CN111966733B - Hot spot knowledge generation method and device - Google Patents
Hot spot knowledge generation method and device Download PDFInfo
- Publication number
- CN111966733B CN111966733B CN202010830261.1A CN202010830261A CN111966733B CN 111966733 B CN111966733 B CN 111966733B CN 202010830261 A CN202010830261 A CN 202010830261A CN 111966733 B CN111966733 B CN 111966733B
- Authority
- CN
- China
- Prior art keywords
- commodity
- knowledge data
- hot spot
- grouping
- knowledge
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 238000004590 computer program Methods 0.000 claims description 16
- 230000002776 aggregation Effects 0.000 claims description 7
- 238000004220 aggregation Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000002372 labelling Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Finance (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a hot spot knowledge generation method and a device, which relate to the technical field of data processing, and the method comprises the following steps: acquiring commodity knowledge data in a time window; the commodity knowledge data comprises hot spot parameters of each commodity; grouping commodity knowledge data according to the hot spot parameters, and writing the commodity knowledge data into a plurality of target files according to grouping results; calculating the heat value of the commodity according to the target file; and generating hot point knowledge in a time window according to the heat value of the commodity. The method can collect commodity knowledge data, perform grouping calculation on the commodity knowledge data according to the hot spot parameters to obtain the hot spot knowledge value of the commodity, and generate the hot spot knowledge in the time window according to the hot spot knowledge value of the commodity.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for generating hot point knowledge.
Background
In a customer service knowledge base of a bank, most hot knowledge is realized through manual labeling, the method is time-consuming and labor-consuming, the process of manually labeling the hot knowledge has certain subjectivity, and the labeling result is inaccurate and cannot reflect the actual use condition of a seat operator, so that the method is a function to be improved in a knowledge base system.
Disclosure of Invention
The invention provides a hot spot knowledge generation method and a hot spot knowledge generation device, which can improve the generation efficiency of hot spot knowledge, save labor cost and improve the data processing speed and result accuracy.
In a first aspect, an embodiment of the present invention provides a hotspot knowledge generating method, where the method includes: acquiring commodity knowledge data in a time window; the commodity knowledge data comprises hot spot parameters of each commodity; grouping the commodity knowledge data according to the hot spot parameters, and writing the commodity knowledge data into a plurality of target files according to grouping results; calculating the heat value of the commodity according to the target file; and generating hot point knowledge in the time window according to the heat value of the commodity.
In a second aspect, an embodiment of the present invention further provides a hotspot knowledge generating apparatus, where the apparatus includes: the acquisition module is used for acquiring commodity knowledge data in the time window; the commodity knowledge data comprises hot spot parameters of each commodity; the grouping module is used for grouping the commodity knowledge data according to the hot spot parameters and writing the commodity knowledge data into a plurality of target files according to grouping results; the calculation module is used for calculating the heat value of the commodity according to the target file; and the generation module is used for generating hot point knowledge in the time window according to the heat value of the commodity.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the hot spot knowledge generating method when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above hotspot knowledge generation method.
The embodiment of the invention has the following beneficial effects: the embodiment of the invention provides a hot spot knowledge generation scheme, which is used for acquiring commodity knowledge data through a time window, wherein the commodity knowledge data comprises hot spot parameters of each commodity, then grouping the commodity knowledge data according to the hot spot parameters, writing the commodity knowledge data into a plurality of target files according to grouping results, calculating the heat value of the commodity in each target file, and finally synthesizing the heat values of the commodity in the plurality of target files to generate hot spot knowledge in the time window. According to the embodiment of the invention, commodity knowledge data can be collected, the commodity knowledge data is calculated in a grouping mode according to the hot spot parameters to obtain the hot value of the commodity, and hot spot knowledge in a time window is generated according to the hot value of the commodity.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a hot spot knowledge generation method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a hot knowledge generating apparatus according to an embodiment of the present invention;
FIG. 3 is a block diagram of another hot knowledge generating apparatus according to an embodiment of the present invention;
Fig. 4 is a block diagram of a grouping module in the hotspot knowledge generating apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the hot spot knowledge generation method and device provided by the embodiment of the invention, indexes such as click quantity, access frequency, evaluation score and the like of a knowledge base background can be collected according to the fixed time window size, and calculation of the hot spot knowledge is completed through sliding window aggregation.
For the sake of understanding the present embodiment, first, a detailed description is given of a hotspot knowledge generating method disclosed in the present embodiment.
The embodiment of the invention provides a hot spot knowledge generation method, which is shown in a flow chart of the hot spot knowledge generation method in FIG. 1, and comprises the following steps:
Step S102, acquiring commodity knowledge data in a time window.
In the embodiment of the present invention, the time window refers to a fixed time length, for example, may be one hour. Knowledge data of the commodity in an hour period is obtained. Wherein, the commodity can be a financial product, such as a financial product. The commodity knowledge data is data of user attention and user use of the commodity. The commodity knowledge data includes hotspot parameters for each commodity. The hotspot parameter is a parameter for describing information such as a user attention degree and a user use frequency, and may include one or more parameter types.
Before acquiring commodity knowledge data in a time window, the operator knowledge base needs to be queried in advance, timestamps of all knowledge points are extracted, the size of the window is divided, and data such as clicking behaviors, query frequency, scores, collection numbers and the like are filtered out so as to improve data acquisition efficiency.
Step S104, grouping commodity knowledge data according to the hot spot parameters, and writing the commodity knowledge data into a plurality of target files according to grouping results.
In the embodiment of the invention, because the knowledge in the knowledge base is massive, in order to count the hottest commodity under each window, the data processing efficiency is improved, and a plurality of commodity knowledge data in the time window can be subjected to grouping processing. The commodity knowledge data can be grouped according to the category of the hot spot parameter or the size of the corresponding numerical value of each category, and a plurality of grouping results are respectively written into a plurality of different target files so as to ensure that the same knowledge is divided into the same files.
Step S106, calculating the heat value of the commodity according to the target file.
In the embodiment of the invention, commodity knowledge data belonging to the same group is stored in each target file, and the heat value of the commodity is calculated for the commodity knowledge data in each target file. The heat value is used to describe the degree of interest of the commodity under the comprehensive consideration of various heat parameters.
Step S108, generating hot point knowledge in a time window according to the commodity heat value.
In the embodiment of the invention, the commodities are ordered according to the heat value, the higher the heat value is, the higher the attention degree of the commodities is, and the hot point knowledge in the time window is determined according to the ordering result.
The embodiment of the invention provides a hot spot knowledge generation scheme, which is used for acquiring commodity knowledge data through a time window, wherein the commodity knowledge data comprises hot spot parameters of each commodity, then grouping the commodity knowledge data according to the hot spot parameters, writing the commodity knowledge data into a plurality of target files according to grouping results, calculating the heat value of the commodity in each target file, and finally synthesizing the heat values of the commodity in the plurality of target files to generate hot spot knowledge in the time window. According to the embodiment of the invention, commodity knowledge data can be collected, the commodity knowledge data is calculated in a grouping mode according to the hot spot parameters to obtain the hot value of the commodity, and hot spot knowledge in a time window is generated according to the hot value of the commodity.
In order to ensure the grouping efficiency, the commodity knowledge data are grouped according to the hot spot parameters, and the commodity knowledge data are written into a plurality of target files according to the grouping result, and the method can be implemented according to the following steps:
Calculating a hash value of commodity knowledge data according to the hot spot parameters; grouping commodity knowledge data according to the hash value and the commodity knowledge data; and writing commodity knowledge data belonging to the same group into the same target file.
In the embodiment of the invention, if n pieces of commodity knowledge data exist in one time window. And obtaining a hash value h from each piece of commodity knowledge data through hash operation, grouping according to the hash value h and n pieces of commodity knowledge data, and writing the commodity knowledge data belonging to the same grouping into the same target file.
The grouping of the commodity knowledge data according to the hash value and the commodity knowledge data may be performed as follows:
Calculating the remainder of the division of the hash value and the commodity knowledge data; and if the remainder corresponding to the first commodity knowledge data and the second commodity knowledge data is the same, determining that the first commodity knowledge data and the second commodity knowledge data belong to the same group.
In the embodiment of the invention, the hash value h is divided by the number n of commodity knowledge data to obtain remainder, and if the remainder corresponding to the first commodity knowledge data and the second commodity knowledge data is the same, the first commodity knowledge data and the second commodity knowledge data are determined to belong to the same group. Thus, it is possible to ensure that the same knowledge is divided into the same target file.
The hot spot parameters at least comprise one or more of click frequency parameters, inquiry frequency parameters, scoring parameters and collection frequency parameters; calculating the heat value of the commodity according to the target file, comprising: acquiring weight values of various hotspot parameters; calculating a hot spot parameter value of the commodity in each target file; and carrying out weighted calculation on the hot spot parameter values according to the weight values to obtain the heat value of the commodity.
In the embodiment of the present invention, the weight value may be set according to actual requirements, which is not particularly limited in the embodiment of the present invention. The hotspot parameter value is a degree of interest value for the type calculated from the hotspot parameter type. For example, according to the hot spot parameter of the type of the hit frequency parameter, the hot spot parameter value is calculated to be 50 times, and according to the hot spot parameter of the type of the query frequency parameter, the hot spot parameter value is calculated to be 3 times per minute. And carrying out weighted calculation on the hot spot parameter values based on the weight values to obtain a commodity heat value which is used for describing the comprehensive attention degree of the commodity in multiple dimensions.
In order to improve the memory processing efficiency, after the commodity knowledge data are grouped according to the hot spot parameters, the following steps may be further executed:
judging whether the memory quantity required by the grouping result is smaller than the idle memory quantity or not; if yes, writing commodity knowledge data into a plurality of target files according to the grouping result; if not, the grouping result is regrouped according to the hot spot parameters, and commodity knowledge data is written into a plurality of target files according to the regrouped result.
In the embodiment of the invention, the grouping result comprises a plurality of commodity knowledge data in a plurality of groups of target files, each group of commodity knowledge data exists in different target files, if the memory amount required by the data amount in one target file during data processing is smaller than or equal to the idle memory amount in the system, the system can be proved to load the target file once, the commodity knowledge data is written into the plurality of target files according to the grouping result, if the memory amount required by the data amount in one target file during data processing is larger than the idle memory amount in the system, the grouping result is regrouped according to the hot spot parameters, so that the memory amount required by the commodity knowledge data during data processing is reduced in the regrouped target files, the commodity knowledge data is written into the plurality of target files according to the regrouped result, and then whether the memory amount required by the regrouped result is smaller than the idle memory amount is judged until the memory amount required by the grouping result is smaller than the idle memory amount.
In order to facilitate global statistics and improve data processing efficiency, after generating hot point knowledge in a time window according to a commodity heat value, the following steps may be further executed:
and generating the aggregated hot knowledge according to the hot knowledge in the plurality of time windows.
In the embodiment of the invention, commodity knowledge data in a time window can be acquired at intervals of a fixed duration, for example, the time window length is 1 hour, and commodity knowledge data in the time window is acquired every 5 minutes, namely, commodity click volumes, inquiry evaluation rates, scores and the like of windows such as [09:00, 10:00) ], [09:05, 10:05) ], [09:10, 10:10) … and the like are counted respectively.
And then, the hottest knowledge is calculated in each window in turn by slicing, sliding window aggregation is performed, the top N commodities with the click rate in each window are output according to each window aggregation, hot point knowledge in the time window is obtained, and finally, the hottest knowledge is calculated by aggregating statistics of each time window.
The embodiment of the invention provides a hot spot knowledge generation method and a device, wherein the method can be used for counting a hot spot knowledge generation task by adopting the idea of sliding window aggregation, and finally generating the hot spot knowledge by aggregation according to the statistical results of each window.
The embodiment of the invention also provides a hot spot knowledge generation device, which is described in the following embodiment. Because the principle of the device for solving the problem is similar to that of the hot spot knowledge generation method, the implementation of the device can refer to the implementation of the hot spot knowledge generation method, and the repetition is not repeated. Referring to the block diagram of the hot knowledge generating apparatus shown in fig. 2, the apparatus includes:
An acquisition module 71, configured to acquire commodity knowledge data in a time window; the commodity knowledge data comprises hot spot parameters of each commodity; a grouping module 72, configured to group the commodity knowledge data according to the hotspot parameters, and write the commodity knowledge data into a plurality of target files according to the grouping result; a calculation module 73 for calculating a heat value of the commodity according to the target file; a generating module 74, configured to generate knowledge of hot spots in the time window according to the heat value of the commodity.
In one embodiment, referring to the block diagram of the grouping module in the hotspot knowledge generating apparatus shown in fig. 4, the grouping module includes: a hash unit 61 for calculating a hash value of the commodity knowledge data according to the hotspot parameters; a data unit 62 for grouping commodity knowledge data according to the hash value and commodity knowledge data; and a writing unit 63 for writing the commodity knowledge data belonging to the same group into the same target file.
In one embodiment, the data unit is specifically configured to: calculating the remainder of the division of the hash value and the commodity knowledge data; and if the remainder corresponding to the first commodity knowledge data and the second commodity knowledge data is the same, determining that the first commodity knowledge data and the second commodity knowledge data belong to the same group.
In one embodiment, the hotspot parameters at least comprise one or more of a click frequency parameter, a query frequency parameter, a scoring parameter and a collection frequency parameter; the computing module is specifically used for: acquiring weight values of various hotspot parameters; calculating a hot spot parameter value of the commodity in each target file; and carrying out weighted calculation on the hot spot parameter values according to the weight values to obtain the heat value of the commodity.
In one embodiment, referring to another structural block diagram of the hotspot knowledge generating apparatus shown in fig. 3, the apparatus further includes a determining module 75, specifically configured to: judging whether the memory quantity required by the grouping result is smaller than the idle memory quantity or not; if yes, writing commodity knowledge data into a plurality of target files according to the grouping result; if not, the grouping result is regrouped according to the hot spot parameters, and commodity knowledge data is written into a plurality of target files according to the regrouped result.
In one embodiment, referring to another hot knowledge generation apparatus structural block diagram shown in fig. 3, the apparatus further includes an aggregation module 76 for: and generating the aggregated hot knowledge according to the hot knowledge in the plurality of time windows.
The embodiment of the present invention further provides a computer device, referring to a schematic block diagram of a computer device structure shown in fig. 5, where the computer device includes a memory 81, a processor 82, and a computer program stored on the memory and capable of running on the processor, and when the processor executes the computer program, the processor implements the steps of any of the hot spot knowledge generating methods described above.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the computer device described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing any one of the hot spot knowledge generation methods.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A hot spot knowledge generation method is characterized by being applied to a customer service knowledge base of a bank and comprising the following steps:
acquiring commodity knowledge data in a time window; the commodity knowledge data comprises hot spot parameters of each commodity; the hot spot parameters at least comprise one or more of clicking frequency parameters, inquiry frequency parameters, scoring parameters and collection frequency parameters;
Grouping the commodity knowledge data according to the hot spot parameters, and writing the commodity knowledge data into a plurality of target files according to grouping results; grouping commodity knowledge data according to the category of the hot spot parameters or the size of corresponding numerical values of each category, and respectively writing a plurality of grouping results into a plurality of different target files;
calculating the heat value of the commodity according to the target file;
Generating hot point knowledge in the time window according to the heat value of the commodity;
the method for calculating the heat value of the commodity according to the target file comprises the following steps:
Acquiring weight values of various hotspot parameters; calculating a hot spot parameter value of the commodity in each target file;
weighting calculation is carried out on the hot spot parameter values according to the weight values, so that a heat value of the commodity is obtained;
Grouping the commodity knowledge data according to the hot spot parameters, and writing the commodity knowledge data into a plurality of target files according to grouping results, wherein the method comprises the following steps:
calculating a hash value of the commodity knowledge data according to the hot spot parameters;
grouping the commodity knowledge data according to the hash value and the commodity knowledge data;
Writing commodity knowledge data belonging to the same group into the same target file;
Wherein after grouping the commodity knowledge data according to the hotspot parameters, the method further comprises:
judging whether the memory quantity required by the grouping result is smaller than the idle memory quantity or not;
if yes, writing the commodity knowledge data into a plurality of target files according to the grouping result;
If not, the grouping result is regrouped according to the hot spot parameters, and the commodity knowledge data is written into a plurality of target files according to the regrouped result;
after generating the hot point knowledge in the time window according to the heat value of the commodity, the method further comprises the following steps:
And generating aggregated hot knowledge according to the hot knowledge in the plurality of time windows.
2. The method of claim 1, wherein grouping the commodity knowledge data according to the hash value and the commodity knowledge data comprises:
calculating the remainder of the division of the hash value and the commodity knowledge data;
And if the remainder corresponding to the first commodity knowledge data and the second commodity knowledge data is the same, determining that the first commodity knowledge data and the second commodity knowledge data belong to the same group.
3. The hot point knowledge generation device is characterized by being applied to a customer service knowledge base of a bank and comprising:
The acquisition module is used for acquiring commodity knowledge data in the time window; the commodity knowledge data comprises hot spot parameters of each commodity; the hot spot parameters at least comprise one or more of clicking frequency parameters, inquiry frequency parameters, scoring parameters and collection frequency parameters;
The grouping module is used for grouping the commodity knowledge data according to the hot spot parameters and writing the commodity knowledge data into a plurality of target files according to grouping results; grouping commodity knowledge data according to the category of the hot spot parameters or the size of corresponding numerical values of each category, and respectively writing a plurality of grouping results into a plurality of different target files;
The calculation module is used for calculating the heat value of the commodity according to the target file;
the generation module is used for generating hot point knowledge in the time window according to the heat value of the commodity;
The computing module is specifically configured to:
Acquiring weight values of various hotspot parameters;
calculating a hot spot parameter value of the commodity in each target file;
weighting calculation is carried out on the hot spot parameter values according to the weight values, so that a heat value of the commodity is obtained;
the grouping module comprises:
The hash unit is used for calculating the hash value of the commodity knowledge data according to the hot spot parameters;
a data unit, configured to group the commodity knowledge data according to the hash value and the commodity knowledge data;
the writing unit is used for writing commodity knowledge data belonging to the same group into the same target file;
the device also comprises a judging module for:
judging whether the memory quantity required by the grouping result is smaller than the idle memory quantity or not;
if yes, writing the commodity knowledge data into a plurality of target files according to the grouping result;
If not, the grouping result is regrouped according to the hot spot parameters, and the commodity knowledge data is written into a plurality of target files according to the regrouped result;
the system further comprises an aggregation module for:
And generating aggregated hot knowledge according to the hot knowledge in the plurality of time windows.
4. The apparatus according to claim 3, characterized in that said data unit is specifically configured to:
calculating the remainder of the division of the hash value and the commodity knowledge data;
And if the remainder corresponding to the first commodity knowledge data and the second commodity knowledge data is the same, determining that the first commodity knowledge data and the second commodity knowledge data belong to the same group.
5. A computer 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 claims 1 to 2 when executing the computer program.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010830261.1A CN111966733B (en) | 2020-08-18 | 2020-08-18 | Hot spot knowledge generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010830261.1A CN111966733B (en) | 2020-08-18 | 2020-08-18 | Hot spot knowledge generation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111966733A CN111966733A (en) | 2020-11-20 |
| CN111966733B true CN111966733B (en) | 2024-05-28 |
Family
ID=73387791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010830261.1A Active CN111966733B (en) | 2020-08-18 | 2020-08-18 | Hot spot knowledge generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111966733B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102385623A (en) * | 2011-10-25 | 2012-03-21 | 曙光信息产业(北京)有限公司 | Catalogue access method in DFS (distributed file system) |
| CN102571575A (en) * | 2011-12-29 | 2012-07-11 | 奇智软件（北京）有限公司 | Session information creation method, device and system |
| CN104063448A (en) * | 2014-06-18 | 2014-09-24 | 华东师范大学 | Distributed type microblog data capturing system related to field of videos |
| CN110659404A (en) * | 2018-06-13 | 2020-01-07 | 腾讯科技（深圳）有限公司 | Information recommendation method and device and storage medium |
| CN110674165A (en) * | 2018-07-03 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Method and device for adjusting sampling rate, storage medium and terminal equipment |
| CN111125561A (en) * | 2019-11-28 | 2020-05-08 | 泰康保险集团股份有限公司 | Network heat display method and device |
| CN111538891A (en) * | 2020-04-21 | 2020-08-14 | 招商局金融科技有限公司 | Hot event monitoring method and device, computer device and readable storage medium |
-
2020
- 2020-08-18 CN CN202010830261.1A patent/CN111966733B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102385623A (en) * | 2011-10-25 | 2012-03-21 | 曙光信息产业(北京)有限公司 | Catalogue access method in DFS (distributed file system) |
| CN102571575A (en) * | 2011-12-29 | 2012-07-11 | 奇智软件（北京）有限公司 | Session information creation method, device and system |
| CN104063448A (en) * | 2014-06-18 | 2014-09-24 | 华东师范大学 | Distributed type microblog data capturing system related to field of videos |
| CN110659404A (en) * | 2018-06-13 | 2020-01-07 | 腾讯科技（深圳）有限公司 | Information recommendation method and device and storage medium |
| CN110674165A (en) * | 2018-07-03 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Method and device for adjusting sampling rate, storage medium and terminal equipment |
| CN111125561A (en) * | 2019-11-28 | 2020-05-08 | 泰康保险集团股份有限公司 | Network heat display method and device |
| CN111538891A (en) * | 2020-04-21 | 2020-08-14 | 招商局金融科技有限公司 | Hot event monitoring method and device, computer device and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于关键词分析的微博群体阅读特征分析;郑天奇;;图书情报研究;20180614(第02期);第62-70页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111966733A (en) | 2020-11-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103729776B (en) | Aggregation of data analysis system | |
| EP2273431B1 (en) | Model determination system | |
| CN109934268B (en) | Abnormal transaction detection method and system | |
| CN112100219B (en) | Report generation method, device, equipment and medium based on database query processing | |
| TW201237665A (en) | Determining preferred categories based on user access attribute values | |
| US11182364B2 (en) | Data analysis support apparatus and data analysis support method | |
| CN111127105A (en) | User hierarchical model construction method and system, and operation analysis method and system | |
| CN106844320B (en) | Financial statement integration method and equipment | |
| CN104517020A (en) | Characteristic extraction method and device used for cause and effect analysis | |
| CN113190426B (en) | Stability monitoring method for big data scoring system | |
| WO2019114754A1 (en) | Join query method and system for multiple time sequences under columnar storage | |
| CN102156641A (en) | Prediction method and system for confidence interval of software cost | |
| CN116579804A (en) | Holiday commodity sales prediction method, holiday commodity sales prediction device and computer storage medium | |
| CN111966733B (en) | Hot spot knowledge generation method and device | |
| CN109408364A (en) | Method for analyzing performance, device, terminal and the computer storage medium of software product | |
| CN112150179B (en) | Information pushing method and device | |
| CN115936875A (en) | Financial product form hanging processing method and device | |
| CN108241643B (en) | Index data analysis method and device for keywords | |
| CN113835965B (en) | Parameter track mark-keeping method and device | |
| CN110069379A (en) | The screening technique and screening plant of monitor control index | |
| CN111694872B (en) | Method and device for providing service handling data scheme | |
| CN111768282B (en) | Data analysis method, device, equipment and storage medium | |
| CN109697203A (en) | Index unusual fluctuation analysis method and equipment, computer storage medium, computer equipment | |
| CN112882854B (en) | Method and device for processing request exception | |
| CN113360553A (en) | Data cold and hot degree evaluation method and server |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |