CN117692350B - Fingerprint-based flow prediction method and system - Google Patents
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Abstract
The invention provides a flow prediction method and a flow prediction system based on fingerprints, which relate to the field of prediction. The image recognition field and the prediction field are combined, data fitting under a large number of conditions is achieved, a large number of historical data are filtered through the classifier, the data calculation amount of image similarity is reduced, and finally flow data are predicted in a data fitting mode.
Description
Technical Field
The invention belongs to the technical field of prediction, and particularly relates to a flow prediction method and system based on fingerprints.
Background
The flow prediction has an important role in network application, and the current mainstream prediction method adopts a machine learning algorithm to predict, divides data into a training set and a testing set, adjusts parameters and finally achieves an ideal prediction effect. However, when the machine learning algorithm is adopted for prediction, the required network traffic data is huge, and a great amount of calculation time is required.
Disclosure of Invention
In order to solve the technical problems, the invention provides a flow prediction method and a flow prediction system based on fingerprints.
The first aspect of the invention discloses a fingerprint-based flow prediction method; the method comprises the following steps:
Step S1, mapping processing is carried out on flow data in a preset time period to generate a plurality of flow trend graphs;
s2, scaling and gray processing are carried out on each flow trend graph to generate a plurality of gray images;
step S3, fingerprint information in each gray level image is calculated to obtain and store a training sample set;
s4, training a preset model by using the training sample set to obtain a classification model;
S5, acquiring a sample set to be detected, and sequentially inputting samples to be detected in the sample set to be detected into the classification model to obtain a corresponding prediction result;
Step S6, calculating the similarity between each training sample in each prediction result and the corresponding sample to be detected in sequence, and obtaining the training sample with the highest similarity to obtain prediction data;
and S7, restoring the original flow data corresponding to the predicted data, and performing scatter diagram fitting to generate a flow prediction curve.
According to the method of the first aspect of the present invention, in said step S1, the time spans and flow ranges of the plurality of flow profiles are kept identical.
According to the method of the first aspect of the present invention, the step S3 specifically includes:
Forming a gray matrix of the image according to the pixel gray values of the image, and performing discrete cosine transform on the matrix to reduce the dimension of the two-dimensional matrix to a one-dimensional array;
And carrying out normalization processing on the one-dimensional array, and taking the normalized data as fingerprint information of the gray level image.
According to the method of the first aspect of the present invention, in the step S4, the preset model includes at least one of a cluster model and a KNN model.
According to the method of the first aspect of the present invention, in the step S5, the step of obtaining the sample set to be measured includes:
acquiring flow data to be measured, and mapping the flow data to be measured to generate a plurality of flow trend graphs to be measured;
Scaling and gray processing are carried out on each flow trend graph to be detected, and then a plurality of gray images to be detected are generated;
and calculating fingerprint information in each gray level image to be detected to obtain a sample set to be detected.
According to the method of the first aspect of the present invention, in the step S6, the similarity between each training sample in each prediction result and the corresponding sample to be tested is calculated by a cosine similarity calculation formula.
According to the method of the first aspect of the present invention, in said step S7, a best fit curve is obtained by means of a least squares method.
The second aspect of the invention discloses a fingerprint-based flow prediction system; the system comprises:
the first processing module is configured to map the flow data in a preset time period to generate a plurality of flow trend graphs;
The second processing module is configured to scale and gray scale each flow trend graph to generate a plurality of gray scale images;
The third processing module is configured to calculate fingerprint information in each gray level image so as to obtain and store a training sample set;
The fourth processing module is configured to train a preset model by using the training sample set to obtain a classification model;
The fifth processing module is configured to acquire a sample set to be detected, and sequentially input samples to be detected in the sample set to be detected into the classification model so as to acquire a corresponding prediction result;
The sixth processing module is configured to sequentially calculate the similarity between each training sample in each prediction result and the corresponding sample to be detected, and acquire the training sample with the highest similarity to obtain prediction data;
And the seventh processing module is configured to restore the original flow data corresponding to the predicted data and perform scatter diagram fitting to generate a predicted curve of the flow.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps in a fingerprint-based flow prediction method of any one of the first aspects of the present disclosure when the processor executes the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a fingerprint-based flow prediction method of any of the first aspects of the present disclosure.
In summary, the scheme provided by the invention has the following technical effects: the method combines the methods of graphic processing, conversion and similarity analysis to predict the flow, firstly, collected flow data form a data set, data in a period of time is adopted to generate an image, then image characteristics are obtained by using an image compression and DCT conversion mode, then images with similar characteristics are classified by classification, new flow data are used to generate image characteristics and classify, corresponding data is obtained, flow data fitting is carried out by utilizing the data with highest similarity, further, flow trend in a period of time is predicted, and flow prediction data are obtained. The image recognition field and the prediction field are combined, data fitting under a large number of conditions is achieved, a large number of historical data are filtered through the classifier, the data calculation amount of image similarity is reduced, and finally flow data are predicted in a data fitting mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a fingerprint-based flow prediction method according to an embodiment of the present invention;
FIG. 2 is a flow chart according to an embodiment of the present invention;
FIG. 3 is a schematic view of image classification according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a data fitting according to an embodiment of the present invention;
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be understood that the terms first, second, etc. as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element. For example, a first image may be referred to as a second image, and similarly, a second image may be referred to as a first image, without departing from the scope of the application. Both the first image and the second image are images, but they are not the same image.
The first aspect of the invention discloses a fingerprint-based flow prediction method; referring to fig. 1, the method includes:
Step S1, mapping processing is carried out on flow data in a preset time period to generate a plurality of flow trend graphs;
in the step S1, collecting flow data of equipment through a probe to form a large number of data samples; the time spans and the flow ranges of the flow trend graphs are kept consistent, namely, the flow data in a certain time period are subjected to drawing processing to generate the flow trend graph, the duration, the acquisition interval and the y-axis unit of the graph of the flow trend graph are kept consistent, and the maximum value of the y-axis is kept consistent.
S2, scaling and gray processing are carried out on each flow trend graph to generate a plurality of gray images;
In this step, the image size needs to be kept consistent, the image is converted into a gray image, and the gray value of each pixel of the image is reduced.
Step S3, fingerprint information in each gray level image is calculated to obtain and store a training sample set;
The step S3 specifically includes:
Forming a gray matrix of the image according to the pixel gray values of the image, and performing Discrete Cosine Transform (DCT) on the matrix to reduce the dimension of the two-dimensional matrix to a one-dimensional array;
And carrying out normalization processing on the one-dimensional array, namely solving the length of the data, dividing each bit of the array by the length, and taking the normalized data as fingerprint information of the gray image.
It will be appreciated that, in order to test the preset model later, part of the data may be separated from the entire fingerprint information as a test set.
S4, training a preset model by using the training sample set to obtain a classification model;
The preset model comprises at least one of a clustering model and a KNN model. Optionally, training is performed by adopting a KNN algorithm, and class labels are generated.
S5, acquiring a sample set to be detected, and sequentially inputting samples to be detected in the sample set to be detected into the classification model to obtain a corresponding prediction result; the prediction result is a training sample of a certain class in class labels.
In the step S5, the step of obtaining a sample set to be tested includes:
acquiring flow data to be measured, and mapping the flow data to be measured to generate a plurality of flow trend graphs to be measured;
Scaling and gray processing are carried out on each flow trend graph to be detected, and then a plurality of gray images to be detected are generated;
and calculating fingerprint information in each gray level image to be detected to obtain a sample set to be detected.
Step S6, calculating the similarity between each training sample in each prediction result and the corresponding sample to be detected in sequence, and obtaining the training sample with the highest similarity to obtain prediction data;
optionally, calculating the similarity between each training sample in each prediction result and the corresponding sample to be tested through a cosine similarity calculation formula.
And S7, restoring the original flow data corresponding to the predicted data, and performing scatter diagram fitting to generate a flow prediction curve. When the original flow data corresponding to the predicted data is restored, the flow data of a period of time after the time point corresponding to the initial flow data can be obtained together.
In the step S7, a best fit curve is obtained by a least square method. And taking out fitting data of the corresponding time points to be output as prediction data so as to complete flow prediction based on fingerprints.
The present invention will be further described with reference to fig. 2, 3 and 4.
Fig. 2 depicts generating an image from raw flow data using a line graph with time on the horizontal axis and flow size on the vertical axis, connecting the acquired data into a smooth curve. The time span and flow range of each graph remain consistent.
Fig. 3 depicts generating an image and classifying new flow data by computing image fingerprint information.
Fig. 4 depicts a graph of predicted data from data calculated by fitting the data to the highest similarity data, predicting data flow over time.
Compared with the prior art, the invention has the innovation that the data is predicted by adopting the image similarity discrimination mode, the image processing algorithm and the machine learning data fitting algorithm are combined, the closest sample data is found out by comparing the similarity of the image fingerprint data, and the flow is predicted by utilizing the historical data.
The second aspect of the invention discloses a fingerprint-based flow prediction system; the system comprises:
the first processing module is configured to map the flow data in a preset time period to generate a plurality of flow trend graphs;
The second processing module is configured to scale and gray scale each flow trend graph to generate a plurality of gray scale images;
The third processing module is configured to calculate fingerprint information in each gray level image so as to obtain and store a training sample set;
The fourth processing module is configured to train a preset model by using the training sample set to obtain a classification model;
The fifth processing module is configured to acquire a sample set to be detected, and sequentially input samples to be detected in the sample set to be detected into the classification model so as to acquire a corresponding prediction result;
The sixth processing module is configured to sequentially calculate the similarity between each training sample in each prediction result and the corresponding sample to be detected, and acquire the training sample with the highest similarity to obtain prediction data;
And the seventh processing module is configured to restore the original flow data corresponding to the predicted data and perform scatter diagram fitting to generate a predicted curve of the flow.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps in a fingerprint-based flow prediction method of any one of the first aspects of the present disclosure when the processor executes the computer program.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the technical solution of the present disclosure is applied, and a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements steps of a fingerprint-based flow prediction method of any of the first aspects of the present disclosure.
In summary, the scheme provided by the invention has the following technical effects: the method combines the methods of graphic processing, conversion and similarity analysis to predict the flow, firstly, collected flow data form a data set, data in a period of time is adopted to generate an image, then image characteristics are obtained by using an image compression and DCT conversion mode, then images with similar characteristics are classified by classification, new flow data are used to generate image characteristics and classify, corresponding data is obtained, flow data fitting is carried out by utilizing the data with highest similarity, further, flow trend in a period of time is predicted, and flow prediction data are obtained. The image recognition field and the prediction field are combined, data fitting under a large number of conditions is achieved, a large number of historical data are filtered through the classifier, the data calculation amount of image similarity is reduced, and finally flow data are predicted in a data fitting mode.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A fingerprint-based flow prediction method, the method comprising:
Step S1, mapping processing is carried out on flow data in a preset time period to generate a plurality of flow trend graphs;
s2, scaling and gray processing are carried out on each flow trend graph to generate a plurality of gray images;
step S3, fingerprint information in each gray level image is calculated to obtain and store a training sample set;
The step S3 specifically includes:
Forming a gray matrix of the image according to the pixel gray values of the image, and performing discrete cosine transform on the matrix to reduce the dimension of the two-dimensional matrix to a one-dimensional array;
normalizing the one-dimensional array, and taking the normalized data as fingerprint information of the gray level image;
s4, training a preset model by using the training sample set to obtain a classification model;
The preset model comprises a KNN model, and training is carried out by adopting the KNN model to generate a class label;
S5, acquiring a sample set to be detected, and sequentially inputting samples to be detected in the sample set to be detected into the classification model to obtain a corresponding prediction result; the prediction result is a training sample of a certain class in the class label;
Step S6, calculating the similarity between each training sample in each prediction result and the corresponding sample to be detected in sequence, and obtaining the training sample with the highest similarity to obtain prediction data;
and S7, restoring the original flow data corresponding to the predicted data, and performing scatter diagram fitting to generate a flow prediction curve.
2. A fingerprint-based flow prediction method according to claim 1, wherein in said step S1, the time spans and flow ranges of a plurality of flow profiles are kept identical.
3. The fingerprint-based traffic prediction method according to claim 1, wherein in the step S5, the step of obtaining a sample set to be measured includes:
acquiring flow data to be measured, and mapping the flow data to be measured to generate a plurality of flow trend graphs to be measured;
Scaling and gray processing are carried out on each flow trend graph to be detected, and then a plurality of gray images to be detected are generated;
and calculating fingerprint information in each gray level image to be detected to obtain a sample set to be detected.
4. The fingerprint-based traffic prediction method according to claim 1, wherein in the step S6, the similarity between each training sample in each prediction result and the corresponding sample to be measured is calculated by a cosine similarity calculation formula.
5. A fingerprint-based flow prediction method according to claim 1, wherein in said step S7, a best fit curve is obtained by means of a least squares method.
6. A fingerprint-based flow prediction system, the system comprising:
the first processing module is configured to map the flow data in a preset time period to generate a plurality of flow trend graphs;
The second processing module is configured to scale and gray scale each flow trend graph to generate a plurality of gray scale images;
the third processing module is configured to calculate fingerprint information in each gray level image so as to obtain and store a training sample set; the third processing module forms a gray matrix of the image according to the gray values of the pixels of the image, and performs discrete cosine transformation on the matrix to reduce the dimension of the two-dimensional matrix to a one-dimensional array; then, carrying out normalization processing on the one-dimensional array, and taking the normalized data as fingerprint information of the gray level image;
The fourth processing module is configured to train a preset model by using the training sample set to obtain a classification model; the preset model comprises a KNN model, and training is carried out by adopting the KNN model to generate a class label;
the fifth processing module is configured to acquire a sample set to be detected, and sequentially input samples to be detected in the sample set to be detected into the classification model so as to acquire a corresponding prediction result; the prediction result is a training sample of a certain class in the class label;
The sixth processing module is configured to sequentially calculate the similarity between each training sample in each prediction result and the corresponding sample to be detected, and acquire the training sample with the highest similarity to obtain prediction data;
And the seventh processing module is configured to restore the original flow data corresponding to the predicted data and perform scatter diagram fitting to generate a predicted curve of the flow.
7. An electronic device comprising a memory storing a computer program and a processor implementing the steps of a fingerprint-based flow prediction method according to any one of claims 1 to 5 when the computer program is executed by the processor.
8. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of a fingerprint based flow prediction method according to any of claims 1 to 5.
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