Control method and apparatus for communication device

Abstract

The present application relates to the technical field of communication, in particular to a kind of control method and device of communication equipment.The communication equipment with the traffic volume distribution with preset tidal distribution characteristics is determined as tidal communication equipment, then according to the historical traffic volume data of tidal communication equipment, the traffic volume prediction value of each time point in first time length is predicted, then the time point with traffic volume prediction value lower than first threshold is determined as the closing point of tidal communication equipment, then each closing point is traversed according to time sequence, the closing point with the time interval greater than second threshold from next closing point is set as closing point, and closing point is used to represent the closing time of tidal communication equipment.Through determining tidal communication equipment and establishing model to predict the traffic volume prediction value of tidal communication equipment, the closing point of tidal communication equipment is determined according to traffic volume prediction value, so that tidal communication equipment is closed when traffic volume is low, and the waste of power resource is avoided.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a control method and apparatus for a communication device. Background Technology

[0002] In today's cities, 4G base stations are spaced less than 300 meters apart, while 5G, using higher frequencies, has an even smaller coverage area per station. To achieve better and more comprehensive coverage, more and denser base stations are needed. If base stations remain operational when their load is relatively low or idle, it will waste a significant amount of electricity.

[0003] Existing solutions typically use threshold settings to start and stop base stations. When the number of users falls below the threshold, power to active equipment is shut off; when the number of users exceeds the threshold, the base station is activated and normal operation resumes. However, fluctuations in the number of users can lead to frequent base station shutdowns, and data acquisition and processing can be delayed, impacting the overall system operation. Summary of the Invention

[0004] This invention provides a control method and apparatus for a communication device. By identifying a tidal communication device and establishing a model to predict the traffic volume of the tidal communication device, the method determines the shutdown point of the tidal communication device based on the traffic volume prediction value, so that the tidal communication device shuts down when the traffic volume is low, thus avoiding the waste of power resources.

[0005] In a first aspect, embodiments of the present invention provide a control method for a communication device, the method comprising:

[0006] Communication devices whose traffic distribution has a preset tidal distribution characteristic are identified as tidal communication devices;

[0007] Based on the historical traffic data of the tidal communication equipment, predict the traffic volume at each time point within the first time length.

[0008] The time point when the predicted traffic volume is lower than the first threshold is determined as the point at which the tidal communication equipment will be shut down.

[0009] The system iterates through each of the points to be closed in chronological order, and sets the points to be closed that have a time interval greater than a second threshold as closed points. The closed points are used to characterize the closing time of the tidal communication device.

[0010] In one implementation, a communication device whose traffic distribution exhibits a preset tidal distribution characteristic is defined as a tidal communication device, including:

[0011] The original traffic volume data of each communication device is obtained according to the first time granularity. The original traffic volume data includes: sampling time, communication device identification, number of user connections, traffic and call volume.

[0012] The converged service volume data of each communication device is determined at a second time granularity, which includes N consecutive first time granularities. The converged service volume data is determined based on the corresponding N original service volume data, where N is a positive integer greater than 2.

[0013] Determine the traffic distribution characteristics of each communication device at a third time granularity, wherein the third time granularity includes M consecutive second time granularities, and the traffic distribution characteristics are determined based on the distribution characteristics of the M corresponding fused traffic data.

[0014] Among them, communication devices whose traffic distribution characteristics have preset tidal distribution characteristics are identified as tidal communication devices.

[0015] In one implementation, determining the fused service volume data at any second time granularity includes:

[0016] The maximum number of user connections among the N original traffic data corresponding to any second time granularity is determined as the comprehensive number of user connections for that second time granularity.

[0017] The sum of the traffic in the N original traffic data corresponding to any second time granularity is determined as the comprehensive traffic of the second time granularity;

[0018] The sum of the call volume in the N original traffic volume data corresponding to any second time granularity is determined as the comprehensive call volume of the second time granularity;

[0019] The number of user connections, the total traffic, and the total call volume are each normalized.

[0020] The sum of the normalized integrated user connection count, the integrated traffic, and the integrated call volume is determined as the fused service volume at any second time granularity. The fused service volume data includes: sampling time, communication device identifier, and the fused service volume.

[0021] In one implementation, the traffic distribution characteristics of each communication device at a third time granularity are determined, and the communication devices whose traffic distribution characteristics have preset tidal distribution characteristics are identified as the tidal communication devices, including:

[0022] For the M converged service volume data contained in the third time granularity of the communication device, if the number of data with converged service volume higher than the first load is greater than the third threshold, and the number of data with converged service volume lower than the second load is greater than the fourth threshold, then the communication device in the third time granularity is identified as a tidal communication device.

[0023] In one implementation, the raw traffic volume data of each communication device is obtained according to the first time granularity, including:

[0024] Obtain the original traffic volume data table used to determine the tidal communication equipment;

[0025] The original traffic data of each communication device is collected from the original traffic data table according to the first time granularity.

[0026] In one implementation, predicting the traffic volume forecast for each time point within a first time period based on the historical traffic volume data of the tidal communication device includes:

[0027] Based on the sampling time, the data of each integrated business volume are classified into multiple data sets according to working days, statutory holidays and other holidays;

[0028] According to the formula A business volume prediction model is established to process the data set to calculate the predicted business volume value, wherein... Here, T(t) is the business volume forecast value, which is used to predict the trend of the business volume within a preset time period, S(t) is the seasonal factor, which is used to predict the regular periodic fluctuations of the business volume, C(t) is the cyclic factor, which is used to predict the irregular periodic continuous changes of the business volume, and I(t) is the irregular change factor, which is used to predict the impact of accidental factors on the business volume.

[0029] According to the formula Calculate the error between the observed and predicted traffic volume, where RMSE is the root mean square error, and y i Traffic volume observations is the predicted business volume value, and m is the number of predicted business volume values.

[0030] In one implementation, according to the formula Calculate the business volume forecast, including:

[0031] The business volume data in the multiple datasets are processed using the weighted moving average method and the breakpoint detection method to obtain M(t), so as to eliminate the influence of S(t) and I(t) on the straight line fitting. M(t) = C(t) + T(t).

[0032] M(t) is fitted according to the formula T(t)=β0+β1t,t=1,2,3… and β0 and β1 are estimated by least squares method, where β0 and β1 represent the slope and intercept of the fitted line, respectively.

[0033] The average or median of the daily business volume is used as the initial value of the seasonal factor for that day. The business volume of M data points in a day is divided by the initial value to obtain M seasonal factors.

[0034] Calculate the cycle factor using the formula C(t) = M(t) - T(t);

[0035] The irregular variation factor is calculated using the formula I(t)=y(t)-T(t)-S(t)-C(t), where y(t) is the observed value of the business volume.

[0036] In a second aspect, embodiments of the present invention provide a control device for a communication device, the device comprising:

[0037] The first determining module is used to determine communication devices whose traffic distribution has a preset tidal distribution characteristic as tidal communication devices;

[0038] The prediction module is used to predict the traffic volume at each time point within a first time length based on the historical traffic volume data of the tidal communication device.

[0039] The second determining module is used to determine the time point when the predicted traffic volume is lower than the first threshold as the point when the tidal communication device is to be shut down.

[0040] The setting module is used to traverse each of the points to be closed in chronological order, and set the points to be closed that have a time interval greater than a second threshold as closed points. The closed points are used to characterize the closing time of the tidal communication device.

[0041] Thirdly, embodiments of the present invention provide an electronic device, characterized in that it includes:

[0042] At least one processor; and

[0043] At least one memory communicatively connected to the processor, wherein:

[0044] The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the method provided in the first aspect.

[0045] Fourthly, embodiments of the present invention provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions that cause the computer to perform the method as provided in the first aspect.

[0046] In this embodiment of the invention, communication devices with traffic distribution exhibiting a preset tidal distribution characteristic are first identified as tidal communication devices. Then, based on historical traffic data of the tidal communication devices, the predicted traffic volume values ​​for each time point within a first time period are predicted. The time points where the predicted traffic volume value is lower than a first threshold are then identified as the shutdown points for the tidal communication devices. Each shutdown point is then traversed in chronological order, and the shutdown point where the time interval between it and the next shutdown point is greater than a second threshold is set as the shutdown point. The shutdown point characterizes the shutdown time of the tidal communication device. By identifying the tidal communication devices and establishing a model to predict their traffic volume, and then determining the shutdown point based on the predicted traffic volume, the tidal communication devices are shut down when traffic is low, thus avoiding waste of power resources. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart of a communication device control method provided in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the structure of a communication equipment control device provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0051] To better understand the technical solutions in this specification, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] It should be understood that the described embodiments are merely some, not all, of the embodiments in this specification. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without inventive effort are within the scope of protection of this specification.

[0053] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0054] Figure 1This is a flowchart illustrating a communication device control method provided in an embodiment of the present invention. This method can be applied to processing devices, such as… Figure 1 The term may include:

[0055] Step 101: The communication equipment whose traffic distribution has a preset tidal distribution characteristic is identified as tidal communication equipment.

[0056] Communication equipment can be categorized by its traffic volume distribution into tidal communication equipment and non-tidal communication equipment. Non-tidal communication equipment has a relatively even distribution of traffic volume throughout the day and does not require shutdown or startup operations. However, tidal communication equipment has an uneven distribution of traffic volume and requires shutdown or startup operations based on its traffic volume distribution. Therefore, it is necessary to classify communication equipment to determine which are tidal communication equipment and which are non-tidal communication equipment.

[0057] In some embodiments, the specific method for identifying tidal communication devices may include: acquiring raw traffic volume data for each communication device according to a first time granularity. The raw traffic volume data may include: sampling time, communication device identifier, number of user connections, traffic volume, and call volume. The first time granularity can be set to 5 minutes, without specific limitation; the sampling time can be the time point at which the raw traffic data is collected; and the number of user connections, traffic volume, and call volume reflect the specific traffic volume of the communication device. Next, the fused traffic volume data for each communication device is determined at a second time granularity. The second time granularity contains N consecutive first time granularities, where N is a positive integer greater than 2. The second time granularity can be set to 1 hour, without specific limitation. Multiple raw traffic volume data points are contained within the second time granularity, and these raw traffic volume data points are consolidated into a single fused traffic volume data point. Then, the traffic volume distribution characteristics of each communication device are determined at a third time granularity. The third time granularity contains M consecutive second time granularities, and the third time granularity can be set to 1 day, without specific limitation. Multiple fused traffic volume data points are contained within the third time granularity, and the traffic volume distribution characteristics of the communication device are determined based on the contained fused traffic volume data. Finally, communication devices whose traffic distribution characteristics at any third time granularity have preset tidal distribution characteristics are identified as the tidal communication devices.

[0058] In some embodiments, a specific method for organizing N raw traffic volume data contained in the second time granularity into a single fused traffic volume data may include: determining the maximum value of the number of user connections among the N raw traffic volume data as the comprehensive number of user connections; determining the sum of the traffic among the N raw traffic volume data as comprehensive traffic; determining the sum of the call volume among the N raw traffic volume data as comprehensive call volume; normalizing the number of user connections, comprehensive traffic, and comprehensive call volume; and determining the sum of the normalized comprehensive number of user connections, comprehensive traffic, and comprehensive call volume as the fused traffic volume. The fused traffic volume data includes: sampling time, communication device identifier, and fused traffic volume.

[0059] In some embodiments, a specific method for determining a communication device whose traffic distribution characteristics in any third time granularity have a preset tidal distribution characteristic as the tidal communication device may include: for M converged traffic data contained in the communication device in the third time granularity, if the number of data with converged traffic volume higher than the first load is greater than a third threshold, and the number of data with converged traffic volume lower than the second load is greater than a fourth threshold, then the communication device in the third time granularity is determined as the tidal communication device.

[0060] In some embodiments, the method for obtaining the raw traffic data of each communication device according to a first time granularity may include: obtaining a raw traffic data table for determining tidal communication devices, and collecting the raw traffic data of each communication device from the raw traffic data table according to a first time granularity.

[0061] Step 102: Based on the historical traffic data of the tidal communication equipment, predict the traffic volume forecast for each time point within the first time period.

[0062] Specific methods may include: based on sampling time, classifying the various converged service volume data into multiple datasets according to weekdays, statutory holidays, and other public holidays. The service volume of the same communication device differs between weekdays and public holidays, requiring separate processing. Then, according to the formula... A business volume forecasting model is established to process multiple data sets to calculate predicted business volume values. The main processing steps may include fitting a business volume forecasting curve by fusing business volume data, and predicting the business volume forecasting value within a first time period based on the trend of the business volume forecasting curve. Here, T(t) represents the predicted business volume, which is a long-term trend factor, indicating a trend or state of continuous development and change in business volume over a relatively long period. S(t) is a seasonal factor, referring to the strictly regular changes caused by the cyclical variations in business volume. C(t) is a cyclical factor, referring to the irregular cyclical and continuous changes in business volume. I(t) is an irregular variation factor, referring to the impact of numerous accidental factors on business volume. Then, according to the formula... Calculate the error between the observed and predicted traffic volume, where RMSE is the root mean square error, and y i Traffic volume observations is the predicted business volume value, and m is the number of predicted business volume values.

[0063] In some embodiments, a method for predicting business volume forecasts within a first time period by fitting a curve using integrated business volume data may include: processing the integrated business volume data within the plurality of datasets using a weighted moving average method and a breakpoint detection method to obtain M(t), thereby eliminating the influence of S(t) and I(t) on the linear fitting, where M(t) = C(t) + T(t). Integrated business volume data may exhibit significant fluctuations, primarily due to the influence of seasonal factors S(t) and irregular variation factors I(t), which reduces the accuracy of the fitted curve. Processing the integrated business volume data using a weighted moving average method can eliminate this influence. Furthermore, since the long-term trend of business volume data may change, potentially due to changes in operational strategies, the trend of the integrated business volume data may be segmented, exhibiting an upward trend in one time period and a downward trend in another. Therefore, a breakpoint detection method is used to segment the data after the weighted moving average. Next, a linear regression model is used to fit the segmented fused business volume data M(t). The specific calculation formula is T(t) = β0 + β1t, t = 1, 2, 3…, thus obtaining the long-term trend factor. Time is set as the independent variable, and the business volume observation value of each segment is the dependent variable. The linear regression model for each segment is obtained, where β0 and β1 represent the slope and intercept of the fitted line, respectively. β0 and β1 can be estimated using the least squares method. The seasonal factor refers to the periodic fluctuation of the data. In this method, the seasonal cycle can be set as the third time granularity. The average or median of the M fused business volumes in the third time granularity is used as the preliminary value of the seasonal factor. The M fused business volumes in the third time granularity are divided by the preliminary value to obtain the M seasonal factors S(t). The M(t) obtained above using the weighted moving average method and the breakpoint detection method includes the long-term trend factor and the cyclic factor. Therefore, the cyclic factor can be calculated using the formula C(t) = M(t) - T(t). Finally, the only unknown quantity is the irregular variation factor I(t), which can be calculated using the formula I(t) = y(t) - T(t) - S(t) - C(t), where y(t) is the observed traffic volume. The fitted straight line T(t) is extended for a first time period based on the linear trend to obtain the initial value of the traffic volume forecast within the first time period. Then, S(t), C(t), and I(t) are applied together to this initial value to obtain a traffic volume forecast curve. The horizontal axis of the traffic volume forecast curve represents time, and the vertical axis represents the predicted traffic volume.

[0064] Step 103: Determine the time point when the predicted traffic volume is lower than the first threshold as the shutdown point for the tidal communication equipment.

[0065] Step 104: Traverse each point to be closed in chronological order, and set the point to be closed that has a time interval greater than the second threshold as the closing point. The closing point is used to characterize the closing time of the tidal communication device.

[0066] In some embodiments, a communication device may contain multiple points to be shut down within a third time granularity. Short-term changes in traffic volume can lead to excessively short time intervals between some of these points, causing frequent shutdowns and restarts of the communication device, impacting system operation. Therefore, it is necessary to merge these points. For example, given three points to be shut down in chronological order: point A, point B, and point C, starting with point A, if the time interval between point A and point B is greater than a second threshold, then point A is set as the shut-down point. Then, starting with point B, if the time interval between point B and point C is less than the second threshold, then point B is not set as the shut-down point. This method iterates through all points to be shut down in chronological order.

[0067] In some embodiments, some communication devices may be involved in major events at specific times and need to remain in an active state for a long time. The above method cannot be used to predict the shutdown point. Therefore, a special data list needs to be set. Each special data item may include: third data time, communication device identifier and reason. The communication device corresponding to the communication device identifier does not set a shutdown point during the third data time.

[0068] In this embodiment of the invention, communication devices with traffic distribution exhibiting a preset tidal distribution characteristic are first identified as tidal communication devices. Then, based on historical traffic data of the tidal communication devices, the predicted traffic volume values ​​for each time point within a first time period are predicted. The time points where the predicted traffic volume value is lower than a first threshold are then identified as the shutdown points for the tidal communication devices. Each shutdown point is then traversed in chronological order, and the shutdown point where the time interval between it and the next shutdown point is greater than a second threshold is set as the shutdown point. The shutdown point characterizes the shutdown time of the tidal communication device. By identifying the tidal communication devices and establishing a model to predict their traffic volume, and then determining the shutdown point based on the predicted traffic volume, the tidal communication devices are shut down when traffic is low, thus avoiding waste of power resources.

[0069] Figure 2 This is a schematic diagram of a control device for a communication device provided in an embodiment of the present invention. The control device for the communication device in this embodiment can be used as a control device for the communication device to implement the control method for the communication device provided in this embodiment. Figure 2 As shown, the control device of the above-mentioned communication equipment may include: a first determining module 210, a prediction module 220, a second determining module 230, and a setting module 240.

[0070] The first determining module 210 is used to determine communication devices whose traffic distribution has a preset tidal distribution characteristic as tidal communication devices.

[0071] The prediction module 220 is used to predict the traffic volume at each time point within a first time length based on the historical traffic volume data of the tidal communication equipment.

[0072] The second determining module 230 is used to determine the time point when the predicted traffic volume is lower than the first threshold as the shutdown point of the tidal communication equipment.

[0073] The setting module 240 is used to traverse each point to be closed in chronological order and set the point to be closed that has a time interval greater than the next point to be closed as the closing point. The closing point is used to characterize the closing time of the tidal communication device.

[0074] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0075] like Figure 3 As shown, the electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 310, memory 330, and communication bus 340 connecting different system components (including memory 330 and processing unit 310).

[0076] Communication bus 340 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0077] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0078] Memory 330 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to the communication bus 340 via one or more data media interfaces. The memory 330 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0079] A program / utility having a set (at least one) of program modules can be stored in memory 330. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of the present invention.

[0080] The electronic device can also communicate with one or more external devices, one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can be performed through communication interface 320. Furthermore, the electronic device can also communicate through a network adapter ( Figure 3 (Not shown) communicates with one or more networks (e.g., Local Area Network (LAN), Wide Area Network (WAN), and / or public networks, such as the Internet). The aforementioned network adapter can communicate with other modules of the electronic device via communication bus 340. It should be understood that, although... Figure 3Not shown, other hardware and / or software modules can be used in conjunction with electronic devices, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Drives; hereinafter referred to as RAID) systems, tape drives, and data backup storage systems.

[0081] The processor 310 executes various functional applications and data processing by running programs stored in the memory 330, such as implementing the control method of the communication device provided in the embodiments of the present invention.

[0082] This invention also provides a computer-readable storage medium storing computer instructions that cause the computer to execute the control method for the communication device provided in this invention.

[0083] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0084] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0085] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0086] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0087] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0088] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0089] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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.

[0090] 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 in the form of hardware plus software functional units.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for a communication device, characterized in that, The method includes: Communication devices whose traffic distribution has a preset tidal distribution characteristic are identified as tidal communication devices; Based on the historical traffic data of the tidal communication equipment, predict the traffic volume at each time point within the first time length. The time point when the predicted traffic volume is lower than the first threshold is determined as the point at which the tidal communication equipment will be shut down. The process involves iterating through each of the points to be closed in chronological order, and setting the points to be closed that have a time interval greater than a second threshold as closed points. The closed points are used to characterize the closing time of the tidal communication device. Based on the historical traffic data of the tidal communication equipment, predict the traffic volume at each time point within the first time period, including: Based on the sampling time, the data of each integrated business volume are classified into multiple data sets according to weekdays, statutory holidays and other holidays; According to the formula A business volume prediction model is established to process the data set to calculate the predicted business volume value, wherein... This is the predicted business volume. This is a long-term trend factor used to predict the trend of the business volume over a preset time period. This is a seasonal factor used to predict regular, cyclical fluctuations in the business volume. This is a cyclical factor used to predict irregular, periodic, continuous variations in the business volume. This is an irregular variation factor used to predict the impact of random factors on the business volume; According to the formula Calculate the error between the observed and predicted traffic volume, where, The root mean square error, Traffic volume observations is the predicted business volume value, and m is the number of predicted business volume values.

2. The method according to claim 1, characterized in that, Communication devices whose traffic distribution exhibits a pre-defined tidal distribution characteristic are defined as tidal communication devices, including: The original traffic volume data of each communication device is obtained according to the first time granularity. The original traffic volume data includes: sampling time, communication device identification, number of user connections, traffic and call volume. The converged service volume data of each communication device is determined at a second time granularity, which includes N consecutive first time granularities. The converged service volume data is determined based on the corresponding N original service volume data, where N is a positive integer greater than 2. Determine the traffic distribution characteristics of each communication device at a third time granularity, wherein the third time granularity includes M consecutive second time granularities, and the traffic distribution characteristics are determined based on the distribution characteristics of the M corresponding fused traffic data. Among them, communication devices whose traffic distribution characteristics have preset tidal distribution characteristics are identified as tidal communication devices.

3. The method according to claim 2, characterized in that, Determining the fused service volume data at any second time granularity includes: The maximum number of user connections among the N original traffic data corresponding to any second time granularity is determined as the comprehensive number of user connections for that second time granularity. The sum of the traffic in the N original traffic data corresponding to any second time granularity is determined as the comprehensive traffic of the second time granularity; The sum of the call volume in the N original traffic volume data corresponding to any second time granularity is determined as the comprehensive call volume of the second time granularity; The number of user connections, the total traffic, and the total call volume are each normalized. The sum of the normalized integrated user connection count, the integrated traffic, and the integrated call volume is determined as the fused service volume at any second time granularity. The fused service volume data includes: sampling time, communication device identifier, and the fused service volume.

4. The method according to claim 3, characterized in that, Determine the traffic distribution characteristics of each communication device at a third time granularity, and identify communication devices whose traffic distribution characteristics have preset tidal distribution characteristics as the tidal communication devices, including: For the M converged service volume data contained in the third time granularity of the communication device, if the number of data with converged service volume higher than the first load is greater than the third threshold, and the number of data with converged service volume lower than the second load is greater than the fourth threshold, then the communication device in the third time granularity is identified as a tidal communication device.

5. The method according to claim 2, characterized in that, Obtain the raw traffic volume data of each communication device at the first-time granularity, including: Obtain the original traffic volume data table used to determine the tidal communication equipment; The original traffic data of each communication device is collected from the original traffic data table according to the first time granularity.

6. The method according to claim 1, characterized in that, According to the formula Calculate the business volume forecast, including: The traffic volume data within the multiple datasets is processed using the weighted moving average method and the breakpoint detection method to obtain... To eliminate and The impact on line fitting, ; According to the formula , right Perform fitting and estimate using the least squares method ,in, These represent the slope and intercept of the fitted line, respectively. The average or median of the daily business volume is used as the initial value of the seasonal factor for that day. The business volume of M data points in a day is divided by the initial value to obtain M seasonal factors. According to the formula Calculate the cycle factor; According to the formula Calculate the irregular variation factor. These are observations of business volume.

7. A control device for a communication equipment, characterized in that, include: The first determining module is used to determine communication devices whose traffic distribution has a preset tidal distribution characteristic as tidal communication devices; The prediction module is used to predict the traffic volume at each time point within a first time length based on the historical traffic volume data of the tidal communication device. The second determining module is used to determine the time point when the predicted traffic volume is lower than the first threshold as the point when the tidal communication device is to be shut down. The setting module is used to traverse each of the points to be closed in chronological order, and set the points to be closed that have a time interval greater than a second threshold as closed points. The closed points are used to characterize the closing time of the tidal communication device. Based on the historical traffic data of the tidal communication equipment, predict the traffic volume at each time point within the first time period, including: Based on the sampling time, the data of each integrated business volume are classified into multiple data sets according to weekdays, statutory holidays and other holidays; According to the formula A business volume prediction model is established to process the data set to calculate the predicted business volume value, wherein... This is the predicted business volume. This is a long-term trend factor used to predict the trend of the business volume over a preset time period. This is a seasonal factor used to predict regular, cyclical fluctuations in the business volume. This is a cyclical factor used to predict irregular, periodic, continuous variations in the business volume. This is an irregular variation factor used to predict the impact of random factors on the business volume; According to the formula Calculate the error between the observed and predicted traffic volume, where, The root mean square error, Traffic volume observations is the predicted business volume value, and m is the number of predicted business volume values.

8. An electronic device, characterized in that, include: At least one processor; as well as At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the method as described in any one of claims 1 to 6 by calling the program instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 6.

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