Cost accounting management system based on big data application

Abstract

The invention relates to the technical field of cost accounting management, in particular to a cost accounting management system based on big data application, and the system comprises the following modules: a data input module which is used for inputting cost data, including direct cost, indirect cost, fixed cost and variable cost; the cost accounting module is used for realizing accurate accounting of the product cost and the project cost according to the cost composition and accounting method of the enterprise, and the cost accounting module supports the cost accounting method including but not limited to a standard cost method and an activity basic cost method; and the cost analysis module is used for providing multi-dimensional cost analysis functions including product cost analysis, department cost analysis and cost difference analysis. According to the method, the requirements of enterprises on cost management and control are better met, the accuracy, efficiency and practicability of cost accounting are improved, the cost management level of the enterprises can be improved, and the enterprises are helped to better control the cost, improve the efficiency and make a more intelligent decision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cost accounting management, in particular to a cost accounting management system based on big data application. BACKGROUND

[0002] The cost accounting management system originates from the demand of enterprises for cost management and control. With the increase of enterprise scale and complexity, the traditional cost accounting system relies on manual operation and simple data processing tools, which is difficult to cope with massive and complex data processing demands in the era of big data. With the rapid development of information technology, it has been unable to meet the demand of enterprises for cost data accuracy, real-time and analysis depth.

[0003] Therefore, we improve it and propose a cost accounting management system based on big data application. SUMMARY

[0004] The purpose of the present application is to solve the problem that the traditional cost accounting system relies on manual operation and simple data processing tools, which is difficult to cope with massive and complex data processing demands in the era of big data, and has been unable to meet the demand of enterprises for cost data accuracy, real-time and analysis depth.

[0005] In order to achieve the above-mentioned purpose of the application, the present application provides a cost accounting management system based on big data application to improve the above-mentioned problems.

[0006] The present application is as follows:

[0007] A cost accounting management system based on big data application, comprising the following modules:

[0008] Data entry module: used for entering cost data, including direct cost, indirect cost, fixed cost and variable cost;

[0009] Cost accounting module: used for realizing accurate accounting of product cost and project cost according to the cost composition and accounting method of the enterprise, and the cost accounting module supports the cost accounting method;

[0010] Cost analysis module: used for providing multi-dimensional cost analysis function, including product cost analysis, department cost analysis and cost variance analysis, and the cost analysis module supports user-defined cost analysis dimension and report format to meet the cost management needs of different enterprises;

[0011] Cost prediction module: used for supporting cost prediction and early warning function based on historical data and cost change trend;

[0012] Cost control module: used for providing cost control tools and reports to help enterprises monitor and control the growth of cost.

[0013] As the preferred technical solution of the present application, the cost accounting method comprises a quota consumption amount proportion method and a machine hour work hour proportion method, the quota consumption amount proportion method comprises the following steps:

[0014] S1, first determine the quota consumption amount of products and projects that need to be cost accounted, i.e. the standard material and labor quantity required by unit product and project;

[0015] S2, then calculate the total amount of fees to be distributed according to the quota consumption amount and unit cost;

[0016] S3, next calculate the distribution rate according to the quota consumption total amount and the total amount of fees to be distributed;

[0017] S4, finally calculate the amount to be distributed, i.e. the distribution rate multiplied by the quota consumption amount, according to the actual output and the number of completed projects, according to the quota consumption amount proportion method.

[0018] As the preferred technical solution of the present application, the machine hour work hour proportion method comprises the following steps:

[0019] S1, first determine the machine hour work hour proportion of products and projects that need to be cost accounted;

[0020] S2, then calculate the total amount of wages to be paid according to the machine hour work hour consumed by the actually produced products and projects;

[0021] S3, next calculate the wages to be distributed according to the machine hour work hour consumed by the actually produced products and projects according to the machine hour work hour proportion;

[0022] S4, finally distribute according to the machine hour work hour proportion method according to the calculated wages to be distributed.

[0023] As the preferred technical solution of the present application, the production cost analysis table comprises a direct material cost analysis table and a direct labor cost analysis table, the direct material cost analysis table comprises the following steps:

[0024] S1, determine the products and projects that need to be cost analyzed;

[0025] S2, collect relevant direct material cost data, including the procurement cost and transportation cost of raw materials;

[0026] S3, calculate the total cost of direct materials according to the production quantity of products and projects;

[0027] S4, classify and summarize the direct material cost according to the format of the cost analysis table, including the name, specification, quantity, unit price and total price information of various raw materials;

[0028] S5. Compare the direct material costs of different products over different time periods, analyze their changes, and identify the reasons affecting cost changes.

[0029] As a preferred technical solution of this application, the direct labor cost analysis table includes the following steps:

[0030] S1. Identify the products and projects that require cost analysis;

[0031] S2. Collect relevant direct labor cost data, including workers' wages, overtime pay, and welfare expenses;

[0032] S3. Calculate the total direct labor cost based on the production quantity of the product or project;

[0033] S4. Classify and summarize direct labor costs according to the format of the cost analysis table, including worker names, working hours, wage standards, and overtime pay information;

[0034] S5. Compare the direct labor costs of different time periods and different products, analyze their changes, and identify the reasons affecting cost changes.

[0035] As a preferred technical solution of this application, it also includes a cost prediction module, which uses deep learning technology to perform cost prediction analysis.

[0036] As a preferred technical solution of this application, the cost prediction module includes the following steps:

[0037] S1. Deep Learning Model Selection: Select a deep learning architecture, including feedforward neural networks, recurrent neural networks, long short-term memory networks, and convolutional neural networks;

[0038] S2, Model Training: Train the deep learning model using the training set data and adjust the model parameters;

[0039] S3. Feature Importance Analysis: Utilize model interpretability tools to analyze which features have the greatest impact on cost prediction;

[0040] S4. Model Evaluation and Optimization: Evaluate the predictive performance of the model on the test set using evaluation metrics, including mean squared error and coefficient of determination.

[0041] S5. Cost Prediction: Use a trained model to predict new and future cost data.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] In the scheme of this application:

[0044] 1. In order to address the problem that existing traditional cost accounting systems rely heavily on manual operation and simple data processing tools, making it difficult to cope with the massive and complex data processing needs of the big data era, and failing to meet enterprises' requirements for the accuracy, real-time performance, and depth of analysis of cost data, this application aims to better meet enterprises' needs for cost management and control, improve the accuracy, efficiency, and practicality of cost accounting, enhance enterprises' cost management level, and help enterprises better control costs, improve efficiency, and make more informed decisions;

[0045] 2. This application integrates multiple data collection technologies and calculation methods to meet users' needs in different scenarios. It features a simple interface, convenient operation, and easy user-friendliness. It also employs advanced security technologies to ensure user data security. Attached Figure Description

[0046] Figure 1 A flowchart illustrating the accounting method of the cost accounting management system based on big data applications provided in this application;

[0047] Figure 2 Production cost analysis charts provided for the cost accounting management system based on big data applications in this application. Detailed Implementation

[0048] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0049] As described in the background section, traditional cost accounting systems rely heavily on manual operation and simple data processing tools, which are insufficient to meet the massive and complex data processing needs of the big data era and can no longer satisfy enterprises' requirements for the accuracy, real-time performance, and depth of analysis of cost data.

[0050] To address this technical problem, the present invention provides a cost accounting management system based on big data applications, which is applied in the field of cost accounting management technology.

[0051] For details, please refer to Figure 1 The cost accounting management system based on big data applications includes the following modules:

[0052] Data entry module: Used to enter cost data, including direct costs, indirect costs, fixed costs, and variable costs;

[0053] Cost accounting module: It is used to accurately calculate product costs and project costs based on the company's cost structure and accounting methods. The cost accounting module supports cost accounting methods, including but not limited to standard costing and activity-based costing.

[0054] Cost Analysis Module: Provides multi-dimensional cost analysis functions, including product cost analysis, department cost analysis, and cost variance analysis. The cost analysis module supports user-defined cost analysis dimensions and report formats to meet the cost management needs of different enterprises.

[0055] Cost forecasting module: Used to support cost forecasting and early warning functions based on historical data and cost change trends;

[0056] Cost control module: Provides tools and reports for cost control, helping businesses monitor and control cost growth.

[0057] This application better meets the needs of enterprises for cost management and control, improves the accuracy, efficiency and practicality of cost accounting, enhances the cost management level of enterprises, and helps enterprises better control costs, improve efficiency and make more informed decisions.

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0059] Example 1, please refer to Figure 1 and Figure 2 A cost accounting management system based on big data applications includes the following modules:

[0060] Data entry module: Used to enter cost data, including direct costs, indirect costs, fixed costs, and variable costs;

[0061] Cost accounting module: It is used to accurately calculate product costs and project costs based on the company's cost structure and accounting methods. The cost accounting module supports cost accounting methods, including but not limited to standard costing and activity-based costing.

[0062] Cost Analysis Module: Provides multi-dimensional cost analysis functions, including product cost analysis, department cost analysis, and cost variance analysis. The cost analysis module supports user-defined cost analysis dimensions and report formats to meet the cost management needs of different enterprises.

[0063] Cost forecasting module: Used to support cost forecasting and early warning functions based on historical data and cost change trends;

[0064] Cost control module: Provides tools and reports for cost control, helping businesses monitor and control cost growth.

[0065] Cost accounting methods include the standard consumption ratio method and the machine time / labor time ratio method. The standard consumption ratio method includes the following steps:

[0066] S1. First, determine the standard consumption of products and projects that need to be cost-accounted for, that is, the standard materials and labor required per unit of product or project.

[0067] S2. Then, based on the quota consumption and unit cost, calculate the total amount of expenses to be allocated;

[0068] S3. Next, calculate the allocation rate based on the total quota consumption and the total amount of expenses to be allocated;

[0069] S4. Finally, based on the actual output and the number of completed projects, calculate the allocation amount according to the quota consumption ratio method, that is, the allocation rate multiplied by the quota consumption.

[0070] The machine-time-labor-time ratio method includes the following steps:

[0071] S1. First, determine the machine time and labor time ratios for the products and projects that require cost accounting;

[0072] S2. Then, based on the actual machine hours and man-hours consumed by the products and projects produced, calculate the total amount of wages payable.

[0073] S3. Next, based on the ratio of machine time to labor time, calculate the wages to be allocated according to the actual machine time and labor time consumed by the products and projects produced.

[0074] S4. Finally, the wages to be allocated are distributed according to the machine time-to-work-hour ratio method based on the calculated amount.

[0075] Example 2 further optimizes the cost accounting management system based on big data applications provided in Example 1. Specifically, the production cost analysis table includes a direct material cost analysis table and a direct labor cost analysis table. The direct material cost analysis table includes the following steps:

[0076] S1. Identify the products and projects that require cost analysis;

[0077] S2. Collect relevant direct material cost data, including the procurement cost and transportation cost of raw materials;

[0078] S3. Calculate the total cost of direct materials based on the production quantity of the product or project;

[0079] S4. Classify and summarize the direct material costs according to the cost analysis table format, including the name, specifications, quantity, unit price and total price information of various raw materials;

[0080] S5. Compare the direct material costs of different products over different time periods, analyze their changes, and identify the reasons affecting cost changes.

[0081] The direct labor cost analysis table includes the following steps:

[0082] S1. Identify the products and projects that require cost analysis;

[0083] S2. Collect relevant direct labor cost data, including workers' wages, overtime pay, and welfare expenses;

[0084] S3. Calculate the total direct labor cost based on the production quantity of the product or project;

[0085] S4. Classify and summarize direct labor costs according to the format of the cost analysis table, including worker names, working hours, wage standards, and overtime pay information;

[0086] S5. Compare the direct labor costs of different time periods and different products, analyze their changes, and identify the reasons affecting cost changes.

[0087] Example 3 further optimizes the cost accounting management system based on big data applications provided in Example 1 or 2. Specifically, it also includes a cost prediction module that uses deep learning technology for cost prediction analysis.

[0088] The cost forecasting module includes the following steps:

[0089] S1. Deep Learning Model Selection: Select a deep learning architecture, including feedforward neural networks, recurrent neural networks, long short-term memory networks, and convolutional neural networks;

[0090] S2, Model Training: Train the deep learning model using the training set data and adjust the model parameters;

[0091] S3. Feature Importance Analysis: Utilize model interpretability tools to analyze which features have the greatest impact on cost prediction;

[0092] S4. Model Evaluation and Optimization: Evaluate the predictive performance of the model on the test set using evaluation metrics, including mean squared error and coefficient of determination.

[0093] S5. Cost Prediction: Use a trained model to predict new and future cost data.

[0094] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A cost accounting management system based on big data applications, characterized in that, Includes the following modules: Data entry module: Used to enter cost data, including direct costs, indirect costs, fixed costs, and variable costs; Cost accounting module: Used to accurately calculate product costs and project costs based on the company's cost structure and accounting methods. The cost accounting module supports cost accounting methods. Cost Analysis Module: Provides multi-dimensional cost analysis functions, including product cost analysis, department cost analysis, and cost variance analysis. The cost analysis module supports user-defined cost analysis dimensions and report formats to meet the cost management needs of different enterprises. Cost forecasting module: Used to support cost forecasting and early warning functions based on historical data and cost change trends; Cost control module: Provides tools and reports for cost control, helping businesses monitor and control cost growth.

2. The cost accounting management system based on big data applications according to claim 1, characterized in that, The cost accounting methods include the standard consumption ratio method and the machine time / labor time ratio method. The standard consumption ratio method includes the following steps: S1. First, determine the standard consumption of products and projects that need to be cost-accounted for, that is, the standard materials and labor required per unit of product or project. S2. Then, based on the quota consumption and unit cost, calculate the total amount of expenses to be allocated; S3. Next, calculate the allocation rate based on the total quota consumption and the total amount of expenses to be allocated; S4. Finally, based on the actual output and the number of completed projects, calculate the allocation amount according to the quota consumption ratio method, that is, the allocation rate multiplied by the quota consumption.

3. The cost accounting management system based on big data applications according to claim 2, characterized in that, The machine time-labor time ratio method includes the following steps: S1. First, determine the machine time and labor time ratios for the products and projects that require cost accounting; S2. Then, based on the actual machine hours and man-hours consumed by the products and projects produced, calculate the total amount of wages payable. S3. Next, based on the ratio of machine time to labor time, calculate the wages to be allocated according to the actual machine time and labor time consumed by the products and projects produced. S4. Finally, the wages to be allocated are distributed according to the machine time-to-work-hour ratio method based on the calculated amount.

4. The cost accounting management system based on big data applications according to claim 3, characterized in that, The production cost analysis table includes a direct material cost analysis table and a direct labor cost analysis table. The direct material cost analysis table includes the following steps: S1. Identify the products and projects that require cost analysis; S2. Collect relevant direct material cost data, including the procurement cost and transportation cost of raw materials; S3. Calculate the total cost of direct materials based on the production quantity of the product or project; S4. Classify and summarize the direct material costs according to the cost analysis table format, including the name, specifications, quantity, unit price and total price information of various raw materials; S5. Compare the direct material costs of different products over different time periods, analyze their changes, and identify the reasons affecting cost changes.

5. A cost accounting management system based on big data applications according to claim 4, characterized in that, The direct labor cost analysis table includes the following steps: S1. Identify the products and projects that require cost analysis; S2. Collect relevant direct labor cost data, including workers' wages, overtime pay, and welfare expenses; S3. Calculate the total direct labor cost based on the production quantity of the product or project; S4. Classify and summarize direct labor costs according to the format of the cost analysis table, including worker names, working hours, wage standards, and overtime pay information; S5. Compare the direct labor costs of different time periods and different products, analyze their changes, and identify the reasons affecting cost changes.

6. A cost accounting management system based on big data applications according to claim 5, characterized in that, It also includes a cost prediction module, which uses deep learning technology to perform cost prediction analysis.

7. A cost accounting management system based on big data applications according to claim 6, characterized in that, The cost prediction module includes the following steps: S1. Deep Learning Model Selection: Select a deep learning architecture, including feedforward neural networks, recurrent neural networks, long short-term memory networks, and convolutional neural networks; S2, Model Training: Train the deep learning model using the training set data and adjust the model parameters; S3. Feature Importance Analysis: Utilize model interpretability tools to analyze which features have the greatest impact on cost prediction; S4. Model Evaluation and Optimization: Evaluate the predictive performance of the model on the test set using evaluation metrics, including mean squared error and coefficient of determination. S5. Cost Prediction: Use a trained model to predict new and future cost data.

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