Intelligent power generation evaluation system and method

Abstract

The invention discloses an intelligent power generation evaluation system and method, and relates to the technical field of intelligent power generation, and the system comprises a hierarchical division unit, an index division unit, a process implementation unit, a weight distribution unit, and a comprehensive evaluation unit. According to the invention, through the hierarchical division unit and the index division unit, three first-level indexes and twenty second-level indexes are finally determined according to large-class division of an intelligent equipment layer, an intelligent control layer and an intelligent management layer, an evaluation index system is constructed, and through the process implementation unit, intelligent degree grades are divided for each index point and a judgment matrix is constructed, so that the evaluation index system is constructed. A weight distribution unit is used for calculating weights according to the judgment matrix by adopting an arithmetic average method, a geometric average method and a characteristic value method, an evaluation index set and an evaluation set are established through a comprehensive evaluation unit, the intelligence degree of the power plant is digitized, a second-level index layer corresponding to a first-level index is evaluated, and after a rating score is obtained, the evaluation index set and the evaluation index set are obtained. And power plant intelligence degree first-level index comprehensive evaluation is carried out, and a basis is provided for intelligence degree evaluation.

Description

Technical Field

[0001] The present invention relates to the field of smart power generation technology, and in particular to a smart power generation evaluation system and method. Background Art

[0002] Smart power generation is the intelligence of the power generation process. It is the basis for building smart power plants and constructing smart energy. Smart power generation mainly targets the power generation process, uses modern technology to replace manual labor, and realizes intelligent monitoring, operation and management of the power generation process according to preset procedures, aiming to improve the utilization rate of energy and resources. In the invention patent with application number CN202311234246.0, it is disclosed that "a smart power plant wind power generation evaluation system includes a wind power generation system, an artificial neural network monitoring system and a human-machine interface. The wind power generation system uses a wind power generation power smoothing control strategy to make the supercapacitor energy storage device change according to the wind power and power load to ensure the stable performance of the wind power generation system. The artificial neural network monitoring system is used for wind power generation system fault evaluation and diagnosis, generator terminal voltage and reactive power control, and excitation to achieve the stability of the power system. The human-machine interface provides a system operation interface for operation and maintenance personnel to monitor and evaluate the operating status of the smart power plant wind power generation evaluation system."

[0003] The above-mentioned documents improve the efficiency and reliability of wind power generation in smart power plants. However, there is still a lack of evaluation on the intelligence level of power plant power generation technology. There is a lack of basis for the weight allocation of evaluation indicators, and the requirements for intelligent construction of existing power plants are not quantified as a score. It is difficult to evaluate the intelligence level of power plants. Since the factors affecting power generation are unclear and numerous, it is not convenient to adjust various indicators, which reduces the power generation rate of power plants and restricts the economic development of power generation technology. Summary of the invention

[0004] The object of the present invention is to provide a smart power generation evaluation system and method to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a smart power generation evaluation system, comprising a hierarchical division unit, wherein the hierarchical division unit divides the smart power generation management and control system of the smart power plant into a three-layer structure of a smart device layer, a smart control layer and a smart management layer, wherein the smart device layer includes on-site measurement and control equipment, and monitors the operating status and data of on-site equipment in real time, the smart control layer performs online control, optimization and diagnosis of production and auxiliary devices, and the smart management layer coordinates and controls various production and management subsystems;

[0006] An indicator division unit, wherein the indicator division unit constructs an evaluation indicator system of two-level indicators, takes the intelligent device layer, the intelligent control layer and the intelligent management layer as the first-level indicators, and divides the three first-level indicators into second-level indicators respectively, and these second-level indicators serve as indicator points for evaluating the level of intelligence;

[0007] A process implementation unit, after determining the evaluation index, the process implementation unit classifies the intelligence level of the index point, and through the classification, each element in the index layer has a different level and importance. After the index classification unit constructs the evaluation index system, a hierarchical model is established through the hierarchical analysis method, a judgment matrix is ​​constructed according to the hierarchical model, and the elements of the judgment matrix are given by the AHP 1-9 scaling method;

[0008] A weight allocation unit, which assigns a corresponding weight to each indicator according to the different characteristics of the importance of each element. The relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1. The hierarchical weight is calculated, and the weight is calculated using the arithmetic mean method, the geometric mean method and the eigenvalue method, and the obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level;

[0009] A comprehensive evaluation unit establishes an evaluation index set and an evaluation set through the obtained weights, and then digitizes the intelligence degree of the power plant, evaluates the secondary indicator layer corresponding to the primary indicator, and after obtaining the rating score, conducts a comprehensive evaluation of the primary indicator of the power plant's intelligence degree, providing a basis for the intelligence degree evaluation.

[0010] Preferably, the indicator division unit includes a first-category division module, a second-category division module and a third-category division module. The first-category division module divides the intelligent device layer downward into 5 second-level indicators, namely communication protocol and interface, measurement base, detection equipment, on-site maintenance and inspection technology, and field-level equipment. The second-category division module divides the intelligent control layer downward into 9 second-level indicators, namely unit automatic start and stop technology, control algorithm, object fault diagnosis and self-healing, system simulation technology, communication network clock synchronization technology, unit energy-saving optimization technology, emission control optimization technology, combustion optimization technology and network source coordination control technology. The third-category division module divides the intelligent management layer downward into 6 second-level indicators, namely plant-level load optimization, fuel management, information system construction, safety protection system, spare parts storage and business indicator monitoring.

[0011] Preferably, the process implementation unit includes a grade division module and a structure division module. After the grade division module determines the evaluation index, it grades the intelligence level of the index point, and divides the index point level into R1, R2, R3 and R4. R1 indicates a very high level of technical intelligence, R2 indicates a high level of technical intelligence, R3 indicates a moderate level of technical intelligence, and R4 indicates a general level of technical intelligence. The grade division makes each element in the index layer have different levels and importance. After the structure division module constructs the evaluation index system, it establishes a hierarchical model through the hierarchical analysis method, and divides the hierarchical model into the top, middle and bottom layers. Between two adjacent layers, the upper layer is the target layer, and the lower layer is the element layer. The relationship between the elements is expressed in a tree form.

[0012] Preferably, the process implementation unit also includes a scale establishment module, which constructs a judgment matrix for the same-layer elements belonging to each element of the upper layer until the lowest layer according to the hierarchical structure model, and obtains the relative importance of the elements by comparing the elements of the same layer in pairs. The elements of the judgment matrix are given by the AHP 1-9 scaling method, and the scale 1 indicates that the two elements are of equal importance, the scale 3 indicates that the former element is slightly more important than the latter element, the scale 5 indicates that the former element is obviously more important than the latter element, the scale 7 indicates that the former element is very important than the latter element, and the scale 9 indicates that the former element is extremely important than the latter element. Scales 2, 4, 6, and 8 represent intermediate values ​​of the above judgment values.

[0013] Preferably, the process implementation unit also includes a scale selection module and a matrix establishment module. Before the scale selection module scales the judgment matrix, it first selects the factors with the best and worst value orientations, and the scales of other factors are between the best and worst factors. The middle factor is selected as a benchmark to measure whether the remaining factors are above or below the state. The matrix establishment module arranges the elements of the same level in the first row and first column in sequence, arranges the results of the pairwise comparisons according to corresponding positions, and only scales the upper triangle of the judgment matrix, thereby reducing the scaling workload. The scale of the lower triangle is the reciprocal of each element of the upper triangle.

[0014] Preferably, the weight allocation unit includes a consistency check module and a condition measurement module. The consistency check module uses arithmetic mean method, geometric mean method and eigenvalue method to calculate the weights, and averages the obtained indicator weight results, which can reduce the deviation caused by a single method. Then, each level is checked for consistency to observe whether the judgment matrix passes the consistency condition. If it does not pass, the judgment matrix is ​​adjusted and the consistency check is performed again. If it passes, a total consistency check is performed to determine the final weight. The condition measurement module judges that the consistency of the matrix is ​​the primary criterion for measuring the quality of the judgment matrix. For all elements in the judgment matrix, if condition a is satisfied, ij =aik *a kj , i,j,k=1,2,…,n, a is an element in the judgment matrix, then the judgment matrix is ​​a completely consistent matrix.

[0015] Preferably, in the comprehensive evaluation unit, a set is established according to the power plant intelligence evaluation index and an evaluation set R={R1, R2, R3, R4} consisting of multiple decisions is established, wherein R1, R2, R3, and R4 are used as the evaluation grade standards of the index points, and a scoring system is used to divide the four grades into specific scores, {R1, R2, R3, and R4} correspond to {v1, v2, v3, and v4} respectively, and v represents the grade of the corresponding evaluation grade;

[0016] The evaluation score formula is:

[0017] C i =W i *V i T

[0018]

[0019] In the formula, the first secondary indicator in the primary indicator meets the evaluation criteria in the evaluation set R, denoted as v i1 , then the result of the first-level index evaluation is V i =(v i1 ,v i2 ,v i3 ,…,v in ), i = 1, 2, 3, the weight set of the secondary indicator layer corresponding to the primary indicator is W i =(w i1 ,w i2 ,w i3 ,…,w in ), C i Indicates the evaluation score of the secondary indicator layer corresponding to the primary indicator, i = 1, 2, 3, represents the weight set calculated by arithmetic mean, geometric mean and eigenvalue methods, δ is the rating score of the power plant’s intelligence level, V T , C T Represent the transposed matrices of V and C respectively.

[0020] A smart power generation evaluation method comprises the following steps:

[0021] S1. Divide the system architecture: The intelligent power generation management and control system of the smart power plant is divided into three layers: the intelligent equipment layer, the intelligent control layer and the intelligent management layer. The intelligent equipment layer includes on-site measurement and control equipment, which monitors the operating status and data of on-site equipment in real time. The intelligent control layer performs online control, optimization and diagnosis of production and auxiliary devices. The intelligent management layer coordinates and controls various production and management subsystems.

[0022] S2. Construct an evaluation index system: Construct a two-level index evaluation index system, taking the intelligent device layer, intelligent control layer and intelligent management layer as the first-level index, and dividing the three first-level indexes into second-level indexes respectively. These second-level indexes serve as indicator points for evaluating the level of intelligence;

[0023] S3, construct judgment matrix: after determining the evaluation index, the intelligence level of the index point is graded, and the level and importance of each element in the index layer are different through the grade division. After the evaluation index system is constructed, the hierarchical structure model is established through the hierarchical analysis method, and the judgment matrix is ​​constructed according to the hierarchical structure model. The AHP 1-9 scaling method is used to give the elements of the judgment matrix;

[0024] S4. Weight allocation: According to the different characteristics of the importance of each element, each indicator is assigned a corresponding weight. The relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1. The stratified weight is calculated, and the weight is calculated using the arithmetic mean method, geometric mean method and eigenvalue method. The obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level;

[0025] S5. Comprehensive evaluation indicators: The evaluation indicator set and evaluation set are established through the obtained weights, and then the intelligence degree of the power plant is digitized. The secondary indicator layer corresponding to the primary indicator is evaluated. After the rating score is obtained, a comprehensive evaluation of the primary indicator of the power plant's intelligence degree is conducted to provide a basis for the intelligence degree evaluation.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention divides the intelligent device layer, intelligent control layer and intelligent management layer into major categories through the hierarchical division unit and the indicator division unit, and finally determines 3 primary indicators and 20 secondary indicators to construct an evaluation indicator system. The process implementation unit divides the intelligence level for each indicator point and constructs a judgment matrix. The weight allocation unit calculates the weight according to the judgment matrix using the arithmetic mean method, the geometric mean method and the eigenvalue method. The three methods are used to reduce the deviation caused by a single method, and the obtained indicator weight results are averaged. The comprehensive evaluation unit is used to establish an evaluation indicator set and an evaluation set, and then the intelligence degree of the power plant is digitized, and the secondary indicator layer corresponding to the primary indicator is evaluated. After obtaining the rating score, a comprehensive evaluation of the primary indicator of the power plant's intelligence is conducted to provide a basis for the evaluation of the intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the overall system flow provided by an embodiment of the present invention;

[0029] Figure 2An internal module block diagram of an indicator division unit provided in an embodiment of the present invention;

[0030] Figure 3 An internal module block diagram of a process implementation unit provided by an embodiment of the present invention;

[0031] Figure 4 A block diagram of the internal modules of a weight distribution unit provided in an embodiment of the present invention;

[0032] In the figure: 1. Hierarchical division unit; 2. Index division unit; 201. One-category division module; 202. Two-category division module; 203. Three-category division module; 3. Process implementation unit; 301. Level division module; 302. Structure division module; 303. Scale establishment module; 304. Scale selection module; 305. Matrix establishment module; 4. Weight allocation unit; 401. Consistency check module; 402. Condition measurement module; 5. Comprehensive evaluation unit. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1-4 , the present invention provides a technical solution: a smart power generation evaluation system, including a hierarchical division unit 1, the hierarchical division unit 1 divides the smart power generation management and control system of the smart power plant into a three-layer structure of a smart device layer, a smart control layer and a smart management layer, the smart device layer includes on-site measurement and control equipment, real-time monitoring of the operating status and data of on-site equipment, the smart control layer performs online control, optimization and diagnosis of production and auxiliary devices, and the smart management layer coordinates and controls various production and management subsystems;

[0035] Indicator division unit 2: Indicator division unit 2 constructs a two-level indicator evaluation indicator system, taking the intelligent device layer, intelligent control layer and intelligent management layer as the first-level indicators, and dividing the three first-level indicators into second-level indicators respectively. These second-level indicators serve as indicator points for evaluating the level of intelligence;

[0036] Process implementation unit 3, after determining the evaluation index, process implementation unit 3 classifies the intelligence level of the index point, and through the classification, each element in the index layer has a different level and importance. After the evaluation index system is constructed in the index classification unit 2, a hierarchical structure model is established through the hierarchical analysis method, and a judgment matrix is ​​constructed according to the hierarchical structure model. The elements of the judgment matrix are given by the AHP 1-9 scaling method;

[0037] The weight allocation unit 4 assigns a corresponding weight to each indicator according to the different characteristics of the importance of each element. The relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1. The hierarchical weight is calculated, and the weight is calculated by arithmetic mean method, geometric mean method and eigenvalue method, and the obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level;

[0038] Comprehensive evaluation unit 5, the comprehensive evaluation unit 5 establishes an evaluation index set and an evaluation set through the obtained weights, and then digitizes the intelligence degree of the power plant, evaluates the secondary indicator layer corresponding to the primary indicator, and after obtaining the rating score, conducts a comprehensive evaluation of the primary indicator of the power plant's intelligence degree, providing a basis for the intelligence degree evaluation.

[0039] The indicator division unit 2 includes a first-class division module 201, a second-class division module 202 and a third-class division module 203. The first-class division module 201 divides the intelligent device layer into 5 second-class indicators, namely, communication protocol and interface, measurement base, detection equipment, on-site maintenance and inspection technology and on-site equipment. The second-class division module 202 divides the intelligent control layer into 9 second-class indicators, namely, unit automatic start-stop technology, control algorithm, object fault diagnosis and self-healing, system simulation technology, communication network clock synchronization technology, unit energy-saving optimization technology, emission control optimization technology, combustion optimization technology and network source coordination control technology. The third-class division module 203 divides the intelligent management layer into 6 second-class indicators, namely, plant-level load optimization, fuel management, information system construction, safety protection system, spare parts storage and business indicator monitoring;

[0040] The process implementation unit 3 includes a level division module 301 and a structure division module 302. After the level division module 301 determines the evaluation index, it divides the intelligence level of the index point into R1, R2, R3 and R4. R1 indicates that the technical intelligence level is very high, R2 indicates that the technical intelligence level is high, R3 indicates that the technical intelligence level is moderate, and R4 indicates that the technical intelligence level is general. Through the level division, each element in the index layer has a different level and importance. After the structure division module 302 constructs the evaluation index system, it establishes a hierarchical model through the hierarchical analysis method, and divides the hierarchical model into the top layer, the middle layer and the bottom layer. Between two adjacent layers, the upper layer is the target layer and the lower layer is the element layer. The relationship between the elements is expressed in a tree form.

[0041] The process implementation unit 3 also includes a scale establishment module 303. The scale establishment module 303 constructs a judgment matrix for the same-layer elements belonging to each element of the upper layer until the lowest layer according to the hierarchical structure model, and obtains the relative importance of the elements by comparing the elements of the same layer with each other. The elements of the judgment matrix are given by the AHP 1-9 scale method. The scale 1 indicates that the two elements are of equal importance, the scale 3 indicates that the former element is slightly more important than the latter element, the scale 5 indicates that the former element is obviously more important than the latter element, the scale 7 indicates that the former element is very important than the latter element, the scale 9 indicates that the former element is extremely important than the latter element, and the scales 2, 4, 6, and 8 indicate the intermediate values ​​of the above judgment values.

[0042] The process implementation unit 3 also includes a scale selection module 304 and a matrix establishment module 305. Before the scale selection module 304 scales the judgment matrix, it first selects the factors with the best and worst value orientations, and the scales of other factors are between the best and worst factors, and selects the middle factor as a benchmark to measure whether the remaining factors are above or below the state. The matrix establishment module 305 arranges the elements of the same level in the first row and first column in sequence, arranges the results of the pairwise comparison according to the corresponding positions, and only scales the upper triangle of the judgment matrix, thereby reducing the workload of scaling. The scale of the lower triangle is the reciprocal of each element of the upper triangle.

[0043] The weight distribution unit 4 includes a consistency check module 401 and a condition measurement module 402. The consistency check module 401 uses arithmetic mean method, geometric mean method and eigenvalue method to calculate the weight, and averages the obtained indicator weight results, which can reduce the deviation caused by a single method. Then, each level is checked for consistency to observe whether the judgment matrix passes the consistency condition. If it does not pass, the judgment matrix is ​​adjusted and the consistency check is performed again. If it passes, a total consistency check is performed to determine the final weight. The condition measurement module 402 judges that the consistency of the matrix is ​​the primary criterion for measuring the quality of the judgment matrix. For all elements in the judgment matrix, if the condition a is satisfied, ij =a ik *a kj , i, j, k = 1, 2, ..., n, a is an element in the judgment matrix, then the judgment matrix is ​​a completely consistent matrix;

[0044] When adjusting the judgment matrix, start with the element with the largest deviation, which can quickly correct the judgment matrix. When adjusting the elements, the minimum value is not less than 1 / 9 and the maximum value is not greater than 9. Calculate the offset distance of each element, filter out the largest element, record the row and column position of the element, and select the closest Replace this element with the number of a is an element in the judgment matrix;

[0045] In the comprehensive evaluation unit 5, a set is established according to the evaluation index of the power plant intelligence degree and an evaluation set R = {R1, R2, R3, R4} composed of multiple decisions is established, where R1, R2, R3, and R4 are used as the evaluation level standards of the indicator points, and a scoring system is used to divide the four levels into specific scores, {R1, R2, R3, R4} correspond to {v1, v2, v3, v4} respectively, and v represents the grade of the corresponding evaluation level;

[0046] The evaluation score formula is:

[0047] C i =W i *V i T

[0048]

[0049] In the formula, the first secondary indicator in the primary indicator meets the evaluation criteria in the evaluation set R, denoted as v i1 , then the result of the first-level index evaluation is V i =(v i1 ,v i2 ,v i3 ,…,v in ), the weight set of the secondary indicator layer corresponding to the primary indicator is W i =(w i1 ,w i2 ,w i3 ,…,w in ), C i Indicates the evaluation score of the secondary indicator layer corresponding to the primary indicator, i = 1, 2, 3, represents the weight set calculated by arithmetic mean, geometric mean and eigenvalue methods, δ is the rating score of the power plant’s intelligence level, V T , C T Represent the transposed matrices of V and C respectively;

[0050] A smart power generation evaluation method comprises the following steps:

[0051] S1. Divide the system architecture: The intelligent power generation management and control system of the smart power plant is divided into three layers: the intelligent equipment layer, the intelligent control layer and the intelligent management layer. The intelligent equipment layer includes on-site measurement and control equipment, which monitors the operating status and data of on-site equipment in real time. The intelligent control layer performs online control, optimization and diagnosis of production and auxiliary devices. The intelligent management layer coordinates and controls various production and management subsystems.

[0052] S2. Construct an evaluation index system: Construct a two-level index evaluation index system, taking the intelligent device layer, intelligent control layer and intelligent management layer as the first-level index, and dividing the three first-level indexes into second-level indexes respectively. These second-level indexes serve as indicator points for evaluating the level of intelligence;

[0053] S3, construct judgment matrix: after determining the evaluation index, the intelligence level of the index point is graded, and the level and importance of each element in the index layer are different through the grade division. After the evaluation index system is constructed, the hierarchical structure model is established through the hierarchical analysis method, and the judgment matrix is ​​constructed according to the hierarchical structure model. The AHP 1-9 scaling method is used to give the elements of the judgment matrix;

[0054] S4. Weight allocation: According to the different characteristics of the importance of each element, each indicator is assigned a corresponding weight. The relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1. The stratified weight is calculated, and the weight is calculated using the arithmetic mean method, geometric mean method and eigenvalue method. The obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level;

[0055] S5. Comprehensive evaluation indicators: The evaluation indicator set and evaluation set are established through the obtained weights, and then the intelligence degree of the power plant is digitized. The secondary indicator layer corresponding to the primary indicator is evaluated. After the rating score is obtained, a comprehensive evaluation of the primary indicator of the power plant's intelligence degree is conducted to provide a basis for the intelligence degree evaluation.

[0056] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0057] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart power generation evaluation system, characterized in that: include: A hierarchical division unit (1), wherein the hierarchical division unit (1) divides the intelligent power generation control system of the smart power plant into a three-layer structure of an intelligent device layer, an intelligent control layer and an intelligent management layer, wherein the intelligent device layer includes on-site measurement and control equipment, and monitors the operating status and data of on-site equipment in real time; the intelligent control layer performs online control, optimization and diagnosis of production and auxiliary devices; and the intelligent management layer coordinates and controls various production and management subsystems; An indicator division unit (2), wherein the indicator division unit (2) constructs a two-level indicator evaluation indicator system, takes the intelligent device layer, the intelligent control layer and the intelligent management layer as the first-level indicators, and divides the three first-level indicators into second-level indicators respectively, and these second-level indicators serve as indicator points for evaluating the level of intelligence; A process implementation unit (3), after determining the evaluation index, the process implementation unit (3) classifies the intelligence level of the index point, so that each element in the index layer has a different level and importance through the classification. After the evaluation index system is constructed by the index classification unit (2), a hierarchical structure model is established through the hierarchical analysis method, a judgment matrix is ​​constructed according to the hierarchical structure model, and the elements of the judgment matrix are given by the AHP 1-9 scaling method; A weight allocation unit (4), wherein the weight allocation unit (4) assigns a corresponding weight to each indicator according to the different characteristics of the importance of each element, wherein the relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1, and the hierarchical weight is calculated, and the weight is calculated by using an arithmetic mean method, a geometric mean method and an eigenvalue method, and the obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level; A comprehensive evaluation unit (5) is provided, wherein the comprehensive evaluation unit (5) establishes an evaluation index set and an evaluation set through the obtained weights, and then digitizes the intelligence degree of the power plant, evaluates the secondary index layer corresponding to the primary index, and after obtaining the rating score, conducts a comprehensive evaluation of the primary index of the power plant intelligence degree, thereby providing a basis for the intelligence degree evaluation.

2. The smart power generation evaluation system according to claim 1, characterized in that: The index division unit (2) comprises a first-class division module (201), a second-class division module (202) and a third-class division module (203). The first-class division module (201) divides the intelligent device layer downward into five second-class indicators, namely communication protocol and interface, measurement base, detection equipment, on-site maintenance and inspection technology and on-site equipment. The second-class division module (202) divides the intelligent control layer downward into nine second-class indicators, namely unit automatic start-stop technology, control algorithm, object fault diagnosis and self-healing, system simulation technology, communication network clock synchronization technology, unit energy-saving optimization technology, emission control optimization technology, combustion optimization technology and network source coordination control technology. The third-class division module (203) divides the intelligent management layer downward into six second-class indicators, namely plant-level load optimization, fuel management, information system construction, safety protection system, spare parts storage and business index monitoring.

3. The smart power generation evaluation system according to claim 1, characterized in that: The process implementation unit (3) comprises a level division module (301) and a structure division module (302). After the level division module (301) determines the evaluation index, it divides the intelligence level of the index point into R1, R2, R3 and R4 levels. R1 level indicates a very high level of technical intelligence, R2 level indicates a high level of technical intelligence, R3 level indicates a moderate level of technical intelligence, and R4 level indicates a general level of technical intelligence. Through the level division, each element in the index layer has a different level and importance. After the structure division module (302) constructs the evaluation index system, it establishes a hierarchical model through the hierarchical analysis method, and divides the hierarchical model into the top layer, the middle layer and the bottom layer. Between two adjacent layers, the upper layer is the target layer and the lower layer is the element layer. The relationship between the elements is expressed in a tree form.

4. The smart power generation evaluation system according to claim 3, characterized in that: The process implementation unit (3) also includes a scale establishment module (303). The scale establishment module (303) constructs a judgment matrix for the same-layer elements belonging to each element of the previous layer until the lowest layer according to the hierarchical structure model, and obtains the relative importance of the elements by comparing the elements of the same layer in pairs. The elements of the judgment matrix are given by the AHP 1-9 scale method. Scale 1 indicates that the two elements are of equal importance, scale 3 indicates that the former element is slightly more important than the latter element, scale 5 indicates that the former element is significantly more important than the latter element, scale 7 indicates that the former element is very important than the latter element, scale 9 indicates that the former element is extremely important than the latter element, and scales 2, 4, 6, and 8 indicate intermediate values ​​of the above judgment values.

5. A smart power generation evaluation system according to claim 4, characterized in that: The process implementation unit (3) also includes a scale selection module (304) and a matrix establishment module (305). Before the scale selection module (304) scales the judgment matrix, it first selects the factors with the best and worst value orientations. The scales of other factors are between the best and worst factors, and the middle factor is selected as a benchmark to measure whether the remaining factors are above or below the state. The matrix establishment module (305) arranges the elements of the same level in the first row and first column in sequence, arranges the results of the pairwise comparison according to the corresponding positions, and only scales the upper triangle of the judgment matrix, thereby reducing the workload of scaling. The scale of the lower triangle is the reciprocal of each element of the upper triangle.

6. The smart power generation evaluation system according to claim 1, characterized in that: The weight allocation unit (4) includes a consistency check module (401) and a condition measurement module (402). The consistency check module (401) calculates the weight using arithmetic mean method, geometric mean method and eigenvalue method, and averages the obtained indicator weight results, which can reduce the deviation caused by a single method. After that, a consistency check is performed on each level to observe whether the judgment matrix passes the consistency condition. If it does not pass, the judgment matrix is ​​adjusted and the consistency check is performed again. If it passes, a total consistency check is performed to determine the final weight. The condition measurement module (402) judges that the consistency of the judgment matrix is ​​the primary criterion for measuring the quality of the judgment matrix. For all elements in the judgment matrix, if the condition a is satisfied, the judgment matrix is ​​equal to the element of the judgment matrix. ij =a ik *a kj , i,j,k=1,2,…,n, a is an element in the judgment matrix, then the judgment matrix is ​​a completely consistent matrix.

7. The smart power generation evaluation system according to claim 1, characterized in that: In the comprehensive evaluation unit (5), a set is established according to the power plant intelligence evaluation index and an evaluation set R = {R1, R2, R3, R4} composed of multiple decisions is established, wherein R1, R2, R3 and R4 are used as the index point evaluation level standards, and a scoring system is used to divide the four levels into specific scores, {R1, R2, R3, R4} correspond to {v1, v2, v3, v4} respectively, and v represents the grade of the corresponding evaluation level.

8. A smart power generation evaluation method, characterized in that: The smart power generation evaluation method comprises the smart power generation evaluation system described in any one of 1 to 7, and comprises the following steps: S1. Divide the system architecture: The intelligent power generation management and control system of the smart power plant is divided into three layers: the intelligent equipment layer, the intelligent control layer and the intelligent management layer. The intelligent equipment layer includes on-site measurement and control equipment, which monitors the operating status and data of on-site equipment in real time. The intelligent control layer performs online control, optimization and diagnosis of production and auxiliary devices. The intelligent management layer coordinates and controls various production and management subsystems. S2. Construct an evaluation index system: Construct a two-level index evaluation index system, taking the intelligent device layer, intelligent control layer and intelligent management layer as the first-level index, and dividing the three first-level indexes into second-level indexes respectively. These second-level indexes serve as indicator points for evaluating the level of intelligence; S3, construct judgment matrix: after determining the evaluation index, the intelligence level of the index point is graded, and the level and importance of each element in the index layer are different through the grade division. After the evaluation index system is constructed, the hierarchical structure model is established through the hierarchical analysis method, and the judgment matrix is ​​constructed according to the hierarchical structure model. The AHP 1-9 scaling method is used to give the elements of the judgment matrix; S4. Weight allocation: According to the different characteristics of the importance of each element, each indicator is assigned a corresponding weight. The relative importance of the indicator is the weight, and the sum of the weight values ​​of each indicator is 1. The stratified weight is calculated, and the weight is calculated using the arithmetic mean method, geometric mean method and eigenvalue method. The obtained indicator weight results are averaged, and then the weight is determined according to the consistency test results of each level; S5. Comprehensive evaluation indicators: The evaluation indicator set and evaluation set are established through the obtained weights, and then the intelligence degree of the power plant is digitized. The secondary indicator layer corresponding to the primary indicator is evaluated. After the rating score is obtained, a comprehensive evaluation of the primary indicator of the power plant's intelligence degree is conducted to provide a basis for the intelligence degree evaluation.

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