CN107948230B - Method and device for determining cache time of data from server - Google Patents
Method and device for determining cache time of data from server Download PDFInfo
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- CN107948230B CN107948230B CN201610895504.3A CN201610895504A CN107948230B CN 107948230 B CN107948230 B CN 107948230B CN 201610895504 A CN201610895504 A CN 201610895504A CN 107948230 B CN107948230 B CN 107948230B
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The application discloses a method and a device for determining cache time of data from a server. The method for determining the cache time of the data from the server comprises the following steps: acquiring a multidimensional factor related to the cache time; establishing a support vector machine algorithm model by using the multi-dimensional factors; obtaining a first cache time through the support vector machine algorithm; and evaluating the validity of the support vector machine algorithm model to obtain a second cache time. The method and the device for determining the cache time of the data from the server can obtain more reasonable cache time, so that the booking success rate is improved, the ticket booking interface calling times are reduced, the time is prevented from being adjusted manually, the operation cost can be reduced, and errors caused by manual adjustment are avoided.
Description
Technical Field
The invention relates to the technical field of civil aviation information, in particular to a method and a device for determining cache time of data from a server.
Background
In applications where transactions are conducted using a remote server, it is often necessary to query the remote data. For example, in a system for booking airline tickets on the internet, querying the price of the airline ticket and the number of remaining tickets requires calling a ticket booking interface of a merchant or an airline company to obtain the price. To optimize query rate and enhance user experience, a cache is typically used to store data, such as flight ticket information, over a period of time. However, because the change frequency of the data such as the air ticket price and the remaining tickets is very high and is uniformly regulated and controlled by an airline company, if the preset cache time is too long, the air ticket price and the number of tickets cannot be updated in time, and the booking failure rate is greatly increased; on the contrary, if the preset caching time is too short, the frequency of calling the ticket booking interface of the merchant or the airline company is increased, the cost is increased, the query speed is reduced, and the user experience is damaged.
At present, the cache time of most air ticket inquiring and booking systems is a dynamic setting function according to the suggestion of each merchant or airline company. Alternatively, it is calculated as a linear function of the departure date from the day's number of days:
F(x)＝wx+b
f (x) is the buffer time, w and b are constants, and the background system can be preset. x is the number of days from the date of departure in minutes.
In the ticket booking mode, the buffer time is calculated only by considering the input factor of the departure date and the day, and the deviation between the calculation result and the actual effect is large due to less consideration factors.
Therefore, there is a need for an improved method and apparatus for determining cache time of data from a server.
The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for determining a cache time of data from a server, which can obtain a more reasonable cache time, thereby improving a booking success rate, reducing a ticket booking interface calling frequency, avoiding time adjustment by a human, reducing an operation cost, and avoiding an error caused by manual adjustment.
Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to an aspect of the present invention, a method for determining a cache time of data from a server is provided, including: acquiring a multidimensional factor related to the cache time; establishing a support vector machine algorithm model by using the multidimensional factors; obtaining a first cache time through a support vector machine algorithm; and evaluating the validity of the support vector machine algorithm model to obtain a second cache time.
In an exemplary embodiment of the present disclosure, further comprising: and dynamically optimizing the second cache time to obtain a third cache time.
In an exemplary embodiment of the present disclosure, the multidimensional factor includes: a hot route factor; a holiday factor; a distance days factor; historical cache hit rate; a historical booking success rate; a characteristic factor; and historical computation cache times.
In an exemplary embodiment of the present disclosure, the establishing of the support vector machine algorithm model by using the multidimensional factor comprises: selecting a regression function of a support vector machine algorithm; and obtaining coefficients of the regression function through fitting.
In an exemplary embodiment of the disclosure, evaluating the validity of the support vector machine algorithm model to obtain the second cache time includes: and evaluating the effectiveness of the support vector machine algorithm model through the historical cache duration.
In an exemplary embodiment of the present disclosure, dynamically optimizing cache time includes: the dynamic optimization factor is obtained using the following formula:
wherein x is a dynamic optimization factor, R is a predetermined success rate threshold, aiBooking success rates for history, biFor the historical cache duration, i is a positive integer, i ═ 1,2.... n), and t is the second cache time.
In an exemplary embodiment of the present disclosure, dynamically optimizing the second cache time to obtain a third cache time includes:
T＝t*x；
wherein T is the third cache time, x is the dynamic optimization factor, and T is the second cache time.
In an exemplary embodiment of the present disclosure, further comprising: the weights of the multidimensional factors are determined.
In an exemplary embodiment of the disclosure, determining the weights of the multidimensional factors includes at least one of: determining the weight of the multidimensional factor by an empirical method; determining the weight of the multidimensional factor by using a system identification method; and determining multidimensional factor weights using big data analysis.
According to an aspect of the present invention, there is provided an apparatus for determining a buffering time of data from a server, including: the acquisition factor module is used for acquiring a multidimensional factor related to the cache time; the model module is used for establishing a support vector machine algorithm model by utilizing the multidimensional factors; the first cache module is used for obtaining first cache time through a support vector machine algorithm; and the second cache module is used for evaluating the effectiveness of the support vector machine algorithm model to obtain second cache time.
In an exemplary embodiment of the present disclosure, further comprising: the dynamic module is used for dynamically optimizing the second cache time to obtain a third cache time; and a weighting module for determining the weight of the multidimensional factor.
According to the method and the device for determining the cache time of the data from the server, more reasonable cache time can be obtained, so that the booking success rate is improved, the ticket booking interface calling times are reduced, the time is prevented from being adjusted manually, the operation cost can be reduced, and errors caused by manual adjustment are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a flow chart illustrating a method of determining cache time of data from a server according to an example embodiment.
FIG. 2 is a flow chart illustrating another method of determining cache time from server data in accordance with an example embodiment.
Fig. 3 is a block diagram illustrating an apparatus for determining a buffering time of data from a server according to an example embodiment.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The following describes a method for determining the cache time of data from the server by taking a system for booking airline tickets through a network as an example. It is easily understood that the technical solution according to the inventive concept can also be applied to other applications that utilize a remote server for transaction processing.
FIG. 1 is a flow diagram illustrating a method of determining cache time for data from a server according to an example embodiment.
As shown in fig. 1, in S102, a multidimensional factor related to the buffering time is obtained.
In the prior art, the buffer time is only determined by the number of days from the departure date to the ticket booking date, and the problem of poor consideration exists. The invention mainly aims to solve the problem that in the prior art, the cache duration and the departure date are in a linear relationship strongly dependent on the number of days of the current day. In the embodiment of the invention, the influence relationship of various conditions on the cache time is comprehensively considered, for example, the influence relationship of hot air route factors, day factors of departure date and the current day, holiday factors, factors of various merchants or airlines, influence relationship of historical cache time, historical preset success rate and other factors on the cache time, and the like. After comprehensive consideration, a plurality of influence factors which are important to influence the cache time are selected as the multidimensional factors which influence the cache time.
In S104, a support vector machine algorithm model is established by using the multidimensional factors.
A Support Vector Machine (SVM) algorithm is a Machine learning method based on a statistical learning theory and developed in the middle of 90 years, the generalization capability of a learning Machine is improved by seeking for the minimum structured risk, and the minimization of experience risk and a confidence range is realized, so that the aim of obtaining a good statistical rule under the condition of less statistical sample quantity is fulfilled. Generally speaking, the method is a two-class classification model, and a basic model of the method is defined as a linear classifier with the maximum interval on a feature space, namely, a learning strategy of a support vector machine is interval maximization, and finally, the method can be converted into the solution of a convex quadratic programming problem.
The support vector machine maps the vectors into a higher dimensional space in which a maximally spaced hyperplane is built and two hyperplanes are built parallel to each other on either side of the hyperplane separating the data. Establishing a suitably oriented split hyperplane maximizes the distance between two hyperplanes parallel thereto, assuming: the larger the distance or difference between the parallel hyperplanes, the smaller the total error of the classifier. The support vector machine method is a method which is established on the basis of VC Dimension (Vapnik-Chervonenkis Dimension) theory of statistical learning theory and the principle of minimum structural risk, seeks the best compromise between the complexity of the model and the learning ability according to limited sample information, and obtains the best popularization ability, and the method is well known by persons in the field and is not repeated herein.
In the embodiment of the invention, a support vector machine pre-algorithm-based algorithm model for calculating the cache time is established by taking the multi-dimensional factors introduced above as input data.
In S106, a first buffer time is obtained by the support vector machine model.
In this embodiment, the first cache time data can be obtained by using a support vector machine model and using multidimensional factors as input data and performing model calculation.
In S108, the validity of the support vector machine algorithm model is evaluated to obtain a second cache time.
And evaluating the effectiveness of the support vector machine algorithm model through the first cache time obtained by the support vector machine algorithm model, wherein the obtained first cache time can be recorded as a second cache time under the condition that the support vector machine algorithm model is effective.
According to the method for determining the cache time of the data from the server, the more reasonable cache time can be obtained, so that the booking success rate is improved, the ticket booking interface calling times are reduced, the time is prevented from being adjusted manually, the operation cost can be reduced, and errors caused by manual adjustment are avoided.
It should be clearly understood that the present disclosure describes how to make and use particular examples, but the principles of the present disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
In an exemplary embodiment of the present disclosure, further comprising: and dynamically optimizing the second cache time to obtain a third cache time.
And with the accumulation of the historical data, further dynamically optimizing the cache time through the relationship between the historical data and the cache time obtained by utilizing the support vector machine algorithm model.
According to the method for determining the cache time of the data from the server side, the dynamic optimization factor is not a fixed numerical value, and the calculation result of the dynamic factor is continuously optimized along with the accumulation of the historical data, so that the calculated cache time is further optimized.
In an exemplary embodiment of the present disclosure, the multidimensional factor includes: a hot route factor; a holiday factor; a distance days factor; historical cache hit rate; a historical booking success rate; a characteristic factor; and historical computation cache times.
The multidimensional factors are a plurality of influence factors which influence the cache time calculated in this embodiment, and in the present invention, the multidimensional factors may be, for example, the following factors:
and (4) counting and summarizing hot air routes of the top300 according to historical query data by using the hot air route factors.
And a holiday factor, which judges whether the departure date is holiday.
Distance days factor, departure date from day days.
Historical cache hit rate, merchant historical cache hit rate, whether the cache hits the log or not is recorded during query, and the hit rate is calculated.
The method comprises the steps of historical booking success rate, merchant historical booking success rate, logging during booking and calculating the booking success rate.
And the characteristic factor is obtained by calculating according to the record of the historical cache time.
And historical calculation of cache time.
According to the method for determining the cache time of the data from the server, the effectiveness and the robustness of the cache setting duration are improved by adding the multi-dimensional factors to the original linear technical solution. Meanwhile, the introduction of the multidimensional factors changes the linear strong dependence relationship between the original cache time and the distance days into a nonlinear relationship, so that the calculated cache time is suitable for more environments, the parameter adjustment by manpower is avoided, the operation cost can be reduced, and the error caused by manual adjustment is avoided.
In an exemplary embodiment of the present disclosure, dynamically optimizing cache time includes: the dynamic optimization factor is obtained using the following formula:
wherein x is a dynamic optimization factor, R is a predetermined success rate threshold, aiBooking success rates for history, biFor the historical cache duration, i is a positive integer, i ═ 1,2.... n), and t is the second cache time.
In the dynamic optimization factor formula in this embodiment, the dynamic factor x is set as a weighting coefficient according to the relationship between the historical booking success rate and the cache duration. The dynamic optimization factor x is not a fixed value and may be recalculated at intervals, for example, with a newly obtained historical predetermined success rate, as historical data accumulates.
Fig. 2 is a flowchart illustrating a method of determining a cache time of data from a server according to another exemplary embodiment. The embodiment of fig. 2 is further described in the embodiment of fig. 1, in which S104 utilizes multidimensional factors to build a support vector machine algorithm model.
As shown in fig. 2, in S202, a regression function supporting the vector machine algorithm is selected.
In the support vector machine algorithm, a large number of functions can be optimized to obtain an optimal solution. In this embodiment, a linear regression function may be selected, for example, as the initial function. In the calculation of the actual support vector machine algorithm, for example, a linear regression function is selected:
F(x)＝wx+b。
in S204, coefficients of the regression function are obtained by fitting.
The linear regression function may be selected, for example, as an initial function of the support vector machine model, and the following function may be selected, for example:
F(x)＝wx+b；
in the above function, fitting data (xi, yi) is required, where i ═ 1,2,3i∈Rn，yiE R, where w and b are the normal vector and offset, respectively, of the linear regression function. In order to ensure good fitting effect of the linear regression function, the basic objective is to obtain a parameter w ═ w (w)1,w1......wn) And b, determining the coefficient to obtain the value of x ═ (x)1,x1......xn)TUnder the input condition, the result is obtained, the process is calculated by using the principle of a least square method, and the formula of the constraint condition is as follows:
by finding the relation wiThe partial derivatives of (a) can be obtained as w and b, and the formula is as follows:
by solving the algebraic equation, w and b can be obtained.
In this embodiment, the cache time to be calculated can be calculated under the condition of multidimensional factors by using the obtained w and b.
In an exemplary embodiment of the disclosure, evaluating the validity of the support vector machine algorithm model to obtain the second cache time includes: and evaluating the effectiveness of the support vector machine algorithm model through the historical cache duration.
The support vector machine model is a two-class classification model. The basic model of the method is a linear classifier with the maximum interval defined on a feature space, and the maximum interval makes the method different from a perceptron; the support vector machine also includes kernel skills, which make it a substantially non-linear classifier. The learning strategy of the support vector machine is interval maximization, can be formalized into a problem of solving convex quadratic programming, and is also equivalent to a minimization problem of a regularized hinge loss function. The learning algorithm of the support vector machine is an optimization algorithm for solving convex quadratic programming.
Due to the setting of the support vector machine algorithm, the result obtained by using the support vector machine algorithm is related to the input data every time, and even under the condition of the same data input, different functions are adopted in the support vector machine algorithm to carry out fitting solution. Therefore, in this embodiment, the first cache time obtained by calculation using the support vector machine algorithm is different. After the first cache time is obtained through calculation, the validity of the current support vector machine model needs to be evaluated. For example, the validity of the support vector machine algorithm model is evaluated through the historical cache duration, if the first cache time obtained by the support vector machine algorithm model at this time is within a certain predetermined range of the historical cache duration, and for example, the first cache time is within ± 100% of the historical cache duration, the support vector machine algorithm model at this time is considered to be a valid model, the first cache time obtained through the calculation at this time is the valid time, and the first cache time is recorded as the second cache time. If the first caching time is not within the range of +/-100% of the historical caching duration, the current support vector machine algorithm model is considered to be an invalid model, the first caching time obtained through the current calculation is invalid time, and the first caching time is not recorded as second caching time. However, the invention is not limited thereto.
In an exemplary embodiment of the present disclosure, the second cache time is dynamically optimized to obtain a third cache time:
T＝t*x；
wherein T is the third caching time, x is the dynamic optimization factor described above, and T is the second caching time obtained by using the effective support vector machine algorithm model.
In an exemplary embodiment of the present disclosure, further comprising: the weights of the multidimensional factors are determined.
The multidimensional factors comprise a plurality of influence factors influencing the cache time, the influence factors influencing the cache time are not completely the same, and the factors influencing the cache time greatly are separated from the factors influencing the cache time less through distributing different weights to the influence factors, so that the more accurate cache time is obtained.
In an exemplary embodiment of the disclosure, determining the weights of the multidimensional factors includes at least one of: determining the weight of the multidimensional factor by an empirical method; determining the weight of the multidimensional factor by a system identification method; and determining the multi-dimensional factor weight by a big data analysis method.
The multidimensional factor weight is determined, for example, empirically; determining the weight of the multidimensional factor by a system identification method; and big data analysis to determine multidimensional factor weights, the invention is not limited thereto.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Fig. 3 is a block diagram illustrating an apparatus for determining a buffering time of data from a server according to an example embodiment.
As shown in fig. 3, the apparatus 30 for determining the buffering time of the data from the server comprises: a get factor module 302, a model module 304, a first cache module 306, a second cache module 308, a dynamic module 310, and a weight module 312.
The get factor module 302 is used to get a multidimensional factor related to the buffering time.
The model module 304 is configured to establish a support vector machine algorithm model according to the multidimensional factor and the historical cache time.
The first buffer module 306 is configured to obtain a first buffer time through a support vector machine algorithm.
The second buffer module 308 is used for evaluating the validity of the support vector machine algorithm model to obtain a second buffer time.
The dynamic module 310 is configured to perform dynamic optimization on the second cache time to obtain a third cache time.
The weight module 312 is used to determine the weight of the multidimensional factor.
From the above detailed description, those skilled in the art will readily appreciate that the method and apparatus for determining cache time of data from a server according to embodiments of the present invention have one or more of the following advantages.
According to the method and the device for determining the cache time of the data from the server, more reasonable cache time can be obtained, so that the booking success rate can be improved, and the ticket booking interface calling times can be reduced.
According to the method for determining the cache time of the data from the server side, the dynamic optimization factor is not a fixed numerical value, and the calculation result of the dynamic factor is continuously optimized along with the accumulation of the historical data, so that the calculated cache time is further optimized.
According to the method for determining the cache time of the data from the server, the effectiveness and the robustness of the cache setting duration are improved by adding the multi-dimensional factors to the original linear technical solution. Meanwhile, the introduction of the multidimensional factors changes the linear strong dependence relationship between the original cache time and the distance days into a nonlinear relationship, so that the calculated cache time is suitable for more environments, the parameter adjustment by manpower is avoided, the operation cost can be reduced, and the error caused by manual adjustment is avoided.
Exemplary embodiments of the present invention are specifically illustrated and described above. It is to be understood that the invention is not limited to the precise construction, arrangements, or instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (7)
1. A method for determining a buffering time of data from a server, comprising:
acquiring a multidimensional factor related to the cache time;
establishing a support vector machine algorithm model by using the multi-dimensional factors;
obtaining a first cache time through the support vector machine algorithm model; and
evaluating the effectiveness of the support vector machine algorithm model through historical cache duration to obtain second cache time;
the dynamic optimization factor is obtained using the following formula,
wherein x is the dynamic optimization factor, R is a predetermined success rate threshold, aiBooking success rates for history, biThe history cache duration is, i is a positive integer, (1,2 … … n), and t is the second cache time; recalculating the dynamic optimization factor once by the newly obtained historical booking success rate at intervals;
dynamically optimizing the second cache time to obtain a third cache time, including:
T＝t*x；
wherein T is the third caching time, x is the dynamic optimization factor, and T is the second caching time.
2. The method of claim 1, wherein the multidimensional factor comprises:
a hot route factor;
a holiday factor;
a distance days factor;
historical cache hit rate;
a historical booking success rate;
a characteristic factor; and
the cache time is calculated historically.
3. The method of claim 1, wherein using the multidimensional factors to build a support vector machine algorithm model comprises:
selecting a regression function of the support vector machine algorithm; and
obtaining coefficients of the regression function through fitting;
and recalculating the dynamic optimization factor once by the newly obtained historical booking success rate at intervals.
4. The method of claim 1, further comprising:
determining a weight of the multidimensional factor.
5. The method of claim 4, wherein determining the weights for the multidimensional factors comprises at least one of:
empirically determining the multidimensional factor weight;
determining the multi-dimensional factor weight by using a system identification method; and
and determining the multi-dimensional factor weight by using a big data analysis method.
6. An apparatus for determining a buffering time of data from a server, comprising:
the acquisition factor module is used for acquiring a multidimensional factor related to the cache time;
the model module is used for establishing a support vector machine algorithm model by utilizing the multidimensional factors;
the first cache module is used for obtaining first cache time through the support vector machine algorithm; and
the second cache module is used for evaluating the effectiveness of the support vector machine algorithm model through historical cache duration to obtain second cache time;
a dynamic module for obtaining a dynamic optimization factor using the following formula,
wherein x is the dynamic optimization factor, R is a predetermined success rate threshold, aiBooking success rates for history, biThe history cache duration is, i is a positive integer, (1,2 … … n), and t is the second cache time;recalculating the dynamic optimization factor once by the newly obtained historical booking success rate at intervals;
dynamically optimizing the second cache time to obtain a third cache time, including:
T＝t*x；
wherein T is the third caching time, x is the dynamic optimization factor, and T is the second caching time.
7. The apparatus of claim 6, further comprising:
and the weight module is used for determining the weight of the multidimensional factors.
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| CN101887384A (en) * | 2010-06-30 | 2010-11-17 | 中山大学 | Markov model-based process timeslice length determining method |
| CN103729353A (en) * | 2012-10-10 | 2014-04-16 | 阿里巴巴集团控股有限公司 | Method for searching for ticket business information and servers |
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