Power transmission and transformation project cost response control method adapting to market element change

Abstract

The invention relates to a power transmission and transformation project cost response control method adapting to market element changes. The method comprises the following steps of collecting market element data and power transmission and transformation project cost element data and performing preprocessing; performing correlation analysis based on the preprocessed market element data and the power transmission and transformation project cost element data; setting a correlation degree threshold value, screening all the market elements and the power transmission and transformation project cost elements of which the correlation degrees are greater than the threshold value, performing global sensitivity analysis, and setting the elements of which the global sensitivity is greater than a preset threshold value as monitoring elements; and setting different control strategies, and executing the different control strategies according to the change of each monitoring element.

Description

Technical Field

[0001] The invention relates to a cost response control method for a power transmission and transformation project adapting to changes in market factors, and belongs to the technical field of power engineering. Background Art

[0002] Cost management of power transmission and transformation projects is a key link in power project construction. Changes in market factors have a significant impact on project costs. At present, the common cost control methods for power transmission and transformation projects have the following shortcomings:

[0003] Existing methods usually conduct cost analysis based on static market data, which is difficult to adapt to the impact of dynamic changes in market factors, resulting in delayed response or prediction deviation;

[0004] Insufficient research on the complex relationship between market factors and cost factors makes it difficult to accurately identify which market factors have a key impact on cost fluctuations;

[0005] Existing technologies lack a comprehensive assessment of the global sensitivity of different factors, which may lead to irrational resource allocation and affect control effectiveness. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention proposes a cost response control method for power transmission and transformation projects that adapts to changes in market factors.

[0007] The technical solution of the present invention is as follows:

[0008] On the one hand, the present invention provides a cost response control method for power transmission and transformation projects that adapts to changes in market factors, comprising the following steps:

[0009] Collect market factor data and power transmission and transformation project cost factor data and perform pre-processing;

[0010] Conduct correlation analysis based on pre-processed market factor data and transmission and transformation project cost factor data;

[0011] Set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements;

[0012] Set different control strategies and execute different control strategies according to the changes of each monitoring factor.

[0013] As a preferred embodiment of the present invention, the market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index, and level advanced index and technology innovation index;

[0014] The cost elements of the power transmission and transformation project include total cost, labor cost, material cost, equipment cost and construction period.

[0015] As a preferred embodiment of the present invention, the grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project cost, and the specific steps are as follows:

[0016] Suppose n data sequences are used to construct the following matrix:

[0017]

[0018] Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element;

[0019] The data in each data sequence is dimensionlessly processed, as shown in the following formula:

[0020]

[0021] Where: x i ′ (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x after dimensionless processing i ′ (k);

[0022] The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula:

[0023]

[0024] Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient;

[0025] The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

[0026] As a preferred embodiment of the present invention, the Sobol method is used to perform global sensitivity analysis, and the specific steps are as follows:

[0027] Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements;

[0028] For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost;

[0029] Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y;

[0030] Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y;

[0031] Calculate the first-order sensitivity index S i :

[0032]

[0033] Calculate the total sensitivity index S Ti :

[0034]

[0035] Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y;

[0036] Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

[0037] On the other hand, the present invention also provides a power transmission and transformation project cost response control system that adapts to changes in market factors, including a data acquisition module, a correlation analysis module, a sensitivity analysis module, and a control strategy management module;

[0038] The data acquisition module is used to collect market factor data and power transmission and transformation project cost factor data and perform preprocessing;

[0039] The relevance analysis module is used to perform relevance analysis based on the pre-processed market factor data and the power transmission and transformation project cost factor data;

[0040] The sensitivity analysis module is used to set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with a correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements;

[0041] The control strategy management module is used to set different control strategies and execute different control strategies according to changes in various monitoring elements.

[0042] As a preferred embodiment of the present invention, the market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index, and level advanced index and technology innovation index;

[0043] The cost elements of the power transmission and transformation project include total cost, labor cost, material cost, equipment cost and construction period.

[0044] As a preferred embodiment of the present invention, the grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project cost, and the specific steps are as follows:

[0045] Suppose n data sequences are used to construct the following matrix:

[0046]

[0047] Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element;

[0048] The data in each data sequence is dimensionlessly processed, as shown in the following formula:

[0049]

[0050] Where: x′ i (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x′ after dimensionless processing i (k);

[0051] The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula:

[0052]

[0053] Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient;

[0054] The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

[0055] As a preferred embodiment of the present invention, the Sobol method is used to perform global sensitivity analysis, and the specific steps are as follows:

[0056] Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements;

[0057] For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost;

[0058] Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y;

[0059] Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y;

[0060] Calculate the first-order sensitivity index S i :

[0061]

[0062] Calculate the total sensitivity index S Ti :

[0063]

[0064] Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y;

[0065] Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

[0066] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0067] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0068] The present invention has the following beneficial effects:

[0069] 1. The present invention introduces the correlation analysis technology of market factors and cost factors, which can accurately identify the potential relationship between the two; by setting the correlation threshold, the factors with significant influence are screened out to improve the pertinence and effectiveness of the analysis; based on the selected key factors, a global sensitivity analysis is further carried out to quantify the influence of each factor on the cost fluctuation. Setting the factors with global sensitivity greater than the preset threshold as monitoring factors is conducive to the accurate allocation of resources and avoids unnecessary redundant monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0073] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0074] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0075] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0076] Embodiment 1:

[0077] See also Figure 1 A cost response control method for power transmission and transformation engineering adapting to changes in market factors comprises the following steps:

[0078] Collect market factor data and power transmission and transformation project cost factor data and perform pre-processing;

[0079] Conduct correlation analysis based on pre-processed market factor data and transmission and transformation project cost factor data;

[0080] Set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements;

[0081] Set different control strategies and execute different control strategies according to the changes of each monitoring factor.

[0082] As a preferred implementation of this embodiment, the market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index (measures the circulation and logistics efficiency of market resources, affecting the procurement cost of materials and equipment), level advanced index and technology innovation index (reflects the level of technological development and its impact on cost), and GDP;

[0083] The cost elements of the power transmission and transformation project include the total cost, labor cost, material cost, equipment cost and construction period, as well as other costs such as site entry fees, enterprise management fees, etc.

[0084] As a preferred implementation of this embodiment, the grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project cost. The specific steps are as follows:

[0085] Suppose n data sequences are used to construct the following matrix:

[0086]

[0087] Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element;

[0088] The data in each data sequence is dimensionlessly processed, as shown in the following formula:

[0089]

[0090] Where: x i ′ (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x after dimensionless processing i ′ (k);

[0091] The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula:

[0092]

[0093] Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient;

[0094] The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

[0095] As a preferred implementation of this embodiment, the Sobol method is used to perform global sensitivity analysis, and the specific steps are as follows:

[0096] Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements;

[0097] For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost;

[0098] Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y;

[0099] Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y;

[0100] Calculate the first-order sensitivity index S i :

[0101]

[0102] Calculate the total sensitivity index S Ti :

[0103]

[0104] Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y;

[0105] Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

[0106] In this embodiment, the following control strategies are set:

[0107] (1) Control strategy

[0108] Cost control: Reduce material and equipment costs by optimizing procurement processes, centralized procurement, and locking in prices in advance.

[0109] Budget control: Strictly implement the budget, conduct detailed analysis and control on the over-budget portion to ensure that the project cost does not exceed the standard.

[0110] Contract management: Specify price adjustment clauses in the contract to ensure that the impact of market fluctuations on the contract cost is within a controllable range.

[0111] (2) Mitigation strategies

[0112] Schedule adjustment: When the market fluctuates drastically, adjust the project schedule appropriately to avoid purchasing large quantities of materials during price peaks.

[0113] Alternatives: Look for alternative materials or technologies to reduce reliance on factors with volatile prices.

[0114] Phased implementation: Implement the project in phases and flexibly adjust the progress and cost control of each phase according to market conditions.

[0115] (3) Perception strategy

[0116] Data analysis: Use big data and AI technology to conduct in-depth analysis of market factors and macroeconomic indicators and predict future trends.

[0117] Market research: Conduct market research regularly to grasp the latest market trends and price changes.

[0118] Information sharing: Establish an information sharing platform to timely release market factor changes and early warning information for reference by relevant departments.

[0119] (4) Transfer strategy

[0120] Risk transfer: Transferring part of the market risk to a third party through financial instruments such as insurance and hedging.

[0121] Supply chain cooperation: Strengthen cooperation with upstream and downstream companies in the supply chain to jointly cope with the risks brought about by market fluctuations.

[0122] Contract transfer: Introduce price adjustment clauses in the contract to transfer part of the market fluctuation risk to the contractor.

[0123] (5) Acceptance strategy

[0124] Reserve funds: Set aside a certain amount of emergency funds in the budget to cope with the additional costs caused by market fluctuations.

[0125] Flexible budget: When preparing the budget, consider market fluctuations and develop a flexible budget plan.

[0126] Manage expectations: Communicate fully with all parties involved in the project, explain the possible impact of market fluctuations in advance, and manage the expectations of all parties.

[0127] Embodiment 2:

[0128] A power transmission and transformation project cost response control system that adapts to changes in market factors, including a data acquisition module, a correlation analysis module, a sensitivity analysis module, and a control strategy management module;

[0129] The data acquisition module is used to collect market factor data and power transmission and transformation project cost factor data and perform preprocessing;

[0130] The relevance analysis module is used to perform relevance analysis based on the pre-processed market factor data and the power transmission and transformation project cost factor data;

[0131] The sensitivity analysis module is used to set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with a correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements;

[0132] The control strategy management module is used to set different control strategies and execute different control strategies according to changes in various monitoring elements.

[0133] As a preferred implementation of this embodiment, the market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index, and level advanced index and technology innovation index;

[0134] The cost elements of the power transmission and transformation project include total cost, labor cost, material cost, equipment cost and construction period.

[0135] As a preferred implementation of this embodiment, the grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project cost. The specific steps are as follows:

[0136] Suppose n data sequences are used to construct the following matrix:

[0137]

[0138] Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element;

[0139] The data in each data sequence is dimensionlessly processed, as shown in the following formula:

[0140]

[0141] Where: x i ′ (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x after dimensionless processing i ′ (k);

[0142] The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula:

[0143]

[0144] Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient;

[0145] The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

[0146] As a preferred implementation of this embodiment, the Sobol method is used to perform global sensitivity analysis, and the specific steps are as follows:

[0147] Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements;

[0148] For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost;

[0149] Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y;

[0150] Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y;

[0151] Calculate the first-order sensitivity index S i :

[0152]

[0153] Calculate the total sensitivity index S Ti :

[0154]

[0155] Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y;

[0156] Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

[0157] Embodiment three:

[0158] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0159] Embodiment 4:

[0160] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0161] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0162] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0165] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A cost response control method for power transmission and transformation projects that adapts to changes in market factors, characterized in that: The following steps are involved: Collect market factor data and power transmission and transformation project cost factor data and perform pre-processing; Conduct correlation analysis based on pre-processed market factor data and transmission and transformation project cost factor data; Set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements; Set different control strategies and execute different control strategies according to the changes of each monitoring factor.

2. A power transmission and transformation project cost response control method that adapts to changes in market factors according to claim 1, characterized in that: The market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index, and level advancement index and technology innovation index; The cost elements of the power transmission and transformation project include total cost, labor cost, material cost, equipment cost and construction period.

3. A power transmission and transformation project cost response control method that adapts to changes in market factors according to claim 2, characterized in that: The grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project. The specific steps are as follows: Suppose n data sequences are used to construct the following matrix: Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element; The data in each data sequence is dimensionlessly processed, as shown in the following formula: Where: x′ i (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x′ after dimensionless processing i (k); The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula: Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient; The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

4. A power transmission and transformation project cost response control method adapted to changes in market factors according to claim 3, characterized in that: The Sobol method is used to perform global sensitivity analysis. The specific steps are as follows: Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements; For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost; Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y; Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y; Calculate the first-order sensitivity index S i : Calculate the total sensitivity index S Ti : Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y; Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

5. A power transmission and transformation project cost response control system that adapts to changes in market factors, characterized in that: It includes data acquisition module, correlation analysis module, sensitivity analysis module and control strategy management module; The data acquisition module is used to collect market factor data and power transmission and transformation project cost factor data and perform preprocessing; The relevance analysis module is used to perform relevance analysis based on the pre-processed market factor data and the power transmission and transformation project cost factor data; The sensitivity analysis module is used to set a correlation threshold, screen all market elements and power transmission and transformation project cost elements with a correlation greater than the threshold for global sensitivity analysis, and set elements with global sensitivity greater than the preset threshold as monitoring elements; The control strategy management module is used to set different control strategies and execute different control strategies according to changes in various monitoring elements.

6. A power transmission and transformation project cost response control system that adapts to changes in market factors according to claim 5, characterized in that: The market factors include labor unit price, material unit price, machinery unit price, land cost, supply chain circulation index, and level advancement index and technology innovation index; The cost elements of the power transmission and transformation project include total cost, labor cost, material cost, equipment cost and construction period.

7. A power transmission and transformation project cost response control system that adapts to changes in market factors according to claim 6, characterized in that: The grey correlation analysis method is used to analyze the correlation of each factor to the total cost of the power transmission and transformation project. The specific steps are as follows: Suppose n data sequences are used to construct the following matrix: Where: X′ n Represents the data sequence corresponding to the nth element, X′ n =[x′ n (1),x′ n (2),…,x′ n (m)]; x′ n (m) represents the mth sample value of the data sequence corresponding to the nth element; The data in each data sequence is dimensionlessly processed, as shown in the following formula: Where: x′ i (k) represents the kth sample value of the data sequence corresponding to the i-th element; x i (k) represents x′ after dimensionless processing i (k); The maximum value of each element in each data sequence is selected to construct a reference sequence X0 = [x0(1), x0(2), ..., x0(m)], and the absolute difference and the extreme difference between each sequence sample and the reference sequence sample are calculated. Then, the correlation coefficient of each sample is calculated based on the absolute difference and the extreme difference, as shown in the following formula: Where: i = 1, 2, ..., n; k = 1, 2, ..., m; ρ represents the resolution coefficient; The mean of the correlation coefficients corresponding to the samples of each element in each data sequence is taken as the correlation degree of the element.

8. A power transmission and transformation project cost response control system that adapts to changes in market factors according to claim 7, characterized in that: The Sobol method is used to perform global sensitivity analysis. The specific steps are as follows: Use the Monte Carlo method to generate random samples of input variables. Let the random samples be [X1, X2, …, X R ], each random sample includes data values ​​corresponding to all market elements with a correlation greater than a threshold value and power transmission and transformation project cost elements; For each random sample, calculate the output variable Y, which is the total cost of the power transmission and transformation project, Y = f(X1, X2, ..., X R ), where f represents the total cost calculation function of the power transmission and transformation project cost; Calculate the total variance of the output variable V(Y) = E[Y 2 ]-(E[Y]) 2 , where E[Y] represents the expected value of Y; Calculate the conditional variance V i (Y|X i ), that is, when the input variable X is fixed i In the case of , the contribution of changes in other input variables to the variance of Y; Calculate the first-order sensitivity index S i : Calculate the total sensitivity index S Ti : Where: V -i (Y|X -i ) means that after fixing all input variables except X i In addition, the contribution of changes in other input variables to the variance of Y; Elements with a total sensitivity index greater than the threshold are taken as monitoring elements.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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