CN109934657A - Business data processing method, device, equipment and medium - Google Patents

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

Business data processing method, device, equipment and medium

technical field

The present invention relates to the field of communications, and in particular, to a method, apparatus, device and computer-readable storage medium for processing service data.

Background technique

In the existing communication network system, there is a huge amount of business data. These business data are sent to each platform side or the Business and Operation Support System (BOSS, Business and Operation Support System) side through the setting of various rules for corresponding processing. configuration to realize the corresponding business functions and meet the needs of users.

Operators have a wide variety of business data, and during business processing, each core system is interlinked and interdependent, and bottlenecks occur in systems with closely integrated upstream and downstream relationships, which will affect the processing performance of the entire system link.

Business data is processed based on experience and online resource utilization. When the amount of business data is large, the entire system will be overwhelmed; when the amount of business data is small, system resources will be idle.

To sum up, due to the mismatch between business data and system resources, the unreasonable use of system resources is caused.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a business data processing method, apparatus, device and computer-readable storage medium, which can ensure that business data matches system resources and ensure rational use of system resources.

According to an aspect of the embodiments of the present invention, a method for processing service data is provided, the method comprising:

The business operation support system BOSS uses the extended Kalman filter model to predict the system resources for processing business data;

Trigger business logs on system resources of business data to record user business data;

Generate an order from the user's business log, activate the service for the service order in the order, and archive the data information of the service order and the data information of the non-service order in the order;

According to the historical information of the database in the BOSS, the data information of the archived service orders and the data information of the archived non-service orders, the user orders are processed.

In one embodiment, BOSS utilizes the extended Kalman filter model to predict system resources for processing business data, including:

BOSS uses the data volume of the business data in the previous cycle to obtain the posterior estimate of the data volume in this cycle by extending the Kalman filter model;

The system resources of the service data are predicted according to the posterior estimated value of the data volume in the current period and the resource occupancy rate of the service data.

In one embodiment, BOSS utilizes the data volume of the business data of the previous cycle to obtain the posterior estimated value of the data volume of this cycle by extending the Kalman filter model, including:

Use the data volume and prediction equation of the business data of the previous cycle to obtain a priori estimate of the current cycle data volume;

Based on the a priori estimate of the data volume of the current cycle, the correction equation and the observed value of the data volume of the current cycle, the a posteriori estimate of the data volume of the current cycle is obtained.

In one embodiment, triggering a business log on a system resource of business data to record the business data of the user includes:

Trigger business logs on system resources of business data through various channels to record user business data.

In one embodiment, according to the historical information of the database in the BOSS, the data information of the archived service order and the data information of the archived non-service order, the user order is processed, including:

According to the data information of the archived service orders and the archived data information of non-service orders, update the historical information of the database in BOSS;

Obtain the historical information corresponding to the user from the historical information of the database in the updated BOSS;

Based on the historical information corresponding to the user, the user order is processed.

In one embodiment, according to the historical information of the database in the BOSS, the data information of the archived service order and the data information of the archived non-service order, after processing the user order, it also includes:

Based on the current system resources in BOSS and the system resources of business data, the system resource utilization status is output in real time.

According to another aspect of the embodiments of the present invention, an apparatus for processing service data is provided, and the apparatus includes:

The prediction module is used for the business operation support system BOSS to use the extended Kalman filter model to predict the system resources for processing business data;

A triggering module, used to trigger the business log on the system resource of the business data to record the business data of the user;

The archiving module is used to generate an order from the user's business log, activate the service for the service order in the order, and archive the data information of the service order and the data information of the non-service order in the order;

The processing module is used to process user orders according to the historical information of the database in BOSS, the data information of the archived service orders and the data information of the archived non-service orders.

In one embodiment, the prediction module includes:

The first acquisition sub-module is used for BOSS to use the data volume of the business data of the previous cycle to obtain the posterior estimated value of the data volume of this cycle by extending the Kalman filter model;

The prediction sub-module is used to predict the system resources of the service data according to the posterior estimated value of the data volume in this period and the resource occupancy rate of the service data.

In one embodiment, the first acquisition submodule is specifically used for:

Use the data volume and prediction equation of the business data of the previous cycle to obtain a priori estimate of the current cycle data volume;

Based on the a priori estimate of the data volume of the current cycle, the correction equation and the observed value of the data volume of the current cycle, the a posteriori estimate of the data volume of the current cycle is obtained.

In one embodiment, the trigger module is specifically used for:

Trigger business logs on system resources of business data through various channels to record user business data.

In one embodiment, the processing module includes:

The update sub-module is used to update the historical information of the database in BOSS according to the data information of the archived service orders and the archived data information of the non-service orders;

The second acquisition sub-module is used to acquire the historical information corresponding to the user from the historical information of the database in the updated BOSS;

The processing sub-module is used to process the user's order based on the historical information corresponding to the user.

In one embodiment, the apparatus for processing service data further includes:

The output module is used to output the system resource utilization status in real time based on the current system resources in the BOSS and the system resources of the business data.

According to yet another aspect of the embodiments of the present invention, there is provided a service data processing device, the device comprising: a processor and a memory storing computer program instructions;

When the processor executes the computer program instructions, the service data processing method provided by the embodiment of the present invention is implemented.

According to yet another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the processing of the service data provided by the embodiments of the present invention is realized. method.

The method, apparatus, device, and computer-readable storage medium for processing business data provided by the embodiments of the present invention can predict system resources for processing business data based on an extended Kalman filter model, and then compare the predicted system resources of business data with The user's order is processed, so as to ensure that the business data matches the system resources and realize the rational use of the system resources.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings required in the embodiments of the present invention will be briefly introduced below. For those of ordinary skill in the art, without creative work, the Additional drawings can be obtained from these drawings.

1 shows a schematic flowchart of a method for processing service data in an embodiment of the present invention;

2 shows a schematic structural diagram of an apparatus for processing service data in an embodiment of the present invention;

FIG. 3 shows a schematic diagram of a hardware structure of a service data processing device in an embodiment of the present invention.

Detailed ways

The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention, and are not configured to limit the present invention. It will be apparent to those skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present invention by illustrating examples of the invention.

It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprises" does not preclude the presence of additional identical elements in a process, method, article, or device that includes the element.

Considering that in the process of processing business data, it is necessary to ensure the stable operation of BOSS and to improve the processing rate, so the staff can predict the business data in advance, and then according to the predicted data volume of the business data, the system resources are processed in advance. Reasonable allocation and scheduling to meet the maximum utilization of system resources and the timeliness of business data processing.

However, most of the existing technologies predict the required system resources based on experience, and the accuracy is very poor. In response to this, an embodiment of the present invention provides a method for processing business data, which uses an extended Kalman filter model to predict system resources required for processing business data, and based on the predicted system resources required for processing business data, analyzes System resources are allocated and scheduled to ensure that business data matches system resources and realize rational use of system resources.

FIG. 1 shows a flowchart of a method for processing service data in an embodiment of the present invention. Referring to FIG. 1 , the method for processing service data in the embodiment of the present invention mainly includes S110 to S140.

S110, the BOSS predicts system resources for processing business data by using an extended Kalman filter model.

In some embodiments, step S110 may specifically include:

S111 , the BOSS obtains a priori estimated value of the data volume of the current cycle by using the data volume of the business data of the previous cycle and the prediction equation.

Specifically: first, the data volume of the service data of several cycles before the current cycle is selected as the sample set, and the data with large fluctuations is used as the noise point. That is, if the data volume of the service data in a certain period changes greatly compared to the data volume of the service data in other periods, the data volume of the service data in this period is regarded as a noise point.

Then, according to the data volume and fluctuation of the business data of several cycles in the sample set, the state value conversion function is established, and the noise input function is established according to the noise points in the sample set, and then the prediction equation is obtained. That is, a prediction equation is established according to the data volume and fluctuation of the business data of several cycles in the sample set.

In some embodiments, the prediction equation can be:

X _k,k-1 =f[X _k-1,k-1 ]+Γ[X _k-1,k-1 ]W _k-1 (1)

In the formula, X _k,k-1 represents the a priori estimate of the data volume of the service data in the k-th cycle, and X _k-1,k-1 represents the a posteriori estimate of the data volume of the service data in the k-1th cycle , f[X] represents the state value transfer function, Γ[X] represents the noise input function, W _k-1 represents the process noise of the k-1th cycle, and W _k-1 is a zero-mean white noise vector. where k is an integer greater than 1.

Finally, by substituting the data volume of the business data of the previous cycle into the prediction equation (1), a priori estimated value of the data volume of the business data of the current cycle can be obtained.

S112 , based on the a priori estimated value of the data volume of the current cycle, the correction equation, and the observed value of the data volume of the current cycle, obtain the a posteriori estimated value of the data volume of the current cycle.

Specifically: First, according to the posterior estimated state vector covariance matrix of the previous cycle, the process noise covariance matrix of the previous cycle, the prior estimated transition matrix of this cycle, and the state vector covariance matrix estimation equation, the current cycle is obtained. The prior estimated state vector covariance matrix of .

In some embodiments, the state vector covariance matrix estimation equation may be:

In the formula, P _k,k-1 represents the a priori estimated state vector covariance matrix of the kth cycle, P _k-1,k-1 represents the a posteriori estimated state vector covariance matrix of the k-1th cycle, Φ _{k, k-1} represents the a priori estimated transition matrix of the kth cycle, represents the transpose matrix of the prior estimated transition matrix of the kth cycle, Γ[X _k-1,k-1 ] represents the noise input matrix of the k-1th cycle, Γ ^T [X _k-1,k-1 ] represents The transposed matrix of the noise input matrix of the k-1th cycle, Q _{k-1, k-1} represents the process noise covariance matrix of the k-1th cycle, and Q _{k-1, k-1} is the diagonal elements larger than A diagonal matrix of 0.

Among them, Φ _{k, k-1} can be obtained by taking the partial derivative of the state value conversion function f[X] at X _{k, k-1} , specifically:

Then, according to the prior estimated state vector covariance matrix of the current cycle, the prior estimated measurement matrix of the previous cycle, the measurement noise covariance matrix of the previous cycle, and the Kalman filter gain matrix update equation, the Kalman filter of the current cycle is obtained. gain matrix.

In some embodiments, the Kalman filter gain matrix update equation may be:

In the formula, K _k,k represents the k-th cycle Kalman filter gain matrix, P _k,k-1 represents the k-th cycle a priori estimated state vector covariance matrix, H _k,k-1 represents the k-th cycle prior The empirically estimated measurement matrix, represents the transpose matrix of the a priori estimated measurement matrix of the kth cycle, R _k-1,k-1 represents the measurement noise covariance matrix of the k-1th cycle, and R _k-1,k-1 are the diagonal elements A diagonal matrix that is all greater than 0.

Among them, H _k,k-1 can be obtained by taking the partial derivative of the observed value conversion function h[X] at X _k,k-1 , specifically:

Finally, the a priori estimated value of the data volume of the business data in this cycle and the observed value of the data volume of the business data in this cycle are substituted into the correction equation to obtain the a posteriori estimated value of the data volume of the business data in this cycle.

In some embodiments, the correction equation may be:

X _k,k =X _k,k-1 +K _k,k [Z _k,k -h[X _k,k-1 ]] (6)

In the formula, X _k,k represents the a posteriori estimate of the data volume of the service data in the kth cycle, X _k,k-1 represents the a priori estimate of the data volume of the service data in the kth cycle, and K _k,k represents The Kalman filter gain matrix of the kth cycle, Z _k,k represents the observed value of the data volume of the business data of the kth cycle, and h[X] represents the conversion function of the observed value.

Among them, Z _k,k can be obtained from the observation equation, and the observation equation is also established according to the data volume and fluctuation of the business data of several cycles in the sample set.

In some embodiments, the observation equation may be:

Z _k,k =h[X _k-1,k-1 ]+V _k-1 (7)

In the formula, Z _k,k represents the observed value of the data volume of the business data in the kth cycle, h[X] represents the observation value conversion function, and X _k-1,k-1 represents the data of the business data in the k-1th cycle. The posterior estimate of the quantity, V _k-1 represents the observation noise of the k-1th cycle.

In some embodiments, after obtaining the a posteriori estimated value of the data volume of the service data in the current period, the state vector covariance matrix update equation can be used to obtain the a posteriori estimated state vector covariance matrix of the current period, which can be used for calculation The state vector covariance matrix is estimated a priori in the next cycle, and then the data volume of the service data in the next cycle is predicted.

In some embodiments, the state vector covariance matrix update equation may be:

P _k,k = [IK _k,k H _k,k ]P _k,k-1 (8)

where P _k,k represents the posterior estimated state vector covariance matrix of the kth cycle, I represents the identity matrix, K _k,k represents the Kalman filter gain matrix of the kth cycle, and H _k,k represents the kth cycle. A posteriori estimated measurement matrix, P _k,k-1 represents the a priori estimated state vector covariance matrix of the kth cycle.

It can be known from the specific processes of the above steps S111 and S112 that in the embodiment of the present invention, the data volume of the service data of the previous cycle and the prediction equation are first used to predict the a priori estimated value of the data volume of the service data of the current cycle. Then, use the observed value of the data volume of the business data in this cycle and the correction equation to correct the predicted a priori estimate of the data volume of the business data in this cycle, so as to obtain the data volume of the business data in this cycle The posterior estimated value of , that is, the data volume of business data in this period is predicted. That is to say, in the embodiment of the present invention, the data volume of the service data of the previous cycle is continuously used to obtain the a posteriori estimated value of the data volume of the service data of the current cycle, that is, a continuous cycle process. Based on this, two points need to be noted:

First, in the process of continuous cycle, only the first cycle is to use the actual value of the data volume of the business data of the previous cycle or the preset value of the data volume of the business data of the previous cycle to obtain the business data of this cycle. A posteriori estimate of the amount of data for the data. That is, during the first cycle, the data volume of the service data of the previous cycle is the actual value of the data volume of the service data of the previous cycle or the preset value of the data volume of the service data of the previous cycle. In the subsequent cycle process, when the data volume of the business data of the previous cycle is used to obtain the posterior estimated value of the data volume of the business data of the current cycle, the data volume of the business data of the previous cycle is, the data volume of the business data of the previous cycle is A posteriori estimate of the data volume of business data.

As an example, the data volume of business data of a mobile business hall from January to June 2017 is known to predict the data volume of business data from July to September 2017. Then, when predicting the data volume of the business data in July, the actual value of the data volume of the business data in June is used. In the subsequent prediction of the data volume of the business data in August and September, the a posteriori estimated value of the data volume of the business data in July and the a posteriori estimated value of the data volume of the business data in August are respectively used.

Second, during the first cycle, various data of the previous cycle involved are all preset values. Moreover, these preset values are all set according to the data volume and fluctuation of the service data of several cycles in the sample set.

As an example, the data volume of business data of a mobile business hall from January to June 2017 is known to predict the data volume of business data from July to September 2017. Then in the first cycle process, when using the state vector covariance matrix calculation equation to obtain the prior estimated state vector covariance matrix of July, the posterior estimated state vector covariance matrix of June will be used, and the June estimated state vector covariance matrix will be used. The process noise covariance matrix of . Then, according to the data volume and fluctuation of business data from January to June 2017, the initial state vector covariance matrix can be set as the posterior estimated state vector covariance matrix and the initial process noise covariance matrix for June. as the process noise covariance matrix for June.

S113: Predict the system resources for processing the service data according to the a posteriori estimated value of the data volume in the current period and the resource occupancy rate of the service data.

In some embodiments, the resource occupancy rate of service data refers to system resources required to process a certain amount of service data. Therefore, after obtaining the data volume of the service data of the current cycle, the system resources required to process the service data of the current cycle can be predicted according to the resource occupancy rate of the service data.

Further, after predicting the system resources required to process the business data of this cycle, if the system resources required to process the business data of this cycle are compared with the system resources required to process the business data of the previous cycle If there is a large fluctuation, for example, the system resources required to process the business data of this cycle increase or decrease sharply compared with the previous cycle, BOSS will send a reminder message to remind the staff to process the business data of this cycle. Pay more attention to the utilization of system resources to avoid mismatch between business data and system resources.

By using the extended Kalman filter model to predict the system resources for processing business data, the system resources to be used can be known in advance, and the system resources can be scheduled in advance, so as to ensure that the business data matches the system resources and realize the rational use of the system resources.

S120, triggering a service log on the system resource of the service data to record the service data of the user.

In some embodiments, a business log can be triggered on a system resource that processes business data through various channels to record the business data of the user. For example, users can handle business in various ways such as business halls, call centers, agents, and banks, so that BOSS will automatically trigger business logs on system resources. The business log records business data information handled by the user.

S130: Generate an order from the user's business log, activate the service for the service order in the order, and archive the data information of the service order and the data information of the non-service order in the order.

In some embodiments, one order can be generated from one transaction transaction record of each user. And according to whether the order needs to be activated, the order can be divided into service orders and non-service orders. For service orders, the corresponding service configuration and activation are performed according to the data information recorded in the order, that is, the service is activated. Further, the data information in all orders can also be archived on the system resources, that is, the data information of the service orders and the data information of the non-service orders in the orders are archived, so as to record the data of each user's data. Changes are stored and synchronized.

S140: Process the user order according to the historical information of the database in the BOSS, the data information of the archived service order, and the data information of the archived non-service order.

Specifically: first, the historical information of the database in the BOSS is updated according to the data information of the archived service orders and the archived data information of the non-service orders.

In some embodiments, the user's profile information may be acquired according to the data information of the archived service order and the archived data information of the non-service order, and then the user's profile information is loaded to update the BOSS The user profile information stored in the database is the historical information of updating the database in BOSS.

Then, obtain the historical information corresponding to the user from the historical information of the database in the updated BOSS.

Finally, the user order is processed based on the historical information corresponding to the user.

In some implementations, after step S140, the business data processing method further includes:

Based on the current system resources in BOSS and the system resources of business data, the system resource utilization status is output in real time.

In some embodiments, after the user's order is processed, the utilization status of the system resources of the business data can also be output in real time according to the current system resources in the BOSS and the predicted system resources of the business data. As an example, the ratio of the current system resources to the predicted system resources of the service data can be used to represent the utilization state of the system resources of the service data.

In the process of business data processing, by outputting the utilization status of system resources in real time, the utilization rate of system resources can be grasped in real time, so as to know in time whether the system resources of the predicted business data are accurate, so as to make corresponding adjustments.

The business data processing method in the embodiment of the present invention predicts the system resources for processing business data by using the extended Kalman filter model, and then processes the user's order on the predicted system resources of the business data. Since the extended Kalman filter model can be used to accurately predict the system resources for processing business data in advance, and the staff can reasonably schedule the system resources in advance according to the predicted system resources of the business data, the business data of the embodiment of the present invention The processing method can ensure that the business data matches the system resources and realize the rational use of the system resources.

The apparatus for processing service data according to the embodiment of the present invention will be described in detail below with reference to FIG. 2 . FIG. 2 shows a schematic structural diagram of an apparatus for processing service data according to another embodiment of the present invention. As shown in FIG. 2, the apparatus 200 of the apparatus for processing service data includes:

The prediction module 210 is used for the BOSS to predict the system resources for processing business data by using the extended Kalman filter model.

The triggering module 220 is configured to trigger the service log on the system resource of the service data to record the service data of the user.

The archiving module 230 is configured to generate an order from the user's business log, activate the service for the service order in the order, and archive the data information of the service order and the data information of the non-service order in the order.

The processing module 240 is configured to process the user order according to the historical information of the database in the BOSS, the data information of the archived service order and the data information of the archived non-service order.

In some embodiments, the prediction module 210 may specifically include:

The first acquisition sub-module is used for the BOSS to use the data volume of the business data of the previous cycle to obtain a posteriori estimated value of the data volume of the current cycle by extending the Kalman filter model.

The prediction sub-module is used to predict the system resources of the service data according to the posterior estimated value of the data volume in this period and the resource occupancy rate of the service data.

In some embodiments, the first acquisition sub-module can be specifically used for:

Using the data volume of the business data of the previous cycle and the prediction equation, a priori estimate of the data volume of the current cycle is obtained.

Based on the a priori estimate of the data volume of the current cycle, the correction equation and the observed value of the data volume of the current cycle, the a posteriori estimate of the data volume of the current cycle is obtained.

In some embodiments, the triggering module 220 can be specifically used to:

Trigger business logs on system resources of business data through various channels to record user business data.

In some embodiments, the processing module 240 may specifically include:

The update sub-module is used to update the historical information of the database in the BOSS according to the data information of the archived service orders and the data information of the archived non-service orders.

The second acquisition sub-module is used to acquire the historical information corresponding to the user from the historical information of the database in the updated BOSS.

The processing sub-module is used to process the user's order based on the historical information corresponding to the user.

In some embodiments, the apparatus for processing service data further includes:

The output module is used to output the system resource utilization status in real time based on the current system resources in the BOSS and the system resources of the business data.

Other details of the apparatus for processing service data according to the embodiment of the present invention are similar to the method for processing service data according to the embodiment of the present invention described above with reference to FIG. 1 , and details are not described herein again.

The apparatus for processing business data provided by the embodiment of the present invention can ensure that the business data matches the system resources and realize the rational use of the system resources.

The method and apparatus for processing service data according to the embodiments of the present invention described in conjunction with FIG. 1 to FIG. 2 may be implemented by a service data processing device. FIG. 3 is a schematic diagram illustrating a hardware structure 300 of a device for processing service data according to an embodiment of the invention.

As shown in FIG. 3 , the service data processing device 300 in this embodiment includes an input device 301 , an input interface 302 , a central processing unit 303 , a memory 304 , an output interface 305 , and an output device 306 . Among them, the input interface 302, the central processing unit 303, the memory 304, and the output interface 305 are connected to each other through the bus 310, and the input device 301 and the output device 306 are respectively connected to the bus 310 through the input interface 302 and the output interface 305. Other components of the processing device 300 are connected.

Specifically, the input device 301 receives input information from the outside, and transmits the input information to the central processing unit 303 through the input interface 302; the central processing unit 303 processes the input information based on the computer-executable instructions stored in the memory 304 to generate output information, temporarily or permanently store the output information in the memory 304, and then transmit the output information to the output device 306 through the output interface 305; the output device 306 outputs the output information to the outside of the service data processing device 300 for the user to use.

That is to say, the service data processing device shown in FIG. 3 can also be implemented to include: a memory storing computer-executable instructions; and a processor, which, when executing the computer-executable instructions, can realize the combination of The method and apparatus for processing service data depicted in FIG. 2 .

In one embodiment, the service data processing device 300 shown in FIG. 3 includes: a memory 304 for storing a program; a processor 303 for running the program stored in the memory to execute the service data processing in the embodiment of the present invention method.

The business data processing device provided by the embodiment of the present invention can ensure that the business data matches the system resources, and realize the rational use of the system resources.

Embodiments of the present invention further provide a computer-readable storage medium, where computer program instructions are stored thereon; when the computer program instructions are executed by a processor, the method for processing service data provided by the embodiments of the present invention is implemented.

It is to be understood that the present invention is not limited to the specific arrangements and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above-described embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the sequence of steps after comprehending the spirit of the present invention.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, elements of the invention are programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted over a transmission medium or communication link by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments may be downloaded via a computer network such as the Internet, an intranet, or the like.

It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be different from the order in the embodiments, or several steps may be performed simultaneously.

The above are only specific implementations of the present invention. Those skilled in the art can clearly understand that, for the convenience and simplicity of the description, the specific working process of the above-described systems, modules and units may refer to the foregoing method embodiments. The corresponding process in , will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should all cover within 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.