WO2016015240A1

WO2016015240A1 - Method and device for estimating gain value of characteristic in network

Info

Publication number: WO2016015240A1
Application number: PCT/CN2014/083297
Authority: WO
Inventors: 周海波; 朱益
Original assignee: 华为技术有限公司
Priority date: 2014-07-30
Filing date: 2014-07-30
Publication date: 2016-02-04
Also published as: CN105519173A; CN105519173B

Abstract

The present invention provides a method and device for estimating a gain value of a characteristic in a network. The method comprises: dividing in advance, in each gain sub-scenario, the value range of gain influence factors corresponding to a characteristic to be estimated; acquiring all gain events within a preset period of time after the characteristic to be estimated is enabled in an office to be tested; estimating a sub-scenario gain value of each gain sub-scenario; calculating the scenario probability of each gain sub-scenario according to the value of each influence factor in each gain event; and calculating, according to the sub-scenario gain value and the scenario probability of each gain sub-scenario, a gain value generated after the characteristic to be estimated is enabled in the office to be tested. The present invention provides a method for estimating a gain value generated after a characteristic to be estimated is enabled in an office to be tested, so that the gain value generated after the characteristic to be estimated is enabled in a specific office in a network can be precisely estimated, and reference is provided for effectively evaluating the gain effect of the characteristic in the specific office.

Description

Method and device for estimating gain size in network

The present invention relates to the field of communications technologies, and in particular, to a method and apparatus for estimating the gain of a characteristic in a network. Background technique

With the continuous development of network technologies, various emerging technical features that are applied to wireless networks to increase the transmission gain of wireless networks are emerging. For example: 64 Quadrature Amplitude Modulation (64QAM), Dual Carrier (DC), and Interference Cancellation (IC), which are used in WCDMA commercial wireless networks, can be used to enhance wireless. The characteristics of network throughput.

As features such as 64QAM, DC, or IC that improve the gain of the transmitted signal in a wireless network, each feature has its own specific application scenario. Each feature can only take effect and improve the transmission of the wireless network in its specific application scenario. The gain of the signal. For example, the IC feature can only be effective and gains in the Full Buffer service, but there is no gain for the Burst service such as web pages. Therefore, before each feature is put into practical use, it is necessary to estimate the gain of the transmitted signal in different network scenarios.

At present, the combination of theoretical estimation of network scenarios, laboratory verification, and measured gains in commercial networks is used to estimate the gain of the transmitted signals in the network scenario. However, for different characteristics, the specific conditions for the activation of the switch are different, and the gain of the signal transmitted in a network scenario is related to the number of users and the service model in the network scenario. Therefore, for the same network scenario, the same feature has different gains for different network sites. The theoretical estimation, laboratory verification, and the measured gain of the site in the commercial network, combined with the estimated characteristics of the gain of the network transmission signal, the range of which is large, only applicable to a large network scenario, in a network scenario. For each of the sites, the estimated gain is not accurate. Summary of the invention

In view of this, the present invention provides a method and apparatus for estimating the gain of a characteristic in a network. Therefore, it is possible to accurately estimate the gain of the specific feature of the to-be-predicted characteristic in a network, and provide a basis for the effective evaluation characteristic to gain effect at a specific site.

A first aspect of the embodiments of the present invention provides an apparatus for estimating a gain of a characteristic in a network, where the apparatus includes:

a dividing unit, configured to pre-divide a value range of a gain influencing factor corresponding to the to-be-predicted characteristic in each of the gain sub-scenarios, where the gain influencing factor is to affect the to-be-predicted characteristic after the to-be-tested point is opened The factor of the resulting gain size;

a first acquiring unit, configured to acquire all gain events in a preset time period after the to-be-predicted feature is turned on in the site to be tested, where each gain event includes one value of each gain influencing factor; a sub-scene gain value for estimating a gain sub-scenario, where the sub-scenario gain value is a range of gain influencing factors of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site The amount of gain produced;

a first calculating unit, configured to calculate a scene probability of each of the gain sub-scenarios according to a value of each of the influencing factors in the respective gain events, where the scene probability is that the to-be-tested site opens the estimated characteristic The probability of a gain sub-scene;

And a second calculating unit, configured to calculate, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-tested feature to be turned on.

In a first possible implementation manner of the first aspect, the first calculating unit includes:

a classification sub-unit, configured to determine a gain sub-scenario to which the value of the gain influencing factor in each gain event belongs;

The probability calculation subunit is configured to calculate a scene probability of each of the gain sub-scene, and the scene probability is a ratio of the number of gain events possessed by the gain sub-scene to the number of acquired gain events.

In a second possible implementation manner of the first aspect of the present disclosure, the second calculating unit includes:

a product subunit, configured to calculate a product of a sub-scene gain value of each gain sub-scene and a scene probability as an estimated gain sub-value of the gain sub-scene;

a summation subunit for finding the sum of the estimated gain sub-values of all gain sub-scenarios as the The measurement point opens the gain value generated by the to-be-predicted characteristic.

In a third possible implementation manner of the first aspect of the present disclosure, the device further includes: a second acquiring unit, configured to obtain a value of an opening influence factor corresponding to the to-be-predicted feature in the site to be tested;

a first determining unit, configured to determine whether the value of the opening influencing factor all meets the opening preset value, and when the value of the opening influencing factor all meets the opening preset value, the to-be-tested site satisfies the opening The condition of the estimated feature.

With reference to the first aspect of the embodiments of the present invention to the third possible implementation of the first aspect, in a fourth possible implementation, the apparatus further includes:

a third obtaining unit, configured to obtain a value of an effective influence factor corresponding to the to-be-predicted feature in the site to be tested;

a second determining unit, configured to determine whether the value of the effective influencing factor meets the effective preset value, and when the value of the effective influencing factor all meets the effective preset value, the to-be-tested site opens the to-be-predetermined The estimated feature can take effect.

A second aspect of the present invention provides an apparatus for estimating a gain of a characteristic in a network, wherein the apparatus comprises:

And a memory, wherein the memory is used to store an instruction, the processor is configured to retrieve an instruction, and the instruction includes:

Obtaining a value of a gain influencing factor corresponding to the to-be-predicted feature in the site to be tested, where the gain influencing factor is a factor that affects a magnitude of a gain generated by the to-be-predicted feature after the to-be-tested site is turned on;

Only one value of the gain influencing factor is changed to generate a gain sub-scenario, and a gain sub-scenario having the same number of the gain influencing factors is generated, and the types of gain influence factors changed in each of the gain sub-scene are different;

Estimating, by each gain sub-scenario, a gain generated by the to-be-predicted characteristic as a sub-field gain value;

Calculating, according to the sub-scene gain value of each of the gain sub-scenarios, a gain value generated by the to-be-measured characteristic of the to-be-tested site.

A third aspect of the embodiments of the present invention provides a method for estimating a gain size of a network in a network. The characteristic range of the gain influencing factor corresponding to the to-be-predicted characteristic in each of the gain sub-scenarios is pre-divided, and the gain influencing factor is that after the opening of the to-be-tested point is affected The factor of the resulting gain size, the method includes:

Obtaining all the gain events in the preset time period after the opening of the to-be-predicted characteristic in the to-be-tested site, and each gain event includes a value of each of the gain influencing factors;

Estimating a sub-scene gain value of each of the gain sub-scenarios, where the sub-scenario gain value is generated after the to-be-predicted characteristic of the to-be-tested site is opened and the value of the gain influencing factor of the gain sub-scene is Gain size

Calculating a scene probability of each of the gain sub-scenarios according to a value of each of the influencing factors in the respective gain events, where the scene probability is a probability that the gain sub-scene occurs after the estimated feature is turned on by the to-be-tested site;

And calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-tested feature to be turned on.

In a first possible implementation manner of the third aspect, the determining, by the value of each influencing factor in each of the gain events, the scenario probability of each of the gain sub-scenarios includes:

Determining a gain sub-scenario to which the value of the gain influencing factor belongs in each gain event; calculating a scene probability of each gain sub-scene, the scene probability being the number of gain events possessed by the gain sub-scene and the acquired gain event The ratio of the number.

In a second possible implementation manner of the third aspect of the embodiments of the present invention, the calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, the gain generated by the to-be-measured feature of the to-be-tested site Values include:

Calculating a product of a sub-scene gain value of each gain sub-scene and a scene probability as an estimated gain sub-value of the gain sub-scene;

The sum of the estimated gain sub-values of all the gain sub-scenarios is obtained as the gain value generated by the to-be-predicted characteristic of the to-be-tested site.

In a third possible implementation manner of the third aspect of the embodiments of the present disclosure, the acquiring a site to be tested obtains a value of an opening influence factor corresponding to the to-be-predicted feature in the site to be tested;

Determining whether the values of the opening influence factors all satisfy the opening preset value; When the values of the opening influencing factors all satisfy the opening preset value, the to-be-measured site satisfies the condition for opening the to-be-predicted characteristic.

With reference to the third aspect of the present invention, in a third possible implementation manner, in the fourth possible implementation manner, the acquiring the to-be-tested site in the preset time period after the opening of the to-be-predicted feature is All pre-gain event methods also include:

Obtaining the value of the effective influencing factor corresponding to the feature to be estimated in the site to be tested;

Determining whether the values of the effective influencing factors all meet the effective preset value;

When the value of the effective influencing factor satisfies the effective preset value, the to-be-predicted feature can be activated.

As can be seen from the above, the present invention has the following beneficial effects:

The present invention provides a method and apparatus for estimating the gain of a characteristic in a network, and pre-dividing a range of values of a gain influencing factor corresponding to a characteristic to be estimated in each gain sub-scenario, wherein the gain influencing factor is an influence Determining a gain gain event generated by the estimated characteristic after the point to be tested is turned on, each gain event includes a value of each gain influencing factor; estimating a sub-scene gain value of each gain sub-scene, The sub-scenario gain value is a magnitude of a gain generated by the range of gain influencing factors of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site; according to each influencing factor of each gain event Calculating a scene probability of each of the gain sub-scenarios, where the scene probability is a probability that the gain sub-scene occurs after the estimated feature is turned on by the to-be-tested site; and the sub-scene gain value according to each of the gain sub-scene Calculating a gain value generated by the to-be-predicted feature of the to-be-measured site by using the scenario probability, and the present invention provides an estimate of the to-be-predicted feature after the site to be tested is opened. The size of the raw gain value method may be implemented to be estimated accurately estimate the magnitude of the gain characteristics of the particular game point after the opening in a network, providing evidence for the effect of the specific gain characteristic evaluation game point. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are to be regarded as In other words, other drawings can be obtained from these drawings without any creative work.

1 is a schematic structural view of an apparatus for estimating the gain of a characteristic in a network according to the present invention;

2 is a schematic diagram of a gain influencing factor affecting the gain of the to-be-predicted characteristic after the opening of the to-be-tested site is performed according to the present invention;

3 is a schematic structural view of a second embodiment of an apparatus for estimating the gain of a characteristic in a network according to the present invention;

4 is a schematic structural view of a third embodiment of an apparatus for estimating the gain of a characteristic in a network according to the present invention;

FIG. 5 is a flow chart of a fourth embodiment of a method for estimating the gain of a characteristic in a network according to the present invention; FIG.

FIG. 6 is a flow chart of a fifth embodiment of a method for estimating the gain of a characteristic in a network according to the present invention. detailed description

The present invention provides a method and a device for estimating the gain of a characteristic in a network, in order to provide an implementation of the estimated size of the gain generated in the network in the network. The preferred embodiments of the invention are to be considered as illustrative and not restrictive. And the first embodiment

FIG. 1 is a schematic structural diagram of an apparatus for estimating the gain of a characteristic in a network according to the present invention. The apparatus includes:

The dividing unit 101 is configured to pre-separate a value range of the gain influencing factor corresponding to the to-be-predicted characteristic in each of the gain sub-scenarios, where the gain influencing factor is to affect the to-be-predicted characteristic to be opened in the to-be-tested site The factor of the gain that is produced afterwards.

The gain influencing factor is a factor that affects the magnitude of the gain generated by the to-be-measured site after the feature to be estimated is turned on. Generally, the gain influencing factor includes the site to be tested. The number of users and the probability of the scene of the business type. In general, the number of users and/or service types of different sites is different, and the number of users and/or service types are different in different time segments of the same site.

According to the actual situation, the range of the gain influencing factors corresponding to the characteristics to be estimated in each gain sub-scene may be pre-divided. Set a value range for all gain influencing factors corresponding to the characteristics to be estimated as a gain sub-scene. The value range of at least one gain influencing factor and the range of values in other gain sub-scenarios in each gain sub-scenario different.

For example, as shown in FIG. 2, there are six factors affecting the gain of the gain to be estimated after the to-be-measured site is turned on. In the first gain sub-scenario, the influencing factor 1 takes the value b; the influencing factor 2 takes the value c; the influencing factor 3 takes the value of the air interface load, and the air interface meets: the actual load-bottom noise>target load-3db; The value of the influencing factor 4 is 2ms, the proportion of TTI users is high, and the proportion of 2ms user load is >50%. The value of the influencing factor 5 is the Full Buffer service. The value of the influencing factor 6 is l≤Full Buffer. The number of users is ≤ 2. Compared with the first gain sub-scene, the second gain sub-scenario has only a different range of influence factors 6 . The sixth gain subscenario is different from the first gain subscenario, influencing factor 4, influencing factor 5 and influencing factor 6.

When the instantaneous value of all the influencing factors in the site to be tested is within the range of the influencing factors of a gain sub-scenario, it indicates that the site to be tested is in this gain sub-scene at this moment. When the values of the gain influencing factors at different times of the site to be tested belong to the same gain sub-scenario, the difference in the magnitude of the gain of the site to be estimated under the feature to be estimated is not large.

It should be noted that different gain sub-scenarios actually describe different ranges of values of the influencing factors of the to-be-predicted characteristics of the to-be-measured site, and different values of each influencing factor may affect the to-be-tested The size of the gain after the feature is to be estimated is opened. Therefore, in different gain sub-scenarios (the range of influencing factors is different), the gains of the estimated characteristics of the site to be tested are different.

The first obtaining unit 102 is configured to acquire all the gain events in the preset time period after the to-be-predicted feature is turned on in the site to be tested, and each gain event includes a value of each of the gain influencing factors.

a gain event is corresponding to the to-be-predicted feature in the site to be tested at a certain moment A general term for the values of all influencing factors, where there is only one value for each influencing factor at a certain moment. That is to say, at a certain moment, the value of all the influencing factors in the site to be tested is used as a description of the gain event occurring at this moment. Obtaining the to-be-tested office in the preset time period, and obtaining a plurality of gain events after the to-be-predicted feature is obtained, that is, obtaining the to-be-measured site and the waiting to be performed at different times in the preset time period The value of the influencing factor after the estimated feature. Generally, the gain influencing factors of all the sites in a network system are stored in information such as the system, the log, and the configuration script, and can be obtained from information such as the system, the log, and the configuration script when needed.

When the gain influencing factor is only in the message information, the value of the gain influencing factor corresponding to the to-be-predicted feature in the site to be tested may be obtained only from the system, and the gain influencing factor is only in the log or configuration script. In the information, the value of the gain influencing factor corresponding to the feature to be estimated in the site to be tested may be obtained only from the log or the configuration script information; when the gain influencing factor is in the information such as the system, the log, and the configuration script information. The value of the gain influencing factor corresponding to the feature to be estimated in the site to be tested needs to be obtained from the information such as the message system, the log, and the configuration script information.

The estimating unit 103 is configured to estimate a sub-scene gain value of each of the gain sub-scenarios, where the sub-scenario gain value is a gain effect of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site The amount of gain produced by the factor range.

The gain generated by the gain sub-scenario to open the to-be-predicted characteristic is predictable, and can be estimated by using the scene theory estimation and the laboratory verification method, and can be estimated in the gain sub-scenario in the The amount of gain generated after the estimated feature is turned on.

The first calculating unit 104 is configured to calculate a scene probability of each of the gain sub-scenarios according to the value of each of the influencing factors in the respective gain events, where the scene probability is that the to-be-tested site opens the estimated characteristic The probability of a gain subscenario.

The first calculating unit 104 includes:

Considering the preset time period as a total network field for the site to be estimated The value of the influencing factor corresponding to the to-be-predicted characteristic at different times may change. The value of the influencing factor at each moment is regarded as a gain event, and the gain sub-scenario to which the gain event belongs is determined according to the range of the influencing factors in the gain sub-scene to which the value of the influencing factor of the gain event belongs. The ratio of the number of gain events possessed by the gain sub-scenario to the total number of gain events acquired in the preset time period is the scene probability of the gain sub-scenario, and the scene probability is that the pre-measurement site opens the pre-measure The probability of the gain sub-scene occurring after the characteristic is estimated.

For example: Suppose that 1000 gain events are obtained within a preset time period, and the gain event of the influence factor in the range of the influence factor of the first gain sub-scene belongs to the first gain sub-scene, which belongs to the first The gain event in the gain sub-scenario is 30, and the scene probability of the first gain sub-scene is 3%. The calculation method of other gain sub-scene is similar to the technical method of the first sub-scenario described above, and will not be described here.

The second calculating unit 105 is configured to calculate, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-measured feature to be turned on.

The second calculating unit 105 includes:

And a summation subunit, configured to find a sum of estimated gain sub-values of all gain sub-scenarios as a gain value generated by the to-be-predicted characteristic of the to-be-tested site.

The first calculation unit 104 calculates the scene probability of each of the gain sub-scenarios. For the same gain sub-scenario, the scene probabilities of different test sites are different. For example, for Shanghai, the scene probability of the highway sub-scenario is 15%, the scene probability of a high-speed rail scene is 85%; for Beijing, the probability of a highway sub-scenario is 75%; for a high-speed rail scene, the probability is 25%.

For the same site to be tested, the probability of the scene of the gain sub-scenario may also change with time. Therefore, the scene probability of each gain sub-scene is for a preset time period and is meaningful within a specific time range.

The product of the sub-scene gain value of each gain sub-scene and the scene probability is calculated as the estimated gain sub-value of the gain sub-scene. The estimated gain sub-values of all the gain sub-scenarios are summed to obtain the gain value generated by the to-be-tested site to open the to-be-predicted characteristic.

As can be seen from the above, the present invention has the following beneficial effects: Determining, in advance, a value range of a gain influencing factor corresponding to the to-be-predicted characteristic in each of the gain sub-scenarios, where the gain influencing factor is a magnitude of a gain generated by the to-be-predicted characteristic after the to-be-tested point is turned on a factor, obtaining all gain events within a preset time period after the opening of the to-be-predicted characteristic in the site to be tested, each gain event including a value of each gain influencing factor; estimating each gain sub-scenario a sub-scenario gain value, where the sub-scene gain value is a magnitude of a gain generated by the range of gain influencing factors of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site; Calculating the probability of each of the gain sub-scenarios by using the value of each of the influencing factors, wherein the scene probability is a probability that the gain sub-scene occurs after the estimated feature is turned on by the to-be-tested site; The sub-scene gain value of the scene and the scene probability are calculated, and the gain value generated by the to-be-predicted characteristic is opened by the to-be-tested site, and the present invention provides an estimated to-be-predicted characteristic in the to-be-tested The method of maximizing the gain value generated after the opening can accurately predict the gain of the specific feature of the to-be-predicted characteristic in a network, and effectively evaluate the gain effect of the characteristic at a specific site. Provide evidence. Embodiment 2

FIG. 3 is a schematic structural diagram of a second embodiment of a device for estimating the gain of a characteristic in a network according to the present invention. The second embodiment further includes a first determining unit and a second determining unit, which are used to measure the device, compared with the first embodiment. Whether the point-to-predicted characteristic satisfies the opening condition and the effective condition, the device includes:

The dividing unit 101 is similar to the first embodiment, and the description of the first embodiment will be omitted. The second obtaining unit 301 is configured to obtain a value of a turn-on impact factor corresponding to the to-be-predicted feature in the site to be tested.

The first determining unit 302 is configured to determine whether the value of the opening influencing factor all meets the opening preset value. If yes, the to-be-tested site satisfies the condition that the to-be-predicted feature is opened, and enters the third acquiring unit 303. If no, the to-be-tested site does not satisfy the condition that the feature to be estimated is opened, and enters the first returning unit 305. The second obtaining unit 301 and the first determining unit 302 mainly detect whether the site to be tested has a network hardware configuration for opening the to-be-estimated feature. Generally, the opening and influencing factors include network configuration such as hardware setting, air interface networking, and terminal type. The site to be tested must have a preset value of all the influencing factors to meet the conditions for opening the to-be-predicted feature. If one of the opening influencing factors is not available, the condition for the to-be-predicted feature is not met. It means that the to-be-tested feature cannot be opened by the to-be-tested site. At this time, the information that does not satisfy the condition for opening the to-be-predicted characteristic is returned, and the staff is reminded to configure all the values of the opening influence factors.

The third obtaining unit 303 is configured to obtain a value of an effective impact factor corresponding to the to-be-predicted feature in the site to be tested.

The second determining unit 304 is configured to determine whether the value of the effective influencing factor satisfies the effective preset value, and the to-be-predicted feature is enabled to be valid, and enters the first acquiring unit 102; The to-be-predicted feature of the to-be-tested site is not valid, and enters the second returning unit 306.

The third obtaining unit 303 and the second determining unit 304 mainly detect whether the site to be tested opened by the feature to be estimated has a software configuration that makes it valid. In general, the effective factors affecting the network software configuration such as air interface load and service type. The site to be tested must meet all the preset values that affect the influencing factors of the feature to be estimated, so as to satisfy the condition that the feature to be estimated is valid at the site to be tested, and there is an effective influencing factor. If the feature to be estimated is not valid at the site to be tested, it indicates that the to-be-predicted feature of the site to be tested cannot be valid. At this time, returning to the station to be tested to open the information that the to-be-predicted characteristic cannot be effective, and reminding the staff to configure all the values of the effective influencing factors.

The first calculating unit 104 is configured to calculate a scene probability of each of the gain sub-scenarios according to the value of each of the influencing factors in the respective gain events, where the scene probability is that the The probability of the gain sub-scene occurring after the characteristic is estimated.

The first calculating unit 104 includes:

The second calculating unit 105 includes:

The first obtaining unit 102, the estimating unit 103, the first calculating unit 104, and the second calculating unit 105 are similar to the first embodiment. Referring to the description of the first embodiment, details are not described herein again.

The first returning unit 305 is configured to return information that does not satisfy the condition for opening the to-be-predicted characteristic.

The second returning unit 306 is configured to return to the information about the to-be-tested site to open the information that the to-be-predicted feature cannot be valid.

Before estimating the gain generated by the to-be-estimated feature of the to-be-tested site, determining whether the site to be tested has a hardware configuration for opening the to-be-predicted feature, and further determining the to-be-tested site Whether the point has a software configuration that makes the feature to be estimated effective, may provide a preliminary determination as to whether the feature to be estimated can be installed at the site to be tested. Embodiment 3

4 is a schematic structural diagram of Embodiment 3 of an apparatus for estimating a gain in a network according to the present invention, where the apparatus includes: The memory 401 is used to store instructions, and the processor 402 is used to retrieve instructions. The instructions include:

Determining, in advance, a value range of a gain influencing factor corresponding to the to-be-predicted characteristic in each of the gain sub-scenarios, where the gain influencing factor is a magnitude of a gain generated by the to-be-predicted characteristic after the to-be-tested point is turned on the elements of;

Calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-measured feature to be turned on;

Optionally, determining the scenario probability of each gain sub-scenario according to the value of each of the influencing factors in each of the gain events includes:

Optionally, the calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, the gain value generated by the to-be-predicted feature of the to-be-tested site to be:

Finding a sum of estimated gain sub-values of all gain sub-scenarios as a gain value generated by the to-be-predicted characteristic of the to-be-tested site;

Optionally, before obtaining the multiple gain events corresponding to the to-be-predicted feature in the site to be tested, the method further includes:

Obtaining a value of an opening influence factor corresponding to the to-be-predicted feature in the site to be tested; Determining whether the values of the opening influence factors all satisfy the opening preset value;

When the values of the opening influencing factors all meet the preset value of the opening, the site to be tested satisfies the condition for opening the to-be-predicted characteristic;

When the value of the effective influencing factor satisfies the effective preset value, the to-be-predicted feature can be activated. Embodiment 4

FIG. 5 is a flowchart of Embodiment 4 of a method for estimating a gain in a network according to the present invention. Embodiment 4 is a method corresponding to the apparatus according to Embodiment 1, and the method includes: Step 501: Pre-division The range of values of the gain influencing factors corresponding to the characteristics to be estimated in each gain sub-scene.

The gain influencing factor is a factor that affects the magnitude of the gain generated by the to-be-predicted characteristic after the point to be tested is turned on. The value range of the gain influencing factors corresponding to the characteristics to be estimated in each gain sub-scene may be pre-divided according to actual conditions. Set a value range for all gain influencing factors corresponding to the characteristics to be estimated as a gain sub-scene. The value range of at least one gain influencing factor and the range of values in other gain sub-scenarios in each gain sub-scenario different.

Step 502: Acquire all the gain events in the preset time period after the to-be-predicted feature is turned on in the site to be tested, and each gain event includes a value of each of the gain influencing factors.

A gain event is a general term for the values of all influencing factors corresponding to the feature to be estimated in the site to be tested at a certain time. That is to say, at a certain moment, the value of all the influencing factors in the site to be tested is used as a description of the gain event occurring at this moment. Obtaining, in a preset time period, a plurality of gain events after the to-be-tested feature is opened, that is, acquiring the to-be-measured site and the to-be-predetermined at different times in the preset time period Estimate the value of the influencing factor after the characteristic. In general, the gain influencing factors of all the sites in a network system are It can be stored in information such as system, log, and configuration scripts. It can be obtained from information such as the system, log, and configuration script when needed.

Step 503: Estimating a sub-scene gain value of each of the gain sub-scenarios, where the sub-scenario gain value is a value range of a gain influencing factor of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site The amount of gain produced below.

Step 504: Calculate a scene probability of each of the gain sub-scenarios according to the value of each of the influencing factors in the respective gain events, where the scene probability is that the to-be-measured site opens the estimated characteristic and the gain sub-scene occurs. Probability.

The calculating the scene probability of each of the gain sub-scene according to the value of each of the influencing factors in each of the gain events includes:

Step 505: Calculate, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-measured feature to be turned on.

And calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scenarios, the gain values generated by the to-be-measured feature to be opened by the to-be-tested site includes:

Steps 501 to 505 are similar to the first embodiment, and the description of the first embodiment will be omitted. Embodiment 5

FIG. 6 is a flowchart of Embodiment 5 of a method for estimating a gain of a characteristic in a network according to the present invention The fifth embodiment is a method corresponding to the device in the second embodiment. The method includes: Step 601: Pre-divide a range of values of gain influencing factors corresponding to the to-be-predicted feature in each of the gain sub-scene.

Step 602: Obtain a value of the opening influence factor corresponding to the to-be-predicted feature in the site to be tested. Step 603: It is determined whether the value of the opening influencing factor all meets the opening preset value. If yes, the to-be-measured site satisfies the condition for opening the to-be-predicted characteristic, and step 604 is performed; if not, the to-be-tested If the site does not satisfy the condition for opening the to-be-predicted feature, step 610 is performed.

Step 602 and step 603 mainly detecting whether the site to be tested has a network hardware configuration for opening the to-be-predicted feature. In general, the opening factors include hardware configuration, network configuration of the air interface, and network type of the terminal. The site to be tested must have a preset value of all the influencing factors to meet the conditions for opening the to-be-predicted feature. If one of the opening influencing factors is not available, the condition for the to-be-predicted feature is not met. It means that the to-be-tested feature cannot be opened by the to-be-tested site. At this time, the information that does not satisfy the condition for opening the to-be-predicted characteristic is returned, and the staff is reminded to configure all the values of the opening influence factors.

Step 604: Obtain a value of an effective influencing factor corresponding to the to-be-predicted feature in the site to be tested. Step 605: Determine whether the value of the effective influencing factor meets the effective preset value. If yes, the to-be-tested feature can be activated, and step 606 is performed; if not, the to-be-tested office The point-to-predict feature cannot be valid, and step 611 is performed.

Step 604 and step 605 mainly detect whether the site to be tested opened by the feature to be estimated has a software configuration that makes it valid. In general, the influencing factors include the network software configuration such as air interface load and service type. The site to be tested must meet all the preset values that affect the influencing factors of the feature to be estimated, so as to satisfy the condition that the feature to be estimated is valid at the site to be tested, and there is an effective influencing factor. If yes, the characteristics to be predicted cannot be If the site to be tested is valid, it indicates that the to-be-predicted feature of the site to be tested cannot be valid. At this time, returning to the station to be tested to open the information that the to-be-predicted characteristic cannot be effective, and reminding the staff to configure all the values of the effective influencing factors.

Step 606: Acquire all the gain events in the preset time period after the to-be-predicted feature is turned on in the site to be tested, and each gain event includes a value of each of the gain influencing factors.

Step 607: Estimating a sub-scene gain value of each of the gain sub-scenarios, where the sub-scenario gain value is a value range of a gain influencing factor of the gain sub-scene after the to-be-predicted characteristic is turned on by the to-be-tested site The amount of gain produced below.

Step 608: Calculate a scene probability of each of the gain sub-scenarios according to the value of each of the influencing factors in the respective gain events, where the scene probability is that the to-be-measured site opens the estimated characteristic and then the gain sub-scene occurs. Probability.

Determining the scene probability of each of the gain sub-scene according to the value of each of the influencing factors in each of the gain events includes:

Step 609: Calculate, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, a gain value generated by the to-be-tested feature to be turned on.

Step 610: Return information that does not satisfy the condition for opening the to-be-predicted characteristic.

Step 611: Return to the information that the to-be-predicted feature cannot be validated by the site to be tested. The description of the second embodiment is similar to that of the second embodiment, and details are not described herein again.

It should be noted that the method and device for estimating the gain of the network in the network are applicable to WCDMA (Wideband Code Division Multiple Access, 3GPP-defined network formats such as LTE (Long Term Evolution, 3GPP Long Term Evolution) and GSM (Global System for Mobile Communications), when it is necessary to deploy features, optimize isochronization , Estimated characteristics of the site and gain size that need to be turned on.

It is apparent that those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Thus, it is intended that the present invention cover the modifications and modifications of the invention

Claims

Rights request

What is claimed is: 1. A device for estimating the magnitude of a gain in a network, wherein the device comprises:

The apparatus according to claim 1, wherein the first calculating unit comprises: a classifying subunit, configured to determine a gain sub-scenario to which a value of a gain influencing factor in each gain event belongs;

The apparatus according to claim 1, wherein the second calculating unit comprises: a product subunit, configured to calculate a product of a sub-scene gain value of each gain sub-scene and a scene probability as the gain sub- The estimated gain sub-value of the scene; a summation subunit, configured to find a sum of estimated gain sub-values of all gain sub-scenarios as a gain value generated by the to-be-predicted characteristic of the to-be-tested site.

The device according to claim 1, wherein the device further comprises: a second acquiring unit, configured to acquire a value of an opening influence factor corresponding to the to-be-predicted feature in the site to be tested;

The device according to any one of claims 1 to 4, wherein the device further comprises:

6. Apparatus for estimating the magnitude of a gain in a network, the apparatus comprising:

Generating a gain sub-scenario by changing only one value of the gain influencing factor, generating a gain sub-scenario having the same number of the gain influencing factors, and varying the types of gain influencing factors in each of the gain sub-scene; Estimating, by each gain sub-scenario, a gain generated by the to-be-predicted characteristic as a sub-scene gain value;

A method for estimating the gain of a characteristic in a network, characterized in that: a value range of a gain influencing factor corresponding to a characteristic to be estimated in each gain sub-scene is pre-divided, and the influence factor of the gain is an influence Determining, by the estimated characteristic, a factor of a gain generated after the to-be-measured site is turned on, the method includes:

The method according to claim 7, wherein the determining the scene probability of each of the gain sub-scene according to the value of each of the influencing factors in each of the gain events comprises:

The method according to claim 7, wherein the calculating, according to the sub-scene gain value and the scene probability of each of the gain sub-scene, the to-be-measured characteristic of the to-be-tested site is opened The raw gain values include:

The method according to claim 7, wherein the obtaining the value of the opening influence factor corresponding to the to-be-predicted feature in the site to be tested is obtained in the site to be tested;

Determining whether the values of the opening influence factors all satisfy the opening preset value;

When the values of the opening influencing factors all satisfy the opening preset value, the to-be-measured site satisfies the condition for opening the to-be-predicted characteristic.

The method according to any one of claims 7 to 10, wherein the obtaining comprises: