4G network networking method and system based on own optical cable line

Abstract

The invention discloses a 4G network networking method and system based on an own optical cable line, and relates to the technical field of mobile communication, and the method comprises the steps: collecting 4G network data, and carrying out the preprocessing of the 4G network data; performing feature extraction on the preprocessed data by using a recursive feature elimination method to obtain a network feature vector; constructing an optical cable path model by adopting a recurrent neural network (RNN), inputting the network feature vector into the optical cable path model, and predicting an optimal transmission path; collecting real-time data, inputting the real-time data into the optical cable path model to obtain a real-time optimal transmission path, and dynamically adjusting parameters of the optical cable path model when detecting that the network state is changed; a judgment function is constructed according to the real-time optimal transmission path, the abnormal state of the line is judged according to the judgment function, and when the line is abnormal, an alarm is triggered immediately; and after the alarm is triggered, corresponding countermeasures are executed.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a 4G network networking method based on a self-owned optical cable line. The method realizes efficient management and optimization of the 4G network through the steps of collecting 4G network data, feature extraction, path prediction, real-time path optimization and anomaly detection, so as to improve the stability of network transmission and the service quality. Background Art

[0002] With the rapid development of mobile communication technology, the fourth generation mobile communication system (4G) has become a core part of modern communication infrastructure. The 4G network has greatly promoted the development of mobile Internet services with its high speed and low latency. However, with the continuous growth of data traffic, how to efficiently optimize the 4G network has become an important challenge. Especially in the 4G network networking based on its own optical cable lines, how to reasonably plan the optical cable path to ensure the reliability of data transmission and handle network anomalies in a timely manner has become a key issue to be solved.

[0003] Traditional 4G network networking methods often focus on static path planning and lack the ability to effectively monitor and dynamically adjust the real-time network conditions. For example, traditional path planning methods usually rely on fixed data sets for path optimization. This static method is difficult to cope with sudden traffic changes in the network. In addition, existing anomaly detection mechanisms are usually based on simple threshold judgments and cannot accurately identify complex network anomaly patterns, which may lead to underreporting and affect the stability of the network. Therefore, it is particularly important to develop a networking method that can monitor network conditions in real time and quickly respond to abnormal events. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a 4G network networking method based on its own optical cable line, aiming to solve the problem that the traditional 4G network networking method relies on fixed data sets for optimization, is difficult to cope with sudden traffic changes in the network, lacks effective monitoring and adjustment capabilities for real-time network conditions, cannot quickly respond to sudden traffic changes in the network, and the anomaly detection mechanism is usually based on simple threshold judgment, which is difficult to accurately identify complex network anomaly patterns, may lead to underreporting of anomalies, and affect the stability of the network.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a 4G network networking method based on a self-owned optical cable line, which includes:

[0008] Collect 4G network data and pre-process it;

[0009] The recursive feature elimination method is used to extract features from the preprocessed data to obtain the network feature vector;

[0010] The recurrent neural network (RNN) is used to build the optical cable path model, and the network feature vector is input into the optical cable path model to predict the optimal transmission path.

[0011] Collect real-time data and input the real-time data into the optical cable path model to obtain the real-time optimal transmission path. When changes in the network status are detected, the parameters of the optical cable path model are dynamically adjusted;

[0012] Build a judgment function based on the real-time optimal transmission path, judge the abnormal state of the line based on the judgment function, and trigger an alarm immediately when the line is abnormal;

[0013] After the alarm is triggered, the corresponding response measures are executed.

[0014] As a preferred solution of the 4G network networking method based on the self-owned optical cable line of the present invention, wherein: the 4G network data is collected and pre-processed, and the specific steps are:

[0015] Set the collected 4G network data set to X;

[0016] Use the Z-score method to detect X and remove outliers. The specific formula is:

[0017]

[0018] Among them, Zi represents the standardized score of the i-th observation, xi represents the i-th observation, μ represents the mean of the sample, and σ represents the standard deviation of the sample;

[0019] Set the threshold TI,

[0020] Zi is a real number with a value range from negative infinity to positive infinity. When | Zi |>TI, the observation is considered to be an outlier and is eliminated, otherwise it is retained.

[0021] As a preferred solution of the 4G network networking method based on the self-owned optical cable line of the present invention, wherein: the recursive feature elimination method is used to extract features from the pre-processed data to obtain the network feature vector, and the specific steps are:

[0022] The recursive feature elimination method is used to extract features from the standardized network data to obtain the network feature vector, which is expressed as:

[0023]

[0024] Among them, Vopt represents the network feature vector, N represents the number of features, I i (x i ) represents the i-th feature x i The importance score of is based on the recurrent neural network model, represents the average importance score of all features, n represents the accumulated variable in the integration process, e represents the base of the natural logarithm, σ represents the standard deviation of the normalized feature importance score, π represents pi, and dN represents the integral of N.

[0025] As a preferred solution of the 4G network networking method based on the self-owned optical cable line of the present invention, wherein: the optical cable path model is constructed by using a recurrent neural network RNN, the network feature vector is input into the optical cable path model, and the optimal transmission path is predicted. The specific steps are:

[0026] The recurrent neural network RNN ​​is used to construct the optical cable path model, and the expression is:

[0027]

[0028] Among them, H represents the predicted optimal transmission path, V opt represents the network feature vector, L represents the current network load matrix, D(e) represents the average time required for the e-th edge to transmit data, F(e) represents the possibility of failure of the e-th edge within a certain period of time, B(e) represents the ratio of the bandwidth currently used on the e-th edge to its maximum available bandwidth, C(e) represents the cost of using the e-th edge, and w D , w F , w B , w C are the weight coefficients of path delay, failure probability, bandwidth utilization and cost, respectively. represents the minimum value of all possible paths from the source point s to the end point t, and P represents one of all possible paths from the source point s to the end point t;

[0029] The feature vector V opt Input into the model to obtain the predicted optimal transmission path H.

[0030] As a preferred solution of the 4G network networking method based on the self-owned optical cable line of the present invention, wherein: the real-time data is collected, the real-time data is input into the optical cable path model, and the real-time optimal transmission path is obtained, and the optical cable path model parameters are dynamically adjusted when the network status is detected to change. The specific steps are:

[0031] Collect real-time data, preprocess it, and use recursive feature elimination to extract features from the preprocessed real-time data to obtain the real-time network feature vector V rt ;

[0032] When a change in the network status is detected, the optical cable path model parameters are dynamically adjusted to optimize the path selection, and the real-time network feature vector V rt Input the optical cable path model to obtain the real-time optimal transmission path H L ;

[0033] For the real-time optimal transmission path H L The comprehensive scoring expression is:

[0034]

[0035] Among them, S represents the comprehensive score; N represents the number of features; I i (x i (H L )) represents the i-th feature x i In real-time optimal transmission path H L The importance rating of the following; represents the average importance score of all features; exp represents the exponential function; x i (H L ) represents the i-th feature in the real-time optimal transmission path H L The actual value under; σ represents the standard deviation in the normalization factor; α i Represents the correction term for the i-th feature.

[0036] As a preferred solution of the 4G network networking method based on the self-owned optical cable line of the present invention, wherein: the judgment function is constructed according to the real-time optimal transmission path, and the abnormal state of the line is judged according to the judgment function. When the line is abnormal, an alarm is triggered immediately. The specific steps are:

[0037] Based on the comprehensive score S, a judgment function J(S) is constructed to distinguish the status of the line. The expression is:

[0038]

[0039] Among them, k is a positive real number, e represents the base of the natural logarithm, and T2 is the set threshold;

[0040] The value range of J(S) is 0 and 1. When J(S) = 0, it means that the optical cable line is in a normal state and no alarm will be triggered. When J(S) = 1, it means that the optical cable line is in an abnormal state and an alarm will be triggered.

[0041] As a preferred solution of the 4G network networking method based on the own optical cable line of the present invention, wherein: after the alarm is triggered, the corresponding response measures are executed, and the specific steps are:

[0042] Define the threshold T low and T high ;

[0043] When T low ≤S≤T high If the optical cable line is slightly abnormal, the fault will be reported to the relevant internal departments immediately, the monitoring frequency will be increased, and the line status will be closely monitored;

[0044] When S>T high When a fault occurs, it is considered that there is a serious abnormality in the optical cable line, and the nearest maintenance team is dispatched to the site, and notifications are sent to affected customers via text messages and emails.

[0045] In a second aspect, the present invention provides a 4G network networking system based on a self-owned optical cable line, comprising:

[0046] Data collection module, used to collect 4G network data and pre-process the data, including outlier detection and removal, to ensure the accuracy of subsequent analysis;

[0047] The feature extraction module is used to extract key features from the preprocessed data using a recursive feature elimination method to form a network feature vector, which provides input for subsequent path prediction;

[0048] The path prediction model uses a recurrent neural network (RNN) to build a cable path model and inputs the network feature vector to predict the optimal transmission path;

[0049] The real-time path dynamic adjustment module is used to collect real-time data and input it into the path prediction model to obtain the real-time optimal transmission path, and dynamically adjust the model parameters according to the changes in network status to optimize path selection;

[0050] The anomaly detection module is used to build a judgment function based on the real-time optimal transmission path to detect whether the line is in an abnormal state and trigger an alarm when an anomaly is detected;

[0051] The countermeasure execution module is used to execute pre-set countermeasures to solve network problems or restore services after receiving an alarm signal.

[0052] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the 4G network networking method based on a private optical cable line as described in the first aspect of the present invention is implemented.

[0053] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the 4G network networking method based on a owned optical cable line as described in the first aspect of the present invention is implemented.

[0054] The beneficial effects of the present invention are: realizing effective collection and preprocessing of 4G network data, extraction of key features, prediction of optimal paths, and optimization and dynamic adjustment of real-time paths. These steps work together to form a highly intelligent and dynamically responsive 4G network networking method, which can effectively cope with various challenges in the network, significantly improve the transmission efficiency, stability and service quality of the 4G network, and provide users with a more reliable and efficient network connection experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 This is a flow chart of the 4G network networking method based on the own optical cable line in Example 1.

[0057] Figure 2 This is a flow chart of the 4G network networking system based on the own optical cable line in Example 1. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a 4G network networking method based on a self-owned optical cable line, comprising the following steps:

[0062] S1 collects 4G network data and pre-processes it;

[0063] Set the collected 4G network data set to X;

[0064] Use the Z-score method to detect X and remove outliers. The specific formula is:

[0065]

[0066] Among them, Zi represents the standardized score of the i-th observation, xi represents the i-th observation, μ represents the mean of the sample, and σ represents the standard deviation of the sample;

[0067] Set the threshold TI,

[0068] Zi is a real number with a range from negative infinity to positive infinity. When | Zi |>TI, the observed value is considered to be an outlier and is removed, otherwise it is retained;

[0069] 4G network data, including signal strength, network traffic, geographic location, delay time, and packet loss rate.

[0070] S2 uses recursive feature elimination method to extract features from the preprocessed data and obtains the network feature vector;

[0071] The recursive feature elimination method is used to extract features from the standardized network data to obtain the network feature vector, which is expressed as:

[0072]

[0073] Among them, V opt represents the network feature vector, N represents the number of features, I i (x i ) represents the i-th feature x i The importance score of is based on the recurrent neural network model, represents the average importance score of all features, n represents the accumulated variable in the integration process, e represents the base of the natural logarithm, σ represents the standard deviation of the normalized feature importance score, π represents pi, and dN represents the integral of N.

[0074] S3 uses a recurrent neural network (RNN) to build a cable path model, inputs the network feature vector into the cable path model, and predicts the optimal transmission path;

[0075] The recurrent neural network RNN ​​is used to construct the optical cable path model, and the expression is:

[0076]

[0077] Among them, H represents the predicted optimal transmission path, V optrepresents the network feature vector, L represents the current network load matrix, D(e) represents the average time required for the e-th edge to transmit data, F(e) represents the possibility of failure of the e-th edge within a certain period of time, B(e) represents the ratio of the bandwidth currently used on the e-th edge to its maximum available bandwidth, C(e) represents the cost of using the e-th edge, and w D , w F , w B , w C are the weight coefficients of path delay, failure probability, bandwidth utilization and cost, respectively. represents the minimum value of all possible paths from the source point s to the end point t, and P represents one of all possible paths from the source point s to the end point t;

[0078] The feature vector V opt Input into the model to obtain the predicted optimal transmission path H.

[0079] S4 collects real-time data, inputs the real-time data into the optical cable path model, obtains the real-time optimal transmission path, and dynamically adjusts the parameters of the optical cable path model when a change in the network status is detected;

[0080] Collect real-time data, preprocess it, and use recursive feature elimination to extract features from the preprocessed real-time data to obtain the real-time network feature vector V rt ;

[0081] When a change in the network status is detected, the optical cable path model parameters are dynamically adjusted to optimize the path selection, and the real-time network feature vector V rt Input the optical cable path model to obtain the real-time optimal transmission path H L ;

[0082] For the real-time optimal transmission path H L The comprehensive scoring expression is:

[0083]

[0084] Among them, S represents the comprehensive score; N represents the number of features; I i (x i (H L )) represents the i-th feature x i In real-time optimal transmission path H L The importance rating of the following; represents the average importance score of all features; exp represents the exponential function; x i (H L ) represents the i-th feature in the real-time optimal transmission path H L The actual value under; σ represents the standard deviation in the normalization factor; α iRepresents the correction term for the i-th feature.

[0085] S5 builds a judgment function based on the real-time optimal transmission path, and judges the abnormal state of the line according to the judgment function. When the line is abnormal, an alarm is triggered immediately;

[0086] Based on the comprehensive score S, a judgment function J(S) is constructed to distinguish the status of the line. The expression is:

[0087]

[0088] Among them, k is a positive real number, e represents the base of the natural logarithm, and T2 is the set threshold;

[0089] The value range of J(S) is 0 and 1. When J(S) = 0, it means that the optical cable line is in a normal state and no alarm will be triggered. When J(S) = 1, it means that the optical cable line is in an abnormal state and an alarm will be triggered.

[0090] After S6 triggers the alarm, corresponding countermeasures are executed;

[0091] Define the threshold T low and T high ;

[0092] When T low ≤S≤T high If the optical cable line is slightly abnormal, the fault will be reported to the relevant internal departments immediately, the monitoring frequency will be increased, and the line status will be closely monitored;

[0093] When S>T high When a fault occurs, it is considered that there is a serious abnormality in the optical cable line, and the nearest maintenance team is dispatched to the site, and notifications are sent to affected customers via text messages and emails.

[0094] This embodiment also provides a 4G network networking system based on a self-owned optical cable line, including:

[0095] Data collection module, used to collect 4G network data and pre-process the data, including outlier detection and removal, to ensure the accuracy of subsequent analysis;

[0096] The feature extraction module is used to extract key features from the preprocessed data using a recursive feature elimination method to form a network feature vector, which provides input for subsequent path prediction;

[0097] The path prediction model uses a recurrent neural network (RNN) to build a cable path model and inputs the network feature vector to predict the optimal transmission path;

[0098] The real-time path dynamic adjustment module is used to collect real-time data and input it into the path prediction model to obtain the real-time optimal transmission path, and dynamically adjust the model parameters according to the changes in network status to optimize path selection;

[0099] The anomaly detection module is used to build a judgment function based on the real-time optimal transmission path to detect whether the line is in an abnormal state and trigger an alarm when an anomaly is detected;

[0100] The countermeasure execution module is used to execute pre-set countermeasures to solve network problems or restore services after receiving an alarm signal.

[0101] This embodiment also provides a computer device, which is suitable for the case of a 4G network networking method based on a private optical cable line, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the 4G network networking method based on a private optical cable line as proposed in the above embodiment.

[0102] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0103] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the 4G network networking method based on a self-owned optical cable line as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, referred to as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), a programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), a read-only memory (Read-Only Memory, referred to as ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0104] In summary, the present invention realizes effective collection and preprocessing of 4G network data, extraction of key features, prediction of optimal paths, and optimization and dynamic adjustment of real-time paths. These steps work together to form a highly intelligent and dynamically responsive 4G network networking method, which can effectively cope with various challenges in the network, significantly improve the transmission efficiency, stability and service quality of the 4G network, and provide users with a more reliable and efficient network connection experience.

[0105] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a 4G network networking method based on a self-owned optical cable line is provided.

[0106] This embodiment selects a typical 4G network environment and simulates different network loads and fault conditions to test the difference in effect between the method of the present invention and the traditional method. First, a 4G network including 100 base stations is selected, covering a medium-sized urban area, and three different network load conditions of high, medium and low are simulated, as well as two network states of normal and abnormal.

[0107] Next, 4G network data was collected, including signal strength, network traffic, geographic location, delay time, and packet loss rate. The Z-score method was used to preprocess the data, outliers were removed, and the threshold TI was set to 3. The recursive feature elimination method was used to extract features from the preprocessed data to obtain the network feature vector. In order to evaluate the effect of feature extraction, signal strength and network traffic were selected as the two most important features for subsequent analysis. The recurrent neural network RNN ​​was used to build the optical cable path model, and the mean square error (MSE) was used as the loss function to train the model to predict the optimal transmission path. The model training used a training set containing 5000 data points and a validation set containing 1000 data points. Real-time data was collected and input into the path prediction model to obtain the real-time optimal transmission path. When a change in network status was detected, the model parameters were dynamically adjusted to optimize the path selection. In the experiment, sudden high-traffic events such as live broadcasts of sports events were simulated to observe the effect of path optimization. A judgment function J(S) was constructed to detect abnormal line status. When the line is abnormal, an alarm is triggered immediately and corresponding countermeasures are implemented. The threshold T2=0.8 was set to trigger the alarm.

[0108] The details are shown in Table 1:

[0109] Table 1 Experimental record table

[0110]

[0111] Through the above experimental data, we can observe the following significant beneficial effects:

[0112] In the fault scenario, the sudden increase in network traffic and the decrease in signal strength lead to increased latency and increased packet loss. By performing path optimization using the method of the present invention, it can be seen that despite the fault, the network is still able to maintain relatively high signal strength, low latency and packet loss. In contrast, the performance of the baseline scenario (without optimization) is poor, especially when the network load is high.

[0113] The method of the present invention can accurately detect changes in network status and trigger an alarm in a timely manner. By triggering an alarm and executing corresponding countermeasures, it is possible to quickly respond to network failures and reduce service interruption time. In a failure scenario, although the network status deteriorates, the method of the present invention can quickly take measures to maintain the continuity of network services.

[0114] The method of the present invention shows significant advantages in handling network failures and optimizing paths. Compared with traditional static path planning methods, the present invention can detect network anomalies more accurately and take timely measures, thereby significantly improving network stability and service quality. For example, in a failure scenario, the delay of the traditional method reaches 55ms and the packet loss rate is 0.03, while the delay of the method of the present invention is 60ms and the packet loss rate is 0.03. However, the method of the present invention can quickly optimize the path and reduce the impact of the failure.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A 4G network networking method based on a self-owned optical cable line, characterized in that: include: Collect 4G network data and pre-process it; The recursive feature elimination method is used to extract features from the preprocessed data to obtain the network feature vector; The recurrent neural network (RNN) is used to build the optical cable path model, and the network feature vector is input into the optical cable path model to predict the optimal transmission path. Collect real-time data and input the real-time data into the optical cable path model to obtain the real-time optimal transmission path. When changes in the network status are detected, the parameters of the optical cable path model are dynamically adjusted; Build a judgment function based on the real-time optimal transmission path, judge the abnormal state of the line based on the judgment function, and trigger an alarm immediately when the line is abnormal; After the alarm is triggered, the corresponding response measures are executed.

2. The 4G network networking method based on the self-owned optical cable line according to claim 1, characterized in that: The 4G network data is collected and preprocessed, and the specific steps are as follows: Set the collected 4G network data set to X; Use the Z-score method to detect X and remove outliers. The specific formula is: Among them, Zi represents the standardized score of the i-th observation, xi represents the i-th observation, μ represents the mean of the sample, and σ represents the standard deviation of the sample; Set the threshold TI, Zi is a real number with a value range from negative infinity to positive infinity. When |Zi|>TI, the observation value is considered to be an outlier and is eliminated, otherwise it is retained.

3. The 4G network networking method based on the self-owned optical cable line according to claim 2, characterized in that: The recursive feature elimination method is used to extract features from the preprocessed data to obtain a network feature vector. The specific steps are: The recursive feature elimination method is used to extract features from the standardized network data to obtain the network feature vector, which is expressed as: Among them, V opt represents the network feature vector, N represents the number of features, I i (x i ) represents the i-th feature x i The importance score of is based on the recurrent neural network model, represents the average importance score of all features, n represents the accumulated variable in the integration process, e represents the base of the natural logarithm, σ represents the standard deviation of the normalized feature importance score, π represents pi, and dN represents the integral of N.

4. The 4G network networking method based on the self-owned optical cable line according to claim 3 is characterized in that: Regarding the use of a recurrent neural network RNN ​​to construct an optical cable path model, the network feature vector is input into the optical cable path model to predict the optimal transmission path, and the specific steps are as follows: The recurrent neural network RNN ​​is used to construct the optical cable path model, and the expression is: Among them, H represents the predicted optimal transmission path, V opt represents the network feature vector, L represents the current network load matrix, D(e) represents the average time required for the e-th edge to transmit data, F(e) represents the possibility of failure of the e-th edge within a certain period of time, B(e) represents the ratio of the bandwidth currently used on the e-th edge to its maximum available bandwidth, C(e) represents the cost of using the e-th edge, and w D , w F , w B , w C are the weight coefficients of path delay, failure probability, bandwidth utilization and cost, respectively. represents the minimum value of all possible paths from the source point s to the end point t, and P represents one of all possible paths from the source point s to the end point t; The feature vector V opt Input into the model to obtain the predicted optimal transmission path H.

5. The 4G network networking method based on the self-owned optical cable line according to claim 4, characterized in that: The real-time data is collected, inputted into the optical cable path model, and the real-time optimal transmission path is obtained. When a change in the network status is detected, the parameters of the optical cable path model are dynamically adjusted. The specific steps are as follows: Collect real-time data, preprocess it, and use recursive feature elimination to extract features from the preprocessed real-time data to obtain the real-time network feature vector V rt ; When a change in the network status is detected, the optical cable path model parameters are dynamically adjusted to optimize the path selection, and the real-time network feature vector V rt Input the optical cable path model to obtain the real-time optimal transmission path H L ; For the real-time optimal transmission path H L The comprehensive scoring expression is: Among them, S represents the comprehensive score; N represents the number of features; I i (x i (H L )) represents the i-th feature x i In real-time optimal transmission path H L The importance rating of the following; represents the average importance score of all features; exp represents the exponential function; x i (H L ) represents the i-th feature in the real-time optimal transmission path H L The actual value under; σ represents the standard deviation in the normalization factor, α i Represents the correction term for the i-th feature.

6. The 4G network networking method based on the self-owned optical cable line according to claim 5, characterized in that: The judgment function is constructed according to the real-time optimal transmission path, and the abnormal state of the line is judged according to the judgment function. When the line is abnormal, an alarm is immediately triggered. The specific steps are as follows: Based on the comprehensive score S, a judgment function J(S) is constructed to distinguish the status of the line. The expression is: Among them, k is a positive real number, e represents the base of the natural logarithm, and T2 is the set threshold; The value range of J(S) is 0 and 1. When J(S) = 0, it means that the optical cable line is in a normal state and no alarm will be triggered. When J(S) = 1, it means that the optical cable line is in an abnormal state and an alarm will be triggered.

7. The 4G network networking method based on the self-owned optical cable line according to claim 6, characterized in that: After the alarm is triggered, corresponding countermeasures are executed, and the specific steps are as follows: Define the threshold T low and T high ; When T low ≤S≤T high If the optical cable line is slightly abnormal, the fault will be reported to the relevant internal departments immediately, the monitoring frequency will be increased, and the line status will be closely monitored; When S>T high When a fault occurs, it is considered that there is a serious abnormality in the optical cable line, and the nearest maintenance team is dispatched to the site, and notifications are sent to affected customers via text messages and emails.

8. A 4G network networking system based on a self-owned optical cable line, based on the 4G network networking method based on a self-owned optical cable line according to any one of claims 1 to 7, characterized in that: include, Data collection module, used to collect 4G network data and pre-process the data, including outlier detection and removal, to ensure the accuracy of subsequent analysis; The feature extraction module is used to extract key features from the preprocessed data using a recursive feature elimination method to form a network feature vector, which provides input for subsequent path prediction; The path prediction model uses a recurrent neural network (RNN) to build a cable path model and inputs the network feature vector to predict the optimal transmission path; The real-time path dynamic adjustment module is used to collect real-time data and input it into the path prediction model to obtain the real-time optimal transmission path, and dynamically adjust the model parameters according to the changes in network status to optimize path selection; The anomaly detection module is used to build a judgment function based on the real-time optimal transmission path to detect whether the line is in an abnormal state and trigger an alarm when an anomaly is detected; The countermeasure execution module is used to execute pre-set countermeasures to solve network problems or restore services after receiving an alarm signal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the 4G network networking method based on the own optical cable line described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the 4G network networking method based on the own optical cable line described in any one of claims 1 to 7 are implemented.

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