CN116957391A - Multi-dimensional evaluation method and system for comprehensive energy operation and maintenance - Google Patents
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Abstract
The application discloses a multidimensional evaluation method and a multidimensional evaluation system for comprehensive energy operation and maintenance, which relate to the technical field of data processing, wherein the method comprises the following steps: determining multiple groups of evaluation index sets, including multiple dimensions of power supply cost, risk and stability; based on a plurality of groups of evaluation index sets, monitoring real-time index characteristic values, subjectively scoring the plurality of groups of evaluation index sets, and building a scoring matrix, wherein the scoring matrix is marked with a first weight; objective scoring is carried out on a plurality of groups of evaluation index sets, a two-item scoring matrix is built, and the two-item scoring matrix is marked with a second weight; and calculating and acquiring an operation and maintenance assessment result based on the first scoring matrix and the second scoring matrix and combining the first weight and the second weight. The method solves the technical problem of low accuracy of operation and maintenance effect evaluation of the comprehensive energy operation and maintenance system in the prior art, and achieves the technical effect of improving the accuracy of operation and maintenance effect evaluation of the comprehensive energy operation and maintenance system through multi-dimensional evaluation.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a multi-dimensional evaluation method and system for comprehensive energy operation and maintenance.
Background
The comprehensive energy operation and maintenance refers to integrating renewable energy, hydrogen energy, energy storage facilities, electrified traffic and the like on the basis of the traditional energy supply of collecting electricity, cold, heat and gas, and breaks through the traditional mode of independent planning, design and operation of different energy varieties. The system is used as a tie for connecting an operation and maintenance unit and an electricity utilization enterprise, can assist the enterprise in eliminating energy consumption dead zones, improves the operation and energy utilization safety of a power distribution system, and reduces the operation cost for the enterprise. However, the operation and maintenance effect evaluation of the existing comprehensive operation and maintenance system is single in face and is not beneficial to optimizing the comprehensive operation and maintenance system.
Disclosure of Invention
The application provides a multi-dimensional evaluation method and a multi-dimensional evaluation system for comprehensive energy operation and maintenance, which are used for solving the technical problem of low accuracy of operation and maintenance effect evaluation of a comprehensive energy operation and maintenance system in the prior art.
In a first aspect of the present application, there is provided a multi-dimensional assessment method for integrated energy operation and maintenance, the method comprising: determining multiple groups of evaluation index sets, including multiple dimensions of power supply cost, risk and stability; monitoring a real-time index characteristic value based on the multiple groups of evaluation index sets; subjective scoring is carried out on the multiple groups of evaluation index sets based on the real-time index characteristic values, and a scoring matrix is built, wherein the scoring matrix is marked with first weights; objectively scoring the plurality of groups of evaluation index sets based on the real-time index characteristic values, and building a two-term scoring matrix, wherein the two-term scoring matrix is marked with a second weight; and calculating and acquiring an operation and maintenance evaluation result based on the one scoring matrix and the two scoring matrices and combining the first weight and the second weight.
In a second aspect of the application, there is provided a multi-dimensional assessment system for integrated energy operation and maintenance, the system comprising: the evaluation index set determining module is used for determining a plurality of groups of evaluation index sets, including a plurality of dimensions of power supply cost, risk and stability; the real-time index characteristic value monitoring module is used for monitoring the real-time index characteristic value based on the multiple groups of evaluation index sets; the scoring matrix building module is used for subjectively scoring the multiple groups of evaluation index sets based on the real-time index characteristic values, and building a scoring matrix, wherein the scoring matrix is marked with a first weight; the two-item scoring matrix building module is used for objectively scoring the multiple groups of evaluation index sets based on the real-time index characteristic values, and building a two-item scoring matrix, wherein the two-item scoring matrix is marked with a second weight; and the operation and maintenance evaluation result acquisition module is used for calculating and acquiring an operation and maintenance evaluation result based on the first scoring matrix and the second scoring matrix and combining the first weight and the second weight.
In a third aspect of the present application, there is provided an electronic apparatus comprising: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the system of any of the first aspects.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the application provides a multi-dimensional evaluation method for comprehensive energy operation and maintenance, which relates to the technical field of data processing, and comprises the steps of determining a plurality of groups of evaluation index sets, including a plurality of dimensions of power supply cost, risk and stability; based on a plurality of groups of evaluation index sets, monitoring real-time index characteristic values, subjectively scoring the plurality of groups of evaluation index sets, and building a scoring matrix, wherein the scoring matrix is marked with a first weight; objective scoring is carried out on a plurality of groups of evaluation index sets, a two-item scoring matrix is built, and the two-item scoring matrix is marked with a second weight; based on the one scoring matrix and the two scoring matrices, the operation and maintenance evaluation result is calculated and obtained by combining the first weight and the second weight, the technical problem that the operation and maintenance effect evaluation accuracy of the comprehensive energy operation and maintenance system in the prior art is low is solved, and the technical effect of improving the operation and maintenance effect evaluation accuracy of the comprehensive energy operation and maintenance system through multi-dimensional evaluation is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a multi-dimensional evaluation method for comprehensive energy operation and maintenance according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of constructing a scoring matrix in the multidimensional evaluation method for comprehensive energy operation and maintenance according to the embodiment of the present application;
fig. 3 is a schematic structural diagram of a multi-dimensional evaluation system for comprehensive energy operation and maintenance according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to the present application.
Reference numerals illustrate: the system comprises an evaluation index set determining module 11, a real-time index characteristic value monitoring module 12, a one-term scoring matrix constructing module 13, a two-term scoring matrix constructing module 14, an operation and maintenance evaluation result obtaining module 15, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304..
Detailed Description
The application provides a multidimensional evaluation method for comprehensive energy operation and maintenance, which is used for solving the technical problem of low accuracy of operation and maintenance effect evaluation of a comprehensive energy operation and maintenance system in the prior art.
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. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented 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 server 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 modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a multi-dimensional evaluation method for comprehensive energy operation and maintenance, the method comprising:
s100: determining multiple groups of evaluation index sets, including multiple dimensions of power supply cost, risk and stability;
specifically, comprehensive energy operation and maintenance evaluation indexes of multiple dimensions are obtained based on Internet big data, the comprehensive energy operation and maintenance evaluation indexes are used as multiple groups of evaluation index sets, the multiple groups of evaluation index sets comprise evaluation indexes of multiple dimensions of power supply cost, risk and stability, the power supply cost comprises power generation cost of various types of energy sources, such as photovoltaic power generation cost, wind power generation cost, thermal power generation cost, power grid exchange loss and the like, the risk refers to energy supply risks of various types of energy sources, including energy supply interruption, equipment abnormality and other risk event factors, and the stability refers to the capability of continuous stable energy supply of various types of energy sources, and can be reflected through energy source coupling efficiency, peak clipping and valley filling states and the like. The multiple groups of evaluation index sets can be used as references for subsequent real-time index feature value detection.
S200: monitoring a real-time index characteristic value based on the multiple groups of evaluation index sets;
specifically, based on the multiple sets of evaluation indexes in the multiple sets of evaluation index sets, that is, the power supply cost evaluation index, the risk evaluation index and the stability evaluation index, multiple sets of evaluation indexes of the target comprehensive energy operation and maintenance system are monitored in real time to obtain multiple sets of evaluation index data of the target comprehensive energy operation and maintenance system in the current operation period, for example, the power generation cost, the power supply stability, the power loss value and the like of the target comprehensive energy operation and maintenance system in the current operation period, and the multiple sets of evaluation index data are used as real-time index characteristic values and can be used as basic data for evaluating the operation and maintenance effects of the target comprehensive energy operation and maintenance system in the current period.
S300: subjective scoring is carried out on the multiple groups of evaluation index sets based on the real-time index characteristic values, and a scoring matrix is built, wherein the scoring matrix is marked with first weights;
specifically, subjective scoring is performed on the multiple sets of evaluation index sets based on the real-time index characteristic values, the subjective scoring is performed on the basis of the completion degrees of all evaluation indexes and index evaluation rules, the index evaluation rules are scoring standards of all evaluation indexes, different scoring grades are set according to the completion degrees of all evaluation indexes, the real-time index characteristic values can reflect the completion degrees of all evaluation indexes, sub-scoring matrixes are built respectively on the basis of all evaluation indexes in the multiple sets of evaluation index sets, and then the multiple sub-scoring matrixes are fused to obtain a scoring matrix, namely a comprehensive energy operation and maintenance effect scoring matrix obtained by calculating the completion degrees of all evaluation indexes, the importance degrees of all evaluation indexes in the one scoring matrix are different, therefore, different weights are occupied, and the first weights are marked and can be used as basic data of the comprehensive energy operation and maintenance effect scoring.
Further, as shown in fig. 2, step S300 of the embodiment of the present application further includes:
s310: constructing an initialization matrix based on the multiple groups of evaluation index sets;
s320: traversing the multiple groups of evaluation index sets, and building multiple index evaluation sub-level matrixes based on the real-time index characteristic values;
s330: based on the index scoring sub-level matrixes, performing weighted calculation on matrix items on each index scoring sub-level matrix, and determining a first index scoring value;
s340: the one term scoring matrix is generated based on the first index scoring values and the initialization matrix.
Specifically, based on the multiple sets of evaluation index sets, building a corresponding initialization matrix for each set of evaluation indexes, traversing the multiple sets of evaluation index sets, substituting the numerical values of the multiple evaluation indexes in the real-time index characteristic values into the corresponding initialization matrices to obtain multiple index evaluation sub-level matrices, distributing corresponding weight coefficients for each evaluation index according to the importance degree of each evaluation index, performing weighted calculation of matrix items on each index evaluation sub-level matrix by using the weight coefficients, taking a weighted average value as a first index evaluation value, and generating the one scoring matrix, namely, a comprehensive energy operation and maintenance effect scoring matrix obtained by calculation according to the completion degree of each evaluation index on the basis of the first index evaluation value and the initialization matrix.
Further, step S320 of the embodiment of the present application further includes:
s321: determining an index judgment factor set based on the multiple groups of evaluation index sets, wherein each evaluation index corresponds to at least one index judgment factor;
s322: and extracting index judgment factors corresponding to all the evaluation indexes based on the index judgment factor set, and constructing a plurality of index evaluation sub-level matrixes in combination with the real-time index characteristic values, wherein the index evaluation sub-level matrixes are orthogonal matrixes based on the index judgment factors corresponding to the evaluation indexes.
Specifically, based on the multiple sets of evaluation index sets, determining an index judgment factor set corresponding to each evaluation index, determining an index judgment factor set, wherein each evaluation index corresponds to at least one index judgment factor, for example, the power supply cost evaluation index corresponds to an index judgment factor such as power generation cost, power grid exchange loss, power transportation loss, power facility maintenance loss, labor cost and the like, the risk evaluation index corresponds to an index judgment factor such as power supply interruption, equipment abnormality, power deficiency and the like, the stability evaluation index corresponds to an index judgment factor such as energy coupling efficiency, peak clipping and valley filling state, generator working state and the like, each evaluation index corresponds to an index judgment factor set, extracting the index judgment factors corresponding to each evaluation index from the index judgment factor set, and building a plurality of index evaluation sub-level matrixes based on an orthogonal matrix of the index judgment factors corresponding to the evaluation indexes, that is, and can reflect real-time index characteristic values of the index judgment factors corresponding to each evaluation index, that is, namely, completion degrees of each index.
S400: objectively scoring the plurality of groups of evaluation index sets based on the real-time index characteristic values, and building a two-term scoring matrix, wherein the two-term scoring matrix is marked with a second weight;
specifically, objective scoring is performed on the multiple groups of evaluation index sets based on the real-time index characteristic values, the objective scoring is based on a sample refining scoring rule, the multiple groups of evaluation index sets are scored by using the scoring rule, two scoring matrixes are built by combining the multiple groups of evaluation index sets, and the two scoring matrixes contain different amounts of all evaluation index data, so that all evaluation index data occupy different weights, and a second weight is marked and can be used as basic data of comprehensive energy operation and maintenance effect scoring.
Further, step S400 of the embodiment of the present application further includes:
s410: taking the multiple groups of evaluation index sets as indexes, collecting and calling a sample data set, wherein the sample data set comprises a sample index characteristic value set, a sample index grading value and a sample index weight;
s420: determining training data based on the sample data set, the sample index scoring values, and the sample index weights;
s430: training the neural network based on the training data to generate an objective scoring model;
s440: inputting the real-time index characteristic values into the objective scoring model, and outputting an index scoring value set;
s450: and performing matrix conversion on the index scoring value set to generate the two-term scoring matrix.
Specifically, each evaluation index in the multiple sets of evaluation index sets is used as an index condition, evaluation data of multiple comprehensive energy operation and maintenance evaluation cases are collected from big data and used as a sample data set, the sample data set comprises a sample index characteristic value set, multiple sample index grading values and multiple sample index weights, the sample index characteristic value set comprises multiple sample index characteristic values, the sample data set, the sample index grading values and the sample index weights are used as construction data, a BP neural network is combined, an objective grading model is constructed, the sample data is randomly divided into a training data set, a verification data set and a test data set, and the objective grading model is subjected to supervision training based on the training data set, the verification data set and the test data set until the objective grading model achieves convergence and meets the preset accuracy requirement, and the objective grading model is obtained. The BP neural network is a multi-layer feedforward neural network trained according to an error reverse propagation algorithm, a mathematical equation of a mapping relation between input and output is not required to be determined in advance, a certain rule is learned only through self training, and a result closest to an expected output value is obtained when an input value is given.
Further, the real-time index characteristic values are input into the objective scoring model, a plurality of index scoring value sets are output by the objective scoring model, the index scoring value sets are subjected to matrix conversion after weight calculation, and the two scoring matrices are obtained and can be used as basic data of comprehensive energy operation and maintenance effect scoring.
Further, step S400 of the embodiment of the present application further includes:
s460: based on the multiple groups of evaluation index sets, counting total sample size;
s470: dividing the sample data set based on the multiple groups of evaluation index sets, counting the sample sizes of the same evaluation index, and obtaining multiple index sample sizes;
s480: traversing the index sample sizes, calculating the ratio of the index sample sizes to the total sample size, and determining the specific gravity of the indexes;
s490: the second weight is determined based on the plurality of index weights.
Specifically, the multiple groups of evaluation index sets are taken as references, the total sample size of the invoked sample data set is counted, the sample data set is divided based on the multiple groups of evaluation index sets, the sample sizes of the same evaluation indexes are counted respectively to obtain sample sizes of multiple evaluation indexes, the sample sizes are taken as multiple index sample sizes, the multiple index sample sizes are traversed, ratio calculation is carried out on the multiple index sample sizes and the total sample size, multiple index specific weights are determined, the specific weights of the multiple index sample sizes account for the total sample size, the weight calculation is carried out based on the multiple index specific weights and the importance degree of each evaluation index is combined, the second weight is obtained, and the second weight can be used as basic data for carrying out subsequent two-item comprehensive score calculation.
S500: and calculating and acquiring an operation and maintenance evaluation result based on the one scoring matrix and the two scoring matrices and combining the first weight and the second weight.
Further, step S500 of the embodiment of the present application further includes:
s510: combining the first weight, carrying out matrix item weighted summation on the scoring matrix to determine a comprehensive score;
s520: combining the second weight, carrying out matrix item weighted summation on the two item scoring matrixes, and determining two item comprehensive scores;
s530: and carrying out mean value calculation on the comprehensive score and the two comprehensive scores to obtain the operation and maintenance evaluation result.
Specifically, the first weight is combined, weighted summation is carried out on each matrix item in the one-item scoring matrix, the weighted average value is used as one-item comprehensive score, the second weight is combined, weighted summation is carried out on each matrix item in the two-item scoring matrix, the weighted average value is used as two-item comprehensive score, average value calculation is carried out on the one-item comprehensive score and the two-item comprehensive score, the operation and maintenance assessment result is obtained, the operation and maintenance assessment result is used as the operation and maintenance effect of the target comprehensive energy operation and maintenance system, and the operation and maintenance effect assessment accuracy of the comprehensive energy operation and maintenance system can be improved.
In summary, the embodiment of the application has at least the following technical effects:
the application determines a plurality of groups of evaluation index sets, including a plurality of dimensions of power supply cost, risk and stability; based on a plurality of groups of evaluation index sets, monitoring real-time index characteristic values, subjectively scoring the plurality of groups of evaluation index sets, and building a scoring matrix, wherein the scoring matrix is marked with a first weight; objective scoring is carried out on a plurality of groups of evaluation index sets, a two-item scoring matrix is built, and the two-item scoring matrix is marked with a second weight; and calculating and acquiring an operation and maintenance assessment result based on the first scoring matrix and the second scoring matrix and combining the first weight and the second weight.
The technical effect of improving the accuracy of the operation and maintenance effect evaluation of the comprehensive energy operation and maintenance system through multi-dimensional evaluation is achieved.
Example two
Based on the same inventive concept as the multi-dimensional evaluation method for comprehensive energy operation and maintenance in the foregoing embodiments, as shown in fig. 3, the present application provides a multi-dimensional evaluation system for comprehensive energy operation and maintenance, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the evaluation index set determining module 11 is configured to determine multiple sets of evaluation index sets, including multiple dimensions of power supply cost, risk and stability;
a real-time index feature value monitoring module 12, where the real-time index feature value monitoring module 12 is configured to monitor a real-time index feature value based on the multiple sets of evaluation index sets;
a scoring matrix building module 13, where the scoring matrix building module 13 is configured to perform subjective scoring on the multiple sets of evaluation index sets based on the real-time index feature values, and build a scoring matrix, where the scoring matrix is identified with a first weight;
the two-term scoring matrix building module 14 is configured to objectively score the multiple sets of evaluation index sets based on the real-time index feature values, and build a two-term scoring matrix, where the two-term scoring matrix is identified with a second weight;
and the operation and maintenance evaluation result acquisition module 15 is used for calculating and acquiring an operation and maintenance evaluation result based on the first scoring matrix and the second scoring matrix and combining the first weight and the second weight.
Further, the one scoring matrix building module 13 is further configured to perform the following steps:
constructing an initialization matrix based on the multiple groups of evaluation index sets;
traversing the multiple groups of evaluation index sets, and building multiple index evaluation sub-level matrixes based on the real-time index characteristic values;
based on the index scoring sub-level matrixes, performing weighted calculation on matrix items on each index scoring sub-level matrix, and determining a first index scoring value;
the one term scoring matrix is generated based on the first index scoring values and the initialization matrix.
Further, the one scoring matrix building module 13 is further configured to perform the following steps:
determining an index judgment factor set based on the multiple groups of evaluation index sets, wherein each evaluation index corresponds to at least one index judgment factor;
and extracting index judgment factors corresponding to all the evaluation indexes based on the index judgment factor set, and constructing a plurality of index evaluation sub-level matrixes in combination with the real-time index characteristic values, wherein the index evaluation sub-level matrixes are selfing matrixes based on the index judgment factors corresponding to the evaluation indexes.
Further, the two-term scoring matrix building module 14 is further configured to perform the following steps:
taking the multiple groups of evaluation index sets as indexes, collecting and calling a sample data set, wherein the sample data set comprises a sample index characteristic value set, a sample index grading value and a sample index weight;
determining training data based on the sample data set, the sample index scoring values, and the sample index weights;
training the neural network based on the training data to generate an objective scoring model;
inputting the real-time index characteristic values into the objective scoring model, and outputting an index scoring value set;
and performing matrix conversion on the index scoring value set to generate the two-term scoring matrix.
Further, the two-term scoring matrix building module 14 is further configured to perform the following steps:
based on the multiple groups of evaluation index sets, counting total sample size;
dividing the sample data set based on the multiple groups of evaluation index sets, counting the sample sizes of the same evaluation index, and obtaining multiple index sample sizes;
traversing the index sample sizes, calculating the ratio of the index sample sizes to the total sample size, and determining the specific gravity of the indexes;
the second weight is determined based on the plurality of index weights.
Further, the operation and maintenance evaluation result obtaining module 15 is further configured to perform the following steps:
combining the first weight, carrying out matrix item weighted summation on the scoring matrix to determine a comprehensive score;
combining the second weight, carrying out matrix item weighted summation on the two item scoring matrixes, and determining two item comprehensive scores;
and carrying out mean value calculation on the comprehensive score and the two comprehensive scores to obtain the operation and maintenance evaluation result.
Example III
Based on the same inventive concept as the multi-dimensional evaluation method for integrated energy operation and maintenance in the foregoing embodiments, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method as in the first embodiment.
Exemplary electronic device
An electronic device of an embodiment of the application is described below with reference to fig. 4.
Based on the same inventive concept as the multi-dimensional evaluation method for comprehensive energy operation and maintenance in the foregoing embodiment, the present application also provides a multi-dimensional evaluation system for comprehensive energy operation and maintenance, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method of embodiment one.
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303 uses any transceiver-like means for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or may be an EEPROM (electrically erasable Programmable read-only memory), a compact disc-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the inventive arrangements, and is controlled by the processor 302 for execution. The processor 302 is configured to execute computer-executable instructions stored in the memory 301, thereby implementing the multi-dimensional assessment for integrated energy operation and maintenance provided by the above-described embodiments of the present application.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (9)
1. A multi-dimensional assessment method for integrated energy operation and maintenance, the method comprising:
determining multiple groups of evaluation index sets, including multiple dimensions of power supply cost, risk and stability;
monitoring a real-time index characteristic value based on the multiple groups of evaluation index sets;
subjective scoring is carried out on the multiple groups of evaluation index sets based on the real-time index characteristic values, and a scoring matrix is built, wherein the scoring matrix is marked with first weights;
objectively scoring the plurality of groups of evaluation index sets based on the real-time index characteristic values, and building a two-term scoring matrix, wherein the two-term scoring matrix is marked with a second weight;
and calculating and acquiring an operation and maintenance evaluation result based on the one scoring matrix and the two scoring matrices and combining the first weight and the second weight.
2. The method of claim 1, wherein the objective scoring is performed on the multiple sets of evaluation index sets based on the real-time index feature values, and a two-term scoring matrix is constructed, the method comprising:
constructing an initialization matrix based on the multiple groups of evaluation index sets;
traversing the multiple groups of evaluation index sets, and building multiple index evaluation sub-level matrixes based on the real-time index characteristic values;
based on the index scoring sub-level matrixes, performing weighted calculation on matrix items on each index scoring sub-level matrix, and determining a first index scoring value;
the one term scoring matrix is generated based on the first index scoring values and the initialization matrix.
3. The method of claim 2, wherein traversing the plurality of sets of evaluation metrics builds a plurality of metric evaluation sub-level matrices based on the real-time metric feature values, the method comprising:
determining an index judgment factor set based on the multiple groups of evaluation index sets, wherein each evaluation index corresponds to at least one index judgment factor;
and extracting index judgment factors corresponding to all the evaluation indexes based on the index judgment factor set, and constructing a plurality of index evaluation sub-level matrixes in combination with the real-time index characteristic values, wherein the index evaluation sub-level matrixes are selfing matrixes based on the index judgment factors corresponding to the evaluation indexes.
4. The method of claim 1, wherein the objective scoring is performed on the multiple sets of evaluation index sets based on the real-time index feature values, and a two-term scoring matrix is constructed, the method comprising:
taking the multiple groups of evaluation index sets as indexes, collecting and calling a sample data set, wherein the sample data set comprises a sample index characteristic value set, a sample index grading value and a sample index weight;
determining training data based on the sample data set, the sample index scoring values, and the sample index weights;
training the neural network based on the training data to generate an objective scoring model;
inputting the real-time index characteristic values into the objective scoring model, and outputting an index scoring value set;
and performing matrix conversion on the index scoring value set to generate the two-term scoring matrix.
5. The method of claim 4, wherein the two-term scoring matrix identifies a second weight, the method comprising:
based on the multiple groups of evaluation index sets, counting total sample size;
dividing the sample data set based on the multiple groups of evaluation index sets, counting the sample sizes of the same evaluation index, and obtaining multiple index sample sizes;
traversing the index sample sizes, calculating the ratio of the index sample sizes to the total sample size, and determining the specific gravity of the indexes;
the second weight is determined based on the plurality of index weights.
6. The method of claim 1, wherein the computing obtains the operation and maintenance assessment result based on the one scoring matrix and the two scoring matrices in combination with the first weight and the second weight, the method comprising:
combining the first weight, carrying out matrix item weighted summation on the scoring matrix to determine a comprehensive score;
combining the second weight, carrying out matrix item weighted summation on the two item scoring matrixes, and determining two item comprehensive scores;
and carrying out mean value calculation on the comprehensive score and the two comprehensive scores to obtain the operation and maintenance evaluation result.
7. A multi-dimensional assessment system for integrated energy operation and maintenance, the system comprising:
the evaluation index set determining module is used for determining a plurality of groups of evaluation index sets, including a plurality of dimensions of power supply cost, risk and stability;
the real-time index characteristic value monitoring module is used for monitoring the real-time index characteristic value based on the multiple groups of evaluation index sets;
the scoring matrix building module is used for subjectively scoring the multiple groups of evaluation index sets based on the real-time index characteristic values, and building a scoring matrix, wherein the scoring matrix is marked with a first weight;
the two-item scoring matrix building module is used for objectively scoring the multiple groups of evaluation index sets based on the real-time index characteristic values, and building a two-item scoring matrix, wherein the two-item scoring matrix is marked with a second weight;
and the operation and maintenance evaluation result acquisition module is used for calculating and acquiring an operation and maintenance evaluation result based on the first scoring matrix and the second scoring matrix and combining the first weight and the second weight.
8. A multi-dimensional assessment system for integrated energy operation and maintenance, comprising: a processor coupled to a memory for storing a program which, when executed by the processor, causes the system to perform the steps of the method of any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the method steps of any of claims 1 to 6.
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| CN117312816A (en) * | 2023-11-28 | 2023-12-29 | 张家港广大特材股份有限公司 | Special steel smelting effect evaluation method and system |
| CN117312816B (en) * | 2023-11-28 | 2024-04-02 | 张家港广大特材股份有限公司 | Special steel smelting effect evaluation method and system |
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