Accounting data management system and method based on big data application

Abstract

The invention belongs to the technical field of accounting data management, and particularly discloses an accounting data management system and method based on big data application. Comprising a model construction module which is used for integrating a data transmission route, determining an execution protocol, calibrating a data generation frequency and generating an accounting data management topology initial model; the execution interval determination module is used for acquiring a required computing power value in historical transmission data, processing a computing power value sorting sequence and determining a resource allocation interval; the computing power distribution module is used for calculating a total evaluation value and a total computing power resource, and distributing the computing power resource based on the ratio sequence; an initial model of a management topological graph is constructed based on network equipment of an accounting data management system, correlation analysis is carried out on computing power characteristics among data processing modules in the initial model, computing power characteristic values are confirmed from historical data, a numerical value section with the most complete characteristic expression is confirmed and selected through the numerical value section, a computing power execution interval is determined, and the calculation power is obtained. Accurate determination of numerical values is realized, and a foundation is laid for subsequent model optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of accounting data management, and in particular, relates to an accounting data management system and method based on big data application. Background Art

[0002] With the rapid development of information technology, corporate accounting data has experienced explosive growth, posing numerous challenges to traditional accounting data management methods. Existing accounting data management systems, when processing massive amounts of data, suffer from low data processing efficiency, irrational resource allocation, and an inability to efficiently handle concurrent multi-process processing. These issues make it difficult for companies to meet their needs for real-time analysis and precise management of accounting data. For example, during the calculation and analysis of multi-dimensional accounting data, improper allocation of computing resources often causes some data processing processes to be delayed, impacting overall data processing efficiency. Furthermore, existing systems fail to fully integrate big data technology to conduct in-depth analysis of the characteristics of accounting data, making it impossible to optimize data processing paths and resulting in a waste of computing resources. Summary of the Invention

[0003] The purpose of the present invention is to provide an accounting data management system and method based on big data application, which solves the problems of low data processing efficiency, unreasonable resource allocation and insufficient multi-process concurrent processing capabilities of traditional accounting data management systems.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] An accounting data management system based on big data application, characterized by comprising:

[0006] Model building module: used to integrate data transmission routes, determine execution protocols, calibrate data generation frequency, and generate an initial model of accounting data management topology;

[0007] Execution interval determination module: used to obtain the required computing power value in the historical transmission data, process the computing power value sorting sequence, and determine the resource allocation interval;

[0008] Computing power allocation module: used to calculate the total evaluation value and total computing power resources, and allocate computing power resources based on the ratio sequence;

[0009] Process adjustment module: used to determine the first ratio sequence and the second ratio sequence, adjust the computing power based on the sequence, handle allocation errors, and ensure that each process is in the optimal processing state.

[0010] Preferably, the specific manner in which the model building module generates the initial accounting data management topology model is:

[0011] Integrate data transmission routes between different data processing modules and confirm the management topology;

[0012] The transmission protocol used in actual data transmission is used as the execution protocol of the corresponding route of the management topology graph;

[0013] Taking the current moment as the calibration moment, each module transmits data according to the actual data generation frequency to complete the initial model construction.

[0014] Preferably, the required computing power value in the execution interval determination module is calibrated as F m-n , where m represents the transmission protocol between data processing modules and n represents the historical data number.

[0015] Preferably, when the execution interval determination module determines the execution interval, f m-n The computing power value sorting sequence is obtained by sorting the values ​​from small to large, and a unique set of characteristic value segments is determined from the sequence, and the characteristic value segments belong to the computing power value sorting sequence.

[0016] Preferably, the characteristic value of the characteristic value segment is confirmed in the following manner:

[0017] The number of values ​​in the characteristic value segment is calibrated as N r Calculate the correlation difference between adjacent values, the next value - the previous value, and take the average value to get D r ;

[0018] Using E r =N r ÷D r Calculate the characteristic value, select the standard numerical segment corresponding to the maximum value, and determine the resource allocation interval.

[0019] Preferably, the specific method of computing power allocation in the computing power allocation module is:

[0020] Mark the execution intervals of multiple processes as pending intervals Q t , calculate the total evaluation value Q of the maximum value of the interval p With total computing power resources Q L ;

[0021] If Q p ≤Q L , the normal execution process; if Q p >Q L , then based on the middle value M of the undetermined interval t The computing power is distributed based on the ratio sequence of .

[0022] Preferably, the readjustment processing in the process adjustment module includes:

[0023] Determine a first ratio sequence and a second ratio sequence based on the minimum endpoint value and the maximum endpoint value of the to-be-determined interval, respectively;

[0024] Adjust the computing power of each process so that the computing power ratio sequence is between the two ratio sequences, and record the changes in transmission rate to calibrate the process to be added.

[0025] An accounting data management method based on big data application includes the following steps:

[0026] Generate an initial topology model of accounting data management based on the data transmission routes between various data processing modules in the accounting data management system;

[0027] Determine the required computing power value from the historical transmission data of the relevant transmission protocol executed by each data processing module, and determine the resource allocation interval as the execution interval of the corresponding data processing module;

[0028] When multiple data processing processes are executed simultaneously, analyze whether the total computing power resources meet the computing power requirements and allocate computing power resources based on the analysis results;

[0029] Re-adjust several data processing processes that have completed the allocation of computing resources to ensure that each process is in the optimal processing state.

[0030] Preferably, the specific method of generating the initial model of accounting data management topology is:

[0031] Integrate data transmission routes between different data processing modules and confirm the management topology;

[0032] The transmission protocol used in actual data transmission is used as the execution protocol of the corresponding route of the management topology graph;

[0033] Taking the current moment as the calibration moment, each module transmits data according to the actual data generation frequency to complete the initial model construction.

[0034] Preferably, the reconditioning process comprises:

[0035] Determine a first ratio sequence and a second ratio sequence based on the minimum endpoint value and the maximum endpoint value of the to-be-determined interval, respectively;

[0036] Adjust the computing power of each process so that the computing power ratio sequence is between the two ratio sequences, and record the changes in transmission rate to calibrate the process to be added.

[0037] The beneficial effects of the present invention are as follows: a preliminary model of a management topology diagram is constructed based on the network equipment (data processing module) of the accounting data management system, and a correlation analysis is performed on the computing power characteristics between the data processing modules in the preliminary model, the computing power characteristic values ​​are confirmed from historical data, the numerical segments are confirmed to select the numerical segments with the most complete characteristic performance, the computing power execution interval is determined, and the numerical values ​​are accurately determined, laying the foundation for subsequent model optimization. When multiple data processing processes are executed synchronously, the computing power resources are analyzed to see whether they meet the demand, and the computing power resources are reasonably allocated based on the analysis results, so that the system processing process is closer to the actual accounting data processing characteristics, the difference is reduced, the data processing efficiency is improved, the computing power allocation effect is optimized, the waste of resources is avoided, and the efficient operation of each data processing process is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a system block diagram of an accounting data management system based on big data application in the present invention.

[0040] Figure 2 This is a flow chart of an accounting data management method based on big data application of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] like Figure 2 As shown, an accounting data management method based on big data application

[0043] Step 1: Build an initial topology model for accounting data management

[0044] S1. Based on the data transmission routes between the data processing modules (such as the voucher processing module, the account book management module, the report generation module, the financial analysis module, etc.) in the accounting data management system, several groups of data transmission routes are integrated to confirm the management topology diagram belonging to the accounting data management system.

[0045] S2. Based on the transmission protocols between the data processing modules in the actual accounting data management process (such as data format conversion protocol, data encryption transmission protocol, etc.), the corresponding transmission protocols are used as the execution protocols of the corresponding transmission routes in the management topology diagram.

[0046] S3. Based on the data generation frequency of each data processing module in the actual accounting data management system (which can be set in advance by the system administrator or obtained from the actual operation data), the current moment is used as the calibration moment, so that the management topology diagram controls each data processing module to use the same data generation frequency for data generation at subsequent moments, and transmits data based on the determined execution protocol to complete the construction of the initial model of the accounting data management topology.

[0047] Step 2: Determine the execution range of the data processing module

[0048] Based on the historical transmission data associated with each data processing module when executing the relevant transmission protocol, the required computing power value is determined from each set of historical transmission data and calibrated as F m-n , where m represents the relevant transmission protocols between different data processing modules, and n represents different historical transmission data. m-n Sorting is performed, and the computing power value sorting sequence is determined in ascending order of values. The characteristic value segment is determined from the computing power value sorting sequence. There is only one set of this characteristic value segment and it belongs to the computing power value sorting sequence. The characteristic value of each characteristic value segment determined by the determination process is confirmed:

[0049] The number of values ​​contained in the characteristic value segment is calibrated as N r , where r represents the characteristic value segment corresponding to different determination processes.

[0050] Determine the correlation difference of adjacent values ​​of the characteristic value segment, the correlation difference = the next group of values ​​of the adjacent values ​​- the previous group of values ​​of the adjacent values, perform mean processing on the confirmed groups of correlation differences and mark them as D r .

[0051] Using E r =N r ÷D r Confirm that the eigenvalue E associated with the corresponding determination process r , from several eigenvalues ​​E r The maximum value is selected, the characteristic numerical segment corresponding to the maximum value is calibrated as the standard numerical segment, and the minimum computing power value and the maximum computing power value are determined from the standard numerical segment, and the resource allocation interval is determined. The resource allocation interval is used as the execution interval of the corresponding data processing module in the initial model of the accounting data management topology.

[0052] Step 3: Allocate computing resources

[0053] Based on the existence of multiple data processing processes, the execution intervals associated with the multiple data processing processes are marked as pending intervals, and the pending intervals are marked as Q t , where t represents different intervals to be determined. t The maximum value of the interval is summed up to determine the total evaluation value Q p , the total computing power resources associated with the initial model of accounting data management topology are calibrated as Q L :

[0054] If Q p ≤Q L , the data processing process will be executed normally without the need for computing power allocation.

[0055] If Q p >Q L , then the computing power resources are allocated. t The middle value is confirmed and marked as M t , several intermediate values ​​M t Perform ratio processing to determine the ratio sequence. Based on the ratio sequence, the total computing power resource Q L Allocate it to each data processing process to complete the allocation of computing resources.

[0056] Step 4: Readjust the data processing process

[0057] The undetermined interval Q determined based on multiple data processing processes t , several undetermined intervals Q t The ratio of several minimum endpoint values ​​is processed to determine the first ratio sequence, and then several undetermined intervals Q are t The ratio of several maximum endpoint values ​​is processed to determine a second ratio sequence. Based on the determined first ratio sequence and second ratio sequence, the computing power associated with the corresponding data processing process is adjusted up or down, and the computing power ratio sequence of different data processing processes after adjustment is limited to be between the first ratio sequence and the second ratio sequence:

[0058] Randomly select a group of data processing processes, adjust the associated computing power downward, and record whether the processing rate associated with this data processing process decreases.

[0059] If it decreases, the computing power value of this data processing process will be readjusted to the initial state, and this data processing process will be marked as a process to be increased.

[0060] If it does not decrease, continue to adjust downward until the processing rate changes, and record the computing power value that has been adjusted downward. Use this method to synchronize adjustments to other data processing processes.

[0061] Based on the multiple sets of computing power values ​​confirmed during the downward adjustment process, these sets of computing power values ​​are sequentially allocated to the processes to be added. During the allocation process, the computing power ratio sequences of the different data processing processes after allocation are restricted to lie between the first ratio sequence and the second ratio sequence. If the computing power exceeds the range associated with the first ratio sequence and the second ratio sequence, the excess computing power is evenly distributed to other data processing processes that are not part of the process to be added, so that the computing power ratio sequences of the different data processing processes lie between the first ratio sequence and the second ratio sequence.

[0062] If, after the excess computing power is allocated, the computing power ratio sequences of different data processing processes cannot be placed between the first ratio sequence and the second ratio sequence, an allocation error signal is generated for display.

[0063] like Figure 1 As shown, an accounting data management system based on big data application

[0064] Model building module

[0065] This module is used to build a preliminary topological model for accounting data management, including integrating data transmission routes, determining execution protocols, and calibrating data generation frequencies. This module integrates the transmission routes between the various data processing modules in the accounting data management system to form a management topology. It also determines the actual transmission protocols to be executed for each transmission route, using the current moment as the calibration moment to ensure that each module transmits data according to the actual data generation frequency, completing the construction of the preliminary model.

[0066] Execution interval determination module

[0067] This module is used to determine the execution interval of the data processing module, including obtaining the required computing power value, processing the computing power value sorting sequence, and determining the resource allocation interval. This module obtains the required computing power value from historical transmission data, sorts it, determines the characteristic value segment, and selects the standard value segment by calculating the characteristic value to determine the resource allocation interval, providing the data processing module with an accurate execution interval.

[0068] Computing power allocation module

[0069] Used for computing resource allocation, including analyzing the relationship between the total evaluation value and total computing resources and allocating computing resources based on a ratio sequence. This module determines whether computing resources need to be allocated by calculating the relationship between the total evaluation value and total computing resources for multiple process execution intervals. If allocation is required, computing resources are allocated reasonably based on the ratio sequence of the intermediate values ​​in the undetermined intervals.

[0070] Process Control Module

[0071] This module is used to readjust data processing processes, including determining ratio sequences, adjusting computing power, and handling allocation errors. This module determines a first ratio sequence and a second ratio sequence, adjusts the computing power of each process based on these sequences, and adjusts the computing power ratio sequence to place it between the two sequences. It also records transmission rate changes to calibrate processes to be added, handles allocation errors, and ensures that each process is in the optimal processing state.

[0072] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. An accounting data management system based on big data application, characterized by: include: Model building module: used to integrate data transmission routes, determine execution protocols, calibrate data generation frequency, and generate an initial model of accounting data management topology; Execution interval determination module: used to obtain the required computing power value in the historical transmission data, process the computing power value sorting sequence, and determine the resource allocation interval; Computing power allocation module: used to calculate the total evaluation value and total computing power resources, and allocate computing power resources based on the ratio sequence; Process adjustment module: used to determine the first ratio sequence and the second ratio sequence, adjust the computing power based on the sequence, handle allocation errors, and ensure that each process is in the optimal processing state.

2. The accounting data management system based on big data application according to claim 1, characterized in that: The specific method for the model building module to generate the initial accounting data management topology model is: Integrate data transmission routes between different data processing modules and confirm the management topology; The transmission protocol used in actual data transmission is used as the execution protocol of the corresponding route of the management topology graph; Taking the current moment as the calibration moment, each module transmits data according to the actual data generation frequency to complete the initial model construction.

3. The accounting data management system based on big data application according to claim 1, characterized in that: The required computing power value in the execution interval determination module is calibrated as F m-n , where m represents the transmission protocol between data processing modules and n represents the historical data number.

4. The accounting data management system based on big data application according to claim 3, characterized in that: When the execution interval determination module determines the execution interval, F m-n The computing power value sorting sequence is obtained by sorting the values ​​from small to large, and a unique set of characteristic value segments is determined from the sequence, and the characteristic value segments belong to the computing power value sorting sequence.

5. The accounting data management system based on big data application according to claim 4, characterized in that: The characteristic value confirmation method of the characteristic value segment is: The number of values ​​in the characteristic value segment is calibrated as N r Calculate the correlation difference between adjacent values, the next value - the previous value, and take the average value to get D r ; Using E r =N r ÷D r Calculate the characteristic value, select the standard numerical segment corresponding to the maximum value, and determine the resource allocation interval.

6. The accounting data management system based on big data application according to claim 1, characterized in that: The specific method of computing power allocation in the computing power allocation module is: Mark the execution intervals of multiple processes as pending intervals Q t , calculate the total evaluation value Q of the maximum value of the interval p With total computing power resources Q L ; If Q p ≤Q L , the normal execution process; if Q p >Q L , then based on the middle value M of the undetermined interval t The computing power is distributed based on the ratio sequence of .

7. The accounting data management system based on big data application according to claim 1, characterized in that: The re-adjustment processing in the process adjustment module includes: Determine a first ratio sequence and a second ratio sequence based on the minimum endpoint value and the maximum endpoint value of the to-be-determined interval, respectively; Adjust the computing power of each process so that the computing power ratio sequence is between the two ratio sequences, and record the changes in transmission rate to calibrate the process to be added.

8. An accounting data management method based on big data application, applied to an accounting data management system based on big data application according to any one of claims 1 to 7, characterized in that: The following steps are involved: Generate an initial topology model of accounting data management based on the data transmission routes between various data processing modules in the accounting data management system; Determine the required computing power value from the historical transmission data of the relevant transmission protocol executed by each data processing module, and determine the resource allocation interval as the execution interval of the corresponding data processing module; When multiple data processing processes are executed simultaneously, analyze whether the total computing power resources meet the computing power requirements and allocate computing power resources based on the analysis results; Re-adjust several data processing processes that have completed the allocation of computing resources to ensure that each process is in the optimal processing state.

9. The accounting data management method based on big data application according to claim 8 is characterized in that: The specific method of generating the initial model of accounting data management topology is: Integrate data transmission routes between different data processing modules and confirm the management topology; The transmission protocol used in actual data transmission is used as the execution protocol of the corresponding route of the management topology graph; Taking the current moment as the calibration moment, each module transmits data according to the actual data generation frequency to complete the initial model construction.

10. The accounting data management method based on big data application according to claim 8, characterized in that: The reconditioning process includes: Determine a first ratio sequence and a second ratio sequence based on the minimum endpoint value and the maximum endpoint value of the to-be-determined interval, respectively; Adjust the computing power of each process so that the computing power ratio sequence is between the two ratio sequences, and record the changes in transmission rate to calibrate the process to be added.

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