Metro communication signal enhancement method and system based on edge calculation

Abstract

The invention discloses a subway communication signal enhancement method and system based on edge computing, and particularly relates to the technical field of subway communication. Software component compatibility abnormal information, load balancing failure information and signal enhancement delay information are obtained when a target edge computing device enhances a subway communication signal; the hidden danger monitoring model of the edge computing equipment is constructed, so that the potential operation hidden danger of the edge computing equipment can be perceived in time before the edge computing equipment has an obvious fault; and obtaining correlation information of subway communication signal enhancement strategies of the target edge computing device and the neighborhood edge computing device and remaining time for the running subway to enter the coverage area of the target edge computing device, constructing a target edge computing device replacement model, and determining whether the target edge computing device is replaced immediately or not. And a scientific basis is provided for replacing the edge computing equipment.

Description

Technical Field

[0001] The present invention relates to the field of subway communication technology, and more specifically, to a subway communication signal enhancement method and system based on edge computing. Background Art

[0002] With the rapid development of urban rail transit systems, the stability and reliability of subway communication signals have become important factors in ensuring passenger safety and improving operational efficiency. The subway environment is complex and the signal attenuation is serious. Traditional signal enhancement methods can hardly meet the high-intensity and dynamically changing communication needs. Edge computing, as an emerging computing architecture, can process and analyze data close to the data source, reduce latency and reduce bandwidth usage. Its application in subway communication systems can achieve real-time signal monitoring and processing through distributed edge computing devices. These devices can not only adaptively adjust signal enhancement strategies, but also effectively share the pressure of the central server, improving the overall response speed and flexibility of the system;

[0003] However, when it comes to replacing edge computing devices, some companies usually wait until the edge computing devices have obvious faults before replacing or upgrading them in order to maximize the utilization of the equipment. When the equipment has obvious faults or is replaced in an emergency, the original signal enhancement strategy may become invalid, thereby affecting the communication quality. During the access process, the new edge computing device needs to be quickly compatible with the existing equipment to ensure the continuity and effectiveness of the signal enhancement strategy. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a subway communication signal enhancement method and system based on edge computing to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A subway communication signal enhancement method based on edge computing includes the following steps:

[0007] Step S1, obtaining software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal;

[0008] Step S2, constructing an edge computing device hidden danger monitoring model, generating an edge computing device hidden danger initial index, and evaluating whether the target edge computing device has an operation hidden danger;

[0009] Step S3, when the target edge computing device has an operating risk, obtain the correlation information between the target edge computing device and the subway communication signal enhancement strategy of the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device;

[0010] Step S4: construct a target edge computing device replacement model, generate an edge computing device replacement immediate index, and determine whether to replace the target edge computing device immediately.

[0011] In a preferred embodiment, the software component compatibility exception information includes a software component compatibility exception coefficient, the load balancing failure information includes a load balancing failure coefficient, and the signal enhancement delay information includes a signal enhancement delay coefficient.

[0012] In a preferred embodiment, the logic for obtaining the software component compatibility anomaly coefficient is as follows:

[0013] Obtain the version number of each software component installed on the edge computing device, compare each software component with its compatible software component, record the software components with mismatched version numbers, add them to the version missing software component set, and mark the version missing software component set as C = {C 1 ,C 2 ,...,C n}, where C 1 ,C 2 ,...,C n They respectively represent different software components in the set of software components with missing versions;

[0014] A component dependency graph is constructed based on different software components in the version missing software component set. The nodes in the component dependency graph represent software components C. i , the edges in the component dependency graph represent dependency relationships. If software component C i Depends on software component C j , then from software component C i To software component C j Draw a directed edge, C i →C j ;

[0015] Create an n*n dependency matrix D[i][j] and initialize the dependency matrix D[i][j], that is, D[i][j] = 0, where i = {1, 2, ..., n}, j = {1, 2, ..., n}; for each dependency relationship C i →C j , mark the corresponding position of the dependency matrix, that is, D[i][j]=1;

[0016] For each software component Ci , calculate the number of direct dependencies

[0017] Calculate the software component compatibility anomaly coefficient zjy, the expression is as follows

[0018] In a preferred implementation, the logic for obtaining the load balancing failure coefficient is as follows:

[0019] Get the request processing volume L of the target edge computing device at different monitoring time points m , m represents different monitoring time points, and the total number of requests Ltotal is calculated as follows m={1,2,...,M}

[0020] Calculate the load ratio p at different monitoring time points m , the expression is as follows

[0021] Calculate the entropy value HS of the target edge computing device, the expression is as follows

[0022] Calculate the maximum entropy value Hmax, the expression is as follows: Hmax = log(M);

[0023] Calculate the load balancing failure coefficient fzx, the expression is as follows

[0024] In a preferred embodiment, the logic for obtaining the signal enhancement delay coefficient is as follows:

[0025] Record the arrival time Tre of the subway communication signal at the target edge computing device and the sending time Tse of the subway communication signal;

[0026] Calculate the communication signal enhancement time Tto, the expression is as follows Tto = Tse-Tre;

[0027] Calculate the average value pf of the communication signal enhancement time, the expression is as follows Where f represents the sequence number of the subway communication signal arriving at the target edge computing device, f = {1, 2, ..., F};

[0028] The communication signal enhancement time of different subway communication signals is compared with the average value of the communication signal enhancement time. If the communication signal enhancement time of the subway communication signal is greater than the average value of the communication signal enhancement time, the communication signal enhancement time of the current subway communication signal is marked as the communication signal enhancement delay time Tty;

[0029] Calculate the signal enhancement delay coefficient xhz, the expression is as follows Wherein b represents the order number in which the communication signal enhancement time is marked as the communication signal enhancement delay time, b={1, 2, ..., B}.

[0030] In a preferred embodiment, the obtained software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient are normalized, and an edge computing device hidden danger monitoring model is constructed according to the normalized software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient to generate an edge computing device hidden danger initial index byjc. The model is based on the following formula: Where a 1 、a 2 、a 3 They represent the preset proportional coefficients of the software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient, respectively, and a 1 、a 2 、a 3 All are greater than 0;

[0031] The edge computing device hidden danger initial emergence index is compared with the preset edge computing device hidden danger initial emergence index threshold. If the edge computing device hidden danger initial emergence index is greater than the edge computing device hidden danger initial emergence index threshold, an operation hidden danger initial emergence signal is generated; if the edge computing device hidden danger initial emergence index is less than or equal to the edge computing device hidden danger initial emergence index threshold, an operation steady-state signal is generated.

[0032] In a preferred embodiment, the correlation information of the subway communication signal enhancement strategy between the target edge computing device and the neighboring edge computing device includes a neighborhood signal enhancement correlation coefficient;

[0033] The logic for obtaining the neighborhood signal enhancement correlation coefficient is as follows:

[0034] Obtain subway communication signal enhancement data of the target edge computing device and the neighboring edge computing device, the subway communication signal enhancement data including but not limited to signal amplification strength difference, spectrum utilization, and effective coverage;

[0035] Calculate the correlation R between the target edge computing device and the neighboring edge computing device in the subway communication signal enhancement strategy. The expression is as follows Where X d represents the subway communication signal enhancement data of the target edge computing device, that is, X 1 Indicates the difference in signal amplification intensity, X 2 represents the spectrum utilization, X 3 Indicates the effective coverage area. represents the average value of the subway communication signal enhancement data of the target edge computing device; Y d represents the subway communication signal enhancement data of the neighborhood edge computing device, that is, Y1 Indicates the difference in signal amplification intensity, Y 2 represents the spectrum utilization, Y 3 Indicates the effective coverage area. It represents the average value of subway communication signal enhancement data of the neighborhood edge computing devices;

[0036] Calculate the neighborhood signal enhancement correlation coefficient lyx, the expression is as follows Where H represents the number of edge computing devices in the neighborhood, R gh Represents the correlation between the target edge computing device and the hth neighboring edge computing device in the subway communication signal enhancement strategy.

[0037] In a preferred embodiment, the remaining time TS for the subway to enter the coverage area of ​​the target edge computing device can be calculated by the following expression: Where JL represents the distance between the current subway train and the coverage range of the target edge computing device, and VS represents the real-time speed of the current subway train.

[0038] In a preferred embodiment, the obtained neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device are normalized, and a target edge computing device replacement model is constructed according to the normalized neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device, and an edge computing device replacement immediate index ghjk is generated. The model is based on the following formula: Where α and β represent the neighborhood signal enhancement correlation coefficient and the preset proportional coefficient of the remaining time for the running subway to enter the coverage area of ​​the target edge computing device, respectively, and α and β are both greater than 0;

[0039] The edge computing device replacement immediate index is compared with the preset edge computing device replacement immediate index threshold. If the edge computing device replacement immediate index is greater than the edge computing device replacement immediate index threshold, an immediate replacement signal is generated to replace the target edge computing device; if the edge computing device replacement immediate index is less than or equal to the edge computing device replacement immediate index threshold, there is no need to generate an immediate replacement signal.

[0040] In a preferred embodiment, a subway communication signal enhancement system based on edge computing includes an operation information acquisition module, a hidden danger monitoring module, a decision response module, and an immediate replacement module;

[0041] The operation information acquisition module is used to obtain software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal;

[0042] The hidden danger monitoring module is used to build an edge computing device hidden danger monitoring model, generate an edge computing device hidden danger initial index, and evaluate whether the target edge computing device has operation hidden dangers;

[0043] A decision response module is used to obtain the correlation information between the subway communication signal enhancement strategy of the target edge computing device and the neighboring edge computing device and the remaining time for the running subway to enter the coverage area of ​​the target edge computing device when there is a hidden danger in the operation of the target edge computing device;

[0044] The immediate replacement module is used to build a target edge computing device replacement model, generate an edge computing device replacement immediate index, and determine whether to replace the target edge computing device immediately.

[0045] Technical effects and advantages of the present invention:

[0046] 1. The present invention obtains software component compatibility exception information, load balancing failure information and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal, constructs an edge computing device hidden danger monitoring model, generates an edge computing device hidden danger initial index, evaluates whether the target edge computing device has operation hidden dangers, effectively improves the timely perception of potential operation hidden dangers of the edge computing device before the edge computing device has obvious failures, identifies potential hidden dangers of the edge computing device in advance, can reduce the frequency of equipment replacement and related costs, and maximize equipment utilization. When the target edge computing device has an operation hidden danger, the correlation information of the subway communication signal enhancement strategy of the target edge computing device and the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device are obtained, and a target edge computing device replacement model is constructed. The edge computing device replacement immediate index is generated to determine whether the target edge computing device is replaced immediately, providing a scientific basis for the replacement of edge computing devices, ensuring the continuity and effectiveness of the signal enhancement strategy, helping to reduce the risk of communication interruption, and improving the safety experience of passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0048] Figure 1 This is a schematic diagram of the structure of the method of Example 1 of the present invention;

[0049] Figure 2 Schematic diagram of the structure of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1: Figure 1 The present invention provides a subway communication signal enhancement method based on edge computing, which includes the following steps:

[0052] Step S1, obtaining software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal;

[0053] Step S2, constructing an edge computing device hidden danger monitoring model, generating an edge computing device hidden danger initial index, and evaluating whether the target edge computing device has an operation hidden danger;

[0054] Step S3, when the target edge computing device has an operating risk, obtain the correlation information between the target edge computing device and the subway communication signal enhancement strategy of the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device;

[0055] Step S4, constructing a target edge computing device replacement model, generating an edge computing device replacement immediate index, and determining whether to replace the target edge computing device immediately;

[0056] Step S1, obtaining software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal;

[0057] The software component compatibility exception information includes a software component compatibility exception coefficient, the load balancing failure information includes a load balancing failure coefficient, and the signal enhancement delay information includes a signal enhancement delay coefficient;

[0058] The software component compatibility anomaly coefficient is used to measure the degree of anomaly in the compatibility between software components of edge computing devices. The higher the software component compatibility anomaly coefficient, the deeper the degree of anomaly in the compatibility between software components of edge computing devices, and the greater the probability of operational risks in edge computing devices. By calculating the software component compatibility anomaly coefficient, the compatibility risk of edge computing devices can be effectively evaluated to ensure its effectiveness and reliability in subway communication signal enhancement.

[0059] The logic for obtaining the software component compatibility anomaly coefficient is as follows:

[0060] Obtain the version number of each software component installed on the edge computing device, compare each software component with its compatible software component, record the software components with mismatched version numbers, add them to the version missing software component set, and mark the version missing software component set as C = {C 1 ,C 2 ,...,C n}, where C 1 ,C 2 ,...,C n They respectively represent different software components in the set of software components with missing versions;

[0061] A component dependency graph is constructed based on different software components in the version missing software component set. The nodes in the component dependency graph represent software components C. i , the edges in the component dependency graph represent dependency relationships. If software component C i Depends on software component C j , then from software component C i To software component C j Draw a directed edge, C i →C j ;

[0062] Create an n*n dependency matrix D[i][j] and initialize the dependency matrix D[i][j], that is, D[i][j] = 0, where i = {1, 2, ..., n}, j = {1, 2, ..., n}; for each dependency relationship C i →C j , mark the corresponding position of the dependency matrix, that is, D[i][j]=1;

[0063] For each software component C i , calculate the number of direct dependencies

[0064] Calculate the software component compatibility anomaly coefficient zjy, the expression is as follows

[0065] The load balancing failure coefficient is used to measure the effectiveness and stability of the load balancing mechanism of the edge computing device when processing requests. A larger load balancing failure coefficient indicates that the load balancing mechanism of the edge computing device may have failure risks when processing requests, which may lead to abnormal operation behavior of the edge computing device during actual operation and increase the probability of its operation risks. By calculating the load balancing failure coefficient, the potential operation risks of the edge computing device can be effectively identified, thereby ensuring the stability and reliability of the edge computing environment.

[0066] The logic for obtaining the load balancing failure coefficient is as follows:

[0067] Get the request processing volume L of the target edge computing device at different monitoring time points m , m represents different monitoring time points, and the total number of requests Ltotal is calculated as follows m={1,2,...,M}

[0068] Calculate the load ratio p at different monitoring time points m , the expression is as follows

[0069] Calculate the entropy value HS of the target edge computing device, the expression is as follows

[0070] Calculate the maximum entropy value Hmax, the expression is as follows: Hmax = log(M);

[0071] Calculate the load balancing failure coefficient fzx, the expression is as follows

[0072] The signal enhancement delay coefficient is used to measure the degree of delay when the edge computing device enhances the subway communication signal. The larger the signal enhancement delay coefficient, the more significant the delay in the communication signal processing of the edge computing device, and the higher the operational risks of the edge computing device. By calculating the signal enhancement delay coefficient, the operational risks of the edge computing device can be effectively identified, the performance bottleneck of the edge computing device in the process of subway communication signal enhancement can be optimized, and potential performance problems can be identified and solved.

[0073] The logic for obtaining the signal enhancement delay coefficient is as follows:

[0074] Record the arrival time Tre of the subway communication signal at the target edge computing device and the sending time Tse of the subway communication signal;

[0075] Calculate the communication signal enhancement time Tto, the expression is as follows Tto = Tse-Tre;

[0076] Calculate the average value pf of the communication signal enhancement time, the expression is as follows Where f represents the sequence number of the subway communication signal arriving at the target edge computing device, f = {1, 2, ..., F};

[0077] The communication signal enhancement time of different subway communication signals is compared with the average value of the communication signal enhancement time. If the communication signal enhancement time of the subway communication signal is greater than the average value of the communication signal enhancement time, the communication signal enhancement time of the current subway communication signal is marked as the communication signal enhancement delay time Tty;

[0078] Calculate the signal enhancement delay coefficient xhz, the expression is as follows Wherein b represents the order number of the communication signal enhancement time marked as the communication signal enhancement delay time, b={1,2,...,B};

[0079] Step S2, constructing an edge computing device hidden danger monitoring model, generating an edge computing device hidden danger initial index, and evaluating whether the target edge computing device has an operation hidden danger;

[0080] The obtained software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient are normalized. According to the normalized software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient, an edge computing device hidden danger monitoring model is constructed to generate the edge computing device hidden danger initial index byjc. The model is based on the following formula: Where a 1 、a 2 、a 3 They represent the preset proportional coefficients of the software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient, respectively, and a 1 、a 2 、a 3 All are greater than 0;

[0081] It should be noted that a 1 、a 2 、a 3 Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0082] It can be seen from the above calculation expression that the larger the software component compatibility anomaly coefficient, the larger the load balancing failure coefficient, and the larger the signal enhancement delay coefficient, the larger the edge computing device hidden danger initial emergence index, indicating that the probability of operation hidden dangers in the edge computing device is higher. Conversely, the smaller the software component compatibility anomaly coefficient, the smaller the load balancing failure coefficient, and the smaller the signal enhancement delay coefficient, the smaller the edge computing device hidden danger initial emergence index, indicating that the probability of operation hidden dangers in the edge computing device is lower.

[0083] Compare the edge computing device hidden danger initial emergence index with the preset edge computing device hidden danger initial emergence index threshold. If the edge computing device hidden danger initial emergence index is greater than the edge computing device hidden danger initial emergence index threshold, it indicates that the edge computing device has an operation hidden danger, and an operation hidden danger initial emergence signal is generated; if the edge computing device hidden danger initial emergence index is less than or equal to the edge computing device hidden danger initial emergence index threshold, it indicates that the edge computing device has no obvious operation hidden danger, and an operation steady-state signal is generated;

[0084] Step S3, when the target edge computing device has an operating risk, obtain the correlation information between the target edge computing device and the subway communication signal enhancement strategy of the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device;

[0085] The correlation information of the subway communication signal enhancement strategy between the target edge computing device and the neighboring edge computing device includes a neighborhood signal enhancement correlation coefficient;

[0086] The neighborhood signal enhancement correlation coefficient is used to measure the correlation between the target edge computing device and its neighboring edge computing device's subway communication signal enhancement strategy. The lower the correlation between the target edge computing device and its neighboring edge computing device in the subway communication signal enhancement strategy, the more mature the time is to replace the target edge computing device with operation risks, and the smaller the impact on the overall subway communication signal enhancement effect;

[0087] The logic for obtaining the neighborhood signal enhancement correlation coefficient is as follows:

[0088] Obtain subway communication signal enhancement data of the target edge computing device and the neighboring edge computing device, the subway communication signal enhancement data including but not limited to signal amplification strength difference, spectrum utilization, and effective coverage;

[0089] The signal amplification strength difference refers to the difference between the strength of the received signal after it is enhanced by the subway communication signal enhancement strategy in the edge computing device and the original received signal strength;

[0090] Spectrum utilization refers to the ratio of actually used spectrum resources to available spectrum resources in a specific frequency band, which reflects the degree of effective utilization of spectrum resources;

[0091] Effective coverage refers to the actual effective coverage of the subway communication signal enhancement strategy of edge computing devices;

[0092] Calculate the correlation R between the target edge computing device and the neighboring edge computing device in the subway communication signal enhancement strategy. The expression is as follows Where X d represents the subway communication signal enhancement data of the target edge computing device, that is, X 1 Indicates the difference in signal amplification intensity, X 2 represents the spectrum utilization, X 3 Indicates the effective coverage area. represents the average value of the subway communication signal enhancement data of the target edge computing device; Y d represents the subway communication signal enhancement data of the neighborhood edge computing device, that is, Y 1 Indicates the difference in signal amplification intensity, Y 2 represents the spectrum utilization, Y 3Indicates the effective coverage area. It represents the average value of subway communication signal enhancement data of the neighborhood edge computing devices;

[0093] It should be noted that before calculating the correlation between the target edge computing device and the neighboring edge computing device in the subway communication signal enhancement strategy, the subway communication signal enhancement data needs to be normalized. Commonly used normalization methods include Min-Max normalization, Z-Score normalization, etc., which will not be repeated again;

[0094] Calculate the neighborhood signal enhancement correlation coefficient lyx, the expression is as follows Where H represents the number of edge computing devices in the neighborhood, R gh Indicates the correlation between the target edge computing device and the hth neighboring edge computing device in terms of subway communication signal enhancement strategy;

[0095] The remaining time for the subway to enter the coverage area of ​​the target edge computing device refers to the time it is expected that the subway train will reach the coverage area of ​​the target edge computing device at the current speed. The remaining time TS for the subway to enter the coverage area of ​​the target edge computing device can be calculated by the following expression: Where JL represents the distance between the current subway train and the coverage range of the target edge computing device, and VS represents the real-time speed of the current subway train;

[0096] Step S4, constructing a target edge computing device replacement model, generating an edge computing device replacement immediate index, and determining whether to replace the target edge computing device immediately;

[0097] The obtained neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device are normalized. The target edge computing device replacement model is constructed based on the normalized neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device, and the edge computing device replacement immediate index ghjk is generated. The model is based on the following formula: Where α and β represent the neighborhood signal enhancement correlation coefficient and the preset proportional coefficient of the remaining time for the running subway to enter the coverage area of ​​the target edge computing device, respectively, and α and β are both greater than 0;

[0098] It should be noted that α and β are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0099] It can be seen from the above calculation expression that the smaller the neighborhood signal enhancement correlation coefficient and the smaller the remaining time for the running subway to enter the coverage range of the target edge computing device, the larger the edge computing device replacement immediacy index is, indicating that the time to replace the target edge computing device is more mature; conversely, the larger the neighborhood signal enhancement correlation coefficient and the larger the remaining time for the running subway to enter the coverage range of the target edge computing device, the smaller the edge computing device replacement immediacy index is, indicating that the time to replace the target edge computing device is less mature;

[0100] Compare the edge computing device replacement immediate index with the preset edge computing device replacement immediate index threshold. If the edge computing device replacement immediate index is greater than the edge computing device replacement immediate index threshold, it means that the time to replace the target edge computing device is the most appropriate at this time, and an immediate replacement signal is generated to replace the target edge computing device; if the edge computing device replacement immediate index is less than or equal to the edge computing device replacement immediate index threshold, it means that the time to replace the target edge computing device is not yet ripe, and there is no need to generate an immediate replacement signal;

[0101] Example 2: This example is an introduction to a subway communication signal enhancement system based on edge computing. Figure 2 As shown, it includes an operation information acquisition module, a hidden danger monitoring module, a decision response module, and an immediate replacement module;

[0102] The operation information acquisition module is used to obtain software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal;

[0103] The hidden danger monitoring module is used to build an edge computing device hidden danger monitoring model, generate an edge computing device hidden danger initial index, and evaluate whether the target edge computing device has operation hidden dangers;

[0104] The decision response module obtains the correlation information between the subway communication signal enhancement strategy of the target edge computing device and the neighboring edge computing device and the remaining time for the running subway to enter the coverage area of ​​the target edge computing device when there is a hidden danger in the operation of the target edge computing device;

[0105] An immediate replacement module is used to build a target edge computing device replacement model, generate an edge computing device replacement immediate index, and determine whether to replace the target edge computing device immediately;

[0106] The present invention obtains software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal, constructs an edge computing device hidden danger monitoring model, generates an edge computing device hidden danger initial index, evaluates whether the target edge computing device has operation hidden dangers, effectively improves the timely perception of potential operation hidden dangers of the edge computing device before the edge computing device has obvious failures, identifies potential hidden dangers of the edge computing device in advance, can reduce the frequency of equipment replacement and related costs, and maximize equipment utilization. When the target edge computing device has an operation hidden danger, the correlation information of the subway communication signal enhancement strategy of the target edge computing device and the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device are obtained, and a target edge computing device replacement model is constructed. The edge computing device replacement immediate index is generated to determine whether the target edge computing device is replaced immediately, provide a scientific basis for edge computing device replacement, ensure the continuity and effectiveness of the signal enhancement strategy, help reduce the risk of communication interruption, and improve the safety experience of passengers;

[0107] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0109] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0111] In several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways.

[0112] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A subway communication signal enhancement method based on edge computing, characterized in that: The steps include: Step S1, obtaining software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal; Step S2, constructing an edge computing device hidden danger monitoring model, generating an edge computing device hidden danger initial index, and evaluating whether the target edge computing device has an operation hidden danger; Step S3, when the target edge computing device has an operating risk, obtain the correlation information between the target edge computing device and the subway communication signal enhancement strategy of the neighboring edge computing device and the remaining time for the running subway to enter the coverage of the target edge computing device; Step S4: construct a target edge computing device replacement model, generate an edge computing device replacement immediate index, and determine whether to replace the target edge computing device immediately.

2. According to the method for enhancing subway communication signals based on edge computing in claim 1, it is characterized in that: The software component compatibility exception information includes a software component compatibility exception coefficient, the load balancing failure information includes a load balancing failure coefficient, and the signal enhancement delay information includes a signal enhancement delay coefficient.

3. According to the method for enhancing subway communication signals based on edge computing in claim 2, it is characterized in that: The logic for obtaining the software component compatibility anomaly coefficient is as follows: Obtain the version number of each software component installed on the edge computing device, compare each software component with its compatible software component, record the software components with mismatched version numbers, add them to the set of missing software components, and mark the set of missing software components as C = {C1, C2, ..., C n }, where C1, C2, ..., C n They respectively represent different software components in the set of software components with missing versions; A component dependency graph is constructed based on different software components in the version missing software component set. The nodes in the component dependency graph represent software components C. i , the edges in the component dependency graph represent dependency relationships. If software component C i Depends on software component C j , then from software component C i To software component C j Draw a directed edge, C i →C j ; Create an n*n dependency matrix D[i][j] and initialize the dependency matrix D[i][j], that is, D[i][j] = 0, where i = {1, 2, ..., n}, j = {1, 2, ..., n}; for each dependency relationship C i →C j , mark the corresponding position of the dependency matrix, that is, D[i][j]=1; For each software component C i , calculate the number of direct dependencies Calculate the software component compatibility anomaly coefficient zjy, the expression is as follows 4. According to a method for enhancing subway communication signals based on edge computing according to claim 2, it is characterized in that: The logic for obtaining the load balancing failure coefficient is as follows: Get the request processing volume L of the target edge computing device at different monitoring time points m , m represents different monitoring time points, and the total number of requests Ltotal is calculated as follows Calculate the load ratio p at different monitoring time points m , the expression is as follows Calculate the entropy value HS of the target edge computing device, the expression is as follows Calculate the maximum entropy value Hmax, the expression is as follows: Hmax = log(M); Calculate the load balancing failure coefficient fzx, the expression is as follows 5. According to a method for enhancing subway communication signals based on edge computing according to claim 2, it is characterized in that: The logic for obtaining the signal enhancement delay coefficient is as follows: Record the arrival time Tre of the subway communication signal at the target edge computing device and the sending time Tse of the subway communication signal; Calculate the communication signal enhancement time Tto, the expression is as follows Tto = Tse-Tre; Calculate the average value pf of the communication signal enhancement time, the expression is as follows Where f represents the sequence number of the subway communication signal arriving at the target edge computing device, f = {1, 2, ..., F}; The communication signal enhancement time of different subway communication signals is compared with the average value of the communication signal enhancement time. If the communication signal enhancement time of the subway communication signal is greater than the average value of the communication signal enhancement time, the communication signal enhancement time of the current subway communication signal is marked as the communication signal enhancement delay time Tty; Calculate the signal enhancement delay coefficient xhz, the expression is as follows Wherein b represents the order number in which the communication signal enhancement time is marked as the communication signal enhancement delay time, b={1, 2, ..., B}.

6. According to a method for enhancing subway communication signals based on edge computing according to claim 2, it is characterized in that: The obtained software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient are normalized. According to the normalized software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient, an edge computing device hidden danger monitoring model is constructed to generate the edge computing device hidden danger initial index byjc. The model is based on the following formula: Wherein a1, a2, and a3 represent preset proportional coefficients of software component compatibility anomaly coefficient, load balancing failure coefficient, and signal enhancement delay coefficient, respectively, and a1, a2, and a3 are all greater than 0; Compare the edge computing device hidden danger initial emergence index with a preset edge computing device hidden danger initial emergence index threshold, and if the edge computing device hidden danger initial emergence index is greater than the edge computing device hidden danger initial emergence index threshold, generate an operation hidden danger initial emergence signal; If the edge computing device hidden danger initial emergence index is less than or equal to the edge computing device hidden danger initial emergence index threshold, an operation steady-state signal is generated.

7. The method for enhancing subway communication signals based on edge computing according to claim 6 is characterized in that: The correlation information of the subway communication signal enhancement strategy between the target edge computing device and the neighboring edge computing device includes a neighborhood signal enhancement correlation coefficient; The logic for obtaining the neighborhood signal enhancement correlation coefficient is as follows: Obtain subway communication signal enhancement data of the target edge computing device and the neighboring edge computing device, the subway communication signal enhancement data including but not limited to signal amplification strength difference, spectrum utilization, and effective coverage; Calculate the correlation R between the target edge computing device and the neighboring edge computing device in the subway communication signal enhancement strategy. The expression is as follows Where X d It represents the subway communication signal enhancement data of the target edge computing device, that is, X1 represents the signal amplification strength difference, X2 represents the spectrum utilization rate, and X3 represents the effective coverage range. represents the average value of the subway communication signal enhancement data of the target edge computing device; Y d It represents the subway communication signal enhancement data of the neighborhood edge computing device, that is, Y1 represents the signal amplification strength difference, Y2 represents the spectrum utilization rate, and Y3 represents the effective coverage range. It represents the average value of subway communication signal enhancement data of the neighborhood edge computing devices; Calculate the neighborhood signal enhancement correlation coefficient lyx, the expression is as follows Where H represents the number of edge computing devices in the neighborhood, R gh Represents the correlation between the target edge computing device and the hth neighboring edge computing device in the subway communication signal enhancement strategy.

8. The method for enhancing subway communication signals based on edge computing according to claim 7 is characterized in that: The remaining time TS for the subway to enter the coverage area of ​​the target edge computing device can be calculated by the following expression: Where JL represents the distance between the current subway train and the coverage range of the target edge computing device, and VS represents the real-time speed of the current subway train.

9. A subway communication signal enhancement method and system based on edge computing according to claim 8, characterized in that: The obtained neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device are normalized. The target edge computing device replacement model is constructed based on the normalized neighborhood signal enhancement correlation coefficient and the remaining time for the subway to enter the coverage of the target edge computing device, and the edge computing device replacement immediate index ghjk is generated. The model is based on the following formula: Where α and β represent the neighborhood signal enhancement correlation coefficient and the preset proportional coefficient of the remaining time for the running subway to enter the coverage area of ​​the target edge computing device, respectively, and α and β are both greater than 0; Compare the edge computing device replacement immediate index with a preset edge computing device replacement immediate index threshold; if the edge computing device replacement immediate index is greater than the edge computing device replacement immediate index threshold, generate an immediate replacement signal to replace the target edge computing device; If the edge computing device replacement immediate index is less than or equal to the edge computing device replacement immediate index threshold, there is no need to generate an immediate replacement signal.

10. A subway communication signal enhancement system based on edge computing, used to implement a subway communication signal enhancement method based on edge computing as described in any one of claims 1 to 9, characterized in that: It includes operation information acquisition module, hidden danger monitoring module, decision response module and immediate replacement module; The operation information acquisition module is used to obtain software component compatibility exception information, load balancing failure information, and signal enhancement delay information of the target edge computing device when enhancing the subway communication signal; The hidden danger monitoring module is used to build an edge computing device hidden danger monitoring model, generate an edge computing device hidden danger initial index, and evaluate whether the target edge computing device has operation hidden dangers; The decision response module obtains the correlation information between the subway communication signal enhancement strategy of the target edge computing device and the neighboring edge computing device and the remaining time for the running subway to enter the coverage area of ​​the target edge computing device when there is a hidden danger in the operation of the target edge computing device; The immediate replacement module is used to build a target edge computing device replacement model, generate an edge computing device replacement immediate index, and determine whether to replace the target edge computing device immediately.

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