CN111932121A - Method, device, terminal and storage medium for evaluating high-quality power investment scheme - Google Patents
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Abstract
The application provides a method, a device, a terminal and a storage medium for evaluating a high-quality power investment scheme, which are based on a basic reliability probability calculated by a level confidence coefficient and a weight coefficient of influence factor measured data, and carry out iterative operation by using a recursive ER algorithm to respectively obtain reliability probability values of the investment scheme information in each level interval; based on an evidence reasoning theory, the utility value of the investment scheme information is obtained by projecting the reliability probability value into a preset utility function, the calculated utility value is used as an evaluation result of the investment scheme, the uncertainty of the information is considered, the fusion of multi-factor information influencing the decision is realized, and the technical problems that the existing investment evaluation mode of high-quality power is easily influenced by artificial subjective factors and has low reliability and accuracy are solved.
Description
Technical Field
The present application relates to the field of high-quality power technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for evaluating a high-quality power investment scenario.
Background
With the technological progress and social development, high-end manufacturing enterprises use a large amount of frequency-variable speed-regulating driving equipment, computer control equipment, precision instruments and other equipment extremely sensitive to voltage sag in the production process to improve the production efficiency and production quality, so that the tolerance of the production line to the voltage sag is sharply reduced, and the consequences such as production interruption, data loss and the like can be caused, which can bring huge direct economic loss to users, and meanwhile, indirect loss of different degrees can be added. Such current situation causes power supply enterprises and users to pay more attention to power quality disturbance represented by voltage sag, and such sensitive users are increasingly eager to obtain power supply superior to the existing standard, i.e., high-quality power.
Because the requirement of the high-quality power service is higher than the standard power service, the input cost of the high-quality power construction is higher than the normal power construction input cost, so that the investment decision of the power company on the high-quality power is more and more emphasized, and the adverse influence on the operation of the power company due to the decision error is avoided.
At present, an investment evaluation mode of a power company for high-quality power adopts an expert analysis mode, and an expert group in related fields analyzes a proposed investment scheme based on empirical data so as to obtain an analysis result.
Disclosure of Invention
The application provides a method, a device, a terminal and a storage medium for evaluating a high-quality power investment scheme, which are used for solving the technical problems that the existing high-quality power investment evaluation mode is easily influenced by human subjective factors and has low reliability and accuracy.
First, a first aspect of the present application provides a method for evaluating a high-quality power investment plan, including:
acquiring investment scheme information to be evaluated, and acquiring influence factor actual measurement data corresponding to index system information from the investment scheme information based on preset index system information;
based on preset influence grade information, comparing the influence factor measured data with a threshold value of each grade interval in the influence grade information, and determining the grade confidence of the influence factor measured data;
obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data;
based on the basic reliability probability, iterative operation is carried out by using a recursive ER algorithm, and reliability probability values of the investment scheme information in all level intervals are respectively obtained;
and based on a utility interval comparison mode, projecting the reliability probability value into a preset utility function to obtain a utility value of the investment scheme information through the operation of the utility function.
Optionally, the process of generating the weight coefficient specifically includes:
respectively calculating a first ratio and a second ratio based on the measured data of the influence factors and preset ideal data of the influence factors, wherein the first ratio is the ratio of the maximum absolute value of the difference between all the measured data of the influence factors and the ideal data of the influence factors to the ideal data of the influence factors, and the second ratio is the ratio of the average value of all the measured data of the influence factors and the ideal data of the influence factors to the ideal data of the influence factors;
and obtaining the weight coefficient of the actually measured data of the influence factors according to the sum of the first ratio and the second ratio of the actually measured data of the influence factors and the ratio of the sum of the first ratio and the second ratio of all the actually measured data of the influence factors.
Optionally, after obtaining the weight coefficient of the measured data of the influencing factor according to the sum of the first ratio and the second ratio of the measured data of the influencing factor and the ratio of the sum of the first ratio and the second ratio of all the measured data of the influencing factor, the method further includes:
and updating the weight coefficient in a geometric mean calculation mode according to the weight coefficient and a reference weight coefficient to obtain an updated weight coefficient, wherein the reference weight coefficient is obtained from a preset expert database and corresponds to the index system information one by one.
Optionally, the method further comprises:
and sequencing the utility values of the plurality of investment scheme information, and determining the optimal investment scheme information corresponding to the maximum utility value according to the sequencing result.
Secondly, the second aspect of the present application provides a high-quality electric power investment scenario evaluation apparatus, comprising:
the system comprises an influence factor data acquisition unit, a data processing unit and a data processing unit, wherein the influence factor data acquisition unit is used for acquiring investment scheme information to be evaluated and acquiring influence factor actual measurement data corresponding to index system information from the investment scheme information based on preset index system information;
the level confidence determining unit is used for comparing the influence factor measured data with the threshold value of each level interval in the influence level information based on preset influence level information to determine the level confidence of the influence factor measured data;
the basic reliability probability calculation unit is used for obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data;
a reliability probability value calculation unit, configured to perform iterative operation by using a recursive ER algorithm based on the basic reliability probability, and obtain reliability probability values of the investment plan information in each level interval respectively;
and the scheme utility value calculating unit is used for projecting the reliability probability value into a preset utility function based on a utility interval comparison mode so as to obtain the utility value of the investment scheme information through the calculation of the utility function.
Optionally, the method further comprises:
an influence factor ratio calculation unit, configured to calculate a first ratio and a second ratio respectively based on the measured influence factor data and preset ideal influence factor data, where the first ratio is a ratio between a maximum absolute value of differences between all the measured influence factor data and the ideal influence factor data, and the second ratio is a ratio between an average value of all the measured influence factor data and the ideal influence factor data;
and the weight coefficient calculation unit is used for obtaining the weight coefficient of the influence factor actual measurement data according to the sum of the first ratio and the second ratio of the influence factor actual measurement data and the ratio of the sum of the first ratio and the second ratio of all the influence factor actual measurement data.
Optionally, the method further comprises:
and the weight coefficient optimization unit is used for updating the weight coefficient in a geometric mean calculation mode according to the weight coefficient and a reference weight coefficient to obtain an updated weight coefficient, wherein the reference weight coefficient is obtained from a preset expert database and corresponds to the index system information one by one.
Optionally, the method further comprises:
and the scheme utility value comparing unit is used for sequencing utility values of the plurality of investment scheme information and determining the optimal investment scheme information corresponding to the maximum utility value according to the sequencing result.
A third aspect of the present application provides a terminal, comprising: a memory and a processor;
the memory is configured to store program code corresponding to the method for assessing a premium power investment scenario of the first aspect of the present application;
the processor is configured to execute the program code.
A fourth aspect of the present application provides a storage medium having stored therein program code corresponding to the method for assessing a premium power investment scenario of the first aspect of the present application.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a method, a device, a terminal and a storage medium for evaluating a high-quality power investment scheme, wherein the method comprises the following steps: acquiring investment scheme information to be evaluated, and acquiring influence factor actual measurement data corresponding to index system information from the investment scheme information based on preset index system information; based on preset influence grade information, comparing the influence factor measured data with a threshold value of each grade interval in the influence grade information, and determining the grade confidence of the influence factor measured data; obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data; based on the basic reliability probability, iterative operation is carried out by using a recursive ER algorithm, and reliability probability values of the investment scheme information in all level intervals are respectively obtained; and based on a utility interval comparison mode, projecting the reliability probability value into a preset utility function to obtain a utility value of the investment scheme information through the operation of the utility function.
The method comprises the steps of calculating basic reliability probability based on level confidence and weight coefficients of influence factor measured data, performing iterative operation by using a recursive ER algorithm, and respectively obtaining reliability probability values of investment scheme information in each level interval; based on an evidence reasoning theory, the utility value of the investment scheme information is obtained by projecting the reliability probability value into a preset utility function, the calculated utility value is used as an evaluation result of the investment scheme, the uncertainty of the information is considered, the fusion of multi-factor information influencing the decision is realized, and the technical problems that the existing investment evaluation mode of high-quality power is easily influenced by artificial subjective factors and has low reliability and accuracy are solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a first embodiment of a method for evaluating a premium power investment scenario provided by the present application;
FIG. 2 is a schematic flow chart illustrating a second embodiment of a method for evaluating a premium power investment scenario provided herein;
fig. 3 is a schematic structural diagram of a first embodiment of a high-quality power investment scenario evaluation apparatus provided in the present application.
Detailed Description
The embodiment of the application provides a method, a device, a terminal and a storage medium for evaluating a high-quality power investment scheme, which are used for solving the technical problems that the existing high-quality power investment evaluation mode is easily influenced by human subjective factors and has low reliability and accuracy
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a first embodiment of the present application provides a method for evaluating a high-quality power investment scenario, which includes:
It should be noted that, first, in this embodiment, according to the acquired to-be-evaluated investment scenario information, based on preset index system information, the measured data of the impact factors corresponding to the index system information is acquired from the investment scenario information. The index system information of this embodiment preferably includes: economic indicators, technical indicators and market indicators.
And 102, comparing the actually measured data of the influence factors with the threshold value of each grade interval in the influence grade information based on preset influence grade information, and determining the grade confidence of the actually measured data of the influence factors.
It should be noted that the influence level information of this embodiment belongs to a part of the index system information, and based on the preset influence level information, the influence factor actual measurement data is compared with the threshold of each level interval in the influence level information, and the level confidence of the influence factor actual measurement data is obtained according to the distribution of the influence factor actual measurement data in each level interval.
And 103, obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data.
And step 104, based on the basic reliability probability, performing iterative operation by using a recursive ER algorithm to respectively obtain the reliability probability values of the investment scheme information in each level interval.
It should be noted that, based on the basic reliability probability calculated in step 103, in combination with the evidence reasoning theory, iterative operation is performed by using a recursive ER algorithm, and reliability probability values of the investment plan information in each level interval are respectively obtained.
And 105, projecting the reliability probability value into a preset utility function based on a utility interval comparison mode to obtain a utility value of the investment scheme information through the operation of the utility function.
It should be noted that the reliability probability value is projected into a preset utility function, so as to obtain a utility value of the investment scheme information through the operation of the utility function, and the utility value is used as an evaluation result of the investment scheme information according to the height of the utility value.
The method comprises the steps of calculating basic reliability probability based on level confidence and weight coefficients of influence factor measured data, performing iterative operation by using a recursive ER algorithm, and respectively obtaining reliability probability values of investment scheme information in each level interval; based on an evidence reasoning theory, the utility value of the investment scheme information is obtained by projecting the reliability probability value into a preset utility function, the calculated utility value is used as an evaluation result of the investment scheme, the uncertainty of the information is considered, the multi-factor information influencing the decision making is fused, and the technical problems that the existing investment evaluation mode of high-quality power is easily influenced by artificial subjective factors and has low reliability and accuracy are solved.
The above is a detailed description of a first embodiment of a method for evaluating a high-quality electric power investment scenario provided by the present application, and the following is a detailed description of a second embodiment of a method for evaluating a high-quality electric power investment scenario provided by the present application.
Referring to fig. 2, a second embodiment of the present application provides a method for evaluating a high-quality power investment scenario based on the first embodiment, which includes:
And 203, obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data.
And step 204, based on the basic reliability probability, performing iterative operation by using a recursive ER algorithm to respectively obtain reliability probability values of the investment scheme information in each level interval.
And step 205, projecting the reliability probability value into a preset utility function based on a utility interval comparison mode to obtain a utility value of the investment scheme information through the operation of the utility function.
And step 206, sequencing the utility values of the plurality of investment scheme information, and determining the optimal investment scheme information corresponding to the maximum utility value according to the sequencing result.
It should be noted that, acquiring the information of the investment plan to be evaluated, assuming that there are L different high-quality power investment plans to be compared and analyzed, each candidate plan is marked as a ═ al},l＝1,2,…,L。
The index system information of the embodiment specifically includes three aspects, an economic index, a technical index and a market index.
The economic indicators preferably include a net Investment present value and an Investment risk, and the net Investment present value (NPV of Investment, NPVI) is an evaluation indicator typically describing profitability of an Investment scheme and is an important factor influencing a high-quality power Investment decision. The benefit is that the profit is directly expressed in monetary value, taking into account the value of capital and time. In the actual use process, whether profit is obtained or not is convenient to visually judge. Generally, the NPVI variation range and the maximum possible value Q1 of the corresponding investment are obtained, the investment risk is a factor which must be considered when investment activities are carried out, and a large risk means that uncertainty of income may be caused, investment will of a user is influenced, and therefore the method is an economic evaluation index. The investment risk generally only obtains its range of variation and the most likely value Q2.
Factors included under the technical index are preferably Premium Power Level (PPL), which is a typical parameter describing the Level of demand of the user for Premium Power, and the sag influence degree. By adopting the PPL as an influencing factor, the high-quality power level provided for the user by different high-quality power investments can be simply and clearly depicted, and the investment decision is influenced. It is divided into three grades: A. AA and AAA, respectively corresponding to normal, high-quality and special grade.
Temporarily decreasing the degree of influence uVSSThe factor represents the overall level of voltage sag suffered by the user, and is represented by the ratio of the sum of the severity of the sag that the user cannot endure to the sum of the severity of the total sag, and is calculated as shown in formula (1):
uvss＝xB/xO (1)
in the formula: x is the number ofBRepresents the sum of the user-unacceptable severity of the voltage sag; x is the number ofORepresenting the sum of the severity of all voltage sags experienced by the user. The sag severity calculation formula is shown in (2):
in the formula: m represents the residual voltage amplitude; mcueve(D) The voltage amplitude corresponding to the F47 curve is for the sag duration time D.
And the factors included in the market index are preferably investment environment, market progressiveness and user credit. The investment environment refers to other factors influencing the high-quality power investment of users, and belongs to factors which cannot be completely controlled. The intuition fuzzy set theory can be introduced to depict to obtain the u value in the intuition fuzzy number to depict the quality of the investment environment. With this index added, the consideration is more comprehensive.
Market-advancement MA is an important factor in expressing the effectiveness of the offered premium power investment schemes into the market and the extent to which they are satisfied by the user. It is divided into three grades of weak (W), medium (M) and strong (S) according to degree, and is expressed as formula (3):
MA＝{W|M|S} (3)
information about sensitive users who have a need for high-quality electricity themselves may also affect the electricity selling companies to provide high-quality electricity services for them. The user credit QU is selected to represent the user credit level and the like, which can be used as the basis for user evaluation and is divided into 5 levels: very poor (VL), poor (L), medium (M), good (G), Very Good (VG), expressed as formula (4):
QU＝{VL|L|M|G|VG} (4)
based on the index system, in actual application, corresponding data is obtained according to preset index system information to obtain influence factor actual measurement data for subsequent use.
The comprehensive condition of the high-quality electric power is expressed by e, and e is determined by T influencing factors and is respectively expressed by e1,e2,…,eTAnd (4) showing. The result of the multi-factor comprehensive evaluation of the high-quality power exists in an evaluation grade H ═ { verylow (H)1),low(H2),average(H3),good(H4),verygood(H5) Among them.
In the evaluation of high-quality power investment, any alternative scheme can be as shown in (5):
S(ai)＝{(ej,k,βj,k)；k＝1,2,…,T},j＝1,2,…,N (5)
wherein the subjects have a total of T influencing factors, ej,kRepresentative factor ekIs judged as Hj grade with grade confidence degree of betaj,k。ωkAre weight coefficients.
And comparing the actually measured data of the influence factors with the threshold value of each grade interval in the influence grade information based on the preset influence grade information, and determining the grade confidence of the actually measured data of the influence factors.
In this embodiment, the determination manner of the level confidence may refer to the following exemplary steps,
based on the above listed influence factors, the method is specifically classified into a quantitative type and a qualitative type, and the embodiment provides respective confidence degree calculation methods according to different factor characteristics.
1) Quantitative type
A quantitative form factor refers to a factor whose descriptive quantity is a particular quantitative data. The grade division is qualitative, and to judge the qualitative of quantitative data, a corresponding standard value needs to be set for the qualitative index grade, so as to calculate the corresponding grade confidence of the quantitative data.
The selection of the standard value needs to cover all the values of the maximum possible value of the scheme to be evaluated, and 5 standard values are uniformly distributed, have consistent gradient and cannot have overlarge span. Interval threshold of different grades is Yn,iN is 1,2, 3, 4, 5, i is the index number corresponding to the index number, and i is 1 as an example. Standard value Y1,1～Y5,1Sequentially and respectively corresponding to the grades H1、H2、H3、H4、H5The standard value of (2). After the standard value is determined, the grade confidence degree betan,i(i ═ 1) the calculation procedure was as follows:
when the quantitative descriptor size M1 of the factor is between the threshold value Y of the grade intervaln,iAnd Yn+1,iCalculating the grade confidence coefficients of the investment net present value factors which belong to n and n +1 respectively by using the following two formulas: beta is an,i，βn+1,i。
The confidence of the remaining three levels is 0. While obtaining the product of Q1The value is exactly equal to the standard value Y of any graden,iThen the level confidence is 1 and the remaining level confidence is 0.
And finally, obtaining factor grade confidence coefficient distribution, namely a factor evaluation evidence set:
S(e1)＝{(H1,β1,1),(H2,β2,1),(H3,β3,1),(H4,β4,1),(H5,β5,1)} (8)
2) shape of setting
Taking a high-quality power level as an example, the available data of the index are 3 qualitative grades, and the evaluation grade has 5 grades, so that the 3 grades can be sequentially corresponding to the 5 grades, namely A is corresponding to H1AA corresponds to H3AAA corresponds to H5. When the obtained data is AA, the obtained measurement data is evaluated to be H3Confidence of (1) and confidence of the remaining 4 levels of (0). A set of evidence can be obtained:
S(e4)＝{(H1,0),(H2,0),(H3,1),(H4,0),(H5,0)} (9)
obtaining the basic reliability probability of the influence factor measured data according to the product of the level confidence of the influence factor measured data and the weight coefficient of the influence factor measured data, wherein the generation process of the weight coefficient comprises the following steps:
it should be noted that, the objective weight calculation method based on the difference driving principle comprehensively utilizes the measured data, and makes the weight more objective and credible through multi-dimensional comparison. The calculation steps are as follows: assuming that an observation matrix formed by n evaluation objects under m evaluation indexes is X ═ X (X)ij)n×mAs follows
Wherein xij(i 1,2, …, n, j 1,2, …, m) represents the measured value of the ith evaluation target at the jth evaluation index, that is, the influence factor measured data. Simultaneously setting a positive ideal evaluation object as X+＝[x1 +,x2 +,…,xm +]I.e. influencing factor ideal data.
The present embodiment considers the magnitude of the objective information provided by the measured value of the jth index from two aspects: j indexAll measured values xijAnd an ideal value xj +The ratio of the maximum value of the absolute difference to the ideal value is recorded as a first ratio Z1j(ii) a J th influence factor measured data and xj +Is averaged with the sum of the absolute values of the differences of (a) and (b), and then is summed with xj +Is recorded as a second ratio Z2j. As shown in formulas (10) and (11):
These two ratios combine overall information and individual information. The objective weight of each index is determined as in equation (12):
meanwhile, on the premise of ensuring certain objectivity, in order to further improve the accuracy of weight coefficient configuration, the embodiment provides a weight coefficient calculation mode for optimizing objective weight by using a subjective weight coefficient, and the specific process includes:
The reference weight coefficient is stored in an expert database, and the subjective weight of each factor is calculated by a common expert survey method. The expert survey method is characterized in that a weight is determined by a digital expert according to high-quality power supply and related investment experience and in consideration of the actual situation of a power enterprise. The method comprises the following specific basic steps:
1) and (5) selecting by experts. According to the understanding of the high-quality power investment, selecting proper expert members giving evaluation influence factor weights from related experts, and detailing the concept and sequence of the weights and a method for recording the weights; 2) and (4) listing. The weight determination made by all experts is given out through sorting, and as shown in table 1, the weight determination comprises the average value and dispersion of the weights of all factors; 3) and (6) re-evaluating. Sending the last obtained table 1 to each expert, providing some supplementary materials, and readjusting the weight of the modification factor by the experts according to information such as the comprehensive table and the like to obtain a new table; repeating the step 3) for multiple times, and repeatedly adjusting and modifying until the dispersion is not more than the expected given value at a certain time, wherein the weight of the factor set is taken as the average value of the corresponding weights.
The subjective weight and the objective weight of each index are respectively obtained by the two methods, the subjective weight and the objective weight are integrated by a geometric mean formula, and the combined weighted geometric mean formula is shown as (13):
in the formula, wjFor updated weight coefficients, w1,jIs a reference weight coefficient, w2,jThe weighting factor for the measured data of the influencing factors calculated in step 202.
After the level confidence coefficient and the weight coefficient of each factor are determined, a basic probability function in a high-quality power investment decision is formed, and the basic confidence probability of the influence factor actual measurement data is obtained according to the product of the level confidence coefficient of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data:
mj,k＝wkβj,k,j＝1,2,…,N (5)
in the formula: m isj,kIs a factor ekThen adding the weight wkThen, the basic credibility probability distribution of Hn grades is judged; m isH,kRepresenting a basic probability distribution of confidence that its rank fraction cannot be determined, this parameter is typically zero,
the basic reliability probability caused by the fact that the error between the weight setting and the real situation exceeds a preset range is not distributed;
the basic credibility probability is not distributed due to the fact that the real result is not within the given standard range of the index system of the embodiment;
the following recursive ER algorithm is then used to combine the basic probability functions:
mj,I(k+1)＝KI(k+1)Mj (16)
Mj＝[mj,I(k)mj,k+1+mH,I(k)mj,k+1+mj,I(k)mH,k+1] (17)
wherein:
mH,I(k+1)＝KI(k+1)MH (19)
according to the above disclosureThe iterative calculation of the formula can obtain the reliability probability value of a certain high-quality power investment scheme. And then according to the reliability probability value and the uncertain reliability probability value of each result level corresponding to the comprehensive factors. Study candidate alThe polymerization result of (2) is present on the evaluation scale j:
study object alRelative to the unknown:
and then, based on a utility interval comparison mode, projecting the reliability probability value into a preset utility function to obtain a utility value of the investment scheme information through the operation of the utility function.
In the step, the advantages and the disadvantages of similar multi-factor evaluation results are accurately compared, and a utility function is set for each different evaluation grade:
projecting the final evaluation result to a secondary utility function to obtain a comprehensive evaluation utility value:
and finally, the comprehensive utility value is used for selecting the high-quality power investment scheme to be evaluated preferentially, and the investment schemes are sorted from large to small according to the comprehensive utility value, wherein the scheme with the large comprehensive utility value has more advantages, namely, the high-quality power investment decision is realized.
The above is a detailed description of the second embodiment of the method for evaluating a high-quality electric power investment scenario provided by the present application, and the following is a detailed description of the first embodiment of the apparatus for evaluating a high-quality electric power investment scenario provided by the present application.
Referring to fig. 3, a third embodiment of the present application provides a device for evaluating a high-quality power investment plan, including:
an influence factor data obtaining unit 301, configured to obtain investment scenario information to be evaluated, and obtain influence factor actual measurement data corresponding to the index scenario information from the investment scenario information based on preset index scenario information;
a level confidence determining unit 302, configured to compare the impact factor measured data with a threshold of each level interval in the impact level information based on preset impact level information, and determine a level confidence of the impact factor measured data;
a basic reliability probability calculating unit 303, configured to obtain a basic reliability probability of the influence factor actual measurement data according to a product of the level confidence of the influence factor actual measurement data and a weight coefficient of the influence factor actual measurement data;
a reliability probability value calculation unit 304, configured to perform iterative operation by using a recursive ER algorithm based on the basic reliability probability, and obtain reliability probability values of the investment scheme information in each level interval respectively;
and the scheme utility value calculating unit 305 is configured to project the reliability probability value into a preset utility function based on a utility interval comparison mode, so as to obtain the utility value of the investment scheme information through the calculation of the utility function.
Further, still include:
the influence factor ratio calculation unit 306 is configured to calculate a first ratio and a second ratio respectively based on the measured influence factor data and preset ideal influence factor data, where the first ratio is a ratio between a maximum absolute value of differences between all the measured influence factor data and the ideal influence factor data, and the second ratio is a ratio between an average value of all the measured influence factor data and the ideal influence factor data;
the weight coefficient calculating unit 307 is configured to obtain a weight coefficient of the influence factor measured data according to a sum of the first ratio and the second ratio of the influence factor measured data and a ratio of the sum of the first ratio and the second ratio of all the influence factor measured data.
Further, still include:
the weight coefficient optimizing unit 308 is configured to update the weight coefficient in a geometric mean calculation manner according to the weight coefficient and a reference weight coefficient, so as to obtain an updated weight coefficient, where the reference weight coefficient is a weight coefficient that is obtained from a preset expert database and corresponds to the index system information one to one.
Further, still include:
and the scheme utility value comparing unit 309 is configured to rank utility values of the plurality of investment scheme information, and determine the optimal investment scheme information corresponding to the maximum utility value according to a ranking result.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method for evaluating a high-quality power investment plan, comprising:
acquiring investment scheme information to be evaluated, and acquiring influence factor actual measurement data corresponding to index system information from the investment scheme information based on preset index system information;
based on preset influence grade information, comparing the influence factor measured data with a threshold value of each grade interval in the influence grade information, and determining the grade confidence of the influence factor measured data;
obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data;
based on the basic reliability probability, iterative operation is carried out by using a recursive ER algorithm, and reliability probability values of the investment scheme information in all level intervals are respectively obtained;
and based on a utility interval comparison mode, projecting the reliability probability value into a preset utility function to obtain a utility value of the investment scheme information through the operation of the utility function.
2. The method according to claim 1, wherein the process of generating the weight coefficients specifically comprises:
respectively calculating a first ratio and a second ratio based on the measured data of the influence factors and preset ideal data of the influence factors, wherein the first ratio is the ratio of the maximum absolute value of the difference between all the measured data of the influence factors and the ideal data of the influence factors to the ideal data of the influence factors, and the second ratio is the ratio of the average value of all the measured data of the influence factors and the ideal data of the influence factors to the ideal data of the influence factors;
and obtaining the weight coefficient of the actually measured data of the influence factors according to the sum of the first ratio and the second ratio of the actually measured data of the influence factors and the ratio of the sum of the first ratio and the second ratio of all the actually measured data of the influence factors.
3. The method according to claim 2, wherein the obtaining the weighting factor of the measured influencing factor data according to the ratio of the sum of the first ratio and the second ratio of the measured influencing factor data and the sum of the first ratio and the second ratio of all the measured influencing factor data further comprises:
and updating the weight coefficient in a geometric mean calculation mode according to the weight coefficient and a reference weight coefficient to obtain an updated weight coefficient, wherein the reference weight coefficient is obtained from a preset expert database and corresponds to the index system information one by one.
4. The method according to claim 1, further comprising:
and sequencing the utility values of the plurality of investment scheme information, and determining the optimal investment scheme information corresponding to the maximum utility value according to the sequencing result.
5. A high-quality electric power investment scenario evaluation apparatus, comprising:
the system comprises an influence factor data acquisition unit, a data processing unit and a data processing unit, wherein the influence factor data acquisition unit is used for acquiring investment scheme information to be evaluated and acquiring influence factor actual measurement data corresponding to index system information from the investment scheme information based on preset index system information;
the level confidence determining unit is used for comparing the influence factor measured data with the threshold value of each level interval in the influence level information based on preset influence level information to determine the level confidence of the influence factor measured data;
the basic reliability probability calculation unit is used for obtaining the basic reliability probability of the influence factor actual measurement data according to the product of the level confidence of the influence factor actual measurement data and the weight coefficient of the influence factor actual measurement data;
a reliability probability value calculation unit, configured to perform iterative operation by using a recursive ER algorithm based on the basic reliability probability, and obtain reliability probability values of the investment plan information in each level interval respectively;
and the scheme utility value calculating unit is used for projecting the reliability probability value into a preset utility function based on a utility interval comparison mode so as to obtain the utility value of the investment scheme information through the calculation of the utility function.
6. The excellent electric power investment scenario evaluation apparatus of claim 5, further comprising:
an influence factor ratio calculation unit, configured to calculate a first ratio and a second ratio respectively based on the measured influence factor data and preset ideal influence factor data, where the first ratio is a ratio between a maximum absolute value of differences between all the measured influence factor data and the ideal influence factor data, and the second ratio is a ratio between an average value of all the measured influence factor data and the ideal influence factor data;
and the weight coefficient calculation unit is used for obtaining the weight coefficient of the influence factor actual measurement data according to the sum of the first ratio and the second ratio of the influence factor actual measurement data and the ratio of the sum of the first ratio and the second ratio of all the influence factor actual measurement data.
7. The excellent electric power investment scenario evaluation apparatus of claim 6, further comprising:
and the weight coefficient optimization unit is used for updating the weight coefficient in a geometric mean calculation mode according to the weight coefficient and a reference weight coefficient to obtain an updated weight coefficient, wherein the reference weight coefficient is obtained from a preset expert database and corresponds to the index system information one by one.
8. The excellent electric power investment scenario evaluation apparatus of claim 5, further comprising:
and the scheme utility value comparing unit is used for sequencing utility values of the plurality of investment scheme information and determining the optimal investment scheme information corresponding to the maximum utility value according to the sequencing result.
9. A terminal, comprising: a memory and a processor;
the memory for storing program code corresponding to the premium power investment scenario evaluation method of any one of claims 1 to 4;
the processor is configured to execute the program code.
10. A storage medium characterized in that a program code corresponding to the method of evaluating a premium power investment scenario of any one of claims 1 to 4 is stored in the storage medium.
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