A load-aware communication energy management system

Abstract

This invention belongs to the field of communication energy-saving management technology, specifically disclosing a load-aware communication energy-saving management system. It includes a historical communication data retrieval module, a communication traffic time distribution analysis module, a communication interference time distribution analysis module, a correlation evaluation module, a full transmission power adjustment module, and a partial transmission power adjustment module. By retrieving historical communication logs from the communication base station and using the communication traffic and interference data in the logs, the system analyzes the time distribution characteristics of communication traffic and interference. Furthermore, by combining the correlation between the time distribution characteristics of communication traffic and interference, the system dynamically adjusts the transmission power over time. This ensures that high-quality communication services are still provided while saving energy, maximizing the user's communication experience and security.

Description

Technical Field

[0001] This invention belongs to the field of communication energy-saving management technology, and specifically discloses a communication energy-saving management system based on load awareness. Background Technology

[0002] Traditional home networks commonly suffer from uneven coverage; the walls of multi-story buildings and the dense access of smart devices make it difficult for traditional routers to meet the demands. Modern whole-house WiFi systems construct a distributed intelligent network architecture by introducing a central communication control device. This central control device plays a core role, not only providing wide signal coverage but also effectively managing communication status through real-time monitoring and dynamic adjustment of network parameters.

[0003] Communication control equipment consumes electrical energy during operation, especially during periods of high traffic, requiring higher transmission power to maintain communication quality. To control energy consumption, current communication control equipment analyzes the temporal distribution characteristics of traffic, increasing transmission power during peak periods and decreasing it during off-peak periods, thus achieving energy management. However, while this management method reduces energy consumption to some extent, it doesn't fully consider the impact of communication interference. Typically, high traffic is accompanied by network congestion, leading to increased communication interference; conversely, low traffic reduces both. However, there are exceptions, such as in adverse weather conditions or when users have high communication demands, where even low traffic may not reduce interference. Therefore, simply reducing transmission power during off-peak periods risks failing to meet user communication needs and negatively impacting the user experience. Furthermore, reducing transmission power during periods of high user demand can lead to decreased communication quality and potentially increase data transmission security risks, such as data leakage and tampering. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this application provides a load-aware communication energy-saving management system. By dynamically adjusting the transmission power over time based on the communication traffic data and communication interference data generated during the operation of the communication control equipment, the system effectively improves the single approach of reducing transmission power during off-peak traffic periods.

[0005] The objective of this invention can be achieved through the following technical solution: a load-aware communication energy-saving management system, comprising: a historical communication data retrieval module, used to retrieve the communication logs of the communication master control device from a selected historical period, divide them into time periods, and extract communication traffic data and communication interference data for each time period.

[0006] The communication traffic time distribution analysis module is used to analyze the time distribution characteristics of communication traffic based on the communication traffic data corresponding to each time period. The time distribution characteristics of communication traffic include peak traffic periods and low traffic periods.

[0007] The communication interference time distribution analysis module is used to analyze the time distribution characteristics of communication traffic based on the communication interference data corresponding to each time period. The communication interference time distribution characteristics include peak interference periods and low interference periods.

[0008] The correlation assessment module is used to compare the time distribution characteristics of communication traffic with the time distribution characteristics of communication interference, assess whether there is a correlation, and determine the correlation category when a correlation is found.

[0009] The transmit power adjustment module is used to increase the transmit power of the communication master control device during peak traffic periods and decrease the transmit power of the communication master control device during off-peak traffic periods when the association category is determined to be fully associated.

[0010] The transmit power adjustment module is used to increase the transmit power of the communication master control equipment during peak traffic periods when the association category is determined to be partial association, and to collect meteorological environment and user communication behavior information during low traffic periods. Based on this, the module performs meteorological environment severity assessment and user communication demand assessment, and dynamically adjusts the transmit power during low traffic periods according to the assessment results.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention retrieves the historical communication logs of the communication master control device, uses the communication traffic data and communication interference data in the logs to analyze the time distribution characteristics of communication traffic and communication interference, and combines the correlation between the time distribution characteristics of communication traffic and communication interference to dynamically adjust the transmission power over time, which can ensure that high-quality communication services can still be provided while saving energy, and maximize the protection of users' communication experience and communication security. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0014] Figure 2 This is a schematic diagram of the communication traffic time distribution characteristic analysis process in this invention.

[0015] Figure 3This is a schematic diagram illustrating the implementation of dynamic adjustment of transmission power in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Reference Figure 1 As shown, a load-aware communication energy-saving management system includes a historical communication data retrieval module, a communication traffic time distribution analysis module, a communication interference time distribution analysis module, a correlation evaluation module, a full transmission power adjustment module, and a partial transmission power adjustment module. The historical communication data retrieval module is connected to both the communication traffic time distribution analysis module and the communication interference time distribution analysis module. Both the communication traffic time distribution analysis module and the communication interference time distribution analysis module are connected to the correlation evaluation module. The correlation evaluation module is connected to both the full transmission power adjustment module and the partial transmission power adjustment module.

[0018] The historical communication data retrieval module is used to retrieve the communication logs of the communication master control device from a selected historical period, divide them into time periods, and extract communication traffic data and communication interference data for each time period.

[0019] In the above-described scheme, the communication log of the central control device is automatically generated during its operation. It records the communication activities and network status between the central control device and user devices. Whenever a user device (such as a mobile phone or tablet) communicates with the central control device, the central control device records the relevant communication activity. Generally, the communication log includes the time period of the communication activity, the user device ID, the type of communication activity (such as voice call, data transmission, SMS, etc.), the user device's IP address, protocol type, the number of transmitted data packets, and the signal-to-noise ratio.

[0020] Further applied to the above scheme, communication logs can be retrieved from the local storage of the central control device.

[0021] It should be noted that retrieving communication logs from a selected historical period is done because recent data is more timely and better reflects the current network operation and user needs. For example, if the selected historical period is 6 months, retrieving communication logs from 6 months ago is more in line with current analysis requirements. Furthermore, the log data generated by the communication control equipment is enormous; retrieving all historical communication logs could increase the complexity of data processing and analysis, consuming significant computing resources and time. By selecting a historical period, the amount of data can be controlled, improving analysis efficiency. Moreover, it provides a dynamic and periodic basis for subsequent communication traffic and interference analysis.

[0022] As a preferred implementation of the above scheme, the time period is divided as follows: the minimum and maximum units of the time period are determined as the time period division boundary, and several time period division intervals are set based on the time period division boundary in multiples of the minimum unit.

[0023] It's important to understand that the purpose of time-segmentation is to analyze communication traffic and interference at different time scales. However, the specific time-segmentation interval cannot be chosen blindly. Therefore, by defining the boundaries of the time-segmentation, several time-segmentation intervals are generated within these boundaries, thereby obtaining the communication log segmentation status under each time-segmentation interval. Finally, the time-segmentation interval with the optimal communication log segmentation status is selected as the final time-segmentation interval.

[0024] In the example above, the smallest unit of time interval can be 0.5 hours, and the largest unit can be 2 hours. Under this example, the resulting time intervals can be 0.5 hours, 1 hour, 1.5 hours, and 2 hours. 0.5 hours is a relatively small unit that provides high temporal resolution, while 2 hours is a larger unit that provides a macroscopic time perspective. However, the choice of the smallest unit can be adjusted according to actual needs, such as choosing 10 minutes or 20 minutes as the smallest unit to accommodate different data volumes and analytical requirements.

[0025] The time interval of a day is pre-divided according to several set time intervals to obtain the time periods divided under each time interval.

[0026] In the specific implementation of the above scheme, the time interval of a day can be from 6:00 AM to 6:00 AM the next day. Using the example of time interval division, when the time interval is 0.5 hours, the time periods are 6:00-6:30, 6:30-7:00, 7:00-7:30, 7:30-8:00, etc.; when the time interval is 1 hour, the time periods are 6:00-7:00, 7:00-8:00, 8:00-9:00, 9:00-10:00, etc.; and when the time interval is 2 hours, the time periods are 6:00-8:00, 8:00-10:00, 10:00-12:00, 12:00-14:00, etc.

[0027] The communication logs retrieved from the selected historical period are aggregated according to the time periods divided by each time period interval. The number of communication logs categorized by each time period interval is then counted, and the minimum number of communication logs under each time period interval is extracted.

[0028] As a specific implementation of the above scheme, when aggregating the retrieved communication logs, the time period of the communication activity in the communication log is compared with the time period divided under each time period interval. If the time period of the communication activity in a certain communication log falls under a certain time period, then the communication log is classified under that time period.

[0029] The minimum number of communication logs under each time period division interval is compared with the pre-configured number of available communication logs. For example, the number of available communication logs can be 20, and the time period division interval that meets the number of available communication logs is selected as the final time period division interval.

[0030] It is important to understand that the minimum number of communication logs is selected for each time period interval because the minimum number of communication logs can reflect the worst-case scenario for each time period interval. By comparing the number of communication logs obtained under the worst-case scenario with the pre-configured number of available communication logs, the time period intervals that meet the number of available communication logs are selected as the final time period intervals. This ensures that each time period has sufficient data to represent it, ensuring the reliability of data analysis and avoiding analytical biases caused by insufficient data.

[0031] Furthermore, if multiple time intervals are selected to match the available communication log quantity, such as 1.5h and 2h, then 1.5h should be chosen as the final time interval. This is because 1.5 hours provides higher time resolution compared to a 2-hour time unit. This means that changes in communication traffic and interference can be captured more precisely within a 1.5-hour time interval.

[0032] The time interval of a day is divided into time periods according to the final time period division interval.

[0033] This invention pre-divides a day's time intervals based on retrieved communication logs, allowing for a systematic evaluation of the communication log distribution at different time granularities. This ensures that all possible time granularities are considered, avoiding the omission of important time patterns, and thereby selecting suitable time intervals to ensure sufficient data for each time period, thus improving the reliability and effectiveness of data analysis.

[0034] As a further preferred implementation of the above scheme, the following process is used to extract communication traffic data and communication interference data for each time period: the retrieved communication logs are classified according to the time period division method to form several communication logs corresponding to each time period.

[0035] The communication traffic data of each communication log corresponding to each time period is used to construct the communication traffic dataset corresponding to each time period.

[0036] The communication interference data for each communication log in each time period are used to construct the communication interference dataset for each time period.

[0037] In the example of the above implementation, the communication traffic data in the communication log mentioned above can be the number of data packets transmitted in the communication log, and the communication interference data in the communication log can be the signal-to-noise ratio in the communication log.

[0038] The communication traffic time distribution analysis module is used to analyze the communication traffic time distribution characteristics based on the communication traffic data corresponding to each time period, wherein the communication traffic time distribution characteristics are peak traffic periods and low traffic periods.

[0039] See above Figure 2 As shown, the analysis of the time distribution characteristics of communication traffic is carried out as follows: The communication traffic data in the corresponding communication traffic dataset for each time period are numbered according to the order in which the communication logs were generated.

[0040] The communication traffic data with the same number are extracted from the communication traffic datasets corresponding to different time periods to form a communication traffic data group.

[0041] In the example above, assuming a 2-hour interval is used as the final time slot division, the resulting time slots are 6:00-8:00, 8:00-10:00, 10:00-12:00, 12:00-14:00, etc. Then, the communication traffic dataset formed during the 6:00-8:00 period is: The communication traffic dataset generated between 8:00 and 10:00 is as follows ,

[0042] The communication traffic dataset generated between 10:00 and 12:00 is as follows The communication traffic dataset generated between 12:00 and 14:00 is as follows ,

[0043] The resulting communication traffic data group is , , , .

[0044] A coordinate system is constructed with time period as the horizontal axis and communication traffic data as the vertical axis. For each communication traffic data group, the corresponding traffic time distribution curve is generated within the constructed coordinate system for the time period to which each piece of communication traffic data belongs.

[0045] Periodic curves are identified for the traffic time distribution curves corresponding to each communication traffic data group, and communication traffic data groups whose traffic time distribution curves are periodic curves are retained as valid communication traffic data groups.

[0046] Specifically, the process for identifying periodic curves of the traffic time distribution curves corresponding to each communication traffic data group is as follows: the traffic time distribution curves corresponding to each communication traffic data group are converted into a spectrum by Fourier transform, and the peak values ​​in the spectrum are used to identify periodic components. If a periodic component can be identified in the spectrum of the transformed traffic time distribution curve corresponding to a certain communication traffic data group, then the traffic time distribution curve corresponding to that communication traffic data group is determined to be a periodic curve.

[0047] It should be noted that converting the flow time distribution curve into a spectrum graph can obtain the energy distribution of the signal at different frequencies. Peak detection algorithms are used to identify peaks in the spectrum graph, where the largest peak corresponds to the periodic component of the signal. This is because the periodic component will produce a significant energy concentration in the spectrum, i.e., the peak in the spectrum. Therefore, by identifying whether there are peaks in the spectrum graph, it can be determined that there are periodic components in the signal.

[0048] This invention, when capturing peak and trough traffic periods based on the traffic time distribution curves corresponding to each communication data traffic group, identifies communication data traffic groups with periodic traffic time distribution curves for further peak and trough traffic capture. This is done because some communication data traffic groups may contain errors or anomalies that cause the resulting traffic time distribution curves to not meet periodicity. These abnormal data may interfere with the analysis results, leading to inaccurate identification of peak and trough traffic periods. Periodic curves have stable periodic characteristics; by selecting periodic curves, the analyzed data can be ensured to have reliable periodicity, thereby improving the accuracy of capturing peak and trough traffic periods.

[0049] The traffic time distribution curves corresponding to each valid communication traffic data group are marked with maximum and minimum values. The periods when these maximum and minimum values ​​are located on the horizontal axis are identified as peak and trough traffic periods, respectively. This is because the maximum value corresponds to the peak on the traffic time distribution curve, indicating that the traffic reaches its maximum value during that period. Therefore, the period when the maximum value is located on the horizontal axis can naturally be considered the peak traffic period. The minimum value corresponds to the trough on the traffic time distribution curve, indicating that the traffic reaches its minimum value during that period. Therefore, the period when the minimum value is located on the horizontal axis can naturally be considered the trough traffic period.

[0050] It should be added that if the traffic flow time distribution curve contains multiple maxima and minima, it indicates that there are multiple traffic peak periods and traffic trough periods in the time distribution. In this case, the multiple traffic peak periods are processed as follows: Merging of consecutive peak periods: No interval: If there is no obvious time interval between multiple traffic peak periods (i.e., adjacent peak periods are closely connected), then these periods are merged into a single overall traffic peak period.

[0051] Intervals: If there are obvious time intervals between multiple traffic peak periods (i.e., there are low traffic periods between adjacent peak periods), then these periods are considered as multiple independent traffic peak periods.

[0052] Merging consecutive low traffic periods: No interval: If there is no obvious time interval between multiple low traffic periods (i.e., adjacent low traffic periods are closely connected), then these periods are merged into a whole low traffic period.

[0053] Intervals: If there are obvious time intervals between multiple low traffic periods (i.e., there are high traffic periods between adjacent low traffic periods), then these periods are considered as multiple independent low traffic periods.

[0054] The peak traffic periods corresponding to each valid communication traffic data group are compared to identify whether there are duplicate peak traffic periods. If duplicate peak traffic periods exist, they are all taken as peak traffic periods within a day.

[0055] In the example above, assume we have three valid communication traffic data groups, with peak traffic periods for each group as follows: Data group 1: peak traffic periods are [8:00-10:00, 18:00-20:00], Data group 2: peak traffic periods are [8:00-10:00, 14:00-16:00], and Data group 3: peak traffic periods are [8:00-10:00, 18:00-20:00]. By comparing and summarizing these peak traffic periods, we find that 8:00-10:00 and 18:00-20:00 overlap, therefore, both 8:00-10:00 and 18:00-20:00 are considered as peak traffic periods within a day.

[0056] This invention reduces misjudgments caused by random fluctuations in individual data sets by identifying and summarizing recurring traffic peak periods. Peak periods that recur in multiple data sets are more likely to reflect the true traffic pattern rather than random noise. At the same time, peak periods that appear consistently in multiple data sets can verify the stability of traffic peak periods and improve the reliability of the analysis results.

[0057] Similarly, by comparing the low traffic periods corresponding to each valid communication traffic data group, the low traffic periods within a day can be obtained.

[0058] The communication interference time distribution analysis module is used to analyze the communication interference time distribution characteristics based on the communication interference data corresponding to each time period, wherein the communication interference time distribution characteristics are the peak interference period and the low interference period.

[0059] The analysis of the temporal distribution characteristics of communication interference described above is similar to the analysis of the temporal distribution characteristics of communication traffic. However, it should be noted that since communication interference data uses the signal-to-noise ratio (SNR) as the analysis indicator, a lower SNR indicates greater communication interference. Therefore, when marking the maximum and minimum values ​​of the traffic interference distribution curves corresponding to each effective communication interference data set, the period when the maximum value is on the horizontal axis represents the interference trough period, and the period when the minimum value is on the horizontal axis represents the interference peak period.

[0060] The correlation evaluation module is used to compare the time distribution characteristics of communication traffic with the time distribution characteristics of communication interference, evaluate whether there is a correlation, and determine the correlation category when a correlation is found.

[0061] Preferably, the process for determining whether a correlation exists is as follows: compare the peak traffic periods with the peak interference periods in the communication traffic time distribution characteristics and the communication interference time distribution characteristics, and compare the low traffic periods with the low interference periods, then substitute these comparisons into the evaluation model. The result of the assessment of whether there is a correlation is obtained. ,in This indicates that there is no correlation. This indicates that there is a correlation. , , , These represent peak traffic periods, peak interference periods, low traffic periods, and low interference periods, respectively. Indicates "and", Indicates "not".

[0062] More preferably, the correlation category is determined as follows: when the peak traffic period coincides with the peak interference period and the low traffic period coincides with the low interference period, this indicates that there is a high positive correlation between traffic and interference, and the correlation category is complete correlation.

[0063] It is important to understand that when there are multiple traffic peak periods and multiple interference peak periods, the matching frequency (i.e., the proportion of consistent periods) between traffic peak periods and interference peak periods, and the matching frequency between traffic trough periods and interference trough periods are counted. If the matching frequency between traffic peak periods and interference peak periods, and the matching frequency between traffic trough periods and interference trough periods both reach the set threshold, then the association category is complete association.

[0064] The determination of the association category in this invention is not based on the above-mentioned multiple traffic peak periods, multiple interference peak periods, multiple traffic trough periods, and multiple interference trough periods.

[0065] When peak traffic periods coincide with peak interference periods, but low traffic periods do not coincide with low interference periods, this indicates that the relationship between traffic and interference is not entirely positively correlated, classifying it as a partial correlation. During low traffic periods, factors such as severe weather, high user communication demands, network maintenance testing, and automatic application updates can lead to significant communication interference even during these periods. This invention adjusts transmission power under partial correlation conditions primarily using severe weather and high user communication demands as representative indicators. This is because severe weather and high user communication demands can be monitored in real-time through meteorological platform data interfaces and user application logs, facilitating data acquisition. In practice, multiple indicators can be used to adjust transmission power.

[0066] It should be emphasized that peak traffic periods and peak interference periods are generally highly consistent. This is because high network load during peak traffic periods may lead to more devices competing for limited spectrum resources at the same time, thereby increasing mutual interference between signals. In addition, high density of user devices may also generate more background noise, further aggravating interference. Therefore, high communication interference is generally accompanied by peak traffic periods. Thus, this invention does not consider the situation where peak traffic periods and peak interference periods are inconsistent.

[0067] The transmit power adjustment module is used to increase the transmit power of the communication master control device during peak traffic periods and decrease the transmit power of the communication master control device during low traffic periods when the correlation category is determined to be fully correlated.

[0068] It should be added that the aforementioned increase or decrease in the transmission power of the communication master control equipment can be based on the historical average transmission power.

[0069] The transmission power adjustment module is used to increase the transmission power of the communication control equipment during peak traffic periods when the association category is determined to be partially associated, and to collect meteorological environment information and user communication behavior information during low traffic periods, thereby conducting meteorological environment severity assessment and user communication demand assessment, and dynamically adjusting the transmission power during low traffic periods based on the assessment results.

[0070] Specifically, the assessment of severe meteorological conditions is as follows: the area covered by the communication control equipment is obtained, and the forecast meteorological conditions for the low-flow period are retrieved from the meteorological platform of the corresponding area every day. This information is then compared with the severe meteorological conditions set by the meteorological authorities. For example, severe meteorological conditions include thunderstorms, heavy rain, and strong winds. If any forecast meteorological condition matches the severe meteorological conditions on a given day, then the meteorological conditions on that day are considered severe during the low-flow period.

[0071] When conducting severe weather environment assessments, the wind speed, rainfall, and lightning intensity extracted from the forecast meteorological environment information can be compared with the warning values ​​of various meteorological information in the severe weather environment information. If any meteorological information reaches the warning value, the meteorological environment is considered severe during the low flow period of that day.

[0072] More specifically, the evaluation of user communication needs is carried out as follows: user application logs during periods of low traffic are retrieved within a selected historical period, user communication access objects are extracted from them, and it is identified whether these access objects are high-bandwidth multimedia content. For example, high-bandwidth multimedia content is video streaming media.

[0073] It's important to know that application logs record the specific applications a user uses during communication activities, including but not limited to the following information: the object accessed during communication (such as playing videos, accessing web pages, etc.); and communication event data (such as video IDs, playback times, etc.).

[0074] The system summarizes the percentage of user application logs whose communication access targets are high-bandwidth multimedia content, and compares this percentage with a preset threshold. For example, the threshold is 0.8. If the threshold is reached or exceeded, it is determined that the user's communication demand is high during the low traffic period.

[0075] It is important to understand that although overall traffic is lower during off-peak hours, if a large number of users are watching video streaming, the network load is still not low because video streaming usually involves long periods of continuous data transmission. This means that users' communication needs remain high, which may cause communication interference to not decrease as expected.

[0076] Furthermore, based on the evaluation results, the transmission power is dynamically adjusted during periods of low traffic as follows: during periods of low traffic, if the evaluation weather conditions are severe or the user's communication demand is high, the transmission power of the communication master control equipment is not reduced; otherwise, the transmission power of the communication master control equipment is reduced.

[0077] It should be added that, as mentioned above, the transmission power of the communication control equipment will not be reduced when both severe weather conditions and high user communication demands exist.

[0078] It should be noted that the aforementioned "not reducing the transmission power of the communication control equipment" can mean maintaining the historical average transmission power or increasing the transmission power, depending on the actual situation, with the primary goal of meeting actual communication needs.

[0079] For the implementation of dynamic adjustment of transmission power mentioned above, please refer to [link / reference]. Figure 3 As shown.

[0080] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A load-aware communication energy-saving management system, characterized in that... ,include: The communication logs of the communication control equipment within the selected historical period are retrieved, divided into time periods, and communication traffic data and communication interference data for each time period are extracted. Based on communication traffic data analysis, the temporal distribution characteristics of communication traffic are identified, including peak traffic periods and low traffic periods. Based on the analysis of communication interference data, the temporal distribution characteristics of communication interference are identified, including peak interference periods and low interference periods. The temporal distribution characteristics of communication traffic are compared with the temporal distribution characteristics of communication interference to determine whether there is a correlation, and if there is a correlation, the correlation category is determined. When the correlation category is determined to be fully correlated, the transmission power of the communication master control equipment is increased during peak traffic periods and decreased during off-peak traffic periods. When the correlation category is determined to be partially correlated, the transmission power of the communication master control equipment is increased during peak traffic periods, and meteorological environment and user communication behavior information are collected during low traffic periods. The severity of the meteorological environment and user communication needs are evaluated, and the transmission power is dynamically adjusted during low traffic periods based on the evaluation results.

2. The load-aware communication energy-saving management system as described in claim 1, characterized in that: The time period is divided as follows: The minimum and maximum units of time periods are determined as the boundaries for dividing time periods, and several time period intervals are set based on the boundaries of time period division in multiples of the minimum unit. The time interval of a day is pre-divided according to a set number of time intervals to obtain the time intervals under each time interval; The communication logs retrieved from the selected historical period are aggregated according to the time periods divided under each time period interval. The number of communication logs categorized under each time period interval is then counted, and the minimum number of communication logs under each time period interval is extracted. The minimum number of communication logs under each time period division interval is compared with the pre-configured number of available communication logs, and the time period division interval that meets the number of available communication logs is selected as the final time period division interval. The time interval of a day is divided into time periods according to the final time period division interval.

3. The load-aware communication energy-saving management system as described in claim 2, characterized in that: The extraction of communication traffic data and communication interference data for each time period is described in the following process: The retrieved communication logs are categorized according to time periods to form several communication logs corresponding to each time period. The communication traffic data of each communication log corresponding to each time period is used to construct the communication traffic dataset corresponding to each time period; The communication interference data for each communication log in each time period are used to construct the communication interference dataset for each time period.

4. The load-aware communication energy-saving management system as described in claim 3, characterized in that: The analysis of the temporal distribution characteristics of communication traffic is implemented as follows: The communication traffic data in the corresponding communication traffic dataset for each time period are numbered according to the order in which the communication logs were generated; The communication traffic data with the same number are extracted from the communication traffic datasets corresponding to different time periods to form a communication traffic data group; A coordinate system is constructed with time period as the horizontal axis and communication traffic data as the vertical axis. For the time period to which each piece of communication traffic data belongs in each communication traffic data group, a traffic time distribution curve corresponding to each communication traffic data group is formed in the constructed coordinate system. Periodic curves are identified for the traffic time distribution curves corresponding to each communication traffic data group, and communication traffic data groups whose traffic time distribution curves are periodic curves are retained as valid communication traffic data groups. The maximum and minimum points of the traffic time distribution curve corresponding to each effective communication traffic data group are marked, and the time periods when the maximum and minimum points are on the horizontal axis are captured as the traffic peak period and traffic trough period, respectively. The peak traffic periods corresponding to each valid communication traffic data group are compared to identify whether there are duplicate peak traffic periods. Then, the frequency of occurrence of each duplicate peak traffic period is summarized, and the peak traffic period with the highest frequency is taken as the peak traffic period of the day. Similarly, by comparing the low traffic periods corresponding to each valid communication traffic data group, the low traffic periods within a day can be obtained.

5. A load-aware communication energy-saving management system as described in claim 4, characterized in that: The process of identifying the periodic curve of the traffic time distribution curve corresponding to each communication traffic data group is as follows: The traffic time distribution curves corresponding to each communication traffic data group are converted into a spectrum by Fourier transform, and the periodic components are identified by the peak values ​​in the spectrum. If the periodic components can be identified in the spectrum of the traffic time distribution curve corresponding to a certain communication traffic data group, then the traffic time distribution curve corresponding to that communication traffic data group is determined to be a periodic curve.

6. The load-aware communication energy-saving management system as described in claim 1, characterized in that: The process for determining whether a correlation exists is as follows: The evaluation model is then developed by comparing the peak traffic periods and peak interference periods in the temporal distribution characteristics of communication traffic with those in the temporal distribution characteristics of communication interference, and by comparing the low traffic periods and low interference periods. The result of the assessment of whether there is a correlation is obtained. ,in This indicates that there is no correlation. This indicates that there is a correlation. , , , These represent peak traffic periods, peak interference periods, low traffic periods, and low interference periods, respectively. Indicates "and", Indicates "not".

7. A load-aware communication energy-saving management system as described in claim 1, characterized in that: The determination of the association category is implemented as follows: When the peak traffic period coincides with the peak interference period and the trough traffic period coincides with the trough interference period, the correlation category is complete correlation. When the peak traffic period coincides with the peak interference period, and the low traffic period does not coincide with the low interference period, the association category is partial association.

8. A load-aware communication energy-saving management system as described in claim 1, characterized in that: The assessment of the severe meteorological environment is as follows: The system obtains the area covered by the communication control equipment and retrieves the forecast meteorological environment information for the low flow period from the meteorological platform of the corresponding area every day. It then compares this information with the pre-set severe meteorological environment information. If any forecast meteorological environment information on a certain day matches the severe meteorological environment information, the system evaluates that the meteorological environment on that day is severe during the low flow period.

9. A load-aware communication energy-saving management system as described in claim 8, characterized in that: The evaluation of user communication needs follows the following process: Within a selected historical period, retrieve user application logs from periods of low traffic, extract user communication access objects, and identify whether these access objects are high-bandwidth multimedia content. The system summarizes the percentage of user application logs that access high-bandwidth multimedia content and compares this percentage with a preset threshold. If the threshold is reached or exceeded, it is determined that user communication demand is high during periods of low traffic.

10. A load-aware communication energy-saving management system as described in claim 9, characterized in that: Based on the evaluation results, the following operations are performed to dynamically adjust the transmission power during periods of low traffic: During periods of low traffic, the transmission power of the communication control equipment will not be reduced when the weather conditions are severe or the user's communication demand is high; conversely, the transmission power of the communication control equipment will be reduced during periods of high traffic.

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