CN115330086A - Photovoltaic land automatic site selection method under multi-condition constraint of spatial big data - Google Patents
Photovoltaic land automatic site selection method under multi-condition constraint of spatial big data Download PDFInfo
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Abstract
The invention is suitable for the technical field of spatial big data, and provides an automatic photovoltaic land location method under the constraint of multiple conditions of the spatial big data, which comprises the following steps: determining a photovoltaic site selection influence factor based on a photovoltaic site selection strategy; on the basis of space big data analysis, dividing a comparison and selection land parcel, performing weight assignment calculation on different influence factors, and overlapping the weight of each influence factor to construct a combined model; performing site selection and multi-scheme comparison according to the combined model to determine an optimal scheme; and realizing the visual display of the spatial data of the optimal scheme according to the geographic information system. The invention reduces the area range of the field reconnaissance, saves the reconnaissance time and reduces the site selection cost; meanwhile, an optimal scheme is determined by constructing a combined model and comparing and selecting the combined model, and finally, the compliance passing rate is greatly improved through visual display.
Description
Technical Field
The invention belongs to the field of space big data analysis, and particularly relates to an automatic photovoltaic land location method under the constraint of multiple conditions of space big data.
Background
The research of new energy power generation in foreign countries starts earlier, and researchers have made a lot of researches on problems such as photovoltaic site selection. The typical situations of site selection of foreign wind power plants and photovoltaic power plants are two. Firstly, the principle of 'near access and near consumption' represented by Germany and Denmark is adopted, but the principle is only suitable for the situation that the distance between related resources and a load center is short in the geographic position, the limitation is large, and the advantage is that the development cost is low; and secondly, a principle of 'enhancing the regulation capability and carrying out large-scale grid connection' represented by the United states and European part of countries is mainly used for developing new energy power generation such as photovoltaic and the like, and meanwhile, a unit with strong regulation capability taking oil and gas as power generation energy is vigorously built, so that the aim of carrying out large-scale grid connection on new energy power generation on the basis of ensuring the power supply reliability is fulfilled. This requires that the grids in each region have the characteristics of close connection, strong power exchange capability, strong regulation capability, and the like.
In the aspect of photovoltaic site selection, a common research idea is to establish a site selection decision model for comparing and selecting a scheme by an analytic hierarchy process, a GIS-based space multi-criterion evaluation method and the like on the basis of summarizing and analyzing site selection influence factors. The most widely used method is SOLARGIS, which is a method developed abroad for evaluation of renewable energy. The method can calculate which renewable energy source is suitable for being developed in a region by comprehensively analyzing the data according to solar energy data, wind energy data, population data, distance data from a power grid and social and economic development conditions of the region, and plan and evaluate station building and site selection. The method is simple and convenient to apply, but mainly analyzes the macroscopic factors in combination with the economic cost, and the consideration factors are too few.
The photovoltaic land location method is mostly based on consideration of construction factors of power stations, land suitability is not analyzed from the perspective of national space planning, the photovoltaic industry is rapidly developed in recent years, land demand is very vigorous, but due to lack of space placement in energy development planning, the joining strength of most photovoltaic projects and the national space planning in the location planning stage is insufficient, problems of planning conflict, space resource mismatch and the like exist, and finally administrative approval processes are long and return is caused in midway.
In the current photovoltaic site selection process, an enterprise and public institution at least needs to butt at least 8 following departments to obtain related data or permission: the departments of the natural resource bureau, the ecological environment bureau, the housing and urban and rural construction bureau, the transportation bureau, the water administration, the agricultural rural bureau, the cultural broadcasting and TV tourism and sports bureau, the forestry bureau and the like take a certain market as an example, and the working time of 4 persons is probably required to be invested for about 15 days. Meanwhile, the prior art cannot be competent for superposition analysis and calculation of provincial million-level image spot number in performance, and generally has high time consumption and low efficiency.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an automatic photovoltaic land selection method under the constraint of multiple conditions of spatial big data, and aims to solve the technical problems of time consumption and low efficiency in planning in the prior art.
The invention adopts the following technical scheme:
the photovoltaic land automatic site selection method under the constraint of multiple conditions of the spatial big data comprises the following steps:
s1, determining a photovoltaic site selection influence factor based on a photovoltaic site selection strategy;
s2, on the basis of space big data analysis, dividing a comparison and selection land block, performing weight assignment calculation on different influence factors, overlapping the weights of the influence factors, and constructing a combined model, wherein the combined model is formed by mixing a hierarchical structure model and an entropy method model;
s3, comparing and selecting the multiple address selection schemes according to the combined model, and determining an optimal address selection scheme;
and S4, realizing the visual display of the spatial data of the optimal site selection scheme according to the geographic information system.
The invention has the beneficial effects that: according to the method, the photovoltaic site selection strategy is determined, and the site selection influence factor is determined, so that the area range of field reconnaissance is reduced, the reconnaissance time is saved, and the site selection cost is reduced; meanwhile, the optimal scheme is determined by constructing the combined model and comparing and selecting the combined model, and finally, the visual display is carried out, so that the method greatly reduces the time of the administrative approval process, greatly improves the compliance passing rate, accelerates the photovoltaic land application efficiency of enterprises and public institutions and saves administrative resources; and the site selection performance is improved to the analytical calculation of the competent provincial-level and million-level image spot quantity, and the administrative range level of site selection based on the current territorial space planning concept is improved.
Drawings
FIG. 1 is a flow chart of an automated photovoltaic land location method under multi-condition constraints of spatial big data provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hierarchical model structure provided by an embodiment of the present invention;
FIG. 3 is an illustration of a summary representation of weights calculated by an entropy model;
FIG. 4 is an example graph of entropy model calculation weight bars.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
As shown in fig. 1, the method for automatically selecting a photovoltaic site under the constraint of multiple conditions of spatial big data provided by the embodiment includes the following steps:
s1, determining a photovoltaic site selection influence factor based on a photovoltaic site selection strategy.
The photovoltaic land location method focuses on photovoltaic land location, and carries out overall photovoltaic land location strategy based on the current situation and problems of photovoltaic industry development planning project land location in terms of spatial layout-resource allocation.
The method comprises the steps of taking user requirements as guidance, researching quantifiable factors influencing photovoltaic site selection based on photovoltaic site selection strategies such as illumination, cost, current conditions, ownership, planning conditions, connectivity levels and the like, and determining photovoltaic site selection influence factors.
Wherein the illumination can be classified according to the sunshine hours level. The grade is shown in table 1 below:
TABLE 1
| Grade | Direction of slope | Degree of suitability |
| 1 | A positive south slope with a slope less than 30 degrees and within 20 degrees of east or west | Preference selection |
| 2 | The slope of the east, west, southeast and southwest is less than 20 degrees | Sub-optimal selection |
| 3 | Due north, northeast and northwest slopes with gradient less than 10 degrees | Basically do not consider |
Therefore, in the step, the site selection influence factor is constructed by factors such as illumination (sunshine duration level), cost, current conditions, gradient and slope (gradient and slope level), ownership, planning conditions, continuity level (continuity level high and low) and the like.
And S2, on the basis of space big data analysis, dividing a comparison and selection land block, performing weight assignment calculation on different influence factors, overlapping the weights of the influence factors, and constructing a combined model, wherein the combined model is formed by mixing a hierarchical structure model and an entropy method model.
Based on planning status data, data such as permanent basic farmlands, ecological protection red lines, town development boundaries, high-standard farmlands, primary protection areas of drinking water source protection areas and the like are removed, and the data are divided into comparison and selection plots according to results. I.e. the addressing alternative.
The method comprises the steps of constructing a combined model for site selection, superposing the weight of an influence factor to obtain a score value, calculating the score value of each alternative scheme of the combined model, and determining an optimal scheme. And constructing a combined model, namely constructing a hierarchical structure model and an entropy method model, and then mixing.
Specifically, the hierarchical structure model is constructed as follows:
(1) And determining a target layer, a criterion layer and a scheme layer of the model, wherein the target layer is an address, the criterion layer is an influence factor, and the scheme layer is a comprehensive weight value of each candidate scheme.
Firstly, analyzing problems, determining requirements, determining evaluation indexes and establishing an evaluation hierarchical relationship. The decision objectives, the factors considered (decision criteria) and the decision objects are divided into a highest layer, an intermediate layer and a lowest layer according to their interrelationships.
As shown in fig. 2, in the automatic addressing for photovoltaic land, firstly, the land addressing is used as a target layer, and an influence factor of the photovoltaic land addressing is used as a criterion layer of the hierarchical structure model, for example, the influence factor includes: illumination, cost, current conditions, gradient and slope directions, ownership, planning conditions, continuity levels and the like. And finally, substituting the quantitative value into the factor corresponding to the candidate scheme through inspection to obtain the comprehensive weight value of each candidate scheme, namely the scheme layer. And the decision maker selects the most appropriate site selection party according to the calculation result of each scheme to complete the site selection target task. Assume that there are m influencing factors, n alternatives.
(2) And constructing a judgment matrix among the influence factors.
The judgment matrix is a comparison showing the relative importance among all the influence factors of the hierarchy. Determining the elements of the matrix by alpha ij The expression, namely the importance comparison result of the ith factor and the jth factor, namely the factor is the influence factor, can be obtained by the 1-9 scale method of Saaty. As shown in table 2 below:
TABLE 2
| Scale | Means of |
| 1 | Showing the same importance of the two factors compared |
| 3 | Indicating that one factor is slightly more important than the other factor when compared to the other factor |
| 5 | Indicating that one factor is significantly more important than the other factor when compared to the other factor |
| 7 | Indicating that one factor is more important than the other factor |
| 9 | Indicating that one factor is extremely important compared to the other |
| 2、4、6、8 | Median value of the above two adjacent judgments |
| Reciprocal of the | Judgment alpha of comparison of factors i and j ij The judgment alpha of the comparison of the factors j and i ji =1/α ij |
The decision matrix is shown in Table 3 below, and the influence factor is W 1 ，…，W m The numbering is carried out in sequence:
TABLE 3
| Illumination (W) 1 ) | Cost (W) 2 ) | Current condition (W) 3 ) | Slope direction (W) 4 ) | Ownership (W) 5 ) | …… | W m | |
| Illumination (W) 1 ) | 1 | W 1 /W 2 | W 1 /W 3 | W 1 /W 4 | W 1 /W 5 | …… | W 1 /W m |
| Cost (W) 2 ) | W 2 /W 1 | 1 | W 2 / W 3 | W 2 /W 4 | W 2 /W 5 | …… | W 2 /W m |
| Current condition (W) 3 ) | W 3 /W 1 | W 3 /W 2 | 1 | W 3 /W 4 | W 3 /W 5 | …… | W 3 /W m |
| …… | …… | …… | …… | …… | …… | …… | …… |
| W m | W m /W 1 | W m /W 2 | W m /W 3 | W m /W 4 | W m /W 5 | …… | 1 |
Comparing the influence of the factors such as illumination, gradient and slope direction, current conditions, etc., and comparing the influence factors pairwise to establish a pairwise comparison matrix, wherein the matrix A = (alpha) is used for all comparison results ij ) And m is expressed by m, A is called a judgment matrix between H and X, and m is the number of the types of the influence factors. Judging diagonal line symmetry element in matrixThe elements are reciprocal to each other and on the diagonal the scale of the factor compared to itself is constant at 1. Let alpha be ij =W i /W j The conversion to matrix formula is:
for the pair-wise comparison matrix a that is not consistent (but within the allowable range), the eigenvector corresponding to the largest eigenvalue λ max is used as the weight vector.
(3) And obtaining relative weight between layers according to the judgment matrix, and carrying out single-level sorting and consistency check and single-level sorting feature vector normalization processing.
The eigenvector corresponding to the largest eigenvalue of a comparison matrix is the final weight vector, but the matrix meets the consistency test. The eigenvectors corresponding to the largest eigenvalue of the decision matrix λ max are normalized (so that the sum of the elements in the vector is equal to 1) and denoted as W.
The elements of W are the sorting weights of the relative importance of the same level factor to a certain factor of the previous level factor, and the process is called level list sorting. The feature vector may be expressed as: w = (W) 1 ，W 2 ，…，W m ) T 。
Calculating a characteristic vector W and a maximum characteristic root lambda max by a square root method, a sum method and a power method:
whether the hierarchical list ordering can be confirmed or not needs to be checked for consistency, and consistency check means to determine an allowable range of inconsistency for a. The simple understanding of the consistency check is that the check is performed according to the following theorem:
(1) The only nonzero characteristic root of the N-order consistent array is N;
(2) The maximum characteristic root lambda max of the N-order positive and negative matrix A is larger than or equal to N, and A is a consistent matrix if and only if lambda max = N.
The feature vector corresponding to the maximum feature value is used as a weight vector of the influence degree of the compared factor on a certain factor of an upper layer, and the larger the inconsistency degree is, the larger the judgment error is caused. The degree of inconsistency of A can be measured by the value of lambda-N.
After the matrix is determined, a consistency criterion needs to be defined for the matrix calculation:
where CI represents the consistency index and λ max is the maximum feature root. When CI =0, judging that the matrix has complete consistency; when CI is close to 0, there is satisfactory consistency; the inconsistency is more severe when CI is larger. In order to measure the magnitude of CI, an average random consistency index RI is introduced, and the expression method is as follows:
randomly constructing 500 pairwise judgment matrixes to obtain a consistency index CI 1 、CI 2 、CI 3 ……CI N Then, it is
According to the calculation result, the following average random consistency index RI value table is concretely shown:
| dimension N | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| |
0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
When N <3, the decision matrix always has complete consistency. The ratio of the matrix consistency index CI to the average random consistency index RI of the same order is called as a random consistency ratio CR, and the formula is expressed as follows:
and (3) consistency test: when CR is reached<At 0.1, the inconsistency degree of the judgment matrix A is considered to be within the allowable range, and the judgment matrix A has satisfactory and acceptable consistency, and the normalized feature vector can be used as a weight vector through consistency test. When CR is more than or equal to 0.10, the judgment matrix A needs to be readjusted and corrected, and for alpha ij Adjusted to satisfy CR<0.10, thereby having satisfactory consistency.
(4) And obtaining the score value of each alternative scheme through the total hierarchical ordering and consistency detection, and representing the score value by B.
And calculating the total weight (total hierarchical ranking) of each layer to the total evaluation target to obtain the evaluation result of each alternative scheme. The weight value of relative importance of all factors of a certain level to the highest level (total target), namely a score value, is calculated and called the total ordering of the level.
Suppose that the m factors of the criterion layer rank the total target as a1, a2, a3 \8230 \8230am, the n alternatives of the scheme layer rank the factor level list in the upper layer as b 1j 、b 2j 、b 3j ……b nj ，j=1、2……m。
The total rank B is:
then the single-level ordering consistency index is CIj:
average random consistency index RIj:
the consistency ratio of the overall ranking of the hierarchy is CRj:
and (3) checking consistency: the consistency check is also carried out on the total hierarchical ordering, and the check is still carried out layer by layer from a high layer to a low layer like the hierarchical single ordering. Although each layer has been subjected to consistency test of single-layer sequencing and each judgment matrix has satisfactory consistency, the inconsistency of each layer may be accumulated during comprehensive examination, resulting in serious inconsistency of final analysis results. When CR < 0.1, the total rank order is considered to pass the consistency test. The total hierarchical ordering has satisfactory consistency, otherwise, the element values of the judgment matrix with high consistency ratio need to be readjusted. And finally making a final decision according to the total ordering of the lowest layer (scheme layer).
For the entropy model, entropy is a mathematical method used to determine the degree of dispersion of an index, and the greater the degree of dispersion, the greater the influence of the index on the comprehensive evaluation. The degree of dispersion of a certain index can be judged by using the entropy value. The entropy method is an objective weighting method, which uses the information entropy thought as reference, and determines the weight of the index according to the influence of the relative change degree of the index on the whole system by calculating the information entropy of the index, namely, the weighting is carried out according to the difference degree of the mark values of each index, thereby obtaining the corresponding weight of each index, and the index with large relative change degree has larger weight. The larger the information quantity is, the smaller the uncertainty is, the smaller the entropy is, and the larger the utility value of the information is; the smaller the information amount, the larger the uncertainty, the larger the entropy, and the smaller the utility value of the information. The entropy method is guided by user requirements, and illumination, cost, current conditions, ownership, planning conditions and connectivity levels are used as influence factors for researching influence on photovoltaic site selection. The specific construction process of the model is as follows:
(1) And forming an original index data matrix, and standardizing the index data. Take 4 samples of 7 indices as an example:
| illumination of light | Cost of | Current condition | Direction of slope | Ownership | Planning conditions | Degree of continuity grade |
| 3 | 5 | 1 | 2 | 1 | 2 | 1 |
| 2 | 2 | 5 | 3 | 3 | 1 | 4 |
| 1 | 2 | 1 | 2 | 2 | 3 | 4 |
| 4 | 1 | 3 | 3 | 4 | 4 | 1 |
Collecting and sorting the original data to be used, for example, selecting 7 indexes, total 4 samples, and forming an original index data matrix, where the indexes are influence factors, and the samples are alternatives:
for a certain index, the greater the degree of dispersion of the sample, the greater the role of the index in the comprehensive evaluation. If the flag values of the index are all equal, it indicates that the index does not function in the comprehensive evaluation.
Because the measurement units and directions of the indexes are not uniform, before the indexes are used for calculating the comprehensive indexes, the indexes are standardized, namely, the absolute values of the indexes are converted into relative values, so that the homogenization problem of the indexes with different qualities is solved. Moreover, since the positive indicator and the negative indicator have different meanings (the higher the positive indicator value is, the better the negative indicator value is), different algorithms are used for data processing of the high and low indicators. To avoid logarithmic meaningless in entropy calculations, a real number of a smaller order of magnitude may be added to each 0 value.
When the larger the index is, the more beneficial the system development is, a forward index calculation method is adopted; when the smaller the index is, the more beneficial the system development is, a negative index calculation method is adopted.
(2) And calculating the proportion of each sample in the current index under each index, wherein the index is an influence factor, and the sample is an alternative scheme.
For example, the specific gravity of the c-th sample in the indicator under the v-th indicator is calculated:
forming a specific gravity matrix:
(3) And calculating the entropy value of the current index.
Calculating the entropy value of the v index:
ensure that e is more than or equal to 0 v 1 or less, i.e. e v The maximum is 1. The entropy value of the v index can thus be expressed as:
if the observed value difference of the v-th index is larger, the entropy value is smaller; conversely, the larger the entropy value.
(4) Calculating the difference coefficient of the current index: and calculating the difference coefficient of the v index.
The information utility value of a certain index depends on the information entropy e of the index v The difference value between the value of the information utility value and 1 directly influences the magnitude of the weight, the larger the information utility value is, the greater the importance of the evaluation is, and the greater the weight is:
if the observed value of the v-th index is larger, the difference coefficient d v The larger the index, the more important the index of the v-th term.
(5) And estimating the weight of each index by using an entropy method.
The weight of each index is estimated by using an entropy method, the essence of the weight is calculated by using a difference coefficient of the index information, and the higher the difference coefficient is, the greater the importance of the evaluation is (the greater the weight is, the greater the contribution to the evaluation result is). Weight of the v index:
one result of estimating the weights of the indicators by using entropy method is shown in fig. 3 and 4.
(6) The score value for each alternative is calculated and is denoted by Z.
The v sample composite level score:
after the hierarchical structure model and the entropy model are obtained, the combined model is mixed according to the following calculation formula:
wherein B is j To obtain the score value, Z, of the j' th alternative by calculation of the hierarchical model j Calculating to obtain the fraction value of the j alternative by an entropy method model, R j The score value of the j-th alternative is calculated for the combination of the two models.
The analytic hierarchy process is to subjectively determine the weight according to the importance degree of each factor and experience to form a matrix to obtain each index weight, and the analytic hierarchy process has reference significance but poor objectivity; the entropy method is used for sorting, calculating and analyzing actual indexes to obtain weights, namely the weights of all evaluation indexes are determined by a judgment matrix formed by the evaluation index values, but the method has the limitations that the collected data of all indexes need to keep the same dimension and the like due to late formation. The calculation results of the two models are subjected to combined optimization weighting, certain superiority is achieved, the influence of subjective aspects and objective factors is comprehensively considered, comprehensive evaluation is enabled to be more reasonable, and the scheme weight calculation precision is improved.
And S3, comparing and selecting the multiple address schemes according to the combined model, and determining the optimal address scheme.
According to the characteristics of two models of an AHP (attitude and heading process) and an entropy method, a combined weighting method combining a subjective weighting method and an objective weighting method is adopted, qualitative and quantitative analysis of subjective factor leading address selection directions is carried out under the condition that reasonable weighting of influence factors is ensured, a combined model suitable for photovoltaic land address selection is constructed, various schemes are obtained through the combined model, an optimal scheme is determined, and the address selection result is real, scientific and credible.
And S4, realizing the visual display of the spatial data of the optimal scheme according to the geographic information system.
Spatial data visualization is a scientific and technical study on the visual manifestation of data. Depending on a geographic information system, the spatial data is visualized, and information is clearly and effectively transmitted and communicated by means of a chart.
Finally, on-site survey needs to be carried out on selection results, the scientificity, accuracy and operability of the method are verified, the conversion from traditional manual blind selection to scientific site selection based on big data analysis is realized, and the healthy development of the photovoltaic industry and the accurate landing of projects are facilitated.
In conclusion, the photovoltaic site selection method based on the large data analysis is based on the concept of 'multi-rule-in-one' of the existing national space planning, achieves the purpose that the photovoltaic site selection is changed from the traditional manual 'blind selection' to the scientific site selection based on the large data analysis, and has important significance for improving the scientificity of the new energy industry layout, promoting the new energy to gather and save the sea land, optimizing the enterprise and business environment, promoting the development of the new energy industry, the energy structure transformation and the green low-carbon development, and achieving the 'double-carbon' goal. Secondly, the invention innovatively utilizes a spatial big data analysis technology to greatly improve the performance efficiency. By identifying the forbidden region and the constructable region in the photovoltaic land automatic site selection influence factor, taking a certain grade city as an example, the traditional identification mode needs 2 workers to work for about 7 days; the invention can finish the efficiency after the efficiency is improved to 2-3 days for a single person, thereby greatly improving the efficiency of data analysis performance. Thirdly, the photovoltaic site selection landing efficiency is greatly improved, and a large amount of economic cost and administrative resources are saved. In the current photovoltaic site selection process, an enterprise and public institution at least needs to butt at least 8 following departments to obtain related data or permission: the departments of the natural resource bureau, the ecological environment bureau, the housing and urban and rural construction bureaus, the transportation bureaus, the water service bureaus, the agricultural and rural bureaus, the cultural radio and television tourism and sports bureaus, the forestry bureaus and the like take a certain grade city as an example, and the working time of 4 persons is probably required to be invested for about 15 days. The invention improves the efficiency to the point that a single worker can finish site selection and landing on the ground on the same day, and has strong practicability. Fourthly, the invention can realize 'one-key address selection' and 'scheme comparison selection' after the deployment is finished, and has simple and convenient operation and friendly interaction. So that non-professionals can operate by hands quickly and the learning cost is saved. Fifthly, the invention can flexibly configure operators and adapt to more service models.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. An automatic photovoltaic land location method under the constraint of multiple conditions of spatial big data is characterized by comprising the following steps:
s1, determining a photovoltaic site selection influence factor based on a photovoltaic site selection strategy;
s2, on the basis of space big data analysis, a comparison and selection land block is drawn, weight assignment calculation is carried out on different influence factors, the weights of the influence factors are combined, and a combined model is constructed, wherein the combined model is formed by mixing a hierarchical structure model and an entropy method model;
s3, comparing and selecting the multiple address selection schemes according to the combined model, and determining an optimal address selection scheme;
and S4, realizing the visual display of the spatial data of the optimal site selection scheme according to the geographic information system.
2. The photovoltaic land automatic site selection method under the constraint of the multiple conditions of the space big data as claimed in claim 1, wherein in the step S1, the influence factors comprise illumination, cost, current conditions, ownership, planning conditions and linkage degree grade.
3. The photovoltaic land automation locating method under the multi-condition constraint of the spatial big data as claimed in claim 2, wherein in the step S2, the specific process of constructing the hierarchical structure model is as follows:
1) Determining a target layer, a criterion layer and a scheme layer of the model, wherein the target layer is an address selection, the criterion layer is an influence factor, and the scheme layer is a comprehensive weight value of each candidate scheme;
2) Constructing a judgment matrix among the influence factors;
3) Obtaining relative weight between layers according to the judgment matrix, and carrying out normalization processing on feature vectors of single-rank order through single-rank order and consistency check;
4) Obtaining the score value of each alternative scheme through the total hierarchical ordering and consistency detection, and expressing the score value by B;
in step S2, the specific process of constructing the entropy model is as follows:
1) Selecting index data to form an original index data matrix, and standardizing the index data;
2) Calculating the proportion of each sample in the current index under each index, wherein the index is an influence factor, and the sample is an alternative scheme;
3) Calculating an entropy value of the current index;
4) Calculating a difference coefficient of the current index;
5) Estimating the weight of each index by using an entropy method;
6) A composite score is calculated for each alternative, denoted by Z.
4. The method for automatically selecting photovoltaic sites under the constraint of multiple conditions of spatial big data as claimed in claim 3, wherein in the step S2, the calculation formula of the combined model is as follows:
where n is the number of alternatives, B j To obtain the score value, Z, of the j' th alternative by calculation of the hierarchical model j Calculating to obtain the fraction value of the j alternative by an entropy method model, R j The score value of the j-th alternative is calculated for the combination of the two models.
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