Scheduling method and device based on LTE (Long Term Evolution) long-distance private network transmission

Abstract

The invention is suitable for the technical field of data communication, and provides a scheduling method and device based on LTE long-distance private network transmission, and the scheduling method based on LTE long-distance private network transmission comprises the steps: obtaining a plurality of historical data volumes of a client, and matching the business characteristics of the client according to the plurality of historical data volumes; obtaining performance indexes of different links; obtaining a weight set corresponding to the client service characteristics; calculating communication quality scores corresponding to different links according to the performance indexes and the weight set; selecting a main link and a standby link from different links according to the communication quality score; scheduling the main link or the standby link to transmit virtual private network data; the data volume of the virtual private network refers to data for data communication by adopting the virtual private network. According to the scheduling method, loads can be balanced among different links, the utilization rate of network resources is maximized, and the scheduling method has remarkable advantages especially in a long-distance transmission scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of data communication, and in particular relates to a scheduling method and device based on LTE long-distance private network transmission. Background Art

[0002] With the continuous development of communication technology, especially the mature application of LTE (Long Term Evolution) technology, long-distance private network transmission has gradually become the preferred solution for many industries. LTE technology has the advantages of wide coverage, high transmission rate, low latency and support for high mobility, and has broad application prospects in public safety, energy, transportation and industrial automation. However, in practical applications, how to efficiently schedule and manage the transmission of long-distance private networks to ensure the stability and reliability of data communication is still a technical challenge that needs to be solved.

[0003] Existing LTE long-distance private network transmission scheduling methods usually rely on a single link selection strategy, which fails to fully consider the performance indicators of different links and the service characteristics of the client. This method may lead to improper selection of transmission links in a changing network environment, thereby affecting the efficiency and reliability of data transmission. In addition, the existing methods lack in-depth mining of historical data volume and service characteristics, and cannot effectively distinguish and schedule different types of services.

[0004] Therefore, it is particularly important to propose a scheduling method based on LTE long-distance private network transmission to solve the shortcomings of the existing technology. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a scheduling method and device based on LTE long-distance private network transmission to solve the technical problem that the existing methods lack in-depth mining of historical data volume and service characteristics and cannot effectively distinguish and schedule different types of services.

[0006] A first aspect of an embodiment of the present invention provides a scheduling method based on LTE long-distance private network transmission, and the scheduling method based on LTE long-distance private network transmission includes:

[0007] Acquire multiple historical data volumes of the client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state;

[0008] Obtaining performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate;

[0009] Obtaining a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights;

[0010] Calculating the communication quality scores corresponding to different links according to the performance indicators and the weight set;

[0011] Selecting a main link and a backup link among different links according to the communication quality score;

[0012] The main link or the standby link is scheduled to transmit virtual private network data; the virtual private network data volume refers to data communicated using a virtual private network.

[0013] Furthermore, the step of obtaining multiple historical data volumes of the client and matching the client service characteristics according to the multiple historical data volumes includes:

[0014] Acquire a service tag of the historical data volume; the service tag includes a real-time data tag and a non-real-time data tag;

[0015] Extracting first historical data volumes corresponding to a plurality of first historical sub-periods in a first historical period;

[0016] Calculating a first average value among a plurality of the first historical data amounts;

[0017] Extracting a first historical sub-period corresponding to a first historical data amount greater than a threshold;

[0018] calculating a first time interval between adjacent first historical sub-periods;

[0019] Extracting a second historical data volume corresponding to a plurality of previous historical sub-periods in a previous historical period; the previous historical period refers to a historical period that is located before and adjacent to the first historical period;

[0020] Extracting a second historical sub-period corresponding to a second historical data amount greater than a threshold;

[0021] Calculating the degree of consistency of data volume distribution between historical periods according to the distribution characteristics of the first historical sub-periods and the second historical sub-periods;

[0022] constructing a current feature vector based on a plurality of the first time intervals, the first average value, the degree of agreement, and the number of the first historical sub-periods;

[0023] According to the current feature vector, matching data volume features;

[0024] The service tag is combined with the data volume feature to obtain the client service characteristic.

[0025] Furthermore, the step of calculating the degree of consistency of data volume distribution between historical periods according to the distribution characteristics of the first historical sub-periods and the second historical sub-periods includes:

[0026] Matching a target historical sub-cycle closest to the second historical sub-cycle among the plurality of first historical sub-cycles;

[0027] Calculating a second time interval between the target historical sub-period and the corresponding second historical sub-period;

[0028] Calculating the variance of the second time intervals corresponding to each of the plurality of second historical sub-periods;

[0029] The variance is taken as the goodness of fit.

[0030] Furthermore, the step of matching the data volume feature according to the current feature vector includes:

[0031] Obtaining a pre-trained target cluster center, and calculating the similarity between the target cluster center and the current feature vector;

[0032] The target cluster center corresponding to the maximum similarity is extracted, and the standard feature corresponding to the target cluster center corresponding to the maximum similarity is used as the data volume feature.

[0033] Furthermore, before the step of obtaining the pre-trained target cluster center and calculating the similarity between the target cluster center and the current feature vector, the method further includes:

[0034] Obtaining standard feature vectors corresponding to multiple client service features; the standard feature vectors are obtained based on prior knowledge;

[0035] Acquire multiple training feature vectors, and perform clustering operation using the multiple standard feature vectors as initial clustering centers to obtain multiple clusters and current clustering centers corresponding to each of the multiple clusters;

[0036] Extract sub-clusters in each cluster;

[0037] Calculating first distances between a plurality of sub-cluster clusters and a plurality of cluster clusters;

[0038] The sub-cluster corresponding to the minimum first distance is merged with the cluster to obtain a new cluster and a target cluster center corresponding to the new cluster.

[0039] Furthermore, the step of extracting sub-clusters in each cluster includes:

[0040] Obtaining a second distance between each training feature vector in the cluster and the cluster center corresponding to the cluster;

[0041] Arrange the plurality of the second distances from largest to smallest, and extract the first N second distances;

[0042] Respectively calculating the third distances between the training feature vectors corresponding to the first N second distances;

[0043] If the third distance is greater than a threshold, the two training feature vectors corresponding to the third distance are respectively used as the first current cluster centers, and the training feature vectors in the cluster whose distance from the first current cluster center is less than a preset value are respectively formed into the sub-cluster clusters;

[0044] If the third distance is not greater than the threshold, the two training feature vectors corresponding to the third distance are merged into a second current cluster center, and the training feature vectors in the cluster whose distance from the second current cluster center is less than a preset value are formed into the sub-cluster cluster.

[0045] Furthermore, the weight set includes weight values ​​corresponding to each of the multiple performance indicators;

[0046] The step of calculating the communication quality scores corresponding to different links according to the performance indicators and the weight set comprises:

[0047] Get multiple value ranges corresponding to each performance indicator type; different value ranges correspond to different type scores;

[0048] Matching the target value range of the performance indicator, and obtaining the type score corresponding to the target value range;

[0049] Multiplying the type score corresponding to each of the performance indicators by the weight value to obtain a result value;

[0050] The result values ​​corresponding to the plurality of performance indicators are added together to obtain the communication quality score.

[0051] A second aspect of an embodiment of the present invention provides a scheduling device based on LTE long-distance private network transmission, including:

[0052] A first acquisition unit is used to acquire multiple historical data volumes of a client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state;

[0053] A second acquisition unit is used to acquire performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate;

[0054] A third acquisition unit is used to acquire a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights;

[0055] A calculation unit, used to calculate the communication quality scores corresponding to different links according to the performance indicator and the weight set;

[0056] A selection unit, configured to select a main link and a backup link from different links according to the communication quality score;

[0057] The communication unit is used to schedule the main link or the backup link to transmit virtual private network data; the virtual private network data volume refers to data used for data communication using a virtual private network.

[0058] The third aspect of an embodiment of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the scheduling method based on LTE long-distance private network transmission described in the first aspect are implemented.

[0059] The fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the scheduling method based on LTE long-distance private network transmission described in the first aspect above.

[0060] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: by obtaining multiple historical data volumes of the client, and matching the business characteristics of the client in combination with the historical data volumes, the reasonable allocation and efficient utilization of network resources are ensured. By obtaining the performance indicators of different links, including bandwidth, network delay, jitter and packet loss rate, and combining a preset weight set to evaluate the communication quality of each link, the accuracy and reliability of link selection are further improved. Through the intelligent selection and scheduling of the main link and the backup link, the system can flexibly respond to network fluctuations and ensure the efficient transmission of virtual private network data, thereby significantly reducing network delays and data packet loss, and improving the stability and reliability of data transmission. This scheduling method can balance the load between different links and maximize the utilization of network resources, especially in long-distance transmission scenarios. It has significant advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A schematic flow chart of a scheduling method based on LTE long-distance private network transmission provided by the present invention is shown;

[0063] Figure 2 A schematic diagram of a scheduling device based on LTE long-distance private network transmission provided by an embodiment of the present invention is shown;

[0064] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0066] The embodiment of the present invention provides a scheduling method and device based on LTE long-distance private network transmission to solve the technical problem that the existing method lacks in-depth mining of historical data volume and service characteristics and cannot effectively distinguish and schedule different types of services.

[0067] First, the present invention provides a scheduling method based on LTE long-distance private network transmission. Figure 1 , Figure 1 FIG. 1 is a schematic flow chart showing a scheduling method based on LTE long-distance private network transmission provided by the present invention. Figure 1 As shown, the scheduling method based on LTE long-distance private network transmission may include the following steps:

[0068] Step 101: Acquire multiple historical data volumes of the client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state;

[0069] Each historical period includes multiple historical sub-periods, for example, the historical period is 24 hours (days), and the historical sub-period is one hour. The multiple historical data volumes include the historical data volumes corresponding to the multiple historical periods (ie, the historical data volumes for N days).

[0070] According to the client business characteristics corresponding to multiple historical data volumes, the client business characteristics include but are not limited to real-time regular flat peaks, real-time regular sparse peaks, real-time irregular sparse peaks, real-time regular dense peaks, real-time irregular dense peaks, non-real-time regular flat peaks, non-real-time regular sparse peaks, non-real-time irregular sparse peaks, non-real-time regular dense peaks, non-real-time irregular dense peaks and no data status, etc.

[0071] The specific logic for matching client business characteristics based on multiple historical data volumes is as follows:

[0072] Specifically, step 101 includes steps A01 to A11:

[0073] Step A01: Acquire the service tag of the historical data volume; the service tag includes a real-time data tag and a non-real-time data tag;

[0074] Each data has a corresponding business tag, which includes but is not limited to real-time data tags and non-real-time data tags. Business tags can be divided according to business functions. Real-time data tags include communication data generated by functions such as live broadcast or video call, and non-real-time data tags include communication data generated by functions such as download or audio and video.

[0075] Step A02: extracting the first historical data volume corresponding to a plurality of first historical sub-periods in the first historical period;

[0076] The first historical period indicates the historical period closest to the current moment, so that subsequent processing results are more in line with data change requirements.

[0077] Step A03: Calculate a first average value among a plurality of the first historical data amounts;

[0078] Step A04: extracting a first historical sub-period corresponding to a first historical data volume greater than a threshold;

[0079] The threshold is used to filter the first historical sub-period with high data volume. It can be understood that the first historical data volume exceeding the threshold is regarded as a data peak period.

[0080] Step A05: calculating a first time interval between adjacent first historical sub-periods;

[0081] The first time interval may be used to analyze distribution characteristics between data peaks to identify density or continuity of data peaks.

[0082] Step A06: extracting the second historical data volume corresponding to a plurality of previous historical sub-periods in a previous historical period; the previous historical period refers to a historical period that is located before and adjacent to the first historical period;

[0083] In order to identify whether there is regularity in the change of client data volume in multiple historical periods, it is necessary to extract the second historical data volume corresponding to multiple previous historical sub-periods in the previous historical period, so as to further analyze the second historical data volume and the first historical data volume.

[0084] Step A07: extracting a second historical sub-period corresponding to a second historical data amount greater than a threshold;

[0085] The threshold is used to filter the second historical sub-period with high data volume. It can be understood that the second historical data volume exceeding the threshold is regarded as a data peak period.

[0086] Step A08: calculating the degree of consistency of data volume distribution between historical periods according to the distribution characteristics of the first historical sub-periods and the second historical sub-periods;

[0087] The degree of fit can be used to determine the regularity of the data peak period between the first historical period and the second historical period. The specific logic is as follows:

[0088] Specifically, step A08 includes steps A081 to A088.

[0089] Step A081: matching a target historical sub-cycle closest to the second historical sub-cycle among multiple first historical sub-cycles;

[0090] Step A082: Calculate a second time interval between the target historical sub-period and the corresponding second historical sub-period;

[0091] Step A083: Calculate the variance of the second time intervals corresponding to each of the plurality of second historical sub-periods;

[0092] Step A084: taking the variance as the goodness of fit.

[0093] The variance can be used to indicate the degree of dispersion between the second time intervals, that is, the gap between the target historical sub-period and the second historical sub-period. The smaller the degree of fit, the smaller the gap between the target historical sub-period and the second historical sub-period, that is, the stronger regularity of the data peak period between the first historical period and the second historical period.

[0094] In this embodiment, by accurately matching the distribution characteristics between multiple historical sub-periods, it is innovatively proposed to use the variance of time intervals to calculate the consistency of the data volume distribution between historical periods. Specifically, the scheme first matches the closest target historical sub-period for each second historical sub-period in multiple first historical sub-periods, and calculates the second time interval between them. By statistically analyzing the time intervals between multiple second historical sub-periods and their corresponding target historical sub-periods, and further calculating the variance of these time intervals, as an indicator to measure the consistency of historical periods. Through this calculation method, the scheme can effectively capture the degree of deviation of data distribution between different historical periods. The smaller the variance, the more consistent the sub-period distribution of different historical periods, and the higher the consistency; conversely, the larger the variance, the greater the distribution difference of the sub-period. This method can carefully characterize the regularity and volatility of historical data distribution, and has high accuracy and stability. Therefore, the technical solution achieves higher accuracy in matching the data volume distribution characteristics, can effectively reflect the similarity and periodicity of historical data changes, and provides a reliable data basis for further client business characteristics analysis and prediction.

[0095] Step A09: constructing a current feature vector based on a plurality of the first time intervals, the first average values, the degree of agreement, and the number of the first historical sub-periods;

[0096] The multiple first time intervals, the first average values, the degrees of agreement and the number of the first historical sub-periods are combined according to preset positions to obtain the current feature vector.

[0097] Step A10: matching data volume features according to the current feature vector;

[0098] Specifically, step A10 includes steps A101 to A102:

[0099] Step A101: obtaining a pre-trained target cluster center, and calculating the similarity between the target cluster center and the current feature vector;

[0100] Step A102: extracting the target cluster center corresponding to the maximum similarity, and taking the standard feature corresponding to the target cluster center corresponding to the maximum similarity as the data volume feature.

[0101] As an optional embodiment of the present application, before step A101, steps B1 to B5 are also included:

[0102] Step B1: obtaining standard feature vectors corresponding to multiple client service features; the standard feature vectors are obtained based on prior knowledge;

[0103] The standard feature vector is artificial prior knowledge preset by the user based on the client business characteristics. It is understandable that since the artificially set standard feature vector has certain differences from the feature vector in the actual application environment, the standard feature vector needs to be corrected and optimized through subsequent clustering.

[0104] Step B2: obtaining a plurality of training feature vectors, and performing clustering operation using the plurality of standard feature vectors as initial clustering centers to obtain a plurality of clusters and current clustering centers corresponding to the plurality of clusters;

[0105] The clustering operation adopts the traditional K-value clustering method, which will not be described here.

[0106] Step B3: extract sub-clusters in each cluster;

[0107] In order to further improve the clustering accuracy, it is necessary to refine the clustering clusters, so it is necessary to extract sub-clustering clusters from the clusters and then optimize the processing. The extraction logic of sub-clustering clusters is as follows:

[0108] Specifically, step B3 specifically includes steps B31 to B35:

[0109] Step B31: obtaining a second distance between each training feature vector in the cluster and the cluster center corresponding to the cluster;

[0110] Step B32: Arrange the plurality of second distances from largest to smallest, and extract the first N second distances;

[0111] In order to further improve the clustering accuracy, the N training feature vectors farthest from the cluster center are obtained in the cluster cluster for subsequent fusion clustering.

[0112] Step B33: respectively calculating the third distances between the training feature vectors corresponding to the first N second distances;

[0113] Step B34: if the third distance is greater than a threshold, the two training feature vectors corresponding to the third distance are respectively used as first current cluster centers, and the training feature vectors in the cluster whose distance from the first current cluster center is less than a preset value are respectively formed into the sub-cluster clusters;

[0114] Step B35: If the third distance is not greater than the threshold, the two training feature vectors corresponding to the third distance are merged into the second current cluster center, and the training feature vectors in the cluster whose distance from the second current cluster center is less than a preset value are formed into the sub-cluster cluster.

[0115] In this embodiment, by obtaining the second distance between each training feature vector and the cluster center in the cluster cluster, and extracting N feature vectors with larger distances, local outliers or boundary points in the cluster cluster can be effectively captured. Then, the third distance between these feature vectors is calculated to further reveal the intrinsic similarities or differences between them. When the third distance exceeds the threshold, the two feature vectors can be regarded as new cluster centers, thereby forming a more accurate sub-cluster cluster, ensuring the structural rationality of the sub-cluster cluster, and effectively controlling redundant information in the clustering process, making the algorithm more efficient when processing high-dimensional data.

[0116] Step B4: calculating the first distances between the plurality of sub-clusters and the plurality of clusters;

[0117] Step B5: Merge the sub-cluster corresponding to the minimum first distance with the cluster cluster to obtain a new cluster cluster and a target cluster center corresponding to the new cluster cluster.

[0118] Since the cluster center of the cluster cluster is obtained by gradual iterative clustering based on the standard feature vector, after multiple clustering iterations, the distance between the cluster center and the corresponding training feature vector may increase, so it is necessary to re-match other cluster clusters for the sub-cluster cluster to further improve the clustering accuracy.

[0119] In this embodiment, the standard feature vector obtained based on prior knowledge is used as the initial cluster center, which can effectively utilize the existing business feature information, reduce the randomness of clustering operation, and thus improve the accuracy of clustering. The introduction of this prior knowledge makes the initial cluster center more representative, and avoids the problem of unstable clustering results caused by randomly selecting the initial cluster center. Using the standard feature vector as the initial cluster center in the clustering operation reduces the time for the model to converge to a suitable cluster center, making the clustering process more efficient. At the same time, the method of extracting sub-cluster clusters in each cluster cluster and optimizing by calculating the distance can further simplify the operation steps and accelerate the convergence speed. By fusing the sub-cluster clusters and cluster clusters corresponding to the minimum first distance, the interference of isolated points or noise data on the clustering results can be effectively avoided, thereby improving the robustness of the model. In addition, this fusion operation helps to ensure that the cluster clusters are more uniform and reasonable in the case of complex data distribution, and enhances the stability of the model. By calculating the similarity between the current feature vector and the target cluster center, the position of the current business feature in the overall cluster can be more accurately measured. This helps to more accurately find the most similar cluster for each client's business feature, improving the accuracy of subsequent business decisions. In summary, this technical solution not only improves the accuracy and efficiency of clustering, but also enhances the system's ability to process multi-dimensional feature data, making the clustering of business features more in line with actual application needs.

[0120] Step A11: Combine the service tag with the data volume feature to obtain the client service characteristic.

[0121] In this embodiment, by obtaining the business tags of the historical data volume, and combining the real-time and non-real-time data tags, the historical data volume in multiple historical periods is deeply analyzed and processed, and the accurate matching of the client business characteristics is achieved. By extracting the data volume of multiple sub-periods in the historical period, calculating the average value of each historical data volume and the time interval between adjacent sub-periods, and screening out the key data volume greater than the threshold, the fluctuation trend and periodic characteristics of the data volume can be effectively captured. Further, by analyzing the consistency of data distribution between different historical periods, a feature vector reflecting the dynamic change of data volume is constructed. The feature vector comprehensively considers multiple data statistical indicators, such as time interval, data volume average value, historical cycle consistency and sub-period number, etc., to ensure the multi-dimensional expression and integrity of data features. Finally, by combining these feature vectors with business tags, the scheme can not only match the data volume characteristics, but also accurately identify and distinguish the business characteristics of different clients according to specific business tags. This method improves the accuracy and efficiency of data analysis.

[0122] Step 102: Obtain performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate;

[0123] Performance indicators include but are not limited to bandwidth, network latency, jitter, and packet loss rate

[0124] Step 103: Obtain a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights;

[0125] Step 104: Calculate the communication quality scores corresponding to different links according to the performance indicators and the weight set;

[0126] Specifically, step 104 includes steps 1041 to 1044:

[0127] Step 1041: obtaining multiple value ranges corresponding to each performance indicator type; different value ranges correspond to different type scores;

[0128] Step 1042: matching the target value range of the performance indicator, and obtaining the type score corresponding to the target value range;

[0129] Step 1043: multiply the type score corresponding to each performance indicator by the weight value to obtain a result value;

[0130] Step 1044: Add the result values ​​corresponding to the plurality of performance indicators to obtain the communication quality score.

[0131] As an optional embodiment of the present application, the communication quality score may also be calculated by the following mathematical formula:

[0132]

[0133] Among them, Q i represents the communication quality score, w ij represents the jth weight value, p j represents the jth performance index, min(p j ) represents the lower limit of the jth performance index, max(p j ) represents the upper limit value of the jth performance indicator.

[0134] Converting performance parameters of different units and magnitudes to the same scale improves the comparability between different performance indicators and avoids excessive impact of certain indicators on the score due to their large magnitude. It is suitable for processing data of multiple performance indicators, such as delay, bandwidth, packet loss rate, etc., to ensure that these indicators can be compared on the same scale. The formula reflects the importance of different performance indicators and data service characteristics through weights, and can flexibly adjust the influence of each performance indicator and service characteristic according to actual needs, thereby improving the accuracy and applicability of the score. In different application scenarios, such as real-time video transmission, file download, VoIP calls, etc., the weights can be adjusted according to specific business needs to obtain the optimal communication link selection.

[0135] Such a calculation method can fully consider the comprehensive impact of the multi-dimensional performance indicators of the communication link and provide a more comprehensive communication quality evaluation.

[0136] Step 105: selecting a main link and a backup link from different links according to the communication quality score;

[0137] The first score and the second score are used as the main link and the backup link respectively.

[0138] Step 106: Schedule the main link or the backup link to transmit virtual private network data; the virtual private network data volume refers to data communicated using a virtual private network.

[0139] The backup link is used to provide switchover when the primary link fails to ensure communication quality.

[0140] In this embodiment, by obtaining multiple historical data volumes of the client and matching the business characteristics of the client in combination with the historical data volumes, the reasonable allocation and efficient use of network resources are ensured. By obtaining the performance indicators of different links, including bandwidth, network delay, jitter and packet loss rate, and combining a preset weight set to evaluate the communication quality of each link, the accuracy and reliability of link selection are further improved. Through the intelligent selection and scheduling of the main link and the backup link, the system can flexibly respond to network fluctuations and ensure the efficient transmission of virtual private network data, thereby significantly reducing network delays and data packet loss, and improving the stability and reliability of data transmission. This scheduling method can balance the load between different links and maximize the utilization of network resources, especially in long-distance transmission scenarios. It has significant advantages.

[0141] like Figure 2 The present invention provides a scheduling device based on LTE long-distance private network transmission, see Figure 2 , Figure 2 A schematic diagram of a scheduling device based on LTE long-distance private network transmission provided by the present invention is shown, Figure 2The scheduling device shown is based on LTE long-distance private network transmission and includes:

[0142] The first acquisition unit 21 is used to acquire multiple historical data volumes of the client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state;

[0143] A second acquisition unit 22 is used to acquire performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate;

[0144] A third acquisition unit 23 is used to acquire a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights;

[0145] A calculation unit 24, configured to calculate the communication quality scores corresponding to different links according to the performance indicator and the weight set;

[0146] A selection unit 25, configured to select a primary link and a backup link from different links according to the communication quality score;

[0147] The communication unit 26 is used to schedule the main link or the backup link to transmit virtual private network data; the virtual private network data volume refers to data used for data communication using a virtual private network.

[0148] The present invention provides a scheduling device based on LTE long-distance private network transmission, which ensures the reasonable allocation and efficient use of network resources by obtaining multiple historical data volumes of the client and matching the business characteristics of the client in combination with the historical data volumes. By obtaining the performance indicators of different links, including bandwidth, network delay, jitter and packet loss rate, and combining a preset weight set to evaluate the communication quality of each link, the accuracy and reliability of link selection are further improved. Through the intelligent selection and scheduling of the main link and the backup link, the system can flexibly respond to network fluctuations and ensure the efficient transmission of virtual private network data, thereby significantly reducing network delays and data packet loss, and improving the stability and reliability of data transmission. This scheduling method can balance the load between different links and maximize the utilization of network resources, especially in long-distance transmission scenarios. It has significant advantages.

[0149] Figure 3Schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a scheduling program based on LTE long-distance private network transmission. When the processor 30 executes the computer program 32, the steps in the above-mentioned scheduling method embodiment based on LTE long-distance private network transmission are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0150] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into the following specific functions of each unit:

[0151] A first acquisition unit is used to acquire multiple historical data volumes of a client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state;

[0152] A second acquisition unit is used to acquire performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate;

[0153] A third acquisition unit is used to acquire a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights;

[0154] A calculation unit, used to calculate the communication quality scores corresponding to different links according to the performance indicator and the weight set;

[0155] A selection unit, configured to select a main link and a backup link from different links according to the communication quality score;

[0156] The communication unit is used to schedule the main link or the backup link to transmit virtual private network data; the virtual private network data volume refers to data used for data communication using a virtual private network.

[0157] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on the terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0158] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0159] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0160] It should be understood that the order of execution of the steps in the above embodiment does not necessarily 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 embodiment of the present invention.

[0161] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0162] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0163] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0164] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0165] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0166] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0167] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0168] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed over multiple network units.

[0170] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0171] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0172] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to monitoring, depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is monitored" or "in response to monitoring [described condition or event]", depending on the context.

[0173] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0174] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0175] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A scheduling method based on LTE long-distance private network transmission, characterized in that: The scheduling method based on LTE long-distance private network transmission includes: Acquire multiple historical data volumes of the client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state; Obtaining performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate; Obtaining a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights; Calculating the communication quality scores corresponding to different links according to the performance indicators and the weight set; Selecting a main link and a backup link among different links according to the communication quality score; The main link or the standby link is scheduled to transmit virtual private network data; the virtual private network data volume refers to data communicated using a virtual private network.

2. The scheduling method based on LTE long-distance private network transmission as claimed in claim 1, characterized in that: The step of obtaining multiple historical data volumes of the client and matching the client service characteristics according to the multiple historical data volumes includes: Acquire a service tag of the historical data volume; the service tag includes a real-time data tag and a non-real-time data tag; Extracting first historical data volumes corresponding to a plurality of first historical sub-periods in a first historical period; Calculating a first average value among a plurality of the first historical data amounts; Extracting a first historical sub-period corresponding to a first historical data amount greater than a threshold; calculating a first time interval between adjacent first historical sub-periods; Extracting a second historical data volume corresponding to a plurality of previous historical sub-periods in a previous historical period; the previous historical period refers to a historical period that is located before and adjacent to the first historical period; Extracting a second historical sub-period corresponding to a second historical data amount greater than a threshold; Calculating the degree of consistency of data volume distribution between historical periods according to the distribution characteristics of the first historical sub-periods and the second historical sub-periods; constructing a current feature vector based on a plurality of the first time intervals, the first average value, the degree of agreement, and the number of the first historical sub-periods; According to the current feature vector, matching data volume features; The service tag is combined with the data volume feature to obtain the client service characteristic.

3. The scheduling method based on LTE long-distance private network transmission as claimed in claim 2, characterized in that: The step of calculating the degree of coincidence of the data volume distribution between the historical periods according to the distribution characteristics of the first historical sub-periods and the second historical sub-periods includes: Matching a target historical sub-cycle closest to the second historical sub-cycle among the plurality of first historical sub-cycles; Calculating a second time interval between the target historical sub-period and the corresponding second historical sub-period; Calculating the variance of the second time intervals corresponding to each of the plurality of second historical sub-periods; The variance is taken as the goodness of fit.

4. The scheduling method based on LTE long-distance private network transmission as claimed in claim 2, characterized in that: The step of matching the data volume feature according to the current feature vector comprises: Obtaining a pre-trained target cluster center, and calculating the similarity between the target cluster center and the current feature vector; The target cluster center corresponding to the maximum similarity is extracted, and the standard feature corresponding to the target cluster center corresponding to the maximum similarity is used as the data volume feature.

5. The scheduling method based on LTE long-distance private network transmission as claimed in claim 4, characterized in that: Before the step of obtaining the pre-trained target cluster center and calculating the similarity between the target cluster center and the current feature vector, the method further includes: Obtaining standard feature vectors corresponding to multiple client service features; the standard feature vectors are obtained based on prior knowledge; Acquire multiple training feature vectors, and perform clustering operation using the multiple standard feature vectors as initial clustering centers to obtain multiple clusters and current clustering centers corresponding to each of the multiple clusters; Extract sub-clusters in each cluster; Calculating first distances between a plurality of sub-cluster clusters and a plurality of cluster clusters; The sub-cluster corresponding to the minimum first distance is merged with the cluster to obtain a new cluster and a target cluster center corresponding to the new cluster.

6. The scheduling method based on LTE long-distance private network transmission as claimed in claim 5, characterized in that: The step of extracting a sub-cluster in each cluster comprises: Obtaining a second distance between each training feature vector in the cluster and the cluster center corresponding to the cluster; Arrange the plurality of the second distances from largest to smallest, and extract the first N second distances; Respectively calculating the third distances between the training feature vectors corresponding to the first N second distances; If the third distance is greater than a threshold, the two training feature vectors corresponding to the third distance are respectively used as the first current cluster centers, and the training feature vectors in the cluster whose distance from the first current cluster center is less than a preset value are respectively formed into the sub-cluster clusters; If the third distance is not greater than the threshold, the two training feature vectors corresponding to the third distance are merged into a second current cluster center, and the training feature vectors in the cluster whose distance from the second current cluster center is less than a preset value are formed into the sub-cluster cluster.

7. The scheduling method based on LTE long-distance private network transmission as claimed in claim 1, characterized in that: The step of calculating the communication quality scores corresponding to different links according to the performance indicators and the weight set comprises: Get multiple value ranges corresponding to each performance indicator type; different value ranges correspond to different type scores; Matching the target value range of the performance indicator, and obtaining the type score corresponding to the target value range; Multiplying the type score corresponding to each of the performance indicators by the weight value to obtain a result value; The result values ​​corresponding to the plurality of performance indicators are added together to obtain the communication quality score.

8. A scheduling device based on LTE long-distance private network transmission, characterized in that: The scheduling device based on LTE long-distance private network transmission includes: A first acquisition unit is used to acquire multiple historical data volumes of a client, and match the client service characteristics according to the multiple historical data volumes; each of the historical data volumes refers to the data volume in a historical sub-period, the historical period includes multiple historical sub-periods, and the multiple historical data volumes include data volumes corresponding to multiple historical periods; the client service characteristics include real-time regular flat peak, real-time regular sparse peak, real-time irregular sparse peak, real-time regular dense peak, real-time irregular dense peak, non-real-time regular flat peak, non-real-time regular sparse peak, non-real-time irregular sparse peak, non-real-time regular dense peak, non-real-time irregular dense peak and no data state; A second acquisition unit is used to acquire performance indicators of different links; the performance indicators include bandwidth, network delay, jitter and packet loss rate; A third acquisition unit is used to acquire a weight set corresponding to the client service characteristics; the weight set includes a plurality of preset weights; A calculation unit, configured to calculate the communication quality scores corresponding to different links according to the performance indicator and the weight set; A selection unit, configured to select a main link and a backup link from different links according to the communication quality score; The communication unit is used to schedule the main link or the backup link to transmit virtual private network data; the virtual private network data volume refers to data used for data communication using a virtual private network.

9. A terminal device, characterized in that: The terminal device includes: a memory, a processor, and a scheduling program based on LTE long-distance private network transmission stored in the memory and executable on the processor, wherein the scheduling program based on LTE long-distance private network transmission is configured to implement the steps in the scheduling method based on LTE long-distance private network transmission as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps in the scheduling method based on LTE long-distance private network transmission as described in any one of claims 1 to 7 are implemented.

Images