Television content updating method and device based on user behaviors, equipment and medium

Abstract

The invention discloses a television content updating method and device based on user behaviors, equipment and a medium. The method comprises the following steps: when a television is started and operation of a user is detected, counting the use condition of a content module of a television end by the user, and obtaining user behavior data; detecting whether the television side is connected with a server side or not, if the television side is connected with the server side, sending the user behavior data to the server side, and receiving a networking sorting list sent by the server side; if the user behavior data is not connected with the server side, performing data calculation and sorting on the user behavior data to generate a local sorting list; and updating the arrangement sequence of the content modules according to the networking arrangement list or the local arrangement list. By implementing the method provided by the invention, the problem that the television content in the prior art cannot meet the personalized requirement of the user can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method, device, equipment and medium for updating television content based on user behavior. Background Art

[0002] With the rapid advancement of smart TV technology, users are increasingly pursuing personalized and intelligent TV experiences. However, traditional TVs generally use static presets for preference settings, which makes it difficult to flexibly adapt to each user's unique viewing habits and preference changes. Although there are methods on the market that rely on network connections to set TV preferences, the methods in the prior art increase the economic burden of data transmission, extend processing time, increase the "window period" for users to wait, and are more likely to cause concerns about user privacy leakage due to improper processing of data during transmission. What is particularly critical is that for users who are offline or face challenges with unstable networks, this personalized service mechanism that relies on the network is almost non-existent, making it difficult for existing TV content to meet the personalized needs of modern users. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and medium for updating television content based on user behavior, aiming to solve the problem in the prior art that television content cannot meet the personalized needs of users.

[0004] In the first aspect, an embodiment of the present invention provides a method for updating television content based on user behavior, on the television side, the method comprising: when the television is started and detects user operation, counting the user's usage of the content module on the television side to obtain user behavior data; detecting whether the television side is connected to the server side, if connected to the server side, sending the user behavior data to the server side, and receiving the networked sorting list sent by the server side; if not connected to the server side, calculating and sorting the user behavior data to generate a local sorting list; updating the arrangement order of the content modules according to the networked sorting list or the local sorting list.

[0005] In the second aspect, an embodiment of the present invention also provides a method for updating television content based on user behavior, which is applied to a server side, and the method includes: receiving user behavior data sent by a television side; determining whether the user behavior data reaches a preset analysis indicator quantity through a preset calculation algorithm, and if so, calculating the user behavior data to obtain the average daily usage time and the average daily usage frequency of different content modules; sorting the content modules according to the average daily usage time and the average daily usage frequency and a preset sorting level to generate a networked sorting list; sending the networked sorting list to the television side to update the arrangement order of the content modules.

[0006] In a third aspect, an embodiment of the present invention further provides a device for updating television content based on user behavior, which includes a unit for executing any method as described in the first aspect, or includes a unit for executing any method as described in the second aspect.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0008] In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and wherein the program instructions can implement the above method when executed by a processor.

[0009] The embodiment of the present invention provides a method, device, equipment and medium for updating TV content based on user behavior. When the method is applied to a TV end, when the TV is started and detects that the user is operating, the user's use of the content module on the TV end is counted to obtain user behavior data; detect whether the TV end is connected to the server end, if it is connected to the server end, send the user behavior data to the server end, and receive the network sorting list sent by the server end; if it is not connected to the server end, calculate and sort the user behavior data to generate a local sorting list; update the arrangement order of the content module according to the network sorting list or the local sorting list. When the method is applied to the server end, receive the user behavior data sent by the TV end; determine whether the user behavior data reaches the preset analysis index number through a preset calculation algorithm, if it reaches it, calculate the user behavior data to obtain the average daily usage time and the average daily usage times of different content modules; sort the content modules according to the average daily usage time and the average daily usage times and the preset sorting level to generate a network sorting list; send the network sorting list to the TV end to update the arrangement order of the content modules. The embodiment of the present invention collects user behavior data when the user uses the television, and analyzes and sorts the user behavior data through the server when the television is connected to the server, so as to obtain an online sorting list and update the arrangement order of the television content modules according to the online sorting list; if the server is not connected, the user behavior data is analyzed and sorted through the television, and a local sorting list is obtained to update the arrangement order of the content modules, thereby realizing personalized preference settings for the content modules, improving the static and lack of personalization problems of the preference settings of traditional television viewers, so that users can enjoy personalized services regardless of whether they are online or not, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0011] Figure 1 A flowchart of a method for updating television content based on user behavior provided by an embodiment of the present invention applied to a television terminal;

[0012] Figure 2 A schematic diagram of a sub-process of applying the method for updating television content based on user behavior provided by an embodiment of the present invention to a television terminal;

[0013] Figure 3 A schematic diagram of a sub-process of applying the method for updating television content based on user behavior provided by an embodiment of the present invention to a television terminal;

[0014] Figure 4 A schematic diagram of a sub-process of applying the method for updating television content based on user behavior provided by an embodiment of the present invention to a television terminal;

[0015] Figure 5 A schematic diagram of a sub-process of applying the method for updating television content based on user behavior provided by an embodiment of the present invention to a television terminal;

[0016] Figure 6 A schematic diagram of a flow chart of a method for updating television content based on user behavior provided by an embodiment of the present invention applied to a server side;

[0017] Figure 7 A schematic diagram of a sub-process of applying the method for updating television content based on user behavior provided by an embodiment of the present invention to a server side;

[0018] Figure 8 A schematic block diagram of a TV content updating device based on user behavior provided by an embodiment of the present invention applied to a TV terminal;

[0019] Fig. 9 A schematic block diagram of a television content updating device based on user behavior provided by an embodiment of the present invention applied to a server side;

[0020] Fig.10 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] See also Figure 1 , Figure 1 The present invention provides a flowchart of a method for updating TV content based on user behavior provided in an embodiment of the present invention applied to a TV end. The method for updating TV content based on user behavior in this embodiment can be applied to a TV end and a server end. The user's usage habits can be collected and fed back to the server end through the data statistics, network feedback and other modules of the TV end, so that the server end can update the TV content according to the received user's usage habits. If the TV end is not connected to the Internet, a local sorting list can be generated through the TV end to update the TV content, thereby improving the static and lack of personalization of the preference settings of traditional TV viewers, so that users can enjoy personalized services regardless of whether they are connected to the Internet, thereby improving the user experience.

[0026] Figure 1 The method for updating TV content based on user behavior provided by an embodiment of the present invention is applied to a TV terminal. As shown in the figure, the method includes the following steps S110-S140.

[0027] S110: When the TV is turned on and detects a user operation, count the user's usage of the content module on the TV and obtain user behavior data.

[0028] In this embodiment, the user behavior data is data of the user's operation on the TV, including but not limited to the time the user watches a certain program, the frequency of switching different content modules, the time spent in a certain module, and whether the user has performed interactive operations such as likes, shares or comments on specific content. When the TV is turned on, the usage time of the TV is counted. It should be noted that the operating pressure of the TV should be considered at this time (i.e., the power-on time of the TV, where the minimum counting unit of the power-on time is set to 60s). When the TV is turned on and detects that the user is operating, the user's use of the content module on the TV is immediately counted, and the user behavior data is obtained together according to the power-on time of the TV and the user's use. Specifically, if the TV is turned on and detects that the user adjusts the viewing channel to the news channel, the user's viewing time of the news channel is immediately recorded to obtain the user behavior data, and the user behavior data is encrypted and stored in the local data, where the minimum counting unit for the use time of a certain frequency band or APP is 60s, and the timer for the power-on time counting is shared to reduce the system memory usage. By obtaining user behavior data when the TV is turned on and the user operates, a data basis is provided for subsequent adjustments to the TV content module.

[0029] S120, detecting whether the TV end is connected to the server end, and if so, sending the user behavior data to the server end, and receiving the network ranking list sent by the server end.

[0030] In this embodiment, the network ranking list is a ranking list of content modules that conform to user behavior habits sent by the server. Detect whether the TV end is connected to the server end, specifically, determine whether the TV end is connected to the network. If the TV end is already connected to the network, it is determined that the TV end is connected to the server, and then the user behavior data is sent to the server end, and the server end re-sorts the arrangement order of the content modules according to the user behavior data to generate a network ranking list and send it to the TV end, and the TV end receives the network ranking list. It should be noted that in order to reduce the pressure on the server, communication is only performed once after the TV is turned on and connected to the network. In the same day, if the TV is turned on multiple times, communication is only performed once. By sending user behavior data to the server end when the TV end is connected to the network, the network ranking list sent by the server end is obtained to reduce the operating pressure of the TV end, and obtain an accurate network ranking list to sort the content modules in the TV.

[0031] In one embodiment, if Figure 2 As shown, the step S120 includes steps S121-S122.

[0032] S121, detecting whether the storage time of the user behavior data is greater than a preset storage time;

[0033] S122: If the storage time is greater than the preset storage time, the user behavior data is uploaded to the server through a preset communication protocol, and the uploaded user behavior data is deleted.

[0034] In this embodiment, the preset storage time is the pre-set storage time of the user behavior data, wherein the preset storage time can be set according to the specific storage space, and there is no limitation on this. In this embodiment, the preset storage time is one day. Detect whether the storage time of the user behavior data is greater than the preset storage time. Specifically, when the TV end is connected to the server end to send the user behavior data, determine whether the storage time of the user behavior data is greater than one day, send the user behavior data with a storage time greater than one day to the server end, that is, feed back the user behavior data before the day to the server end, and delete the uploaded user behavior data to reduce the storage pressure of the TV end, and communicate the data by using the preset HTTPS protocol and post request. The post request includes a token authentication token for identity authentication, thereby achieving completely reliable data transmission. By sending the user behavior data greater than the preset storage time to the server end, the server end can re-sort the content modules in the TV according to the previous data.

[0035] In one embodiment, if Figure 3 As shown, step S120 includes steps S1201-S1202.

[0036] S1201, detecting whether the server has updated a preset data upload cycle and a preset sorting level;

[0037] S1202: If the server updates the preset data upload cycle and the preset sorting level, the user behavior data is uploaded or sorted according to the updated preset data upload cycle and the preset sorting level.

[0038] In this embodiment, the preset data upload cycle is the cycle sent by the server side during which data can be uploaded; the preset sorting level is the sorting level of the content module sent by the server side. Specifically, when the TV side sorts the TV content module, it will follow the sorting level of the content module. If the sorting level is not set, it will be sorted according to the default sorting level. For example, the APP pushed by the server side can be set as the highest sorting level, the average APP usage time is used as the secondary sorting level, and the function items and the average number of APP clicks are used as the lowest sorting level. For example, the APP set by the server side is placed at the first place in the online sorting list, and the level is sorted according to the user's usage time, that is, the remaining unset APPs are sorted according to the average daily usage time first, and are ranked after the APP set by the server side. If there are APPs with the same average usage time, they will be sorted again according to the average daily usage times, and finally a sorting list is generated, wherein the sorting of the function items does not have the sorting parameter P1, and the P1 indicator will be ignored when sorting the menu function items. If the server updates the preset data upload cycle and the preset sorting level, the user behavior data is uploaded according to the updated preset data upload cycle, and the content modules are sorted according to the preset sorting level when the server is not connected. By uploading or sorting the user behavior data according to the preset data upload cycle and the preset sorting level updated by the server, it is ensured that the data synchronization cycle of the TV end and the server end is the same, and the sorting methods of the TV content modules at both ends are the same.

[0039] S130: If the server is not connected, the user behavior data is calculated and sorted to generate a local sorting list.

[0040] In this embodiment, the local sorting list is a sorting list of TV content modules that conform to user habits, which is analyzed by the TV end based on user behavior data. If the TV end is not connected to the network, it is determined that it is not connected to the server end. At this time, the TV end performs data calculation and sorting based on the user behavior data to generate a local sorting list. Specifically, the user behavior data can be analyzed and calculated to obtain information such as the user's usage time and frequency of use for different APPs, and a sorting list of content modules can be generated based on the preset sorting level sent by the server end obtained when connecting to the network and the usage time and frequency of the APP. By performing data calculation and sorting on the user behavior data, a local sorting list that conforms to the user's usage habits is generated, so that the content modules can still be sorted when not connected to the network, so that users can enjoy personalized services and improve the user experience.

[0041] In one embodiment, if Figure 4 As shown, the step S130 includes steps S131-S132.

[0042] S131, determining whether the user behavior data reaches a first preset analysis indicator quantity, and if so, calculating the user behavior data to obtain the average daily usage duration and average daily usage frequency of different content modules;

[0043] S132: Sort the different content modules according to the average daily usage duration, the average daily usage times and the preset sorting levels to generate the local sorting list.

[0044] In this embodiment, the first preset analysis index number is the number that the user data should reach when calculating the user behavior data. For example, if only one day's user behavior data is calculated, accurate user habits cannot be inferred. Therefore, it is necessary to calculate the user behavior data of the preset analysis index number, for example, to calculate the user behavior data of seven days. It should be noted that the data of seven days is not the data of seven consecutive days, but the data corresponding to the number of days of using the TV for a total of 7 days. If the user uses the TV only twice in seven days, the user behavior data of seven days cannot be obtained. At this time, it is necessary to expand the statistical cycle of the user behavior data until the user behavior data of seven days can be obtained. It is judged whether the user behavior data reaches the preset analysis index number. If it reaches, the user behavior data is calculated to obtain the average daily usage time and the average daily usage frequency of different content modules. Specifically, all the usage time and all the usage frequency of different content modules in 7 days are obtained according to the user behavior data, and all the usage time and all the usage frequency are averaged to obtain the average daily usage time and the average daily usage frequency of different content modules. The preset sorting level is a pre-set level for sorting different content modules. The preset sorting level is the same as the above-mentioned sorting method and is not described here. The different content modules are sorted according to the average daily usage time, the average daily usage times and the preset sorting level to generate the local sorting list that conforms to the user's behavior habits.

[0045] S140: Update the arrangement order of the content modules according to the online arrangement list or the local arrangement list.

[0046] In this embodiment, the online sorting list is a sorting list obtained when the network is connected, and the local sorting list is a sorting list obtained when the network is not connected. It can be understood that only one of the online sorting list or the local sorting list can be obtained at the same time. If the network is connected, the arrangement order of the content module is updated according to the online sorting list, and if the network is not connected, the content module is updated according to the arrangement order in the local sorting list. By updating the arrangement order of the content module according to the online sorting list or the local sorting list, users can enjoy personalized services regardless of whether they are connected to the network, thereby improving the user experience.

[0047] In one embodiment, if Figure 5 As shown, step S140 includes steps S141-S143.

[0048] S141, obtaining the interval between the last time the arrangement order of the content modules was updated and the current time;

[0049] S142, determining whether the update interruption time interval is greater than a preset update threshold;

[0050] S143: If the value is greater than the preset update threshold, the arrangement order of the content modules is updated.

[0051] In this embodiment, the update interval is the time interval between the last sorting and updating date and the current date. Specifically, the update interval is obtained by performing digital calculation based on the recorded date of the last update of the arrangement order of the content module and the current date, and judging whether the update interval is greater than a preset update threshold, the preset update threshold is the time threshold for updating the arrangement order of the content module. If the update interval is greater than the preset update threshold, the arrangement order of the content module is updated. For example, if the update interval is one month and the preset update threshold is 15 days, the arrangement order of the content module is updated. By judging whether the update interval is greater than the preset update threshold, it is decided whether to update the arrangement order of the content module, thereby avoiding the situation where frequent updates of the arrangement of the content modules lead to a lot of user dissatisfaction and reduce the user experience.

[0052] In addition, the method also includes: displaying user usage data so that users can view their usage statistics, adjust preference settings, or manage multiple user accounts on the interface. It can also provide users with instant operation feedback through visual and sound feedback mechanisms. It also has a health reminder function. Specifically, according to the user's usage time and habits, it reminds the user to rest or do eye exercises in time, pays attention to the user's eye health, and thus improves the user's overall usage experience.

[0053] Figure 6 The figure is a flow chart of the method for updating TV content based on user behavior provided by an embodiment of the present invention applied to a server side. As shown in the figure, the method includes the following steps S210-S240.

[0054] S210: Receive user behavior data sent by the TV terminal.

[0055] In this embodiment, the server receives the user behavior data sent by the TV, wherein the user behavior data includes but is not limited to the number of times a menu function item is used, the number of times and duration of use of the homepage APP, the boot time of the day, etc. The TV provides a data basis for generating a sorted list of content modules on the TV by receiving the user behavior data.

[0056] S220: Determine whether the user behavior data reaches a second preset analysis indicator quantity; if so, calculate the user behavior data to obtain the average daily usage duration and the average daily usage frequency of different content modules.

[0057] In this embodiment, the second preset analysis index number is the number that the user data should reach when the server calculates the user behavior data. If only one day's user behavior data is calculated, accurate user habits cannot be inferred. Therefore, it is necessary to calculate the user behavior data of the preset analysis index number. For example, the preset analysis index number can be seven days of user behavior data. It should be noted that the seven-day user behavior data is not the data for seven consecutive days, but the data corresponding to the cumulative number of days of using the TV for seven days. If the user uses the TV only twice within seven days, the seven-day user behavior data cannot be obtained. At this time, it is necessary to receive more user behavior data sent by the TV end until the seven-day user behavior data can be obtained. It is judged whether the user behavior data reaches the preset analysis index number. If it reaches, the user behavior data is calculated to obtain the average daily usage time and the average daily usage times of different content modules. Specifically, all the usage time and all the usage times of different content modules within seven days are obtained according to the user behavior data, and all the usage time and all the usage times are averaged to obtain the average daily usage time and the average daily usage times of different content modules. By obtaining the average daily usage time and average daily usage times of different content modules, data support is provided for the subsequent sorting of content modules.

[0058] S230: Sort the content modules according to the average daily usage duration, the average daily usage times and a preset sorting level to generate a network sorting list.

[0059] In this embodiment, the preset sorting level is a pre-set level for sorting different content modules. The preset sorting level can be limited according to specific usage conditions, and there is no limitation on this. In this embodiment, the preset sorting level can be specifically: the push APP set by itself can be set as the highest sorting level, the average APP usage time as the secondary sorting level, and the function items and the average number of APP clicks as the lowest sorting level. By default, the highest sorting level, the secondary sorting level, and the lowest sorting level are prioritized. By sorting the different content modules according to the average daily usage time, the average daily usage times, and the preset sorting level, the online sorting list that conforms to the user's behavior habits is generated.

[0060] In one embodiment, if Figure 7 As shown, step S230 includes steps S2301-S2303.

[0061] S2301, receiving identification information sent by the TV terminal;

[0062] S2302, customizing a personalized update list according to the brand information and location information in the identification information;

[0063] S2303, sending the personalized update list to the TV end.

[0064] In this embodiment, the TV end sends the user behavior data to the server end and also sends the identification information of the TV end to the server end. The identification information can show the detailed information of the TV end, including but not limited to the TV brand, the TV unique serial SN number, the TV generation order number, and the TV MAC address. The above identification information is sent to the server end together with the user behavior data. The server end receives the identification information sent by the TV end, and customizes the personalized update list according to the brand information and the location information in the identification information. Specifically, the server end searches for matching update resources in the background database according to the brand information and model. These resources may include the latest operating system version, application updates, user interface optimization, etc. At the same time, the system will also refer to the location information to ensure that the updated content provided complies with the local language environment, laws and regulations, and cultural habits, and integrate this information to generate a personalized update list, which is tailored for the TV end. The personalized update list is sent to the TV end together with the network sorting list through a secure and reliable communication network (such as an encrypted network connection), wherein the server end pushes and adjusts according to the actual experience effect and data feedback from the TV end, so that the whole link forms feedback adjustment. By customizing the personalized update list according to the brand information and location information in the identification information, it can be ensured that the television device obtains the update content that is most suitable for its brand and location, thereby providing a better user experience.

[0065] S240: Send the online sorting list to the TV end to update the arrangement order of the content modules.

[0066] In this embodiment, the online ranking list is a preset ranking list of content modules with user usage habits generated based on the user behavior data, and the online ranking list is sent to the TV end through a communication protocol. After receiving the list, the TV end will adjust the arrangement order of the content modules accordingly according to the information in the list. By sending the online ranking list to the TV end, it is convenient for the TV end to make adjustments, ensuring that users can more conveniently and quickly access the content modules they are most interested in, thereby improving the user experience and increasing user satisfaction and loyalty.

[0067] Corresponding to the above TV content update based on user behavior, the present invention also provides a TV content update device based on user behavior. The TV content update device based on user behavior includes a unit for executing the above TV content update method based on user behavior, and the device can be configured in a desktop computer, tablet computer, laptop computer, etc. Figure 8 is a schematic block diagram 300 of a TV content updating device based on user behavior provided by an embodiment of the present invention applied to a TV terminal. Fig. 9It is a schematic block diagram 400 of a television content updating device based on user behavior provided by an embodiment of the present invention applied to a server side.

[0068] like Figure 8 As shown, the device for updating television content based on user behavior when applied to a television terminal includes a statistical unit 310 , a detection unit 320 , a generation unit 330 and an updating unit 340 .

[0069] The statistical unit 310 is used to count the usage of the content module on the TV side by the user and obtain the user behavior data when the TV is started and detects the user's operation.

[0070] The detection unit 320 is used to detect whether the TV end is connected to the server end. If it is connected to the server end, the user behavior data is sent to the server end, and the network ranking list sent by the server end is received.

[0071] In one embodiment, the detection unit 320 includes a first detection unit and an uploading unit.

[0072] A first detection unit, used to detect whether the storage time of the user behavior data is greater than a preset storage time;

[0073] The uploading unit is used to upload the user behavior data to the server through a preset communication protocol if the storage time is greater than the preset storage time, and delete the uploaded user behavior data.

[0074] In one embodiment, the detection unit 320 includes a second detection unit and a sorting unit.

[0075] A second detection unit, used to detect whether the server has updated the preset data upload cycle and the preset sorting level;

[0076] A sorting unit is used to upload or sort the user behavior data according to the updated preset data upload cycle and the preset sorting level if the server side updates the preset data upload cycle and the preset sorting level.

[0077] The generating unit 330 is used to perform data calculation and sorting on the user behavior data to generate a local sorting list if the server is not connected.

[0078] In one embodiment, the generating unit 330 includes a first calculating unit and a generating sub-unit.

[0079] A first calculation unit, used to determine whether the user behavior data reaches a first preset analysis index number, and if so, calculate the user behavior data to obtain an average daily usage duration and an average daily usage frequency of different content modules;

[0080] A generating subunit is used to sort the different content modules according to the average daily usage duration, the average daily usage times and the preset sorting levels to generate the local sorting list.

[0081] The updating unit 340 is configured to update the arrangement order of the content modules according to the online arrangement list or the local arrangement list.

[0082] In one embodiment, the updating unit 340 includes an interval unit, a determination unit, and an updating sub-unit.

[0083] An interval unit, used to obtain the interval between the last time the arrangement order of the content modules was updated and the current time;

[0084] A judging unit, used to judge whether the time interval of the interruption of updating is greater than a preset updating threshold;

[0085] The updating subunit is used to update the arrangement order of the content modules if the value is greater than the preset update threshold.

[0086] like Fig. 9 As shown, the device for updating television content based on user behavior when applied to a server side includes a first receiving unit 410 , a second calculating unit 420 , a network generating unit 430 and a sending unit 440 .

[0087] The first receiving unit 410 is used to receive user behavior data sent by the TV terminal.

[0088] The second calculation unit 420 is used to determine whether the user behavior data reaches a second preset analysis indicator quantity. If so, the user behavior data is calculated to obtain the average daily usage duration and the average daily usage frequency of different content modules.

[0089] The network generation unit 430 is used to sort the content modules according to the average daily usage duration, the average daily usage times and a preset sorting level to generate a network sorting list.

[0090] In one embodiment, the network generation unit 430 includes a spacing unit, a determination unit, and an updating subunit.

[0091] A second receiving unit, used to receive identification information sent by the TV terminal;

[0092] A customization unit, configured to customize a personalized update list according to the brand information and the location information in the identification information;

[0093] A list sending unit is used to send the personalized update list to the TV end.

[0094] The sending unit 440 is used to send the online sorting list to the TV end to update the arrangement order of the content modules.

[0095] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned user behavior-based TV content updating device 300, 400 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0096] The above-mentioned TV content updating device based on user behavior can be implemented in the form of a computer program. The computer program can be used in a computer program such as Fig.10 Runs on the computer device shown.

[0097] See also Fig.10 , Fig.10 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0098] See also Fig.10 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0099] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a method for updating television content based on user behavior.

[0100] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0101] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for updating television content based on user behavior.

[0102] The network interface 505 is used to communicate with other devices over the network. Fig.10The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0104] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0105] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0106] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0107] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0108] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit 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.

[0110] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0112] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for updating television content based on user behavior, characterized in that: The method is applied to a television terminal, and the method comprises: When the TV is turned on and detects user operations, the user's usage of the content module on the TV is counted to obtain user behavior data; Detecting whether the TV end is connected to the server end, and if so, sending the user behavior data to the server end, and receiving the network ranking list sent by the server end; If the server is not connected, the user behavior data is calculated and sorted to generate a local sorting list; The arrangement order of the content modules is updated according to the online arrangement list or the local arrangement list.

2. The method according to claim 1, characterized in that The step of sending the user behavior data to the server side if the connection to the server side is established comprises: Detecting whether the storage time of the user behavior data is greater than a preset storage time; If it is greater than the preset storage time, the user behavior data is uploaded to the server through a preset communication protocol, and the uploaded user behavior data is deleted.

3. The method according to claim 1, characterized in that After the step of receiving the networking sorting list sent by the server, the method further includes: Detecting whether the server has updated the preset data upload cycle and the preset sorting level; If the server updates the preset data upload cycle and the preset sorting level, the user behavior data is uploaded or sorted according to the updated preset data upload cycle and the preset sorting level.

4. The method according to claim 3, characterized in that The step of calculating and sorting the user behavior data to generate a local sort list also includes: Determine whether the user behavior data reaches a first preset analysis indicator quantity, and if so, calculate the user behavior data to obtain the average daily usage duration and average daily usage frequency of different content modules; The different content modules are sorted according to the average daily usage duration, the average daily usage times and the preset sorting levels to generate the local sorting list.

5. The method according to claim 4, characterized in that The step of updating the arrangement order of the content modules according to the online arrangement list or the local arrangement list comprises: Obtaining the time interval between the last time the arrangement order of the content modules was updated and the current time; Determine whether the update interruption time interval is greater than a preset update threshold; If it is greater than the preset update threshold, the arrangement order of the content modules is updated.

6. A method for updating television content based on user behavior, characterized in that: The method is applied to the server side, and the method comprises: Receive user behavior data sent by the TV end; Determine whether the user behavior data reaches a second preset analysis indicator quantity, and if so, calculate the user behavior data to obtain the average daily usage duration and the average daily usage frequency of different content modules; Sorting the content modules according to the average daily usage duration, the average daily usage times and a preset sorting level to generate a network sorting list; The online sorting list is sent to the television end to update the arrangement order of the content modules.

7. The method according to claim 6, characterized in that After the step of sorting the content modules according to the average daily usage duration, the average daily usage times and the preset sorting levels to generate a network sorting list, the method further includes: Receiving identification information sent by the TV terminal; Customize a personalized update list according to the brand information and location information in the identification information; The personalized update list is sent to the TV terminal.

8. A device for updating television content based on user behavior, characterized in that: It comprises a TV end and a server end, wherein the TV end comprises a unit for executing the method as claimed in any one of claims 1 to 5, and the server end comprises a unit for executing the method as claimed in any one of claims 6 to 7.

9. A computer device, characterized in that: The computer device includes a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to execute the steps of the method as described in any one of claims 1 to 5 or to execute the steps of the method as described in any one of claims 6 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, they can implement the steps of the method as described in any one of claims 1 to 5, or implement the steps of the method as described in any one of claims 6 to 7.

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