A wireless network selection method, system and terminal for 4G mobile communication

Abstract

The present application discloses a wireless network selection method, system and terminal for 4G communication, which belongs to the field of wireless communication technology and can solve the technical problem that when a user moves to a different location, the currently connected operator may not provide the best service quality. The method includes: when the terminal device is in a network connection state, collecting multiple device network parameters in real time; preprocessing the device network parameters to generate a preprocessing score corresponding to each device network parameter; performing a weighted comprehensive evaluation calculation based on each preprocessing score to obtain each weighted comprehensive evaluation calculation result; judging whether the current network connection state is in the optimal network connection state according to the weighted comprehensive evaluation calculation result, and executing network switching if the judgment result is not optimal.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to the field of 4G communication network selection. Background Art

[0002] In the existing 4G communication network environment, user terminals usually connect to a fixed operator network and maintain the connection until the signal is extremely poor or completely disconnected. This network selection mechanism mainly depends on signal strength, and is usually determined by the user manually selecting the operator or by preset priority rules. Although this method is simple, it does not perform well in a complex and dynamically changing network environment.

[0003] As users' requirements for network connection stability, speed and energy consumption continue to increase, the existing network selection mechanism is difficult to adapt to various complex environments. For example, when users move to different locations, the current connected operator may not be able to provide the best service quality. In addition, frequent network switching will also lead to increased power consumption and shorten the battery life of terminal devices.

[0004] Therefore, there is an urgent need for a network selection method that can dynamically adapt to environmental changes to improve user experience and achieve energy-saving optimization of terminal devices. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a wireless network selection method, system and terminal for 4G communication, which can solve the problem that when a user moves to a different location, the currently connected operator may not be able to provide the best service quality.

[0006] In order to solve the above technical problems, the embodiments of the present application are implemented as follows:

[0007] In the first aspect, the present application provides a wireless network selection method for 4G communication. The method includes: when the terminal device is in a network connection state, collecting multiple device network parameters in real time; preprocessing the device network parameters to generate a preprocessing score corresponding to each device network parameter; performing a weighted comprehensive evaluation calculation based on each preprocessing score to obtain each weighted comprehensive evaluation calculation result; judging whether the current network connection state is in the optimal network connection state according to the weighted comprehensive evaluation calculation result, and executing network switching if the judgment result is not optimal; wherein the device network parameters include: the first device network parameter, the second device network parameter...the Nth device network parameter.

[0008] Furthermore, each of the device network parameters includes signal strength, network delay, network congestion status, device power and user preferences, and the signal strength, network delay, network congestion status, device power and user preferences are all collected by the terminal device.

[0009] Furthermore, the preprocessing is fuzzy logic reasoning, and the fuzzy logic reasoning process includes:

[0010] Grouping the network parameters of each device, wherein the signal strength is a first group, the network delay and network congestion status are a second group, and the device power and user preference are a third group;

[0011] A fuzzy set is created for the device network parameters in each of the groups, wherein the fuzzy set includes specific elements; the fuzzy set exists in the first group, the fuzzy set of the signal strength is strong, medium, and weak, and the strong, medium, and weak are the elements of the first group; the fuzzy set exists in the second group, wherein the fuzzy set of the network delay is: low delay, medium delay, and high delay, and the low delay, medium delay, and high delay are the elements of the second group; the fuzzy set of the network congestion status is: unobstructed, moderate, and congested, and the unobstructed, moderate, and congested are the elements of the second group, and the The fuzzy set of the second group is the set of the fuzzy set of the network delay and the fuzzy set of the network congestion state; the third group has the fuzzy set, the fuzzy set of the device power state is: low power, medium power, high power, the low power, medium power, high power are the elements of the third group, the fuzzy set of the user preference is: low priority, medium priority, high priority, the low priority, medium priority, high priority are the elements of the third group, the fuzzy set of the second group is the set of the fuzzy set of the device power and the fuzzy set of the user preference;

[0012] Based on the fuzzy set of each of the groups, a corresponding fuzzy inference rule base is defined, wherein the rule base includes the elements of each group and the corresponding scores of the elements, and the set of the elements and the set of the corresponding scores of the elements are in a bijective relationship; the rule base of the first group includes the corresponding scores of the elements of the first group and the elements of the first group, the rule base of the second group includes the corresponding scores of the elements of the second group and the elements of the second group, and the rule base of the third group includes the corresponding scores of the elements of the third group and the elements of the third group;

[0013] Based on the network parameters of each device and the fuzzy set of the group, the membership of each group is obtained through a membership function, the parameters of the membership function of each group are determined by the elements in the group, the input independent variable of the membership function of the first group is the signal strength, and the obtained dependent variable is the first group of membership, the input independent variables of the membership function of the second group are the network delay and the network congestion degree, and the obtained dependent variable is the second group of membership, and the input independent variables of the membership function of the third group are the device power and the user preference, and the obtained dependent variable is the third group of membership;

[0014] Each of the groups obtains the preprocessing score through defuzzification, and the defuzzification uses a defuzzification formula, and the defuzzification formula parameters are the corresponding scores of each of the elements in the rule base; the first group uses the defuzzification formula as the input independent variable for the signal strength, and the obtained dependent variable is the first group of preprocessing scores; the second group uses the defuzzification formula as the input independent variables for the network delay and the network congestion level, and the obtained dependent variable is the second group of preprocessing scores; the third group uses the defuzzification formula as the input independent variables for the device power and the user preference, and the obtained dependent variable is the third group of preprocessing scores.

[0015] Furthermore, the weighted comprehensive evaluation calculation specifically includes:

[0016] Standardization processing, wherein the standardization processing standardizes the preprocessing score of each of the groups to obtain a standardized result of each of the groups, wherein the standardization uses a standardization function, wherein the first group obtains the first group of standardized results based on the first group of preprocessing scores through the standardization function, the second group obtains the second group of standardized results based on the second group of preprocessing scores through the standardization function, and the third group obtains the third group of standardized results based on the third group of preprocessing scores through the standardization function;

[0017] Assign weights, wherein the assigned weights are weight values ​​assigned to the standardized results of each group, the sum of the weight values ​​of each group is 1, the first group of standardized results is assigned to obtain a first group of weight values, the second group of standardized results is assigned to obtain a second group of weight values, and the third group of standardized results is assigned to obtain a third group of weight values;

[0018] The weighted comprehensive evaluation calculation result of each device network parameter is obtained through comprehensive scoring calculation, and the comprehensive scoring calculation is to obtain a first group of weighted standard values ​​by multiplying the standardized result of the first group by the weight value of the first group, obtain a second group of weighted standard values ​​by multiplying the standardized result of the second group by the weight value of the second group, obtain a third group of weighted standard values ​​by multiplying the standardized result of the third group by the weight value of the third group, and finally obtain the weighted comprehensive evaluation calculation result by adding the first group of weighted standard values, the second group of weighted standard values, and the third group of weighted standard values.

[0019] Furthermore, the method also includes, after executing the network switching, applying a network optimization strategy, wherein the network optimization strategy includes reducing the signal scanning frequency and switching to a low power consumption mode.

[0020] Furthermore, the method also includes, after switching to the optimal network, continuously monitoring dynamic changes in the network environment, and adjusting the weighted comprehensive evaluation calculation according to the dynamic changes.

[0021] In the second aspect, the present application provides a wireless network selection system for 4G communication, which includes a device network parameter monitoring module, a preprocessing module, a comprehensive evaluation module, and a network switching module: the device network parameter monitoring module is used to collect multiple device network parameters in real time when the terminal device is in a network connection state; the preprocessing module is used to preprocess the device network parameters and generate a preprocessing score corresponding to each device network parameter; the comprehensive evaluation module performs a weighted comprehensive evaluation calculation based on each preprocessing score to obtain each weighted comprehensive evaluation calculation result; the network switching module is used to judge whether the current network connection state is in the optimal network connection state according to the weighted comprehensive evaluation calculation result, and if the judgment result is non-optimal, execute network switching; wherein the device network parameters include: a first device network parameter, a second device network parameter...the Nth device network parameter.

[0022] In a third aspect, an embodiment of the present application provides a terminal for implementing a step of selecting a wireless network for 4G communication.

[0023] Beneficial effects of the present invention:

[0024] This application collects network parameters of multiple devices in real time and performs weighted comprehensive evaluation. This method can dynamically evaluate the current network connection status and ensure that the terminal device always maintains the optimal network connection status. Compared with the traditional network selection method that only relies on signal strength, this method can better adapt to complex network environments and reduce connection problems caused by signal fluctuations or network congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of a wireless network selection method for 4G communication provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram of the pretreatment process provided in the embodiment of the present application;

[0027] Figure 3 A schematic diagram of the processing process of weighted comprehensive parity calculation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0030] The present embodiment provides a wireless network selection method for 4G communication, which collects device network parameters in real time when a terminal device is in a network connection state; preprocesses the device network parameters to generate a preprocessing score for each device network parameter; performs a weighted comprehensive evaluation calculation based on the preprocessing score to obtain a weighted comprehensive evaluation calculation result; determines whether the current network connection state is in an optimal network connection state based on the weighted comprehensive evaluation calculation result, and executes network switching if it is not in the optimal network connection state.

[0031] The following, in conjunction with the accompanying drawings, describes in detail the wireless network selection method, system and medium for 4G communication provided by the embodiments of the present application through specific embodiments and application scenarios.

[0032] like Figure 1 As shown, the present application provides a wireless network selection method for 4G communication, which may include the following steps S100 to S600.

[0033] Step S100 collects network parameters of the device in real time when the terminal device is in a network connection state.

[0034] Among them, the terminal device continuously monitors and collects data on key network parameters in real time through multiple monitoring modules configured to ensure the accuracy and reliability of subsequent network selection. The device network parameters include signal strength, network delay, network congestion status, device power status and user preferences, which are monitored by the following modules respectively;

[0035] Optionally, a signal strength monitoring module is provided, which can detect the signal strength of different frequency bands and is particularly suitable for LTE / 4G networks. The signal strength monitoring module collects the output signal strength value data in dBm in the current environment in real time.

[0036] Optionally, a network delay monitoring module is provided, which can obtain network delay data in real time. By sending a test data packet and measuring its round trip time (RTT), the network delay monitoring module can accurately calculate the current network delay.

[0037] Optionally, a network congestion status monitoring module is provided. The network congestion status monitoring module evaluates the current network congestion situation in real time by analyzing the ratio of the actual data transmission rate to the theoretical maximum rate. The module can generate percentage data of network utilization.

[0038] Optionally, the device power status monitoring module is responsible for detecting the power status of the terminal device. The module can accurately track the battery power percentage so that the power status of the device can be considered during the network selection process to achieve energy saving optimization.

[0039] Optionally, the user preference monitoring module, the user preference data is set by the user through the device interface, or automatically inferred by analyzing the user's historical behavior data. The user preference monitoring module continuously tracks and updates the user's preference settings, and this information directly affects the decision-making process of the terminal device when selecting a network.

[0040] In this embodiment, through the collaborative work of the above-mentioned monitoring modules, the terminal device can obtain and update key device network parameter data in real time. These data provide accurate and timely basic information for subsequent fuzzy logic processing and weighted comprehensive evaluation, thereby ensuring that the terminal device can always maintain the best network connection status in different network environments.

[0041] Step S200 pre-processes the device network parameters to generate a pre-processing score for each device network parameter.

[0042] The device network parameters include: a first device network parameter, a second device network parameter, ... an Nth device network parameter.

[0043] In this embodiment, the first device network parameter, the second device network parameter...the Nth device network parameter refer to the device network parameters in the operator network received by the terminal device. The terminal device can accept network parameters in multiple operator networks and mark them as the first device network parameter, the second device network parameter...the Nth device network parameter. After receiving the network parameters in the operator network, these network parameters will be preprocessed.

[0044] Specifically, if Figure 2 The preprocessing process shown can be decomposed into multiple steps. S201 is that when receiving network parameters from an operator network, the terminal device first groups the collected network parameters, where signal strength is the first group; network congestion and network delay are the second group; device power and user preference are the third group. This grouping is to perform effective fuzzy reasoning for different parameter combinations.

[0045] Further, such as Figure 2 The pre-processing process S202 shown is to create a fuzzy set for the device network parameters in each group after the grouping is completed. The fuzzy set includes specific elements. The first group has a fuzzy set, and the fuzzy set of signal strength is "strong, medium, weak", and strong, medium, and weak are elements of the first group; the second group has a fuzzy set, in which the fuzzy set of network delay is: "low delay, medium delay, high delay". Low delay, medium delay, and high delay are elements of the second group, and the fuzzy set of network congestion status is: "smooth, moderate, and crowded". Smooth, moderate, and crowded are elements of the second group, and the fuzzy set of the second group is the collection of the fuzzy set of network delay and the fuzzy set of network congestion status; the third group has a fuzzy set, and the fuzzy set of device power status is: "low power, medium power, and high power". Low power, medium power, and high power are elements of the third group, and the fuzzy set of user preference is: "low priority, medium priority, and high priority". Low priority, medium priority, and high priority are elements of the third group, and the fuzzy set of the second group is the collection of the fuzzy set of device power and user preference;

[0046] Further, such as Figure 2 The pre-processing process S203 shown is to define the relevant rule base and scoring mechanism after the grouping and fuzzy sets are established.

[0047] In this embodiment, the grouping rule base is as follows:

[0048] Group 1: Signal Strength

[0049] Rule Number Signal Strength score Rule 1 powerful 80 Rule 2 middle 60 Rule 3 weak 40

[0050] Group 2: Network latency and network congestion

[0051] Rule Number Network latency Network congestion score Rule 4 Low latency Smooth 80 Rule 5 Low latency Moderate 70 Rule 6 Low latency Crowded 60 Rule 7 Medium delay Smooth 70 Rule 8 Medium delay Moderate 60 Rule 9 Medium delay Crowded 50 Rule 10 High latency Smooth 60 Rule 11 High latency Moderate 50 Rule 12 High latency Crowded 40

[0052] Group 3: Device power and user preferences

[0053]

[0054]

[0055] Optionally, in this embodiment, as Figure 2 shown, the preprocessing process S204 is to perform fuzzy inference through a triangular membership function to obtain the membership degrees of relevant network parameters within each group.

[0056] Specifically, the triangular membership function is a commonly used membership function in fuzzy logic systems, used to represent the degree to which an element in a fuzzy set belongs to a certain fuzzy set. The triangular membership function is usually represented by a triangle and is a continuous, linearly increasing and decreasing function on the graph. The function can be defined by three parameters: the left endpoint a, the peak point b, and the right endpoint c, where x is the input independent variable of the function, and the form of the function is as follows:

[0057]

[0058] Exemplarily, in the first group, for the calculation of the membership degree of the signal strength, the independent variable is the signal strength S;

[0059] For the calculation of "weak" in the fuzzy set, where a is the left endpoint of the "weak" range, b is the peak point in the "weak" range, c is the right endpoint of the "weak" range, and a < b < c, the membership degree "Low" of the signal strength in the fuzzy set with respect to "weak" is obtained through the triangular membership function; for the calculation of "medium" in the fuzzy set, where a is the left endpoint of the "medium" range, b is the peak point in the "medium" range, c is the right endpoint of the "medium" range, and a < b < c, the membership degree "Medium" of the signal strength in the fuzzy set with respect to "medium" is obtained through the triangular membership function; for the calculation of "strong" in the fuzzy set, where a is the left endpoint of the "strong" range, b is the peak point in the "strong" range, c is the right endpoint of the "strong" range, and a < b < c, the membership degree "High" of the signal strength in the fuzzy set with respect to "strong" is obtained through the triangular membership function.

[0060] Furthermore, the membership degree of the signal strength in the first group calculated through the triangular membership function is μ(S) = {Low, Medium, High}.

[0061] In the second group, for the calculation of the membership degree of network latency, the independent variable is network latency L; for the calculation of "low latency" in the fuzzy set, where a is the left endpoint of the "low latency" range, b is the peak point in the "low latency" range, and c is the right endpoint of the "low latency" range, and a < b < c, the membership degree of the signal strength with respect to "low latency" in the fuzzy set, "Low Latency", is obtained through the triangular membership function; for the calculation of "medium latency" in the fuzzy set, where a is the left endpoint of the "medium latency" range, b is the peak point in the "medium latency" range, and c is the right endpoint of the "medium latency" range, and a < b < c, the membership degree of the signal strength with respect to "medium latency" in the fuzzy set, "Medium Latency", is obtained through the triangular membership function; for the calculation of "high latency" in the fuzzy set, where a is the left endpoint of the "high latency" range, b is the peak point in the "high latency" range, and c is the right endpoint of the "high latency" range, and a < b < c, the membership degree of the signal strength with respect to "high latency" in the fuzzy set, "High Latency", is obtained through the triangular membership function.

[0062] Furthermore, the membership degree of network latency in the second group is calculated through the triangular membership function as μ(L) = {Low Latency, Medium Latency, High Latency}.

[0063] In the calculation of the membership degree of network congestion level, the independent variable is network congestion level C; for the calculation of "free flow" in the fuzzy set, where a is the left endpoint of the "free flow" range, b is the peak point in the "free flow" range, and c is the right endpoint of the "free flow" range, and a < b < c, the membership degree of the signal strength with respect to "free flow" in the fuzzy set, "Free Flow", is obtained through the triangular membership function; for the calculation of "moderate" in the fuzzy set, where a is the left endpoint of the "moderate" range, b is the peak point in the "moderate" range, and c is the right endpoint of the "moderate" range, and a < b < c, the membership degree of the signal strength with respect to "moderate" in the fuzzy set, "Moderate", is obtained through the triangular membership function; for the calculation of "congested" in the fuzzy set, where a is the left endpoint of the "congested" range, b is the peak point in the "congested" range, and c is the right endpoint of the "congested" range, and a < b < c, the membership degree of the signal strength with respect to "congested" in the fuzzy set, "Congested", is obtained through the triangular membership function.

[0064] Furthermore, the membership degree of network congestion level in the second group is calculated through the triangular membership function as μ(L) = {Free Flow, Moderate, Congested}.

[0065] Among them, there are two network parameters in the second group, and the corresponding rules in the rule base are 3*3=9 rules. The membership of the second group adopts the product method, and the two membership vectors of network delay and network congestion are outer-producted to obtain the membership matrix. The membership of the second group can be expressed as:

[0066]

[0067] In this embodiment, the process of calculating the membership of the third group by using the triangle membership function is the same as that of the second group, which will not be described in detail here.

[0068] Optional, such as Figure 2 After obtaining three groups of membership data, the preprocessing process S205 uses the centroid method to defuzzify and obtain the preprocessing score in step S200. The centroid method formula is as follows:

[0069]

[0070] Specifically, x* is the final deterministic output value; x i is a set of discrete possible output values, which represent the possible results of fuzzy system inference based on fuzzy rules; μ(x i ) represents the possible output value x i The degree of belonging to the fuzzy set; n is the possible output value x i It represents the total number of different output values ​​inferred from fuzzy rules in a fuzzy system.

[0071] For example, in the first group, there are 3 signal strength rules, n=3, when i=3, x i Score rule 3, μ(x i ) is a membership degree of "low"; when i = 2, x i Score rule 2, μ(x i ) is the membership degree "medium"; when i = 1, x i Score rule 1, μ(x i ) is a membership degree of “high”; x* is calculated by the centroid method, where x* is the first set of preprocessing scores.

[0072] The second set of network delay and network congestion rules has 9 rules in total, so n = 9,

[0073] When i=1, x i Score rule 4, μ(x i ) is the membership degree μ(Low Latency)×μ(Free Flow); when i=2, x i Score rule 5, μ(x i) is the membership degree μ(Low Latency)×μ(Moderate); when i=3, x i Score rule 6, μ(x i ) is the membership degree μ(Low Latency)×μ(Congested); when i=4, x i Score rule 7, μ(x i ) is μ(Medium Latency)×μ(Free Flow); when i=5, x i Score rule 8, μ(x i ) is the membership degree μ(Medium Latency)×μ(Moderate); when i=6, x i Score rule 9, μ(x i ) is the membership degree μ(Medium Latency)×μ(Congested); when i=7, x i Score rule 10, μ(x i ) is the membership μ(High Latency)×μ(Free Flow); when i=8, x i Score rule 11, μ(x i ) is the membership μ(High Latency)×μ(Moderate); when i=9, x i Score rule 12, μ(x i ) is the membership μ(HighLatency)×μ(Congested); x* is calculated by the centroid method, where x* is the second group of preprocessing scores.

[0074] In this embodiment, the preprocessing scoring process of the third group obtained by defuzzification using the centroid method is consistent with that of the second group, which will not be repeated here.

[0075] like Figure 3 The step S300 shown uses a weighted comprehensive evaluation calculation to process the three groups of pre-processing scores. The processing process includes S301 standardization, S302 weight allocation, and S303 comprehensive weighted calculation to obtain a weighted comprehensive evaluation calculation result. The specific weighted comprehensive evaluation calculation can be the following steps:

[0076] In this embodiment, S301 performs standardization processing on the three groups of pre-processing scores in step S200. The standardization processing process is to standardize the pre-processing scores of each device network parameter to obtain a standardized result. The calculation formula for maximizing parameter standardization is as follows:

[0077]

[0078] For example, when the X variable is the first set of preprocessing scores, X max is the first set of highest preprocessing scores in the first set of historical evaluation cycles, X min is the first group of lowest pre-processing scores in the first group of historical evaluation cycles, X′ is the first group of standardized results; when the X variable is the second group of pre-processing scores, X max is the second highest pre-processing score in the second historical evaluation period, X min is the second group's lowest pre-processing score in the second group's historical evaluation cycle, X′ is the second group's standardized result; when the X variable is the third group's pre-processing score, X max is the third highest pre-processing score in the third historical evaluation period, X min is the lowest pre-processing score of the third group in the third historical evaluation cycle, and X′ is the standardized result of the third group.

[0079] Furthermore, S302 assigns weights to the standardized results. In the weighted comprehensive evaluation calculation, the weight assignment needs to meet the following conditions:

[0080] w1+w2+…+w n =1

[0081] In this embodiment, the weight obtained by the first group allocation is w1, the weight obtained by the second group allocation is w2, the weight obtained by the first group allocation is w3, and the sum of the weights obtained by the three groups allocation is 1.

[0082] Furthermore, the comprehensive score of the S303 device network is calculated by weighting the standardized results of the first, second and third groups for comprehensive evaluation. The specific calculation formula is as follows:

[0083]

[0084] Among them, X i ′ is the standardized result of group i, w i The weight assigned to the i-th group, S i is the comprehensive score of the network parameters of the i-th device.

[0085] In step S400, it is determined whether the current network connection state is in the optimal network connection state through the weighted comprehensive evaluation calculation result. If it is not in the optimal network connection state, network switching is performed.

[0086] Specifically, the optimal network connection is the network with the highest device network parameter weighted comprehensive score.

[0087] In step S500, after the best network is selected and the network switching is performed, a network optimization strategy is applied, which includes reducing the signal scanning frequency and switching to a low power consumption mode.

[0088] In step S600, after the network optimization is performed, the dynamic changes of the device network parameters in the network environment are continuously monitored, and the weighted comprehensive evaluation calculation is adjusted according to the dynamic changes of the device network parameters.

[0089] For example, since the device power level in the device network parameters will change over time, the weight assigned to the device power level in the weighted comprehensive evaluation calculation needs to be changed according to the specific power level situation.

[0090] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0091] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in, including several instructions for a terminal (which can be a communication module, a handheld terminal, a computer, a server or a network device, etc.) to execute the methods of each embodiment of the present application.

[0092] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A wireless network selection method for 4G communication, characterized in that: The method comprises: S100, when the terminal device is in a network connection state, collecting multiple device network parameters in real time; S200, preprocessing the device network parameters to generate a preprocessing score corresponding to each device network parameter; S300, performing a weighted comprehensive evaluation calculation based on each of the preprocessing scores to obtain each weighted comprehensive evaluation calculation result; S400, judging whether the current network connection state is in an optimal network connection state according to the weighted comprehensive evaluation calculation result, and if the judgment result is not optimal, performing network switching; The device network parameters include: a first device network parameter, a second device network parameter, ... an Nth device network parameter.

2. The wireless network selection method for 4G communication according to claim 1, characterized in that In S100, each of the device network parameters includes signal strength, network delay, network congestion status, device power and user preferences, and the signal strength, network delay, network congestion status, device power and user preferences are all collected by the terminal device.

3. The wireless network selection method for 4G communication according to claim 2, characterized in that In S200, the preprocessing is fuzzy logic reasoning, and the fuzzy logic reasoning process includes: S201, grouping the network parameters of each device, wherein the signal strength is a first group, the network delay and network congestion status are a second group, and the device power and user preference are a third group; S202, creating a fuzzy set for the device network parameters in each of the groups, wherein the fuzzy set includes specific elements; The first group has the fuzzy set, the fuzzy set of the signal strength is strong, medium, and weak, and the strong, medium, and weak are the elements of the first group; The second group has the fuzzy set, wherein the fuzzy set of the network delay is: low delay, medium delay, high delay, and the low delay, medium delay, and high delay are the elements of the second group, and the fuzzy set of the network congestion state is: unobstructed, moderate, and congested, and the unobstructed, moderate, and congested are the elements of the second group, and the fuzzy set of the second group is the collection of the fuzzy set of the network delay and the fuzzy set of the network congestion state; The third group contains the fuzzy set, the fuzzy set of the device power state is: low power, medium power, high power, and the low power, medium power, and high power are the elements of the third group, the fuzzy set of the user preference is: low priority, medium priority, and high priority, and the low priority, medium priority, and high priority are the elements of the third group, and the fuzzy set of the second group is the set of the fuzzy set of the device power and the fuzzy set of the user preference; S203, based on the fuzzy set of each of the groups, define a corresponding fuzzy inference rule base, wherein the rule base includes the elements of each group and the corresponding scores of the elements, and the set of the elements and the set of the corresponding scores of the elements are in a bijective relationship; The rule base of the first group includes corresponding scores of elements of the first group and elements of the first group, the rule base of the second group includes corresponding scores of elements of the second group and elements of the second group, and the rule base of the third group includes corresponding scores of elements of the third group and elements of the third group; S204, based on the network parameters of each device and the fuzzy set of the group, obtaining the membership of each group through a membership function, the parameters of the membership function of each group are determined by the elements in the group, the input independent variable of the membership function of the first group is the signal strength, and the obtained dependent variable is the first group of membership, the input independent variables of the membership function of the second group are the network delay and the network congestion level, and the obtained dependent variable is the second group of membership, the input independent variables of the membership function of the third group are the device power and the user preference, and the obtained dependent variable is the third group of membership; S205, each of the groups obtains the preprocessing score by defuzzification, wherein the defuzzification uses a defuzzification formula, and a parameter of the defuzzification formula is a corresponding score of each element in the rule base; The first group uses the defuzzification formula to input an independent variable as the signal strength, and the resulting dependent variable is the first group of preprocessing scores; The second group of independent variables inputted by the defuzzification formula are the network delay and the network congestion level, and the obtained dependent variables are the second group of preprocessing scores; The third group of independent variables input using the defuzzification formula are the device power and the user preference, and the obtained dependent variables are the third group of preprocessing scores.

4. The wireless network selection method for 4G communication according to claim 3, characterized in that In S300, the weighted comprehensive evaluation calculation specifically includes: S301, standardization processing, wherein the standardization processing standardizes the preprocessing score of each of the groups to obtain a standardized result of each of the groups, wherein the standardization uses a standardization function, wherein the first group obtains the first group of standardized results based on the first group of preprocessing scores through the standardization function, the second group obtains the second group of standardized results based on the second group of preprocessing scores through the standardization function, and the third group obtains the third group of standardized results based on the third group of preprocessing scores through the standardization function; S302, assigning weights, wherein the weight assignment is to assign a weight value to the standardized result of each group, the sum of the weight values ​​of each group is 1, the first group of standardized results is assigned to obtain a first group of weight values, the second group of standardized results is assigned to obtain a second group of weight values, and the third group of standardized results is assigned to obtain a third group of weight values; S303. Obtain the weighted comprehensive evaluation calculation result of each device network parameter through comprehensive scoring calculation, wherein the comprehensive scoring calculation is to obtain a first group of weighted standard values ​​by multiplying the standardized result of the first group by the weight value of the first group, obtain a second group of weighted standard values ​​by multiplying the standardized result of the second group by the weight value of the second group, obtain a third group of weighted standard values ​​by multiplying the standardized result of the third group by the weight value of the third group, and finally obtain the weighted comprehensive evaluation calculation result by adding the first group of weighted standard values, the second group of weighted standard values, and the third group of weighted standard values.

5. The wireless network selection method for 4G communication according to claim 1, characterized in that: Also includes step S500; After executing the network switching, S500 applies a network optimization strategy, which includes reducing the signal scanning frequency and switching to a low power consumption mode.

6. The wireless network selection method for 4G communication according to claim 1, characterized in that: Also includes step S600; S600, after switching to the optimal network, continuously monitors dynamic changes in the network environment and adjusts the weighted comprehensive evaluation calculation according to the dynamic changes.

7. A 4G communication wireless network selection system used in the 4G communication wireless network selection method according to any one of claims 1 to 6, characterized in that: The system includes a device network parameter monitoring module, a preprocessing module, a comprehensive evaluation module, and a network switching module: The device network parameter monitoring module is used to collect multiple device network parameters in real time when the terminal device is in a network connection state; The preprocessing module is used to preprocess the device network parameters and generate a preprocessing score corresponding to each device network parameter; The comprehensive evaluation module performs a weighted comprehensive evaluation calculation based on each of the preprocessing scores to obtain each weighted comprehensive evaluation calculation result; The network switching module is used to determine whether the current network connection state is in an optimal network connection state according to the weighted comprehensive evaluation calculation result, and if the determination result is not optimal, perform network switching; The device network parameters include: a first device network parameter, a second device network parameter, ... an Nth device network parameter.

8. A 4G communication wireless network selection system according to claim 7, characterized in that: Each of the device network parameters includes signal strength, network delay, network congestion status, device power and user preferences, and the signal strength, network delay, network congestion status, device power and user preferences are all collected by the device network parameter monitoring module of the terminal device.

9. A terminal, characterized in that: The terminal is used to implement the step of selecting a wireless network for 4G communication as described in any one of claims 1 to 6.

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