CN116186142A - Cross-border tax data service management system based on Internet of things - Google Patents
Cross-border tax data service management system based on Internet of things Download PDFInfo
- Publication number
- CN116186142A CN116186142A CN202310215954.3A CN202310215954A CN116186142A CN 116186142 A CN116186142 A CN 116186142A CN 202310215954 A CN202310215954 A CN 202310215954A CN 116186142 A CN116186142 A CN 116186142A
- Authority
- CN
- China
- Prior art keywords
- preset
- database
- data
- memory
- specific gravity
- 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.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 claims abstract description 109
- 238000007726 management method Methods 0.000 claims abstract description 21
- 230000005484 gravity Effects 0.000 claims description 102
- 230000005540 biological transmission Effects 0.000 claims description 93
- 238000012937 correction Methods 0.000 claims description 33
- 238000013500 data storage Methods 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 8
- 230000006855 networking Effects 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 11
- 230000000007 visual effect Effects 0.000 abstract description 5
- 239000000047 product Substances 0.000 description 21
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 206010033799 Paralysis Diseases 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 239000013589 supplement Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012015 optical character recognition Methods 0.000 description 1
Images
Classifications
-
- 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
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- 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
- G06F16/25—Integrating or interfacing systems involving database management systems
-
- 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
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/282—Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/906—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/123—Tax preparation or submission
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Software Systems (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Telephonic Communication Services (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to the technical field of the Internet of things, in particular to a cross-border tax data service management system based on the Internet of things. The first analysis unit divides the data into corresponding first-level databases according to the first-level tags in the single data, determines third-level tags according to the data content and the service, and judges to divide the data into corresponding second-level databases according to the product type fitting degree in the third-level tags of the data; the first analysis unit classifies the submitted document in detail so that the observation of the historical data is more visual when a user invokes the target data; the second analysis unit adjusts the reserved memory proportion of the secondary database to a corresponding value according to the average frequency and the memory growth rate of the historical data received by the single secondary database in a preset period, and determines a planning mode aiming at the memory of the server according to the duty ratio of the memory of the adjusted secondary database to the total memory of the primary database, so that the processing efficiency of the data to be processed is improved, and the safe and stable operation of the system is ensured.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to a cross-border tax data service management system based on the Internet of things.
Background
Today, where global commerce is continually deepened, cross-border purchasing of goods is one of many merchant or consumer options. Through cross-border buying and selling, not only can richer commodities be provided or enjoyed, but also relatively objective income can be brought to merchants, and certain tax fee is usually required to be paid to relevant countries in realizing cross-border buying and selling.
Chinese patent publication No.: CN114154473a. The invention discloses a cross-border tax system control method, a device, a system and a readable storage medium, relating to an electronic commerce technology, wherein the method comprises the following steps: acquiring user portrait information; calling a corresponding data supplement list according to the user image information; receiving submitted data which is supplemented by a user according to the data supplement list by using a preset server; performing target detection on the submitted data to classify the documents so as to obtain target documents of all types; performing character content recognition on the target document by utilizing an optical character recognition technology to acquire tax payment country and preset type information; calling a preset submitting mode corresponding to the tax payment country from a preset database according to the tax payment country; filling the preset type information into the corresponding position of the preset template document to obtain a document to be submitted, wherein the document to be submitted at least comprises a tax application form; submitting the document to be submitted by using the called preset submitting mode; it follows that the following problems are present: the submitted documents are not classified in detail, visual observation of historical data is affected when a user invokes target data, and the storage space of the data is not managed, so that the system running speed is slow or even the system is paralyzed due to unreasonable memory allocation, the processing efficiency of the data to be processed is greatly affected, and a certain hidden danger is caused to the safe and stable operation of the system.
Disclosure of Invention
Therefore, the invention provides a cross-border tax data service management system based on the Internet of things, which is used for solving the problems that in the prior art, submitted documents are not classified in detail, visual observation of historical data is affected when a user invokes target data, and the storage space of the data is not managed, so that the running speed of a system is slow and even the system is paralyzed due to unreasonable memory allocation, the processing efficiency of data to be processed is greatly affected, and a certain hidden trouble is caused to the safe and stable operation of the system.
In order to achieve the above object, the present invention provides a cross-border tax data service management system based on the internet of things, comprising:
the server comprises a plurality of primary databases for classifying data storage by using the primary storage space, and each primary database comprises a plurality of secondary databases for classifying data storage by using the secondary storage space;
the first analysis unit is connected with the server and is used for generating a plurality of three-level labels according to the product keywords of the data and the keywords of the product stored in the cloud platform, arranging the three-level labels in a descending order according to the superposition quantity of the product keywords and the keywords of the product stored in the cloud platform, and selecting a single three-level label with the largest superposition quantity of the keywords as a second-level label of the data so as to transmit the data into a corresponding second-level database;
The second analysis unit is connected with the first analysis unit and is used for adjusting the reserved memory proportion of the secondary database to a first corresponding proportion according to the average frequency of the historical data received by a single secondary database in a preset period, secondarily adjusting the reserved memory proportion of the secondary database to a second corresponding proportion according to the acquired memory growth rate in the preset duration of the secondary database, adjusting the transmission rate of the corresponding secondary database to a corresponding transmission rate according to the data storage amount of the corresponding secondary database in the preset period, and determining a planning mode for the memory of the server according to the ratio of the memory of the adjusted secondary database to the total memory of the primary database;
the proportion of reserved memory of the secondary database is the proportion of reserved memory of the secondary database accounting for the total memory of the primary database.
Further, the first analysis unit generates a first-level label according to single data content input by a user, the first analysis unit generates a plurality of third-level labels according to the superposition amount of product keywords of the data and keywords of the product stored in the cloud platform, the third-level labels are arranged in a descending order according to the superposition amount of the product keywords and the keywords of the product stored in the cloud platform, and a single third-level label with the largest superposition amount of the keywords is selected as a second-level label of the data.
Further, the second analysis unit determines a setting mode of the reserved memory of the secondary database corresponding to the single secondary label to the specific weight of the reserved memory of the secondary database corresponding to the single secondary label to the total memory of the primary database according to the total data amount in the single secondary database, wherein,
the first setting mode is that the second analysis unit takes the first preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the first setting mode meets the condition that the total data amount in a single secondary database is smaller than or equal to a first preset total data amount;
the second setting mode is that the second analysis unit takes the second preset specific gravity as the specific gravity of the reserved memory of the secondary database in the total memory of the primary database; the second setting mode meets the condition that the total data volume in a single secondary database is larger than the first preset data volume and smaller than or equal to the second preset total data volume;
the third setting mode is that the second analysis unit takes the third preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the third setting mode satisfies that the total data amount in the single secondary database is larger than the second preset total data amount.
Further, the second analysis unit determines a regulation mode of the proportion of the reserved memory of the secondary database to the total memory of the primary database according to the average frequency of the historical data received by the single secondary database in a preset period, wherein,
The first specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to a preset specific gravity; the first correction mode meets the condition that the average frequency is smaller than or equal to a first preset average frequency;
the second specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to the first specific gravity by using a first specific gravity adjusting coefficient; the second correction mode meets the condition that the average frequency is larger than the first preset average frequency and smaller than or equal to the second preset average frequency;
the third specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to a second specific gravity by using a second specific gravity adjusting coefficient; the third correction mode satisfies that the average frequency is larger than the second preset average frequency;
wherein the first preset average frequency is smaller than the second preset average frequency.
Further, the second analysis unit determines whether the running state of the server accords with the judging mode of the preset standard according to the data transmission rate of the single data in the acquired primary database to the corresponding secondary database, wherein,
the first judging mode is that the second analyzing unit judges that the running state of the server meets the preset standard, and the second analyzing unit controls the server to run with the current running parameters; the first judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is larger than a second preset transmission rate;
The second judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located has fluctuation, and detects the transmission rate of the server aiming at other secondary databases so as to further judge the running state of the server; the second judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the second preset transmission rate and larger than the first preset transmission rate;
the third judging mode is that the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the specific gravity to a corresponding value according to the increasing rate of the data quantity in the secondary database; the third judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the first preset transmission rate;
wherein the first preset transmission rate is smaller than the preset second transmission rate.
Further, the second analysis unit determines a determination mode of whether the network environment where the server is located meets the requirements according to the obtained difference value between the average transmission rate of the primary database to the secondary database and the average transmission rate under the second preset condition, wherein,
The first network environment judging mode is that the second analyzing unit judges that the network environment where the server is located does not have fluctuation, the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the proportion to a corresponding value according to the increasing rate of the data quantity in the secondary database; the first network environment meets the condition that the difference value between the transmission rate of the primary database aiming at the secondary database and the average transmission rate is smaller than or equal to a first preset difference value;
the second network environment judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located does not have fluctuation, and secondarily judges the network environment where the server is located according to the data storage quantity of a single secondary database in a preset period; the second network environment meets the condition that the difference value between the transmission rate and the average transmission rate of the primary database aiming at the secondary database is smaller than or equal to a second preset difference value and larger than the first preset difference value;
the third network environment judging mode is that the second analyzing unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; the third network environment satisfies that the difference between the transmission rate of the primary database for the secondary database and the average transmission rate is larger than the second preset difference;
Wherein the first preset difference value is smaller than the second preset difference value;
and the second preset condition is that the second analysis unit finishes judging the running state of the server by using a second judging mode.
Further, the second analyzing unit sequentially obtains the data storage space Sj of the server to the jth secondary database in a preset period under a third preset condition, calculates the network environment score G according to the Sj, and determines a correction coefficient adjusting mode of the transmission rate of the primary database to the jth secondary database according to the Sj, wherein,
the first correction coefficient is adjusted by the second analysis unit using a first preset coefficient alpha j1 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the first correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a first preset storage amount;
the second correction coefficient is adjusted by the second analysis unit using a second preset coefficient alpha j2 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the second correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a second preset storage amount and larger than the first preset storage amount;
The third correction coefficient is adjusted by the second analysis unit using a third preset coefficient alpha j3 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the third correction coefficient adjusting mode meets the condition that the data storage amount is larger than the second preset storage amount;
the first preset storage amount is smaller than the second preset storage amount;
the second analysis unit is based on alpha ji Obtaining the network environment score G and setting
Wherein i=1, 2,3, v 0 Data transmission rate, V j The method comprises the steps of setting a beta=100 for the transmission rate of a first-level database to the j-th second-level database data, wherein j is the total number of the second-level labels in the first-level database, j=1, 2,3 … n and beta is a grading correction parameter;
and the third preset condition is that the second analysis unit completes the judgment of the network environment where the server is located by using a second network environment judgment mode.
Further, the second analyzing unit determines a secondary judging mode of whether the network environment fluctuates according to the network environment score G under the third preset condition, wherein,
the second analysis unit judges that the network environment where the server is located does not have fluctuation, the second analysis unit judges that the running state of the server does not accord with a preset standard, and the second analysis unit adjusts the data quantity in the secondary database to a corresponding value according to the increasing rate of the data quantity; the first network environment secondary judgment mode meets the condition that the network environment score is larger than a preset standard network environment score;
The second analysis unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; and the second network environment secondary judgment mode meets the condition that the network environment score is smaller than or equal to the preset standard network environment score.
Further, the second analysis unit determines a secondary adjustment mode of the specific gravity of the memory of the secondary database and the total memory of the primary database according to the memory growth rate in the preset duration of the obtained secondary database under a fourth preset condition, wherein,
the first specific gravity secondary regulation mode is that the second analysis unit regulates the specific gravity to a preset specific gravity; the first adjustment mode meets the condition that the memory growth rate is smaller than or equal to a first preset memory growth rate;
the second specific gravity secondary adjustment mode is that the second analysis unit uses the first specific gravity secondary adjustment coefficient to secondarily adjust the specific gravity to a third specific gravity; the second adjustment mode satisfies that the memory growth rate is smaller than or equal to a second preset memory growth rate and larger than the first memory growth rate;
the third specific gravity secondary adjustment mode is that the second analysis unit uses a second specific gravity secondary adjustment coefficient to secondarily adjust the specific gravity to a fourth specific gravity; the third adjustment mode satisfies that the memory growth rate is greater than the second preset memory growth rate;
Wherein the first predetermined memory growth rate is less than the second predetermined memory growth rate;
and the fourth preset condition is that the second analysis unit judges that the running state of the server does not accord with a preset standard.
Further, the second analysis unit determines a planning mode for the memory of the server according to the ratio of the memory of the adjusted secondary database to the total memory of the primary database under the fourth preset condition, wherein,
the first planning mode is that the second analysis unit uses the second corresponding proportion as the proportion of the reserved memory of the secondary database to the total memory of the primary database; the first planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is smaller than or equal to the preset standard ratio;
the second planning mode is that the second analysis unit uses preset adjustment parameters to adjust the proportion of the storage space of the primary database corresponding to the secondary database to the storage space of the server to a corresponding value; the second planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is larger than the preset standard ratio.
Compared with the prior art, the invention has the beneficial effects that the invention relates to the technical field of the Internet of things, in particular to a cross-border tax data service management system based on the Internet of things. The first analysis unit divides the data into corresponding first-level databases according to the first-level tags in the single data, determines third-level tags according to the data content and the service, and judges to divide the data into corresponding second-level databases according to the product type fitting degree in the third-level tags of the data; the first analysis unit classifies the submitted document in detail so that the observation of the historical data is more visual when a user invokes the target data; the second analysis unit adjusts the reserved memory proportion of the secondary database to a corresponding value according to the average frequency and the memory growth rate of the historical data received by the single secondary database in a preset period, and determines a planning mode aiming at the memory of the server according to the duty ratio of the memory of the adjusted secondary database to the total memory of the primary database, so that the processing efficiency of the data to be processed is improved, and the safe and stable operation of the system is ensured.
Further, the first analysis unit generates a first-level label according to single data content input by a user, the first analysis unit generates a plurality of third-level labels according to the superposition amount of product keywords of the data and keywords of the product stored in the cloud platform, the third-level labels are arranged in descending order according to the superposition amount of the product keywords and the keywords of the product stored in the cloud platform, a single third-level label with the largest superposition amount of the keywords is selected as a second-level label of the data, the first analysis unit transmits the data to a corresponding database according to the first-level label, and the first database is controlled to transmit the data to the second-level database corresponding to the second-level label of the data, so that a plurality of data submitted by the user are respectively stored in the corresponding database, and visual observation of historical data is further improved when the user calls the target data while the detailed classification of the plurality of data is guaranteed.
Further, the second analysis unit adjusts the proportion of the reserved memory of the secondary database corresponding to the data to the total memory of the primary database to a corresponding value according to the total data amount stored in the single secondary database, and the second analysis unit manages the storage space of the data according to the actual use condition of the server, so that the processing efficiency of the server to the data to be processed and the stability of the system operation are further improved while the preset memory of the secondary database is ensured to be set to be a reasonable range.
Further, the second analysis unit corrects the proportion of the reserved memory of the secondary database to the total memory of the primary database to a corresponding value according to the average frequency of the historical data received by the single secondary database in a preset period, the second analysis unit further accurately corrects the reserved memory of the secondary database, the reserved memory of the secondary database is determined according to the operating parameters of the server in multiple angles, and the processing efficiency of the server to the data to be processed and the stability of the system operation are further improved while the preset memory of the secondary database is ensured to be set to be in a reasonable range.
Further, the second analysis unit determines the running state of the server according to the data transmission rate of the single data to the corresponding secondary database, and when the second analysis unit determines that the transmission rate is greater than a second preset transmission rate, the running state of the server is determined to meet the preset standard; when the second analysis unit judges that the transmission rate is smaller than or equal to the second preset transmission rate and larger than the first preset transmission rate, the second analysis unit preliminarily judges that the reason for the slow data transmission rate is that the network has fluctuation; when the transmission rate of the primary database is smaller than or equal to the first preset transmission rate, the central control module judges that the running state of the server does not meet the preset standard, and the reason why the data actually occupy the memory in the secondary database is larger than the preset occupied memory of the secondary database is that the data actually occupy the memory of the secondary database, and the running state of the server is monitored, so that the reason why the running state of the server does not meet the preset standard is found in time, and meanwhile, the processing efficiency of the server to the data to be processed and the running stability of the system are further improved.
Further, when the second analysis unit preliminarily judges that the network environment where the server is located has fluctuation, the average transmission rate of the primary database to the secondary database is obtained to further fluctuation whether the network environment where the server is located has fluctuation, and the processing efficiency of the server to the data to be processed and the stability of system operation are further improved while the reasons that the operation effect of the server does not meet the preset standard are determined in all aspects.
Further, the second analysis unit calculates and obtains the network score according to a preset scoring formula according to the data storage capacity, the data transmission rate of the server to a single secondary database in a preset period and the data transmission rate of the primary database to the j secondary database, so that the processing efficiency of the server to the data to be processed and the stability of the system operation are further improved while the condition of the network environment where the server is located is ensured to be accurately determined.
Further, the further judgment of the running state of the server is completed according to the grading, when the grading of the network environment is larger than the grading of the network environment with the preset standard, the second analysis unit judges that the network environment where the server is located does not fluctuate, the running state of the server is judged to be not in accordance with the preset standard by the second analysis unit, and the reasons that the running state of the server is not in accordance with the preset standard are that the actually occupied memory of the data in the secondary database is larger than the preset occupied memory of the secondary database, when the grading of the network environment is smaller than the grading of the network environment with the preset standard, the second analysis unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment.
Further, the second analysis unit adjusts the ratio of the memory of the secondary database to the total memory of the primary database to a corresponding value according to the memory growth rate of the secondary database within a preset time period under the condition that the running state of the server is not in accordance with a preset standard, and then adaptively adjusts the memory of the secondary database according to the characteristics of the data stored by a user, so that the processing efficiency of the server to the data to be processed and the running stability of the system are further improved while the running state of the server is maintained in an optimal state.
Further, the second analysis unit determines whether to adjust the proportion of the storage space of the primary database corresponding to the secondary database to the storage space of the server to a corresponding value according to the ratio of the memory of the adjusted secondary database to the total memory of the primary database, so that the operation state of the server is maintained in an optimal state, and the processing efficiency of the server to the data to be processed and the stability of the system operation are further improved.
Drawings
FIG. 1 is a block diagram of a cross-border tax data service management system based on the Internet of things in an embodiment of the invention;
FIG. 2 is a flow chart of a specific gravity setting mode of a cross-border tax data service management system based on the Internet of things according to the embodiment of the invention;
FIG. 3 is a flow chart of a specific gravity adjustment mode of a cross-border tax data service management system based on the Internet of things according to an embodiment of the invention;
fig. 4 is a flowchart of a method for determining a server running state of a cross-border tax data service management system based on the internet of things according to an embodiment of the invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, fig. 2, fig. 3, and fig. 4, which are respectively a structural block diagram, a specific gravity setting mode flowchart, a specific gravity adjusting mode flowchart, and a flowchart of a method for determining an operation state of a server according to an embodiment of the present invention; the embodiment of the invention discloses a cross-border tax data service management system based on the Internet of things, which comprises the following steps:
the server comprises a plurality of primary databases for classifying data storage by using the primary storage space, and each primary database comprises a plurality of secondary databases for classifying data storage by using the secondary storage space;
The first analysis unit is connected with the server and is used for generating a plurality of three-level labels according to the product keywords of the data and the keywords of the product stored in the cloud platform, arranging the three-level labels in a descending order according to the superposition quantity of the product keywords and the keywords of the product stored in the cloud platform, and selecting a single three-level label with the largest superposition quantity of the keywords as a second-level label of the data so as to transmit the data into a corresponding second-level database;
the second analysis unit is connected with the first analysis unit and is used for adjusting the reserved memory proportion of the secondary database to a first corresponding proportion according to the average frequency of the historical data received by a single secondary database in a preset period, secondarily adjusting the reserved memory proportion of the secondary database to a second corresponding proportion according to the acquired memory growth rate in the preset duration of the secondary database, adjusting the transmission rate of the corresponding secondary database to a corresponding transmission rate according to the data storage amount of the corresponding secondary database in the preset period, and determining a planning mode for the memory of the server according to the ratio of the memory of the adjusted secondary database to the total memory of the primary database;
The proportion of reserved memory of the secondary database is the proportion of reserved memory of the secondary database accounting for the total memory of the primary database.
Specifically, the first analysis unit generates a first-level label according to single data content input by a user, the first analysis unit generates a plurality of third-level labels according to the superposition amount of product keywords of the data and keywords of the product stored in the cloud platform, the third-level labels are arranged in a descending order according to the superposition amount of the product keywords and the keywords of the product stored in the cloud platform, and a single third-level label with the largest superposition amount of the keywords is selected as a second-level label of the data.
Specifically, the second analysis unit determines a setting mode of the reserved memory of the secondary database corresponding to the single secondary label accounting for the proportion of the total memory of the primary database according to the total data amount in the single secondary database, wherein,
the first setting mode is that the second analysis unit takes the first preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the first setting mode meets the condition that the total data amount in a single secondary database is smaller than or equal to a first preset total data amount;
The second setting mode is that the second analysis unit takes the second preset specific gravity as the specific gravity of the reserved memory of the secondary database in the total memory of the primary database; the second setting mode meets the condition that the total data volume in a single secondary database is larger than the first preset data volume and smaller than or equal to the second preset total data volume;
the third setting mode is that the second analysis unit takes the third preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the third setting mode satisfies that the total data amount in a single secondary database is larger than the second preset total data amount;
wherein the first preset total data amount is smaller than the second preset total data amount.
Specifically, the second analysis unit determines an adjustment mode of the specific weight of the reserved memory of the secondary database to the total memory of the primary database according to the average frequency of the historical data received by the single secondary database in a preset period, wherein,
the first specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to a preset specific gravity; the first correction mode meets the condition that the average frequency is smaller than or equal to a first preset average frequency;
the second specific gravity adjusting mode is that the second analyzing unit uses the first specific gravity adjusting coefficient to adjust the specific gravity to be higher than the first specific gravity; the second correction mode meets the condition that the average frequency is larger than the first preset average frequency and smaller than or equal to the second preset average frequency;
The third specific gravity adjusting mode is that the second analyzing unit uses a second specific gravity adjusting coefficient to adjust the specific gravity to be higher than the second specific gravity; the third correction mode satisfies that the average frequency is larger than the second preset average frequency;
the first preset average frequency is smaller than the second preset average frequency, and the first specific gravity adjusting coefficient is smaller than the second specific gravity adjusting coefficient.
Specifically, the second analysis unit determines whether the running state of the server accords with the judging mode of the preset standard according to the data transmission rate of single data in the acquired primary database to the corresponding secondary database, wherein,
the first judging mode is that the second analyzing unit judges that the running state of the server meets the preset standard, and the second analyzing unit controls the server to run with the current running parameters; the first judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is larger than a second preset transmission rate;
the second judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located has fluctuation, and detects the transmission rate of the server aiming at other secondary databases so as to further judge the running state of the server; the second judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the second preset transmission rate and larger than the first preset transmission rate;
The third judging mode is that the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the specific gravity to a corresponding value according to the increasing rate of the data quantity in the secondary database; the third judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the first preset transmission rate;
wherein the first preset transmission rate is smaller than the preset second transmission rate.
Specifically, the second analysis unit determines, under a second preset condition, a determination mode of whether the network environment in which the server is located meets the requirement according to the obtained difference value between the average transmission rate of the primary database to the secondary database and the average transmission rate, wherein,
the first network environment judging mode is that the second analyzing unit judges that the network environment where the server is located does not have fluctuation, the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the proportion to a corresponding value according to the increasing rate of the data quantity in the secondary database; the first network environment meets the condition that the difference value between the transmission rate of the primary database aiming at the secondary database and the average transmission rate is smaller than or equal to a first preset difference value;
The second network environment judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located does not have fluctuation, and secondarily judges the network environment where the server is located according to the data storage quantity of a single secondary database in a preset period; the second network environment meets the condition that the difference value between the transmission rate and the average transmission rate of the primary database aiming at the secondary database is smaller than or equal to a second preset difference value and larger than the first preset difference value;
the third network environment judging mode is that the second analyzing unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; the third network environment satisfies that the difference between the transmission rate of the primary database for the secondary database and the average transmission rate is larger than the second preset difference;
wherein the first preset difference value is smaller than the second preset difference value;
and the second preset condition is that the second analysis unit finishes judging the running state of the server by using a second judging mode.
Specifically, the second analysis unit sequentially acquires the data storage space Sj of the server to the jth secondary database in a preset period under a third preset condition, calculates the network environment score G according to the Sj, determines a correction coefficient adjustment mode of the transmission rate of the primary database to the jth secondary database according to the Sj, wherein,
The first correction coefficient is adjusted by the second analysis unit using a first preset coefficient alpha j1 The transmission rate of the first-level database to the j-th second-level database is increased to a corresponding value; the first correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a first preset storage amount;
the second correction coefficient is adjusted by the second analysis unit using a second preset coefficient alpha j2 Couple the first level database to the j second levelThe transmission rate of the level database is increased to a corresponding value; the second correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a second preset storage amount and larger than the first preset storage amount;
the third correction coefficient is adjusted by the second analysis unit using a third preset coefficient alpha j3 Reducing the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the third correction coefficient adjusting mode meets the condition that the data storage amount is larger than the second preset storage amount;
the first preset storage amount is smaller than the second preset storage amount, and the first preset coefficient is larger than the second preset coefficient and larger than the third preset coefficient;
the second analysis unit is based on alpha ji Obtaining the network environment score G and setting
Wherein i=1, 2,3, v 0 Data transmission rate, V j The method comprises the steps of setting a beta=100 for the transmission rate of a first-level database to the j-th second-level database data, wherein j is the total number of the second-level labels in the first-level database, j=1, 2,3 … n and beta is a grading correction parameter;
and the third preset condition is that the second analysis unit completes the judgment of the network environment where the server is located by using a second network environment judgment mode.
Specifically, the second analysis unit determines a secondary determination mode of whether the network environment fluctuates according to the network environment score G under the third preset condition, wherein,
the second analysis unit judges that the network environment where the server is located does not have fluctuation, the second analysis unit judges that the running state of the server does not accord with a preset standard, and the second analysis unit adjusts the specific gravity to a corresponding value according to the increasing rate of the data quantity in the secondary database; the first network environment secondary judgment mode meets the condition that the network environment score is larger than a preset standard network environment score;
the second network environment secondary judgment mode is that the second analysis unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; and the second network environment secondary judgment mode meets the condition that the network environment score is smaller than or equal to the preset standard network environment score.
Specifically, the second analysis unit determines a secondary adjustment mode of the specific gravity of the memory of the secondary database and the total memory of the primary database according to the memory growth rate in the preset duration of the obtained secondary database under a fourth preset condition, wherein,
the first specific gravity secondary regulation mode is that the second analysis unit regulates the specific gravity to a preset specific gravity; the first adjustment mode meets the condition that the memory growth rate is smaller than or equal to a first preset memory growth rate;
the second specific gravity secondary regulation mode is that the second analysis unit uses the first specific gravity secondary regulation coefficient to secondarily regulate the specific gravity to a third specific gravity; the second adjustment mode satisfies that the memory growth rate is smaller than or equal to a second preset memory growth rate and larger than the first memory growth rate;
the third specific gravity secondary regulation mode is that the second analysis unit uses a second specific gravity secondary regulation coefficient to secondarily regulate the specific gravity to a fourth specific gravity; the third adjustment mode satisfies that the memory growth rate is greater than the second preset memory growth rate;
the first preset memory growth rate is smaller than the second preset memory growth rate, and the first specific gravity secondary adjustment coefficient is smaller than the second specific gravity secondary adjustment coefficient;
And the fourth preset condition is that the second analysis unit judges that the running state of the server does not accord with a preset standard.
Specifically, the second parsing unit determines a planning mode for the memory of the server according to the duty ratio of the memory of the adjusted secondary database and the total memory of the primary database under the fourth preset condition, wherein,
the first planning mode is that the second analysis unit uses the second corresponding proportion as the proportion of the reserved memory of the secondary database to the total memory of the primary database; the first planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is smaller than or equal to the preset standard ratio;
the second planning mode is that the second analysis unit uses preset adjustment parameters to adjust the proportion of the storage space of the primary database corresponding to the secondary database to the storage space of the server to a corresponding value; the second planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is larger than the preset standard ratio.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a cross-border tax data service management system based on thing networking which characterized in that includes:
the server comprises a plurality of primary databases for classifying and storing data by using the primary storage space, and each primary database comprises a plurality of secondary databases for classifying and storing data by using the secondary storage space;
the first analysis unit is connected with the server and is used for generating a plurality of three-level labels according to the product keywords of the data and the keywords of the product stored in the cloud platform, arranging the three-level labels in a descending order according to the superposition quantity of the product keywords and the keywords of the product stored in the cloud platform, and selecting a single three-level label with the largest superposition quantity of the keywords as a second-level label of the data so as to transmit the data into a corresponding second-level database;
The second analysis unit is connected with the first analysis unit and is used for adjusting the reserved memory proportion of the secondary database to a first corresponding proportion according to the average frequency of the historical data received by a single secondary database in a preset period, secondarily adjusting the reserved memory proportion of the secondary database to a second corresponding proportion according to the acquired memory growth rate in the preset duration of the secondary database, adjusting the transmission rate of the corresponding secondary database to a corresponding transmission rate according to the data storage amount of the corresponding secondary database in the preset period, and determining a planning mode for the memory of the server according to the ratio of the memory of the adjusted secondary database to the total memory of the primary database;
the proportion of reserved memory of the secondary database is the proportion of reserved memory of the secondary database accounting for the total memory of the primary database.
2. The internet of things-based cross-border tax data service management system of claim 1, wherein the first analysis unit generates a first-level tag according to single data content input by a user, the first analysis unit generates a plurality of third-level tags according to the superposition amount of product keywords of the data and keywords of the product stored in a cloud platform, the third-level tags are arranged in descending order according to the superposition amount of the product keywords and the keywords of the product stored in the cloud platform, and a single third-level tag with the largest superposition amount of the keywords is selected as a second-level tag of the data.
3. The internet of things-based cross-border tax data service management system of claim 2, wherein the second parsing unit determines a setting manner of a specific gravity of reserved memory of the secondary database corresponding to the single secondary tag to the total memory of the primary database according to the total data amount in the single secondary database,
the first setting mode is that the second analysis unit takes the first preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the first setting mode meets the condition that the total data amount in a single secondary database is smaller than or equal to a first preset total data amount;
the second setting mode is that the second analysis unit takes the second preset specific gravity as the specific gravity of the reserved memory of the secondary database in the total memory of the primary database; the second setting mode meets the condition that the total data volume in a single secondary database is larger than the first preset data volume and smaller than or equal to the second preset total data volume;
the third setting mode is that the second analysis unit takes the third preset specific gravity as the specific gravity of the reserved memory of the secondary database accounting for the total memory of the primary database; the third setting mode satisfies that the total data amount in the single secondary database is larger than the second preset total data amount.
4. The Internet of things-based cross-border tax data service management system as claimed in claim 3, wherein the second parsing unit determines a manner of adjusting a specific gravity of reserved memory of the secondary database to a total memory of the primary database according to an average frequency of historical data received by the acquired single secondary database within a preset period,
the first specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to a preset specific gravity; the first correction mode meets the condition that the average frequency is smaller than or equal to a first preset average frequency;
the second specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to the first specific gravity by using a first specific gravity adjusting coefficient; the second correction mode meets the condition that the average frequency is larger than the first preset average frequency and smaller than or equal to the second preset average frequency;
the third specific gravity adjusting mode is that the second analyzing unit adjusts the specific gravity to a second specific gravity by using a second specific gravity adjusting coefficient; the third correction mode satisfies that the average frequency is larger than the second preset average frequency;
wherein the first preset average frequency is smaller than the second preset average frequency.
5. The internet of things-based cross-border tax data service management system of claim 4, wherein the second parsing unit determines whether the running state of the server meets a preset standard according to the data transmission rate of the single data in the acquired primary database to the corresponding secondary database, wherein,
The first judging mode is that the second analyzing unit judges that the running state of the server meets the preset standard, and the second analyzing unit controls the server to run with the current running parameters; the first judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is larger than a second preset transmission rate;
the second judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located has fluctuation, and detects the transmission rate of the server aiming at other secondary databases so as to further judge the running state of the server; the second judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the second preset transmission rate and larger than the first preset transmission rate;
the third judging mode is that the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the specific gravity to a corresponding value according to the increasing rate of the data quantity in the secondary database; the third judging mode meets the condition that the transmission rate of the primary database aiming at the secondary database is smaller than or equal to the first preset transmission rate;
wherein the first preset transmission rate is smaller than the preset second transmission rate.
6. The internet of things-based cross-border tax data service management system of claim 5, wherein the second parsing unit determines a determination mode of whether the network environment where the server is located meets the requirements according to a difference between the average transmission rate of the primary database to the secondary database and the average transmission rate under a second preset condition, wherein,
the first network environment judging mode is that the second analyzing unit judges that the network environment where the server is located does not have fluctuation, the second analyzing unit judges that the running state of the server does not accord with a preset standard, and the second analyzing unit adjusts the proportion to a corresponding value according to the increasing rate of the data quantity in the secondary database; the first network environment meets the condition that the difference value between the transmission rate of the primary database aiming at the secondary database and the average transmission rate is smaller than or equal to a first preset difference value;
the second network environment judging mode is that the second analyzing unit preliminarily judges that the network environment where the server is located does not have fluctuation, and secondarily judges the network environment where the server is located according to the data storage quantity of a single secondary database in a preset period; the second network environment meets the condition that the difference value between the transmission rate and the average transmission rate of the primary database aiming at the secondary database is smaller than or equal to a second preset difference value and larger than the first preset difference value;
The third network environment judging mode is that the second analyzing unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; the third network environment satisfies that the difference between the transmission rate of the primary database for the secondary database and the average transmission rate is larger than the second preset difference;
wherein the first preset difference value is smaller than the second preset difference value;
and the second preset condition is that the second analysis unit finishes judging the running state of the server by using a second judging mode.
7. The internet of things-based cross-border tax data service management system of claim 6, wherein the second parsing unit sequentially obtains data storage amounts Sj of the server to the j-th secondary database in a preset period under a third preset condition, calculates a network environment score G according to the Sj, and determines a correction coefficient adjustment mode of a transmission rate of the primary database to the j-th secondary database according to the Sj, wherein,
the first correction coefficient is adjusted by the second analysis unit using a first preset coefficient alpha j1 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value The method comprises the steps of carrying out a first treatment on the surface of the The first correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a first preset storage amount;
the second correction coefficient is adjusted by the second analysis unit using a second preset coefficient alpha j2 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the second correction coefficient adjusting mode meets the condition that the data storage amount is smaller than or equal to a second preset storage amount and larger than the first preset storage amount;
the third correction coefficient is adjusted by the second analysis unit using a third preset coefficient alpha j3 Adjusting the transmission rate of the first-level database to the j-th second-level database to a corresponding value; the third correction coefficient adjusting mode meets the condition that the data storage amount is larger than the second preset storage amount;
the first preset storage amount is smaller than the second preset storage amount;
the second analysis unit is based on alpha ji Obtaining the network environment score G and setting
Wherein i=1, 2,3, v 0 Data transmission rate, V j The method comprises the steps of setting a beta=100 for the transmission rate of a first-level database to the j-th second-level database data, wherein j is the total number of the second-level labels in the first-level database, j=1, 2,3 … n and beta is a grading correction parameter;
and the third preset condition is that the second analysis unit completes the judgment of the network environment where the server is located by using a second network environment judgment mode.
8. The internet of things-based cross-border tax data service management system of claim 7, wherein the second parsing unit determines a secondary decision mode of whether the network environment fluctuates according to the network environment score G under the third preset condition, wherein,
the second analysis unit judges that the network environment where the server is located does not have fluctuation, the second analysis unit judges that the running state of the server does not accord with a preset standard, and the second analysis unit adjusts the specific gravity to a corresponding value according to the increasing rate of the data quantity in the secondary database; the first network environment secondary judgment mode meets the condition that the network environment score is larger than a preset standard network environment score;
the second network environment secondary judgment mode is that the second analysis unit judges that the network environment where the server is located fluctuates, and the alarm unit sends out fault alarm information aiming at the network environment; and the second network environment secondary judgment mode meets the condition that the network environment score is smaller than or equal to the preset standard network environment score.
9. The internet of things-based cross-border tax data service management system of claim 8, wherein the second parsing unit determines a secondary adjustment mode of specific gravity of a memory of the secondary database and a total memory of the primary database according to a memory growth rate in a preset duration of the obtained secondary database under a fourth preset condition, wherein,
The first specific gravity secondary regulation mode is that the second analysis unit regulates the specific gravity to a preset specific gravity; the first adjustment mode meets the condition that the memory growth rate is smaller than or equal to a first preset memory growth rate;
the second specific gravity secondary adjustment mode is that the second analysis unit uses the first specific gravity secondary adjustment coefficient to secondarily adjust the specific gravity to a third specific gravity; the second adjustment mode satisfies that the memory growth rate is smaller than or equal to a second preset memory growth rate and larger than the first memory growth rate;
the third specific gravity secondary adjustment mode is that the second analysis unit uses a second specific gravity secondary adjustment coefficient to secondarily adjust the specific gravity to a fourth specific gravity; the third adjustment mode satisfies that the memory growth rate is greater than the second preset memory growth rate;
wherein the first predetermined memory growth rate is less than the second predetermined memory growth rate;
and the fourth preset condition is that the second analysis unit judges that the running state of the server does not accord with a preset standard.
10. The internet of things-based cross-border tax data service management system of claim 9, wherein the second parsing unit determines a planning mode for a server memory according to a ratio of the adjusted memory of the secondary database to a total memory of the primary database under the fourth preset condition, wherein,
The first planning mode is that the second analysis unit uses the second corresponding proportion as the proportion of the reserved memory of the secondary database to the total memory of the primary database; the first planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is smaller than or equal to the preset standard ratio;
the second planning mode is that the second analysis unit uses preset adjustment parameters to adjust the proportion of the storage space of the primary database corresponding to the secondary database to the storage space of the server to a corresponding value; the second planning mode meets the condition that the ratio of the memory of the secondary database to the total memory of the primary database after adjustment is larger than the preset standard ratio.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310215954.3A CN116186142B (en) | 2023-03-08 | 2023-03-08 | Cross-border tax data service management system based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310215954.3A CN116186142B (en) | 2023-03-08 | 2023-03-08 | Cross-border tax data service management system based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116186142A true CN116186142A (en) | 2023-05-30 |
| CN116186142B CN116186142B (en) | 2023-12-22 |
Family
ID=86452112
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310215954.3A Active CN116186142B (en) | 2023-03-08 | 2023-03-08 | Cross-border tax data service management system based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116186142B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116933138A (en) * | 2023-07-26 | 2023-10-24 | 广州淦源智能科技有限公司 | Intelligent racing product-based athletic control system |
| CN117237133A (en) * | 2023-08-07 | 2023-12-15 | 深圳欧税通技术有限公司 | Financial tax data intelligent management method, system and readable storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080082587A1 (en) * | 2006-09-29 | 2008-04-03 | Oki Electric Industry Co., Ltd. | Computer maintenance support system and analysis server |
| US20100161569A1 (en) * | 2008-12-18 | 2010-06-24 | Sap Ag | Method and system for dynamically partitioning very large database indices on write-once tables |
| CN103218360A (en) * | 2012-01-18 | 2013-07-24 | 中国石油天然气集团公司 | Method of industrial real-time database for realizing dynamic memory management by adopting memory pool technology |
| US20180157729A1 (en) * | 2016-12-06 | 2018-06-07 | Electronics And Telecommunications Research Institute | Distributed in-memory database system and method for managing database thereof |
| US20200026780A1 (en) * | 2018-07-17 | 2020-01-23 | International Business Machines Corporation | Memory compaction for append-only formatted data in a distributed storage network |
| KR102145579B1 (en) * | 2019-12-30 | 2020-08-18 | 남호영 | Data transfer system between server and clients |
| CN112130995A (en) * | 2020-09-18 | 2020-12-25 | 苏州浪潮智能科技有限公司 | Real-time data processing method and device |
| CN114979122A (en) * | 2022-05-31 | 2022-08-30 | 冀锋 | Industrial internet-based big data storage rate optimization method |
| CN115202588A (en) * | 2022-09-14 | 2022-10-18 | 云和恩墨(北京)信息技术有限公司 | Data storage method and device and data recovery method and device |
-
2023
- 2023-03-08 CN CN202310215954.3A patent/CN116186142B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080082587A1 (en) * | 2006-09-29 | 2008-04-03 | Oki Electric Industry Co., Ltd. | Computer maintenance support system and analysis server |
| US20100161569A1 (en) * | 2008-12-18 | 2010-06-24 | Sap Ag | Method and system for dynamically partitioning very large database indices on write-once tables |
| CN103218360A (en) * | 2012-01-18 | 2013-07-24 | 中国石油天然气集团公司 | Method of industrial real-time database for realizing dynamic memory management by adopting memory pool technology |
| US20180157729A1 (en) * | 2016-12-06 | 2018-06-07 | Electronics And Telecommunications Research Institute | Distributed in-memory database system and method for managing database thereof |
| US20200026780A1 (en) * | 2018-07-17 | 2020-01-23 | International Business Machines Corporation | Memory compaction for append-only formatted data in a distributed storage network |
| KR102145579B1 (en) * | 2019-12-30 | 2020-08-18 | 남호영 | Data transfer system between server and clients |
| CN112130995A (en) * | 2020-09-18 | 2020-12-25 | 苏州浪潮智能科技有限公司 | Real-time data processing method and device |
| CN114979122A (en) * | 2022-05-31 | 2022-08-30 | 冀锋 | Industrial internet-based big data storage rate optimization method |
| CN115202588A (en) * | 2022-09-14 | 2022-10-18 | 云和恩墨(北京)信息技术有限公司 | Data storage method and device and data recovery method and device |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116933138A (en) * | 2023-07-26 | 2023-10-24 | 广州淦源智能科技有限公司 | Intelligent racing product-based athletic control system |
| CN116933138B (en) * | 2023-07-26 | 2024-03-19 | 广州淦源智能科技有限公司 | Intelligent racing product-based athletic control system |
| CN117237133A (en) * | 2023-08-07 | 2023-12-15 | 深圳欧税通技术有限公司 | Financial tax data intelligent management method, system and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116186142B (en) | 2023-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116186142B (en) | Cross-border tax data service management system based on Internet of things | |
| CN110163427B (en) | Method for optimizing storeroom of storeroom | |
| CN109063945A (en) | A kind of 360 degree of customer portrait construction methods of sale of electricity company based on Value accounting system | |
| CN108846547A (en) | A kind of Enterprise Credit Risk Evaluation method of dynamic adjustment | |
| CN112529491B (en) | Inventory management method and device | |
| CN107657500A (en) | Stock recommends method and server | |
| CN111340581B (en) | Data processing method and device, readable storage medium and electronic equipment | |
| CN114548723A (en) | Real-time vehicle and goods matching and path recommendation method and system based on cloud edge cooperation | |
| CN109146663A (en) | The processing method and system of flowing water information | |
| CN116128600A (en) | Recommendation information generation method based on AI | |
| CN111062806A (en) | Personal finance credit risk evaluation method, system and storage medium | |
| CN109740036A (en) | OTA platform hotel's sort method and device | |
| CN116402399A (en) | Business data processing method and system based on artificial intelligence and electronic mall | |
| US11429644B2 (en) | Systems and methods for computer modeling using incomplete data | |
| CN113869700A (en) | Performance index prediction method and device, electronic equipment and storage medium | |
| CN110040411B (en) | Intelligent box selecting and packing line collection area parameter optimization method | |
| CN116777592A (en) | Financial business approval method, device, equipment and storage medium | |
| CN110750549A (en) | Vehicle inventory management system based on big data | |
| CN115983753A (en) | Logistics freight price adjusting method based on big data | |
| CN113379497B (en) | Order regulation method, order regulation device, computer equipment and computer readable storage medium | |
| CN115719194A (en) | Big data prediction based material purchasing method and system | |
| CN108765092A (en) | A kind of transaction security air control method and system | |
| CN113407827A (en) | Information recommendation method, device, equipment and medium based on user value classification | |
| Morales et al. | Credit rating using fuzzy algorithms | |
| CN114997970B (en) | Purchasing plan management and control method based on cloud platform and ERP system |
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 |