CN113888047A - Technical improvement project investment scale prediction method and system considering regional investment capacity - Google Patents
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Abstract
The invention discloses a technical improvement project investment scale prediction method and system considering regional investment capacity, relating to the technical field of power grid investment planning and having the technical key points that: acquiring influence factor indexes of the power grid investment capacity in a target area, and constructing a power grid investment capacity influence factor index model according to the influence factors; performing relevance analysis on the power grid investment capacity influence factor index model by adopting a bivariate relevance analysis model, performing grey relevance clustering analysis on influence factor indexes, and selecting a plurality of indexes influencing the front rank as representative indexes; constructing a power grid investment capacity calculation model according to the representative indexes; and constructing a technical improvement project investment scale prediction model based on asset depreciation calculation. The invention avoids the problem of excessive or short investment of technical improvement projects, and realizes the accurate investment of the capital of the power grid enterprise, thereby coping with the current complex internal and external operation situation and realizing the operation development target of the enterprise.
Description
Technical Field
The invention relates to the technical field of power grid investment planning, in particular to a method and a system for predicting investment scale of a technical improvement project by considering regional investment capacity.
Background
The power grid company is a natural enterprise with dense assets and technology, has a large number of power grid physical assets and a large variety, and has high technical improvement investment management complexity. For an asset-intensive enterprise, the performance of the enterprise is directly and closely linked with the condition and the use efficiency of the asset, and the benefits of a power grid company are mainly derived from the stable and continuous operation of equipment and are closely related with the cost control of the equipment. In recent years, the social electricity demand is increasing along with the economic development, and the investment scale of power grid enterprises is gradually increased. Meanwhile, under the influence of national industrial structure adjustment, power grid enterprises face large uncertainty in investment management, the enterprise benefit fluctuation is large, newly added asset efficiency cannot play an effective role, and the contradiction between input and output structures is prominent. Therefore, on the premise of ensuring safe and stable operation of the power grid, the investment capacity of the power grid enterprise is accurately mastered, investment structure distribution is optimized, the economy of power grid construction is improved, reasonable resource allocation and enterprise sustainable development are realized, and the problem that power grid investment benefits are stably improved to become a urgent need for further research of the power grid enterprise is solved.
From the external environment of a power grid enterprise, along with the steady development of national economy of China, the living standard of people is improved year by year, the demand of the whole society on electric power is increased day by year, the investment scale of a power grid is also increased continuously, and technical improvement investment management is improved gradually, but the problems that a power grid manager still has insufficient scientific basis when a technical improvement investment strategy is customized, the influence of the development demand on the technical improvement investment strategy is not comprehensively and systematically evaluated, and the technical improvement investment scale of part of the power grid is excessive or deficient are also gradually exposed.
From the view of internal operation of power grid enterprises, the power grid enterprise investment capacity measurement and calculation is widely applied in the national grid range, but more, the investment capacity of the power grid enterprises is evaluated from the perspective of unit finance, investment capacity influence elements influencing investment strategies are not scientifically, systematically and comprehensively sorted and evaluated and analyzed, the current situation and trend research of company assets and power transmission and distribution cost is not combined, and the management and control of investment risks are further improved.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the investment scale of a technical improvement project by considering regional investment capacity so as to solve the problem of excessive or insufficient technical improvement investment scale in the prior art.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a method for forecasting investment scale of a technical improvement project considering regional investment capacity, comprising the steps of:
acquiring influence factor indexes of the power grid investment capacity in a target area, and constructing a power grid investment capacity influence factor index model according to the influence factors;
performing relevance analysis on the power grid investment capacity influence factor index model by adopting a bivariate relevance analysis model, performing grey relevance clustering analysis on influence factor indexes, and selecting a plurality of indexes influencing the front rank as representative indexes;
constructing a power grid investment capacity calculation model according to the representative indexes;
and constructing a technical improvement project investment scale prediction model based on asset depreciation calculation.
The invention comprehensively analyzes the investment capacity of power grid enterprises from the aspects of social economic development on the influence of power demand, power demand increase on the requirement of investment scale, enterprise financial capacity supporting investment, market economic environment on return of investment and the like, scientifically evaluates the investment capacity of the enterprises, constructs a technical improvement investment scale measuring and calculating model based on an asset depreciation measuring algorithm, provides decision support for power grid enterprise planning and investment, realizes accurate investment of power grid enterprise funds, avoids the problem of excessive or deficient technical improvement investment scale, and accordingly, the invention can meet the current complex internal and external operation situation and realize the enterprise operation development target.
Furthermore, the influence factor indexes of the power grid investment capacity comprise three influence factor indexes of depreciation, net profit and financing, a power grid investment capacity influence factor index model is constructed according to the three influence factor indexes of depreciation, net profit and financing, and a power grid investment capacity influence factor fishbone diagram is obtained according to the index model.
Further, a bivariate correlation analysis method is adopted to calculate a correlation coefficient between the influence factors and the historical investment capacity, and influence factor indexes which are obviously related to the historical investment capacity are screened out;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
Further, according to the business income of the power grid in the target area, the enterprise operation coefficient, the self-owned fund ratio in investment and the return on investment rate, a power grid investment capacity calculation model is constructed as follows:
wherein, ItThe method is characterized in that the method represents the investment capacity of the power grid enterprise in the t-th year, alpha represents the operation coefficient of the power grid enterprise, S represents the business income of the enterprise in the initial year, gamma represents the return on investment, beta represents the proportion of the own fund of the power grid enterprise to the total investment, and t is the investment age limit.
Further, calculating the proportion of the technically improved investment in the original value of the depreciated asset at the beginning of the year, and calculating the weighted average value of the technically improved investment;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
In a second aspect, the present invention provides a technical improvement project investment scale prediction system considering regional investment capacity, which is used for implementing the prediction method provided in the first aspect, and comprises an obtaining unit, an analyzing unit, a calculating unit and a predicting unit;
the acquisition unit is used for acquiring the influence factor indexes of the power grid investment capacity in the target area and constructing a power grid investment capacity influence factor index model according to the influence factors;
the analysis unit is used for performing correlation analysis on the power grid investment capacity influence factor index model by adopting a bivariate correlation analysis model, performing grey correlation clustering analysis on influence factor indexes, and selecting a plurality of indexes influencing the front row of the sequence as representative indexes;
the computing unit is used for constructing a power grid investment capacity computing model according to the representative indexes;
and the prediction unit is used for constructing a technical improvement project investment scale prediction model based on asset depreciation calculation.
Furthermore, the obtaining unit is further configured to identify three influence factor indexes of depreciation, net profit and financing, construct a power grid investment ability influence factor index model according to the three influence factor indexes of depreciation, net profit and financing, and obtain a power grid investment ability influence factor fishbone diagram according to the index model.
Furthermore, the analysis unit is also used for calculating a correlation coefficient between the influence factors and the historical investment capacity by adopting a bivariate correlation analysis method, and screening out the influence factor indexes which are obviously related to the historical investment capacity;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
Further, the calculation unit is further configured to construct a power grid investment capacity calculation model according to the business income, the enterprise operation coefficient, the own capital proportion in investment and the return on investment rate of the power grid in the target area, where the power grid investment capacity calculation model is:
wherein, ItThe investment capacity of the power grid enterprise in the t year is represented, alpha represents the operation coefficient of the power grid enterprise, S represents the business income of the enterprise in the initial year, gamma represents the return on investment, and beta represents the total investment of the own fund of the power grid enterpriseT is the investment age.
Further, the prediction unit is also used for calculating the proportion of the technical improvement investment in the original value of the depreciated asset in the beginning of the year and calculating the weighted average value of the technical improvement investment;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
Compared with the prior art, the invention has the following beneficial effects:
firstly, identifying the influence factors of the investment capacity, and constructing an index system of the influence factors of the investment capacity of the power grid from the aspects of depreciation, net profit, financing and the like; extracting the representative indexes of main influence by applying bivariate correlation analysis and grey correlation clustering analysis, and using the representative indexes as basic parameters for constructing an investment capacity model; comprehensively analyzing the investment capacity of the power grid enterprise from the aspects of the influence of social economic development on the power demand, the requirement of the power demand on the investment scale, the support of the financial capacity of the enterprise on the investment, the return of the investment by the market economic environment and the like, and constructing an investment capacity measuring and calculating model based on the financial indexes of the enterprise and the market economic environment indexes by combining the main influence indexes of the investment capacity; measuring and calculating the reasonable scale of the technically improved investment by using an asset depreciation measuring algorithm; the method comprises the steps of comprehensively analyzing the investment capacity of power grid enterprises from the aspects of influence of social economic development on power demand, requirement of power demand increase on investment scale, support of enterprise financial capacity on investment, return of market economic environment on investment and the like, scientifically evaluating the investment capacity of the enterprises, constructing a technically improved investment scale measuring and calculating model based on an asset depreciation measuring algorithm, and providing decision support for power grid enterprise planning and investment.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of a prediction method according to an embodiment of the present invention;
fig. 2 is a fishbone diagram of the influence factors of the investment capacity of the power grid according to an embodiment of the invention;
fig. 3 is a technical model diagram of power grid investment capacity provided by an embodiment of the invention;
fig. 4 is a block diagram of a system architecture according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings that is solely for the purpose of facilitating the description and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and is therefore not to be construed as limiting the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Examples
As shown in fig. 1, the method for forecasting investment scale of a technical improvement project considering regional investment ability comprises the steps of,
step S10, obtaining influence factor indexes of the power grid investment capacity in the target area, and constructing a power grid investment capacity influence factor index model according to the influence factors;
step S20, a bivariate correlation analysis model is adopted to carry out correlation analysis on the power grid investment capacity influence factor index model, grey correlation clustering analysis is carried out on the influence factor indexes, and a plurality of indexes influencing the front row of sequencing are selected as representative indexes;
step S30, constructing a power grid investment capacity calculation model according to the representative indexes;
and step S40, constructing a technical improvement project investment scale prediction model based on asset depreciation calculation.
Specifically, in step S10, the main influence elements of the grid investment capacity in the target area, which may be a certain grid company or all grid companies in a certain city, are first identified, and a grid investment capacity influence factor index model is constructed from depreciation, net profit, financing and other aspects to obtain a grid investment capacity influence factor fishbone map. Step S20, adopting a bivariate correlation analysis model to analyze whether the correlation between the influencing factor index variable and the investment ability dependent variable exists or not; and then performing grey correlation clustering analysis on the indexes, more scientifically and precisely simplifying the indexes, and refining the indexes of the representative influence factors of the main influence for the next step of constructing an investment capacity calculation model of the power grid company. And step S30, determining a power grid investment capacity calculation model according to the influence factor indexes such as business income, enterprise operation coefficients, self-owned capital occupation ratio in investment, return on investment and the like, and calculating the power grid investment capacity of a power grid company through the power grid investment capacity calculation model. And step S40, calculating the technical improvement investment scale of the enterprise in the next year by combining the asset depreciation measuring algorithm. According to the embodiment of the application, the influence of social economic development on the power demand is met, the power demand is increased, the requirement on the investment scale is met, the financial capacity of an enterprise supports investment, the return of the investment by the market economic environment is comprehensively analyzed in the aspects of return of the investment and the like, the investment capacity of the enterprise is scientifically evaluated, a technically improved investment scale measuring and calculating model based on an asset depreciation measuring algorithm is constructed, decision support is provided for power grid enterprise planning and investment, accurate investment of power grid enterprise funds is realized, the current complex internal and external operation situation is met, and the enterprise operation development target is realized.
Based on the above embodiment, in a further embodiment of the present application, as shown in fig. 2, the influence factor indexes of the power grid investment capacity include three influence factor indexes of depreciation, net profit, and financing, a power grid investment capacity influence factor index model is constructed according to the three influence factor indexes of depreciation, net profit, and financing, and a fishbone diagram of the power grid investment capacity influence factor is obtained according to the index model.
Specifically, the power grid investment capacity refers to an upper limit of an investment scale which can be supported by the company finance under the conditions of target profit, a limit value of an asset liability rate and an assumed increase of power selling amount, namely, the investment capacity formed by the fact that the cash of operation and financing activities is completely used for investment on the premise of keeping safe fund payment. The investment ability mainly consists of three aspects of depreciation, profit and financing, and is influenced by many external factors such as economy, policy and the like. The income of a company, the composition of the income and the future development trend are researched through the aspects of electricity price, electric quantity and the like; the operating cost of the company is analyzed from factors such as the power grid development stage and the age composition of the equipment, and the income and the cost are combined to form the profit of the company. The current age and composition and size of the assets of the company are studied, the depreciation of the assets is determined, and the financing capacity and approach of the company under the current circumstances. As shown in FIG. 2, the investment ability fishbone map is made from three impact indexes of depreciation, net profit and financing.
On the basis of the above embodiment, in a further embodiment of the present application, a bivariate correlation analysis method is used to calculate a correlation coefficient between the influencing factors and the historical investment capacity, and the influencing factor indexes which are significantly correlated with the historical investment capacity are screened out;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
Specifically, the investment ability influence factor identification in the above embodiment is combined, a bivariate correlation analysis method is adopted to calculate a correlation coefficient between the remaining factors and the historical investment ability, factors which are significantly related to the historical investment ability are screened out, an improved gray cluster analysis method is adopted to cluster the influence factors to form factor groups, a factor with a strong representativeness is selected from each type of factor group, and the second round screening of the influence factors is realized.
Bivariate correlation analysis is used as a basic correlation analysis method for performing correlation analysis between two or more variables, such as giving two-by-two correlation analysis results for a plurality of variables. The bivariate correlation analysis is simple and easy to use, and the applicability is strong. Whether repeated indexes or a plurality of indexes are closely related in the evaluation indexes is checked, so that the indexes can be represented by using the comprehensive average of the indexes or one of the indexes.
The grey associative clustering is mainly used for merging the same type of factors so as to simplify the complex system. Through grey correlation clustering, whether a plurality of factors are closely related or not can be checked, so that the factors can be represented by a comprehensive average index of the factors or one of the factors, and information is not seriously lost. The gray whitening weight function clustering is mainly used for checking whether the observed objects belong to different preset categories so as to be treated differently. The grey associative clustering is mainly used for merging the same type of factors so as to simplify the complex system. Through grey correlation clustering, whether a plurality of factors are closely related or not can be checked, so that the factors can be represented by a comprehensive average index of the factors or one of the factors, and information is not seriously lost.
And calculating a correlation coefficient between each influencing factor and the historical investment capacity. If the correlation coefficient is greater than 0.8, it indicates that at a confidence level of 0.01, there is a significant correlation between the bivariables, i.e., there is a strong correlation between the two. It can be seen that the factor has a greater degree of correlation with the investment capacity, indicating that it has a greater degree of influence on the investment capacity.
The main objective of correlation analysis (correlation analysis) is to study the closeness of the relationship between variables, such as height and weight, wire and tower material, main transformer capacity and distribution equipment. In statistical analysis, correlation is generally referred to as "linear correlation", and the degree of closeness thereof is represented by a correlation coefficient. The correlation coefficient is generally stated as taking a value between-1 and +1, with the closer the absolute value is to 1, the tighter the relationship between the explanatory variables. The absolute value is equal to 1, which indicates that the two variables are completely correlated, and the value of the variable A is known to obtain the value of the variable B. The correlation coefficient is positive, which indicates that the variable B is increased simultaneously when the variable A is increased, and the variable A and the variable B are in positive correlation; conversely, the B variable is decreased when the A variable is increased, and the A variable and the B variable are in a negative correlation relationship. For different types of variables, the calculation formula of the correlation coefficient is also different, and three common correlation coefficient calculation methods are briefly described below:
1) pearson simple correlation coefficient
The Pearson simple correlation coefficient is used for measuring the linear correlation relation of the distance variable and is most widely applied to calculation of the correlation coefficient. The calculation formula is as follows:
wherein n is the number of samples, xiAnd yiRespectively, the values of the two variables in different samples. The formula for calculating the Pearson simple correlation coefficient is also referred to as product-distance correlation coefficient because it is exactly the matrix product form. After the expression is changed, the correlation coefficient can be expressed as xiAnd yiAfter normalization, the n products are averaged.
After the correlation coefficient is obtained from the variable characteristics, it can be analyzed. When r is 0, it means that there is no linear correlation between the two variables; when 0< | r | < 0.3, the two are weakly correlated; when 0.3< | r | < 0.5, the two are low-degree correlated; when 0.5< | r | < 0.8, the two are obviously related; 0.8< | r | <1, the two are highly correlated; when r is 1, the two are completely linearly related. In the analysis of the influence factors of the construction cost of the power transmission and transformation project, the foundation is laid for the subsequent multi-factor dimension reduction, key factor screening and the like by judging whether the factors have obvious linear relation.
2) Hypothesis testing
When the correlation analysis is performed on the X variable and the Y variable, the joint distribution of the two variables is preset to be two-dimensional normal distribution: when X takes any value, the condition distribution of Y is normal distribution; when Y takes any value, the conditional distribution of X is normal distribution. However, due to the randomness of sampling and the small sample size, the result obtained by sampling cannot be directly used to describe the whole, and needs to be inferred by a hypothesis testing method. The method comprises the following steps: the original assumption is provided, and no obvious linear correlation relationship exists between the two variables;
test statistics are constructed. The test statistic of the Pearson correlation coefficient is T statistic, T-T (n-2),
3) calculating an observed value of the test statistic; the observations were compared to the level of significance. If the significance level is less than the significance level, the original hypothesis is rejected, and the significant linear correlation relationship exists between the two variables. If it is negative, the original hypothesis is accepted. Note that: if the correlation coefficient of the two variables is known to be positive or negative, one-sided test can be carried out, and the effect is better.
And (3) performing grey correlation clustering analysis, wherein n observation objects are arranged, and each object observes m characteristic data to obtain a sequence as follows:
Xi＝(xi(1),xi(2),…,xi(n)) from Xi、XjThe generated initial point zero-ization image Xi 0、Xj 0The following were used:
Xi 0＝(xi 0(1),xi 0(2),…,xi 0(n))，
wherein x isi 0(k)＝xi(k)-xi(1) Let us order
Then XiAnd XjHas an absolute grey correlation of
Thereby obtaining an upper triangular matrix A
Wherein epsilonii＝1，i＝1,2,…,m。
The critical value tau (0 < tau ≦ 1) can be determined according to the requirements of practical problems, and tau is generally required>0.5. The closer τ is to 1, the finer the classification, the fewer features in each class; the smaller the value of τ, the coarser the classification, and the more features in each class. When epsilonijWhen t is greater than or equal to T, then X is looked atiAnd XjHomogeneous features at level τ. Thus obtaining the characteristic X1,X2,…,XnOne classification at level τ.
Since when X is presentiAnd XjWhen they are positively correlated, their corresponding S values are of the same sign (both positive or both negative), and | Si-SjLess, | XiAnd XjThe degree of association of (2) is large; when X is presentiAnd XjWhen the correlation is negative, the corresponding S value has different sign, | Si-SjGreater, XiAnd XjThe degree of association of (a) is small. Thus, XiAnd XjThe two are considered to be positively correlated when they are of the same type at level τ.
The absolute correlation degree meets the normative in the grey correlation axiom, the even-pair symmetry and the proximity, but does not meet the integrity. And has the following 8 properties:
1)0＜εij≤1；
2)εijonly with XiAnd XjGeometry-dependent, independent of the others, or translation does not change the value of the absolute correlation;
3) neither sequence is absolutely independent, i.e.. epsilonijIs constantly not zero;
4)Xiand XjThe greater the geometric similarity, εijThe larger;
5) when X is presentiOr XjAny one of the observed data changes, epsilonijWill change accordingly;
6)Xiand XjChange in length,. epsilonijThe value of (c) will also change;
7)εii＝εjj＝1；
8)εij＝εji。
according to the results of the bivariate correlation analysis and the factor gray clustering analysis of the embodiment, the internal factors have a large influence on the investment capacity of the power grid, most of the screened important factors are internal financial index factors, and the factors which most directly influence the investment capacity and have large influence coefficients are business income, own capital occupation ratio, return on investment rate and enterprise operation coefficients.
Based on the above embodiments, in a further embodiment of the present application, as shown in fig. 3, a power grid investment capacity calculation model constructed according to the revenue of the power grid in the target area, the business operation coefficient, the own capital proportion in investment, and the return on investment rate is as follows:
wherein, ItThe method is characterized in that the method represents the investment capacity of the power grid enterprise in the t-th year, alpha represents the operation coefficient of the power grid enterprise, S represents the business income of the enterprise in the initial year, gamma represents the return on investment, beta represents the proportion of the own fund of the power grid enterprise to the total investment, and t is the investment age limit.
Specifically, the power grid enterprise investment capacity measuring and calculating model mainly relates to indexes such as business income, enterprise operation coefficients, self-owned fund proportion in investment, investment return rate and the like, and the business income is a main financial index for reflecting enterprise operation level and determining investment capacity; the enterprise operation coefficient represents the proportion of the fund available for investment of the enterprise to the available fund on the premise that the enterprise and the industrial operation are not influenced. Due to the characteristics of the power grid industry, the return on investment of the power grid industry is regulated to a certain extent. Accordingly, an investment capacity measuring and calculating model of the power grid enterprise is constructed, and is shown in fig. 3.
On the basis of the above embodiment, in a further embodiment of the present application, the proportion of the technical improvement investment in the original value of the depreciated asset at the beginning of the year is calculated, and a weighted average value of the technical improvement investment is calculated;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
Specifically, the investment scale of the technical improvement of the company in the next year is calculated by combining an asset depreciation measuring algorithm. In financial accounting, all fixed assets have corresponding depreciation years, all the fixed assets which are depreciated and still in use should be taken into consideration of technical improvement replacement, and a relatively stable relationship exists between the annual technical improvement investment and the scale of the depreciated fixed assets at the beginning of the year. Therefore, the proportion can be used for measuring and calculating the size of the technically improved investment, namely the asset depreciation measuring algorithm.
The technological improvement investment scale measuring and calculating process based on the asset depreciation measuring algorithm comprises the following 2 steps. Step a: calculating the relationship between the improvement investment and the improvement of depreciated assets. Calculating the ratio of the technological improvement investment scale of the last 3 years to the original value of the depreciated assets which is already improved at the beginning of the year, and further calculating a weighted average value; step b: and calculating the technical improvement investment scale of the company. And (c) calculating the technological improvement investment scale of the company in the next year according to the original value of the depreciated fixed assets in the beginning of the next year and the ratio measured in the step (a).
By combining the technical scheme of the embodiment, the technical improvement investment scale is taken as a research object by combining related research results and sample data, the problem that quantitative reasonable basis is lacked in current power grid technical improvement investment scale determination is solved, the self investment capacity of a power grid enterprise is taken as a basic reference, investment structure distribution is optimized, the power grid technical improvement investment scale is scientifically and reasonably determined, the economy of power grid construction is improved, and reasonable allocation of resources and enterprise sustainable development are achieved.
As shown in fig. 4, the present embodiment further provides a system for predicting investment scale of a technical improvement project considering area investment capacity, so as to implement the prediction method provided by the foregoing embodiment, including an obtaining unit 210, an analyzing unit 220, a calculating unit 230, and a predicting unit 240;
the obtaining unit 210 is configured to obtain an influence factor index of the power grid investment capacity in the target area, and construct a power grid investment capacity influence factor index model according to the influence factor;
the analysis unit 220 is configured to perform correlation analysis on the power grid investment capacity influence factor index model by using a bivariate correlation analysis model, perform gray correlation clustering analysis on the influence factor indexes, and select multiple indexes affecting the front row of the sequence as representative indexes;
the computing unit 230 is used for constructing a power grid investment capacity computing model according to the representative indexes;
the prediction unit 240 is configured to construct a technical improvement project investment scale prediction model based on asset depreciation calculation.
Specifically, the obtaining unit 210 first identifies the main influencing elements of the investment capacity, and constructs an index model of the power grid investment capacity influencing elements from the aspects of depreciation, net profit, financing and the like to obtain a fishbone diagram of the power grid investment capacity influencing elements. The analysis unit 220 analyzes the correlation of the influencing factor index variable and the investment ability dependent variable by adopting a bivariate correlation analysis method; and then carrying out grey correlation clustering analysis on the influence factor indexes, more scientifically and precisely simplifying the indexes, and refining the representative influence factor indexes mainly influenced for subsequently constructing an investment capacity calculation model of the power grid company. The calculating unit 230 determines an investment capacity calculating model according to the influence factor indexes such as the business income, the enterprise operation coefficient, the self-owned capital occupation ratio in investment, the return on investment and the like, and calculates the power grid investment capacity of the power grid company through the power grid investment capacity calculating model. And the prediction unit 240 calculates the technical improvement investment scale of the enterprise in the next year by combining the asset depreciation measurement algorithm. The embodiment comprehensively analyzes the investment capacity of the power grid enterprise from the aspects of social economic development on the power demand, power demand increase on the investment scale, enterprise financial capacity supporting investment, return of investment by market economic environment and the like, scientifically evaluates the investment capacity of the enterprise, constructs a technically improved investment scale measuring and calculating model based on an asset depreciation measuring algorithm, provides decision support for power grid enterprise planning and investment, and realizes accurate investment of power grid enterprise funds, so that the current complex internal and external operation situation is responded, and the enterprise operation development target is realized.
On the basis of the above embodiment, in a further embodiment of the present application, the obtaining unit 210 is further configured to identify three impact factor indexes of power grid investment capacity, namely depreciation, net profit, and financing, construct a power grid investment capacity impact factor index model according to the three impact factor indexes of depreciation, net profit, and financing, and obtain a fishbone diagram of the power grid investment capacity impact factor according to the index model.
Specifically, the obtaining unit 210 is further configured to obtain a fishbone map of the influence factor of the power grid investment capacity, which is the same as that in the foregoing embodiment of the prediction method and will not be described again.
On the basis of the foregoing embodiment, in a further embodiment of the present application, the analysis unit 220 is further configured to calculate a correlation coefficient between the influencing factor and the historical investment capacity by using a bivariate correlation analysis method, and screen out an influencing factor index significantly related to the historical investment capacity;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
Specifically, the analysis unit 220 obtains representative influence factors by using bivariate correlation analysis and gray clustering analysis, which are the same as those in the above embodiment of the prediction method and will not be described again.
On the basis of the above embodiments, the present application further providesIn an embodiment, the calculating unit 230 is further configured to construct a power grid investment capacity calculating model according to the operating income, the enterprise operating coefficient, the own capital investment ratio, and the return on investment of the power grid in the target area, where the power grid investment capacity calculating model is:
wherein, ItThe method is characterized in that the method represents the investment capacity of the power grid enterprise in the t-th year, alpha represents the operation coefficient of the power grid enterprise, S represents the business income of the enterprise in the initial year, gamma represents the return on investment, beta represents the proportion of the own fund of the power grid enterprise to the total investment, and t is the investment age limit.
Specifically, the computing unit 230 constructs a power grid investment capacity computing model according to the enterprise operation coefficient, the return on investment, and the like, and the description herein is the same as that in the above prediction method embodiment, and therefore, the description is omitted.
On the basis of the above embodiment, in a further embodiment of the present application, the prediction unit 240 is further configured to calculate a proportion of the improved investment in the original value of the depreciated asset at the beginning of the year, and calculate a weighted average of the improved investment;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
Specifically, the prediction unit 240 uses an asset depreciation measurement algorithm to obtain the next-year technically improved investment scale, which is the same as the prediction method in the embodiment described above, and therefore, the description is omitted.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The technical improvement project investment scale prediction method considering regional investment capacity is characterized by comprising the following steps:
acquiring influence factor indexes of the power grid investment capacity in a target area, and constructing a power grid investment capacity influence factor index model according to the influence factors;
performing relevance analysis on the power grid investment capacity influence factor index model by adopting a bivariate relevance analysis model, performing grey relevance clustering analysis on influence factor indexes, and selecting a plurality of influence factor indexes with the front influence sequence as representative indexes;
constructing a power grid investment capacity calculation model according to the representative indexes;
and constructing a technical improvement project investment scale prediction model based on asset depreciation calculation.
2. The method of claim 1, wherein the impact factor indicators of the grid investment capacity include depreciation, net profit, and financing, the grid investment capacity impact factor indicator model is constructed according to the depreciation, net profit, and financing impact factor indicators, and a grid investment capacity impact factor fishbone map is obtained according to the indicator model.
3. The method for predicting the investment scale of a technical improvement project considering regional investment capacity according to claim 1, wherein a bivariate correlation analysis method is adopted to calculate a correlation coefficient between an influence factor and historical investment capacity, and an influence factor index which is obviously related to the historical investment capacity is screened out;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
4. The method of claim 1, wherein the power grid investment capacity calculation model is constructed according to the business income, the business operation coefficient, the self-owned capital occupation ratio in investment and the return on investment of the power grid in the target areaThe power grid investment capacity calculation model is as follows:
wherein, ItThe method is characterized in that the method represents the investment capacity of the power grid enterprise in the t-th year, alpha represents the operation coefficient of the power grid enterprise, S represents the business income of the enterprise in the initial year, gamma represents the return on investment, beta represents the proportion of the own fund of the power grid enterprise to the total investment, and t is the investment age limit.
5. The method of claim 1, wherein the proportion of the improvement investment in the original value of the depreciated assets at the beginning of the year is calculated, and the weighted average value of the improvement investment is calculated;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
6. The technical improvement project investment scale prediction system considering the regional investment capacity is characterized by comprising an acquisition unit, an analysis unit, a calculation unit and a prediction unit;
the acquisition unit is used for acquiring the influence factor indexes of the power grid investment capacity in the target area and constructing a power grid investment capacity influence factor index model according to the influence factors;
the analysis unit is used for performing correlation analysis on the power grid investment capacity influence factor index model by adopting a bivariate correlation analysis model, performing grey correlation clustering analysis on influence factor indexes, and selecting a plurality of indexes influencing the front row of the sequence as representative indexes;
the computing unit is used for constructing a power grid investment capacity computing model according to the representative indexes;
and the prediction unit is used for constructing a technical improvement project investment scale prediction model based on asset depreciation calculation.
7. The system of claim 6, wherein the obtaining unit is further configured to identify three impact factor indicators of grid investment capacity including depreciation, net profit, and financing, construct a grid investment capacity impact factor indicator model according to the three impact factor indicators of depreciation, net profit, and financing, and obtain a grid investment capacity impact factor fishbone map according to the indicator model.
8. The system of claim 6, wherein the analysis unit is further configured to calculate a correlation coefficient between the influencing factors and the historical investment capacity by using a bivariate correlation analysis method, and screen out an influencing factor indicator that is significantly correlated with the historical investment capacity;
and clustering the influence factor indexes by adopting a gray clustering analysis method to generate factor groups, and respectively selecting representative influence factor indexes from each type of factor groups.
9. The system of claim 6, wherein the computing unit is further configured to construct a power grid investment capacity computing model according to the revenue of the power grid in the target area, the business operation coefficient, the proportion of capital owned by the investment, and the return on investment, wherein the power grid investment capacity computing model is:
wherein It represents the power grid enterprise investment capacity of the t-th year, alpha represents the power grid enterprise operation coefficient, S represents the enterprise business income of the initial year, gamma represents the return on investment, beta represents the proportion of the own funds of the power grid enterprise to the total investment, and t is the investment age limit.
10. The system of claim 6, wherein the prediction unit is further configured to calculate a proportion of the technically improved investment in the original value of the depreciated asset at the beginning of the year and calculate a weighted average of the technically improved investment;
and obtaining the next-year technical improvement investment scale according to the proportion of the original value of the depreciated fixed asset which is already advanced at the beginning of the next year and the technical improvement investment in the original value of the depreciated asset which is already advanced at the beginning of the next year.
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| CN115829272B (en) * | 2022-12-08 | 2023-07-28 | 国网江苏省电力有限公司南通供电分公司 | Method for extracting key influence factors of electric quantity demand in industry |
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