CN104427534A - Detection method and movable detection device of long-term evolution software acqusition - Google Patents
Detection method and movable detection device of long-term evolution software acqusition Download PDFInfo
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Abstract
The invention discloses a detection method and a movable detection device of long-term evolution software acqusition, and aims to solve the problem that the accuracy of the software acqusition cannot be detected in the prior art. According to the method and the device, the signaling transmission accuracy of the software acqusition is detected according to received full-volume signaling data by receiving the full-volume signaling data acquired through an SCA (Signaling Convergence Adapter); the received full-volume signaling data is decoded, and the signaling effectiveness of the software acqusition is detected according to decoded signaling data and the received full-volume signaling data; the decoded signaling data is associated into signaling data of each user or each service; original signaling data transmitted by an air interface of a testing terminal UE (User Equipment) is received, the signaling accuracy of the software acqusition is detected according to the associated signaling data and the received original signaling data, so that accuracy detection can be effectively performed on the software acqusition in multiple aspects; thus, the problem that the accuracy of the software acqusition cannot be detected in the prior art can be solved.
Description
Technical Field
The invention relates to a mobile communication system detection technology, in particular to a detection method of Long Term Evolution (LTE) soft acquisition and a movable detection device.
Background
In order to meet the requirement of LTE network operation and maintenance, the LTE system optimization work provides network monitoring and maintenance based on signaling soft mining of LTE network element equipment. Soft acquisition refers to that signaling data completes signaling acquisition work through corresponding software convergence when passing through a switch, and is different from signaling acquisition performed by methods such as hardware, high resistance bridging or copy shunting on the existing signaling link physical transmission. After the LTE network is flattened, the conventional Abis/Iub interface disappears, and the signaling monitoring optimization means based on the Abis/Iub interface cannot be used continuously in 4G, so a signaling soft-acquisition technical scheme based on the network element device outputting the full original signaling is provided.
Fig. 1 shows a block diagram of a soft mining architecture of LTE, and the system includes: the system comprises a soft mining layer, a sharing layer and an application layer, wherein the soft mining layer comprises network element equipment such as a traffic convergence adapter (SCA), a mobility management device (MME), a service gateway (S-GW), a PDN gateway (PDN-GW), a policy and charging control unit (PCRF) and the like, the SCA collects the whole amount of original Signaling data transmitted on the network element equipment through a mirror image port on the network element equipment, and sends the collected Signaling and data to the sharing layer through an IF1 interface; the sharing layer synthesizes the received full original signaling data into related user service records, carries out data mining and processing on the user records according to different service analysis requirements, and realizes application presentation on an application layer, such as application of signaling monitoring, flow analysis, performance alarm, cell analysis, accurate marketing and the like.
In the above LTE soft-acquisition architecture, to implement effective application analysis and presentation, accurate full-amount original signaling data needs to be acquired first, but at present, the accuracy of soft acquisition cannot be detected.
Disclosure of Invention
The embodiment of the invention provides a detection method for LTE soft mining and a movable detection device, which are used for solving the problem that the accuracy of the soft mining cannot be detected in the prior art.
The technical scheme of the embodiment of the invention is as follows:
a detection method for LTE soft mining comprises the following steps: receiving the full signaling data collected by a traffic convergence adapter SCA, and detecting the signaling transmission accuracy of soft mining according to the received full signaling data; decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data; and associating the decoded signaling data into the signaling data of each user or each service, receiving original signaling data sent by an air interface of the test terminal UE, and detecting the signaling accuracy of soft mining according to the associated signaling data and the received original signaling data.
The method for detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data specifically comprises the following steps: analyzing the associated signaling data to determine the message type and the message quantity of the associated signaling data, analyzing the received original signaling data to determine the message type and the message quantity of the acquired original signaling data, and determining the message type and the message quantity of all the tested UEs in the associated signaling data; and detecting the signaling completeness of the soft mining and/or determining the message error rate of the soft mining according to the associated message types and message quantities of the signaling data of all the UE to be tested and the received message types and message quantities of the original signaling data.
Specifically, detecting signaling completeness of soft mining according to the message types and message quantities of signaling data of all associated test UEs and the message types and message quantities of received original signaling data, specifically including: and comparing the associated message types of the signaling data of all the UE to be tested with the acquired message types of the original signaling data, and comparing the associated message quantity of the signaling data of all the UE to be tested with the acquired message quantity of the original signaling data, and determining that the soft acquisition has signaling completeness under the condition that the compared message contents are the same and the compared message quantities are the same.
Determining a soft-error message rate according to the associated message type and message quantity of the signaling data of the test UE and the message type and message quantity of the received original signaling data, specifically comprising: determining a cell level message error rate, a user level message error rate, a message level message error rate, or a joint error rate; wherein the cell level message error rate is determined according to the following formula:
determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
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wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message level message error rate, RjntFor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the message type in the kth of the jth user in the ith cell after association, and M' is the total number of the received messages of the original signaling data.
Further, detecting signaling accuracy of soft mining further comprises: and generating a service detail record xDR according to the decoded signaling data, and determining the association success rate according to the xDR and the associated signaling data.
The method for detecting the signaling transmission accuracy of the soft mining specifically comprises the following steps: and detecting whether the soft mining supports a serial data transmission protocol, detecting the number of ACK/NACK (acknowledgement/negative acknowledgement) data packets, the number of retransmitted data packets and the number of received data packets, and determining the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link.
The detecting the signaling effectiveness of the soft mining according to the decoded signaling data and the received full signaling data specifically includes: and determining the decoding success rate of soft mining according to the number of successfully decoded signaling data packets and the number of the received full signaling data packets.
Furthermore, the method also determines a key performance index KPI of the soft mining according to the correlated signaling data and xDR, receives a current network KPI from a current network manager or a signaling analysis platform, and detects the KPI accuracy of the current network manager or the signaling analysis platform by comparing the KPI of the soft mining with the current network KPI, that is, for the same type of KPI, the error rate of the KPI of the type is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining. Wherein the KPI comprises at least: the radio resource controls the RRC connection success rate, the RRC reestablishment ratio, the RRC establishment success times, the RRC connection reestablishment success times and the RRC connection establishment request times.
The method also carries out optimization analysis on the object area of soft mining according to the result of detecting the KPI accuracy of the current network management or signaling analysis platform, xDR and the received full signaling data; and carrying out network quality evaluation according to the result of the optimization analysis.
A movable detection device for LTE soft mining, which is a movable independent device, comprises: the receiving module is used for receiving the full signaling data collected by the traffic aggregation adapter SCA; receiving original signaling data sent by an air interface of a test terminal UE; the first detection module is used for detecting the signaling transmission accuracy of soft mining according to the received full amount of signaling data; a decoding module, configured to decode the received full amount of signaling data; the second detection module is used for detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full amount of signaling data; the association module is used for associating the decoded signaling data into the signaling data of each user or each service; and the third detection module is used for detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data.
Wherein, the third detection module specifically includes: the analysis submodule is used for analyzing the associated signaling data to determine the message type and the message quantity of the associated signaling data, analyzing the received original signaling data to determine the message type and the message quantity of the acquired original signaling data; the determining submodule is used for determining the message types and the message quantity of all the tested UEs in the associated signaling data; the first detection submodule is used for detecting the signaling completeness of the soft mining according to the message types and the message quantity of the associated signaling data of all the UE to be tested and the message types and the message quantity of the received original signaling data; and the second detection submodule is used for determining the soft-mining message error rate according to the message types and the message quantity of the associated signaling data of all the tested UEs and the message types and the message quantity of the received original signaling data.
The first detection submodule is specifically configured to: and comparing the associated message types of the signaling data of all the UE to be tested with the acquired message types of the original signaling data, and comparing the associated message quantity of the signaling data of all the UE to be tested with the acquired message quantity of the original signaling data, and determining that the soft acquisition has signaling completeness under the condition that the compared message contents are the same and the compared message quantities are the same.
The second detection submodule is specifically configured to: determining a cell level message error rate, a user level message error rate, a message level message error rate, or a joint error rate; wherein the cell level message error rate is determined according to the following formula: <math>
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determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
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wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message level message error rate, RjntFor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the message type in the kth of the jth user in the ith cell after association, and M' is the total number of the received messages of the original signaling data.
Further, the association module is further configured to generate a service detail record xDR according to the decoded signaling data; then, the third detection module further includes: and the third detection sub-module is used for determining the association success rate according to the xDR and the associated signaling data.
The first detection module is specifically configured to: and detecting whether the soft mining supports a serial data transmission protocol, detecting the number of ACK/NACK (acknowledgement/negative acknowledgement) data packets, the number of retransmitted data packets and the number of received data packets, and determining the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link.
The second detection module is specifically configured to: and determining the decoding success rate of soft mining according to the number of successfully decoded signaling data packets and the number of the received full signaling data packets.
Preferably, the receiving module is further configured to receive an existing network KPI from an existing network manager or a signaling analysis platform;
the device further comprises: the fourth detection module is used for determining a key performance index KPI of soft mining according to the correlated signaling data and xDR, and detecting the KPI accuracy of a network manager or a signaling analysis platform of the current network by comparing the KPI of soft mining with the KPI of the current network; specifically, for the same type of KPI, the error rate of the KPI of this type is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining; wherein the KPI comprises at least: the radio resource controls the RRC connection success rate, the RRC reestablishment ratio, the RRC establishment success times, the RRC connection reestablishment success times and the RRC connection establishment request times.
Preferably, the apparatus further comprises: the optimization analysis module is used for performing optimization analysis according to the KPI of the soft mining determined by the fourth detection module, the xDR determined by the correlation module and the full signaling data received by the receiving module; and the quality evaluation module is used for carrying out network quality evaluation according to the result of the optimization analysis.
According to the technical scheme provided by the embodiment of the invention, the signaling transmission accuracy of soft mining is detected by receiving the full signaling data acquired by the SCA and according to the received full signaling data; decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data; and associating the decoded signaling data into the signaling data of each user or each service, receiving the original signaling data sent by the air interface of the test terminal UE, detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data, and effectively detecting the accuracy of the soft mining in multiple aspects, thereby solving the problem that the accuracy of the soft mining cannot be detected in the prior art.
In addition, the embodiment of the invention also determines the key performance index KPI of the soft mining according to the associated signaling data, receives the current network KPI from the current network management or the signaling analysis platform, and detects the KPI accuracy of the current network management or the signaling analysis platform according to the KPI of the soft mining and the current network KPI, thereby further carrying out the accuracy detection of a deeper level; and performing optimization analysis on the soft mining object area and performing network quality evaluation according to the result of detecting the KPI accuracy of the current network management or signaling analysis platform, the xDR and the received full signaling data, and providing basis and reference for network optimization.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
Fig. 1 is a structural block diagram of a soft mining architecture of LTE;
fig. 2 is a flowchart of a method for detecting LTE soft mining according to an embodiment of the present invention;
fig. 3 is another work flow chart of the detection method for LTE soft mining according to the embodiment of the present invention;
fig. 4 is a block diagram of a mobile detection apparatus for LTE soft mining according to an embodiment of the present invention;
FIG. 5 is a block diagram of a third detection module shown in FIG. 4;
fig. 6 is another structural block diagram of a mobile detection apparatus for LTE soft mining according to an embodiment of the present invention;
fig. 7 is a structural block diagram of a mobile detection device for LTE soft mining according to an embodiment of the present invention in a specific application.
Detailed Description
The embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that the embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.
Aiming at the problem that the accuracy of soft mining cannot be detected in the prior art, the embodiment of the invention provides a detection method and a movable detection device for LTE soft mining, which are used for solving the problem.
The method detects the accuracy of LTE soft mining from two levels, wherein the first level is the accuracy of detecting the transmission of bottom data, and the second level is the accuracy of detecting the application of an upper layer. The first layer comprises the accuracy of detecting the signaling transmission of the soft mining, the effectiveness of the signaling of the soft mining and the accuracy of the signaling of the soft mining, and the second layer comprises the accuracy of detecting the Key Performance Indicator (KPI) of the current network management or signaling analysis platform. That is, the accuracy of bottom data transmission is detected in a relatively comprehensive range by the first level, and the accuracy of the upper application is detected by the second level, so that the accuracy of soft mining can be reliably and effectively detected, and the detection requirements in multiple aspects can be met.
The technical solution of the embodiment of the present invention is explained in detail below.
Fig. 2 shows a work flow diagram of a detection method for LTE soft mining provided by an embodiment of the present invention, which may be executed in a device independent of a soft mining system, and the method includes:
step 201, receiving the total signaling data collected by the SCA, and detecting the signaling transmission accuracy of soft mining according to the received total signaling data;
specifically, whether the soft mining supports a serial data transmission protocol is detected, the number of ACK/NACK (acknowledgement/negative acknowledgement) data packets, the number of retransmission data packets and the number of received data packets are detected, and the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link are determined; the method for detecting whether the soft mining supports the serial data transmission protocol, detecting the number of ACK/NACK data packets, the number of retransmission data packets and the number of received data packets, and determining the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link is realized by a related method in the prior art;
step 202, decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data;
specifically, the decoding success rate of soft mining is determined according to the number of successfully decoded signaling data packets and the number of received full-amount signaling data packets; wherein, determining the decoding success rate can be realized by a related method in the prior art;
step 203, associating the decoded signaling data with signaling data of each user or each service, receiving original signaling data sent by an air interface of the test terminal UE, and detecting signaling accuracy of soft mining according to the associated signaling data and the received original signaling data.
Specifically, the signaling accuracy of detecting soft mining can be detected by: detecting the association success rate, detecting the signaling completeness of soft mining, and/or determining the message error rate of soft mining;
detecting the association success rate, generating a service detailed record (xDR) according to the decoded signaling data, and determining the association success rate according to the service detailed record and the associated signaling data; the form and content of the xDR can refer to the form and content of the xDR in the prior art, and the determination of the association success rate can be realized by a related method in the prior art;
before detecting the signaling completeness of the soft mining and/or determining the message error rate of the soft mining, analyzing the associated signaling data to determine the message type and the message quantity of the associated signaling data, analyzing the received original signaling data to determine the message type and the message quantity of the acquired original signaling data, and determining the message type and the message quantity of all UE (user equipment) to be tested in the associated signaling data; then, detecting the signaling completeness of soft mining according to the message types and the message quantity of the signaling data of all the associated test UEs and the message types and the message quantity of the received original signaling data;
specifically, the message types of the signaling data of all the associated test UEs and the message types of the acquired original signaling data are compared, the message quantity of the signaling data of all the associated test UEs and the message quantity of the acquired original signaling data are compared, and under the condition that the compared message contents are the same and the compared message quantities are the same, it is determined that the soft mining has signaling completeness;
in particular, the message error rate may include a cell-level message error rate, a user-level message error rate, a message-level message error rate, or a joint error rate;
the cell level message error rate is determined according to the following formula:
determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
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wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message level message error rate, RjntFor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the kth message type of the jth user in the ith cell after association, and M' is the total amount of the received original signaling data;
the Number of messages of the UE is determined in the associated signaling data, and may be determined according to a user Identifier (a Radio Network Temporary Identifier (RNTI), a Temporary Identifier (TMSI), a Temporary Mobile Subscriber Identity (IMSI)), a Cell Number may be determined according to a Cell Identifier (a Physical Cell Identifier (PCI) or a Cell Global Identifier (CGI)), a Number of messages of a certain message type may be determined, and may be determined according to a message type;
through the processing process, the signaling transmission accuracy of soft mining is detected by receiving the total signaling data acquired by the SCA and according to the received total signaling data; decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data; and associating the decoded signaling data into the signaling data of each user or each service, receiving the original signaling data sent by the air interface of the test terminal UE, detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data, and effectively detecting the accuracy of the soft mining in multiple aspects, thereby solving the problem that the accuracy of the soft mining cannot be detected in the prior art.
Preferably, as shown in fig. 3, on the basis of the method shown in fig. 2, the detection method for LTE soft mining provided by the embodiment of the present invention further includes:
step 204, determining a Key Performance Indicator (KPI) of soft mining according to the correlated signaling data and xDR, receiving a KPI of an existing network from an existing network management or signaling analysis platform (such as an OMC network management or a sharing platform of a soft mining system), and detecting the KPI accuracy of the existing network management or signaling analysis platform by comparing the KPI of the soft mining with the KPI of the existing network;
wherein the KPI comprises at least: a Radio Resource Control (RRC) connection success rate, an RRC reestablishment proportion, an RRC establishment success frequency, an RRC connection reestablishment success frequency and an RRC connection establishment request frequency;
specifically, for the same type of KPI, the error rate of the KPI of this type is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining;
step 205, performing optimization analysis on the object area of soft mining according to the result of detecting the KPI accuracy of the current network management or signaling analysis platform, the xDR and the received full signaling data;
and step 206, evaluating the network quality according to the optimization analysis result.
Through the processing procedure shown in fig. 3, the embodiment of the present invention can also detect the KPI accuracy of the existing network management or signaling analysis platform, and can further perform accuracy detection of the upper layer application; and optimization analysis and network quality evaluation are carried out, so that basis and reference can be provided for network optimization.
Therefore, according to the processing procedures shown in fig. 2 and fig. 3, the accuracy of the bottom layer data transmission can be comprehensively detected, and the accuracy of the upper layer application can be more deeply detected, so that the accuracy of soft mining can be reliably and effectively detected, and the detection requirements in various aspects can be met.
Based on the same inventive concept, the embodiment of the invention also provides a movable detection device for LTE soft mining, which is a movable independent device.
Fig. 4 shows a block diagram of a mobile detection apparatus for LTE soft mining according to an embodiment of the present invention, where the apparatus includes:
a receiving module 41, configured to receive full signaling data acquired by the traffic aggregation adapter SCA; receiving original signaling data sent by an air interface of a test terminal UE;
a first detecting module 42, connected to the receiving module 41, for detecting signaling transmission accuracy of soft mining according to the full amount of signaling data received by the receiving module 41;
specifically, the first detection module 42 detects whether the soft mining supports the serial data transmission protocol, and detects the number of ACK/NACK data packets, the number of retransmitted data packets, and the number of received data packets, to determine the packet error rate, the retransmission rate, and the soft mining transmission delay of the soft mining link;
a decoding module 43, connected to the receiving module 41, for decoding the full amount of signaling data received by the receiving module 41;
a second detecting module 44, connected to the decoding module 43, for detecting the signaling validity of the soft mining according to the signaling data decoded by the decoding module 43 and the received full signaling data;
specifically, the second detection module 44 determines the decoding success rate of soft mining according to the number of successfully decoded signaling data packets and the number of received full-amount signaling data packets;
a correlation module 45, connected to the decoding module 43, for correlating the signaling data decoded by the decoding module 43 into the signaling data of each user or each service;
and a third detecting module 46, connected to the associating module 45, for detecting the signaling accuracy of the soft mining according to the signaling data associated by the associating module 45 and the received original signaling data.
As shown in fig. 5, the third detection module 46 includes: an analysis submodule 461, a determination submodule 462, a first detection submodule 463, a second detection submodule 464 and a third detection submodule 465;
the analyzing submodule 461, connected to the associating module 45, is configured to analyze the signaling data associated by the associating module 45 to determine the message type and the message quantity of the associated signaling data, and analyze the received original signaling data to determine the message type and the message quantity of the acquired original signaling data;
a determining submodule 462, connected to the parsing submodule 461, for determining the message types and the message quantities of all the tested UEs in the associated signaling data;
a first detection sub-module 463, connected to the determining sub-module 462 and the receiving module 41, for detecting the signaling completeness of soft mining according to the message types and message quantities of the signaling data of all the associated test UEs and the message types and message quantities of the received original signaling data;
specifically, the first detection sub-module 463 compares the message types of the signaling data of all the associated test UEs and the message types of the acquired original signaling data, and compares the message number of the signaling data of all the associated test UEs and the message number of the acquired original signaling data, and determines that the soft mining has signaling completeness when the compared message contents are the same and the compared message numbers are the same;
and a second detection submodule 464, connected to the determination submodule 462 and the receiving module 41, configured to determine a soft-sampling message error rate according to the message types and the message quantities of the associated signaling data of all the test UEs and the message types and the message quantities of the received original signaling data.
Specifically, the second detection submodule 464 is specifically configured to:
determining a cell level message error rate, a user level message error rate, a message level message error rate, or a joint error rate; wherein,
the cell level message error rate is determined according to the following formula:
determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
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wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message level message error rate, RjntFor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the kth message type of the jth user in the ith cell after association, and M' is the total amount of the received original signaling data;
the association module 45 is further configured to generate an xDR according to the decoded signaling data; then the process of the first step is carried out,
and the third detection sub-module 465 is connected to the association module 45, and configured to determine the association success rate according to the xDR generated by the association module 45 and the associated signaling data.
According to the device shown in fig. 4, by receiving the total signaling data collected by the SCA, the signaling transmission accuracy of soft mining is detected according to the received total signaling data; decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data; and associating the decoded signaling data into the signaling data of each user or each service, receiving the original signaling data sent by the air interface of the test terminal UE, detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data, and effectively detecting the accuracy of the soft mining in multiple aspects, thereby solving the problem that the accuracy of the soft mining cannot be detected in the prior art.
Preferably, as shown in fig. 6, on the basis of the apparatus shown in fig. 4, the movable detection apparatus for LTE soft mining provided by the embodiment of the present invention further includes: a fourth detection module 47, an optimization analysis module 48 and a quality assessment module 49;
the receiving module 41 is further configured to receive a current network KPI from a current network manager or a signaling analysis platform;
a fourth detection module 47, connected to the association module 45 and the receiving module 41, configured to determine a KPI of soft mining according to the signaling data and xDR associated by the association module 45, and detect a KPI accuracy of an existing network manager or a signaling analysis platform according to the KPI of soft mining and an existing network KPI received by the receiving module 41;
wherein the KPI comprises at least: the RRC connection success rate, the RRC reestablishment proportion, the RRC establishment success times, the RRC connection reestablishment success times and the RRC connection establishment request times;
specifically, for the same type of KPI, the error rate of the KPI of this type is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining.
An optimization analysis module 48, connected to the fourth detection module 47, the association module 45 and the receiving module 41, configured to perform optimization analysis according to the KPI of the soft mining determined by the fourth detection module 47, the xDR determined by the association module 45 and the full amount of signaling data received by the receiving module 41;
and the quality evaluation module 49 is connected to the optimization analysis module 48 and is used for carrying out network quality evaluation according to the result of the optimization analysis module 48.
According to the device shown in FIG. 6, the accuracy detection of the upper layer application can be further carried out; and optimization analysis and network quality evaluation are carried out, so that basis and reference can be provided for network optimization.
Therefore, according to the device shown in fig. 4 and fig. 6, the accuracy of the bottom layer data transmission can be comprehensively detected, and the accuracy of the upper layer application can be more deeply detected, so that the accuracy of soft mining can be reliably and effectively detected, and the detection requirements in various aspects can be met.
The following describes a specific application of the embodiments of the present invention.
Fig. 7 is a block diagram illustrating a structure of a mobile detection apparatus for LTE soft mining in a specific application, where the apparatus includes: a processing application unit 71 and a detection unit 72;
the processing application unit 711 includes a data receiving module 711, a data storage module 712, a signaling decoding module 713, a signaling association module 714, a KPI index statistics module 715, and a network optimization analysis and presentation module 716, and the detection unit 72 includes an interface transmission detection module 721, a signaling validity detection module 722, a signaling accuracy detection module 723, a KPI index accuracy detection module 724, a network quality assessment module 725, and an interface module 726.
Wherein, the data receiving module 711 corresponds to a functional portion of the receiving module 41 in fig. 2 or fig. 3 for receiving the whole signaling data collected by SCA, the signaling decoding module 713 corresponds to the decoding module 73 in fig. 2 or fig. 3, the signaling correlation module 714 corresponds to the correlation module 75 in fig. 2 or fig. 3, the KPI index statistics module 715 corresponds to the fourth detecting module 47 in fig. 3 for determining a functional portion of the key performance index KPI of soft mining according to the correlated signaling data and xDR, the network optimization analysis and presentation module 716 corresponds to the optimization analysis module 48 in fig. 3, the interface transmission detecting module 721 corresponds to the first detecting module 72 in fig. 2 or fig. 3, the signaling validity detecting module 722 corresponds to the second detecting module 74 in fig. 2 or fig. 3, the signaling accuracy detecting module 723 corresponds to the third detecting module 76 in fig. 3 or fig. 3, and the network accuracy detecting module 724 corresponds to the fourth detecting module 47 in fig. 3 for detecting KPI of soft mining and the current network KPI A functional part for managing KPI accuracy of the signaling analysis platform, the network quality evaluation module 725 corresponds to the quality evaluation module 49 in fig. 3, and the interface module 726 corresponds to a functional part for receiving original signaling data sent by an air interface of the test terminal UE and receiving a KPI of a current network from a network manager of the current network or the signaling analysis platform of the current network by the receiving module 41 in fig. 2 or fig. 3.
The data receiving module 711 supports a Serial Data Transfer (SDTP) protocol, and can receive the full amount of signaling data from the SCA, and the signaling encapsulation format conforms to the interface requirements of the IF1 standard.
A data storage module 712 connected to the data receiving module 711, for storing the whole amount of signaling data received by the data receiving module 711;
a signaling decoding module 713, connected to the data storage module 712, for decoding the full amount of signaling data, implementing the functions of decapsulation of the IF1 interface and analysis of the original protocol signaling, and outputting the decoded message data;
a signaling association module 714, connected to the signaling decoding module 713, for associating signaling messages from the same user or the same service, outputting associated signaling data, and outputting a service detail record (xDR);
a KPI indicator statistic module 715, connected to the signaling correlation module 714, for calculating KPI indicators of the network, such as RRC connection success rate, RRC reestablishment ratio, RRC connection establishment success frequency, RRC connection reestablishment success frequency, RRC connection establishment request frequency, and the like, based on the xDR output by the signaling correlation module 714;
a network optimization analysis and presentation module 716, which is connected to the KPI index statistics module 715 and the signaling correlation module 714, and performs upper layer application analysis on the KPI index obtained by the KPI index statistics module 715, the xDR record output by the signaling correlation module 714, and the decoded signaling data, and implements interface and Geographic Information System (GIS) presentation;
the interface transmission detection module 721 is connected to the data receiving module 711, and configured to detect the data stream status transmitted by the IF1 interface at regular time, verify whether the host device supports the SDTP transmission protocol, generate a detection report of the error rate, transmission delay, and throughput index of the IF1 interface, and output an alarm message when the index is abnormal.
A signaling validity detection module 722, connected to the signaling decoding module 713, for receiving the signaling output from the signaling decoding module 713 and generating a detection report of the decoding success rate indicator;
the interface module 726 is used for receiving the original signaling data from the test terminal, and performing interface adaptation on the original signaling data to obtain the original signaling data with a data format meeting the requirements; the KPI from the existing network management OMC is also received, and the KPI is subjected to interface adaptation to obtain the KPI with a data format meeting the requirements;
a signaling accuracy detection module 723, connected to the signaling correlation module 714 and the interface module 726, for analyzing the xDR from the signaling correlation module 714 and counting a success rate index of correlation of the signaling; analyzing the accuracy of the soft mining signaling, that is, analyzing the signaling completeness and the message error rate according to the signaling data associated by the signaling association module 714 and the original signaling data received by the interface module 726; in the process of analyzing the message error rate, the user-level error rate, the cell-level error rate, the message-level error rate, or the joint error rate may be analyzed according to the difference in statistical granularity of the signaling data. The user-level error rate is counted by taking user identifiers (RNTI, TMSI and IMSI) as indexes, the cell-level error rate is counted by taking cell identifiers (PCI and CGI) as indexes, the Message-level error rate is counted by taking a Message Type (Message Type) as an index, and the joint error rate is counted by taking the user identifiers, the cell identifiers and the Message types as indexes;
a KPI indicator accuracy detection module 724, connected to the KPI indicator statistics module 715 and the interface module 726, for comparing the KPI indicators from the KPI indicator statistics module 715 with the KPI indicators from the current network gateway received by the interface module 726, and determining the KPI accuracy;
and the network quality evaluation module 725 is connected to the network optimization analysis and presentation module 716, and performs network quality evaluation according to the optimization analysis result of the network optimization analysis and presentation module 716 to obtain a network quality evaluation report of the soft mining acquisition area.
The operation of the apparatus shown in fig. 7 will be described with reference to a specific application scenario.
Scene one
In this scenario, a test terminal (e.g., a multi-UE emulator) initiates a service, an SCA collects full Signaling data sent by the multi-UE emulation, and a Mobile Signaling express Equipment (MSE for short) for LTE soft mining shown in fig. 7 performs accuracy detection of bottom layer data transmission on the full Signaling data for soft mining, where the detection processing procedure includes:
step one, a multi-UE simulator initiates a service, and the multi-UE simulator records an original signaling interacted by each simulated user in a communication process;
step two, SCA receives the full amount of signaling data output by the mirror image port of E-NodeB or MME;
step three, the data receiving module 711 of the MSE shown in fig. 7 receives the full amount of signaling data from the SCA through the IF1 interface;
step four, the interface transmission detection module 721 of the MSE detects whether the SCA supports the serial data transmission protocol by detecting the communication process with the SCA, and detects the number of ACK messages and NACK messages and the number of retransmission packets to detect the packet error rate, retransmission rate, and transmission delay;
step five, decoding the full amount of signaling data received by the data receiving module 711 of the signaling decoding module 713 of MSE;
step six, the signaling validity detection module 722 of the MSE determines the detection decoding rate of the signaling data decoded by the signaling decoding module 713;
seventhly, the signaling correlation module 714 of the MSE correlates the signaling data decoded by the signaling decoding module 713, and obtains xDR;
step eight, the interface module 726 of MSE receives the original signaling data from the multi-UE emulator;
step nine, the signaling accuracy detection module 723 of the MSE analyzes the correlated signaling data and xDR from the signaling correlation module 714 and the original signaling data from the interface module 726 to obtain an analysis result table as shown in table 1, and counts the message error rate according to the table;
table 1 includes the analysis record number, the cell identity (PCI), the user identity (RNTI), the message type, the reference number (i.e., the number of original signaling data received by the interface module 726), and the soft data statistics (i.e., the number of signaling data associated by the signaling association module 714);
TABLE 1
| Record number | Cell identity | User identification | Message type | Reference number | Statistics of soft sampling |
| 1 | 46 | 32 | RRC Connenct Requset | M_1 | C_1 |
| 2 | 46 | 12 | Measurement Report | M_2 | C_2 |
| 3 | 48 | 18 | Measurement Report | M_3 | C_3 |
| … | … | … | … | … | … |
| N | 72 | 12 | RRC Conn Release | M_N | C_N |
Specifically, cell level error rate: taking the cell identification as a statistic dimension, and counting the error rate after combining other dimensions, Rcell=(|(C_1+C_2)-(M_1+M_2)|+|C_3-M_3|+…+|C_N-M_N|)/(M_1+M_2+M_3…+M_N)；
User-level error rate: taking the user identification as a statistical dimension, and counting the error rate after combining other dimensions, RUE=(|C_1-M_1|+|(C_2+C_N)-(M_2+M_N)|…+|C_3-M_3|)/(M_1+M_2+M_3…+M_N)
Message level error rate: taking the message type as a statistical dimension, and counting the error rate after combining other dimensions, RMSG=(|C_1-M_1|+|(C_2+C3)-(M_2+M_3)|…+|C_N-M_N|)/(M_1+M_2+M_3…+M_N)；
Joint error rate: taking the three dimensions of cell-user-message joint statistics, requiring the number of items jointly determined by each identification to be consistent with the check data, the index having the highest requirement on accuracy, Rjnt=(|C_1-M_1|+|C_2-M_2|+|C_3-M_3|…+|C_N-M_N|)/(M_1+M_2+M_3…+M_N)。
The accuracy of bottom layer data transmission can be detected for soft mining through the process.
Scene two
In a second scenario, KPI accuracy of soft mining of a provincial level soft mining system is detected in an external field, and the detection process includes steps one to nine as in the first scenario, and further includes the following steps:
step ten, a KPI statistic module 715 of MSE performs KPI statistics according to the signaling data associated by the signaling association module 714 to obtain KPI of soft mining;
step eleven, an interface module 726 of MSE receives a current network KPI of a current network OMC for initiating service statistics in the provincial mobile network for a multi-UE simulator;
twelfth, the KPI indicator accuracy detection module 724 receives the soft acquisition KPI obtained by the KPI indicator statistics module 715 and the current network KPI received by the interface module 726, and obtains a KPI record table as shown in table 2, where table 2 includes a record number, a KPI type, a current network KPI, and a soft acquisition KPI, and determines an error rate of the KPI according to the content shown in table 2;
TABLE 2
| Record number | KPI type | Existing network KPI | Soft mining KPI |
| 1 | RRC connection success rate | X_1 | Y_1 |
| … | … | … | … |
| N | RRC reestablishment ratio | X_N | Y_N |
Specifically, the error rate of the RRC connection success rate = (X _ 1-Y-1)/Y _1, the RRC reestablishment proportion error rate = (X _ N-Y _ N)/Y _ N;
thirteen, the network optimization analysis and presentation module 716 of the MSE performs optimization analysis according to the soft mining KPI obtained by the KPI index statistic module 715, the xDR obtained by the signaling correlation module 714 and the full signaling data received by the data receiving module 711 to obtain an optimization analysis result, and presents the optimization analysis result;
fourteen, the network quality evaluation module 725 performs network quality evaluation according to the optimization analysis obtained by the network optimization analysis and presentation module 716, and generates a network quality evaluation report.
And through the processing process of the scene two, the accuracy of the upper application aspect of soft mining can be detected.
Therefore, the accuracy of soft mining can be reliably and effectively detected through the first scene and the second scene, and the detection requirements in multiple aspects can be met.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (20)
1. A detection method for Long Term Evolution (LTE) soft acquisition is characterized by comprising the following steps:
receiving the full signaling data collected by a traffic convergence adapter SCA, and detecting the signaling transmission accuracy of soft mining according to the received full signaling data;
decoding the received full signaling data, and detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full signaling data;
and associating the decoded signaling data into the signaling data of each user or each service, receiving original signaling data sent by an air interface of the test terminal UE, and detecting the signaling accuracy of soft mining according to the associated signaling data and the received original signaling data.
2. The method according to claim 1, wherein detecting the signaling accuracy of the soft-handoff according to the associated signaling data and the received original signaling data specifically comprises:
analyzing the associated signaling data to determine the message type and the message quantity of the associated signaling data, analyzing the received original signaling data to determine the message type and the message quantity of the acquired original signaling data, and determining the message type and the message quantity of all the tested UEs in the associated signaling data;
and detecting the signaling completeness of the soft mining and/or determining the message error rate of the soft mining according to the associated message types and message quantities of the signaling data of all the UE to be tested and the received message types and message quantities of the original signaling data.
3. The method according to claim 1, wherein detecting the signaling transmission accuracy of soft mining specifically comprises:
and detecting whether the soft mining supports a serial data transmission protocol, detecting the number of ACK/NACK (acknowledgement/negative acknowledgement) data packets, the number of retransmitted data packets and the number of received data packets, and determining the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link.
4. The method according to claim 1, wherein detecting the signaling validity of the soft-handoff according to the decoded signaling data and the received full amount of signaling data specifically comprises:
and determining the decoding success rate of soft mining according to the number of successfully decoded signaling data packets and the number of the received full signaling data packets.
5. The method according to claim 2, wherein detecting signaling completeness of soft handover according to the associated message types and message quantities of all signaling data of the test UE and the received message types and message quantities of the original signaling data specifically comprises:
and comparing the associated message types of the signaling data of all the UE to be tested with the acquired message types of the original signaling data, and comparing the associated message quantity of the signaling data of all the UE to be tested with the acquired message quantity of the original signaling data, and determining that the soft acquisition has signaling completeness under the condition that the compared message contents are the same and the compared message quantities are the same.
6. The method according to claim 4, wherein determining the soft error rate according to the message type and the message number of the associated signaling data of the test UE and the message type and the message number of the received original signaling data specifically comprises:
determining a cell level message error rate, a user level message error rate, a message level message error rate, or a joint error rate; wherein,
the cell level message error rate is determined according to the following formula:
determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
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</mrow>
</math>
wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message-level message error rate and,Rjntfor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the message type in the kth of the jth user in the ith cell after association, and M' is the total number of the received messages of the original signaling data.
7. The method of claim 1, wherein detecting signaling accuracy of soft mining further comprises:
and generating a service detail record xDR according to the decoded signaling data, and determining the association success rate according to the xDR and the associated signaling data.
8. The method of claim 7, further comprising:
determining a key performance index KPI of soft mining according to the correlated signaling data and xDR, receiving a current network KPI from a current network manager or a signaling analysis platform, and detecting the KPI accuracy of the current network manager or the signaling analysis platform by comparing the KPI of soft mining with the current network KPI;
wherein the KPI comprises at least: the radio resource controls the RRC connection success rate, the RRC reestablishment ratio, the RRC establishment success times, the RRC connection reestablishment success times and the RRC connection establishment request times.
9. The method according to claim 8, wherein detecting the accuracy of the KPI of soft mining specifically comprises:
for the same type of KPI, the error rate for that type of KPI is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining.
10. The method of claim 8, further comprising:
performing optimization analysis on the soft mining object area according to the result of detecting the KPI accuracy of the current network management or the signaling analysis platform, the xDR and the received full signaling data;
and carrying out network quality evaluation according to the result of the optimization analysis.
11. A movable detection device for Long Term Evolution (LTE) soft mining is characterized in that the device is a movable independent device and comprises:
the receiving module is used for receiving the full signaling data collected by the traffic aggregation adapter SCA; receiving original signaling data sent by an air interface of a test terminal UE;
the first detection module is used for detecting the signaling transmission accuracy of soft mining according to the received full amount of signaling data;
a decoding module, configured to decode the received full amount of signaling data;
the second detection module is used for detecting the signaling effectiveness of soft mining according to the decoded signaling data and the received full amount of signaling data;
the association module is used for associating the decoded signaling data into the signaling data of each user or each service;
and the third detection module is used for detecting the signaling accuracy of the soft mining according to the associated signaling data and the received original signaling data.
12. The apparatus according to claim 11, wherein the third detecting module specifically includes:
the analysis submodule is used for analyzing the associated signaling data to determine the message type and the message quantity of the associated signaling data, analyzing the received original signaling data to determine the message type and the message quantity of the acquired original signaling data;
the determining submodule is used for determining the message types and the message quantity of all the tested UEs in the associated signaling data;
the first detection submodule is used for detecting the signaling completeness of the soft mining according to the message types and the message quantity of the associated signaling data of all the UE to be tested and the message types and the message quantity of the received original signaling data;
and the second detection submodule is used for determining the soft-mining message error rate according to the message types and the message quantity of the associated signaling data of all the tested UEs and the message types and the message quantity of the received original signaling data.
13. The apparatus of claim 11, wherein the first detection module is specifically configured to:
and detecting whether the soft mining supports a serial data transmission protocol, detecting the number of ACK/NACK (acknowledgement/negative acknowledgement) data packets, the number of retransmitted data packets and the number of received data packets, and determining the packet error rate, the retransmission rate and the soft mining transmission delay of a soft mining link.
14. The apparatus of claim 11, wherein the second detection module is specifically configured to:
and determining the decoding success rate of soft mining according to the number of successfully decoded signaling data packets and the number of the received full signaling data packets.
15. The apparatus of claim 12, wherein the first detection submodule is specifically configured to:
and comparing the associated message types of the signaling data of all the UE to be tested with the acquired message types of the original signaling data, and comparing the associated message quantity of the signaling data of all the UE to be tested with the acquired message quantity of the original signaling data, and determining that the soft acquisition has signaling completeness under the condition that the compared message contents are the same and the compared message quantities are the same.
16. The apparatus of claim 12, wherein the second detection submodule is specifically configured to:
determining a cell level message error rate, a user level message error rate, a message level message error rate, or a joint error rate; wherein,
the cell level message error rate is determined according to the following formula:
determining a user-level message error rate according to the following formula:
the message level message error rate is determined according to the following formula:
determining a joint message error rate according to the following formula: <math>
<mrow>
<msub>
<mi>R</mi>
<mi>jnt</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>r</mi>
</munderover>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>s</mi>
</munderover>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</munderover>
<mrow>
<mo>(</mo>
<mo>|</mo>
<msub>
<mi>M</mi>
<mi>ijk</mi>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mi>ijk</mi>
</msub>
<mo>|</mo>
<mo>)</mo>
</mrow>
</mrow>
<msup>
<mi>M</mi>
<mo>′</mo>
</msup>
</mfrac>
<mo>;</mo>
</mrow>
</math>
wherein, i is the cell number of soft mining determined according to the associated signaling data, j is the UE number of soft mining determined according to the associated signaling data, k is the number of the message types of soft mining determined according to the associated signaling data, and RcellIs the cell level message error rate, RUETo the user-level message error rate, RMESIs the message level message error rate, RjntFor joint message error rates, MijkThe number of messages of the kth message type of the jth user in the ith cell in the received original signaling data, CijkThe number of messages of the message type in the kth of the jth user in the ith cell after association, and M' is the total number of the received messages of the original signaling data.
17. The apparatus of claim 11, wherein the association module is further configured to generate a service detail record xDR according to the decoded signaling data; then the process of the first step is carried out,
the third detection module further comprises:
and the third detection sub-module is used for determining the association success rate according to the xDR and the associated signaling data.
18. The apparatus of claim 17, wherein the receiving module is further configured to receive an existing network KPI from an existing network manager or a signaling analysis platform;
the device further comprises:
the fourth detection module is used for determining a key performance index KPI of soft mining according to the correlated signaling data and xDR, and detecting the KPI accuracy of a network manager or a signaling analysis platform of the current network by comparing the KPI of soft mining with the KPI of the current network;
wherein the KPI comprises at least: the radio resource controls the RRC connection success rate, the RRC reestablishment ratio, the RRC establishment success times, the RRC connection reestablishment success times and the RRC connection establishment request times.
19. The apparatus of claim 18, wherein the fourth detection module is specifically configured to:
for the same type of KPI, the error rate for that type of KPI is: the difference value of the KPI of the current network and the KPI of the soft mining is the quotient of the KPI of the soft mining.
20. The apparatus of claim 18, further comprising:
the optimization analysis module is used for performing optimization analysis according to the KPI of the soft mining determined by the fourth detection module, the xDR determined by the correlation module and the full signaling data received by the receiving module;
and the quality evaluation module is used for carrying out network quality evaluation according to the result of the optimization analysis.
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