Dynamic cost distribution system based on operation cost method

Abstract

The invention relates to the technical field of enterprise cost management, in particular to a dynamic cost distribution system based on an operation cost method, which comprises a data acquisition module, a cost motivation analysis module, a dynamic distribution module and a result output module, the data acquisition module acquires resource consumption data and workload data in an enterprise operation process and transmits the data to the cost motivation analysis module, and the cost motivation analysis module performs cost motivation importance sorting on the acquired data based on a decision tree algorithm and outputs key cost motivation to the dynamic allocation module. The dynamic distribution module calculates the dynamic cost distribution rate of each operation center according to the key cost factors and completes cost distribution, and the result output module receives the distribution result of the dynamic distribution module and generates a cost distribution report. According to the invention, through integration of multi-system data, C4.5 algorithm screening causes, dynamic optimization of the allocation rate and generation of a multi-dimensional report, accurate, flexible and visual cost management and data continuity are realized.

Description

Technical Field

[0001] This invention relates to the field of enterprise cost management technology, and in particular to a dynamic cost allocation system based on activity-based costing. Background Technology

[0002] Cost management, as a core aspect of enterprise operations, can be traced back to the cost ledger method used in 16th-century Italian textile mills, which systematically recorded cost data to lay the foundation for profit calculation. With changes in the global economic environment and intensified market competition, the importance of cost management has become increasingly prominent, prompting in-depth discussions in academia regarding the construction and practical application of cost management systems. Research indicates that reasonable financial cost management helps enterprises reduce costs and increase efficiency, thereby improving economic benefits. From a business environment perspective, a favorable business environment can reduce cost stickiness and optimize cost management. At the industry application level, cost management has permeated multiple sectors, including healthcare, non-ferrous metals, engineering construction, manufacturing, foreign trade, and animal feed. Some argue that public hospitals need to strengthen cost control of medical consumables, non-ferrous metal enterprises should enhance cost management, and the engineering construction sector needs to propose improvement measures to address cost control issues. Some studies have found that managers with manufacturing backgrounds can reduce enterprise cost stickiness, while others point out that refined cost management is of great significance for the whole-process cost control of animal feed enterprises.

[0003] With the advancement of digital transformation, cost management concepts are constantly evolving and moving towards intelligentization. Research suggests the need for dynamic adjustments to cost management concepts, promoting deep integration with intelligent cost management principles. Related discussions indicate that the application of next-generation digital technologies in industry brains can reduce costs and improve efficiency. Exploration has revealed the practical value of new pathways for energy cost management based on the Internet of Things (IoT). Analysis shows that the architecture and advantages of e-commerce companies' dynamic cube cost management system can provide a reference for the industry. These studies collectively demonstrate that the embedding of intelligent technologies is reshaping cost management models, making it possible for enterprises to build efficient and accurate cost management systems.

[0004] Activity-based costing (ABC), an important method of cost management, originated in the late 1930s when the high proportion of indirect costs in hydropower plants was observed, leading to questions about traditional cost allocation methods. The emergence of flexible production systems in the 1970s challenged traditional cost accounting, and subsequent research propelled ABC's theoretical breakthroughs, attracting widespread attention. Practice has proven that ABC can track cost accounting objects in real time. Analysis of relevant factors shows that the proportion of indirect costs, market competition pressure, and product diversification influence companies' decisions to implement ABC. In application areas, ABC has made progress in industries such as healthcare, real estate, and construction, proposing improved cost accounting solutions for hospitals, real estate projects, and construction projects. Compared with traditional methods, it provides more accurate cost information, promoting effective product cost control.

[0005] Decision tree algorithms, as a data mining technique, are widely used in enterprise management. Related applications show that they can be used to analyze a company's working capital situation, dividend distribution decisions in agricultural enterprises, and to build personal credit assessment models for commercial banks. In sales management, combining decision trees with logistic regression can build sales forecasting models, and applying specific algorithms to predict factors influencing automobile sales can help companies formulate sales strategies. In customer management, analyzing customer complaint data using classification algorithms can extract typical characteristics of customer churn, and customer credit assessment models built using neural networks can be analyzed and validated from multiple dimensions such as company size, operational level, and market competitiveness. These applications demonstrate the significant value of decision tree algorithms in financial management, sales forecasting, and customer management.

[0006] Despite the significant achievements in cost management research and the deepening application of activity-based costing (ABC) and decision tree algorithms, existing research still has significant shortcomings. Exploration of ABC applications in certain industries is relatively limited, particularly regarding the accuracy of cost driver identification and the dynamic adjustment of cost allocation, which lack systematic solutions. Meanwhile, the potential of decision tree algorithms, as powerful data analysis tools, in cost management has not been fully realized. Existing research largely focuses on single stages or specific industries, failing to deeply integrate with ABC to create synergy. This results in room for improvement in the scientific rigor of cost driver analysis and the dynamism of cost allocation. These issues constrain the effectiveness of cost management systems, making it difficult to meet the needs of enterprises for refined and dynamic cost control in complex market environments. Summary of the Invention

[0007] The purpose of this invention is to overcome the above-mentioned problems and provide a dynamic cost allocation system based on activity-based costing. To achieve the above objective, this invention adopts the following technical solution:

[0008] A dynamic cost allocation system based on activity-based costing includes a data acquisition module, a cost driver analysis module, a dynamic allocation module, and a result output module. The data acquisition module is connected to the cost driver analysis module, which is also connected to the dynamic allocation module. The dynamic allocation module is connected to the result output module. The data acquisition module collects resource consumption data and workload data from the enterprise's work processes and transmits them to the cost driver analysis module. The cost driver analysis module uses a decision tree algorithm to rank the collected data by cost driver importance and outputs key cost drivers to the dynamic allocation module. The dynamic allocation module calculates the dynamic cost allocation rate for each work center based on the key cost drivers and completes the cost allocation. The result output module receives the allocation results from the dynamic allocation module and generates a cost allocation report.

[0009] Furthermore, the data acquisition module connects to the enterprise's ERP system, MES system, and production equipment sensor network through an industrial data gateway. The collected resource consumption data includes direct material consumption, direct labor hours, equipment runtime, and indirect cost incurred. The workload data includes the number of operations, product output, and batch quantity of each work center. After performing format verification, duplicate value removal, and missing value imputation on the collected data, the data acquisition module pushes it to the cost driver analysis module in real time through a standardized data interface.

[0010] Furthermore, the cost driver analysis module connects to the data acquisition module, receives the output structured data, and performs the following steps:

[0011] Step S11: Receive structured data transmitted by the data acquisition module, eliminate dimensional differences through standardization processing, and use the range transformation method to map the original data to the [0,1] interval;

[0012] Step S12: Construct a cost driver analysis model based on the C4.5 decision tree algorithm, using the cost deviation rate as the target variable, and calculate the information gain value by traversing all cost driver features. The formula for calculating the information gain value is as follows:

[0013] IG(A) = H(D) - H(D|A)

[0014] Where IG(A) is the information gain of feature A, H(D) is the empirical entropy of dataset D, and H(D|A) is the empirical conditional entropy of feature A given conditions;

[0015] Step S13: Sort the cost drivers in descending order of information gain value, select the key cost drivers, and output them to the dynamic allocation module through the data bus.

[0016] Furthermore, the cost driver analysis module and the dynamic allocation module interact through a two-way data channel. The dynamic allocation module feeds back the allocation results to the cost driver analysis module. The cost driver analysis module adjusts the splitting threshold of the decision tree model based on the feedback results. When the fluctuation of the information gain value of the key cost driver exceeds the preset range, the model reconstruction process is triggered.

[0017] Furthermore, the dynamic allocation module executes dynamic allocation logic based on the key cost drivers and weights output by the cost driver analysis module, with the following steps:

[0018] Step S21: Receive the key cost drivers and weights output by the cost driver analysis module. The weights are obtained by normalizing the information gain value.

[0019] Step S22: Based on the baseline cost allocation rate of the activity center, calculate the dynamic allocation rate by combining the standardized values ​​of key cost drivers. The formula for calculating the dynamic allocation rate is:

[0020]

[0021] Among them, R j R is the dynamic cost allocation rate for the j-th work center. j0 Let M be the baseline cost allocation rate for the j-th work center, M be the number of key cost drivers, and ω be the base cost allocation rate. m X represents the importance weight of the m-th key cost driver. jm Let m be the standardized value of the m-th key cost driver in the j-th work center;

[0022] Step S23: Allocate indirect costs to each work center according to the dynamic allocation rate, and synchronize the allocation results to the result output module.

[0023] Furthermore, the dynamic allocation module has a built-in periodic update mechanism. Through timer triggering and cost driver analysis module update process, steps S11-S13 and S21-S23 are re-executed every preset accounting period. The updated key cost driver weights and dynamic allocation rates are stored in the cache module of the dynamic allocation module, and the activity cost allocation rate is adjusted in real time.

[0024] Furthermore, after receiving the cost allocation results transmitted by the dynamic allocation module, the result output module generates a multi-dimensional cost allocation report that includes a comparison table of dynamic allocation rates for each work center, an analysis of the contribution ratio of key cost drivers, and a cost difference trend curve. The report is then pushed to the management terminal and the financial system through the enterprise's internal data platform.

[0025] Furthermore, a data caching module is set up between the data acquisition module and the cost driver analysis module. When the data acquisition data transmission link is interrupted, the data acquisition module will temporarily store the acquired data in the data caching module, and the data will be automatically resumed to the cost driver analysis module after the link is restored.

[0026] The advantages of this invention are:

[0027] 1. This invention integrates resource consumption and workload data from enterprise ERP system, MES system and production equipment sensor network through data acquisition module, and combines cost driver analysis module with cost driver importance ranking based on C4.5 decision tree algorithm to realize intelligent and accurate cost driver screening, effectively improve the efficiency of key cost driver identification, and provide a reliable data foundation for dynamic cost allocation.

[0028] 2. This invention utilizes the built-in periodic update mechanism of the dynamic allocation module, combined with the allocation results fed back from the cost driver analysis module, to continuously optimize the splitting threshold of the decision tree model, thereby achieving dynamic adjustment of the activity cost allocation rate. This ensures that the cost allocation results can respond in real time to changes in the enterprise's operational processes, improving the flexibility and adaptability of cost management.

[0029] 3. This invention generates a multi-dimensional report through the result output module, which includes a dynamic allocation rate comparison of the contribution ratio of key cost drivers and a cost difference trend curve. At the same time, it uses a data caching module to ensure automatic resume transmission when the data transmission link is interrupted, thereby realizing the intuitive presentation of cost allocation results and the continuity of data processing, enhancing the scientific nature of cost management decisions and the reliability of system operation. Attached Figure Description

[0030] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application.

[0031] In the attached diagram:

[0032] Figure 1 This is a system framework diagram of a dynamic cost allocation system based on activity-based costing in Example 1.

[0033] Figure 2 This is a flowchart of a dynamic cost allocation system based on activity-based costing in Example 1. Detailed Implementation

[0034] The present invention will now be described in detail and specifically through specific embodiments to enable a better understanding of the invention. However, the following embodiments do not limit the scope of protection of the present invention.

[0035] Example 1

[0036] like Figure 1-2 As shown, a dynamic cost allocation system based on activity-based costing includes a data acquisition module, a cost driver analysis module, a dynamic allocation module, and a result output module. The data acquisition module is connected to the cost driver analysis module, which is also connected to the dynamic allocation module. The dynamic allocation module is connected to the result output module. The data acquisition module collects resource consumption data and workload data from the enterprise's work processes and transmits them to the cost driver analysis module. The cost driver analysis module uses a decision tree algorithm to rank the collected data by cost driver importance and outputs key cost drivers to the dynamic allocation module. The dynamic allocation module calculates the dynamic cost allocation rate for each work center based on the key cost drivers and completes the cost allocation. The result output module receives the allocation results from the dynamic allocation module and generates a cost allocation report.

[0037] In a specific embodiment, the system adopts a modular, sequential design. The data acquisition module, cost driver analysis module, dynamic allocation module, and result output module are connected sequentially through data interfaces, forming a complete management chain from raw data collection to cost report generation. The data acquisition module focuses on capturing basic data in the enterprise's operational processes, transmitting resource consumption and workload data to the cost driver analysis module in real time, providing underlying data support for cost driver identification. The cost driver analysis module introduces a decision tree algorithm for in-depth data mining, filtering key influencing factors by ranking cost drivers by importance, ensuring that cost allocation logic closely aligns with core driving elements. The dynamic allocation module calculates the dynamic allocation rate for each work center based on key cost drivers, accurately allocating indirect costs, and then synchronizes the results to the result output module. The result output module transforms the allocation data into an intuitive cost report, helping management clearly understand the cost composition. The orderly connection of each module shortens the data flow path and improves the timeliness and accuracy of cost allocation.

[0038] Furthermore, the data acquisition module connects to the enterprise's ERP system, MES system, and production equipment sensor network through an industrial data gateway. The collected resource consumption data includes direct material consumption, direct labor hours, equipment runtime, and indirect cost incurred. The workload data includes the number of operations, product output, and batch quantity of each work center. After performing format verification, duplicate value removal, and missing value imputation on the collected data, the data acquisition module pushes it to the cost driver analysis module in real time through a standardized data interface.

[0039] In a specific embodiment, the data acquisition module constructs a multi-source data access network through an industrial data gateway, enabling real-time data interaction with the enterprise's ERP system, MES system, and sensor network of production equipment. The acquisition scope covers two major dimensions: resource consumption and workload. Resource consumption data includes direct material consumption, direct labor hours, equipment runtime, and indirect costs. Workload data covers the number of operations at the work center, product output, and batch quantity, comprehensively covering the basic information required for cost accounting. To ensure data quality, the module incorporates a three-level preprocessing mechanism: first, it filters non-standard data through format validation; second, it removes duplicate records to avoid data redundancy; and finally, it uses missing value imputation to complete the data chain. The processed standardized data is pushed to the cost driver analysis module in real time through a dedicated interface, providing high-quality input for algorithm analysis and reducing error interference in model calculations.

[0040] Furthermore, the cost driver analysis module connects to the data acquisition module, receives the output structured data, and performs the following steps:

[0041] Step S11: Receive structured data transmitted by the data acquisition module, eliminate dimensional differences through standardization processing, and use the range transformation method to map the original data to the [0,1] interval;

[0042] Step S12: Construct a cost driver analysis model based on the C4.5 decision tree algorithm, using the cost deviation rate as the target variable, and calculate the information gain value by traversing all cost driver features. The formula for calculating the information gain value is as follows:

[0043] IG(A) = H(D) - H(D|A)

[0044] Where IG(A) is the information gain of feature A, H(D) is the empirical entropy of dataset D, and H(D|A) is the empirical conditional entropy of feature A given conditions;

[0045] Step S13: Sort the cost drivers in descending order of information gain value, select the key cost drivers, and output them to the dynamic allocation module through the data bus.

[0046] In a specific embodiment, the cost driver analysis module and the data acquisition module establish a stable data receiving link to directly acquire pre-processed structured data. After the data enters the module, it first undergoes standardization processing, using a range transformation method to uniformly map the original data to the 0-1 range, eliminating dimensional differences between different indicators and making various cost drivers horizontally comparable. Subsequently, an analysis model is constructed based on the C4.5 decision tree algorithm, using the cost deviation rate as the target variable, and calculating the information gain value by traversing all cost driver features. The information gain value is quantified by the difference between empirical entropy and conditional entropy, accurately reflecting the degree of influence of each feature on cost fluctuations. Based on the descending order of the information gain values, the system automatically filters key cost drivers and transmits them to the dynamic allocation module via the data bus, achieving precise positioning of cost driving factors and providing a scientific basis for dynamic allocation.

[0047] Furthermore, the cost driver analysis module and the dynamic allocation module interact through a two-way data channel. The dynamic allocation module feeds back the allocation results to the cost driver analysis module. The cost driver analysis module adjusts the splitting threshold of the decision tree model based on the feedback results. When the fluctuation of the information gain value of the key cost driver exceeds the preset range, the model reconstruction process is triggered.

[0048] In a specific embodiment, a bidirectional data channel is designed between the cost driver analysis module and the dynamic allocation module to form a closed-loop optimization mechanism of analysis, allocation, and feedback. The dynamic allocation module transmits the cost allocation results back to the cost driver analysis module each time. The module automatically adjusts the splitting threshold of the decision tree model based on the feedback data, so that the model can adapt to the changes in data characteristics during the actual allocation process. When the fluctuation of the information gain value of the key cost driver exceeds the preset range, the system triggers the model reconstruction process to retrain the decision tree to maintain the analysis accuracy. This dynamic adjustment mechanism enables the cost driver analysis to continuously adapt to changes in enterprise operations and improves the robustness of the cost allocation model.

[0049] Furthermore, the dynamic allocation module executes dynamic allocation logic based on the key cost drivers and weights output by the cost driver analysis module, with the following steps:

[0050] Step S21: Receive the key cost drivers and weights output by the cost driver analysis module. The weights are obtained by normalizing the information gain value.

[0051] Step S22: Based on the baseline cost allocation rate of the activity center, calculate the dynamic allocation rate by combining the standardized values ​​of key cost drivers. The formula for calculating the dynamic allocation rate is:

[0052]

[0053] Among them, R j R is the dynamic cost allocation rate for the j-th work center. j0 Let M be the baseline cost allocation rate for the j-th work center, M be the number of key cost drivers, and ω be the base cost allocation rate. m X represents the importance weight of the m-th key cost driver. jm Let m be the standardized value of the m-th key cost driver in the j-th work center;

[0054] Step S23: Allocate indirect costs to each work center according to the dynamic allocation rate, and synchronize the allocation results to the result output module.

[0055] In a specific embodiment, the dynamic allocation module executes allocation logic based on the key cost drivers and weights output by the cost driver analysis module. The module first receives the key cost drivers and their corresponding weight values. The weights are obtained through information gain value normalization to ensure a unified standard for measuring the importance of different drivers. During the allocation calculation phase, the system dynamically adjusts the allocation based on the benchmark cost allocation rate of each work center, combining the standardized values ​​and weights of the key cost drivers. The dynamic cost allocation rate for each work center is calculated through weighted averages. In the formula, the benchmark allocation rate serves as the basic coefficient, and the standardized values ​​and weights of the key cost drivers together constitute the adjustment factor, achieving refined calibration of the allocation rate. After calculation, the module allocates indirect costs to each work center according to the dynamic allocation rate and synchronizes the final results to the result output module, ensuring the transparency and traceability of the cost allocation process.

[0056] Furthermore, the dynamic allocation module has a built-in periodic update mechanism. Through timer triggering and cost driver analysis module update process, steps S11-S13 and S21-S23 are re-executed every preset accounting period. The updated key cost driver weights and dynamic allocation rates are stored in the cache module of the dynamic allocation module, and the activity cost allocation rate is adjusted in real time.

[0057] In a specific embodiment, the dynamic allocation module incorporates a built-in periodic update mechanism, using a timer to periodically optimize cost allocation parameters. The system automatically triggers the data update process with the cost driver analysis module at preset accounting intervals, re-executing core steps such as data standardization, feature filtering, weight calculation, and allocation rate calibration. The updated key cost driver weights and dynamic allocation rates are stored in the module's built-in cache module, ensuring efficient data retrieval. This periodic update mechanism allows the cost allocation rate to dynamically adjust with the company's operating cycle, avoiding allocation deviations caused by long-term use of fixed parameters and improving the timeliness of cost management.

[0058] Furthermore, after receiving the cost allocation results transmitted by the dynamic allocation module, the result output module generates a multi-dimensional cost allocation report that includes a comparison table of dynamic allocation rates for each work center, an analysis of the contribution ratio of key cost drivers, and a cost difference trend curve. The report is then pushed to the management terminal and the financial system through the enterprise's internal data platform.

[0059] In a specific embodiment, after receiving the cost allocation results transmitted by the dynamic allocation module, the result output module initiates a multi-dimensional report generation process. The system automatically integrates the dynamic allocation rate data of each work center to generate a comparative analysis table. It reveals the cost composition through the contribution ratio analysis of key cost drivers and intuitively displays the cost fluctuation pattern by combining the cost difference trend curve. After the report is generated, it is pushed to the management terminal and financial system through the enterprise's internal data platform, realizing cross-departmental sharing of cost information. The multi-dimensional report presentation method meets the decision-making needs of managers at different levels, and the access of the data platform ensures the security and efficiency of information transmission.

[0060] Furthermore, a data caching module is set up between the data acquisition module and the cost driver analysis module. When the data acquisition data transmission link is interrupted, the data acquisition module will temporarily store the acquired data in the data caching module, and the data will be automatically resumed to the cost driver analysis module after the link is restored.

[0061] In a specific embodiment, a data caching module is added between the data acquisition module and the cost driver analysis module to build a secure data transmission mechanism. When the data acquisition and transmission link is interrupted, the data acquisition module automatically stores the real-time data in the caching module to avoid data loss. After the link is restored, the system starts the automatic resume transmission process, and the cached data is pushed to the cost driver analysis module in the original transmission order to ensure the integrity of the data chain. The caching module solves the problem of unstable data transmission in industrial environments and ensures the continuity of the cost analysis process.

[0062] The specific embodiments of the present invention have been described in detail above, but they are merely examples, and the present invention is not equivalent to the specific embodiments described above. For those skilled in the art, any equivalent modifications and substitutions to the present invention are also within the scope of the present invention. Therefore, all equivalent transformations and modifications made without departing from the spirit and scope of the present invention should be covered within the scope of the present invention.

Claims

1. A dynamic cost allocation system based on activity-based costing, characterized in that, The system includes a data acquisition module, a cost driver analysis module, a dynamic allocation module, and a result output module. The data acquisition module is connected to the cost driver analysis module, which is connected to the dynamic allocation module, and the dynamic allocation module is connected to the result output module. The data acquisition module collects resource consumption data and workload data from the enterprise's operational processes and transmits them to the cost driver analysis module. The cost driver analysis module uses a decision tree algorithm to rank the collected data by cost driver importance and outputs key cost drivers to the dynamic allocation module. The dynamic allocation module calculates the dynamic cost allocation rate for each operational center based on the key cost drivers and completes the cost allocation. The result output module receives the allocation results from the dynamic allocation module and generates a cost allocation report.

2. The dynamic cost allocation system based on activity-based costing according to claim 1, characterized in that, The data acquisition module connects to the enterprise's ERP system, MES system, and production equipment sensor network through an industrial data gateway. The collected resource consumption data includes direct material consumption, direct labor hours, equipment operating time, and indirect cost incurred. The workload data includes the number of operations, product output, and batch quantity of each work center. After performing format verification, duplicate value removal, and missing value imputation on the collected data, the data acquisition module pushes the data to the cost driver analysis module in real time through a standardized data interface.

3. A dynamic cost allocation system based on activity-based costing according to claim 2, characterized in that, The cost driver analysis module is connected to the data acquisition module, receives the output structured data, and performs the following steps: Step S11: Receive structured data transmitted by the data acquisition module, and eliminate dimensional differences through standardization processing. The standardization processing uses the range transformation method to map the original data to the [0,1] interval. Step S12: Construct a cost driver analysis model based on the C4.5 decision tree algorithm, using the cost deviation rate as the target variable, and calculate the information gain value by traversing all cost driver features. The formula for calculating the information gain value is as follows: IG(A) = H(D) - H(D)|A) Where IG(A) is the information gain of feature A, H(D) is the empirical entropy of dataset D, and H(D|A) is the empirical conditional entropy of feature A given conditions; Step S13: Sort the cost drivers in descending order of information gain value, select the key cost drivers, and output them to the dynamic allocation module through the data bus.

4. A dynamic cost allocation system based on activity-based costing according to claim 3, characterized in that, The cost driver analysis module and the dynamic allocation module interact through a two-way data channel. The dynamic allocation module feeds back the allocation results to the cost driver analysis module. The cost driver analysis module adjusts the splitting threshold of the decision tree model based on the feedback results. When the fluctuation range of the information gain value of the key cost driver exceeds the preset range, the model reconstruction process is triggered.

5. A dynamic cost allocation system based on activity-based costing according to claim 4, characterized in that, The dynamic allocation module executes dynamic allocation logic based on the key cost drivers and weights output by the cost driver analysis module, and the steps are as follows: Step S21: Receive the key cost drivers and weights output by the cost driver analysis module, wherein the weights are obtained through information gain value normalization processing; Step S22: Calculate the dynamic allocation rate based on the baseline cost allocation rate of the work center and the standardized values ​​of key cost drivers. The formula for calculating the dynamic allocation rate is as follows: Among them, R j R is the dynamic cost allocation rate for the j-th work center. j0 Let M be the baseline cost allocation rate for the j-th work center, M be the number of key cost drivers, and ω be the base cost allocation rate. m X represents the importance weight of the m-th key cost driver. jm The standardized value of the m-th key cost driver in the j-th work center; Step S23: Allocate indirect costs to each work center according to the dynamic allocation rate, and synchronize the allocation results to the result output module.

6. A dynamic cost allocation system based on activity-based costing according to claim 5, characterized in that, The dynamic allocation module has a built-in periodic update mechanism. Through timer triggering and cost driver analysis module update process, steps S11-S13 and S21-S23 are re-executed every preset accounting period. The updated key cost driver weights and the dynamic allocation rate are stored in the cache module of the dynamic allocation module, and the activity cost allocation rate is adjusted in real time.

7. A dynamic cost allocation system based on activity-based costing according to claim 6, characterized in that, After receiving the cost allocation results transmitted by the dynamic allocation module, the result output module generates a multi-dimensional cost allocation report that includes a comparison table of dynamic allocation rates for each work center, an analysis of the contribution ratio of key cost drivers, and a cost difference trend curve. The report is then pushed to the management terminal and the financial system through the enterprise's internal data platform.

8. A dynamic cost allocation system based on activity-based costing according to claim 7, characterized in that, A data caching module is set between the data acquisition module and the cost driver analysis module. When the data acquisition data transmission link is interrupted, the data acquisition module will temporarily store the acquired data in the data caching module, and automatically resume transmission to the cost driver analysis module after the link is restored.

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