Power consumption adjustment method, device and equipment based on FTTR and medium

Abstract

The invention discloses an FTTR-based power consumption adjustment method, device, equipment and medium, the power consumption control method can be realized on the basis of an FTTR system, an FTTR-B system or a broadband fusion terminal, the uplink and downlink data volumes of all network equipment in a plurality of historical unit periods are acquired, the uplink and downlink data volumes are analyzed, and the power consumption of the network equipment is adjusted. The method comprises the steps of determining data fluctuation degrees of all network devices in each time period in a plurality of historical unit periods, matching a corresponding use scene label for each user by using a preset label association rule based on the data fluctuation degrees and user behavior data, and adopting corresponding different power consumption adjustment strategies for all network devices according to the use scene labels. According to the method and the device, the data fluctuation degrees in different time periods are determined by analyzing the uplink and downlink data volumes of all the network devices, and then the corresponding use scene label is matched for each user by using the preset label association rule, so that more accurate power consumption adjustment is quickly realized in different time periods, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method, device, equipment and medium for adjusting power consumption based on FTTR. Background Art

[0002] With the continuous development of smart home devices and FTTR technology, power consumption management of various devices in home networks has become one of the key issues for energy saving and improving user experience. Especially in the Mesh network architecture, the data transmission and communication mode between the main network devices and the sub-network devices is very complex and affected by many factors, such as user activity patterns, network load, and device operation status.

[0003] Currently, in traditional wireless network power consumption management, many methods focus on static power consumption control, usually adjusting power consumption based on the working status of the device or the transmission load. However, this static management method ignores the regularity of user behavior and cannot adapt to the impact of factors such as different time periods on network usage patterns. Therefore, how to quickly achieve more accurate power consumption adjustment in different time periods is a technical problem that needs to be solved urgently. Summary of the invention

[0004] Based on this, it is necessary to address the above technical issues. The embodiments of the present invention provide a power consumption adjustment method, device, equipment and medium based on FTTR to solve the problem that the prior art cannot quickly achieve more accurate power consumption adjustment in different time periods.

[0005] A first aspect of an embodiment of the present application provides a power consumption adjustment method based on FTTR, and the power consumption adjustment method based on FTTR includes: Obtaining the uplink and downlink data volumes of all network devices in multiple historical unit periods, wherein the historical unit periods include working days and holidays; Analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles to determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles; Based on the data fluctuation degree and user behavior data, a corresponding usage scenario label is matched for each user using a preset label association rule, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays; According to the usage scenario label, different corresponding power consumption adjustment strategies are adopted for all the network devices.

[0006] A second aspect of an embodiment of the present application provides a power consumption adjustment device based on FTTR, and the power consumption adjustment device based on FTTR includes: An acquisition module, used to acquire the uplink and downlink data volumes of all network devices within a plurality of historical unit periods, wherein the historical unit periods include working days and holidays; An analysis module, used to analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles, and determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles; A matching module, for matching a corresponding usage scenario label for each user using a preset label association rule based on the data fluctuation degree and the user behavior data, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays; The adjustment module is used to adopt different power consumption adjustment strategies for all the network devices according to the usage scenario labels.

[0007] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the power consumption adjustment method based on FTTR as described in the first aspect is implemented.

[0008] In a fourth aspect, a readable storage medium is provided, wherein the readable storage medium stores a program, and when the program is executed by a processor, the power consumption adjustment method based on FTTR as described in the first aspect is implemented.

[0009] In summary, the present invention provides a power consumption adjustment method, device, equipment and medium based on FTTR. The power consumption control method can be implemented based on an FTTR system, an FTTR-B system or a broadband converged terminal, and the uplink and downlink data volumes of all network devices in multiple historical unit periods are obtained, wherein the historical unit periods include weekdays and holidays. The uplink and downlink data volumes in different time periods in the multiple historical unit periods are analyzed to determine the data fluctuation degree of all network devices in each time period in the multiple historical unit periods. Based on the data fluctuation degree and user behavior data, a corresponding usage scenario label is matched for each user using a preset label association rule, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays and an irregular usage scenario label on holidays. According to the usage scenario label, different power consumption adjustment strategies are adopted for all network devices. It can be seen that the present application analyzes the uplink and downlink data volumes of all network devices to determine the data fluctuation degree of all network devices in each time period in multiple historical unit cycles, and based on the data fluctuation degree and user behavior data, uses preset label association rules to match the corresponding usage scenario label for each user, and adopts different power consumption adjustment strategies for different usage scenario labels, so as to quickly achieve more accurate power consumption adjustment in different time periods, making the power consumption adjustment more targeted and flexible, and further improving the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0011] Figure 1 It is a flowchart of a power consumption adjustment method based on FTTR provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a power consumption adjustment device based on FTTR provided by one embodiment of the present invention; Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “uponce” or “in response to determining”, depending on the context. Similarly, the phrases “if it is determined” or “if matched to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “uponce matched to [described condition or event]” or “in response to matching to [described condition or event]”, depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence 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.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is a flow chart of a power consumption adjustment method based on FTTR provided by an embodiment of the present invention, such as Figure 1 As shown, the power consumption adjustment method based on FTTR can be implemented by the following steps.

[0021] S201: Obtaining uplink and downlink data volumes of all network devices within a plurality of historical unit periods, wherein the historical unit periods include working days and holidays.

[0022] In step S201, since different users have certain regularity in their daily activities at home, for example, they may spend more time in the living room or study during the day, and have strong regularity on weekdays, and may spend more time in the bedroom at night, and the amount of data used by users on their mobile phones or computers every day generally fluctuates within a fixed range, the power consumption of all network devices can be controlled based on this user usage pattern. Among them, all network devices include the main network device and all sub-network devices connected to the main network device, and all network devices include but are not limited to Mesh devices or broadband converged terminals, etc. The unit period is a preset unit time period, generally 24 hours a day as a unit period, and the historical unit period is divided into weekdays and holidays. The uplink and downlink data volume is the uplink data volume and the downlink data volume. The uplink data volume, also known as the upload data volume, refers to the amount of data sent from the user's device (such as a computer, mobile phone or router) to the Internet service provider (ISP) or server. This includes files, photos, videos uploaded by users, and information sent to the server when browsing the web. Uplink data volume is mainly used to send information, such as social media posting, online chat, cloud storage synchronization, etc. In some cases, such as live video or large data uploads, uplink data volume may become the main part of network usage and may be subject to special attention or billing by network service providers; Downlink data volume, also known as download data volume, refers to the amount of data returned from the Internet or service provider to the user's device, which includes emails received by users, application updates downloaded, online videos watched or updated applications, etc. Downlink data volume is mainly used to receive information and services, such as receiving emails, news push, online entertainment content, etc. Since most Internet services are content acquisition mainly for user consumption, downlink data volume usually dominates network usage, and network service providers often charge more for downlink data volume. By using MQTT, HTTP, FTTR or other protocols to collect uplink and downlink data volume in 24 time periods corresponding to multiple historical days from all network devices in real time, users' network usage patterns in different time periods can be analyzed, so as to provide users with more accurate network services or optimize network bandwidth allocation and power consumption control strategies.

[0023] In the embodiment of the present application, the terminal device is connected to all network devices by using FTTR technology. Once the connection is successful, the upstream and downstream data volumes of all network devices within multiple historical unit cycles are measured and recorded. Among them, FTTR technology is used to provide the infrastructure of the high-speed backbone network, connecting each room through optical fiber to ensure that each room has a high-quality network connection. On the basis of a high-speed backbone network (such as FTTR), Mesh nodes can be deployed in each room, and Mesh network technology can be used to wirelessly transmit high-speed network signals to every corner to ensure wireless signal coverage and stability throughout the building, thereby providing users with high-speed, low-latency network access services, greatly improving the quality and stability of the network. FTTR technology can not only support more devices online at the same time, but also effectively solve the bandwidth bottleneck problem in traditional wireless access technology through the high bandwidth advantage of optical fiber.

[0024] For example, during the daytime hours on weekdays, users may use computers or mobile phones in the living room or study room to work and study, and the uplink and downlink data volumes may be relatively high. In the evening hours, users may mainly use mobile phones in the bedroom for entertainment and social activities, and the downlink data volume may be higher, while the uplink data volume is relatively low. By regularly collecting data, the uplink and downlink data volumes of all network devices within a specified number of days can be obtained, thereby ensuring the comprehensiveness of the data, so that the data can be analyzed later, so as to better understand the user's network usage behavior and provide users with more personalized network services, thereby making reasonable power consumption adjustments.

[0025] S202: Analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles to determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles.

[0026] In step S202, after obtaining the uplink and downlink data volumes of all network devices in multiple historical unit periods (such as days, weeks, months, etc.), the collected uplink and downlink data volumes are classified according to network devices and time periods, and a database table or data structure (such as a dictionary nested list in Python) can be used to store data. For example, a dictionary is used, in which the key is the identifier of the network device, and the value is a list, and each element in the list corresponds to the uplink and downlink data volume of a time period. Then, the uplink and downlink data volumes of different time periods in multiple historical unit periods are analyzed to determine the data fluctuation degree of all network devices in each time period in multiple historical unit periods. The data fluctuation degree is a measure that describes the size of the change in data within a period of time or within a specific set. It reflects the degree of dispersion of data points relative to their average or median. The magnitude of the data fluctuation degree is crucial to understanding the stability and reliability of the data. The standard deviation, variance, range, and coefficient of variation can be used to measure the data fluctuation degree, and this application does not make any restrictions on this.

[0027] In one embodiment of the invention, determining the data fluctuation degree of all network devices in each time period of the plurality of historical unit cycles includes: Preprocessing the uplink and downlink data volumes in different time periods in a plurality of historical unit cycles to obtain the uplink and downlink data volumes in different time periods in each historical unit cycle after preprocessing; Determine a data volume curve corresponding to each historical unit period according to the uplink and downlink data volumes in different time periods in each historical unit period after the preprocessing; The data volume curves are fitted to obtain a target data volume curve, and the data volume data corresponding to different time periods on the target data volume curve are used as the data fluctuation degree of different time periods within a unit period.

[0028] Specifically, by preprocessing the uplink and downlink data volumes in different time periods in multiple historical unit cycles, that is, removing abnormal values ​​or missing values, this can be achieved by setting a reasonable data range threshold or using statistical methods (such as the 3σ principle), and then obtaining the uplink and downlink data volumes in different time periods in each historical unit cycle after preprocessing, and by using the uplink and downlink data volumes in different time periods in each historical unit cycle after preprocessing, the corresponding data volume curves of each historical unit cycle are drawn, and the least squares method is used to fit each data volume curve to obtain the target data volume curve, and the data volume data corresponding to different time periods on the target data volume curve is determined. The degree of data fluctuation in different time periods within the unit cycle can be improved. It should be noted that charts (such as bar charts and line charts) can also be used to show the degree of data fluctuation in each time period. Different time periods can be used as the horizontal axis, and the degree of fluctuation index (such as standard deviation) can be used as the vertical axis to draw the changes in the degree of fluctuation in different time periods. This application does not make any restrictions on this. So that we can analyze the possible reasons for the high data fluctuation period by observing the visualization results. For example, the period of high data fluctuation may be due to the peak of network usage. User behavior (such as large file downloads, video playback, etc.) causes drastic changes in the amount of upstream and downstream data. We can compare the fluctuations of different network devices and find out the network devices that may have problems (such as instability), so as to further check their hardware, software or network connection status. Through the above steps, through data preprocessing and curve fitting, we can reduce the impact of these abnormal points on the results, and can intuitively display the fluctuation of data in different periods, so as to obtain a more accurate measurement of the degree of fluctuation.

[0029] In this embodiment, by analyzing the amount of uplink and downlink data in different time periods in multiple historical unit cycles, the degree of data fluctuation of all network devices in each time period can be quickly and accurately determined, reducing the power consumption adjustment error caused by abnormal fluctuations of a single device, so that power consumption optimization strategies can be formulated accordingly to improve network performance and user experience.

[0030] S203: Based on the data fluctuation degree and user behavior data, a preset tag association rule is used to match a corresponding usage scenario tag for each user, wherein the usage scenario tags include regular usage scenario tags on weekdays, irregular usage scenario tags on weekdays, regular usage scenario tags on holidays, and irregular usage scenario tags on holidays.

[0031] In step S203, the user's behavior data in different time periods are collected, including browsing, clicking, purchasing, commenting, etc. These data are pre-processed, including deduplication, cleaning, formatting, etc., to ensure the accuracy and consistency of the user behavior data. The usage scenario label is pre-defined according to the scenario, so as to accurately reflect the power consumption demand of the device in the period. The usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays. Among them, the regular usage scenario label on weekdays refers to the regular usage behavior of users on weekdays (such as use at a fixed time every day); the irregular usage scenario label on weekdays refers to the irregular usage behavior of users on weekdays (such as irregular time or frequency); the regular usage scenario label on holidays refers to the regular usage behavior of users on holidays (such as use at a fixed time on each holiday); the irregular usage scenario label on holidays refers to the irregular usage behavior of users on holidays (such as irregular time or frequency). Then, according to the user behavior data and the degree of data fluctuation, the corresponding usage scenario label is matched for each user using the preset label association rules, so as to better understand the user behavior pattern and improve the user experience.

[0032] It should be noted that the preset label association rules can be formulated based on statistical methods, machine learning algorithms or deep learning models, and this application does not impose any limitations on this.

[0033] In one embodiment of the invention, based on the data fluctuation degree and user behavior data, a preset tag association rule is used to match a corresponding usage scenario tag for each user, including: When the data fluctuation degree is the first data fluctuation degree of all network devices in each period of multiple working days, determining whether the first data fluctuation degree is less than a first preset fluctuation degree threshold; If the first data fluctuation degree is less than a first preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding weekday regular usage scenario label is matched for each user using a preset label association rule; If the first data fluctuation degree is greater than a first preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding weekday irregular usage scenario tag for each user.

[0034] Specifically, when the data fluctuation degree is the first data fluctuation degree of all network devices in each period of multiple working days, it is determined whether the first data fluctuation degree is less than the first preset fluctuation degree threshold. If the first data fluctuation degree is less than the first preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and the preset label association rule is used to match the corresponding working day regular use scenario label for each user. If the first data fluctuation degree is greater than the first preset fluctuation degree threshold, it is determined that the user behavior data does not have regularity, and the preset label association rule is used to match the corresponding working day irregular use scenario label for each user. That is, when obtaining the first data fluctuation degree of all network devices in each period of multiple working days, if the first data fluctuation degree is less than the first preset fluctuation degree threshold, it can be indicated that the user's use data in this time period has a certain regularity and is maintained within a certain data range. At this time, the use scenario label can be determined as a working day regular use scenario label. If the first data fluctuation degree is greater than the first preset fluctuation degree threshold, it can be indicated that the use data in this time period has poor regularity. At this time, the use scenario label can be determined as a working day irregular use scenario label. Through the above steps, the power consumption strategy can be dynamically adjusted according to different needs of the time period. Especially for different usage patterns on weekdays, it can automatically switch the corresponding power consumption control strategy to maximize energy saving effects while ensuring network stability and response speed.

[0035] For example, during the period from Monday to Friday, if the data fluctuation degree at 8 o'clock in the morning is 5, which is less than the first preset fluctuation degree threshold of 10, the user's behavior in this period is judged to be regular, and the weekday regular usage scenario label is matched for it. If the data fluctuation degree at 8 o'clock in the evening is 15, which is greater than the threshold 10, the user's behavior in this period is judged to be irregular, and the weekday irregular usage scenario label is matched for it.

[0036] It should be noted that the first preset fluctuation degree threshold can be set according to actual conditions, and this application does not impose any limitation on this.

[0037] In an embodiment of the invention, based on the data fluctuation degree and the user behavior data, a corresponding usage scenario tag is matched for each user using a preset tag association rule, further comprising: When the data fluctuation degree is the second data fluctuation degree of all network devices in each period of multiple holidays, determining whether the second data fluctuation degree is less than a second preset fluctuation degree threshold; If the second data fluctuation degree is less than a second preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding holiday regular usage scenario label is matched for each user by using a preset label association rule; If the second data fluctuation degree is greater than a second preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding holiday irregular usage scenario tag for each user.

[0038] Specifically, when the data fluctuation degree is the second data fluctuation degree of all network devices in each period of multiple holidays, it is determined whether the second data fluctuation degree is less than the second preset fluctuation degree threshold. If the second data fluctuation degree is less than the second preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and the preset label association rule is used to match the corresponding holiday regular use scenario label for each user. If the second data fluctuation degree is greater than the second preset fluctuation degree threshold, it is determined that the user behavior data does not have regularity, and the preset label association rule is used to match the corresponding holiday irregular use scenario label for each user. That is, when obtaining the second data fluctuation degree of all network devices in each period of multiple holidays, if the second data fluctuation degree is less than the second preset fluctuation degree threshold, it can be indicated that the user's use data in this time period has a certain regularity and is maintained within a certain data range. At this time, the use scenario label can be determined as a holiday regular use scenario label. If the second data fluctuation degree is greater than the second preset fluctuation degree threshold, it can be indicated that the use data in this time period has poor regularity. At this time, the use scenario label can be determined as a holiday irregular use scenario label. Through the above steps, the power consumption strategy can be dynamically adjusted according to different needs of the time period. Especially for different usage modes during holidays, it can automatically switch the corresponding power consumption control strategy to maximize energy saving effects while ensuring network stability and response speed.

[0039] For example, if the data fluctuation degree on New Year's Eve is 20, which is greater than the second preset fluctuation degree threshold of 15, it is judged that the user's behavior in this period is irregular during holidays, and the holiday irregular usage scenario label is matched for it; if the data fluctuation degree at 8 o'clock in the morning of the New Year's Day is 10, which is less than the threshold of 15, it is judged that the user's behavior in this period is regular during holidays. Considering that this is the traditional time for New Year's greetings, it can be matched with a holiday regular usage scenario label.

[0040] It should be noted that the second preset fluctuation degree threshold can be set according to actual conditions, and this application does not impose any limitation on this.

[0041] In this embodiment, by extracting and analyzing the data fluctuation degree and user behavior data, the robustness of the system in complex environments can be effectively enhanced, and the power consumption adjustment error caused by abnormal fluctuations of a single device can be reduced. The preset label association rules are used to match the corresponding usage scenario labels for each user according to the different needs of the time period, so as to dynamically adjust the power consumption strategy in the future, thereby improving the accuracy and stability of the overall control.

[0042] S204: Adopting corresponding different power consumption adjustment strategies for all the network devices according to the usage scenario labels.

[0043] In step S204, according to the user behavior data and the degree of data fluctuation, a corresponding usage scenario label is matched for each user, such as regular usage scenario on weekdays, irregular usage scenario on weekdays, regular usage scenario on holidays, irregular usage scenario on holidays, etc. Then, corresponding power consumption adjustment strategies are formulated for different usage scenario labels. These strategies may include adjusting the working mode of the device, optimizing the network connection, reducing the power consumption of non-essential functions, etc., and according to the formulated power consumption adjustment strategy, the network device is configured and optimized to achieve power consumption reduction.

[0044] In an embodiment of the invention, different power consumption adjustment strategies are adopted for all the network devices according to the usage scenario labels, including: When the usage scenario tag is a regular usage scenario tag on a weekday, obtaining a first data volume of each network device; Determine a first data weight of each network device according to a proportion of the first data volume of each network device in the uplink and downlink data volume; According to the first data weight of each network device, setting a corresponding first power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on a weekday, obtaining a second data volume of each network device; Determine in turn whether the second data volume of each network device is lower than the first preset data volume threshold; Reducing the power consumption parameter of the network device below the first preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices that are higher than the first preset data volume threshold are maintained at the original power consumption parameters.

[0045] Specifically, when the usage scenario label is a regular usage scenario label on weekdays, the first data volume of each network device is obtained, and then the first data weight of each network device is calculated according to the proportion of the first data volume of each network device in the uplink and downlink data volume, and then the corresponding first power consumption is set for each network device according to the first data weight of each network device. The network device with a higher weight may need to maintain a higher power consumption to support more data transmission or processing, while the network device with a lower weight can reduce the power consumption. When the usage scenario label is an irregular usage scenario label on weekdays, the second data volume of each network device is obtained, and it is determined in turn whether the second data volume of each network device is lower than the first preset data volume threshold, and the power consumption parameter of the network device lower than the first preset data volume threshold is reduced to the preset low power consumption parameter threshold to save energy, and the power consumption parameter of the network device higher than the first preset data volume threshold is maintained at the original power consumption parameter to ensure the stability of data transmission and processing. For example, for network devices with less than 50MB (such as cameras used only for monitoring rooms that are not often used), their power consumption parameters are reduced to the preset low power consumption parameter threshold, such as 1W, and for network devices with more than 50MB (such as network devices in entertainment areas frequently used by family members), their power consumption parameters are maintained at the original power consumption parameter level, such as 5W.

[0046] It should be noted that the first preset data volume threshold and the preset low power consumption parameter threshold can be set according to actual conditions, and this application does not impose any limitation on this.

[0047] In the embodiment of the present application, the power consumption is dynamically adjusted for different usage scenarios, and the power consumption control strategies for different time periods during the working day are different. By reducing the inactive network devices to the preset power consumption, the energy waste of the devices at low load can be effectively reduced, thereby significantly improving the energy efficiency of the home network. And by calculating the proportion of the data volume of each network management device in the total data volume, combined with the user's usage pattern, the power consumption of each device is reasonably set. In particular, for usage scenarios with small fluctuations in data volume and strong regularity, power consumption can be effectively reduced, and during periods of large fluctuations in usage data, power consumption is maintained at a reasonable level to avoid excessive reduction that affects user experience.

[0048] In an embodiment of the invention, according to the usage scenario label, adopting corresponding different power consumption adjustment strategies for all the network devices further includes: When the usage scenario tag is a holiday regular usage scenario tag, obtaining a third data volume of each network device; Determine the second data weight of each network device according to the proportion of the third data volume of each network device in the uplink and downlink data volume; According to the second data weight of each network device, setting a corresponding second power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on holidays, obtaining a fourth data amount of each network device; Determining in turn whether the fourth data volume of each network device is lower than the second preset data volume threshold; Reducing the power consumption parameter of the network device below the second preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices whose data volume exceeds the second preset data volume threshold are maintained at the original power consumption parameters.

[0049] Specifically, when the usage scenario label is a regular usage scenario label on holidays, the third data volume of each network device is obtained, and then the second data weight of each network device is calculated according to the proportion of the third data volume of each network device in the uplink and downlink data volume, and then the corresponding second power consumption is set for each network device according to the second data weight of each network device. The network device with a higher weight may need to maintain a higher power consumption to support more data transmission or processing, while the network device with a lower weight can reduce the power consumption. When the usage scenario label is an irregular usage scenario label on holidays, the fourth data volume of each network device is obtained, and it is determined in turn whether the fourth data volume of each network device is lower than the second preset data volume threshold, and the power consumption parameter of the network device lower than the second preset data volume threshold is reduced to the preset low power consumption parameter threshold to save energy, and the power consumption parameter of the network device higher than the second preset data volume threshold is maintained at the original power consumption parameter to ensure the stability of data transmission and processing. For example, for network devices with less than 30MB (such as cameras used only for monitoring rooms that are not often used), their power consumption parameters are reduced to the preset low power consumption parameter threshold, such as 8W, and for network devices with more than 30MB (such as network devices in entertainment areas frequently used by family members), their power consumption parameters are maintained at the original power consumption parameter level, such as 10W.

[0050] It should be noted that the second preset data volume threshold and the preset low power consumption parameter threshold can be set according to actual conditions, and this application does not impose any limitation on this.

[0051] In the embodiment of the present application, the power consumption is dynamically adjusted for different usage scenarios, and the power consumption control strategies for different time periods during holidays are different. By reducing the inactive network devices to the preset power consumption, the energy waste of the devices at low load can be effectively reduced, thereby significantly improving the energy efficiency of the home network. And by calculating the proportion of the data volume of each network management device in the total data volume, combined with the user's usage pattern, the power consumption of each device is reasonably set. In particular, for usage scenarios with small fluctuations in data volume and strong regularity, power consumption can be effectively reduced, and during periods of large fluctuations in usage data, power consumption is maintained at a reasonable level to avoid excessive reduction that affects user experience.

[0052] In the embodiments of the present application, the user's usage patterns can be accurately identified based on the differences in the user's behavior patterns on weekdays and holidays. For data fluctuations that show regularity in certain time periods, the solution can accurately maintain the device power consumption to ensure that the device always maintains a good response speed and stability. For irregular usage scenarios with large fluctuations, the solution can flexibly adjust the device power consumption to avoid unnecessary energy consumption without interfering with the user's normal use, thereby improving the overall user experience.

[0053] In an embodiment of the invention, after adopting corresponding different power consumption adjustment strategies for all the network devices, the method includes: Get the number of external devices connected to all network devices; Determine whether the number of devices is greater than a preset device number threshold; If the number of devices is greater than a preset device number threshold, the power consumption level is adjusted based on a preset rule.

[0054] Specifically, for each network device, the number of all external devices currently connected to it is obtained through its management interface or API interface, and a preset device number threshold is set to determine whether the number of external devices is too large. This threshold can be set according to the processing capacity, network resources and business requirements of the network device, and the number of external devices obtained for each network device is compared with the preset device number threshold. If the number of external devices of a certain network device is greater than the preset device number threshold, the power consumption level of the network device is adjusted according to the preset rules, which may include reducing power consumption to reduce resource occupation, or increasing power consumption to support more external devices (although this may increase energy consumption, it may be necessary in some cases, such as ensuring the stable operation of key devices). That is, in some family gatherings or other scenarios, facing a surge in the number of people and data demand, at this time, the number of devices currently connected to all network devices can be obtained to increase the power consumption of the device. Specifically, different power consumption increase levels are set for different numbers of devices. For example: add two, increase one level, add four, increase two levels, and so on. Through the above steps, we can dynamically adjust the power consumption level of the network device according to the number of external devices, that is, flexibly configure it according to the specific usage of different home users. Regardless of the number and type of devices in the home, or the user's usage habits and activity patterns, we can provide personalized power consumption management solutions, thereby ensuring stable system operation while optimizing energy efficiency.

[0055] It should be noted that the preset rules can be defined according to actual conditions, and this application does not impose any limitation on this.

[0056] In this embodiment, as the number of home devices increases and the network environment changes, the system can dynamically adjust power consumption for different network load conditions (such as high traffic periods or low traffic periods) to ensure that the device provides sufficient bandwidth guarantee when the load is high and saves more energy when the load is low. In addition, by analyzing the degree of data fluctuation and user usage patterns, the system can dynamically adjust power consumption for different usage scenarios. On weekdays and holidays, the power consumption control strategies for different time periods are different. By reducing the power consumption of inactive hosts or slaves to the preset power consumption, the energy waste of devices at low loads can be effectively reduced, thereby significantly improving the energy efficiency of the home network.

[0057] In summary, the present invention provides a power consumption adjustment method, device, equipment and medium based on FTTR. The power consumption control method can be implemented based on an FTTR system, an FTTR-B system or a broadband converged terminal, and the uplink and downlink data volumes of all network devices in multiple historical unit periods are obtained, wherein the historical unit periods include weekdays and holidays. The uplink and downlink data volumes in different time periods in the multiple historical unit periods are analyzed to determine the data fluctuation degree of all network devices in each time period in the multiple historical unit periods. Based on the data fluctuation degree and user behavior data, a corresponding usage scenario label is matched for each user using a preset label association rule, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays and an irregular usage scenario label on holidays. According to the usage scenario label, different power consumption adjustment strategies are adopted for all network devices. It can be seen that the present application analyzes the uplink and downlink data volumes of all network devices to determine the data fluctuation degree of all network devices in each time period in multiple historical unit cycles, and based on the data fluctuation degree and user behavior data, uses preset label association rules to match the corresponding usage scenario label for each user, and adopts different power consumption adjustment strategies for different usage scenario labels, so as to quickly achieve more accurate power consumption adjustment in different time periods, making the power consumption adjustment more targeted and flexible, and further improving the overall user experience.

[0058] See also Figure 2 , Figure 2 is a schematic diagram of the structure of the power consumption adjustment device based on FTTR provided by an embodiment of the present invention. The power consumption adjustment device based on FTTR corresponds to the power consumption adjustment method based on FTTR in the above embodiment. Figure 1 as well as Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 2 The power consumption adjustment device 20 based on FTTR includes: an acquisition module 21, an analysis module 22, a matching module 23, and an adjustment module 24.

[0059] An acquisition module 20 is used to acquire the uplink and downlink data volumes of all network devices in a plurality of historical unit periods, wherein the historical unit periods include working days and holidays; An analysis module 21, configured to analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles, and determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles; A matching module 22, configured to match a corresponding usage scenario label for each user based on the data fluctuation degree and the user behavior data by using a preset label association rule, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays; The adjustment module 23 is used to adopt different power consumption adjustment strategies for all the network devices according to the usage scenario labels.

[0060] Optionally, the analysis module 21 is specifically used for: Preprocessing the uplink and downlink data volumes in different time periods in a plurality of historical unit cycles to obtain the uplink and downlink data volumes in different time periods in each historical unit cycle after preprocessing; Determine a data volume curve corresponding to each historical unit period according to the uplink and downlink data volumes in different time periods in each historical unit period after the preprocessing; The data volume curves are fitted to obtain a target data volume curve, and the data volume data corresponding to different time periods on the target data volume curve are used as the data fluctuation degree of different time periods within a unit period.

[0061] Optionally, the matching module 23 is specifically used for: When the data fluctuation degree is the first data fluctuation degree of all network devices in each period of multiple working days, determining whether the first data fluctuation degree is less than a first preset fluctuation degree threshold; If the first data fluctuation degree is less than a first preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding weekday regular usage scenario label is matched for each user using a preset label association rule; If the first data fluctuation degree is greater than a first preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding weekday irregular usage scenario tag for each user.

[0062] Optionally, the matching module 23 is further used for: When the data fluctuation degree is the second data fluctuation degree of all network devices in each period of multiple holidays, determining whether the second data fluctuation degree is less than a second preset fluctuation degree threshold; If the second data fluctuation degree is less than a second preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding holiday regular usage scenario label is matched for each user by using a preset label association rule; If the second data fluctuation degree is greater than a second preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding holiday irregular usage scenario tag for each user.

[0063] Optionally, the adjustment module 24 is specifically used for: When the usage scenario tag is a regular usage scenario tag on a weekday, obtaining a first data volume of each network device; Determine a first data weight of each network device according to a proportion of the first data volume of each network device in the uplink and downlink data volume; According to the first data weight of each network device, setting a corresponding first power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on a weekday, obtaining a second data volume of each network device; Determine in turn whether the second data volume of each network device is lower than the first preset data volume threshold; Reducing the power consumption parameter of the network device below the first preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices that are higher than the first preset data volume threshold are maintained at the original power consumption parameters.

[0064] Optionally, the adjustment module 24 is further used for: When the usage scenario tag is a holiday regular usage scenario tag, obtaining a third data volume of each network device; Determine the second data weight of each network device according to the proportion of the third data volume of each network device in the uplink and downlink data volume; According to the second data weight of each network device, setting a corresponding second power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on holidays, obtaining a fourth data amount of each network device; Determining in turn whether the fourth data volume of each network device is lower than the second preset data volume threshold; Reducing the power consumption parameter of the network device below the second preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices whose data volume exceeds the second preset data volume threshold are maintained at the original power consumption parameters. Optionally, the adjustment module 24 is specifically used for: Get the number of external devices connected to all network devices; Determine whether the number of devices is greater than a preset device number threshold; If the number of devices is greater than a preset device number threshold, the power consumption level is adjusted based on a preset rule.

[0065] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0066] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device of this embodiment includes: at least one processor ( Figure 3 Only one is shown in the figure), a memory, and a program stored in the memory and executable on at least one processor, and when the processor executes the program, the steps in any of the above-mentioned power consumption adjustment method embodiments based on FTTR are implemented.

[0067] The electronic device may include, but is not limited to, a processor and a memory. It can be understood by those skilled in the art that Figure 3 These are merely examples of electronic devices and do not constitute limitations on the electronic devices. The electronic devices may include more or fewer components than those shown in the figures, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input system.

[0068] In one embodiment, a readable storage medium is provided, and when the instructions in the readable storage medium are executed by a processor in an electronic device, the electronic device can perform the steps of any embodiment of a power consumption adjustment method based on FTTR disclosed in the present invention, which will not be repeated here. The readable storage medium can be non-volatile or volatile.

[0069] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) 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, etc.

[0070] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of an electronic device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of an electronic device, and in other embodiments, it may also be an external storage device of the electronic device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory may also include both an internal storage unit of the electronic device and an external storage device. The memory is used to store an operating system, a cooperative application, a boot loader (BootLoader), data, and other programs, such as the program code of the program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0071] It can be understood by ordinary technical personnel in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0072] The technical business in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0073] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A power consumption adjustment method based on FTTR, characterized in that: include: Obtaining the uplink and downlink data volumes of all network devices in multiple historical unit periods, wherein the historical unit periods include working days and holidays; Analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles to determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles; Based on the data fluctuation degree and user behavior data, a corresponding usage scenario label is matched for each user using a preset label association rule, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays; According to the usage scenario label, different corresponding power consumption adjustment strategies are adopted for all the network devices.

2. The power consumption adjustment method based on FTTR according to claim 1, characterized in that: The matching of the corresponding usage scenario tag for each user by using a preset tag association rule based on the data fluctuation degree and the user behavior data includes: When the data fluctuation degree is the first data fluctuation degree of all network devices in each period of multiple working days, determining whether the first data fluctuation degree is less than a first preset fluctuation degree threshold; If the first data fluctuation degree is less than a first preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding weekday regular usage scenario label is matched for each user using a preset label association rule; If the first data fluctuation degree is greater than a first preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding weekday irregular usage scenario tag for each user.

3. The power consumption adjustment method based on FTTR according to claim 1, characterized in that: The method of matching a corresponding usage scenario tag for each user based on the data fluctuation degree and the user behavior data using a preset tag association rule also includes: When the data fluctuation degree is the second data fluctuation degree of all network devices in each period of multiple holidays, determining whether the second data fluctuation degree is less than a second preset fluctuation degree threshold; If the second data fluctuation degree is less than a second preset fluctuation degree threshold, it is determined that the user behavior data has regularity, and a corresponding holiday regular usage scenario label is matched for each user by using a preset label association rule; If the second data fluctuation degree is greater than a second preset fluctuation degree threshold, it is determined that the user behavior data is irregular, and a preset tag association rule is used to match a corresponding holiday irregular usage scenario tag for each user.

4. The power consumption adjustment method based on FTTR according to claim 1, characterized in that: The adopting corresponding different power consumption adjustment strategies for all the network devices according to the usage scenario labels includes: When the usage scenario tag is a regular usage scenario tag on a weekday, obtaining a first data volume of each network device; Determine a first data weight of each network device according to a proportion of the first data volume of each network device in the uplink and downlink data volume; According to the first data weight of each network device, setting a corresponding first power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on a weekday, obtaining a second data volume of each network device; Determine in turn whether the second data volume of each network device is lower than the first preset data volume threshold; Reducing the power consumption parameter of the network device below the first preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices that are higher than the first preset data volume threshold are maintained at the original power consumption parameters.

5. The method for adjusting power consumption based on FTTR according to claim 1, characterized in that: The adopting corresponding different power consumption adjustment strategies for all the network devices according to the usage scenario labels also includes: When the usage scenario tag is a holiday regular usage scenario tag, obtaining a third data volume of each network device; Determine the second data weight of each network device according to the proportion of the third data volume of each network device in the uplink and downlink data volume; According to the second data weight of each network device, setting a corresponding second power consumption for each network device; When the usage scenario tag is an irregular usage scenario tag on holidays, obtaining a fourth data amount of each network device; Determining in turn whether the fourth data volume of each network device is lower than the second preset data volume threshold; Reducing the power consumption parameter of the network device below the second preset data volume threshold to a preset low power consumption parameter threshold; The power consumption parameters of the network devices whose data volume exceeds the second preset data volume threshold are maintained at the original power consumption parameters.

6. The method for adjusting power consumption based on FTTR according to claim 1, characterized in that: The analyzing the uplink and downlink data volumes in different time periods in a plurality of historical unit cycles to determine the data fluctuation degree of all network devices in each time period in the plurality of historical unit cycles includes: Preprocessing the uplink and downlink data volumes in different time periods in a plurality of historical unit cycles to obtain the uplink and downlink data volumes in different time periods in each historical unit cycle after preprocessing; Determine a data volume curve corresponding to each historical unit period according to the uplink and downlink data volumes in different time periods in each historical unit period after the preprocessing; The data volume curves are fitted to obtain a target data volume curve, and the data volume data corresponding to different time periods on the target data volume curve are used as the data fluctuation degree of different time periods within a unit period.

7. The method for adjusting power consumption based on FTTR according to claim 1, characterized in that: After adopting corresponding different power consumption adjustment strategies for all the network devices, the method includes: Get the number of external devices connected to all network devices; Determine whether the number of devices is greater than a preset device number threshold; If the number of devices is greater than a preset device number threshold, the power consumption level is adjusted based on a preset rule.

8. A power consumption adjustment device based on FTTR, characterized in that: include: An acquisition module, used to acquire the uplink and downlink data volumes of all network devices within a plurality of historical unit periods, wherein the historical unit periods include working days and holidays; An analysis module, configured to analyze the uplink and downlink data volumes in different time periods in the multiple historical unit cycles, and determine the data fluctuation degree of all network devices in each time period in the multiple historical unit cycles; A matching module, for matching a corresponding usage scenario label for each user using a preset label association rule based on the data fluctuation degree and the user behavior data, wherein the usage scenario label includes a regular usage scenario label on weekdays, an irregular usage scenario label on weekdays, a regular usage scenario label on holidays, and an irregular usage scenario label on holidays; The adjustment module is used to adopt different power consumption adjustment strategies for all the network devices according to the usage scenario labels.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the power consumption adjustment method based on FTTR as claimed in any one of claims 1 to 7 is implemented.

10. A readable storage medium storing a program, characterized in that: When the program is executed by a processor, the power consumption adjustment method based on FTTR as claimed in any one of claims 1 to 7 is implemented.

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