Mountainous area tobacco leaf production administrator demand prediction method

Abstract

A mountainous area tobacco leaf production administrator demand prediction method comprises the following steps: collecting X groups of data, including tobacco leaf production administrator compilation number, tobacco plan purchase amount, planting area, hundred mu contiguous sheet rate, tobacco grower number, per household planting area, average flue-cured tobacco yield of purchase site, planting administrative village distribution and tobacco area dispersion; the method comprises the following steps: by taking a tobacco production administrator compilation number as a dependent variable and the others as independent variables, performing correlation analysis by using SPSS, screening the independent variables related to the administrator compilation number, calculating a tobacco production management coefficient K, and clustering the tobacco production management coefficient K by using a K-means clustering analysis method; and predicting the number of tobacco production managers in each region in combination with industrial standards and clustering results. The scientificity and reliability of the prediction result are improved, and the more accurate number of tobacco leaf production managers is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel demand forecasting, and particularly to a method for forecasting the demand for tobacco leaf production administrators in mountainous areas. Background Art

[0002] Tobacco leaf production administrators mainly undertake the responsibilities of tobacco leaf planting, production and purchase, and related management in the tobacco industry, ensuring the standardization, standardization and high efficiency of tobacco leaf production to meet the demand for high-quality tobacco leaves in the tobacco industry. Due to the obvious seasonality of tobacco leaf production, the work intensity of tobacco leaf production administrators is relatively high during the planting and harvesting periods. Since the soil and climate conditions vary in different regions, the tobacco leaf planting technology and management methods need to be adjusted according to local conditions. Tobacco leaf production is highly technical, and tobacco leaf production administrators need to possess professional knowledge of tobacco leaf planting and management.

[0003] Currently, the tobacco leaf production in the whole country is mainly concentrated in the southwestern mountainous areas such as Yunnan, Guizhou, Sichuan, and Chongqing, accounting for more than 80% of the country. In the southwestern mountainous areas of our country, due to the wide distribution of tobacco-growing areas, unbalanced urban and rural distribution, relatively wide jurisdiction areas of grass-roots staff, and very difficult tobacco leaf production management, there are great differences in the number of tobacco farmers, average household planting area, and contiguous area rate per 100 mu in each county or district, and the demand for tobacco leaf production administrators in each county or district is also different. Since tobacco leaf production administrators are highly professional, if talent recruitment activities are carried out only when there is a shortage of tobacco leaf production administrators in the unit, the time required is relatively long, which is likely to affect the production and operation activities of the unit. Therefore, it is necessary to scientifically predict the demand for tobacco leaf production administrators, but how to improve the scientificity and accuracy of the demand for tobacco leaf production administrators in the mountainous tobacco industry is a common technical problem existing in the employers in the current mountainous tobacco industry. Summary of the Invention

[0004] Based on the above problems, the present invention aims to provide a method for forecasting the demand for tobacco leaf production administrators in mountainous areas, so as to solve the problem that the existing demand for tobacco leaf production administrators in the tobacco industry is unscientific and inaccurate.

[0005] The technical solution adopted by the present invention is as follows: A method for forecasting the demand for tobacco leaf production administrators in mountainous areas, comprising the following steps:

[0006] Step 1, collect X groups of data of counties (or county-level cities, districts), and each group of data includes the existing administrator establishment number, planned flue-cured tobacco purchase volume, planting area, contiguous area rate per 100 mu, number of tobacco farmers, average household planting area, average flue-cured tobacco yield of purchase stations, distribution of planting administrative villages, and tobacco-growing area dispersion degree within the current administrative region;

[0007] Step 2: Using the existing number of tobacco leaf production administrators as the dependent variable, and the planned tobacco purchase volume, planting area, percentage of contiguous plots of 100 mu or more, number of tobacco farmers, average planting area per household, average tobacco leaf production per purchasing station, distribution of administrative villages for planting, and dispersion degree of tobacco-growing areas as independent variables, use the SPSS tool to conduct a correlation analysis on the dependent variable and independent variables of the X groups of data;

[0008] Step 3: According to the results of the correlation analysis, select the independent variables with a Pearson correlation coefficient greater than 0.5 to obtain n associated variables;

[0009] Step 4: Determine the tobacco leaf production management coefficient K for the X groups based on the n associated variables obtained in Step 3;

[0010] Step 5: Use the K-means clustering analysis method in SPSS to cluster the tobacco leaf production management coefficient K of the X groups to obtain the clustering results;

[0011] Step 6: Based on the industry's guiding standards for the allocation of tobacco leaf production administrators, combine the results of Step 5 to calculate the corresponding quota standards for each clustering category;

[0012] Step 7: Predict the required number of tobacco leaf production administrators for each group according to the quota standards.

[0013] Furthermore, in Step 1, the planned tobacco purchase volume refers to the annual purchase plan issued by the superior tobacco company based on the order connection with relevant industrial enterprises; the planting area refers to the area agreed upon in the planting contract; the percentage of contiguous plots of 100 mu or more refers to the proportion of tobacco fields with contiguous planting areas reaching or exceeding 100 mu in a certain area to the planting area of that area, and the percentage of contiguous plots of 100 mu or more = contiguous area of 100 mu or more / total planting area; the number of tobacco farmers refers to the total number of farmers who have signed tobacco leaf planting contracts with the tobacco company and are engaged in tobacco leaf planting; the average planting area per household refers to the average value of the planting area and the number of tobacco farmers in a certain area, that is, the average area of tobacco leaf planting; the average tobacco leaf production per purchasing station refers to the average amount of tobacco leaves purchased by each purchasing station; the distribution of administrative villages for planting refers to the number and distribution of administrative villages where tobacco is planted; the concentration degree of tobacco-growing areas refers to the degree of concentration or dispersion of tobacco-growing areas. A high concentration degree indicates that the planting area is relatively concentrated, while a low concentration degree indicates that the planting area is relatively dispersed. The concentration degree of tobacco-growing areas = number of administrative villages of 1000 mu (units) / number of administrative villages with tobacco planting plans (units).

[0014] Furthermore, in Step 3, after screening out n associated variables according to the Pearson correlation coefficient, use the expert scoring method to score the selected variables to further optimize the variable selection and obtain the final associated variables.

[0015] Furthermore, the formula for determining the tobacco leaf production management coefficient K for each group in Step 4 is:

[0016] ki = associated variable i / average value of associated variable i;

[0017]

[0018] where K: tobacco leaf production management coefficient;

[0019] ki: proportion coefficient of associated variable i;

[0020] qi: weight of associated variable i;

[0021] n: number of associated variables;

[0022] The correlation coefficient of associated variable i is obtained by performing correlation analysis in step two.

[0023] Further, when using K-means clustering analysis of SPSS in step five, the value of K is set to 3 - 5 categories.

[0024] Further, the guiding standard for the allocation of tobacco leaf production administrators in the industry in step six is to allocate 1 tobacco leaf production administrator for every 500 mu of mountain land.

[0025] Further, after obtaining the corresponding quota and staffing standards for each clustering category in step six, the expert scoring method is used to score the corresponding quota and staffing standards for each clustering category to determine the final clustering category and the corresponding quota and staffing standards.

[0026] Advantages of the present invention: By collecting multiple variables and using the SPSS tool to perform correlation analysis on the independent variables related to the number of tobacco leaf production administrators, the present invention can more accurately capture the differences in the demand for tobacco leaf production administrators in different regions. Through K-means clustering analysis of SPSS, combined with the clustering analysis results, the corresponding quota and staffing standards for different values of K are calculated; based on the guiding standard in the industry, and then through organizing expert discussion and analysis, the tobacco leaf production administrators are determined. The use of the SPSS tool improves the scientificity and reliability of the analysis results of the independent variables related to the tobacco leaf production administrators. Using the expert scoring method and combining with the actual situation further improves the reliability and scientificity of the decision-making, and finally obtains more accurate tobacco leaf production administrators. This enables the enterprise to plan in advance during recruitment and avoid temporary recruitment from affecting the enterprise's production and business activities. Specific implementation manner

[0027] A method for predicting the demand for tobacco leaf production administrators in mountainous areas, comprising the following steps:

[0028] Step 1: Collect data. Through preliminary research and data collection, collect data on the existing establishment numbers of tobacco leaf production administrators, planned flue-cured tobacco purchase volumes, planting areas, percentage of contiguous plots of 100 mu or more, number of tobacco farmers, average planting area per household, average flue-cured tobacco output of purchase stations, distribution of planting administrative villages, and degree of dispersion of tobacco-growing areas in 57 "tobacco and cigarettes" county-level bureaus (branch companies) under the Guizhou Provincial Tobacco Company.

[0029] Planned flue-cured tobacco purchase volume: Refers to the annual purchase plan issued by the superior tobacco company based on the docking situation of orders with relevant industrial enterprises. The planned purchase volume is issued annually and reflects market demand and production targets. Planting area: Refers to the land area used for planting flue-cured tobacco in accordance with the area stipulated in the planting contract, measuring the scale of flue-cured tobacco production.

[0030] Percentage of contiguous plots of 100 mu or more: Refers to the proportion of tobacco fields with contiguous planting areas reaching or exceeding 100 mu in a certain area to the total planting area in that area, reflecting the degree of planting concentration and facilitating management and mechanized operations.

[0031] Percentage of contiguous plots of 100 mu or more = Area of contiguous plots of 100 mu or more / Total planting area. Number of tobacco farmers: Refers to the total number of farmers engaged in flue-cured tobacco planting who have signed flue-cured tobacco planting contracts with the tobacco company in each year, reflecting the group scale of flue-cured tobacco production.

[0032] Average planting area per household: Refers to the average value of the planting area and the number of tobacco farmers in a certain area, that is, the average area for planting flue-cured tobacco, measuring the large-scale planting of tobacco farmers.

[0033] Average flue-cured tobacco output of purchase stations: Refers to the average quantity of flue-cured tobacco purchased by each purchase station, reflecting the workload of the station and the regional output distribution.

[0034] Distribution of planting administrative villages: Refers to the number and distribution of administrative villages where flue-cured tobacco is planted, reflecting the geographical scope of flue-cured tobacco planting.

[0035] Degree of concentration of tobacco-growing areas: Refers to the degree of concentration or dispersion of flue-cured tobacco planting areas. A high degree of concentration indicates that the planting areas are relatively concentrated, while a low degree indicates that the planting areas are relatively dispersed. Degree of concentration of tobacco-growing areas = Number of administrative villages of 1000 mu (units) / Number of administrative villages with flue-cured tobacco planting plans (units).

[0036] Step 2: Through preliminary research and data collection, using the existing establishment numbers of tobacco leaf production administrators in 57 "tobacco and cigarettes" county-level bureaus (branch companies) in Guizhou Province as the dependent variable, and using 8 factors such as "planned flue-cured tobacco purchase volume, planting area, percentage of contiguous plots of 100 mu or more, number of tobacco farmers, average planting area per household, average flue-cured tobacco output of purchase stations, distribution of planting administrative villages, degree of dispersion of tobacco-growing areas" as independent variables, use the SPSS tool to conduct a correlation analysis on the dependent variable and independent variables.

[0037] Table 1 Results of correlation analysis between the number of tobacco leaf production administrators and various factors

[0038]

[0039]

[0040] Step 3: Determine the final associated variables based on the results of the correlation analysis;

[0041] Through analysis, several factors such as "planned purchase volume of flue-cured tobacco, percentage of contiguous plots of 100 mu, average planting area per household, average flue-cured tobacco yield at purchase stations, dispersion degree of tobacco-growing areas, and planting area" are all somewhat correlated with the number of tobacco leaf production administrators.

[0042] Based on the correlation analysis, the expert scoring method is used to screen the associated variables again. Internal experts in the tobacco leaf line are organized to conduct centralized discussions and scoring, and it is considered that: A. The mountainous area in Guizhou accounts for 92%, and the natural endowment for tobacco leaf planting is poor. The levels of "percentage of contiguous plots of 100 mu" and "dispersion degree of tobacco-growing areas" have a greater impact on the difficulty of tobacco leaf production management work; B. The smaller the "average planting area per household" and the lower the "average flue-cured tobacco yield at purchase stations" indicate a poor concentration and a high dispersion degree in the tobacco-growing areas, which have a greater impact on the workload, time, and energy input of tobacco leaf production management personnel. Determine the final associated variables as "percentage of contiguous plots of 100 mu", "average planting area per household", "average flue-cured tobacco yield at purchase stations", and "dispersion degree of tobacco-growing areas".

[0043] Step 4: Determine the tobacco leaf production management coefficient K based on the final associated variables obtained in Step 3;

[0044]

[0045] ki = associated variable i / average value of associated variable i;

[0046]

[0047] Where K: tobacco leaf production management coefficient;

[0048] ki: proportion coefficient of associated variable i;

[0049] qi: weight of associated variable i;

[0050] n: number of associated variables;

[0051] It is known from Step 3 that n = 4;

[0052] k1: coefficient of percentage of contiguous plots of 100 mu;

[0053] k1 = percentage of contiguous plots of 100 mu in the whole county / average percentage of contiguous plots of 100 mu in the whole province, percentage of contiguous plots of 100 mu = area of contiguous plots of 100 mu / total planting area;

[0054] k2: coefficient of average planting area per household;

[0055] k2 = average planting area per household in the whole county / average planting area per household in the whole province;

[0056] k3: Coefficient of average flue-cured tobacco yield at acquisition sites

[0057] k3 = Average flue-cured tobacco yield of county-wide acquisition sites / Average flue-cured tobacco yield of province-wide acquisition sites

[0058] k4: Coefficient of tobacco-growing area dispersion

[0059] k4 = Dispersion of tobacco-growing area / Average dispersion of province-wide tobacco-growing areas, where the dispersion of tobacco-growing area = Number of administrative villages in the tobacco-growing area (units) / Planned flue-cured tobacco purchase volume (ten thousand dan)

[0060] Table 2 Correlation coefficients and weights of the final associated variables

[0061]

[0062] Finally, we get: K = k1 * 20% + k2 * 30% + k3 * 30% + k4 * 20%

[0063] Step 5: Use the K-means clustering analysis method in SPSS to cluster the 57 county-level "tobacco and cigarette" bureaus (branch companies) in the province according to the tobacco leaf production management coefficient K, and obtain the clustering results; see Tables 3, 4, and 5 for details

[0064] Cluster analysis of "tobacco leaf production management coefficient" - divided into 3 categories

[0065] Table 3 Cluster analysis table of three categories of tobacco leaf production management coefficients

[0066]

[0067]

[0068] ② Cluster analysis of "tobacco leaf production management coefficient" - divided into 4 categories

[0069] Table 4 Cluster analysis table of four categories of tobacco leaf production management coefficients

[0070]

[0071] ③ Cluster analysis of "tobacco leaf production management coefficient" - divided into 5 categories

[0072] Table 5 Cluster analysis table of five categories of tobacco leaf production management coefficients

[0073]

[0074] Step 6: Based on the industry's guiding standards for the allocation of tobacco leaf production administrators, combine the clustering analysis results to calculate the corresponding quota and staffing standards for each category

[0075] Calculation of quota and staffing standards

[0076] Based on the industry guiding standard of allocating one tobacco leaf production administrator for every 500 mu, combined with the results of cluster analysis, calculate the corresponding quota and staffing standards for each category.

[0077] Table 6 Calculation Table of Quota and Staffing Standards for Tobacco Leaf Production Administrators

[0078]

[0079] Step 7: Determine the final cluster results;

[0080] Based on the above results of cluster analysis, use the expert scoring method to determine the final cluster categories and quota and staffing standards, and organize internal experts in the tobacco leaf line to conduct discussions and analyses. After discussion, it is considered that the cluster result of clustering into 3 categories is more in line with the actual situation of Guizhou Tobacco, but the classification of individual county bureaus (branch companies) should be slightly adjusted, and the quota and staffing standards should be rounded according to the arithmetic progressive rule.

[0081] Table 7 Calculation Table of Final Quota and Staffing Standards for Tobacco Leaf Production Administrators

[0082]

[0083]

[0084] Predict the number of tobacco leaf production administrators according to the final cluster results.

[0085] Table 8 Predicted Number of Tobacco Leaf Production Administrators in County Bureaus (Branch Companies) of Qiannan Prefecture, Guizhou Province

[0086]

[0087] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for predicting demand for tobacco production managers in mountainous areas, characterized in that: The following steps are involved: Step 1: collect data of X groups of counties (or county-level cities, districts), each group of data includes the number of existing administrators in the current administrative area, the planned purchase volume of flue-cured tobacco, the planting area, the 100-mu contiguous rate, the number of tobacco farmers, the average planting area per household, the average flue-cured tobacco yield at the purchase station, the distribution of planting administrative villages, and the dispersion of tobacco areas; Step 2: With the number of existing tobacco production managers as the dependent variable, the planned purchase volume of flue-cured tobacco, the planting area, the 100-mu contiguous rate, the number of tobacco farmers, the average planting area per household, the average flue-cured tobacco yield at the purchase site, the distribution of planting administrative villages and the dispersion of tobacco areas as independent variables, SPSS tools were used to conduct correlation analysis on the dependent and independent variables of Group X data; Step 3: According to the correlation analysis results, select the independent variables with a Pearson correlation coefficient greater than 0.5 to obtain n associated variables; Step 4, determining the production management coefficient K of the tobacco leaves of group X according to the n associated variables obtained in step 3; Step 5, using the K-means cluster analysis method of SPSS, cluster the tobacco production management coefficient K of group X to obtain the clustering results; Step 6: Based on the industry's guidance standards for the allocation of tobacco production managers, combined with the results of step 5, calculate the corresponding quota staffing standards for each cluster category; Step seven, predict the number of tobacco production managers required for each group based on the quota staffing standards.

2. The method for predicting demand for tobacco production managers in mountainous areas according to claim 1, characterized in that: The planned purchase volume of flue-cured tobacco in the step 1 refers to the annual purchase plan issued by the superior tobacco company based on the order docking situation with relevant industrial enterprises; the planting area refers to the area agreed in the planting contract; the 100-mu contiguous rate refers to the proportion of tobacco fields with a contiguous planting area of ​​100 mu or more in a certain area to the planting area in the area, and the 100-mu contiguous rate = 100-mu contiguous area / total planting area; the number of tobacco farmers refers to the total number of farmers who have signed flue-cured tobacco planting contracts with the tobacco company in each year and are engaged in flue-cured tobacco planting; the average planting area per household refers to the average of the planting area and the number of tobacco farmers in a certain area, that is, the average area of ​​flue-cured tobacco planting; the average flue-cured tobacco output of the purchasing station refers to the average amount of flue-cured tobacco purchased by each purchasing station; the distribution of administrative villages for planting refers to the number and distribution of administrative villages for planting flue-cured tobacco; the concentration of tobacco areas refers to the concentration or dispersion of the flue-cured tobacco planting area. A high concentration means that the planting area is relatively concentrated, and a low concentration means that the planting area is relatively dispersed. The concentration of tobacco areas = the number of administrative villages per thousand mu (individuals) / the number of administrative villages with flue-cured tobacco planting plans (individuals).

3. The method for predicting demand of tobacco production managers in mountainous areas according to claim 1, characterized in that: In the step three, after n associated variables are screened out according to the Pearson correlation coefficient, the screened variables are scored using the expert scoring method to further optimize the variable selection and obtain the final associated variables.

4. The method for predicting demand for tobacco production managers in mountainous areas according to claim 1, characterized in that: The formula for determining the production management coefficient K of each group of tobacco leaves in step 4 is: ki = associated variable i / average value of associated variable i; qi=correlation coefficient of associated variable i / Where K: tobacco production management coefficient; ki: coefficient of association variable i; qi: weight of associated variable i; n: the number of associated variables; The correlation coefficient of associated variable i is obtained by the correlation analysis in step 2.

5. The method for predicting demand of tobacco production managers in mountainous areas according to claim 1, characterized in that: When using the K-means cluster analysis of SPSS in step 5, the K value is set to 3-5 categories.

6. The method for predicting demand for tobacco production managers in mountainous areas according to claim 1, characterized in that: The industry's tobacco production manager configuration guidance standard in step six is ​​to have one tobacco production manager for every 500 acres of mountain land.

7. The method for predicting demand for tobacco production managers in mountainous areas according to claim 1, characterized in that: After obtaining the quota and staffing standards corresponding to each cluster category in step 6, the expert scoring method is used to score the quota and staffing standards corresponding to each cluster category to determine the final cluster category and the corresponding quota and staffing standards.

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