CN115375089A - Modeling method and application of industrial chain ecological big data model - Google Patents

Modeling method and application of industrial chain ecological big data model Download PDF

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CN115375089A
CN115375089A CN202210815036.XA CN202210815036A CN115375089A CN 115375089 A CN115375089 A CN 115375089A CN 202210815036 A CN202210815036 A CN 202210815036A CN 115375089 A CN115375089 A CN 115375089A
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big data
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刘远
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Shanghai Lixin University Of Accounting And Finance
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    • G06Q10/067—Enterprise or organisation modelling
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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Abstract

The invention discloses a modeling method of an industrial chain ecological big data model and application thereof, wherein the modeling method of the industrial chain ecological big data model comprises the following steps: the method comprises the steps of obtaining multiple groups of data of each index for judging the economic benefit of an enterprise in a certain time period and the corresponding economic benefit of the enterprise in the time period, wherein the indexes comprise product sales rate, employee labor productivity, raw material cost difference rate and production equipment consumption difference rate, constructing an independent index model related to the economic benefit of the enterprise for each index, setting the maximum level limit and the minimum level limit of the corresponding index under the condition that the economic benefit of the enterprise is in a certain level in the index model and constructing a multi-element nonlinear model of product quality and each index model based on each constructed index model through nonlinear analysis.

Description

Modeling method and application of industrial chain ecological big data model
Technical Field
The invention relates to the technical field of industrial chains, in particular to a modeling method and application of an ecological big data model of an industrial chain.
Background
If any operation program in any department of the company enterprise is lost, the quality of the products or the services manufactured by the company enterprise can be influenced, and further the reputation and the overall operating profit of the company enterprise can be influenced; therefore, any employee in the company enterprise can propose an enterprise improvement project plan to suggest an improvement scheme if any loss occurs in the current system or product process of the company enterprise, so that the products or services provided by the company enterprise can be improved to a degree completely satisfactory to the client as much as possible, and the company enterprise can achieve the maximum profit target.
An important task in enterprise improvement project planning is to evaluate the economic benefits that can be generated. Basically, any operation loss or product loss in a company enterprise will result in the so-called loss cost, so that each enterprise improvement project plan will first perform the economic benefit assessment before actually executing, thereby pre-assessing the loss cost that can be reduced by each enterprise improvement project plan.
Conventionally, the economic performance evaluation method of enterprise improvement project plan is to collect data related to missing cost of each related operation department in a written and manual manner, such as indirect labor cost, cost of purchasing raw material parts, waste disposal cost, machine manufacturing cost, etc., and to perform the sorting and calculation work by manpower, thereby evaluating the missing cost of enterprise improvement project plan which can be reduced.
However, the conventional method for evaluating the economic benefits of the enterprise improvement project plan has the disadvantages that the arrangement and calculation work needs to be partially performed manually; thus making the overall enterprise improvement project execution task time consuming and inefficient.
Disclosure of Invention
The invention aims to solve the problems in the background art by aiming at the existing device, namely a modeling method of an industrial chain ecological big data model and application thereof.
In order to solve the technical problems, the invention provides the following technical scheme: a modeling method of an industrial chain ecological big data model comprises the following steps:
s1, obtaining multiple groups of data of various indexes for judging enterprise economic benefits in a certain time period and corresponding enterprise economic benefits in the time period, wherein the indexes comprise product sales rate, staff labor productivity, raw material cost difference rate and production equipment consumption rate;
s2, constructing an independent index model related to the enterprise economic benefits for the indexes, and setting the maximum level limit and the minimum level limit of the corresponding indexes according to the condition that the enterprise economic benefits in the index model are in a certain level.
And S3, constructing a multivariate nonlinear model of the product quality and each index model through nonlinear analysis based on the constructed index models.
The invention further explains that in the step S1, a certain time period is segmented by year, month or day.
The invention further illustrates that, in the step S1, the specific steps of obtaining the data of the product sales rate are as follows:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring a total production value and an enterprise sales production value of each small time period;
and dividing the enterprise sales output value in each small time period by the total production output value of the enterprise to obtain the product sales rate in each small time period.
The invention further explains that the specific steps of constructing the independent index model for each index are as follows:
in the step S1, the specific steps of acquiring data of the labor productivity of the staff are as follows:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring the number of products produced in each small time period;
the number of products produced in each small time period is divided by the time period to obtain the employee labor productivity.
The invention further illustrates that, in the step S1, the data of the raw material cost difference rate is obtained by the following specific steps:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring the cost difference of initial material accumulation, the cost difference of the material in the warehouse for the current period, the planned cost of the material in the warehouse for the initial period and the planned cost of the material in the warehouse for the current period;
the ratio of the cost difference of the initial storage materials, the cost difference of the warehousing materials accepted at the current period, the plan cost of the initial storage materials and the plan cost of the warehousing materials accepted at the current period is the raw material cost difference rate in the small time period.
The invention further illustrates that, in the step S1, the specific steps for obtaining the consumption rate of the production equipment are as follows:
dividing a certain time period into a plurality of small time periods with equal intermission, and acquiring the quantity consumed by production equipment in the certain time period;
and dividing the consumption quantity of the production equipment in each small time period by the time period to obtain the consumption rate of the production equipment.
The invention further illustrates that, in the step S2, the specific steps of constructing the individual index model related to the enterprise economic benefit for each index are as follows:
taking a plurality of groups of product sales rates/staff labor productivity/raw material cost difference rates/production equipment consumption rates as input values, taking enterprise economic benefits corresponding to the input values as output target values, and training through a neural network to obtain a trained prediction model of the enterprise economic benefits-the product sales rates, the enterprise economic benefits-the staff labor productivity, the enterprise economic benefits-the raw material cost difference rates and the enterprise economic benefits-the production equipment consumption rates;
the neural network adopts double layers of hidden layers, each hidden layer is provided with 5 Tanh-Sigmoid activation functions, and a gradient descent method is adopted for training.
Further, the invention further illustrates that, in the step S3, based on the constructed index models, the specific steps of constructing the multivariate linear model of the product quality and the index models through linear analysis are as follows:
carrying out linear fitting on prediction models of enterprise economic benefits-product sales rate, enterprise economic benefits-employee labor productivity, enterprise economic benefits-raw material cost difference rate and enterprise economic benefits-production equipment consumption rate to obtain a unitary regression model, and artificially synthesizing a multivariate nonlinear model;
and comparing results obtained by different fitting formulas according to the principle that the fitting error is minimum to obtain the multivariate nonlinear model of the enterprise economic benefit and each index model.
The present invention is further illustrated, wherein the results obtained by comparing different fitting formulas according to the principle of minimum fitting error are specifically as follows: introducing goodness of fit value R 2 And the square root mean square error RMSE judges the quality degree of each model fitting test data, R 2 The value range is [0,1 ]]，R 2 The closer to 1 the better the fitting, otherwise R 2 The model matching degree is not ideal when the value is close to 0, the smaller the RMSE value is, the better the model fitting effect is, and R 2 The formula for RMSE is as follows:
Figure BDA0003742002080000041
wherein, y i Represents the ith original data point and the ith original data point,
Figure BDA0003742002080000042
is the average value of the original data,
Figure BDA0003742002080000043
for model calculation, n is the number of raw data points.
An industrial chain ecological big data model constructed by a modeling method of the industrial chain ecological big data model is applied to enterprise economic management.
Compared with the prior art, the invention has the following beneficial effects: according to the modeling method of the industrial chain ecological big data model, the economic benefit of an enterprise and the multivariate nonlinear model for evaluating each index of the economic benefit of the enterprise can be visually and intuitively known in a certain time period and the reason for directly analyzing the economic benefit of the enterprise, so that an enterprise manager can timely adjust each department of the enterprise, and the good circulation of the economic benefit of the enterprise is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a modeling method of an industrial chain ecological big data model according to the present invention;
FIG. 2 is a flowchart of step S3 of the modeling method of the ecological big data model of the industrial chain according to the present invention;
FIG. 3 is a training flowchart of a neural network of a modeling method of an industrial chain ecological big data model according to the present invention;
fig. 4 is a model interface display schematic diagram constructed by the modeling method of the industrial chain ecological big data model of the invention.
Detailed Description
The present invention will be described in further non-limiting detail with reference to the following preferred embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a modeling method of an industrial chain ecological big data model and application thereof, which can analyze and measure the economic benefit of an enterprise through the data model, improve the efficiency, provide enterprise managers to adjust each part of the enterprise in time and ensure the good circulation of the economic benefit of the enterprise.
Referring to fig. 1-3, the method for modeling an ecological big data model of an industry chain includes:
the method comprises the steps of S1, obtaining multiple groups of data of various indexes for judging enterprise economic benefits in a certain time period and corresponding enterprise economic benefits in the time period, wherein the indexes comprise product sales rate, staff labor productivity, raw material cost difference rate and production equipment consumption rate, and in the embodiment, the certain time period is segmented by year, month or day.
The specific steps for acquiring the data of the product sales rate are as follows: dividing a certain time period into a plurality of equally-interrupted small time periods (for example, a month is taken as a period of time, then 30 days of the month is equally divided into 5 parts, each part is 6 days, and every 6 days is taken as an hour period, and 5 groups of data are respectively obtained), and obtaining the total output value of enterprise production and the enterprise sales output value of each hour period, and dividing the enterprise sales output value in each small time period by the total output value of enterprise production to obtain the product sales rate in each hour period.
The specific steps for acquiring the data of the labor productivity of the staff are as follows: the method comprises the steps of dividing a certain time period into a plurality of equally-interrupted small time periods, obtaining the number of products produced in each small time period, and dividing the number of the products produced in each small time period by the time period to obtain the labor productivity of staff.
The specific steps of the data acquisition of the raw material cost difference rate are as follows: dividing a certain time period into a plurality of equally-interrupted small time periods, and obtaining the ratio of the cost difference of the initial settlement materials, the cost difference of the income checking warehouse materials in the current period, the plan cost of the initial settlement materials, the plan cost of the warehousing materials in the current period, the cost difference of the initial settlement materials, the cost difference of the warehousing materials in the current period, the plan cost of the warehousing materials in the current period, and the plan cost of the warehousing materials in the current period, namely the raw material cost difference rate in the small time period.
The specific steps for obtaining the consumption rate of the production equipment are as follows: dividing a certain time period into a plurality of small time periods which are equally interrupted, obtaining the consumption quantity of the production equipment in the certain time period, and dividing the consumption quantity of the production equipment in each small time period by the time period to obtain the consumption rate of the production equipment.
S2, constructing an independent index model related to the enterprise economic benefit for each index, and setting a maximum level limit and a minimum level limit of the corresponding index according to the condition that the enterprise economic benefit in the index model is at a certain level.
Specifically, as shown in fig. 3, a plurality of sets of product sales rate/employee labor productivity/raw material cost difference rate/production equipment consumption rate are used as input values, and enterprise economic benefit corresponding to the input values is used as an output target value, and the training is performed through a neural network, so as to obtain a trained prediction model of enterprise economic benefit-product sales rate, enterprise economic benefit-employee labor productivity, enterprise economic benefit-raw material cost difference rate, and enterprise economic benefit-production equipment consumption rate;
the neural network adopts double layers of hidden layers, each hidden layer is provided with 5 Tanh-Sigmoid activation functions, and a gradient descent method is adopted for training.
And S3, constructing a multivariate nonlinear model of the product quality and each index model through nonlinear analysis based on the constructed index models.
Specifically, a univariate regression model obtained by linearly fitting a prediction model of the enterprise economic benefit-product sales rate, the enterprise economic benefit-employee labor productivity, the enterprise economic benefit-raw material cost difference rate and the enterprise economic benefit-production equipment consumption rate is subjected to artificial synthesis to obtain a multivariate nonlinear model;
and comparing results obtained by different fitting formulas according to the principle of minimum fitting error to obtain the multivariate nonlinear model of the enterprise economic benefit and each index model.
The result obtained by comparing different fitting formulas according to the principle of minimum fitting error is specifically as follows: introducing goodness of fit value R 2 And evaluating the quality degree of each model fitting test data by the root mean square error RMSE, R 2 The value range is [0,1 ]]，R 2 The closer to 1 the better the fit, otherwise R 2 The model matching degree is not ideal when the value is close to 0, the smaller the RMSE value is, the better the model fitting effect is, and R 2 The formula for RMSE is as follows:
Figure BDA0003742002080000071
wherein, y i Represents the ith original data point and represents the ith original data point,
Figure BDA0003742002080000072
is the average value of the original data,
Figure BDA0003742002080000073
calculated for the model, n is the original data pointThe number of the cells.
The model display diagram constructed by the modeling method of the industrial chain ecological big data model shown in fig. 4 is further described below, when the economic benefit of an enterprise in a period of time is evaluated (a time value can be set according to the length of time), the indexes in the period of time, namely, the product sales rate, the employee labor productivity, the raw material cost difference rate and the production equipment consumption rate, are input into the model, so that the model diagram shown in fig. 4 can be displayed, the economic benefit in the period of time can be displayed in the model, the model can automatically generate the maximum level limit and the minimum level limit corresponding to each index according to the economic benefit in the period of time, when a certain index exceeds the maximum level limit or is lower than the minimum level limit, the index is unreasonable, and an enterprise manager can analyze and further adjust the corresponding problems of the department according to the abnormal index.
According to the modeling method of the industrial chain ecological big data model, the economic benefit of an enterprise and the multivariate nonlinear model for evaluating each index of the economic benefit of the enterprise can be visually and intuitively known in a certain time period and the reason for directly analyzing the economic benefit of the enterprise, so that an enterprise manager can adjust all departments of the enterprise in time, and the good circulation of the economic benefit of the enterprise is ensured.
The invention also provides an industrial chain ecological big data model constructed by the industrial chain ecological big data model modeling method, which is applied to enterprise economic management.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description of the present invention, and do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions may be made in some technical features thereof, without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A modeling method of an industrial chain ecological big data model is characterized by comprising the following steps:
s1, acquiring multiple groups of data of various indexes for judging enterprise economic benefits in a certain time period and corresponding enterprise economic benefits in the time period, wherein the indexes comprise product sales rate, staff labor productivity, raw material cost difference rate and production equipment consumption rate;
s2, constructing an independent index model related to the enterprise economic benefits for the indexes, and setting the maximum level limit and the minimum level limit of the corresponding indexes according to the condition that the enterprise economic benefits in the index model are in a certain level.
And S3, constructing a multivariate nonlinear model of the product quality and each index model through nonlinear analysis based on the constructed index models.
2. The modeling method of the industrial chain ecological big data model according to claim 1, wherein in the step S1, a certain time period is segmented by year, month or day.
3. The modeling method of the industrial chain ecological big data model according to claim 1, wherein in the step S1, the specific steps of obtaining the product sales rate data are as follows:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring a total production value and an enterprise sales production value of each small time period;
and dividing the enterprise sales output value in each small time end by the total production output value of the enterprise to obtain the product sales rate in each small time period.
4. The modeling method of the industrial chain ecological big data model according to claim 1, wherein in the step S1, the specific steps of obtaining the data of the employee labor productivity are as follows:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring the number of products produced in each small time period;
the number of products produced in each hour period is divided by the time period to obtain the employee labor productivity.
5. The modeling method of the ecological big data model of the industrial chain according to claim 1, wherein in the step S1, the specific steps of obtaining the data of the raw material cost difference rate are as follows:
dividing a certain time period into a plurality of equally-interrupted small time periods, and acquiring the cost difference of the initial stockpiling material, the cost difference of the warehousing material accepted in the current period, the planned cost of the initial stockpiling material and the planned cost of the warehousing material accepted in the current period in the small time period;
the ratio of the cost difference of the initial stockpiling material, the cost difference of the warehousing material accepted at the current period, the planned cost of the initial stockpiling material and the planned cost of the warehousing material accepted at the current period is the raw material cost difference rate within the small time period.
6. The modeling method of the industrial chain ecological big data model according to claim 1, wherein in the step S1, the specific steps of obtaining the consumption rate of the production equipment are as follows:
dividing a certain time period into a plurality of small time periods with equal intermission, and acquiring the quantity consumed by production equipment in the certain time period;
and dividing the consumption amount of the production equipment in each small time period by the time period to obtain the consumption rate of the production equipment.
7. The modeling method of the ecological big data model of the industrial chain according to claim 1, wherein in the step S2, the specific steps of constructing the individual index model related to the economic benefit of the enterprise for each index are as follows:
taking a plurality of groups of product sales rates/staff labor productivity/raw material cost difference rates/production equipment consumption rates as input values, taking enterprise economic benefits corresponding to the input values as output target values, and training through a neural network to obtain a trained prediction model of the enterprise economic benefits-the product sales rates, the enterprise economic benefits-the staff labor productivity, the enterprise economic benefits-the raw material cost difference rates and the enterprise economic benefits-the production equipment consumption rates;
the neural network adopts double layers of hidden layers, each hidden layer is provided with 5 Tanh-Sigmoid activation functions, and a gradient descent method is adopted for training.
8. The modeling method of the industrial chain ecological big data model according to claim 1, wherein in the step S3, based on the index models constructed, the specific steps of constructing the multivariate linear model of the product quality and the index models through linear analysis are as follows:
carrying out linear fitting on the prediction models of the enterprise economic benefits, the product sales rate, the enterprise economic benefits, the employee labor productivity, the enterprise economic benefits, the raw material cost difference rate and the enterprise economic benefits, the production equipment consumption rate to obtain a unitary regression model, and artificially synthesizing a multivariate nonlinear model;
and comparing results obtained by different fitting formulas according to the principle that the fitting error is minimum to obtain the multivariate nonlinear model of the enterprise economic benefit and each index model.
9. The modeling method of the industrial chain ecological big data model according to claim 8, wherein the result obtained by comparing different fitting formulas according to the principle of minimum fitting error is as follows: introducing goodness of fit value R 2 Number of fitting tests to each model with root mean square error RMSEJudging according to the degree of quality, R 2 The value range is [0,1 ]]，R 2 The closer to 1 the better the fitting, otherwise R 2 The model matching degree is not ideal when the value is close to 0, the smaller the RMSE value is, the better the model fitting effect is, and R 2 The formula for RMSE is as follows:
Figure FDA0003742002070000031
wherein, y i Represents the ith original data point and represents the ith original data point,
Figure FDA0003742002070000032
is the average value of the original data,
Figure FDA0003742002070000033
for model calculation, n is the number of raw data points.
10. An industrial chain ecological big data model constructed based on the modeling method of the industrial chain ecological big data model according to any one of claims 1-9, and applied to enterprise economic management.
CN202210815036.XA 2022-07-12 2022-07-12 Modeling method and application of industrial chain ecological big data model Pending CN115375089A (en)

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