Layout optimization method and device of relay equipment, equipment, medium and program product

Abstract

The invention provides a layout optimization method and device of relay equipment, equipment, a medium and a program product, and relates to the technical field of communication, the method comprises the following steps: analyzing real-time data in a mobile communication network to obtain user position information and equipment use data; generating a popularity map of mobile communication based on the user position information and the equipment use data; and optimizing the layout scheme of the relay equipment based on the popularity map and the performance evaluation result of the relay equipment. Through real-time data collection and analysis and dynamic popularity map generation, quick response to user behaviors and network states is ensured, network optimization is more timely and accurate, meanwhile, the relay equipment layout is optimized through the popularity map and the performance evaluation result of the relay equipment, resource waste can be avoided, and the network optimization efficiency is improved. And the efficiency, the performance and the reliability of the mobile communication network are improved, so that the requirements of users are better met.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, and in particular to a relay device layout optimization method, device, equipment, medium and program product. BACKGROUND

[0002] With the rapid development of mobile communication technology, relay devices play an increasingly important role in expanding network coverage and improving communication quality. Currently, relay device layout involves mobile terminal group information collection and resource allocation of relay devices. In collecting mobile terminal group information, multiple wireless transmission technologies are involved, but wireless transmission technologies have limitations. In addition, when allocating resources through relay devices, there are problems of energy consumption and complexity of task completion mode. When multiple relays coexist, inefficient allocation methods can lead to greater energy consumption, and different task completion modes have different energy consumptions, so selecting the appropriate task completion mode to optimize energy is a challenge. Therefore, how to realize the layout optimization of relay devices has become a problem to be solved. SUMMARY

[0003] The present application provides a relay device layout optimization method, device, equipment, medium and program product to solve the layout optimization defects of relay devices in the prior art, avoid resource waste, improve the efficiency, performance and reliability of mobile communication networks, and better meet the needs of users.

[0004] The present application provides a relay device layout optimization method, comprising the following steps: Analyzing real-time data in a mobile communication network to obtain user location information and device usage data; Generating a mobile communication heat map based on the user location information and the device usage data; Optimizing the layout scheme of the relay device based on the heat map and the performance evaluation result of the relay device.

[0005] According to the relay device layout optimization method provided by the present application, the layout scheme of the relay device is optimized based on the heat map and the performance evaluation result of the relay device, which comprises: Determining the change information of the hot spot area in the heat map over time based on a particle swarm algorithm; Determining the performance evaluation result of the relay device in the layout scheme based on the change information of the hot spot area over time; In the case where the performance evaluation result does not meet the preset condition, the layout scheme of the relay device is optimized.

[0006] The method comprises the following steps of: determining the change information of a hotspot region in a heat map over time based on a particle swarm algorithm, wherein the heat map is generated based on user location information and device usage data. initializing the location and speed of the user in the heat map; iteratively updating the location and speed of the user; determining the user distribution information and traffic change information in the heat map after each iteration update is completed; determining the hotspot region based on the user distribution information; determining the change information of the hotspot region over time based on the traffic change information.

[0007] The method comprises the following steps of: determining the change information of a hotspot region in a heat map over time based on a particle swarm algorithm, wherein the heat map is generated based on user location information and device usage data. inputting the user location information and the device usage data into a Gaussian mixture model to obtain a probability density estimation result of data points output by the Gaussian mixture model; obtaining the distribution of data in space based on the probability density estimation result; generating the heat map of the mobile communication based on the distribution of data in space; The Gaussian mixture model is trained based on user location sample information and device usage sample data.

[0008] The method comprises the following steps of: determining the change information of a hotspot region in a heat map over time based on a particle swarm algorithm, wherein the heat map is generated based on user location information and device usage data. determining the priority coverage region of the relay device based on the heat map; analyzing the signal transmission of the priority coverage region to determine the signal coverage range and quality of the priority coverage region; determining the layout scheme of the relay device based on network topology data and the signal coverage range and quality of the priority coverage region.

[0009] The method comprises the following steps of: determining the change information of a hotspot region in a heat map over time based on a particle swarm algorithm, wherein the heat map is generated based on user location information and device usage data. determining the potential placement position of the relay device based on the heat map; optimizing the layout scheme of the relay device based on the signal propagation characteristics, topography and building shielding information of the potential placement position.

[0010] The application further provides a relay device layout optimization device comprising the following modules: an analysis module configured to analyze real-time data in the mobile communication network to obtain user location information and device usage data; a heat map generation module configured to generate a heat map of the mobile communication based on the user location information and the device usage data; a layout optimization module configured to optimize a layout scheme of the relay device based on the heat map and a performance evaluation result of the relay device.

[0011] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the layout optimization method of the relay device according to any one of the above when executing the computer program.

[0012] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program is executable on a processor to implement the layout optimization method of the relay device according to any one of the above.

[0013] The application further provides a computer program product comprising a computer program, and the computer program is executable on a processor to implement the layout optimization method of the relay device according to any one of the above.

[0014] The layout optimization method, device, equipment, medium and program product of the relay device provided by the application obtain user location information and device usage data by analyzing real-time data in the mobile communication network, generate a heat map of the mobile communication based on the user location information and the device usage data, and optimize a layout scheme of the relay device based on the heat map and a performance evaluation result of the relay device. The application ensures quick response to user behavior and network state through real-time data collection and analysis and dynamic heat map generation, makes network optimization more time-efficient and accurate, and optimizes the relay device layout through the heat map and the performance evaluation result of the relay device, so that resource waste can be avoided, the efficiency, performance and reliability of the mobile communication network are improved, and the needs of users are better met. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0016] Figure 1 is a flowchart of the layout optimization method of the relay device provided by the application.

[0017] Figure 2It is a structural schematic diagram of the data acquisition device provided by the application.

[0018] Figure 3 It is a flowchart of the relay device optimization layout method based on the mobile communication heat map provided by the application.

[0019] Figure 4 It is a structural schematic diagram of the relay device layout optimization apparatus provided by the application.

[0020] Figure 5 It is a structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0022] In the related art, the relay device layout methods mainly include laying out the relay device by collecting mobile terminal group information and allocating resources by the relay device to realize the layout of the relay device. However, the above layout methods have the following problems: For laying out the relay device by collecting mobile terminal group information, the current wireless transmission technologies include WiFi, Bluetooth, Zigbee, etc. WiFi has the advantages of fast communication speed and high real-time performance, and is suitable for mobile group communication, but has the problem of high power consumption, which is not conducive to the battery life of the mobile terminal. Bluetooth has low power consumption, but the number of mobile terminals that can be connected is limited, and usually only a few links can be established, so it is not suitable for group communication with a large number of mobile terminals. Zigbee also has the advantage of low power consumption, and can automatically and flexibly form a network, and can support a large number of devices in the network, and is suitable for mobile group communication, but has the problems of long network access time and slow communication speed, resulting in poor real-time performance of group communication, and the communication efficiency decreases significantly as the number of mobile terminals increases.

[0023] Allocating resources by the relay device, the demand of mobile devices for solving complex application computing is increasing. However, the mobile device is small in size, which means that its computing power is limited, and how to complete the complex application computing in time still needs to face some challenges. First, when multiple relay devices coexist, inefficient allocation methods may cause greater energy consumption. Second, different task completion methods have different energy consumption, and different task completion methods will bring additional complexity. How to choose the best way from different task completion modes to optimize energy consumption requires a good allocation method.

[0024] To address the aforementioned issues, this invention proposes a method for optimizing the layout of relay devices, specifically a method for optimizing the layout of relay devices based on a mobile communication heat map.

[0025] The following is combined Figures 1-5 The present invention describes a method, apparatus, device, medium, and program product for optimizing the layout of relay devices.

[0026] Figure 1 This is a flowchart illustrating the layout optimization method for relay devices provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 101: Analyze the real-time data in the mobile communication network to obtain user location information and device usage data.

[0027] Pre-deploy data acquisition equipment, the structural diagram of which is shown below. Figure 2 As shown. Real-time data is collected from mobile communication networks using deployed data acquisition devices. This real-time data can be obtained through network monitoring devices, base station logs, user equipment location information, and other channels. For example, network monitoring devices can monitor data transmission in the mobile communication network in real time, thereby capturing network data packets and recording information such as data flow direction, traffic volume, and transmission speed. Base station logs contain connection information between the base station and mobile devices, such as connection time, signal strength, and the approximate location of the mobile device (based on base station positioning technology). Location information is obtained through the mobile device's own positioning functions, such as GPS (Global Positioning System), A-GPS (Assisted Global Positioning System), Wi-Fi positioning, and base station positioning technologies.

[0028] Optionally, real-time data may include user location data, network connection data, device status data, user behavior data, etc.

[0029] Big data analytics is used to process and analyze collected real-time data in real time to accurately reflect changes in user distribution and popularity, eliminating outliers and redundant information. Big data analytics includes various methods such as data mining, machine learning, and statistical analysis. For example, in data mining, clustering algorithms can be used to group data points with similar characteristics together to discover user distribution patterns. For instance, the K-Means clustering algorithm can divide users into different groups based on location information, with each group representing a user clustering area. In machine learning, regression analysis can be used to predict trends in user popularity. For example, by analyzing historical and current real-time data, a regression model can be built to predict the number of users or the frequency of device usage in a certain area within a future time period.

[0030] Through analysis of a large amount of real-time location data, a distribution map of users in different areas can be drawn. User heat is usually related to factors such as the number of users, device usage frequency, and data traffic, and by analyzing these indicators in real-time data, the heat changes of different areas can be determined.

[0031] After big data analysis and processing, user location information and device usage data are obtained. The user location information includes the geographic location of the user, the user movement trajectory, and the area aggregation situation, and can reflect the real distribution of the user in different time periods. The device usage data includes the application usage frequency and duration, data traffic usage, and device status information, and can clearly present the usage mode of the device, such as the usage frequency of different applications, the peak and trough time periods of data traffic, etc.

[0032] In step 102, a heat map of mobile communication is generated based on the user location information and the device usage data.

[0033] Specifically, according to the processed user location information and device usage data, a heat map of mobile communication is generated, which can reflect the user distribution density, data traffic situation, and communication demand in different areas in real time. At the same time, the heat map can help identify high-density areas and communication hotspots, providing a reference for the layout of relay devices.

[0034] In one embodiment, the user location information and device usage data are input into a Gaussian mixture model to obtain a probability density estimation result of data points output by the Gaussian mixture model; based on the probability density estimation result, the distribution of data in space is obtained; based on the distribution of data in space, a heat map of mobile communication is generated; wherein the Gaussian mixture model is trained based on user location sample information and device usage sample data.

[0035] It can be understood that for complex data distribution of user location information and device usage data, a single Gaussian distribution cannot be well fitted, and a Gaussian mixture model (Gaussian Mixture Model, GMM) can more accurately describe the distribution of data through the combination of multiple Gaussian distributions. The Gaussian mixture model GMM includes the following contents: The Gaussian mixture model refers to a random variable with the following form of distribution: ; wherein, is the probability density function of the random variable under the condition that the parameter is given; is a random variable, representing an observed data point; For all the parameters of the mixture components (including the mean vector and the covariance matrix ), the expression is: ; The probability density function of each mixture component is: ; where is the probability density function of the th Gaussian component, representing the probability density of the random variable given the mean and the covariance matrix .

[0036] denotes that the Gaussian mixture distribution is composed of Gaussian mixture components, is the mixing coefficient of the Gaussian mixture component, and since the Gaussian mixture distribution is also a distribution, we have: ; that is, .

[0037] After inputting the user location information and device usage data into the GMM, the GMM will fit these data into a combination of multiple Gaussian distributions, each with parameters such as mean and covariance. For example, the user's location and device usage data in certain specific areas (such as business districts and office areas) may exhibit a relatively concentrated distribution, while in other areas (such as suburbs) they may exhibit another distribution, and the GMM can model these different types of distributions separately.

[0038] The GMM outputs the probability of each data point belonging to each Gaussian distribution by processing the input data, and these probabilities form the probability density estimation result. For user location information and device usage data, the probability density estimation result can reflect the likelihood of data appearing in different locations. For example, in a certain location, the user's appearance and device usage probability is higher, while in other locations the probability is lower.

[0039] Based on the probability density estimation result, the distribution of data in space can be obtained. Further, based on the distribution of data in space, the area with high probability density and the area with low probability density can be determined to draw a heat map. Specifically, the area with high probability density means that the frequency of user appearance and the frequency of device use are high in this area, which will be represented as a "hot spot" area on the heat map. For example, if in the business district of the city center, the probability density obtained after processing the user location information and device use data by GMM is high, the business district will be displayed as red (representing a hot spot) on the heat map. On the contrary, in some remote areas, the probability density is low, which will be displayed as blue (representing a cold spot) on the heat map.

[0040] The heat map of mobile communication can intuitively reflect the user distribution density and device use situation in different areas, which is crucial for the planning and optimization of mobile communication network. For example, in the hot spot area, the number of base stations can be increased or the network bandwidth can be improved to meet the communication needs of users; while in the cold spot area, network resources can be reasonably configured to avoid waste of resources.

[0041] By generating the heat map of mobile communication, the subsequent relay device layout optimization can be based on the heat map to ensure that the relay device is placed in the most needed place, so that it can fully play its role and handle the communication traffic of the corresponding area, reducing the situation of idle or overload of the device. At the same time, the performance of the entire mobile communication network can be improved, including the expansion of network coverage, the improvement of signal quality, the acceleration of data transmission speed, etc., to provide better communication experience for users.

[0042] In step 103, the layout scheme of the relay device is optimized based on the heat map and the performance evaluation result of the relay device.

[0043] According to the heat map and the performance evaluation result of the relay device, an optimization algorithm is used to calculate the optimized layout scheme of the relay device, so that the relay device can better meet the communication needs of users, while improving the use efficiency of the device and the stability of the network. At the same time, through simulation and experimental verification, it is ensured that the optimization algorithm can effectively improve the network coverage and communication quality, while avoiding resource waste and performance bottleneck.

[0044] In one embodiment, the optimization algorithm can use a particle swarm algorithm. Specifically, based on the particle swarm algorithm, the change information of the hot spot area in the heat map over time is determined; based on the change information of the hot spot area over time, the performance evaluation result of the relay device in the layout scheme is determined; and in the case that the performance evaluation result does not meet the preset condition, the layout scheme of the relay device is optimized.

[0045] It can be understood that in the particle swarm algorithm, particles will move and gather with the change of user data traffic, and each particle can represent a certain amount of user data traffic or communication demand. By dynamically tracking these particles, the change of the hotspot area over time can be obtained, which refers to the area with high user distribution density, large data traffic and high communication demand in the mobile communication network.

[0046] The traditional heat map can only reflect the user distribution and communication demand at a certain moment, while the change information obtained by the particle swarm algorithm can help users understand the hotspot change rule at different time periods within a day or a week. For example, for a city, the hotspots during the day may be concentrated in business districts and schools, while at night they may shift to residential areas. This time-varying information can help users more accurately plan the layout of relay devices.

[0047] Based on the change information of the hotspot area over time, the performance evaluation result of the relay device in the layout scheme can be determined. For example, based on the change information of the hotspot area over time, the working condition of each relay device at different time periods is simulated and analyzed to obtain the corresponding performance evaluation result. The performance evaluation factors can include: 1) Network capacity: based on the change information of the hotspot area over time, it is evaluated whether the relay device can handle the maximum data traffic in the corresponding area at different time periods. For example, during the peak of the hotspot, whether the relay device has enough capacity to support the data transmission of a large number of users.

[0048] 2) Load balancing: check whether the load between different relay devices is reasonable. If the hotspot shifts over time, it may cause some relay devices to be overloaded at certain time periods, while others are idle.

[0049] 3) Stability: consider the network stability of the relay device during the switching of the hotspot area to ensure that users do not experience frequent network interruptions when the hotspot changes.

[0050] In the case where the performance evaluation result does not meet the preset conditions, the layout scheme of the relay device is optimized. The preset conditions include that the network capacity reaches a certain threshold, the load balancing coefficient is within a reasonable range, and the network stability reaches a certain standard. For example, the preset condition can be that the network congestion rate of the relay device during the peak of the hotspot cannot exceed 10%.

[0051] The way to optimize the layout scheme of the relay device includes: Position adjustment: if the performance evaluation result shows that some relay devices cannot meet the demand in the hotspot area, the position of these devices can be adjusted to be closer to the hotspot. For example, the relay device with insufficient performance in the residential area at night when the hotspot is moved to a more suitable position.

[0052] Quantity Adjustment: Adjust the number of repeater devices in a specific area based on the size of the hotspot and the intensity of communication demand. For example, add temporary repeater devices during events held at large venues.

[0053] Equipment upgrade: If it is not suitable to adjust the location or number of relays, consider upgrading the relay equipment to improve its processing capacity and network coverage.

[0054] The present invention provides a method for determining hotspot changes based on the particle swarm optimization algorithm and evaluating and optimizing the layout of relay devices accordingly, which can effectively improve the performance of mobile communication networks and user experience.

[0055] In one embodiment, the location and speed of users in the heat map are initialized; the location and speed of users are iteratively updated; after each iteration update, the user distribution information and traffic change information in the heat map are determined; based on the user distribution information, hotspot areas are determined; based on the traffic change information, the change information of hotspot areas over time is determined.

[0056] As is understood, Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. In this embodiment of the invention, users or data traffic in a mobile communication network can be regarded as particles. Each particle has attributes such as position (corresponding to its location in the network) and velocity (corresponding to the changing trend of users or data traffic). By iteratively calculating the position and velocity of these particles at different times, the changes in hotspot areas over time can be simulated. For example, the distribution of users in a city changes at different times of the day, and PSO can track these changes.

[0057] The particle swarm optimization algorithm includes the following: Assuming in a In the heat map search space of Dimension, there are Users form a community, and users can be abstracted as particles (points) without mass and volume, and this can be extended to... 3D space. 1.1) User Location: (The rest of the text appears to be incomplete and requires further context.) Individual users The position vector in 3D space is represented as: ; in, For the first Individual users The location in 3D space.

[0058] 1.2) User speed: Individual users The velocity vector in 3D space is represented as: ; where, is the velocity of the th user in the th dimension.

[0059] (2) Iterative update: 2.1) Position update: When the th user in the th generation evolves to the th generation, the position update formula is: ; where, denotes the th dimension; the position of the next time (the th generation) is equal to the position of the current time (the th generation) plus the velocity of the next time. .

[0060] 2.2) Velocity update: The velocity update formula is: ; where, is the velocity of the user in the th generation; is the inertia weight, which determines the degree of inheritance of the current velocity of the particle; and are two parameters, is the individual learning coefficient, is the global learning coefficient; and are two random numbers; is the most ideal point (individual optimal position) found by the user in the initial direction in the th generation; is the point that all users think is the most ideal (global optimal position) in the th generation; is the position of the current user in the th generation.

[0061] ​When the user's location and speed are updated, the user's position in the heat map changes. By counting the positions of all users in the heat map, the distribution information of the users can be obtained. Since the movement (speed) of the users affects the data traffic demand (for example, users moving to different areas may have different data usage behaviors), the change information of the data traffic can be inferred according to the movement of the users. After the update and statistics of the user's position are completed, the hot spot area can be determined by analyzing the aggregation degree of the users in the heat map. If a large number of users are aggregated in a certain area, the area is a hot spot area.

[0062] The traffic change information reflects the data demand situation of different areas at different times. When the users move in the heat map, the data traffic demand also changes. By tracking these traffic changes, the change of the hot spot area over time can be determined. For example, if the traffic of a certain area gradually increases over a period of time, the area may be becoming a new hot spot area; on the contrary, if the traffic of a certain hot spot area gradually decreases, the area may no longer be a hot spot area.

[0063] The method based on the particle swarm algorithm can effectively simulate the dynamic behavior of the users in the heat map, and further determine the hot spot area and its change over time, providing a strong basis for the optimization of the layout of the relay device.

[0064] The method for optimizing the layout of the relay device provided by the embodiments of the present application obtains user position information and device usage data by analyzing real-time data in a mobile communication network; generates a heat map of the mobile communication based on the user position information and the device usage data; and optimizes the layout scheme of the relay device based on the heat map and the performance evaluation result of the relay device. The present application ensures fast response to user behavior and network state through real-time data collection and analysis and dynamic heat map generation, making network optimization more timely and accurate. At the same time, the layout of the relay device can be optimized through the heat map and the performance evaluation result of the relay device, which can avoid resource waste and improve the efficiency, performance and reliability of the mobile communication network, thereby better meeting the needs of users.

[0065] Based on the above embodiments, the layout scheme of the relay device is determined in the following manner: Step 110, determining the priority coverage area of the relay device based on the heat map; Step 111, analyzing the signal transmission of the priority coverage area to determine the signal coverage range and quality of the priority coverage area; Step 112, determining the layout scheme of the relay device based on the network topology data and the signal coverage range and quality of the priority coverage area.

[0066] The heat map is an intuitive presentation of the user distribution density, data flow condition and communication demand in a mobile communication network. In the heat map, the area with higher heat value is determined as the priority coverage area. For example, in the central business district of a city, there are a large number of users for shopping, office activities and the like during the day, and the data flow is large and the communication demand is high. These areas will show higher heat in the heat map, and thus will be determined as the priority coverage area of the relay device.

[0067] For the priority coverage area, it is necessary to analyze how far the signal can be transmitted from the existing device (such as a base station or other relay device) or the location where the relay device is to be placed. For example, according to the free space propagation model or the modified propagation model considering the actual environment (buildings, terrain, etc.), the intensity of the signal at different distances is calculated to determine the geographical range that can be effectively covered by the signal. By understanding the boundary of the signal coverage provided by the existing or potential relay device in the priority coverage area, the blind area of the signal coverage or the area with weak signal can be found.

[0068] The signal quality depends not only on the signal strength, but also on the stability of the signal, the interference condition and the like. The signal quality can be evaluated by monitoring the error rate, signal-to-noise ratio and the like. For example, in some areas with a large number of electronic device interferences, such as electronic markets, although the signal strength can be sufficient, the signal quality can be poor due to the many interference factors. By determining the distribution of the signal quality in the priority coverage area, the area with poor signal quality can be found, so that the signal quality of these areas can be improved when the relay device is laid out, for example, by adjusting the device location, increasing the signal enhancement device and the like.

[0069] The network topology data includes the location, connection mode, transmission link bandwidth and the like of the relay device (such as a base station, an access point and the like) in the existing network, which reflects the basic architecture and connection relationship of the network. By combining the signal coverage range and quality of the priority coverage area, the network topology data is used to determine the layout scheme of the relay device. For example, if the signal coverage range of a priority coverage area is insufficient, and according to the network topology, it is found that there is a suitable transmission link and installation location nearby, a relay device can be added at this location to expand the coverage range. At the same time, considering the signal quality problem, if the poor signal quality in a certain area is caused by interference or excessive load of the existing device, the device location can be adjusted, new devices can be added to share the load or anti-interference technology can be used to improve the signal quality in the layout scheme. For example, in the area with serious signal interference, a suitable frequency band or shielding technology can be selected to optimize the layout of the relay device and improve the signal quality.

[0070] The embodiment of the present application can accurately find the place where the user communication demand is the most vigorous by determining the priority coverage area through the heat map, avoid over-investment of resources in some areas with less users and low communication demand, and concentrate resources in the hotspot area that really needs, thereby improving the pertinence and effectiveness of the relay device coverage.

[0071] Based on the above embodiment, after generating the heat map of mobile communication based on the user location information and the device usage data, the method further comprises: Step 120, determining the potential placement position of the relay device based on the heat map; Step 121, optimizing the layout scheme of the relay device based on the signal propagation characteristics, topography and building shielding information of the potential placement position.

[0072] Specifically, according to the heat map, the hotspot area is taken as the key consideration object to determine the potential placement position of the relay device.

[0073] Different environments have different effects on signal propagation. By analyzing the signal propagation characteristics of the environment around the potential placement position, the transmission power, antenna direction and height of the relay device and other parameters can be adjusted. If the signal attenuation around a certain potential position is fast, the transmission power of the device may need to be increased or the antenna direction can be adjusted to ensure that the signal can cover the expected range. At the same time, the signal reflection characteristics can be used to reasonably arrange the relay device, so that the signal can reach some signal blind areas through reflection, such as using the wall of a building to reflect the signal to cover the other side of the street.

[0074] Topography such as mountains, rivers, valleys and other natural topography will have a significant impact on signal propagation. Mountains may block signal propagation, forming a signal shadow area; while valleys may cause repeated reflection of signals in them, causing signal interference. In areas with mountains and other obstacles, the relay device can be placed on the top of the mountain or on the slope to overcome the obstacles for signal propagation. For complex topography such as valleys, a distributed relay device layout can be used to pass the signal through multiple devices in relay mode to reduce signal interference and ensure signal coverage in the entire area.

[0075] Buildings are an important factor affecting signal propagation, tall buildings can cause signal shielding, forming a signal blind area behind it. At the same time, different building materials have different signal penetration capabilities, for example, the signal penetration capability of concrete structure buildings is weak, while the glass curtain wall building is relatively good. According to the height, distribution and structure information of the building, the layout of the relay device is optimized, if there is a tall building in front of a potential placement position, the device position may need to be adjusted to be located on the top or side of the building to reduce the shielding effect. For large building groups, indoor and outdoor collaborative coverage can be used, and relay devices are reasonably arranged inside and around the building, signals are penetrated from outdoor devices to indoor, and small relay devices are set in the indoor to enhance signal coverage, such as setting signal enhancers on each floor of large office buildings.

[0076] The embodiment of the application determines the potential placement position of the relay device through the heat map, can accurately focus on the area where the user is dense and the communication demand is strong, ensures that the relay device is mainly deployed in the place where the network coverage really needs to be strengthened, avoids wasting resources in the area where the user is few, and realizes efficient coverage of the hot area.

[0077] In order to further analyze and describe the layout optimization method of the relay device provided by the application, the following embodiments are referred to.

[0078] The embodiment of the application specifically proposes a relay device optimization layout method based on mobile communication heat map. The method collects real-time data in the mobile communication network by deploying real-time data collection devices, and uses big data analysis technology to process and analyze the real-time data in real time to accurately reflect the change of user distribution and heat; according to the processed user position information and device use data, a mobile communication heat map is generated, wherein the mobile communication heat map can help identify high-density areas and communication hotspots; according to the heat map and the performance evaluation result of the relay device, an optimization algorithm is used to calculate the optimization layout scheme of the relay device. The application ensures the rapid response to user behavior and network state through real-time data collection and analysis and dynamic heat map generation, makes the network optimization more time-effective and accurate, considers the signal propagation characteristics, topography and building shielding and other factors, optimizes the layout of the relay device, so that it can maximize the coverage of the target area, thereby improving the network coverage range and communication quality.

[0079] Reference Figure 3 The relay device optimization layout method based on mobile communication heat map mainly includes the following processing procedures: Step one: Data collection and processing: First, deploy real-time data collection equipment, then collect real-time data in mobile communication networks through pre-deployed data collection equipment, such as through network monitoring equipment, base station logs, user equipment location information, etc. to obtain real-time data, and use big data analysis technology to process and analyze real-time data in real time to accurately reflect the changes in user distribution and heat, and eliminate abnormal values and redundant information in the data.

[0080] Step two: Generate heat map: According to the processed user location information and device usage data, generate a mobile communication heat map that can reflect the user distribution density, data flow situation and communication demand of different areas in real time. The heat map can help identify high-density areas and communication hotspots, providing a reference for the layout of relay devices.

[0081] Step three: Relay device location planning: Combine network topology data to comprehensively evaluate the layout scheme of relay devices, and use data-driven methods to optimize the layout of relay devices based on real-time data and comprehensive consideration of indicators to avoid resource waste and performance problems.

[0082] Step four: Optimize layout calculation: According to the heat map and relay device performance evaluation results, use optimization algorithms to calculate the optimized layout scheme of relay devices to better meet the communication needs of users and improve the efficiency of devices and the stability of the network. At the same time, through simulation and experimental verification, it is ensured that the optimization algorithm can effectively improve the network coverage range and communication quality, while avoiding resource waste and performance bottlenecks.

[0083] Step five: Implementation and adjustment of layout scheme: Once the layout scheme of relay devices is determined, the calculated optimized layout scheme can be applied to the actual network, and real-time adjustments can be made according to network operation and user feedback to ensure that the relay device layout is always in the optimal state to adapt to changes in user demand and network environment.

[0084] Step six: Establish a mechanism for continuous optimization: Regularly evaluate and adjust the layout scheme of relay devices to adapt to the rapid development of mobile communication technology and changes in user demand, and establish a network monitoring system to monitor network performance and user experience in real time, and timely discover and solve problems to ensure the continuous optimization of relay device layout and the stable operation of the network.

[0085] Optionally, the data collection and processing in step one also includes: User data: Collect mobile user location information, call and data transmission volume, and device usage data; Network data: Obtain the connection between base stations, network topology, and signal propagation characteristics; Environmental data: Consider geographical environmental factors such as terrain and the impact of buildings on signal propagation.

[0086] Optionally, the generation of the heat map in step two also includes the following processing: Data processing: Process and analyze the collected data to generate a heat map; Map visualization: Visualize the heat map to display information such as user density, data flow and signal strength in different areas.

[0087] Optionally, the relay device location planning in step three also includes: Priority area: Determine the priority coverage area according to the heat map, high user density area and high flow area; Signal propagation analysis: Use signal propagation models to analyze the signal coverage range and quality of candidate locations; Topology optimization: Consider the network topology structure to select the best relay device location to ensure the continuity of the coverage range and maximize the coverage rate.

[0088] Optionally, the priority area also includes determining the potential placement location of the relay device based on the target area and the heat map; considering factors such as signal propagation characteristics, topography, building shielding, etc., to optimize the layout of the relay device.

[0089] Optionally, the optimization algorithm in step four uses a particle swarm algorithm: (1) Assume that in a dimensional heat map search space, there are users forming a colony, which can be abstracted as particles (points) without mass and volume, and extended to dimensional space. 1.1) User position: The position vector of the th user in the dimensional space is represented as: ; Where, is the position of the th user in the dimensional space.

[0090] 1.2) User speed: The speed vector of the th user in the dimensional space is represented as: ; Where, is the speed of the th user in the dimensional space.

[0091] (2) Iterative update: 2.1) Position update: At the t-th time step, the position of the i-th user is updated as follows: where d represents the dimensionality of the space; ; the position of the i-th user at the next time step (t+1) is equal to the position of the i-th user at the current time step (t) plus the velocity of the i-th user at the next time step.

[0092] 2.2) Velocity update: The velocity of the i-th user at the t-th time step is updated as follows: where v represents the velocity of the i-th user at the t-th time step; is the inertia weight, which determines the degree of inheritance of the particle to the current velocity; are two parameters, is the individual learning coefficient, is the global learning coefficient; are two random numbers;

[0093] Optionally, the generating the heat map in step ii) further comprises using a Gaussian Mixture Model (GMM) to estimate the probability density of the continuous data points, to more accurately capture the user distribution and traffic changes. The Gaussian Mixture Model (GMM) comprises the following contents: The Gaussian Mixture Model refers to a random variable X having a distribution of the following form: where f(x|θ) represents the probability density function of the random variable X given the parameters θ; are parameters of all mixed components (including mean vector​​​​​​​​​​​​​​​​​​​​​​​​​​​ a set of covariance matrices, , the expression of the Gaussian mixture distribution is: ; The probability density function of each mixed component is: ; where, is the probability density function of the m-th Gaussian component, which represents the probability density of the random variable given the mean and the covariance matrix .

[0094] indicates that the Gaussian mixture distribution is composed of Gaussian mixture components, is the mixing coefficient of the Gaussian mixture component, since the Gaussian mixture distribution is also a distribution, so: ; that is, .

[0095] The embodiment of the application ensures quick response to user behavior and network status through real-time data collection and analysis and dynamic heat map generation, making network optimization more timely and accurate; by considering factors such as signal propagation characteristics, topography and building shielding, the layout of the relay device is optimized to maximize coverage of the target area and reduce the occurrence of signal weak points and signal blind areas, thereby improving network coverage and communication quality; through the application of intelligent layout algorithms, resource waste can be avoided, such as avoiding over-deployment of relay devices or deploying them in unnecessary areas; by continuously monitoring network performance and user experience, the relay device layout scheme can be adjusted in a timely manner to improve network coverage and communication quality, meet the growing communication needs of users, and at the same time, maximize the avoidance of resource waste and performance bottleneck problems.

[0096] The layout optimization device of the relay device provided by the application will be described below. The layout optimization device of the relay device described below can be mutually referred to the layout optimization method of the relay device described above.

[0097] With reference to Figure 4 , the layout optimization device of the relay device provided by the application includes an analysis module 401, a heat map generation module 402 and a layout optimization module 403.

[0098] The analysis module 401 is used for analyzing real-time data in a mobile communication network to obtain user location information and device usage data. ​​The heat map generation module 402 is configured to generate a heat map of mobile communication based on the user position information and the device usage data. The layout optimization module 403 is configured to optimize a layout scheme of the relay device based on the heat map and a performance evaluation result of the relay device.

[0099] The layout optimization apparatus of the relay device provided by the embodiment of the present application can obtain user position information and device usage data by analyzing real-time data in a mobile communication network, generate a heat map of mobile communication based on the user position information and the device usage data, and optimize a layout scheme of the relay device based on the heat map and a performance evaluation result of the relay device. The present application can ensure quick response to user behavior and network state through real-time data collection and analysis and dynamic heat map generation, and make network optimization more time-efficient and accurate. Meanwhile, the relay device layout can be optimized based on the heat map and the performance evaluation result of the relay device, so that resource waste can be avoided, and the efficiency, performance and reliability of the mobile communication network can be improved, thereby better meeting the needs of users.

[0100] In one embodiment, the layout optimization module 403 is specifically configured to: determine the change information of the hotspot area in the heat map over time based on a particle swarm algorithm, determine the performance evaluation result of the relay device in the layout scheme based on the change information of the hotspot area over time, and optimize the layout scheme of the relay device in the case that the performance evaluation result does not meet a preset condition.

[0101] In one embodiment, the layout optimization module 403 is specifically configured to: initialize the position and speed of a user in the heat map, iteratively update the position and speed of the user, determine user distribution information and traffic change information in the heat map after each iteration update is completed, determine the hotspot area based on the user distribution information, and determine the change information of the hotspot area over time based on the traffic change information.

[0102] In one embodiment, the heat map generation module 402 is specifically configured to: input the user position information and the device usage data into a Gaussian mixture model to obtain a probability density estimation result of data points output by the Gaussian mixture model, obtain the distribution of data in space based on the probability density estimation result, and generate the heat map of mobile communication based on the distribution of data in space, wherein the Gaussian mixture model is trained based on user position sample information and device usage sample data.

[0103] In one embodiment, the layout scheme of the relay device is determined based on the following manner: Based on the heat map, a priority coverage area of the relay device is determined; signal transmission of the priority coverage area is analyzed to determine signal coverage range and quality of the priority coverage area; based on network topology data and the signal coverage range and quality of the priority coverage area, a layout scheme of the relay device is determined.

[0104] In one embodiment, the layout optimization module 403 is further configured to: Based on the heat map, a potential placement position of the relay device is determined; based on signal propagation characteristics, topography and building shielding information of the potential placement position, the layout scheme of the relay device is optimized.

[0105] Figure 5 An example of a schematic diagram of a physical structure of an electronic device is shown in Figure 5 As shown, the electronic device can include a processor 510, a communications interface 520, a memory 530 and a communications bus 540, wherein the processor 510, the communications interface 520 and the memory 530 communicate with each other through the communications bus 540. The processor 510 can invoke the logical instructions in the memory 530 to execute a layout optimization method of a relay device, the method including: analyzing real-time data in a mobile communication network to obtain user location information and device usage data; based on the user location information and the device usage data, generating a heat map of the mobile communication; based on the heat map and performance evaluation results of the relay device, optimizing a layout scheme of the relay device.

[0106] In addition, the logical instructions in the memory 530 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0107] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a non-transitory computer readable storage medium, and the computer program, when executed by a processor, enables a computer to perform the method of optimizing the layout of a relay device provided by any of the above methods, which comprises: analyzing real-time data in a mobile communication network to obtain user location information and device usage data; generating a heat map of mobile communication based on the user location information and the device usage data; and optimizing a layout scheme of the relay device based on the heat map and performance evaluation results of the relay device.

[0108] In another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method of optimizing the layout of a relay device provided by any of the above methods, which comprises: analyzing real-time data in a mobile communication network to obtain user location information and device usage data; generating a heat map of mobile communication based on the user location information and the device usage data; and optimizing a layout scheme of the relay device based on the heat map and performance evaluation results of the relay device.

[0109] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0110] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0111] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the layout of relay equipment, characterized in that, include: Analyze real-time data in mobile communication networks to obtain user location information and device usage data; A mobile communication heat map is generated based on the user location information and the device usage data. Based on the heat map and the performance evaluation results of the relay equipment, the layout scheme of the relay equipment is optimized.

2. The method for optimizing the layout of relay equipment according to claim 1, characterized in that, The optimization of the relay equipment layout scheme based on the heat map and the performance evaluation results of the relay equipment includes: Based on the particle swarm optimization algorithm, the changes in hotspot areas in the heat map over time are determined; Based on the information about the changes in the hotspot area over time, the performance evaluation results of the relay equipment in the layout scheme are determined; If the performance evaluation results do not meet the preset conditions, the layout scheme of the relay equipment is optimized.

3. The method for optimizing the layout of relay equipment according to claim 2, characterized in that, The determination of the changes in hotspot areas in the heat map over time based on the particle swarm optimization algorithm includes: Initialize the user's location and speed in the heat map; Iteratively update the user's position and speed; After each iteration update, the user distribution information and traffic change information in the heat map are determined; Based on the user distribution information, the hotspot areas are determined; Based on the traffic change information, the changes in the hotspot area over time are determined.

4. The method for optimizing the layout of relay equipment according to claim 1, characterized in that, The step of generating a mobile communication heat map based on the user location information and the device usage data includes: The user location information and the device usage data are input into a Gaussian mixture model to obtain the probability density estimation results of the data points output by the Gaussian mixture model. Based on the probability density estimation results, the spatial distribution of the data is obtained; Based on the spatial distribution of the data, a heat map of the mobile communication is generated. The Gaussian mixture model is trained based on user location sample information and device usage sample data.

5. The method for optimizing the layout of relay equipment according to claim 1, characterized in that, The layout scheme of the relay equipment was determined based on the following method: Based on the heat map, the priority coverage area of ​​the relay device is determined; The signal transmission in the priority coverage area is analyzed to determine the signal coverage range and quality of the priority coverage area; Based on network topology data and the signal coverage range and quality of the priority coverage area, the layout scheme of the relay equipment is determined.

6. The method for optimizing the layout of relay equipment according to claim 1, characterized in that, After generating a mobile communication heat map based on the user location information and the device usage data, the method further includes: Based on the heat map, the potential placement location of the relay device is determined; Based on the signal propagation characteristics, terrain, and building obstruction information of the potential placement locations, the layout scheme of the relay equipment is optimized.

7. A layout optimization device for relay equipment, characterized in that, include: The analysis module is used to analyze real-time data in the mobile communication network to obtain user location information and device usage data; The heat map generation module is used to generate a heat map of mobile communication based on the user location information and the device usage data; The layout optimization module is used to optimize the layout scheme of the relay equipment based on the heat map and the performance evaluation results of the relay equipment.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the layout optimization method for the relay device as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the layout optimization method for the relay device as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the layout optimization method for the relay device as described in any one of claims 1 to 6.

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