A method for generating time-dependent service data

Abstract

This invention discloses a method for generating time-dependent business data, relating to the field of communication network simulation. The method includes: synthesizing first variable data following a normal distribution based on sine waves of different frequencies, and synthesizing second variable data following a normal distribution that is orthogonal to the first variable data based on cosine waves; establishing data following a target distribution combination based on the first and second variable data; and selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario to establish a business traffic model. Compared to existing technologies, this invention can generate time-dependent business data for different business types even in the absence of real traffic datasets. It can more accurately and effectively simulate actual user traffic data, adapt to multiple business scenarios, and can be used for tasks such as traffic analysis, business classification, and business prediction, reducing research costs.

Description

Technical Field

[0001] This invention relates to the field of communication network simulation technology, and more specifically, to a method for generating time-dependent service data. Background Technology

[0002] With the rapid development of mobile communication technology and the widespread application of technologies such as the Internet of Things (IoT), cloud computing, and virtual reality, mobile communication data and traffic are experiencing explosive growth. To meet soaring user demand and reduce operating costs, cell-level traffic forecasting plays a crucial role in mobile networks. Many mobile applications rely on real-time or near-real-time radio access network traffic analysis. Accurately predicting the future traffic load of multiple base stations can help them implement sleep strategies to reduce energy consumption, optimize network resources for better allocation and utilization, enable the network to automatically adapt to changes in service traffic, enhance network flexibility and the ability to respond to emergencies, and improve the service experience for mobile users. Therefore, the importance of traffic forecasting is increasingly evident.

[0003] Currently, intelligent machine learning methods can achieve good prediction accuracy for traffic flow forecasting. Machine learning models rely on large datasets for training and validation; however, collecting traffic data is relatively difficult and costly. In mobile network environments, traffic data collection not only requires high-precision equipment and complex technical support but may also involve user privacy and data security issues, which to some extent limits data acquisition and use. Currently, publicly available live network traffic datasets are relatively few, especially for 5G service data, which is even scarcer.

[0004] Therefore, most industry research generates simulation data based on the distributions that different services follow, using this data to simulate actual data, thus reducing research costs and improving development efficiency. Existing methods for generating simulated traffic data mainly employ Monte Carlo methods to generate a large number of random numbers based on the variables and parameters of different service models to simulate the corresponding service distributions. For example, simple discrete random distributions are typically generated using the inverse transform method, which involves solving for the inverse function of the cumulative distribution function of the target distribution. For complex distribution functions, rejection sampling is usually used, sampling random numbers from a reference distribution and determining whether to accept the sampled value by comparing it to the probability density function of the target distribution, ultimately simulating the corresponding probability density function. While this method can quickly generate data that conforms to a certain service statistical distribution, this data lacks temporal correlation. In practical applications, mobile network traffic exhibits significant spatiotemporal dependence, meaning that traffic patterns may differ significantly across different time periods and geographical locations. Therefore, existing methods for generating simulated service traffic data using random numbers cannot serve traffic prediction; a more accurate simulation of the temporal correlation of actual network traffic data is needed. Summary of the Invention

[0005] To overcome the shortcomings of existing methods for generating simulated traffic data using random numbers, which cannot be used for traffic prediction, this invention provides a method for generating traffic data with time correlation.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] Firstly, a method for generating time-related business data includes:

[0008] The first variable data, which follows a normal distribution, is synthesized from sine waves of different frequencies, and the second variable data, which follows a normal distribution and is orthogonal to the first variable data, is synthesized from cosine waves.

[0009] Based on the first variable data and the second variable data, establish data that follows a target distribution combination;

[0010] Based on the business scenario, target business data is selected from the data that follows the target distribution combination, the first variable data, and / or the second variable data to establish a business traffic model.

[0011] In a second aspect, a computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by a processor to implement the method as described in the first aspect.

[0012] Thirdly, a computer program product includes a computer program or computer-executable instructions, which, when executed by a processor, implement the method described in the first aspect.

[0013] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0014] This invention provides a method for generating time-dependent business data. It synthesizes first and second variable data following a normal distribution using sine waves of different frequencies, thereby establishing data that follows a target distribution combination. Target business data is then selected based on the business scenario to build a business traffic model. Compared to existing technologies, this invention can generate time-dependent business data for different business types even in the absence of real traffic datasets. It can more accurately and effectively simulate actual user traffic data, adapt to multiple business scenarios, and can be used for tasks such as traffic analysis, business classification, and business prediction, while reducing research costs. Attached Figure Description

[0015] Figure 1This is a flowchart illustrating a time-related business data generation method according to Embodiment 1 of this application.

[0016] Figure 2 This is another flowchart illustrating a time-related business data generation method according to Embodiment 1 of this application.

[0017] Figure 3 This is a schematic diagram of a time-dependent normal distribution in Embodiment 1 of this application.

[0018] Figure 4 This is a schematic diagram of a normal distribution that does not have time correlation in Embodiment 1 of this application.

[0019] Figure 5 This is a schematic diagram of the 8K UHD wireless screen projection service traffic model established in Embodiment 1 of this application.

[0020] Figure 6 This is a schematic diagram of the packet layer traffic model for shared bicycle communication services established in Embodiment 1 of this application. Detailed Implementation

[0021] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the description of embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of units is not necessarily limited to those units, but may include other units not explicitly listed or inherent to those processes, methods, products, or apparatuses. The term "determine" broadly covers a wide variety of actions, including acquiring, calculating, processing, deriving, investigating, searching (e.g., searching in a table, database, or other data structure), probing, and similar actions; it may also include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), and similar actions; it may also include generating, creating, establishing, and similar actions; and parsing, selecting, choosing, and similar actions, etc. Definitions of other terms will be given in the following description.

[0022] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. Furthermore, in the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if there is transmission of electrical signals or data between the connected objects.

[0023] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0024] To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions;

[0025] It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0026] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] Example 1

[0028] This embodiment provides a method for generating business data with time correlation. (See attached document.) Figure 1 ,include:

[0029] The first variable data, which follows a normal distribution, is synthesized from sine waves of different frequencies, and the second variable data, which follows a normal distribution and is orthogonal to the first variable data, is synthesized from cosine waves.

[0030] Based on the first variable data and the second variable data, establish data that follows a target distribution combination;

[0031] Based on the business scenario, target business data is selected from the data that follows the target distribution combination, the first variable data, and / or the second variable data to establish a business traffic model.

[0032] In some preferred embodiments, the synthesis of a first variable data following a normal distribution based on sine waves of different frequencies, and the synthesis of a second variable data following a normal distribution that is orthogonal to the first variable data based on cosine waves, include:

[0033] Set the highest frequency f m Used to characterize the periodicity of data, and in (0,f) m Randomly select N-1 frequencies within the frequency range of f, and compare them with the highest frequency f. m Together they form N reference frequencies; within a certain time granularity, the highest frequency f m This is reflected in the relevance of business data; a low frequency indicates a strong correlation between the preceding and following business data, while a high frequency indicates a weak correlation.

[0034] Randomly select N phases;

[0035] Based on the central limit theorem, using the selected N reference frequencies and N phases, N sine waves with random frequencies and random phases and corresponding N cosine waves are constructed.

[0036] Set time granularity ts The data sampling interval used to characterize specific business operations is based on the time granularity t. s The first variable data, which is approximately normally distributed, is synthesized by sampling on N sine waves and synthesized on N cosine waves, based on a preset duration of the simulated service. The second variable data is synthesized by sampling on N cosine waves. The two normally distributed data already have time correlation.

[0037] It should be noted that the temporal correlation of business data is influenced by various factors such as user behavior habits, time, space, weather, and major events. Typically, this type of time-series data can be divided into three components: trend, seasonal, and random. The seasonal component is mainly affected by the rhythm of population activity, exhibiting a certain periodicity. The random component primarily reflects short-term user activity patterns. This embodiment borrows the method of simulating Rayleigh channels using Jakes' Model. It uses the highest frequency of a sine wave to reflect the seasonal component and combines sine waves with multiple random frequencies and phases to characterize the random component. Utilizing the central limit theorem, a normal distribution is synthesized from multiple sine waves with random phases and frequencies. Then, through parameter transformation and acceptance / rejection sampling methods, data following target distributions such as Weibull or mean distributions are obtained, thus simulating business data with a certain degree of temporal correlation.

[0038] In some specific implementation processes, eight sine waves of different frequencies are used for synthesis to obtain an approximately normally distributed variable v1, i.e., the first variable data. The highest frequency determines the strength of the data correlation; the higher the frequency, the weaker the correlation between data over the same time period. The obtained normally distributed data is as follows: Figure 3 As shown. In contrast, normally distributed random numbers generated using classical methods are as follows: Figure 4 As shown, the latter lacks temporal relevance and is difficult to use for prediction tasks.

[0039] By synthesizing cosine waves with the same frequency combination, a variable v2, which is orthogonal to v1 in step one, is obtained, which is the second variable data.

[0040] In some preferred embodiments, the target distribution combination includes at least one of the following: exponential distribution, uniform distribution, Weibull distribution, Gumel distribution (also called Largest Extreme Value distribution), log-normal distribution, Pareto distribution, Gamma distribution, and Poisson distribution.

[0041] In some alternative embodiments, data following an exponential and / or uniform distribution are obtained based on the Box-Muller transform method, including:

[0042] The first variable data and the second variable data can be regarded as mutually orthogonal standard normal distributions. Taking the first variable data and the second variable data as two bases of the complex plane respectively, the square of the amplitude is used to obtain data that follows an exponential distribution, and / or the phase angle is used to obtain data that follows a uniform distribution.

[0043] Furthermore, using the data that follows an exponential and / or uniform distribution, data that follows a Weibull, Gumel, log-normal, and / or Pareto distribution are obtained based on parameter transformation methods, and data that follows a Gamma and / or Poisson distribution are obtained based on rejection sampling methods.

[0044] Furthermore, when the business scenario is an eMBB business traffic scenario, the business traffic model includes one or more of the following: 8K UHD wireless projection business traffic model, video stream packet layer traffic model, video stream session layer traffic model, gaming downlink business packet layer traffic model, and gaming uplink business packet layer traffic model.

[0045] The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes:

[0046] The parameters of the first variable data or the second variable data that follow a normal distribution are transformed, and then the maximum value is truncated to obtain the 8K UHD wireless screen projection service traffic model.

[0047] The data packet interval time is determined based on data that follows a Gamma distribution and satisfies preset distribution parameters, and the video stream packet layer traffic model is established according to the preset data packet size; wherein, the data that follows a Gamma distribution is obtained by rejecting sampling (also called "reject-receive sampling" or "receive-reject sampling") on data that follows a uniform distribution and the first variable data or the second variable data that follows a normal distribution;

[0048] The frame data size is determined based on data that follows a Weibull distribution, and a video stream session layer traffic model is established by combining the preset frame time interval; wherein, the data that follows a Weibull distribution is obtained by parametric transformation of data that follows an exponential distribution.

[0049] The initial arrival time interval of the Gaming downlink service data packet is obtained by scaling the data that follows a uniform distribution. The data that follows a uniform distribution is then transformed by parameters to obtain data that follows a Gumel distribution, which are then used as the subsequent arrival time interval and data packet size, respectively. The Gaming downlink service packet layer traffic model is established by combining the initial arrival time interval of the Gaming downlink service data packet.

[0050] And / or,

[0051] The initial arrival time interval of Gaming uplink service data packets is obtained by scaling the data that follows a uniform distribution. The data that follows a uniform distribution is then transformed to obtain data that follows a Gumel distribution, which are used as the subsequent arrival time interval and data packet size, respectively. Based on the initial arrival time interval of the Gaming uplink service data packets, the Gaming uplink service packet layer traffic model is established.

[0052] In some specific implementations, for 8K UHD wireless projection services, the service packet interval is fixed at 1 / 16200s, and the data packet size follows a normal distribution with a mean of 36.76Kbytes and a standard deviation of 1.35Kbytes, with the maximum value truncated to 368.640Kbytes. By transforming the parameters using data following a standard normal distribution (i.e., the first variable data or the second variable data) and then truncating the maximum value, the data for this service can be obtained. The corresponding 8K UHD wireless projection service traffic model is as follows: Figure 5 As shown.

[0053] In some specific implementations, for the packet layer of video streaming services (BV6, 15.6Mbps), the packet size is fixed at 500 bytes, the packet interval follows a Gamma distribution with distribution coefficients k = 0.2463 and theta = 60.227. Data following this distribution can be obtained from data following standard uniform and standard normal distributions using the rejection-acceptance sampling method. For the session layer of video streaming services, the frame interval is fixed at 400ms, and the frame data size is sampled from data following a Weibull distribution to establish a traffic model for the video streaming session layer.

[0054] Furthermore, when the business scenario is a uRLLC business traffic scenario, the business traffic model includes one or more of the following: substation automatic detection business packet layer traffic model, substation maintenance business traffic model, ambulance and hospital two-way video business packet layer traffic model, ambulance and hospital medical equipment interconnection communication packet layer traffic model, and enhanced driving event triggered business packet layer traffic model.

[0055] The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes:

[0056] The data that follows a uniform distribution is scaled to determine the packet size. Combined with the preset packet interval, a periodic arrival model is established as the substation automatic detection service packet layer traffic model.

[0057] A substation maintenance service session layer flow model is established based on Pareto distribution data or uniform distribution data that meet preset distribution parameters and undergo parameter transformation. The corresponding session duration is determined based on Pareto distribution data. A substation maintenance service group layer flow model is established based on Poisson distribution data, with constant group size and group time interval obtained based on exponential distribution data. The substation maintenance service session layer flow model and the substation maintenance service group layer flow model are combined into a substation maintenance service flow model.

[0058] The packet layer traffic model of the two-way video service between the ambulance and the hospital is obtained by sampling and fitting data that follows a uniform distribution based on the receive-reject sampling method, the corresponding packet time interval is determined based on data that follows an exponential distribution, and the packet data size is determined based on data that follows a Gamma distribution.

[0059] Based on data that follows an exponential distribution and satisfies a preset mean, the grouping time interval is determined, and the group data packet size is determined based on a preset fixed value. A packet-layer traffic model for interconnection communication between ambulances and hospital medical equipment is established.

[0060] And / or,

[0061] Based on data that follows an exponential distribution, the grouping time interval is determined and a fixed additional preset bias is applied. Based on data that follows a uniform distribution, the grouping size is determined, and an enhanced driving event-triggered business grouping layer traffic model is established.

[0062] In some specific implementation processes, the substation automatic detection service packet layer traffic model is a periodic arrival model with a fixed packet interval of 2 seconds, and the packet data size is determined from the data that follows a uniform distribution.

[0063] In some specific implementation processes, for substation maintenance services, the session layer is divided into on / off states. The on state follows a Pareto distribution with distribution parameters (1.6, 1.6), and the session duration also follows a Pareto distribution. The packet layer follows a Poisson arrival model, with a constant packet size of 85 bytes, and the packet time interval is determined based on data that follows an exponential distribution.

[0064] In some specific implementation processes, for the packet layer traffic model of interconnection communication between ambulances and hospital medical equipment, the packet time interval is determined based on data that follows an exponential distribution with a mean of 60 seconds, and the packet data packet size is 85 bytes.

[0065] In some specific implementation processes, for the traffic model of the packet layer of enhanced driving event-triggered services, the packet time interval follows an exponential distribution with a mean of 50ms, and a fixed additional 50ms bias is added. The packet size is uniformly distributed as [200:200:2000] bytes.

[0066] Furthermore, when the business scenario is an mMTC business traffic scenario, the business traffic model includes one or more of the following: smart grid triggered business packet layer traffic model, wireless retail business packet layer traffic model, and shared bicycle communication business packet layer traffic model;

[0067] The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes:

[0068] Based on data that follows an exponential distribution and satisfies a preset mean, the grouping time interval is determined through scaling transformation; a smart grid trigger-type business grouping layer flow model is established based on the preset group size and the determined grouping time interval.

[0069] The grouping period is determined based on Pareto distribution data that meets preset distribution parameters, the grouping size is determined based on uniform distribution data, and a wireless retail business grouping layer traffic model is established based on the determined grouping period and grouping size.

[0070] And / or,

[0071] Based on data that follows a uniform distribution, the grouping time interval is determined, and based on data that follows a Pareto distribution, the grouping size is determined, thus establishing a packet-layer traffic model for shared bicycle communication services.

[0072] In some specific implementation processes, for the smart grid trigger-type business group layer traffic model, the group time interval is an exponential distribution with a mean of 1 / 60s. It can be generated by scaling data that follows an exponential distribution. The group size is always 135 bytes and is uniformly distributed.

[0073] In some specific implementation processes, for the wireless retail business packet layer traffic model, the packet period is determined based on data satisfying a Pareto distribution with parameters (shape=10, minimum=1), and the packet size is determined based on data following a uniform distribution.

[0074] In some specific implementation processes, the packet layer traffic model for shared bicycle communication services established for the packet layer traffic is as follows: Figure 6 As shown, the time intervals between groupings are uniformly distributed, and their sizes follow a Pareto distribution.

[0075] Example 2

[0076] This embodiment provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor, causing the processor to perform some or all of the steps of the method provided in Embodiment 1 of this application.

[0077] It is understood that the storage medium can be transient or non-transient. Exemplarily, the storage medium includes, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] By way of example, the processor may be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0079] By way of example, the read-only memory includes, but is not limited to, MASK ROM, PROM, EPROM, EEPROM, Flash, etc.

[0080] By way of example, the random access memory includes, but is not limited to, DRAM, SRAM, SDRAM, DDR SDRAM, etc.

[0081] In some examples, a computer program product is provided, which can be implemented by hardware, software, or a combination thereof. As a non-limiting example, the computer program product can be embodied in the storage medium, or it can be embodied in a software product, such as an SDK (Software Development Kit).

[0082] As a non-limiting example, a computer program product is provided, comprising a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and executes the computer-executable instructions, causing the electronic device to perform some or all of the steps of the method described in the embodiments of this application.

[0083] In some examples, a computer program is provided that includes computer-readable code, wherein, when the computer-readable code is run in a computer device, a processor in the computer device performs some or all of the steps for implementing the method.

[0084] This embodiment also proposes an electronic device, including a memory and a processor. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor executes the at least one instruction, at least one program, code set, or instruction set, it implements some or all of the steps of the method described in Embodiment 1.

[0085] In some examples, a hardware entity of the electronic device is provided, including: a processor, a memory, and a communication interface; wherein the processor typically controls the overall operation of the electronic device; the communication interface is used to enable the electronic device to communicate with other terminals or servers via a network; the memory is configured to store instructions and applications executable by the processor, and may also cache data to be processed or already processed (including but not limited to image data, audio data, voice communication data, and video communication data) to be processed by the processor and various modules in the electronic device, and may be implemented using flash memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or random access memory (RAM).

[0086] A processor may include one or more processing elements. Therefore, a processor may include one or more integrated circuits (ICs) configured to perform the functions of the processor. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, and other circuitry) configured to perform the functions of the processor.

[0087] Furthermore, data can be transferred between the processor, communication interface, and memory via a bus, which can include any number of interconnected buses and bridges, connecting various circuits of one or more processors and memories together.

[0088] It is understood that the options in Embodiment 1 above also apply to this embodiment, so they will not be described again here.

[0089] The same or similar labels correspond to the same or similar parts;

[0090] The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this application.

[0091] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0092] In different specific implementations, the methods or systems described in this application can be implemented in software, hardware, or a combination thereof. Furthermore, the order of the method steps can be changed, and various elements can be added, reordered, combined, omitted, or modified.

[0093] Obviously, the above embodiments of this application are merely examples for clearly illustrating this application, and are not intended to limit the implementation of this application, nor are they intended to limit this application. For those skilled in the art, other variations or modifications can be made based on the above description. The separate structural / functional modules or units can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. The structure and function of the separate components can be implemented as a combined structure or component. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of the claims of this application.

Claims

1. A method for generating business data with time correlation, characterized in that, include: The first variable data, which follows a normal distribution, is synthesized from sine waves of different frequencies, and the second variable data, which follows a normal distribution and is orthogonal to the first variable data, is synthesized from cosine waves. Based on the first variable data and the second variable data, establish data that follows a target distribution combination; Based on the business scenario, target business data is selected from the data that follows the target distribution combination, the first variable data, and / or the second variable data to establish a business traffic model; The first variable data, which is synthesized from sine waves of different frequencies and follows a normal distribution, and the second variable data, which is synthesized from cosine waves and follows a normal distribution and is orthogonal to the first variable data, include: Set the highest frequency Used to characterize the periodicity of data, and in Randomly select within the frequency range Each frequency, and the highest frequency Together One reference frequency; Random selection One phase; Based on the central limit theorem, using the selected N reference frequencies and Each of the aforementioned phases, construct A sine wave with random frequency and random phase and its corresponding... A cosine wave; Set time granularity The data sampling interval used to characterize specific business operations, based on the time granularity. and the preset duration of the simulated service, in The first variable data, which is approximately normally distributed by upsampling and synthesizing the sine wave, is then... The cosine wave upsampling is used to synthesize the second variable data; The target distribution combination includes at least one of the following: exponential distribution, uniform distribution, Weibull distribution, Gumel distribution, log-normal distribution, Pareto distribution, Gamma distribution, and Poisson distribution; Data obtained based on the Box-Muller transform method that follows an exponential and / or uniform distribution include: Using the first variable data and the second variable data as two bases of the complex plane, the square of the amplitude is taken to obtain data that follows an exponential distribution, and / or the phase angle is taken to obtain data that follows a uniform distribution; Using the data that follows an exponential and / or uniform distribution, data that follows a Weibull, Gumel, log-normal, and / or Pareto distribution are obtained based on parameter transformation methods, and data that follows a Gamma and / or Poisson distribution are obtained based on rejection sampling methods; When the business scenario is an eMBB business traffic scenario, the business traffic model includes one or more of the following: 8K UHD wireless projection business traffic model, video stream packet layer traffic model, video stream session layer traffic model, gaming downlink business packet layer traffic model, and gaming uplink business packet layer traffic model; The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes: The parameters of the first variable data or the second variable data that follow a normal distribution are transformed, and then the maximum value is truncated to obtain the 8K UHD wireless screen projection service traffic model. The data packet interval time is determined based on data that follows a Gamma distribution and satisfies preset distribution parameters, and the video stream packet layer traffic model is established according to the preset data packet size; wherein, the data that follows a Gamma distribution is obtained by rejecting sampling of data that follows a uniform distribution and the first variable data or the second variable data that follows a normal distribution; The frame data size is determined based on data that follows a Weibull distribution, and a video stream session layer traffic model is established by combining the preset frame time interval; wherein, the data that follows a Weibull distribution is obtained by parametric transformation of data that follows an exponential distribution; The initial arrival time interval of the Gaming downlink service data packets is obtained by scaling the uniformly distributed data. The data following the uniform distribution is then transformed to obtain data following a Gumel distribution, which is used as the subsequent arrival time interval and data packet size, respectively. Based on the initial arrival time interval of the Gaming downlink service data packets, a packet-layer traffic model for the Gaming downlink service is established; and / or, The initial arrival time interval of the Gaming uplink service data packets is obtained by scaling the data that follows a uniform distribution. The data that follows a uniform distribution is then transformed to obtain data that follows a Gumel distribution, which are used as the subsequent arrival time interval and data packet size, respectively. Based on the initial arrival time interval of the Gaming uplink service data packets, the Gaming uplink service packet layer traffic model is established.

2. The method for generating time-related business data according to claim 1, characterized in that, When the business scenario is a uRLLC business traffic scenario, the business traffic model includes one or more of the following: substation automatic detection business packet layer traffic model, substation maintenance business traffic model, ambulance and hospital two-way video business packet layer traffic model, ambulance and hospital medical equipment interconnection communication packet layer traffic model, and enhanced driving event triggered business packet layer traffic model. The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes: The data that follows a uniform distribution is scaled to determine the packet size. Combined with the preset packet interval, a periodic arrival model is established as the substation automatic detection service packet layer traffic model. A substation maintenance service session layer flow model is established based on Pareto distribution data or uniform distribution data that meet preset distribution parameters and undergo parameter transformation. The corresponding session duration is determined based on Pareto distribution data. A substation maintenance service group layer flow model is established based on Poisson distribution data, with constant group size and group time interval obtained based on exponential distribution data. The substation maintenance service session layer flow model and the substation maintenance service group layer flow model are combined into a substation maintenance service flow model. The traffic model of the bidirectional video service between the ambulance and the hospital is obtained by sampling and fitting data that follows a uniform distribution using the rejection sampling method. The corresponding time interval of the group is determined based on the data that follows an exponential distribution, and the size of the group data packet is determined based on the data that follows a Gamma distribution. Based on data following an exponential distribution with a preset mean, the grouping time interval is determined; based on a preset fixed value, the packet data packet size is determined; and a packet-layer traffic model for interconnection communication between ambulances and hospital medical equipment is established; and / or, Based on data that follows an exponential distribution, the grouping time interval is determined and a fixed additional preset bias is applied. Based on data that follows a uniform distribution, the grouping size is determined, and an enhanced driving event-triggered business grouping layer traffic model is established.

3. The method for generating time-related business data according to claim 1, characterized in that, When the business scenario is an mMTC business traffic scenario, the business traffic model includes one or more of the following: smart grid triggered business packet layer traffic model, wireless retail business packet layer traffic model, and shared bicycle communication business packet layer traffic model; The step of selecting target business data from the data following the target distribution combination, the first variable data, and / or the second variable data according to the business scenario, and establishing a business traffic model includes: Based on data that follows an exponential distribution and satisfies a preset mean, the grouping time interval is determined through scaling transformation; a smart grid trigger-type business grouping layer flow model is established based on the preset group size and the determined grouping time interval. The grouping period is determined based on Pareto distribution data that satisfies preset distribution parameters; the group size is determined based on uniform distribution data; and a wireless retail business grouping layer traffic model is established based on the determined grouping period and group size; and / or, Based on data that follows a uniform distribution, the grouping time interval is determined, and based on data that follows a Pareto distribution, the grouping size is determined, thus establishing a packet-layer traffic model for shared bicycle communication services.

4. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, which is loaded and executed by a processor to implement the method as described in any one of claims 1-3.

5. A computer program product, comprising a computer program or computer-executable instructions, characterized in that, When the computer program or computer-executable instructions are executed by a processor, the method described in any one of claims 1-3 is implemented.

Images