CN113159462A - Sea-filling project integration degree prediction method and system - Google Patents
Sea-filling project integration degree prediction method and system Download PDFInfo
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Abstract
The invention discloses a sea-filling project integration degree prediction method and a system, wherein the method comprises the following steps: firstly, determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data; calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index; determining evaluation scores corresponding to the secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator; determining the weight corresponding to each secondary index and the weight corresponding to the primary index; determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index; determining the aggregation degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index; and predicting the sea filling project integration according to the to-be-filled project integration score. The invention realizes the prediction of the intensive degree of the sea-filling projects of different scales by taking the principles of saving the sea, intensive sea, protecting the shoreline and overall planning of land and sea.
Description
Technical Field
The invention relates to the technical field of aggregation prediction, in particular to a method and a system for predicting the aggregation of a sea-filling project.
Background
With the development of marine economy in China, the sea scale of various industries is rapidly expanded, and in order to process the relationship between guarantee development and resource protection, a rough sea using mode must be changed to insist on intensive sea using and scientifically and reasonably allocate sea resources. One of the important contents of intensive sea use is the quota use and quantitative management of sea areas, but the technical means of sea function zoning, sea environment evaluation, sea area use argument, sea reclamation plan, coastal zone protection, utilization planning and the like established in China all focus on solving the problem of whether a certain sea area can be reclaimed, and the theoretical method and the technical standard for considering how large area sea and how long shoreline are occupied by a certain project from the industrial point of view and the standard specification specially determined for the consolidation degree of the sea reclamation project are lacked. In this background, a method for predicting the level of integration of a sea-filling project is urgently needed in scientific research, enterprise production and management.
The existing patents aim at the intensive utilization of land, the ecological environment of sea areas and the development and prediction method of sea areas, but a scheme for directly predicting the intensive use of sea areas of a sea filling project is lacked.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the consolidation degree of a sea-filling project, so as to predict the consolidation degree of the sea-filling projects with different scales.
In order to achieve the above object, the present invention provides a method for predicting the aggregation of a sea-filling project, including:
determining the sea area use type of the project to be predicted; when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data;
determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index;
calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index;
determining evaluation scores corresponding to the secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator;
determining the weight corresponding to each secondary index and the weight corresponding to the primary index;
determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index;
determining the aggregation degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index;
and predicting the sea filling project integration according to the to-be-filled project integration score.
Optionally, the determining the weight corresponding to each secondary indicator and the weight corresponding to the primary indicator specifically includes:
constructing a discrimination matrix between the first-level indexes or a discrimination matrix between the second-level indexes by adopting a 1-9 scale method;
calculating a characteristic vector corresponding to the maximum characteristic value in the discrimination matrix;
calculating an index of consistency based on the maximum feature value;
calculating the ratio of the index of the consistency to the correction coefficient;
judging whether the ratio is smaller than a set value or not; if the ratio is smaller than a set value, the characteristic vector is normalized through consistency check, and the normalized characteristic vector is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio is larger than or equal to the set value, the consistency check is not passed, and the method returns to the step of constructing the discrimination matrix between the first-level indexes or the discrimination matrix between the second-level indexes by adopting a 1-9 scale method.
Optionally, the determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index is performed by a specific formula:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the ith level under the kth level indexWeight corresponding to index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
Optionally, the evaluation score corresponding to each secondary index is determined according to the fuzzy relation matrix by using a fuzzy matrix synthesis operator, and the specific formula is as follows:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index, akijThe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating the likelihood that the ith secondary indicator is rated as the jth rating.
Optionally, the method for determining the aggregation level score of the project to be crammed according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index comprises the following steps:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
The invention also provides a sea-filling project integration degree prediction system, which comprises:
the secondary index numerical value calculation module is used for determining the sea area use type of the project to be predicted; when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data;
the evaluation grade determining module is used for determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index;
the fuzzy relation matrix calculation module is used for calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index;
the first evaluation score determining module is used for determining evaluation scores corresponding to all secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator;
the weight determining module is used for determining the weight corresponding to each secondary index and the weight corresponding to the primary index;
the second evaluation score determining module is used for determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index;
the system comprises a to-be-landfilled project integrity score determining module, a to-be-landfilled project integrity score determining module and a to-be-landfilled project integrity score determining module, wherein the to-be-landfilled project integrity score determining module is used for determining the to-be-landfilled project integrity score according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index;
and the prediction module is used for predicting the sea filling project integration degree according to the to-be-filled project integration degree score.
Optionally, the weight determining module specifically includes:
the judgment matrix construction unit is used for constructing a judgment matrix among the first-level indexes or a judgment matrix among the second-level indexes by adopting a 1-9 scale method;
the characteristic vector calculation unit is used for calculating a characteristic vector corresponding to the maximum characteristic value in the discrimination matrix;
a consistency index calculation unit for calculating a consistency index based on the maximum feature value;
a ratio calculation unit for calculating a ratio of the index of the consistency to a correction coefficient;
the judging unit is used for judging whether the ratio is smaller than a set value or not; if the ratio is smaller than a set value, the characteristic vector is normalized through consistency check, and the normalized characteristic vector is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio is larger than or equal to the set value, the consistency check is not passed, and a judgment matrix construction unit is returned.
Optionally, the determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index is performed by a specific formula:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the weight corresponding to the ith secondary index under the kth primary index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
Optionally, the evaluation score corresponding to each secondary index is determined according to the fuzzy relation matrix by using a fuzzy matrix synthesis operator, and the specific formula is as follows:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index, akijThe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating the likelihood that the ith secondary indicator is rated as the jth rating.
Optionally, the method for determining the aggregation level score of the project to be crammed according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index comprises the following steps:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a sea-filling project integration degree prediction method and a system, wherein the method comprises the following steps: firstly, determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data; calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index; determining evaluation scores corresponding to the secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator; determining the weight corresponding to each secondary index and the weight corresponding to the primary index; determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index; determining the aggregation degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index; and predicting the sea filling project integration according to the to-be-filled project integration score. The invention realizes the prediction of the intensive degree of the sea-filling projects of different scales by taking the principles of saving the sea, intensive sea, protecting the shoreline and overall planning of land and sea.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flowchart of a method for forecasting the aggregation of a sea-filling project according to an embodiment of the present invention;
fig. 2 is a structural diagram of a sea-filling project aggregation prediction system according to embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for predicting the consolidation degree of a sea-filling project, so as to predict the consolidation degree of the sea-filling projects with different scales.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1
As shown in fig. 1, the present invention discloses a method for predicting the aggregation of a sea-filling project, which is characterized in that the method comprises:
step S1: determining the sea area use type of the project to be predicted; and when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data.
Step S2: and determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index.
Step S3: and calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index.
Step S4: and determining the evaluation score corresponding to each secondary index according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator.
Step S5: and determining the weight corresponding to each secondary index and the weight corresponding to the primary index.
Step S6: and determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index.
Step S7: and determining the intensive degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index.
Step S8: and predicting the sea filling project integration according to the to-be-filled project integration score.
The individual steps are discussed in detail below:
step S1: determining the sea area use type of the project to be predicted; when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data; the conventional sea area use types include fishery sea, industrial sea, transportation sea, travel and recreation sea and land making engineering sea. If the sea area usage type of the project to be predicted is novel and cannot be directly matched with the classification result in the table 2, subsequent prediction can be performed according to the relatively close sea area usage type according to the actual construction process and sea usage mode of the project to be predicted.
The invention obtains the data of the sea-filling project based on the sea-using conditions of different types of sea-filling projects; the sea-filling project data comprises a plurality of primary indexes and a plurality of secondary indexes corresponding to the primary indexes; the plurality of first-level indexes comprise input indexes, efficiency indexes and layout structures; the secondary indicators include: investment intensity, sea area utilization rate, shoreline utilization rate, volume rate, proportion of administrative office and living service facilities, proportion of marine ecological area, proportion of land-to-land enclosed sea and development yielding distance; wherein the input index comprises investment intensity; the efficiency index comprises sea area utilization rate and shoreline utilization rate; the layout structure comprises a volume ratio, the proportion of administrative office and living service facilities, the proportion of marine ecological area, the proportion of land-to-sea reclamation and development and concession distance.
The concept of sea level of integration is abstract, and the invention adopts 8 indexes with physical significance to decompose the process of predicting the sea level project integration level, thereby facilitating direct measurement, calculation and evaluation. The data of the specific sea-filling project is detailed in table 1.
Table 1 sea filling project data sheet
1. Sea area utilization rate
The sea area utilization rate is the proportion of the effective utilization area in the sea filling range of the project to the sea filling area of the project.
Calculating the formula: sea area utilization rate is effective utilization area ÷ sea reclamation area × 100%.
The effective utilization area is equal to the sum of the sea surface volumes for various buildings, structures for production and direct service for production, open-air facilities, storage yards, operation yards, and the like. Road squares, greenbelts, reserved areas, landscape facilities, entertainment facilities, etc. are not taken into consideration for effective utilization of the area.
2. Utilization ratio of shoreline
The shoreline utilization rate is the ratio of the length of a new shoreline formed by sea filling to the length of an occupied original shoreline (including a natural shoreline and an artificial shoreline).
Calculating the formula: the shore line utilization rate is the new coastline length divided by the original coastline length.
3. Area ratio of marine ecological space
The ratio of the marine ecological space area is the ratio of the sum of the marine ecological space area in the sea filling range of the project to the sea filling area.
Calculating the formula: the area ratio of the marine ecological space is divided by the total area of the marine ecological space divided by the sea filling area multiplied by 100 percent.
The area of the marine ecological space comprises the sum of the areas of the constructed wetland, the water system, the greenbelt and the like in the project sea filling range. The greenbelts include public greenbelts, protection greenbelts, greenbelts around buildings (structures), and the like.
4. Intensity of investment
The investment intensity refers to the fixed asset investment amount per unit area in the project sea filling range. The unit is ten thousand yuan/hectare.
Calculating the formula: the investment intensity is the total investment of the fixed assets of the project and the total sea area of the project.
The total investment of the project fixed assets comprises the utilization rate of the sea area, the sea reclamation cost (the expenses of engineering investigation design, demonstration and environmental assessment and other assessment, sea reclamation, sea check compensation and the like), the land yield, the capital construction cost, the facility and equipment cost and the like.
For a construction project sea using sea and land or a project sea for supporting projects, the investment intensity is calculated according to the whole project.
5. Volume fraction
The volume ratio refers to the ratio of the total building area to the reclamation area in the sea filling range of the project.
Calculating the formula: volume ratio is total building area divided by sea reclamation area.
When the height of a building layer exceeds 8 meters, the building area of the layer is calculated by doubling when the volume ratio is calculated.
6. Proportion of occupied area of administrative office and living service facilities
The proportion of the occupied area of the administrative office and living service facilities is the proportion of the sea area (or the allocated sea area) of the administrative office and living service facilities to the sea-filling area within the sea-filling range of the project.
Calculating the formula: the proportion of the occupied area of the administrative office and the living service facilities is divided into the area of the sea area occupied by the administrative office and the living service facilities and the area of the land reclamation by sea filling multiplied by 100 percent.
When the area of the administrative office and the living service facilities occupying the sea area cannot be independently calculated, the calculated proportion of the building area of the administrative office and the living service facilities occupying the total building area can be adopted to replace the allocated sea area.
7. Development of back-off distance
The development retreat distance is a retreat distance of a building on the sea side for a construction project from a newly formed coastline, and is equal to a width from an outer edge line of a vertical projection of the building on the sea side to a top line of a sea-filling slope.
The distance between the top line of the sea filling slope and the underwater outer edge line is not counted as the development yielding distance. Except for buildings necessary for public safety and services or projects that must be sea bound.
8. Sea-to-land ratio of enclosed filling
The land-to-sea ratio refers to the ratio of sea filling area to sea area in the sea area of the project. The area of the sea reclamation land utilization mode accounts for the proportion of the total area of all sea utilization modes (sea reclamation land, structures, sea reclamation and open type) of the sea.
Calculating the formula: the land-filling ratio of the enclosing sea is the total sea filling area of the project divided by the total sea area of the project.
Step S2: and determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index.
If V is { V ═ V1,V2,...V5And the comment grade set is composed of various total evaluation results which can be made by the evaluator on the evaluated object. Wherein, VjRepresenting the jth evaluation level, and each comment has a corresponding score of ai. And scoring 8 index calculation results of the evaluated sea-filling projects, comparing the numerical values of all secondary indexes in the sea-filling project data with the control index values in the tables 2 and 3, and making an intensive evaluation of each index by combining the actual situation of the sea-filling projects. The evaluation result is divided into 5 grades, wherein the grades are respectively { the degree of integration is extremely high, the degree of integration is higher, the degree of integration is general, the degree of integration is lower and the degree of integration is extremely low }, and the corresponding score is { a }1:100,a2:80,a3:60,a4:40,a5:20}. Taking the sea area utilization rate index of a certain fishery project as an example: through the calculation of the step S1, the sea area utilization rate of the project is 80%, the sea area utilization rate control index value of the fishery project with the lookup table is 65%, and the participating appraisers need to select descriptions which meet the sea area utilization rate index of the project from the comment level set { the degree of integration is extremely high, the degree of integration is general, the degree of integration is low, and the degree of integration is extremely low }.
TABLE 2 Main control index value of sea area for construction project
TABLE 3 sea investment intensity control index values for construction projects
Note (Unit: Wanyuan/hectare)
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Step S3: calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index, wherein the specific formula is as follows:
wherein R represents a fuzzy relation matrix, RijThis indicates the likelihood that the ith secondary indicator is rated as the jth evaluation level, i.e., the ith secondary indicator is rated as the membership of the jth evaluation level, i 1, 2.
Calculating rijThe specific mode is as follows:
wherein W represents the total number of the evaluated persons, QijIndicating the number of persons who selected the ith secondary index as the jth evaluation level.
Step S4: and determining the evaluation scores corresponding to the secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator, wherein the specific formula is as follows:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index,akijthe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating the likelihood that the ith secondary indicator is rated as the jth rating.
In the above example, if 10 evaluators are counted, 4 evaluators consider the degree of aggregation to be extremely high, 4 evaluators consider the degree of aggregation to be high, and 2 evaluators consider the degree of aggregation to be general, the fuzzy relation of the sea area usage is [0.4,0.4,0.2,0,0 ]. The sea area utilization is calculated by the synthesis operator to be 84.
Step S5: determining the weight corresponding to each secondary index and the weight corresponding to the primary index, specifically comprising:
P:U＝{P1,P2,P3efficiency index, investment index, layout structure. Efficiency index A in secondary index1:{P11Sea area utilization ratio, P12Shore line utilization, the input index A2:{P21Investment intensity, layout and structure
In the index system of the present invention, the efficiency index A1Two secondary indexes are included, and the importance of the two indexes is compared to ensure that the two indexes are assigned with the weight sum of 1, namely (p)11+p121). Input index A2Corresponding to a second-level index investment intensity without distributing weight (p)211). Therefore, the first-order indexes P and A need to be constructed3Two discrimination matrices corresponding to the secondary indices.
Step S51: a1-9 scaling method is adopted to construct a discrimination matrix C between first-level indexes or a discrimination matrix C between second-level indexes, and the specific formula is as follows:
wherein, cqpThe relative importance value between the secondary index q and the secondary index p is 1/cqpOr c isqpThe relative importance value between the primary index q and the primary index p is 1/cpqAnd 1 is the same importance of the two indexes, 9 represents that a secondary index q is extremely important compared with a secondary index p, H represents the total number of each secondary index in a primary index or the total number in the primary index, p is more than or equal to 1 and less than or equal to H, q is more than or equal to 1 and less than or equal to H, and p and q are positive integers.
Step S52: calculating the maximum eigenvalue lambda of the discrimination matrix CmaxThe corresponding feature vector omega.
Step S53: based on the maximum eigenvalue lambdamaxAnd calculating the index CI of consistency, wherein the specific formula is as follows:
wherein λ ismaxRepresents the maximum eigenvalue, CI represents an index of consistency, and n represents the matrix order.
Step S54: and calculating the ratio CR of the index CI of consistency and the correction coefficient RI, wherein the specific formula is as follows:
the correction coefficient RI is looked up by table 4.
TABLE 4 correction coefficient RI table corresponding to matrix orders n when different
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.52 |
Step S55: judging whether the ratio CR is smaller than a set value or not; if the ratio CR is smaller than a set value, the characteristic vector omega is normalized through consistency check, and the normalized characteristic vector omega is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio CR is greater than or equal to the set value, it indicates that the consistency check is not passed, and the process returns to step S51 to reconstruct the decision matrix C.
The evaluation index weight distribution obtained by the above method is shown in table 5.
TABLE 5 intensive evaluation index weight distribution table for sea-filling projects
Step S6: determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index, wherein the specific formula is as follows:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the weight corresponding to the ith secondary index under the kth primary index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
Step S7: determining the intensive degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index, wherein the specific formula is as follows:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
Step S8: and predicting the sea filling project integration according to the to-be-filled project integration score.
Example 2
As shown in fig. 2, the present invention further provides a system for forecasting the aggregation of a sea-filling project, the system comprising:
a secondary index numerical value calculation module 201, configured to determine a sea area usage type of the sea filling project to be predicted; and when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data.
And the evaluation grade determining module 202 is configured to determine an evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index.
And the fuzzy relation matrix calculating module 203 is configured to calculate a fuzzy relation matrix based on the evaluation level corresponding to each secondary index.
And the first evaluation score determining module 204 is configured to determine, by using a fuzzy matrix synthesis operator, an evaluation score corresponding to each secondary index according to the fuzzy relation matrix.
The weight determining module 205 is configured to determine a weight corresponding to each secondary indicator and a weight corresponding to each primary indicator.
And a second evaluation score determining module 206, configured to determine the evaluation score of the corresponding primary indicator according to the weight corresponding to each secondary indicator and the evaluation score corresponding to each secondary indicator.
And the to-be-landfilled project integrity score determining module 207 is configured to determine an integrity score of the to-be-landfilled project according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index.
And the prediction module 208 is used for predicting the sea filling item integration according to the sea filling item integration score.
Optionally, the weight determining module 205 specifically includes:
and the discrimination matrix construction unit is used for constructing the discrimination matrix between the first-level indexes or the discrimination matrix between the second-level indexes by adopting a 1-9 scale method.
And the eigenvector calculation unit is used for calculating the eigenvector corresponding to the maximum eigenvalue in the discrimination matrix.
And the consistent index calculating unit is used for calculating the consistent index based on the maximum characteristic value.
And the ratio calculation unit is used for calculating the ratio of the index of the consistency and the correction coefficient.
The judging unit is used for judging whether the ratio is smaller than a set value or not; if the ratio is smaller than a set value, the characteristic vector is normalized through consistency check, and the normalized characteristic vector is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio is larger than or equal to the set value, the consistency check is not passed, and a judgment matrix construction unit is returned.
As an optional implementation manner, in the present invention, the evaluation score of the corresponding primary indicator is determined according to the weight corresponding to each secondary indicator and the evaluation score corresponding to each secondary indicator, and a specific formula is as follows:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the weight corresponding to the ith secondary index under the kth primary index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
As an optional implementation manner, the evaluation scores corresponding to the secondary indexes are determined according to the fuzzy relation matrix by using a fuzzy matrix synthesis operator, and the specific formula is as follows:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index, akijThe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating that the ith secondary indicator was rated as the jth ratingThe likelihood of a rank.
As an optional implementation manner, in the present invention, the aggregation score of the project to be crammed is determined according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index, and a specific formula is as follows:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for forecasting the aggregation of sea-filling projects, the method comprising:
determining the sea area use type of the project to be predicted; when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data;
determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index;
calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index;
determining evaluation scores corresponding to the secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator;
determining the weight corresponding to each secondary index and the weight corresponding to the primary index;
determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index;
determining the aggregation degree score of the project to be filled in the sea according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index;
and predicting the sea filling project integration according to the to-be-filled project integration score.
2. The method according to claim 1, wherein the determining the weight corresponding to each secondary indicator and the weight corresponding to each primary indicator specifically comprises:
constructing a discrimination matrix between the first-level indexes or a discrimination matrix between the second-level indexes by adopting a 1-9 scale method;
calculating a characteristic vector corresponding to the maximum characteristic value in the discrimination matrix;
calculating an index of consistency based on the maximum feature value;
calculating the ratio of the index of the consistency to the correction coefficient;
judging whether the ratio is smaller than a set value or not; if the ratio is smaller than a set value, the characteristic vector is normalized through consistency check, and the normalized characteristic vector is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio is larger than or equal to the set value, the consistency check is not passed, and the method returns to the step of constructing the discrimination matrix between the first-level indexes or the discrimination matrix between the second-level indexes by adopting a 1-9 scale method.
3. The method for predicting the integrity of a sea-filling project according to claim 1, wherein the evaluation score of the corresponding primary index is determined according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index, and the specific formula is as follows:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the weight corresponding to the ith secondary index under the kth primary index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
4. The method for predicting the integrity of a sea-filling project according to claim 1, wherein the evaluation scores corresponding to the secondary indexes are determined according to the fuzzy relation matrix by using a fuzzy matrix synthesis operator, and the specific formula is as follows:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index, akijThe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating the likelihood that the ith secondary indicator is rated as the jth rating.
5. The method for predicting the integrity of a sea-filling project according to claim 1, wherein the integrity score of the sea-filling project is determined according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index, and the specific formula is as follows:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
6. A system for forecasting the aggregation of sea-filling projects, the system comprising:
the secondary index numerical value calculation module is used for determining the sea area use type of the project to be predicted; when the sea area use type of the sea filling project to be predicted belongs to the conventional sea area use type, calculating the numerical value of each secondary index in the sea filling project data;
the evaluation grade determining module is used for determining the evaluation grade corresponding to each secondary index according to the numerical value of each secondary index in the sea-filling project data and the control index value corresponding to each secondary index;
the fuzzy relation matrix calculation module is used for calculating a fuzzy relation matrix based on the evaluation level corresponding to each secondary index;
the first evaluation score determining module is used for determining evaluation scores corresponding to all secondary indexes according to the fuzzy relation matrix by adopting a fuzzy matrix synthesis operator;
the weight determining module is used for determining the weight corresponding to each secondary index and the weight corresponding to the primary index;
the second evaluation score determining module is used for determining the evaluation score of the corresponding primary index according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index;
the system comprises a to-be-landfilled project integrity score determining module, a to-be-landfilled project integrity score determining module and a to-be-landfilled project integrity score determining module, wherein the to-be-landfilled project integrity score determining module is used for determining the to-be-landfilled project integrity score according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index;
and the prediction module is used for predicting the sea filling project integration degree according to the to-be-filled project integration degree score.
7. The system of claim 6, wherein the weight determination module comprises:
the judgment matrix construction unit is used for constructing a judgment matrix among the first-level indexes or a judgment matrix among the second-level indexes by adopting a 1-9 scale method;
the characteristic vector calculation unit is used for calculating a characteristic vector corresponding to the maximum characteristic value in the discrimination matrix;
a consistency index calculation unit for calculating a consistency index based on the maximum feature value;
a ratio calculation unit for calculating a ratio of the index of the consistency to a correction coefficient;
the judging unit is used for judging whether the ratio is smaller than a set value or not; if the ratio is smaller than a set value, the characteristic vector is normalized through consistency check, and the normalized characteristic vector is used as the weight corresponding to each secondary index or the weight corresponding to each primary index; if the ratio is larger than or equal to the set value, the consistency check is not passed, and a judgment matrix construction unit is returned.
8. The system of claim 6, wherein the evaluation score of the corresponding primary index is determined according to the weight corresponding to each secondary index and the evaluation score corresponding to each secondary index, and the specific formula is as follows:
wherein, bkiRepresents the evaluation score, p, corresponding to the ith secondary index under the kth primary indexkiRepresents the weight corresponding to the ith secondary index under the kth primary index, BkAnd (4) expressing the evaluation score corresponding to the kth primary index, and expressing the total number of the secondary indexes under each primary index.
9. The system of claim 6, wherein the fuzzy matrix synthesis operator is used to determine the evaluation score corresponding to each secondary index according to the fuzzy relation matrix, and the specific formula is:
wherein the content of the first and second substances,
representing fuzzy matrix composition operators, bkiRepresents the evaluation score corresponding to the ith secondary index under the kth primary index, akijThe evaluation score corresponding to the j evaluation grade of the ith secondary index under the kth primary index is shown, m represents the total number of the evaluation grades, rijIndicating the likelihood that the ith secondary indicator is rated as the jth rating.
10. The system of claim 6, wherein the aggregation degree score of the project to be crammed is determined according to the weight corresponding to each primary index and the evaluation score corresponding to each primary index, and the specific formula is as follows:
wherein B represents the intensive score of the project to be filled in the sea, pkRepresents the weight corresponding to the kth primary index, BkAnd expressing the evaluation score corresponding to the kth primary index.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114091618A (en) * | 2021-11-30 | 2022-02-25 | 重庆允成互联网科技有限公司 | Industrial equipment health state diagnosis management method and device and server |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491877A (en) * | 2017-08-18 | 2017-12-19 | 国网上海市电力公司 | A kind of power network construction project Budget Performance method based on fuzzy overall evaluation |
| CN112101700A (en) * | 2020-07-24 | 2020-12-18 | 国网上海市电力公司 | Evaluation method of multi-station fusion site selection index system |
| AU2020103059A4 (en) * | 2020-10-28 | 2020-12-24 | Sichuan Agricultural University | An Evaluation Method for the Economic Feasibility of Renewable Energy-saving Technology |
-
2021
- 2021-05-25 CN CN202110569309.2A patent/CN113159462A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491877A (en) * | 2017-08-18 | 2017-12-19 | 国网上海市电力公司 | A kind of power network construction project Budget Performance method based on fuzzy overall evaluation |
| CN112101700A (en) * | 2020-07-24 | 2020-12-18 | 国网上海市电力公司 | Evaluation method of multi-station fusion site selection index system |
| AU2020103059A4 (en) * | 2020-10-28 | 2020-12-24 | Sichuan Agricultural University | An Evaluation Method for the Economic Feasibility of Renewable Energy-saving Technology |
Non-Patent Citations (2)
| Title |
|---|
| 王晗;徐伟;岳奇;: "我国主要海洋产业填海项目海域集约利用评价研究" * |
| 罗先香;朱永贵;张龙军;杨建强;: "集约用海对海洋生态环境影响的评价方法" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114091618A (en) * | 2021-11-30 | 2022-02-25 | 重庆允成互联网科技有限公司 | Industrial equipment health state diagnosis management method and device and server |
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