CN118278743A - Method and device for analyzing website operation risk data - Google Patents
Method and device for analyzing website operation risk data Download PDFInfo
- Publication number
- CN118278743A CN118278743A CN202410542205.6A CN202410542205A CN118278743A CN 118278743 A CN118278743 A CN 118278743A CN 202410542205 A CN202410542205 A CN 202410542205A CN 118278743 A CN118278743 A CN 118278743A
- Authority
- CN
- China
- Prior art keywords
- risk data
- data
- risk
- index
- network point
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004590 computer program Methods 0.000 claims description 27
- 238000007405 data analysis Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 9
- 238000013075 data extraction Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012502 risk assessment Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 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
- 230000008520 organization Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for analyzing website operation risk data, wherein the method comprises the following steps: determining each operation index related to the network point from a pre-constructed network point operation index library; extracting risk data corresponding to each operation index; calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes; counting the number of business of the network points and the number of corresponding business labels, comparing the number of business of the network points with a preset business number threshold value, and comparing the number of business labels of the network points with the preset business label number threshold value; determining business risk data of the network points; and determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point. The method and the system can improve the accuracy of analysis of the website operation risk data, measure and grade the website operation risk without relying on manpower, reduce the cost and improve the risk assessment efficiency.
Description
Technical Field
The invention relates to the technical field of finance, in particular to a website operation risk data analysis method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The operational risk of a large number of sites is currently a common problem faced by large commercial banks. The difficulty of website operation risk measurement and grading is mainly in two aspects: firstly, the actual daily operation conditions of the network points between different areas and the network points between different service types are huge, the measurement is difficult to be simply aligned, and the huge difference of the network points in the actual service operation cannot be considered in grading, so that the unfairness of the network point operation risk assessment result is caused; secondly, the network site operation risk cases are rare, development modeling is difficult to carry out based on big data or artificial intelligence technology, and measurement grading is carried out on network site operation risks by means of manpower.
Disclosure of Invention
The embodiment of the invention provides a website operation risk data analysis method, which is used for improving the accuracy of website operation risk analysis, reducing labor cost and improving risk analysis efficiency, and comprises the following steps:
determining each operation index related to the network point from a pre-constructed network point operation index library;
Extracting risk data corresponding to each operation index from operation data of the network points;
calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes;
counting the number of the business of the network points and the number of corresponding business labels, comparing the number of the business of the network points with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network points with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service;
Determining business risk data of the network points according to the first comparison result and the second comparison result;
And determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point.
The embodiment of the invention also provides a website operation risk data analysis device, which is used for improving the accuracy of website operation risk analysis, reducing labor cost and improving risk analysis efficiency, and comprises the following steps:
The operation index determining module is used for determining each operation index related to the network point from a pre-constructed network point operation index library;
the risk data extraction module of the operation indexes is used for extracting risk data corresponding to each operation index from the operation data of the network points;
The operation risk data calculation module of the network point is used for calculating operation risk data of the network point according to the preset weights corresponding to the operation indexes and the risk data corresponding to the operation indexes;
The comparison result acquisition module is used for counting the service quantity of the network points and the corresponding service label quantity, comparing the service quantity of the network points with a preset service quantity threshold value to obtain a first comparison result, and comparing the service label quantity of the network points with the preset service label quantity threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service;
The business risk data determining module of the network point is used for determining business risk data of the network point according to the first comparison result and the second comparison result;
And the operation risk level determining module is used for determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the website operation risk data analysis method.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the website operation risk data analysis method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the website operation risk data analysis method when being executed by a processor.
In the embodiment of the invention, each operation index related to the network point is determined from a pre-constructed network point operation index library; extracting risk data corresponding to each operation index from operation data of the network points; calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes; counting the number of the business of the network points and the number of corresponding business labels, comparing the number of the business of the network points with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network points with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service; determining business risk data of the network points according to the first comparison result and the second comparison result; and determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point. In the process, the embodiment of the invention combines the operation risk data of the network point with the business risk data of the network point to determine the final network point operation risk level, thereby improving the accuracy of network point operation risk data analysis, avoiding the need of manually grading the network point operation risk by measuring, reducing the labor cost and improving the risk assessment efficiency.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flowchart of a method for analyzing website operation risk data according to an embodiment of the present invention;
Fig. 2 is a flowchart of extracting risk data corresponding to each operation index in the embodiment of the present invention;
fig. 3 is a flowchart of setting weights of various operation indexes in the embodiment of the present invention;
FIG. 4 is a flowchart of determining an operation risk level of a website according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a website operation risk data analysis device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a specific website operation risk data analysis device in 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 embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Fig. 1 is a flowchart of a method for analyzing website operation risk data according to an embodiment of the present invention, where the method includes:
step 101, determining each operation index related to the network point from a pre-constructed network point operation index library;
102, extracting risk data corresponding to each operation index from operation data of the network points;
step 103, calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes;
step 104, counting the number of the business of the network point and the number of the corresponding business labels, comparing the number of the business of the network point with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network point with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service;
Step 105, determining business risk data of the network point according to the first comparison result and the second comparison result;
and step 106, determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point.
Each step is described in detail below.
In step 101, each operation index related to the website is determined from a website operation index library constructed in advance.
In a specific embodiment, according to the specific access logic of each operation index, extracting, cleaning and standardizing the detail data required by each operation index from each scattered service system, and writing the detail data into a detail database table subordinate to the hierarchy. Current operation indexes include: organization-like indicators, personnel-like indicators, and business-like indicators. The mechanism indexes comprise 9 indexes in total, the personnel indexes comprise 17 indexes in total, the service indexes comprise 35 indexes in total and 61 indexes in total. As shown in table 1.
TABLE 1
| Index code | Index name | Class of business | Business subclass |
| INDEXxxxx | R1-case and major risk event | Organization type | Compliance culture |
| .. (9 In total) | ... | ... | ... |
| INDEXxxxx | Q7-inverse transaction duty cycle | Personnel class | Accounting mass |
| .. (Total of 17) | ... | ... | ... |
| INDEXxxxx | F47-Manual marketing number of important article | Service class | Accounting element |
| .. (Total of 35) | ... | ... | ... |
In step 102, risk data corresponding to each operation index is extracted from operation data of the website.
As shown in fig. 2, in an embodiment, extracting risk data corresponding to each operation index from operation data of a website includes:
Step 201, classifying each operation index to obtain various data source tables; the data source tables comprise a transaction list, an account list, a case-related list, a risk event monitoring list and a risk event list which are generated according to operation data of network points;
step 202, counting the ratio of the data of various data source tables to the total data of all the data source tables;
Step 203, taking the counted ratio as an index value, and establishing an index value detail table according to the logic relation between the index value and the risk data of each operation index; the index value detail table comprises index values and corresponding risk data of each operation index;
step 204, determining risk data of each operation index from the index value detail table.
In a specific embodiment, the data source table takes the detail database table as a data source, takes index detail data in the current statistical range, marks and records according to specific indexes, and writes the index detail data into the class A table. The concept of class a tables is supplemented here: all data sources are defined as 5 basic types (transaction details, account details, case-related lists, monitoring risk events, risk events), and accordingly can be normalized and written into 5 types of data source tables: "transaction details table (A1)", "account details table (A2)", "case-related list table (A3)", "monitoring risk event details table (A4)", and risk event details table (A5). These source tables are all the basic data that needs to be prepared before each metric calculation.
In one embodiment, determining risk data for each operation index from the index value detail table includes:
And determining risk data of each operation index from the index value detail table by utilizing a box division algorithm and an attenuation algorithm.
In a specific embodiment, a box division algorithm, an attenuation algorithm or a ticket overrule algorithm is utilized to obtain risk data of each network point in determining each operation index, and a result set is written into a preset risk data analysis table.
Compared with the traditional risk data analysis scheme, a sorting and scoring algorithm such as a box-sorting algorithm, an attenuation algorithm and the like is introduced, so that risk data of the network points on each operation index are scientifically measured and analyzed. The algorithm parameters can be dynamically adjusted according to the actual service conditions, and relatively reasonable and satisfactory risk analysis results are obtained in the debugging process.
In step 103, operation risk data of the website is calculated according to the preset weight corresponding to each operation index and the risk data corresponding to each operation index.
As shown in fig. 3, in an embodiment, the method further includes:
Step 301, setting importance of each operation index according to actual requirements;
step 302, setting weights corresponding to the operation indexes based on the importance of the operation indexes.
In step 104 to step 105, counting the number of the business of the network point and the number of the corresponding business labels, comparing the number of the business of the network point with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network point with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service; and determining business risk data of the network points according to the first comparison result and the second comparison result.
In a specific embodiment, the counter traffic volume of the website, the number of personal accounts of the website, the number of public accounts of the website, the number of operators of the website and the like can directly reflect the situation of the website business risk. Based on the counter service operation log of the network point, the counter service and the kind information thereof which actually occur in the network point in the statistical period are obtained, and then a plurality of service labels are marked on the network point, as shown in the table 2. And counting the number of service labels of each network point. The number of service labels truly reflects the complexity of the service class of a website and the condition of the type of the service born.
TABLE 2
And dividing the mass first-line mesh points into three groups of large mesh points, medium mesh points and small mesh points by combining the statistical information calculated in the previous two steps, and storing the classification result data in a table C2. An expert judgment rule parameter table is attached.
Table C2
In step 106, the operation risk level of the website is determined according to the operation risk data of the website and the business risk data of the website.
As shown in fig. 4, in an embodiment, determining the operation risk level of the website according to the operation index risk data of the website and the service risk data of the website includes:
step 401, determining final website operation risk data according to website operation index risk data and website business risk data by adopting an Euclidean distance algorithm;
Step 402, determining the operation risk level of the website according to the final website operation risk data.
In one embodiment, the final website operational risk data is calculated according to the following formula:
in a specific embodiment, the operation risk class of the website is classified into A, B, C, D, E five classes. The operation risk level assessment can be regulated and controlled according to the operation risk level name proportion parameters of each branch, so that the flexibility and the expandability of the rating result are ensured, and compared with the traditional technical scheme, the operation risk level assessment is easier to use and more practical. The adjustment parameter representation is shown in table 3, for example.
TABLE 3 Table 3
The embodiment of the invention also provides a website operation risk data analysis device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the website operation risk data analysis method, the implementation of the device can be referred to the implementation of the website operation risk data analysis method, and the repetition is omitted. As shown in fig. 5, the apparatus includes:
An operation index determining module 501, configured to determine each operation index related to a website from a website operation index library constructed in advance;
the risk data extraction module 502 of the operation index is configured to extract risk data corresponding to each operation index from operation data of the website;
An operation risk data calculation module 503 of the website is configured to calculate operation risk data of the website according to the preset weights corresponding to the operation indexes and the risk data corresponding to the operation indexes;
The comparison result obtaining module 504 is configured to count the number of services of the mesh point and the number of corresponding service labels, compare the number of services of the mesh point with a preset service number threshold to obtain a first comparison result, and compare the number of service labels of the mesh point with the preset service label number threshold to obtain a second comparison result; the service tag is used for identifying the category of the service;
the business risk data determining module 505 of the website is configured to determine business risk data of the website according to the first comparison result and the second comparison result;
The operation risk level determining module 506 of the website is configured to determine an operation risk level of the website according to the operation risk data of the website and the business risk data of the website.
In an embodiment, the risk data extraction module 502 of the operation index is specifically configured to:
Classifying each operation index to obtain various data source tables; the data source tables comprise a transaction list, an account list, a case-related list, a risk event monitoring list and a risk event list which are generated according to operation data of network points;
counting the ratio of the data of various data source tables to the total data of all the data source tables;
Taking the counted ratio as an index value, and establishing an index value detail table according to the logic relation between the index value and the risk data of each operation index; the index value detail table comprises index values and corresponding risk data of each operation index;
And determining risk data of each operation index from the index value detail table.
In an embodiment, the risk data extraction module 502 of the operation index is specifically configured to:
And determining risk data of each operation index from the index value detail table by utilizing a box division algorithm and an attenuation algorithm.
As shown in fig. 6, in an embodiment, the system further includes an operation index weight setting module 601, specifically configured to:
Setting importance of each operation index according to actual requirements;
and setting weights corresponding to the operation indexes based on the importance of the operation indexes.
In an embodiment, the operation risk level determining module 506 of the website includes:
Determining final network point operation risk data according to network point operation index risk data and network point business risk data by adopting an Euclidean distance algorithm;
and determining the operation risk level of the network point according to the final network point operation risk data.
In one embodiment, the final website operational risk data is calculated according to the following formula:
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the website operation risk data analysis method.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the website operation risk data analysis method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the website operation risk data analysis method when being executed by a processor.
In the embodiment of the invention, each operation index related to the network point is determined from a pre-constructed network point operation index library; extracting risk data corresponding to each operation index from operation data of the network points; calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes; counting the number of the business of the network points and the number of corresponding business labels, comparing the number of the business of the network points with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network points with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service; determining business risk data of the network points according to the first comparison result and the second comparison result; and determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point. In the process, the embodiment of the invention combines the operation risk data of the network point with the business risk data of the network point to determine the final network point operation risk level, thereby improving the accuracy of network point operation risk data analysis, avoiding the need of manually grading the network point operation risk by measuring, reducing the labor cost and improving the risk assessment efficiency.
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.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A website operation risk data analysis method, comprising:
determining each operation index related to the network point from a pre-constructed network point operation index library;
Extracting risk data corresponding to each operation index from operation data of the network points;
calculating to obtain operation risk data of the network points according to preset weights corresponding to the operation indexes and risk data corresponding to the operation indexes;
counting the number of the business of the network points and the number of corresponding business labels, comparing the number of the business of the network points with a preset business number threshold value to obtain a first comparison result, and comparing the number of the business labels of the network points with the preset business label number threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service;
Determining business risk data of the network points according to the first comparison result and the second comparison result;
And determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point.
2. The method of claim 1, wherein extracting risk data corresponding to each operation index from operation data of the website comprises:
Classifying each operation index to obtain various data source tables; the data source tables comprise a transaction list, an account list, a case-related list, a risk event monitoring list and a risk event list which are generated according to operation data of network points;
counting the ratio of the data of various data source tables to the total data of all the data source tables;
Taking the counted ratio as an index value, and establishing an index value detail table according to the logic relation between the index value and the risk data of each operation index; the index value detail table comprises index values and corresponding risk data of each operation index;
And determining risk data of each operation index from the index value detail table.
3. The method of claim 2, wherein determining risk data for each of the operation indicators from the indicator value detail table comprises:
And determining risk data of each operation index from the index value detail table by utilizing a box division algorithm and an attenuation algorithm.
4. The method as recited in claim 1, further comprising:
Setting importance of each operation index according to actual requirements;
and setting weights corresponding to the operation indexes based on the importance of the operation indexes.
5. The method of claim 1, wherein determining the operational risk level for the node based on the operational indicator risk data for the node and the traffic risk data for the node comprises:
Determining final network point operation risk data according to network point operation index risk data and network point business risk data by adopting an Euclidean distance algorithm;
and determining the operation risk level of the network point according to the final network point operation risk data.
6. The method of claim 5, wherein the final site operation risk data is calculated according to the following formula:
7. a website operation risk data analysis device, characterized by comprising:
The operation index determining module is used for determining each operation index related to the network point from a pre-constructed network point operation index library;
the risk data extraction module of the operation indexes is used for extracting risk data corresponding to each operation index from the operation data of the network points;
The operation risk data calculation module of the network point is used for calculating operation risk data of the network point according to the preset weights corresponding to the operation indexes and the risk data corresponding to the operation indexes;
The comparison result acquisition module is used for counting the service quantity of the network points and the corresponding service label quantity, comparing the service quantity of the network points with a preset service quantity threshold value to obtain a first comparison result, and comparing the service label quantity of the network points with the preset service label quantity threshold value to obtain a second comparison result; the service tag is used for identifying the category of the service;
The business risk data determining module of the network point is used for determining business risk data of the network point according to the first comparison result and the second comparison result;
And the operation risk level determining module is used for determining the operation risk level of the network point according to the operation risk data of the network point and the service risk data of the network point.
8. 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 6 when executing the computer program.
9. 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 6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410542205.6A CN118278743A (en) | 2024-04-30 | 2024-04-30 | Method and device for analyzing website operation risk data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410542205.6A CN118278743A (en) | 2024-04-30 | 2024-04-30 | Method and device for analyzing website operation risk data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118278743A true CN118278743A (en) | 2024-07-02 |
Family
ID=91634696
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410542205.6A Pending CN118278743A (en) | 2024-04-30 | 2024-04-30 | Method and device for analyzing website operation risk data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118278743A (en) |
-
2024
- 2024-04-30 CN CN202410542205.6A patent/CN118278743A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110634080B (en) | Abnormal electricity utilization detection method, device, equipment and computer readable storage medium | |
| CN109583722A (en) | A kind of channel data analysis system and method | |
| CN109635007B (en) | Behavior evaluation method and device and related equipment | |
| CN109934268B (en) | Abnormal transaction detection method and system | |
| CN105824870A (en) | Classification and quality inspection method and system based on verification rules | |
| CN111340606B (en) | Whole-flow income auditing method and device | |
| CN110163683B (en) | Value user key index determination method, advertisement delivery method and device | |
| CN109711424A (en) | A kind of rule of conduct acquisition methods, device and equipment based on decision tree | |
| CN107766500A (en) | The auditing method of fixed assets card | |
| CN111695979A (en) | Method, device and equipment for analyzing relation between raw material and finished product | |
| CN107358519A (en) | Invoice monitoring method and system | |
| CN114612194A (en) | Product recommendation method and device, electronic equipment and storage medium | |
| CN107480188B (en) | Audit service data processing method and computer equipment | |
| CN115545652A (en) | Comprehensive personnel evaluation method, device, equipment and storage medium | |
| CN112950359B (en) | User identification method and device | |
| CN111966733A (en) | Hotspot knowledge generation method and device | |
| CN110796381A (en) | Method and device for processing evaluation indexes of modeling data, terminal equipment and medium | |
| CN111160929A (en) | Method and device for determining client type | |
| CN118278743A (en) | Method and device for analyzing website operation risk data | |
| CN115271514A (en) | Communication enterprise monitoring method and device, electronic equipment and storage medium | |
| CN115983898A (en) | Electricity charge income prediction method and system based on Markov state transition matrix | |
| CN113393169B (en) | Financial industry transaction system performance index analysis method based on big data technology | |
| CN115237970A (en) | Data prediction method, device, equipment, storage medium and program product | |
| CN111861725B (en) | Three-party data source cost accounting method and system | |
| CN114897345A (en) | Method and device for automatically generating index scores based on employee data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |