Network course pricing method and device and electronic equipment

Abstract

The invention provides a network course pricing method and device and electronic equipment, and belongs to the field of computer data processing. The method comprises the steps of obtaining a to-be-priced network course and a free watching condition, opening the to-be-priced network course based on the free watching condition, obtaining evaluation information of a preset number of free watching users on the to-be-priced network course, analyzing the evaluation information by using artificial intelligence to obtain a preliminary price interval, and selling the to-be-priced network course according to the preliminary price interval. And setting a preliminary selling price of the to-be-priced network course according to the preliminary selling price interval, entering a pricing stage to sell the to-be-priced network course according to the preliminary selling price, analyzing evaluation information of a purchaser, obtaining a selling price interval of each pricing stage, and setting a selling price. And for a seller, whether the selling price is appropriate or not can be timely mastered. For a purchaser, it is considered that the network courses purchased at the selling price are matched, the value of the network courses can be accepted by social groups, and the credibility and persuasion are improved.

Description

Technical Field

[0001] The present invention belongs to the field of computer data processing and relates to a method, device and electronic device for pricing online courses. Background Art

[0002] Since the 21st century, due to the popularization of the media, more and more users will teach knowledge and experience through various software platforms for other users to learn. As a result, there has emerged a phenomenon where users organize their knowledge or experiences into courses and promote and sell them through live broadcasts or short videos. However, in the existing live broadcast and short video course sales industry, the prices of most online courses are set by the publishers themselves, which easily leads to the following phenomena: On the one hand, online publicity is related to the number of fans. Even if the quality of the online course is excellent, due to the limited number of fans, it will not attract much attention, and thus the material rewards obtained by the publisher will decrease, which will also affect the publisher's creative motivation. On the other hand, since the price of the online course is self-defined by the publisher, during the sales process, the value of the publisher's course is difficult to be referred to by the social group, and for consumers, the trust and persuasion are relatively low. Summary of the Invention

[0003] The purpose of the present invention is to address the above problems existing in the prior art and propose a method for pricing online courses to solve the problems reflected in the above background art.

[0004] The purpose of the present invention can be achieved by the following technical solutions: A method for pricing online courses, comprising:

[0005] Obtain the online course to be priced and the free viewing conditions;

[0006] Open the online course to be priced based on the free viewing conditions, and obtain the evaluation information of the preset number of free viewing users for the online course to be priced;

[0007] Use artificial intelligence to analyze the evaluation information to obtain a preliminary price range, and set a preliminary price for the online course to be priced according to the preliminary price range;

[0008] Sell the online course to be priced according to the preliminary price in the pricing stage, and analyze the evaluation information of the purchasers to obtain the price range of each pricing stage and set the selling price;

[0009] When the number of purchasers reaches the preset total number of purchasers, based on the current selling price, analyze the evaluation information of all purchasers to determine the final price range and set the final selling price.

[0010] As an alternative implementation of the present invention, sell the network course to be priced in the pricing stage according to the preliminary selling price, analyze the evaluation information of the purchasers, obtain the selling price range for each pricing stage and set the selling price, including:

[0011] When the number of purchasers of the network course to be priced reaches the preset number of purchasers in the current pricing stage, analyze the evaluation information of the purchasers and the selling price in the current pricing stage, obtain the selling price range corresponding to the next pricing stage and set the next selling price of the network course to be priced from it.

[0012] As an alternative implementation of the present invention, it further includes:

[0013] Obtain the fixed selling price set by the seller for the network course to be priced;

[0014] Obtain and analyze the evaluation information of the purchasers who purchase the network course to be priced according to the fixed selling price, determine the recommended range of the fixed selling price and send it to the seller;

[0015] The seller determines whether to use the fixed selling price as the preliminary selling price to enter the pricing stage to determine the final selling price according to the recommended range of the fixed selling price.

[0016] As an alternative implementation of the present invention, it further includes:

[0017] Obtain the regular free viewing conditions set by the seller for the network course to be priced;

[0018] Open the network course to be priced for free according to the regular free viewing conditions, and collect and count the evaluation information of the viewing users;

[0019] During the viewing time period of the regular free viewing conditions, when the evaluation information reaches the preset data volume, analyze the evaluation information, determine the recommended range of the release selling price and send it to the seller, and the seller determines the final selling price according to the recommended range of the release selling price.

[0020] As an alternative implementation of the present invention, after obtaining the fixed selling price set by the seller for the network course to be priced, it further includes:

[0021] Analyze the network course to be priced based on the real-time video dataset. When the network course to be priced is a video relay, retrieve the corresponding real-time video or historical video;

[0022] Blur the key information in the video relay according to the real-time video or the historical video.

[0023] As an alternative implementation of the present invention, after blurring the key information in the video relay, it further includes:

[0024] Create interactive questions based on the key information and send them to the purchaser with a time limit for answering. After the purchaser answers the interactive questions, the corresponding key information is clearly displayed.

[0025] As an alternative implementation of the present invention, before opening the network course to be priced based on the free viewing condition and obtaining the evaluation information of the network course to be priced from a preset number of free viewing users, it further includes:

[0026] Analyze the copyright situation of the network course to be priced. When it is determined that the network course to be priced requires copyright protection, guide the seller to protect the copyright of the network course to be priced;

[0027] When it is determined that the network course to be priced meets the copyright product, return a prompt message indicating that it cannot be sold to the seller.

[0028] As an alternative implementation of the present invention, it further includes: the pricing models can be switched arbitrarily.

[0029] The present invention also provides a device for pricing network courses, including:

[0030] A receiving module, configured to obtain a network course to be priced and free viewing conditions;

[0031] An information collection module, configured to open the network course to be priced based on the free viewing condition and obtain the evaluation information of the network course to be priced from a preset number of free viewing users;

[0032] A preliminary selling price determination module, configured to use artificial intelligence to analyze the evaluation information to obtain a preliminary selling price range, and set the preliminary selling price of the network course to be priced according to the preliminary selling price range;

[0033] A multi-stage pricing module, configured to sell the network course to be priced according to the preliminary selling price in the pricing stage, analyze the evaluation information of the purchaser, obtain the selling price range of each pricing stage and set the selling price;

[0034] A final selling price determination module, configured to when the number of purchasers reaches a preset total number of purchasers, based on the current selling price, analyze the evaluation information of all purchasers, determine the final selling price range and set the final selling price.

[0035] The present invention also provides an electronic device, including:

[0036] A processor;

[0037] A memory for storing instructions executable by the processor;

[0038] Among them, when the processor is configured to execute the executable instructions, the method for pricing network courses described above is implemented.

[0039] Compared with the prior art, for the seller, it is possible to timely grasp whether the selling price is appropriate and adjust it in a timely manner according to the analysis of the evaluation information by the server. At the same time, it can also ensure the feedback of substances. For the buyer, it is considered that the selling price for purchasing the network course is matched. The buyer can learn the content shared by the seller from it, and the value of the network course can be recognized by the social group. Brief Description of the Drawings

[0040] Figure 1 is a flowchart of the method for pricing network courses according to an embodiment of the present invention;

[0041] Figure 2 is a block diagram of the device for pricing network courses according to an embodiment of the present invention. Detailed Embodiment

[0042] The following are specific embodiments of the present invention and in combination with the accompanying drawings, the technical solutions of the present invention are further described, but the present invention is not limited to these embodiments.

[0043] Embodiment 1

[0044] Figure 1 A flowchart showing the method for pricing network courses according to an embodiment of the present application is shown. Through this method, a reference for a suitable selling price is provided for the seller, so that the buyer feels that the goods are worth the price, and the seller can also obtain material feedback. As Figure 1 shown, the flowchart includes:

[0045] S1. Obtain the network course to be priced and the free viewing conditions;

[0046] S2. Open the network course to be priced based on the free viewing conditions, and obtain the evaluation information of the preset number of free viewing users for the network course to be priced;

[0047] S3. Use artificial intelligence to analyze the evaluation information to obtain a preliminary price range, and set the preliminary selling price of the network course to be priced according to the preliminary price range;

[0048] S4. Enter the pricing stage according to the preliminary selling price to sell the network course to be priced, and analyze the evaluation information of the buyers to obtain the price range of each pricing stage and set the selling price;

[0049] S5. When the number of buyers reaches the preset total number of buyers, based on the current selling price, analyze the evaluation information of all buyers, determine the final price range and set the final selling price.

[0050] In this embodiment, the seller creates works based on real emotional experiences, learning experiences, social experiences, etc., and uploads them to the server. The server combines artificial intelligence technology and uses the works uploaded by the seller as network courses to be priced, allowing the seller to select a pricing model. In this embodiment, there are three pricing models, namely, the comprehensive scoring pricing model, the fixed price selling model, and the regular free pricing model.

[0051] When the seller selects the comprehensive scoring pricing model, the seller is prompted to set the free viewing conditions, such as setting the segments that can be viewed for free for Network Course 1, including the content and introduction, etc., as well as the date range for viewing. The server first publishes Network Course 1 to each social platform according to the current settings. Users can view the free-viewable parts of Network Course 1 through the social platform and give evaluations or scores. The server collects the evaluations or scores of each user for Network Course 1 and integrates them into evaluation information. When the number of users collected reaches the number of free-viewing users, the evaluation information of each user is analyzed. It should be noted that the number of free-viewing users is set by the server through artificial intelligence technology, not by the seller. That is, the server analyzes the same type of network courses being sold on the network through artificial intelligence technology, and determines the number of free-viewing users according to the proportional relationship between the number of purchasing users and the corresponding evaluation information of the network courses ranked relatively high in the ranking, so that the number of free-viewing users and the evaluation information are objective.

[0052] After the server collects the evaluation information of the set number of users, it uses artificial intelligence to analyze this evaluation information, mainly through the text recognition results and score evaluation results. Based on the selling prices of the same type of popular network courses when containing similar evaluation information and score evaluations, combined with the selling prices of multiple network courses of the same type, a preliminary selling price range is formed for the seller to choose. For example, after analysis, the preliminary selling price range of Network Course 1 is 120 - 150, and the preliminary selling price set from this preliminary selling price range is objectively reasonable. The seller can choose 135 as the preliminary selling price.

[0053] After the seller determines the preliminary selling price, the server publishes Network Course 1 to each social platform and enters the pricing stage, continuously monitoring the number of users purchasing Network Course 1. At this time, users who want to view need to purchase according to the preliminary selling price.

[0054] That is, in the first pricing stage, the selling price of online course 1 is 135. When the number of purchasers in this stage reaches the set value of the server in this pricing stage, for example, 50 people, collect the evaluation information of these 50 purchasers on online course 1 and analyze it. Through the analysis, it can be known whether the current selling price is too high or too low for online course 1, and then determine the selling price range in the second pricing stage. For example, when it is determined that the current selling price is too high for online course 1, set the selling price range in the second pricing stage as 110 - 130, and remind the seller to set the selling price in the second pricing stage, and so on to set the selling price of each pricing stage. It should be noted that the selling price ranges between each pricing stage are not an absolute relationship and change with the number of purchasers and evaluation information. That is, the selling price range and selling price of each pricing stage do not decrease or increase with the increase in the number of pricing stages, but are related to the evaluation information of the purchasers.

[0055] The server will continuously count the number of purchasers of online course 1 from the first pricing stage to the current pricing stage according to the total number of purchasers set in the comprehensive scoring pricing mode. For example, set the total number of purchasers as 2000. When the number of purchasers reaches 2000, analyze the analysis information of these 2000 purchasers based on the current selling price to obtain the final selling price range, and prompt the user to set the final selling price from the final selling price range. By setting multiple pricing stages and then analyzing the evaluation information of the purchasers, the final selling price has a certain objectivity. It should be noted that the number of purchasers set by the server in each pricing stage can be different, and the number of purchasers in the next pricing stage can be determined according to the evaluation information until the number of purchasers in all pricing stages reaches the total number of purchasers.

[0056] Preferably, sell the to-be-priced online course according to the preliminary selling price and enter the pricing stage, and analyze the evaluation information of the purchasers to obtain the selling price range of each pricing stage and set the selling price, including: when the number of purchasers of the to-be-priced online course reaches the preset number of purchasers in the current pricing stage, analyze the evaluation information of the purchasers and the selling price in the current pricing stage, obtain the selling price range corresponding to the next pricing stage and set the next selling price of the to-be-priced online course from it.

[0057] For example, when the server sets the number of purchasers to 50 in the first pricing stage, after 50 users purchase the online course 1, based on the current selling price, such as 135 yuan, analyze the evaluation information of these 50 purchasers to determine whether the selling price should be decreased or increased in the second pricing stage, and provide a suitable price range to the seller. For example, if the seller determines that the selling price in the second pricing stage is 140, and in the second pricing stage, the server sets the number of purchasers to 100, then after 100 users purchase the online course 1 priced at 140 yuan, analyze the evaluation information of the newly added 100 purchasers in combination with the number of purchasers in the first pricing stage, that is, analyze the evaluation information of these 150 purchasing users to determine the selling price range and selling price in the third pricing stage, and so on. The analysis of multiple pricing stages reduces instability and improves objectivity.

[0058] Preferably, it further includes: obtaining the fixed selling price set by the seller for the to-be-priced online course, obtaining and analyzing the evaluation information of the purchasers who purchase the to-be-priced online course according to the fixed selling price, determining the fixed selling price recommendation range and sending it to the seller, and the seller selects whether to use the fixed selling price as the preliminary selling price to enter the pricing stage to determine the final selling price according to the fixed selling price recommendation range.

[0059] When the seller selects the fixed-price selling mode, for example, for the online course 2, input the fixed selling price desired by the seller, and the server uploads the online course 2 to each social platform for sale according to the input fixed selling price. Different from the existing situation, the server will continuously collect the evaluation information of the purchasers for the online course 2, and conduct a comprehensive analysis in combination with the time range of the evaluation information and the current time to analyze whether the fixed selling price is conducive to the sale of the online course 2, determine the fixed selling price recommendation range and send it to the seller. For example, for the online course 2, the seller sets the fixed selling price to 150. The server can collect the evaluation information of the online course 2 after a period of time or continuously. For example, collect the evaluation information after one week. Through analysis, within one week, the number of purchasers is relatively small, and the corresponding evaluation information is also relatively small. By analyzing the evaluation information of similar online courses, etc., it is considered that the fixed selling price is too high and the fixed selling price should be decreased. After the seller receives the analysis of the server, the seller can determine a new fixed selling price from the fixed selling price recommendation range. The seller can still use the current fixed selling price for sale, or can also switch to the comprehensive scoring pricing mode, use the current fixed selling price as the preliminary selling price, and obtain a more objective final selling price range through the pricing stage to determine the final selling price. Compared with the existing situation, it can enable the seller to objectively understand the rationality of setting the selling price, help the seller change the selling price in time when the selling price is unreasonable, and play the role of the online course.

[0060] Preferably, it further includes: obtaining the regular free viewing conditions set by the seller for the network course to be priced, opening the network course to be priced for free according to the regular free viewing conditions, collecting and statistically analyzing the evaluation information of the viewing users, and during the viewing time period of the regular free viewing conditions, when the evaluation information reaches the preset data volume, analyzing the evaluation information, determining the recommended release price range and sending it to the seller, and the seller determines the final selling price according to the recommended release price range.

[0061] When the seller selects the regular free pricing model, for example, for Network Course 3, the seller is prompted to set the regular free viewing conditions, which include the number of free viewers, free viewing duration, region, free viewing date restrictions, etc. The number of free viewers set in the regular free pricing model is different from the number of free viewing users set by the server in the comprehensive scoring pricing model. That is, in the regular free pricing model, the number of free viewers is set by the seller; in the comprehensive scoring pricing model, the number of free viewing users is set by the server side. The server collects the evaluation information of Network Course 3 according to the regular free viewing conditions, and analyzes whether the evaluation information reaches the data volume that can be analyzed during the free viewing date restrictions set by the seller. For example, if the evaluation information is too little, it is difficult to analyze a suitable selling price based on the free viewing of Network Course 3. In this case, the seller is recommended to relax the free viewing date restrictions to collect evaluation information that meets the data volume. When the data volume of the evaluation information reaches the data volume, analyze this evaluation information, determine a suitable recommended release price range and let the seller determine the final selling price from it.

[0062] Preferably, after obtaining the fixed selling price set by the seller for the network course to be priced, it further includes: analyzing the network course to be priced based on the real-time video dataset. When the network course to be priced is a video relay, retrieving the corresponding real-time video or historical video, and blurring the key information in the video relay according to the real-time video or the historical video.

[0063] When the server analyzes the network course uploaded by the seller, it determines whether the network course belongs to the video relay of the live broadcast scenario based on the real-time video dataset. The real-time video dataset includes the live and historical live video data of the network platform, and determines whether the network course corresponds to the real-time live broadcast or historical live broadcast of a certain project according to the similarity of the video data, and then blurs the key information of the video relay. For example, when the seller conducts a live broadcast or relay of a ball game event and sets a fixed selling price, users can purchase and view the event live broadcast or relay by viewing the profile information. For the event live broadcast or relay, the score belongs to the key information. Then, when playing, the area where the score is located is blurred according to the real-time live video or historical live video.

[0064] Preferably, after blurring the key information in the live broadcast of the picture, it further includes: making an interactive question based on the key information, setting a time limit for answering and sending it to the purchaser, and after the purchaser answers the interactive question, clearly displaying the corresponding key information.

[0065] For example, after blurring the score, an interactive question about the subsequent development of the event is made based on the score and the event content, and sent to the purchaser in the form of a pop-up window. For example, the interactive question is whether a certain team scores or not. The purchaser needs to answer within a short time range. After answering, whether the answer is correct or not, the blurred score will be clearly displayed. For purchasers who miss the live broadcast of the event and do not want to watch the replay with the mood of already knowing the result of the game, this can greatly improve the participation and achieve the effect of watching the live broadcast in real time, and reproduce the passion and fun of watching the event. When the interactive question is popped up, an option to pay to skip the interactive question and display the subsequent score can also be provided. If the purchaser does not want to answer the interactive question at this time, they can pay to know the score situation, and the subsequent score will no longer be blurred. That is, when the purchaser purchases to view the live broadcast or replay of the event, they can choose to watch in the blurred score mode or the non-blurred score mode. When choosing to watch in the blurred score mode, during the viewing process, when answering the interactive question, if you don't want to answer, you can pay to view the subsequent score. Similarly, when the seller sets an interactive question related to the content in the online course, when the purchaser uses the online course, they will receive the interactive question set by the seller, and the purchaser can answer the interactive question according to the situation of learning the online course to improve the knowledge acceptance of the online course.

[0066] Preferably, before opening the to-be-priced online course based on the free viewing condition and obtaining the evaluation information of the to-be-priced online course by the preset number of free viewing users, it further includes: analyzing the copyright situation of the to-be-priced online course. When it is judged that the to-be-priced online course needs copyright protection, guiding the seller to protect the copyright of the to-be-priced online course; when it is judged that the to-be-priced online course meets the copyright product, returning a prompt message that it cannot be sold to the seller.

[0067] When the server receives the online courses uploaded by the seller, it uses artificial intelligence or manual labor to review whether the online courses need copyright protection. If copyright protection is required, it guides the seller to apply for copyright protection. In this embodiment, the server can call the copyright protection link to remind the seller that the online courses can be protected by copyright by clicking the copyright protection entry, and play the specific content of the online courses within a specific time period, so as to minimize the enthusiasm of the seller for delaying the selling time due to applying for copyright protection. If the seller chooses not to protect the copyright, the seller is allowed to choose a pricing model. If the online course is an existing product protected by copyright, an information indicating that it cannot be sold is returned to inform the seller that pricing cannot be carried out. If the online course is not an existing product protected by copyright, the server will analyze the content of the online course and, based on the real-time time, analyze whether there are online courses with high similarity before the real-time time. If there are online courses with high similarity, it is determined that the seller's online course has the risk of involving others' copyright, and an information indicating that it cannot be sold is returned to inform the seller that pricing cannot be carried out.

[0068] Preferably, it further includes that the pricing models can be switched arbitrarily.

[0069] After selecting one of the pricing models to determine the final selling price, it can also be switched to the other pricing models for pricing again. For example, after determining the final selling price according to the comprehensive scoring pricing model, the fixed selling price can be changed to the fixed price pricing model based on the final selling price, and the fixed selling price is analyzed again to see if it is reasonable through the fixed price pricing model. It can also be that after the seller selects the comprehensive scoring pricing model, the server reminds the seller that the time period of this pricing model is longer than that of other pricing models, and the seller can choose to switch to the fixed price pricing model or the regular free pricing model according to the time requirement to determine the final selling price more quickly.

[0070] In this embodiment, the server further includes a bonus setting module, which uses the comprehensive scoring pricing model to complete the competition, reducing manpower and material resources. For example, a certain unit needs to hold a photography competition, and the participants are the employees of the unit. After the competition initiator selects the bonus setting module, the server reminds the competition initiator to set the competition rules, winning conditions, and scoring rules; the competition rules mainly include eligible participants and competition requirements, etc., the winning conditions are to determine the winning personnel according to the final value ranking, and the scoring rules are that the employees in the unit who do not participate in the competition need to have their personal information identified before making comments or scoring to prevent non-participating personnel from making comments or scoring. After setting, it reminds the competition initiator to recharge the bonus, and then the participating employees can upload their photographic works.

[0071] When the server receives a photographic work, it enters the comprehensive scoring and pricing mode. The server will compare the photographic work with existing similar photographic works and analyze the value of the photographic work, that is, the initial price at which the photographic work can be sold, by combining evaluation information such as the scores and comments of other employees. The initial prices of each photographic work are sorted, and the corresponding bonus amounts are distributed according to the winning conditions. For example, the top ten contestants win the prize, and the server distributes the bonus according to the information provided by the contestants when participating in the competition. After that, these photographic works can enter the pricing stage, reminding the photographer to set a price and sell the photographic works.

[0072] Through the above pricing mode, for the seller, they can timely grasp whether the selling price is appropriate and adjust it in time according to the analysis of the evaluation information by the server, and at the same time, it can ensure the material feedback. For the buyer, they think that the selling price is matched to purchase the online course, and the buyer can learn the content shared by the seller from it. This is beneficial to both of them.

[0073] Embodiment 2

[0074] Based on the same principle as the foregoing method, a device 100 for pricing online courses is also proposed, as Figure 2 shown, including:

[0075] A receiving module 110, configured to obtain the to-be-priced online course and the free viewing conditions;

[0076] An information collection module 120, configured to open the to-be-priced online course based on the free viewing conditions and obtain the evaluation information of the to-be-priced online course by a preset number of free viewing users;

[0077] A preliminary selling price determination module 130, configured to use artificial intelligence to analyze the evaluation information to obtain a preliminary selling price range, and set the preliminary selling price of the to-be-priced online course according to the preliminary selling price range;

[0078] A multi-stage pricing module 140, configured to enter the pricing stage to sell the to-be-priced online course according to the preliminary selling price, analyze the evaluation information of the buyers, obtain the selling price range of each pricing stage and set the selling price;

[0079] A final selling price determination module 150, configured to, when the number of buyers reaches the preset total number of purchasers, analyze the evaluation information of all buyers based on the current selling price, determine the final selling price range and set the final selling price.

[0080] Embodiment 3

[0081] Furthermore, an electronic device is proposed, including:

[0082] A processor;

[0083] A memory for storing processor-executable instructions;

[0084] Wherein, when the processor is configured to execute the executable instructions, the method for pricing network courses described in Embodiment 1 is implemented.

[0085] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the attached drawings). If the specific posture changes, the directional indications will also change accordingly.

[0086] In addition, it should be noted that in the present invention, descriptions such as "first", "second", "one" are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. Terms such as "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and can be the communication inside two elements or the interaction relationship between two elements, unless otherwise clearly defined. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0087] In addition, the technical solutions between various embodiments of the present invention can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0088] The specific embodiments described herein are only illustrative of the spirit of the present invention. Those skilled in the art to which the present invention pertains can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A method for pricing online courses, characterized in that, Including: Obtain the network course to be priced and the free viewing conditions; Open the network course to be priced based on the free viewing conditions, and obtain the evaluation information of the preset number of free viewing users for the network course to be priced; Use artificial intelligence to analyze the evaluation information to obtain a preliminary price range, and set the preliminary price of the network course to be priced according to the preliminary price range; Sell the network course to be priced according to the preliminary price in the pricing stage, analyze the evaluation information of the purchasers, obtain the price range of each pricing stage and set the selling price; When the number of purchasers reaches the preset total number of purchasers, based on the current selling price, analyze the evaluation information of all purchasers, determine the final price range and set the final selling price.

2. The method for pricing online courses according to claim 1, wherein Sell the network course to be priced according to the preliminary price in the pricing stage, analyze the evaluation information of the purchasers, obtain the price range of each pricing stage and set the selling price, including: When the number of purchasers of the network course to be priced reaches the preset number of purchasers in the current pricing stage, analyze the evaluation information of the purchasers and the selling price in the current pricing stage, obtain the price range corresponding to the next pricing stage and set the next selling price of the network course to be priced from it.

3. A method for pricing online courses according to claim 1, characterized in that, Also including: Obtain the fixed price set by the seller for the network course to be priced; Obtain and analyze the evaluation information of the purchasers who purchase the network course to be priced according to the fixed price, determine the recommended range of the fixed price and send it to the seller; The seller selects whether to use the fixed price as the preliminary price to enter the pricing stage to determine the final selling price according to the recommended range of the fixed price.

4. A method for pricing online courses according to claim 1, characterized in that, Also including: Obtain the regular free viewing conditions set by the seller for the network course to be priced; Open the network course to be priced for free according to the regular free viewing conditions, and collect and count the evaluation information of the viewing users; During the viewing time period of the regular free viewing conditions, when the evaluation information reaches the preset data volume, analyze the evaluation information, determine the recommended range of the release price and send it to the seller, and the seller determines the final selling price according to the recommended range of the release price.

5. A method for pricing online courses according to claim 3, characterized in that, After obtaining the fixed price set by the seller for the network course to be priced, it also includes: Analyze the network course to be priced based on the real-time video data set. When the network course to be priced is a video relay, retrieve the corresponding real-time video or historical video; Blur the key information in the video relay according to the real-time video or the historical video.

6. A method for pricing online courses according to claim 5, characterized in that, After blurring the key information in the video relay, it also includes: Make interactive questions according to the key information, set a time limit for answering and send them to the purchasers. After the purchasers answer the interactive questions, the corresponding key information will be clearly displayed.

7. A method for pricing online courses according to claim 6, characterized by Before opening the network course to be priced based on the free viewing conditions and obtaining the evaluation information of the preset number of free viewing users for the network course to be priced, it also includes: Analyze the copyright situation of the network course to be priced. When it is judged that the network course to be priced requires copyright protection, guide the seller to protect the copyright of the network course to be priced; When it is determined that the network course to be priced meets the copyright product, a prompt message indicating that it cannot be sold is returned to the seller.

8. A method for pricing online courses according to claim 1, characterized in that, It further includes: The pricing modes can be switched arbitrarily.

9. An apparatus for pricing online courses, characterized in that, It includes: A receiving module, configured to obtain a network course to be priced and free viewing conditions; An information collection module, configured to open the network course to be priced based on the free viewing conditions, and obtain evaluation information of the network course to be priced by the preset number of free viewing users; A preliminary selling price determination module, configured to use artificial intelligence to analyze the evaluation information to obtain a preliminary selling price range, and set a preliminary selling price of the network course to be priced according to the preliminary selling price range; A multi-stage pricing module, configured to enter the pricing stage to sell the network course to be priced according to the preliminary selling price, analyze the evaluation information of the purchasers, obtain the selling price range of each pricing stage, and set the selling price; A final selling price determination module, configured to, when the number of purchasers reaches the preset total number of purchasers, analyze the evaluation information of all purchasers based on the current selling price, determine the final selling price range, and set the final selling price.

10. An electronic device, including: A processor; A memory for storing processor-executable instructions; Wherein, when the processor is configured to execute the executable instructions, the method for pricing a network course according to any one of claims 1-8 above is implemented.

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