the homepage display score, and the personal information score.

3. The video data evaluation method according to claim 1, characterized in that: The target dimension includes an execution dimension, the business capability score includes an execution score, and the step of evaluating the video platform data of the enterprise account based on the preset operation diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the execution dimension from the video platform data of the enterprise account, wherein the video features under the execution dimension include the first live broadcast data and work data of the enterprise account; Based on the first live broadcast data, detecting whether the average live broadcast duration of the enterprise account has reached the standard and whether the number of live broadcasts has reached the standard, obtaining a first live broadcast score of the enterprise account; Based on the work data, detecting whether the number of new works of the enterprise account has reached the standard obtains the cover title score of the enterprise account; The execution score of the enterprise account is obtained by combining the first live broadcast score and the cover title score.

4. The video data evaluation method according to claim 1, characterized in that: The target dimension includes an operational capability dimension, the business capability score includes an operational capability score, and the step of evaluating the video platform data of the enterprise account based on a preset operational diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the operational power dimension from the video platform data of the enterprise account, wherein the video features under the operational power dimension include the second live broadcast data and work playback data of the enterprise account; Based on the second live broadcast data, the average live broadcast viewing time, the average number of live broadcast viewers per game, the live broadcast exposure entry rate, and the number of new live broadcast fans of the enterprise account are detected to obtain a second live broadcast score of the enterprise account; Detecting the newly added playback volume of the works of the enterprise account based on the play data of the works to obtain the play score of the works of the enterprise account; The operational capability score of the enterprise account is obtained by combining the second live broadcast score and the work playback score.

5. The video data evaluation method according to claim 4, characterized in that: The target dimension includes a customer acquisition dimension, the business capability score includes a customer acquisition score, and the step of evaluating the video platform data of the enterprise account based on a preset operation diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the customer acquisition power dimension from the video platform data of the enterprise account, wherein the video features under the customer acquisition power dimension include background private message data, live broadcast customer acquisition data, and call retention data of the enterprise account; Detecting the private message retention rate of the enterprise account based on the background private message data to obtain the background private message score of the enterprise account; Based on the live broadcast customer acquisition data, the average number of customers acquired in the live broadcast of the enterprise account is detected to obtain the live broadcast customer acquisition score of the enterprise account; Based on the call data, the number of new calls sent by private messages to the enterprise account and the total number of new calls received are detected to obtain a call score; The customer acquisition score of the enterprise account is obtained by combining the background private message score, the live broadcast customer acquisition score and the call retention score.

6. The video data evaluation method according to claim 4, characterized in that: The target dimension includes a conversion power dimension, the business capability score includes a conversion power score, and the step of evaluating the video platform data of the enterprise account based on a preset operation diagnosis standard to obtain the business capability score of the enterprise account further includes: Extracting video features under the conversion power dimension from the video platform data of the enterprise account, wherein the video features under the conversion power dimension include subscription information data and visitor data of the enterprise account; Based on the subscription information data, the number of new subscriptions in the enterprise account during the period is detected to obtain a new subscription score; Based on the visitor data, the number of new visitors to the enterprise account during the period is detected to obtain a new visit score; The conversion power score of the enterprise account is obtained by combining the newly added subscription score and the newly added visit score.

7. The video data evaluation method according to claim 1, characterized in that: After the step of generating the comprehensive business capability score of the enterprise account set, the following step is further included: Compare the comprehensive business capability score with the industry median, and obtain the score decline dimension corresponding to the score below the industry median; Determine the feature compliance status of each video feature in the score reduction dimension under the enterprise account set, and generate a diagnostic suggestion for the score reduction dimension based on the feature compliance status and a preset corpus.

8. An electronic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the video data evaluation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the video data evaluation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which implements the steps of the video data evaluation method according to any one of claims 1 to 6 when executed by a processor.

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