WO2017045229A1 - 一种基站流量数据的处理方法及基站 - Google Patents
一种基站流量数据的处理方法及基站 Download PDFInfo
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- WO2017045229A1 WO2017045229A1 PCT/CN2015/091354 CN2015091354W WO2017045229A1 WO 2017045229 A1 WO2017045229 A1 WO 2017045229A1 CN 2015091354 W CN2015091354 W CN 2015091354W WO 2017045229 A1 WO2017045229 A1 WO 2017045229A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/10—Flow control between communication endpoints
Definitions
- the present invention relates to the field of electronic device technologies, and in particular, to a method and a base station for processing base station traffic data.
- the peak rate of a single base station/single-carrier fan of LTE is more than 10 times that of a 3G base station, and the peak rate is 4-6 times the mean rate. Congestion is more likely to occur than 3G.
- the existing congestion control scheme performs quality of service (QoS) QoS control on the base station side and the core network side, and performs hierarchical queue scheduling capability for different base stations and different services to ensure smooth communication of important base stations and important data.
- QoS quality of service
- the maintenance personnel need to upgrade the logic of the base station and the core network element, and pass a large number of tests, with a long cycle and a large workload.
- the peak since the peak is objective, it originates from the demand of a large number of mobile terminals through the cellular network.
- QoS control can only guarantee the transmission of important data. The cost is to actively block and abandon the lower priority traffic, which will cause some data to be discarded. The package affects the user experience on the terminal side.
- the present invention provides a method for processing base station traffic data and a base station, so as to improve the peak data processing efficiency of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- a first aspect of the embodiments of the present invention provides a method for processing base station traffic data, including:
- the base station analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period;
- the base station determines a data late event probability according to the traffic peak event probability
- the base station broadcasts, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent,
- the data delay event probability execution delays the preset interval duration to send the data packet Operation.
- the base station analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, including:
- the base station analyzes the obtained traffic data in the specified time period to generate the statistical data corresponding to the traffic data, and generates the traffic mode corresponding to the specified time period based on the generated statistical data, where the statistical data includes at least the traffic average The value, the variance of the flow, the dispersion of the flow, the proportion of the large flow period, and the proportion of the flow hour.
- the base station determines the traffic The peak event probability of the traffic corresponding to the mode, including:
- the base station matches the probability that the i-th traffic peak event probability of the M traffic peak event probability is the traffic peak event probability of the traffic mode, where i is a positive integer less than or equal to the M.
- the base station acquires the M historical history that matches the specified time period.
- M traffic peak event probability of the traffic mode and the M historical historical traffic patterns including:
- the base station extracts N historical synchronous traffic data of N historical synchronization periods that match the specified time period, and creates a relationship between X historical synchronous traffic data and traffic peak events in the N historical synchronous traffic data.
- the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is greater than the a positive integer of M, the X being a positive integer less than or equal to N;
- the base station analyzes the N historical concurrent traffic data to generate N sets of statistical data, and divides the N sets of statistical data into M data sets, wherein each of the M data sets includes at least one set Statistical data, and the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the base station determines, according to the traffic peak event probability, a data late event probability ,include:
- the second mapping relationship is a proportional relationship.
- a second aspect of the embodiments of the present invention provides a base station, including:
- An analyzing unit configured to analyze traffic data in a specified time period to obtain a traffic mode corresponding to the specified time period
- a determining unit configured to determine a traffic peak event probability corresponding to the traffic mode acquired by the analyzing unit
- the determining unit is further configured to: if it is determined that the traffic peak event probability is greater than a preset probability threshold, determine a data late event probability according to the traffic peak event probability;
- a sending unit configured to broadcast, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent And performing an operation of delaying the preset interval duration to send the data packet with the data late event probability.
- the analyzing unit is specifically configured to:
- the traffic data in the specified time period to generate the statistical data corresponding to the traffic data, and generating, according to the generated statistical data, a traffic mode corresponding to the specified time period, where the statistical data includes at least a traffic average value and a traffic variance , the flow dispersion, the proportion of the large flow period, and the proportion of the flow hour.
- the determining unit includes:
- an acquiring unit configured to acquire M historical peak traffic events that match the specified time period and M traffic peak event probability of the M historical synchronous traffic patterns, where the M is a positive integer;
- a matching unit configured to match an ith traffic peak event of the M traffic peak event probability
- the piece probability is a flow peak event probability of the flow mode, and the i is a positive integer less than or equal to the M.
- the acquiring unit is specifically configured to:
- the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is greater than the M. a positive integer, the X being a positive integer less than or equal to N;
- the N historical concurrent traffic data is analyzed to generate N sets of statistical data, and the N sets of statistical data are divided into M data sets, wherein each of the M data sets includes at least one set of statistical data. And the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the determining unit determines a data late event according to the traffic peak event probability
- the specific way of probability is:
- mapping relationship Determining, according to a second mapping relationship between the pre-stored traffic peak event probability and the data late event probability, and the traffic peak event probability, the data late event probability corresponding to the traffic peak event probability, wherein the second The mapping relationship is a proportional relationship.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than the preset probability threshold.
- the data delay event probability is determined according to the traffic peak event probability.
- the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal moves after receiving the source suppression packet.
- the terminal can perform the operation of delaying the transmission of the data packet by delaying the preset interval duration in the case that the presence of the data packet needs to be transmitted, and can reduce the data transmission of the mobile terminal in the sector range of the base station in the same time period.
- the quantity is beneficial to improve the peak processing efficiency of the data flow of the base station and avoid discarding the datagram. Improve the user experience of the mobile terminal.
- FIG. 1 is a schematic structural diagram of a network architecture disclosed in a first embodiment of the present invention
- FIG. 2 is a schematic flowchart of a method for processing base station traffic data according to a second embodiment of the present invention
- FIG. 3 is a schematic flowchart of a method for processing base station traffic data according to a third embodiment of the present invention.
- FIG. 4 is a schematic flowchart of a method for processing base station traffic data according to a fourth embodiment of the present invention.
- FIG. 5 is a schematic structural diagram of a base station according to a fifth embodiment of the present invention.
- FIG. 6 is a schematic structural diagram of a base station according to a sixth embodiment of the present invention.
- references to "an embodiment” herein mean that a particular feature, structure, or characteristic described in connection with the embodiments can be It is included in at least one embodiment of the invention.
- the appearances of the phrases in various places in the specification are not necessarily referring to the same embodiments, and are not exclusive or alternative embodiments that are mutually exclusive. Those skilled in the art will understand and implicitly understand that the embodiments described herein can be combined with other embodiments.
- the embodiment of the invention discloses a method for processing base station traffic data and a base station, which is beneficial to improving the peak data processing efficiency of the base station, avoiding discarding data reports, and improving the user experience of the mobile terminal.
- FIG. 1 is a schematic structural diagram of a network architecture according to an embodiment of the present invention.
- the network architecture diagram may include a base station and a mobile terminal, where the base station may include a server, a service host, a service system, a service platform, and the like, and the mobile terminal may be, but not limited to, a mobile phone, a mobile computer, a tablet computer, Personal Digital Assistant (PDA), media player, smart TV, smart watch, smart glasses, smart bracelet, etc.
- PDA Personal Digital Assistant
- the base station can communicate with the mobile terminal through the Internet.
- FIG. 2 is a schematic structural diagram of a method for processing base station traffic data according to a second embodiment of the present invention. As shown in the figure, the method in the embodiment of the present invention includes:
- the base station analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period.
- the base station determines a traffic peak event probability corresponding to the traffic mode.
- the base station determines a data late event probability according to the traffic peak event probability.
- the base station broadcasts, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent. And performing an operation of delaying the preset interval duration to send the data packet with the data late event probability.
- the preset interval duration is greater than or equal to the first preset interval duration ⁇ t1 and less than or equal to the second preset interval duration ⁇ t2, and the ⁇ t1 is smaller than the ⁇ t2.
- the above ⁇ t1 may take a value of 50 ms
- the above ⁇ t2 may take a value of 500 ms.
- the specific implementation manner that the base station analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period may be:
- the base station analyzes the obtained traffic data in the specified time period to generate the statistical data corresponding to the traffic data, and generates the traffic mode corresponding to the specified time period based on the generated statistical data, where the statistical data includes at least the traffic average The value, the variance of the flow, the dispersion of the flow, the proportion of the large flow period, and the proportion of the flow hour.
- the specific implementation manner of determining, by the base station, the traffic peak event probability corresponding to the traffic mode may be:
- the base station matches the probability that the i-th traffic peak event probability of the M traffic peak event probability is the traffic peak event probability of the traffic mode, where i is a positive integer less than or equal to the M.
- the specific implementation manner in which the base station acquires the M historical peak traffic patterns that match the specified time period and the M traffic peak event probability of the M historical historical synchronous traffic modes may be:
- the base station extracts N historical synchronous traffic data of N historical synchronization periods that match the specified time period, and creates a relationship between X historical synchronous traffic data and traffic peak events in the N historical synchronous traffic data.
- the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is greater than the a positive integer of M, the X being a positive integer less than or equal to N;
- the base station analyzes the N historical concurrent traffic data to generate N sets of statistical data, and divides the N sets of statistical data into M data sets, wherein each of the M data sets includes at least one set Statistical data, and the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the M data sets include a first data set and a second data set
- the M The historical synchronous traffic mode includes a first historical synchronous traffic mode corresponding to the first data set and a second historical synchronous traffic mode corresponding to the second data set
- the base station is based on the M data sets and the The first mapping relationship calculates a probability of M traffic peak events that match the M historical historical traffic patterns, including:
- the base station Determining, by the base station, the number b1 of the statistical data matching the first mapping relationship in the a1 group statistics, and determining the number b2 of the statistical data matching the first mapping relationship in the a2 group statistics.
- the b1 is a positive integer less than or equal to a1
- the b2 is a positive integer less than or equal to a2;
- the base station calculates, according to the a1 and the b1, a first traffic peak event probability that matches the first historical synchronous traffic pattern, and calculates a matching with the second historical synchronous traffic pattern according to the a2 and the b2 The first traffic peak event probability.
- the specific implementation manner of determining, by the base station, a data late event probability according to the traffic peak event probability may be:
- the second mapping relationship is a proportional relationship.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than In the case of the preset probability threshold, the data late event probability is determined according to the traffic peak event probability. Finally, the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal receives the source suppression.
- the mobile terminal can perform the operation of delaying the transmission of the data packet by the delay time of the preset interval in the case of detecting that the data packet needs to be transmitted, and can reduce the mobile terminal within the sector range of the base station in the same time period.
- the number of data packets sent is beneficial to improve the peak processing efficiency of data traffic of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- FIG. 3 is a flowchart of a method for processing base station traffic data according to a third embodiment of the present invention. As shown in the figure, the method in the embodiment of the present invention includes:
- the base station analyzes the acquired traffic data in a specified time period to generate corresponding to the traffic data. Statistical data.
- the base station generates, according to the generated statistics, a traffic mode corresponding to the specified time period, where the statistics includes at least a traffic average, a traffic variance, a traffic dispersion, a large traffic volume, and a traffic hour segment. Any of the ratios.
- the base station acquires M historical peak traffic events and the M traffic peak event probability of the M historical synchronous traffic patterns that match the specified time period, where the M is a positive integer.
- the specific implementation manner in which the base station acquires the M historical peak traffic patterns that match the specified time period and the M traffic peak event probability of the M historical historical synchronous traffic modes may be:
- the base station extracts N historical synchronous traffic data of N historical synchronization periods that match the specified time period, and creates a relationship between X historical synchronous traffic data and traffic peak events in the N historical synchronous traffic data.
- the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is greater than the a positive integer of M, the X being a positive integer less than or equal to N;
- the base station analyzes the N historical concurrent traffic data to generate N sets of statistical data, and divides the N sets of statistical data into M data sets, wherein each of the M data sets includes at least one set Statistical data, and the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the M data sets include a first data set and a second data set
- the M historical synchronous traffic patterns include a first historical synchronous traffic pattern and the second corresponding to the first data set
- the second historical synchronous traffic mode corresponding to the data set, the base station calculates, according to the M data sets and the first mapping relationship, M traffic peak event probabilityes that match the M historical synchronous traffic patterns, including:
- Determining, by the base station, statistical data that matches the first mapping relationship in the a1 group statistics The number of groups b1, the number b2 of the statistical data matching the first mapping relationship in the a2 group statistical data is determined, the b1 is a positive integer less than or equal to a1, and the b2 is a positive value less than or equal to a2 Integer
- the base station calculates, according to the a1 and the b1, a first traffic peak event probability that matches the first historical synchronous traffic pattern, and calculates a matching with the second historical synchronous traffic pattern according to the a2 and the b2 The first traffic peak event probability.
- the base station matches an event that the i-th traffic peak event probability of the M traffic peak event probability is a traffic peak event probability of the traffic mode, where the i is a positive integer less than or equal to the M.
- the base station performs a second mapping relationship between the pre-stored traffic peak event probability and the data late event probability, and the traffic peak event probability. Determining a data late event probability corresponding to the traffic peak event probability, wherein the second mapping relationship is a proportional relationship.
- the base station broadcasts, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent. And performing an operation of delaying the preset interval duration to send the data packet with the data late event probability.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than In the case of the preset probability threshold, the data late event probability is determined according to the traffic peak event probability. Finally, the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal receives the source suppression.
- the mobile terminal can perform the operation of delaying the transmission of the data packet by the delay time of the preset interval in the case of detecting that the data packet needs to be transmitted, and can reduce the mobile terminal within the sector range of the base station in the same time period.
- the number of data packets sent is beneficial to improve the peak processing efficiency of data traffic of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- FIG. 4 is a flowchart of a method for processing base station traffic data according to a fourth embodiment of the present invention. As shown in the figure, the method in the embodiment of the present invention includes:
- the base station analyzes the acquired traffic data in a specified time period to generate statistical data corresponding to the traffic data.
- the base station generates, according to the generated statistics, a traffic mode corresponding to the specified time period, where the statistics include at least a traffic average, a traffic variance, a traffic dispersion, a large traffic volume ratio, and a traffic hour segment. Any of the ratios.
- the base station extracts N historical synchronous traffic data of N historical synchronization periods that match the specified time period, and creates between X historical synchronous traffic data and traffic peak events in the N historical synchronous traffic data.
- the first mapping relationship, the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is a positive integer greater than the M, the X being a positive integer less than or equal to N;
- the base station analyzes the N historical concurrent traffic data to generate N sets of statistical data, and divides the N sets of statistical data into M data sets, where each of the M data sets includes at least a set of statistical data, and the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the base station generates, according to the M data sets, M historical time-synchronous traffic patterns of the base station, and calculates, according to the M data sets and the first mapping relationship, the M-type historical synchronous traffic mode. M traffic peak event probability.
- the M data sets include a first data set and a second data set
- the M historical synchronous traffic patterns include a first historical synchronous traffic pattern and the second corresponding to the first data set
- the second historical synchronous traffic mode corresponding to the data set, the base station calculates, according to the M data sets and the first mapping relationship, M traffic peak event probabilityes that match the M historical synchronous traffic patterns, including:
- the base station Determining, by the base station, the number b1 of the statistical data matching the first mapping relationship in the a1 group statistics, and determining the number b2 of the statistical data matching the first mapping relationship in the a2 group statistics.
- the b1 is a positive integer less than or equal to a1
- the b2 is a positive integer less than or equal to a2;
- the base station calculates, according to the a1 and the b1, a first traffic peak event probability that matches the first historical synchronous traffic pattern, and calculates a matching with the second historical synchronous traffic pattern according to the a2 and the b2 The first traffic peak event probability.
- the base station matches an ith traffic peak event of the M traffic peak event probability.
- the piece probability is a flow peak event probability of the flow mode, and the i is a positive integer less than or equal to the M.
- the base station is configured according to a second mapping relationship between the pre-stored traffic peak event probability and the data late event probability, and the traffic peak event probability. Determining a data late event probability corresponding to the traffic peak event probability, wherein the second mapping relationship is a proportional relationship.
- the base station broadcasts, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent. And performing an operation of delaying the preset interval duration to send the data packet with the data late event probability.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than In the case of the preset probability threshold, the data late event probability is determined according to the traffic peak event probability. Finally, the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal receives the source suppression.
- the mobile terminal can perform the operation of delaying the transmission of the data packet by the delay time of the preset interval in the case of detecting that the data packet needs to be transmitted, and can reduce the mobile terminal within the sector range of the base station in the same time period.
- the number of data packets sent is beneficial to improve the peak processing efficiency of data traffic of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- FIG. 5 is a schematic structural diagram of a base station end according to a fifth embodiment of the present invention.
- the base station in the embodiment of the present invention includes:
- the analyzing unit 501 is configured to analyze the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period;
- a determining unit 502 configured to determine a traffic peak event probability corresponding to the traffic mode acquired by the analyzing unit;
- the determining unit 502 is further configured to: if it is determined that the traffic peak event probability is greater than a preset probability threshold, determine a data late event probability according to the traffic peak event probability;
- the sending unit 503 is configured to broadcast, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal needs to detect the presence of a data packet In the case of transmitting, the operation of delaying the preset interval duration to transmit the data packet is performed with the data late event probability.
- the analyzing unit 501 is specifically configured to:
- the traffic data in the specified time period to generate the statistical data corresponding to the traffic data, and generating, according to the generated statistical data, a traffic mode corresponding to the specified time period, where the statistical data includes at least a traffic average value and a traffic variance , the flow dispersion, the proportion of the large flow period, and the proportion of the flow hour.
- the determining unit 502 may further include:
- an acquiring unit configured to acquire M historical peak traffic events that match the specified time period and M traffic peak event probability of the M historical synchronous traffic patterns, where the M is a positive integer;
- a matching unit configured to match an i-th traffic peak event probability of the M traffic peak event probability to a traffic peak event probability of the traffic mode, where i is a positive integer less than or equal to the M.
- the acquiring unit is specifically configured to:
- the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is greater than the M. a positive integer, the X being a positive integer less than or equal to N;
- the N historical concurrent traffic data is analyzed to generate N sets of statistical data, and the N sets of statistical data are divided into M data sets, wherein each of the M data sets includes at least one set of statistical data. And the data matching degree of any two sets of statistical data of the plurality of sets of statistical data in the same data set is greater than a preset threshold;
- the specific manner in which the determining unit 502 determines the probability of the late event of the data according to the probability of the peak event of the traffic may be:
- the functions of the functional modules of the base station in this embodiment may be specifically implemented according to the method in the foregoing method embodiment.
- Some or all of the functional modules in the base station may be implemented by hardware circuits, and some or all of the functional modules in the base station may also be implemented by a processor (such as a digital signal processor) by executing code or instructions.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than In the case of the preset probability threshold, the data late event probability is determined according to the traffic peak event probability. Finally, the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal receives the source suppression.
- the mobile terminal can perform the operation of delaying the transmission of the data packet by the delay time of the preset interval in the case of detecting that the data packet needs to be transmitted, and can reduce the mobile terminal within the sector range of the base station in the same time period.
- the number of data packets sent is beneficial to improve the peak processing efficiency of data traffic of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- FIG. 6 is a schematic structural diagram of another mobile terminal according to a sixth embodiment of the present invention.
- the apparatus can include at least one processor 601, such as a CPU, at least one receiver 603, at least one memory 604, at least one transmitter 605, and at least one communication bus 602.
- the communication bus 602 is used to implement connection communication between these components.
- the receiver 603 and the transmitter 605 of the device in the embodiment of the present invention may be a wired sending port, or may be a wireless device, for example, including an antenna device, for performing signaling or data communication with other node devices.
- the memory 604 may be a high speed RAM memory or a non-volatile memory such as at least one disk memory.
- the memory 604 can optionally also be at least one storage device located remotely from the aforementioned processor 601.
- a set of program codes is stored in the memory 604, and the processor 601 is configured to call program code stored in the memory for performing the following operations:
- the processor 601 analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period.
- the processor 601 determines a traffic peak event probability corresponding to the traffic mode.
- the processor 601 root The probability of the late event of the data is determined according to the probability of the peak event of the traffic.
- the processor 601 broadcasts, to the mobile terminal, a source suppression packet including the data late event probability and a preset interval duration, where the source suppression packet is used to indicate that the mobile terminal detects that a data packet needs to be sent. And performing an operation of delaying the preset interval duration to send the data packet with the data late event probability.
- the preset interval duration is greater than or equal to the first preset interval duration 1 and less than or equal to the second preset interval duration 2, and the ⁇ t1 is smaller than the ⁇ t2.
- the specific implementation manner that the processor 601 analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period may be:
- the processor 601 analyzes the obtained traffic data in the specified time period to generate the statistical data corresponding to the traffic data, and generates a traffic mode corresponding to the specified time period based on the generated statistical data, where the statistical data includes at least The average of the flow rate, the variance of the flow, the dispersion of the flow, the proportion of the large flow period, and the proportion of the flow hour.
- the specific implementation manner of the processor 601 determining the traffic peak event probability corresponding to the traffic mode may be:
- the processor 601 acquires M historical peak traffic events that match the specified time period and M traffic peak event probability of the M historical synchronous traffic patterns, where the M is a positive integer;
- the processor 601 matches the probability that the i-th traffic peak event probability of the M traffic peak event probability is the traffic peak event probability of the traffic mode, where i is a positive integer less than or equal to the M.
- a specific implementation manner in which the processor 601 acquires M historical peak traffic patterns that match the specified time period and M traffic peak event probability of the M historical synchronous traffic patterns may be :
- the processor 601 extracts N historical synchronous flow data of N historical synchronization periods that match the specified time period, and creates between X historical synchronous flow data and traffic peak events in the N historical synchronous flow data.
- the first mapping relationship, the traffic peak event is that the traffic value of the traffic data acquired by the base station after any one of the X historical synchronization periods is greater than a preset traffic threshold, where the N is a positive integer greater than the M, the X being a positive integer less than or equal to N;
- the processor 601 analyzes the N historical concurrent traffic data to generate N sets of statistical data, and
- the N sets of statistical data are divided into M data sets, wherein each of the M data sets includes at least one set of statistical data, and any two sets of statistical data of multiple sets of statistical data in the same data set
- the data matching degree is greater than a preset threshold
- the processor 601 generates M historical historical synchronization traffic patterns of the base station based on the M data sets, and calculates, according to the M data sets and the first mapping relationship, the M historical time synchronization traffic patterns. M traffic peak event probability.
- the M data sets include a first data set and a second data set
- the M historical synchronous traffic patterns include a first historical synchronous traffic pattern and the second corresponding to the first data set a second historical synchronous traffic pattern corresponding to the data set
- the processor 601 calculating, according to the M data sets and the first mapping relationship, M traffic peak event probabilityes that match the M historical synchronous traffic patterns, include:
- the processor 601 determines the number of groups a1 and a2 of the statistical data included in the first data set and the second data set, where a1 and a2 are positive integers;
- the processor 601 determines a group number b1 of statistical data that matches the first mapping relationship in the a1 group statistical data, and determines a group of statistical data that matches the first mapping relationship in the a2 group statistical data. a number b2, the b1 being a positive integer less than or equal to a1, the b2 being a positive integer less than or equal to a2;
- the processor 601 calculates, according to the a1 and the b1, a first traffic peak event probability that matches the first historical synchronous traffic pattern, and calculates the second historical synchronous traffic mode according to the a2 and the b2. The probability of matching the first traffic peak event.
- the specific implementation manner of the processor 601 determining the probability of the data late event according to the traffic peak event probability may be:
- the processor 601 determines, according to a second mapping relationship between a pre-stored traffic peak event probability and a data late event probability, and the traffic peak event probability, a data late event probability corresponding to the traffic peak event probability, where
- the second mapping relationship is a proportional relationship.
- the functions of the functional modules of the base station in this embodiment may be specifically implemented according to the method in the foregoing method embodiment.
- Some or all of the functional modules in the base station may be implemented by hardware circuits, and some or all of the functional modules in the base station may also be implemented by a processor (such as a digital signal processor) by executing code or instructions.
- the base station first analyzes the traffic data in the specified time period to obtain the traffic mode corresponding to the specified time period, and secondly, the base station determines the traffic peak event probability corresponding to the traffic mode, and determines that the traffic peak event probability is greater than In the case of the preset probability threshold, the data late event probability is determined according to the traffic peak event probability. Finally, the source suppression packet including the data late event probability and the preset interval duration is broadcast to the mobile terminal, and the mobile terminal receives the source suppression.
- the mobile terminal can perform the operation of delaying the transmission of the data packet by the delay time of the preset interval in the case of detecting that the data packet needs to be transmitted, and can reduce the mobile terminal within the sector range of the base station in the same time period.
- the number of data packets sent is beneficial to improve the peak processing efficiency of data traffic of the base station, avoid dropping datagrams, and improve the user experience of the mobile terminal.
- the program may be stored in a computer readable storage medium, and the storage medium may include: Flash disk, read-only memory (English: Read-Only Memory, referred to as: ROM), random accessor (English: Random Access Memory, referred to as: RAM), disk or optical disk.
- ROM Read-Only Memory
- RAM Random Access Memory
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Abstract
本发明实施例公开了一种基站流量数据的处理方法及基站，包括：基站分析指定时段内的流量数据以获取指定时段对应的流量模式；基站确定流量模式对应的流量峰值事件概率；若判断出流量峰值事件概率大于预设概率阈值，则基站根据流量峰值事件概率确定数据迟发事件概率；基站向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，源抑制包用于指示移动终端在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作。采用本发明实施例，有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
Description
本发明涉及电子设备技术领域，尤其涉及一种基站流量数据的处理方法及基站。
LTE的单基站/单载扇的峰值速率达到3G基站的10倍以上，而且峰值速率是均值速率的4-6倍。和3G相比，更容易发生拥塞。现有的拥塞控制方案是在基站侧、核心网侧进行服务质量(Quality of Service，QoS)QoS控制，针对不同基站和不同业务执行层次化的队列调度能力，确保重要基站和重要数据的畅通。
上述对基站侧、核心网侧进行QoS控制中，维护人员需要升级基站、核心网网元的逻辑，并要通过大量测试，周期长，工作量大。同时，由于峰值是客观存在的，源自大量移动终端通过蜂窝网络的上网需求，QoS控制只能保证重要数据的传输，代价是主动阻塞、放弃优先级较低的流量，这会造成丢弃一部分数据包，影响终端侧的用户体验。
发明内容
本发明提供一种基站流量数据的处理方法及基站，以期提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
本发明实施例第一方面提供了一种基站流量数据的处理方法，包括：
基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式；
所述基站确定所述流量模式对应的流量峰值事件概率；
若判断出所述流量峰值事件概率大于预设概率阈值，则所述基站根据所述流量峰值事件概率确定数据迟发事件概率；
所述基站向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包
的操作。
在本发明实施例第一方面的第一种可能的实现方式中，所述基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式，包括：
所述基站分析获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
结合本发明实施例第一方面或本发明实施例第一方面的第一种可能的实现方式，在本发明实施例第一方面的第二种可能的实现方式中，所述基站确定所述流量模式对应的流量峰值事件概率，包括：
所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；
所述基站匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
结合本发明实施例第一方面的第二种可能的实现方式，在本发明实施例第一方面的第三种可能的实现方式中，所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，包括：
所述基站提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
所述基站分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
所述基站基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量
模式的M个流量峰值事件概率。
结合本发明实施例第一方面第三种可能的实现方式，在本发明实施例第一方面的第四种可能的实现方式中，所述基站根据所述流量峰值事件概率确定数据迟发事件概率，包括：
所述基站根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
本发明实施例第二方面提供一种基站，包括：
分析单元，用于分析指定时段内的流量数据以获取所述指定时段对应的流量模式；
确定单元，用于确定所述分析单元获取的所述流量模式对应的流量峰值事件概率；
所述确定单元，还用于若判断出所述流量峰值事件概率大于预设概率阈值，则根据所述流量峰值事件概率确定数据迟发事件概率；
发送单元，用于向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
在本发明实施例第二方面的第一种可能的实现方式中，所述分析单元具体用于：
获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
结合本发明实施例第二方面或本发明实施例第二方面的第一种可能的实现方式，在本发明实施例第二方面的第二种可能的实现方式中，所述确定单元包括：
获取单元，用于获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；
匹配单元，用于匹配出所述M个流量峰值事件概率中的第i个流量峰值事
件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
结合本发明实施例第二方面的第二种可能的实现方式，在本发明实施例第二方面的第三种可能的实现方式中，所述获取单元具体用于：
提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
结合本发明实施例第二方面第三种可能的实现方式，在本发明实施例第二方面的第四种可能的实现方式中，所述确定单元根据所述流量峰值事件概率确定数据迟发事件概率的具体方式为：
根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
实施本发明实施例，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，
提升移动终端用户体验。
为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明第一实施例公开的一种网络构架的结构示意图；
图2是本发明第二实施例公开的一种基站流量数据的处理方法的流程示意图；
图3是本发明第三实施例公开的一种基站流量数据的处理方法的流程示意图；
图4是本发明第四实施例公开的一种基站流量数据的处理方法的流程示意图；
图5是本发明第五实施例公开的一种基站的结构示意图；
图6是本发明第六实施例公开的一种基站的结构示意图。
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”和“第三”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可
以包含在本发明的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
本发明实施例公开了一种基站流量数据的处理方法及基站，有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
下面结合附图及具体实施方式，对本发明实施例的技术方案进行详细说明。
为了更好理解本发明实施例公开的一种基站流量数据的处理方法及基站，下面先对本发明实施例适用的网络构架进行描述。请参阅图1，图1是本发明实施例公开的一种网络构架的结构示意图。如图1所示，该网络构架示意图可以包括基站和移动终端，其中，基站可以包括服务器、服务主机、服务系统以及服务平台等，而移动终端可以但不限于移动电话、移动电脑、平板电脑、个人数字助理(Personal Digital Assistant,PDA)、媒体播放器、智能电视、智能手表、智能眼镜、智能手环等。其中，基站可以通过互联网与移动终端进行通信连接。
基于图1所示的网络构架，本发明实施例公开了的一种基站流量数据的处理方法。请参考图2，图2是本发明第二实施例提出的一种基站流量数据的处理方法的结构示意图。如图所示，本发明实施例中的方法包括：
S201、基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式。
S202、所述基站确定所述流量模式对应的流量峰值事件概率。
S203、若判断出所述流量峰值事件概率大于预设概率阈值，则所述基站根据所述流量峰值事件概率确定数据迟发事件概率。
S204、所述基站向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
可以理解的，所述预设间隔时长大于或等于第一预设间隔时长Δt1且小于或等于第二预设间隔时长Δt2，所述Δt1小于所述Δt2。
例如，上述Δt1可以取值为50ms，上述Δt2可以取值为500ms。
作为一种可选的实施方式，所述基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式的具体实施方式可以是：
所述基站分析获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
作为一种可选的实施方式，所述基站确定所述流量模式对应的流量峰值事件概率的具体实施方式可以是：
所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；
所述基站匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
作为一种可选的实施方式，所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率的具体实施方式可以是：
所述基站提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
所述基站分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
所述基站基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
举例来说，所述M个数据集合包括第一数据集合和第二数据集合，所述M
种历史同期流量模式包括与所述第一数据集合对应的第一历史同期流量模式和所述第二数据集合对应的第二历史同期流量模式，所述基站基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式匹配的M个流量峰值事件概率，包括：
所述基站确定所述第一数据集合和所述第二数据集合中包括的统计数据的组数a1和a2，所述a1和a2为正整数；
所述基站确定所述a1组统计数据中与所述第一映射关系匹配的统计数据的组数b1，确定所述a2组统计数据中与所述第一映射关系匹配的统计数据的组数b2，所述b1为小于或等于a1的正整数，所述b2为小于或等于a2的正整数；
所述基站根据所述a1和所述b1计算与所述第一历史同期流量模式匹配的第一流量峰值事件概率，根据所述a2和所述b2计算与所述第二历史同期流量模式匹配的第一流量峰值事件概率。
作为一种可选的实施方式，所述基站根据所述流量峰值事件概率确定数据迟发事件概率的具体实施方式可以是：
所述基站根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
可以看出，本发明实施例中，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
请参考图3，图3是本发明第三实施例提出的一种基站流量数据的处理方法的流程图。如图所示，本发明实施例中的方法包括：
S301、基站分析获取的指定时段内的流量数据以生成所述流量数据对应的
统计数据。
S302、所述基站基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
S303、所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数。
作为一种可选的实施方式，所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率的具体实施方式可以是：
所述基站提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
所述基站分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
所述基站基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
举例来说，所述M个数据集合包括第一数据集合和第二数据集合，所述M种历史同期流量模式包括与所述第一数据集合对应的第一历史同期流量模式和所述第二数据集合对应的第二历史同期流量模式，所述基站基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式匹配的M个流量峰值事件概率，包括：
所述基站确定所述第一数据集合和所述第二数据集合中包括的统计数据的组数a1和a2，所述a1和a2为正整数；
所述基站确定所述a1组统计数据中与所述第一映射关系匹配的统计数据的
组数b1，确定所述a2组统计数据中与所述第一映射关系匹配的统计数据的组数b2，所述b1为小于或等于a1的正整数，所述b2为小于或等于a2的正整数；
所述基站根据所述a1和所述b1计算与所述第一历史同期流量模式匹配的第一流量峰值事件概率，根据所述a2和所述b2计算与所述第二历史同期流量模式匹配的第一流量峰值事件概率。
S304、所述基站匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
S305、若判断出所述流量峰值事件概率大于预设概率阈值，则所述基站根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
S306、所述基站向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
可以看出，本发明实施例中，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
请参考图4，图4是本发明第四实施例提出的一种基站流量数据的处理方法的流程图。如图所示，本发明实施例中的方法包括：
S401、基站分析获取的指定时段内的流量数据以生成所述流量数据对应的统计数据。
S402、所述基站基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
S403、所述基站提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
S404、所述基站分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
S405、所述基站基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
举例来说，所述M个数据集合包括第一数据集合和第二数据集合，所述M种历史同期流量模式包括与所述第一数据集合对应的第一历史同期流量模式和所述第二数据集合对应的第二历史同期流量模式，所述基站基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式匹配的M个流量峰值事件概率，包括：
所述基站确定所述第一数据集合和所述第二数据集合中包括的统计数据的组数a1和a2，所述a1和a2为正整数；
所述基站确定所述a1组统计数据中与所述第一映射关系匹配的统计数据的组数b1，确定所述a2组统计数据中与所述第一映射关系匹配的统计数据的组数b2，所述b1为小于或等于a1的正整数，所述b2为小于或等于a2的正整数；
所述基站根据所述a1和所述b1计算与所述第一历史同期流量模式匹配的第一流量峰值事件概率，根据所述a2和所述b2计算与所述第二历史同期流量模式匹配的第一流量峰值事件概率。
S406、所述基站匹配出所述M个流量峰值事件概率中的第i个流量峰值事
件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
S407、若判断出所述流量峰值事件概率大于预设概率阈值，则所述基站根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
S408、所述基站向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
可以看出，本发明实施例中，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
请参考图5，图5是本发明第五实施例提出的基站端的结构示意图。如图所示，本发明实施例中的基站包括：
分析单元501，用于分析指定时段内的流量数据以获取所述指定时段对应的流量模式；
确定单元502，用于确定所述分析单元获取的所述流量模式对应的流量峰值事件概率；
所述确定单元502，还用于若判断出所述流量峰值事件概率大于预设概率阈值，则根据所述流量峰值事件概率确定数据迟发事件概率；
发送单元503，用于向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需
要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
具体实现中，所述分析单元501具体用于：
获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
具体实现中，所述确定单元502还可以进一步包括：
获取单元，用于获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；
匹配单元，用于匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
具体实现中，所述获取单元具体用于：
提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
具体实现中，所述确定单元502根据所述流量峰值事件概率确定数据迟发事件概率的具体方式可以是：
根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件
概率，其中，所述第二映射关系为正比例关系。
可以理解的是，本实施例的基站的各功能模块的功能可根据上述方法实施例中的方法具体实现，其具体实现过程可以参照上述方法实施例的相关描述，此处不再赘述。基站中的部分或全部功能模块可由硬件电路实现，基站中的部分或全部功能模块也可由处理器(如数字信号处理器)通过完成执行代码或指令来实现。
可以看出，本发明实施例中，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
图6是本发明第六实施例提出的另一种移动终端的结构示意图。如图所示，该装置可以包括：至少一个处理器601，例如CPU，至少一个接收器603，至少一个存储器604，至少一个发送器605，至少一个通信总线602。其中，通信总线602用于实现这些组件之间的连接通信。其中，本发明实施例中装置的接收器603和发送器605可以是有线发送端口，也可以为无线设备，例如包括天线装置，用于与其他节点设备进行信令或数据的通信。存储器604可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器604可选的还可以是至少一个位于远离前述处理器601的存储装置。存储器604中存储一组程序代码，且处理器601用于调用存储器中存储的程序代码，用于执行以下操作：
所述处理器601分析指定时段内的流量数据以获取所述指定时段对应的流量模式。
所述处理器601确定所述流量模式对应的流量峰值事件概率。
若判断出所述流量峰值事件概率大于预设概率阈值，则所述处理器601根
据所述流量峰值事件概率确定数据迟发事件概率。
所述处理器601向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
可以理解的，所述预设间隔时长大于或等于第一预设间隔时长1且小于或等于第二预设间隔时长2，所述Δt1小于所述Δt2。
作为一种可选的实施方式，所述处理器601分析指定时段内的流量数据以获取所述指定时段对应的流量模式的具体实施方式可以是：
所述处理器601分析获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
作为一种可选的实施方式，所述处理器601确定所述流量模式对应的流量峰值事件概率的具体实施方式可以是：
所述处理器601获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；
所述处理器601匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
作为一种可选的实施方式，所述处理器601获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率的具体实施方式可以是：
所述处理器601提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；
所述处理器601分析所述N个历史同期流量数据以生成N组统计数据，将
所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；
所述处理器601基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
举例来说，所述M个数据集合包括第一数据集合和第二数据集合，所述M种历史同期流量模式包括与所述第一数据集合对应的第一历史同期流量模式和所述第二数据集合对应的第二历史同期流量模式，所述处理器601基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式匹配的M个流量峰值事件概率，包括：
所述处理器601确定所述第一数据集合和所述第二数据集合中包括的统计数据的组数a1和a2，所述a1和a2为正整数；
所述处理器601确定所述a1组统计数据中与所述第一映射关系匹配的统计数据的组数b1，确定所述a2组统计数据中与所述第一映射关系匹配的统计数据的组数b2，所述b1为小于或等于a1的正整数，所述b2为小于或等于a2的正整数；
所述处理器601根据所述a1和所述b1计算与所述第一历史同期流量模式匹配的第一流量峰值事件概率，根据所述a2和所述b2计算与所述第二历史同期流量模式匹配的第一流量峰值事件概率。
作为一种可选的实施方式，所述处理器601根据所述流量峰值事件概率确定数据迟发事件概率的具体实施方式可以是：
所述处理器601根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
可以理解的是，本实施例的基站的各功能模块的功能可根据上述方法实施例中的方法具体实现，其具体实现过程可以参照上述方法实施例的相关描述，此处不再赘述。基站中的部分或全部功能模块可由硬件电路实现，基站中的部分或全部功能模块也可由处理器(如数字信号处理器)通过完成执行代码或指令来实现。
可以看出，本发明实施例中，基站首先分析指定时段内的流量数据以获取指定时段对应的流量模式，其次，基站确定流量模式对应的流量峰值事件概率，并在判断出流量峰值事件概率大于预设概率阈值的情况下，根据流量峰值事件概率确定数据迟发事件概率，最后，向移动终端广播包括数据迟发事件概率和预设间隔时长的源抑制包，移动终端在接收到该源抑制包之后，移动终端能够在在检测到存在数据包需要发送的情况下，以数据迟发事件概率执行推迟预设间隔时长发送数据包的操作，能够减少同一时段基站的扇区范围内的移动终端发送数据包的数量，从而有利于提升基站的数据流量峰值处理效率，避免丢弃数据报，提升移动终端用户体验。
需要说明的是，对于前述的各个方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某一些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本发明所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详细描述的部分，可以参见其他实施例的相关描述。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：闪存盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取器(英文：Random Access Memory，简称：RAM)、磁盘或光盘等。
以上对本发明实施例所提供的基站流量数据的处理方法及相关设备进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。
Claims (10)
- 一种基站流量数据的处理方法，其特征在于，所述方法包括：基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式；所述基站确定所述流量模式对应的流量峰值事件概率；若判断出所述流量峰值事件概率大于预设概率阈值，则所述基站根据所述流量峰值事件概率确定数据迟发事件概率；所述基站向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
- 根据权利要求1所述的基站流量数据的处理方法，其特征在于，所述基站分析指定时段内的流量数据以获取所述指定时段对应的流量模式，包括：所述基站分析获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
- 根据权利要求1或2所述的基站流量数据的处理方法，其特征在于，所述基站确定所述流量模式对应的流量峰值事件概率，包括：所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；所述基站匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
- 根据权利要求3所述的基站流量数据的处理方法，其特征在于，所述基站获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，包括：所述基站提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整 数；所述基站分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；所述基站基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
- 根据权利要求4所述的基站流量数据的处理方法，其特征在于，所述基站根据所述流量峰值事件概率确定数据迟发事件概率，包括：所述基站根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
- 一种基站，其特征在于，包括：分析单元，用于分析指定时段内的流量数据以获取所述指定时段对应的流量模式；确定单元，用于确定所述分析单元获取的所述流量模式对应的流量峰值事件概率；所述确定单元，还用于若判断出所述流量峰值事件概率大于预设概率阈值，则根据所述流量峰值事件概率确定数据迟发事件概率；发送单元，用于向移动终端广播包括所述数据迟发事件概率和预设间隔时长的源抑制包，所述源抑制包用于指示所述移动终端在检测到存在数据包需要发送的情况下，以所述数据迟发事件概率执行推迟所述预设间隔时长发送所述数据包的操作。
- 根据权利要求6所述的基站，其特征在于，所述分析单元具体用于：获取的指定时段内的流量数据以生成所述流量数据对应的统计数据，基于生成的所述统计数据生成所述指定时段对应的流量模式，其中，所述统计数据至少包括流量平均值、流量方差、流量离差、流量大时段占比、流量小时段占比中的任意一种。
- 根据权利要求6或7所述的基站，其特征在于，所述确定单元包括：获取单元，用于获取与所述指定时段匹配的M种历史同期流量模式和所述M种历史同期流量模式的M个流量峰值事件概率，所述M为正整数；匹配单元，用于匹配出所述M个流量峰值事件概率中的第i个流量峰值事件概率为所述流量模式的流量峰值事件概率，所述i为小于或等于所述M的正整数。
- 根据权利要求8所述的基站，其特征在于，所述获取单元具体用于：提取与所述指定时段匹配的N个历史同期时段的N个历史同期流量数据，并创建所述N个历史同期流量数据中的X个历史同期流量数据与流量峰值事件之间的第一映射关系，所述流量峰值事件是指所述基站在所述X个历史同期时段中的任意一个历史同期时段之后所获取的流量数据的流量值大于预设流量阈值，所述N为大于所述M的正整数，所述X为小于或等于N的正整数；分析所述N个历史同期流量数据以生成N组统计数据，将所述N组统计数据分成M个数据集合，其中，所述M个数据集合中的每一个数据集合至少包括一组统计数据，且同一个数据集合中的多组统计数据的任意两组统计数据的数据匹配度大于预设阈值；基于所述M个数据集合生成所述基站的M种历史同期流量模式，并基于所述M个数据集合和所述第一映射关系，计算与所述M种历史同期流量模式的M个流量峰值事件概率。
- 根据权利要求9所述的基站，其特征在于，所述确定单元根据所述流量峰值事件概率确定数据迟发事件概率的具体方式为：根据预存的流量峰值事件概率与数据迟发事件概率之间的第二映射关系，以及所述流量峰值事件概率，确定所述流量峰值事件概率对应的数据迟发事件概率，其中，所述第二映射关系为正比例关系。
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