5G base station intelligent control power saving system based on Internet of Things technology

Abstract

The invention discloses a 5G base station intelligent control power saving system based on the Internet of Things technology, and relates to the technical field of 5G communication, the system comprises a data acquisition module, a user movement track analysis module, a service demand evaluation module, a dynamic carrier distribution and scheduling module and a base station equipment control module; through cooperative work of the data acquisition module, the user movement track analysis module and the service demand evaluation module, accurate capture of user behaviors and dynamic evaluation of service demands are realized, the flexibility and accuracy of 5G base station resource allocation are improved, and a traditional base station often adopts a fixed resource allocation mode, so that the resource allocation efficiency is greatly improved. In the prior art, due to the fact that real-time adjustment cannot be conducted according to actual needs of users, resources are wasted, and energy efficiency is low, the system dynamically adjusts carrier allocation and base station transmitting power by monitoring the positions and moving tracks of the users in real time and combining a service requirement evaluation model, energy consumption is reduced, and user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of 5G communication technology, and specifically to a 5G base station intelligent control power saving system based on Internet of Things technology. Background Art

[0002] With the rapid development and widespread application of 5G technology, the energy consumption problem of 5G base stations, as key infrastructure supporting the operation of 5G networks, has become increasingly prominent. Compared with 4G base stations, 5G base stations provide higher speeds, larger capacities and lower latency, but also bring higher energy consumption challenges. In order to meet this challenge, an intelligent control power-saving system based on the Internet of Things technology has emerged, aiming to optimize the energy consumption of 5G base stations through intelligent management and achieve the goal of energy conservation and emission reduction.

[0003] Traditional 5G base station energy consumption management mainly relies on static configuration and manual adjustment. This method has many shortcomings. First, static configuration cannot be dynamically adjusted according to actual user needs and the base station operating environment, resulting in energy waste. Second, manual adjustment is not only inefficient, but also difficult to accurately grasp the energy consumption of base stations and cannot make timely and effective adjustments. In addition, traditional technology lacks in-depth analysis of user movement trajectories and business needs, resulting in unreasonable allocation of base station resources, further exacerbating the energy consumption problem.

[0004] Therefore, a 5G base station intelligent control and power saving system based on Internet of Things technology is developed to address the shortcomings of traditional technology and promote the intelligent development of 5G base station energy consumption management. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a 5G base station intelligent control and power saving system based on the Internet of Things technology. Through data collection, user movement trajectory analysis, business demand assessment, dynamic carrier allocation and scheduling, and base station equipment control module, it realizes the intelligent allocation of 5G base station resources and the dynamic adjustment of equipment power consumption, effectively reduces the energy consumption of 5G base stations, improves resource utilization, and provides strong support for the sustainable development of 5G communication technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a 5G base station intelligent control power saving system based on Internet of Things technology, the system includes: a data acquisition module, a user movement trajectory analysis module, a service demand assessment module, a dynamic carrier allocation and scheduling module and a base station equipment control module; Data collection module: By deploying base station positioning sensors, Wi-Fi sniffers, and Bluetooth detectors in the base station coverage area, the user location information and device connection status data are collected, and the data is transmitted to the cloud platform for storage in real time using the Internet of Things communication protocol; User movement trajectory analysis module: obtains newly collected user location data from the cloud platform, groups users using clustering algorithms, clusters user groups with similar movement patterns together, processes user location data using a comprehensive movement route evaluation formula, finds out the main movement routes and frequently stayed areas, determines the movement routes, stay times, and frequently active areas of users in different time periods, and combines the user category information obtained by clustering with the movement route importance score based on path analysis to build a user movement trajectory model; Business demand assessment module: Combine the data from the user movement trajectory analysis module to determine the number of users in different areas. According to the different environments and movement trends of users, the business demand intensity of each area is calculated through the user business demand intensity quantification formula. The business demand intensity is , the business type set is , each business type The weight is , users have a strong interest in the business The probability of using ,business The standard resource requirement coefficient is ,area The number of users is , the calculation formula is: At the same time, according to the user mobility trend, the business demand change rate formula based on the user mobility trend is used to capture the change trend of business demand. Time zone The business demand intensity is , after a time interval The business demand intensity after , the business demand change rate is , considering the changes in business needs caused by user mobility, introducing the user inflow rate and outflow rate , and business demand adjustment factors , the calculation formula is: ; Dynamic carrier allocation and scheduling module: Based on the business demand assessment information, dynamic carrier allocation strategies are formulated according to the frequency characteristics, coverage and coordination between different base stations of the carriers in areas with high business demand and dense users. At the same time, by monitoring the business types and traffic of users on the carriers, statistics are collected on the resource usage of each carrier and changes in user mobility, and the carrier resource utilization rate is dynamically adjusted using the formula to allocate idle and low-utilization carrier resources. Base station equipment control module: adjusts the base station transmission power according to the user movement trajectory analysis results and dynamic carrier allocation, increases the power in the area where users are close to ensure signal coverage quality, reduces the power when users leave the area to reduce energy consumption, and determines whether the base station equipment is on or off in a certain area based on the business demand intensity and the predicted value of the user's stay time.

[0007] Furthermore, in the user movement trajectory analysis module, the similarity between users is measured by the user clustering attraction similarity formula to cluster users with similar movement patterns together. and The position coordinates are and , in the area There are attractive points with coordinates , the attraction weight is , according to the user and The clustering similarity Divide, the calculation formula is: in, Indicates user To point The Euclidean distance of is the distance attenuation coefficient.

[0008] Furthermore, the user movement trajectory analysis module determines the importance of the user's movement route through a comprehensive evaluation formula of the movement route, finds out the main movement routes and the areas where the user frequently stays, and assumes that the user The position sequence of the time is , from the position arrive The transition probability is , the residence time is , define path entropy for: , let the residence time weight function be: , the importance score of the user's mobile route in different time periods for: .

[0009] Furthermore, the user movement trajectory analysis module combines the user category information obtained by the clustering algorithm with the movement route importance score based on path analysis to construct a user movement trajectory model. Suppose the user category obtained by the clustering algorithm is , each category The user collection included is , for users , which at the time The location is , calculated according to the clustering similarity formula The similarity with the category center is , according to the mobile route importance score formula At the moment The route importance score of In the user movement trajectory model, the user In the future Appear in location Probability The calculation formula is: ,in, , is the Kronecker function, hour , otherwise 0.

[0010] Furthermore, the business demand assessment module assesses the user's business type based on the user's environment determined by the user movement trajectory analysis module: for users in commercial areas, the business types are high-definition video browsing, large file downloads, and frequent mobile payment operations; in residential areas, user business types tend to be video entertainment and smart home control; and the business needs in office areas are large amounts of file transfers and video conferencing.

[0011] Furthermore, the dynamic carrier allocation and scheduling module calculates the number of carriers to be allocated to each area through the carrier allocation quantity formula, and assumes that the number of carriers allocated to the area The number of carriers is , the business demand intensity is , the effective resource capacity of a single carrier is , the resource redundancy coefficient is , the matching coefficient between service demand and carrier resources is , the calculation formula is: .

[0012] Furthermore, the dynamic carrier allocation and scheduling module optimizes the allocation of carrier resources by dynamically adjusting the carrier resource utilization formula, assuming that the area The carrier resource utilization is , the amount of carrier resources used is , the total amount of allocated carrier resources is , the change rate of user business demand is , the carrier resource adjustment speed coefficient is , the calculation formula is: .

[0013] Furthermore, the base station equipment control module dynamically adjusts the base station transmission power through the transmission power adjustment formula, assuming that the base station is in the area The transmission power is , the initial transmission power is , the current number of users in the region is , the user number threshold is , the power adjustment coefficient is , the user moving speed vector is , the signal propagation environment influencing factor is , the formula is: .

[0014] Furthermore, the base station equipment control module determines the on or off state of the base station equipment in a certain area through the equipment switch decision formula. The opening probability is , the business demand intensity is , the device startup threshold is , the predicted user stay time is , the device opening time coefficient is , the calculation formula is: .

[0015] Compared with the existing technology, the 5G base station intelligent control power saving system based on the Internet of Things technology has the following beneficial effects: 1. The present invention realizes the accurate capture of user behavior and dynamic evaluation of business needs through the collaborative work of the data acquisition module, the user movement trajectory analysis module and the business demand assessment module, thereby improving the flexibility and accuracy of 5G base station resource allocation. Traditional base stations often adopt a fixed resource allocation method and cannot be adjusted in real time according to the actual needs of users, resulting in resource waste and low energy efficiency. This system monitors user location and movement trajectory in real time, combines the business demand assessment model, and dynamically adjusts carrier allocation and base station transmission power, which not only reduces energy consumption but also improves user experience.

[0016] 2. The present invention can intelligently allocate carrier resources and base station equipment status to adapt to changing business needs by introducing a dynamic adjustment formula for carrier resource utilization and a device switch decision formula. This resource scheduling method not only improves resource utilization, but also reduces operation and maintenance costs. At the same time, the system can adjust the base station transmission power and equipment startup status in advance according to user mobility trends and predicted dwell time values, thereby further reducing energy consumption while ensuring signal coverage quality. This not only conforms to the current development trend of green communications, but also provides a useful reference for the intelligent management of future 5G networks.

[0017] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a flow chart of the 5G base station intelligent control power saving system based on the Internet of Things technology; Figure 2 Flowchart of the business needs assessment module; Figure 3 This is a flow chart of the dynamic carrier allocation and scheduling module. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0021] Large commercial center scene Data collection: Base station positioning sensors, Wi-Fi sniffers and Bluetooth detectors are deployed in and around large commercial centers. Base station positioning sensors obtain user location information by measuring the signal propagation parameters between the base station and the user device. The Wi-Fi sniffer monitors the surrounding Wi-Fi signals, obtains the Wi-Fi hotspot information connected to the user device, and determines the relative distance between the user and the hotspot based on the attenuation law of signal strength with distance. The Bluetooth detector receives the Bluetooth signal of the user device and calculates the user location based on the signal strength and propagation time. The collected data is quickly transmitted to the cloud platform for storage through the Internet of Things communication protocol. The cloud platform classifies and stores the data according to the regional divisions within the commercial center. Each data record contains metadata information such as the sensor identifier, collection accuracy and error range.

[0022] User movement trajectory analysis, after obtaining the user location data of the commercial center from the cloud platform, data cleaning is performed to remove outliers (such as erroneous location information caused by signal interference), and clustering algorithms are used to cluster users according to the length of stay and similarity of movement paths in different store areas. Users who frequently visit the fashion clothing area and beauty area are clustered into one group, and users who are more active in the dining area are clustered into another group. In the clustering algorithm, assume that users and The position coordinates are and , in the area attractive points (such as shops and elevator entrances), whose coordinates are , the attraction weight is (determined by the popularity and business importance of the location), based on the user and The clustering similarity The clusters are divided and the calculation formula is: ,in, Indicates user To point The Euclidean distance of is the distance attenuation coefficient, and the mobile route comprehensive evaluation formula is used to determine the user's mobile route in different time periods on weekdays, weekends, and holidays. The position sequence of the time is , from the position arrive The transition probability is , the residence time is , define path entropy for: , let the residence time weight function be: , the importance score of the user's mobile route in different time periods for: According to the importance of mobile routes, we find out the main mobile routes and frequently stayed areas. Based on these analyses, we build a user mobile trajectory model to predict the changes in user density in each area in different time periods. We combine the user category information obtained by the clustering algorithm with the importance score of the mobile route based on path analysis to build a user mobile trajectory model. Let the user category obtained by the clustering algorithm be , each category The user collection included is , for users , which at the time The location is , calculated according to the clustering similarity formula The similarity with the category center is , according to the mobile route importance score formula At the moment The route importance score of In the user movement trajectory model, the user In the future Appear in location Probability The calculation formula is: ,in, , is the Kronecker function, hour , otherwise 0.

[0023] Business demand assessment, combined with the user movement trajectory model, determines the number of users in different areas of the commercial center (such as each floor, store cluster area) ,For the business type characteristics of users in commercial areas, such as high-definition video browsing (for watching product promotion videos and live broadcasts), large file downloads (such as coupons and electronic map downloads), and frequent mobile payment operations, the business type set is , for business type , whose weight is (determined based on the impact of the service on network performance and the expected frequency of use, while taking into account the user's service preference index factor), user preference for the service The probability of using ,business The standard resource requirement coefficient is (For high-definition video services, the bandwidth resource demand coefficient is related to the video resolution, frame rate, and encoding format). The service demand intensity of each region is calculated using the service demand intensity quantification formula. The formula is: , consider the user movement trend, such as the flow of users from the mall entrance to the event area during the promotion period, and introduce the user inflow rate and outflow rate , and business demand adjustment factors ,The business demand change rate formula based on user mobility trends captures the changing trend of business demand. The formula is: , and adjust the assessment of business needs in a timely manner.

[0024] Dynamic carrier allocation and scheduling: Based on the results of business demand assessment, more carrier resources are allocated to areas with dense users and high business demand (such as venues for promotional activities and areas with a concentration of popular restaurants), taking into account carrier frequency characteristics, coverage, and coordination of different base stations. The number of carriers is , the business demand intensity is , the effective resource capacity of a single carrier is , the resource redundancy coefficient is , (dynamically adjusted according to network congestion and service demand stability), the matching coefficient between service demand and carrier resources is , the number of carriers to be allocated to each area is calculated using the carrier allocation formula, which is: , allocate more 5G carriers to the promotional event site to meet the needs of a large number of users for simultaneous high-definition video live viewing and real-time social sharing, and count the amount of carrier resources used by real-time monitoring of user service types and traffic on the carrier. and the total amount of allocated carrier resources , use the carrier resource utilization dynamic adjustment formula to allocate idle and low-utilization carrier resources in other areas to this area. The calculation formula is: ,in Adjust the speed factor for carrier resources to ensure a smooth user network experience during the event while avoiding wasting resources in other areas.

[0025] Base station equipment control, based on the user movement trajectory analysis results and dynamic carrier allocation, adjust the base station transmission power and set the base station in the area The transmission power is , the initial transmission power is , the current number of users in the region is , the user number threshold is , the power adjustment proportional coefficient is ,area The user moving speed vector is (calculated through the continuous change of user location data), the signal propagation environment impact factor is (Taking into account factors such as building shielding and electromagnetic interference), the base station transmit power is dynamically adjusted according to the base station transmit power adjustment formula. The formula is: Before the promotion begins, when a large number of users flock to the mall entrance and the surrounding areas of the event site, the base station transmission power in these areas is increased to ensure the quality of signal coverage. After the event ends, users gradually leave and the transmission power in the corresponding areas is reduced. At the same time, according to the intensity of business demand , the device startup threshold is , the predicted user stay time is , the device opening time coefficient is , the device switch decision formula is used to determine whether the base station device is turned on or off in a certain area. The calculation formula is: For example, after the mall closes late at night, most non-essential base station equipment is turned off. Before the mall opens, the equipment is turned on in advance based on the predicted user arrival trend, realizing intelligent energy-saving operation of base station equipment, ensuring that energy consumption is reduced while meeting user network needs.

[0026] To sum up, in the scenario of a large commercial center, the data collection, user movement trajectory analysis, business demand assessment, dynamic carrier allocation and scheduling, and base station equipment control modules of the present invention work together. Data collection provides a foundation, trajectory analysis helps to grasp user behavior, business assessment accurately quantifies demand, carrier allocation adjusts resources as needed, and equipment control intelligently saves energy, ultimately meeting diverse business needs, achieving intelligent control and power saving, and improving network service quality and operational efficiency. Example

[0027] Large sports venues Data collection: base station positioning sensors, Wi-Fi sniffers and Bluetooth detectors are deployed in the venue and surrounding areas. The base station positioning sensors use multi-base station signal positioning technology to accurately obtain the location information of spectators and staff in and around the venue, such as the locations in different areas of the stands, venue passages, and parking lots. Wi-Fi sniffers monitor the use of Wi-Fi hotspots in and around the venue to assist in locating users and determine their activity range. Bluetooth detectors scan the Bluetooth signals of user devices to enhance positioning accuracy, especially in crowded areas. The collected data is transmitted to the cloud platform in real time through the Internet of Things communication protocol. The cloud platform classifies and stores the data according to different functional areas in the venue (such as stands, competition venues, rest areas, and dining areas). Each data record contains metadata information such as sensor identifier, collection accuracy, and error range.

[0028] User movement trajectory analysis, after obtaining user location data from the cloud platform, clean the data to remove erroneous information, and use clustering algorithms to cluster users according to their activity patterns in the venue (such as the seating of spectators in different areas of the stands and the distribution of staff working areas in the venue). For example, spectators watching the game in the stands near the rostrum are clustered into one group, and staff responsible for event operations in the venue are clustered into another group. In the clustering algorithm, assume that users and The position coordinates are and , in the area attractive points (such as the competition area in the stadium, the service facilities in the venue), whose coordinates are , the attraction weight is (Determined by the attractiveness of the location to the user, such as the attractiveness of the competition area has a higher weight), according to the user and The clustering similarity The clusters are divided and the calculation formula is: ,in, Indicates user To point The Euclidean distance of is the distance attenuation coefficient. At the same time, the time series analysis method is used to determine the user's movement routes at different stages of the event (such as before, during, and after the event). The position sequence of the time is , from the position arrive The transition probability is , the residence time is , define path entropy for: , let the residence time weight function be , the importance score of the user's mobile route in different time periods for: , build a user movement trajectory model, predict the changes in personnel distribution in each area during the event, and combine the user category information obtained by the clustering algorithm with the importance score of the movement route based on path analysis to build a user movement trajectory model. Suppose the user category obtained by the clustering algorithm is , each category The user collection included is , for users , which at the time The location is , calculated according to the clustering similarity formula The similarity with the category center is , according to the mobile route importance score formula At the moment The route importance score of In the user movement trajectory model, the user In the future Appear in location Probability The calculation formula is: ,in, , is the Kronecker function, hour , otherwise 0.

[0029] Business demand assessment, combined with the user movement trajectory model, determines the number of users in different areas of the venue , for user service needs in sports event scenarios, such as a large number of viewers simultaneously watching high-definition video live broadcasts, taking real-time photos and uploading and sharing them, and querying event information online, the service type set is , for business type , whose weight is (determined based on the impact of the service on network performance and the expected frequency of use, while taking into account the priority factors of event-related services), user's service The probability of using ,business The standard resource requirement coefficient is (For example, the bandwidth resource requirement coefficient of high-definition video live broadcast service is related to video quality related parameters). The service demand intensity of each region is calculated by the service demand intensity quantification formula. The calculation formula is: , consider the user movement trend, such as the flow of spectators from the stands to the dining area and toilets during the halftime break, and introduce the user inflow rate and outflow rate , and business demand adjustment factors , the business demand change rate is calculated according to the business demand change rate formula based on user mobility trends. The calculation formula is: , so as to adjust the assessment of business needs in a timely manner.

[0030] Dynamic carrier allocation and scheduling, based on the business demand assessment results, targets areas with high business demand such as event sites (stands, surrounding areas of competition venues) and crowded areas (dining areas, souvenir shops), and comprehensively considers carrier frequency characteristics, coverage range, and coordination between different base stations to allocate carrier resources. The number of carriers is , the business demand intensity is , the effective resource capacity of a single carrier is , the resource redundancy coefficient is , (its value can be adjusted dynamically according to the changes in network load during the event), the matching coefficient between business demand and carrier resources is , the number of carrier allocations is determined by the carrier allocation number calculation formula, which is: For example, before the game starts, appropriate carrier resources are allocated to the stands based on the predicted audience attendance and service needs to ensure that the audience can watch the game live in high definition. By real-time monitoring of user service types and traffic on the carrier, the amount of carrier resources used can be counted. and the total amount of allocated carrier resources , the allocation of carrier resources is optimized by using the dynamic adjustment formula of carrier resource utilization. The calculation formula is: ,in, Adjust the speed coefficient for carrier resources and reallocate idle and low-utilization carrier resources in other areas to areas with increased demand. For example, when spectators flow from the stands to the exits after a game, reallocate carrier resources from some stands to the exits and surrounding areas, thereby improving carrier resource utilization efficiency and ensuring that users have a good network experience during the event.

[0031] Base station equipment control, adjust the base station transmission power according to the user movement trajectory analysis results and dynamic carrier allocation, set the base station in the area The transmission power is , the initial transmission power is , the current number of users in the region is , the user number threshold is , the power adjustment proportional coefficient is , the user moving speed vector is (calculated through the continuous change of user location data), the signal propagation environment impact factor is (Taking into account the venue building structure and surrounding electromagnetic environment factors), according to the base station transmission power adjustment formula, the formula for dynamically adjusting the base station transmission power is: Before the game starts, when a large number of spectators rush into the venue, the base station transmission power at the venue entrance and surrounding areas is increased. After the game ends and the spectators leave, the transmission power in the corresponding area is reduced. At the same time, according to the intensity of business demand , the device startup threshold is , the predicted user stay time is , the device opening time coefficient is , the base station equipment switch decision formula is used to determine the on or off state of the base station equipment. The calculation formula is: For example, during the preparation of an event, the corresponding equipment is turned on according to the staff's activity area and business needs. After the event, non-essential equipment is turned off to achieve intelligent energy-saving operation of base station equipment, reduce base station energy consumption, and ensure that user network needs are met during the event.

[0032] To sum up, in large-scale sports venues, the various modules of the system work closely together, and after collecting data, trajectory analysis is used to grasp the movement of people. Business evaluation accurately responds to event needs, carrier allocation ensures network smoothness, and equipment control saves energy while ensuring services. The overall optimization of base station resource allocation and energy-saving operation are achieved, thereby improving users' event network experience.

[0033] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. The 5G base station intelligent control power saving system based on Internet of Things technology is characterized by: The system includes: a data acquisition module, a user movement trajectory analysis module, a service demand assessment module, a dynamic carrier allocation and scheduling module, and a base station equipment control module; Data collection module: By deploying base station positioning sensors, Wi-Fi sniffers, and Bluetooth detectors in the base station coverage area, the user location information and device connection status data are collected, and the data is transmitted to the cloud platform for storage in real time using the Internet of Things communication protocol; User movement trajectory analysis module: obtains newly collected user location data from the cloud platform, groups users using clustering algorithms, clusters user groups with similar movement patterns together, processes user location data using a comprehensive movement route evaluation formula, finds out the main movement routes and frequently stayed areas, determines the movement routes, stay times, and frequently active areas of users in different time periods, and combines the user category information obtained by clustering with the movement route importance score based on path analysis to build a user movement trajectory model; Business demand assessment module: Combine the data from the user movement trajectory analysis module to determine the number of users in different areas. According to the different environments and movement trends of users, the business demand intensity of each area is calculated through the user business demand intensity quantification formula. The business demand intensity is , the business type set is , each business type The weight of , users have a strong interest in the business The probability of using ,business The standard resource requirement coefficient is ,area The number of users is , the calculation formula is: At the same time, according to the user mobility trend, the business demand change rate formula based on the user mobility trend is used to capture the change trend of business demand. Time zone The business demand intensity is , after a time interval The business demand intensity after , the business demand change rate is , considering the changes in business needs caused by user mobility, introducing the user inflow rate and outflow rate , and business demand adjustment factors , the calculation formula is: ; Dynamic carrier allocation and scheduling module: Based on the business demand assessment information, dynamic carrier allocation strategies are formulated according to the frequency characteristics, coverage and coordination between different base stations of the carriers in areas with high business demand and dense users. At the same time, by monitoring the business types and traffic of users on the carriers, statistics are collected on the resource usage of each carrier and changes in user mobility, and the carrier resource utilization rate is dynamically adjusted using the formula to allocate idle and low-utilization carrier resources. Base station equipment control module: adjusts the base station transmission power according to the user movement trajectory analysis results and dynamic carrier allocation, increases the power in the area where users are close to ensure signal coverage quality, reduces the power when users leave the area to reduce energy consumption, and determines whether the base station equipment is on or off in a certain area based on the business demand intensity and the predicted value of the user's stay time.

2. According to claim 1, the 5G base station intelligent control power saving system based on Internet of Things technology is characterized in that: In the user movement trajectory analysis module, the user clustering attraction similarity formula is used to measure the similarity between users and cluster users with similar movement patterns. and The position coordinates are and , in the area There are attractive points with coordinates , the attraction weight is , according to the user and The clustering similarity Divide, the calculation formula is: ,in, Indicates user To point The Euclidean distance of is the distance attenuation coefficient.

3. According to claim 1, the 5G base station intelligent control power saving system based on Internet of Things technology is characterized in that: The user movement trajectory analysis module determines the importance of the user's movement route through the comprehensive evaluation formula of the movement route, finds out the main movement routes and the areas where the user frequently stays, and assumes that the user The position sequence of the time is , from the position arrive The transition probability is , the residence time is , define path entropy for: , let the residence time weight function be: , the importance score of the user's mobile route in different time periods for: .

4. According to claim 1, the 5G base station intelligent control power saving system based on Internet of Things technology is characterized in that: The user movement trajectory analysis module combines the user category information obtained by the clustering algorithm with the movement route importance score based on path analysis to construct a user movement trajectory model. Suppose the user category obtained by the clustering algorithm is , each category The user collection included is , for users , which at the time The location is , calculated according to the clustering similarity formula The similarity with the category center is , according to the mobile route importance score formula At the moment The route importance score of In the user movement trajectory model, the user In the future Appear in location Probability The calculation formula is: ,in, , is the Kronecker function, hour , otherwise 0.

5. According to claim 1, the 5G base station intelligent control power saving system based on Internet of Things technology is characterized in that: The business demand assessment module assesses the user's business type based on the user's environment determined by the user movement trajectory analysis module: for users in commercial areas, the business types are high-definition video browsing, large file downloads, and frequent mobile payment operations; in residential areas, user business types tend to be video entertainment and smart home control; and in office areas, the business needs are large amounts of file transfers and video conferencing.

6. The 5G base station intelligent control power saving system based on Internet of Things technology according to claim 1 is characterized in that: The dynamic carrier allocation and scheduling module calculates the number of carriers to be allocated to each area through the carrier allocation quantity formula, and sets the number of carriers allocated to the area The number of carriers is , the business demand intensity is , the effective resource capacity of a single carrier is , the resource redundancy coefficient is , the matching coefficient between service demand and carrier resources is , the calculation formula is: .

7. The 5G base station intelligent control power saving system based on Internet of Things technology according to claim 1 is characterized in that: The dynamic carrier allocation and scheduling module optimizes the allocation of carrier resources by dynamically adjusting the carrier resource utilization formula. The carrier resource utilization is , the amount of carrier resources used is , the total amount of allocated carrier resources is , the change rate of user business demand is , the carrier resource adjustment speed coefficient is , the calculation formula is: .

8. The 5G base station intelligent control power saving system based on Internet of Things technology according to claim 1 is characterized in that: The base station equipment control module dynamically adjusts the base station transmission power through the transmission power adjustment formula. The transmission power is , the initial transmission power is , the current number of users in the region is , the user number threshold is , the power adjustment proportional coefficient is , the user moving speed vector is , the signal propagation environment influencing factor is , the formula is: .

9. The 5G base station intelligent control power saving system based on Internet of Things technology according to claim 1 is characterized in that: The base station equipment control module determines the on or off state of the base station equipment in a certain area through the equipment switch decision formula. The opening probability is , the business demand intensity is , the device startup threshold is , the predicted user stay time is , the device opening time coefficient is , the calculation formula is: .

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