Cloud platform rights management method and system

Abstract

The embodiment of the present application provides a permission management method and system for a cloud platform, the method comprising: the cloud platform obtains a login request from user A, and extracts the platform level and historical purchase record corresponding to the user A after verifying the login request; the cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, the purchase record comprising: free viewing time, purchase amount, number of free viewings, video clarity and viewing network type; the cloud platform constructs a purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity value between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extracts the free viewing time t1 and clarity in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, and configures the free viewing time t1 and clarity of the first video for user A.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and specifically relates to a permission management method and system for a cloud platform. Background Art

[0002] Yunpintai is a computing service platform based on cloud computing technology, which is used to provide flexible, scalable and shareable computing resources and system services. The video portal based on the cloud platform is one of the important applications of the cloud platform. The users of the cloud platform of the video website have multiple permissions, such as VIP, SVIP, etc. However, due to the requirements of the video copyright holders, many videos are paid videos. For paid videos, the free viewing time corresponding to all permissions is the same. This makes it impossible for the cloud platform to perform detailed management of permissions for different users, affecting the consumption of paid videos. Summary of the invention

[0003] The present application provides a permission management method and system for a cloud platform, which can perform detailed management for different users and promote the consumption of paid videos.

[0004] In a first aspect, the present application provides a permission management method for a cloud platform, the method comprising the following steps:

[0005] The cloud platform obtains a login request from user A, verifies the login request, and extracts the platform level and historical purchase records corresponding to user A;

[0006] The cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, video definition, and viewing network type;

[0007] The cloud platform constructs a purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extracts the free viewing time t1 and the clarity in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, and configures the free viewing time t1 and the clarity of the first video for user A.

[0008] In a second aspect, a rights management system for a cloud platform is provided, the system comprising:

[0009] A communication unit, used to obtain a login request from user A;

[0010] A verification unit, used for verifying the login request;

[0011] A processing unit is used to extract the platform level and historical purchase records corresponding to the user A; obtain the first video selected by the user A, and when determining that the first video is a paid video, extract the first type corresponding to the first video, and extract the purchase record of the first type from the historical purchase records, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, video clarity, and viewing network type; construct a purchase vector corresponding to the purchase record, construct multiple consumption vectors of the first video, calculate the similarity value between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extract the free viewing time t1 and clarity in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, and configure the free viewing time t1 and clarity of the first video for the user A.

[0012] In a third aspect, the present application provides a computer storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute some or all of the steps described in the first aspect of the present application.

[0013] The embodiments of the present application have the following beneficial effects:

[0014] The technical solution provided by the present application is that the cloud platform obtains the login request of user A, and after verifying the login request, extracts the platform level and historical purchase records corresponding to the user A; the cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, and the purchase record includes: free viewing time, purchase amount, number of free viewings and viewing network type; the cloud platform constructs the purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity value between the purchase vector and the multiple consumption vectors, extracts the free viewing time t1 in the first consumption vector with the highest similarity value from the multiple sales vectors, and configures the free viewing time of user A in the first video to be t1. In this way, the cloud platform can dynamically configure the free viewing time of the first video selected by the user based on the user's consumption record and user level, and then dynamically manage the permissions of the videos on the cloud platform, thereby realizing fine-grained permission management of the cloud platform and increasing the consumption amount of the cloud product platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the architecture of a cloud platform provided in an embodiment of the present application;

[0017] Figure 2 It is a flowchart of a permission management method for a cloud platform provided in an embodiment of the present application;

[0018] Figure 3 It is a flowchart of a method for managing permissions on a cloud platform provided in Example 1 of the present application;

[0019] Figure 4 It is a structural block diagram of a cloud platform rights management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, systems, products or devices.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] The following is an introduction to the relevant terms involved in this application.

[0024] VIP (Very Important Person) is a classification of visitors by an organization, party, society, country, etc.

[0025] SVIP (Super Very Important Person) is a classification of visitors by an organization, party, society, country, etc., which usually exceeds the VIP level.

[0026] Cosine Similarity: Cosine similarity measures the cosine value of the angle between two feature vectors, ranging from -1 to 1. It ignores the absolute size of the vector and only focuses on the direction between the feature vectors. The closer the cosine similarity is to 1, the more similar the two feature vectors are.

[0027] The following describes the architecture of the cloud platform and blockchain involved in the embodiments of the present application.

[0028] like Figure 1 As shown, the present application also provides a schematic diagram of the architecture of a cloud platform, the cloud platform comprising: a plurality of servers, wherein the plurality of servers are connected to each other by communication to form the cloud platform; wherein,

[0029] The server 10 includes at least one processor and a memory, and may also include a communications interface, a display screen, and a bus. Among them, the processor, the memory, the display screen, and the communications interface can communicate with each other through the bus. The communication interface can transmit information, and the above-mentioned communication interface can have a wireless communication function, and the above-mentioned wireless communication function can be a short-range wireless communication or a long-range communication function (such as NR mode). The processor can call the logic instructions in the memory to execute or support the method in the embodiment of the present application.

[0030] In addition, the logical instructions in the above storage can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0031] The memory, as a computer-readable storage medium, can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor executes functional applications and data processing by running the software programs, instructions or modules stored in the memory, that is, implementing or supporting the methods in the embodiments of the present application.

[0032] For the management of existing cloud platform members, such as video websites based on cloud platforms, the management of members is only based on level management. Even in some documents, the management of members is not completely based on level management. Take video A as an example. There are 3 users, and the levels of the 3 users are SVIP, VIP, and ordinary. For video A, it is a paid video, so the trial time of SVIP, VIP, and ordinary is the same. The platform cannot manage user rights according to different levels and different videos, and thus cannot subdivide user rights. For example, regardless of the consumption level of the 3 users, their free viewing time for the purchased video is 6 minutes, but their actual

[0033] The specific methods are introduced in detail below.

[0034] See also Figure 2 , This application also provides a permission management method for a cloud platform, Figure 2 A flowchart of a permission management method for a cloud platform provided in this application, the method can be used in Figure 1 The cloud platform shown in the figure is executed under the framework of Figure 2 As shown, the above method may include the following steps:

[0035] Step S201: The cloud platform obtains a login request from user A, verifies the login request, and then extracts the platform level and historical purchase records corresponding to user A;

[0036] The verification of the login request of the above user A includes but is not limited to: account and password verification; identity recognition verification, and the above identity recognition verification includes but is not limited to: fingerprint verification, face verification or vein verification, etc.

[0037] The above platform levels may specifically include: SVIP, VIP, ordinary, etc.

[0038] Step S202: The cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, video definition, and viewing network type;

[0039] Step S203: The cloud platform constructs a purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extracts the free viewing time t1 and the clarity of the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, configures the free viewing time t1 and the clarity of the first video for user A to be t1, that is, configures the clarity of the first video to be the clarity corresponding to the first consumption vector.

[0040] The technical solution provided by the present application is that the cloud platform obtains the login request of user A, and after verifying the login request, extracts the platform level and historical purchase records corresponding to the user A; the cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, and the purchase record includes: free viewing time, purchase amount, number of free viewings and viewing network type; the cloud platform constructs the purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity value between the purchase vector and the multiple consumption vectors, extracts the free viewing time t1 in the first consumption vector with the highest similarity value from the multiple sales vectors, and configures the free viewing time of user A in the first video to be t1. In this way, the cloud platform can dynamically configure the free viewing time of the first video selected by the user based on the user's consumption record and user level, and then dynamically manage the permissions of the videos on the cloud platform, thereby realizing fine-grained permission management of the cloud platform and increasing the consumption amount of the cloud product platform.

[0041] For user A, the free viewing time of the corresponding type plays a decisive role in his consumption when purchasing videos. Most people decide whether to purchase paid videos after watching the free time of paid videos. Therefore, the above-mentioned multiple consumption vectors are mainly obtained by adjusting the free viewing time. This can better match user A and achieve the purpose of increasing user A's purchase of paid videos.

[0042] For example, the above-mentioned construction of multiple consumption vectors of the first video may specifically include:

[0043] Extract the first amount of the first video and the network type of the current device, extract the number of times c1 that user A clicks on the first video this time, obtain multiple free viewing times that the cloud platform can provide according to the platform level of user A, extract one free viewing time t0 from the multiple free viewing times, fill t0, the first amount, the number of times c1, the clarity, and the network type value into the 5 element positions of a consumption vector in order to obtain a consumption vector, and traverse multiple free viewing times to obtain multiple consumption vectors.

[0044] The following is an actual example to illustrate the method of constructing multiple consumption vectors. Here, it is assumed that the first amount is 6 yuan, the number of times is the second time, the network type is wifi, the corresponding type value is 5, and the multiple time values ​​are 4, 5, 6, and 7 respectively. Then the four consumption vectors are constructed respectively:

[0045] 4,6,2,540,5;

[0046] 5,6,2,720,5;

[0047] 6,6,2,1024,5;

[0048] 7,6,2,1024,5;

[0049] Different values ​​can be configured for different network types, for example, the type value of wifi is 5, the type value of 5G is 4, the type value of 4G is 3, and so on.

[0050] The clarity mentioned above specifically refers to the resolution of the video, for example, standard definition is 720P, and high definition resolution is 1024P, etc. Here, only numerical values ​​are taken to form a vector.

[0051] For example, the calculation of the similarity between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values ​​may specifically include:

[0052] A consumption vector is selected from multiple consumption vectors, a normalization operation is performed on a purchase vector and a consumption vector to obtain n normalized vectors, a normalized vector corresponding to a consumption vector is extracted from the n vectors, the similarity between the normalized vector and the remaining n-1 vectors is calculated to obtain n-1 similarity values, and an average value of the n-1 similarity values ​​is calculated as a similarity value corresponding to a consumption vector; and multiple consumption vectors are traversed to obtain multiple similarity values ​​corresponding to the multiple consumption vectors.

[0053] The above-mentioned vector similarity can be calculated by using a cosine similarity calculation method. Of course, other similarity calculation methods can also be used in practical applications.

[0054] For example, the purchase vector may be a vector formed by the user's historical purchase records. For example, if the user has purchased three videos in history, then three purchase vectors are obtained by extracting parameters corresponding to the three purchased videos (the same parameters as the consumption vector).

[0055] For example, the above method may further include:

[0056] If user A initiates a purchase request within the free viewing time t1, the cloud platform receives the purchase request, extracts the preferential ratio of the corresponding permission of user A, and initiates a charging request to user A based on the preferential ratio and the purchase amount.

[0057] The charging amount of the above charging request may be the purchase amount × the discount ratio.

[0058] The above method can reflect the preferential amount of user rights, which is more in line with the user's authority level.

[0059] By way of example, the above-mentioned verification of the login request may specifically include: the cloud platform receives a vein picture of the target object collected by the electronic device, performs vein recognition on the vein picture to obtain a first identity corresponding to the vein picture, and if the first identity is user A, determines that the verification is successful, otherwise, determines that the verification fails.

[0060] For example, performing vein recognition on the vein image to obtain the first identity corresponding to the vein image may specifically include:

[0061] Extract vein pixel points of the vein image to obtain a feature image, connect continuous pixel points in the feature image to obtain multiple line segments, extract intersection points between the multiple line segments to obtain m intersection points and the number of m line segments corresponding to each of the m intersection points; select a first value, a second value and a third value with unique values ​​from the m number of line segments, extract a point o1 corresponding to the first value, a point o2 corresponding to the second value and a point o3 corresponding to the third value from the m intersection points, connect point o1 to point o2 to obtain vector 1, connect point o1 to point o3 to obtain vector 2, and calculate the relationship between vector 1 and vector 2. The angle between them is obtained by connecting point o2 to point o1 to obtain vector 3, connecting point o2 to point o3 to obtain vector 4, calculating the angle between vector 3 and vector 4 to obtain α2, connecting point o3 to point o1 to obtain vector 5, connecting point o3 to point o2 to obtain vector 6, calculating the angle between vector 5 and vector 6 to obtain α3, extracting the first template image, performing the above processing on the first template image to obtain three angles β1, β2, and β3, if α1=β1, α2=β2 and α3=β3, then determining that the first identity is the identity corresponding to the first template image.

[0062] For example, if m=7, there are 7 line segment numbers, which are 2, 3, 4, 4, 5, 5, and 6, and the 3 values ​​with unique values ​​are 2, 3, and 6. That is, the value of the line segment number needs to be a unique value among the m line segment numbers, so as to avoid the problem that the corresponding point is not a unique point, resulting in the constructed vector being multiple vectors.

[0063] The above vein recognition method can avoid the influence of the recognition angle on the recognition accuracy, thereby improving the recognition accuracy. The recognition here is in two aspects. First, the template image needs to have the same first value, second value and third value. The second recognition method is the recognition of three angles. Since the angle here is the angle between the vein segments, no matter how the vein image is rotated, the angle between the connection vectors of the intersection of the vein segments will not change. Therefore, the influence of the acquisition angle on vein recognition can be completely filtered out, improving the recognition accuracy.

[0064] Embodiment 1

[0065] Embodiment 1 of the present application provides a method for managing permissions on a cloud platform. Figure 1 The method is executed under the construction of the cloud platform shown in FIG. 1 , and the terminal device used by user A has the vein collection function. Figure 3 As shown, the following steps are included:

[0066] Step S301: The cloud platform obtains a login request from user A, which includes an account number and a vein image. The cloud platform identifies the vein image to determine a first identity. After the first identity is associated with the account number, the cloud platform extracts the platform level and historical purchase records corresponding to user A.

[0067] Step S302: The cloud platform obtains the first paid video selected by user A, identifies the type of the first paid video to obtain the first type, extracts three purchase records of user A in the first type, and constructs three purchase vectors corresponding to the three purchase records.

[0068] The construction method of the above purchase vector can refer to the construction method of the consumption vector, which will not be repeated here.

[0069] Step S302, the cloud platform constructs multiple consumption vectors of the first paid video, calculates the similarity value between each consumption vector and the three purchase vectors to obtain multiple similarity values, selects the free viewing time t1 and clarity Q1 in the consumption vector corresponding to the maximum similarity value among the multiple similarity values, configures the free viewing time of the first paid video to t1, and configures the clarity of the first paid video to Q1.

[0070] The technical solution provided by the present application is that the cloud platform obtains the login request of user A, and after verifying the login request, extracts the platform level and historical purchase records corresponding to the user A; the cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, and the purchase record includes: free viewing time, purchase amount, number of free viewings and viewing network type; the cloud platform constructs the purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity value between the purchase vector and the multiple consumption vectors, extracts the free viewing time t1 in the first consumption vector with the highest similarity value from the multiple sales vectors, and configures the free viewing time of user A in the first video to be t1. In this way, the cloud platform can dynamically configure the free viewing time of the first video selected by the user based on the user's consumption record and user level, and then dynamically manage the permissions of the videos on the cloud platform, thereby realizing fine-grained permission management of the cloud platform and increasing the consumption amount of the cloud product platform.

[0071] See also Figure 4 , Figure 4 A rights management system for a cloud platform is provided, the system is applied to the cloud platform, and the system includes:

[0072] Communication unit 401, used to obtain a login request from user A;

[0073] Verification unit 402, used to verify the login request;

[0074] Processing unit 403 is used to extract the platform level and historical purchase records corresponding to the user A; obtain the first video selected by user A, and when determining that the first video is a paid video, extract the first type corresponding to the first video, and extract the purchase record of the first type from the historical purchase record, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, and viewing network type; construct a purchase vector corresponding to the purchase record, construct multiple consumption vectors of the first video, calculate the similarity value between the purchase vector and each consumption vector in the multiple consumption vectors to obtain multiple similarity values, extract the free viewing time t1 and clarity in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, and configure the free viewing time t1 and clarity of the first video for user A.

[0075] For example,

[0076] The processing unit is specifically used to extract the first amount of the first video and the network type of the current device, extract the number of times c1 that user A clicks on the first video this time, obtain multiple free viewing times that the cloud platform can provide according to the platform level of user A, extract one free viewing time t0 from the multiple free viewing times, fill t0, the first amount, the number of times c1, the clarity and the network type value into the 5 element positions of a consumption vector in sequence to obtain a consumption vector, and traverse multiple free viewing times to obtain multiple consumption vectors.

[0077] For example,

[0078] The processing unit is specifically used to select a consumption vector from multiple consumption vectors, perform a normalization operation on a purchase vector and a consumption vector to obtain n normalized vectors, extract a normalized vector corresponding to a consumption vector from the n vectors, calculate the similarity between the normalized vector and the remaining n-1 vectors to obtain n-1 similarity values, calculate the average value of the n-1 similarity values ​​as the similarity value corresponding to a consumption vector; and traverse multiple consumption vectors to obtain multiple similarity values ​​corresponding to the multiple consumption vectors.

[0079] For example,

[0080] The processing unit is also used to control the communication unit to receive the purchase request if user A initiates a purchase request within the free viewing time t1, extract the preferential ratio of the corresponding authority of user A, and the cloud platform initiates a charging request to user A based on the preferential ratio and the purchase amount.

[0081] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0082] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0083] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0084] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0085] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0089] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a volatile memory or a non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM) and direct RAM bus RAM (DR RAM). Various media that can store program code are available.

[0090] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A cloud platform rights management method, characterized in that: The method comprises the following steps: The cloud platform obtains a login request from user A, verifies the login request, and extracts the platform level and historical purchase records corresponding to user A; The cloud platform obtains the first video selected by user A, and when determining that the first video is a paid video, extracts the first type corresponding to the first video, and extracts the purchase record of the first type from the historical purchase record, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, video definition, and viewing network type; The cloud platform constructs a purchase vector corresponding to the purchase record, constructs multiple consumption vectors of the first video, calculates the similarity between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extracts the free viewing time t1 and the clarity Q1 in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, configures the free viewing time of user A in the first video to be t1, and configures the clarity of the first video to be Q1.

2. The cloud platform rights management method according to claim 1, characterized in that: The step of constructing a plurality of consumption vectors of the first video specifically includes: Extract the first amount of the first video and the network type of the current device, extract the number of times c1 that user A clicks on the first video this time, obtain multiple free viewing times that the cloud platform can provide according to the platform level of user A, extract one free viewing time t0 from the multiple free viewing times, fill t0, the first amount, the number of times c1, the clarity, and the network type value into the 5 element positions of a consumption vector in order to obtain a consumption vector, and traverse multiple free viewing times to obtain multiple consumption vectors.

3. The cloud platform rights management method according to claim 2, characterized in that: The calculating of the similarity between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values ​​specifically includes: A consumption vector is selected from multiple consumption vectors, a normalization operation is performed on a purchase vector and a consumption vector to obtain n normalized vectors, a normalized vector corresponding to a consumption vector is extracted from the n vectors, the similarity between the normalized vector and the remaining n-1 vectors is calculated to obtain n-1 similarity values, and an average value of the n-1 similarity values ​​is calculated as a similarity value corresponding to a consumption vector; and multiple consumption vectors are traversed to obtain multiple similarity values ​​corresponding to the multiple consumption vectors.

4. The cloud platform rights management method according to claim 1, characterized in that: The method further comprises: If user A initiates a purchase request within the free viewing time t1, the cloud platform receives the purchase request, extracts the preferential ratio of the corresponding permission of user A, and initiates a charging request to user A based on the preferential ratio and the purchase amount.

5. A rights management system for a cloud platform, characterized in that: The system is applied to a cloud platform, and the system includes: A communication unit, used to obtain a login request from user A; A verification unit, used for verifying the login request; A processing unit is used to extract the platform level and historical purchase records corresponding to the user A; obtain the first video selected by the user A, and when determining that the first video is a paid video, extract the first type corresponding to the first video, and extract the purchase record of the first type from the historical purchase record, wherein the purchase record includes: free viewing time, purchase amount, number of free viewings, video clarity, and viewing network type; construct a purchase vector corresponding to the purchase record, construct multiple consumption vectors of the first video, calculate the similarity value between the purchase vector and each of the multiple consumption vectors to obtain multiple similarity values, extract the free viewing time t1 and clarity Q1 in the first consumption vector with the highest similarity value from the multiple similarity values ​​corresponding to the multiple sales vectors, configure the free viewing time of the user A in the first video to be t1, and configure the clarity of the first video to be Q1.

6. The cloud platform rights management system according to claim 5, characterized in that: The processing unit is specifically used to extract the first amount of the first video and the network type of the current device, extract the number of times c1 that user A clicks on the first video this time, obtain multiple free viewing times that the cloud platform can provide according to the platform level of user A, extract one free viewing time t0 from the multiple free viewing times, fill t0, the first amount, the number of times c1, the clarity and the network type value into the 5 element positions of a consumption vector in sequence to obtain a consumption vector, and traverse multiple free viewing times to obtain multiple consumption vectors.

7. The cloud platform rights management system according to claim 6, characterized in that: The processing unit is specifically used to select a consumption vector from multiple consumption vectors, perform a normalization operation on a purchase vector and a consumption vector to obtain n normalized vectors, extract a normalized vector corresponding to a consumption vector from the n vectors, calculate the similarity between the normalized vector and the remaining n-1 vectors to obtain n-1 similarity values, calculate the average value of the n-1 similarity values ​​as the similarity value corresponding to a consumption vector; and traverse multiple consumption vectors to obtain multiple similarity values ​​corresponding to the multiple consumption vectors.

8. The cloud platform rights management system according to claim 5, characterized in that: The processing unit is also used to control the communication unit to receive the purchase request if user A initiates a purchase request within the free viewing time t1, extract the preferential ratio of the corresponding authority of user A, and the cloud platform initiates a charging request to user A based on the preferential ratio and the purchase amount.

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