CN114866540B - Enterprise data operation management system - Google Patents
Enterprise data operation management system Download PDFInfo
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- CN114866540B CN114866540B CN202210786994.9A CN202210786994A CN114866540B CN 114866540 B CN114866540 B CN 114866540B CN 202210786994 A CN202210786994 A CN 202210786994A CN 114866540 B CN114866540 B CN 114866540B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
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Abstract
The invention discloses an enterprise data operation management system, which acquires and analyzes use data of each account in a plurality of working cycles to acquire use habits of each account, determines a high-probability login monitoring section of the corresponding account, acquires and analyzes the data of each high-probability login monitoring section to acquire an operation file type of the corresponding account in each high-probability login time period, downloads part of file types in advance in a subsequent working process, and avoids the problems of data congestion and unsmooth data transmission caused by the transmission of a large amount of data in a peak period, so that smooth data transmission between a peak operation management module and a cloud platform can be ensured under the conditions of limited network construction cost and limited network transmission capacity, and good data processing experience is provided.
Description
Technical Field
The invention belongs to the technical field of information management, and particularly relates to an enterprise data operation management system.
Background
In the development process of enterprises, the operation data needs to be recorded and sorted, so that the operation conditions of the enterprises in the past period are obtained, diagnosis and analysis are carried out according to the operation conditions, the operation problems in the past period are found, and the future operation decision is assisted.
In the prior art, an enterprise data operation management platform is built based on a cloud platform, when a terminal device logs in an operation management system to perform data query, update and download, a large amount of data can be transmitted between the operation management system and the cloud platform, but due to the influence of network construction cost and enterprise development, peak data congestion is easy to occur, the transmission efficiency is low, and the problem of blocking is easy to occur.
Disclosure of Invention
The invention aims to provide an enterprise data operation management system, which solves the problems that the enterprise data operation management system in the prior art has data congestion and data transmission is blocked in a peak period.
The purpose of the invention can be realized by the following technical scheme:
an enterprise data operations management system, comprising:
the operation management module is used for providing functional units related to enterprise operation management;
the cloud platform is used for storing the data acquired by the operation management module and is in communication connection with the operation management module through the Internet;
the account management module is used for monitoring the account use time and the account execution action;
the data temporary storage module is used for storing the data transmitted by the cloud platform;
the working method of the enterprise data operation management system comprises the following steps:
the method comprises the following steps that firstly, a high-probability login monitoring section corresponding to an account is obtained through an account management module;
specifically, the method comprises the following steps:
dividing the time of a working cycle into n log-in monitoring sections with equal time length;
in a login monitoring section, acquiring login time of an account in the login monitoring section in k working cycles, and sequentially marking the login time as t1, t2,. and tk;
according to the formula
Calculating discrete values L of k login durations, comparing the discrete values L with a preset value L1, if L is larger than L1, considering that the discrete values L of the group of data are too large, and sequentially deleting corresponding ti values from large to small according to the | ti-tp | until L is less than or equal to L1; wherein i is more than or equal to 1 and less than or equal to k, and tp is the average value of ti values participating in the calculation of the corresponding discrete value L;
counting the number of the residual ti values when L is less than or equal to L1, and when the ratio of the number of the residual ti values to k is greater than a preset ratio, considering the corresponding login monitoring section as a high-probability login monitoring section;
calculating in sequence to obtain an account number corresponding to the low-probability login monitoring section and the high-probability login monitoring section;
step two, the account management module monitors the execution action of the account in each high-probability login monitoring section and acquires real download data, real update data and real query data corresponding to one account;
the specific method for acquiring the file corresponding to each execution action comprises the following steps:
acquiring a file type aimed at by an account executing action in a corresponding high-probability login monitoring section, and acquiring a data type of the file type, wherein the data type comprises download data, update data and query data;
in k working cycles, acquiring the times of marking the file type as each data type in the corresponding high-probability login monitoring section, and if the ratio of the times of marking the file type as a certain data type to k is more than or equal to a preset value omega, considering the file type as real download data, real update data or real query data in the corresponding high-probability login monitoring section;
step three, calculating according to the methods in the step one and the step two to obtain the high-probability login monitoring sections corresponding to the accounts and the real download data, the real update data and the real query data in the high-probability login monitoring sections;
and step four, selecting a time period with good network condition within a preset time T1 range before a high-probability login monitoring section corresponding to an account number, downloading real download data and real query data of the account number in the high-probability login monitoring section into a data temporary storage module of the account number binding terminal, logging in an operation management module through the bound terminal, and directly reading the downloaded data by the data temporary storage module after inputting a corresponding viewing instruction.
As a further scheme of the present invention, the k work cycles are traced back to the past by taking the current work cycle as a starting point.
As a further aspect of the present invention, the method for marking the data type in step two is: if the account number executes data downloading on one file type in a working period, the corresponding file type is marked as downloading data, if the account number executes data updating on the file type in the working period, the corresponding file type is marked as updating data, and if the viewing time of the account number on the file type in the working period exceeds the preset time, the corresponding file type is marked as query data.
As a further aspect of the invention, the same file category may be multiple data types at the same time.
As a further scheme of the present invention, in the fourth step, if the data temporary storage module is within the preset time T2, the downloaded data in the data temporary storage module is deleted if the corresponding account does not check the downloaded data;
and after the corresponding account views the data downloaded from the data temporary storage module, deleting the data after the preset time T3.
As a further scheme of the present invention, the step four is followed by:
calculating the activity of an account according to a formula H = beta 1S + beta 2 f, wherein beta 1 and beta 2 are preset coefficients, S is the average online login duration of the account in k working cycles, and f is the average online login frequency of the account in k working cycles;
calculating a priority value Y of one account for viewing a certain file type according to a formula Y = gamma 1 × H + C1+ C2, wherein gamma 1 is a preset coefficient, C1 and C2 are preset parameters, C1 is a preset parameter of different accounts according to job level, and C2 is a preset parameter of the file type to be viewed according to security level;
when the transmission capability is not enough to support normal data transmission, the communication transmission capability is distributed according to the priority value Y of each account, so that the command with high priority value can be completed preferentially and smoothly.
The invention has the beneficial effects that:
(1) according to the method, the use data of each account in a plurality of working cycles is collected and analyzed, the use habit of each account is obtained, the high-probability login monitoring section of the corresponding account is determined, then the data of each high-probability login monitoring section is collected and analyzed, the operation file type of the corresponding account in each high-probability login time period is obtained, and in the subsequent working process, part of file types are downloaded in advance, so that online downloading and checking after login of a user are avoided, the smoothness of data transmission is effectively guaranteed, the requirement on network construction is lowered, and the network construction cost is lowered on the premise that the use experience is not influenced;
(2) according to the method and the device, part of common data is pre-downloaded, so that the problems that data congestion is caused by the fact that a large amount of data is transmitted in a peak period, and data transmission is unsmooth are avoided, and therefore smooth data transmission between the operation management module and the cloud platform in the peak period can be guaranteed under the conditions that the network construction cost is limited and the network transmission capability is limited, and good data processing experience is provided.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a framework structure of an enterprise data operation management platform according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An enterprise data operations management system, as shown in fig. 1, comprising:
the operation management module is used for providing functional units related to enterprise operation management, and the corresponding functional units can be executed after the connection between the terminal and the operation management module is established;
the cloud platform is used for storing the data acquired by the operation management module and is in communication connection with the operation management module through the Internet;
the account management module is used for executing account identification, account permission distribution and account use state monitoring, wherein the account use state comprises account use time and account execution actions;
the data temporary storage module is used for pre-storing data transmitted by the cloud platform, and in one embodiment of the invention, the data temporary storage module is arranged on the terminal equipment by binding the account number with the corresponding terminal equipment;
the working method of the enterprise data operation management system comprises the following steps:
a user logs in an operation management module through terminal equipment and an account management module, monitors the use state of an account through the account management module, and acquires the time period and duration for logging in the operation management module by the account;
specifically, the method comprises the following steps:
dividing the time of a working cycle into n log-in monitoring sections with equal time duration, and sequentially marking the n log-in monitoring sections in the working cycle as D1, D2,. Dn;
taking a login monitoring section as an example, acquiring login time of an account in the login monitoring section in k work cycles, and sequentially marking the login time as t1, t2,. and tk; in an embodiment of the present invention, the k work cycles refer to backtracking k work cycles from the current work cycle as a starting point; in one embodiment of the invention, one working cycle is one day, and the time length of one login monitoring section is 20 min;
according to the formula
Calculating discrete values L of k login durations, comparing the calculated discrete values L with a preset value L1, if L is larger than L1, considering that the discrete values L of the group of data are too large, deleting corresponding ti values and calculating the discrete values L of the remaining ti values in turn according to the sequence of the ti-tp from large to small until L is not larger than L1; wherein i is more than or equal to 1 and less than or equal to k, and tp is the average value of ti values participating in the calculation of the corresponding discrete value L;
counting the number of the residual ti values when L is less than or equal to L1, when the ratio of the number of the residual ti values to k is less than or equal to a preset ratio, considering the corresponding login monitoring section as a low-probability login monitoring section, and when the ratio of the number of the residual ti values to k is greater than the preset ratio, considering the corresponding login monitoring section as a high-probability login monitoring section
Sequentially calculating whether each login monitoring section belongs to a low-probability login monitoring section or a high-probability login monitoring section for one account;
step two, acquiring each high-probability login monitoring section of an account, monitoring the execution action of the account in each high-probability login monitoring section through an account management module, and acquiring real download data, real update data and real query data corresponding to the account;
the specific method for acquiring the file corresponding to each execution action comprises the following steps:
acquiring a file type aimed at by the account executing action in a corresponding high-probability login monitoring segment; specifically, if the account performs data downloading on one file type in one working cycle, the corresponding file type is marked as downloaded data, if the account performs data updating on one file type in one working cycle, the corresponding file type is marked as updated data, and if the viewing time of the account on one file type in one working cycle exceeds preset time, the corresponding file type is marked as query data;
in k working cycles, acquiring the times of marking the file type as each data type in the corresponding high-probability login monitoring section, and if the ratio of the times of marking the file type as a certain data type to k is more than or equal to a preset value omega, considering the file type as real download data, real update data or real query data in the corresponding high-probability login monitoring section;
the data types comprise download data, update data and query data;
it should be noted that the same file category can be multiple data types at the same time;
in the step, the files are divided into a plurality of subclasses according to factors such as contents, corresponding products, corresponding departments and the like, the files of one subclass can be a comprehensive icon or a group of a plurality of documents, for example, purchasing reimbursement data of one department is used as one subclass;
the execution action comprises data updating, data query and data downloading;
step three, calculating according to the methods in the step one and the step two to obtain a high-probability login monitoring section corresponding to each account and real download data, real update data and real query data in each high-probability login monitoring section;
selecting a time period with good network condition within a preset time T1 range before a high-probability login monitoring section corresponding to an account number, downloading real download data and real query data of the account number in the high-probability login monitoring section into a data temporary storage module of the account number binding terminal device, after the corresponding account number logs in an operation management module through the bound terminal device, inputting a corresponding viewing instruction, and directly reading the downloaded data by the data temporary storage module, thereby avoiding long-time and large-quantity data transmission between the operation management module and a cloud platform;
in the range of the preset time T2, if the corresponding account does not check the data downloaded from the temporary data storage module, the temporary data storage module automatically deletes the downloaded data;
after the data downloaded from the data temporary storage module is checked by the corresponding account and preset time T3, the data temporary storage module automatically deletes the data;
in the step, the problems that data jam and unsmooth data transmission are caused by the fact that a large amount of data are transmitted in a peak period are solved by pre-downloading part of common data, so that smooth data transmission between the peak operation management module and the cloud platform can be guaranteed under the conditions that network construction cost is limited and network transmission capacity is limited, and good data processing experience is provided.
In an embodiment of the present invention, if a situation that a part of files still have insufficient network transmission capability by downloading in advance, it is necessary to assign a priority for instruction execution according to the authority of an account and the confidentiality of a viewed file, and by limiting an instruction with a low priority, enable an instruction with a high priority to be preferentially executed, specifically, the following steps are included:
calculating the activity of an account according to a formula H = beta 1 × S + beta 2 × f, wherein both beta 1 and beta 2 are preset coefficients, S is the average online login duration of the account in k working cycles, and f is the average online login frequency of the account in k working cycles;
calculating a priority value Y of one account for viewing a certain file type according to a formula Y = gamma 1 × H + C1+ C2, wherein gamma 1 is a preset coefficient, C1 and C2 are preset parameters, C1 is a preset parameter of different accounts according to job level, and C2 is a preset parameter of the file type to be viewed according to security level;
when the network state is poor, the communication transmission capability is distributed according to the priority value Y of each account, so that the instruction with high priority value can be completed preferentially and smoothly.
The invention acquires and analyzes the use data of each account in k working cycles, acquires the use habit of each account, determines the commonly used login monitoring section of the corresponding account, acquires and analyzes the data of each commonly used login monitoring section, acquires the operation file type of the corresponding account in each high-probability login time section, downloads part of the file types in advance in the subsequent working process, and avoids online downloading and checking after the user logs in, thereby effectively ensuring the smoothness of data transmission and reducing the requirement on network construction.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is illustrative and explanatory only and is not intended to be exhaustive or to limit the invention to the precise embodiments described, and various modifications, additions, and substitutions may be made by those skilled in the art without departing from the scope of the invention or exceeding the scope of the claims.
Claims (6)
1. An enterprise data operations management system, comprising:
the operation management module is used for providing a functional unit related to enterprise operation management;
the cloud platform is used for storing the data acquired by the operation management module and is in communication connection with the operation management module through the Internet;
the account management module is used for monitoring the account use time and the account execution action;
the data temporary storage module is used for storing the data transmitted by the cloud platform;
the working method of the enterprise data operation management system comprises the following steps:
the method comprises the following steps that firstly, a high-probability login monitoring section corresponding to an account is obtained through an account management module;
specifically, the method comprises the following steps:
dividing the time of a working cycle into n log-in monitoring sections with equal time length;
in a login monitoring section, acquiring login time of an account in the login monitoring section in k working cycles, and sequentially marking the login time as t1, t2,. and tk;
according to the formula
Calculating discrete values L of k login durations, comparing the discrete values L with a preset value L1, if L is larger than L1, considering that the discrete values L of the group of data are too large, and sequentially deleting corresponding ti values from large to small according to the | ti-tp | until L is less than or equal to L1; wherein i is more than or equal to 1 and less than or equal to k, and tp is the average value of ti values participating in the calculation of the corresponding discrete value L;
counting the number of the residual ti values when L is less than or equal to L1, and when the ratio of the number of the residual ti values to k is greater than a preset ratio, considering the corresponding login monitoring section as a high-probability login monitoring section;
calculating in sequence to obtain an account number corresponding to the low-probability login monitoring section and the high-probability login monitoring section;
step two, the account management module monitors the execution action of the account in each high-probability login monitoring section, and acquires real download data, real update data and real query data corresponding to one account;
the specific method for acquiring the file corresponding to each execution action comprises the following steps:
acquiring a file type aimed at by an account executing action in a corresponding high-probability login monitoring section, and acquiring a data type of the file type, wherein the data type comprises download data, update data and query data;
in k working cycles, acquiring the times of marking the file type as each data type in the corresponding high-probability login monitoring section, and if the ratio of the times of marking the file type as a certain data type to k is more than or equal to a preset value omega, considering the file type as real download data, real update data or real query data in the corresponding high-probability login monitoring section;
step three, calculating according to the methods in the step one and the step two to obtain the high-probability login monitoring sections corresponding to the accounts and the real download data, the real update data and the real query data in the high-probability login monitoring sections;
and step four, selecting a time period with good network condition within a preset time T1 range before a high-probability login monitoring section corresponding to an account number, downloading real download data and real query data of the account number in the high-probability login monitoring section to a data temporary storage module of the account number binding terminal device, logging in an operation management module through the bound terminal device when the corresponding account number passes through the bound terminal device, and directly reading the downloaded data by the data temporary storage module after inputting a corresponding viewing instruction.
2. The system of claim 1, wherein the k work cycles are traced back to the past by taking a current work cycle as a starting point.
3. The system of claim 2, wherein the method for marking the data type in step two comprises: if the account number executes data downloading on one file type in a working period, the corresponding file type is marked as downloading data, if the account number executes data updating on the file type in the working period, the corresponding file type is marked as updating data, and if the viewing time of the account number on the file type in the working period exceeds the preset time, the corresponding file type is marked as query data.
4. The system of claim 3, wherein the same file category comprises multiple data types.
5. The enterprise data operation management system according to claim 4, wherein in step four, if the data temporary storage module is within a preset time T2, the downloaded data is deleted if the corresponding account does not check the data downloaded from the data temporary storage module;
and after the corresponding account views the data downloaded from the data temporary storage module, deleting the data after the preset time T3.
6. The enterprise data operation management system of claim 1, further comprising after step four:
calculating the activity of an account according to a formula H = beta 1S + beta 2 f, wherein beta 1 and beta 2 are preset coefficients, S is the average online login duration of the account in k working cycles, and f is the average online login frequency of the account in k working cycles;
calculating a priority value Y of one account for viewing a certain file type according to a formula Y = gamma 1 × H + C1+ C2, wherein gamma 1 is a preset coefficient, C1 and C2 are preset parameters, C1 is a preset parameter of different accounts according to job level, and C2 is a preset parameter of the file type to be viewed according to security level;
when the transmission capability is not enough to support normal data transmission, the communication transmission capability is distributed according to the priority value Y of each account, so that the command with high priority value can be completed preferentially and smoothly.
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| CN113919927A (en) * | 2021-10-13 | 2022-01-11 | 集美大学 | Auditing platform based on data processing |
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| CN111741112A (en) * | 2020-06-22 | 2020-10-02 | 中国平安财产保险股份有限公司 | File downloading method, device, equipment and storage medium based on artificial intelligence |
| CN113919927A (en) * | 2021-10-13 | 2022-01-11 | 集美大学 | Auditing platform based on data processing |
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