CN113837547A - Feasibility analysis method for reconstructing and establishing new range based on old village - Google Patents
Feasibility analysis method for reconstructing and establishing new range based on old village Download PDFInfo
- Publication number
- CN113837547A CN113837547A CN202110986128.XA CN202110986128A CN113837547A CN 113837547 A CN113837547 A CN 113837547A CN 202110986128 A CN202110986128 A CN 202110986128A CN 113837547 A CN113837547 A CN 113837547A
- Authority
- CN
- China
- Prior art keywords
- score
- old
- feasibility
- land
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000010606 normalization Methods 0.000 claims abstract description 24
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000008569 process Effects 0.000 claims abstract description 11
- 238000010276 construction Methods 0.000 claims description 40
- 238000011161 development Methods 0.000 claims description 31
- 238000012549 training Methods 0.000 claims description 22
- 230000008859 change Effects 0.000 claims description 17
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 230000004927 fusion Effects 0.000 claims description 12
- 230000009466 transformation Effects 0.000 claims description 11
- 230000010354 integration Effects 0.000 claims description 8
- 230000002093 peripheral effect Effects 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000005065 mining Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000000306 recurrent effect Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000013461 design Methods 0.000 abstract description 2
- 238000012986 modification Methods 0.000 description 8
- 230000004048 modification Effects 0.000 description 8
- 238000004445 quantitative analysis Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000005211 surface analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The invention relates to the technical field of rural planning and design, and discloses a feasibility analysis method for reconstructing and building a new range based on an old village, which comprises the following steps: s1, receiving, collecting and reading related multi-source data; s2, setting a feasibility analysis index system of the old village reconstruction new range, wherein the feasibility analysis index system comprises 4 target layers, 6 standard layers and 12 index layers; s3, carrying out data normalization standardization processing on each index layer to obtain a normalization standardization value of each index layer; s4, respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process; and S5, obtaining a feasibility result of the new range built in the old village according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer, so as to judge whether the new range built in the old village is feasible. The invention provides a feasibility analysis method for reconstructing a new range based on old villages, so that the feasibility analysis result has the advantage of accuracy.
Description
Technical Field
The invention relates to the technical field of rural planning and design, in particular to a feasibility analysis method for reconstructing and constructing a new range based on an old village.
Background
The old village reconstruction is a comprehensive deployment for researching future development of the village, reasonably distributing the village and arranging various project constructions of the village, is a blueprint for the development of the village in a certain period, is an important component part for village management and is a basis for the village construction and management, so that the old village reconstruction has certain requirements at present. At present, a feasibility analysis method based on the old village reconstruction new range is carried out through qualitative analysis, only the current data is described and subjected to surface analysis, and whether the old village reconstruction new range is feasible or not can not be accurately judged, so that a feasibility analysis result is influenced.
Disclosure of Invention
The purpose of the invention is: the invention provides a feasibility analysis method for reconstructing a new range based on old villages, so that the feasibility analysis result has the advantage of accuracy.
In order to achieve the above object, the present invention provides a feasibility analysis method for reconstructing a new range based on old villages, which comprises the following steps:
s1, preprocessing the obtained multi-source data by receiving, collecting and reading related multi-source data, and obtaining parameters required by analysis indexes after screening, repairing and mining;
s2, setting a feasibility analysis index system of a new range of old village reconstruction, wherein the feasibility analysis index system of the new range of old village reconstruction takes regional development feasibility, district construction feasibility, population gathering feasibility and land intensive conservation and utilization feasibility as target layers, the regional development feasibility is divided into two standard layers of an urban core position and a traffic convenience level according to the property of the regional development feasibility, the urban core position is divided into an index layer of the distance between the center of an administrative village and the center of the administrative center according to the property of the urban core position, and the traffic convenience level is divided into an index layer of the number of peripheral traffic trunk roads according to the property of the traffic convenience level;
the district construction feasibility is divided into a standard layer of land urbanization level according to the property of the district construction feasibility, and the land urbanization level is divided into two index layers of current construction land occupation ratio and planning construction land occupation ratio according to the property of the land urbanization level;
the population gathering feasibility is divided into a criterion layer of population number conditions according to the properties of the population gathering feasibility, and the population number conditions are divided into an index layer of population density change indexes according to the properties of the population number conditions;
the land intensive conservation and utilization feasibility is divided into two criterion layers of land utilization strength and land integration difficulty degree according to the property of the land utilization feasibility, the land utilization strength is divided into four index layers of current volume fraction, reconstructed house net volume fraction, reconstructed building density and re-fusion ratio according to the property of the land utilization strength, and the land integration difficulty degree is divided into three index layers of three old pattern spot continuous sheet indexes, three old pattern spot shape rule indexes and potential development land indexes according to the property of the land utilization strength;
s3, carrying out data normalization standardization processing on each index layer to obtain a normalization standardization value of each index layer;
s4, respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process;
and S5, obtaining a feasibility result of the old village reconstruction new range according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer, so as to judge whether the old village reconstruction new range is feasible.
In some embodiments of the present application, inIn the above-mentioned S3, the formula is used
Carrying out data normalization standardization processing on each index layer to obtain a normalization standardized value of each index layer;
wherein x is*And normalizing the normalized value of each index layer, wherein x is the value of each index layer, and max is the maximum value of each index layer.
In some embodiments of the present application, in S2, the step of obtaining the score of the distance between the center of the administrative village and the center of the administrative center is:
s21, acquiring the distance between the central point of the administrative village and the central point of the administrative center;
s22, performing score analysis on the distance between the central point of the administrative village and the central point of the administrative center by using the trained first score model to obtain a score of the distance between the central point of the administrative village and the central point of the administrative center;
the method comprises the following steps of:
s23, acquiring the number of the traffic trunk roads within a preset distance range from the central point of the administrative village;
s24, analyzing the number of the traffic trunk roads by using a second score model obtained through training, and obtaining the weight of the number of the traffic trunk roads;
the method comprises the following steps of:
s25, acquiring the current construction land area of the administrative village;
s26, analyzing the area of the current construction land by using a trained third score model to obtain the score of the current construction land;
the method comprises the following steps of:
s27, acquiring the area for planning and constructing the administrative village;
s28, analyzing the area of the planned construction land by using a fourth score model obtained through training, and obtaining the score of the planned construction land;
the method for acquiring the score of the population density change comprises the following steps:
s29, acquiring the predicted population density change of the modified administrative village;
s210, analyzing the population density change by using a fifth value model obtained through training to obtain a value of the population density change;
the method for acquiring the value of the current volume ratio comprises the following steps:
s211, acquiring the current volume rate of the administrative village;
s212, analyzing the current volume rate by using a trained sixth score model to obtain the weight of the current volume rate;
the weight obtaining step of the net volume ratio of the modified house comprises the following steps:
s213, acquiring the net volume rate of the modified house;
s214, analyzing the net volume rate of the modified house by using a trained seventh score model to obtain a score of the net volume rate of the modified house;
the method for obtaining the score of the density of the modified building comprises the following steps:
s215, obtaining the modified building density;
s216, analyzing the modified building density by using an eighth score model obtained through training to obtain a score of the modified building density;
the method comprises the following steps of:
s217, obtaining the complex fusion ratio;
s218, analyzing the complex fusion ratio by using a ninth score model obtained through training to obtain a score of the complex fusion ratio;
the method comprises the following steps of obtaining the score of the three-old-pattern-spot connected-sheet index:
s219, obtaining the connectivity of the three old image spots;
s220, analyzing the three old image spot continuity by using a tenth value model obtained through training to obtain the scores of the three old image spot continuity;
the method comprises the following steps of obtaining scores of three old pattern spot shape rule indexes:
s221, obtaining the shape rule of the three old patterns;
s222, analyzing the three old pattern spot shape rules by using an eleventh score model obtained through training to obtain scores of the three old pattern spot shape rules;
the potential development land index score obtaining step comprises the following steps:
s223, acquiring the potential development land index;
and S224, analyzing the potential development land index by using the twelfth score model obtained through training to obtain the score of the potential development land index.
In some embodiments of the present application, the first score model, the second score model, the third score model, the fourth score model, the fifth score model, the sixth score model, the seventh score model, the eighth score model, the ninth score model, the tenth score model, the eleventh score model, and the twelfth score model are established through one of a machine learning algorithm, a convolutional neural network algorithm, a recurrent neural network algorithm, a decision tree, and a deep learning algorithm.
In some embodiments of the present application, in S4, the method specifically includes the following steps:
s41, constructing a judgment matrix A for pairwise comparison of importance degrees of the factors according to the factors of the target layer, the criterion layer and the index layer:
s42 using formula a ω ═ λmaxOmega calculates the maximum characteristic root lambda of the judgment matrix AmaxCarrying out normalization processing on the characteristic vector omega to obtain importance ranking of each factor;
s43, using formula
And CI ═ λMAX-n)/(n-1), judging the consistency index of the judgment matrix a, if CR is less than 0.1, the judgment matrix a is true, and proceeding to S24; if CR is more than or equal to 0.1, the judgment matrix A is not established, and the process is ended;
s44, the feature vector omega after normalization processing is the weight vector of the target layer, the criterion layer and the index layer.
The embodiment of the invention provides a feasibility analysis method for reconstructing and establishing a new range based on old villages, which has the following beneficial effects compared with the prior art:
according to the feasibility analysis method based on the old village reconstruction new range, disclosed by the embodiment of the invention, a feasibility analysis index system of the old village reconstruction new range is set, the feasibility analysis index system comprises 4 target layers, 6 standard layers and 12 indication layers, and then data normalization standardization processing is carried out on each index layer to obtain a normalization standardized value of each index layer; respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process; finally, obtaining a feasibility result of the old village reconstruction new range according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer so as to judge whether the old village reconstruction new range is feasible or not; therefore, whether the old village reconstruction new range is feasible or not is analyzed through quantitative analysis, the accuracy of the feasibility judgment result of the old village reconstruction new range is greatly improved, and the feasibility analysis result has the advantage of accuracy.
Drawings
Fig. 1 is a schematic flow chart of a feasibility analysis method for building a new range based on old village transformation according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a feasibility analysis index system of an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "left", "right", "top", "bottom", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
As shown in fig. 1 and 2, the present invention provides a feasibility analysis method for building a new range based on old village transformation, which comprises the following steps:
s1, preprocessing the obtained multi-source data by receiving, collecting and reading related multi-source data, and obtaining parameters required by analysis indexes after screening, repairing and mining;
s2, setting a feasibility analysis index system of the old village reconstruction new range, wherein the feasibility analysis index system of the old village reconstruction new range takes region development feasibility, district construction feasibility, population gathering feasibility and land intensive saving utilization feasibility as target layers, the region development feasibility is divided into two standard layers of an urban core position and a traffic convenience level according to the property of the feasibility, the urban core position is divided into an index layer of the distance between the center of an administrative village and the center of the administrative center according to the property of the urban core position, and the traffic convenience level is divided into an index layer of the number of peripheral traffic trunk roads according to the property of the urban core position;
the plot construction feasibility is divided into a standard layer of land urbanization level according to the property of the plot construction feasibility, and the land urbanization level is divided into two index layers of current construction land occupation ratio and planning construction land occupation ratio according to the property of the plot construction feasibility;
the population gathering feasibility is divided into a criterion layer of population number conditions according to the properties of the population gathering feasibility, and the population number conditions are divided into an index layer of population density change indexes according to the properties of the population number conditions;
the land intensive conservation and utilization feasibility is divided into two criterion layers of land utilization strength and land integration difficulty degree according to the property of the land intensive conservation and utilization feasibility, the land utilization strength is divided into four index layers of current volume ratio, reconstructed house net volume ratio, reconstructed building density and re-fusion ratio according to the property of the land utilization strength, and the land integration difficulty degree is divided into three index layers of three old pattern patch indexes, three old pattern patch shape rule indexes and potential development land indexes according to the property of the land integration difficulty degree;
s3, carrying out data normalization standardization processing on each index layer to obtain a normalization standardization value of each index layer;
s4, respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process;
and S5, obtaining a feasibility result of the new range built in the old village according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer, so as to judge whether the new range built in the old village is feasible.
Based on the above arrangement, the feasibility analysis method based on the old village reconstruction new range according to the embodiment of the invention sets the feasibility analysis index system of the old village reconstruction new range, wherein the feasibility analysis index system comprises 4 target layers, 6 standard layers and 12 indication layers, and then performs data normalization standardization processing on each index layer to obtain a normalization value of each index layer; respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process; finally, obtaining a feasibility result of the old village reconstruction new range according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer so as to judge whether the old village reconstruction new range is feasible or not; therefore, whether the old village reconstruction new range is feasible or not is analyzed through quantitative analysis, the accuracy of the feasibility judgment result of the old village reconstruction new range is greatly improved, and the feasibility analysis result has the advantage of accuracy.
In some embodiments, as shown in fig. 1 and 2, in order to obtain the value of each index layer, in S2, the score from the administrative center is obtained by:
s21, acquiring the distance between the central point of the administrative village and the central point of the administrative center;
s22, performing score analysis on the distance between the central point of the administrative village and the central point of the administrative center by using the trained first score model to obtain the score of the distance between the central point of the administrative village and the central point of the administrative center; wherein, the shorter the distance between the central point of the administrative village and the central point of the administrative center, the higher the score value is;
the method for acquiring the score of the number of the peripheral traffic main roads comprises the following steps:
s23, acquiring the number of the traffic main roads within a preset distance range from the central point of the administrative village;
s24, analyzing the number of the traffic main roads by using the trained second score model, and acquiring the weight of the number of the traffic main roads; the more the number of the traffic main roads is, the higher the traffic convenience degree is, the higher the transformation feasibility is, and therefore, the score is higher;
the method for acquiring the score of the current construction land comprises the following steps:
s25, acquiring the current construction land area of the administrative village;
s26, analyzing the area of the current construction land by using the trained third score model to obtain the score of the current construction land; the higher the current construction land area is, the higher the urbanization level is, the higher the transformation feasibility is, and therefore, the score is higher;
the method for obtaining the score of the planned construction land comprises the following steps:
s27, acquiring the area for planning and constructing the administrative village;
s28, analyzing the area of the planned construction land by using a fourth score model obtained through training to obtain a score of the planned construction land; the higher the planned construction land area is, the higher the future urbanization level is, the higher the transformation feasibility of the land area is, and therefore, the score of the land area is higher;
the method for acquiring the score of the population density change comprises the following steps:
s29, acquiring the predicted population density change of the reformed administrative village;
s210, analyzing population density change by using a fifth value model obtained through training to obtain a value of the population density change; wherein, the larger the change of the population density is, the stronger the ability of the aggregated population of the modified village is, the higher the modification feasibility of the village is, and therefore, the score is higher;
the method for obtaining the current volume ratio comprises the following steps:
s211, acquiring the current volume rate of the administrative village;
s212, analyzing the current volume rate by using a trained sixth score model to obtain the weight of the current volume rate; the current volume ratio is (current building quantity/new building range), and the smaller the current volume ratio is, the greater the transformation feasibility is, and therefore, the score is higher;
the weight obtaining step of the net volume ratio of the modified house comprises the following steps:
s213, acquiring the net volume rate of the modified house;
s214, analyzing the net volume rate of the modified house by using a trained seventh score model to obtain a score of the net volume rate of the modified house; the net volume ratio of the modified house (the building volume of the modified house/the residential land) is smaller, the modification feasibility is higher, and therefore the score is higher;
the method for obtaining the score of the density of the reconstructed building comprises the following steps:
s215, obtaining the building density after modification;
s216, analyzing the modified building density by using the eighth score model obtained through training to obtain a score of the modified building density; the building density of the old villages is higher, the open greening space is reduced, the building height of the old villages is higher after the old villages are modified, the open greening space is more, the building density is reduced, and therefore the smaller the modified building density is, the higher the modification feasibility is, and the score is higher;
the method comprises the following steps of:
s217, obtaining a complex fusion ratio;
s218, analyzing the complex fusion ratio by using a ninth score model obtained through training to obtain a score of the complex fusion ratio; wherein, the maximum value of the complex fusion ratio is 1, the larger the complex fusion ratio is, the better the balance of the modified building scale is, the higher the modification feasibility is, and therefore, the score is higher;
the method comprises the following steps of obtaining the score of the three-old-pattern-spot-connected-sheet index:
s219, obtaining the continuity of the three old image spots;
s220, analyzing the three old image spots by using the trained tenth value model to obtain the values of the three old image spots; the three-old-image-spot continuity includes that firstly, the number of three-old-image spots is calculated, secondly, an ARCGIS buffer area tool is used, the number of three-old-image spots related to the buffer area is calculated by setting the buffer area in a certain range with the image spot with the largest area as a central point, the fewer the three-old-image spots are, the more the three-old-image spots related to the buffer area are, the better the continuity of the three-old-image spots is, and therefore, the fewer the three-old-image spots are, the higher the score of the three-old-image-spot continuity is;
the three steps of obtaining the score of the regular index of the spot shape of the old figure are as follows:
s221, acquiring three old pattern spot shape rules;
s222, analyzing the three-old-pattern-spot shape rule by using an eleventh score model obtained through training to obtain scores of the three-old-pattern-spot shape rule; the three-old pattern spot shape rule index is the three-old pattern spot perimeter/the three-old pattern spot area, the shape rule index standard value is the shape rule index/the maximum shape rule index, the closer the three-old pattern spot shape rule index is to the standard value, the more regular the shape of the three-old pattern spots is, and therefore, the smaller the error between the three-old pattern spot shape and the standard value is, the higher the score of the three-old pattern spot shape rule index is;
the method for obtaining the score of the potential development land index comprises the following steps:
s223, potential development land indexes are obtained;
s224, analyzing the potential development land index by using the twelfth score model obtained through training to obtain the score of the potential development land index; the potential development land index is larger, the larger the potential development land index is, the larger the space for the particles is, the smaller the control constraint of planning suffered by project development is, the larger the development potential is, the higher the transformation feasibility is, and the higher the score is.
wherein x is*And normalizing the normalized value of each index layer, wherein x is the value of each index layer, and max is the maximum value of each index layer. In addition, x is*Is in the range of [0, 1 ]]。
In some embodiments, as shown in fig. 1 and 2, to facilitate the building of the first score model, the second score model, the third score model, the fourth score model, the fifth score model, the sixth score model, the seventh score model, the eighth score model, the ninth score model, the tenth score model, the eleventh score model, and the twelfth score model, the first score model, the second score model, the third score model, the fourth score model, the fifth score model, the sixth score model, the seventh score model, the eighth score model, the ninth score model, the tenth score model, the eleventh score model, and the twelfth score model are built by one of a machine learning algorithm, a convolutional neural network algorithm, a recurrent neural network algorithm, a decision tree, and a deep learning algorithm.
In some embodiments, as shown in fig. 1 and 2, in order to obtain the weights of the target layer, the criterion layer, and the index layer, in S4, the following steps are specifically included:
s41, constructing a judgment matrix A for pairwise comparison of importance degrees of the factors according to the factors of the target layer, the criterion layer and the index layer:
wherein, ai，aj(i, j ═ 1, 2, …, n) denotes the factor aijDenotes aiTo ajRelative importance measure of;
s42 using formula a ω ═ λmaxMaximum characteristic root lambda of omega calculation judgment matrix AmaxCarrying out normalization processing on the characteristic vector omega to obtain importance ranking of each factor;
s43, using formula
And CI ═ λMAX-n)/(n-1), judging the consistency index of the judgment matrix A, if CR is less than 0.1, judging that the matrix A is established, and entering S24; if CR is more than or equal to 0.1, judging that the matrix A is not established, and ending the process;
and S44, the feature vector omega after normalization processing is the weight vector of the target layer, the standard layer and the index layer. After the treatment of the analytic hierarchy process, the weights of the development feasibility, the district construction feasibility, the population aggregation feasibility and the land intensive saving and utilization feasibility of a target layer area are respectively 0.2, 0.1 and 0.5, the weights of the core position and the traffic convenience level of a criterion layer city are respectively 0.5 and 0.5, the weight of the distance between the center of an administrative village and the center of an administrative center is 1, and the weight of the number of peripheral traffic trunk roads is 1;
the weight of the land urbanization level is 1, and the weight of the current construction land occupation ratio and the weight of the planned construction land occupation ratio are 0.5 and 0;
the weight of the population number condition is 1, and the weight of the population density change index is 1;
the weights of the land utilization intensity and the land integration difficulty degree are respectively 0.6 and 0.4, the weights of the current volume fraction, the net volume fraction of the modified house, the building density after modification and the composite fusion ratio are respectively 0.3, 0.4, 0.15 and 0.15, and the weights of the three-old-pattern-patch index, the three-old-pattern-patch shape rule index and the potential development land index are respectively 0.4, 0.3 and 0.3.
To sum up, according to the feasibility analysis method for building a new range based on old village transformation, a feasibility analysis index system for building a new range based on old village transformation is set, the feasibility analysis index system comprises 4 target layers, 6 standard layers and 12 indication layers, and then data normalization standardization processing is performed on each index layer to obtain a normalization value of each index layer; respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process; finally, obtaining a feasibility result of the old village reconstruction new range according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer so as to judge whether the old village reconstruction new range is feasible or not; therefore, whether the old village reconstruction new range is feasible or not is analyzed through quantitative analysis, the accuracy of the feasibility judgment result of the old village reconstruction new range is greatly improved, and the feasibility analysis result has the advantage of accuracy.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.
Claims (5)
1. A feasibility analysis method for reconstructing a new range based on old villages is characterized by comprising the following steps:
s1, preprocessing the obtained multi-source data by receiving, collecting and reading related multi-source data, and obtaining parameters required by analysis indexes after screening, repairing and mining;
s2, setting a feasibility analysis index system of a new range of old village reconstruction, wherein the feasibility analysis index system of the new range of old village reconstruction takes regional development feasibility, district construction feasibility, population gathering feasibility and land intensive conservation and utilization feasibility as target layers, the regional development feasibility is divided into two standard layers of an urban core position and a traffic convenience level according to the property of the regional development feasibility, the urban core position is divided into an index layer of the distance between the center of an administrative village and the center of the administrative center according to the property of the urban core position, and the traffic convenience level is divided into an index layer of the number of peripheral traffic trunk roads according to the property of the traffic convenience level;
the district construction feasibility is divided into a standard layer of land urbanization level according to the property of the district construction feasibility, and the land urbanization level is divided into two index layers of current construction land occupation ratio and planning construction land occupation ratio according to the property of the land urbanization level;
the population gathering feasibility is divided into a criterion layer of population number conditions according to the properties of the population gathering feasibility, and the population number conditions are divided into an index layer of population density change indexes according to the properties of the population number conditions;
the land intensive conservation and utilization feasibility is divided into two criterion layers of land utilization strength and land integration difficulty degree according to the property of the land utilization feasibility, the land utilization strength is divided into four index layers of current volume fraction, reconstructed house net volume fraction, reconstructed building density and re-fusion ratio according to the property of the land utilization strength, and the land integration difficulty degree is divided into three index layers of three old pattern spot continuous sheet indexes, three old pattern spot shape rule indexes and potential development land indexes according to the property of the land utilization strength;
s3, carrying out data normalization standardization processing on each index layer to obtain a normalization standardization value of each index layer;
s4, respectively calculating the weights of the target layer, the standard layer and the index layer by using an analytic hierarchy process;
and S5, obtaining a feasibility result of the old village reconstruction new range according to the normalized value of the index layer, the weight of the criterion layer and the weight of the target layer, so as to judge whether the old village reconstruction new range is feasible.
2. The method for analyzing feasibility of reconstructing a new range based on old villages according to claim 1, wherein in said S3, a formula is used
Number of lines per index layerObtaining a normalized value of each index layer according to normalization processing;
wherein x is*And normalizing the normalized value of each index layer, wherein x is the value of each index layer, and max is the maximum value of each index layer.
3. The feasibility analysis method for building a new range based on old village transformation according to claim 2, wherein in said S2, the score of the distance between the center of said administrative village and the center of said administrative center is obtained by:
s21, acquiring the distance between the central point of the administrative village and the central point of the administrative center;
s22, performing score analysis on the distance between the central point of the administrative village and the central point of the administrative center by using the trained first score model to obtain a score of the distance between the central point of the administrative village and the central point of the administrative center;
the method comprises the following steps of:
s23, acquiring the number of the traffic trunk roads within a preset distance range from the central point of the administrative village;
s24, analyzing the number of the traffic trunk roads by using a second score model obtained through training, and obtaining the weight of the number of the traffic trunk roads;
the method comprises the following steps of:
s25, acquiring the current construction land area of the administrative village;
s26, analyzing the area of the current construction land by using a trained third score model to obtain the score of the current construction land;
the method comprises the following steps of:
s27, acquiring the area for planning and constructing the administrative village;
s28, analyzing the area of the planned construction land by using a fourth score model obtained through training, and obtaining the score of the planned construction land;
the method for acquiring the score of the population density change comprises the following steps:
s29, acquiring the predicted population density change of the modified administrative village;
s210, analyzing the population density change by using a fifth value model obtained through training to obtain a value of the population density change;
the method for acquiring the value of the current volume ratio comprises the following steps:
s211, acquiring the current volume rate of the administrative village;
s212, analyzing the current volume rate by using a trained sixth score model to obtain the weight of the current volume rate;
the weight obtaining step of the net volume ratio of the modified house comprises the following steps:
s213, acquiring the net volume rate of the modified house;
s214, analyzing the net volume rate of the modified house by using a trained seventh score model to obtain a score of the net volume rate of the modified house;
the method for obtaining the score of the density of the modified building comprises the following steps:
s215, obtaining the modified building density;
s216, analyzing the modified building density by using an eighth score model obtained through training to obtain a score of the modified building density;
the method comprises the following steps of:
s217, obtaining the complex fusion ratio;
s218, analyzing the complex fusion ratio by using a ninth score model obtained through training to obtain a score of the complex fusion ratio;
the method comprises the following steps of obtaining the score of the three-old-pattern-spot connected-sheet index:
s219, obtaining the connectivity of the three old image spots;
s220, analyzing the three old image spot continuity by using a tenth value model obtained through training to obtain the scores of the three old image spot continuity;
the method comprises the following steps of obtaining scores of three old pattern spot shape rule indexes:
s221, obtaining the shape rule of the three old patterns;
s222, analyzing the three old pattern spot shape rules by using an eleventh score model obtained through training to obtain scores of the three old pattern spot shape rules;
the potential development land index score obtaining step comprises the following steps:
s223, acquiring the potential development land index;
and S224, analyzing the potential development land index by using the twelfth score model obtained through training to obtain the score of the potential development land index.
4. The method of analyzing feasibility of building a new range based on old village transformation according to claim 3, wherein said first score model, said second score model, said third score model, said fourth score model, said fifth score model, said sixth score model, said seventh score model, said eighth score model, said ninth score model, said tenth score model, said eleventh score model and said twelfth score model are built by one of machine learning algorithm, convolutional neural network algorithm, recurrent neural network algorithm, decision tree and deep learning algorithm.
5. The feasibility analysis method for reconstructing a new range based on old villages according to claim 1, wherein in said S4, the method specifically comprises the following steps:
s41, constructing a judgment matrix A for pairwise comparison of importance degrees of the factors according to the factors of the target layer, the criterion layer and the index layer:
s42 using formula a ω ═ λmaxOmega calculates the maximum characteristic root lambda of the judgment matrix AmaxCarrying out normalization processing on the characteristic vector omega to obtain importance ranking of each factor;
s43, using formula
And CI ═ λMAX-n)/(n-1), judging the consistency index of the judgment matrix a, if CR is less than 0.1, the judgment matrix a is true, and proceeding to S24; if CR is more than or equal to 0.1, the judgment matrix A is not established, and the process is ended;
s44, the feature vector omega after normalization processing is the weight vector of the target layer, the criterion layer and the index layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110986128.XA CN113837547A (en) | 2021-08-25 | 2021-08-25 | Feasibility analysis method for reconstructing and establishing new range based on old village |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110986128.XA CN113837547A (en) | 2021-08-25 | 2021-08-25 | Feasibility analysis method for reconstructing and establishing new range based on old village |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113837547A true CN113837547A (en) | 2021-12-24 |
Family
ID=78961241
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110986128.XA Pending CN113837547A (en) | 2021-08-25 | 2021-08-25 | Feasibility analysis method for reconstructing and establishing new range based on old village |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113837547A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115577058A (en) * | 2022-09-23 | 2023-01-06 | 中国测绘科学研究院 | Small pattern spot competition splitting method considering global and local optimal influences |
| CN116644886A (en) * | 2023-05-06 | 2023-08-25 | 广州市城市规划勘测设计研究院 | Method, device, terminal and medium for evaluating transformation value of stock land |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105005690A (en) * | 2015-07-07 | 2015-10-28 | 同济大学建筑设计研究院(集团)有限公司 | Grey correlation-analytic hierarchy process based evaluation method for potential index of spongy urban area |
| CN108520351A (en) * | 2018-04-04 | 2018-09-11 | 宿州学院 | Rural Land Circulation pattern Performance Evaluation Methods, system, equipment, storage medium |
| CN112348404A (en) * | 2020-11-26 | 2021-02-09 | 广州市白云区城市规划设计研究所 | Village planning implementation evaluation system |
| CN112598268A (en) * | 2020-12-18 | 2021-04-02 | 广州市城市规划设计所 | Urban village updating and transformation potential evaluation method, device and storage medium thereof |
| CN112906978A (en) * | 2021-03-19 | 2021-06-04 | 中国科学院地理科学与资源研究所 | Regional industry ecological suitability selection technology system |
-
2021
- 2021-08-25 CN CN202110986128.XA patent/CN113837547A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105005690A (en) * | 2015-07-07 | 2015-10-28 | 同济大学建筑设计研究院(集团)有限公司 | Grey correlation-analytic hierarchy process based evaluation method for potential index of spongy urban area |
| CN108520351A (en) * | 2018-04-04 | 2018-09-11 | 宿州学院 | Rural Land Circulation pattern Performance Evaluation Methods, system, equipment, storage medium |
| CN112348404A (en) * | 2020-11-26 | 2021-02-09 | 广州市白云区城市规划设计研究所 | Village planning implementation evaluation system |
| CN112598268A (en) * | 2020-12-18 | 2021-04-02 | 广州市城市规划设计所 | Urban village updating and transformation potential evaluation method, device and storage medium thereof |
| CN112906978A (en) * | 2021-03-19 | 2021-06-04 | 中国科学院地理科学与资源研究所 | Regional industry ecological suitability selection technology system |
Non-Patent Citations (4)
| Title |
|---|
| SPONGE: "旧村全面改造项目可行性分析的若干要点", Retrieved from the Internet <URL:https://zhuanlan.zhihu.com/p/356605990> * |
| 石全: "《系统决策与建模》", 31 December 2016, 国防工业出版社, pages: 104 * |
| 祁鹏: "莱芜市城中村改造可行性评价研究", 中国优秀硕士学位论文全文数据库, no. 2, 15 February 2018 (2018-02-15), pages 25 * |
| 陈松林: "《中国金融安全问题研究》", 31 December 2002, 中国金融出版社, pages: 211 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115577058A (en) * | 2022-09-23 | 2023-01-06 | 中国测绘科学研究院 | Small pattern spot competition splitting method considering global and local optimal influences |
| CN116644886A (en) * | 2023-05-06 | 2023-08-25 | 广州市城市规划勘测设计研究院 | Method, device, terminal and medium for evaluating transformation value of stock land |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110472882B (en) | Urban development land suitability evaluation method based on principal component analysis | |
| CN109636150A (en) | A kind of method for building up and its system of smart city " more rule unifications " appraisement system | |
| CN113837547A (en) | Feasibility analysis method for reconstructing and establishing new range based on old village | |
| CN110889562B (en) | Dynamic city model system based on scenario planning | |
| CN110263111B (en) | Priori knowledge-based space-time monitoring method for land utilization/coverage information | |
| CN109063976A (en) | Intelligence manufacture capability maturity evaluation method based on Fuzzy AHP | |
| CN106355334A (en) | Farmland construction area determining method | |
| CN110852532A (en) | Natural protected area system integration and boundary delineation method | |
| CN102646164B (en) | A kind of land use change survey modeling method in conjunction with spatial filtering and system thereof | |
| CN113360989B (en) | City green roof planning method | |
| CN107766825A (en) | Land-based area province cities and counties' space planning 3rd area recognition methods based on space function unit | |
| CN112766800A (en) | Urban green traffic system evaluation method based on fuzzy analytic hierarchy process | |
| Tian et al. | Suburban identification based on multi-source data and landscape analysis of its construction land: A case study of Jiangsu Province, China | |
| CN113033081A (en) | Runoff simulation method and system based on SOM-BPNN model | |
| CN115841225A (en) | Road maintenance priority ranking method in road network | |
| CN109933901B (en) | MCR city expansion simulation method for optimizing service value of ecosystem | |
| CN116720750B (en) | Comprehensive energy station equipment capacity planning method, device, terminal and storage medium | |
| CN115310878B (en) | Power space planning design method based on homeland space element data | |
| CN111160719A (en) | Two-network fused multi-index evaluation method | |
| Zhu et al. | Rural road network planning based on 5g and traffic big data | |
| CN116451963B (en) | Multi-factor pension service facility optimal configuration method based on ensemble learning | |
| AU2021104004A4 (en) | Double-layer Domain-oriented Intelligent Optimal Allocation Method for Rural Residential Areas | |
| Song et al. | Modeling land use change prediction using multi-model fusion techniques: A case study in the Pearl River Delta, China | |
| Tiwari et al. | Predicting Future Urban Growth in Bhopal, India using Artificial Intelligence based Modelling | |
| Zou et al. | Benefit Evaluation of Preventive Maintenance of Highway Bridges Based on Fuzzy Neural Network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |