Internet live broadcast-based visitor flow regulation and control method, device and equipment

Abstract

The invention discloses a visitor flow regulation and control method, device and equipment based on Internet live broadcast, and the method comprises the steps: introducing a user behavior classification mechanism, firstly calculating a better proportion of different types of users in each live broadcast scene based on historical data as a reference, then carrying out the real-time statistics of the actual proportion of different types of users in each live broadcast scene in a current time period, and carrying out the real-time statistics of the actual proportion of different types of users in each live broadcast scene; and finally, the traffic quota of each live broadcast scene is dynamically adjusted according to the deviation, so that the user structure change in each live broadcast scene is accurately sensed, the traffic distribution is dynamically optimized, the requirements of different user groups are better met, the traffic resource is inclined to the user groups with higher value, and the user experience is improved. User experience and platform business income are improved, and healthy development of live broadcast content ecology is promoted.

Description

Technical Field

[0001] This invention relates to the field of network traffic control, and particularly to a method, apparatus, and device for regulating visitor traffic based on Internet live streaming. Background Technology

[0002] In the operation of comprehensive live streaming platforms, the rationality and real-time nature of traffic allocation directly impact user experience and platform revenue. Different users exhibit varying behavioral patterns towards live streaming content. For example, some users focus on a particular live streaming scenario for an extended period, while others frequently switch between multiple scenarios, and still others follow specific streamers across different scenarios. However, existing traffic control technologies typically allocate traffic based solely on overall popularity metrics (such as the number of online users and total viewing time), failing to fully consider the differences in traffic value generated by different user types. Furthermore, when user proportions change, especially when the proportion of important user types shifts, existing solutions struggle to detect and dynamically optimize traffic allocation in a timely manner, thus affecting user experience and the health of the platform ecosystem. Summary of the Invention

[0003] This invention provides a method, apparatus, and device for controlling visitor traffic based on Internet live streaming, which solves the technical problems mentioned above.

[0004] A first aspect of this invention provides a method for controlling visitor traffic based on internet live streaming, comprising the following steps: Step 1: Collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types for each live streaming scenario; Step 2: Obtain the real-time percentage of each visitor type in each live streaming scenario during the current time period, and calculate the current traffic supply and demand balance for each live streaming scenario based on the basic percentage. Step 3: Adjust the global traffic based on the current traffic supply and demand balance.

[0005] A second aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for controlling visitor traffic based on Internet live streaming.

[0006] A third aspect of the present invention provides a visitor traffic control device based on Internet live streaming, including a computer-readable storage medium and a processor, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the visitor traffic control method based on Internet live streaming described above.

[0007] A fourth aspect of this invention provides a visitor traffic control device based on internet live streaming, comprising a first calculation module, a second calculation module, and a control module. The first calculation module is used to collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types in each live streaming scenario; The second calculation module is used to obtain the real-time proportion of each visitor type in each live streaming scenario in the current time period, and calculate and generate the current traffic supply and demand balance degree corresponding to each live streaming scenario in combination with the basic proportion; The control module is used to control the global traffic based on the current traffic supply and demand balance.

[0008] The beneficial effects of this invention are as follows: This invention provides a method, apparatus, and device for controlling visitor traffic based on internet live streaming. It introduces a user behavior classification mechanism. First, it calculates the optimal proportion of different types of users in each live streaming scenario based on historical data as a benchmark. Then, it statistically analyzes the actual proportion of different types of users in each live streaming scenario in the current time period and calculates the deviation from the optimal benchmark. Finally, it dynamically adjusts the traffic quota for each live streaming scenario based on the deviation, thereby accurately sensing changes in user structure in each live streaming scenario and dynamically optimizing traffic allocation, which better meets the needs of different user groups. At the same time, traffic resources are tilted towards higher-value user groups, improving user experience and platform commercial revenue, and promoting the healthy development of the live streaming content ecosystem.

[0009] To make the above-mentioned objects, features and advantages of the invention more apparent and understandable, preferred embodiments of the invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the visitor traffic control method based on Internet live streaming provided in Example 1; Figure 2 This is a schematic diagram of the visitor traffic control device based on Internet live streaming provided in Embodiment 2; Figure 3 This is a schematic diagram of the visitor traffic control device based on Internet live streaming provided in Example 3. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0013] It should be noted that, unless otherwise specified, the various features in the embodiments of this invention can be combined with each other, all of which are within the protection scope of this invention. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this invention do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0014] Figure 1 This is a flowchart illustrating a visitor traffic control method based on internet live streaming provided in Example 1. Figure 1 As shown, it includes the following steps: Step 1: Collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types for each live streaming scenario; Step 2: Obtain the real-time percentage of each visitor type in each live streaming scenario during the current time period, and calculate the current traffic supply and demand balance for each live streaming scenario based on the basic percentage. Step 3: Adjust the global traffic based on the current traffic supply and demand balance.

[0015] The live streaming scenarios referred to in this invention include educational live streaming, e-commerce live streaming, game live streaming, entertainment live streaming, and / or competition live streaming, etc. Visitor types include: the first type of visitor who has a high level of focus and loyalty to a certain live streaming scenario and basically does not pay attention to other scenarios; the second type of visitor who has a high level of focus on a certain streamer and will switch between scenarios according to the streamer's behavior; the third type of visitor who has a high level of focus on a certain scenario and is also willing to explore other scenarios; and the fourth type of visitor who does not have a particular preference when switching between multiple scenarios.

[0016] The above embodiments provide a visitor traffic control method based on internet live streaming. It introduces a user behavior classification mechanism. First, it calculates the optimal proportion of different user types in each live streaming scenario based on historical data as a benchmark. Then, it statistically analyzes the actual proportion of different user types in each live streaming scenario during the current time period and calculates the deviation from the optimal benchmark. Finally, it dynamically adjusts the traffic quota for each live streaming scenario based on the deviation, thereby accurately sensing changes in user structure in each live streaming scenario and dynamically optimizing traffic allocation to better meet the needs of different user groups. Simultaneously, it tilts traffic resources towards higher-value user groups, improving user experience and platform revenue, and promoting the healthy development of the live streaming content ecosystem.

[0017] The following specific embodiments will be used to describe each step of the above method in detail.

[0018] In one specific embodiment, generating the basic proportion of different visitor types for each live streaming scenario specifically involves: Step 101: Collect historical access data within a preset time range, filter the historical access data according to the control objectives, and generate categorized data corresponding to each live streaming scenario. For example, you can choose the better historical access data when the traffic is relatively balanced and the user experience is high, or the better historical access data when the proportion of user types is relatively moderate and the platform revenue is high.

[0019] Step 102: Randomly extract sample data for each live streaming scene according to a preset ratio, and calculate the focus of each historical visitor on the corresponding live streaming scene and / or streamer in the sample data based on a distributed method. Specifically, the ratio of sample data extraction can be set according to the average number or average importance of different live streaming scenes in multiple time periods. When setting the ratio, factors such as holidays, competition days, winter and summer vacations can also be considered to set a fluctuation coefficient to improve accuracy.

[0020] Step 103: Invoke the preset judgment conditions and identify the visitor type of each historical visitor based on the focus level, and generate the basic proportion of each visitor type in each live streaming scenario.

[0021] Specifically, the initial focus level of any visitor on the live stream is calculated as follows: Step 1021: Calculate the percentage of effective viewing time for any visitor u in the live streaming scenario c based on the sample data. Average stay duration percentage Interest Breadth Punishment and interaction frequency And standardize it.

[0022] For example, here is the percentage of effective viewing time. The effective viewing time of visitor u in live streaming scenario c / total viewing time across all live streaming scenarios; average dwell time percentage. The average dwell time of visitor u in live stream scene c when a scene switching action occurs / the average dwell time across all live stream scenes; interest breadth penalty. =1 / total number of live stream scenarios attended by visitors; interaction frequency ratio The average interaction frequency of visitor u in live streaming scenario c / the average interaction frequency across all live streaming scenarios. Interaction behaviors include liking, sending flowers, commenting, tipping, etc.

[0023] Step 1022: Calculate the first focus level of any visitor u on the live streaming scene c using the first preset formula. : , , in, , , , These are the corresponding standardized results. , , , These are preset weighting coefficients, which can be set based on historical data.

[0024] For example, in a specific embodiment, calculating the second level of focus of any visitor on the streamer is specifically as follows: Step 1023: Calculate the percentage of effective viewing time, scene coverage, and cross-scene response time for any visitor u to at least one target streamer s based on the sample data. The target streamer is the streamer with the highest focus or interaction level among all the streamers followed by visitor u. The cross-scene response time represents the interval when a visitor follows the target streamer into a new live streaming scene after the target streamer switches from the daily live streaming scene to a new live streaming scene due to participation in activities, streamer collaboration, etc., and the time spent in the new live streaming scene exceeds a preset threshold.

[0025] Step 1024: Generate an initial focus level based on the effective viewing time percentage, and generate a follow-up coefficient based on scene coverage and cross-scene response time; Step 1025: Optimize the initial focus using the following coefficient to generate a second focus for any visitor on the target streamer. .

[0026] Of course, in other embodiments, clustering algorithms or neural network models such as LSTM can be used to set judgment conditions and classify visitors based on user behavior, which will not be elaborated here.

[0027] In a further preferred embodiment, a preset judgment condition is invoked and the visitor type of each historical visitor is identified based on the focus, specifically: Step 1031, a first focus vector for each visitor on the live streaming scene and a second focus vector for a preset number of anchors are established; Step 1032: Obtain the second maximum value of the visitor's focus, and determine whether the second maximum value of the focus is greater than or equal to the first preset threshold. If so, determine that it is the first visitor type; otherwise, proceed to step 1033. Step 1033: Obtain the first maximum focus value of the visitor. If the first maximum focus value is greater than or equal to A1, proceed to step 1034; otherwise, proceed to step 1035. Step 1034: If all other first focus levels are less than A2 and A2 < A1, then it is determined to be the second visitor type; otherwise, it is determined to be the third visitor type. Step 1035: Calculate the difference between the first maximum focus value and the first minimum focus value. If the difference is less than the second preset threshold, it is determined to be the fourth visitor type; otherwise, it is determined to be the third visitor type.

[0028] Of course, in other embodiments, other judgment conditions can be set as needed, and visitor types can be specifically classified.

[0029] In a preferred embodiment, the current traffic supply and demand balance degree corresponding to each live streaming scenario is calculated. Specifically: , in, , 'c' represents the live streaming scenario, and 'K' represents the number of visitor types. and This represents the real-time and base percentages of visitor type k in live streaming scenario c. This indicates the target number of live streaming rooms for live streaming scenario C during the current time period. This represents the baseline number of live streaming rooms in live streaming scenario C during the same time period. and This indicates the weights to be calculated and adjusted based on historical data.

[0030] In another embodiment, global traffic is regulated based on the current traffic supply-demand balance, specifically as follows: Step 301: Construct the target balance degree for each live streaming scenario based on the control objectives. The target balance degree can also be set by referring to historical data of composite control requirements.

[0031] Step 302: Calculate the difference between the current traffic supply and demand balance and the target balance for each live streaming scenario; Step 303: Adjust the current traffic quota of at least one live streaming scenario to minimize the overall balance deviation of all live streaming scenarios. The specific process can adopt a variety of optimization methods, all of which are within the protection scope of this embodiment.

[0032] In a preferred embodiment, a fluctuation identification step is further included, specifically: Step 304: Obtain the fluctuation impact characteristics of the current time period, including periodic fluctuation characteristics, such as evening rush hour, summer and winter vacations, and short-term event characteristics, such as official events, major competitions, holiday promotions, etc.

[0033] Step 305: Generate a fluctuation coefficient based on the fluctuation impact characteristics. Specifically, the coefficient can be obtained by looking up a table or querying a pre-established prediction model. Then, generate the target balance fluctuation range for multiple live streaming scenarios based on the fluctuation coefficient. Step 306: Obtain the number of scenarios where the current traffic supply and demand balance exceeds the corresponding target balance fluctuation range. When the number of scenarios exceeds the preset number, perform traffic regulation in step 3 to eliminate normal fluctuations, ensure the effectiveness of regulation, and avoid resource waste.

[0034] In a preferred embodiment, in addition to adjusting the traffic quotas for multiple live streaming scenarios, the traffic quotas for multiple live streaming rooms within a single live streaming scenario can also be adjusted. For example, the recommended traffic quotas for live streaming rooms with different attributes, including in-depth content, popular entertainment, and new streamers, can be adjusted based on the current proportion of various visitor types.

[0035] It should be understood that the sequence number of each step in the above embodiments does not imply 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 invention.

[0036] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for controlling visitor traffic based on internet live streaming.

[0037] Figure 2 This is a schematic diagram of the visitor traffic control device based on Internet live streaming provided in Embodiment 2, as shown below. Figure 2 As shown, it includes a first calculation module 100, a second calculation module 200, and a control module 300. The first calculation module 100 is used to collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types in each live streaming scenario; The second calculation module 200 is used to obtain the real-time proportion of each visitor type in each live streaming scenario in the current time period, and calculate and generate the current traffic supply and demand balance degree corresponding to each live streaming scenario in combination with the basic proportion; The control module 300 is used to control the global traffic based on the current traffic supply and demand balance.

[0038] The above embodiments provide a visitor traffic control device based on internet live streaming. It introduces a user behavior classification mechanism. First, it calculates the optimal proportion of different types of users in each live streaming scenario based on historical data as a benchmark. Then, it counts the actual proportion of different types of users in each live streaming scenario in real time and calculates the deviation from the optimal benchmark. Finally, it dynamically adjusts the traffic quota for each live streaming scenario based on the deviation, thereby accurately sensing changes in user structure in each live streaming scenario and dynamically optimizing traffic allocation to better meet the needs of different user groups. At the same time, traffic resources are tilted towards higher-value user groups, improving user experience and platform business revenue, and promoting the healthy development of the live streaming content ecosystem.

[0039] In a preferred embodiment, the first computing module 100 specifically includes: The data processing unit is used to collect historical access data within a preset time range, filter the historical access data according to the control target, and generate classification data corresponding to each live streaming scenario. A distributed computing unit is used to randomly extract sample data for each live streaming scene according to a preset ratio, and calculate the focus of each historical visitor on the corresponding live streaming scene and / or the streamer based on a distributed method. The classification and statistics unit is used to call preset judgment conditions and identify the visitor type of each historical visitor based on the focus level, and generate the basic proportion of each visitor type in each live streaming scenario.

[0040] In a preferred embodiment, the control module 300 specifically includes: The building unit is used to construct the target equilibrium degree corresponding to each live streaming scenario based on the control objectives. The calculation unit is used to calculate the difference between the current traffic supply and demand balance and the target balance for each live streaming scenario. The control unit is used to adjust the current traffic quota for at least one live streaming scenario in order to minimize the overall balance deviation across all live streaming scenarios.

[0041] In a preferred embodiment, the control module 300 further includes a fluctuation identification unit. The fluctuation identification unit is used to obtain the fluctuation impact characteristics of the current time period, generate a fluctuation coefficient based on the fluctuation impact characteristics, generate target balance fluctuation ranges for multiple live streaming scenarios, and obtain the number of scenarios where the current traffic supply and demand balance exceeds the corresponding target balance fluctuation range. When the number of scenarios exceeds a preset number, the control unit is invoked to execute traffic control steps, thereby eliminating normal fluctuations, ensuring the effectiveness of control, and avoiding resource waste.

[0042] It should be noted that the foregoing explanation of the embodiment of the visitor traffic control method based on Internet live streaming also applies to the visitor traffic control device based on Internet live streaming in the above embodiment, and will not be repeated here.

[0043] This invention also provides a visitor traffic control device based on Internet live streaming, including a computer-readable storage medium and a processor. When the processor executes a computer program on the computer-readable storage medium, it implements the steps of the visitor traffic control method based on Internet live streaming described above.

[0044] Figure 3 This is a schematic diagram of the visitor traffic control device based on Internet live streaming provided in Embodiment 3 of the present invention, as shown below. Figure 3 As shown, the visitor traffic control device 8 based on internet live streaming in this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various method embodiments described above, for example... Figure 1 The steps shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module in the above-described device embodiments, for example... Figure 2 The functions of the module shown.

[0045] For example, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 82 in the Internet-based live streaming visitor traffic control device 8.

[0046] The visitor traffic control device 8 based on internet live streaming may include, but is not limited to, a processor 80 and a readable storage medium 81. Those skilled in the art will understand that... Figure 3This is merely an example of a visitor traffic control device 8 based on internet live streaming, and does not constitute a limitation on the visitor traffic control device 8 based on internet live streaming. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the visitor traffic control device based on internet live streaming may also include a power management module, a computing processing module, input / output devices, network access devices, buses, etc.

[0047] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0048] The readable storage medium 81 can be an internal storage unit of the internet live streaming visitor traffic control device 8, such as a hard drive or memory. The readable storage medium 81 can also be an external storage device of the internet live streaming visitor traffic control device 8, such as a plug-in hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the readable storage medium 81 can include both internal and external storage units of the internet live streaming visitor traffic control device 8. The readable storage medium 81 is used to store the computer program and other programs and data required by the internet live streaming visitor traffic control device. The readable storage medium 81 can also be used to temporarily store data that has been output or will be output.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0050] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0051] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0052] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0054] Furthermore, 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0055] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. A method for controlling visitor traffic based on internet live streaming, characterized in that, Includes the following steps: Step 1: Collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types for each live streaming scenario; Step 2: Obtain the real-time percentage of each visitor type in each live streaming scenario during the current time period, and calculate the current traffic supply and demand balance for each live streaming scenario based on the basic percentage. Step 3: Adjust the global traffic based on the current traffic supply and demand balance.

2. The visitor traffic control method based on Internet live streaming according to claim 1, characterized in that, The live streaming scenarios include educational live streaming, e-commerce live streaming, game live streaming, entertainment live streaming, and / or competition live streaming; the generation of the basic proportion of different visitor types under each live streaming scenario is specifically as follows: Step 101: Collect historical access data within a preset time range, filter the historical access data according to the control target, and generate classification data corresponding to each live streaming scenario; Step 102: Randomly extract sample data for each live streaming scene according to a preset ratio, and calculate the focus of each historical visitor on the corresponding live streaming scene and / or the streamer in the sample data based on a distributed method. Step 103: Invoke the preset judgment conditions and identify the visitor type of each historical visitor based on the focus level, and generate the basic proportion of each visitor type in each live streaming scenario.

3. The visitor traffic control method based on Internet live streaming according to claim 2, characterized in that, The initial focus level of any visitor on the live stream is calculated as follows: Step 1021: Calculate the percentage of effective viewing time for any visitor u in the live streaming scenario c based on the sample data. Average stay duration percentage Interest Breadth Punishment and interaction frequency And standardize it; Step 1022: Calculate the first focus level of any visitor u on the live streaming scene c using the first preset formula. : ， ， in, , , , These are the corresponding standardized results. , , , These are the preset weighting coefficients.

4. The visitor traffic control method based on Internet live streaming according to claim 2, characterized in that, The second level of focus for any visitor on the streamer is calculated as follows: Step 1023: Calculate the percentage of effective viewing time, scene coverage, and cross-scene response time for any visitor u to the target anchor s based on the sample data. Step 1024: Generate an initial focus level based on the effective viewing time percentage, and generate a follow-up coefficient based on scene coverage and cross-scene response time; Step 1025: Optimize the initial focus using the following coefficient to generate a second focus for any visitor on the target streamer. .

5. The visitor traffic control method based on Internet live streaming according to claim 2, characterized in that, The system invokes preset judgment conditions and identifies the visitor type of each historical visitor based on the stated level of focus, specifically as follows: Step 1031: Establish the first focus vector of each visitor to the live streaming scene and the second focus vector to a preset number of streamers; Step 1032: Obtain the second maximum value of the visitor's focus, and determine whether the second maximum value of the focus is greater than or equal to the first preset threshold. If so, determine that it is the first visitor type; otherwise, proceed to step 1033. Step 1033: Obtain the first maximum focus value of the visitor. If the first maximum focus value is greater than or equal to A1, proceed to step 1034; otherwise, proceed to step 1035. Step 1034: If all other first focus levels are less than A2 and A2 < A1, then it is determined to be the second visitor type; otherwise, it is determined to be the third visitor type. Step 1035: Calculate the difference between the first maximum focus value and the first minimum focus value. If the difference is less than the second preset threshold, it is determined to be the fourth visitor type; otherwise, it is determined to be the third visitor type.

6. The visitor traffic control method based on Internet live streaming according to any one of claims 1-5, characterized in that, Calculate the current traffic supply and demand balance for each live streaming scenario. Specifically: , in, , 'c' represents the live streaming scenario, and 'K' represents the number of visitor types. and This represents the real-time and base percentages of visitor type k in live streaming scenario c. This indicates the target number of live streaming rooms for live streaming scenario C during the current time period. This represents the baseline number of live streaming rooms in live streaming scenario C during the same time period. and This indicates the calculation of weights.

7. The visitor traffic control method based on Internet live streaming according to claim 6, characterized in that, The global traffic is regulated based on the current traffic supply and demand balance, specifically as follows: Step 301: Construct the target equilibrium degree corresponding to each live streaming scenario based on the control objectives; Step 302: Calculate the difference between the current traffic supply and demand balance and the target balance for each live streaming scenario; Step 303: Adjust the current traffic quota for at least one live streaming scenario to minimize the overall balance deviation across all live streaming scenarios.

8. The visitor traffic control method based on Internet live streaming according to claim 7, characterized in that, It also includes a fluctuation identification step, specifically: Obtain the characteristics of the fluctuation impact in the current time period; A fluctuation coefficient is generated based on the fluctuation impact characteristics, and a target balance fluctuation range for multiple live streaming scenarios is generated based on the fluctuation coefficient. Obtain the number of scenarios where the current traffic supply and demand balance exceeds the corresponding target balance fluctuation range. When the number of scenarios exceeds the preset number, perform traffic regulation in step 3.

9. A visitor traffic control device based on internet live streaming, characterized in that, It includes a first calculation module, a second calculation module, and a control module. The first calculation module is used to collect historical access data for different live streaming scenarios, identify user behavior based on the historical access data, and generate the basic proportion of different visitor types in each live streaming scenario; The second calculation module is used to obtain the real-time proportion of each visitor type in each live streaming scenario in the current time period, and calculate and generate the current traffic supply and demand balance degree corresponding to each live streaming scenario in combination with the basic proportion; The control module is used to control the global traffic based on the current traffic supply and demand balance.

10. A visitor traffic control device based on Internet live streaming, comprising a computer-readable storage medium and a processor, characterized in that, When the processor executes the computer program on the computer-readable storage medium, it implements the steps of the visitor traffic control method based on Internet live streaming as described in any one of claims 1-8.

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