CN109934657A - Processing method, device, equipment and the medium of business datum - Google Patents
Processing method, device, equipment and the medium of business datum Download PDFInfo
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Abstract
The invention discloses the processing method of business datum, device, equipment and media.This method comprises: BOSS utilizes the system resource of Extended Kalman filter model prediction processing business data；Business diary is triggered in the system resource of business datum to record the business datum of user；The business diary of user is generated into order, is the service class order turn up service in order, while the data information of the non-serving class order in the data information and order of archiving services class order；According to the data information of the service class order after the historical information of database, filing in BOSS and the data information of the non-serving class order after filing, user's order is handled.Processing method, device, equipment and the computer readable storage medium of the business datum provided according to embodiments of the present invention can guarantee that business datum is matched with system resource, the reasonable employment of safeguards system resource.
Description
Technical field
The present invention relates to the communications fields more particularly to a kind of processing method of business datum, device, equipment and computer can
Read storage medium.
Background technique
In existing communications network system, there is quite huge business datum amount, these business datums pass through more
The setting of kind rule, is sent to each platform side or service operation supports system (BOSS, Business and Operation
Support System) side configured accordingly, to realize corresponding business function, met the needs of users.
The business datum of operator is many kinds of, and when business handling, and each core system is all linked with one another, mutually interdependent
Rely, bottleneck problem occurs in the system that upstream-downstream relationship is combined closely, will affect the process performance of whole system link.
Empirically with resource utilization processing business data on line, when business datum amount is larger, can crush entire system
System；When business datum amount is smaller, then cause system resource idle.
To sum up, since business datum and system resource mismatch, the unreasonable use of system resource is caused.
Summary of the invention
The embodiment of the invention provides a kind of processing method of business datum, device, equipment and computer-readable storage mediums
Matter can guarantee that business datum is matched with system resource, the reasonable employment of safeguards system resource.
One side according to an embodiment of the present invention provides a kind of processing method of business datum, this method comprises:
Service operation supports system BOSS to utilize the system resource of Extended Kalman filter model prediction processing business data；
Business diary is triggered in the system resource of business datum to record the business datum of user；
The business diary of user is generated into order, is the service class order turn up service in order, while archiving services class
The data information of non-serving class order in the data information and order of order；
Data information according to the service class order after the historical information of database, filing in BOSS and the non-clothes after filing
The data information of business class order, handles user's order.
In one embodiment, BOSS utilizes the system resource of Extended Kalman filter model prediction processing business data,
Include:
BOSS obtained this period by Extended Kalman filter model using the data volume of the business datum in a upper period
The posterior estimate of data volume；
According to the posterior estimate of this cycle data amount and the resources occupation rate of business datum, the system for predicting business datum
Resource.
In one embodiment, BOSS passed through Extended Kalman filter using the data volume of the business datum in a upper period
Model obtains the posterior estimate of this cycle data amount, comprising:
Using the data volume and predictive equation of the business datum in a upper period, the prior estimate of this cycle data amount was obtained
Value；
The observation of priori estimates, correction equation and this cycle data amount based on this cycle data amount, obtains this week
The posterior estimate of phase data volume.
In one embodiment, business diary is triggered in the system resource of business datum to record the business number of user
According to, comprising:
Business diary is triggered in the system resource of business datum by multiple channel to record the business datum of user.
In one embodiment, according to the data letter of the service class order after the historical information of database, filing in BOSS
The data information of non-serving class order after breath and filing, handles user's order, comprising:
According to the data information of the service class order after filing and the data information of the non-serving class order after filing, update
The historical information of database in BOSS；
The historical information of database in BOSS in the updated obtains the corresponding historical information of user；
Based on the corresponding historical information of user, user's order is handled.
In one embodiment, according to the data letter of the service class order after the historical information of database, filing in BOSS
The data information of non-serving class order after breath and filing, after handling user's order, further includes:
Based on the system resource of current system resource and business datum in BOSS, real-time output system utilization of resources shape
State.
According to another aspect of an embodiment of the present invention, a kind of processing unit of business datum is provided, which includes:
Prediction module supports system BOSS to utilize Extended Kalman filter model prediction processing business number for service operation
According to system resource；
Trigger module, for triggering business diary in the system resource of business datum to record the business datum of user；
Profiling module is the service class order turn up service in order, together for the business diary of user to be generated order
When archiving services class order data information and order in non-serving class order data information；
Processing module, for the data information according to the service class order after the historical information of database, filing in BOSS
With the data information of the non-serving class order after filing, user's order is handled.
In one embodiment, prediction module includes:
First acquisition submodule passed through spreading kalman for BOSS using the data volume of the business datum in a upper period
Filtering Model obtains the posterior estimate of this cycle data amount；
Predict submodule, for the resources occupation rate of posterior estimate and business datum according to this cycle data amount, in advance
Survey the system resource of business datum.
In one embodiment, the first acquisition submodule is specifically used for:
Using the data volume and predictive equation of the business datum in a upper period, the prior estimate of this cycle data amount was obtained
Value；
The observation of priori estimates, correction equation and this cycle data amount based on this cycle data amount, obtains this week
The posterior estimate of phase data volume.
In one embodiment, trigger module is specifically used for:
Business diary is triggered in the system resource of business datum by multiple channel to record the business datum of user.
In one embodiment, processing module includes:
Submodule is updated, for the non-serving class order after the data information according to the service class order after filing and filing
Data information, update BOSS in database historical information；
Second acquisition submodule obtains that user is corresponding to go through for the historical information of database in BOSS in the updated
History information；
Submodule is handled, for being based on the corresponding historical information of user, user's order is handled.
In one embodiment, the processing unit of business datum, further includes:
Output module, for the system resource based on current system resource and business datum in BOSS, output in real time is
System resource utilization status.
It is according to an embodiment of the present invention in another aspect, provide a kind of processing equipment of business datum, which includes: processing
Device and the memory for being stored with computer program instructions；
Processor realizes the processing method of business datum provided in an embodiment of the present invention when executing computer program instructions.
It is according to an embodiment of the present invention in another aspect, provide a kind of computer readable storage medium, computer-readable storage
It is stored with computer program instructions on medium, is realized when computer program instructions are executed by processor provided in an embodiment of the present invention
The processing method of business datum.
Processing method, device, equipment and the computer readable storage medium of business datum provided in an embodiment of the present invention, can
To predict the system resource of processing business data based on Extended Kalman filter model, and then in the business datum predicted
The order of user is handled in system resource, so as to guarantee that business datum matches with system resource, realizes system
The reasonable employment of resource.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, will make below to required in the embodiment of the present invention
Attached drawing is briefly described, for those of ordinary skill in the art, without creative efforts, also
Other drawings may be obtained according to these drawings without any creative labor.
Fig. 1 shows the flow diagram of the processing method of business datum in the embodiment of the present invention；
Fig. 2 shows the structural schematic diagrams of the processing unit of business datum in the embodiment of the present invention；
Fig. 3 shows the hardware structural diagram of the processing equipment of business datum in the embodiment of the present invention.
Specific embodiment
The feature and exemplary embodiment of various aspects of the invention is described more fully below, in order to make mesh of the invention
, technical solution and advantage be more clearly understood, with reference to the accompanying drawings and embodiments, the present invention is further retouched in detail
It states.It should be understood that specific embodiment described herein is only configured to explain the present invention, it is not configured as limiting the present invention.
To those skilled in the art, the present invention can be real in the case where not needing some details in these details
It applies.Below the description of embodiment is used for the purpose of better understanding the present invention to provide by showing example of the invention.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence " including ... ", it is not excluded that including
There is also other identical elements in the process, method, article or equipment of the element.
In view of that should guarantee BOSS sound and stable operation, improve processing speed again in the treatment process of business datum, then
Staff then can in advance predict business datum, and then according to the data volume of the business datum predicted, right in advance
System resource is reasonably distributed, is dispatched, to meet the peak use rate of system resource and the timeliness of business data processing.
However, the prior art is to predict that required system resource, accuracy are excessively poor based on experience mostly.It is directed to
This, the embodiment of the invention provides a kind of processing methods of business datum, handle industry using Extended Kalman filter model prediction
System resource needed for data of being engaged in, and based on system resource needed for the processing business data predicted, in advance to system resource
It is allocated, dispatches, to guarantee that business datum matches with system resource, realize the reasonable employment of system resource.
Fig. 1 shows the flow chart of the processing method of business datum in one embodiment of the invention.Referring to Fig.1, the present invention is real
The processing method for applying business datum in example mainly includes S110 to S140.
S110, BOSS utilize the system resource of Extended Kalman filter model prediction processing business data.
In some embodiments, step S110 can specifically include:
S111, BOSS obtained this cycle data amount using the data volume and predictive equation of the business datum in a upper period
Priori estimates.
Specifically: firstly, the data volume of the business datum in several periods before choosing this period is as sample set, and
Biggish data will wherein be fluctuated as noise spot.Even industry of the data volume of the business datum in certain period compared to other periods
The data volume amplitude of variation for data of being engaged in is larger, then using the data volume of the business datum in the period as noise spot.
Then, according to the data volume of the business datum in several periods in sample set and fluctuation situation, state value is established
Transfer function establishes noise inputs function according to the noise spot in sample set, and then obtains predictive equation.I.e. according in sample set
The data volume and fluctuation situation of the business datum in several periods, establish predictive equation.
In some embodiments, predictive equation can be with are as follows:
Xk,k-1=f [Xk-1,k-1]+Γ[Xk-1,k-1]Wk-1 (1)
In formula, Xk,k-1Indicate the priori estimates of the data volume of the business datum in kth period, Xk-1,k-1Indicate -1 week of kth
The posterior estimate of the data volume of the business datum of phase, f [X] indicate that state value transfer function, Γ [X] indicate noise inputs letter
Number, Wk-1Indicate the process noise in -1 period of kth, and Wk-1For zero-mean white noise vector.Wherein, k is the integer greater than 1.
Finally, the data volume of the business datum in a upper period is substituted into predictive equation (1), the industry in this period can be obtained
The priori estimates of the data volume for data of being engaged in.
S112, the observation of priori estimates, correction equation and this cycle data amount based on this cycle data amount, obtains
The posterior estimate of this cycle data amount.
Specifically: firstly, being made an uproar according to the process of the Posterior estimator state vector covariance matrix in a upper period, a upper period
Sound covariance matrix, the prior estimate transfer matrix in this period and state vector covariance matrix calculate equation, obtain this week
The prior estimation state vector covariance matrix of phase.
In some embodiments, state vector covariance matrix calculates that equation can be with are as follows:
In formula, Pk,k-1Indicate the prior estimation state vector covariance matrix in kth period, Pk-1,k-1Indicate -1 period of kth
Posterior estimator state vector covariance matrix, Φk,k-1Indicate the prior estimate transfer matrix in kth period,Indicate the
The transposed matrix of the prior estimate transfer matrix in k period, Γ [Xk-1,k-1] indicate -1 period of kth noise inputs matrix, ΓT
[Xk-1,k-1] indicate -1 period of kth noise inputs matrix transposed matrix, Qk-1,k-1Indicate the process noise association in -1 period of kth
Variance matrix, and Qk-1,k-1It is the diagonal matrix that diagonal element is all larger than 0.
Wherein, Φk,k-1Can by state value transfer function f [X] in Xk,k-1Place asks local derviation to obtain, specifically:
Then, square was measured according to the prior estimation state vector covariance matrix in this period, the prior estimate in a upper period
Battle array, the measurement noise covariance matrix in a upper period and Kalman filtering gain matrix renewal equation, obtain the card in this period
Germania gain matrix.
In some embodiments, Kalman filtering gain matrix renewal equation can be with are as follows:
In formula, Kk,kIndicate the Kalman filtering gain matrix in kth period, Pk,k-1Indicate the prior estimation state in kth period
Vector covariance matrix, Hk,k-1Indicate the prior estimate measurement matrix in kth period,Indicate the prior estimate in kth period
The transposed matrix of measurement matrix, Rk-1,k-1Indicate the measurement noise covariance matrix in -1 period of kth, and Rk-1,k-1It is diagonal element
It is all larger than 0 diagonal matrix.
Wherein, Hk,k-1Can by observation transfer function h [X] in Xk,k-1Place asks local derviation to obtain, specifically:
Finally, by the priori estimates of the data volume of the business datum in this period, the data volume of the business datum in this period
Observation substitute into correction equation, obtain the posterior estimate of the data volume of the business datum in this period.
In some embodiments, correction equation can be with are as follows:
Xk,k=Xk,k-1+Kk,k[Zk,k-h[Xk,k-1]] (6)
In formula, Xk,kIndicate the posterior estimate of the data volume of the business datum in kth period, Xk,k-1Indicate the industry in kth period
The priori estimates of the data volume for data of being engaged in, Kk,kIndicate the Kalman filtering gain matrix in kth period, Zk,kIndicate the kth period
Business datum data volume observation, h [X] indicate observation transfer function.
Wherein, Zk,kIt can be obtained by observational equation, and observational equation is also the industry according to several periods in sample set
What the data volume and fluctuation situation for data of being engaged in were established.
In some embodiments, observational equation can be with are as follows:
Zk,k=h [Xk-1,k-1]+Vk-1 (7)
In formula, Zk,kIndicating the observation of the data volume of the business datum in kth period, h [X] indicates observation transfer function,
Xk-1,k-1Indicate the posterior estimate of the data volume of the business datum in -1 period of kth, Vk-1Indicate the observation noise in -1 period of kth.
In some embodiments, after the posterior estimate for obtaining the data volume of the business datum in this period, it can use shape
State vector covariance matrix renewal equation, to obtain the Posterior estimator state vector covariance matrix in this period, for calculating
The prior estimation state vector covariance matrix in next period, and then the data volume of the business datum in next period is carried out pre-
It surveys.
In some embodiments, state vector covariance matrix renewal equation can be with are as follows:
Pk,k=[I-Kk,kHk,k]Pk,k-1 (8)
In formula, Pk,kIndicate that the Posterior estimator state vector covariance matrix in kth period, I indicate unit matrix, Kk,kIt indicates
The Kalman filtering gain matrix in kth period, Hk,kIndicate the Posterior estimator measurement matrix in kth period, Pk,k-1Indicate the kth period
Prior estimation state vector covariance matrix.
By the detailed process of above-mentioned steps S111 and S112 it is found that the embodiment of the present invention was the business first with a upper period
The data volume and predictive equation of data, come predict this period business datum data volume priori estimates.Then, it recycles
The observation and correction equation of the data volume of the business datum in this period, come the data of the business datum to this period predicted
The priori estimates of amount are modified, to obtain the posterior estimate of the data volume of the business datum in this period, that is, are predicted
The data volume of the business datum in this period.That is, the embodiment of the present invention is constantly to utilize the business datum in a upper period
Data volume, come obtain this period business datum data volume posterior estimate, be the process constantly recycled.Base
In this, need to illustrate two o'clock:
First, only circulation is to utilize the data of the business datum in a upper period for the first time during continuous circulation
The preset value of the data volume of the business datum in the true value of amount or a upper period, come obtain this period business datum data volume
Posterior estimate.I.e. in first time cyclic process, the data volume of the business datum in a upper period was the business number in a upper period
According to data volume true value or the business datum in a upper period data volume preset value.And in subsequent cyclic process,
When the data volume of the business datum using a upper period obtains the posterior estimate of the data volume of the business datum in this period, on
The data volume of the business datum in one period was the posterior estimate of the data volume of the business datum in a upper period.
As an example, it is known that the data volume of the business datum in certain China Mobile Service Hall in January, 2017 to June, to predict
In July, 2017 to September business datum data volume.Then when predicting the data volume of business datum in July, the industry in June is utilized
The true value of the data volume for data of being engaged in.And in the data volume of subsequent prediction August and the business datum of September, it is utilized respectively July
The posterior estimate of the data volume of the business datum of the posterior estimate and August of the data volume of business datum.
Second, the various data in an involved upper period are all preset value in first time cyclic process.Also,
These preset values are arranged according to the data volume and fluctuation situation of the business datum in several periods in sample set.
As an example, it is known that the data volume of the business datum in certain China Mobile Service Hall in January, 2017 to June, to predict
In July, 2017 to September business datum data volume.Then in first time cyclic process, state vector covariance matrix is utilized
When calculating prior estimation state vector covariance matrix of the equation to obtain July, the Posterior estimator state vector in June can be used
Covariance matrix and the process noise covariance matrix in June.It then can be in advance according to the business datum in January, 2017 to June
Data volume and fluctuation situation, Posterior estimator state vector of the initial state vector covariance matrix as June is arranged
The process noise covariance matrix of covariance matrix, initial process noise covariance matrix as June.
S113 predicts processing business according to the posterior estimate of this cycle data amount and the resources occupation rate of business datum
The system resource of data.
In some embodiments, the resources occupation rate of business datum, which refers to needed for a certain number of business datums of processing, is
System resource.It therefore,, can be pre- according to the resources occupation rate of business datum after obtaining the data volume of business datum in this period
Measure the system resource used needed for the business datum for handling this period.
Further, after predicting the system resource used needed for the business datum for handling this period, if processing is originally
The system resource used needed for the business datum in period, compared to the system used needed for the business datum for handling upper a cycle
Resource has biggish fluctuation, such as handles the system resource used needed for the business datum in this period and increased severely compared to the last period
Or reduce sharply, then BOSS can issue prompting message, to remind staff that need to add to pay close attention to when handling the business datum in this period
The utilization rate of system resource, in order to avoid there is business datum and system resource mismatch.
By the system resource using Extended Kalman filter model prediction processing business data, needed for can knowing in advance
The system resource used, is in advance scheduled system resource, real so as to guarantee that business datum matches with system resource
The reasonable employment of existing system resource.
S120 triggers business diary in the system resource of business datum to record the business datum of user.
In some embodiments, business diary can be triggered in the system resource of processing business data by multiple channel
To record the business datum of user.For example, user can pass through a variety of sides such as business hall, call center, agent and bank
Formula transacting business, so that BOSS can automatically trigger business diary in system resource.Wherein, business diary records user and does
The service data information of reason.
The business diary of user is generated order, is the service class order turn up service in order, while filing clothes by S130
The data information for the non-serving class order being engaged in the data information and order of class order.
In some embodiments, the business handling of each user can be recorded and generates an order.And according to
Whether order needs turn up service, and order can be divided into service class order and non-serving class order.For servicing class order, press
Corresponding service configuration and activation, i.e. turn up service are executed according to the data information recorded in order.It further, can also be
The data information in all orders is subjected to Data storage in system resource, i.e., by the data information of the service class order in order
All carry out Data storage with the data information of non-serving class order, by the change of each subscriber data stored with it is synchronous.
S140, after the data information of the service class order after the historical information of database, filing in BOSS and filing
Non-serving class order data information, user's order is handled.
Specifically: firstly, according to the non-serving class order after the data information of the service class order after filing and filing
Data information updates the historical information of database in BOSS.
It in some embodiments, can the first data information according to the service class order after filing and the non-serving after filing
Then the data information of class order again loads the data information of user to obtain the data information of user, to update
The subscriber information message stored in BOSS database updates the historical information of database in BOSS.
Then, in BOSS in the updated database historical information, obtain the corresponding historical information of user.
Finally, being based on the corresponding historical information of user, user's order is handled.
In some implementations, the processing method of business datum is after step s 140, further includes:
Based on the system resource of current system resource and business datum in BOSS, real-time output system utilization of resources shape
State.
It in some embodiments, can also be according to the current system in BOSS after the order to user is handled
Resource, and the system resource of business datum predicted, carry out the utilization state of the system resource of real-time outgoing traffic data.Make
For an example, current system resource and the ratio between the system resource of business datum predicted can use, to indicate business number
According to system resource utilization state.
During business data processing, by the utilization state of real-time output system resource, it can grasp in real time and be
The utilization rate for resource of uniting, so can the system resource of business datum that predicts of timely learning it is whether accurate, it is corresponding to carry out
Adjustment.
The processing method of business datum in the embodiment of the present invention is handled by being predicted using Extended Kalman filter model
The system resource of business datum, and then the order of user is handled in the system resource of the business datum predicted.By
Accurate Prediction it can go out to handle the system resource of business datum in advance in using Extended Kalman filter model, and staff
According to the system resource of the business datum predicted, system resource reasonably can be dispatched in advance, therefore the present invention is real
The business data processing method for applying example can guarantee that business datum matches with system resource, realize rationally making for system resource
With.
The processing unit of the business datum of the embodiment of the present invention is discussed in detail below with reference to Fig. 2.Fig. 2 shows according to this hair
The structural schematic diagram of the processing unit for the business datum that bright another embodiment provides.As shown in Fig. 2, the processing unit of business datum
Device 200 include:
Prediction module 210 utilizes the system resource of Extended Kalman filter model prediction processing business data for BOSS.
Trigger module 220, for triggering business diary in the system resource of business datum to record the business number of user
According to.
Profiling module 230 opens clothes for the business diary of user to be generated order for the service class order in order
Business, while the data information of the non-serving class order in the data information and order of archiving services class order.
Processing module 240, for the data letter according to the service class order after the historical information of database, filing in BOSS
The data information of non-serving class order after breath and filing, handles user's order.
In some embodiments, prediction module 210 can specifically include:
First acquisition submodule passed through spreading kalman for BOSS using the data volume of the business datum in a upper period
Filtering Model obtains the posterior estimate of this cycle data amount.
Predict submodule, for the resources occupation rate of posterior estimate and business datum according to this cycle data amount, in advance
Survey the system resource of business datum.
In some embodiments, the first acquisition submodule specifically can be used for:
Using the data volume and predictive equation of the business datum in a upper period, the prior estimate of this cycle data amount was obtained
Value.
The observation of priori estimates, correction equation and this cycle data amount based on this cycle data amount, obtains this week
The posterior estimate of phase data volume.
In some embodiments, trigger module 220 specifically can be used for:
Business diary is triggered in the system resource of business datum by multiple channel to record the business datum of user.
In some embodiments, processing module 240 can specifically include:
Submodule is updated, for the non-serving class order after the data information according to the service class order after filing and filing
Data information, update BOSS in database historical information.
Second acquisition submodule obtains that user is corresponding to go through for the historical information of database in BOSS in the updated
History information.
Submodule is handled, for being based on the corresponding historical information of user, user's order is handled.
In some embodiments, the processing unit of business datum, further includes:
Output module, for the system resource based on current system resource and business datum in BOSS, output in real time is
System resource utilization status.
The other details of the processing unit of business datum according to an embodiment of the present invention combine the basis of Fig. 1 description with more than
The method of the processing of the business datum of the embodiment of the present invention is similar, and details are not described herein.
The processing unit of business datum provided in an embodiment of the present invention can guarantee business datum and system resource phase
Match, realizes the reasonable employment of system resource.
In conjunction with Fig. 1 to Fig. 2 describe it is according to embodiments of the present invention in the treating method and apparatus of business datum can be by
The processing equipment of business datum is realized.Fig. 3 is the hardware knot for the processing equipment for showing the business datum according to inventive embodiments
300 schematic diagram of structure.
As shown in figure 3, the processing equipment 300 of the business datum in the present embodiment includes input equipment 301, input interface
302, central processing unit 303, memory 304, output interface 305 and output equipment 306.Wherein, input interface 302, center
Processor 303, memory 304 and output interface 305 are connected with each other by bus 310, input equipment 301 and output equipment
306 are connect by input interface 302 and output interface 305 with bus 310 respectively, so with the processing equipment of business datum 300
Other assemblies connection.
Specifically, input equipment 301 is received from external input information, and will input information by input interface 302
It is transmitted to central processing unit 303；Central processing unit 303 is based on the computer executable instructions stored in memory 304 to input
Information is handled to generate output information, and output information is temporarily or permanently stored in memory 304, is then passed through
Output information is transmitted to output equipment 306 by output interface 305；Output information is output to business datum by output equipment 306
The outside of processing equipment 300 is for users to use.
That is, the processing equipment of business datum shown in Fig. 3 also may be implemented as include: be stored with computer can
The memory executed instruction；And processor, the processor may be implemented to combine Fig. 1 extremely when executing computer executable instructions
The treating method and apparatus of the business datum of Fig. 2 description.
In one embodiment, the processing equipment 300 of business datum shown in Fig. 3 includes: memory 304, for storing
Program；Processor 303, the program for being stored in run memory, to execute the processing side of business datum of the embodiment of the present invention
Method.
The processing equipment of business datum provided in an embodiment of the present invention can guarantee business datum and system resource phase
Match, realizes the reasonable employment of system resource.
The embodiment of the present invention also provides a kind of computer readable storage medium, is stored on the computer readable storage medium
Computer program instructions；The computer program instructions realize business datum provided in an embodiment of the present invention when being executed by processor
Processing method.
It should be clear that the invention is not limited to specific configuration described above and shown in figure and processing.
For brevity, it is omitted here the detailed description to known method.In the above-described embodiments, several tools have been described and illustrated
The step of body, is as example.But method process of the invention is not limited to described and illustrated specific steps, this field
Technical staff can be variously modified, modification and addition after understanding spirit of the invention, or suitable between changing the step
Sequence.
Functional block shown in structures described above block diagram can be implemented as hardware, software, firmware or their group
It closes.When realizing in hardware, it may, for example, be electronic circuit, specific integrated circuit (ASIC), firmware appropriate, insert
Part, function card etc..When being realized with software mode, element of the invention is used to execute program or the generation of required task
Code section.Perhaps code segment can store in machine readable media program or the data-signal by carrying in carrier wave is passing
Defeated medium or communication links are sent." machine readable media " may include any medium for capableing of storage or transmission information.
The example of machine readable media includes electronic circuit, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), soft
Disk, CD-ROM, CD, hard disk, fiber medium, radio frequency (RF) link, etc..Code segment can be via such as internet, inline
The computer network of net etc. is downloaded.
It should also be noted that, the exemplary embodiment referred in the present invention, is retouched based on a series of step or device
State certain methods or system.But the present invention is not limited to the sequence of above-mentioned steps, that is to say, that can be according in embodiment
The sequence referred to executes step, may also be distinct from that the sequence in embodiment or several steps are performed simultaneously.
The above description is merely a specific embodiment, it is apparent to those skilled in the art that,
For convenience of description and succinctly, the system, module of foregoing description and the specific work process of unit can refer to preceding method
Corresponding process in embodiment, details are not described herein.It should be understood that scope of protection of the present invention is not limited thereto, it is any to be familiar with
Those skilled in the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or substitutions,
These modifications or substitutions should be covered by the protection scope of the present invention.
Claims (14)
1. a kind of processing method of business datum characterized by comprising
Service operation supports system BOSS to utilize the system resource of Extended Kalman filter model prediction processing business data；
Business diary is triggered in the system resource of the business datum to record the business datum of user；
The business diary of user is generated into order, is the service class order turn up service in the order, while filing institute
State the data information of the non-serving class order in the data information and the order of service class order；
According to the data information of the service class order after the historical information of database, filing in BOSS and described after filing
The data information of non-serving class order handles user's order.
2. the processing method of business datum according to claim 1, which is characterized in that the BOSS is filtered using spreading kalman
The system resource of wave pattern prediction processing business data, comprising:
The BOSS obtained this by the Extended Kalman filter model using the data volume of the business datum in a upper period
The posterior estimate of cycle data amount；
According to the posterior estimate of described cycle data amount and the resources occupation rate of business datum, the business datum is predicted
System resource.
3. the processing method of business datum according to claim 2, which is characterized in that the BOSS utilized the industry in a upper period
The data volume of business data obtains the posterior estimate of this cycle data amount by the Extended Kalman filter model, comprising:
Using the data volume and predictive equation of the business datum in a upper period, the prior estimate of this cycle data amount was obtained
Value；
The observation of priori estimates, correction equation and this cycle data amount based on described cycle data amount obtains described
The posterior estimate of this cycle data amount.
4. the processing method of business datum according to claim 1, which is characterized in that the system in the business datum
Business diary is triggered in resource to record the business datum of user, comprising:
Business diary is triggered in the system resource of the business datum by multiple channel to record the business datum of user.
5. the processing method of business datum according to claim 1, which is characterized in that database goes through in the foundation BOSS
The data information of the data information of the service class order after history information, filing and the non-serving class order after filing,
User's order is handled, comprising:
The data information of the service class order after foundation filing and the data information of the non-serving class order after filing,
Update the historical information of database in the BOSS；
The historical information of database in the BOSS in the updated, obtains the corresponding historical information of the user；
Based on the corresponding historical information of the user, user's order is handled.
6. the processing method of business datum according to claim 1, which is characterized in that database goes through in the foundation BOSS
The data information of the data information of the service class order after history information, filing and the non-serving class order after filing,
After handling user's order, further includes:
Based on the system resource of current system resource and the business datum in BOSS, real-time output system utilization of resources shape
State.
7. a kind of processing unit of business datum, which is characterized in that described device includes:
Prediction module supports system BOSS using Extended Kalman filter model prediction processing business data for service operation
System resource；
Trigger module, for triggering business diary in the system resource of the business datum to record the business datum of user；
Profiling module opens clothes for the business diary of user to be generated order for the service class order in the order
Business, while filing the data information of the non-serving class order in the data information and the order for servicing class order；
Processing module, for the data information according to the service class order after the historical information of database, filing in BOSS
With the data information of the non-serving class order after filing, user's order is handled.
8. the processing unit of business datum according to claim 7, which is characterized in that the prediction module includes:
First acquisition submodule passed through the expansion card for the BOSS using the data volume of the business datum in a upper period
Kalman Filtering model obtains the posterior estimate of this cycle data amount；
Predict submodule, for the resources occupation rate of posterior estimate and business datum according to described cycle data amount, in advance
Survey the system resource of the business datum.
9. the processing unit of business datum according to claim 8, which is characterized in that first acquisition submodule is specifically used
In:
Using the data volume and predictive equation of the business datum in a upper period, the prior estimate of this cycle data amount was obtained
Value；
The observation of priori estimates, correction equation and this cycle data amount based on described cycle data amount obtains described
The posterior estimate of this cycle data amount.
10. the processing unit of business datum according to claim 7, which is characterized in that the trigger module is specifically used for:
Business diary is triggered in the system resource of the business datum by multiple channel to record the business datum of user.
11. the processing unit of business datum according to claim 7, which is characterized in that the processing module includes:
Submodule is updated, for the non-serving class after the data information according to the service class order after filing and filing
The data information of order updates the historical information of database in the BOSS；
It is corresponding to obtain the user for the historical information of database in the BOSS in the updated for second acquisition submodule
Historical information；
Submodule is handled, for being based on the corresponding historical information of the user, user's order is handled.
12. the processing unit of business datum according to claim 7, which is characterized in that further include:
Output module, for the system resource based on current system resource and the business datum in BOSS, output in real time is
System resource utilization status.
13. a kind of processing equipment of business datum, which is characterized in that the equipment includes: processor and is stored with computer
The memory of program instruction；
The processor realizes business datum as claimed in any one of claims 1 to 6 when executing the computer program instructions
Processing method.
14. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program instruction, the computer program instructions realize business as claimed in any one of claims 1 to 6 when being executed by processor
The processing method of data.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1347253A (en) * | 2001-11-23 | 2002-05-01 | 杨大成 | Algorithm for scheduling and distributing packet data service resources in mobile environment |
| US20070226042A1 (en) * | 2006-03-27 | 2007-09-27 | Business Objects, S.A. | Apparatus and method for improved forecasting |
| CN101360319A (en) * | 2007-07-30 | 2009-02-04 | 鼎桥通信技术有限公司 | Resource reservation method and apparatus based on traffic |
| CN101686553A (en) * | 2008-09-24 | 2010-03-31 | 中国移动通信集团公司 | Resource adapting method, device and system |
| CN101753344A (en) * | 2008-12-12 | 2010-06-23 | 华为技术有限公司 | Method, device and system for logging |
| CN102025512A (en) * | 2009-09-14 | 2011-04-20 | 中国移动通信集团北京有限公司 | Service operation support system, service fulfillment method and device |
| CN102177504A (en) * | 2008-10-10 | 2011-09-07 | 阿尔卡特朗讯美国公司 | Method and system of traffic processor selection for broadcast/multicast service in a wireless network |
| CN102308540A (en) * | 2011-06-15 | 2012-01-04 | 华为技术有限公司 | Scheduling method and device for business processing resources |
| CN102361515A (en) * | 2011-07-22 | 2012-02-22 | 中国联合网络通信集团有限公司 | Assessment method and device based on utilization of wireless network resources |
| CN103338461A (en) * | 2013-06-18 | 2013-10-02 | 中国联合网络通信集团有限公司 | Method and device for network planning based on prediction of volume of business |
| CN103561428A (en) * | 2013-10-10 | 2014-02-05 | 东软集团股份有限公司 | Method and system for elastically distributing nodes in short message gateway cluster system |
| CN104219167A (en) * | 2013-05-31 | 2014-12-17 | 中国电信股份有限公司 | Network resource scheduling method and server |
| CN105392154A (en) * | 2014-09-05 | 2016-03-09 | 中兴通讯股份有限公司 | Resource occupation prediction method and system |
| CN106411392A (en) * | 2016-09-26 | 2017-02-15 | 中央军委装备发展部第六十三研究所 | Satellite communication system based on communication traffic prediction and wireless resource dynamic allocation |
| CN107135323A (en) * | 2017-07-04 | 2017-09-05 | 中国联合网络通信集团有限公司 | The processing method and system of prepayment service |
| CN107205248A (en) * | 2016-03-17 | 2017-09-26 | 中国移动通信集团内蒙古有限公司 | A kind of resource allocation methods and system |
-
2017
- 2017-12-19 CN CN201711373301.9A patent/CN109934657A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1347253A (en) * | 2001-11-23 | 2002-05-01 | 杨大成 | Algorithm for scheduling and distributing packet data service resources in mobile environment |
| US20070226042A1 (en) * | 2006-03-27 | 2007-09-27 | Business Objects, S.A. | Apparatus and method for improved forecasting |
| CN101360319A (en) * | 2007-07-30 | 2009-02-04 | 鼎桥通信技术有限公司 | Resource reservation method and apparatus based on traffic |
| CN101686553A (en) * | 2008-09-24 | 2010-03-31 | 中国移动通信集团公司 | Resource adapting method, device and system |
| CN102177504A (en) * | 2008-10-10 | 2011-09-07 | 阿尔卡特朗讯美国公司 | Method and system of traffic processor selection for broadcast/multicast service in a wireless network |
| CN101753344A (en) * | 2008-12-12 | 2010-06-23 | 华为技术有限公司 | Method, device and system for logging |
| CN102025512A (en) * | 2009-09-14 | 2011-04-20 | 中国移动通信集团北京有限公司 | Service operation support system, service fulfillment method and device |
| CN102308540A (en) * | 2011-06-15 | 2012-01-04 | 华为技术有限公司 | Scheduling method and device for business processing resources |
| CN102361515A (en) * | 2011-07-22 | 2012-02-22 | 中国联合网络通信集团有限公司 | Assessment method and device based on utilization of wireless network resources |
| CN104219167A (en) * | 2013-05-31 | 2014-12-17 | 中国电信股份有限公司 | Network resource scheduling method and server |
| CN103338461A (en) * | 2013-06-18 | 2013-10-02 | 中国联合网络通信集团有限公司 | Method and device for network planning based on prediction of volume of business |
| CN103561428A (en) * | 2013-10-10 | 2014-02-05 | 东软集团股份有限公司 | Method and system for elastically distributing nodes in short message gateway cluster system |
| CN105392154A (en) * | 2014-09-05 | 2016-03-09 | 中兴通讯股份有限公司 | Resource occupation prediction method and system |
| CN107205248A (en) * | 2016-03-17 | 2017-09-26 | 中国移动通信集团内蒙古有限公司 | A kind of resource allocation methods and system |
| CN106411392A (en) * | 2016-09-26 | 2017-02-15 | 中央军委装备发展部第六十三研究所 | Satellite communication system based on communication traffic prediction and wireless resource dynamic allocation |
| CN107135323A (en) * | 2017-07-04 | 2017-09-05 | 中国联合网络通信集团有限公司 | The processing method and system of prepayment service |
Non-Patent Citations (2)
| Title |
|---|
| TIANLELONG: "中国移动BOSS业务规范（正式版）pdf186", 《道客巴巴》 * |
| 杜翠凤，蒋仕宝: "4G时代一种市场评估选择的方法研究", 《移动通信》 * |
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