CN113837467A - Point-to-point energy project evaluation method and device, computer equipment and storage medium - Google Patents
Point-to-point energy project evaluation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a point-to-point energy project evaluation method, a point-to-point energy project evaluation device, computer equipment and a storage medium. The method comprises the following steps: responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes; according to the evaluation index set, carrying out data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set; carrying out maximum type conversion processing on the initial data to obtain corresponding maximum type data; and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal. By adopting the method, the accuracy of point-to-point energy project evaluation can be improved.
Description
Technical Field
The present application relates to the field of distributed power generation technologies, and in particular, to a point-to-point energy project assessment method, apparatus, computer device, and storage medium.
Background
With the development of distributed power generation technology, more and more distributed energy resources are developed, and thus, the distributed power generation is traded in the electric power market gradually and frequently. The point-to-point energy retail market can establish a flexible and autonomous distributed market on the power distribution network level, and the problem that the traditional power trading market is difficult to integrate distributed power generation main bodies with large quantity and scattered regional distribution is solved. The point-to-point energy retail market has a profound influence on the current electric power transaction service, and needs to evaluate point-to-point energy projects and formulate a corresponding solution policy according to an evaluation result so as to deal with the future large-scale electric power transaction service.
At present, the point-to-point energy project evaluation methods mainly include a questionnaire survey method and a rating scale method, but the two point-to-point energy project evaluation methods have large subjective factors and cannot objectively and accurately evaluate the point-to-point energy project.
Disclosure of Invention
In view of the above, it is desirable to provide a peer-to-peer energy project assessment method, apparatus, computer device and storage medium capable of accurately assessing a peer-to-peer energy project.
A point-to-point energy project assessment method, the method comprising:
responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
according to the evaluation index set, performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set;
performing maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal.
In one embodiment, in response to an evaluation request for a point-to-point energy project sent by an energy management terminal, generating an evaluation index set corresponding to the evaluation request includes:
responding to an evaluation request aiming at the point-to-point energy project sent by the energy management terminal, and determining at least three first-layer indexes corresponding to the evaluation request;
acquiring at least one second-layer evaluation index corresponding to each first-layer index to obtain at least three second-layer evaluation indexes serving as the at least three evaluation indexes;
and generating an evaluation index set corresponding to the evaluation request according to the at least three evaluation indexes.
In one embodiment, before performing data extraction processing on the item data of the point-to-point energy item according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set, the method further includes:
receiving project data aiming at the point-to-point energy project sent by the energy management terminal;
the data extraction processing is performed on the project data of the point-to-point energy project according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set, and the method comprises the following steps:
and finding out data matched with each evaluation index in the evaluation index set from the project data as the initial data.
In one embodiment, before performing maximum size conversion processing on the initial data to obtain corresponding maximum size data, the method further includes:
if the initial data comprises numerical data, performing ratio conversion processing on the numerical data to obtain ratio data corresponding to the numerical data;
and updating the numerical data in the initial data into the ratio data to obtain target data.
In one embodiment, the maximum type conversion processing on the initial data to obtain corresponding maximum type data includes:
judging whether the target data contains extremely small data or not;
and if the target data contains the extremely small data, subtracting the extremely small data from the upper bound value of the extremely small data to obtain the extremely large data.
In one embodiment, the maximum data includes maximum data corresponding to each evaluation index in the evaluation index set;
inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, wherein the evaluation result comprises:
determining the weight of each evaluation index through the project evaluation model, and performing weighted summation on the maximum data corresponding to each evaluation index according to the weight of each evaluation index to obtain an evaluation result corresponding to the point-to-point energy project.
In one embodiment, the method further comprises:
dividing the extremely large data according to a preset period to obtain a plurality of groups of periodic data;
inputting each group of the periodic data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project respectively to obtain a plurality of periodic evaluation results of the point-to-point energy project;
generating an analysis report according to the plurality of periodic evaluation results, and returning the analysis report to the energy management terminal; the analysis report is used for representing comparison results of a plurality of period evaluation results of the point-to-point energy project in the preset period.
A peer-to-peer energy project assessment apparatus, the apparatus comprising:
the index construction module is used for responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
the data acquisition module is used for acquiring the project data of the point-to-point energy project according to the evaluation index set, and performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set;
the data conversion module is used for carrying out maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and the result acquisition module is used for inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project and returning the evaluation result to the energy management terminal.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
according to the evaluation index set, performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set;
performing maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
according to the evaluation index set, performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set;
performing maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal.
According to the point-to-point energy project assessment method, the point-to-point energy project assessment device, the computer equipment and the storage medium, an assessment index set corresponding to the assessment request is generated by responding to an assessment request aiming at a point-to-point energy project sent by an energy management terminal, then data extraction processing is carried out on project data of the point-to-point energy project according to the assessment index set, initial data of each assessment index in the assessment index set is obtained, then maximum conversion processing is carried out on the initial data, corresponding maximum data are obtained, then the maximum data and the assessment index set are input into a project assessment model corresponding to the point-to-point energy project, an assessment result of the point-to-point energy project is obtained, and finally the assessment result is returned to the energy management terminal. By adopting the method, the corresponding evaluation index set can be generated according to the change of the evaluation request of the point-to-point energy project, so that the evaluation result is more consistent with the real evaluation request, the evaluation standards of different evaluation indexes in the evaluation index set are unified through the extremely large processing of initial data, and the evaluation is carried out by utilizing the project evaluation model corresponding to the point-to-point energy project, so that the accuracy of the obtained evaluation result is improved, and the accuracy of the point-to-point energy project is further improved.
Drawings
FIG. 1 is a diagram of an exemplary application environment of a peer-to-peer energy project evaluation method;
FIG. 2 is a flow diagram of a peer-to-peer energy project assessment method according to an embodiment;
FIG. 3 is a diagram illustrating point-to-point energy project assessment indicators, according to an embodiment;
FIG. 4 is a flowchart illustrating the step of dividing the maximum data according to a predetermined period in one embodiment;
FIG. 5 is a flowchart illustrating a point-to-point energy project assessment method according to another embodiment;
FIG. 6 is a block diagram of an embodiment of a peer-to-peer energy project evaluation apparatus;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The point-to-point energy project evaluation method provided by the application can be applied to the application environment shown in fig. 1. Wherein the energy management terminal 102 communicates with the server 104 through a network. Specifically, referring to fig. 1, in a peer-to-peer energy project assessment scenario, a server 104 responds to an assessment request for a peer-to-peer energy project sent by an energy management terminal 102, and generates an assessment index set corresponding to the assessment request; according to the evaluation index set, carrying out data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set; carrying out maximum type conversion processing on the initial data to obtain corresponding maximum type data; and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal 102. The energy management terminal 102 is a terminal for a technology development unit or a technician to manage a point-to-point energy project and for a general worker to perform an operation of a point-to-point energy transaction, and may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices; the server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a peer-to-peer energy project evaluation method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S201, responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes.
The point-to-point energy project is a transaction project which takes wind, light and other distributed clean energy as main energy and takes a power distribution network as a main energy transaction channel to realize a new energy retail mode. The evaluation request comprises an evaluation dimension which needs to be evaluated on the point-to-point energy project and is used for requesting to evaluate the point-to-point energy project.
The evaluation index set refers to a set of evaluation indexes of an evaluation request aiming at a point-to-point energy project, and comprises at least three evaluation indexes of evaluation indexes such as power generation prediction accuracy, power utilization prediction accuracy, price fluctuation, default rate, total volume of transaction increase rate, market subject profit condition, wind curtailment and light curtailment rate, carbon emission reduction rate, distributed power generation quantity increase rate, power selling company quantity increase rate, user satisfaction degree, active user proportion, complaint user proportion, platform new increase users, customer acquisition conversion rate, platform function completeness, platform information safety and the like. The evaluation criteria of the evaluation indexes are different, for example, the evaluation criterion of the user satisfaction index is the higher the numerical value is, the better the evaluation criterion is, and the evaluation criterion of the complaint user proportion index is the lower the numerical value is, the better the evaluation criterion is.
Specifically, the energy management terminal responds to point-to-point energy project evaluation operation of technicians to generate evaluation requests for point-to-point energy projects, sends the evaluation requests for the point-to-point energy projects to the server, the server receives and analyzes the evaluation requests to obtain a plurality of corresponding evaluation dimensions, the number of the evaluation dimensions is at least three, the server generates evaluation indexes corresponding to the evaluation dimensions, the number of the evaluation indexes corresponding to the evaluation dimensions is at least one, and the server constructs a set of all the evaluation indexes to obtain an evaluation index set corresponding to the evaluation requests. Therefore, after the server acquires the evaluation index set, the server executes the subsequent data extraction step by taking the evaluation index set as a processing basis.
And S202, according to the evaluation index set, performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set.
Wherein the project data is a large amount of data about transaction traffic of the point-to-point energy project. The data extraction processing is to extract target data corresponding to the evaluation index from a data source by a big data extraction technique. The initial data is obtained according to the evaluation indexes in the evaluation index set, that is, each set of initial data includes the initial data corresponding to each evaluation index in the evaluation index set. The evaluation indexes in the evaluation index set correspond to multiple sets of initial data, and the initial data comprises thousands of sets or more than thousands of sets of initial data.
Specifically, after the server obtains the evaluation index set, the server calls a corresponding data extraction instruction according to the evaluation indexes contained in the evaluation index set, and performs data extraction operation on the transaction service item data of the point-to-point energy item to obtain multiple sets of initial data of each evaluation index in the evaluation index set, wherein each set of initial data corresponds to one evaluation index in the evaluation index set. Thus, after the server acquires the initial data, the subsequent maximum type conversion step is executed by using the initial data as a processing basis.
Step S203, perform maximum transformation processing on the initial data to obtain corresponding maximum data.
The dimensional units of the initial data are different, and the evaluation criteria of the initial data are the same as the corresponding evaluation indexes, so that the evaluation criteria of the initial data are also different. The maximum data is data whose evaluation is better if the index value is larger, for example, the increase rate of the total volume of the deal is the maximum data, that is, the larger the value of the increase rate of the total volume of the deal is, the better the total volume of the deal is.
Specifically, after the server obtains the initial data, in order to unify the evaluation criteria of each data in the initial data, and accurately evaluate the point-to-point energy project, all evaluation indexes need to be converted into the same direction, for example, the evaluation indexes are converted into forward direction, that is, the evaluation indexes are converted into very large indexes in a unified manner. The server acquires a preset maximum conversion instruction, and according to the preset maximum conversion instruction, if the evaluation index is the minimum data, the maximum conversion operation is performed on all initial data corresponding to the evaluation index to obtain the maximum data corresponding to the initial data of each evaluation index in the evaluation index set. Therefore, after the server acquires the extremely large data, the server executes the subsequent project evaluation step by taking the extremely large data as a processing basis.
And S204, inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal.
Specifically, after all the evaluation indexes are converted into extremely large data, the server acquires a stored project evaluation model corresponding to the point-to-point energy project from the database, then inputs the extremely large data and the evaluation indexes into the project evaluation model corresponding to the point-to-point energy project in a set manner to obtain evaluation results of multiple sets of initial data, then adds all the evaluation results to obtain a final evaluation result of the point-to-point energy project, sends the evaluation result to the energy management terminal, and the energy management terminal receives and displays the evaluation result so that technicians can check the evaluation result in time.
According to the point-to-point energy project assessment method, an assessment index set corresponding to an assessment request is generated by responding to the assessment request aiming at a point-to-point energy project sent by an energy management terminal, then data extraction processing is carried out on project data of the point-to-point energy project according to the assessment index set, initial data of each assessment index in the assessment index set is obtained, then maximum conversion processing is carried out on the initial data, corresponding maximum data are obtained, then the maximum data and the assessment index set are input into a project assessment model corresponding to the point-to-point energy project, assessment results of the point-to-point energy project are obtained, and finally the assessment results are returned to the energy management terminal. By adopting the method, the corresponding evaluation index set can be generated according to the change of the evaluation request of the point-to-point energy project, so that the evaluation result is more consistent with the real evaluation request, the evaluation standards of different evaluation indexes in the evaluation index set are unified through the extremely large processing of initial data, and the evaluation is carried out by utilizing the project evaluation model corresponding to the point-to-point energy project, so that the accuracy of the obtained evaluation result is improved, and the accuracy of the point-to-point energy project is further improved.
In an embodiment, in step S201, in response to an evaluation request for a peer-to-peer energy project sent by an energy management terminal, generating an evaluation index set corresponding to the evaluation request specifically includes: responding to an evaluation request aiming at the point-to-point energy item sent by an energy management terminal, and determining at least three first-layer indexes corresponding to the evaluation request; acquiring at least one second-layer evaluation index corresponding to each first-layer index to obtain at least three second-layer evaluation indexes serving as at least three evaluation indexes; and generating an evaluation index set corresponding to the evaluation request according to the at least three evaluation indexes.
The first-layer index is determined according to the evaluation dimension of the evaluation request, the first-layer index includes market operation, market subject service and trading platform service, and the first-layer index may also be changed according to the actual situation of the evaluation request, which is not limited specifically herein. And the second-layer index is evaluation dimension subdivision based on the first-layer index, and the second-layer index is used for point-to-point energy project evaluation.
Specifically, the server carries out demand analysis on an evaluation request aiming at a point-to-point energy project sent by an energy management terminal to obtain at least three evaluation dimensions of the evaluation request, and determines at least three first-layer indexes according to the evaluation dimensions, wherein the number of the first-layer indexes is the same as that of the evaluation dimensions; then the server calls a detailed demand analysis instruction corresponding to the point-to-point energy project evaluation request, further analysis is conducted on each first-layer index, each first-layer index at least obtains one corresponding second-layer index, the server can obtain at least three second-layer indexes, and the obtained second-layer indexes serve as evaluation indexes; and the server gathers the evaluation indexes and generates a set which is used as an evaluation index set corresponding to the evaluation request.
For example, the server performs demand analysis on an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, the demand of the evaluation request is that the point-to-point energy project is evaluated from three dimensions of point-to-point energy retail market operation, market subject service and platform service, therefore, the market operation, the market subject service and the platform service are used as first-layer indexes, the server further performs demand analysis on each first-layer index to obtain indexes corresponding to the market operation index, such as power generation prediction accuracy a, power utilization prediction accuracy b, price fluctuation c, default rate d, total volume of transaction increase rate e, market subject profit condition f, wind curtailment rate g and carbon emission reduction rate h, and the second-layer indexes corresponding to the market subject service index comprise distributed power generation quantity increase rate i, number of electricity selling companies increase rate j, power consumption company number increase rate j, power consumption rate h and the like, The second-layer evaluation indexes corresponding to the service indexes of the trading platform include indexes such as platform added users o, customer obtaining conversion rate p, platform function completeness q, platform information safety r and the like, and the obtained evaluation indexes are specifically shown in fig. 3. And finally, generating an evaluation index set S according to all the second-layer indexes:
S＝{a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r} (1)
in the embodiment, the corresponding at least three first-layer indexes are determined by analyzing the evaluation request, then the corresponding second-layer indexes are obtained from the first-layer indexes, and the evaluation index set is generated according to the second-layer indexes, so that the evaluation result is more fit with the real evaluation request, the situation that the obtained evaluation result cannot reflect real and comprehensive evaluation point-to-point energy projects due to the deviation between the evaluation result and the evaluation indexes is avoided, and the comprehensiveness and accuracy of the evaluation point-to-point energy projects are improved.
In one embodiment, before performing data extraction processing on the item data of the point-to-point energy item according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set, the method further includes: receiving project data aiming at a point-to-point energy project sent by an energy management terminal; then, in step S202, according to the evaluation index set, performing data extraction processing on the item data of the point-to-point energy item to obtain initial data of each evaluation index in the evaluation index set, which specifically includes: and finding out data matched with each evaluation index in the evaluation index set from the project data as initial data.
Specifically, after obtaining an evaluation index set corresponding to an evaluation request, the server generates a project data request of a corresponding point-to-point energy project according to the evaluation request, and sends the project data request to the energy management terminal, the energy management terminal receives and responds to the request, and sends the project data of the point-to-point energy project to the server, and the server receives the project data. And then the server constructs a data query statement, finds out data matched with each evaluation index of the evaluation index set in the project data, and extracts the matched data through a data extraction method in a big data technology to serve as initial data of the evaluation point-to-point energy project.
In this embodiment, data matching each evaluation index in the evaluation index set is found from the project data of the point-to-point energy project by a data extraction method in the big data technology, and is used as initial data. By adopting the method, the project evaluation is carried out only by adopting the data related to each evaluation index in the project data without using the whole point-to-point energy project data, so that the accuracy of evaluating the point-to-point energy project is improved, and the efficiency of evaluating the point-to-point energy project can be improved.
In an embodiment, before performing maximum size conversion processing on the initial data to obtain corresponding maximum size data, the step S203 further includes: if the initial data comprises numerical data, performing ratio conversion processing on the numerical data to obtain ratio data corresponding to the numerical data; and updating the numerical data in the initial data into the ratio data to obtain target data.
Wherein the numerical data consists of numbers, decimal points, signs and letters E, and the numerical data can be integers, decimals and negatives, such as 10, 3.9, -12. Ratiometric data is the ratio between the index value and the upper limit of the value, e.g., 50%, 90%, 12%.
Specifically, after the server acquires the initial data, the server calls a corresponding data type detection instruction to detect the data type of the initial data, if the initial data includes numerical data, the server calls a preset ratio conversion instruction to divide the numerical data by the upper limit of the numerical data, so as to obtain the ratio data corresponding to the numerical data, and then converts all the numerical data in the initial data into the ratio data in the same manner, so that the server obtains the target data.
For example, if α is numerical data and β is an upper bound of α, then the ratio data Φ of the numerical data α can be calculated:
in this embodiment, the server converts all numerical data in the initial data into ratio-type data, and then obtains target data for point-to-point project evaluation, so that dimension units of the data in the target data are unified, subsequent extremely large conversion processing of the server is simpler and more convenient, and evaluation efficiency of the point-to-point energy project can be improved.
In one embodiment, the maximum type conversion processing is performed on the initial data to obtain corresponding maximum type data, and specifically includes: judging whether the target data has extremely small data or not; if the target data contains the extremely small data, the upper limit value of the extremely small data is subtracted from the extremely small data to obtain the extremely large data.
The maximum size conversion processing means converting the minimum size data into the maximum size data. The minimum data refers to data with a smaller numerical value and better evaluation, for example, the complaint user scale index is minimum data, that is, the smaller the data of the complaint user scale index, the better the corresponding evaluation result.
Specifically, the server acquires a preset maximum conversion instruction, judges whether the evaluation index contains the extremely small data according to the preset maximum conversion instruction, if a certain evaluation index is the extremely small data, performs maximum conversion operation on target data corresponding to the evaluation index, and subtracts the extremely small data from an upper bound value of the target data, so as to obtain the maximum data corresponding to initial data of each evaluation index in the evaluation index set; therefore, after the server acquires the extremely large data, the server performs the subsequent project evaluation step by taking the extremely large data as the evaluation basis. And if the target data does not comprise the extremely small data, inputting the target data into the project evaluation model corresponding to the point-to-point energy project.
For example, the complaint user ratio N is very small data, and since the target data N ' corresponding to the complaint user ratio N is ratio-type data, the upper bound value of the target data N ' corresponding to the complaint user ratio N is 1, and the very large data N corresponding to the target data N ' is:
N＝1-n' (3)
in this embodiment, the server determines whether the target data includes the extremely small data, and if so, converts the extremely small data into the extremely large data, so that the data input into the project evaluation model corresponding to the point-to-point energy project is the extremely large data, and at this time, the higher the numerical value of the evaluation result is, the better the evaluation of the point-to-point energy project is, thereby unifying the evaluation standards of the evaluation indexes in the evaluation project set, facilitating accurate evaluation of the point-to-point energy project, and improving the accuracy of the point-to-point energy project evaluation.
In one embodiment, the maximum data includes maximum data corresponding to each evaluation index in the evaluation index set; then, in step S204, the extremely large data and the evaluation index set are input to the project evaluation model corresponding to the point-to-point energy project, so as to obtain an evaluation result of the point-to-point energy project, which specifically includes: determining the weight of each evaluation index through a project evaluation model, and carrying out weighted summation on the maximum data corresponding to each evaluation index according to the weight of each evaluation index to obtain an evaluation result corresponding to the point-to-point energy project, wherein the obtained evaluation result is also the maximum data.
Specifically, the server distributes a weight to each evaluation index in an evaluation index set through a project evaluation model, adds the weight of each evaluation index and the result obtained by multiplying each group of corresponding extremely-large data to obtain the weighted sum result of a plurality of groups of evaluation index sets, and finally adds the weighted sum results to obtain the total result value as the evaluation result corresponding to the point-to-point energy project.
For example, the server constructs a project evaluation model based on an analytic hierarchy process, and evaluates the transaction service of the point-to-point energy project through the project evaluation model. The method comprises the steps of firstly determining the weight of each evaluation index in an evaluation index set through a project evaluation model, then calculating the weighted summation result of single extremely large data, and finally summing the weighted summation results of all the extremely large data to obtain the final evaluation result of the point-to-point energy project.
In order to determine the weight of each evaluation index in the evaluation index set, a judgment matrix C of the point-to-point energy evaluation project needs to be constructed, and C is assumedxyIs a first layer index DxRelative to the first layer index DyThe importance of (2) is to satisfy:
Cxy＞0,Cxy＝1/Cyx,Cxx＝1(1≤x,y≤3) (4)
the judgment matrix C of the point-to-point energy evaluation project is as follows:
suppose zpqIs the second layer index VpRelative to the second layer index VqThe degree of importance of the second layer index, and a judgment matrix Z between the second layer indexesδWhere δ (δ is 1,2,3) represents the ordinal number of the determination matrix of the first-layer index, and the degree of importance zpqIt is also required to satisfy:
zpq＞0,zpq＝1/zqp,zpp＝1 (6)
the judgment matrix of the first-layer index is as follows:
wherein gamma represents the ordinal number of the second layer index,
represents a p second layer index V corresponding to the delta first layer indexpWith respect to the q-th second layer index VqThe degree of importance of.
Degree of importance CxyGeometric mean of
And degree of importance
Geometric mean of
Comprises the following steps:
the first layer index has a weight of
The second layer index is weighted relative to the first layer index by
The formula is as follows:
the weight ω of the pth second-tier index in the point-to-point energy itempComprises the following steps:
according to the second layer index VpCorrespond toOf very large data vηpAnd the weight ω of the second layer indexpThe weighted summation result P of a single group of extremely large data can be calculatedε：
Wherein, PεRepresenting the result of weighted summation of the epsilon-th very large data, eta representing the number of very large data, vηpAnd a numerical value representing the eta maximum data corresponding to the p second-layer index.
Adding the weighted summation results of all the extremely large data to obtain a final point-to-point energy project evaluation result P':
the value range of P 'is [0,1], and the larger the value of P' is, the better the transaction service of the point-to-point energy project is.
In the embodiment, the evaluation result of the point-to-point energy project is obtained by constructing the project evaluation model corresponding to the point-to-point energy project, so that the evaluation result is more objective and accurate, thereby avoiding the one-sided evaluation of the point to the point energy project caused by subjective factors, and further improving the accuracy rate of evaluating the point to point energy project.
In this embodiment, as shown in fig. 4, the method divides the extremely large data according to the preset period, and specifically includes the following steps:
step S401, according to a preset period, dividing the extremely large data to obtain a plurality of groups of periodic data.
The preset period is a project evaluation period set by a technical research and development unit or a technician according to evaluation requirements of point energy projects, such as one month, one quarter, one year, and the like, and may be adjusted according to actual conditions, which is not specifically limited herein.
Specifically, the energy management terminal responds to periodic evaluation operation of technicians for point-to-point energy projects, generates a periodic evaluation request for the point-to-point energy projects, the periodic evaluation request comprises preset periodic information, then sends the periodic evaluation request to the server, the server receives the evaluation request to obtain a preset period, and then divides data of the maximum model according to the preset period to obtain multiple groups of periodic data. After multiple divisions, if the number of the last remaining extremely large data is less than the preset period, the server calls a data generation instruction, predicts according to the remaining extremely large data to obtain the extremely large data less than the preset period, and combines the predicted data with the remaining extremely large data to obtain the last group of periodic data. Therefore, after the server acquires the plurality of groups of periodic data, the subsequent periodic evaluation step is executed by taking the plurality of groups of periodic data as processing bases.
And S402, inputting each group of periodic data and evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain a plurality of periodic evaluation results of the point-to-point energy project.
Specifically, each set of periodic data includes periodic data corresponding to each evaluation index in the set of evaluation indexes. The server acquires a project evaluation model corresponding to the point-to-point energy project from the stored project evaluation models, then respectively inputs each group of periodic data and an evaluation index set into the project evaluation model, the project evaluation model calculates the period weight of each evaluation index corresponding to each group of periodic data, then obtains the weighted summation result of single periodic data in each group according to the period weight and the periodic data, and adds all the weighted summation results in each group to obtain the period evaluation result of each group.
Step S403, generating an analysis report according to the multiple periodic evaluation results, and returning the analysis report to the energy management terminal; the analysis report is used for representing comparison results of a plurality of period evaluation results of the point-to-point energy project in a preset period.
Specifically, after the server obtains a plurality of cycle evaluation results, the cycle evaluation results are transversely compared, an analysis report is generated according to the transverse comparison result, then the analysis report is returned to the energy management terminal, and the energy management terminal receives and displays the analysis report so as to be convenient for a technician to check, so that the technician can know the condition of the point-to-point energy project in each cycle, the technician can timely find problems in the point-to-point energy project, and a solution is provided.
In this embodiment, a plurality of groups of periodic data are obtained by dividing very large data according to a preset period, each group of periodic data and an evaluation index are respectively input into a project evaluation model to obtain a period evaluation result, an analysis report is generated according to the period evaluation result and returned to an energy management terminal, so that a technician can obtain a total evaluation result of a point-to-point energy project and an analysis report of the point-to-point energy project in each period through the energy management terminal, and further can directly and comprehensively analyze the total evaluation result and a plurality of period evaluation results of the point-to-point energy project, and further discover advantages and disadvantages in the point-to-point energy project, so that a solution can be provided to further improve the point-to-point energy project.
In one embodiment, as shown in fig. 5, another peer-to-peer energy project evaluation method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S501, responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and determining at least three first-layer indexes corresponding to the evaluation request.
Step S502, at least one second-layer evaluation index corresponding to each first-layer index is obtained, and at least three second-layer evaluation indexes are obtained and used as at least three evaluation indexes.
Step S503, generating an evaluation index set corresponding to the evaluation request according to at least three evaluation indexes.
Step S504, project data aiming at the point-to-point energy project sent by the energy management terminal is received.
Step S505 is to find out data matching each evaluation index in the evaluation index set from the project data as initial data.
In step S506, if the initial data includes numerical data, ratio conversion processing is performed on the numerical data to obtain ratio data corresponding to the numerical data.
Step S507, updating the numerical data in the initial data to the ratio data to obtain the target data.
Step S508, judge whether there is the extremely small data in the target data; if the target data contains the extremely small data, the upper limit value of the extremely small data is subtracted from the extremely small data to obtain the extremely large data.
Step S509, determining a weight of each evaluation index through the project evaluation model, and performing weighted summation on the maximum data corresponding to each evaluation index according to the weight of each evaluation index to obtain an evaluation result corresponding to the point-to-point energy project.
The point-to-point energy project evaluation method can achieve the following technical effects: (1) by analyzing the evaluation request, a plurality of more objective project evaluation indexes can be obtained, and the comprehensiveness of point-to-point energy project evaluation results is improved; (2) the point-to-point energy project is evaluated by using the extremely large data, so that the unification of evaluation standards of evaluation indexes is facilitated, and the accuracy of evaluation results is improved; (3) the assessment is carried out through the project assessment model corresponding to the point-to-point energy project, the one-sidedness of the assessment result of the point-to-point energy project caused by subjective factors is avoided, and therefore the accuracy of the point-to-point energy project assessment is further improved.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 6, there is provided a peer-to-peer energy project evaluation apparatus 600, including: an index construction module 601, a data acquisition module 602, a data conversion module 603, and a result acquisition module 604, wherein:
the index building module 601 is configured to generate an evaluation index set corresponding to an evaluation request in response to the evaluation request for the point-to-point energy project sent by the energy management terminal; the evaluation index set comprises at least three evaluation indexes.
The data obtaining module 602 is configured to perform data extraction processing on the project data of the point-to-point energy project according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set.
The data conversion module 603 is configured to perform maximum type conversion processing on the initial data to obtain corresponding maximum type data.
And the result obtaining module 604 is configured to input the extremely large data and the evaluation index set to the project evaluation model corresponding to the point-to-point energy project, obtain an evaluation result of the point-to-point energy project, and return the evaluation result to the energy management terminal.
In one embodiment, the index building module 601 is further configured to determine, in response to an evaluation request for a point-to-point energy project sent by an energy management terminal, at least three first-layer indexes corresponding to the evaluation request; acquiring at least one second-layer evaluation index corresponding to each first-layer index to obtain at least three second-layer evaluation indexes serving as at least three evaluation indexes; and generating an evaluation index set corresponding to the evaluation request according to the at least three evaluation indexes.
In one embodiment, the peer-to-peer energy project assessment apparatus 600 further includes a data receiving module, configured to receive project data for the peer-to-peer energy project sent by the energy management terminal.
In one embodiment, the data obtaining module 602 is further configured to find, from the project data, data that matches each evaluation index in the evaluation index set as initial data.
In one embodiment, the peer-to-peer energy project evaluating apparatus 600 further includes a rate conversion module, configured to perform a rate conversion process on the numerical data if the initial data includes the numerical data, so as to obtain a rate data corresponding to the numerical data; and updating numerical data in the initial data into ratio data to obtain target data.
In one embodiment, the peer-to-peer energy project evaluation apparatus 600 further includes a maximum conversion module for determining whether there is minimum data in the target data; if the target data contains the extremely small data, the upper limit value of the extremely small data is subtracted from the extremely small data to obtain the extremely large data.
In an embodiment, the maximum data includes maximum data corresponding to each evaluation index in the evaluation index set, and the result obtaining module 604 is further configured to determine a weight of each evaluation index through the project evaluation model, and perform weighted summation on the maximum data corresponding to each evaluation index according to the weight of each evaluation index to obtain an evaluation result corresponding to the point-to-point energy project.
In one embodiment, the point-to-point energy project evaluation apparatus 600 further includes a period analysis module, configured to divide the extremely large data according to a preset period to obtain multiple sets of periodic data; respectively inputting each group of periodic data and evaluation index set to a project evaluation model corresponding to the point-to-point energy project to obtain a plurality of periodic evaluation results of the point-to-point energy project; generating an analysis report according to a plurality of periodic evaluation results, and returning the analysis report to the energy management terminal; the analysis report is used for representing comparison results of a plurality of period evaluation results of the point-to-point energy project in a preset period.
For specific limitations of the peer-to-peer energy project assessment apparatus, reference may be made to the above limitations of the peer-to-peer energy project assessment method, which are not described herein again. The modules in the point-to-point energy project evaluation device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing project data, initial data, extremely large data, evaluation results and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a peer-to-peer energy project assessment method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A peer-to-peer energy project assessment method, the method comprising:
responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal, and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
according to the evaluation index set, performing data extraction processing on the project data of the point-to-point energy project to obtain initial data of each evaluation index in the evaluation index set;
performing maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project, and returning the evaluation result to the energy management terminal.
2. The method according to claim 1, wherein the generating, in response to an evaluation request for a point-to-point energy project sent by an energy management terminal, an evaluation index set corresponding to the evaluation request comprises:
responding to an evaluation request aiming at the point-to-point energy project sent by the energy management terminal, and determining at least three first-layer indexes corresponding to the evaluation request;
acquiring at least one second-layer evaluation index corresponding to each first-layer index to obtain at least three second-layer evaluation indexes serving as the at least three evaluation indexes;
and generating an evaluation index set corresponding to the evaluation request according to the at least three evaluation indexes.
3. The method according to claim 1, before performing data extraction processing on the project data of the point-to-point energy project according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set, further comprising:
receiving project data aiming at the point-to-point energy project sent by the energy management terminal;
the data extraction processing is performed on the project data of the point-to-point energy project according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set, and the method comprises the following steps:
and finding out data matched with each evaluation index in the evaluation index set from the project data as the initial data.
4. The method of claim 1, further comprising, before performing maximum size conversion processing on the initial data to obtain corresponding maximum size data:
if the initial data comprises numerical data, performing ratio conversion processing on the numerical data to obtain ratio data corresponding to the numerical data;
and updating the numerical data in the initial data into the ratio data to obtain target data.
5. The method according to claim 4, wherein said performing maximum size conversion processing on the initial data to obtain corresponding maximum size data comprises:
judging whether the target data contains extremely small data or not;
and if the target data contains the extremely small data, subtracting the extremely small data from the upper bound value of the extremely small data to obtain the extremely large data.
6. The method according to claim 1, wherein the very large data includes very large data corresponding to each evaluation index in the evaluation index set;
the step of inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project includes:
determining the weight of each evaluation index through the project evaluation model, and performing weighted summation on the maximum data corresponding to each evaluation index according to the weight of each evaluation index to obtain an evaluation result corresponding to the point-to-point energy project.
7. The method of any one of claims 1 to 6, further comprising:
dividing the extremely large data according to a preset period to obtain a plurality of groups of periodic data;
inputting each group of the periodic data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project respectively to obtain a plurality of periodic evaluation results of the point-to-point energy project;
generating an analysis report according to the plurality of periodic evaluation results, and returning the analysis report to the energy management terminal; the analysis report is used for representing comparison results of a plurality of period evaluation results of the point-to-point energy project in the preset period.
8. An ad hoc energy project assessment apparatus, comprising:
the index construction module is used for responding to an evaluation request aiming at a point-to-point energy project sent by an energy management terminal and generating an evaluation index set corresponding to the evaluation request; the evaluation index set comprises at least three evaluation indexes;
the data acquisition module is used for performing data extraction processing on the project data of the point-to-point energy project according to the evaluation index set to obtain initial data of each evaluation index in the evaluation index set;
the data conversion module is used for carrying out maximum type conversion processing on the initial data to obtain corresponding maximum type data;
and the result acquisition module is used for inputting the extremely large data and the evaluation index set into a project evaluation model corresponding to the point-to-point energy project to obtain an evaluation result of the point-to-point energy project and returning the evaluation result to the energy management terminal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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