Communication energy-saving management system based on load awareness

Abstract

The invention belongs to the technical field of communication energy-saving management, and particularly discloses a communication energy-saving management system based on load awareness, which comprises a historical communication data calling module, a communication flow time distribution analysis module, a communication interference time distribution analysis module, an association judgment module, a transmitting power complete adjustment module and a transmitting power partial adjustment module, time distribution characteristic analysis of communication flow and communication interference is carried out by calling a historical communication log of a communication base station and by means of communication flow data and communication interference data in the log, and dynamic adjustment of transmitting power in time is carried out by combining relevance of the communication flow and the communication interference time distribution characteristic. High-quality communication service can still be provided while energy is saved, and the communication experience and the communication safety of a user are guaranteed to the maximum extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication energy-saving management, and specifically discloses a communication energy-saving management system based on load perception. Background Art

[0002] Traditional home networks generally suffer from uneven coverage. Walls in multi-story homes and the dense concentration of smart devices make traditional routers unable to meet demand. Modern whole-home WiFi systems utilize a master communication control device to build a distributed intelligent network architecture. This control device plays a core role, not only providing extensive signal coverage but also effectively managing communication status through real-time monitoring and dynamic adjustment of network parameters.

[0003] Communication control equipment consumes energy during operation, especially during periods of high traffic volume, when higher transmit power is required to maintain communication quality. To control energy consumption, current communication control equipment analyzes the temporal distribution of traffic volume, increasing transmit power during peak traffic periods and reducing it during off-peak traffic periods, thus achieving energy management. However, while this management approach reduces energy consumption to a certain extent, it does not fully consider the impact of communication interference. Typically, high traffic volume is accompanied by network congestion and increased communication interference; whereas low traffic volume reduces network congestion and communication interference. However, there are special circumstances, such as inclement weather conditions or when users have high communication demands, in which communication interference may not be reduced even with low traffic volume. Therefore, simply reducing transmit power during off-peak traffic periods risks failing to meet user communication needs and potentially impacting the user experience. Furthermore, reducing transmit 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] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a communication energy-saving management system based on load perception, which effectively improves the single practice of reducing the transmission power during low traffic periods by dynamically adjusting the transmission power over time according to the communication traffic data and communication interference data generated during the operation of the communication master control equipment.

[0005] The purpose of the present invention can be achieved through the following technical solution: a communication energy-saving management system based on load perception, including: a historical communication data retrieval module, which is used to retrieve the communication log of the communication master control device from a selected historical period and divide it into time periods, and extract the communication traffic data and communication interference data of each time period.

[0006] 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, where the communication traffic time distribution characteristics are traffic peak period and traffic valley period.

[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, where the communication interference time distribution characteristics are the interference peak period and the interference valley period.

[0008] The correlation evaluation module is used to compare the time distribution characteristics of communication traffic with the time distribution characteristics of communication interference, judge whether there is correlation, and determine the correlation category when it is judged that there is correlation.

[0009] The transmission power full adjustment module is used to increase the transmission power of the communication control device during peak traffic hours and reduce the transmission power of the communication control device during low traffic hours when the association category is determined to be full association.

[0010] The transmission power partial adjustment module is used to increase the transmission power of the communication control equipment during peak traffic hours when the association category is determined to be partial association, and to collect information on the meteorological environment and user communication behavior during low traffic hours, thereby evaluating the severity of the meteorological environment and user communication needs, and dynamically adjusting the transmission power during low traffic hours based on the evaluation results.

[0011] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention retrieves the historical communication log of the communication master control device and uses the communication traffic data and communication interference data in the log to analyze the time distribution characteristics of communication traffic and communication interference, and dynamically adjusts the transmission power over time based on the correlation between the time distribution characteristics of communication traffic and communication interference, which can ensure that high-quality communication services can be provided while saving energy, and maximize the user's communication experience and communication security. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0014] Figure 2 Schematic diagram of the communication traffic time distribution feature analysis process in the present invention.

[0015] Figure 3This is a schematic diagram of the implementation of dynamic adjustment of transmit power in the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] Reference Figure 1 As shown, a communication energy-saving management system based on load perception includes a historical communication data retrieval module, a communication traffic time distribution analysis module, a communication interference time distribution analysis module, an association evaluation module, a transmission power full adjustment module and a transmission power partial adjustment module, wherein the historical communication data retrieval module is respectively connected to the communication traffic time distribution analysis module and the communication interference time distribution analysis module, the communication traffic time distribution analysis module and the communication interference time distribution analysis module are both connected to the association evaluation module, and the association evaluation module is respectively connected to the transmission power full adjustment module and the transmission power partial adjustment module.

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

[0019] In the above solution, the communication log of the master control device is automatically generated during operation. It records the communication activities and network status between the master control device and user devices. Whenever a user device (such as a mobile phone or tablet) communicates with the master control device, the master 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), the user device IP address, the protocol type, the number of transmitted data packets, and the signal-to-noise ratio.

[0020] Further applied to the above solution, the communication log can be retrieved from the local storage of the master control device.

[0021] It's important to note that communication logs are retrieved from a selected historical period because recent data is more timely and can better reflect current network operations and user needs. For example, if the selected historical period is six months, retrieving communication logs from six months ago will better meet current analysis needs. Furthermore, the volume of log data generated by the communication master control equipment is enormous. Retrieving all historical communication logs can increase the complexity of data processing and analysis, consuming significant computing resources and time. By selecting a historical period, the data volume can be controlled, improving analysis efficiency. Furthermore, this provides a dynamic and periodic basis for retrieving communication logs for subsequent communication traffic and interference analysis.

[0022] As a preferred implementation of the above solution, the time period division process is as follows: determining the minimum unit and the maximum unit of the time period as the time period division boundaries, and setting a number of time period division intervals based on the time period division boundaries 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 determining the time segmentation boundary, several time segmentation intervals are generated within this boundary, and the communication log segmentation status at each time segmentation interval is obtained. Ultimately, the time segmentation interval that provides the optimal communication log segmentation status is selected as the final time segmentation interval.

[0024] In the above implementation example, the minimum unit of the time period can be 0.5 hours, and the maximum unit of the time period can be 2 hours. The time period division intervals obtained in this example can be 0.5 hours, 1 hour, 1.5 hours, and 2 hours. Among them, 0.5 hours is a relatively small time unit that can provide a higher time resolution, and 2 hours is a larger time unit that can provide a macro time perspective. However, the choice of the minimum unit can be adjusted according to actual needs. For example, 10 minutes or 20 minutes can be selected as the minimum unit to adapt to different data volumes and analysis requirements.

[0025] The time interval of a day is pre-divided into time periods according to a number of set time period division intervals to obtain time periods divided under each time period division interval.

[0026] In the specific implementation of the above solution, the time interval of a day can be from 6:00 a.m. to 6:00 a.m. the next day. In the above example of time period division interval, when the time period division 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 period division 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.; when the time period division 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 under each time period division interval, thereby counting the number of communication logs classified under each time period division interval, and extracting the minimum number of communication logs under each time period division interval.

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

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

[0030] It should be understood that the minimum number of communication logs under each time period is selected because the minimum number of communication logs can reflect the worst case scenario under each time period. By comparing the number of communication logs obtained in the worst case scenario with the pre-configured number of available communication logs, and then screening out the time period interval that meets the number of available communication logs as the final time period interval, it can ensure that each time period has sufficient data representation, ensure the reliability of data analysis, and avoid analysis bias caused by insufficient data.

[0031] Furthermore, if multiple time intervals are selected that match the number of available communication logs, for example, 1.5-hour and 2-hour time intervals are selected, 1.5-hour interval is preferred as the final time interval. This is because 1.5-hour intervals provide higher temporal resolution than 2-hour intervals. This means that changes in communication traffic and interference can be captured in greater detail within a 1.5-hour interval.

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

[0033] The present invention pre-divides the time interval of a day into different time period division intervals based on the retrieved communication logs, and can systematically evaluate the distribution of communication logs at different time granularities, ensuring that all possible time granularities are taken into account, avoiding missing important time patterns, and then screening out time period division intervals that meet the requirements to ensure that each time period has sufficient data, thereby improving the reliability and effectiveness of data analysis.

[0034] As a further preferred implementation of the above solution, the communication traffic data and communication interference data of each time period are extracted by the following process: the retrieved communication logs are classified according to the time period division method to form a number of communication logs corresponding to each time period.

[0035] The communication flow data corresponding to each communication log in each time period is used to form a communication flow data set corresponding to each time period.

[0036] The communication interference data corresponding to each communication log in each time period constitutes a communication interference data set corresponding to each time period.

[0037] In the above implementation example, the communication traffic data in the communication log mentioned above may be the number of transmitted data packets in the communication log, and the communication interference data in the communication log may 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 traffic peak time period and traffic valley time period.

[0039] See above Figure 2 As shown, the communication traffic time distribution feature analysis is implemented as follows: the communication traffic data in the communication traffic data set corresponding to each time period are numbered in the order of the communication log generation time.

[0040] The communication flow data with the same number is extracted from the communication flow data sets corresponding to different time periods to form a communication flow data group.

[0041] In the above implementation example, assuming that 2 hours is used as the final time period division interval, the time periods obtained under this division interval are 6:00-8:00, 8:00-10:00, 10:00-12:00, 12:00-14:00, etc., then the communication traffic data set constructed under 6:00-8:00 is ,The communication traffic data set constructed from 8:00-10:00 is ,

[0042] The communication traffic data set constructed between 10:00 and 12:00 is ,The communication traffic data set constructed from 12:00 to 14:00 is ,

[0043] The communication traffic data group thus formed is 、 、 、 .

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

[0045] The traffic time distribution curve corresponding to each communication traffic data group is identified as a periodic curve, and the communication traffic data group whose traffic time distribution curve is a periodic curve is retained as a valid communication traffic data group.

[0046] Specifically, the traffic time distribution curve corresponding to each communication traffic data group is subjected to the following process for periodic curve identification: the traffic time distribution curve corresponding to each communication traffic data group is Fourier transformed into a spectrum diagram, and the peak value in the spectrum diagram is used to identify the periodic component. If the periodic component can be identified in the spectrum diagram obtained by transforming the traffic time distribution curve corresponding to a certain communication traffic data group, then the traffic time distribution curve corresponding to the 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 diagram can obtain the energy distribution of the signal at different frequencies. By using a peak detection algorithm to identify the peak in the spectrum diagram, the maximum peak corresponds to the periodic component of the signal. This is because the periodic component will produce a significant energy concentration in the spectrum, that is, the peak in the spectrum. Therefore, by identifying whether there is a peak in the spectrum diagram, it can be determined whether there is a periodic component in the signal.

[0048] When capturing traffic peak periods and traffic trough periods based on the traffic time distribution curves corresponding to each communication data traffic group, the present invention screens out the communication data traffic group whose traffic time distribution curve is a periodic curve from the communication data traffic group through the identification of the periodic curve, and then performs the next step of capturing traffic peak periods and traffic trough periods. The purpose of doing so is to take into account the errors and anomalies in the data of individual communication traffic data groups, which cause the formed traffic time distribution curves to not meet the periodic properties. These abnormal data may interfere with the analysis results, resulting in inaccurate identification of traffic peak and trough periods. The periodic curve has a stable periodic characteristic. By screening out the periodic curve, it can be ensured that the analyzed data has reliable periodicity, thereby improving the accuracy of capturing traffic peak and trough periods.

[0049] The traffic time distribution curve corresponding to each valid communication traffic data group is marked with maximum and minimum points, and the time periods with maximum and minimum points on the horizontal axis are captured as traffic peak period and traffic trough period respectively. This is because the maximum point corresponds to the peak value on the traffic time distribution curve, indicating that the traffic reaches the maximum value during this period. Therefore, the time period with the maximum point on the horizontal axis can naturally be regarded as the traffic peak period. The minimum point corresponds to the valley value on the traffic time distribution curve, indicating that the traffic reaches the minimum value during this period. Therefore, the time period with the minimum point on the horizontal axis can naturally be regarded as the traffic trough period.

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

[0051] With intervals: If there are obvious time intervals between multiple peak traffic periods (that is, there are low traffic periods between adjacent peak periods), these periods are regarded as multiple independent peak traffic periods.

[0052] Merging of 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), these periods will be merged into an overall low traffic period.

[0053] With intervals: If there are obvious time intervals between multiple traffic trough periods (that is, there are high traffic periods between adjacent low traffic periods), these periods are regarded as multiple independent traffic trough periods.

[0054] The traffic peak periods corresponding to each valid communication traffic data group are compared to identify whether there are repeated traffic peak periods. If there are repeated traffic peak periods, the repeated traffic peak periods are regarded as the traffic peak periods within a day.

[0055] In the example of the above operation, suppose we have three valid communication traffic data groups, and the traffic peak hours of each data group are as follows: Data Group 1: traffic peak hours are [8:00-10:00, 18:00-20:00], Data Group 2: traffic peak hours are [8:00-10:00, 14:00-16:00], Data Group 3: traffic peak hours are [8:00-10:00, 18:00-20:00]. By comparing and summarizing these traffic peak hours, 8:00-10:00 and 18:00-20:00 are repeated, so 8:00-10:00 and 18:00-20:00 are both regarded as traffic peak hours in a day.

[0056] By identifying and summarizing repeated traffic peak periods, the present invention can reduce misjudgments caused by accidental fluctuations in individual data groups. Peak periods that appear repeatedly in multiple data groups are more likely to reflect real traffic patterns rather than random noise. At the same time, peak periods that appear consistently in multiple data groups can verify the stability of traffic peak periods and improve the reliability of analysis results.

[0057] The low traffic periods corresponding to each valid communication traffic data group are compared similarly to obtain the low traffic periods within a day.

[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 interference peak time period and the interference valley time 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 is analyzed using the signal-to-noise ratio (SNR), a lower SNR indicates greater communication interference. Therefore, when marking the maximum and minimum points of the traffic interference distribution curve corresponding to each valid communication interference data set, the period with the maximum value on the horizontal axis represents the low interference period, and the period with the minimum value on the horizontal axis represents the peak interference period.

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

[0061] Preferably, the following process is used to judge whether there is a correlation: compare the traffic peak period with the interference peak period, and the traffic valley period with the interference valley period in the communication traffic time distribution characteristics and the communication interference time distribution characteristics, and substitute them into the judgment model Get the judgment result of whether there is a correlation ,in Indicates that there is no correlation. Indicates that there is a correlation. 、 、 、 They represent the traffic peak period, interference peak period, traffic valley period, and interference valley period respectively. Indicates that Indicates no.

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

[0063] It should be understood that when there are multiple traffic peak periods and multiple interference peak periods, the matching frequency of traffic peak periods and interference peak periods (that is, the consistent period proportions) and the matching frequency of traffic low periods and interference low periods are counted. If the matching frequency of traffic peak periods and interference peak periods and the matching frequency of traffic low periods and interference low periods both reach the set critical values, the association category is fully associated.

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

[0065] When the peak traffic period coincides with the peak interference period and the low traffic period does not coincide with the low interference period, this indicates that the relationship between traffic and interference is not completely positively correlated. The correlation category is partial correlation. During the low traffic period, it may be affected by severe weather conditions, high user communication demands, network maintenance testing, and automatic application updates, resulting in high communication interference even during the low traffic period. The present invention adjusts the transmission power in the case of partial correlation mainly based on severe weather conditions and high user communication demands as representative indicators. This is because severe weather conditions and user communication demands can be monitored in real time through the meteorological platform data interface and user application logs, which facilitates data acquisition. In actual operation, the above-mentioned multiple indicators can be used to adjust the transmission power.

[0066] It should be emphasized that, generally speaking, there is a high degree of consistency between peak traffic periods and peak interference periods. This is because high network load during peak traffic periods may cause more devices to compete for limited spectrum resources at the same time, thereby increasing mutual interference between signals. In addition, high-density user devices may also generate more background noise, further exacerbating interference. Therefore, peak traffic periods are generally accompanied by higher communication interference. Therefore, the present invention does not consider the situation where peak traffic periods are inconsistent with peak interference periods.

[0067] The transmission power full adjustment module is used to increase the transmission power of the communication control device during peak traffic hours and reduce the transmission power of the communication control device during low traffic hours when the association category is determined to be full association.

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

[0069] The transmission power partial 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 partial association, and to collect meteorological environment information and user communication behavior information during low traffic periods, thereby evaluating the severity of the meteorological environment and the user communication needs, and dynamically adjusting the transmission power during low traffic periods based on the evaluation results.

[0070] Specifically, the evaluation process of severe meteorological environment is as follows: obtain the area covered by the communication master control equipment, and retrieve the forecast meteorological environment information for the low traffic period from the meteorological platform of the corresponding area every day, and compare it with the severe meteorological environment information set by the meteorological system. For example, the severe meteorological environment information includes lightning, heavy rain, strong wind, etc. If any one of the forecast meteorological environment information on a certain day meets the severe meteorological environment information, then the meteorological environment of that day is evaluated to be severe during the low traffic period.

[0071] When evaluating a severe meteorological environment, 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 meteorological environment information. If any meteorological information reaches the warning value, it is evaluated that the meteorological environment during the low traffic period on that day is severe.

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

[0073] It is important to note that application logs record the specific applications used by users in communication activities, including but not limited to the following information: communication access objects (such as playing videos, visiting web pages, etc.) and communication event data (such as video ID, playback time, etc.).

[0074] The proportion of user application logs whose communication access objects are high-bandwidth multimedia content is summarized and compared with a preset critical value. For example, the critical value is 0.8. If the critical value is reached or exceeded, it is determined that the user communication demand is high during the low traffic period.

[0075] It should be understood that although the overall traffic is low during low-traffic periods, if there are a large number of users watching video streaming, since video streaming usually involves long-term continuous data transmission, this will occupy more network resources, resulting in a high network load, and the user's communication needs are still very high, which may cause communication interference to not be reduced as expected.

[0076] Furthermore, based on the evaluation results, the transmission power is dynamically adjusted during the low traffic period as follows: during the low traffic period, when the weather environment is evaluated to be bad or the user communication demand is high, the transmission power of the communication control equipment will not be reduced; otherwise, the transmission power of the communication control equipment will be reduced.

[0077] It should be added that, in the above evaluation, when both the weather environment is bad and the user communication demand is high, the transmission power of the communication master control equipment is not reduced.

[0078] It should be pointed out that the above-mentioned non-reduction of the transmission power of the communication master control equipment can be to maintain the historical average transmission power or to increase the transmission power, which generally depends on the actual situation and is mainly based on meeting actual communication needs.

[0079] For the implementation of the above-mentioned dynamic adjustment of transmit power, see Figure 3 shown.

[0080] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A communication energy-saving management system based on load perception, characterized in that ,include: Retrieve the communication logs of the communication master control device from the selected historical period and divide them into time periods, from which the communication flow data and communication interference data of each time period are extracted; Analyze the time distribution characteristics of communication traffic based on communication traffic data, where the time distribution characteristics are traffic peak period and traffic valley period; Analyze the time distribution characteristics of communication interference based on communication interference data, where the time distribution characteristics of interference are peak interference period and low interference period; Compare the time distribution characteristics of communication traffic with the time distribution characteristics of communication interference to determine whether there is a correlation, and determine the correlation category if there is a correlation; When the association type is determined to be complete association, the transmission power of the communication control device is increased during peak traffic hours, and the transmission power of the communication control device is reduced during low traffic hours; When the association category is determined to be partial association, the transmission power of the communication control equipment is increased during peak traffic hours, and information on the meteorological environment and user communication behavior is collected during low traffic hours. An evaluation of the severity of the meteorological environment and user communication needs is conducted, and the transmission power is dynamically adjusted during low traffic hours based on the evaluation results.

2. The load-aware communication energy-saving management system according to claim 1, wherein: The time period is divided into the following process: Determine the minimum unit and maximum unit of the time period as the time period division boundary, and set a number of time period division intervals based on the time period division boundary in multiples of the minimum unit; The time interval of a day is pre-divided into time periods according to a number of set time period division intervals, and the time periods divided under each time period division interval are obtained; The communication logs retrieved from the selected historical period are aggregated according to the time periods divided by each time period division interval, thereby counting the number of communication logs classified in each time period division interval, and extracting the minimum number of communication logs in each time period division interval; Compare the minimum number of communication logs under each time period division interval with the pre-configured number of available communication logs, and select the time period division interval that meets the number of available communication logs as the final time period division interval; Divide the time interval of a day into time periods according to the final time period division interval.

3. The load-aware communication energy-saving management system according to claim 2, wherein: The process of extracting the communication traffic data and communication interference data of each time period is as follows: Classify the retrieved communication logs according to the time period to form a number of communication logs corresponding to each time period; The communication flow data corresponding to each communication log in each time period is used to form a communication flow data set corresponding to each time period; The communication interference data corresponding to each communication log in each time period constitutes a communication interference data set corresponding to each time period.

4. The load-aware communication energy-saving management system according to claim 3, wherein: The analysis of the time distribution characteristics of communication traffic is implemented as follows: Number the communication flow data in the communication flow data set corresponding to each time period in the order of the communication log generation time; Extracting communication flow data with the same number from corresponding communication flow data sets in different time periods to form a communication flow data group; A coordinate system is constructed with the time period as the horizontal axis and the communication traffic data as the vertical axis, and a traffic time distribution curve corresponding to each communication traffic data group is formed within the constructed coordinate system for the time period to which each communication traffic data piece belongs in each communication traffic data group; Perform periodic curve identification on the traffic time distribution curve corresponding to each communication traffic data group, and retain the communication traffic data group whose traffic time distribution curve is a periodic curve as the valid communication traffic data group; The traffic time distribution curve corresponding to each valid communication traffic data group is marked with the maximum and minimum points, and the time periods of the maximum and minimum points on the horizontal axis are captured as the traffic peak period and traffic valley period respectively; Compare the traffic peak periods corresponding to each valid communication traffic data group to identify whether there are repeated traffic peak periods, and then summarize the occurrence frequency of each repeated traffic peak period, and take the traffic peak period with the highest occurrence frequency as the traffic peak period of the day; Similarly, the traffic valley periods corresponding to each valid communication traffic data group are compared to obtain the traffic valley periods within a day.

5. The load-aware communication energy-saving management system according to 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 curve corresponding to each communication traffic data group is converted into a spectrum diagram by Fourier transform, and the peak value in the spectrum diagram is used to identify the periodic component. If the periodic component can be identified in the spectrum diagram of the traffic time distribution curve corresponding to a certain communication traffic data group, then the traffic time distribution curve corresponding to the communication traffic data group is determined to be a periodic curve.

6. The load-aware communication energy-saving management system according to claim 1, wherein: The process of judging whether there is a correlation is as follows: Compare the traffic peak period with the interference peak period, and the traffic valley period with the interference valley period in the communication traffic time distribution characteristics and the communication interference time distribution characteristics, and substitute them into the evaluation model. Get the judgment result of whether there is a correlation ,in Indicates that there is no correlation. Indicates that there is a correlation. 、 、 、 They represent the traffic peak period, interference peak period, traffic valley period, and interference valley period respectively. Indicates that Indicates no.

7. The load-aware communication energy-saving management system according to claim 1, wherein: The determination of the association category is implemented as follows: When the traffic peak period coincides with the interference peak period and the traffic valley period coincides with the interference valley period, the correlation category is complete correlation; When the traffic peak period coincides with the interference peak period and the traffic valley period does not coincide with the interference valley period, the correlation type is partial correlation.

8. The load-aware communication energy-saving management system according to claim 1, wherein: The evaluation process of the severe meteorological environment is as follows: Obtain the area covered by the communication master control equipment, and retrieve the forecast meteorological environment information for the low traffic period from the meteorological platform of the corresponding area every day, and compare it with the pre-set severe meteorological environment information. If any of the forecast meteorological environment information on a certain day meets the severe meteorological environment information, then the meteorological environment during the low traffic period on that day is evaluated to be severe.

9. The load-aware communication energy-saving management system according to claim 8, characterized in that: The user communication demand evaluation process is as follows: Retrieving user application logs during low-traffic periods within a selected historical period, extracting the user's communication access objects, and identifying whether these access objects are high-bandwidth multimedia content; The proportion of user application logs accessing high-bandwidth multimedia content is summarized and compared with a preset critical value. If the critical value is reached or exceeded, it is determined that the user communication demand is high during the low traffic period.

10. The load-aware communication energy-saving management system according to claim 9, characterized in that: The following operations are performed to dynamically adjust the transmit power during the low traffic period based on the evaluation results: During the low traffic period, when the weather environment is bad or the user communication demand is high, the transmission power of the communication control equipment will not be reduced. Otherwise, the transmission power of the communication control equipment will be reduced.

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