Mobile phone antenna supervision data intelligent processing system

Abstract

The invention relates to the technical field of communication data processing, in particular to an intelligent processing system for mobile phone antenna supervision data, which comprises the following steps of: identifying an operation signal value in an area to obtain an abnormal signal, analyzing mobile equipment at the position according to the position of the abnormal signal to determine a signal abnormal area, and sending the signal abnormal area to a server; then identifying mobile devices in the signal abnormal area, acquiring characteristic values of data signals of the mobile devices, analyzing the characteristic values, dividing the mobile devices in the signal abnormal area into a plurality of antenna receiving groups, analyzing the signal receiving rate of the mobile devices in each antenna receiving group, determining compensation parameters, and finally, determining the signal receiving rate of the mobile devices in each antenna receiving group on the basis of the compensation parameters. According to the invention, the method has strong adaptive capability, can adapt to different signal environments and mobile equipment distribution conditions, and improves the automation level and efficiency of signal processing.

Description

Technical Field

[0001] The present invention relates to the technical field of communication data processing, and particularly to an intelligent processing system for mobile phone antenna supervision data. Background Art

[0002] With the continuous iterative development of communication technologies, mobile phones are no longer limited to voice calls and text message sending and receiving functions, but have become intelligent terminals integrating functions such as high-speed Internet access, short videos, mobile games, and live broadcasts.

[0003] The prior art CN117278073A discloses an ultra-wideband antenna signal automatic adjustment method, which includes collecting ambient temperature values and ambient humidity values through real-time monitoring, and introducing data processing and analysis algorithms at the backend to perform time-series collaborative analysis of the ambient temperature values and ambient humidity values, so as to perform adaptive adjustment of signal transmission power. In this way, the performance and stability of the ultra-wideband communication system can be improved, enabling it to adapt to communication requirements under different environmental conditions.

[0004] When there are a large number of mobile phones in a regional scope, a large number of mobile phones simultaneously make data signal requests, which will cause network data congestion. However, the signal sensing capabilities of mobile phones of different operators are inconsistent when in use. When in a multi-communication device area, for mobile phones of different operators, the intensity of the received antenna signals will be different. When there are differences in the mobile phone antenna signal levels in the area, the activity experience of users in this area will be reduced. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background art, and to propose an intelligent processing system for mobile phone antenna supervision data.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] An intelligent processing system for mobile phone antenna supervision data, comprising:

[0008] A signal detection module, based on the data signals in a fixed area, determines the real-time position where the data signals are located. At the same time, it takes the data signals within a unit evaluation time as the running signal values, compares the running signal values to determine abnormal signals, sets the real-time position of the abnormal signals as the key area, and analyzes the mobile devices in the key area to determine the signal abnormal area;

[0009] A signal recognition module that performs feature recognition on the data signals in the signal anomaly area, determines the feature values of each mobile device, integrates the feature values of the same mobile device to obtain a feature set, then calculates and analyzes the similarity of the feature sets between mobile devices to determine a comprehensive similarity value, and based on the comprehensive similarity value, divides the mobile devices in the signal anomaly area into multiple antenna reception groups;

[0010] A comprehensive processing module that analyzes the signal reception rate of the mobile devices in each antenna reception group based on the area information of the fixed area to determine the signal reception base value, and then determines the compensation parameter based on the signal reception base value;

[0011] A device compensation module that adjusts the parameters of the operating base station device based on the compensation parameter.

[0012] As a further solution of the present invention, the method for determining an abnormal signal includes:

[0013] S1: Based on the received data signals in the fixed area, use the positioning analysis method to determine the real-time positions of the mobile devices in the fixed area. The positioning analysis method selects the GPS positioning method. The mobile device refers to the communication device carried by the user in the fixed area. Further, the data signal in the signal detection module is set to the data transmission rate;

[0014] S2: Set an evaluation unit time. According to the evaluation unit time, obtain the real-time data signals of each mobile device within the evaluation unit time and mark them as running signal values. Compare the obtained running signal values with the standard signal values. If the running signal value is greater than or equal to the standard signal value, it means that the running state of the mobile device is smooth, and at this time, normal monitoring of this mobile device will continue. Otherwise, if the running signal value is less than the standard signal value, an abnormal signal is generated.

[0015] As a further solution of the present invention, the method for determining the signal anomaly area includes:

[0016] When an abnormal signal is detected, identify the real-time position where the abnormal signal is located, and then set a key area based on the real-time position of the abnormal signal;

[0017] Obtain the area range of the key area, identify the number of mobile devices within the area range, and divide the number of mobile devices by the area of the key area to obtain the area device density ρ;

[0018] Monitor the data signals of the mobile devices in the key area to obtain the running signal values corresponding to different mobile devices within the evaluation unit time;

[0019] Compare the running signal values with the standard signal values, mark the mobile devices with running signal values less than the standard signal values as abnormal devices, and count the total number of devices of abnormal devices within this evaluation unit time. Then, obtain the running signal values of different mobile devices within the next evaluation unit time, and process them according to the above method to obtain the total number of devices of abnormal devices within the next evaluation unit time;

[0020] Repeat the above steps to obtain the total number of devices of abnormal devices within n consecutive evaluation unit times in the key area, and mark it as Bi, where i represents the number corresponding to different evaluation unit times, and i ∈ [1, n];

[0021] S4: Use the formula to obtain the signal smoothness value Lc of the key area, where r1 and r2 are constant coefficients respectively, and r1 > 1, 0 < r2 < 1;

[0022] Compare the signal smoothness value Lc with the standard threshold. If the signal smoothness value Lc is less than the standard threshold, then mark the key area as a signal abnormal area. On the contrary, if the signal smoothness value Lc is greater than or equal to the standard threshold, then continue to collect data signals for the key area.

[0023] As a further solution of the present invention, the method for determining the feature set includes:

[0024] SS1: Identify the mobile devices in the signal abnormal area again, and obtain the data signals of each mobile device within m consecutive time periods t1, where m and t1 are both thresholds. Further, in the signal recognition module, the data signals include signal strength, transmission rate of frequency bands, and signal interference ability;

[0025] SS2: Use the feature processing method to calculate the features of the data signals of each mobile device within each consecutive time period t1, and integrate the obtained feature values to obtain a feature set. Among them, in the feature set, there are multiple subsets, each subset represents different data types in the data signals, and each element in the subset represents the feature value of the corresponding data type within different consecutive time periods.

[0026] As a further solution of the present invention, the method for determining the comprehensive similarity value includes:

[0027] Use the Manhattan distance algorithm to calculate the similarity of the feature sets between any two mobile devices in the signal abnormal area to obtain the feature similarity value Xj, where j represents different data types in the data signals, and j is set to take values of 1, 2, and 3, respectively representing signal strength, transmission rate of frequency bands, and signal interference ability in the data signals;

[0028] Use the formula The comprehensive similarity value Xz between mobile devices is calculated, where αj is the weight coefficient corresponding to different data in the data signal.

[0029] As a further solution of the present invention, the method for determining the antenna receiving group includes:

[0030] Compare the comprehensive similarity value Xz with the similarity threshold Yx. If Xz < Yx, mark the corresponding two mobile devices as different devices from each other. On the contrary, if Xz ≥ Yx, mark the corresponding two mobile devices as associated devices with each other;

[0031] According to the labels between two mobile devices, integrate the mobile devices that are associated with each other into a group, and mark the integrated result as the antenna receiving group. At this time, there will be multiple antenna receiving groups in a signal abnormal area.

[0032] As a further solution of the present invention, the method for determining the signal receiving base value includes:

[0033] Set the antenna receiving group as the target group in turn, obtain the signal receiving rate of each mobile device in the target group, then select the data with the signal receiving rate less than the standard receiving threshold from the signal receiving rates, and perform mean processing on all the signal receiving rates less than the standard receiving threshold in the target group to obtain the signal receiving base value.

[0034] As a further solution of the present invention, the method for determining the compensation parameter includes: select the minimum value in the signal receiving base value, then subtract the minimum value from the standard receiving threshold, and mark the obtained result as the compensation parameter.

[0035] As a further solution of the present invention, the data signal in the fixed area is obtained by the signal acquisition module and transmitted to the signal detection module.

[0036] As a further solution of the present invention, the area information in the fixed area is collected by the area information collection module and transmitted to the comprehensive processing module.

[0037] Compared with the existing technology, the advantages of the present invention are:

[0038] The present invention identifies abnormal signals by recognizing the operating signal values within a region, analyzes the mobile devices at the positions where the abnormal signals are located based on the positions of the abnormal signals to determine the signal abnormal region, then identifies the mobile devices in the signal abnormal region, obtains the characteristic values of the data signals of the mobile devices and analyzes them, divides the mobile devices in the signal abnormal region into multiple antenna receiving groups, then analyzes the signal reception rates of the mobile devices in each antenna receiving group to determine the compensation parameters, and finally adjusts the parameters of the base station equipment based on the compensation parameters. By calculating and analyzing the similarity of the characteristic sets between mobile devices to divide the antenna receiving groups, it is beneficial to perform signal processing and optimization on different groups targeted, improve the efficiency and accuracy of signal processing. At the same time, the present invention has strong adaptability, can adapt to different signal environments and mobile device distribution situations, and improves the automation level and efficiency of signal processing. Description of the Drawings

[0039] Figure 1 It is a schematic structural diagram of the system of the present invention. Detailed Embodiment

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0041] Referring to Figure 1 , an intelligent processing system for mobile phone antenna supervision data, including a regional information collection module, a signal acquisition module, a signal detection module, a signal recognition module, a comprehensive processing module, and an equipment compensation module;

[0042] The regional information collection module is used to collect regional information within a fixed region. Among them, the specific regional scope of the fixed region is set by those skilled in the art. The regional information includes the distribution positions of the base stations in the target region and the basic operating parameters of the base stations. Then, the regional information collection module transmits the collected regional information to the comprehensive processing module;

[0043] The signal acquisition module is used to collect data signals within the fixed region and transmit the collected data signals to the signal detection module;

[0044] The signal detection module is used to receive the data signals within the fixed region and detect the received data signals to determine the signal abnormal region. The specific method for determining the signal abnormal region includes:

[0045] S1: Based on the data signals within the received fixed area, use the positioning analysis method to determine the real-time location of the mobile device within the fixed area. Among them, the positioning analysis method in this embodiment is selected as the GPS positioning method. The mobile device refers to the communication device carried by the user within the fixed area. Further, in this embodiment, the communication device is referred to as a mobile phone;

[0046] It should be further noted that in this embodiment, the data signal in the signal detection module is set as the data transmission rate;

[0047] S2: Set the evaluation unit time. Then, according to the evaluation unit time, obtain the real-time data signals of each mobile device within the evaluation unit time and mark them as running signal values. Compare the obtained running signal values with the standard signal values. If the running signal value is greater than or equal to the standard signal value, it indicates that the running state of the mobile device is smooth. At this time, continue to monitor this mobile device normally. On the contrary, if the running signal value is less than the standard signal value, an abnormal signal is generated;

[0048] It should be further noted that the specific evaluation unit time and the standard signal value are respectively set by those skilled in the art according to big data experience. In this embodiment, the evaluation unit time is set to 1 s;

[0049] S3: When an abnormal signal is detected, identify the real-time location where the abnormal signal is located. Then, based on the real-time location of the abnormal signal, set a key area. Among them, the area of the key area is set by those skilled in the art according to big data experience;

[0050] Obtain the area range of the key area, identify the number of mobile devices within the area range. Then divide the number of mobile devices by the area of the key area, and mark the obtained result as the area device density ρ;

[0051] Then, monitor the data signals of the mobile devices within the key area, obtain the running signal values corresponding to different mobile devices within the evaluation unit time, and compare the running signal values with the standard signal values. Mark the mobile devices with running signal values less than the standard signal values as abnormal devices, and count the total number of abnormal devices within this evaluation unit time. Then, obtain the running signal values of different mobile devices within the next evaluation unit time and process them according to the above method to obtain the total number of abnormal devices within the next evaluation unit time;

[0052] Repeat the above steps to obtain the total number of abnormal devices within n consecutive evaluation unit times within the key area and mark it as Bi. i represents the number corresponding to different evaluation unit times, and i ∈ [1, n]. Further, the specific value of n is set by those skilled in the art according to big data experience;

[0053] S4: Use the formula to obtain the signal smoothness value Lc of the key area, where r1 and r2 are constant coefficients respectively, and r1 > 1, 0 < r2 < 1. Further, the specific values of r1 and r2 are obtained by those skilled in the art through big data operations;

[0054] Compare the signal smoothness value Lc with the standard threshold. If the signal smoothness value Lc is less than the standard threshold, then mark the key area as a signal abnormal area. On the contrary, if the signal smoothness value Lc is greater than or equal to the standard threshold, then continue to collect data signals for the key area, where the specific value of the standard threshold is obtained by those skilled in the art through big data operations;

[0055] After the signal abnormal area is determined, the signal detection module then transmits the signal abnormal area to the signal recognition module;

[0056] The signal recognition module is used to obtain the signal abnormal area and perform feature recognition on the data signals in the signal abnormal area to determine the antenna receiving group. The specific method for determining the antenna receiving group includes:

[0057] SS1: Identify the mobile devices in the signal abnormal area again and obtain the data signals of each mobile device within m consecutive time periods t1, where m and t1 are both thresholds, and the specific values of m and t1 are set by those skilled in the art according to big data experience. It should be further noted that in the signal recognition module, the data signals include signal strength, transmission rate of frequency bands, and signal interference ability;

[0058] SS2: Use the feature processing method to calculate the features of the data signals of each mobile device within each consecutive time period t1, and integrate the obtained feature values to obtain a feature set. Among them, in the feature set, there are multiple subsets, each subset represents different data types in the data signals, and each element in the subset represents the feature value of the corresponding data type within different consecutive time periods. For example, when m is set to 5, select the subset corresponding to signal strength {0.3, 0.5, 0.3, 0.4, 0.5} in the feature set. At this time, the first 0.3 is the feature value of the first consecutive time period t1, the second 0.5 is the feature value of the second consecutive time period t1,...;

[0059] The feature processing method in this embodiment is selected as the principal component analysis method. The specific processing process of the principal component analysis method belongs to the prior art and will not be elaborated here;

[0060] SS3: Then, using the similarity algorithm, calculate the similarity value Xj between the feature sets of any two mobile devices within the signal anomaly area, where j represents different data types in the data signal. In this embodiment, j is set to 1, 2, and 3, representing the signal strength, transmission rate of the frequency band, and signal interference ability in the data signal respectively;

[0061] It should be further noted that the similarity algorithm in this embodiment selects the Manhattan distance algorithm. Specifically, the Manhattan distance algorithm belongs to the prior art and will not be elaborated here;

[0062] After that, use the formula to calculate the comprehensive similarity value Xz between the mobile devices, where αj is the weight coefficient corresponding to different data in the data signal, and the specific value of αj is obtained by those skilled in the art through big data operations;

[0063] Compare the comprehensive similarity value Xz with the similarity threshold Yx. If Xz < Yx, then mark the corresponding two mobile devices as different devices. On the contrary, if Xz ≥ Yx, then mark the corresponding two mobile devices as associated devices, where the specific value of the similarity threshold Yz is obtained by those skilled in the art through big data operations;

[0064] SS4: According to the labels between the two mobile devices, collectively integrate the mobile devices that are associated devices, and mark the integrated result as the antenna receiving group. At this time, there will be multiple antenna receiving groups within a signal anomaly area;

[0065] For example, within a signal anomaly area, there are mobile devices b1, b2, b3, b4, b5, b6, b7, and b8. Then, calculate the comprehensive similarity value between any two mobile devices respectively according to the above method, and based on the comprehensive similarity value, set labels between the mobile devices. Among them, the labels between mobile devices b1, b3, and b4 are associated devices, b2 and b8 are associated devices with each other, and b6 and b7 are associated devices with each other. At this time, set mobile devices b1, b3, and b4 as the antenna receiving group Q1, set mobile devices b2 and b8 as the antenna receiving group Q2, and set b6 and b7 as the antenna receiving group Q3;

[0066] After the antenna receiving group is determined, the signal recognition module then transmits the antenna receiving group to the comprehensive processing module;

[0067] The comprehensive processing module is used to obtain the antenna receiving group and at the same time obtain the area information, combine and analyze the area information with the antenna receiving group to determine the compensation parameters of the signal anomaly area. The specific analysis method of the compensation parameters of the signal anomaly area includes:

[0068] Arbitrarily select an antenna receiving group and label it as the target group. Taking the target group as an example, obtain the signal reception rate of each mobile device in the target group. Then, among the signal reception rates, select the data with a signal reception rate less than the standard reception threshold, and perform an average process on all the signal reception rates less than the standard reception threshold in the target group to obtain the signal reception base value. Among them, the specific value of the standard reception threshold is set by those skilled in the art according to big data experience;

[0069] Successively set all the antenna receiving groups as the target group and process them according to the above method to obtain the signal reception base value of each antenna receiving group. Then, select the minimum value among the signal reception base values, and then subtract the minimum value from the standard reception threshold. Mark the obtained result as the compensation parameter and transmit it to the device compensation module by the comprehensive processing module;

[0070] The device compensation module is used to receive the compensation parameter and, based on the compensation parameter, adjust the parameters of the operating base station device. The specific parameter adjustment method belongs to the prior art and will not be elaborated here.

[0071] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A mobile phone antenna monitoring data intelligent processing system, characterized in that: include: The signal detection module determines the real-time location of the data signal based on the data signal in the fixed area, and takes the data signal within the unit evaluation time as the operation signal value, compares the operation signal values, determines the abnormal signal, sets the real-time location of the abnormal signal as the key area, analyzes the mobile devices in the key area, and determines the signal abnormal area; The signal recognition module performs feature recognition on the data signal in the signal anomaly area, determines the feature value of each mobile device, integrates the feature values ​​of the same mobile device to obtain a feature set, and then calculates and analyzes the similarity of the feature sets between mobile devices to determine a comprehensive similarity value. Based on the comprehensive similarity value, the mobile devices in the signal anomaly area are divided into multiple antenna receiving groups; The comprehensive processing module analyzes the signal reception rate of the mobile device in each antenna receiving group based on the area information of the fixed area, determines the signal reception base value, and then determines the compensation parameter based on the signal reception base value; The equipment compensation module adjusts the parameters of the running base station equipment based on the compensation parameters.

2. The mobile phone antenna supervision data intelligent processing system according to claim 1 is characterized in that: Methods for determining abnormal signals include: S1: Based on the received data signal in the fixed area, the real-time position of the mobile device in the fixed area is determined by using a positioning analysis method, the positioning analysis method uses a GPS positioning method, and the mobile device refers to a communication device carried by a user in the fixed area. Furthermore, the data signal in the signal detection module is set to a data transmission rate; S2: Set the evaluation unit time, obtain the real-time data signal of each mobile device within the evaluation unit time according to the evaluation unit time, and mark it as the operation signal value, and compare the obtained operation signal value with the standard signal value. If the operation signal value is greater than or equal to the standard signal value, it means that the operation status of the mobile device is smooth. At this time, normal monitoring of this mobile device will continue. On the contrary, if the operation signal value is less than the standard signal value, an abnormal signal is generated.

3. The mobile phone antenna supervision data intelligent processing system according to claim 2 is characterized in that: Methods for determining signal abnormality areas include: When an abnormal signal is detected, the real-time location of the abnormal signal is identified, and then the key area is set based on the real-time location of the abnormal signal; Obtain the area range of the key area, identify the number of mobile devices within the area, and divide the number of mobile devices by the area of ​​the key area to obtain the regional device density ρ; Monitor the data signals of mobile devices in key areas and obtain the corresponding operating signal values ​​of different mobile devices within the evaluation unit time; Compare the operating signal value with the standard signal value, mark the mobile device whose operating signal value is less than the standard signal value as an abnormal device, and count the total number of abnormal devices in this evaluation unit time, then obtain the operating signal values ​​of different mobile devices in the next evaluation unit time, and process them according to the above method to obtain the total number of abnormal devices in the next evaluation unit time; Repeat the above steps to obtain the total number of abnormal devices in the key area for n consecutive evaluation unit times, and mark them as Bi, where i represents the number corresponding to different evaluation unit times, and i∈[1,n]; S4: Utilizing the formula The signal fluency value Lc of the key area is obtained, where r1 and r2 are constant coefficients, and r1>1, 0<r2<1; The signal fluency value Lc is compared with the standard threshold. If the signal fluency value Lc is less than the standard threshold, the key area is marked as a signal abnormality area. Conversely, if the signal fluency value Lc is greater than or equal to the standard threshold, data signal collection continues for the key area.

4. The mobile phone antenna monitoring data intelligent processing system according to claim 1 is characterized in that: Methods for determining feature sets include: SS1: Identify the mobile devices in the signal abnormality area again, and obtain the data signal of each mobile device in m consecutive time periods t1, where m and t1 are both thresholds. Furthermore, in the signal identification module, the data signal includes signal strength, transmission rate of the frequency band, and signal interference capability; SS2: Using the feature processing method, the features of the data signal of each mobile device in each continuous time period t1 are calculated, and the obtained feature values ​​are integrated to obtain a feature set, wherein the feature set includes multiple subsets, each subset represents a different data type in the data signal, and each element in the subset represents the feature value of the corresponding data type in different continuous time periods.

5. The mobile phone antenna monitoring data intelligent processing system according to claim 4 is characterized in that: Methods for determining the comprehensive similarity value include: Using the Manhattan distance algorithm, the similarity of the feature sets between any two mobile devices in the signal anomaly area is calculated to obtain the feature similarity value Xj, where j represents different data types in the data signal, and the value of j is set to 1, 2, and 3, which respectively represent the signal strength, frequency band transmission rate, and signal interference capability in the data signal; Using the formula The comprehensive similarity value Xz between the mobile devices is calculated, where αj is the weight coefficient corresponding to different data in the data signal.

6. The mobile phone antenna monitoring data intelligent processing system according to claim 5 is characterized in that: Methods for determining the antenna receiving group include: Compare the comprehensive similarity value Xz with the similarity threshold Yx. If Xz<Yx, the corresponding two mobile devices are marked as different devices. Otherwise, if Xz≥Yx, the corresponding two mobile devices are marked as associated devices. According to the tags between two mobile devices, the mobile devices that are related to each other are collectively integrated, and the integrated results are marked as antenna receiving groups. At this time, there will be multiple antenna receiving groups in one signal abnormality area.

7. The mobile phone antenna monitoring data intelligent processing system according to claim 1 is characterized in that: The method for determining the signal reception base value includes: The antenna receiving groups are set as the target groups in turn, and the signal receiving rate of each mobile device in the target group is obtained. Then, among the signal receiving rates, the data with a signal receiving rate less than the standard receiving threshold are selected, and the signal receiving rates of all the signal receiving rates in the target group less than the standard receiving threshold are averaged to obtain the signal receiving base value.

8. The mobile phone antenna monitoring data intelligent processing system according to claim 7 is characterized in that: The method for determining the compensation parameter includes: selecting the minimum value among the signal reception base values, then subtracting the minimum value from the standard reception threshold, and marking the obtained result as the compensation parameter.

9. The mobile phone antenna monitoring data intelligent processing system according to claim 1, characterized in that: The data signal in a fixed area is acquired by the signal acquisition module and transmitted to the signal detection module.

10. The mobile phone antenna monitoring data intelligent processing system according to claim 1, characterized in that: The regional information within a fixed area is collected by the regional information collection module and transmitted to the comprehensive processing module.

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