Communication signal optimization method and device, server and computer storage medium

Abstract

The invention relates to the technical field of communication, in particular to a communication signal optimization method and device, a server and a computer storage medium. According to the invention, when it is monitored that the load size of the network node set is greater than the preset load threshold value, the historical traffic data corresponding to the target object is obtained, the network traffic mode of the target object is determined based on the historical traffic data, and when the network node set receives the network request of the target object, the network traffic mode of the target object is determined. Acquiring signal quality data and external environment data of the target object at the current moment, and predicting target signal intensity of the target object at the next moment according to current signal intensity in the signal quality data and the external environment data, and optimizing a signal search strategy of the target object based on the current signal strength, the target signal strength and the network flow mode, thereby realizing optimization of the signal search strategy to optimize a communication signal of the target object, and further reducing power consumption of the target object.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a communication signal optimization method, device, server and computer storage medium. Background Art

[0002] 5G technology is the fifth generation of mobile communication technology, which is a further upgrade and evolution of the previous four generations of mobile communication technology. Compared with previous generations of technology, 5G technology has higher data transmission rate, lower latency, greater connection density and higher network capacity.

[0003] With the popularization of 5G networks, users' demands for smartphone signal quality and stability are increasing. When users are using smartphones, when the phone detects that the current signal is getting weak, it can frequently search for surrounding wireless signal sources to automatically switch to a stronger signal source, ensuring that users always get the best network experience. However, high-intensity signal search consumes additional battery power, resulting in increased energy consumption of the phone. Summary of the invention

[0004] In view of the above, the present application provides a communication signal optimization method, device, server and computer storage medium to optimize the communication signal of a target object to reduce the power consumption of the target object.

[0005] A first aspect of the present application provides a communication signal optimization method, the method comprising: When the load size of the network node set is monitored to be greater than a preset load threshold, the historical traffic data corresponding to the target object is obtained; Determine a network traffic pattern of the target object based on the historical traffic data; When the network node set receives the network request of the target object, acquiring the signal quality data and external environment data of the target object at the current moment, wherein the signal quality data includes the current signal strength; Predicting the target signal strength of the target object at the next moment according to the current signal strength and the external environment data; The signal search strategy for the target object is optimized based on the current signal strength, the target signal strength and the network traffic pattern.

[0006] In an optional implementation, the optimizing the signal search strategy for the target object based on the current signal strength, the target signal strength, and the network traffic pattern includes: Determining a strength difference between the current signal strength and the target signal strength, and a mode type of the network traffic mode; The signal search strategy is optimized according to the strength difference and the mode type.

[0007] In an optional implementation, the optimizing the signal search strategy according to the strength difference and the mode type includes: When the target signal strength is greater than the current signal strength, the strength difference is greater than a preset strength threshold, and the mode type is the first mode type, the search frequency of the signal search strategy is reduced, and the search range of the signal search strategy is narrowed; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, performing the operation of reducing the search frequency of the signal search strategy; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the first mode type, the search frequency of the signal search strategy is increased, and the search range of the signal search strategy is expanded; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, the operation of increasing the search frequency of the signal search strategy is performed.

[0008] In an optional embodiment, the method further comprises: Determine, according to the network traffic pattern, contention resources in the network resources corresponding to the set of network nodes; The resource allocation strategy of the target object is optimized according to the signal difference and the contention resource.

[0009] In an optional implementation, the optimizing the resource allocation strategy of the target object according to the signal difference and the contention resource includes: Determining current availability of the contention resource at the current moment; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the first availability, increasing the first resource allocation ratio of the contention resource in the resource allocation strategy; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the second availability situation, reducing the first resource allocation ratio; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the first availability situation, maintaining the first resource allocation ratio; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the second availability situation, the second resource allocation ratio of non-contention resources in the resource allocation strategy is adjusted.

[0010] In an optional embodiment, the method further comprises: Determining network congestion data of the target object; Determine the antenna direction and antenna angle of the target object according to the signal quality data, the external environment data and the network congestion data; The antenna of the target object is adjusted according to the antenna direction and the antenna angle.

[0011] In an optional embodiment, the method further comprises: Determine spectrum usage data of the target object, and determine spectrum utilization efficiency and spectrum usage power consumption of each segment of used spectrum based on the spectrum usage data; Switching the frequency band used by the target object at the current moment based on the spectrum utilization efficiency and the spectrum usage power consumption to obtain a target frequency band; The target object is controlled to communicate using the target frequency band.

[0012] A second aspect of the present application provides a communication signal optimization device, the device comprising: A first acquisition module is used to acquire historical traffic data of a target network node corresponding to a target object when it is monitored that the load size of the network node set is greater than a preset load threshold; A data determination module, configured to determine a network traffic pattern of the target network node based on the historical traffic data; A second acquisition module is used to acquire signal quality data and external environment data of the target object at a current moment when receiving a network request of the target object, wherein the signal quality data includes current signal strength; A signal prediction module, used to predict the target signal strength of the target object at the next moment according to the current signal strength and the external environment data; A signal optimization module is used to optimize the signal search strategy of the target object based on the current signal strength, the target signal strength and the network traffic pattern.

[0013] A third aspect of the present application provides a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the communication signal optimization method when executing the computer program.

[0014] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned communication signal optimization method when executed by a processor.

[0015] In summary, the communication signal optimization method, device, server and computer storage medium provided by the present application, because high load may cause signal conflict and interference, increase communication power consumption, therefore, by monitoring whether the load size of the network node set is greater than the preset load threshold, if the load size is greater than the preset load threshold, the historical traffic data of the target object is obtained, and the network traffic pattern of the target object is determined based on the historical traffic data, when the network node set receives the network request of the target object, the signal quality data and external environment data of the current moment are obtained, and the target signal strength of the target object at the next moment is predicted using the current signal strength and the external environment data, which helps to adjust the signal search strategy in advance and avoid power consumption loss of the target object. Finally, the signal search strategy of the target object is optimized based on the current signal strength, the target signal strength and the network traffic pattern, so that the target object can perform signal search by optimizing the signal search strategy, avoid unnecessary signal search and reconnection, reduce the number of communications with the network node set, and thus reduce power consumption. In addition, the optimized signal search strategy can also ensure that the target object communicates under better network conditions, improve communication efficiency, and further reduce power consumption. In general, by real-time monitoring of network load, analyzing historical traffic data, predicting the signal strength of the target object based on signal quality data and external environment data, and optimizing the signal search strategy of the target object based on the above data, the power consumption of the target object can be reduced while maintaining communication quality. Secondly, it not only improves the utilization efficiency of network resources, but also improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a communication signal optimization method shown in an embodiment of the present application; Figure 2 It is a functional module diagram of a communication signal optimization device shown in an embodiment of the present application; Figure 3 It is a schematic diagram of a virtual structure of a server shown in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0018] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0019] Reference Figure 1 As shown, it is a flowchart of a communication signal optimization method shown in an embodiment of the present application, and the communication signal method includes the following steps.

[0020] S11, when it is monitored that the load size of the network node set is greater than a preset load threshold, historical traffic data corresponding to the target object is obtained.

[0021] The target object refers to the subject that needs to optimize the communication signal. In some embodiments, the target object can be a communication device, such as a mobile phone, a tablet computer and other mobile devices, a base station, a relay station and other fixed communication devices, etc. In other embodiments, the target object can also be a vehicle communication device, such as an autonomous vehicle, an unmanned vehicle, etc., and the target object can also be an industrial automation device, such as an industrial Internet of Things device (e.g., a sensor, an actuator, etc.), a smart home device (e.g., a smart light bulb, a smart remote control, etc.).

[0022] In order to more clearly understand the inventive concept of the present application, the target object in the embodiment of the present application and the target object in the following embodiments are explained taking a 5G smartphone as an example.

[0023] During the use of 5G smartphones, network nodes are required to send communication signals to 5G smartphones so that 5G smartphones can communicate normally, that is, in the 5G communication network, the network node set is responsible for providing communication services for 5G smartphones. Therefore, the server can detect the corresponding multiple network nodes (base stations) in real time, obtain the network node set, and compare the load size of the network node set with the preset load threshold to determine whether the network node set is in a network overload state. Among them, the load size can be the number of communication devices connected to the network node set, or it can be the data flow in the area covered by the network node set. When the load size is the number of communication devices connected to the network node set, the preset load threshold is the preset device number threshold. When the number of communication devices connected to the network node set is greater than the preset device number threshold, it is determined that the network node set is in a network overload state. When the load size is the data flow in the area covered by the network node set, the preset load threshold is the preset data flow threshold. When the data flow in the area covered by the network node set is greater than the preset data flow threshold, it is determined that the network node set is in a network overload state. When it is determined that the network node set is in a network overload state, the server can further obtain the historical traffic data of the 5G smartphone, for example, the historical traffic data within a preset time period (for example, 7 days). The historical traffic data refers to the network traffic usage, which may include traffic usage timestamp, total traffic volume, traffic peak, traffic trend, traffic usage location information and network type, etc.

[0024] In some embodiments, after acquiring the historical traffic data, the server may pre-process the historical traffic data (e.g., removing outliers, erroneous data, and duplicates, etc.) to improve data quality, and then integrate the pre-processed historical traffic data according to the traffic usage timestamp to facilitate execution of step S12.

[0025] S12: Determine the network traffic pattern of the target object based on the historical traffic data.

[0026] The network traffic mode may include two types of modes, namely, the first traffic mode (high bandwidth demand type) and the second traffic mode (low latency demand type). The high bandwidth demand type may include video streaming, file downloading, etc., and the low latency demand type may include real-time voice / video calls, etc.

[0027] In some embodiments, after obtaining the historical traffic data, the server can determine the network traffic pattern of the 5G smartphone based on the historical traffic data. Specifically, the server first extracts key features from the historical traffic data, such as traffic peaks, traffic trends, traffic fluctuations, and application traffic proportions, and uses data analysis tools to perform regression analysis on the key features to preliminarily determine the network traffic pattern of the 5G smartphone. Furthermore, the preliminarily identified network traffic pattern is verified using unused and independent historical traffic data. When the verification passes, it can be determined that the network traffic pattern is the network traffic pattern of the 5G smartphone at the current moment.

[0028] S13, when the network node set receives the network request of the target object, obtains the signal quality data and external environment data of the target object at the current moment, wherein the signal quality data includes the current signal strength.

[0029] When the 5G smartphone initiates a network request, the server collects in real time the signal quality data of the communication signal transmitted from the network node set to the 5G smartphone and the external environment data corresponding to the current location, wherein the signal quality data may include signal strength and signal-to-noise ratio, etc., and the external environment data may include current location information, network congestion, building density, weather conditions, etc. In some embodiments, the server may use a signal strength meter to obtain the signal strength (current signal strength) of the 5G smartphone at the current moment, use GPS to obtain the current location information of the 5G smartphone, use speed measurement software or query the real-time data of the network service provider of the network node set to obtain network congestion, use GIS to obtain building density, and use a weather application or meteorological data platform to obtain weather conditions, etc.

[0030] In some embodiments, after acquiring the external environment data, the server may pre-process the external environment data, such as normalization or standardization, to ensure the accuracy of subsequent predictions based on the external environment data.

[0031] S14: predicting the target signal strength of the target object at the next moment according to the current signal strength and the external environment data.

[0032] When the current signal strength and external environment data of the 5G smartphone at the current moment are obtained, the server can input the current signal strength and external environment data into the signal strength prediction model pre-trained based on machine learning to predict the signal strength (target signal strength) of the 5G smartphone at the next moment. In specific implementation, the signal strength prediction model can be a model obtained by supervised learning training based on the Long Short-Term Memory (LSTM) network. The server first collects historical signal strength and historical external environment data, and labels them, and inputs the historical signal strength and historical external environment into the initial signal strength prediction model built based on LSTM. Based on the difference between the output result and the standard information, the loss function of the initial signal strength prediction model is continuously optimized until the loss function approaches convergence. The initial signal strength prediction model is called the pre-trained signal strength prediction model.

[0033] S15, optimizing the signal search strategy for the target object based on the current signal strength, the target signal strength and the network traffic pattern.

[0034] Among them, the signal search strategy refers to the method or strategy adopted by 5G smartphones when searching for communication signals, such as search range, search frequency, frequency band selection and base station selection.

[0035] When determining the current signal strength and network traffic pattern of the 5G smartphone at the current moment, and the target signal strength at the next moment, the server can optimize the signal search strategy currently used by the 5G smartphone based on the current signal strength, target signal strength and network traffic pattern, so as to provide a personalized optimized signal search strategy for the target object, thereby improving communication quality and user experience, and further reducing the energy consumption generated by the 5G smartphone during signal search.

[0036] In an optional implementation, the optimizing the signal search strategy for the target object based on the current signal strength, the target signal strength, and the network traffic pattern includes: Determining a strength difference between the current signal strength and the target signal strength, and a mode type of the network traffic mode; The signal search strategy is optimized according to the strength difference and the mode type.

[0037] In some embodiments, the server first compares the current signal strength with the target signal strength to determine whether the signal strength of the 5G smartphone is stronger at the current moment or at the next moment. When the target signal strength is greater than the current signal strength, that is, the signal strength at the next moment is stronger than the signal strength at the current moment, the strength difference = target signal strength - current signal strength. When the current signal strength is greater than the target signal strength, that is, the signal strength at the next moment is weaker than the signal strength at the current moment, the strength difference = current signal strength - target signal strength. Further, the mode type of the network traffic mode is obtained to determine whether the network traffic mode is the first traffic mode or the second traffic mode, that is, to determine whether the network traffic mode is a high bandwidth demand type or a low latency demand type. When determining the strength difference between the current signal strength and the target signal strength, and the mode type of the network traffic mode, the server can optimize the signal search strategy based on the strength difference and the mode type. Specifically, the electronic pre-sets a strength threshold for determining the difference in the strength difference between the current signal strength and the target signal strength. When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type of the network traffic mode is the first mode type (i.e., high bandwidth demand class), the server can reduce the search frequency in the signal search strategy of the 5G smartphone and narrow the search range in the signal search strategy. When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type of the network traffic mode is the second mode type (i.e., low latency demand class), the server can reduce the search frequency in the signal search strategy of the 5G smartphone. When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type of the network traffic mode is the first mode type (i.e., high bandwidth demand class), the server can increase the search frequency in the signal search strategy of the 5G smartphone and expand the search range in the signal search strategy. When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type of the network traffic mode is the second mode type (i.e., low latency demand class), the server can increase the search frequency in the signal search strategy of the 5G smartphone.

[0038] After determining the signal search strategy currently being used by the 5G smartphone and the optimized signal search strategy obtained by optimizing the signal search strategy based on the strength difference and mode type, the server can switch the signal search strategy currently being used to the optimized signal search strategy, so that the 5G smartphone can perform signal search according to the optimized signal search strategy.

[0039] Through the above optional implementation, by comprehensively considering the current signal strength, target signal strength and mode type of the network traffic pattern, dynamic adjustment of the signal search strategy is achieved. When it is detected that the target signal strength is stronger and the network traffic pattern is a high bandwidth requirement, the search frequency is reduced and the search range is narrowed, thereby improving the search efficiency and reducing unnecessary power consumption. In the low latency demand mode, even if the target signal strength is stronger, only the search frequency is reduced to maintain the real-time nature of the communication. On the contrary, when the target signal strength is weak, the continuity and stability of the communication are ensured by increasing the search frequency and / or expanding the search range. In general, by intelligently adjusting the signal search strategy, the power consumption of 5G smartphones is effectively reduced and the user experience is improved.

[0040] In an optional embodiment, the method further comprises: Determine, according to the network traffic pattern, contention resources in the network resources corresponding to the set of network nodes; The resource allocation strategy of the target object is optimized according to the signal difference and the contention resource.

[0041] In some embodiments, after identifying the network traffic pattern of the 5G smartphone at the current moment, the server can identify the contention resources in the network node set according to the network traffic pattern. The contention resources refer to the resources that may be competed for by multiple 5G smartphones at the same time under the current network traffic pattern, such as frequency resources, power resources, bandwidth resources, etc.

[0042] The resource allocation strategy refers to how to reasonably allocate network resources to 5G smartphones by controlling the use of network resources, so that 5G smartphones can provide better service quality and efficient resource utilization, which may include the allocation of frequency resources, the allocation of power resources, and the allocation of bandwidth resources. According to the same embodiment as above, the server can determine the strength difference between the current signal strength and the target signal strength, and after determining the strength difference and the competitive resources, the resource allocation strategy currently being used by the 5G smartphone can be optimized to obtain an optimized resource allocation strategy. Specifically, the server first needs to determine the availability of competitive resources at the current moment (currently available), and the current available situation may include two available situations, namely, the first available situation (resource-rich category) and the second available situation (resource-tight category). When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current available situation of the competitive resources is the first available situation (that is, the resource-rich category), the server can increase the first resource allocation ratio of the competitive resources in the resource allocation strategy to ensure that the 5G smartphone can make full use of high-quality signals and abundant resources, and improve the efficiency and quality of data transmission. When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability of the competitive resources is the second availability (i.e., resource shortage), the server can reduce the first resource allocation ratio to avoid excessive resource competition and congestion, and ensure the overall performance and stability of the network. When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability of the competitive resources is the first availability, the server can keep the first resource allocation ratio unchanged, that is, the signal quality has decreased, but the competitive resources are still sufficient, and there is no need to adjust the first resource allocation ratio. When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability of the competitive resources is the second availability, the server can adjust the second resource allocation ratio of non-competitive resources in the resource allocation strategy, that is, the competitive resources are limited, and it is necessary to compensate by adjusting the resource allocation of non-competitive resources to maintain the stability and quality of 5G smartphone communications.

[0043] Through the above optional implementation, the competitive resources are determined by identifying the network traffic pattern, and the resource allocation strategy of the 5G smartphone is optimized according to the signal difference and the current availability of competitive resources. It can dynamically adapt to changes in network status, reasonably allocate limited network resources, avoid excessive resource competition and congestion, and ensure that 5G smartphones can provide higher service quality and more stable communication experience in complex network environments. At the same time, the lack of competitive resources can be compensated by adjusting the allocation of non-competitive resources, further improving resource utilization efficiency. In general, it not only improves communication quality and efficiency, reduces power consumption, but also improves user experience.

[0044] In an optional embodiment, the method further comprises: Determining network congestion data of the target object; Determine the antenna direction and antenna angle of the target object according to the signal quality data, the external environment data and the network congestion data; The antenna of the target object is adjusted according to the antenna direction and the antenna angle.

[0045] In some embodiments, the server can measure and collect network congestion data of the 5G smartphone through a network diagnostic tool, wherein the network congestion data may include network delay, packet loss rate, and throughput, etc. According to the same embodiment of step S13, the server can obtain the signal quality data and external environment data of the 5G smartphone. After determining the network congestion data, signal quality data, and external environment data of the 5G smartphone, the server can determine the antenna direction and antenna angle of the 5G smartphone based on the network congestion data, signal quality data, and external environment data, and adjust the antenna of the 5G smartphone at the current moment based on the antenna direction and antenna angle. Specifically, first pre-process the network congestion data, signal quality data, and external environment data, and extract key features from the pre-processed network congestion data, signal quality data, and external environment data, such as signal attenuation, interference source location, and signal strength distribution, and further use statistical analysis or machine learning methods to identify key factors affecting communication quality. For example, by comparing signal quality data at different locations and different time periods, the impact of buildings or terrain on signal propagation can be identified. A pre-trained model is selected based on key features and key factors, so that the current network congestion data, signal quality data, and external environment data are used as model inputs, and the model can output antenna direction and antenna angle. The server can generate corresponding antenna adjustment instructions based on the antenna direction and antenna angle output by the model, and send the antenna adjustment instructions to the 5G smartphone, so that the 5G smartphone can receive and execute the antenna adjustment instructions.

[0046] Through the above-mentioned optional implementation manner, by combining the network congestion data, signal quality data and external environment data of the 5G smartphone, the antenna direction and angle of the 5G smartphone can be determined and adjusted more accurately, thereby improving communication quality and alleviating network congestion.

[0047] In an optional embodiment, the method further comprises: Determine spectrum usage data of the target object, and determine spectrum utilization efficiency and spectrum usage power consumption of each segment of used spectrum based on the spectrum usage data; Switching the frequency band used by the target object at the current moment based on the spectrum utilization efficiency and the spectrum usage power consumption to obtain a target frequency band; The target object is controlled to communicate using the target frequency band.

[0048] In some embodiments, the server can collect the spectrum usage data of the 5G smartphone at the current moment in real time through the built-in spectrum analysis tool of the 5G smartphone or the external spectrum monitoring device. The spectrum usage data may include spectrum occupancy, frequency band utilization, signal quality, etc. Then, the server can determine the spectrum utilization efficiency of each segment of the spectrum based on the Shannon formula and the signal quality and modulation coding method in the spectrum usage data. Further, the server can determine the spectrum usage power consumption of each segment of the spectrum by measuring the power consumption of the 5G smartphone in different frequency bands and combining the frequency band occupancy time in the spectrum usage data. After determining the spectrum utilization efficiency and spectrum usage power consumption of each segment of the spectrum, the server can set corresponding weights for the spectrum utilization efficiency and the spectrum usage power consumption, wherein the spectrum utilization efficiency reflects the maximum information transmission rate of the frequency band at the current moment, and the spectrum usage power consumption is the energy consumption required for communication in the frequency band. Therefore, in order to ensure that the information transmission rate is large while the energy consumption is small, the weights of the spectrum utilization efficiency and the spectrum usage power consumption can be set to 0.5 respectively. Furthermore, a weighted sum is performed according to the set weights to obtain a comprehensive evaluation index, and the determined spectrum utilization efficiency and spectrum power consumption of each segment of the used spectrum are input into the comprehensive evaluation index to obtain a comprehensive performance score corresponding to each segment of the used spectrum. Each used frequency band is sorted from low to high according to the comprehensive performance score, and the used frequency band corresponding to the first one in the sort is determined as the target used frequency band, and the used frequency band of the 5G smartphone at the current moment is switched to the target used frequency band, so that the 5G smartphone can use the target used frequency band for communication.

[0049] Through the above optional implementation, by collecting spectrum usage data of 5G smartphones in real time, calculating spectrum utilization efficiency and spectrum power consumption, and setting weights based on spectrum utilization efficiency and spectrum power consumption to make frequency band switching decisions, it is possible to dynamically select the frequency band with the best performance at the current moment for communication, which not only ensures a high information transmission rate, but also reduces energy consumption, thereby improving the communication efficiency and energy efficiency of 5G smartphones. At the same time, it also improves the utilization rate of spectrum resources, optimizes network performance, and provides 5G smartphone users with a better communication experience.

[0050] In some embodiments, the server can pre-set a dynamic power management system to monitor the power consumption of each component of the 5G smartphone, and dynamically adjust the power consumption mode of each component according to the current usage and the remaining battery capacity to minimize power consumption and extend the battery life of the phone. In some embodiments, the server can also introduce a user feedback mechanism to collect the user's signal experience and battery consumption in different environments to optimize the communication signal according to the user experience.

[0051] Reference Figure 2 Shown is a functional module diagram of a communication signal optimization device shown in an embodiment of the present application.

[0052] In some embodiments, the communication signal optimization device 20 may include multiple functional modules composed of computer program segments. The computer programs of the various program segments of the communication signal optimization device 20 may be stored in the memory of the server and executed by at least one processor to perform communication signal optimization (see Figure 1 Description).

[0053] In this embodiment, the communication signal optimization device 20 can be divided into multiple functional modules according to the functions performed. The functional modules may include: a first acquisition module 201, a data determination module 202, a second acquisition module 203, a signal prediction module 204 and a signal optimization module 205. The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0054] The first acquisition module 201 is used to acquire historical traffic data of a target network node corresponding to a target object when it is monitored that the load size of a network node set is greater than a preset load threshold.

[0055] The data determination module 202 is used to determine the network traffic pattern of the target network node based on the historical traffic data.

[0056] The second acquisition module 203 is configured to acquire the signal quality data and external environment data of the target object at the current moment when receiving the network request of the target object, wherein the signal quality data includes the current signal strength.

[0057] The signal prediction module 204 is used to predict the target signal strength of the target object at the next moment according to the current signal strength and the external environment data.

[0058] The signal optimization module 205 is used to optimize the signal search strategy of the target object based on the current signal strength, the target signal strength and the network traffic pattern.

[0059] The signal optimization module 205 is further specifically used to: determine the strength difference between the current signal strength and the target signal strength, and the mode type of the network traffic mode; and optimize the signal search strategy according to the strength difference and the mode type.

[0060] The signal optimization module 205 is also specifically used for: when the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the first mode type, reducing the search frequency of the signal search strategy and narrowing the search range of the signal search strategy; when the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, performing the operation of reducing the search frequency of the signal search strategy; when the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the first mode type, increasing the search frequency of the signal search strategy and expanding the search range of the signal search strategy; when the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, performing the operation of increasing the search frequency of the signal search strategy.

[0061] The signal optimization module 205 is further used to: determine the contention resources in the network resources corresponding to the network node set according to the network traffic pattern; and optimize the resource allocation strategy of the target object according to the signal difference and the contention resources.

[0062] The signal optimization module 205 is also specifically used to: determine the current availability of the contention resource at the current moment; when the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the first availability, increase the first resource allocation ratio of the contention resource in the resource allocation strategy; when the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the second availability, reduce the first resource allocation ratio; when the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the first availability, maintain the first resource allocation ratio; when the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the second availability, adjust the second resource allocation ratio of the non-contention resource in the resource allocation strategy.

[0063] The signal optimization module 205 is also used to: determine the network congestion data of the target object; determine the antenna direction and antenna angle of the target object according to the signal quality data, the external environment data and the network congestion data; and adjust the antenna of the target object according to the antenna direction and the antenna angle.

[0064] The signal optimization module 205 is further used to: determine the spectrum usage data of the target object, and determine the spectrum utilization efficiency and spectrum power consumption of each segment of the used spectrum based on the spectrum usage data; switch the frequency band used by the target object at the current moment based on the spectrum utilization efficiency and the spectrum power consumption to obtain a target frequency band; and control the target object to communicate using the target frequency band.

[0065] It should be understood that the various variations and specific embodiments of the communication signal optimization method provided in the above-mentioned embodiments are also applicable to the communication signal optimization device of this embodiment. Through the above-mentioned detailed description of the communication signal optimization method, those skilled in the art can clearly know the implementation method of the communication signal optimization device in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0066] Reference Figure 3 FIG. 2 is a schematic diagram of a virtual structure of a server shown in an embodiment of the present application. In a preferred embodiment of the present application, the server 3 can be set in the target object, and the server 3 includes a memory 31, at least one processor 32 and at least one communication bus 33.

[0067] Those skilled in the art should understand that Figure 3The structure of the server shown does not constitute a limitation of the embodiments of the present application, and can be either a bus structure or a star structure. The server 3 can also include more or less other hardware or software than shown in the figure, or a different component arrangement.

[0068] It should be noted that the server 3 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0069] In some embodiments, a computer program is stored in the memory 31, and when the computer program is executed by the at least one processor 32, all or part of the steps in the communication signal optimization method as described above are implemented.

[0070] In some embodiments, the at least one processor 32 is the control core (ControlUnit) of the server 3, and uses various interfaces and lines to connect the various components of the entire server 3, and executes various functions and processes data of the server 3 by running or executing the program or module stored in the memory 31, and calling the data stored in the memory 31. For example, when the at least one processor 32 executes the computer program stored in the memory, it implements all or part of the steps of the communication signal optimization method described above in the embodiment of the present application; or implements all or part of the functions of the communication signal optimization device. The at least one processor 32 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips.

[0071] In some embodiments, the at least one communication bus 33 is configured to implement connection and communication between the memory 31 and the at least one processor 32, etc.

[0072] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a server (which can be a personal computer, server, or network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0074] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A communication signal optimization method, characterized in that: The method comprises: When the load size of the network node set is monitored to be greater than a preset load threshold, the historical traffic data corresponding to the target object is obtained; Determine a network traffic pattern of the target object based on the historical traffic data; When the network node set receives the network request of the target object, acquiring the signal quality data and external environment data of the target object at the current moment, wherein the signal quality data includes the current signal strength; Predicting the target signal strength of the target object at the next moment according to the current signal strength and the external environment data; The signal search strategy for the target object is optimized based on the current signal strength, the target signal strength and the network traffic pattern.

2. The communication signal optimization method according to claim 1, characterized in that: The optimizing the signal search strategy of the target object based on the current signal strength, the target signal strength and the network traffic pattern includes: Determining a strength difference between the current signal strength and the target signal strength, and a mode type of the network traffic mode; The signal search strategy is optimized according to the strength difference and the mode type.

3. The communication signal optimization method according to claim 2, characterized in that: The optimizing the signal search strategy according to the intensity difference and the mode type comprises: When the target signal strength is greater than the current signal strength, the strength difference is greater than a preset strength threshold, and the mode type is the first mode type, the search frequency of the signal search strategy is reduced, and the search range of the signal search strategy is narrowed; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, performing the operation of reducing the search frequency of the signal search strategy; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the first mode type, the search frequency of the signal search strategy is increased, and the search range of the signal search strategy is expanded; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the mode type is the second mode type, the operation of increasing the search frequency of the signal search strategy is performed.

4. The communication signal optimization method according to claim 2, characterized in that: The method further comprises: Determine, according to the network traffic pattern, contention resources in the network resources corresponding to the set of network nodes; The resource allocation strategy of the target object is optimized according to the signal difference and the contention resource.

5. The communication signal optimization method according to claim 4, characterized in that: The optimizing the resource allocation strategy of the target object according to the signal difference and the contention resource comprises: Determining current availability of the contention resource at the current moment; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability is the first availability, increasing the first resource allocation ratio of the contention resource in the resource allocation strategy; When the target signal strength is greater than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the second availability situation, reducing the first resource allocation ratio; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the first availability situation, maintaining the first resource allocation ratio; When the target signal strength is less than the current signal strength, the strength difference is greater than the preset strength threshold, and the current availability situation is the second availability situation, the second resource allocation ratio of non-contention resources in the resource allocation strategy is adjusted.

6. The communication signal optimization method according to any one of claims 1 to 5, characterized in that: The method further comprises: Determining network congestion data of the target object; Determine the antenna direction and antenna angle of the target object according to the signal quality data, the external environment data and the network congestion data; The antenna of the target object is adjusted according to the antenna direction and the antenna angle.

7. The communication signal optimization method according to any one of claims 1 to 5, characterized in that: The method further comprises: Determine spectrum usage data of the target object, and determine spectrum utilization efficiency and spectrum usage power consumption of each segment of used spectrum based on the spectrum usage data; Switching the frequency band used by the target object at the current moment based on the spectrum utilization efficiency and the spectrum usage power consumption to obtain a target frequency band; The target object is controlled to communicate using the target frequency band.

8. A communication signal optimization device, characterized in that: The device comprises: A first acquisition module is used to acquire historical traffic data of a target network node corresponding to a target object when it is monitored that the load size of the network node set is greater than a preset load threshold; A data determination module, configured to determine a network traffic pattern of the target network node based on the historical traffic data; A second acquisition module is used to acquire signal quality data and external environment data of the target object at a current moment when receiving a network request of the target object, wherein the signal quality data includes current signal strength; A signal prediction module, used to predict the target signal strength of the target object at the next moment according to the current signal strength and the external environment data; A signal optimization module is used to optimize the signal search strategy of the target object based on the current signal strength, the target signal strength and the network traffic pattern.

9. A server, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the communication signal optimization method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the communication signal optimization method according to any one of claims 1 to 7 are implemented.

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