A method and system for evaluating a backhaul mode based on mobile backhaul technology

Abstract

The present invention discloses a method and system for evaluating a backhaul mode based on mobile backhaul technology, which is applied to the field of mobile backhaul technology. The method comprises: establishing a backhaul mode feature library by performing transmission feature analysis on wireless backhaul, microwave backhaul and optical fiber backhaul. The type of mobile equipment, device density and device peak time period of the target area are collected to obtain target device information. Transmission feature analysis is performed based on the target device information to determine the target mobile transmission feature. Based on the backhaul mode feature library, a difference analysis is performed on the transmission feature set of each backhaul mode to determine the difference feature. The difference feature and the target mobile transmission feature are input into a backhaul mode evaluation model for evaluation to determine the best feature combination. According to the best feature combination, the corresponding backhaul mode combination is matched. The technical problem that the mobile backhaul technology in the prior art has low intelligence in backhaul mode selection, resulting in high mobile backhaul cost is solved.

Description

Technical Field

[0001] This invention relates to the field of backhaul technology, and in particular to a method and system for evaluating backhaul modes based on mobile backhaul technology. Background Technology

[0002] Mobile backhaul is a signal transmission network connecting base stations, base station controllers, and mobile devices. Known mobile backhaul methods include wireless backhaul, microwave backhaul, and fiber optic backhaul. However, in existing technologies, mobile backhaul primarily relies on fiber optic transmission for signal transmission, resulting in a limited range of backhaul methods and an inability to evaluate backhaul methods based on actual needs, ultimately leading to high mobile backhaul costs.

[0003] Therefore, the existing mobile backhaul technology has a low level of intelligence in the selection of backhaul methods, resulting in high mobile backhaul costs. Summary of the Invention

[0004] This application provides a method and system for evaluating backhaul methods based on mobile backhaul technology, which solves the technical problem of low intelligence in the selection of backhaul methods in existing mobile backhaul technologies, resulting in high mobile backhaul costs.

[0005] This application provides a backhaul mode evaluation method based on mobile backhaul technology. The method includes: performing transmission characteristic analysis on wireless backhaul, microwave backhaul, and fiber optic backhaul respectively, and establishing a backhaul mode feature library, which contains the transmission characteristic sets of each backhaul mode; collecting mobile device types, device density, and peak device time periods in a target area to obtain target device information; performing transmission characteristic analysis based on the target device information to determine target mobile transmission characteristics; performing difference analysis on the transmission characteristic sets of each backhaul mode based on the backhaul mode feature library to determine difference characteristics; inputting the difference characteristics and the target mobile transmission characteristics into a backhaul mode evaluation model for evaluation to determine the optimal feature combination; and matching the corresponding backhaul mode combination according to the optimal feature combination.

[0006] This application also provides a backhaul mode evaluation system based on mobile backhaul technology. The system includes: a feature library construction module, used to perform transmission feature analysis on wireless backhaul, microwave backhaul, and fiber optic backhaul respectively, and establish a backhaul mode feature library, which contains the transmission feature sets of each backhaul mode; a target device information acquisition module, used to collect the mobile device type, device density, and peak device time period of the target area to obtain target device information; a transmission feature determination module, used to perform transmission feature analysis based on the target device information to determine the target mobile transmission characteristics; a difference feature determination module, used to perform difference analysis on the transmission feature sets of each backhaul mode based on the backhaul mode feature library to determine the difference features; an optimal feature combination acquisition module, used to input the difference features and the target mobile transmission characteristics into a backhaul mode evaluation model for evaluation to determine the optimal feature combination; and a backhaul mode combination acquisition module, used to match the corresponding backhaul mode combination according to the optimal feature combination.

[0007] This application also provides an electronic device, including:

[0008] Memory, used to store executable instructions;

[0009] When the processor executes the executable instructions stored in the memory, it implements the backhaul mode evaluation method based on mobile backhaul technology provided in the embodiments of this application.

[0010] This application provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, it implements a backhaul mode evaluation method based on mobile backhaul technology provided in this application.

[0011] This application proposes a backhaul mode evaluation method and system based on mobile backhaul technology. This method establishes a backhaul mode feature library. It collects data on mobile device types, device density, and peak time periods in the target area to obtain target device information. Based on this information, it analyzes transmission characteristics to determine the target mobile transmission characteristics. Using the backhaul mode feature library, it analyzes the differences in transmission feature sets for each backhaul mode to identify distinguishing features. These distinguishing features and the target mobile transmission characteristics are then input into a backhaul mode evaluation model to determine the optimal feature combination. Based on this optimal feature combination, it matches the corresponding backhaul mode combination. This achieves intelligent evaluation and selection of different backhaul modes, obtaining the optimal combination, thereby improving backhaul efficiency while further reducing enterprise mobile backhaul operating costs. This solves the technical problem of low intelligence in backhaul mode selection in existing mobile backhaul technologies, leading to high mobile backhaul costs.

[0012] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Obviously, the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure.

[0014] Figure 1 A flowchart illustrating a backhaul mode evaluation method based on mobile backhaul technology provided in this application embodiment;

[0015] Figure 2 A flowchart illustrating a backhaul mode evaluation method based on mobile backhaul technology to obtain a combined evaluation result of backhaul modes, provided in an embodiment of this application;

[0016] Figure 3 A schematic diagram illustrating the process of constructing a transmission feature set based on mobile backhaul technology, provided in an embodiment of this application;

[0017] Figure 4 A schematic diagram of the system structure of a backhaul mode evaluation method based on mobile backhaul technology provided in this application embodiment;

[0018] Figure 5 This is a schematic diagram of the structure of a system electronic device for evaluating a backhaul mode based on mobile backhaul technology, provided in an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures: Feature library construction module 11, target device information acquisition module 12, transmission feature determination module 13, difference feature determination module 14, optimal feature combination acquisition module 15, return transmission mode combination acquisition module 16, processor 31, memory 32, input device 33, output device 34. Detailed Implementation

[0020] Example 1

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references are made to “some embodiments”, which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0024] While this application makes various references to certain modules of the system according to embodiments of this application, any number of different modules may be used and run on user terminals and / or servers. These modules are merely illustrative, and different aspects of the system and method may use different modules.

[0025] This application uses flowcharts to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously, as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0026] like Figure 1 As shown in the figure, this application provides a method for evaluating backhaul modes based on mobile backhaul technology, the method comprising:

[0027] S10: Perform transmission characteristic analysis on wireless backhaul, microwave backhaul and fiber optic backhaul respectively, and establish a backhaul mode feature library. The backhaul mode feature library contains the transmission feature set of each backhaul mode.

[0028] S20: Collect information on the type, density, and peak time periods of mobile devices in the target area to obtain target device information;

[0029] S30: Perform transmission feature analysis based on the target device information to determine the target mobile transmission characteristics;

[0030] Specifically, the mobile backhaul network is a signal transmission network connecting base stations, base station controllers, and mobile devices. Known mobile backhaul methods include wireless backhaul, microwave backhaul, and fiber optic backhaul. When evaluating mobile backhaul technologies, transmission characteristics are analyzed for wireless, microwave, and fiber optic backhaul. This analysis examines various descriptive indicators such as backhaul speed, backhaul type, backhaul parameter loss, backhaul cost, and backhaul coverage area for different backhaul methods, establishing a backhaul method feature database. Subsequently, mobile device types, device density, and peak device time periods in the target area are collected to obtain target device information. Mobile device type refers to the specific category of backhaul method supported by each device; device density refers to the distribution density of devices within a unit area; and peak device time periods are the time periods when device density is highest. This information is used to obtain target device information. Further, transmission characteristic analysis is performed based on the target device information to determine the target mobile transmission characteristics. When determining the target mobile transmission characteristics, the specific categories of backhaul methods supported by each device are obtained. Since some user mobile devices only support a single backhaul method, selection of the backhaul method is not required in this case. For devices supporting multiple backhaul methods, target device information is acquired, and corresponding mobile transmission feature identifiers are created using manual data identification. This yields device information and corresponding mobile transmission feature identifiers for devices supporting multiple backhaul methods, including specific backhaul loss requirements and backhaul speed requirements. Subsequently, supervised training of a neural network model is performed using the acquired device information and corresponding mobile transmission feature identifiers until the model's output mobile transmission features meet a certain accuracy requirement, thus completing the construction of the transmission feature analysis model. Finally, the transmission feature analysis model is used to analyze the transmission features of the target device information, thereby determining the target mobile transmission characteristics.

[0031] like Figure 3 As shown, the method S10 provided in this application embodiment further includes:

[0032] S11: Perform characteristic analysis on the backhaul speed, backhaul type, backhaul parameter loss, backhaul cost, and backhaul coverage area of ​​each backhaul method, and determine the characteristic values ​​of the characteristic indicators of each backhaul method.

[0033] S12: Based on the description indicators of each backhaul method, align the feature values ​​of the description indicators of each backhaul method, establish the mapping relationship between each backhaul method and the feature values ​​of its description indicators, and construct a transmission feature set.

[0034] Specifically, when establishing the backhaul method feature library, big data analysis is used to examine the characteristics of various descriptive indicators for each backhaul method, including backhaul speed, backhaul type, backhaul parameter loss, backhaul cost, and backhaul coverage area. This yields specific parameters for the descriptive indicator features and determines the descriptive indicator feature values ​​for each backhaul method. Subsequently, based on the descriptive indicators of each backhaul method, the feature values ​​are aligned. This involves sorting the descriptive indicators according to a pre-defined order, ensuring that the descriptive indicators for each backhaul method are arranged in a specific sequence, facilitating subsequent differentiation analysis of the indicator features of each backhaul method. Finally, a mapping relationship is established between each backhaul method and its corresponding descriptive indicator feature values, constructing a transmission feature set.

[0035] The method S10 provided in this application embodiment further includes:

[0036] S13: Calculate the deviation based on the characteristic values ​​of the descriptive indicators of each return method to obtain the relative deviation value of the indicators of each return method;

[0037] S14: Sort the indicators from largest to smallest based on their relative deviation values ​​and establish a representative sequence of indicators for each feedback method;

[0038] S15: Select key indicators for the representative sequence of each return method according to the preset screening conditions, and determine the differences between each return method.

[0039] Specifically, based on the constructed transmission feature set, deviations are calculated according to the indicator characteristics between different backhaul methods. When calculating the deviation of indicator characteristics, the indicator characteristics of one backhaul method are used as the benchmark to calculate the deviation values ​​of the indicator characteristics of other backhaul methods. Taking backhaul cost as an example, the cost deviations between microwave backhaul and wireless backhaul, and between microwave backhaul and fiber optic backhaul, are calculated to obtain the cost deviation values ​​of wireless backhaul, fiber optic backhaul, and microwave backhaul, thus obtaining the relative deviation values ​​of the cost indicators for each backhaul method. Deviations are then calculated for the descriptive indicator characteristic values ​​of each backhaul method to obtain the relative deviation values ​​of the indicators for each backhaul method. Subsequently, based on the relative deviation values, the indicators are sorted from largest to smallest to establish a representative sequence of indicators for each backhaul method. Finally, according to preset screening conditions, key indicators are screened from the representative sequence of indicators for each backhaul method to identify the more important key indicators and determine the differences between the backhaul methods.

[0040] The method S15 provided in this application embodiment further includes:

[0041] S151: Based on the big data feedback case, obtain the difference description step size of the feature values ​​of each descriptive indicator, where the difference description step size is the smallest descriptive unit of indicator difference;

[0042] S152: Determine the adjustment coefficient based on the step size of the difference description, and perform relative deviation value correction and sorting adjustment on the index representative sequence;

[0043] S153: Based on the representative sequence of the aforementioned indicators, determine the number of key indicators to be screened. Specifically, based on the returned cases obtained from big data, obtain the difference in feature values ​​of each descriptive indicator.

[0044] The step size for describing differences is defined as the smallest descriptive unit of indicator differences, i.e., the smallest unit of indicator characteristic deviation under different feedback methods. Finally, an adjustment coefficient is determined based on the step size, which is the ratio of the deviation value to the step size. A higher adjustment coefficient corresponds to a higher relative deviation value and a more significant difference, indicating a higher level of indicator criticality; conversely, a lower relative deviation value indicates a less significant difference and a lower level of indicator criticality. Finally, the relative deviation values ​​of the representative indicator sequence are corrected and the ranking is adjusted based on the adjustment coefficient. Based on the representative indicator sequence, a selection quantity is determined for key indicator screening, eliminating indicators ranked after a preset order in the representative indicator sequence, thus completing the screening of key indicators.

[0045] S40: Based on the backhaul mode feature library, perform a difference analysis on the transmission feature sets of each backhaul mode to determine the difference features;

[0046] S50: Input the difference features and the target mobile transmission features into the feedback mode evaluation model for evaluation, and determine the optimal feature combination;

[0047] S60: Match the corresponding return transmission method combination according to the optimal feature combination.

[0048] Specifically, based on the constructed backhaul method feature library, the difference in transmission feature sets of each backhaul method is analyzed to determine the difference features. Further, the difference features and the target mobile transmission features are input into the backhaul method evaluation model for evaluation. Based on the transmission target, transmission constraints, and difference features, feature combination optimization is performed, and the optimal feature combination is determined based on the optimization results. Finally, based on the obtained optimal feature combination, the corresponding backhaul method combination is matched in the established backhaul method feature library. This enables intelligent evaluation and selection of different backhaul methods, obtaining the optimal backhaul method combination, thereby improving backhaul efficiency while further reducing enterprise mobile backhaul operating costs.

[0049] The method S50 provided in this application embodiment further includes:

[0050] S51: Based on the target mobile transmission characteristics, perform transmission constraint analysis to determine the transmission constraints;

[0051] S52: Analyze the transmission target based on the target's mobile transmission characteristics to determine the transmission target;

[0052] S53: Based on the transmission target, transmission constraints, and differential features, construct an optimization space model, perform global optimization, and determine the optimal feature combination.

[0053] Specifically, transmission constraints are analyzed based on the target mobile transmission characteristics to determine the transmission constraints. That is, transmission constraints are determined based on the acquired target mobile transmission characteristics, such as determining the return speed of the corresponding return speed method based on the return speed requirement. Subsequently, transmission target analysis is performed based on the target mobile transmission characteristics to determine the transmission target and obtain the return speed methods acceptable to the transmission target. Finally, an optimization space model is constructed based on the transmission target, transmission constraints, and differential characteristics. That is, based on the transmission target, the differential characteristics of each return speed method are matched with the transmission constraints. During matching, for matching return coverage area and other return features that can be combined and superimposed, a combination and addition method can be used for matching, such as superimposing the return coverage areas of multiple different return speed methods, thereby completing the matching of transmission constraints. The matching results of transmission constraints and differential characteristics of each return speed method are obtained. The matched differential characteristics are then used to perform global optimization of the differential characteristics, and the optimal feature combination is determined.

[0054] like Figure 2 As shown, the method S60 provided in this application embodiment further includes:

[0055] S61: Determine the return node based on the combination of the return methods;

[0056] S62: Evaluate the backhaul effect of each backhaul node and determine the node's backhaul effect;

[0057] S63: Based on the return transmission effect of all nodes, obtain the evaluation result of the combination of return transmission methods.

[0058] Specifically, based on the acquired combination of return transmission methods, the return transmission nodes for that combination are determined, where each return transmission node is a different return transmission device within the combination. The return transmission effect of each node is evaluated by assessing the loss value between the data transmitted from the output end and the data received by the node, thus determining the node's return transmission effect. Finally, based on the return transmission effects of all nodes, the evaluation result of the return transmission method combination is obtained, thereby completing the evaluation of the return transmission effect of the combination and facilitating timely detection of return transmission anomalies by staff.

[0059] The method S60 provided in this embodiment further includes:

[0060] S64: Obtain the output terminal information parameters;

[0061] S65: Obtain the return information parameters of the return node;

[0062] S66: Obtain the deviation parameter and parameter deviation value based on the output end information parameter and the return information parameter of the return node;

[0063] S67: Based on the output end information parameters and deviation parameters, perform correlation analysis to determine the influence coefficient of the deviation parameters;

[0064] S68: Obtain the node feedback effect based on the deviation parameter influence coefficient and parameter deviation value.

[0065] Specifically, the outgoing end information parameters and the return information parameters of the return node are obtained. The outgoing end information parameters are the specific parameters of the data output from the signal output end, such as the data volume, while the return information parameters of the return node are the information parameters received by the return node. Based on the outgoing end information parameters and the return information parameters of the return node, deviation parameters and parameter deviation values ​​are obtained. Subsequently, a correlation analysis is performed based on the outgoing end information parameters and deviation parameters; that is, the deviation rate of the return information parameters is calculated using the outgoing end information parameters, and the ratio of the deviation parameter to the outgoing end information parameters is obtained to determine the deviation parameter influence coefficient. Finally, a preset deviation parameter influence coefficient and a preset parameter deviation value are set. The deviation parameter influence coefficient and parameter deviation value are compared with the preset deviation parameter influence coefficient and preset parameter deviation value. When the deviation parameter influence coefficient and parameter deviation value are greater than the preset deviation parameter influence coefficient and preset parameter deviation value, the deviation generated during the return process is large, and the corresponding return effect is poor; conversely, the deviation generated during the return process is small, and the corresponding return effect is good, thus obtaining the node return effect. To enable the evaluation of the backhaul process and ensure the transmission effect of the backhaul process.

[0066] The technical solution provided in this invention establishes a backhaul mode feature library by analyzing the transmission characteristics of wireless backhaul, microwave backhaul, and fiber optic backhaul. It collects information on the type, density, and peak time periods of mobile devices in the target area to obtain target device information. Based on this information, it analyzes transmission characteristics to determine the target mobile transmission characteristics. Using the backhaul mode feature library, it analyzes the differences in the transmission feature sets of each backhaul mode to identify the distinguishing features. These distinguishing features and the target mobile transmission characteristics are then input into a backhaul mode evaluation model for evaluation to determine the optimal feature combination. Finally, it matches the corresponding backhaul mode combination based on the optimal feature combination. This solves the technical problem of low intelligence in backhaul mode selection in existing mobile backhaul technologies, leading to high mobile backhaul costs. Furthermore, it enables intelligent evaluation and selection of different backhaul modes, obtaining the optimal backhaul mode combination, thereby improving backhaul efficiency while further reducing enterprise mobile backhaul operating costs.

[0067] Example 2

[0068] Based on the same inventive concept as the backhaul mode evaluation method based on mobile backhaul technology in the foregoing embodiments, this invention also provides a system for the backhaul mode evaluation method based on mobile backhaul technology. The system can be implemented in hardware and / or software, and is generally integrated into an electronic device to execute the method provided in any embodiment of this invention. Figure 4 As shown, the system includes:

[0069] The feature library construction module 11 is used to perform transmission feature analysis on wireless backhaul, microwave backhaul and optical fiber backhaul respectively, and establish a backhaul mode feature library, which contains the transmission feature set of each backhaul mode.

[0070] The target device information acquisition module 12 is used to collect information on the type of mobile devices, device density, and peak time period of devices in the target area to obtain target device information.

[0071] The transmission feature determination module 13 is used to perform transmission feature analysis based on the target device information to determine the target mobile transmission features;

[0072] The difference feature determination module 14 is used to perform difference analysis on the transmission feature set of each backhaul method based on the backhaul method feature library to determine the difference features.

[0073] The optimal feature combination acquisition module 15 is used to input the difference features and the target mobile transmission features into the feedback mode evaluation model for evaluation, and determine the optimal feature combination.

[0074] The return transmission mode combination acquisition module 16 is used to match the corresponding return transmission mode combination based on the optimal feature combination.

[0075] Furthermore, the return transmission mode combination acquisition module 16 is also used for:

[0076] Based on the combination of the backhaul methods, the backhaul node is determined;

[0077] Evaluate the return transmission effect of each return node to determine the return transmission effect of the node;

[0078] Based on the feedback results of all nodes, an evaluation result of the combination of feedback methods is obtained.

[0079] Furthermore, the return transmission mode combination acquisition module 16 is also used for:

[0080] Obtain the output terminal information parameters;

[0081] Obtain the return information parameters from the return node;

[0082] Based on the outgoing end information parameters and the return information parameters of the return node, the deviation parameters and parameter deviation values ​​are obtained.

[0083] Based on the output end information parameters and deviation parameters, a correlation analysis is performed to determine the influence coefficient of the deviation parameters;

[0084] The node feedback effect is obtained based on the influence coefficient of the deviation parameter and the parameter deviation value.

[0085] Furthermore, the feature library construction module 11 is also used for:

[0086] The characteristics of each backhaul method are analyzed, including backhaul speed, backhaul type, backhaul parameter loss, backhaul cost, and backhaul coverage area. The characteristic values ​​of the descriptive indicators for each backhaul method are determined.

[0087] Based on the description indicators of each backhaul method, the feature values ​​of the description indicators of each backhaul method are aligned, and a mapping relationship between each backhaul method and its description indicator feature values ​​is established to construct a transmission feature set.

[0088] Furthermore, the feature library construction module 11 is also used for:

[0089] Based on the characteristic values ​​of the descriptive indicators of each return method, the deviation is calculated to obtain the relative deviation value of the indicators of each return method.

[0090] Based on the relative deviation values ​​of the indicators, sort them from largest to smallest to establish a representative sequence of indicators for each feedback method;

[0091] According to the preset screening criteria, the key indicators of the representative sequence of each backhaul method are screened to determine the differences in characteristics of each backhaul method.

[0092] Furthermore, the feature library construction module 11 is also used for:

[0093] Based on big data feedback cases, the difference description step size of the feature values ​​of each descriptive indicator is obtained, and the difference description step size is the smallest descriptive unit of the indicator difference.

[0094] Based on the step size described in the difference description, the adjustment coefficient is determined, and the relative deviation value of the index representative sequence is corrected and the sorting is adjusted;

[0095] Based on the sequence represented by the aforementioned indicators, the number of key indicators to be screened is determined.

[0096] Furthermore, the optimal feature combination acquisition module 15 is also used for:

[0097] Based on the target mobile transmission characteristics, transmission constraint conditions are analyzed to determine the transmission constraints.

[0098] Based on the target's mobile transmission characteristics, the transmission target is analyzed to determine the transmission target;

[0099] Based on the transmission target, transmission constraints, and differential features, an optimization space model is constructed, and global optimization is performed to determine the optimal feature combination.

[0100] The various units and modules included are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0101] Example 3

[0102] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 5 As shown, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 5 Taking a processor 31 as an example, the processor 31, memory 32, input device 33, and output device 34 in an electronic device can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0103] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the backhaul mode evaluation method based on mobile backhaul technology in this embodiment of the invention. The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 32, thereby realizing the aforementioned backhaul mode evaluation method based on mobile backhaul technology.

[0104] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for evaluating a backhaul mode based on mobile backhaul technology, characterized in that: The method comprises: Performing transmission feature analysis on wireless backhaul, microwave backhaul and optical fiber backhaul respectively, establishing a backhaul mode feature library, wherein the backhaul mode feature library contains transmission feature sets of each backhaul mode; wherein the performing transmission feature analysis on wireless backhaul, microwave backhaul and optical fiber backhaul respectively, establishing a backhaul mode feature library, comprises: performing feature analysis on each descriptive index of each backhaul mode, including backhaul speed, backhaul type and backhaul parameter loss, backhaul cost and backhaul coverage area, determining the descriptive index feature value of each backhaul mode, aligning the descriptive index feature value of each backhaul mode based on the descriptive index of each backhaul mode, establishing a mapping relationship between each backhaul mode and its descriptive index feature value, and constructing a transmission feature set; Collect the types of mobile devices, device density, and device peak time periods in the target area to obtain target device information; Performing transmission characteristic analysis based on the target device information to determine the target mobile transmission characteristics; Based on the backhaul mode feature library, performing difference analysis on the transmission feature set of each backhaul mode to determine the difference features, including: performing deviation calculation based on the descriptive indicator feature values ​​of each backhaul mode to obtain the indicator relative deviation value of each backhaul mode, sorting the indicators from large to small based on the indicator relative deviation values, establishing the indicator representative sequence of each backhaul mode, and performing key indicator screening on the indicator representative sequence of each backhaul mode according to preset screening conditions to determine the difference features of each backhaul mode; Inputting the difference feature and the target mobile transmission feature into a backhaul mode evaluation model for evaluation to determine the best feature combination, including: performing transmission constraint analysis based on the target mobile transmission feature to determine the transmission constraint, performing transmission target analysis based on the target mobile transmission feature to determine the transmission target, constructing an optimization space model based on the transmission target, the transmission constraint, and the difference feature, performing global optimization, and determining the best feature combination; According to the optimal feature combination, a corresponding return mode combination is matched.

2. The method according to claim 1, characterized in that The method further comprises: Determining a return node based on the return mode combination; Evaluate the return effect of each return node to determine the node return effect; According to the feedback effects of all nodes, the combined evaluation results of the feedback methods are obtained.

3. The method according to claim 2, characterized in that The step of evaluating the return effect of each return node to determine the return effect of the node includes: Get outgoing end information parameters; Get the return information parameters of the return node; Obtaining a deviation parameter and a parameter deviation value according to the outgoing end information parameter and the return information parameter of the return node; Perform correlation analysis based on the outgoing end information parameter and the deviation parameter to determine the deviation parameter influence coefficient; The node feedback effect is obtained according to the deviation parameter influence coefficient and the parameter deviation value.

4. The method according to claim 1, characterized in that The key indicator screening of the indicator representative sequence of each return mode according to the preset screening conditions includes: Based on the big data feedback case, the difference description step of each descriptive indicator characteristic value is obtained, and the difference description step is the minimum description unit of the indicator difference; Determine the adjustment coefficient according to the difference description step length, and perform indicator relative deviation value correction and ranking adjustment on the indicator representative sequence; Based on the representative sequence of the indicators, the screening quantity is determined to perform key indicator screening.

5. A backhaul mode evaluation system based on mobile backhaul technology, characterized in that: The system comprises: The feature library construction module is used to analyze the transmission characteristics of wireless backhaul, microwave backhaul and optical fiber backhaul respectively, and establish a backhaul method feature library, wherein the backhaul method feature library contains the transmission feature set of each backhaul method; the feature library construction module is also used to: analyze the characteristics of each descriptive index of each backhaul method, such as backhaul speed, backhaul type and backhaul parameter loss, backhaul cost and backhaul coverage area, and determine the descriptive index characteristic value of each backhaul method; align the descriptive index characteristic value of each backhaul method based on the descriptive index of each backhaul method, and establish a mapping relationship between each backhaul method and its descriptive index characteristic value, and construct a transmission feature set; the feature library construction module is also used to: calculate the deviation based on the descriptive index characteristic value of each backhaul method, obtain the relative deviation value of the index of each backhaul method, sort them from large to small based on the relative deviation value of the index, establish the representative sequence of the index of each backhaul method, and screen the key index of the representative sequence of the index of each backhaul method according to the preset screening conditions to determine the difference characteristics of each backhaul method; The target device information acquisition module is used to collect the mobile device type, device density, and device peak time period in the target area to obtain the target device information; A transmission characteristic determination module, used to perform transmission characteristic analysis according to the target device information to determine the target mobile transmission characteristics; A difference feature determination module, configured to perform difference analysis on the transmission feature sets of each backhaul mode based on the backhaul mode feature library to determine the difference features; The optimal feature combination acquisition module is used to input the difference feature and the target mobile transmission feature into the backhaul mode evaluation model for evaluation to determine the optimal feature combination; the optimal feature combination acquisition module is also used to: perform transmission constraint analysis based on the target mobile transmission feature to determine the transmission constraint, perform transmission target analysis based on the target mobile transmission feature to determine the transmission target, construct an optimization space model based on the transmission target, transmission constraint and difference feature, perform global optimization and determine the optimal feature combination; The return mode combination acquisition module is used to match the corresponding return mode combination according to the optimal feature combination.

6. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the backhaul mode evaluation method based on mobile backhaul technology as described in any one of claims 1 to 4 when executing the executable instructions stored in the memory.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a backhaul mode evaluation method based on mobile backhaul technology as described in any one of claims 1 to 4 is implemented.

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