Intelligent cost analysis and optimization method and system and medium

Abstract

The invention provides an intelligent cost analysis and optimization method and system and a medium. The method comprises the following steps: acquiring operation characteristic data of a target service line in a first preset time period before optimization, and processing through a preset cost structure evaluation model according to cost characteristic data and income characteristic data to obtain first cost analysis data, processing through a preset cost benefit evaluation model according to the cost characteristic data and the income characteristic data to obtain second cost analysis data, and processing through a preset profitability evaluation model according to the cost characteristic data, the cost characteristic data and the income characteristic data to obtain third cost analysis data, and performing weighting processing according to the first cost analysis data, the second cost analysis data and the third cost analysis data to obtain a cost availability coefficient, and correspondingly taking optimization measures, thereby realizing the intelligent cost analysis and optimization technology.

Description

Technical Field

[0001] This application relates to the technical field of expenses, and more specifically, to methods, systems, and media for intelligent expense analysis and optimization. Background Art

[0002] In today's enterprise operations and project management, expense management faces many challenges. Traditional expense analysis methods mainly rely on manual statistics and simple data reports, making it difficult to quickly and accurately extract potential information and patterns from a large amount of expense data. This results in enterprises being unable to promptly detect expense anomalies, accurately predict expense trends, and thus difficult to formulate effective expense optimization strategies, affecting the enterprise's cost control and economic benefits.

[0003] In view of the above problems, there is an urgent need for effective technical solutions. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and medium for intelligent expense analysis and optimization. By obtaining the operation characteristic data of the target business line in the first preset time period before optimization, processing it through a preset cost structure evaluation model based on the cost characteristic data and revenue characteristic data to obtain the first expense analysis data, processing it through a preset expense-benefit evaluation model based on the expense characteristic data and revenue characteristic data to obtain the second expense analysis data, processing it through a preset profitability evaluation model based on the cost characteristic data, expense characteristic data, and revenue characteristic data to obtain the third expense analysis data, and performing weighted processing on the first expense analysis data, the second expense analysis data, and the third expense analysis data to obtain the expense utilization coefficient, and accordingly taking optimization measures to achieve the technology of intelligent expense analysis and optimization.

[0005] This application also provides a method for intelligent expense analysis and optimization, including the following steps:

[0006] Obtain the operation characteristic data of the target business line in the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data;

[0007] Process the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first expense analysis data;

[0008] Process the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model to obtain the second expense analysis data;

[0009] Process the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain the third expense analysis data;

[0010] Perform weighted processing on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures.

[0011] Optionally, in the cost intelligent analysis and optimization method described in this application, the operation characteristic data of the target business line within the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data, includes:

[0012] The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data;

[0013] The expense characteristic cost data includes marketing expense data, R & D expense data, and total input expense data;

[0014] The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output expense data, and total asset data.

[0015] Optionally, in the cost intelligent analysis and optimization method described in this application, the processing of the cost characteristic data and the revenue characteristic data through a preset cost structure evaluation model to obtain the first cost analysis data includes:

[0016] Perform statistical processing on the raw material cost data and the operating revenue data to obtain raw material cost rate data;

[0017] Perform statistical processing on the direct labor cost data and the operating revenue data to obtain direct labor cost rate data;

[0018] Process the raw material cost rate data and the direct labor cost rate data through a preset cost structure evaluation model to obtain the first cost analysis data.

[0019] Optionally, in the cost intelligent analysis and optimization method described in this application, the processing of the expense characteristic data and the revenue characteristic data through a preset expense - benefit evaluation model to obtain the second cost analysis data includes:

[0020] Perform statistical processing on the R & D expense data and the operating revenue data to obtain R & D investment ratio data;

[0021] Perform statistical processing on the marketing expense data and the sales revenue data to obtain marketing expense output ratio data;

[0022] Perform statistical processing on the total output expense data and the total input expense data to obtain input - output ratio data;

[0023] Processing the R & D investment ratio data, marketing expense output ratio data, and input-output ratio data through a preset cost-benefit evaluation model to obtain second cost analysis data.

[0024] Optionally, in the cost intelligent analysis and optimization method described in this application, the processing of the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain third cost analysis data includes:

[0025] Performing statistical and extraction processing on the cost characteristic data, expense characteristic data, and revenue characteristic data to obtain gross profit margin data, net profit margin data, and return on assets data;

[0026] Processing the gross profit margin data, net profit margin data, and return on assets data through a preset profitability evaluation model to obtain third cost analysis data.

[0027] Optionally, in the cost intelligent analysis and optimization method described in this application, the weighted processing of the first cost analysis data, second cost analysis data, and third cost analysis data to obtain a cost utilization coefficient and accordingly taking optimization measures includes:

[0028] Performing weighted processing on the first cost analysis data, second cost analysis data, and third cost analysis data to obtain a cost utilization coefficient;

[0029] Comparing the cost utilization coefficient with a preset cost utilization threshold to obtain a corresponding threshold comparison result;

[0030] Judging whether the cost allocation of the target business line is reasonable according to the threshold comparison result;

[0031] If the threshold comparison result is less than the preset threshold, the cost allocation of the target business line is unreasonable and corresponding optimization measures need to be taken.

[0032] In a second aspect, this application provides a cost intelligent analysis and optimization system, which includes: a memory and a processor. The memory includes a program of the cost intelligent analysis and optimization method. When the program of the cost intelligent analysis and optimization method is executed by the processor, the following steps are implemented:

[0033] Obtaining the operation characteristic data of the target business line within a first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data;

[0034] Processing the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain first cost analysis data;

[0035] Process the cost characteristic data and revenue characteristic data through a preset cost-benefit evaluation model to obtain second cost analysis data;

[0036] Process the cost characteristic data, cost characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain third cost analysis data;

[0037] Perform weighted processing on the first cost analysis data, second cost analysis data, and third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures.

[0038] Optionally, in the cost intelligent analysis and optimization system described in this application, the obtaining of the operation characteristic data of the target business line within the first preset time period before optimization, including cost characteristic data, cost characteristic data, and revenue characteristic data, includes:

[0039] The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data;

[0040] The cost characteristic cost data includes marketing cost data, R & D cost data, and total investment cost data;

[0041] The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output cost data, and total assets data.

[0042] Optionally, in the cost intelligent analysis and optimization system described in this application, the obtaining of the first cost analysis data by processing the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model includes:

[0043] Perform statistical processing on the raw material cost data and operating revenue data to obtain raw material cost rate data;

[0044] Perform statistical processing on the direct labor cost data and operating revenue data to obtain direct labor cost rate data;

[0045] Process the raw material cost rate data and direct labor cost rate data through a preset cost structure evaluation model to obtain first cost analysis data.

[0046] In a third aspect, the present application also provides a computer-readable storage medium, in which a program for the cost intelligent analysis and optimization method is stored. When the program for the cost intelligent analysis and optimization method is executed by a processor, the steps of the cost intelligent analysis and optimization method described in any one of the above are implemented.

[0047] As described above, the cost intelligent analysis and optimization method, system and medium disclosed in the present invention obtain the operation characteristic data of the target business line in the first preset time period before optimization, including cost characteristic data, expense characteristic data and revenue characteristic data, process the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first expense analysis data, process the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model to obtain the second expense analysis data, process the cost characteristic data, expense characteristic data and revenue characteristic data through a preset profitability evaluation model to obtain the third expense analysis data, perform weighted processing on the first expense analysis data, the second expense analysis data and the third expense analysis data to obtain the expense utilization coefficient, and accordingly take optimization measures, so as to realize the technology of cost intelligent analysis and optimization.

[0048] Other features and advantages of the present application will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the embodiments of the present application. The objectives and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written specification and the drawings. Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.

[0050] Figure 1 It is a flowchart of the cost intelligent analysis and optimization method provided by the embodiment of the present application;

[0051] Figure 2 It is a flowchart of obtaining the first expense analysis data of the cost intelligent analysis and optimization method provided by the embodiment of the present application;

[0052] Figure 3 It is a flowchart of obtaining the second expense analysis data of the cost intelligent analysis and optimization method provided by the embodiment of the present application;

[0053] Figure 4 It is a flowchart of obtaining the third expense analysis data of the cost intelligent analysis and optimization method provided by the embodiment of the present application. Detailed Description of the Embodiments

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0055] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0056] Please refer to Figure 1 , Figure 1 which is a flowchart of the intelligent cost analysis and optimization method in some embodiments of the present application. The intelligent cost analysis and optimization method is used in terminal devices, such as computer, mobile phone terminals, etc. The intelligent cost analysis and optimization method includes the following steps:

[0057] S11. Obtain the operation characteristic data of the target business line in the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data;

[0058] S12. Process the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first cost analysis data;

[0059] S13. Process the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model to obtain the second cost analysis data;

[0060] S14. Process the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain the third cost analysis data;

[0061] S15. Perform weighted processing on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures.

[0062] It should be noted that in today's enterprise operation and project management, expense management faces many challenges. Traditional expense analysis methods mainly rely on manual statistics and simple data reports, making it difficult to quickly and accurately mine potential information and patterns from a large amount of expense data. This results in the enterprise's inability to promptly detect expense anomalies, accurately predict expense trends, and thus difficult to formulate effective expense optimization strategies, affecting the enterprise's cost control and economic benefits. Therefore, a more intelligent and efficient expense analysis and optimization method is needed. In this embodiment, first, operation characteristic data within a first preset time period before optimization of the target business line is obtained, including cost characteristic data, expense characteristic data, and revenue characteristic data. The first expense analysis data is obtained by processing the cost characteristic data and the revenue characteristic data through a preset cost structure evaluation model. The second expense analysis data is obtained by processing the expense characteristic data and the revenue characteristic data through a preset expense-benefit evaluation model. The third expense analysis data is obtained by processing the cost characteristic data, the expense characteristic data, and the revenue characteristic data through a preset profitability evaluation model. The expense utilization coefficient is obtained by performing weighted processing on the first expense analysis data, the second expense analysis data, and the third expense analysis data, and corresponding optimization measures are taken, thereby realizing the technology of intelligent expense analysis and optimization.

[0063] According to an embodiment of the present invention, the obtaining of the operation characteristic data within a first preset time period before optimization of the target business line, including cost characteristic data, expense characteristic data, and revenue characteristic data, includes:

[0064] The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data;

[0065] The expense characteristic cost data includes marketing expense data, R & D expense data, and total input expense data;

[0066] The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output expense data, and total asset data.

[0067] It should be noted that to better evaluate the expense utilization situation of the target business line before optimization, relevant expense data needs to be collected and obtained first, including cost, expense, and revenue data. Among them, the cost characteristic data includes raw material cost, direct labor cost data, and operating cost. The expense characteristic cost data includes marketing expense, R & D expense, and total input expense data. The revenue characteristic data includes sales revenue, operating revenue, net profit, total output expense, and total asset data.

[0068] Please refer to Figure 2 , Figure 2It is a flowchart for obtaining the first cost analysis data of the intelligent cost analysis and optimization method in some embodiments of the present application. According to the embodiments of the present invention, processing the cost feature data and revenue feature data through a preset cost structure evaluation model to obtain the first cost analysis data includes:

[0069] S21. Perform statistical processing on the raw material cost data and operating revenue data to obtain raw material cost rate data;

[0070] S22. Perform statistical processing on the direct labor cost data and operating revenue data to obtain direct labor cost rate data;

[0071] S23. Process the raw material cost rate data and direct labor cost rate data through a preset cost structure evaluation model to obtain the first cost analysis data.

[0072] It should be noted that to determine whether the cost utilization of a business line is reasonable, it is first necessary to evaluate whether the cost structure is reasonable. Therefore, statistical processing is performed on the raw material cost data and operating revenue data to obtain raw material cost rate data, that is, raw material cost ÷ operating revenue × 100%, which reflects the proportion of raw material cost in operating revenue. Statistical processing is performed on the direct labor cost data and operating revenue data to obtain direct labor cost rate data, that is, direct labor cost ÷ operating revenue × 100%, which reflects the proportion of the labor cost directly involved in production or service in business revenue. Then, the raw material cost rate data and direct labor cost rate data are processed through a preset cost structure evaluation model to obtain the first cost analysis data. Among them, the cost structure evaluation model belongs to a neural network model, and is trained based on a large amount of historical raw material cost rate data and direct labor cost rate data to obtain a trained cost structure evaluation model.

[0073] Please refer to Figure 3 , Figure 3 It is a flowchart for obtaining the second cost analysis data of the intelligent cost analysis and optimization method in some embodiments of the present application. According to the embodiments of the present invention, processing the cost feature data and revenue feature data through a preset cost-benefit evaluation model to obtain the second cost analysis data includes:

[0074] S31. Perform statistical processing on the R & D cost data and operating revenue data to obtain R & D investment ratio data;

[0075] S32. Perform statistical processing on the marketing cost data and sales revenue data to obtain marketing cost output ratio data;

[0076] S33. Perform statistical processing on the total output cost data and the total input cost data to obtain input-output ratio data;

[0077] S34. Process the R & D investment ratio data, marketing cost output ratio data, and input-output ratio data through a preset cost-benefit evaluation model to obtain second cost analysis data.

[0078] It should be noted that, on the other hand, it is necessary to evaluate the cost-benefit to determine whether the cost is utilized effectively and whether it can bring benefits. Among them, statistical processing is performed on the R & D cost data and the operating income data to obtain the R & D investment ratio data, that is, R & D cost ÷ operating income × 100%, which reflects the degree of emphasis and investment in innovation and technology development of the business line. Statistical processing is performed on the marketing cost data and the sales revenue data to obtain the marketing cost output ratio data, that is, sales revenue ÷ marketing cost, which reflects the sales revenue brought by every one yuan of marketing cost input. The higher the ratio, the better the benefit of the marketing cost. Statistical processing is performed on the total output cost data and the total input cost data to obtain the input-output ratio data, that is, total output ÷ total input × 100%, which comprehensively measures the input and output benefits of the business line within a certain period. If this index is greater than 1, it indicates that the business line is profitable, and the larger the value, the better the benefit. Finally, the R & D investment ratio data, marketing cost output ratio data, and input-output ratio data are processed through a preset cost-benefit evaluation model to obtain second cost analysis data. Among them, the cost-benefit evaluation model belongs to a neural network model, and the initialized cost-benefit evaluation model is trained based on a large amount of historical R & D investment ratio data, marketing cost output ratio data, and input-output ratio data to obtain a trained cost-benefit evaluation model.

[0079] Please refer to Figure 4 , Figure 4 is a flowchart of obtaining third cost analysis data of the cost intelligent analysis and optimization method in some embodiments of the present application. According to an embodiment of the present invention, the processing of the cost feature data, cost feature data, and revenue feature data through a preset profitability evaluation model to obtain third cost analysis data includes:

[0080] S41. Perform statistical and extraction processing on the cost feature data, cost feature data, and revenue feature data to obtain gross profit margin data, net profit margin data, and return on assets data;

[0081] S42. Process the gross profit margin data, net profit margin data, and return on assets data through a preset profitability evaluation model to obtain third cost analysis data.

[0082] It should be noted that profitability is a crucial indicator for evaluating the utilization of business line expenses. Only a business line with a certain level of profitability is qualified. Therefore, based on cost characteristic data, expense characteristic data, and revenue characteristic data, statistical and extraction processing is carried out to obtain gross profit margin data, net profit margin data, and return on assets data. Among them, the gross profit margin = gross profit ÷ operating revenue × 100%, which shows the proportion of gross profit in each unit of operating revenue and reflects the initial profitability and cost control ability of products or services. The net profit margin = net profit ÷ operating revenue × 100%, which reflects the proportion of each unit of operating revenue that can ultimately be converted into net profit, comprehensively reflecting the profitability and cost and expense control level. The return on assets = net profit ÷ average total assets × 100%, which measures the ability of the business line to obtain profits by using all assets and reflects the comprehensive effect of asset utilization. Then, based on the above data, processing is carried out through a preset profitability evaluation model to obtain the third expense analysis data. Among them, the profitability evaluation model belongs to a neural network model, which is trained based on a large amount of historical gross profit margin data, net profit margin data, and return on assets data to obtain a trained profitability evaluation model.

[0083] According to an embodiment of the present invention, the weighted processing is performed on the first expense analysis data, the second expense analysis data, and the third expense analysis data to obtain an expense utilization coefficient, and corresponding optimization measures are taken, including:

[0084] The weighted processing is performed on the first expense analysis data, the second expense analysis data, and the third expense analysis data to obtain an expense utilization coefficient;

[0085] The obtained expense utilization coefficient is compared with a preset expense utilization threshold to obtain a corresponding threshold comparison result;

[0086] Based on the threshold comparison result, it is judged whether the expense allocation of the target business line is reasonable;

[0087] If the threshold comparison result is less than the preset threshold, the expense allocation of the target business line is unreasonable, and corresponding optimization measures need to be taken.

[0088] It should be noted that based on the obtained first expense analysis data, second expense analysis data, and third expense analysis data, weighted processing is performed to obtain an expense utilization coefficient, and then it is compared with a preset expense utilization threshold to obtain a corresponding threshold comparison result, and it is judged whether the expense allocation of the target business line is reasonable. If the threshold comparison result is less than the preset threshold, the expense allocation of the target business line is unreasonable, and corresponding optimization measures need to be taken.

[0089] According to an embodiment of the present invention, it further includes:

[0090] Obtain the optimized revenue feature data of the target business line within the second preset time period, including optimized operating revenue data and optimized net profit data;

[0091] Perform a comparison process based on the optimized operating revenue data and the operating revenue data to obtain the operating revenue growth rate data;

[0092] Perform a comparison process based on the optimized net profit data and the net profit data to obtain the net profit growth rate data;

[0093] Perform a weighted process based on the operating revenue growth rate data and the net profit growth rate data to obtain the development potential coefficient.

[0094] It should be noted that for the optimized business line, the optimization effect is evaluated through the profit growth situation. Among them, obtain the optimized operating revenue data and optimized net profit data of the target business line within the second preset time period. The second preset time period has the same time cycle as the first preset time period, and then perform statistical processing with the corresponding data before optimization to obtain the operating revenue growth rate data and the net profit growth rate data. Among them, the operating revenue growth rate = (optimized operating revenue - operating revenue before optimization) ÷ operating revenue before optimization × 100%, which reflects the market expansion speed and scale growth trend of the business line. The higher the growth rate, the faster the business development. The net profit growth rate = (optimized net profit - net profit before optimization) ÷ net profit before optimization × 100%, which reflects the growth trend of the profitability of the business line and is an important indicator for evaluating the development potential. Then, perform a weighted process based on the operating revenue growth rate data and the net profit growth rate data to obtain the development potential coefficient.

[0095] According to an embodiment of the present invention, it further includes:

[0096] Compare the development potential coefficient with a preset development potential threshold to obtain a corresponding second threshold comparison result;

[0097] Judge whether the optimization effect meets the expected value according to the second threshold comparison result;

[0098] If the second threshold comparison result is greater than or equal to the preset threshold, the optimization effect meets the expected value;

[0099] If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the expected value, and it is necessary to optimize the expenses of the target business line again.

[0100] It should be noted that the optimization effect is judged to meet the expectation by comparing the development potential indicators, that is, by comparing the development potential coefficient with the preset development potential threshold, obtaining the corresponding second threshold comparison result, and judging whether the optimization effect meets the expected value. If the second threshold comparison result is greater than or equal to the preset threshold, the optimization effect meets the expected value. If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the expected value, and the cost of the target business line needs to be optimized again.

[0101] In a second aspect, the present invention also discloses a cost intelligent analysis and optimization system, including a memory and a processor. The memory includes a cost intelligent analysis and optimization method program. When the cost intelligent analysis and optimization method program is executed by the processor, the following steps are implemented:

[0102] Obtain the operation characteristic data of the target business line within the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data;

[0103] Process the cost characteristic data and the revenue characteristic data through a preset cost structure evaluation model to obtain the first cost analysis data;

[0104] Process the expense characteristic data and the revenue characteristic data through a preset expense-benefit evaluation model to obtain the second cost analysis data;

[0105] Process the cost characteristic data, the expense characteristic data, and the revenue characteristic data through a preset profitability evaluation model to obtain the third cost analysis data;

[0106] Perform weighted processing on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures.

[0107] It should be noted that in today's enterprise operation and project management, expense management faces many challenges. Traditional expense analysis methods mainly rely on manual statistics and simple data reports, making it difficult to quickly and accurately extract potential information and patterns from a large amount of expense data. This results in the enterprise's inability to promptly detect expense anomalies, accurately predict expense trends, and thus difficult to formulate effective expense optimization strategies, affecting the enterprise's cost control and economic benefits. Therefore, a more intelligent and efficient expense analysis and optimization method is needed. In this embodiment, first, operation characteristic data of the target business line in the first preset time period before optimization is obtained, including cost characteristic data, expense characteristic data, and revenue characteristic data. The first expense analysis data is obtained by processing the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model. The second expense analysis data is obtained by processing the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model. The third expense analysis data is obtained by processing the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model. The expense utilization coefficient is obtained by performing weighted processing on the first expense analysis data, the second expense analysis data, and the third expense analysis data, and corresponding optimization measures are taken, thereby realizing the technology of intelligent expense analysis and optimization.

[0108] According to an embodiment of the present invention, the obtaining of the operation characteristic data of the target business line in the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data, includes:

[0109] The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data;

[0110] The expense characteristic cost data includes marketing expense data, R & D expense data, and total input expense data;

[0111] The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output expense data, and total asset data.

[0112] It should be noted that to better evaluate the expense utilization of the target business line before optimization, relevant expense data needs to be collected and obtained first, including cost, expense, and revenue data. Among them, the cost characteristic data includes raw material cost, direct labor cost data, and operating cost. The expense characteristic cost data includes marketing expense, R & D expense, and total input expense data. The revenue characteristic data includes sales revenue, operating revenue, net profit, total output expense, and total asset data.

[0113] According to an embodiment of the present invention, the obtaining of the first expense analysis data by processing the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model includes:

[0114] Statistical processing is performed based on the raw material cost data and the operating income data to obtain raw material cost rate data;

[0115] Statistical processing is performed based on the direct labor cost data and the operating income data to obtain direct labor cost rate data;

[0116] Processing is performed on the raw material cost rate data and the direct labor cost rate data through a preset cost structure evaluation model to obtain first expense analysis data.

[0117] It should be noted that whether the expense utilization of the business line is reasonable first requires evaluating whether the cost structure is reasonable. Therefore, statistical processing is performed based on the raw material cost data and the operating income data to obtain raw material cost rate data, that is, raw material cost ÷ operating income × 100%, which reflects the proportion of raw material cost in the operating income. Statistical processing is performed based on the direct labor cost data and the operating income data to obtain direct labor cost rate data, that is, direct labor cost ÷ operating income × 100%, which reflects the proportion of the labor cost directly involved in production or service in the business income. Then, processing is performed on the raw material cost rate data and the direct labor cost rate data through a preset cost structure evaluation model to obtain first expense analysis data. Among them, the cost structure evaluation model belongs to a neural network model, and is trained on a large amount of historical raw material cost rate data and direct labor cost rate data to obtain a trained cost structure evaluation model.

[0118] According to an embodiment of the present invention, the processing of the expense feature data and the income feature data through a preset expense-benefit evaluation model to obtain second expense analysis data includes:

[0119] Statistical processing is performed based on the R & D expense data and the operating income data to obtain R & D investment ratio data;

[0120] Statistical processing is performed based on the marketing expense data and the sales income data to obtain marketing expense output ratio data;

[0121] Statistical processing is performed based on the total output expense data and the total input expense data to obtain input-output ratio data;

[0122] Processing is performed on the R & D investment ratio data, the marketing expense output ratio data, and the input-output ratio data through a preset expense-benefit evaluation model to obtain second expense analysis data.

[0123] It should be noted that, on the other hand, it is necessary to conduct a benefit evaluation of the costs to determine whether the costs are utilized effectively and whether they can bring benefits. Among them, statistical processing is performed based on the R & D cost data and the operating income data to obtain the R & D investment ratio data, that is, R & D costs ÷ operating income × 100%, which reflects the degree of emphasis and investment in innovation and technological development of the business line. Statistical processing is performed based on the marketing cost data and the sales revenue data to obtain the marketing cost output ratio data, that is, sales revenue ÷ marketing costs, which reflects the sales revenue brought by every one yuan of marketing cost invested. The higher the ratio, the better the benefit of the marketing cost. Statistical processing is performed based on the total output cost data and the total input cost data to obtain the input-output ratio data, that is, total output ÷ total input × 100%, which comprehensively measures the input and output benefits of the business line within a certain period. If this indicator is greater than 1, it indicates that the business line is profitable, and the larger the value, the better the benefit. Finally, the second cost analysis data is obtained by processing the R & D investment ratio data, the marketing cost output ratio data, and the input-output ratio data through a preset cost-benefit evaluation model. Among them, the cost-benefit evaluation model belongs to a neural network model, and is trained based on a large amount of historical R & D investment ratio data, marketing cost output ratio data, and input-output ratio data to obtain a trained cost-benefit evaluation model.

[0124] According to an embodiment of the present invention, the processing of the cost feature data, the expense feature data, and the revenue feature data through a preset profitability evaluation model to obtain the third cost analysis data includes:

[0125] Statistical and extraction processing is performed on the cost feature data, the expense feature data, and the revenue feature data to obtain gross profit margin data, net profit margin data, and return on assets data;

[0126] The third cost analysis data is obtained by processing the gross profit margin data, the net profit margin data, and the return on assets data through a preset profitability evaluation model.

[0127] It should be noted that profitability is a crucial indicator for evaluating the utilization of business line expenses. Only a business line with a certain level of profitability is qualified. Therefore, based on cost characteristic data, expense characteristic data, and revenue characteristic data, statistical and extraction processing is carried out to obtain gross profit margin data, net profit margin data, and return on assets data. Among them, the gross profit margin = gross profit ÷ operating revenue × 100%, which shows the proportion of gross profit in each unit of operating revenue and reflects the initial profitability and cost control ability of products or services. The net profit margin = net profit ÷ operating revenue × 100%, which reflects the proportion of each unit of operating revenue that can ultimately be converted into net profit, comprehensively reflecting the profitability and cost and expense control level. The return on assets = net profit ÷ average total assets × 100%, which measures the ability of the business line to generate profits using all assets and reflects the comprehensive effect of asset utilization. Then, based on the above data, processing is carried out through a preset profitability evaluation model to obtain the third expense analysis data. Among them, the profitability evaluation model belongs to a neural network model, which is trained on a large amount of historical gross profit margin data, net profit margin data, and return on assets data to obtain a trained profitability evaluation model.

[0128] According to an embodiment of the present invention, the first expense analysis data, the second expense analysis data, and the third expense analysis data are weighted to obtain an expense utilization coefficient, and corresponding optimization measures are taken, including:

[0129] The first expense analysis data, the second expense analysis data, and the third expense analysis data are weighted to obtain an expense utilization coefficient;

[0130] The expense utilization coefficient is compared with a preset expense utilization threshold to obtain a corresponding threshold comparison result;

[0131] Based on the threshold comparison result, it is judged whether the expense allocation of the target business line is reasonable;

[0132] If the threshold comparison result is less than the preset threshold, the expense allocation of the target business line is unreasonable, and corresponding optimization measures need to be taken.

[0133] It should be noted that based on the obtained first expense analysis data, second expense analysis data, and third expense analysis data, weighting is carried out to obtain an expense utilization coefficient, then it is compared with a preset expense utilization threshold to obtain a corresponding threshold comparison result, and it is judged whether the expense allocation of the target business line is reasonable. If the threshold comparison result is less than the preset threshold, the expense allocation of the target business line is unreasonable, and corresponding optimization measures need to be taken.

[0134] According to an embodiment of the present invention, it further includes:

[0135] Obtain the optimized revenue characteristic data of the target business line within the second preset time period, including optimized operating revenue data and optimized net profit data;

[0136] Perform a comparison process based on the optimized operating revenue data and the operating revenue data to obtain the operating revenue growth rate data;

[0137] Perform a comparison process based on the optimized net profit data and the net profit data to obtain the net profit growth rate data;

[0138] Perform a weighting process based on the operating revenue growth rate data and the net profit growth rate data to obtain the development potential coefficient.

[0139] It should be noted that for the optimized business line, the optimization effect is evaluated through the profit growth situation. Among them, obtain the optimized operating revenue data and optimized net profit data of the target business line within the second preset time period. The second preset time period has the same time cycle as the first preset time period, and then perform statistical processing with the corresponding data before optimization to obtain the operating revenue growth rate data and the net profit growth rate data. Among them, the operating revenue growth rate = (optimized operating revenue - operating revenue before optimization) ÷ operating revenue before optimization × 100%, which reflects the market expansion speed and scale growth trend of the business line. The higher the growth rate, the faster the business development. The net profit growth rate = (optimized net profit - net profit before optimization) ÷ net profit before optimization × 100%, which reflects the growth trend of the profitability of the business line and is an important indicator for evaluating the development potential. Then, perform a weighting process based on the operating revenue growth rate data and the net profit growth rate data to obtain the development potential coefficient.

[0140] According to an embodiment of the present invention, it further includes:

[0141] Compare the development potential coefficient with a preset development potential threshold to obtain a corresponding second threshold comparison result;

[0142] Judge whether the optimization effect meets the expected value according to the second threshold comparison result;

[0143] If the second threshold comparison result is greater than or equal to the preset threshold, the optimization effect meets the expected value;

[0144] If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the expected value, and it is necessary to optimize the expenses of the target business line again.

[0145] It should be noted that the optimization effect is judged by comparing the development potential indicators, that is, by comparing the development potential coefficient with the preset development potential threshold, obtaining the corresponding second threshold comparison result, and judging whether the optimization effect meets the expected value. If the second threshold comparison result is greater than or equal to the preset threshold, the optimization effect meets the expected value. If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the expected value, and the cost of the target business line needs to be optimized again.

[0146] The third aspect of the present invention provides a readable storage medium, in which a program for the intelligent cost analysis and optimization method is stored. When the program for the intelligent cost analysis and optimization method is executed by a processor, the steps of the intelligent cost analysis and optimization method as described in any one of the above are implemented.

[0147] The intelligent cost analysis and optimization method, system and medium disclosed in the present invention obtain the operation characteristic data of the target business line in the first preset time period before optimization, including cost characteristic data, expense characteristic data and revenue characteristic data, process the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first cost analysis data, process the expense characteristic data and revenue characteristic data through a preset cost-benefit evaluation model to obtain the second cost analysis data, process the cost characteristic data, expense characteristic data and revenue characteristic data through a preset profitability evaluation model to obtain the third cost analysis data, perform weighted processing on the first cost analysis data, the second cost analysis data and the third cost analysis data to obtain a cost utilization coefficient, and take corresponding optimization measures, thereby realizing the technology of intelligent cost analysis and optimization.

[0148] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be electrical, mechanical, or other forms.

[0149] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in the embodiments of the present invention may all be integrated into one processing unit, or each unit may be separately taken as one unit, or two or more units may be integrated into one unit; the above-mentioned integrated unit may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0151] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks and other various media that can store program codes.

[0152] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

Claims

1. An intelligent cost analysis and optimization method, characterized in that, It includes the following steps: Obtain the operation characteristic data of the target business line within the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data; Process the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first expense analysis data; Process the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model to obtain the second expense analysis data; Process the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain the third expense analysis data; Perform weighted processing on the first expense analysis data, the second expense analysis data, and the third expense analysis data to obtain the expense utilization coefficient, and accordingly take optimization measures.

2. The intelligent cost analysis and optimization method according to claim 1, wherein The obtaining of the operation characteristic data of the target business line within the first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data, includes: The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data; The expense characteristic cost data includes marketing expense data, R & D expense data, and total input expense data; The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output expense data, and total asset data.

3. The intelligent expense analysis and optimization method according to claim 2, wherein The processing of the cost characteristic data and revenue characteristic data through a preset cost structure evaluation model to obtain the first expense analysis data includes: Perform statistical processing on the raw material cost data and operating revenue data to obtain the raw material cost rate data; Perform statistical processing on the direct labor cost data and operating revenue data to obtain the direct labor cost rate data; Process the raw material cost rate data and the direct labor cost rate data through a preset cost structure evaluation model to obtain the first expense analysis data.

4. The intelligent expense analysis and optimization method according to claim 3, wherein The processing of the expense characteristic data and revenue characteristic data through a preset expense-benefit evaluation model to obtain the second expense analysis data includes: Perform statistical processing on the R & D expense data and operating revenue data to obtain the R & D investment ratio data; Perform statistical processing on the marketing expense data and sales revenue data to obtain the marketing expense output ratio data; Perform statistical processing on the total output expense data and total input expense data to obtain the input-output ratio data; Process the R & D investment ratio data, the marketing expense output ratio data, and the input-output ratio data through a preset expense-benefit evaluation model to obtain the second expense analysis data.

5. The intelligent expense analysis and optimization method according to claim 4, wherein The processing of the cost characteristic data, expense characteristic data, and revenue characteristic data through a preset profitability evaluation model to obtain the third expense analysis data includes: Perform statistical and extraction processing on the cost characteristic data, expense characteristic data, and revenue characteristic data to obtain the gross profit margin data, net profit margin data, and return on assets data; Process the gross profit margin data, net profit margin data, and return on assets data through a preset profitability evaluation model to obtain the third expense analysis data.

6. The cost intelligent analysis and optimization method according to claim 5, wherein Perform weighted processing based on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures, including: Perform weighted processing based on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient; Compare the cost utilization coefficient with a preset cost utilization threshold to obtain a corresponding threshold comparison result; Judge whether the cost allocation of the target business line is reasonable according to the threshold comparison result; If the threshold comparison result is less than the preset threshold, the cost allocation of the target business line is unreasonable, and corresponding optimization measures need to be taken.

7. An intelligent expense analysis and optimization system, characterized in that, The system includes: a memory and a processor. The memory includes a program for the cost intelligent analysis and optimization method. When the program for the cost intelligent analysis and optimization method is executed by the processor, the following steps are implemented: Obtain the operation characteristic data of the target business line within a first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data; Process the cost characteristic data and the revenue characteristic data through a preset cost structure evaluation model to obtain first cost analysis data; Process the expense characteristic data and the revenue characteristic data through a preset expense-benefit evaluation model to obtain second cost analysis data; Process the cost characteristic data, the expense characteristic data, and the revenue characteristic data through a preset profitability evaluation model to obtain third cost analysis data; Perform weighted processing based on the first cost analysis data, the second cost analysis data, and the third cost analysis data to obtain a cost utilization coefficient, and accordingly take optimization measures.

8. The intelligent cost analysis and optimization system according to claim 7, wherein The obtaining of the operation characteristic data of the target business line within a first preset time period before optimization, including cost characteristic data, expense characteristic data, and revenue characteristic data, includes: The cost characteristic data includes raw material cost data, direct labor cost data, and operating cost data; The expense characteristic cost data includes marketing expense data, R & D expense data, and total input expense data; The revenue characteristic data includes sales revenue data, operating revenue data, net profit data, total output expense data, and total asset data.

9. The intelligent expense analysis and optimization system according to claim 8, wherein The processing of the cost characteristic data and the revenue characteristic data through a preset cost structure evaluation model to obtain first cost analysis data includes: Perform statistical processing on the raw material cost data and the operating revenue data to obtain raw material cost rate data; Perform statistical processing on the direct labor cost data and the operating revenue data to obtain direct labor cost rate data; Process the raw material cost rate data and the direct labor cost rate data through a preset cost structure evaluation model to obtain first cost analysis data.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for the cost intelligent analysis and optimization method. When the program for the cost intelligent analysis and optimization method is executed by the processor, the steps of the cost intelligent analysis and optimization method according to any one of claims 1 to 6 are implemented.

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