Method and device for estimating purchase quantity and reliability of electric power engineering materials and medium

Abstract

The invention relates to an electric power engineering material purchase quantity and reliability estimation method and device and a medium, and the method comprises the following steps: obtaining influence factors of the demand quantity of each to-be-added project material, constructing an influence factor matrix Xn + 1 of each to-be-added project material, and inputting the influence factor matrix Xn + 1 into a binary estimation model, predicting and outputting the demand quantity and reliability of each to-be-added project material; the execution process of the binary estimation model comprises the following steps; inputting the influence factor matrix Xn + 1 into a preset regression formula, and estimating a confidence interval of the demand quantity of each item material to be newly added; and according to the confidence interval estimation result, estimating the purchase quantity of each item material to be added, and further estimating the reliability to obtain a reliability estimation result. Compared with the prior art, the method has the advantages of realizing joint estimation of demand reliability and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power material forecasting, and in particular to a method, device and medium for estimating the purchase quantity and reliability of electric power engineering materials. Background Art

[0002] At present, the mainstream methods for forecasting the demand for power engineering materials are linear regression, exponential smoothing, time series, grey prediction, neural network and Markov chain. Exponential smoothing, time series, grey prediction, neural network and Markov chain are not prediction algorithms based on engineering characteristics, and the prediction accuracy is irrelevant to the actual situation of the project to be estimated, so the reliability of the prediction results cannot be evaluated.

[0003] Patent application CN118313580A discloses a method and system for predicting the demand for electric power materials, which includes determining the associated information of electric power materials based on big data analysis; setting up an expert system to determine a set of rules to establish a prediction model based on a monotonic reasoning model; obtaining historical data and outputting material prediction data through the prediction model, so as to construct a scientific and reasonable demand prediction model that conforms to the characteristics of the power grid, and realize intelligent prediction of material framework bidding categories and quantities. Patent CN112614011B discloses a method and device for predicting the demand for power distribution network materials, a storage medium and an electronic device, which includes: preprocessing the usage record parameters of the historical distribution network materials in the target area, the distribution network historical planning data and the distribution network area economic development data to obtain a preprocessed data set of the distribution network materials in the target area; dividing the preprocessed data set to obtain a preprocessed sub-data set; clustering the preprocessed sub-data set to obtain a data clustering cluster; inputting the data clustering cluster into the material estimation model to output the estimated material usage required for the target area to operate the power distribution network construction material project. Although the above methods have certain advantages, they do not consider the reliability issue. There is a certain relationship between the required quantity and the purchased quantity, so the joint solution problem of the purchase quantity and reliability is not solved. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device and medium for estimating the procurement quantity and reliability of power engineering materials to achieve a joint solution of procurement quantity and reliability.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for estimating the purchase quantity and reliability of power engineering materials comprises the following steps:

[0007] Obtain the influencing factors of the demand for materials of each new project, and construct the influencing factor matrix X of each new project n+1, and input into the binary estimation model to predict the demand and reliability of materials for each new project;

[0008] The execution process of the binary estimation model includes:

[0009] The influencing factor matrix X n+1 Enter the preset regression formula to estimate the confidence interval of the demand for materials for each new project;

[0010] According to the confidence interval estimation results, the procurement volume of materials for each of the new projects is estimated, and the reliability is further estimated to obtain the reliability estimation results.

[0011] Furthermore, the preset process of the regression formula includes:

[0012] Obtain the historical data of power materials in the project set, wherein the historical data of power materials includes the actual demand for a certain material Y={y1, y2, ..., y n} and m factors affecting demand;

[0013] The m influencing factors of the demand are constructed as an influencing factor matrix X ij =(x ij ), 1≤i≤n, 1≤j≤m, where x ij is the mth influencing factor value of the nth item;

[0014] The initial regression formula is selected as: Y = βX + ε, where β = (β0, β1, ..., β m ) T is the unknown coefficient, ε is the error term;

[0015] The actual demand Y={y1, y2, ..., y n} and influencing factor matrix X ij Input the initial regression formula to solve the unknown coefficient β=(X T X) -1 X T Y, get the preset regression formula.

[0016] Furthermore, the preset process of the regression formula also includes:

[0017] The t-test method is used to perform hypothesis testing on the solved unknown coefficient β.

[0018] Furthermore, the process of performing hypothesis testing includes:

[0019] (1) Unbiased estimate of standard deviation:

[0020]

[0021] Where s is the sample standard deviation;

[0022] (2) Calculation of the test statistic t for the i-th influencing factor:

[0023]

[0024] Where N(0,1) is the standard normal distribution, is a chi-square distribution with nm-1 degrees of freedom, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β i is the coefficient of the ith time;

[0025] (3) Set the significance level α for the hypothesis test and calculate the probability of the test statistic t value

[0026] (4) Determine the probability Is it lower than α? If so, it indicates that there is a linear relationship between the i-th influencing factor and the demand, and the hypothesis test passes. If not, it indicates that there is no linear relationship. If the hypothesis test passes, the i-th influencing factor is eliminated and the regression analysis is performed again to set the regression formula.

[0027] Furthermore, there are two ways to estimate the confidence interval, namely: average confidence interval estimation and individual confidence interval estimation, wherein the estimation expression of the average confidence interval is:

[0028]

[0029] Where Y1 is the estimated average confidence interval, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β is the unknown coefficient, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1 The elements in , X is m kinds of influencing factors to construct the influencing factor matrix, α is the significance level of hypothesis test, s is the sample standard deviation;

[0030] The estimated expression of the individual confidence interval is:

[0031]

[0032] Where Y2 is the estimated individual confidence interval, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β is the unknown coefficient, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1The elements in , X is the influencing factor matrix constructed by m influencing factors, α is the significance level of hypothesis test, and s is the sample standard deviation.

[0033] Furthermore, the reliability estimation process includes:

[0034] According to the upper limit of the confidence interval, determine the purchase volume d of materials for the new project:

[0035]

[0036] Based on the purchase volume d, combined with the hypothesis test significance level and the reliability probability satisfaction condition, the reliability probability is calculated and used as the reliability estimation result, wherein the satisfaction condition is:

[0037]

[0038] In the formula, is the average value of all influencing factors, is the critical value of t distribution, α is the significance level of hypothesis test, s is the sample standard deviation, n is the number of items, and p is the reliability probability.

[0039] Furthermore, it also includes: estimating the total purchase quantity of all materials for the projects to be newly added according to the confidence interval of the demand quantity of the materials for each project to be newly added.

[0040] Furthermore, the total purchase volume is estimated using two methods: interval confidence estimation and quantity confidence estimation, wherein the interval confidence estimation expression is:

[0041]

[0042] Where, d total is the total purchase volume, is the average value of all influencing factors, k is the number of items to be hung, t n-1 is, p is the probability of supply reliability, s is the sample standard deviation, and n is the number of items;

[0043] The quantitative confidence estimate expression is:

[0044]

[0045] Where, d total is the total purchase quantity, k is the number of items to be hung, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1 The elements in , X is the influencing factor matrix constructed by m influencing factors, α is the significance level of hypothesis test, s is the sample standard deviation, Y is the actual demand for a certain material, X n+kFor n-m-1 is a Student's distribution with nm-1 degrees of freedom.

[0046] The present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method for estimating the procurement quantity and reliability of power engineering materials as described above.

[0047] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the method for estimating the procurement quantity and reliability of power engineering materials as described above.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] (1) Based on the theoretical framework of linear regression method, the present invention adopts confidence interval analysis for improvement and optimization, and constructs a binary estimation model of power engineering material procurement quantity and reliability. According to the influencing factors of material demand, the procurement quantity can be estimated according to the confidence interval of demand, and the reliability can be estimated at the same time, thus realizing the joint solution of procurement quantity and reliability.

[0050] (2) The present invention uses the t-test method to perform hypothesis testing on the coefficient β in the regression formula to determine whether there is indeed a linear relationship in the regression formula, which provides a good foundation for the subsequent estimation process and helps to improve the accuracy of the estimation.

[0051] (3) The present invention can be fully realized by computer automation without human intervention, thus avoiding the adverse effects of subjective experience in supply chain forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0053] Figure 2 A two-dimensional “number-probability” curve generated for the present invention. DETAILED DESCRIPTION

[0054] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0055] Example 1

[0056] This embodiment provides a method for estimating the purchase quantity and reliability of power engineering materials. Figure 1 As shown, the method comprises the following steps:

[0057] S1. Conduct regression analysis on historical sample items.

[0058] In the analysis sample consisting of n items, the actual demand for a certain material is Y = {y1, y2, ..., y n}, there are m factors affecting demand, and the value is matrix X ij =(x ij ), 1≤i≤n, 1≤j≤m, where x ij is the mth factor value of the nth item. Simple regression analysis, establish the linear equation Y = βX + ε, the coefficient to be determined β = (β0, β1, ..., β m ) T . Solve the model's unknown coefficient β=(X T X) -1 X T Y, thus we get the preliminary regression formula. The regression formula only solves the line closest to the sample point, but the sample may not have a linear relationship, so we need to do a hypothesis test on the regression formula (linear coefficient), generally using the t-test method:

[0059] (1) Unbiased Estimation of Standard Deviation

[0060] (2) Test statistic for the i-th factor Where N(0,1) is the standard normal distribution, is a chi-square distribution with nm-1 degrees of freedom, t n-m-1 is a Student's distribution with nm-1 degrees of freedom.

[0061] (3) Set the significance level α for the hypothesis test, which indicates the probability that the i-th factor is linearly independent of the demand. Compare it with the significance value. If the t probability is lower than the significance, it means that the i-th factor has a linear relationship with the demand.

[0062] (4) Factors that fail the hypothesis test should be eliminated and the regression analysis should be repeated.

[0063] S2. Confidence interval for estimating the demand for materials for new projects.

[0064] After solving the regression formula, we can use it to estimate the confidence interval of the material demand for the new project. There are two types of confidence interval estimation, namely average confidence interval and individual confidence interval. The average confidence interval is based on the influencing factor value X of the new project. n+1 , estimate the interval of the mean value of Y at the confidence level α, the estimation formula is The individual confidence interval is based on the influencing factor value X n+1, estimate the interval of individual values ​​of Y, the estimation formula is When applying a specific algorithm, the confidence interval estimation method should be reasonably selected based on whether the prediction object is a batch of projects or a single project.

[0065] S3. Estimate purchase volume and reliability.

[0066] In the "demand-reliability" binary estimation model based on confidence interval estimation, the larger the demand, the higher the supply chain reliability. Conversely, in order to improve the reliability of the supply chain, the supply quantity must be increased. The confidence curve is a specific quantification of this law. In the demand forecasting scenario of the material planning stage, the supply chain reliability is used as a predetermined indicator to solve the demand forecast quantity of supporting materials and serve as the basis for determining the demand quantity in the procurement stage. In the agreed inventory allocation scenario of the material fulfillment stage, the probability that the remaining quantity of a certain material purchase will meet the hanging material demand within a certain period of time in the future is estimated as the decision-making basis for initiating the next round of procurement process.

[0067] Generally, as long as the theoretical forecast value of demand quantity is greater than or equal to the actual value, the supply chain can meet the actual demand and the purchase quantity The reliability probability of supply is denoted as p, and the confidence level and reliability probability satisfy In supply chain management practice, a purchase often corresponds to multiple project requirements, and the confidence curves of all projects need to be horizontally superimposed to obtain the total purchase volume of all materials for the new projects to be added. For k projects to be added, the interval confidence curve is Quantitative confidence curve

[0068] In summary, the method of this embodiment is based on the theoretical framework of linear regression method, and adopts confidence interval analysis for improvement and optimization, and establishes a binary estimation model for the demand and reliability of power engineering materials. The binary estimation model adopts the confidence interval analysis method to quantify the uncertainty of demand as risk probability, so that power engineering managers can clearly understand the reliability of the current procurement volume. The advantages of the binary estimation model are that in addition to allocating power grid materials according to reliability goals and realizing the forward analysis of "probability to quantity", it can also measure the agreement inventory satisfaction rate within a certain period of time and realize the reverse analysis of "quantity to probability".

[0069] The implementation method of the present invention is computer program development, deployment and application, and is more suitable for big data analysis platforms such as Python, Matlab, SAS, etc. The algorithm formula can be designed as a program, and the sample is imported from external structured data and at least contains engineering characteristic factors. The output result can be a set of "quantity-probability" binary arrays, or a "quantity-probability" two-dimensional curve, such as Figure 2 shown.

[0070] Example 2

[0071] This embodiment provides an electronic device, including: one or more processors; a memory; and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method for estimating the procurement quantity and reliability of power engineering materials as described in Example 1.

[0072] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. 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 invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0073] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for estimating the purchase quantity and reliability of power engineering materials, characterized in that: The following steps are involved: Obtain the influencing factors of the demand for each new project material, and construct the influencing factor matrix X of each new project material n+1 , and input into the binary estimation model to predict the demand and reliability of materials for each new project; The execution process of the binary estimation model includes: The influencing factor matrix X n+1 Enter the preset regression formula to estimate the confidence interval of the demand for materials for each new project; According to the confidence interval estimation results, the procurement volume of materials for each of the new projects is estimated, and the reliability is further estimated to obtain the reliability estimation results.

2. A method for estimating the purchase volume and reliability of power engineering materials according to claim 1, characterized in that: The preset process of the regression formula includes: Obtain the historical data of power materials in the project set, wherein the historical data of power materials includes the actual demand for a certain material Y = {y1, y2, ..., y n } and m factors affecting demand; The m influencing factors of the demand are constructed as an influencing factor matrix X ij =(x ij ), 1≤i≤n,1≤j≤m, where x ij is the mth influencing factor value of the nth item; The initial regression formula is selected as: Y = βX + ε, where β = (β0, β1, …, β m ) T is the unknown coefficient, ε is the error term; The actual demand Y={y1,y2,…,y n } and influencing factor matrix X ij Input the initial regression formula to solve the unknown coefficient β=(X T X) -1 X T Y, get the preset regression formula.

3. A method for estimating the purchase volume and reliability of power engineering materials according to claim 2, characterized in that: The preset process of the regression formula also includes: The t-test method is used to perform hypothesis testing on the solved unknown coefficient β.

4. A method for estimating the purchase volume and reliability of power engineering materials according to claim 3, characterized in that: The process of conducting hypothesis testing includes: (1) Unbiased estimate of standard deviation: In the formula, s is the sample standard deviation; (2) Calculation of the test statistic t for the i-th influencing factor: Where N(0,1) is the standard normal distribution, is a chi-square distribution with nm-1 degrees of freedom, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β i is the i-th coefficient; (3) Set the significance level α for the hypothesis test and calculate the probability of the test statistic t value (4) Determine the probability Is it lower than α? If so, it indicates that there is a linear relationship between the i-th influencing factor and the demand, and the hypothesis test passes. If not, it indicates that there is no linear relationship. If the hypothesis test passes, the i-th influencing factor is eliminated and the regression analysis is performed again to set the regression formula.

5. The method for estimating the purchase quantity and reliability of power engineering materials according to claim 1, characterized in that: There are two ways to estimate the confidence interval, namely: average confidence interval estimation and individual confidence interval estimation, where the estimation expression of the average confidence interval is: Where Y1 is the estimated average confidence interval, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β is the unknown coefficient, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1 The elements in , X is the influencing factor matrix constructed by m influencing factors, α is the significance level of hypothesis test, and s is the sample standard deviation; The estimated expression of the individual confidence interval is: Where Y2 is the estimated individual confidence interval, t n-m-1 is a Student distribution with nm-1 degrees of freedom, β is the unknown coefficient, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1 The elements in , X is the influencing factor matrix constructed by m influencing factors, α is the significance level of hypothesis test, and s is the sample standard deviation.

6. A method for estimating the purchase volume and reliability of power engineering materials according to claim 1, characterized in that: The reliability estimation process includes: According to the upper limit of the confidence interval, determine the purchase volume d of materials for the new project: Based on the purchase volume d, combined with the hypothesis test significance level and the reliability probability satisfaction condition, the reliability probability is calculated and used as the reliability estimation result, wherein the satisfaction condition is: In the formula, is the average value of all influencing factors, is the critical value of t distribution, α is the significance level of hypothesis test, s is the sample standard deviation, n is the number of items, and p is the reliability probability.

7. A method for estimating the purchase volume and reliability of power engineering materials according to claim 1, characterized in that: Also includes: The total purchase quantity of all materials for the new projects is estimated based on the confidence interval of the demand quantity of the materials for each new project.

8. A method for estimating the purchase volume and reliability of power engineering materials according to claim 7, characterized in that: There are two ways to estimate the total purchase volume: interval confidence estimation and quantity confidence estimation. The interval confidence estimation expression is: Where, d total is the total purchase volume, is the average value of all influencing factors, k is the number of items to be hung, t n-1 is, p is the probability of supply reliability, s is the sample standard deviation, and n is the number of items; The quantitative confidence estimate expression is: Where, d total is the total purchase quantity, k is the number of items to be hung, X n+1 is the influencing factor matrix of the materials to be added, x n+1 For X n+1 The elements in , X is the influencing factor matrix constructed by m influencing factors, α is the significance level of hypothesis test, s is the sample standard deviation, Y is the actual demand for a certain material, X n+k For n-m-1 is a Student's distribution with nm-1 degrees of freedom.

9. An electronic device, characterized in that: include: one or more processors; Memory; and One or more programs stored in a memory, wherein the one or more programs include instructions for executing the method for estimating the procurement quantity and reliability of electric power engineering materials as claimed in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: It includes one or more programs executed by one or more processors of an electronic device, and the one or more programs include instructions for executing the method for estimating the procurement quantity and reliability of power engineering materials as described in any one of claims 1-8.

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