CN117033467A - Data mining method and device based on pre-calculation and storage medium - Google Patents

Abstract

This specification discloses a data mining method, device and storage medium based on precomputation, which obtains data to be mined, responds to the user's business request, precomputes the data to be mined according to the business request, and obtains intermediate results. , and store it; according to the business request, determine whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set. If not, when the pre-calculation is completed, based on the intermediate result, all Perform mining analysis on the data to be mined, obtain the mining analysis results corresponding to the business request, and display them. This method performs mining analysis through pre-calculated intermediate results, reduces the time for mining and analyzing the data to be mined, and improves the efficiency of data mining analysis.

Description

A data mining method, device and storage medium based on precomputation

Technical field

This specification relates to the field of computers, and in particular to a data mining method, device and storage medium based on precomputation.

Background technique

With the development of Internet technology and changes in user needs, computers need to process more and more data. When the computer processes data, it can analyze the data to mine effective information in the data, and by increasing computing resources, such as increasing memory, it can obtain data mining analysis results in real time during data mining analysis. However, while increasing computing resources, it also increases the cost of data maintenance and hardware. Moreover, when there is a large amount of data or the data mining analysis algorithm is more complex, the data mining analysis results cannot be obtained in real time simply by increasing computing resources, resulting in low data mining efficiency.

Based on this, this specification provides a data mining method based on precomputation.

Contents of the invention

This specification provides a data mining method, device, storage medium and electronic device based on precomputation to partially solve the above problems existing in the existing technology.

This manual adopts the following technical solutions:

This manual provides a data mining method based on precomputation, including:

Obtain data to be mined;

In response to the user's business request, precompute the data to be mined according to the business request, obtain intermediate results, and store them; according to the business request, determine whether the data to be mined exists in the stored mining analysis result set according to the business request. Mining analysis results corresponding to business requests;

If not, when the pre-calculation is completed, perform mining analysis on the data to be mined based on the intermediate result, obtain the mining analysis result corresponding to the business request, and display it.

Optionally, according to the business request, precompute the data to be mined to obtain intermediate results, which specifically includes:

According to the business request, among several pre-built pre-computing models, determine the pre-computing model matching the business request;

The data to be mined is input into the pre-computation model to obtain an intermediate result output by the pre-computation model, where the pre-computation includes data cleaning and feature extraction.

Optionally, pre-build a pre-computed model, including:

For each business, obtain the data of the business;

According to the data of the business, configure the pre-computed model parameters to build the pre-computed model.

Optionally, after obtaining the intermediate result, the method further includes:

For each intermediate result, an index of the service request and the intermediate result is established and stored.

Optionally, perform mining analysis on the data to be mined based on the intermediate results, specifically including:

Determine the intermediate result of the service request according to the index;

According to the intermediate results, the data to be mined is mined and analyzed.

Optionally, the method also includes:

The mining analysis results are stored, and the corresponding relationship between the service request and the mining analysis results is stored.

Optionally, according to the business request, determine whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set, specifically including:

According to the corresponding relationship between the business request and the stored mining analysis result set, it is determined whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set.

This manual provides a data mining device based on precomputation, including:

Data acquisition module, used to obtain data to be mined;

The response module is used to respond to the user's business request, perform pre-calculation on the data to be mined according to the business request, obtain intermediate results, and store them; according to the business request, in the stored mining analysis result set, Determine whether there is a mining analysis result corresponding to the business request;

The result acquisition module is configured to, if not, perform mining analysis on the data to be mined according to the intermediate result when the pre-calculation is completed, obtain the mining analysis result corresponding to the business request, and display it.

Optionally, the response module is specifically configured to, according to the business request, determine a pre-computing model matching the business request among several pre-built pre-computing models; and input the data to be mined into the pre-computing model. Calculate the model to obtain the intermediate results output by the pre-computation model, where the pre-computation includes data cleaning and feature extraction.

Optionally, the device also includes:

The model building module is used to obtain the data of each business and configure the pre-computed model parameters based on the data of the business to build a pre-computed model.

Optionally, the device also includes:

An index creation module is used to establish an index of the service request and the intermediate result for each intermediate result, and store the index.

Optionally, the result acquisition module is specifically configured to determine the intermediate result of the service request according to the index; and perform mining analysis on the data to be mined according to the intermediate result.

Optionally, the device also includes:

A storage module, configured to store the mining analysis results and store the corresponding relationship between the business request and the mining analysis results.

Optionally, the result acquisition module is specifically configured to determine whether there is a mining corresponding to the business request in the stored mining analysis result set based on the corresponding relationship between the business request and the stored mining analysis result set. Analyze the results.

This specification provides a computer-readable storage medium. The storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned precomputation-based data mining method is implemented.

This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned precomputation-based data mining method.

At least one of the above technical solutions adopted in this manual can achieve the following beneficial effects:

It can be seen from the data mining method based on precomputation provided in this manual that this method precomputes the data to be mined, and at the same time, determines whether there is a result corresponding to the current business request in the stored mining analysis results. If not, use the precalculated The intermediate results of the calculation are mined and analyzed on the data to be mined to obtain the results corresponding to the current business request. Mining and analyzing through pre-computed intermediate results reduces the time for mining and analyzing the data to be mined, and improves the efficiency of data mining and analysis.

Description of the drawings

The drawings described here are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation of this specification. In the attached picture:

Figure 1 is a schematic flow chart of a data mining method based on precomputation provided in this specification;

Figure 2 is a schematic diagram of the structure of a data mining device based on precomputation provided in this specification;

FIG. 3 is a schematic structural diagram of an electronic device corresponding to FIG. 1 provided in this specification.

Detailed ways

In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only some of the embodiments of this specification, but not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this specification.

The technical solutions provided by each embodiment of this specification will be described in detail below with reference to the accompanying drawings.

Figure 1 is a schematic flowchart of a precomputation-based data mining method provided in this manual, including the following steps:

S100: Obtain the data to be mined.

Real-time data mining analysis emphasizes the timeliness of mining analysis results, and usually adopts the computing upgrade idea, that is, increasing computer resources, and then through reasonable allocation of computing resources such as CPU and memory to achieve low response time data mining analysis. However, this has resulted in increasing costs related to data maintenance and hardware. In addition, only relying on computing upgrades, when the data scale is large or the algorithm is complex, there is still a low response problem that is difficult to support analysis, resulting in low data mining efficiency. Therefore, this description is a data mining analysis method based on precomputation.

The execution subject of this instruction can be a server used for data mining analysis, or other electronic equipment that can be used for data mining analysis. For the convenience of explanation, this specification uses the server as the execution subject to describe the precomputation-based data mining method provided in this specification.

In order to perform data mining and analysis, the server can first obtain the data to be mined, and the data to be mined can be data in a database.

S102: In response to the user's service request, precompute the data to be mined according to the service request, obtain intermediate results, and store them.

In one or more embodiments of this specification, services include shortest paths, association rules, subgraph matching, etc. For different businesses, the data to be mined for the business is different, so the corresponding pre-calculation is different. Therefore, according to the service request, the server can determine a pre-computing model matching the service request among several pre-built pre-computing models. The server can determine the pre-computing model that matches its own business request through the identifier of the business request. For example, if the identifier of the business request is 1, then if the pre-computation model identifier is also 1, then the pre-computation model matches the business request. This specification does not limit the manner in which the precomputed model that matches the business request is determined.

Then the data to be mined is input into the pre-computation model, and the intermediate result output by the pre-computation model is obtained and stored. When the business is to determine the shortest path, the intermediate results include the shortest path between any two nodes in the network graph. When the business is a subgraph match, the intermediate results include the data graph and subgraph that match the business. For different businesses, the intermediate results are different. Wherein, the pre-calculation includes data cleaning and feature extraction. Data cleaning includes processing missing values, deleting duplicate data, correcting format errors, removing noise, etc. to improve data quality. When the business is subgraph matching, feature extraction includes extracting features such as vertex/edge embeddings for nodes and edges.

It should be noted that due to different businesses, different data to be mined, and different pre-computing models. For different types of businesses, different precomputing models need to be built. Among them, different types of businesses refer to different businesses and/or different data to be mined. For example, there is a path graph, which includes four nodes A, B, C, and D. The edges of the graph are the paths between the nodes. Business 1 is to determine the shortest path between A and B. Business 2 is to determine the shortest path between C and B. The shortest path between D, since both business 1 and business 2 are determined shortest paths, and the data to be mined are all path graphs, business 1 and business 2 can use the same pre-computation model.

In addition, due to differences in precomputed models, in order to improve the efficiency of data mining, precomputed models can be built in advance.

Specifically, for each business, the data of the business is obtained, and the pre-computing model parameters are configured according to the data of the business to build the pre-computing model. That is to say, for each business, obtain the data in the database of the business, select the mining algorithm for precomputation based on the business and the data, configure the precomputation model parameters, and build the precomputation model. When the business is to determine the shortest path, select any algorithm among the algorithms that support precomputation, such as the Breadth-First Search algorithm (Breadth-First Search, BFS), and configure the precomputation model parameters based on the graph that determines the shortest path and the algorithm. , build a precomputed model.

S104: According to the business request, determine whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set. If yes, execute S106. If not, execute S108.

After the server requests the mining analysis results corresponding to the business request, it can store the mining analysis results and store the corresponding relationship between the business requests and the mining analysis results, so that when the same business request is subsequently executed, the stored mining analysis results can be directly used. Analyze the results. Therefore, when determining the data mining analysis result, the server can determine whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set according to the business request.

Specifically, based on the corresponding relationship between the business request and the stored mining analysis result set, it is determined whether there is a mining analysis result corresponding to the business request in the stored mining analysis result set. This specification does not limit the method of determining the correspondence between business requests and mining analysis results.

S106: Return the mining analysis results corresponding to the service request to the user.

S108: When the pre-calculation is completed, perform mining analysis on the data to be mined based on the intermediate results, obtain the mining analysis results corresponding to the business request, and display them.

If there is no mining analysis result corresponding to the business request in the stored mining analysis result set, it can also be determined whether the pre-calculation is completed, so as to use the intermediate results to obtain the mining analysis results and improve the efficiency of data mining analysis.

Specifically, when the pre-calculation is completed, the server performs mining analysis on the data to be mined based on the intermediate result, obtains the mining analysis result corresponding to the business request, and displays it. It should be noted that after obtaining the intermediate results, for each intermediate result, an index of the business request and the intermediate results is established and stored. According to the index, an intermediate result of the service request is determined, and according to the intermediate result, the data to be mined is mined and analyzed. When the business is to determine the shortest path, the shortest path of the business is selected from the intermediate results according to the index, that is, from the shortest path between any two nodes in the graph, and the mining analysis result corresponding to the business request is obtained.

Based on the precomputation-based data mining method shown in Figure 1, the data to be mined is precomputed. At the same time, it is judged whether there is a result corresponding to the current business request in the stored mining analysis results. If not, the precomputed intermediate result is used. Conduct mining and analysis on the data to be mined to obtain results corresponding to the current business request. Mining and analyzing through pre-computed intermediate results reduces the time for mining and analyzing the data to be mined, and improves the efficiency of data mining and analysis.

It should be noted that shortest path, association rules, and subgraph matching can be applied in various fields, such as product recommendation. The shortest path can find the user's nearest neighbor, association rules can discover product associations, and subgraph matching can detect user behavior patterns. Abnormal transaction detection, shortest path identification of suspicious related transactions, association rules to find high-risk indicators, and subgraph matching to discover abnormal behavior graph patterns. Logistics and transportation, shortest path planning of logistics routes, association rules optimization of warehousing and distribution, subgraph matching and analysis of transportation networks. Network security, shortest path identification of attack paths, association rules to discover vulnerability associations, subgraph matching to detect attack patterns, etc. The data to be mined are data from actual business scenarios, and will not be described in detail in this manual.

Regarding step S108, following the example in S102, if the server has obtained the mining analysis results of business 1, then when executing business 2, it can obtain the mining analysis results of business 2 based on the intermediate results of business 1. That is to say, for similar businesses, the mining analysis results of the current business request can be obtained based on the intermediate results of the mining analysis results corresponding to the acquired business requests.

The above is one or more implementation methods of this specification. Based on the flow diagram of the pre-computation-based data mining method shown in Figure 1, this specification also provides a corresponding pre-computation-based data mining device, as shown in Figure 2 .

Figure 2 is a schematic diagram of a precomputation-based data mining device provided in this specification, including:

Data acquisition module 200, used to acquire data to be mined;

The response module 202 is configured to respond to the user's business request, precompute the data to be mined according to the business request, obtain intermediate results, and store them; according to the business request, in the stored mining analysis result set , determine whether there is a mining analysis result corresponding to the business request;

The result acquisition module 204 is configured to, if not, perform mining analysis on the data to be mined according to the intermediate result when the pre-calculation is completed, obtain the mining analysis result corresponding to the business request, and display it.

Optionally, the response module 202 is specifically configured to, according to the business request, determine a pre-computing model matching the business request among several pre-built pre-computing models; and input the data to be mined into the Precompute the model to obtain the intermediate results output by the precomputation model, where the precomputation includes data cleaning and feature extraction.

Optionally, the device also includes:

The model building module 206 is used for obtaining the data of each business and configuring the pre-calculated model parameters according to the data of the business to build a pre-calculated model.

Optionally, the device also includes:

The index creation module 208 is configured to create an index of the service request and the intermediate result for each intermediate result, and store the index.

Optionally, the result acquisition module 204 is specifically configured to determine the intermediate result of the service request according to the index; and perform mining analysis on the data to be mined according to the intermediate result.

Optionally, the device also includes:

The storage module 210 is used to store the mining analysis results, and store the correspondence between the business request and the mining analysis results.

Optionally, the result acquisition module 204 is specifically configured to determine, based on the corresponding relationship between the business request and the stored mining analysis result set, whether there is a data corresponding to the business request in the stored mining analysis result set. Mining analysis results.

This specification also provides a computer-readable storage medium that stores a computer program. The computer program can be used to execute a precomputation-based data mining method provided in Figure 1 above.

This specification also provides a schematic structural diagram of the electronic device shown in FIG. 3 corresponding to FIG. 1 . As shown in Figure 3, at the hardware level, the electronic device includes a processor, internal bus, network interface, memory and non-volatile memory, and of course may also include other hardware required for business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the pre-computation-based data mining method described in Figure 1 above.

Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logical unit, and may also be hardware or logic device.

In the 1990s, improvements in a technology could be clearly distinguished as hardware improvements (for example, improvements in circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements in method processes). However, with the development of technology, many improvements in today's method processes can be regarded as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement of a method flow cannot be implemented using hardware entity modules. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit, and its logic function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system on a PLD, instead of asking chip manufacturers to design and produce dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. Before compiling, The original code must also be written in a specific programming language, which is called Hardware Description Language (HDL). There is not only one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language) , AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., the most commonly used ones currently are VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that by simply logically programming the method flow using the above-mentioned hardware description languages and programming it into the integrated circuit, the hardware circuit that implements the logical method flow can be easily obtained.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (eg, software or firmware) executable by the (micro)processor. , logic gates, switches, Application Specific Integrated Circuit (ASIC), programmable logic controllers and embedded microcontrollers. Examples of controllers include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the memory control logic. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the controller can be completely programmed with logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded logic by logically programming the method steps. Microcontroller, etc. to achieve the same function. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the means for implementing various functions can be considered as structures within hardware components as well as software modules implementing the methods.

The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or A combination of any of these devices.

For the convenience of description, when describing the above device, the functions are divided into various units and described separately. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and/or hardware.

Those skilled in the art will understand that embodiments of the present specification may be provided as methods, systems, or computer program products. Thus, the present description may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The specification is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in one process or multiple processes of the flowchart and/or one block or multiple blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in computer-readable media, random access memory (RAM), and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer-readable media includes both persistent and non-volatile, removable and non-removable media that can be implemented by any method or technology for storage of information. Information may be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory. (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cassettes, tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes Other elements are not expressly listed or are inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or device that includes the stated element.

Those skilled in the art will appreciate that embodiments of the present specification may be provided as methods, systems, or computer program products. Thus, the present description may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

This specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present description may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through communications networks. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Each embodiment in this specification is described in a progressive manner. The same and similar parts between the various embodiments can be referred to each other. Each embodiment focuses on its differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For relevant details, please refer to the partial description of the method embodiment.

The above descriptions are only examples of this specification and are not intended to limit this specification. Various modifications and variations may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification shall be included in the scope of the claims of this specification.

Claims (10)

1. A pre-calculation based data mining analysis method, the method comprising:

acquiring data to be mined;

responding to a service request of a user, pre-calculating the data to be mined according to the service request, obtaining an intermediate result, and storing the intermediate result; judging whether a mining analysis result corresponding to the service request exists in a stored mining analysis result set according to the service request;

if not, when pre-calculation is completed, mining and analyzing the data to be mined according to the intermediate result, obtaining mining and analyzing results corresponding to the service request, and displaying the mining and analyzing results.

2. The method of claim 1, wherein the pre-computing the data to be mined according to the service request to obtain an intermediate result specifically comprises:

according to the service request, determining a pre-calculation model matched with the service request from a plurality of pre-calculation models which are pre-constructed;

inputting the data to be mined into the pre-calculation model to obtain an intermediate result output by the pre-calculation model, wherein the pre-calculation comprises data cleaning and feature extraction.

3. The method according to claim 2, characterized in that the pre-calculation model is pre-built, in particular comprising:

for each service, acquiring data of the service;

according to the data of the service, pre-calculation model parameters are configured to construct a pre-calculation model.

4. The method of claim 1, wherein after obtaining the intermediate result, the method further comprises:

for each intermediate result, an index of the service request and the intermediate result is established and stored.

5. The method of claim 4, wherein the mining analysis is performed on the data to be mined according to the intermediate result, specifically comprising:

determining an intermediate result of the service request according to the index;

and mining and analyzing the data to be mined according to the intermediate result.

6. The method of claim 1, wherein the method further comprises:

and storing the mining analysis result, and storing the corresponding relation between the service request and the mining analysis result.

7. The method of claim 6, wherein determining whether there is a mining analysis result corresponding to the service request in the stored mining analysis result set according to the service request, specifically comprises:

judging whether the mining analysis result corresponding to the service request exists in the stored mining analysis result set according to the corresponding relation between the service request and the stored mining analysis result set.

8. A pre-calculation based data mining analysis apparatus, the apparatus comprising:

the data acquisition module is used for acquiring data to be mined;

the response module is used for responding to a service request of a user, pre-calculating the data to be mined according to the service request, obtaining an intermediate result and storing the intermediate result; judging whether a mining analysis result corresponding to the service request exists in a stored mining analysis result set according to the service request;

and the result acquisition module is used for carrying out mining analysis on the data to be mined according to the intermediate result when the pre-calculation is finished if not, so as to obtain mining analysis results corresponding to the service request and display the mining analysis results.

9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-7 when executing the program.