Enterprise cost control method and system based on big data

Abstract

The invention relates to the technical field of cost management, in particular to an enterprise cost control method and system based on big data, and the method comprises the following steps: detecting the calling record and use frequency of enterprise business resources, and recognizing key fund inflow and outflow nodes according to the flow direction frequency of a fund flow path to obtain cost classification boundary data. According to the invention, dynamic management and optimization of real-time cost data are realized through comprehensive analysis of enterprise resource calling data and fund flow direction, and tracking accuracy of resource utilization is improved through real-time monitoring of calling frequency and fund flow direction of service nodes, so that a cost control process is more transparent and predictable. According to the method, the cost items can be dynamically matched and optimized by using the time stamp and the related data of the fund flow, the accurate adjustment of the cost control is realized, in addition, the introduction of the dynamic cost distribution index allows the enterprise to quickly respond in the changing market environment, and the cost management error caused by the market fluctuation is effectively reduced.

Description

Technical Field

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

[0002] The field of cost management technology includes the refined management methods of resource consumption, financial expenditure and operating costs of enterprises in the process of production and operation. The core content of this technical field includes cost accounting, budget control, cost analysis, and cost optimization, involving multiple interdisciplinary subjects such as financial management, data analysis, and information technology. With the development of information technology, big data, artificial intelligence, and cloud computing are gradually applied to the field of cost management, enabling enterprises to more accurately monitor cost changes, predict cost trends, and formulate more scientific cost control strategies. This technical field systematically covers multiple aspects such as enterprise cost structure analysis, cost forecasting and monitoring, financial statement analysis, and cost saving strategy formulation, and has been widely used in various enterprise management information systems.

[0003] Among them, the enterprise cost control method and system based on big data refers to the use of big data technology to achieve effective control of enterprise costs. The technical matters targeted include data collection, data storage and data analysis. By collecting financial data, operational data and market data within the enterprise, storing data in a central database or cloud platform, analyzing data to identify cost-saving opportunities and potential risk points, and using big data technology to process and analyze large amounts of data, it provides decision-making support for the enterprise, thereby achieving the purpose of cost control.

[0004] Existing technologies lack a rapid response mechanism to real-time market changes, and show obvious lags in handling sudden market events, which makes it impossible for enterprises to adjust their cost strategies in a timely manner, thus facing the risk of budget overruns. For example, traditional methods are difficult to immediately reflect changes when raw material prices fluctuate, making cost budgets based on outdated data, which in turn affects the accuracy of decision-making. In addition, existing technologies fail to fully utilize modern information technology in cost data processing, resulting in insufficient data processing efficiency and accuracy. Especially in a large-scale data environment, insufficient processing leads to incorrect cost classification and budget allocation, increasing the complexity and risk of enterprise operations. Summary of the invention

[0005] In order to solve the problem that the existing technology lacks a rapid response mechanism to real-time market changes, and exhibits obvious lags in handling sudden market events, resulting in the company's inability to adjust its cost strategy in a timely manner, thus facing the risk of budget overruns. For example, traditional methods are difficult to immediately reflect changes when raw material prices fluctuate, so that cost budgets are based on outdated data, which in turn affects the accuracy of decision-making. In addition, the existing technology fails to fully utilize modern information technology in cost data processing, resulting in insufficient data processing efficiency and accuracy, especially in large-scale data environments. Insufficient processing leads to incorrect cost classification and budget allocation, increasing the complexity and risk of enterprise operations. The embodiment of the present invention provides an enterprise cost control method and system based on big data. The technical solution is as follows: On the one hand, a method for enterprise cost control based on big data is provided, comprising the following steps: S1: Detect the call records and usage frequency of the enterprise's business resources, identify the key capital inflow and outflow nodes according to the flow frequency of the capital flow path, and obtain the cost classification boundary data; S2: using the cost classification boundary data, monitoring the resource call frequency and capital inflow ratio of the business node, defining the time span and node set range, and generating context window dynamic indicators; S3: According to the context window dynamic indicator, the difference between the amount consumed by calling the business node and the amount of the cost item is calculated, the valley data of the difference node is screened, and the cost matching optimization coefficient is established; S4: Based on the cost matching optimization coefficient, the operating time of the production equipment and the change in the residence time of the logistics node are obtained, and the allocation weight offset is calculated according to the consumption level distribution value to generate a dynamic cost allocation index; S5: Call the dynamic cost allocation indicator, compare the allocation result with the budget and resource ratio, adjust the allocation parameters, analyze the budget execution and resource call efficiency, and generate a cost control optimization plan.

[0006] On the other hand, the cost classification boundary data includes time sensitivity, capital liquidity rating, and key business deadline indicators. The context window dynamic indicators include the resource usage frequency adjustment range, the capital inflow proportion update value, and the number of active business nodes. The cost matching optimization coefficient includes the optimal node selection, frequency consistency score, and time synchronization index. The dynamic cost allocation indicators include the running time adjustment analysis results, the residence time change, and the cost response sensitivity. The cost control optimization plan includes the allocation weight adjustment value, the resource call optimization parameter, and the budget deviation correction value.

[0007] On the other hand, the steps for acquiring the cost classification boundary data are specifically: S101: based on the enterprise business resource call records and usage frequency, sort the call time, extract the key time nodes of resource call, analyze the resource usage efficiency of each time node, and generate a resource node priority table; S102: Based on the resource node priority table, the source and destination of funds in the fund flow path are obtained, the inflow node and the outflow node are identified by counting the frequency data of the fund flow, the distribution ratio of funds between each node is determined, and a fund node distribution map is generated; S103: Based on the capital node distribution diagram, referring to the time span parameter of the enterprise business process, calculating the correlation between the time span and the capital node distribution, dividing the cost classification categories, and generating cost classification boundary data.

[0008] On the other hand, the steps for obtaining the context window dynamic indicator are specifically: S201: Based on the cost classification boundary data, real-time monitoring of business nodes is performed, resource call frequency of each node is captured regularly, changes in the proportion of capital inflow are recorded, real-time status of resource utilization and capital allocation is marked, and call and capital monitoring results are generated; S202: Based on the call and fund monitoring results, determine the change trend of resource utilization and the change node of fund inflow, adjust the time span of the context window, optimize the selection and classification of fund nodes, and generate time span optimization parameters; S203: Based on the time span optimization parameter, obtain node call frequency data, divide the data into intervals according to the time change trend, set the time span range, filter the business nodes that meet the frequency standard, regroup them, and generate context window dynamic indicators.

[0009] On the other hand, the steps for obtaining the cost matching optimization coefficient are specifically: S301: Based on the context window dynamic indicator, the consumption amount of the business node and the corresponding cost item amount are obtained, the amount difference data of each node is calculated, and a difference screening list is generated; S302: Based on the difference screening list, analyzing the calling frequency of the screening node and the occurrence frequency of the cost item, calculating the frequency matching degree between the two, and generating a matching frequency node table; S303: Based on the matching frequency node table, extract the timestamp data of the nodes and cost items, analyze the overlap degree of the timestamp range, filter the node set with a large time overlap, and generate a cost matching optimization coefficient.

[0010] On the other hand, the timestamp data of the extracted nodes and cost items are analyzed to determine the degree of overlap of the timestamp ranges, using the formula: ; Filter the set of nodes with critical time overlap and generate cost matching optimization coefficients; in, Representative The time overlap of nodes is and Respectively represent The node in The start and end time of the sub-time interval, Representative The node in The weight distribution value within the time interval, Representative The node in Cost offset value for the sub-time interval, Representative The total number of time intervals for each node.

[0011] On the other hand, the steps for obtaining the dynamic cost allocation index are specifically: S401: Based on the cost matching optimization coefficient, the operation time record of the production equipment is obtained, the change of the operation time is calculated, the residence time data of the logistics node is extracted, and the change range is calculated to generate a change overview; S402: Based on the change overview, the data cumulative offset value is calculated according to the change amplitude of the running time, and the change amount of the residence time is statistically analyzed in time series, and the weighted combination is performed according to the weight proportion of the time series distribution and the cumulative offset value to calculate the apportioned offset value and generate an offset value index; S403: Based on the offset value index, the matching degree between the consumption and the apportionment weight offset value is analyzed, the weight allocation parameter is adjusted in combination with the consumption distribution trend calculation, and the apportionment weight is corrected according to the matching result to generate a dynamic cost allocation index.

[0012] On the other hand, the weight distribution parameter is calculated and adjusted in combination with the consumption distribution trend, using the formula: ; And according to the matching results, the allocation weights are modified to generate dynamic cost allocation indicators; in, represents the adjusted weight distribution parameter, represents the real-time resource consumption of the i-th business node, Represents the resource consumption target value of the node in the business goal. represents the real-time resource utilization ratio of the i-th node, Represents the resource utilization ratio of the node in the business goal. Represents the real-time allocation offset value of the i-th node, Represents the target allocation offset value of the node. represents the time span of the ith node, Represents the total number of nodes.

[0013] On the other hand, the steps for obtaining the cost control optimization solution are specifically: S501: Based on the dynamic cost allocation indicator, the allocation result is compared with the budget range in the business goal item by item, the budget deviation value is calculated, the allocation items that do not conform to the budget range are recorded, and the distribution interval of the abnormal budget items is identified to generate the budget deviation result; S502: Based on the budget deviation result, analyzing the resource call data in the business node and the resource utilization ratio in the business target, comparing the call data with the target ratio, identifying the deviation item of resource call efficiency, and calculating the offset value of utilization efficiency to generate an efficiency offset index; S503: Based on the efficiency deviation index, the allocation parameters are adjusted, the data weights of the budget and resource utilization ratio are reallocated, the deviation values ​​in the allocation parameters are corrected, and a cost control optimization plan is generated.

[0014] On the other hand, a big data-based enterprise cost control system is provided, which is applied to a big data-based enterprise cost control method, including: The cost identification module identifies key capital inflow and outflow nodes based on the enterprise business resource call records and capital flow, and classifies costs based on the business process time span to obtain cost classification boundary data; The resource monitoring module monitors the resource call frequency of the business node and the change of the capital inflow ratio based on the cost classification boundary data, and generates contextual dynamic indicators; The cost matching optimization module calculates the difference between the consumption amount of the business node and the cost item based on the context dynamic indicator, filters the low-end data, analyzes the nodes with overlapping timestamps, and obtains the cost matching optimization coefficient; The allocation weight module obtains the change in the operating time of the production equipment and the residence time of the logistics node based on the cost matching optimization coefficient, calculates the allocation weight offset value, and integrates it with the consumption level distribution value to obtain a dynamic cost allocation index; The budget control module compares the allocation results with the budget and resource utilization ratio based on the dynamic cost allocation index, analyzes the budget execution and resource call efficiency, and obtains a cost control optimization plan.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By comprehensively analyzing the enterprise resource call data and capital flow, dynamic management and optimization of real-time cost data is achieved. By real-time monitoring of the call frequency of business nodes and capital flow, the accuracy of tracking resource utilization is improved, making the cost control process more transparent and predictable. In particular, by using timestamps and relevant data on capital flow, cost items can be dynamically matched and optimized to achieve precise adjustment of cost control. In addition, the introduction of dynamic cost allocation indicators allows enterprises to respond quickly in a changing market environment, providing a more flexible cost management strategy and effectively reducing cost management errors caused by market fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a main step flow chart of the present invention; Figure 2 is a flow chart of the steps of S1 of the present invention; Figure 3 is a flow chart of the steps of S2 of the present invention; Figure 4 is a flow chart of the steps of S3 of the present invention; Figure 5 is a flow chart of the steps of S4 of the present invention; Figure 6 is a flow chart of the steps of S5 of the present invention; Figure 7 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides an enterprise cost control method based on big data, such as Figure 1 As shown, the following steps are included: S1: According to the enterprise business resource call records and usage frequency, match the priority of the call time node, use the flow frequency of the capital flow path to identify the key capital inflow and outflow nodes, and divide the categories based on the time span of the business process to obtain cost classification boundary data; S2: Based on the cost classification boundary data, monitor the resource call frequency and capital inflow ratio changes of the business nodes, define the time span and node set range of the context window, and generate context window dynamic indicators; S3: Based on the context window dynamic indicators, the difference between the consumption amount of the business node and the cost item is calculated, the valley data of the difference node is screened, the calling frequency of the business node is compared with the occurrence frequency of the cost item, and the nodes with overlapping timestamps are analyzed to generate the cost matching optimization coefficient; S4: Based on the cost matching optimization coefficient, obtain the change in the operating time of the production equipment and the residence time of the logistics node, calculate the apportionment weight offset value, and weight the offset value with the consumption level distribution value to generate a dynamic cost allocation indicator; S5: Based on dynamic cost allocation indicators, compare the allocation results with the budget range and resource utilization ratio in the business objectives, analyze the budget execution and resource call efficiency, adjust the allocation parameters, and generate a cost control optimization plan.

[0024] Cost classification boundary data include time sensitivity, capital liquidity rating, and key business deadline indicators. Context window dynamic indicators include resource usage frequency adjustment range, capital inflow proportion update value, and number of active business nodes. Cost matching optimization coefficients include optimal node selection, frequency consistency score, and time synchronization index. Dynamic cost allocation indicators include runtime adjustment analysis results, residence time change, and cost response sensitivity. Cost control optimization plans include allocation weight adjustment values, resource call optimization parameters, and budget deviation correction values.

[0025] like Figure 2 As shown in the figure, the steps for obtaining cost classification boundary data are as follows: S101: based on the enterprise business resource call records and usage frequency, sort the call time, extract the key time nodes of resource call, analyze the resource usage efficiency of each time node, and generate a resource node priority table; Arrange the call time in ascending order to ensure that the timing logic of resource calls is clearly displayed. Use the sorting function in Excel or the database to sort the time. Generate the sorting order by comparing the timestamps one by one. Use the sorting results to extract the key time nodes of resource calls. The equipment on the production line frequently calls resources at multiple time points, mark the time points as key nodes, calculate the resource utilization efficiency of each node, and use the ratio of resource consumption to resource call frequency to represent the resource utilization efficiency. If the resource consumption at a certain time node is 500 units and the call frequency is 10 times, then the resource utilization efficiency is 50 units / time. Compare the resource utilization efficiency of each node, sort them according to their efficiency, and generate a resource node priority table.

[0026] S102: Based on the resource node priority table, the source and destination of funds in the fund flow path are obtained, the inflow node and the outflow node are identified by counting the frequency data of the fund flow, the distribution ratio of funds between each node is determined, and a fund node distribution map is generated; The source and destination of funds can be obtained by analyzing the capital flow path. The capital flow path refers to the process of funds flowing between departments or projects within the enterprise. Relevant information can be extracted through transaction records in the enterprise financial system. By statistically analyzing the frequency data of capital flow, the proportion of each node in the capital flow and its frequency can be obtained. In order to calculate the frequency of flow, the capital flow in each time period can be summarized. In a certain month, the capital inflow to a node is 10 million yuan, and the capital outflow is 8 million yuan. The capital flow frequency of the node can be measured by the ratio of inflow to outflow. Assuming that the inflow-outflow ratio is 5:4, the capital flow frequency can be expressed by a similar formula: Capital flow frequency = inflow / (inflow + outflow). According to the statistical frequency data, the inflow node and outflow node are further identified, and the distribution ratio of funds between the nodes is calculated. In the production and sales links of the enterprise, the inflow node is the sales department, and the outflow node is the raw material procurement department. Calculating the capital distribution ratio between the two helps the enterprise understand the dominant position of the department in the overall capital flow and obtain the capital node distribution map.

[0027] S103: Based on the capital node distribution diagram, referring to the time span parameters of the enterprise business process, calculating the correlation between the time span and the capital node distribution, dividing the cost classification categories, and generating cost classification boundary data.

[0028] Combined with the time span parameters of the enterprise's business process, the correlation between the capital flow node and the time span is analyzed. The time span parameter represents the time difference from resource call to capital flow. The production cycle of the enterprise is 7 days and the sales cycle is 14 days. The correlation between the time span and the distribution of capital nodes can be calculated through correlation analysis. By comparing the time points of the production cycle and capital inflow and outflow, if the capital inflow occurs at the end of the production cycle, it can be inferred that the flow of funds at the end of production has a significant correlation. When dividing the cost classification categories, considering the correlation between the time nodes of capital inflow and outflow and the business process, the internal resources of the enterprise can be divided into different categories. The purchase of raw materials in the production process belongs to direct costs, while the capital inflow after sales belongs to indirect costs. The standard basis for setting cost classification is not only the nodes of capital inflow and outflow, but also its time characteristics and position in the business process. If the frequency of capital flow nodes in the production link is more significant, they are classified as high-frequency cost nodes, which more clearly define the boundaries of various types of costs and generate cost classification boundary data.

[0029] like Figure 3 As shown in the figure, the steps for obtaining the context window dynamic indicator are as follows: S201: Based on the cost classification boundary data, the business nodes are monitored in real time, the resource call frequency of each node is captured regularly, the change in the proportion of capital inflow is recorded, the real-time status of resource utilization and capital allocation is marked, and the call and capital monitoring results are generated; By regularly capturing the resource call frequency of each business node, using monitoring software or data acquisition system to extract the call record of each node, the resource call frequency of the equipment on the production line is recorded in real time through sensors or data acquisition system. If the equipment calls resources 50 times per hour, then the call frequency of the node is 50 times / hour, and the changes in the proportion of capital inflow are recorded. The capital inflow ratio refers to the ratio of the capital inflow of each node to the total capital flow of the node. If the total capital flow of a node is 1 million yuan and the inflow of funds is 600,000 yuan, then the capital inflow ratio of the node is 60%. In order to comprehensively mark the real-time status of resource utilization and capital allocation, the data acquisition system dynamically updates the real-time status of each node according to the changes in the resource call frequency and the capital inflow ratio, and generates call and capital monitoring results.

[0030] S202: Based on the call and fund monitoring results, determine the change trend of resource utilization and the change node of fund inflow, adjust the time span of the context window, optimize the selection and classification of fund nodes, and generate time span optimization parameters; By comparing the call frequency and capital inflow ratio data in different time periods, the changing trend of resource utilization can be extracted. If the resource call frequency of a node has been on an upward trend while the capital inflow ratio has been on a downward trend in the past week, it can be determined that the resource utilization of the node is over-consumed and the funds are insufficient, and the resource allocation strategy needs to be adjusted. After identifying the changing trend, the time span of the context window needs to be adjusted. The time span refers to the time range for monitoring data collection. For example, if the resource call frequency of a node changes dramatically and the change occurs at a specific time of the day, the time span of the context window can be extended from daily to hourly to capture the node's fluctuation information in more detail. By adjusting the time span, the selection and classification of capital nodes can be optimized, so that the system can capture changes in capital flow and resource calls within a suitable time range, thereby accurately classifying and scheduling business nodes and generating time span optimization parameters.

[0031] S203: Based on the time span optimization parameters, the node call frequency data is obtained, the data is divided into intervals according to the time change trend, and the time span range is set, the business nodes that meet the frequency standard are screened and regrouped, and the context window dynamic index is generated.

[0032] The time period is divided into different intervals, such as the high-frequency interval (call frequency greater than 80 times / hour), the medium-frequency interval (call frequency of 40-80 times / hour) and the low-frequency interval (call frequency less than 40 times / hour). According to the interval division, the call frequency data of the node is set to a time span range. For example, the time span is set to 1 hour in the high-frequency interval to ensure that the hourly data changes are monitored in real time. In the low-frequency interval, the time span can be set to 3 hours to avoid over-refining and meaningless fluctuations. Business nodes that meet the frequency standards are screened and regrouped. Nodes with similar frequencies can be grouped together according to the resource call frequency and capital inflow data of the nodes for centralized resource scheduling and capital optimization. For example, nodes with significant frequencies can be allocated resources first, while low-frequency nodes can reduce resource allocation to generate context window dynamic indicators.

[0033] like Figure 4 As shown, the steps for obtaining the cost matching optimization coefficient are as follows: S301: Based on the dynamic indicator of the context window, the consumption amount of the business node and the amount of the corresponding cost item are obtained, the amount difference data of each node is calculated, and a difference screening list is generated; According to the consumption amount of each business node and the corresponding cost item amount, the consumption amount refers to the cost corresponding to the resource consumption of the business node within a certain period of time, such as the power consumption of the equipment during operation, or the raw material consumption during the operation of the production line. The cost item amount is the summary of all cost expenses related to the node, fixed cost and variable cost. The resource consumption amount of a certain node in each month is 500,000 yuan, and its cost item amount is 450,000 yuan. By calculating the amount data, the amount difference data of each node is obtained. The difference calculation formula is: amount difference = consumption amount - cost item amount. Assuming that the consumption amount of a node is 500,000 yuan and the cost item amount is 450,000 yuan, then the difference of the node is 50,000 yuan. By calculating the difference for each node, a difference screening list is generated.

[0034] S302: Based on the difference screening list, analyzing the calling frequency of the screening node and the occurrence frequency of the cost item, calculating the frequency matching degree between the two, and generating a matching frequency node table; Analyze the relationship between the call frequency and the cost item occurrence frequency of each screening node. The call frequency refers to the frequency of resource calls made by the node within a certain time range, while the cost item occurrence frequency refers to the frequency of cost occurrence related to the node. If a node calls resources 50 times in a day, and the cost item related to the node occurs 30 times in the same day, then the call frequency and cost item occurrence frequency are 50 times and 30 times respectively. To calculate the frequency matching degree between the two, the matching degree formula can be used: matching degree = (call frequency × cost item occurrence frequency) / (call frequency + cost item occurrence frequency). Assuming that the call frequency of a node is 50 times and the cost item occurrence frequency is 30 times, its matching degree is: (50×30) / (50+30)=1500 / 80=18.75, which represents the matching degree between the call frequency and the cost item occurrence frequency of the node. By calculating the matching degree of each node, a matching frequency node table is generated.

[0035] S303: Based on the matching frequency node table, extract the timestamp data of the nodes and cost items, analyze the degree of overlap of the timestamp range, filter the node set with a large time overlap, and generate a cost matching optimization coefficient.

[0036] Extract the timestamp data of nodes and cost items, analyze the overlap of timestamp ranges, and use the formula: ; Filter the set of nodes with critical time overlap and generate cost matching optimization coefficients; in, Representative The time overlap of nodes is and Respectively represent The node in The start and end time of the time interval, in hours. Representative The node in The weight distribution value in the time interval is a dimensionless value. Representative The node in The cost offset value of the time interval, in thousands of yuan. Representative The total number of time intervals for each node; Assume that the first node has three time intervals, namely 10:00 to 14:00, 15:00 to 18:00, and 20:00 to 22:00. Assume that its allocation weight and cost offset values ​​are 1.2 and 300 yuan, 1.5 and 250 yuan, and 1.1 and 400 yuan, respectively. Substitute them into the formula: ; Calculate the parameters for each time period: For the first time interval, the start time is 10 hours, the end time is 14 hours, the weight is 1.2, and the cost offset value is 300 yuan: ; ; ; For the second time interval, the start time is 15 hours, the end time is 18 hours, the weight is 1.5, and the cost offset value is 250 yuan; ; ; ; For the third time interval, the start time is 20 hours, the end time is 22 hours, the weight is 1.1, and the cost offset value is 400 yuan: ; ; ; Calculate the numerator: ; Calculate the denominator: ; calculate: ; Result 213.3 shows the overlap intensity of the first node within the timestamp range. The greater the overlap, the higher the importance of the node allocation adjustment weight, which needs to be further processed in combination with cost matching optimization.

[0037] like Figure 5 As shown in the figure, the steps for obtaining the dynamic cost allocation index are as follows: S401: Based on the cost matching optimization coefficient, the operation time record of the production equipment is obtained, the change of the operation time is calculated, the residence time data of the logistics node is extracted, and the change range is calculated to generate an overview of the change; The operating time record refers to the operating time of the equipment within a certain time range. For example, equipment A runs for 8 hours on one day and only runs for 6 hours on another day, with a change of 2 hours. The change is calculated by comparing the operating time of the equipment in different time periods. The residence time data of the logistics node is extracted and its change range is calculated. The residence time refers to the length of time a logistics node stays during the transmission process. For example, the time a transport vehicle stays at a certain station. The logistics node stays for 3 hours in one day and 4 hours on another day, then the change range is 1 hour. The change in the equipment's operating time is combined with the change range of the logistics node's residence time to generate an overview of the change.

[0038] S402: Based on the overview of the change amount, the data cumulative offset value is calculated according to the change amplitude of the running time, and the change amount of the residence time is statistically analyzed in time series. The weighted combination of the weight proportion of the time series distribution and the cumulative offset value is calculated to calculate the apportioned offset value and generate an offset value index; According to the change in the running time, the cumulative data offset value is calculated. The cumulative data offset value refers to the cumulative result of the fluctuation of the equipment running time in multiple time periods. For example, the equipment running time increased by 2 hours on the first day and decreased by 1 hour on the second day, so the cumulative offset value is 1 hour. The change in residence time is subjected to time series statistics. Time series statistics refers to the summary of the changes in residence time at each time point or time period. For example, the residence time of logistics nodes in a week is 3 hours, 4 hours, 2 hours, 5 hours, 6 hours, 3 hours and 4 hours respectively. Then the time series statistics can sum or average the data by day, analyze its fluctuation trend, and perform weighted combination based on the weight ratio of the time series distribution and the cumulative offset value. The weight ratio of the time series distribution can be allocated according to the flow or importance of each time period. The residence time in some time periods is more critical, so it can be given a weight. The weighted combination formula can be expressed as: weighted combination value = Σ (offset value of each time period × weight ratio of the time period). By calculating the value after weighted combination, the apportioned offset value is obtained, and the offset value index is generated.

[0039] S403: Based on the offset value index, the matching degree between the consumption and the apportionment weight offset value is analyzed, and the weight allocation parameters are adjusted in combination with the consumption distribution trend calculation, and the apportionment weight is corrected according to the matching result to generate a dynamic cost allocation index.

[0040] Combined with the consumption distribution trend, the weight distribution parameters are calculated and adjusted using the formula: ; And according to the matching results, the allocation weights are modified to generate dynamic cost allocation indicators; in, represents the adjusted weight distribution parameter, represents the real-time resource consumption of the i-th business node, Represents the resource consumption target value of the node in the business goal. represents the real-time resource utilization ratio of the i-th node, Represents the resource utilization ratio of the node in the business goal. Represents the real-time allocation offset value of the i-th node, Represents the target allocation offset value of the node. represents the time span of the ith node, Represents the total number of nodes; The real-time resource consumption for setting up the first node (such as production equipment) is 50,000 yuan. The resource consumption target of this node is planned to be 45,000 yuan. , the real-time resource utilization ratio of the first node is 60%, , the resource utilization ratio of this node in the target is 50%, , the real-time allocation offset value of the first node is 5,000 yuan, , the allocation offset value in the target plan is 3,000 yuan. , the time span of the first node is 30 days, , assuming there are 3 nodes for calculation, .

[0041] According to the above definition, the relevant value of the first node is substituted into the formula for calculation: ; Substitute the value of the first node into the formula: ; Perform the calculation: ; ; ; ; Adjustment weight of the first node About 0.204; Similarly, perform similar operations on the second and third nodes and sum them: Assume that the relevant parameters of the second node are: ; Substitute it into the formula and calculate: ; ; Assume that the relevant parameters of the third node are: ; calculate: ; ; Add the adjusted weights of each node to get the adjusted weight distribution parameter : ; Calculated It represents the overall adjustment weight allocation level in the three business nodes. The result reflects the difference between the real-time resource consumption and the target resource consumption, as well as the impact of the offset value and time span factors.

[0042] like Figure 6 As shown in the figure, the steps for obtaining the cost control optimization plan are as follows: S501: Based on the dynamic cost allocation index, the allocation results are compared item by item with the budget range in the business objective, the budget deviation value is calculated, the allocation items that do not conform to the budget range are recorded, and the distribution range of abnormal budget items is identified to generate the budget deviation result; The budget range refers to the upper and lower limits set for the allocation of various resources in the budget plan. A project allocates 1 million yuan in the budget for production equipment, but the actual allocation result is 1.2 million yuan, which exceeds the budget range. By comparing the values ​​of each allocation item one by one, the budget deviation value can be calculated, that is, the difference between the actual allocation result and the budget range. The calculation formula is: Budget deviation value = actual allocation value - budget target value. If the actual allocation value is greater than the budget target value, it means budget overspending; if it is less than the budget target value, it means budget savings. For non-compliant allocation items, they need to be recorded and marked to capture the intervals where the allocation items do not match the budget range and identify the distribution intervals of the non-compliant allocation items. If the deviation value of a certain allocation item is large, the time and area where the item occurs can be analyzed to determine whether the deviation is concentrated in certain specific time periods or areas, and generate budget deviation results.

[0043] S502: Based on the budget deviation result, analyze the resource call data in the business node and the resource utilization ratio in the business target, compare the call data with the target ratio, identify the deviation item of resource call efficiency, and calculate the deviation value of utilization efficiency to generate an efficiency deviation index; Resource call data refers to the amount of resources actually called by each business node within a certain period of time, such as the actual electricity or man-hours used by production line equipment. Resource utilization ratio refers to the ratio of resources allocated to each node according to business objectives. The business objective sets the resource utilization ratio of a node to 30%, and the actual call volume to 40%. By comparing the call data with the target ratio, the deviation item of resource call efficiency can be identified and the deviation value of resource call efficiency can be calculated. The calculation formula for the deviation value is: efficiency deviation value = actual call ratio - target call ratio. If the actual call ratio of a node is 40% and the target call ratio is 30%, then the efficiency deviation value is 40% - 30% = 10%. By calculating the deviation values ​​of all nodes, the resource utilization efficiency of the node can be identified and its resource allocation can be adjusted. By measuring the offset value of utilization efficiency, the degree of deviation can be further quantified. The calculation of the offset value can be achieved by comparing the resource call data of each node, evaluating the fluctuation of its resource utilization efficiency, and generating an efficiency offset indicator.

[0044] S503: Based on the efficiency deviation index, the allocation parameters are adjusted, the data weights of the budget and resource utilization ratio are reallocated, the deviation values ​​in the allocation parameters are corrected, and a cost control optimization plan is generated.

[0045] According to the efficiency shift index, the data weights of the budget and resource utilization ratio are reallocated. The resource utilization efficiency of some nodes is low, and it is necessary to increase the resource allocation of the nodes to improve the utilization efficiency; while for nodes with significant efficiency, their resource allocation is reduced. For the adjustment of the allocation parameters, the weighted average method can be used to combine the resource utilization efficiency of each node with its actual resource consumption, and recalculate its allocation weight. The revised allocation parameters will reflect the new budget and resource utilization ratio, and generate a cost control optimization plan.

[0046] like Figure 7 As shown, an enterprise cost control system based on big data includes: The cost identification module identifies key capital inflow and outflow nodes based on the enterprise business resource call records and capital flow, and classifies costs based on the business process time span to obtain cost classification boundary data; The resource monitoring module monitors the resource call frequency and capital inflow ratio changes of business nodes based on cost classification boundary data, and generates contextual dynamic indicators; The cost matching optimization module calculates the difference between the consumption amount of the business node and the cost item based on the contextual dynamic indicators, filters the low-end data, analyzes the nodes with overlapping timestamps, and obtains the cost matching optimization coefficient; The allocation weight module obtains the change in the operating time of production equipment and the residence time of logistics nodes based on the cost matching optimization coefficient, calculates the apportionment weight offset value, and integrates it with the consumption level distribution value to obtain the dynamic cost allocation index; The budget control module is based on dynamic cost allocation indicators, compares the allocation results with the budget and resource utilization ratio, analyzes the budget execution and resource call efficiency, and obtains the cost control optimization plan.

[0047] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. B can be singular or plural. In addition, the character " / " in this article generally indicates that the preceding and following related objects are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the preceding and following contexts.

[0048] In the present invention, "at least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, a b or at least one of c, which can represent: a b c ab ac bc or abc, where a b c can be single or multiple.

[0049] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0051] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0052] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0055] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0056] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for enterprise cost control based on big data, characterized in that: The method comprises: S1: Detect the call records and usage frequency of the enterprise's business resources, identify the key capital inflow and outflow nodes according to the flow frequency of the capital flow path, and obtain the cost classification boundary data; S2: using the cost classification boundary data, monitoring the resource call frequency and capital inflow ratio of the business node, defining the time span and node set range, and generating context window dynamic indicators; S3: According to the context window dynamic indicator, the difference between the amount consumed by calling the business node and the amount of the cost item is calculated, the valley data of the difference node is screened, and the cost matching optimization coefficient is established; S4: Based on the cost matching optimization coefficient, the operating time of the production equipment and the change in the residence time of the logistics node are obtained, and the allocation weight offset is calculated according to the consumption level distribution value to generate a dynamic cost allocation index; S5: Call the dynamic cost allocation indicator, compare the allocation result with the budget and resource ratio, adjust the allocation parameters, analyze the budget execution and resource call efficiency, and generate a cost control optimization plan.

2. The enterprise cost control method based on big data according to claim 1 is characterized in that: The cost classification boundary data includes time sensitivity, capital liquidity rating, and key business deadline indicators. The context window dynamic indicators include the resource usage frequency adjustment range, the capital inflow proportion update value, and the number of active business nodes. The cost matching optimization coefficient includes the optimal node selection, frequency consistency score, and time synchronization index. The dynamic cost allocation indicators include the running time adjustment analysis results, the residence time change, and the cost response sensitivity. The cost control optimization plan includes the allocation weight adjustment value, the resource call optimization parameter, and the budget deviation correction value.

3. The enterprise cost control method based on big data according to claim 1 is characterized in that: The steps for obtaining the cost classification boundary data are specifically: S101: based on the enterprise business resource call records and usage frequency, sort the call time, extract the key time nodes of resource call, analyze the resource usage efficiency of each time node, and generate a resource node priority table; S102: Based on the resource node priority table, the source and destination of funds in the fund flow path are obtained, the inflow node and the outflow node are identified by counting the frequency data of the fund flow, the distribution ratio of funds between each node is determined, and a fund node distribution map is generated; S103: Based on the capital node distribution diagram, referring to the time span parameter of the enterprise business process, calculating the correlation between the time span and the capital node distribution, dividing the cost classification categories, and generating cost classification boundary data.

4. The enterprise cost control method based on big data according to claim 1 is characterized in that: The steps for obtaining the context window dynamic indicator are specifically: S201: Based on the cost classification boundary data, real-time monitoring of business nodes is performed, resource call frequency of each node is captured regularly, changes in the proportion of capital inflow are recorded, real-time status of resource utilization and capital allocation is marked, and call and capital monitoring results are generated; S202: Based on the call and fund monitoring results, determine the change trend of resource utilization and the change node of fund inflow, adjust the time span of the context window, optimize the selection and classification of fund nodes, and generate time span optimization parameters; S203: Based on the time span optimization parameter, obtain node call frequency data, divide the data into intervals according to the time change trend, set the time span range, filter the business nodes that meet the frequency standard, regroup them, and generate context window dynamic indicators.

5. The enterprise cost control method based on big data according to claim 1 is characterized in that: The steps for obtaining the cost matching optimization coefficient are specifically: S301: Based on the context window dynamic indicator, the consumption amount of the business node and the corresponding cost item amount are obtained, the amount difference data of each node is calculated, and a difference screening list is generated; S302: Based on the difference screening list, analyzing the calling frequency of the screening node and the occurrence frequency of the cost item, calculating the frequency matching degree between the two, and generating a matching frequency node table; S303: Based on the matching frequency node table, extract the timestamp data of the nodes and cost items, analyze the degree of overlap of the timestamp ranges, filter the node sets with larger time overlap, and generate cost matching optimization coefficients.

6. The enterprise cost control method based on big data according to claim 5 is characterized in that: The timestamp data of the extracted nodes and cost items are analyzed to determine the degree of overlap of the timestamp ranges, using the formula: ； Filter the set of nodes with critical time overlap and generate cost matching optimization coefficients; in, Representative The time overlap of nodes is and Respectively represent The node in The start and end time of the time interval. Representative The node in The weight distribution value within the time interval, Representative The node in Cost offset value for the sub-time interval, Representative The total number of time intervals for each node.

7. The enterprise cost control method based on big data according to claim 1 is characterized in that: The steps for obtaining the dynamic cost allocation index are specifically: S401: Based on the cost matching optimization coefficient, the operation time record of the production equipment is obtained, the change of the operation time is calculated, the residence time data of the logistics node is extracted, and the change range is calculated to generate an overview of the change; S402: Based on the change overview, the data cumulative offset value is calculated according to the change amplitude of the running time, and the change amount of the residence time is statistically analyzed in time series, and the weighted combination is performed according to the weight proportion of the time series distribution and the cumulative offset value to calculate the apportioned offset value and generate an offset value index; S403: Based on the offset value index, the matching degree between the consumption and the apportionment weight offset value is analyzed, the weight allocation parameter is adjusted in combination with the consumption distribution trend calculation, and the apportionment weight is corrected according to the matching result to generate a dynamic cost allocation index.

8. The enterprise cost control method based on big data according to claim 7 is characterized in that: The weight distribution parameters are calculated and adjusted in combination with the consumption distribution trend, using the formula: ； And according to the matching results, the allocation weights are modified to generate dynamic cost allocation indicators; in, represents the adjusted weight distribution parameter, represents the real-time resource consumption of the i-th business node, Represents the resource consumption target value of the node in the business goal. represents the real-time resource utilization ratio of the i-th node, Represents the resource utilization ratio of the node in the business goal. Represents the real-time allocation offset value of the i-th node, Represents the target allocation offset value of the node. represents the time span of the ith node, Represents the total number of nodes.

9. The enterprise cost control method based on big data according to claim 1 is characterized in that: The steps for obtaining the cost control optimization solution are specifically: S501: Based on the dynamic cost allocation indicator, the allocation result is compared with the budget range in the business goal item by item, the budget deviation value is calculated, the allocation items that do not conform to the budget range are recorded, and the distribution interval of the abnormal budget items is identified to generate the budget deviation result; S502: Based on the budget deviation result, analyzing the resource call data in the business node and the resource utilization ratio in the business target, comparing the call data with the target ratio, identifying the deviation item of resource call efficiency, and calculating the offset value of utilization efficiency to generate an efficiency offset index; S503: Based on the efficiency deviation index, the allocation parameters are adjusted, the data weights of the budget and resource utilization ratio are reallocated, the deviation values ​​in the allocation parameters are corrected, and a cost control optimization plan is generated.

10. An enterprise cost control system based on big data, wherein the enterprise cost control system based on big data is used to implement the enterprise cost control method based on big data as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The cost identification module identifies key capital inflow and outflow nodes based on the enterprise business resource call records and capital flow, and classifies costs based on the business process time span to obtain cost classification boundary data; The resource monitoring module monitors the resource call frequency of the business node and the change of the capital inflow ratio based on the cost classification boundary data, and generates contextual dynamic indicators; The cost matching optimization module calculates the difference between the consumption amount of the business node and the cost item based on the context dynamic indicator, filters the low-end data, analyzes the nodes with overlapping timestamps, and obtains the cost matching optimization coefficient; The allocation weight module obtains the change in the operating time of the production equipment and the residence time of the logistics node based on the cost matching optimization coefficient, calculates the allocation weight offset value, and integrates it with the consumption level distribution value to obtain a dynamic cost allocation index; The budget control module compares the allocation results with the budget and resource utilization ratio based on the dynamic cost allocation index, analyzes the budget execution and resource call efficiency, and obtains a cost control optimization plan.

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