Urban development talent demand prediction method and device, and storage medium

Abstract

The invention relates to a city development talent demand prediction method and device, and a storage medium. The method comprises the following steps: selecting city research time nodes, input elements and output elements; collecting corresponding input element data and output element data of the city at the research time node; based on the input element data and the output element data, adopting a Cobb-Douglas production function to determine the output elasticity of each input element of the city; determining marginal output data and average output data of each input element according to the output elasticity; drawing a total output curve, an element marginal output curve and an average output curve of the city according to the total output data, the marginal output data and the average output data of each research time node of the city; according to the curve position relation, the urban talent development stage is determined, and a prediction result of the talent demand is generated. According to the method, the effects of capital, labor force and technical progress factors in the production process are described from the macroscopic perspective, and the influence of the talent number and quality on urban development is quantified.

Description

A method, device, and storage medium for predicting talent demand in urban development. Technical Field

[0001] This application belongs to the field of urban research and data science technology, and specifically relates to a method, device and storage medium for predicting the talent demand of urban development. Background Technology

[0002] Talent is a key factor driving urban economic development. Quantifying the role of talent in urban economic development is the foundation and prerequisite for judging the stage of urban talent development, predicting future development directions, and achieving effective resource allocation.

[0003] Existing methods for analyzing and forecasting talent demand include: regression analysis, such as constructing linear equations with the number of jobs and the number of employees at each educational level in the labor market as dependent and independent variables, respectively, solving for the corresponding parameters of employees at each educational level through regression fitting, and then predicting the future number of talents based on the parameters; principal component analysis, such as using principal component analysis to select important variables and their weights, and then predicting the number of talents based on the variables and their weights; and time series analysis, such as selecting several variables and using historical data for time series analysis, and then predicting the future number of talents based on the parameters.

[0004] However, many factors influence the demand for talent, and existing methods for analyzing talent demand cannot cover them all, resulting in serious variable omissions. The demand for talent is dynamic, but existing technologies assume static parameters. The demand for talent may be affected by current and future economic conditions, but existing technologies can usually only reflect the current economic situation and cannot accurately predict future trends. Existing technologies only predict the quantity of talent from a micro perspective, without considering the impact of talent efficiency, technological progress, talent quantity, and talent quality on urban economic development, and cannot determine the overall stage of talent development in a city. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method, device and storage medium for predicting the talent demand of urban development, which aims to quantify the impact of talent quantity and quality on urban economic development, taking into account factors such as capital and technological progress.

[0006] The first aspect of this application provides a method for predicting the talent demand in urban development, including:

[0007] The study selects timeframes, input factors, and output factors for urban research; the input factors include: urban talent quality indicators, urban talent quantity indicators, and urban capital stock indicators; the output factors include: urban total output indicators.

[0008] Collect input and output factor data for the city at the corresponding research time points;

[0009] Based on the input and output data, the output elasticity of each input factor in the city is determined using the Cobb-Douglas production function.

[0010] The marginal output data and average output data of each input factor are determined based on the output elasticity.

[0011] Based on the total output data, marginal output data, and average output data of the city at each research time point, plot the city's total output curve, factor marginal output curve, and average output curve;

[0012] Based on the positional relationship of the curves shown in the city's total output curve, factor marginal output curve, and average output curve, the stage of urban talent development is determined and the predicted results of talent demand are generated.

[0013] Optionally, the urban talent quality index is obtained by using the city's labor productivity at the corresponding time point;

[0014] The indicator of urban talent quantity is obtained by measuring the number of the city's working-age population at the corresponding time point;

[0015] The urban capital stock indicator is obtained by taking the total fixed capital formation of a city at the corresponding time point;

[0016] The city output indicator is obtained by taking the city’s GDP at the corresponding time point.

[0017] Optionally, labor productivity can be determined based on the average wage level of various industries in the city, the proportion of employees in each industry, and the number of industries.

[0018]

[0019] Among them, Q it w represents the labor productivity of city i in period t; ito g represents the average wage level of industry o in city i during period t; ito N represents the proportion of employees in industry o in city i during period t who have a bachelor's degree or above; N represents the number of industries in city i during period t.

[0020] Optionally, the Cobb-Douglas production function is:

[0021] Y it =A it L it α Q it β Kit γ

[0022] Among them, Y it A represents the output of city i in period t, which is the regional GDP at the corresponding time point; it L represents the coefficient of technological progress of city i in period t; it Q represents the number of working-age people in city i during period t; it K represents the quality of talent in city i during period t, i.e., labor productivity; it Let α represent the capital stock of city i in period t, i.e., the gross fixed capital formation; α, β, and γ represent the output elasticities of the quantity of talent, the quality of talent, and the capital stock, respectively.

[0023] The method of using the Cobb-Douglas production function to determine the output elasticity of various input factors in a city includes:

[0024] The Cobb-Douglas production function is transformed into the following transformation equation:

[0025] lnY it =lnA it +αlnL it +βlnQ it +γlnK it +ε it

[0026] Where, ε it Represents a random disturbance term;

[0027] Regression calculations are performed on the transformation equations to determine the output elasticities α, β, and y of each production factor in the city.

[0028] Optionally, determining the marginal output data and average output data of each input factor based on output elasticity includes:

[0029] Based on the transformation equation, the technological progress coefficient A is further calculated:

[0030]

[0031] in,

[0032] The marginal output of each input factor is determined using the following formula:

[0033] MP L =αAL α-1 Q β K γ

[0034] MP Q =βALα Q β-1 K γ

[0035] MP K =γAL α Q β K γ-1

[0036] Among them, MP L MP represents the marginal product of the number of talents; Q MP represents the marginal output of talent quality; K Marginal output represents the capital stock;

[0037] The average output of each input factor is determined using the following formula:

[0038] AP L =AL α-1 Q β K γ

[0039] AP Q =AL α Q β-1 K γ

[0040] AP K =AL α Q β K γ-1

[0041] Among them, AP L AP represents the average output per unit of talent; Q AP represents the average output based on the level of education of the talent; K It represents the average output of capital stock.

[0042] Optionally, this also includes: determining the marginal rate of substitution and the returns to scale of urban development;

[0043] The marginal rate of substitution among input factors is determined using the following formula:

[0044]

[0045]

[0046]

[0047] The following formula is used to determine the returns to scale of a city:

[0048] Given α+β+γ=1, the scale returns of the city remain constant.

[0049] Under the condition α+β+γ>1, the city's scale returns are determined to be increasing;

[0050] When α+β+γ<1, the diminishing returns to scale of cities are determined.

[0051] Optionally, the step of determining the urban talent development stage and generating talent demand predictions based on the positional relationships of the curves shown in the urban total output curve, factor marginal output curve, and average output curve includes:

[0052] In MP L >AP L In this case, the city's talent development stage is determined to be a shortage of talent, leading to a prediction that an increase in talent introduction is needed.

[0053] In MP L <AP L Under these circumstances, the city's talent development stage is determined to be one of talent saturation, leading to the prediction that it needs to seek high-level talent.

[0054] Optionally, in MP L <AP L In this case, it also includes:

[0055] For the Cobb-Douglas production function Y it =A it L it α Q it β K it γ To expand further:

[0056] Y it =A it LB it lb B it b LM it lm M it m LD it ld D it d K γ

[0057] Among them, LB it B represents the number of people with bachelor's degrees in city i during period t; it LM represents the labor productivity of individuals with bachelor's degrees in city i during period t; it M represents the number of people with master's degrees in city i during period t; itLD represents the labor productivity of the working-age population with master's degrees in city i during period t; it D represents the number of people with doctoral degrees in city i during period t; it This represents the labor productivity of the working-age population with doctoral degrees in city i during period t.

[0058] Expand and calculate the marginal and average outputs for LB, LM, and LD respectively;

[0059] In MP LB >MP LB In the case of a shortage of undergraduate degree holders, the prediction is that there is a need to introduce undergraduate degree holders; conversely, the prediction is that the undergraduate degree holder pool is already saturated.

[0060] In MP LM >MP LM In the case of a shortage of master's degree holders, the prediction is that master's degree holders need to be recruited; conversely, the prediction is that the number of master's degree holders is already saturated.

[0061] In MP LD >MP LD In the given situation, if the current stage of talent development is determined to be a shortage of doctoral degree holders, a prediction is made that doctoral degree holders need to be recruited; conversely, if the current stage is not a shortage, a prediction is made that the number of doctoral degree holders is already saturated.

[0062] A second aspect of this application provides a device for predicting the talent demand for urban development, comprising a memory and a processor, wherein the memory stores a computer program, which, when executed by the processor, implements the method for predicting the talent demand for urban development according to any of the above-described methods.

[0063] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for predicting talent demand for urban development according to any of the above-described methods.

[0064] The method for predicting urban development talent demand provided in this application selects urban research time points, input factors, and output factors; collects input factor data and output factor data corresponding to the urban research time points; based on the input factor data and output factor data, uses the Cobb-Douglas production function to determine the output elasticity of each input factor in the urban area; determines the marginal output data and average output data of each input factor based on the output elasticity; plots the urban total output curve, factor marginal output curve, and average output curve based on the total output data, marginal output data, and average output data of the urban area at each research time point; and determines the urban talent development stage and generates a prediction result of talent demand based on the positional relationship of the curves shown in the urban total output curve, factor marginal output curve, and average output curve. The Cobb-Douglas production function used in this application describes the role of factors such as capital, labor, and technological progress in the production process from a macro perspective, thereby quantifying the impact of the quantity and quality of talent on urban development; it judges the stage of urban talent development from the perspective of production efficiency, avoiding the involvement of too many variables and factors; through the output curve, it can understand the output level that can be achieved under different talent inputs, thereby determining the quantity and quality of talent required for optimal output.

[0065] In addition, this application also provides a device and storage medium for predicting urban development talent demand with the aforementioned technical effects. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings.

[0067] Figure 1 is a flowchart of a specific implementation of the urban development talent demand prediction method provided in this application;

[0068] Figure 2 is a schematic diagram of the city's total output curve, factor marginal output curve, and average output curve;

[0069] Figure 3 is a structural block diagram of the urban development talent demand prediction device provided in this application. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. It should be noted that, unless otherwise specified, the implementation methods and features in the implementation methods in this disclosure can be combined, separated, interchanged, and / or rearranged. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “the” are intended to include the plural forms as well. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0072] Current talent demand analysis typically takes a micro-level approach, selecting a few variables to construct linear equations for regression or time series analysis. It uses historical data to solve for parameters and then calculates future talent needs based on these parameters. This approach struggles to encompass all influencing factors, fails to capture the impact of macroeconomic changes, and neglects the correlations or substitutability between various elements or factors. Furthermore, current technologies neglect the assessment of productivity brought by talent, failing to determine whether the current quantity and quality of talent have reached the optimal state required for urban development, and how adjustments should be made to achieve this optimal state.

[0073] The traditional Cobb-Douglas production function assumes a homogeneous workforce and does not adequately consider the quality of talent. Furthermore, previous limitations in statistical data made data acquisition and the quantification of talent quality difficult. However, with advancements in statistical methods and data collection technologies, education level can now be used to measure talent quality. Therefore, this application extends the traditional Cobb-Douglas production function to predict the talent demand for urban development.

[0074] A flowchart of a specific implementation of the urban development talent demand prediction method provided in this application is shown in Figure 1. The method includes:

[0075] S101: Select the time frame, input factors, and output factors for urban research.

[0076] The input factors include: urban talent quality indicators, urban talent quantity indicators, and urban capital stock indicators; the output factors include: urban total output indicators.

[0077] The indicators for urban talent quality may include indicators of education level, skill level, and innovation capability. As a specific implementation method, this application uses the city's labor productivity at a corresponding time point.

[0078] Specifically, labor productivity can be determined based on the average wage level of various industries in the city, the proportion of employees in each industry, and the number of industries:

[0079]

[0080] Among them, Q it w represents the labor productivity of city i in period t; ito g represents the average wage level of industry o in city i during period t; ito N represents the proportion of employees in industry o in city i during period t who have a bachelor's degree or above; N represents the number of industries in city i during period t.

[0081] The number of talents in a city can be measured by the number of employed people or the number of experts in a specific industry. In this embodiment, the number of the working-age population of the city at the corresponding time point is used to obtain the data.

[0082] Urban capital stock indicators can be measured using total fixed asset investment and investment in technology and equipment. This application's embodiment uses the total fixed capital formation of the city at the corresponding time point.

[0083] The city output indicator is obtained by taking the city’s GDP at the corresponding time point.

[0084] S102: Collect input and output factor data for the city at the research time point.

[0085] Input and output data for the city at the research time points can be obtained from statistical yearbooks, economic survey reports, education and labor market data, etc., and the data should be ensured to cover all selected research time points.

[0086] As a specific implementation method, wage level data for various cities and industries can be obtained from the "China Labor Statistics Yearbook"; the number of employees in various cities and industries can be obtained from the "China Labor Statistics Yearbook"; and the education level data of employees in various industries can be obtained from the city's "Population Census"; and then labor productivity data can be further calculated based on the above labor productivity calculation formula.

[0087] One specific implementation method is to obtain data on the number of working-age people in each city from the city's statistical yearbook.

[0088] As a specific implementation method, data on the gross fixed capital formation of each city can be obtained from the city's statistical yearbook.

[0089] One specific implementation method is to obtain the gross domestic product (GDP) data of each city from the city's statistical yearbook.

[0090] After obtaining the data, it can be further cleaned and standardized to facilitate linear regression analysis of the Cobb-Douglas production function.

[0091] S103: Based on the input factor data and output factor data, the output elasticity of each input factor in the city is determined using the Cobb-Douglas production function.

[0092] The Cobb-Douglas production function is:

[0093] Y it =A it L it α Q it β K it γ

[0094] Among them, Y it A represents the output of city i in period t, which is the regional GDP at the corresponding time point; it L represents the coefficient of technological progress of city i in period t; it Q represents the number of working-age people in city i during period t; it K represents the quality of talent in city i during period t, i.e., labor productivity; it Let α represent the capital stock of city i in period t, i.e., gross fixed capital formation; α, β, and γ represent the output elasticities of the quantity and quality of talent, and the capital stock, respectively. Let γ describe how output changes when the corresponding factor of production changes by one unit, i.e., the factor's output share.

[0095] Understandably, the Cobb-Douglas production function in this application further extends the traditional function to consider the impact of urban talent quality on urban economic development.

[0096] The method of using the Cobb-Douglas production function to determine the output elasticity of various input factors in a city includes:

[0097] The Cobb-Douglas production function is transformed into the following transformation equation:

[0098] lnY it =lnA it +αlnL it +βlnQ it +γlnK it +ε it

[0099] Where, ε it Represents a random disturbance term;

[0100] Regression calculations are performed on the transformation equation to determine the output elasticities α, β, and γ of each production factor in the city. Specifically, statistical software can be used for regression analysis; the model is input, and the regression is run to obtain the values ​​of output elasticities α, β, and γ.

[0101] S104: Determine the marginal output data and average output data of each input factor based on the output elasticity.

[0102] Based on the transformation equation, the technological progress coefficient A is further calculated:

[0103]

[0104] in,

[0105] The calculation methods for various urban element indicators are as follows (subscript 'it' omitted):

[0106] The marginal output of each input factor is determined using the following formula:

[0107] MP L =αAL α-1 Q β K γ

[0108] MP Q =βALαQβ- 1 Kγ

[0109] MP K =γAL α Q β K γ-1

[0110] Among them, MP L MP represents the marginal product of the number of talents; Q MP represents the marginal output of talent quality; K Marginal output represents the capital stock;

[0111] The average output of each input factor is determined using the following formula:

[0112] AP L =AL α-1 Q β K γ

[0113] AP Q =AL α Q β-1 K γ

[0114] AP K =AL α Q β K γ-1

[0115] Among them, AP L AP represents the average output per unit of talent; Q AP represents the average output based on the level of education of the talent; K It represents the average output of capital stock.

[0116] In addition, it also includes: determining the marginal rate of substitution and the returns to scale of urban development;

[0117] The marginal rate of substitution among input factors is determined using the following formula:

[0118]

[0119]

[0120]

[0121] The following formula is used to determine the returns to scale of a city:

[0122] Given α+β+γ=1, the scale returns of the city remain constant.

[0123] Given α+β+γ>1, the city's returns to scale are determined to be increasing.

[0124] When α+β+γ<1, the diminishing returns to scale of cities are determined.

[0125] S105: Based on the total output data, marginal output data, and average output data of the city at each research time point, plot the city's total output curve, factor marginal output curve, and average output curve.

[0126] Specifically, plotting software or tools can be used to plot the city's total output curve, factor marginal output curve, and average output curve. Figure 2 shows a schematic diagram of the city's total output curve, factor marginal output curve, and average output curve, where the horizontal axis represents the number of talents and the vertical axis represents total output.

[0127] As shown in Figure 2, the total urban output curve reveals that with the increase in the number of talents, the total urban output gradually increases, but after reaching a certain point, the output growth rate begins to decline. This reflects that under certain economic and technological conditions, increasing input will lead to an increase in output, but after exceeding the optimal point, further increases in input gradually reduce the marginal benefit of output growth. (The marginal product curve is shown in Figure 2.) L =αAL α-1 Q β K γ The graph shows the increase in output for each additional unit of labor input. As can be seen from the graph, marginal product initially increases with increasing labor input, reaches a peak, and then begins to decline. The average product curve (AP) L =AL α-1 Q β K γ The curve represents the average output curve, which is the average output generated per unit of labor input. This curve is below the marginal output curve and initially increases before decreasing as labor input increases.

[0128] S106: Based on the positional relationship of the curves shown in the city's total output curve, factor marginal output curve, and average output curve, determine the city's talent development stage and generate a forecast of talent demand.

[0129] In MP L >AP L In this case, the city's talent development stage is determined to be one of insufficient talent, leading to a prediction that an increase in talent introduction is needed.

[0130] Referring to stage one in Figure 2, when MP L >AP L That is, when the marginal product of talent is greater than the average product, for every additional unit of talent, AP increases. L As average output (AP) continues to rise, the increase in the number of talents leads to an increase in the growth rate of total output. In other words, the increase in the number of talents improves overall output efficiency. Therefore, there is still room for improvement in the number of talents in the system (talent shortage). The number of talents introduced should be increased until AP is reached. L Reaching the extreme value, i.e., MP L =APL (Marginal output = average output, the boundary between stage one and stage two in the diagram).

[0131] In MP L <AP L Under these circumstances, the city's talent development stage is determined to be one of talent saturation, leading to the prediction that it needs to seek high-level talent.

[0132] Corresponding to stage two in the diagram, when MP L <AP L That is, when the marginal output of talent is less than the average output, for every additional unit of talent, AP... L When average output begins to decline, the increase in the number of talents leads to a decrease in the growth rate of total output. In other words, the increase in the number of talents reduces the overall output efficiency. Therefore, the number of talents in the system is already saturated. The speed of talent introduction should be reduced, and the quality of talents should be further improved to avoid entering stage three.

[0133] By observing the positional relationships of the curves in the city's total output curve, factor marginal output curve, and average output curve, we can intuitively see the relationship between the number of talents and output, and how to optimize output efficiency by adjusting labor input, thereby helping to formulate more effective talent development strategies and economic policies.

[0134] Understandably, the Cobb-Douglas production function is often used to measure the relationship between urban development and key production factors. Labor, as one of the production factors, is a crucial factor influencing urban development. Traditional Cobb-Douglas production functions typically assume a homogeneous labor force and do not consider labor quality. However, in reality, there are significant differences in labor skills, educational backgrounds, and experience. Labor quality (talent quality) directly impacts productivity and innovation capabilities. Furthermore, according to endogenous growth theory, human capital accumulation, especially the improvement of talent quality, is a significant driver of economic growth. Therefore, considering talent quality in the production function aligns with theoretical development.

[0135] Considering the differences in talent quality among different cities, this application constructs a method for calculating urban labor productivity, using the labor productivity of undergraduate and above workers in various industries to measure the talent quality of that industry, thereby calculating the talent quality of the city.

[0136] This application considers talent quality, making the production function more closely reflect reality and improving the model's explanatory power for real-world economic phenomena. It helps governments and businesses better understand the impact of human capital investment on economic growth, thereby enabling them to formulate more effective education and talent development policies. In the knowledge economy era, the role of talent is increasingly important; incorporating talent quality into the model helps analyze the relationship between technological progress and economic growth.

[0137] Based on the above embodiments, when the number of talents reaches saturation, the quality of talents can be predicted by classifying them. Specifically, the Cobb-Douglas production function Y can be... it =A it L it α Q it β K it γ To further expand this, talents can be categorized according to their educational background, namely:

[0138] Y it =A it LB it lb B it b LM it lm M it m LD it ld D it d K γ

[0139] Among them, LB it B represents the number of people with bachelor's degrees in city i during period t; it LM represents the labor productivity of individuals with bachelor's degrees in city i during period t; it M represents the number of people with master's degrees in city i during period t; it LD represents the labor productivity of the working-age population with master's degrees in city i during period t; it D represents the number of people with doctoral degrees in city i during period t; it This represents the labor productivity of the working population with doctoral degrees in city i during period t.

[0140] Expand and calculate the marginal and average outputs corresponding to LB, LM, and LD respectively (subscript it is omitted below);

[0141] In MP LB >MP LB In the case of a shortage of undergraduate degree holders, the prediction is that there is a need to introduce undergraduate degree holders; conversely, the prediction is that the undergraduate degree holder pool is already saturated.

[0142] In MP LM >MP LM In the case of a shortage of master's degree holders, the prediction is that master's degree holders need to be recruited; conversely, the prediction is that the number of master's degree holders is already saturated.

[0143] In MP LD >MP LD In the given situation, if the current stage of talent development is determined to be a shortage of doctoral degree holders, a prediction is made that doctoral degree holders need to be recruited; conversely, if the current stage is not a shortage, a prediction is made that the number of doctoral degree holders is already saturated.

[0144] In addition, this application also provides a device for predicting the demand for talent in urban development. As shown in the structural block diagram of the device for predicting the demand for talent in urban development provided in this application in Figure 3, the device specifically includes a memory 31 and a processor 32. The memory 31 stores a computer program, and when the computer program is executed by the processor 32, it implements any of the above-mentioned methods for predicting the demand for talent in urban development.

[0145] The device for predicting the demand for talent in urban development provided in this application corresponds to the method for predicting the demand for talent in urban development mentioned above. The specific implementation process can be referred to the above description and will not be repeated here.

[0146] The Cobb-Douglas production function used in this application describes the role of factors such as capital, labor, and technological progress in the production process from a macro perspective, thereby quantifying the impact of the quantity and quality of talent on urban development; it judges the stage of urban talent development from the perspective of production efficiency, avoiding the involvement of too many variables and factors; through the output curve, it can understand the output level that can be achieved under different talent inputs, thereby determining the quantity and quality of talent required for optimal output.

[0147] In addition, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for predicting talent demand for urban development.

[0148] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0149] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0150] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0151] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting the talent demand for urban development, characterized in that, include: The study selects urban research time points, input factors, and output factors. The input factors include: urban talent quality indicators, urban talent quantity indicators, and urban capital stock indicators. The output factors include: urban total output indicators. Input and output factor data for the city at the specified research time points are collected. Based on these data, the Cobb-Douglas production function is used to determine the output elasticity of each input factor. Marginal and average output data for each input factor are determined based on the output elasticity. The city's total output curve, factor marginal output curve, and average output curve are plotted based on the total output data, marginal output data, and average output data for each research time point. The city's talent development stage is determined based on the positional relationships of the curves shown in the total output curve, factor marginal output curve, and average output curve, and a prediction of talent demand is generated.

2. The method for predicting urban development talent demand according to claim 1, characterized in that, The city's talent quality indicator is obtained from the city's labor productivity at the corresponding time point; the city's talent quantity indicator is obtained from the city's labor force at the corresponding time point; the city's capital stock indicator is obtained from the city's gross fixed capital formation at the corresponding time point; and the city's output indicator is obtained from the city's GDP at the corresponding time point.

3. The method for predicting urban development talent demand according to claim 2, characterized in that, Labor productivity is determined based on the average wage level of various industries in the city, the proportion of employees in each industry, and the number of industries: Among them, Q it w represents the labor productivity of city i in period t; ito g represents the average wage level of industry o in city i during period t; ito N represents the proportion of employees in industry o in city i during period t who have a bachelor's degree or above; N represents the number of industries in city i during period t.

4. The method for predicting urban development talent demand according to any one of claims 1 to 3, characterized in that, The Cobb-Douglas production function is: Y it =A it L it α Q it β K it γ in, Y it A represents the output of city i in period t, which is the regional GDP at the corresponding time point; it L represents the coefficient of technological progress of city i in period t; it Q represents the number of working-age people in city i during period t; it K represents the quality of talent in city i during period t, i.e., labor productivity; it Let represent the capital stock of city i in period t, i.e., gross fixed capital formation; α, β, and γ represent the output elasticities of the quantity, quality, and capital stock of talent, respectively; the determination of the output elasticities of each input factor of the city using the Cobb-Douglas production function includes: the Cobb-Douglas production function is transformed into the transformation equation: lnY it =lnA it +αlnL it +βlnQ it +γlnK it +ε it Where, ε it The random disturbance term is represented by α, β, and y. The transformation equation is subjected to regression calculation to determine the output elasticities α, β, and y of each production factor in the city.

5. The method for predicting urban development talent demand according to claim 4, characterized in that, The determination of marginal output data and average output data of each input factor based on output elasticity includes: further calculating the technological progress coefficient A based on the transformation equation. in, The marginal output of each input factor is determined using the following formula: MP L =αAL α-1 Q β K γ MP Q =βAL α Q β-1 K γ MP K =γAL α Q β K γ-1 Among them, MP L MP represents the marginal product of the number of talents; Q MP represents the marginal output of talent quality; K This represents the marginal output of the capital stock; the average output of each input factor is determined using the following formula: AP L =AL α-1 Q β K γ AP Q =AL α Q β-1 K γ AP K =AL α Q β K γ-1 Among them, AP L AP represents the average output per unit of talent; Q AP represents the average output based on the level of education of the talent; K It represents the average output of capital stock.

6. The method for predicting urban development talent demand according to claim 5, characterized in that, Also includes: Determine the marginal rate of substitution and the returns to scale of urban development; The marginal rate of substitution among input factors is determined using the following formula: The following formula is used to determine the returns to scale of a city: when α+β+γ=1, the returns to scale of the city are constant; when α+β+γ>1, the returns to scale of the city are increasing; when α+β+γ<1, the returns to scale of the city are decreasing.

7. The method for predicting urban development talent demand according to claim 5, characterized in that, The method of determining the urban talent development stage and generating talent demand predictions based on the positional relationships of the curves shown in the urban total output curve, factor marginal output curve, and average output curve includes: in MP L >AP L In this context, the city's talent development stage is determined to be one of insufficient talent, leading to a forecast that an increase in talent introduction is needed; in MP L <AP L Under these circumstances, the city's talent development stage is determined to be one of talent saturation, leading to the prediction that it needs to seek high-level talent.

8. The method for predicting urban development talent demand according to claim 7, characterized in that, In MP L <AP L In this case, it also includes: the Cobb-Douglas production function Y it =A it L it α Q it β K it γ To expand further: Y it =A it LB it lb B it b LM it lm M it m LD it ld D it d K γ Among them, LB it B represents the number of people with bachelor's degrees in city i during period t; it LM represents the labor productivity of individuals with bachelor's degrees in city i during period t; it M represents the number of people with master's degrees in city i during period t; it LD represents the labor productivity of the working-age population with master's degrees in city i during period t; it D represents the number of people with doctoral degrees in city i during period t; it Let LB, LM, and LD represent the labor productivity of the working-age population with doctoral degrees in city i during period t; expand to find the marginal and average outputs corresponding to LB, LM, and LD respectively; in MP LB >MP LB In the current talent development stage, if it is determined that there is a shortage of undergraduate degree holders, a prediction is made that there is a need to introduce undergraduate degree holders; conversely, if there is a shortage, a prediction is made that the undergraduate degree holder pool is saturated; in MP LM >MP LM In the current talent development stage, if it is determined that there is a shortage of master's degree holders, a prediction is made that there is a need to introduce master's degree holders; conversely, if there is a shortage, a prediction is made that the number of master's degree holders is already saturated; in MP LD >MP LD In the given situation, if the current stage of talent development is determined to be a shortage of doctoral degree holders, a prediction is made that doctoral degree holders need to be recruited; conversely, if the current stage is not a shortage, a prediction is made that the number of doctoral degree holders is already saturated.

9. A device for predicting talent demand in urban development, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method for predicting urban development talent demand according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for predicting urban development talent demand according to any one of claims 1-8.

Images