WO2022268098A1 - 异常小区识别方法、装置和电子设备 - Google Patents

异常小区识别方法、装置和电子设备 Download PDF

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Publication number
WO2022268098A1
WO2022268098A1 PCT/CN2022/100290 CN2022100290W WO2022268098A1 WO 2022268098 A1 WO2022268098 A1 WO 2022268098A1 CN 2022100290 W CN2022100290 W CN 2022100290W WO 2022268098 A1 WO2022268098 A1 WO 2022268098A1
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Prior art keywords
target cell
target
information
electronic device
abnormal
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PCT/CN2022/100290
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English (en)
French (fr)
Inventor
曹杭挺
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维沃移动通信有限公司
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Application filed by 维沃移动通信有限公司 filed Critical 维沃移动通信有限公司
Priority to EP22827591.3A priority Critical patent/EP4362554A1/en
Publication of WO2022268098A1 publication Critical patent/WO2022268098A1/zh
Priority to US18/393,658 priority patent/US20240129770A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H04L43/0811—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/02—Access restriction performed under specific conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/20—Selecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management
    • H04W76/10—Connection setup
    • H04W76/15—Setup of multiple wireless link connections

Definitions

  • the present application belongs to the technical field of communications, and in particular relates to an abnormal cell identification method, device and electronic equipment.
  • the cell can be identified before the electronic device accesses the cell, so that the cell with normal network can be preferentially accessed and the cell with abnormal network can be avoided.
  • some cells are regarded as abnormal cells based on the abnormal situation reported by the user that the user cannot connect to the network during the use of the electronic device.
  • the purpose of the embodiments of the present application is to provide a method for identifying abnormal cells, which can solve the problem that fewer abnormal cells are covered by the method for identifying abnormal cells in the prior art.
  • the embodiment of the present application provides a method for identifying an abnormal cell, the method includes: obtaining the relative growth rate of the failure rate of the target cell, the relative growth rate of the cut-off user ratio, and the proportion of low network speed; wherein, the failure rate The relative growth rate of the failure rate, the relative growth rate of the disconnection user ratio, and the low network speed ratio are all related to: at least one of time information and scene information; the relative growth rate of the failure rate and the disconnection rate in the target cell When the sum of the stream user ratio relative growth rates is greater than a first threshold, determine that the target cell has the first abnormal attribute; when the low network speed ratio of the target cell is greater than a second threshold, determine that the target cell The cell has a second abnormal attribute.
  • an abnormal cell identification device which includes: an acquisition module, configured to acquire the relative growth rate of the target cell's failure rate, the relative growth rate of the cut-off user ratio, and the proportion of low network speed; Wherein, the relative growth rate of the failure rate, the relative growth rate of the cut-off user ratio, and the low network speed ratio are all associated with: at least one of time information and scene information; When the sum of the relative growth rate of the failure rate of the target cell and the relative growth rate of the cut-off user ratio is greater than the first threshold, it is determined that the target cell has the first abnormal attribute; the second determination module is configured to: When the low network speed ratio of the cell is greater than the second threshold, it is determined that the target cell has the second abnormal attribute.
  • an embodiment of the present application provides an electronic device, the electronic device includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, and the program or instruction is The processor implements the steps of the method described in the first aspect when executed.
  • an embodiment of the present application provides a readable storage medium, on which a program or an instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented .
  • the embodiment of the present application provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions, so as to implement the first aspect the method described.
  • an embodiment of the present application provides a computer program product, the program product is stored in a non-volatile storage medium, and the program product is executed by at least one processor to implement the computer program product described in the first aspect. method.
  • the embodiment of the present application provides a communication device configured to execute the method as described in the first aspect.
  • a large number of user terminal equipments can obtain the relative growth rate of the failure rate of the target cell, the relative growth rate of the disconnection user ratio, and the low network speed of the target cell. Proportion. Among them, the sum of the relative growth rate of the failure rate and the relative growth rate of the outage user ratio is used to indicate the proportion information of the abnormal situation of the network interruption in the target cell, and the proportion of low network speed is used to indicate the abnormality of the low-speed network in the target cell percentage information.
  • the obtained two proportion information are compared with the corresponding first threshold and the second threshold respectively, correspondingly, when one of the proportion information is greater than the first threshold, it is considered that the target cell is more prone to disconnection network In abnormal cases, it is determined that the target cell has the first abnormal attribute; when the other proportion information is greater than the second threshold, it is considered that the target cell is more prone to low-rate network abnormalities, and the target cell is determined to have the second abnormal attribute. It can be seen that based on the abnormal cell identification method of the present application, at least two types of abnormal cells with the first abnormal attribute and the second abnormal attribute can be identified more precisely, compared with the prior art that can only generally identify one abnormal cell Cells, greatly increasing the coverage area of abnormal cells.
  • Fig. 1 is the flowchart of the abnormal cell identification method of the embodiment of the present application.
  • FIG. 2 is a block diagram of an abnormal cell identification device according to an embodiment of the present application.
  • FIG. 3 is one of the schematic diagrams of the hardware structure of the electronic device according to the embodiment of the present application.
  • FIG. 4 is a second schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
  • FIG. 1 shows a flow chart of an abnormal cell identification method according to an embodiment of the present application.
  • the method is applied to electronic equipment, including:
  • Step S1 Obtain the relative growth rate of failure rate, the relative growth rate of cut-off user ratio, and the proportion of low network speed in the target cell.
  • the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio are used to represent the proportion information of the first type of network abnormality in the target cell.
  • the first type of network abnormality may be a disconnected network abnormality.
  • the proportion of low network speed is used to indicate the proportion information of the second type of network abnormality in the target cell.
  • the second type of network abnormality may be a low-rate network abnormality.
  • the relative growth rate of failure rate, the relative growth rate of outage user ratio, and the proportion of low network speed are all related to at least one of time information and scene information.
  • the relative growth rate of failure rate, the relative growth rate of outage user ratio, and the proportion of low network speed of the target cell can be obtained.
  • the preset time information is one week, and the relative growth rate of the failure rate, the relative growth rate of the cut-off user ratio, and the proportion of low network speed of the target cell in the past week can be obtained.
  • the relative growth rate of failure rate, the relative growth rate of outage user ratio, and the proportion of low network speed of the target cell can be obtained.
  • the preset scene information is a subway scene
  • the relative growth rate of failure rate, the relative growth rate of outage user ratio, and the proportion of low network speed in the target cell can be obtained in the subway scene.
  • Step S2 When the sum of the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio of the target cell is greater than a first threshold, determine that the target cell has the first abnormal attribute.
  • the relative growth rate of the failure rate of the target cell and the relative growth rate of the cut-off user ratio are combined in an additive manner to represent the proportion information of the first type of network abnormality in the target cell.
  • the sum of the relative growth rate of the failure rate and the relative growth rate of the outage user ratio obtained in the target cell is compared with the preset proportion information (ie, the first threshold), if the target cell If the sum of the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio is greater than the first threshold, it is determined that the target cell has the first abnormal attribute. That is to say, the target cell is a cell with frequent outages and network abnormalities, which is defined as a "highly abnormal cell" in this application.
  • the first threshold is 30%.
  • the value of the first threshold is actually adjusted according to requirements, so as to limit the strictness of the definition of "highly abnormal cells".
  • Step S3 When the low network speed ratio of the target cell is greater than the second threshold, determine that the target cell has a second abnormal attribute.
  • the target cell Before this step, compare the low network speed ratio of the obtained target cell with the preset ratio information (ie, the second threshold), and if the low network speed ratio of the target cell is greater than the second threshold, then determine The target cell has a second abnormal attribute. That is to say, the target cell is a cell with frequent low-speed network abnormalities, which is defined as a "low-network speed cell" in this application.
  • the second threshold is 50%.
  • the value of the second threshold is actually adjusted according to requirements, so as to limit the strictness of the definition of the "low network speed cell".
  • a third threshold may be set to define an abnormal condition of a low-rate network. That is, when the network rate is lower than the third threshold, it is considered as a low-rate network abnormality.
  • the third threshold is 300 kilobytes per second (KB/S).
  • At least abnormal cells with the first abnormal attribute and abnormal cells with the second abnormal attribute can be identified.
  • the abnormal cell with the first abnormal attribute when there is an abnormal situation of disconnection of the network, the electronic device cannot connect to the network, so that the user cannot access the Internet normally;
  • the abnormal cell with the second abnormal attribute when there is a low
  • the electronic device can connect to the network, but the network speed is extremely low, making it impossible for users to access the Internet normally.
  • the target cell may have at least one of the first abnormal attribute and the second abnormal attribute.
  • the electronic device in this embodiment may be a user terminal device.
  • the server can obtain historical crowdsourcing data composed of network abnormalities reported by a large number of user terminal devices, and the user terminal devices can obtain historical crowdsourcing data from the server, and then undergo relevant training to obtain the target cell with At least one of the first abnormal attribute and the second abnormal attribute.
  • the electronic device in this embodiment may also be a server.
  • the server can obtain historical crowdsourcing data composed of network abnormalities reported by a large number of user terminal devices, and after relevant training, it can be obtained that the target cell has at least one of the first abnormal attribute and the second abnormal attribute. A sort of.
  • the target cell in this embodiment needs to meet the requirement that the number of electronic devices accessed within a day is greater than or equal to 5.
  • a large number of user terminal equipments can obtain the relative growth rate of the failure rate of the target cell, the relative growth rate of the disconnection user ratio, and the low network speed of the target cell. Proportion. Among them, the sum of the relative growth rate of the failure rate and the relative growth rate of the outage user ratio is used to indicate the proportion information of the abnormal situation of the network interruption in the target cell, and the proportion of low network speed is used to indicate the abnormality of the low-speed network in the target cell percentage information.
  • the obtained two proportion information are compared with the corresponding first threshold and the second threshold respectively, correspondingly, when one of the proportion information is greater than the first threshold, it is considered that the target cell is more prone to disconnection network In abnormal cases, it is determined that the target cell has the first abnormal attribute; when the other proportion information is greater than the second threshold, it is considered that the target cell is more prone to low-rate network abnormalities, and the target cell is determined to have the second abnormal attribute. It can be seen that based on the abnormal cell identification method of the present application, at least two types of abnormal cells with the first abnormal attribute and the second abnormal attribute can be identified more precisely, compared with the prior art that can only generally identify one abnormal cell Cells, greatly increasing the coverage area of abnormal cells.
  • step S2 the method further includes:
  • Step A1 During the process of detecting that the electronic device accesses the target cell, prohibiting access to the target cell.
  • the electronic device may be automatically set to: disable the target cell.
  • the target cell frequently suffers from frequent network outages, which are not easy to avoid. Therefore, the method of disabling the target cell can be used to avoid the abnormal cell, so as to ensure that the electronic equipment is connected to the cell. After that, there will be no frequent interruption of network abnormalities, so as to ensure that users can surf the Internet normally.
  • step A1 includes:
  • Sub-step B1 In the process of detecting that the electronic device accesses the target cell, acquire the target time information where the electronic device is located.
  • Sub-step B2 When the target time information matches the time information associated with the first abnormal attribute, prohibit access to the target cell.
  • the target time information includes any one of early time period information, middle time period information and late time period information; and/or, any one of weekday information and rest day information.
  • the number of connected devices is more during the morning and evening peak hours, and the number of connected devices is less during the noon period. Therefore, the frequency of reporting network abnormalities during the morning and evening periods is significantly higher than that during the noon period. Frequency of reporting network anomalies.
  • step S1 based on certain time information, the relative growth rate of failure rate and the relative growth rate of outage user ratio of the target cell may be obtained.
  • a certain time information may be any one of early time period information, middle time period information and late time period information.
  • 7:00-10:00 (early time period information), 11:00-14:00 (middle time period information), 17:00-20:00 (late time period information) in a day can be divided to obtain The relative growth rate of the failure rate of the target cell and the relative growth rate of the cut-off user ratio in the three time information.
  • the certain time information may be any one of weekday information and holiday information.
  • Monday-Friday (working day information) and Saturday-Sunday (rest day information) of a week can be divided to obtain the relative growth rate of the failure rate and outage rate of the target cell at these two time information respectively.
  • the electronic device after the target time information where the electronic device is located is acquired, if the target time information matches the acquired time information associated with the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio, the electronic device is considered to be The device is just in the time when the target cell is prone to disconnection and network abnormalities, so the target cell is directly disabled.
  • the target time information is a hypothetical corresponding time after accessing the target cell. Considering that the difference between the current time and the time to be accessed is extremely short, the target time information in this step can be understood as the current time information.
  • the current target time information may be determined according to the current specific time point.
  • the current specific time point is: 7:30, which is determined as the early time slot information. Based on the obtained relative growth rate of the failure rate of the target cell in the early period and the relative growth rate of the cut-off user ratio, it can be determined that the information of the target cell in the early period has the first abnormal attribute, thereby prohibiting access to the target cell on the electronic device.
  • a plurality of time information is pre-divided to more finely analyze the outage network abnormality in the target cell, so as to determine whether the target cell has the first time information at different time information. Exception properties. Therefore, the electronic device can match the time information associated with the case where the target cell has the first abnormal attribute according to the current target time information, so that the electronic device can adopt different network conditions according to the target cell information at different times. Corresponding measures can be used to avoid abnormal cells more flexibly.
  • the accuracy rate of judging whether the target cell has the first abnormal attribute can also be improved.
  • the overall frequency of network outages in the target community during the day is not high, but the frequency of network outages at certain times is relatively high. Based on this embodiment, the overall situation will not be directly targeted The cell is determined not to have the first abnormal attribute.
  • Sub-step B3 In the process of detecting that the electronic device accesses the target cell, acquire the target scene information where the electronic device is located.
  • Sub-step B4 When the target context information matches the context information associated with the first abnormal attribute, prohibit access to the target cell.
  • the target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the frequency of reporting network abnormalities is different, and the types of network abnormalities reported are also different.
  • the frequency of reporting network abnormalities in the former is significantly higher than that in the latter.
  • the frequency of the exception is significantly higher than that in the latter.
  • step S1 the relative growth rate of failure rate and the relative growth rate of outage user ratio of the target cell may be obtained based on certain scene information.
  • a certain scene information may be any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the relative growth rate of the failure rate and the relative growth rate of the outage user ratio of the target cell in the four scenarios are obtained respectively.
  • the electronic device after acquiring the target scene information where the electronic device is located, if the target scene information matches the acquired scene information associated with the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio, the electronic device is considered to be The device happens to be in a scene where the target cell is prone to disconnection and network abnormalities, so the target cell is directly disabled.
  • the target scene information is scene information corresponding to a hypothetical access to the target cell. Considering that the difference between the current time and the time to be accessed is extremely short, the target scene information in this step can be understood as the current scene information.
  • the target scene information where it is located may be determined according to the current specific location.
  • the current specific location is: on the XX highway, which is determined as high-speed scene information. Based on the obtained relative growth rate of the failure rate of the target cell in the high-speed scene information and the relative growth rate of the cut-off user ratio, it can be determined that the target cell has the first abnormal attribute in the high-speed scene information, thereby disabling the target cell on the electronic device.
  • a plurality of scene information is pre-divided to more finely analyze the outage network abnormality in the target cell, and it is possible to determine whether the target cell has the first scene information in different scene information. Exception properties. Therefore, the electronic device can match the scene information associated with the case where the target cell has the first abnormal attribute according to the current target scene information, so that the electronic device can take different network conditions according to the target cell in different scenes. Corresponding measures can be used to avoid abnormal cells more flexibly.
  • the accuracy rate of judging whether the target cell has the first abnormal attribute can also be improved.
  • the frequency of abnormal network outages is not high, but the frequency of abnormal network outages in some scenarios is relatively high. Based on this embodiment, it will not focus on the overall situation.
  • the target cell is directly determined as not having the first abnormal attribute.
  • the relative growth rate of the failure rate and the relative growth rate of the outage user ratio when obtaining the relative growth rate of the failure rate and the relative growth rate of the outage user ratio, it may only be for a certain time information or a certain scene information, or it may be for a certain time information and a certain A scene information.
  • step S3 the method further includes:
  • Step C1 In the process of detecting that the electronic device accesses the target cell, access the target cell, and set the connection mode of the electronic device to a dual connection mode.
  • the electronic device can be automatically set to a dual connection mode.
  • the target cell is the non-independent networking mode of the 5G mobile network (New Radio 5G Non-standalone, NR5G-NSA for short), and correspondingly, setting the connection mode of the electronic device to the dual connection mode is as follows: on the electronic device Enable E-UTRAN New Radio-Dual Connectivity (E-UTRAN New Radio–Dual Connectivity, EN-DC for short) mode.
  • 5G mobile network New Radio 5G Non-standalone, NR5G-NSA for short
  • setting the connection mode of the electronic device to the dual connection mode is as follows: on the electronic device Enable E-UTRAN New Radio-Dual Connectivity (E-UTRAN New Radio–Dual Connectivity, EN-DC for short) mode.
  • some cells are configured with 5G mobile network independent networking mode (New Radio5G standalone, NR5G-SA for short) equipment, and some cells are configured with NR5G-NSA equipment.
  • 5G mobile network independent networking mode New Radio5G standalone, NR5G-SA for short
  • NR5G-NSA equipment For the latter, after some 4G electronic devices are connected, under the influence of the 5G electronic devices connected together, low-speed network abnormalities will appear in the 4G electronic devices, and low-speed network abnormalities will be reported.
  • the NR5G-NSA cell is the anchor cell of major operators. Therefore, when the target cell is an anchor cell, determine whether the target cell has the second abnormal attribute, and if the target cell has the second abnormal attribute, access the target cell and enable EN-DC to allow The device simultaneously accesses Long Term Evaluation (LTE for short, specifically referring to 4G mobile network) and 5G on the same frequency band.
  • LTE Long Term Evaluation
  • step C1 includes:
  • Sub-step D1 In the process of detecting that the electronic device accesses the target cell, acquire the target time information where the electronic device is located.
  • Sub-step D2 When the target time information matches the time information associated with the second abnormal attribute, access the target cell, and set the connection mode of the electronic device to the dual connection mode.
  • the target time information includes any one of early time period information, middle time period information and late time period information; and/or, any one of weekday information and rest day information.
  • the number of connected devices is more during the morning and evening peak hours, and the number of connected devices is less during the noon period. Therefore, the frequency of reporting network abnormalities during the morning and evening periods is significantly higher than that during the noon period. Frequency of reporting network anomalies.
  • step S1 the low network speed ratio of the target cell can be obtained based on certain time information.
  • a certain time information may be any one of early time period information, middle time period information and late time period information.
  • 7:00-10:00 (early time period information), 11:00-14:00 (middle time period information), 17:00-20:00 (late time period information) in a day can be divided to obtain Low network speed ratio of the target cell in the three time information.
  • the certain time information may be any one of weekday information and holiday information.
  • Monday-Friday (working day information) and Saturday-Sunday (rest day information) of a week may be divided to obtain the proportion of low network speed of the target community in these two time information respectively.
  • the target time information matches the acquired time information associated with the proportion of low network speed, it is considered that the electronic device is just in the target cell and is prone to low network speed.
  • the speed network is abnormal, it can access the target cell, and set the dual connection mode on the electronic device at the same time.
  • the target time information is a hypothetical corresponding time after accessing the target cell. Considering that the difference between the current time and the time to be accessed is extremely short, the target time information in this step can be understood as the current time information.
  • the current target time information may be determined according to the current specific time point.
  • the current specific time point is: 7:30, which is determined as the early time slot information. Based on the obtained low network speed ratio of the target cell in the early period information, it can be determined that the target cell has the second abnormal attribute in the early period information, thereby accessing the target cell, and setting the dual connection mode on the electronic device at the same time.
  • a plurality of time information is pre-divided to analyze the low-rate network abnormality in the target cell in a more precise manner, so as to determine whether the target cell has the second time information at different time information. Exception properties. Therefore, the electronic device can match the time information associated with the case where the target cell has the second abnormal attribute according to the current target time information, so that the electronic device can take actions according to different network conditions of the target cell at different time information. Corresponding measures can be used to avoid abnormal cells more flexibly.
  • the accuracy of judging whether the target cell has the second abnormal attribute can also be improved.
  • the frequency of low-speed network abnormalities in the target cell is not high in a day, but the frequency of low-speed network abnormalities is relatively high at certain times. Based on this embodiment, it will not directly target the overall situation. The cell is determined not to have the second abnormal attribute.
  • Sub-step D3 In the process of detecting that the electronic device accesses the target cell, acquire the target scene information where the electronic device is located.
  • Sub-step D4 When the target scene information matches the scene information associated with the second abnormal attribute, access the target cell, and set the connection mode of the electronic device to the dual connection mode.
  • the target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the frequency of reporting network abnormalities is different, and the types of network abnormalities reported are also different.
  • the frequency of reporting network abnormalities in the former is significantly higher than that in the latter.
  • the frequency of the exception is significantly higher than that in the latter.
  • step S1 based on certain scene information, the low network speed ratio of the target cell can be obtained.
  • a certain scene information may be any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the proportions of low network speeds of the target cell in the four scenarios are obtained respectively.
  • the target scene information matches the acquired scene information associated with the proportion of low network speed, it is considered that the electronic device is just in the target cell and is prone to low network speed.
  • the target cell is accessed, and the connection mode of the electronic device is set to the dual connection mode.
  • the target scene information is scene information corresponding to a hypothetical access to the target cell. Considering that the difference between the current time and the time to be accessed is extremely short, the target scene information in this step can be understood as the current scene information.
  • the target scene information where it is located may be determined according to the current specific location.
  • the current specific location is: on the XX highway, which is determined as high-speed scene information. Based on the obtained low network speed ratio of the target cell in the high-speed scene information, it can be determined that the target cell has the second abnormal attribute in the high-speed scene information, thereby accessing the target cell, and setting the connection mode of the electronic device to the dual connection mode.
  • a plurality of scene information is pre-divided to analyze the low-rate network abnormality in the target cell more precisely, and it is possible to determine whether the target cell has the second Exception properties. Therefore, the electronic device can match the scene information associated with the case where the target cell has the second abnormal attribute according to the current target scene information, so that the electronic device can adopt different network conditions of the target cell in different scenes. Corresponding measures can be used to avoid abnormal cells more flexibly.
  • the accuracy rate of judging whether the target cell has the second abnormal attribute can also be improved.
  • the frequency of low-rate network anomalies is not high, but the frequency of low-rate network anomalies in some scenarios is relatively high. Based on this embodiment, it will not focus on the overall situation.
  • the target cell is directly determined as not having the second abnormal attribute.
  • the proportion of low network speed in the case of acquiring the proportion of low network speed, it may only be for a certain time information or a certain scene information, and may also be for a certain time information and a certain scene information.
  • the application can identify abnormal cells for more network abnormalities, so as to increase the coverage of abnormal cells; in the second aspect, the application can take different measures for different network abnormalities , to avoid abnormal cells, or to avoid network abnormalities in abnormal cells, so as to more flexibly improve users’ online experience; in the third aspect, this application can selectively avoid abnormal cells at certain times and in certain scenarios , to avoid blind evasion.
  • the target cell in the first case, the target cell only has the first abnormal attribute, and the corresponding measures are: directly disable; in the second case, the target cell only has the second abnormal attribute, The corresponding measures are: access, and set the dual connection mode on the electronic device at the same time; in the third case, the target cell has the first abnormal attribute and the second abnormal attribute at the same time, and the measure of disabling is prioritized to give priority to disconnecting the network abnormal situation.
  • the electronic device accesses the target cell multiple times, the time information and scene information may be different. Therefore, the above embodiments are all for a single access. Re-determine whether it is necessary to take corresponding measures to avoid abnormal cells, or to avoid network abnormalities in abnormal cells.
  • corresponding measures are taken in time based on the abnormal attributes of the target cell.
  • corresponding measures may be taken in time based on abnormal attributes of the target cell after it is detected that the electronic device has just accessed the target cell and before network data transmission.
  • the electronic device can be connected to an ideal cell in time before network data transmission, so as to improve the user's online experience.
  • the execution subject is the user terminal device by default.
  • step S1 includes:
  • Sub-step E1 According to the outage event of the target cell, obtain the failure rate and the outage user ratio of the target cell.
  • the formulas involved in this step include:
  • Failure rate total outage times/total number of connected devices.
  • Cut-off user ratio total number of cut-off devices/total number of connected devices.
  • the total disconnection times indicates: within the first period of time, all the devices connected to the target cell report the total number of disconnection network abnormalities.
  • the total number of cut-off devices means: within the first time period, among all the devices connected to the target cell, the number of devices that report the abnormality of the cut-off network.
  • the total number of connected devices represents: the number of all devices connected to the target cell within the first time period.
  • the identification information is unique, and can be regarded as one device.
  • the device accesses the target cell multiple times on the same day, it will only occupy a number of the number of access devices on that day; if it is on different days, the device accesses the target cell every day , respectively occupy a quantity of the number of connected devices per day. Or, if in the same day, the device reports the abnormality of the disconnected network many times, it will only occupy an amount of the number of disconnected times of the day; A number of break times per day.
  • the first time period may be within the past week.
  • the total number of access devices is used to represent: the total number of all devices accessing the target cell within a week, wherein, for a certain day, A device accesses the target cell multiple times, and the device occupies a number of the total number of access devices on the day. For two consecutive days, if one device accesses the target cell respectively, the device occupies two quantities of the total number of access devices in the two days.
  • the first time period may be within the past week.
  • the total number of outage devices is used to represent: the total number of all devices reporting abnormal network outages within one week, where, for a certain day, A device has repeatedly reported disconnected network abnormalities, and this device occupies a number of the total disconnected devices of the day. For two consecutive days, if a device reports an outage network abnormality respectively, this device occupies two numbers of the total number of outage devices in the two days.
  • the failure rate of the target cell and the ratio of disconnected users are respectively obtained, which can effectively avoid interference caused by personal equipment problems.
  • this embodiment does not directly use the quantity of a single item, but uses the ratio of the quantity of a single item to the total quantity, combined with the overall situation, to accurately evaluate the actual situation.
  • cell 2 is obviously better than cell 1.
  • Sub-step E2 According to the outage events in the target area where the target cell is located, obtain the total failure rate and the total outage user ratio in the target area.
  • a city is used as the target area to determine whether the target cell is an abnormal cell compared with the overall situation of the city.
  • formulas 1 and 2 are used to obtain the total failure rate and the total cut-off user ratio in the target area.
  • Sub-step E3 According to the failure rate and the total failure rate, obtain the relative growth rate of the failure rate of the target cell, and obtain the growth rate of the disconnection user ratio of the target cell according to the cut-off user ratio and the total cut-off user ratio.
  • the formulas involved in this step include:
  • Relative growth rate of failure rate (failure rate-total failure rate)/total failure rate.
  • the relative frequency of the abnormal situation of the disconnected network in the target cell is obtained, thereby providing a regional comparison method, so that the final judgment result obtained more practical.
  • the relative growth rate of the failure rate and the growth rate of the cut-off user ratio are added to obtain a result, which can be used to evaluate the probability of an abnormal network cut-off event after the electronic device accesses the target cell.
  • a method for obtaining the relative growth rate of the failure rate and the growth rate of the cut-off user ratio of the target cell is provided.
  • the number of outage events and the number of devices reporting outage events are used to evaluate the abnormal situation of the outage network, and the interference caused by personal device problems is eliminated;
  • the second aspect is combined with the connection
  • the total number of imported devices can be used to evaluate the abnormal situation of the cut-off network by using the ratio, avoiding the direct use of a certain parameter, which is not representative;
  • the third aspect is to combine the calculated absolute value results of the target community with the overall situation of the area, The relative value result is obtained, and the interference caused by the abnormality of the whole area is excluded.
  • the above method is also applicable to the acquisition of the relative growth rate of the failure rate and the growth rate of the cut-off user ratio of a certain time information (or a certain scene information).
  • the relative growth rate of failure rate and the ratio growth rate of outage users in the corresponding time information (such as early time period information) in the first time period can be obtained; as another example, the corresponding scene information (such as subway Scenario information) the relative growth rate of the failure rate and the growth rate of the cut-off user ratio.
  • Sub-step E4 According to the event that the network speed of the target cell is lower than the third threshold, obtain the low network speed access times and the total access times of the target cell.
  • Sub-step E5 Determine the ratio of the low network speed access times to the total access times as the low network speed proportion.
  • the formulas involved in this step include:
  • the number of times of low network speed access means in the first period of time, among all the devices connected to the target cell, the total number of times of reporting low-speed network abnormalities; the total number of times of access means: in the first time period The total number of times all devices access the target cell within the time period.
  • the number of low-speed network accesses is compared with the total number of accesses to obtain a result, which can be used to evaluate the probability of an abnormal low-speed network event occurring after the electronic device accesses the target cell.
  • the above method is also applicable to the acquisition of the low network speed ratio of a certain time information (or a certain scene information).
  • the low network speed ratio of corresponding time information (such as early time period information) in the first time period can be obtained; as another example, the low network speed ratio of corresponding scene information (such as subway scene information) in the first time period can be obtained Proportion.
  • a method for obtaining the low network speed ratio of the target cell is provided.
  • the proportion of low network speeds is obtained, and the low-speed network abnormalities are evaluated to avoid using a certain parameter directly. It is representative and can effectively and accurately determine whether the target cell is an abnormal cell where low network speed frequently occurs.
  • the first time period may be a time period within the last week, and the amount of data of a week can not only prevent the obtained results from being highly accidental, but also avoid processing too much historical data.
  • the abnormal cell identification method in another embodiment of the present application when it is determined based on the methods in the above embodiments that the target cell has a certain abnormal attribute at a certain time information (or a certain scene information), for further verification, you can Based on the method in the previous embodiment, it is determined whether the target cell has the abnormal attribute in the remaining time (or remaining scene) except the time information (or the scene information). If the target cell does not have the abnormal attribute in the remaining time (or the remaining scene) except the time information (or the scene information), it is further verified that the target cell has a certain time information (or a certain scene information) An abnormal property.
  • the remaining time except for this period refers to all the time except three hours in the early period of the day; All times except Saturday and Sunday are removed.
  • the information of the target cell has an obviously high abnormal phenomenon at the corresponding time, that is, it has the first abnormal attribute.
  • the proportion of low network speed corresponding to the remaining time is less than 35%, it is determined that the target cell has an obvious low network speed phenomenon at the corresponding time, that is, the second abnormal attribute.
  • the abnormal attributes possessed by it can also be characterized by time, that is, there is an obvious abnormal phenomenon under certain time information, and it is relatively normal at other times; And/or, when it is determined that the target cell has abnormal attributes, the abnormal attributes possessed by it can also be made to have spatial characteristics, that is, there are obvious abnormal phenomena in certain scene information, and relatively normal in other scenes.
  • a method for generating an abnormal cell list is provided, so that the electronic device can directly determine the attributes of the target cell from the abnormal cell list.
  • Table 1 is a combination of the first table, the second table and the third table.
  • the first table an overall result can be obtained for the network abnormality of each cell in a certain city within the first time period. That is, in this table, factors such as time and space are not considered, and the interference caused by the abnormal situation of the cell network is not considered.
  • the results corresponding to each time information can be obtained for the network abnormalities of different time information (such as early time information, etc.) of each cell in a certain city within the first time period.
  • the network abnormality of the community is more related to the flow of people, and the different flow of people can be reflected through the time dimension, so that the results corresponding to the two time information such as rest days and working days can be obtained separately, as well as the time information of morning, middle and evening.
  • the results corresponding to the three time information can be obtained for the network abnormalities of different time information (such as early time information, etc.) of each cell in a certain city within the first time period.
  • the network abnormality of the community is more related to the flow of people, and the different flow of people can be reflected through the time dimension, so that the results corresponding to the two time information such as rest days and working days can be obtained separately, as well as the time information of morning, middle and evening.
  • the results corresponding to each scene information can be obtained for the network abnormalities of different scene information (such as high-speed rail scene information, etc.) in each cell in a certain city within the first time period.
  • scene information such as high-speed rail scene information, etc.
  • network abnormalities in a community are more related to the scene where people live, and different scenes can be reflected through the spatial dimension, so that the corresponding results of the four scene information such as subway, high-speed rail, high-speed, and shopping malls can be obtained separately.
  • the merging process is exemplarily as follows: for one of the sub-districts, if it only appears in the first table, after merging, its corresponding time condition (hour), time condition, and space condition are all "No”; if it appears in The first table and the second table, or only appear in the second table, after the combination, at least one of the corresponding time conditions (hours) and/or time conditions (days) is "yes", and its corresponding All the spatial conditions are "No”; if it appears in the first table and the third table, or only appears in the third table, after the combination, at least one of the corresponding spatial conditions is "Yes", and its Both the corresponding time condition (hour) and time condition (day) are "No”; At least one of the conditions of (hour) and/time condition (day) is "yes", and at least one of the corresponding spatial conditions is "yes".
  • the server can generate an abnormal cell list, and then send the abnormal cell list to the user terminal equipment, so that when the user terminal equipment is about to access the target cell, it can identify the target in the abnormal cell list according to its current time and space characteristics. cell, and judge whether the time condition and space condition corresponding to the target cell are met, so as to further determine whether corresponding actions need to be performed.
  • An optional solution is: when judging whether the time condition and space condition corresponding to the target cell are satisfied, if at least one of the three categories of time condition (hour), time condition (day) and space condition is satisfied, it means Corresponding actions need to be performed to avoid abnormal cells in more realistic situations, or to avoid network abnormalities in abnormal cells.
  • Another option is: when judging whether the time condition and space condition corresponding to the target cell are satisfied, if the three categories of time condition (hour), time condition (day) and space condition are satisfied at the same time, it means Corresponding actions need to be performed to help users avoid abnormal cells in a more accurate reality, thereby reducing the probability of misexecution of actions.
  • the "early and late" sub-conditions of the time condition (hour) are both "yes", according to the current own time characteristics, if any sub-condition of "early and late” is satisfied, Then the time condition (hours) is considered to be met.
  • the relationship between the time condition (hour), time condition (day) and space condition may be an "AND” relationship, and each internal relationship may be an "or” relationship.
  • the condition that the first cell needs to meet is [early or late]; the condition that the second cell needs to meet is [(morning or evening) and high-speed rail]; the conditions to be met in the third community are [weekdays and subway].
  • each cell is distinguished by a cell identifier.
  • the cell identification includes Public Land Mobile Network (PLMN for short), Radio Access Type (Radio Access Type, RAT for short), Tracking Area Code (Tracking Area Code, TAC), cell ID (CELLID), physical Cell identification (Physical Cell Identifier, referred to as PCI) and other information.
  • PLMN Public Land Mobile Network
  • Radio Access Type Radio Access Type, RAT for short
  • Tracking Area Code Tracking Area Code
  • TAC Tracking Area Code
  • CELLID cell ID
  • Physical Cell identification Physical Cell Identifier, referred to as PCI
  • the abnormal cell Carry out more detailed analysis of abnormal network phenomena, and at the same time dig out more abnormal cells, so that a fine and comprehensive list of abnormal cells can be generated, so that electronic devices can reasonably perform corresponding actions based on the list of abnormal cells, so as to Avoid abnormal cells, or avoid network abnormalities in abnormal cells.
  • a new table can also be added, which can screen out the corresponding abnormal cells based on the device model, and further, merge the table into Table 1 to Carry out a finer network abnormality analysis on the abnormal cells that have been listed; at the same time, the coverage of abnormal cells can be further increased.
  • the cells in the list of anchor cells of major operators can be obtained in a targeted manner to obtain the proportion of low network speed in each cell, so as to ensure that the subsequent settings on the electronic device are dual Validity of the connection mode.
  • the network anomaly in the electronic device is sometimes not caused by the signal or the device, or it may be that the cell itself connected to the electronic device has a problem. Therefore, some simple strategies can be used to improve the user's network experience, such as placing a set of generalized rules locally, identifying some abnormal communities through the historical big data in the background, and sending them to electronic devices through the server, so as to ensure that the electronic devices Before network abnormalities occur, access to other cells to avoid them.
  • this application not only considers the abnormality of the disconnected network, but also considers the abnormality of the low-speed network, so as to increase the coverage of abnormal cells; the second aspect, for different types of network abnormalities, Different measures have been taken to effectively evade; the third aspect, taking into account the influence of time and space dimensions, can more accurately avoid abnormal cells.
  • the abnormal cell identification method provided in the embodiment of the present application may be executed by an abnormal cell identification device, or a control module in the abnormal cell identification device for executing the abnormal cell identification method.
  • the method for identifying an abnormal cell performed by the abnormal cell identifying device is taken as an example to illustrate the abnormal cell identifying device provided in the embodiment of the present application.
  • FIG. 2 shows a block diagram of an abnormal cell identification device according to another embodiment of the present application, which device includes:
  • the obtaining module 10 is used to obtain the relative growth rate of failure rate, the relative growth rate of cut-off user ratio, and the proportion of low network speed of the target cell; wherein, the relative growth rate of failure rate, the relative growth rate of cut-off user ratio, and the proportion of low network speed are The ratio is associated with: at least one of time information and scene information;
  • the first determining module 20 is used to determine that the target cell has a first abnormal attribute when the sum of the relative growth rate of the failure rate of the target cell and the relative growth rate of the cut-off user ratio is greater than a first threshold;
  • the second determining module 30 is configured to determine that the target cell has a second abnormal attribute when the low network speed ratio of the target cell is greater than a second threshold.
  • a large number of user terminal equipments can obtain the relative growth rate of the failure rate of the target cell, the relative growth rate of the disconnection user ratio, and the low network speed of the target cell. Proportion. Among them, the sum of the relative growth rate of the failure rate and the relative growth rate of the outage user ratio is used to indicate the proportion information of the abnormal situation of the network interruption in the target cell, and the proportion of low network speed is used to indicate the abnormality of the low-speed network in the target cell percentage information.
  • the obtained two proportion information are compared with the corresponding first threshold and the second threshold respectively, correspondingly, when one of the proportion information is greater than the first threshold, it is considered that the target cell is more prone to disconnection network In abnormal cases, it is determined that the target cell has the first abnormal attribute; when the other proportion information is greater than the second threshold, it is considered that the target cell is more prone to low-rate network abnormalities, and the target cell is determined to have the second abnormal attribute. It can be seen that based on the abnormal cell identification method of the present application, at least two types of abnormal cells with the first abnormal attribute and the second abnormal attribute can be identified more precisely, compared with the prior art that can only generally identify one abnormal cell Cells, greatly increasing the coverage area of abnormal cells.
  • the device also includes:
  • the first execution module is configured to prohibit access to the target cell when the electronic device is detected to access the target cell.
  • the first execution module includes:
  • the first obtaining unit is used to obtain the target time information where the electronic device is located during the process of detecting that the electronic device accesses the target cell;
  • the first matching unit is configured to prohibit access to the target cell when the target time information matches the time information associated with the first abnormal attribute
  • the target time information includes any one of early time period information, middle time period information and late time period information; and/or, any one of weekday information and rest day information;
  • the second acquisition unit is configured to acquire target scene information where the electronic device is located during the process of detecting that the electronic device accesses the target cell;
  • the second matching unit is configured to prohibit access to the target cell when the target scene information matches the scene information associated with the first abnormal attribute
  • the target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the device also includes:
  • the second execution module is configured to access the target cell when the electronic device is detected to be connected to the target cell, and set the connection mode of the electronic device to a dual connection mode.
  • the second execution module includes:
  • the third obtaining unit is used to obtain the target time information where the electronic device is located during the process of detecting that the electronic device accesses the target cell;
  • the third matching unit is configured to access the target cell when the target time information matches the time information associated with the second abnormal attribute, and set the connection mode of the electronic device to a dual connection mode;
  • the target time information includes any one of early time period information, middle time period information and late time period information; and/or, any one of weekday information and rest day information;
  • the fourth obtaining unit is used to obtain the target scene information where the electronic device is located during the process of detecting that the electronic device accesses the target cell;
  • the fourth matching unit is used to access the target cell when the target scene information matches the scene information associated with the second abnormal attribute, and set the connection mode of the electronic device to a dual connection mode;
  • the target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the acquisition module 10 includes:
  • the fifth obtaining unit is used to obtain the failure rate and the cut-off user ratio of the target cell according to the cut-off event of the target cell;
  • the sixth acquisition unit is used to obtain the total failure rate and the total disconnection user ratio in the target area according to the outage event in the target area where the target cell is located;
  • the seventh obtaining unit is used to obtain the relative growth rate of the failure rate of the target cell according to the failure rate and the total failure rate, and obtain the growth rate of the cut-off user ratio of the target cell according to the cut-off user ratio and the total cut-off user ratio;
  • the eighth obtaining unit is used to obtain the low network speed access times and the total access times of the target cell according to the event that the network speed of the target cell is lower than the third threshold;
  • the proportion determination unit is configured to determine the ratio of the number of low network speed accesses to the total number of accesses as the proportion of low network speeds.
  • the device for identifying an abnormal cell in the embodiment of the present application may be a device, or a component, an integrated circuit, or a chip in a terminal.
  • the device may be a mobile electronic device or a non-mobile electronic device.
  • the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant).
  • non-mobile electronic devices can be servers, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (television, TV), teller machine or self-service machine, etc., this application Examples are not specifically limited.
  • Network Attached Storage NAS
  • personal computer personal computer, PC
  • television television
  • teller machine or self-service machine etc.
  • the device for identifying an abnormal cell in the embodiment of the present application may be a device with an action system.
  • the action system may be an Android (Android) action system, an ios action system, or other possible action systems, which are not specifically limited in this embodiment of the present application.
  • the device for identifying an abnormal cell provided in the embodiment of the present application can realize each process implemented in the above method embodiment, and to avoid repetition, details are not repeated here.
  • the embodiment of the present application further provides an electronic device 100, including a processor 101, a memory 102, and programs or instructions stored in the memory 102 and operable on the processor 101,
  • an electronic device 100 including a processor 101, a memory 102, and programs or instructions stored in the memory 102 and operable on the processor 101,
  • the program or instruction is executed by the processor 101, each process of any one of the abnormal cell identification method embodiments described above can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.
  • FIG. 4 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.
  • the electronic device 1000 includes, but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010, etc. part.
  • the electronic device 1000 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 1010 through the power management system, so that the management of charging, discharging, and function can be realized through the power management system. Consumption management and other functions.
  • a power supply such as a battery
  • the structure of the electronic device shown in FIG. 4 does not constitute a limitation to the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here. .
  • the processor 1010 is configured to obtain the relative growth rate of the failure rate, the relative growth rate of the disconnection user ratio, and the proportion of low network speed of the target cell; wherein, the relative growth rate of the failure rate and the relative growth rate of the disconnection user ratio are rate and the proportion of the low network speed are all associated with: at least one of time information and scene information; the sum of the relative growth rate of the failure rate and the relative growth rate of the cut-off user ratio in the target cell is greater than the first threshold In this case, it is determined that the target cell has a first abnormal attribute; when the low network speed ratio of the target cell is greater than a second threshold, it is determined that the target cell has a second abnormal attribute.
  • a large number of user terminal equipments can obtain the relative growth rate of the failure rate of the target cell, the relative growth rate of the disconnection user ratio, and the low network speed of the target cell. Proportion. Among them, the sum of the relative growth rate of the failure rate and the relative growth rate of the outage user ratio is used to indicate the proportion information of the abnormal situation of the network interruption in the target cell, and the proportion of low network speed is used to indicate the abnormality of the low-speed network in the target cell percentage information.
  • the obtained two proportion information are compared with the corresponding first threshold and the second threshold respectively, correspondingly, when one of the proportion information is greater than the first threshold, it is considered that the target cell is more prone to disconnection network In abnormal cases, it is determined that the target cell has the first abnormal attribute; when the other proportion information is greater than the second threshold, it is considered that the target cell is more prone to low-rate network abnormalities, and the target cell is determined to have the second abnormal attribute. It can be seen that based on the abnormal cell identification method of the present application, at least two types of abnormal cells with the first abnormal attribute and the second abnormal attribute can be identified more precisely, compared with the prior art that can only generally identify one abnormal cell Cells, greatly increasing the coverage area of abnormal cells.
  • the processor 1010 is further configured to, in a process of detecting that the electronic device accesses the target cell, prohibit access to the target cell.
  • the processor 1010 is further configured to acquire target time information where the electronic device is located during the process of detecting that the electronic device accesses the target cell; when the target time information matches the first In the case of time information associated with abnormal attributes, access to the target cell is prohibited; wherein, the target time information includes any one of early time period information, middle time period information and late time period information; and/or weekday information and rest day information; or/and, in the process of detecting that the electronic device accesses the target cell, acquire the target scene information where the electronic device is located; when the target scene information matches the target cell In the case of scene information associated with the first abnormal attribute, access to the target cell is prohibited; wherein, the target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information, and shopping mall scene information.
  • the processor 1010 is further configured to, in a process of detecting that the electronic device accesses the target cell, access the target cell, and set the connection mode of the electronic device to a dual connection mode.
  • the processor 1010 is further configured to acquire target time information where the electronic device is located during the process of detecting that the electronic device accesses the target cell; when the target time information matches the second In the case of time information associated with abnormal attributes, access the target cell, and set the connection mode of the electronic device to dual connection mode; wherein, the target time information includes early time period information, middle time period information and late time period information and/or, any one of weekday information and rest day information; or/and, in the process of detecting that the electronic device accesses the target cell, acquire the location where the electronic device is located Target scene information; when the target scene information matches the scene information associated with the second abnormal attribute, access the target cell, and set the connection mode of the electronic device to a dual connection mode; wherein, the The target scene information includes any one of high-speed rail scene information, subway scene information, high-speed scene information and shopping mall scene information.
  • the processor 1010 is further configured to obtain the failure rate and user ratio of the target cell according to the disconnection event of the target cell; , to obtain the total failure rate and the total cut-off user ratio of the target area; according to the failure rate and the total failure rate, obtain the relative growth rate of the failure rate of the target cell, and according to the cut-off user ratio Compared with the total cut-off users, the growth rate of the cut-off users ratio of the target cell is obtained; according to the event that the network speed of the target cell is lower than the third threshold, the number of low network speed access times of the target cell is obtained and the total access times; determining the ratio of the low network speed access times to the total access times as the low network speed proportion.
  • this application not only considers the abnormality of the disconnected network, but also considers the abnormality of the low-speed network, so as to increase the coverage of abnormal cells; the second aspect, for different types of network abnormalities, Different measures have been taken to effectively evade; the third aspect, taking into account the influence of time and space dimensions, can more accurately avoid abnormal cells.
  • the input unit 1004 may include a graphics processor (Graphics Processing Unit, GPU) 10041 and a microphone 10042, and the graphics processor 10041 is used by the image capture device in the video image capture mode or the image capture mode (such as a camera) to process the image data of still pictures or video images.
  • the display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 1007 includes a touch panel 10071 and other input devices 10072 .
  • the touch panel 10071 is also called a touch screen.
  • the touch panel 10071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 10072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and action sticks, which will not be repeated here.
  • the memory 1009 can be used to store software programs as well as various data, including but not limited to application programs and motion systems.
  • the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes motion systems, user interfaces, and application programs, and the modem processor mainly processes wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 1010 .
  • the embodiment of the present application also provides a readable storage medium, the readable storage medium stores a program or an instruction, and when the program or instruction is executed by the processor, the various processes of the above-mentioned embodiment of the abnormal cell identification method are realized, and can achieve The same technical effects are not repeated here to avoid repetition.
  • the processor is the processor in the electronic device described in the above embodiments.
  • the readable storage medium includes computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
  • the embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above-mentioned embodiment of the abnormal cell identification method Each process, and can achieve the same technical effect, in order to avoid repetition, will not repeat them here.
  • chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.

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Abstract

本申请公开了一种异常小区识别方法、装置和电子设备，属于通信技术领域。其中，所述异常小区识别方法包括：获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。

Description

异常小区识别方法、装置和电子设备
相关申请的交叉引用
本申请要求在2021年06月25日提交中国专利局、申请号为202110713676.5、名称为“异常小区识别方法、装置和电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于通信技术领域，具体涉及一种异常小区识别方法、装置和电子设备。
背景技术
目前，人们在生活和工作中频繁使用电子设备，例如，使用电子设备通话、上网等。通常，用户在使用电子设备中的过程中，需要网络支持。而不同基站设备厂家对通信协议的理解存在差异、基站配置也存在差异、不同小区的设备配置也存在差异、不同小区接入的设备数量也存在差异，等等，从而可能出现一些网络异常的小区，当电子设备接入这些小区时，导致用户无法正常使用电子设备。
为了确保用户正常使用电子设备，可在电子设备接入小区之前，对小区进行识别，从而可以优先接入网络正常的小区、规避网络异常的小区。在现有技术中，基于用户在使用电子设备过程中，上报的无法连接网络的异常情况，将一些小区作为异常小区。
可见，基于现有技术中的异常小区的识别方法，覆盖的异常小区较少。
发明内容
本申请实施例的目的是提供一种异常小区识别方法，能够解决基于现有技术中的异常小区的识别方法，覆盖的异常小区较少的问题。
第一方面，本申请实施例提供了一种异常小区识别方法，该方法包括：获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；在所 述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。
第二方面，本申请实施例提供了一种异常小区识别装置，该装置包括：获取模块，用于获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；第一确定模块，用于在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；第二确定模块，用于在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。
第三方面，本申请实施例提供了一种电子设备，该电子设备包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤。
第四方面，本申请实施例提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤。
第五方面，本申请实施例提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的方法。
第六方面，本申请实施例提供了一种计算机程序产品，所述程序产品被存储在非易失的存储介质中，所述程序产品被至少一个处理器执行以实现如第一方面所述的方法。
第七方面，本申请实施例提供了一种通信设备，所述通信设备被配置成用于执行如第一方面所述的方法。
这样，在本申请的实施例中，基于历史数据中，大量用户终端设备针对目标小区上报的网络异常情况，可以获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。其中，失效率相对增长率和断流用户比相对增长率的总和用于表示目标小区出现断流网络异常情况的占比信息，低网速占比用于表示目标小区出现低速率网络异常情况的占比信息。进一步地，将得到的两个占比信息，分别与 对应的第一阈值和第二阈值进行比较，对应地，当其中一个占比信息大于第一阈值时，认为目标小区较易出现断流网络异常情况，确定目标小区具有第一异常属性；当另外一个占比信息大于第二阈值时，认为目标小区较易出现低速率网络异常情况，确定目标小区具有第二异常属性。可见，基于本申请的异常小区识别方法，至少可以更精细地识别出具有第一异常属性和第二异常属性这两种异常小区，相比于现有技术中仅能笼统地识别出一种异常小区，大大增加了异常小区的覆盖面积。
附图说明
图1是本申请实施例的异常小区识别方法的流程图；
图2是本申请实施例的异常小区识别装置的框图；
图3是本申请实施例的电子设备的硬件结构示意图之一；
图4是本申请实施例的电子设备的硬件结构示意图之二。
具体实施例
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”等所区分的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”，一般表示前后关联对象是一种“或”的关系。
下面结合附图，通过具体的实施例及其应用场景对本申请实施例提供的异常小区识别方法进行详细地说明。
参见图1，示出了本申请一个实施例的异常小区识别方法的流程图，该方法应用于电子设备，包括：
步骤S1：获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。
在该步骤中，失效率相对增长率和断流用户比相对增长率相结合，用于表示目标小区出现第一类网络异常情况的占比信息。
在本申请中，第一类网络异常情况可以是断流网络异常情况。
低网速占比用于表示目标小区出现第二类网络异常情况的占比信息。
在本申请中，第二类网络异常情况可以是低速率网络异常情况。
其中，失效率相对增长率、断流用户比相对增长率、低网速占比均关联于：时间信息和场景信息中的至少一种。
一方面，可基于预设的时间信息，获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。
例如，预设的时间信息为一周，可获取近一周目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。
另一方面，可基于预设的场景信息，获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。
例如，预设的场景信息为地铁场景，可获取在地铁场景下，目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。
步骤S2：在目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定目标小区具有第一异常属性。
在该步骤中，以相加的方式，将目标小区的失效率相对增长率和断流用户比相对增长率进行结合，以用于表示目标小区出现第一类网络异常情况的占比信息。
对应地，在该步骤之前，将得到的目标小区的失效率相对增长率和断流用户比相对增长率的总和，与预设的占比信息(即第一阈值)，进行比较，若目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值，则确定目标小区具有第一异常属性。也就是说，目标小区为频繁出现断流网络异常情况的小区，本申请定义为“高异常小区”。
可选地，第一阈值为30％。
可选地，实际根据需求来调整第一阈值的取值，以限定对“高异常小区”的定义的严格程度。
步骤S3：在目标小区的低网速占比大于第二阈值的情况下，确定目标小区具有第二异常属性。
在该步骤之前，将得到的目标小区的低网速占比，与预设的占比信息(即第二阈值)，进行比较，若目标小区的低网速占比大于第二阈值，则确定目标小区具有第二异常属性。也就是说，目标小区为频繁出现低速率网络异常情况的小区，本申请定义为“低网速小区”。
可选地，第二阈值为50％。
可选地，实际根据需求来调整第二阈值的取值，以限定对“低网速小区”的定义的严格程度。
其中，可设定第三阈值，以定义低速率网络异常情况。即，当网络速率低于第三阈值时，认为是低速率网络异常情况。
可参考地，第三阈值为300千字节/每秒(KB/S)。
可见，在本申请中，至少可以识别出具有第一异常属性的异常小区和具有第二异常属性的异常小区。其中，对于具有第一异常属性的异常小区而言，当出现断流网络异常情况时，电子设备无法连接网络，使得用户无法正常上网；对于具有第二异常属性的异常小区而言，当出现低速率网络异常情况时，电子设备可以连接网络，但网速极低，使得用户无法正常上网。
其中，对于目标小区而言，可以具有第一异常属性和第二异常属性中的至少一种。
可选地，本实施例中的电子设备可以是用户终端设备。
对应地，在步骤S1之前，服务器可以获取由大量用户终端设备上报的网络异常情况组成的历史众包数据，用户终端设备可从服务器中获取历史众包数据，再经过相关训练，得到目标小区具有第一异常属性和第二异常属性中的至少一种。
可选地，本实施例中的电子设备还可以是服务器。
对应地，在步骤S1之前，服务器可以获取由大量用户终端设备上报的网络异常情况组成的历史众包数据，再经过相关训练，可以得到目标小区具有第一异常属性和第二异常属性中的至少一种。
其中，为了确保对目标小区的准确评估，本实施例中的目标小区需满足：一天内接入的电子设备的数量大于或者等于5。
这样，在本申请的实施例中，基于历史数据中，大量用户终端设备针对目标小区上报的网络异常情况，可以获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。其中，失效率相对 增长率和断流用户比相对增长率的总和用于表示目标小区出现断流网络异常情况的占比信息，低网速占比用于表示目标小区出现低速率网络异常情况的占比信息。进一步地，将得到的两个占比信息，分别与对应的第一阈值和第二阈值进行比较，对应地，当其中一个占比信息大于第一阈值时，认为目标小区较易出现断流网络异常情况，确定目标小区具有第一异常属性；当另外一个占比信息大于第二阈值时，认为目标小区较易出现低速率网络异常情况，确定目标小区具有第二异常属性。可见，基于本申请的异常小区识别方法，至少可以更精细地识别出具有第一异常属性和第二异常属性这两种异常小区，相比于现有技术中仅能笼统地识别出一种异常小区，大大增加了异常小区的覆盖面积。
在本申请另一个实施例的异常小区识别方法的流程中，在步骤S2之后，方法还包括：
步骤A1：在检测到电子设备接入目标小区的过程中，禁止接入目标小区。
在该步骤中，在检测到电子设备接入目标小区的过程中，若目标小区具有第一异常属性，电子设备可自动设置为：禁用目标小区。
在本实施例中，目标小区频繁出现断流网络异常情况，因现断流网络异常情况本身不易规避，因此，可以采用禁用目标小区的方法，来规避异常小区，以确保电子设备在接入小区后，不会频繁出现断流网络异常情况，从而确保用户可以正常上网。
在本申请另一个实施例的异常小区识别方法的流程中，步骤A1，包括：
子步骤B1：在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标时间信息。
子步骤B2：在目标时间信息匹配于第一异常属性关联的时间信息的情况下，禁止接入目标小区。
其中，目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种。
通常，对于一个小区而言，在一天当中，不同的时段内，对于接入该小区的设备来说，上报网络异常情况的频率不一样，上报网络异常情况的类型也不一样。
比如，对于一个小区而言，早晚高峰期接入的设备数量较多，中午时段接入的设备数量较少，因此，在早晚时段内，上报网络异常情况的频率明显高于，中午时段内，上报网络异常情况的频率。
因此，在步骤S1中，可基于某一时间信息，获取目标小区的失效率相对增长率、断流用户比相对增长率。
其中，某一时间信息可以是早时段信息、中时段信息和晚时段信息中的任一种。
例如，可划分出一天当中的7:00-10:00(早时段信息)、11:00-14:00(中时段信息)、17:00-20:00(晚时段信息)，以分别获取目标小区在这三个时间信息的失效率相对增长率、断流用户比相对增长率。
另外，某一时间信息还可以是工作日信息和休息日信息中的任一种。
又如，可划分出一周当中的周一-周五(工作日信息)、周六-周日(休息日信息)，以分别获取目标小区在这两个时间信息的失效率相对增长率、断流用户比相对增长率。
从而，在本实施例中，获取电子设备所处的目标时间信息后，若目标时间信息与获取的失效率相对增长率、断流用户比相对增长率关联的时间信息是匹配的，则认为电子设备刚好处于目标小区易出现断流网络异常情况的时间内，从而直接禁用目标小区。
其中，目标时间信息为假设的接入目标小区后对应的时间。考虑到当前时间与即将接入的时间相差极短，因此，该步骤中的目标时间信息，可以理解为当前的时间信息。
具体地，可根据当前所处的具体时间点，确定其所处的目标时间信息。
举例说明，当前的具体时间点为：7:30，确定为早时段信息。基于获取的目标小区在早时段信息的失效率相对增长率、断流用户比相对增长率，可以确定目标小区在早时段信息具有第一异常属性，从而在电子设备上禁止接入目标小区。
在本实施例中，从时间维度考虑，预先划分了多个时间信息，以更精细地对目标小区中出现的断流网络异常情况进行分析，以分别确定目标小区在不同时间信息是否具有第一异常属性。从而，电子设备可根据当前所处的目标时间信息，去匹配目标小区具有第一异常属性 的情况下所关联的时间信息，从而使得电子设备可根据目标小区在不同时间信息的不同网络情况，采取相应的措施，进而更灵活地规避异常小区。
另外，基于本实施例，还可以提高对目标小区是否具有第一异常属性的判断准确率。例如，目标小区一天下来，整体出现断流网络异常情况的频率并不高，但某些时间出现断流网络异常情况的频率较高，基于本实施例，并不会针对整体情况，直接将目标小区确定为不具有第一异常属性。
或/和，
子步骤B3：在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标场景信息。
子步骤B4：在目标场景信息匹配于第一异常属性关联的场景信息的情况下，禁止接入目标小区。
其中，目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
通常，对于一个小区而言，在不同场景中，对于接入该小区的设备来说，上报网络异常情况的频率不一样，上报网络异常情况的类型也不一样。
比如，对于一个小区而言，电子设备在高铁、地铁、商场、高速等场景中接入，相比于电子设备在其它场景中接入，前者上报网络异常情况的频率明显高于后者上报网络异常情况的频率。
因此，在步骤S1中，可基于某一场景信息，获取目标小区的失效率相对增长率、断流用户比相对增长率。
其中，某一场景信息可以是高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
例如，分别获取目标小区在这四个场景信息的失效率相对增长率、断流用户比相对增长率。
从而，在本实施例中，获取电子设备所处的目标场景信息后，若目标场景信息与获取的失效率相对增长率、断流用户比相对增长率关联的场景信息是匹配的，则认为电子设备刚好处于目标小区易出现断流网络异常情况的场景中，从而直接禁用目标小区。
其中，目标场景信息为假设的接入目标小区后对应的场景信息。 考虑到当前时间与即将接入的时间相差极短，因此，该步骤中的目标场景信息，可以理解为当前的场景信息。
具体地，可根据当前所处的具体位置，确定其所处的目标场景信息。
举例说明，当前的具体位置为：XX高速公路上，确定为高速场景信息。基于获取的目标小区在高速场景信息的失效率相对增长率、断流用户比相对增长率，可以确定目标小区在高速场景信息具有第一异常属性，从而在电子设备上禁用目标小区。
在本实施例中，从空间维度考虑，预先划分了多个场景信息，以更精细地对目标小区中出现的断流网络异常情况进行分析，可以分别确定目标小区在不同场景信息是否具有第一异常属性。从而，电子设备可根据当前所处的目标场景信息，去匹配目标小区具有第一异常属性的情况下所关联的场景信息，进而使得电子设备可根据目标小区在不同场景中的不同网络情况，采取相应的措施，进而更灵活地规避异常小区。
另外，基于本实施例，还可以提高对目标小区是否具有第一异常属性的判断准确率。例如，目标小区在各个场景中，综合下来出现断流网络异常情况的频率并不高，但某些场景出现断流网络异常情况的频率较高，基于本实施例，并不会针对整体情况，直接将目标小区确定为不具有第一异常属性。
需要说明的是，本实施例中，获取失效率相对增长率、断流用户比相对增长率的情况下，可仅针对某一时间信息或者某一场景信息，还可针对某一时间信息和某一场景信息。
在本申请另一个实施例的异常小区识别方法的流程中，在步骤S3之后，方法还包括：
步骤C1：在检测到电子设备接入目标小区的过程中，接入目标小区，并设置电子设备的连接模式为双连接模式。
在该步骤中，在检测到电子设备接入目标小区的过程中，若目标小区具有第二异常属性，电子设备可自动设置为：双连接模式。
在本实施例中，目标小区为5G移动网络的非独立组网模式(New Radio 5G Non-standalone，简称NR5G-NSA)，对应地，设置电子设备的连接模式为双连接模式为：在电子设备中开启E-UTRAN新无线电— 双连接(E-UTRAN New Radio–Dual Connectivity，简称EN-DC)模式。
本实施例中，针对其中一种低速率网络异常情况，提供了相应的采取措施。
目前，部分小区配置的是5G移动网络的独立组网模式(New Radio5G standalone，简称NR5G-SA)设备，也有部分小区配置的是NR5G-NSA设备。对于后者，一些4G电子设备接入后，在一同接入的5G电子设备的影响下，4G电子设备中就会出现低速率网络异常情况，从而会上报低速率网络异常情况。
其中，NR5G-NSA小区为各大运营商的锚点小区。因此，可在目标小区为锚点小区的情况下，确定目标小区是否具有第二异常属性，并在目标小区具有第二异常属性的情况下，接入目标小区，并开启EN-DC，以允许设备在相同的频段上同时接入长期演进(Long Term Evaluation，简称LTE，具体指4G移动网络)和5G。
在本实施例中，对于频繁出现低速率网络异常情况的目标小区，针对目标小区所具备的一些特征，可以接入目标小区，同时，在电子设备中开启双连接模式，以在电子设备端提高网速，从而确保用户可以正常使用电子设备。
在本申请另一个实施例的异常小区识别方法的流程中，步骤C1，包括：
子步骤D1：在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标时间信息。
子步骤D2：在目标时间信息匹配于第二异常属性关联的时间信息的情况下，接入目标小区，并设置电子设备的连接模式为双连接模式。
其中，目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种。
通常，对于一个小区而言，在一天当中，不同的时段内，对于接入该小区的设备来说，上报网络异常情况的频率不一样，上报网络异常情况的类型也不一样。
比如，对于一个小区而言，早晚高峰期接入的设备数量较多，中午时段接入的设备数量较少，因此，在早晚时段内，上报网络异常情况的频率明显高于，中午时段内，上报网络异常情况的频率。
因此，在步骤S1中，可基于某一时间信息，获取目标小区的低网 速占比。
其中，某一时间信息可以是早时段信息、中时段信息和晚时段信息中的任一种。
例如，可划分出一天当中的7:00-10:00(早时段信息)、11:00-14:00(中时段信息)、17:00-20:00(晚时段信息)，以分别获取目标小区在这三个时间信息的低网速占比。
另外，某一时间信息还可以是工作日信息和休息日信息中的任一种。
又如，可划分出一周当中的周一-周五(工作日信息)、周六-周日(休息日信息)，以分别获取目标小区在这两个时间信息的低网速占比。
从而，在本实施例中，获取电子设备所处的目标时间信息后，若目标时间信息与获取的低网速占比关联的时间信息是匹配的，则认为电子设备刚好处于目标小区易出现低速率网络异常情况的时间内，从而接入目标小区，同时在电子设备上设置双连接模式。
其中，目标时间信息为假设的接入目标小区后对应的时间。考虑到当前时间与即将接入的时间相差极短，因此，该步骤中的目标时间信息，可以理解为当前的时间信息。
具体地，可根据当前所处的具体时间点，确定其所处的目标时间信息。
举例说明，当前的具体时间点为：7:30，确定为早时段信息。基于获取的目标小区在早时段信息的低网速占比，可以确定目标小区在早时段信息具有第二异常属性，从而接入目标小区，同时在电子设备上设置双连接模式。
在本实施例中，从时间维度考虑，预先划分了多个时间信息，以更精细地对目标小区中出现的低速率网络异常情况进行分析，以分别确定目标小区在不同时间信息是否具有第二异常属性。从而，电子设备可根据当前所处的目标时间信息，去匹配目标小区具有第二异常属性的情况下所关联的时间信息，从而使得电子设备可根据目标小区在不同时间信息的不同网络情况，采取相应的措施，进而更灵活地规避异常小区。
另外，基于本实施例，还可以提高对目标小区是否具有第二异常 属性的判断准确率。例如，目标小区一天下来，整体出现低速率网络异常情况的频率并不高，但某些时间出现低速率网络异常情况的频率较高，基于本实施例，并不会针对整体情况，直接将目标小区确定为不具有第二异常属性。
或/和，
子步骤D3：在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标场景信息。
子步骤D4：在目标场景信息匹配于第二异常属性关联的场景信息的情况下，接入目标小区，并设置电子设备的连接模式为双连接模式。
其中，目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
通常，对于一个小区而言，在不同场景中，对于接入该小区的设备来说，上报网络异常情况的频率不一样，上报网络异常情况的类型也不一样。
比如，对于一个小区而言，电子设备在高铁、地铁、商场、高速等场景中接入，相比于电子设备在其它场景中接入，前者上报网络异常情况的频率明显高于后者上报网络异常情况的频率。
因此，在步骤S1中，可基于某一场景信息，获取目标小区的低网速占比。
其中，某一场景信息可以是高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
例如，分别获取目标小区在这四个场景信息的低网速占比。
从而，在本实施例中，获取电子设备所处的目标场景信息后，若目标场景信息与获取的低网速占比关联的场景信息是匹配的，则认为电子设备刚好处于目标小区易出现低速率网络异常情况的场景中，从而接入目标小区，并设置电子设备的连接模式为双连接模式。
其中，目标场景信息为假设的接入目标小区后对应的场景信息。考虑到当前时间与即将接入的时间相差极短，因此，该步骤中的目标场景信息，可以理解为当前的场景信息。
具体地，可根据当前所处的具体位置，确定其所处的目标场景信息。
举例说明，当前的具体位置为：XX高速公路上，确定为高速场景 信息。基于获取的目标小区在高速场景信息的低网速占比，可以确定目标小区在高速场景信息具有第二异常属性，从而接入目标小区，并设置电子设备的连接模式为双连接模式。
在本实施例中，从空间维度考虑，预先划分了多个场景信息，以更精细地对目标小区中出现的低速率网络异常情况进行分析，可以分别确定目标小区在不同场景信息是否具有第二异常属性。从而，电子设备可根据当前所处的目标场景信息，去匹配目标小区具有第二异常属性的情况下所关联的场景信息，进而使得电子设备可根据目标小区在不同场景中的不同网络情况，采取相应的措施，进而更灵活地规避异常小区。
另外，基于本实施例，还可以提高对目标小区是否具有第二异常属性的判断准确率。例如，目标小区在各个场景中，综合下来出现低速率网络异常情况的频率并不高，但某些场景出现低速率网络异常情况的频率较高，基于本实施例，并不会针对整体情况，直接将目标小区确定为不具有第二异常属性。
需要说明的是，本实施例中，获取低网速占比的情况下，可仅针对某一时间信息或者某一场景信息，还可针对某一时间信息和某一场景信息。
基于以上实施例，第一方面，本申请可以针对更多的网络异常情况，去识别异常小区，以增加异常小区的覆盖面；第二方面，本申请可以针对不同的网络异常情况，采取不同的措施，以规避异常小区，或者规避异常小区中的网络异常情况，以更灵活地改善用户上网体验；第三方面，本申请可以选择性地在某些时间、某些场景中，对异常小区进行规避，避免盲目规避。
其中，对目标小区的评估，会出现三种情况：第一种情况，目标小区仅具有第一异常属性，对应地措施为：直接禁用；第二种情况，目标小区仅具有第二异常属性，对应地措施为：接入，同时在电子设备上设置为双连接模式；第三种情况，目标小区同时具有第一异常属性和第二异常属性，优先采取禁用的措施，以优先考虑断流网络异常情况。
需要说明的是，电子设备多次接入目标小区时，所处的时间信息、场景信息可能不同，因此，以上实施例都是针对单次接入而言的，到 下次接入时，可重新确定是否需要采取相应的措施，以规避异常小区，或者规避异常小区中的网络异常情况。
另外，在以上实施例中，在检测到电子设备接入目标小区的过程中，即在接入目标小区之前，基于目标小区所具有的异常属性，及时采取相应措施。而在更多的实施例中，还可以在检测到电子设备刚刚接入目标小区之后、以及进行网络数据传输之前，基于目标小区所具有的异常属性，及时采取相应措施。以上，无论是哪个实施例，均能够使得电子设备在进行网络数据传输之前，及时接入理想的小区，提升用户上网体验。
可选地，以上针对在检测到电子设备接入目标小区的过程中，各个实施例所实现的各个步骤，默认为执行主体为用户终端设备。
在本申请另一个实施例的异常小区识别方法的流程中，步骤S1，包括：
子步骤E1：根据目标小区的断流事件，获取目标小区的失效率和断流用户比。
该步骤中所涉及的公式包括：
公式一：失效率＝总断流次数/总接入设备数。
公式二：断流用户比＝总断流设备数/总接入设备数。
其中，在公式一中，总断流次数表示：在第一时间段内，所有接入目标小区的设备中，上报断流网络异常情况的总次数。
在公式二中，总断流设备数表示：在第一时间段内，所有接入目标小区的设备中，上报断流网络异常情况的设备数量。
在公式一和公式二中，总接入设备数表示：在第一时间段内，所有接入目标小区的设备数量。
其中，对于同一设备，识别信息是唯一的，可认为是一个设备。
需要说明的是，对于同一设备，若在同一天里，该设备多次接入目标小区，则仅占用当天接入设备数的一个数量；若在不同天里，该设备每天均接入目标小区，则分别占用每天接入设备数的一个数量。或者，若在同一天里，该设备多次上报断流网络异常情况，则仅占用当天断流次数的一个数量；若在不同天里，该设备每天均上报断流网络异常情况，则分别占用每天断流次数的一个数量。
例如，第一时间段可以是近一周内，在公式一和公式二中，总接 入设备数用于表示：一周内，接入目标小区的所有设备的总数，其中，对于某一天而言，一个设备多次接入目标小区，该设备占用当天的总接入设备数的一个数量。对于连续两天而言，一个设备分别接入目标小区，该设备占用两天内的总接入设备数的两个数量。
又如，第一时间段可以是近一周内，在公式二中，总断流设备数用于表示：一周内，上报断流网络异常情况的所有设备的总数，其中，对于某一天而言，一个设备多次上报断流网络异常情况，该设备占用当天的总断流设备数的一个数量。对于连续两天而言，一个设备分别上报断流网络异常情况，该设备占用两天内的总断流设备数的两个数量。
在该步骤中，分别获取目标小区的失效率和断流用户比数这两个参数，可以有效避免因个人设备问题造成的干扰。
另外，本实施例没有直接利用某单项的数量，而是利用单项的数量与总数量的比值，结合了整体情况，可对实际情况进行准确评估。
例如，同样发生了100次断流，小区1接入了100个设备，小区2接入了10000个设备，那么明显小区2是好于小区1的。
子步骤E2：根据目标小区所处的目标区域范围的断流事件，获取目标区域范围的总失效率和总断流用户比。
可选地，以一个城市作为目标区域范围，判断目标小区相比于该城市的整体情况，是否为异常小区。
可选地，在该步骤中，利用公式一和公式二，获取目标区域范围的总失效率和总断流用户比。
子步骤E3：根据失效率和总失效率，获取目标小区的失效率相对增长率，以及根据断流用户比和总断流用户比，获取目标小区的断流用户比增长率。
该步骤中所涉及的公式包括：
公式三：失效率相对增长率＝(失效率-总失效率)/总失效率。
公式四：断流用户比增长率＝(断流用户比-总断流用户比)/总断流用户比。
在本实施例中，结合目标小区所在的目标区域范围内的整体情况，得到目标小区出现断流网络异常情况的相对频繁程度，从而提供了一种区域性的比较方法，使得最终得到的判断结果更具有实际意义。
在该步骤中，将失效率相对增长率和断流用户比增长率进行相加，得到一个结果，该结果可以用于评估电子设备接入目标小区后，出现断流网络异常事件的概率。
在本实施例中，提供了一种获取目标小区的失效率相对增长率和断流用户比增长率的方法。在该方法中，第一方面，结合断流事件的次数和上报断流事件的设备数，对断流网络异常情况进行评估，排除个人设备问题对评估造成的干扰；第二方面，结合了接入的设备总数，以利用比值对断流网络异常情况进行评估，避免直接使用某项参数，不具有代表性；第三方面，将计算得来的目标小区的绝对值结果，结合区域整体情况，得到相对值结果，排除因整体区域异常对评估造成的干扰。综上，基于上述方法，可有效地、准确地确定目标小区是否为断流现象频繁出现的异常小区。
需要说明的是，上述方法同样适用于某一时间信息(或者某一场景信息)的失效率相对增长率和断流用户比增长率的获取。例如，可获取第一时间段中的对应时间信息(如早时段信息)的失效率相对增长率和断流用户比增长率；又如，可获取第一时间段中的对应场景信息(如地铁场景信息)的失效率相对增长率和断流用户比增长率。
子步骤E4：根据目标小区的网速低于第三阈值的事件，获取目标小区的低网速接入次数和总接入次数。
子步骤E5：将低网速接入次数与总接入次数的比值，确定为低网速占比。
该步骤中所涉及的公式包括：
公式五：低网速占比＝低网速接入次数/总接入次数。
其中，在公式五中，低网速接入次数表示：在第一时间段内，所有接入目标小区的设备中，上报低速率网络异常情况的总次数；总接入次数表示：在第一时间段内，所有设备接入目标小区的总次数。
在该步骤中，将低网速接入次数与总接入次数进行相比，得到一个结果，该结果可以用于评估电子设备接入目标小区后，出现低速率网络异常事件的概率。
需要说明的是，上述方法同样适用于某一时间信息(或者某一场景信息)的低网速占比的获取。例如，可获取第一时间段中的对应时间信息(如早时段信息)的低网速占比；又如，可获取第一时间段中 的对应场景信息(如地铁场景信息)的低网速占比。
在本实施例中，提供了一种获取目标小区的低网速占比的方法。在该方法中，基于低速率网络异常情况的上报次数，结合所有设备接入目标小区的总次数，得到低网速占比，对低速率网络异常情况进行评估，避免直接使用某项参数，不具有代表性，可有效地、准确地确定目标小区是否为低网速现象频繁发生的异常小区。
其中，本实施例中，第一时间段可以是近一周内的时间段，一周的数据量，既可以避免得到的结果具有较大的偶然性，还可以避免处理太多的历史数据。
在本申请另一个实施例的异常小区识别方法中，当基于以上实施例中的方法，确定目标小区在某一时间信息(或者某一场景信息)具有某一异常属性时，为了进一步验证，可以继续基于上一实施例中的方法，确定除了该时间信息(或者该场景信息)以外的剩余时间(或者剩余场景)，目标小区是否具有该异常属性。若除了该时间信息(或者该场景信息)以外的剩余时间(或者剩余场景)，目标小区不具有该异常属性，则进一步验证为：目标小区在某一时间信息(或者某一场景信息)具有某一异常属性。
例如，对于早时段信息，除掉该时段外的剩余时间就是指一天中除去早时段三个小时外的所有时间；又如，对于休息日信息，除掉该时段外的剩余时间就是指一周中去除周六和周日以外的所有时间。
示例性地，若剩余时间对应的失效率相对增长率和断流用户比增长率总和小于15％，则确定目标小区在对应时间信息具有明显的高异常现象，即具有第一异常属性。
示例性地，若剩余时间对应的低网速占比小于35％，则确定目标小区在对应时间具有明显的低网速现象，即第二异常属性。
从而，基于上述验证，在确定目标小区具有异常属性的情况下，还可以使其所具备的异常属性是具有时间特征的，即在某时间信息下存在明显异常现象，并且在其它时间相对正常；和/或，在确定目标小区具有异常属性的情况下，还可以使其所具备的异常属性是具有空间特征的，即在某场景信息下存在明显异常现象，并且在其它场景相对正常。
在本申请另一个实施例中，可结合以上各个实施例，提供一种异 常小区列表的生成方法，从而电子设备可直接从异常小区列表中，确定目标小区具有的属性。
Figure PCTCN2022100290-appb-000001
表1
以下以表1所示方案为例，对本实施例进行详细解释。
表1是由第一表格、第二表格和第三表格合并而来的。
在第一表格中，可针对某城市内的各个小区在第一时间段内的网络异常情况，得到一个整体的结果。即在这一表格中，不考虑时间、 空间等因素，对小区网络异常情况造成的干扰。
在第二表格中，可针对某城市内的各个小区在第一时间段内的不同时间信息(如早时段信息等)的网络异常情况，得到每个时间信息对应的结果。通常，小区的网络异常情况与人流量较为相关，而不同的人流量又可以通过时间维度体现出来，从而可以单独得到如休息日和工作日这两个时间信息对应的结果，以及早中晚这三个时间信息对应的结果。
在第三表格中，可针对某城市内的各个小区在第一时间段内的不同场景信息(如高铁场景信息等)的网络异常情况，得到每个场景信息对应的结果。通常，小区的网络异常情况与人所处的场景较为相关，而不同的场景又可以通过空间维度体现出来，从而可以单独得到如地铁、高铁、高速、商场这四个场景信息对应的结果。
进一步地，将三个表格进行合并。合并的过程示例性地为：对于其中一个小区，若只出现在第一表格中，则合并后，其对应的时间条件(小时)、时间条件、空间条件中均为“否”；若出现在第一表格和第二表格，或者只出现在第二表格中，则合并后，其对应的时间条件(小时)和/时间条件(天)中至少有一项条件为“是”，且其对应的空间条件中均为“否”；若出现在第一表格和第三表格，或者只出现在第三表格中，则合并后，其对应的空间条件中至少有一项条件为“是”，且其对应的时间条件(小时)和时间条件(天)中均为“否”；若分别出现在第二表格和第三表格，或者分别出现在三个表格中，则合并后，其对应的时间条件(小时)和/时间条件(天)中至少有一项条件为“是”，且其对应的空间条件中至少有一项条件为“是”。
例如，基于某一时间信息，得到任意小区具有第一异常属性时，则在表1中，该小区所在一行中，对应时间条件中的某一些为“是”，对应的类型为“高异常”。
又如，基于某一场景信息，得到任意小区具有第二异常属性时，则在表1中，该小区所在一行中，对应的空间条件中的某项为“是”，对应的类型为“低网速”。
示例性地，可由服务器生成异常小区列表，再将异常小区列表下发至用户终端设备，从而用户终端设备即将接入目标小区时，根据当前自身的时间及空间特征，在异常小区列表中识别目标小区，并判断 是否满足目标小区对应的时间条件、空间条件，以进一步确定是否需要执行相应的动作。
一种可选方案为：判断是否满足目标小区对应的时间条件、空间条件时，时间条件(小时)、时间条件(天)、空间条件三个大类之间，至少满足其中一类，则表示需要执行相应的动作，以在更多的现实情况规避异常小区，或者规避异常小区中的网络异常情况。
例如，在表1中的第二个小区，时间条件(小时)和空间条件中，均出现了“是”，根据当前自身的时间及空间特征，如果满足时间条件(小时)和空间条件中的至少一项时，则表示需要执行相应的动作。
另一种可选方案为：判断是否满足目标小区对应的时间条件、空间条件时，时间条件(小时)、时间条件(天)、空间条件三个大类之间，同时满足三类，则表示需要执行相应的动作，以在更精准的现实情况中，帮助用户规避异常小区，从而可以降低动作误执行的概率。
例如，在表1中的第二个小区，时间条件(小时)和空间条件中，均出现了“是”，根据当前自身的时间及空间特征，如果同时满足时间条件(小时)和空间条件时，则表示需要执行相应的动作。
其中，对于任一类条件中的各个子条件，比如时间条件(小时)中的“早、中、晚”三个子条件，判断是否满足时，满足其中一项，则认为满足该类条件。
例如，在表1中的第一个小区，时间条件(小时)的“早、晚”子条件均为“是”，根据当前自身的时间特征，如果满足“早、晚”任一个子条件，则认为满足时间条件(小时)。
可见，在表1中，时间条件(小时)、时间条件(天)和空间条件，之间可以是“与”的关系，各自内部是“或”的关系。基于表1中列举的几个小区若要执行相应的动作，则当前自身的时间特征和空间特征，第一个小区需满足的条件为【早or晚】；第二个小区需满足的条件为【(早or晚)and高铁】；第三个小区需满足的条件为【工作日and地铁】。
另外，在表1中，通过小区标识来区分各个小区。其中，小区标识包括公共陆地移动网(Public Land Mobile Network，简称PLMN)、无线接入类型(Radio Access Type，简称RAT)、跟踪区域码(Tracking Area Code，TAC)、小区ID(CELLID)、物理小区标识(Physical Cell  Identifier，简称PCI)等信息。
在本实施例中，基于网络异常类型这一维度进行考虑，针对不同的异常类型，执行不同的动作；同时，基于对网络异常有影响作用的时间和空间两个维度进行了考虑，对异常小区进行更精细地网络异常现象的分析，同时还挖掘出了更多的异常小区，从而可以生成精细、全面的异常小区列表，进而使得电子设备基于异常小区列表，能够合理地执行相应的动作，以规避异常小区，或者规避异常小区中的网络异常情况。
在本申请更多的实施例中，如果历史众包数据充足，还可以新增一个表格，该表格可以基于设备型号筛选出对应的异常小区，进一步地，将该表格合并至表1中，以对已列入的异常小区进行更精细地网络异常情况分析；同时，可以进一步增加异常小区的覆盖面。
另外，在生成异常小区列表的过程中，可针对性获取各大运营商的锚点小区列表中的小区，以获取各个小区中的低网速占比，从而确保后续在电子设备上设置为双连接模式的有效性。
综上，电子设备中出现网络异常情况，有时并非是信号或者设备的原因，也有可能是电子设备接入的小区本身就有问题。因此，可以通过一些简单的策略来改善用户的网络体验，比如在本地放一套泛化的规则，通过后台的历史大数据识别出一些异常小区，通过服务器下发给电子设备，从而确保电子设备在发生网络异常情况之前，接入其它小区，进而进行规避。
在现有技术中，主要都是针对断流这类网络异常情况，并且采取的各种措施最后都可以归结为重选小区(禁小区、禁频点等)。但随着5G的发展，特别是NR5G-NSA的出现，导致出现了很多可以上网却低网速的4G锚点小区(因为资源被5G小区共享了)，对于这部分小区，我们同样可以采取别的措施提高用户的网络体验。同时，小区的好坏跟时间和空间是强相关的，比如某些地铁站的小区，可能在早晚高峰因为高人流而导致出现异常，但是其它时间段非常正常。如果简单地基于大数据统计，对于这部分小区，就会有两种结果：1)由于只有个别时间段高异常，总指标并不高，最终没有被识别出来；2)即便只有个别时间段高异常，总指标依然很高，被识别出来了，电子设备就会在非高峰时期禁用这些其实是正常的小区。这样不仅没有规避 异常小区，还有可能将用户设备从一个正常小区引导到一个异常小区。
本申请相比于现有技术，第一方面，不仅考虑了断流网络异常情况，还考虑了低速率网络异常情况，以增加异常小区的覆盖面；第二方面，针对不同类型的网络异常情况，采取了不同的措施，来进行有效规避；第三方面，考虑了时间和空间维度的影响，能够更加精准地进行异常小区的规避。
需要说明的是，本申请实施例提供的异常小区识别方法，执行主体可以为异常小区识别装置，或者该异常小区识别装置中的用于执行异常小区识别方法的控制模块。本申请实施例中以异常小区识别装置执行异常小区识别方法为例，说明本申请实施例提供的异常小区识别装置。
图2示出了本申请另一个实施例的异常小区识别装置的框图，该装置包括：
获取模块10，用于获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，失效率相对增长率、断流用户比相对增长率、低网速占比均关联于：时间信息和场景信息中的至少一种；
第一确定模块20，用于在目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定目标小区具有第一异常属性；
第二确定模块30，用于在目标小区的低网速占比大于第二阈值的情况下，确定目标小区具有第二异常属性。
这样，在本申请的实施例中，基于历史数据中，大量用户终端设备针对目标小区上报的网络异常情况，可以获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。其中，失效率相对增长率和断流用户比相对增长率的总和用于表示目标小区出现断流网络异常情况的占比信息，低网速占比用于表示目标小区出现低速率网络异常情况的占比信息。进一步地，将得到的两个占比信息，分别与对应的第一阈值和第二阈值进行比较，对应地，当其中一个占比信息大于第一阈值时，认为目标小区较易出现断流网络异常情况，确定目标小区具有第一异常属性；当另外一个占比信息大于第二阈值时，认为目标小区较易出现低速率网络异常情况，确定目标小区具有第二异 常属性。可见，基于本申请的异常小区识别方法，至少可以更精细地识别出具有第一异常属性和第二异常属性这两种异常小区，相比于现有技术中仅能笼统地识别出一种异常小区，大大增加了异常小区的覆盖面积。
可选地，装置还包括：
第一执行模块，用于在检测到电子设备接入目标小区的过程中，禁止接入目标小区。
可选地，第一执行模块，包括：
第一获取单元，用于在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标时间信息；
第一匹配单元，用于在目标时间信息匹配于第一异常属性关联的时间信息的情况下，禁止接入目标小区；
其中，目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
或/和，
第二获取单元，用于在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标场景信息；
第二匹配单元，用于在目标场景信息匹配于第一异常属性关联的场景信息的情况下，禁止接入目标小区；
其中，目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
可选地，装置还包括：
第二执行模块，用于在检测到电子设备接入目标小区的过程中，接入目标小区，并设置电子设备的连接模式为双连接模式。
可选地，第二执行模块，包括：
第三获取单元，用于在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标时间信息；
第三匹配单元，用于在目标时间信息匹配于第二异常属性关联的时间信息的情况下，接入目标小区，并设置电子设备的连接模式为双连接模式；
其中，目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
或/和，
第四获取单元，用于在检测到电子设备接入目标小区的过程中，获取电子设备所处的目标场景信息；
第四匹配单元，用于在目标场景信息匹配于第二异常属性关联的场景信息的情况下，接入目标小区，并设置电子设备的连接模式为双连接模式；
其中，目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
可选地，获取模块10，包括：
第五获取单元，用于根据目标小区的断流事件，获取目标小区的失效率和断流用户比；
第六获取单元，用于根据目标小区所处的目标区域范围的断流事件，获取目标区域范围的总失效率和总断流用户比；
第七获取单元，用于根据失效率和总失效率，获取目标小区的失效率相对增长率，以及根据断流用户比和总断流用户比，获取目标小区的断流用户比增长率；
第八获取单元，用于根据目标小区的网速低于第三阈值的事件，获取目标小区的低网速接入次数和总接入次数；
占比确定单元，用于将低网速接入次数与总接入次数的比值，确定为低网速占比。
本申请实施例中的异常小区识别装置可以是装置，也可以是终端中的部件、集成电路、或芯片。该装置可以是移动电子设备，也可以为非移动电子设备。示例性的，移动电子设备可以为手机、平板电脑、笔记本电脑、掌上电脑、车载电子设备、可穿戴设备、超级移动个人计算机(ultra-mobile personal computer，UMPC)、上网本或者个人数字助理(personal digital assistant，PDA)等，非移动电子设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)、个人计算机(personal computer，PC)、电视机(television，TV)、柜员机或者自助机等，本申请实施例不作具体限定。
本申请实施例中的异常小区识别装置可以为具有动作系统的装置。该动作系统可以为安卓(Android)动作系统，可以为ios动作系统，还可以为其他可能的动作系统，本申请实施例不作具体限定。
本申请实施例提供的异常小区识别装置能够实现上述方法实施例实现的各个过程，为避免重复，这里不再赘述。
可选地，如图3所示，本申请实施例还提供一种电子设备100，包括处理器101，存储器102，存储在存储器102上并可在所述处理器101上运行的程序或指令，该程序或指令被处理器101执行时实现上述任一异常小区识别方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
需要说明的是，本申请实施例中的电子设备包括上述所述的移动电子设备和非移动电子设备。
图4为实现本申请实施例的一种电子设备的硬件结构示意图。
该电子设备1000包括但不限于：射频单元1001、网络模块1002、音频输出单元1003、输入单元1004、传感器1005、显示单元1006、用户输入单元1007、接口单元1008、存储器1009、以及处理器1010等部件。
本领域技术人员可以理解，电子设备1000还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器1010逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图4中示出的电子设备结构并不构成对电子设备的限定，电子设备可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
其中，处理器1010，用于获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。
这样，在本申请的实施例中，基于历史数据中，大量用户终端设备针对目标小区上报的网络异常情况，可以获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比。其中，失效率相对增长率和断流用户比相对增长率的总和用于表示目标小区出现断流网络异常情况的占比信息，低网速占比用于表示目标小区出现低速率网 络异常情况的占比信息。进一步地，将得到的两个占比信息，分别与对应的第一阈值和第二阈值进行比较，对应地，当其中一个占比信息大于第一阈值时，认为目标小区较易出现断流网络异常情况，确定目标小区具有第一异常属性；当另外一个占比信息大于第二阈值时，认为目标小区较易出现低速率网络异常情况，确定目标小区具有第二异常属性。可见，基于本申请的异常小区识别方法，至少可以更精细地识别出具有第一异常属性和第二异常属性这两种异常小区，相比于现有技术中仅能笼统地识别出一种异常小区，大大增加了异常小区的覆盖面积。
可选地，处理器1010，还用于在检测到电子设备接入所述目标小区的过程中，禁止接入所述目标小区。
可选地，处理器1010，还用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；在所述目标时间信息匹配于所述第一异常属性关联的时间信息的情况下，禁止接入所述目标小区；其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；或/和，在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；在所述目标场景信息匹配于所述第一异常属性关联的场景信息的情况下，禁止接入所述目标小区；其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
可选地，处理器1010，还用于在检测到电子设备接入所述目标小区的过程中，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式。
可选地，处理器1010，还用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；在所述目标时间信息匹配于所述第二异常属性关联的时间信息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；或/和，在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；在所述目标场景信息匹配于所述第二异常属性关联的场景信 息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
可选地，处理器1010，还用于根据所述目标小区的断流事件，获取所述目标小区的失效率和断流用户比；根据所述目标小区所处的目标区域范围的断流事件，获取所述目标区域范围的总失效率和总断流用户比；根据所述失效率和所述总失效率，获取所述目标小区的失效率相对增长率，以及根据所述断流用户比和所述总断流用户比，获取所述目标小区的断流用户比增长率；根据所述目标小区的网速低于第三阈值的事件，获取所述目标小区的低网速接入次数和总接入次数；将所述低网速接入次数与所述总接入次数的比值，确定为所述低网速占比。
本申请相比于现有技术，第一方面，不仅考虑了断流网络异常情况，还考虑了低速率网络异常情况，以增加异常小区的覆盖面；第二方面，针对不同类型的网络异常情况，采取了不同的措施，来进行有效规避；第三方面，考虑了时间和空间维度的影响，能够更加精准地进行异常小区的规避。
应理解的是，本申请实施例中，输入单元1004可以包括图形处理器(Graphics Processing Unit，GPU)10041和麦克风10042，图形处理器10041对在视频图像捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频图像的图像数据进行处理。显示单元1006可包括显示面板10061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板10061。用户输入单元1007包括触控面板10071以及其他输入设备10072。触控面板10071，也称为触摸屏。触控面板10071可包括触摸检测装置和触摸控制器两个部分。其他输入设备10072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、动作杆，在此不再赘述。存储器1009可用于存储软件程序以及各种数据，包括但不限于应用程序和动作系统。处理器1010可集成应用处理器和调制解调处理器，其中，应用处理器主要处理动作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器1010中。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述异常小区识别方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的电子设备中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述异常小区识别方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片、系统芯片、芯片系统或片上系统芯片等。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (17)

  1. 一种异常小区识别方法，其中，所述方法包括：
    获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；
    在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；
    在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。
  2. 根据权利要求1所述的方法，其中，在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性之后，所述方法还包括：
    在检测到电子设备接入所述目标小区的过程中，禁止接入所述目标小区。
  3. 根据权利要求2所述的方法，其中，在检测到电子设备接入所述目标小区的过程中，禁止接入所述目标小区，包括：
    在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；
    在所述目标时间信息匹配于所述第一异常属性关联的时间信息的情况下，禁止接入所述目标小区；
    其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
    或/和，
    在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；
    在所述目标场景信息匹配于所述第一异常属性关联的场景信息的情况下，禁止接入所述目标小区；
    其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
  4. 根据权利要求1所述的方法，其中，在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性之后，所述方法还包括：
    在检测到电子设备接入所述目标小区的过程中，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式。
  5. 根据权利要求4所述的方法，其中，在检测到电子设备接入所述目标小区的过程中，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式，包括：
    在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；
    在所述目标时间信息匹配于所述第二异常属性关联的时间信息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；
    其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
    或/和，
    在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；
    在所述目标场景信息匹配于所述第二异常属性关联的场景信息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；
    其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
  6. 根据权利要求1所述的方法，其中，所述获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比，包括：
    根据所述目标小区的断流事件，获取所述目标小区的失效率和断流用户比；
    根据所述目标小区所处的目标区域范围的断流事件，获取所述目标区域范围的总失效率和总断流用户比；
    根据所述失效率和所述总失效率，获取所述目标小区的失效率相 对增长率，以及根据所述断流用户比和所述总断流用户比，获取所述目标小区的断流用户比增长率；
    根据所述目标小区的网速低于第三阈值的事件，获取所述目标小区的低网速接入次数和总接入次数；
    将所述低网速接入次数与所述总接入次数的比值，确定为所述低网速占比。
  7. 一种异常小区识别装置，其中，所述装置包括：
    获取模块，用于获取目标小区的失效率相对增长率、断流用户比相对增长率、低网速占比；其中，所述失效率相对增长率、所述断流用户比相对增长率、所述低网速占比均关联于：时间信息和场景信息中的至少一种；
    第一确定模块，用于在所述目标小区的失效率相对增长率和断流用户比相对增长率的总和大于第一阈值的情况下，确定所述目标小区具有第一异常属性；
    第二确定模块，用于在所述目标小区的低网速占比大于第二阈值的情况下，确定所述目标小区具有第二异常属性。
  8. 根据权利要求7所述的装置，其中，所述装置还包括：
    第一执行模块，用于在检测到电子设备接入所述目标小区的过程中，禁止接入所述目标小区。
  9. 根据权利要求8所述的装置，其中，所述第一执行模块，包括：
    第一获取单元，用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；
    第一匹配单元，用于在所述目标时间信息匹配于所述第一异常属性关联的时间信息的情况下，禁止接入所述目标小区；
    其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
    或/和，
    第二获取单元，用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；
    第二匹配单元，用于在所述目标场景信息匹配于所述第一异常属 性关联的场景信息的情况下，禁止接入所述目标小区；
    其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
  10. 根据权利要求7所述的装置，其中，所述装置还包括：
    第二执行模块，用于在检测到电子设备接入所述目标小区的过程中，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式。
  11. 根据权利要求10所述的装置，其中，所述第二执行模块，包括：
    第三获取单元，用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标时间信息；
    第三匹配单元，用于在所述目标时间信息匹配于所述第二异常属性关联的时间信息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；
    其中，所述目标时间信息包括早时段信息、中时段信息和晚时段信息中的任一种；和/或，工作日信息和休息日信息中的任一种；
    或/和，
    第四获取单元，用于在检测到电子设备接入所述目标小区的过程中，获取所述电子设备所处的目标场景信息；
    第四匹配单元，用于在所述目标场景信息匹配于所述第二异常属性关联的场景信息的情况下，接入所述目标小区，并设置所述电子设备的连接模式为双连接模式；
    其中，所述目标场景信息包括高铁场景信息、地铁场景信息、高速场景信息和商场场景信息中的任一种。
  12. 根据权利要求7所述的装置，其中，所述获取模块，包括：
    第五获取单元，用于根据所述目标小区的断流事件，获取所述目标小区的失效率和断流用户比；
    第六获取单元，用于根据所述目标小区所处的目标区域范围的断流事件，获取所述目标区域范围的总失效率和总断流用户比；
    第七获取单元，用于根据所述失效率和所述总失效率，获取所述目标小区的失效率相对增长率，以及根据所述断流用户比和所述总断流用户比，获取所述目标小区的断流用户比增长率；
    第八获取单元，用于根据所述目标小区的网速低于第三阈值的事件，获取所述目标小区的低网速接入次数和总接入次数；
    占比确定单元，用于将所述低网速接入次数与所述总接入次数的比值，确定为所述低网速占比。
  13. 一种电子设备，其中，包括处理器，存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1-6任一项所述的异常小区识别方法的步骤。
  14. 一种可读存储介质，其中，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1-6任一项所述的异常小区识别方法的步骤。
  15. 一种芯片，其中，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如权利要求1-6任一项所述的异常小区识别方法。
  16. 一种计算机程序产品，其中，所述程序产品被存储在非易失的存储介质中，所述程序产品被至少一个处理器执行以实现如权利要求1-6任一项所述的异常小区识别方法。
  17. 一种通信设备，其特征在于，所述通信设备被配置成用于执行如权利要求1-6任一项所述的异常小区识别方法。
PCT/CN2022/100290 2021-06-25 2022-06-22 异常小区识别方法、装置和电子设备 WO2022268098A1 (zh)

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