CN116366730A - Data compression distribution method and device under high concurrency scene of course selection in colleges and universities - Google Patents
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Abstract
The invention relates to the technical field of data compression and distribution, and discloses a data compression and distribution method and device in a high concurrence scene of a college course selection, wherein the method comprises the following steps: inputting the class set to be selected obtained by the control server into a data compression model; solving the data compression model by using a multiple iteration method to obtain a data compression result and a summary mapping table; the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all class selection servers; and solving an optimal request distribution objective function according to the address of the student request client, transmitting the static js file to a client browser by using an optimal request distribution course selection server, and completing data decompression at the client. The data compression method can provide extremely high compression ratio, greatly lighten the transmission data volume of a network, and greatly reduce the requirement of server hardware by decompression performed at a client.
Description
Technical Field
The invention relates to the technical field of data compression and distribution, in particular to a data compression and distribution method and device under a high concurrence scene of a college course selection.
Background
At present, in colleges and universities, especially in comprehensive colleges and universities, the number of on-line lessons selecting people is more than ten thousands in peak time, and on average, almost millions of clicks are performed per minute, and each large lesson selecting system provider generally solves the throughput problem in high concurrency by adopting a mode of increasing server composition clusters and increasing entrance bandwidth.
On one hand, huge cost is increased by stacking the servers, and the servers are released after the course selection is finished, so that the maintenance cost of the servers is increased; on the other hand, the access quantity is very large, so that the network transmission pressure is huge, and the bandwidth requirement is extremely high. Aiming at the problem, the patent provides a data compression and distribution method under a college course selection high concurrence scene, which provides network guarantee for college course selection.
Disclosure of Invention
In view of this, the invention provides a data compression and distribution method under a high concurrence scene of a college course selection, which aims at: encoding the class set to be selected into a coding matrix form by utilizing an encoding mode, carrying out iterative compression calculation on the coding matrix, respectively extracting a data compression result and a summary mapping table of the class set to be selected, further encoding and compressing the class set to be selected, providing an extremely high compression ratio according to the characteristics of a class of a university, greatly reducing the transmission data volume of a network, decompressing the class set to be selected at a client, greatly reducing the pressure of a server, greatly reducing the requirement of server hardware, designing a simpler decoding calculation flow, realizing rapid decoding at the client, and reducing the hardware requirement of student computer equipment; and in the iterative compression calculation process, a multiple iteration mode is adopted to carry out iterative calculation on the data compression result and the abstract mapping table respectively, the final compression result is ensured to be better decoded into a class set to be selected based on the abstract mapping table, meanwhile, in the iteration process of the abstract mapping table, the abstract mapping table is updated by utilizing a prime matrix on the premise of ensuring the expression capacity of the abstract mapping table, and the correlation and redundancy among column vectors of the abstract mapping table are reduced, so that the data compression speed is improved, and the coding compression result of the class set to be selected is rapidly obtained.
The invention provides a data compression and distribution method under a college course selection high concurrence scene, which comprises the following steps:
s1: the control server acquires a class set to be selected;
s2: the control server builds a data compression model, inputs the class set to be selected obtained by the control server into the data compression model, wherein the input of the data compression model is the class set to be selected, and outputs a data compression result and a summary mapping table;
s3: solving the data compression model by using a multiple iteration method to obtain a data compression result of the class set to be selected and a summary mapping table;
s4: the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all class selection servers;
s5: constructing an optimal request distribution objective function, and solving the optimal request distribution objective function according to the address of the student request client to obtain an optimal request distribution class selection server;
s6: and the optimal request distribution course selection server transmits the static js file to a browser of a student request client, completes data decompression at the client, and displays a course set to be selected in the browser.
As a further improvement of the present invention:
optionally, the step S1 of obtaining the set of classes to be selected includes:
the control server acquires a class set to be selected before class selection, wherein the format of the class set to be selected is as follows:
X＝[X 1 ,X 2 ,…,X i ,…,X n ] T
wherein:
t represents a transpose;
x represents a class to be selected set formed by all class to be selected information, the form of X is a matrix form, and each row of data in the matrix represents class selecting information data of each class;
X i the ith row data in X is shown, namely class selection information of the ith class; in the embodiment of the invention, the class selection information data of each class includes class numbers, class names, teacher names, class numbers, class times and class learning times.
Optionally, the step S2 of constructing a data compression model, and inputting the set of classes to be selected obtained by the control server into the data compression model includes:
constructing a data compression model, wherein the data compression model is in the form of:
wherein:
y represents the data compression result, P represents the abstract mapping table;
||·|| 2 represents an L2 norm;
and inputting the class set to be selected obtained by the control server into a data compression model.
Optionally, in the step S3, a multiple iteration method is used to solve the data compression model to obtain a data compression result of the class set to be selected and a summary mapping table, including:
solving a data compression model by using a multiple iteration method to obtain a data compression result of a class set to be selected and a summary mapping table, wherein the solving process of the data compression model is as follows:
s31: the data compression model is converted into the following equation:
equivalent to solving the extremum of the following function:
wherein:
t represents a transpose;
s32: initializing data compression result Y 0 Digest mapping table P 0 Initializing the residual as
Corresponding initialization gradient +.>
Let the current iteration number of the multiple iteration method be k, the maximum iteration number be Max, and the initial value of k be 1, the data compression result and the abstract mapping table obtained by the kth iteration are Y respectively k P k ；
S33: calculating an update step length of the data compression result in the kth iteration:
wherein:
α k ,β k ,d k representing the update step length of the data compression result in the kth iteration;
i represent L1 norm;
s34: and (3) carrying out the kth iteration on the abstract mapping table, wherein the formula of the kth iteration is as follows:
Q＝Q 1 Q 2 ...Q m
wherein:
P k representing a summary mapping table obtained by the kth iteration;
q represents a transformation matrix, Q m The values representing the mth column and the mth row are respectively delta, 1-delta, delta represents a transformation parameter, m represents the number of columns of the class set X to be selected, and m=8 and delta=0.4 in the embodiment of the invention;
s35: the kth iteration is carried out on the data compression result:
Y k ＝Y k-1 +α k d k
s36: calculating gradient g of the kth iteration k If g k =0 or k=max, then Y will be k P k Data compression result Y obtained as solving * Digest mapping table P * Otherwise let k=k+1, return to step S33.
Optionally, in the step S4, the control server generates a static js file from the decompression algorithm, the data compression result and the summary mapping table, and distributes the static js file to all class selection servers, including:
the control server compresses the decompression algorithm and the data to obtain the result Y * Digest mapping table P * Generating a static js file, wherein the decompression algorithm flow is as follows:
s41: based on the data compression model, the method calculates and obtains
Wherein->
Representing the coding result of the class set to be selected;
s42: decoding the coding result of the class set to be selected to obtain the class set to be selected, wherein the decoding process is the inverse operation of a single-heat coding mode, and the class information value corresponding to the coding result is determined according to the register bit of the coding result;
and distributing the static js file to all the course selection servers.
Optionally, constructing an optimal request distribution objective function in the step S5 includes:
constructing an optimal request distribution objective function:
wherein:
F(u j loc) indicates the optimal request distribution objective function value of the j-th course selection server in the course selection server cluster after receiving the course selection request, u j The J-th course selection server in the course selection server cluster is represented, J represents the total number of course selection servers in the course selection server cluster, and loc represents the IP address of the client side sending the course selection request;
link(u j loc) represents from loc to u j Is a communication link distance of the IP address of (a);
Optionally, in the step S5, the solving the optimal request distribution objective function according to the address of the student request client side, to obtain an optimal request distribution class selection server, includes:
the course selecting server cluster receives a course selecting request sent by a client, wherein the client sending the course selecting request is a student requesting client, the IP address of the student requesting client is substituted into an optimal request distribution objective function, and the course selecting server corresponding to the maximum objective function value is used as an optimal request distribution course selecting server.
Optionally, in the step S6, the optimal request distributing course selecting server transmits the static js file to a browser of a client side of the student request, completes data decompression at the client side and performs course set to be selected and display, and includes:
the optimal request distribution course selection server transmits the static js file to a student client browser, the student client analyzes the static js file to obtain a decompression algorithm, a data compression result and a summary mapping table, the data compression result is decompressed by the decompression algorithm based on the summary mapping table to obtain a course set to be selected, and the course set to be selected is displayed in the client browser.
In order to solve the above problems, the present invention provides a data compression and distribution device in a college and university class-selecting high concurrence scene, which is characterized in that the device comprises:
the control server is used for constructing a data compression model, inputting the class set to be selected obtained by the control server into the data compression model, solving the data compression model by utilizing a multiple iteration method to obtain a data compression result and a summary mapping table of the class set to be selected, generating a static js file by a decompression algorithm, the data compression result and the summary mapping table, and distributing the static js file to all class selection servers;
and the lesson selecting server is used for receiving the lesson selecting request sent by the client, constructing an optimal request distribution objective function, solving the optimal request distribution objective function according to the address of the student request client to obtain the optimal request distribution lesson selecting server, and transmitting the static js file to the browser of the student request client by the optimal request distribution lesson selecting server.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the data compression distribution method under the college course-selecting high concurrence scene.
In order to solve the above problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored, where the at least one instruction is executed by a processor in an electronic device to implement the data compression distribution method under the college and university class selection high concurrence scenario described above.
Compared with the prior art, the invention provides a data compression and distribution method under a high concurrence scene of a college course selection, and the technology has the following advantages:
firstly, the scheme provides a data compression distribution mode, and a data compression model is constructed, wherein the data compression model is in the form of:
wherein:
the method comprises the steps of representing the coding result of a class set to be selected, wherein the coding mode of the class set to be selected is to code each value in the class set to be selected by utilizing a single-heat coding mode; y represents the data compression result, P represents the abstract mapping table; I.I 2 Represents an L2 norm; inputting the class set to be selected obtained by the control server into a data compression model to solve to obtain a data compression result, and decompressing by the control serverThe algorithm, the data compression result and the abstract mapping table generate static js files, and distribute the static js files to all lesson selecting servers, so as to construct an optimal request distribution objective function:
wherein: f (u) j Loc) indicates the optimal request distribution objective function value of the j-th course selection server in the course selection server cluster after receiving the course selection request, u j The J-th course selection server in the course selection server cluster is represented, J represents the total number of course selection servers in the course selection server cluster, and loc represents the IP address of the client side sending the course selection request; link (u) j Loc) represents from loc to u j Is a communication link distance of the IP address of (a);
represents u j Is +.>
Represents u j Network bandwidth utilization of (a). The class selecting server cluster receives a class selecting request sent by a client, wherein the client sending the class selecting request is a student request client, an IP address of the student request client is substituted into an optimal request distribution objective function, the class selecting server corresponding to the maximum objective function value is used as an optimal request distribution class selecting server, and the optimal request distribution class selecting server can most efficiently transmit a static js file to a browser of the student request client. The optimal request distribution course selection server transmits the static js file to a student client browser, the student client analyzes the static js file to obtain a decompression algorithm, a data compression result and a summary mapping table, the data compression result is decompressed by the decompression algorithm based on the summary mapping table to obtain a course set to be selected, and the course set to be selected is displayed in the client browser. The scheme utilizes the coding mode to code the class set to be selected into a coding matrix form, and codes the coding matrixAnd carrying out iterative compression calculation, respectively extracting a data compression result and a summary mapping table of the class set to be selected, further encoding and compressing the class set to be selected, providing an extremely high compression ratio according to the classroom characteristics of a university, greatly reducing the transmission data volume of a network, decompressing at a client, greatly reducing the pressure of a server, greatly reducing the requirement of server hardware, designing a simpler decoding calculation flow, and realizing quick decoding at the client to reduce the hardware requirement on student computer equipment.
Meanwhile, the scheme provides a solving method of a data compression model, which solves the data compression model by using a multiple iteration method to obtain a data compression result of a class set to be selected and a summary mapping table, wherein the solving flow of the data compression model is as follows: the data compression model is converted into the following equation:
equivalent to solving the extremum of the following function:
wherein: t represents a transpose; initializing data compression result Y 0 Digest mapping table P 0 Initializing the residual as
Corresponding initialization gradient +.>
Let the current iteration number of the multiple iteration method be k, the maximum iteration number be Max, and the initial value of k be 1, the data compression result and the abstract mapping table obtained by the kth iteration are Y respectively k P k The method comprises the steps of carrying out a first treatment on the surface of the Calculating an update step length of the data compression result in the kth iteration:
wherein: alpha k ,β k ,d k Representing the update step length of the data compression result in the kth iteration; i represent L1 norm; and (3) carrying out the kth iteration on the abstract mapping table, wherein the formula of the kth iteration is as follows:
Q＝Q 1 Q 2 ...Q m
wherein: p (P) k Representing a summary mapping table obtained by the kth iteration; q represents a transformation matrix, Q m The values representing the first row and the m row of the m column are delta, 1-delta, delta represents transformation parameters, and m represents the column number of the class set X to be selected; the kth iteration is carried out on the data compression result:
Y k ＝Y k-1 +α k d k
calculating gradient g of the kth iteration k If g k =0 or k=max, then Y will be k P k Data compression result Y obtained as solving * Digest mapping table P * Otherwise let k=k+1, return to the above step. In the invention, a multiple iteration mode is adopted in the iterative compression calculation process, the iterative calculation is respectively carried out on the data compression result and the abstract mapping table, the final compression result is ensured to be better decoded into a class set to be selected based on the abstract mapping table, and meanwhile, in the iteration process of the abstract mapping table, the abstract mapping table is updated and iterated by utilizing the primary matrix on the premise of ensuring the expression capacity of the abstract mapping table, so that the abstract is reducedAnd the correlation and redundancy among column vectors of the mapping table are improved, so that the data compression speed is increased, and the coding compression result of the class set to be selected is obtained rapidly.
Drawings
Fig. 1 is a schematic flow chart of a data compression and distribution method under a high concurrence scene of a college course selection according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a data compression and distribution device in a college course selection high concurrence scene according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an operation of a data compression and distribution device in a college course selection and concurrence scenario according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing a data compression and distribution method in a high concurrence scenario of a college course selection according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a data compression and distribution method in a high concurrence scene of a college course selection. The execution subject of the data compression distribution method in the college course-selection high concurrence scene includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the data compression distribution method under the college course selection high concurrency scene can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
s1: and the control server acquires the class collection to be selected.
The step S1 of obtaining the class set to be selected comprises the following steps:
the control server acquires a class set to be selected before class selection, wherein the format of the class set to be selected is as follows:
X＝[X 1 ,X 2 ,…,X i ,…,X n ] T
wherein:
t represents a transpose;
x represents a class to be selected set formed by all class to be selected information, the form of X is a matrix form, and each row of data in the matrix represents class selecting information data of each class;
X i the ith row data in X is shown, namely class selection information of the ith class; in the embodiment of the invention, the class selection information data of each class includes class numbers, class names, teacher names, class numbers, class times and class learning times.
S2: the control server builds a data compression model, inputs the class set to be selected obtained by the control server into the data compression model, wherein the input of the data compression model is the class set to be selected, and outputs a data compression result and a summary mapping table.
And S2, constructing a data compression model, and inputting the class set to be selected obtained by the control server into the data compression model, wherein the method comprises the following steps:
constructing a data compression model, wherein the data compression model is in the form of:
wherein:
y represents the data compression result, P represents the abstract mapping table;
||·|| 2 represents L2A norm;
and inputting the class set to be selected obtained by the control server into a data compression model.
S3: and solving the data compression model by using a multiple iteration method to obtain a data compression result of the class set to be selected and a summary mapping table.
And step S3, solving the data compression model by using a multiple iteration method to obtain a data compression result of the class set to be selected and a summary mapping table, wherein the method comprises the following steps:
solving a data compression model by using a multiple iteration method to obtain a data compression result of a class set to be selected and a summary mapping table, wherein the solving process of the data compression model is as follows:
s31: the data compression model is converted into the following equation:
equivalent to solving the extremum of the following function:
wherein:
t represents a transpose;
s32: initializing data compression result Y 0 Digest mapping table P 0 Initializing the residual as
Corresponding initialization gradient +.>
Let the current iteration number of the multiple iteration method be k, the maximum iteration number be Max, and the initial value of k be 1, the data compression result and the abstract mapping table obtained by the kth iteration are Y respectively k P k ；
S33: calculating an update step length of the data compression result in the kth iteration:
wherein:
α k ,β k ,d k representing the update step length of the data compression result in the kth iteration;
i represent L1 norm;
s34: and (3) carrying out the kth iteration on the abstract mapping table, wherein the formula of the kth iteration is as follows:
Q＝Q 1 Q 2 ...Q m
wherein:
P k representing a summary mapping table obtained by the kth iteration;
q represents a transformation matrix, Q m The values representing the mth column and the mth row are respectively delta, 1-delta, delta represents a transformation parameter, m represents the number of columns of the class set X to be selected, and m=8 and delta=0.4 in the embodiment of the invention;
s35: the kth iteration is carried out on the data compression result:
Y k ＝Y k-1 +α k d k
s36: calculating gradient g of the kth iteration k If g k =0 or k=max, then Y will be k P k Data compression result Y obtained as solving * Digest mapping table P * Otherwise let k=k+1, return to step S33.
S4: and the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all the course selection servers.
And S4, the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all class selection servers, wherein the method comprises the following steps:
the control server compresses the decompression algorithm and the data to obtain the result Y * Digest mapping table P * Generating a static js file, wherein the decompression algorithm flow is as follows:
s41: based on the data compression model, the method calculates and obtains
Wherein->
Representing the coding result of the class set to be selected;
s42: decoding the coding result of the class set to be selected to obtain the class set to be selected, wherein the decoding process is the inverse operation of a single-heat coding mode, and the class information value corresponding to the coding result is determined according to the register bit of the coding result;
and distributing the static js file to all the course selection servers.
Optionally, constructing an optimal request distribution objective function in the step S5 includes:
constructing an optimal request distribution objective function:
wherein:
F(u j loc) indicates the optimal request distribution objective function value of the j-th course selection server in the course selection server cluster after receiving the course selection request, u j Represents the J-th course selection server in the course selection server cluster, J represents the total number of course selection servers in the course selection server cluster, and loc tableShowing the IP address of the client side sending the course selection request;
link(u j loc) represents from loc to u j Is a communication link distance of the IP address of (a);
S5: and constructing an optimal request distribution objective function, and solving the optimal request distribution objective function according to the address of the student request client to obtain the optimal request distribution class selection server.
And in the step S5, constructing an optimal request distribution objective function, which comprises the following steps:
constructing an optimal request distribution objective function:
wherein:
F(u j loc) indicates the optimal request distribution objective function value of the j-th course selection server in the course selection server cluster after receiving the course selection request, u j The J-th course selection server in the course selection server cluster is represented, J represents the total number of course selection servers in the course selection server cluster, and loc represents the IP address of the client side sending the course selection request;
link(u j loc) represents from loc to u j Is a communication link distance of the IP address of (a);
In the step S5, the optimal request distribution objective function is solved according to the address of the student request client to obtain an optimal request distribution course selection server, which comprises the following steps:
the course selecting server cluster receives a course selecting request sent by a client, wherein the client sending the course selecting request is a student requesting client, the IP address of the student requesting client is substituted into an optimal request distribution objective function, and the course selecting server corresponding to the maximum objective function value is used as an optimal request distribution course selecting server.
S6: and the optimal request distribution course selection server transmits the static js file to a browser of a student request client, completes data decompression at the client, and displays a course set to be selected in the browser.
In the step S6, the optimal request distribution course selection server transmits the static js file to a browser of a student request client, completes data decompression at the client and displays a course set to be selected, and comprises the following steps:
the optimal request distribution course selection server transmits the static js file to a student client browser, the student client analyzes the static js file to obtain a decompression algorithm, a data compression result and a summary mapping table, the data compression result is decompressed by the decompression algorithm based on the summary mapping table to obtain a course set to be selected, and the course set to be selected is displayed in the client browser.
Example 2:
as shown in fig. 2, the functional block diagram of the data compression and distribution device under the high concurrence scene of the colleges and universities provided by an embodiment of the present invention can implement the data compression and distribution method under the high concurrence scene of the colleges and universities in embodiment 1; fig. 3 is a flowchart illustrating an operation of the data compression and distribution device in a high concurrence scenario for a college course selection according to an embodiment of the present invention;
the data compression and distribution device 100 in the high concurrence scene of the colleges and universities can be installed in electronic equipment. According to the implemented functions, the data compression and distribution device in the college course selection high concurrence scene can comprise a control server 101 and a course selection server 102. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The control server 101 is configured to construct a data compression model, input the set of classes to be selected acquired by the control server into the data compression model, solve the data compression model by using a multiple iteration method to obtain a data compression result and a summary mapping table of the set of classes to be selected, generate a static js file by using a decompression algorithm, the data compression result and the summary mapping table, and distribute the static js file to all the class selection servers;
the course selection server 102 is configured to receive a course selection request sent by a client, construct an optimal request distribution objective function, solve the optimal request distribution objective function according to an address of a student request client, obtain an optimal request distribution course selection server, and transmit a static js file to a browser of the student request client;
in detail, the modules in the data compression and distribution device 100 under the high-class concurrency scenario of colleges and universities in the embodiment of the present invention adopt the same technical means as the data compression and distribution method under the high-class concurrency scenario of colleges and universities described in fig. 1, and can generate the same technical effects, which are not described herein again.
Example 3:
fig. 4 is a schematic structural diagram of an electronic device for implementing a data compression and distribution method in a high concurrence scenario of a college course selection according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing data compression distribution, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 4 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
the control server acquires a class set to be selected;
the control server builds a data compression model, and inputs the class set to be selected obtained by the control server into the data compression model;
solving the data compression model by using a multiple iteration method to obtain a data compression result of the class set to be selected and a summary mapping table;
the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all class selection servers;
constructing an optimal request distribution objective function, and solving the optimal request distribution objective function according to the address of the student request client to obtain an optimal request distribution class selection server;
and the optimal request distribution course selection server transmits the static js file to a browser of a student request client, completes data decompression at the client, and displays a course set to be selected in the browser.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 4, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (9)
1. A data compression distribution method and device under a high concurrence scene of course selection in a college are characterized in that the method comprises the following steps:
s1: the control server acquires a class set to be selected;
s2: the control server builds a data compression model, inputs the class set to be selected obtained by the control server into the data compression model, wherein the input of the data compression model is the class set to be selected, and outputs a data compression result and a summary mapping table;
s3: solving the data compression model by using a multiple iteration method to obtain a data compression result of the class set to be selected and a summary mapping table;
s4: the control server generates a static js file from the decompression algorithm, the data compression result and the abstract mapping table, and distributes the static js file to all class selection servers;
s5: constructing an optimal request distribution objective function, and solving the optimal request distribution objective function according to the address of the student request client to obtain an optimal request distribution class selection server;
s6: and the optimal request distribution course selection server transmits the static js file to a browser of a student request client, completes data decompression at the client, and displays a course set to be selected in the browser.
2. The data compression and distribution method in a college course selection high concurrence scene as claimed in claim 1, wherein the step S1 of obtaining the set of courses to be selected includes:
the control server acquires a class set to be selected before class selection, wherein the format of the class set to be selected is as follows:
X＝[X 1 ,X 2 ,…,X i ,…,X n ] T
wherein:
t represents a transpose;
x represents a class to be selected set formed by all class to be selected information, the form of X is a matrix form, and each row of data in the matrix represents class selecting information data of each class;
X i the i-th line data in X is the lesson selection information indicating the i-th lesson.
3. The data compression and distribution method under a college course selection high concurrence scene as claimed in claim 2, wherein the step S2 is to construct a data compression model, and input the set of courses to be selected obtained by the control server into the data compression model, and the method comprises the steps of:
constructing a data compression model, wherein the data compression model is in the form of:
wherein:
y represents the data compression result, P represents the abstract mapping table;
||·|| 2 represents an L2 norm;
and inputting the class set to be selected obtained by the control server into a data compression model.
4. The data compression and distribution method under the high concurrency scene of course selection in colleges and universities as claimed in claim 3, wherein the step S3 is to solve the data compression model by using a multiple iteration method to obtain a data compression result and a summary mapping table of the set of courses to be selected, and includes:
solving a data compression model by using a multiple iteration method to obtain a data compression result of a class set to be selected and a summary mapping table, wherein the solving process of the data compression model is as follows:
s31: the data compression model is converted into the following equation:
equivalent to solving the extremum of the following function:
wherein:
t represents a transpose;
s32: initializing data compression result Y 0 Digest mapping table P 0 Initializing the residual as
Corresponding initialization gradient +.>
Let the current iteration number of the multiple iteration method be k, the maximum iteration number be Max, and the initial value of k be 1, the data compression result and the abstract mapping table obtained by the kth iteration are Y respectively k P k ；
S33: calculating an update step length of the data compression result in the kth iteration:
wherein:
α k ，β k ，d k representing the update step length of the data compression result in the kth iteration;
i represent L1 norm;
s34: and (3) carrying out the kth iteration on the abstract mapping table, wherein the formula of the kth iteration is as follows:
Q＝Q 1 Q 2 ...Q m
wherein:
P k representing a summary mapping table obtained by the kth iteration;
q represents a transformation matrix, Q m The values representing the first row and the m row of the m column are delta, 1-delta, delta represents transformation parameters, and m represents the column number of the class set X to be selected;
s35: the kth iteration is carried out on the data compression result:
Y k ＝Y k-1 +α k d k
s36: calculating gradient g of the kth iteration k If g k =0 or k=max, then Y will be k P k Data compression result Y obtained as solving * Digest mapping table P * Otherwise let k=k+1, return to step S33.
5. The method for data compression and distribution in a college course selection high concurrence scenario as claimed in claim 1, wherein the step S4 is characterized in that the control server generates a static js file from a decompression algorithm, a data compression result and a summary mapping table, and distributes the static js file to all course selection servers, and comprises:
the control server compresses the decompression algorithm and the data to obtain the result Y * Digest mapping table P * Generating a static js file, wherein the decompression algorithm flow is as follows:
s41: based on the data compression model, the method calculates and obtains
Wherein->
Representing the coding result of the class set to be selected;
s42: decoding the coding result of the class set to be selected to obtain the class set to be selected, wherein the decoding process is the inverse operation of a single-heat coding mode, and the class information value corresponding to the coding result is determined according to the register bit of the coding result;
and distributing the static js file to all the course selection servers.
6. The data compression and distribution method in a college course-selection high concurrence scenario of claim 1, wherein the constructing an optimal request distribution objective function in step S5 includes:
constructing an optimal request distribution objective function:
wherein:
F(u j loc) means that after receiving the course selection request, the course selection uniformOptimal request distribution objective function value of jth lesson selection server in server cluster, u j The J-th course selection server in the course selection server cluster is represented, J represents the total number of course selection servers in the course selection server cluster, and loc represents the IP address of the client side sending the course selection request;
link(u j loc) represents from Ioc to u j Is a communication link distance of the IP address of (a);
7. The data compression distribution method under the high concurrence scene of colleges and universities in the invention of claim 6, wherein in the step S5, the optimal request distribution objective function is solved according to the address of the student request client to obtain the optimal request distribution course selection server, which comprises the following steps:
the course selecting server cluster receives a course selecting request sent by a client, wherein the client sending the course selecting request is a student requesting client, the IP address of the student requesting client is substituted into an optimal request distribution objective function, and the course selecting server corresponding to the maximum objective function value is used as an optimal request distribution course selecting server.
8. The method for data compression and distribution under a college course selection high concurrence scene as claimed in claim 7, wherein the optimal course selection server for requesting distribution in step S6 transmits static js file to a student request client browser, completes data decompression at the client and performs course set selection presentation, and comprises the following steps:
the optimal request distribution course selection server transmits the static js file to a student client browser, the student client analyzes the static js file to obtain a decompression algorithm, a data compression result and a summary mapping table, the data compression result is decompressed by the decompression algorithm based on the summary mapping table to obtain a course set to be selected, and the course set to be selected is displayed in the client browser.
9. A data compression distribution device in a college course selection high concurrence scene, the device comprising:
the control server is used for constructing a data compression model, inputting the class set to be selected obtained by the control server into the data compression model, solving the data compression model by utilizing a multiple iteration method to obtain a data compression result and a summary mapping table of the class set to be selected, generating a static js file by a decompression algorithm, the data compression result and the summary mapping table, and distributing the static js file to all class selection servers;
the system comprises a course selection server, a student request client browser, a student request client and a data compression distribution method under the high concurrency scene of colleges and universities, wherein the course selection server is used for receiving a course selection request sent by the client, constructing an optimal request distribution objective function, and solving the optimal request distribution objective function according to the address of the student request client to obtain the optimal request distribution course selection server, and the optimal request distribution course selection server transmits static js files to the student request client browser so as to realize the data compression distribution method under the high concurrency scene of colleges and universities according to the claims 1-8.
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