TWI646808B - Request traffic prediction method - Google Patents

Abstract

The invention relates to a method for requesting traffic prediction, which mainly performs a prediction, and then signs the voucher status according to the requested traffic prediction value, so as to obtain a more accurate pre-signature voucher state quantity, and achieve the load of the reply signature subsystem. balance.

Description

Request traffic prediction method

The invention relates to a method for requesting traffic prediction.

At present, the online voucher status communication protocol method still mainly uses real-time online inquiry voucher and reply voucher, but the current reply signature system will take more processing time when signing the signature, which will further cause a reply. Delays in the client device. In addition, the request traffic may cause an unpredictable burden on the credential server and the reply signature system due to the increase in the time period. When the load cannot be loaded, the most serious will cause the system to crash.

In order to avoid the above situation, some network traffic prediction methods have been implemented in the current technology. The current network traffic prediction methods mainly use statistical methods and grey theory to estimate, but these technologies require a large amount of statistics, and Because the user's request traffic varies greatly, these techniques are likely to get larger request traffic estimation errors, resulting in inaccurate predictions.

For the technology of traffic forecasting, refer to the technology of the Republic of China Patent No. I234974 "Improving the Mechanism of Decentralized Blocking Service Attacks Based on Grey Prediction", which mainly uses data collection and classification modules, combined with grey theory and prevention. The strategy analyzes and predicts the network traffic pattern to judge and defend against decentralized blocking service attacks. However, although this method can perform network traffic prediction and prevent network attacks in the implementation process, it uses gray theory and need To conduct a large number of statistics, and to withstand the possibility of excessive variability in network traffic, it is not quite accurate to empirically estimate its network traffic.

And even if the system has predicted the possible flow value for a certain period of time, how to understand the load during peak hours, there is still no matching processing mode in the prior art.

To sum up, it is proposed that an efficient and systematic forecasting voucher request flow system or method, and pre-processing a voucher of a certain time period may be a high load, which is an extremely urgent subject in the field to which the present invention pertains.

The present invention comprises a voucher pre-signing system for predicting request traffic, which is composed of the following system and database: wherein the voucher pre-signing system for predicting request traffic of the present invention comprises a voucher database, the voucher database is through the network. The road is connected with a voucher status communication protocol server outside the system, and the function of the voucher database is to store a record in which the plurality of voucher is requested, and the record of each of the voucher requests is a status of the plurality of terminal devices external to the system to the voucher status. The time point of the communication protocol server requesting the voucher, that is, the voucher database stores the time data of the terminal device requesting a voucher in all time periods.

The voucher pre-signing system for predicting request traffic of the present invention comprises a request traffic prediction subsystem, wherein the request traffic prediction subsystem obtains a record of each voucher requested in the voucher database, and groups the records by requesting a traffic grouping method. Or directly using the record data, performing an analysis record and establishing a complex neural network to predict the requested traffic of each voucher at a specific time period to generate a request traffic prediction value; the request traffic prediction subsystem can pass a request traffic Grouping method, grouping records, and training each of these types of nerves Network, or directly use the record data to train each of these neural networks to retain the higher accuracy of the prediction; the request traffic prediction subsystem can also find out the requested request by analyzing the records of each of the credentials. A voucher with a higher flow rate is used to predict these high-valued documents.

The request traffic prediction value is transmitted to a reply signature subsystem, and the reply signature subsystem pre-signs the corresponding number of the certificates according to the requested traffic prediction value to generate a plurality of pre-signature voucher states. That is, the reply signature subsystem pre-certifies the voucher for the quantity that a voucher will be requested at a certain time period to cope with the voucher demand at the arrival of the time period; and the reply signature subsystem will The pre-signature voucher status is stored to the voucher database, and the voucher database provides each of the pre-signature voucher statuses to the voucher status communication protocol server for use by external terminal devices to request credentials.

The present invention also includes a method for generating a voucher pre-signing system according to the foregoing predicted request flow, the steps of which mainly include: 1. The request traffic prediction subsystem obtains a record of each voucher being requested from the voucher database; The request traffic prediction subsystem separately counts the traffic of each voucher according to the time period in which each voucher is requested for each voucher, and sorts the traffic according to each time slot of the voucher to select each voucher of the high request traffic. 3. The request traffic prediction subsystem randomly establishes each of the neural networks according to the selected each of the credentials to predict the requested traffic of each of the credentials; 4. The request traffic prediction subsystem is requested according to each of the credentials. The record trains each of these neural networks, which requests the traffic prediction subsystem and analyzes the prediction accuracy of each such neural network to preserve the high accuracy of each such class. Neural network; 5. The request traffic prediction subsystem generates the requested traffic prediction value according to the prediction of each of the neural networks and transmits the predicted traffic to the reply signature subsystem; 6. The reply signature subsystem receives the requested traffic prediction And pre-signing the corresponding number of the voucher according to the requested traffic prediction value to generate each of the pre-signed voucher states; and 7. the reply signature subsystem stores the pre-signature voucher status to the voucher The database is available for external terminal devices.

In order to further train each of the neural networks, the request traffic prediction subsystem may perform a request traffic grouping method to group the historical request traffic data into the training, and the request traffic grouping is the request traffic. The prediction subsystem dataizes the traffic of each time period of each voucher according to the time period into a cluster, and calculates and gradually merges the clusters. The request traffic grouping method mainly includes the following steps: 1. setting an initial clustering step, each of which will be The request flow record of each credential in the voucher request and the query record is regarded as a request traffic set, and all the sets in a single time period are used as a cluster; 2. The standard deviation step is calculated, and the foregoing cluster is calculated The standard deviation of each request traffic set; 3. The calculation distance step is to calculate the distance of the request traffic set between the clusters; 4. The calculation distance standard deviation step is to calculate the standard deviation of the distance of the request traffic set between the clusters. ;5. Similar cluster consolidation step, in a cluster in the foregoing cluster, the internal standard deviation is greater than the In the case of a standard deviation of the distance between the cluster and another cluster, merge the cluster with the other cluster and calculate the cluster set And then the cluster center; and 6. Repeat the merge step, repeating the aforementioned similar cluster merge steps until no clusters meet the mergeable condition.

As described above, the request traffic prediction subsystem is configured to generate a request traffic prediction method for requesting traffic prediction values, which can be further described as follows: First, the request traffic prediction method can be divided into two phases, respectively, a training phase. And the implementation phase, wherein the training phase can include two steps: 1. randomly generating a plurality of neural networks: mainly by setting a random neural network group algorithm parameter value and reading the voucher status request to the voucher database Query historical records of records to randomly establish r neural network models; 2. Retain multiple neural networks with high prediction accuracy: predict accuracy and accuracy of randomly generated r-like neural network models The thresholds are compared to exclude neural network models below the threshold of accuracy, and g-like neural networks are retained. If the accuracy of any neural network-free model is higher than the accuracy threshold When the value is returned, it will return to the first step, reset the threshold, and retrain the random neural network. The implementation phase can also include two steps: 1. Entering the real-time data to the neural network with high prediction accuracy retained in the training phase: obtaining an instant voucher status request query record, and inputting to the training phase according to this Preserved g-like neural network models for predictive computation; 2. Weighted average to produce predicted values: Finally, the request traffic prediction subsystem will retain the predicted values generated by each of these neural networks, and apply the training phase. The correct rate obtained at the time as a weight, the weight is flat Both are used to generate the requested traffic prediction value.

The above-mentioned voucher pre-signing system and method for predicting request traffic, wherein the reply signature subsystem implements a voucher status pre-signature method for pre-signature, which can also be mainly divided into four steps:

1. Receiving the voucher information waiting for the pre-signature: the reply signature subsystem can receive the voucher information and the aforementioned predicted traffic prediction value.

2. detecting the off-peak period: in order to save system resources and reduce the load during peak hours, the reply signature subsystem obtains a record of each requested voucher from the voucher database to statistically analyze that each voucher is requested less frequently The off-peak period, and can pre-sign the voucher status during the off-peak period to achieve the effect of the diversion.

3. For the voucher status production signature value: the corresponding number of voucheres are pre-signed for the plurality of voucher predicted by the predicted traffic flow value to generate a plurality of pre-signed voucher status.

4. Store pre-signature voucher status: Store the data of the pre-signature voucher status to the voucher database for subsequent client device query.

As described above, it can be seen that the present invention is a system for predicting request traffic based on historical data, and for pre-signing a voucher, and a method thereof, when pre-signing the voucher with a large amount of usage, and training the evolution prediction again and again Accuracy is an important part of a highly efficient voucher management system.

100‧‧‧Client equipment

101‧‧‧Online Voucher Status Protocol Server

102‧‧‧Request Traffic Prediction Subsystem

103‧‧‧Voucher database

104‧‧‧Reply signature subsystem

S201~S205‧‧‧Step procedure

S301~S307‧‧‧Step procedure

S401~S402‧‧‧Step procedure

S4011~S4012‧‧‧Step process

S4021~S4022‧‧‧Step procedure

S601~S602‧‧‧Step procedure

S701~S704‧‧‧Step procedure

1 is an overall system of a voucher pre-signing system for predicting request traffic according to the present invention Schematic diagram of the architecture.

FIG. 2 is a schematic flow chart of steps of a method for pre-signing a voucher for predicting request traffic according to the present invention.

FIG. 3 is a schematic flow chart of steps of a method for requesting traffic grouping according to the present invention.

FIG. 4 is a schematic flow chart showing the steps of the method for requesting traffic prediction according to the present invention.

FIG. 5 is a schematic diagram of a method for requesting traffic prediction according to a neural network model 1 of the present invention.

FIG. 6 is a schematic flow chart showing an example of an implementation stage of a request traffic prediction method according to the present invention.

FIG. 7 is a flow chart showing the steps of a method for pre-signing a voucher state in a request traffic prediction method according to the present invention.

The invention will be further illustrated by the following examples in conjunction with the drawings.

The present invention is a detailed online certificate status protocol (OCSP) pre-signing method and system according to the request traffic prediction; in view of the prior art, the method of the voucher status communication protocol still operates mainly on the live line. The way of querying and replying, but since the reply signature subsystem will take a lot of processing time when signing, the method of the prior art will cause delays in replying to the client device, and in addition, as the request traffic increases, Gradually burdening the voucher status protocol server and the reply signing subsystem, and then crashing when the system is unable to load.

In addition, in the prior art, the network traffic prediction method generally needs to be obtained after a large number of statistical processes, and the user requests the traffic variation process. The degree of the request flow estimation error obtained by using the prior art may be large.

Therefore, the present invention mainly collects and analyzes the set of requested traffic of each voucher in each time of day, and then combines the requested traffic of the similar time period into a group by using the request traffic grouping method, and divides the data into a plurality of groups. Subsequent different groups use the request traffic prediction method to predict, and finally sign the voucher status according to the requested traffic prediction value to obtain a more accurate pre-signature voucher status quantity, and reach a reply signature Load balancing of the system.

First, referring to FIG. 1, the system of the present invention includes at least one client device 100, an online certificate status communication protocol (OCSP) server 101, a reply signature subsystem 104, a voucher database 103, and a The request traffic prediction subsystem 102, the modes and steps of its mutual operation will be described in detail in the following paragraph; please refer to the following list 1 and Table 1 for an example to illustrate 2014/07/01 to 2014/07/28 The voucher request query record during the period, if exemplified by FIG. 1, wherein one of the plurality of client devices 100 is required to confirm the credential C1 status when 00:00:29 on 2014/07/01 At this time, the request is sent to the online voucher status communication protocol server 101, and the online voucher status communication protocol server 101 queries and obtains the voucher C1 status from the voucher database 103, and leaves a request inquiry record in the voucher database 103, that is, As shown in the first row of Table 1, the online voucher status communication protocol server 101 subsequently transmits the voucher C1 status to the reply signature subsystem 104 for signature, and the signed certificate C1. shape Responding to the client device D1 in the client device 100 to complete the entire voucher request and signature action; and so on, each client device will query for the voucher required by itself, and each request is queried. The record will be in the voucher database Stored.

The method of the present invention can separately count the request query record set according to the period and the time period, that is, the request traffic prediction subsystem 102 takes the document of the whole 2014/07/01~2014/07/28 period shown in Table 1 as the requested query record as an example. The set is further statistically calculated to calculate the number of voucher states to be pre-signed and transmitted to the reply signature subsystem 104, and the pre-signed voucher status is stored by the reply signature subsystem 104 to the voucher database 103; When the certificate queried by the client device 100 already has the status of the pre-signature voucher, the online voucher status communication protocol server 101 directly queries and obtains the voucher status of the pre-signed certificate by the method of the present invention. The signature voucher status is replied to the client device 100, the voucher database 103 and the pre-signed voucher status that has been removed is destroyed.

The following table is Table 1:

The method flow of the present invention is shown in FIG. 2, and the method may include five steps, namely: requesting traffic collection and statistics of the voucher in step S201, Step S202 acquires the certificate of the highly requested flow rate, the step S203 implements the request flow grouping method, the step S204 implements the request flow rate prediction method, and the step S205 implements the voucher state pre-signature method. The method has the above-mentioned steps, the main purpose of which is to obtain the credentials of the highly requested traffic before performing the request traffic prediction method, and separately perform the statistics and pre-signature processing on the highly requested traffic credentials, and the method can be combined with the request traffic grouping method. The request flow records for each credential are grouped according to the time period for training to increase the prediction correctness rate. Hereinafter, the steps will be described in detail in conjunction with the embodiment.

Firstly, the first step S201, the request flow collection and statistics step of the voucher: the request flow prediction subsystem of the present invention obtains the request query record of the voucher from the voucher database, as shown in Table 1 2014/07/01~2014/ The request query record during the period of 07/28, the request traffic prediction subsystem according to the period (in this embodiment, the week is the period unit), the time period (in the present embodiment, the day is used as the time unit), and the time point (in the present embodiment) In the example, the hourly time unit is used to count the number of request query records at each time point, and the request query record set of multiple time periods, plural time periods, and multiple time points can be obtained, as shown in Table 2 below. The records are compiled as shown.

The following table is Table 2:

Then, in the second step S202, the step of obtaining the certificate of the highly requested traffic is as follows: as shown in Table 2, after obtaining the number of requests for each credential according to the request of each time period, the request traffic prediction subsystem of the present invention will The number of requesting query records is sorted from high to low, and the voucher of the higher ranked request traffic can be obtained, which is the more frequently requested voucher, and can also be interpreted as the invention can selectively delay the need for further decompression. The voucher of the situation.

Taking the period from 2014/07/01 to 2014/07/28 in Table 1 above as an example, the request traffic prediction subsystem can obtain the total number of requested flows for each credential, and then sort the traffic according to the requested traffic from high to low. list three shown; to remove the plurality of credentials requested altitude information flow for subsequent analysis, in this embodiment, since the line to which the request credentials C ₁ Total flow rate of 208,728 times, the height of the request for the certificate most traffic, so Examples Example C ₁ certificate will in this embodiment, the requesting subsystem traffic forecasts for subsequent clustering method request traffic, traffic prediction method requests, pre-signature certificate status method.

Table 3 is as follows:

Then, the third step S203 is to implement a request traffic grouping method step: requesting the traffic prediction subsystem to obtain a request traffic set for each period of the credential to be analyzed, and initially treating the request traffic set of each time period as a cluster. Calculate the standard deviation of the request traffic set in the group, the distance between the request traffic sets of the group, and the standard deviation of the request traffic set between the groups, and then merge and recalculate the clusters of the similar request traffic set until no cluster can be merged.

The fourth step S204 is to implement the request traffic prediction method step: the request traffic prediction subsystem obtains the request traffic set of each period of the voucher to be analyzed, and randomly establishes a plurality of neural networks in the training phase, and then performs historical data. Train and analyze the accuracy of request traffic prediction for each type of neural network, and retain a plurality of highly accurate class-like neural networks; in the implementation phase, the request traffic prediction subsystem inputs the immediate request traffic set into the training phase. The plurality of highly accurate neural networks are retained, and the predicted traffic prediction values are respectively obtained, and then the weighted average is obtained to obtain the final requested traffic prediction value, and the requested traffic prediction value is transmitted to a reply signature subsystem.

Finally, the fifth step S205 is a method for implementing a voucher status pre-signature method: the reply signature subsystem receives the request traffic prediction value, and the voucher information system predicts the voucher information to be pre-signed for the predicted traffic prediction value. Signature value, and store the status of the pre-signature voucher to the voucher database; in addition, in order to reduce the traffic load, the reply signature subsystem can be pre-signed The certificate database queries the time period distribution of the request traffic to analyze the off-peak period and then performs the pre-signature process at the off-peak time with a small load.

The flow of the voucher pre-signing method for predicting request traffic of the present invention includes the foregoing request flow grouping method, and the flow chart of the method is as shown in FIG. 3; mainly includes six steps, which are listed as follows: Step S301 sets the initial Clustering, step S302 calculates the standard deviation of the set of request traffic in the group, step S303 calculates the distance of the inter-group request traffic set, step S304 calculates the standard deviation calculation of the inter-group request traffic set distance, similar cluster merging in step S305, and calculates the cluster center The traffic collection is requested, and step S306 confirms whether there is a cluster uncalculated merge to repeat the calculation to no cluster mergeable. If not, the process proceeds to step S307, which will be explained in detail in the following paragraphs.

Step 1 of the requesting traffic grouping method is to set an initial cluster: the requesting traffic prediction subsystem sets the request traffic set of each time point in each time slot unit as a cluster, or It is possible to aggregate the requested traffic of simultaneous segments in each cycle as a cluster to calculate the center of each cluster. Taking the request traffic of the period from 2014/07/01 to 2014/07/28 in Table 3 as an example, the set of request traffic of the certificate C1 can be sorted as shown in the following list four, wherein the first cycle of the first cycle of the certificate C ₁ Request flow at the first time (ie 0:00 am) for the time period (ie Tuesday, July 24, 2014) 0, in this embodiment, the logic indicating the requested flow value is q _{document number, cycle number, time slot number, time point number} , and in the same way, the seventh period of the fourth cycle of the certificate C ₁ (ie 2014/) Monday, 07/28) Request traffic at the 24th time (ie at 23:00 pm) It is 82, and in this embodiment, the set of time request traffic in the same time period is represented as a Q _{document number, a cycle number, a time slot number, a time point number} , and the request flow rate of the first time period of the first cycle of the certificate C ₁ is ={ , , ,..., }={0,0,...,99}.

Table 4 is as follows:

In this embodiment, the time period unit set by the request traffic prediction subsystem of the present invention has n cycles, m time slots, and o time points, and the system can aggregate the request traffic sets of the same time period in each cycle into one. For clustering, for example, on Tuesday, the request traffic collection on the first week of Tuesday 07/01, the second week of Tuesday 07/08, the third week of Tuesday 07/15, and the fourth week of Tuesday 07/22 can be clustered into one cluster. Coming soon , , , Gather into a cluster and calculate the group center using the following formula (1) as an example The calculation result is shown, for example, in the formula (2); and by the same calculation, the group center based on the simultaneous segment in each cycle can be expressed as , ,..., The results are shown in Table 5 below.

(1) shown below formula, which is calculated based group for example C ₁ voucher center within the j-th cycle; wherein, Representative groups based document center C ₁ of the j th cycle of the first period, the same type of said logical notations:

According to the example of the above formula (1), the formula (2) for calculating the result of the group center of the first cycle of the C ₁ certificate is as follows: Among them, each value represents the group center of each time period in the first cycle, and so on.

Table 5 is as follows:

Step 2: S302 is a standard deviation of the set of request traffic in the group: the request traffic prediction subsystem calculates a standard deviation of the set of request traffic in each cluster; in the embodiment, the following formula (3) is used. For example, the standard deviation of the set of request traffic within the cluster is calculated separately, and the result of the calculation is shown in the following table 6.

Equation (3) is as follows, where σ represents the standard deviation and μ represents the average. This formula calculates the standard deviation of the set in the first cycle of the C ₁ certificate: ,among them, .

Table 6 is as follows:

Step 3: S303 is a distance for calculating a set of inter-cluster request traffic: the request traffic prediction subsystem calculates a distance value or a similarity value of a request traffic set between each cluster and other clusters classified by the foregoing period or time period, and the like. In the present embodiment, the following formula (4) is used to calculate the distance value of each time point vector value of the inter-cluster request traffic set. And as shown in Table 7, Take the calculation result of distance from other clusters as an example.

Equation (4) shown below significance, the calculation equation of the j-th certificate C ₁ cycle cluster groups with C ₁ voucher center of a cluster to the cluster center cycle distance value:

Table 7 is shown below, is the calculation group center With other clusters The result of the distance between:

The step S304 of the request traffic grouping method is a standard deviation calculation of the inter-group request traffic set distance: the request traffic prediction subsystem calculates a standard deviation of the request traffic set distance between each cluster and other clusters; in this embodiment, the following Equation (5) calculates the standard deviation of the set traffic distance between clusters respectively. As shown in Table 8, the group center With other clusters The calculation result of the distance is taken as an example to calculate the standard deviation.

Equation (5) is as follows: among them, .

Table 8 is shown below, is the calculation group center With other clusters The result of the standard deviation between the distances:

Step 5: Requesting a Traffic Clustering Method S305 merges and computes a clustered request traffic set for a similar cluster: the request traffic prediction subsystem determines a standard difference value of the cluster internal request traffic set and a distance between the cluster and another cluster request traffic set. The standard deviation, if the standard deviation of the set of request traffic within the cluster is greater than the standard deviation of the set traffic distance between the cluster and another cluster, then the cluster is judged to be similar to another cluster, so The two clusters merge and calculate the center of the merged cluster. In this embodiment, it can be observed that the group center is With the group center The standard deviation between clusters is relatively small (standard deviation is 40.74), and the group center Standard deviation of the cluster Therefore, the group center can be judged Cluster and group center For similar clusters, will be merged into the cluster center The cluster is merged into the group center Cluster, and use the following formula (6) to calculate the center of the merged cluster to get a new cluster center. And will be merged into the group center The cluster is deleted and the results are shown in the following list nine.

The formula (6) is as follows, the group center is The cluster is merged into the group center The calculation method of the cluster, in which the difference between before and after the calculation of the group center of the first period is performed in the following formulas and tables. Representing the first cycle group center after the merger:

Table 9 is as follows. It is calculated that the certificate C ₁ request traffic after the period of 2014/07/01~2014/07/28 is merged and the group center is merged in the first round. The columns indicate the cluster in the first column as the group center. , the group center of each time period:

Step 6 of requesting traffic grouping method to confirm whether there is a cluster uncalculated merge, to repeatedly calculate to no cluster can be merged: the request traffic prediction subsystem will repeatedly calculate the standard deviation of the clustered request traffic set of each cluster, between clusters Request traffic collection distance, and inter-cluster request traffic collection The standard deviation of the distances is used for merging of similar clusters, and the set of request traffic for the new cluster center is calculated until no similar clusters can be merged, that is, the process proceeds to step S307.

In this embodiment, in the occurrence of the foregoing list 9, the group center is Cluster and group center After the cluster is merged, it will be based on the standard deviation. Order of size Cluster and cluster center Cluster merge (in this case It represents the original 1st and 7th cycle group centers that have been merged. It is inevitable that after many mergers, the symbolic representation is too complicated and difficult to distinguish. After the merger, the 1st and Xth cycle group centers are represented as , only the meaning it represents is different); after the merger, the new group center Cluster and group center Cluster consolidation, and new cluster center Cluster and group center Cluster merge; after the above merge step, due to the cluster center Cluster and group center The distance between the cluster and other clusters is too large (distance is not less than its internal standard deviation), so it can be judged that each cluster is independent of one group, and finally, after completing all the merge operations, the original 7 can be The cluster is merged into 3 groups. It is: a cluster can be divided into a cluster from Monday to Friday, a separate cluster on Saturday, and a separate cluster on Sunday.

In addition, the request traffic grouping method of the present invention is also used to perform grouping on a group center mode based on simultaneous points in each period. In this embodiment, it can be concluded that the cluster can be divided into a cluster from 0:00 to 6:00. And 6:00 to 24:00 is another cluster. Then, the request traffic prediction subsystem of the present invention can use the result data after the grouping to train a plurality of neural network models of the request traffic prediction method to improve The prediction accuracy rate of the system.

So far, after the end of step S307, the step of implementing the request flow grouping method step of the present invention ends.

The flow of the voucher pre-signing method for predicting request traffic of the present invention includes the foregoing request traffic prediction method, and the flow chart of the method is shown in FIG. 4, which mainly includes two phases, namely, training phase S401 and implementation. Stage S402 will be described in detail as follows.

In the training phase S401, the method may include two steps: step S4011 randomly generates a plurality of neural networks, and step S4012 retains a plurality of neural networks with high prediction accuracy.

Wherein, step S4011 in the training stage S401 randomly generates a plurality of neural network systems: the request traffic prediction subsystem can be set to a parameter value of the stochastic neural network group algorithm, and request the traffic prediction subsystem to the voucher database The voucher is requested to query the historical data of the record to randomly establish r neural network models.

First, the developer sets the relevant parameter values of the stochastic neural network group algorithm in the request traffic prediction subsystem, including the number of established neural network models (in the case of r ), and the neural network model. The maximum number of hidden layers (taking h _max as an example), the number of maximum neurons in each hidden layer in the neural network model (taking c _max as an example), and the number of training data in the training neural network model accounted for the total training phase. The ratio of the number of data (the following description will take ρ% as an example) and the threshold of the correct rate (taking w _threshold as an example); in this embodiment, a total of 10 neural network models will be set up (ie, r = 10). The maximum number of hidden layers in the neural network model is 5 (ie, h _max = 5), and the number of largest neurons in each hidden layer in the neural network model is 7 (ie, c _max = 7), training neural network. The ratio of the training data of the road model to the total number of training data is 60% (ie, ρ%=60%), and the correct rate threshold is 0.945 (ie, w _threshold = 0.945, which is 94.5%). This embodiment will Based on the aforementioned parameter values to generate 10 neural network models for request traffic prediction.

In this embodiment, the requested traffic of the credential C ₁ is taken as an example for description; first, the request traffic prediction subsystem reads the voucher status request query history data (ie, the data shown in Table 4) to the voucher database, The set of request traffic for the first period of the first period (ie, Tuesday, July 24, 2014) is ={ , , ,..., }={0,0,...,99}. And the sum of the request traffic collections in the next time period of this data collection is 10537, so the developer will set the input value to the time period request traffic collection. And the target output value should be the sum value 10537 of the set of request traffic for a period below the input period. In addition, if the request traffic grouping method has been performed before, the training and calculation of the stochastic neural network group algorithm are performed separately according to the cluster.

According to the parameter values of the stochastic neural network group algorithm set, the request traffic prediction subsystem should randomly generate 10 neural network models, and the maximum number of hidden layers in the neural network model is 5 and The number of neurons in each hidden layer in the neural network model is 7, which means that the number of hidden layers in each type of neural network model must be between 0 and 5, and the number of neurons in each hidden layer will be From 0 to 7, the results produced by the present embodiment according to the settings are shown in Table 10 below.

Table 10 is as follows:

In addition, see Table 10, please also refer to Figure 5, which is a schematic diagram of the neural network model 1 as an example. The hidden layer of the neural network model 1 is 1 layer (the hidden layer number of the second field). The number of neurons in the hidden layer of this layer is 2 (the set of neuron numbers in the third column {2}); the hidden layer of the neural network model 2 has 2 layers, and the number of neurons in the hidden layer of the first layer is 3, and the number of neurons in the hidden layer of the 2nd layer is 4 (the set of neuron numbers in the third column {3, 4}); and so on, to obtain a total of 10 neural network models. Moreover, since the number of training materials of the set training neural network model accounts for 60% of the total number of data in the training phase, if Table 4 is taken as an example, the total number of data in the training phase is 20, so each one The neural network model will randomly take 12 samples of data for training neural network model learning, and the remaining 8 training stages (Testing Data in Training Stage, TDTRS) will be used as the training phase. The neural network model is used for verification; in this step, each set of 12 data obtained by each type of neural network model is randomly generated, and each type of neural network model will obtain different data sets to be repeated. Train and learn.

After completing the training of all the above-mentioned neural network models, the request traffic prediction subsystem can use the remaining 8 data to verify each type of neural network model to calculate the average correct rate as each type of neural network. The weight of the road model; taking the neural network model 1 as an example, all the test data in the training phase are input into the trained neural network model 1 to calculate the correct rate. For example, when requesting traffic set input, the predicted value is 10911, and then the correct formula is calculated by the following formula: the formula is 1-(|correct value-predicted value|/correct value), and the result is 1-(|10537- 10911|/10537)=96.45%; according to this side By analogy calculation, the correct rate of the test data (TDTRS) in the eight training stages can be obtained, and then the average correct rate is calculated. In this embodiment, the average correct rate of the neural network model 1 is 93.23%. The average correct rate corresponding to all 10 neural network models is listed below, as shown in Table 11.

Table 11 shows the following:

Wherein, in step S401, the training phase of a plurality of correctly predicted retention S4012 high-based neural network as: traffic prediction subsystem requests generating the random number r accuracy of the neural network model with the correct rate threshold w _threshold Perform an alignment to exclude a neural network model below this threshold (ie, a model with a low correct rate), and leave the remaining g neural network models; if there is no neural network model, the correct rate is higher than the threshold. When you return to the previous S4011 step, reset the threshold to retrain the random neural network group. In the present embodiment, the requesting subsystem will analyze the average traffic prediction accuracy of each neural network model, and the accuracy is lower than the threshold w _threshold (i.e., 94.5% in Example of the present embodiment set) was filtered off, please Refer to Table 11 for six types: neural network model 1, neural network model 3, neural network model 4, neural network model 6, neural network model 9, neural network model 10, etc. It will be filtered out, leaving the remaining 4 neural network models (ie g value 4) and their respective weight values to be used in the implementation phase.

In the foregoing request flow prediction method, the implementation stage S402 may further comprise two steps: inputting the real-time data to the training stage in step S4021 The plurality of neural network networks with high prediction accuracy are reserved in step S4022, and the predicted values generated by the plurality of neural networks are weighted and averaged to obtain the final predicted value.

In the implementation stage S402, the step S4021 inputs the instant data to the plurality of neural network steps with high prediction accuracy rate retained in the training phase: requesting the traffic prediction subsystem to obtain an instant credential request query record for inputting to The g -like neural network models retained during the training phase are used for predictive calculations. Please refer to FIG. 6 , for example, the request flow set of the document C ₁ on the date of 2014/07/28 is ={ , ,..., }={1,2,...,82}, as the input data of the random neural network group, input the instant request traffic set in step S601, and input them to the remaining 4 in the training phase respectively. Neural network model (class neural network model 2, neural network model 5, neural network model 7, neural network model 8) to obtain the target 2014/07/29 date certificate C ₁ request Traffic forecast value.

In the implementation stage S402, the step S4022 performs a weighted average of the predicted values generated by the plurality of neural networks to obtain the final predicted value: the predicted values generated by the g neural network models are used for training. The correct rate obtained at the stage is used as a weight, and a weighted average is performed to obtain a final predicted flow rate. The request traffic prediction subsystem inputs the data into each of the remaining neural network models (ie, neural network model 2, neural network model 5, neural network model 7, neural network model 8, such as step 10 shown) in S601, after, visible in FIG. 6, 7, the neural network model is derived by the neural network model 2, 5 neural network models, neural network models C ₁ certificate request traffic 8 The predicted values are 12716, 12582, 12565, and 12401, respectively, as shown in the following list 12. Finally, step S602 is performed to perform weighted averaging on the predicted values according to the weight of each type of neural network model (the neural network model 2 It is 94.90%, the neural network model 5 is 94.61%, the neural network model 7 is 94.93%, and the neural network model 8 is 95.21%) to obtain the final flow rate predicted value 12566 of the certificate C1.

Table 12 shows the following:

The flow of the voucher pre-signing method for predicting request traffic of the present invention includes the voucher status pre-signature method performed by the foregoing reply signature subsystem, and the flow chart of the method is shown in FIG. 7 and mainly includes four The steps are: step S701 receives the voucher information to be pre-signed, step S702 detects the peak time period, step S703 records the voucher status production signature, and step S704 stores the pre-signed voucher status; the voucher status pre-signature The method can perform voucher state production at the off-peak time to balance the load.

The step S701 receives the voucher information of the pre-signature is: the reply signature subsystem receives the voucher information and the voucher status request traffic prediction value, and takes the 2014/07/29 date in the embodiment as an example, requesting traffic prediction subsystem certificate C ₁ stars state prediction by traffic prediction traffic prediction method is 12566 request, reply signature subsystem receives this request 12566 is the predicted value of the flow rate.

The step S702 detects the peak time period: the reply signature subsystem can obtain the history certificate from the voucher database and request the query record to count and analyze the off-peak period; in this embodiment, 2014/07/ The voucher for the period 01~2014/07/28 was requested to be inquired for the record, and the peak period was from 0:00 am to 6:00 pm.

The step S703 for producing a signature value for the voucher state is: the reply signature subsystem pre-signs the specific voucher status according to the requested traffic prediction value, and generates data of the pre-signature voucher status; in this embodiment , due to _a predicted state of the document C is 12566 traffic prediction request, it will respond to the C state certificate signature subsystem ₁ pre signatures, to produce a pre-signed certificate 12566 pen status; end of this pre-step signature Looking at whether the foregoing step S702 is performed, if the step S702 is executed and the off-peak period is obtained, the reply signature subsystem can perform the voucher state production at the off-peak time for load balancing.

The step of storing the status of the pre-signature voucher in step S704 is: in this embodiment, the reply signature subsystem will store the data of the 12566 pre-signature voucher status to the voucher database for the client device to query and After the client device queries and obtains the status of the pre-signature voucher, the voucher database will destroy the status of the pre-signed voucher that is taken, that is, when the client device takes one pen, the voucher database will have 12565 remaining. The pre-signature voucher status is completed until the pre-signature voucher status is completed, and the voucher database has no remaining pre-signed voucher status.

So far, the flow of the voucher pre-signing method for predicting request traffic of the present invention has been completed in combination with the embodiments, the drawings, and the list. The detailed description of the present invention is intended to be illustrative of the preferred embodiments of the present invention. It is included in the patent scope of this case.

In summary, the present invention is truly innovative in terms of technical ideas, fully complies with the statutory invention patent requirements such as novelty and progressiveness, and submits a patent application according to law, and invites you to approve the invention patent application to encourage invention, to the sense Will.

Claims (1)

A method for requesting traffic prediction includes the following steps: a request traffic prediction subsystem obtains a record of a plurality of voucher requests from a voucher database; and the request traffic prediction subsystem sets a parameter related to a stochastic neural network group algorithm a value, respectively, randomly establishing a plurality of neural networks based on each of the credentials to predict the requested traffic of each of the credentials; the request traffic prediction subsystem trains each of the neural networks according to the requested records of the credentials; the request The traffic prediction subsystem determines whether to retain each of the neural networks according to whether the prediction accuracy of each of the neural networks passes a correct rate threshold; and the neural network that the request traffic prediction subsystem will retain The respective generated predicted values are weighted averaged to finally generate a requested traffic predicted value.