A network signal intelligent perception method and system based on big data

Abstract

The present invention provides a method and system for intelligent perception of network signals based on big data, which belongs to the field of network communication technology, and specifically includes: utilizing the changes of network characteristic signals in different dimensions to determine the associated base station equipment of the network signal, determining the changes of the historical operation data of the associated base station equipment of the target base station based on the historical operation data of the associated base station equipment of the target base station, and when it is determined that there is no need to adopt a preset perception strategy to perform perception processing of the network signal of the target base station through the changes of the historical operation data of the associated base station equipment, determining the matching perception strategy of the network signal of the target base station in different time periods based on the changes of the operation data of the associated base station equipment of the target base station in different time periods and the network access user data, thereby improving the efficiency of the perception processing of the network signal.

Description

Technical Field

[0001] The present invention belongs to the field of network communication technology, and in particular relates to a network signal intelligent perception method and system based on big data. Background Art

[0002] In order to realize the perception processing of network signals, the invention patent application CN202210974795.0 "Network resource switching method, device and base station based on user terminal" switches the user to a target cell with low load and good network signal, thereby increasing the possibility that the user can quickly obtain a large amount of time-frequency resources and thus obtain a higher data transmission rate, and solves the problem of low perception rate of users with high data transmission rate. However, there are the following technical problems:

[0003] When perceiving network signals, there is often a certain correlation between the network signals and the operating data of the network devices. Therefore, when perceiving network signals, if the perception strategy cannot be determined based on the analysis of the operating data of the network devices, the real-time performance of the network signal perception processing cannot be guaranteed.

[0004] In response to the above technical problems, the present invention provides a method and system for intelligent perception of network signals based on big data. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for intelligently sensing network signals based on big data is provided.

[0007] A network signal intelligent perception method based on big data, specifically comprising:

[0008] S1 obtains network access user data of a target base station in different time periods, and when it is determined that it is not necessary to use a preset perception strategy to perform perception processing of a network signal of the target base station based on changes in the network access user data, obtains historical perception data of network signals of different base stations;

[0009] S2 determines, based on the historical perception data, changes in network characteristic signals of different dimensions when the operating data of different base station devices change, and determines the associated base station device of the network signal by using the changes in the network characteristic signals of different dimensions;

[0010] S3: determining a change in the historical operation data of the base station device associated with the target base station based on the historical operation data of the base station device associated with the target base station;

[0011] When S4 determines that there is no need to adopt a preset perception strategy to perform perception processing of the network signal of the target base station through changes in the historical operating data of the associated base station equipment, the matching perception strategy of the network signal of the target base station in different time periods is determined based on the changes in the operating data of the associated base station equipment and the network access user data of the target base station in different time periods.

[0012] A further technical solution is that the network access user data includes the number of network access users and access durations of different network access users.

[0013] A further technical solution is that the change of the network access user data is determined based on the network access user data at different time periods on different dates.

[0014] A further technical solution is that the method for determining the matching perception strategy of the network signal in the time period is:

[0015] Determine the number of network access users of the target base station in the time period based on the change of network access user data of the target base station in the time period, and determine the access user change coefficient and the operation data change coefficient on different dates based on the number of network access users on different dates in the time period and the operation data of the associated base station device;

[0016] Determine the comprehensive variation coefficients of different dates according to the access user variation coefficients and the operation data variation coefficients of different dates, and use the comprehensive variation coefficients to determine the data variation dates in the dates;

[0017] The matching perception strategy of the network signal of the target base station in the time period is determined according to the proportion of the number of data change dates.

[0018] A further technical solution is that the access user variation coefficient of the date is determined according to the deviation rate between the number of network access users on the date and the benchmark access user number.

[0019] A further technical solution is that the coefficient of variation of the operating data on the date is determined based on the deviation rate between the operating data on the date and the benchmark operating data.

[0020] A further technical solution is to determine the matching perception strategy of the network signal of the target base station in the time period according to the number ratio of the data change date, which specifically includes:

[0021] Based on the quantity ratio of the data change dates, determine the preset perception strategy corresponding to the quantity ratio;

[0022] Based on the preset perception strategy corresponding to the number ratio, a matching perception strategy for the network signal of the target base station in the time period is determined.

[0023] A further technical solution is that the matching perception strategy includes using different perception cycles to perform perception processing of network signals.

[0024] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method of intelligent perception of network signals based on big data when running the computer program.

[0025] The beneficial effects of the present invention are:

[0026] The changes in network characteristic signals in different dimensions are used to determine the associated base station devices of the network signals, thereby realizing the determination of associated base station devices that are associated with the network signals based on the changes in the network characteristic signals and the changes in the base station devices. This also lays the foundation for further combining the changes in the operating data of the associated base station devices to generate differentiated perception strategies for different target base stations, thereby improving the efficiency and accuracy of the perception processing of network signals.

[0027] Based on the operating data of the associated base station equipment of the target base station in different time periods and the changes in the network access user data, the matching perception strategy of the network signal of the target base station in different time periods is determined, which takes into account both the changes in the operating data of the associated base station equipment of the target base station in the said time period and the changes in the network access user data of the target base station in the said time period, thereby realizing the determination of the perception strategy of the network signal in different time periods from multiple angles and ensuring the reliability of the perception processing of the network signal of the target base station.

[0028] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0031] Figure 1 It is a flow chart of a method for intelligent perception of network signals based on big data;

[0032] Figure 2is a flow chart to identify methods that do not require the use of a preset perception strategy;

[0033] Figure 3 is a flow chart of a method for determining an associated base station device of a network signal;

[0034] Figure 4 It is a flow chart of a method for determining a matching perception strategy for network signals in a time period;

[0035] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0037] When perceiving network signals, there is often a certain correlation between the network signals and the operating data of the network devices. Therefore, when perceiving network signals, it is necessary to determine the perception strategy based on the analysis of the operating data of the network devices to improve the real-time performance of network signal perception processing.

[0038] Based on the changes in network access user data, it is determined that there is no need to adopt a preset perception strategy to perform perception processing on the network signal of the target base station: the user number change time periods on different dates are determined according to the number of network access users in different time periods, and the user number change dates in the dates are determined based on the number of user number change time periods on different dates; when the number of user number change dates is within the preset date number range, it is determined that the preset perception strategy needs to be adopted.

[0039] Associated base station equipment of network signals: Based on the historical changes in the operating data of the base station equipment, the data is divided into different historical change times. According to the changes in the network characteristic signals in different historical change times, the number of changes in the network characteristic signals in different historical change times is determined. The number of historical changes with a number of changes greater than a preset number of changes is taken as the number of change associations. When the number of change associations is greater than the preset number threshold, the base station equipment is determined to be an associated base station equipment.

[0040] Matching perception strategy of network signals of target base stations in different time periods: determine the number of network access users of the target base station in the time period based on the changes in the network access user data of the target base station in the time period, determine the access user change coefficient and the operation data change coefficient on different dates based on the number of network access users in the time period on different dates and the operation data of the associated base station equipment, determine the comprehensive change coefficient on different dates based on the average of the access user change coefficient and the operation data change coefficient on different dates, take the date with a comprehensive change coefficient greater than 0.7 as the data change date, determine the preset perception strategy corresponding to the number ratio based on the number ratio of the data change date, and determine the matching perception strategy of the network signal of the target base station in the time period based on the preset perception strategy corresponding to the number ratio.

[0041] Embodiment 1 To solve the above-mentioned problem, according to one aspect of the present invention, Figure 1 As shown, a network signal intelligent perception method based on big data is provided, which specifically includes:

[0042] S1 obtains network access user data of a target base station in different time periods, and when it is determined that it is not necessary to use a preset perception strategy to perform perception processing of a network signal of the target base station based on changes in the network access user data, obtains historical perception data of network signals of different base stations;

[0043] Furthermore, the network access user data includes the number of network access users and access durations of different network access users.

[0044] Specifically, the change of the network access user data is determined according to the network access user data in different time periods on different dates.

[0045] It should be noted that if Figure 2 As shown, it is determined that there is no need to adopt the preset perception strategy, including:

[0046] Based on the network access user data of different time periods, determine the number of network access users in different time periods, and determine the user number change period on different dates according to the number of network access users in different time periods;

[0047] Determine the user quantity change date in the date based on the number of user quantity change periods on different dates;

[0048] Determine whether to adopt a preset perception strategy based on the number of dates on which the number of users changes.

[0049] Furthermore, the user quantity change period is determined according to a deviation between the number of network access users in the period and a benchmark number of users.

[0050] It should be noted that the user quantity change date is a date when the number of user quantity change periods is greater than the number of preset change periods.

[0051] Further, whether to adopt a preset perception strategy is determined according to the number of user quantity change dates, including:

[0052] When the number of user quantity change dates is within a preset date quantity interval, it is determined that a preset perception strategy needs to be adopted.

[0053] Optionally, determining that a preset perception strategy does not need to be adopted includes:

[0054] Based on the network access user data of different time periods, determine the number of network access users in different time periods, and determine the user number change period on different dates according to the number of network access users in different time periods;

[0055] The total number of the user quantity change period is calculated based on the number of user quantity change periods on different dates, and it is determined whether a preset perception strategy needs to be adopted according to the total number of the user quantity change period.

[0056] Optionally, determining that a preset perception strategy does not need to be adopted includes:

[0057] S11 determines the number of network access users in different time periods based on the network access user data in different time periods, and determines the user number change period on different dates according to the number of network access users in different time periods;

[0058] Optionally, the above step S11 includes the following contents:

[0059] S111 determines the number of network access users in different time periods based on the network access user data in different time periods, determines the user number change period on different dates based on the number of network access users in different time periods, and determines the total number of user number change period based on the number of user number change period on different dates;

[0060] S112: When the total number of users in the period of time during which the number of users changes does not meet the requirement, it is determined that the target base station needs to adopt a preset sensing strategy. When the total number of users in the period of time during which the number of users changes meets the requirement, the process proceeds to step S113;

[0061] S113 determines the date of the period of user number change according to the period of user number change of the target base station on different dates, and when the number of dates of the period of user number change does not meet the requirement, proceeds to step S114; when the number of dates of the period of user number change meets the requirement, proceeds to step S12;

[0062] S114: When the total number of users in the period of change in the number of users is within the preset period number range, it is determined that the target base station needs to adopt a preset sensing strategy; when the total number of users in the period of change in the number of users is not within the preset period number range, proceed to step S12.

[0063] S12 determines the user number change coefficient for different dates according to the user number change period for different dates and the deviation between the number of network access users and the benchmark number of users during the user number change period;

[0064] Optionally, the above step S12 includes the following contents:

[0065] When it is determined in step S121 that there is a date in which the period of change of the number of users is greater than the preset period number threshold according to the period of change of the number of users on different dates, the process proceeds to step S122; when there is no date in which the period of change of the number of users is greater than the preset period number threshold, the process proceeds to step S123;

[0066] S122: when the number of dates during which the user quantity change period is greater than the preset period quantity threshold does not meet the requirement, it is determined that the target base station needs to adopt a preset sensing strategy; when the number of dates during which the user quantity change period is greater than the preset period quantity threshold meets the requirement, the process proceeds to step S123;

[0067] S123 determines the user quantity change coefficients for different dates according to the user quantity change periods for different dates and the deviations between the number of network access users and the number of benchmark users during the user quantity change periods. If there is a date where the user quantity change coefficient does not meet the requirement, the process proceeds to step S124. If there is no date where the user quantity change coefficient does not meet the requirement, the process proceeds to step S13.

[0068] S124 When the number of dates on which the user quantity change coefficient does not meet the requirements is greater than the preset change date number threshold, it is determined that the target base station needs to adopt a preset perception strategy; when the number of dates on which the user quantity change coefficient does not meet the requirements is not greater than the preset change date number threshold, proceed to step S13.

[0069] S13 determines a comprehensive variation coefficient based on the user quantity variation coefficients on different dates, and determines whether a preset perception strategy needs to be adopted according to the comprehensive variation coefficient.

[0070] S2 determines, based on the historical perception data, changes in network characteristic signals of different dimensions when the operating data of different base station devices change, and determines the associated base station device of the network signal by using the changes in the network characteristic signals of different dimensions;

[0071] Furthermore, the network characteristic signals of different dimensions include channel delay, bit error rate, propagation delay, transmission delay, queuing delay and router execution time.

[0072] Specifically, Figure 3 As shown, the method for determining the associated base station device of the network signal is:

[0073] Based on the historical changes of the operating data of the base station equipment, the data is divided into different historical change times;

[0074] Determine the number of changes in the network characteristic signal at different historical change times according to the change of the network characteristic signal at different historical change times;

[0075] The change association times in the historical change times are determined based on the change quantity, and whether the base station device is an associated base station device is determined according to the change association times.

[0076] It should be noted that the change amount of the network characteristic signal is determined according to the change amounts of different network characteristic signals.

[0077] Optionally, a method for determining an associated base station device of the network signal is:

[0078] Based on the historical changes of the operating data of the base station equipment, the data is divided into different historical change times;

[0079] According to the change of the network characteristic signal in different historical change times, determine the network characteristic signal that has changed in the historical change times, and use it as the signal change times;

[0080] Whether the base station device is an associated base station device is determined based on the proportion of the signal change times in the historical change times.

[0081] Optionally, a method for determining an associated base station device of the network signal is:

[0082] S21: dividing the operation data of the base station device into different historical change times based on the historical change situation, and determining the change amount of the network characteristic signal in different historical change times according to the change situation of the network characteristic signal in different historical change times;

[0083] S22 determines the change correlation coefficients of network characteristic signals of different dimensions at different historical change times based on the change amounts of network characteristic signals at different historical change times and the change amounts of operation data of base station equipment, and determines the signal correlation coefficients of characteristic signals of different dimensions based on the change correlation coefficients of network characteristic signals of different dimensions at different historical change times;

[0084] S23 determines a comprehensive correlation coefficient using signal correlation coefficients of characteristic signals of different dimensions, and determines whether the base station device is an associated base station device based on the comprehensive correlation coefficient.

[0085] S3: determining a change in the historical operation data of the base station device associated with the target base station based on the historical operation data of the base station device associated with the target base station;

[0086] Further, determining that it is not necessary to use a preset sensing strategy to sense the network signal of the target base station by associating changes in historical operation data of the base station device specifically includes:

[0087] Determine the historical operation data of the associated base station equipment at different time periods on different dates based on the change of the historical operation data of the associated base station equipment, and determine the operation data change time period on different dates using the historical operation data of different time periods;

[0088] Determining the data change device in the associated base station device based on the number of operation data change periods;

[0089] Whether it is necessary to adopt a preset perception strategy to perform perception processing of the network signal of the target base station is determined by the number of data change devices.

[0090] Furthermore, the operation data change period is determined based on the deviation between the historical operation data and the benchmark operation data. Specifically, the period in which the deviation from the benchmark operation data does not meet the requirements is used as the operation data change period.

[0091] Furthermore, the preset sensing strategy is to sense the network signal of the target base station in real time.

[0092] Specifically, the data change device is an associated base station device whose number of running data change time periods does not meet the requirement.

[0093] It is understandable that when the number of the data change devices does not meet the requirement, it is determined that a preset sensing strategy needs to be adopted to perform sensing processing of the network signal of the target base station.

[0094] In another embodiment, determining that it is not necessary to use a preset sensing strategy to sense the network signal of the target base station by associating changes in historical operation data of the base station device specifically includes:

[0095] Determine the historical operation data of the associated base station equipment at different time periods on different dates based on the change of the historical operation data of the associated base station equipment, and determine the operation data change time period on different dates using the historical operation data of different time periods;

[0096] When there is no operation data change period for any of the associated base station devices of the target base station: it is determined that there is no need to use a preset sensing strategy to perform sensing processing of the network signal of the target base station;

[0097] When the target base station has an associated base station device with an operation data change period:

[0098] The associated base station devices with operation data change periods are regarded as change period base station devices. When the number of change period base station devices is within the preset base station device number range:

[0099] Obtaining the number of operation data change periods of base station devices in different change periods, when the number of operation data change periods of base station devices in different change periods are all within the preset change period number range: determining that there is no need to adopt the preset perception strategy to perform perception processing of the network signal of the target base station;

[0100] When the number of base station devices in the change period is not within the preset range of base station device numbers or there are base station devices in the change period whose number in the operation data change period is not within the preset range of base station device numbers:

[0101] Determine the data change device in the associated base station device based on the number of running data change time periods, and when the number of data change base station devices is greater than a preset base station device number threshold, determine that a preset perception strategy needs to be used to perceive the network signal of the target base station;

[0102] When the number of data change base station devices is not greater than the preset base station device number threshold:

[0103] Determine the total number of operation data change periods based on the number of operation data change periods of the associated base station device of the target base station, and when the total number of operation data change periods does not meet the requirement, determine that a preset perception strategy needs to be adopted to perform perception processing of the network signal of the target base station;

[0104] When the total number of operation data change periods meets the requirements:

[0105] Whether it is necessary to adopt a preset perception strategy to perform perception processing of the network signal of the target base station is determined by the number of data change devices and the number of operating data change periods of different data change devices.

[0106] When S4 determines that there is no need to adopt a preset perception strategy to perform perception processing of the network signal of the target base station through changes in the historical operating data of the associated base station equipment, the matching perception strategy of the network signal of the target base station in different time periods is determined based on the changes in the operating data of the associated base station equipment and the network access user data of the target base station in different time periods.

[0107] Specifically, Figure 4 As shown, the method for determining the matching perception strategy of the network signal in the time period is:

[0108] Determine the number of network access users of the target base station in the time period based on the change of network access user data of the target base station in the time period, and determine the access user change coefficient and the operation data change coefficient on different dates based on the number of network access users on different dates in the time period and the operation data of the associated base station device;

[0109] Determine the comprehensive variation coefficients of different dates according to the access user variation coefficients and the operation data variation coefficients of different dates, and use the comprehensive variation coefficients to determine the data variation dates in the dates;

[0110] The matching perception strategy of the network signal of the target base station in the time period is determined according to the proportion of the number of data change dates.

[0111] Furthermore, the access user variation coefficient of the date is determined according to the deviation rate between the number of network access users on the date and the benchmark access user number.

[0112] It should be noted that the coefficient of variation of the operating data on the date is determined according to the deviation rate between the operating data on the date and the benchmark operating data.

[0113] In one embodiment, determining the matching perception strategy of the network signal of the target base station in the time period according to the number ratio of the data change date specifically includes:

[0114] Based on the quantity ratio of the data change dates, determine the preset perception strategy corresponding to the quantity ratio;

[0115] Based on the preset perception strategy corresponding to the number ratio, a matching perception strategy for the network signal of the target base station in the time period is determined.

[0116] Furthermore, the matching perception strategy includes using different perception cycles to perform perception processing of network signals.

[0117] Embodiment 2 The second aspect, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for intelligent perception of network signals based on big data when running the computer program.

[0118] Optionally, the above step S21 includes the following contents:

[0119] S211: Based on the historical changes of the operation data of the base station device, the operation data is divided into different historical change times. According to the changes of the network characteristic signals in the different historical change times, when it is determined that the network characteristic signals have not changed in the different historical change times, it is determined that the base station device does not belong to the associated base station device. When it is determined that there are historical change times in which the network characteristic signals have changed, the process proceeds to step S212;

[0120] S212 uses the number of historical changes in which the network characteristic signal changes as the number of screening changes. When the proportion of the screening change number in the number of historical changes is greater than the preset number proportion, it is determined that the base station device belongs to an associated base station device. When the proportion of the screening change number in the number of historical changes is not greater than the preset number proportion, the process proceeds to step S213.

[0121] S213 determines the number of change associations in the historical change times according to the number of changes in the network characteristic signal in different historical change times. When the proportion of the change association times in the historical change times is greater than the preset proportion threshold, it is determined that the base station device belongs to an associated base station device. When the proportion of the change association times in the historical change times is not greater than the preset proportion threshold, proceed to step S22.

[0122] Optionally, the above step S22 includes the following contents:

[0123] S221 determines the change correlation coefficients of network characteristic signals of different dimensions at different historical change times based on the change amounts of network characteristic signals at different historical change times and the change amounts of operation data of base station equipment. When the change correlation coefficients of network characteristic signals of different dimensions at different historical change times are all less than a preset correlation coefficient threshold, it is determined that the base station equipment does not belong to the associated base station equipment. When the change correlation coefficients of network characteristic signals of different dimensions at different historical change times are not all less than the preset correlation coefficient threshold, the process proceeds to step S222.

[0124] S222: determining the average value of the change correlation coefficients of different historical change times by using the change correlation coefficients in different historical change times, and taking it as the average value of the correlation coefficients; when there is a network characteristic signal whose average value of the correlation coefficient is greater than a preset coefficient threshold, determining that the base station device does not belong to the associated base station device; when there is no network characteristic signal whose average value of the correlation coefficient is greater than the preset coefficient threshold, proceeding to step S223;

[0125] S223 determines the signal correlation coefficients of the characteristic signals of different dimensions based on the change correlation coefficients of the network characteristic signals of different dimensions in different historical change times. When there is a characteristic signal whose signal correlation coefficient is greater than the preset signal coefficient threshold, the process proceeds to step S224. When there is no characteristic signal whose signal correlation coefficient is greater than the preset signal coefficient threshold, the process proceeds to step S23.

[0126] S224 When the characteristic signal whose signal correlation coefficient is greater than the preset signal coefficient threshold is within the preset characteristic signal quantity interval, it is determined that the base station device belongs to an associated base station device; when the characteristic signal whose signal correlation coefficient is greater than the preset signal coefficient threshold is not within the preset characteristic signal quantity interval, proceed to step S23.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0128] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A network signal intelligent perception method based on big data, characterized in that: Specifically include: Acquire network access user data of the target base station in different time periods, and when it is determined that it is not necessary to use a preset perception strategy to perform perception processing of the network signal of the target base station based on changes in the network access user data, acquire historical perception data of network signals of different base stations; Based on the historical perception data, determine the changes of network characteristic signals of different dimensions when the operating data of different base station devices change, and use the changes of network characteristic signals of different dimensions to determine the associated base station device of the network signal; Determine, based on the historical operation data of the base station device associated with the target base station, changes in the historical operation data of the base station device associated with the target base station; When it is determined through changes in historical operating data of associated base station equipment that there is no need to adopt a preset perception strategy to perform perception processing of the network signal of the target base station, the matching perception strategy of the network signal of the target base station in different time periods is determined based on changes in the operating data of the associated base station equipment and network access user data of the target base station in different time periods.

2. The method for intelligently sensing network signals based on big data according to claim 1, characterized in that: The network access user data includes the number of network access users and the access duration of different network access users.

3. The network signal intelligent perception method based on big data according to claim 1, characterized in that: The change of the network access user data is determined based on the network access user data in different time periods on different dates.

4. The method for intelligently sensing network signals based on big data according to claim 1, characterized in that: Determine that the default perception strategy does not need to be adopted, including: Based on the network access user data of different time periods, determine the number of network access users in different time periods, and determine the user number change period on different dates according to the number of network access users in different time periods; Determine the user quantity change date in the date based on the number of user quantity change periods on different dates; Determine whether to adopt a preset perception strategy based on the number of dates on which the number of users changes.

5. The method for intelligently sensing network signals based on big data according to claim 4, characterized in that: The user quantity change period is determined according to the deviation between the number of network access users in the period and the benchmark number of users.

6. The network signal intelligent perception method based on big data according to claim 1, characterized in that: Determine whether to adopt a preset perception strategy based on the number of user change dates, including: When the number of user quantity change dates is within a preset date quantity interval, it is determined that a preset perception strategy needs to be adopted.

7. The method for intelligently sensing network signals based on big data according to claim 1, characterized in that: Determining that it is not necessary to use a preset sensing strategy to sense the network signal of the target base station by associating changes in historical operation data of the base station device specifically includes: Determine the historical operation data of the associated base station equipment at different time periods on different dates based on the change of the historical operation data of the associated base station equipment, and determine the operation data change time period on different dates using the historical operation data of different time periods; Determining the data change device in the associated base station device based on the number of operation data change periods; Whether it is necessary to adopt a preset perception strategy to perform perception processing of the network signal of the target base station is determined by the number of data change devices.

8. The method for intelligently sensing network signals based on big data according to claim 7, characterized in that: The operation data change period is determined by the deviation between the historical operation data and the benchmark operation data. Specifically, the period in which the deviation from the benchmark operation data does not meet the requirements is used as the operation data change period.

9. The method for intelligently sensing network signals based on big data according to claim 7, characterized in that: The data change device is an associated base station device whose number of running data change time periods does not meet the requirement.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a network signal intelligent perception method based on big data as described in any one of claims 1 to 9 is executed.

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