CN1968490A - Cell load forecasting method - Google Patents
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- CN1968490A CN1968490A CNA2006100905536A CN200610090553A CN1968490A CN 1968490 A CN1968490 A CN 1968490A CN A2006100905536 A CNA2006100905536 A CN A2006100905536A CN 200610090553 A CN200610090553 A CN 200610090553A CN 1968490 A CN1968490 A CN 1968490A
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Abstract
The invention relates to a method for predicting the region load, which comprises that: a, collecting the history data of region load and randomly generating weight and bias; b, using the weight and bias to calculate out the predicted value of region load, and the difference between predicted value and real output value; c, using iterate formula to calculate out the adjusting value of weight and bias, refreshing weight and bias; d, judging if the iterate times is over preset time, or judging if the difference between predicted value and output value is lower than preset value; if it is lower, using refreshed weight and bias to calculate the predicted value, ending, or else, entering into step b. The invention can improve the predict accuracy with better track property.
Description
Technical field
The present invention relates to mobile communication wireless resource control field, relate in particular to a kind of realization cell load forecast method.
Background technology
Because Wideband Code Division Multiple Access (WCDMA) (WCDMA, Wideband Code Division Multiple Access) system is a self-interference system, any one user in the system is other user's potential interference, for the ease of system is planned, must know the situation of forward direction and reverse various interference, reverse link before the size of calculation plot and the balance sysmte then, and actual cell size is by the propagation model of this area and the common decision of the endurable path loss of preceding reverse link, therefore when carrying out Radio Resource control, the up-downgoing load is an important factor of evaluation, usually adopt the load control mode to assess the up-downgoing load of sub-district at present, load control comprises load detecting, the control of co-frequency cell load balance, access control and potential user's control etc., wherein, load monitoring the most commonly, described load monitoring is exactly the up-downgoing load of periodically measuring the sub-district, for access control and other load control algolithm provide discriminative information.Yet load monitoring is just assessed the up-downgoing load of current time sub-district, can not assess cell load constantly in future, the mode of cell load prediction therefore just occurred.
The cell load prediction is exactly to obtain following cell load information constantly according to history data of region load, can make things convenient for the Radio Resource control system that cell load is controlled by cell load is predicted, the load of the sub-district that therefore how to calculate to a nicety is a subject matter that constitutes control precision.A kind of cell load forecast method of the prior art is: the received signal of Node B (NodeB) the periodic report up-downgoing by the base station or the overall strength that transmits, obtain the situation of change of cell load again by the mode of alpha filtering, and then reach the purpose that obtains the following load of prediction by historical data.This cell load Forecasting Methodology is at first carried out filtering to unusual cell load, and then calculates cell load predicted value more accurately, but the filter factor that is provided with can influence the situation of change of real load.
Below in conjunction with the situation of change of table 1 explanation filter factor and cell load, the span of filter factor is between 0 and 1 usually, and table 1 is the test result of above-mentioned prior art to the ascending load data of NodeB.
| Filter factor | 1 | 0.5 | 0.25 | 0.0625 |
| Average | 4.0840 | 4.1225 | 4.3505 | 5.3607 |
| Standard value | 4.2219 | 4.3040 | 4.7503 | 6.8192 |
Table 1
Normally characterize the cell load predicted value with the average of the difference of the predicted value of cell load and real output value and the statistical property of standard value, average is big more, the predicted value of cell load and the difference of real output value are just big more, and the predicted value precision of cell load is just poor more so; Standard value is big more, and the fluctuation of the predicted value of cell load and the difference of real output value is just big more, and then it is not high to draw the precision of cell load predicted value.As shown in Table 1, the alpha filter factor is big more, and the precision of cell load predicted value is just high more, and wherein filter factor is 1 o'clock, and the precision of cell load predicted value is best.But filter factor is provided with when excessive, and the historical data that needs to measure will be more, thereby causes tracking characteristics to reduce, and then causes the cell load accuracy for predicting not high; Filter factor is provided with when too small, it is 1 o'clock cell load predicted value height that the precision of cell load predicted value does not only have filter factor, and carrying out cell load when prediction, and then cause the cell load accuracy for predicting not high also than the influence that is easier to be subjected to exceptional value.Comprehensive above reason, the precision of sub-district load estimation value is not high in the above-mentioned prior art, and excessive filter factor is set can causes the load tracking delay, too small filter factor is set can makes load estimation be subjected to the influence of exceptional value easily.
Summary of the invention
The technical problem to be solved in the present invention provides a kind of realization cell load forecast method, to reach the purpose that improves cell load predicted value precision.
For solving the problems of the technologies described above, the invention provides a kind of realization cell load forecast method, this method may further comprise the steps:
A, collection history data of region load, and generate weights and deviation at random;
B, utilize weights and deviation iterative computation to obtain the predicted value of cell load, calculate the poor of predicted value and real output value again;
C, calculate the adjusted value obtain weights and deviation, refreshing weight and deviation again according to iterative formula;
Whether D, judgement carry out the number of times of iterative computation greater than presetting number of times, whether the difference of perhaps judging cell load predicted value and real output value is less than preset value, if, utilize weights and the deviation upgraded to recomputate the predicted value that obtains cell load, process ends, otherwise return execution in step B.
Wherein, collecting history data of region load in the steps A specifically comprises:
The measured value of cell load before radio network controller or the Node B record current time, Node B is the measured value of periodic report cell load again.
Wherein, when described method adopts the second order interative computation, generate weights at random in the steps A and deviation specifically may further comprise the steps:
A1, intermediate variable is set, the measured value of regeneration cell load and weights between the intermediate variable and deviation, described weights and deviation are set to the weights and the deviation on first rank;
Weights and deviation between the predicted value of A2, generation intermediate variable and cell load, described again weights and deviation are set to the weights and the deviation on second rank.
Wherein, when described method adopts the single order interative computation, generate weights at random in the steps A and deviation specifically comprises:
Intermediate variable is set, weights between the predicted value of regeneration intermediate variable and cell load and deviation, described again weights and deviation are set to the weights and the deviation of single order.
Wherein, further comprise before the step B:
Wait for cell load message, judge whether loaded cell arrives, if, obtain the real output value of the cell load of current time, execution in step B again, otherwise continue wait cell load message.
Wherein, when described method adopts the second order interative computation, calculate among the step B and obtain predicted value and specifically may further comprise the steps:
B1, the measured value of each cell load and the product of the weights between this measured value and the intermediate variable are carried out addition, again the result of addition and the deviation on first rank are carried out addition, then the variation relation on the result of addition and first rank being multiplied each other obtains the output valve of intermediate variable;
B2, the product of the weights between the predicted value of each intermediate variable output valve and each intermediate variable and cell load is carried out addition, again the result of addition and the deviation on second rank are carried out addition, then the variation relation on the result of addition and second rank being multiplied each other obtains the predicted value of cell load.
Wherein, when described method adopts the second order interative computation, calculate the adjusted value of obtaining weights and deviation among the step C and specifically may further comprise the steps:
C10, with the difference of the predicted value of cell load and real output value with adjust multiplication, multiply each other with the weights on first rank and the weights on second rank simultaneously, then multiplied result being multiplied each other to the first derivative of predicted value to the first derivative of middle variable output valve and the second rank variation relation with the first rank variation relation simultaneously obtains the adjusted value of the first rank weights again;
C11, with the difference of predicted value and real output value with adjust multiplication, multiplied result is multiplied each other to the first derivative of predicted value to the first derivative of middle variable output valve and the second rank variation relation with the first rank variation relation simultaneously obtains the adjusted value of the first rank deviation again;
C12, with the difference of predicted value and real output value with adjust multiplication, the weights with second rank multiply each other again, then multiplied result and the second rank variation relation being multiplied each other to the first derivative of predicted value obtains the adjusted value of the second rank weights;
C13, with the difference of predicted value and real output value with adjust multiplication, multiplied result and the second rank variation relation are multiplied each other to the first derivative of predicted value obtains the adjusted value of the second rank deviation again.
Wherein, when described method adopts the single order interative computation, calculate among the step B and obtain predicted value and specifically may further comprise the steps:
The product of the weights between the predicted value of each intermediate variable output valve and each intermediate variable and cell load is carried out addition, again the result of addition and the deviation of single order iteration are carried out addition, then the result of addition and single order variation relation being multiplied each other obtains predicted value.
Wherein, when described method adopts the single order interative computation, calculate the adjusted value of obtaining weights and deviation among the step C and specifically may further comprise the steps:
C20, with the difference of the predicted value of cell load and real output value with adjust multiplication, multiply each other with weights again, then multiplied result and variation relation being multiplied each other to the first derivative of predicted value obtains the adjusted value of weights;
C21, with the difference of predicted value and real output value with adjust multiplication, multiplied result and variation relation are multiplied each other to the first derivative of predicted value obtains the adjusted value of deviation again.
Wherein, refreshing weight and deviation specifically comprise among the step C:
The adjusted value of weights and the weights of last time are carried out addition obtain new weights, again the adjusted value of deviation and the deviation of last time are carried out addition and obtain new deviation.
Above technical scheme as can be seen, because the present invention dynamically adjusts weights and deviation earlier, utilize adjusted weights and deviation calculation to obtain the predicted value of cell load again, on identical prognoses system, compare with the alpha filtering mode prediction cell load of prior art, the average of the difference of the predicted value of cell load and real output value is littler among the present invention, standard deviation is also lower, thereby has improved the precision of cell load predicted value; Further, because the present invention makes the predicted value of cell load approach mutually with real output value by interative computation repeatedly, compare with the alpha filtering mode prediction cell load of prior art, the present invention can obtain better tracking characteristics.
Description of drawings
Fig. 1 method main process of the present invention figure;
First kind of execution mode flow chart of Fig. 2 the inventive method;
Second kind of execution mode flow chart of Fig. 3 the inventive method.
Embodiment
The invention provides a kind of realization cell load forecast method, the main thought of this method is: collect history data of region load, and obtain the real output value of cell load, carry out iterative computation and obtain the predicted value of cell load, the predicted value of calculation plot load and real output value is poor again, then obtain the adjusted value of weights and deviation according to iterative formula, refreshing weight and deviation again, when carrying out the iterative computation number of times when presetting number of times, when perhaps the difference of cell load predicted value and real output value is less than preset value, utilize new weights and deviation to carry out the predicted value that computing obtains cell load.
According to the basic thought of said method, in conjunction with the accompanying drawings the concrete technical scheme of the inventive method is elaborated again below.
With reference to Fig. 1, Fig. 1 is the main process figure of the inventive method, and this method mainly may further comprise the steps:
Whether step 105～step 106, judgement carry out the number of times of iterative computation greater than presetting number of times, whether the difference of perhaps judging cell load predicted value and real output value is less than preset value, if, error between cell load predicted value and the real output value is lower, just the cell load predicted value is approached mutually with real output value, weights that utilize to upgrade again and deviation are carried out computing and are drawn the cell load predicted value, process ends, otherwise return execution in step 103.
With reference to Fig. 2, Fig. 2 is first kind of execution mode flow chart of the inventive method, and this execution mode specifically may further comprise the steps:
Wherein, in the formula (1)
Expression is carried out addition with the measured value of each cell load and the product of the weights between this measured value and the intermediate variable j,
Expression is carried out addition with the product of the weights between each intermediate variable output valve and each intermediate variable and k the predicted value, and Mi represents the measured value of i cell load.
The predicted value A2k of step 212, calculation plot load and real output value Tk's is poor, and formula (2) is as follows:
E(k)＝A2k-Tk
ΔW1ij＝Lr*(A2k-Tk)*W2jk*W1ij*F2′(A2k)*F1′(A1j)
ΔB1j＝Lr*(A2k-Tk)*F2′(A2k)*F1′(A1j)；
ΔW2jk＝Lr*(A2k-Tk)*W2jk*F2′(A2k)
ΔB2k＝Lr*(A2k-Tk)*F2′(A2k)；
Wherein, Lr represents to adjust coefficient, and preferably mode is to adjust coefficient in [0,0.1] interval value, and F1 ' (A1j) represents the first derivative of F1 to middle variable output valve, and F2 ' (A2k) represents the first derivative of F2 to predicted value.
W1ij＝W1ij+ΔW1ij B1j＝B1j+ΔB1j；
W2jk＝W2jk+ΔW2jk B2k＝B2k+ΔB2k；
Whether step 215～step 216, judgement carry out the number of times of iterative computation greater than presetting number of times ME, judge that perhaps whether E (k) is less than preset value, this preset value is got 0.1Tk usually, if, utilize weights and the deviation upgraded, calculate the predicted value of obtaining cell load according to formula (1) again, otherwise, execution in step 211 returned.
The predicted value of step 217, recording cell load is upgraded measurement data, carries out the next round prediction.
In the present embodiment, weights of Sheng Chenging such as W1ij and W2jk are usually in (0,1) interval value at random, and deviation of Sheng Chenging such as B1j and B2k are also in (0,1) interval value at random.In addition, the first derivative of variation relation F1 and F2 be generally F1 ' (x)=x* (1-x), F2 ' is (x)=1.
Below in conjunction with the situation of change of adjusting coefficient and cell load in the table 2 explanation present embodiment, table 2 is present embodiment test results to the ascending load data of NodeB.M=5 wherein, N=20 presets number of times ME=5, and the system of prediction is identical with the system of the alpha filtering mode prediction of prior art.
| Adjust coefficient Lr | 0.05 | 0.075 |
| Average | 2.5494 | 2.7289 |
| Standard value | 3.5119 | 3.6015 |
Table 2
Table 1 and table 2 are compared, method of the present invention can obtain than the better tracking characteristics of alpha filtering mode, from the average of the difference of predictive metrics value and real output value and the statistical property of standard deviation, the average of the inventive method is littler, deviation is also lower, just the deviation of cell load is littler, and it is also lower to fluctuate, and then the precision of cell load predicted value is higher.
From the above, present embodiment is the better embodiment of the inventive method, in addition, such as the intermediate variable number N of the cell load number M of collecting, setting, adjust the precision that a value among coefficient Lr, the iterations ME that presets and E (the k)＜aTk etc. all can have influence on the cell load predicted value.The intermediate variable number N that is provided with is many more, and the number M of the cell load of collection is many more, and the precision of cell load predicted value also can be high more.Usually adjust coefficient Lr and ask value in [0,0.1], but also be not precluded within other scope value, the Lr value is few more, and the precision of cell load predicted value is high more.In addition, the iterations ME that presets is in the certain hour scope, and iterations is many more, and the cell load accuracy of predicting is just high more, but exceeds in the certain hour scope, and iterations is many more, can make to follow the tracks of to postpone, and then cause precision of prediction to reduce; Theoretically, a value among E (k)＜aTk is few more, and the deviation of predicted value and real output value is just low more, if but a value is few more, and need the data of tracking just more, thereby make tracking time longer.
With reference to Fig. 3, Fig. 3 is second kind of execution mode flow chart of the inventive method, and this execution mode specifically may further comprise the steps:
Step 301～step 302, by the measured value of M cell load before radio network controller (RNC) or the NodeB record current time, follow the measured value of NodeB periodic report cell load;
Step 303～step 304, N intermediate variable is set, generate weights and deviation between intermediate variable and the predicted value at random, variation relation between intermediate variable and the predicted value is set again, suppose that weights between j intermediate variable and k the predicted value and deviation are remembered respectively and make W2jk and B2k, variation relation note between j intermediate variable and k the predicted value is made F2, wherein, weights that generate at random and deviation are usually in (0,1) interval value.
Step 305～step 307 is waited for cell load message, judges whether loaded cell arrives, if obtain the cell load real output value of current time, otherwise return execution in step 305;
A1j＝Mi
Wherein, in the formula
Expression is carried out addition with the product of the weights between each intermediate variable output valve and each intermediate variable and k the predicted value, and Mi represents the measured value of i cell load.
E(k)＝A2k-Tk
ΔW2jk＝Lr*(A2k-Tk)*W2jk*F2′(A2k)
ΔB2k＝Lr*(A2k-Tk)*F2′(A2k)
Wherein Lr represents to adjust coefficient, and preferably mode is to adjust coefficient in [0,0.1] interval value, and F2 ' (A2k) represents the first derivative of F2 to predicted value.
W2jk＝W2jk+ΔW2jk
B2k＝B2k+ΔB2k
Whether step 312～step 313, judgement carry out the number of times of iterative computation greater than presetting number of times ME, judge that perhaps whether E (k) is less than preset value, usually preset value is got 0.1Tk, if, utilize new weights and deviation to recomputate the predicted value that obtains cell load, otherwise, return execution in step 308.
By above-mentioned execution mode as can be known, present embodiment is that with the difference of first kind of execution mode the exponent number of iteration is different, first kind of execution mode is to adopt the second order iterative relation to calculate the predicted value of cell load, and second kind of execution mode is to adopt the single order iterative relation to calculate the predicted value of cell load.Predict that in identical system the precision of prediction of present embodiment does not have the precision of prediction height of first kind of execution mode.Hence one can see that, when the prediction cell load, the exponent number that carries out interative computation is high more, the precision of cell load predicted value is just high more, wherein when carrying out the high-order interative computation, can analogize the iterative formula of the adjusted value that obtains asking cell load predicted value and weights and deviation according to above-mentioned two kinds of execution modes.
More than a kind of realization cell load forecast method provided by the present invention is described in detail, used specific case herein principle of the present invention and execution mode are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (10)
1, a kind of realization cell load forecast method is characterized in that this method may further comprise the steps:
A, collection history data of region load, and generate weights and deviation at random;
B, utilize weights and deviation iterative computation to obtain the predicted value of cell load, calculate the poor of predicted value and real output value again;
C, calculate the adjusted value obtain weights and deviation, refreshing weight and deviation again according to iterative formula;
Whether D, judgement carry out the number of times of iterative computation greater than presetting number of times, whether the difference of perhaps judging cell load predicted value and real output value is less than preset value, if, utilize weights and the deviation upgraded to recomputate the predicted value that obtains cell load, process ends, otherwise return execution in step B.
2, realization cell load forecast method as claimed in claim 1 is characterized in that, collects history data of region load in the steps A and specifically comprises:
The measured value of cell load before radio network controller or the Node B record current time, Node B is the measured value of periodic report cell load again.
3, realization cell load forecast method as claimed in claim 1 or 2 is characterized in that, when described method adopts the second order interative computation, generates weights at random in the steps A and deviation specifically may further comprise the steps:
A1, intermediate variable is set, the measured value of regeneration cell load and weights between the intermediate variable and deviation, described weights and deviation are set to the weights and the deviation on first rank;
Weights and deviation between the predicted value of A2, generation intermediate variable and cell load, described again weights and deviation are set to the weights and the deviation on second rank.
4, realization cell load forecast method as claimed in claim 1 or 2 is characterized in that, when described method adopts the single order interative computation, generates weights at random in the steps A and deviation specifically comprises:
Intermediate variable is set, weights between the predicted value of regeneration intermediate variable and cell load and deviation, described again weights and deviation are set to the weights and the deviation of single order.
5, realization cell load forecast method as claimed in claim 1 is characterized in that, further comprises before the step B:
Wait for cell load message, judge whether loaded cell arrives, if, obtain the real output value of the cell load of current time, execution in step B again, otherwise continue wait cell load message.
6, as claim 1,2 or 5 described realization cell load forecast method, it is characterized in that, when described method adopts the second order interative computation, calculate among the step B and obtain predicted value and specifically may further comprise the steps:
B1, the measured value of each cell load and the product of the weights between this measured value and the intermediate variable are carried out addition, again the result of addition and the deviation on first rank are carried out addition, then the variation relation on the result of addition and first rank being multiplied each other obtains the output valve of intermediate variable;
B2, the product of the weights between the predicted value of each intermediate variable output valve and each intermediate variable and cell load is carried out addition, again the result of addition and the deviation on second rank are carried out addition, then the variation relation on the result of addition and second rank being multiplied each other obtains the predicted value of cell load.
7, as claim 1,2 or 5 described realization cell load forecast method, it is characterized in that, when described method adopts the second order interative computation, calculate the adjusted value of obtaining weights and deviation among the step C and specifically may further comprise the steps:
C10, with the difference of the predicted value of cell load and real output value with adjust multiplication, multiply each other with the weights on first rank and the weights on second rank simultaneously, then multiplied result being multiplied each other to the first derivative of predicted value to the first derivative of middle variable output valve and the second rank variation relation with the first rank variation relation simultaneously obtains the adjusted value of the first rank weights again;
C11, with the difference of predicted value and real output value with adjust multiplication, multiplied result is multiplied each other to the first derivative of predicted value to the first derivative of middle variable output valve and the second rank variation relation with the first rank variation relation simultaneously obtains the adjusted value of the first rank deviation again;
C12, with the difference of predicted value and real output value with adjust multiplication, the weights with second rank multiply each other again, then multiplied result and the second rank variation relation being multiplied each other to the first derivative of predicted value obtains the adjusted value of the second rank weights;
C13, with the difference of predicted value and real output value with adjust multiplication, multiplied result and the second rank variation relation are multiplied each other to the first derivative of predicted value obtains the adjusted value of the second rank deviation again.
8, as claim 1,2 or 5 described realization cell load forecast method, it is characterized in that, when described method adopts the single order interative computation, calculate among the step B and obtain predicted value and specifically may further comprise the steps:
The product of the weights between the predicted value of each intermediate variable output valve and each intermediate variable and cell load is carried out addition, again the result of addition and the deviation of single order iteration are carried out addition, then the result of addition and single order variation relation being multiplied each other obtains predicted value.
9, as claim 1,2 or 5 described realization cell load forecast method, it is characterized in that, when described method adopts the single order interative computation, calculate the adjusted value of obtaining weights and deviation among the step C and specifically may further comprise the steps:
C20, with the difference of the predicted value of cell load and real output value with adjust multiplication, multiply each other with weights again, then multiplied result and variation relation being multiplied each other to the first derivative of predicted value obtains the adjusted value of weights;
C21, with the difference of predicted value and real output value with adjust multiplication, multiplied result and variation relation are multiplied each other to the first derivative of predicted value obtains the adjusted value of deviation again.
10, realization cell load forecast method as claimed in claim 1 is characterized in that refreshing weight and deviation specifically comprise among the step C:
The adjusted value of weights and the weights of last time are carried out addition obtain new weights, again the adjusted value of deviation and the deviation of last time are carried out addition and obtain new deviation.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012149749A1 (en) * | 2011-09-19 | 2012-11-08 | 华为技术有限公司 | Load prediction method, apparatus and energy-saving control communication system |
| WO2013174179A1 (en) * | 2012-05-23 | 2013-11-28 | 华为技术有限公司 | Cell handover method and communication device |
| CN103955266A (en) * | 2014-05-22 | 2014-07-30 | 东北林业大学 | Low power consumption design method based on Android mobile Sink load prediction |
| CN104581779A (en) * | 2014-12-11 | 2015-04-29 | 华为技术有限公司 | Business processing method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FI964707A (en) * | 1996-11-26 | 1998-05-27 | Nokia Telecommunications Oy | Method for load control and radio system |
| ES2229559T3 (en) * | 1998-12-18 | 2005-04-16 | Nokia Corporation | METHOD FOR THE CONTROL OF TRAFFIC LOAD IN A TELECOMMUNICATIONS NETWORK. |
| CN100499422C (en) * | 2003-12-10 | 2009-06-10 | 华为技术有限公司 | Method for forecasting load of self-adaptive CDMA system |
| CN100370876C (en) * | 2004-09-29 | 2008-02-20 | 华为技术有限公司 | Downlink load estimating method in mobile communication system |
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2006
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012149749A1 (en) * | 2011-09-19 | 2012-11-08 | 华为技术有限公司 | Load prediction method, apparatus and energy-saving control communication system |
| WO2013174179A1 (en) * | 2012-05-23 | 2013-11-28 | 华为技术有限公司 | Cell handover method and communication device |
| CN103955266A (en) * | 2014-05-22 | 2014-07-30 | 东北林业大学 | Low power consumption design method based on Android mobile Sink load prediction |
| CN103955266B (en) * | 2014-05-22 | 2016-09-14 | 东北林业大学 | The low power consumption design method of Sink load estimation is moved based on Android |
| CN104581779A (en) * | 2014-12-11 | 2015-04-29 | 华为技术有限公司 | Business processing method and device |
| CN104581779B (en) * | 2014-12-11 | 2018-11-30 | 华为技术有限公司 | A kind of method for processing business and device |
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