CN113268704A - Fuzzy analytic hierarchy process based data center site selection method and system - Google Patents
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Abstract
The invention provides a data center site selection method and system based on a fuzzy analytic hierarchy process, which are characterized in that relevant influence factors needing to reach a total target are analyzed, a hierarchical relation among the influence factors is established, each influence factor is regarded as an evaluation index, a fuzzy theory is adopted for performance of different schemes to obtain a priority membership value aiming at the same evaluation index, then an analytic hierarchy process is applied to obtain the weight of relative importance degree of different evaluation indexes of a next layer relative to the evaluation indexes of a previous layer, then the priority membership degree is weighted and summed for a scheme to be selected to obtain the evaluation indexes of the previous layer, and so on, the priority membership value of the evaluation index of the top layer is finally obtained and a scheme is selected preferentially.
Description
Technical Field
The invention belongs to the technical field of multi-station fusion data center site selection, belongs to the technical research of planning and construction key of a multi-station-in-one transformer substation based on the ubiquitous power internet of things, and particularly relates to a data center site selection method and system based on a fuzzy analytic hierarchy process.
Background
The multi-station integration refers to the resources such as the existing space of the transformer substation, manpower, energy supply and automatic management, new energy is consumed nearby, overall construction schemes such as the transformer substation, the data center station, the energy storage station, the 5G base station and the Beidou ground reinforcing station are designed and optimized overall, and a novel energy information body construction mode for achieving overall energy saving and efficiency improvement is achieved through measures such as one-station multi-purpose, resource integration and business mode innovation of the transformer substation.
At present, a relatively mature scheme and application experience are provided for site selection of a transformer substation, but most site selection characteristics of a data center constructed integrally with the transformer substation are on the level of subjective analysis according to relevant specifications, and a scientific and reliable planning method is lacked.
In the prior art, subjective factors occupy larger components, which is very unfavorable for subsequent large-scale popularization and construction of fusion stations, and the application and the commercialization operation of a data center in the later period are influenced due to unreasonable site selection, so that resource waste is caused.
Disclosure of Invention
Considering that a relatively mature scheme and application experience exist for site selection of a transformer substation at present, most site selection characteristics of a data center constructed integrally with the transformer substation are in the aspect of subjective analysis according to relevant specifications, and a scientific and reliable planning method is lacked.
In the prior art, subjective factors occupy larger components, which is very unfavorable for subsequent large-scale popularization and construction of fusion stations, and the application and the commercialization operation of a data center in the later period are influenced due to unreasonable site selection, so that resource waste is caused.
In view of the above problems, the invention provides a multi-station fusion data center module site selection method and system based on a fuzzy analytic hierarchy process, wherein relevant influence factors required to reach a total target are firstly analyzed, a hierarchical relation among the influence factors is established, each influence factor is regarded as an evaluation index, a fuzzy theory is adopted to obtain priority membership value of different schemes aiming at the same evaluation index, then the hierarchical analytic process is used to obtain the weight of relative importance degree of different evaluation indexes of the next layer relative to the evaluation indexes of the previous layer, then the priority membership weighted summation is carried out on the schemes to be selected to obtain the evaluation indexes of the previous layer, and so on, the priority membership value of the evaluation index of the top layer is finally obtained and a scheme is selected preferentially.
The technical scheme is as follows:
a data center address selection method based on a fuzzy analytic hierarchy process is characterized by comprising the following steps:
step S1: analyzing the influence factors of the data center site selection to obtain the influence factors;
step S2: establishing a hierarchical relationship of data center site selection influence factors;
step S3: sorting different schemes by a fuzzy method aiming at the single influence factor of the bottommost layer from top to bottom respectively to obtain respective priority membership value;
step S4: obtaining the weight proportion of different influence factors of the same layer to the influence factor of the previous layer by adopting an analytic hierarchy process;
step S5: multiplying the weight by the corresponding priority membership value to obtain the ranking and comprehensive priority membership value of the influence factors of the upper layer;
step S6: circularly executing the step S3 to the step S5, and sequentially calculating the comprehensive goodness value of the influence index layer by layer;
step S7: and taking the scheme with the maximum priority membership value of the final top-level comprehensive index as a recommendation scheme.
Preferably, in step S1, analyzing the obtained influence factors includes: weather, land price, electricity price, network resources, policy environment and peer development.
Preferably, in step S2, the impact factors are analyzed to establish a hierarchical relationship between the impact factors.
Preferably, according to step S3, obtaining different scheme priority membership values corresponding to each influence factor at the bottom layer;
step S3 specifically includes the following steps;
step S31: obtaining a priority relation matrix of the influence factors:
for the indexes which cannot be quantified specifically, a priority relation matrix is obtained by a qualitative method:
elements in the matrix are obtained by adopting a nine-scale expert scoring method;
for influence factors of specific data targets which can obtain different schemes, a priority relation matrix is obtained by calculation through a quantitative method, and the calculation is divided into two calculation steps of index value normalization and fuzzy priority membership value:
normalization formula:
fuzzy priority membership value calculation formula:
the following priority relationship matrix is obtained according to the above formula:
step S32: on the basis of obtaining the priority relation matrix, obtaining a fuzzy consistent matrix:
in the above matrix
Step S33: on the basis of obtaining the fuzzy consistent matrix, calculating the priority membership value of different schemes under a certain influence factor according to the following formula:
1, 2.. n, where Yij is the ith row and jth column value in the fuzzy consensus matrix.
Preferably, in step S4, an analytic hierarchy process is used to obtain the weight of the lowest-layer influence factor to the previous-layer influence factor, and the solving step is specifically as follows:
step S41: and (3) obtaining a judgment matrix A by adopting a nine-scale method:
in the formula, m is the number of the lower-layer influence factors, aij is the importance degree of the influence factor i relative to the influence factor j, and a nine-scale method is adopted for estimation;
step S42: solving the maximum eigenvalue lambda max of the matrix A;
step S43: calculating a decision matrixDeviation uniformity index of (2):
step S44: judging whether the consistency meets the following formula:
wherein the RI values are shown in the following table:
| m | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
step S45: if the consistency is satisfied, the step S46 is entered, if not, the judgment matrix is adjusted until the consistency check is satisfied;
step S46: the weight values of different influence factors are calculated according to the following formula:
W＝[W1,W2,...,Wm]
preferably, in step S5, the weights of the different lower-layer impact factors to the upper-layer impact factors obtained in step S4 are multiplied by the different priority membership values of the different schemes for the same impact factor in step S3 to obtain the comprehensive priority membership values of the different schemes for the upper-layer impact factors:
1, 2., n, where n is the number of candidate options;
k 1, 2.., m, where m is the number of lower layer impact factors;
ti is the comprehensive priority membership value of the ith scheme to the upper-layer index;
si: the preferential membership value of the ith scheme to the kth index of the lower layer;
wk: the weight of the kth index of the lower layer relative to the index of the upper layer.
Preferably, in step S6, the step S5 is looped until the priority membership values of the different schemes under the topmost combination index are obtained.
And a site selection system according to the above fuzzy analytic hierarchy process based data center site selection method, characterized in that the computer system based site selection system comprises:
the input module is used for inputting the original data corresponding to each influence factor;
the memory is used for storing the original data corresponding to each influence factor and the calculation result data of each step;
the nine-scale method input module is used for inputting scale data obtained by a nine-scale expert scoring method;
the calculation module is used for executing the operation corresponding to the preset formula in each step;
and the display module is used for displaying and outputting the calculation result.
The invention and the optimal selection scheme thereof are based on the computer technology, can extract corresponding priority information from a plurality of objective factors influencing site selection of the transformer substation, finally form a reliable and effective evaluation result through corresponding operation, have important guiding value for site selection of the transformer substation, especially site selection of a multi-station fusion data center, and realize cost reduction and efficiency improvement.
Compared with the prior art, the method has the following advantages:
(1) site selection influence factors influencing the multi-station fusion data center module and a hierarchical relation model among the site selection influence factors are established, and comprehensiveness and accuracy of judgment of the site selection influence factors are improved.
(2) The fuzzy theory is adopted to solve the priority membership value of the bottom layer influence factors under different schemes to be selected, the difficulty of multi-scheme comparison is reduced through quantitative comparison, and the judgment error caused by subjectivity is reduced.
(3) And solving the weight ratio of the lower-layer influence factors to the upper-layer influence factors by adopting an analytic hierarchy process, and ensuring the overall logic uniformity through consistency test.
(4) The qualitative and quantitative combination improves the scientificity of site selection of the multi-station fusion data center module.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of the overall steps of a fuzzy analytic hierarchy process according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a hierarchical relationship between impact factors according to an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
the overall procedure of the ambiguity analysis method proposed in this example is shown in fig. 1:
(1) analyzing the special influence factors of the data center module in the multi-station fusion station, wherein the special influence factors mainly comprise weather, land price, electricity price, network resources, policy environment and peer development conditions.
(2) Analyzing the influence factors, and establishing a hierarchical relationship among the influence factors, wherein the hierarchical relationship is divided into four layers from top to bottom as shown in fig. 2, the comprehensive index comprises cost and income, the cost is divided into construction cost and operation cost, the construction cost is mainly limited by land price and policy environment, and the time and labor cost spent on the early examination and approval of the urban engineering supported by the policy in the data center are relatively superior; the operation cost is mainly limited by weather and electricity price, and areas with low weather temperature are beneficial to natural heat dissipation and reduce the power consumption; the income is limited by network resources and peer development, and areas with rich network resources and better peer development generally indicate that the leasing market of the data center has vigorous demand, high leasing rate and relatively better income.
(3) And respectively solving the priority membership values of different schemes by adopting a fuzzy method aiming at each single influence factor from bottom to top and sequencing the priority membership values. The method comprises the following specific steps:
1) determining a priority matrix of impact factors
The method comprises two schemes of qualitative and quantitative:
firstly, for the situation that the policy environment, the network resources, the same-row development and the like can not obtain specific quantitative indexes, a qualitative method can be adopted to obtain a priority relation matrix:
The elements in the matrix are obtained by adopting a 0.1-0.9 nine-scale expert scoring method, and are shown in the following table:
| scale value | Scale definition |
| Xij＝0.1 | The j scheme is extremely much better than the i scheme |
| Xij＝0.2 | The j scheme is much better than the i scheme |
| Xij＝0.3 | The j scheme is obviously superior to the i scheme |
| Xij＝0.4 | The j scheme is superior to the i scheme |
| Xij＝0.5 | The scheme i and the scheme j are good |
| Xij＝0.6 | i scheme is superior to j scheme |
| Xij＝0.7 | The i scheme is obviously superior to the j scheme |
| Xij＝0.8 | i scheme is much better than j scheme |
| Xij＝0.9 | i scheme is extremely much better than j scheme |
Secondly, for the influence factors of the specific data standard marks of different schemes such as land price, weather and electricity price, a priority relation matrix can be obtained by calculation through a quantitative method, and the method comprises the following two steps of index value normalization and fuzzy priority membership value calculation:
normalization formula:
Fuzzy priority membership value calculation formula:
the following priority matrix can be obtained according to the above formula:
2) On the basis of obtaining the priority relation matrix, obtaining a fuzzy consistent matrix:
In the above matrix
3) On the basis of obtaining the fuzzy consistent matrix, calculating the priority membership value of different schemes under a certain influence factor according to the following formula:
(i 1, 2.. n, where Yij is the ith row and jth column value in the fuzzy consensus matrix)
(4) And (4) solving different scheme priority membership values corresponding to each influence factor (including land price, policy environment, weather, electricity price, network resource and peer development) at the bottom layer according to the step (3).
(5) The weight of the influence factor of the bottom layer to the influence factor of the upper layer is solved by adopting an analytic hierarchy process, and the weight of the ground price, the policy environment to the construction cost, the weight of the weather price and the electricity price to the operation cost, the weight of the construction cost and the operation cost to the cost, and the weight of the network resource and the peer development to the income are specifically included. The solving steps are as follows:
adopting a nine-scale method in the steps (3) -phi to obtain a judgment matrix A:
(where m is the number of the lower layer influence factors, and aij is the degree of importance of the influence factor i relative to the influence factor j, estimated by a 9-scale method)
And obtaining the maximum eigenvalue lambada max of the matrix A.
Calculating the deviation consistency index of the judgment matrix:
judging whether the consistency meets the following formula:
wherein the RI values are shown in the following table:
| m | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
if the consistency is satisfied, entering a step (c), if not, adjusting the judgment matrix until the consistency test is satisfied.
Weight values of different influence factors are calculated according to the following formula:
W＝[W1,W2,...,Wm]
(6) and (4) calculating the weight of the lower-layer different influence factors to the upper-layer influence factors according to the step (5), and multiplying the weight by different priority membership values of different schemes aiming at the same influence factor in the step (4) to obtain the comprehensive priority membership values of different schemes aiming at the upper-layer influence factors.
1, 2., n, where n is the number of candidate options;
k 1, 2.., m, where m is the number of lower layer impact factors;
ti is the comprehensive priority membership value of the ith scheme to the upper-layer index;
si: the preferential membership value of the ith scheme to the kth index of the lower layer;
wk: the weight of the kth index of the lower layer relative to the index of the upper layer.
(7) And (6) circulating the step until the priority membership values of different schemes under the topmost comprehensive index are obtained, and selecting the scheme with the maximum membership value as the recommended site selection scheme.
The embodiment proposes a specific implementation scheme of the method based on a computer system, and the specific implementation scheme comprises the following steps:
the input module is used for inputting the original data corresponding to each influence factor;
the memory is used for storing the original data corresponding to each influence factor and the calculation result data of each step;
the nine-scale method input module is used for inputting scale data obtained by a nine-scale expert scoring method;
the calculation module is used for executing the operation corresponding to the preset formula in each step;
and the display module is used for displaying and outputting the calculation result.
The following uses a specific embodiment to further explain the contents of the present embodiment:
assuming that there are three different addressing schemes, the basic conditions are as follows:
the method provided by the patent comprises the following steps:
1. establishing an influence factor model as shown in FIG. 2;
2. according to the step (3) -the second step, a priority relation matrix of the land price influence factors is obtained:
3. calculating a fuzzy consistent matrix of the land price influence factors according to the step (3) -2):
4. and (4) according to the steps (3) -3), the priority membership value of the land price influence factor in three schemes: land price: [0.22680.37150.4017]
5. In the same way, the priority membership values respectively corresponding to the policy environment, the annual average temperature, the industrial electricity price, the network environment and the peer development under the three schemes can be respectively obtained:
policy context: [0.26500.34480.3902]
Average annual temperature: [0.39630.22350.3682]
Industrial electricity price: [0.24850.31820.4213]
Network environment: [0.38080.31300.3130]
And (3) carrying out development in the same line: [0.39110.28910.3231]
6. And (5) calculating the weight of the land price and the policy environment to the construction cost.
Firstly, acquiring a judgment matrix:
secondly, acquiring a characteristic value of lambda max which is 0.8;
calculating the deviation consistency index of the judgment matrix: CI ═ 1.2;
judging the consistency:
meets the requirements;
fifthly, the weight ratio of the land price and the policy environment to the construction cost is calculated to be [ 0.750.25 ].
7. And (4) according to the step (6), obtaining the priority membership value of the different schemes to the construction cost of the influence factors as [ 0.23640.36480.3988 ].
8. By analogy, the following can be calculated:
the weight of the meteorological and electric prices to the operation cost is [ 0.430.66 ], and the priority membership value of different schemes to the operation cost of the influence factor is [ 0.33440.30610.4364 ];
the weight of the construction cost and the operation cost to the total cost is [ 0.710.35 ], and the priority membership value of different schemes to the cost is [ 0.28490.36620.4359 ];
thirdly, the weight of the network resource and the peer development to the profit is [ 0.710.35 ], and the priority membership value of different schemes to the profit is [ 0.40730.32340.3354 ];
the weight of the cost and the income to the comprehensive index is [ 0.350.71 ], and the priority membership value of different schemes to the comprehensive index is [ 0.38890.35780.3907 ].
9. According to the final comprehensive index priority membership value, the station site C is an optimal scheme, the station site A is a suboptimal scheme, and the station site B scheme is relatively not suitable for recommendation.
The present invention is not limited to the above preferred embodiments, and various other forms of fuzzy analytic hierarchy process based data center location methods and systems can be derived by anyone in light of the present patent disclosure.
Claims (8)
1. A data center address selection method based on a fuzzy analytic hierarchy process is characterized by comprising the following steps:
step S1: analyzing the influence factors of the data center site selection to obtain the influence factors;
step S2: establishing a hierarchical relationship of data center site selection influence factors;
step S3: sorting different schemes by a fuzzy method aiming at the single influence factor of the bottommost layer from top to bottom respectively to obtain respective priority membership value;
step S4: obtaining the weight proportion of different influence factors of the same layer to the influence factor of the previous layer by adopting an analytic hierarchy process;
step S5: multiplying the weight by the corresponding priority membership value to obtain the ranking and comprehensive priority membership value of the influence factors of the upper layer;
step S6: circularly executing the step S3 to the step S5, and sequentially calculating the comprehensive goodness value of the influence index layer by layer;
step S7: and taking the scheme with the maximum priority membership value of the final top-level comprehensive index as a recommendation scheme.
2. The method of claim 1, wherein the method comprises: in step S1, analyzing the obtained influence factors includes: weather, land price, electricity price, network resources, policy environment and peer development.
3. The method of claim 1, wherein the method comprises: in step S2, the influence factors are analyzed to establish a hierarchical relationship between the influence factors.
4. The fuzzy analytic hierarchy process-based data center location method of claim 3, wherein:
according to the step S3, different scheme priority membership values corresponding to each influence factor at the bottom layer are obtained;
step S3 specifically includes the following steps;
step S31: obtaining a priority relation matrix of the influence factors:
for the indexes which cannot be quantified specifically, a priority relation matrix is obtained by a qualitative method:
elements in the matrix are obtained by adopting a nine-scale expert scoring method;
for influence factors of specific data targets which can obtain different schemes, a priority relation matrix is obtained by calculation through a quantitative method, and the calculation is divided into two calculation steps of index value normalization and fuzzy priority membership value:
normalization formula:
fuzzy priority membership value calculation formula:
the following priority relationship matrix is obtained according to the above formula:
step S32: on the basis of obtaining the priority relation matrix, obtaining a fuzzy consistent matrix:
in the above matrix
Step S33: on the basis of obtaining the fuzzy consistent matrix, calculating the priority membership value of different schemes under a certain influence factor according to the following formula:
1, 2.. n, where Yij is the ith row and jth column value in the fuzzy consensus matrix.
5. The method of claim 4, wherein the method comprises:
in step S4, an analytic hierarchy process is used to obtain the weight of the bottom-layer influence factor to the previous-layer influence factor, and the solving steps are as follows:
step S41: and (3) obtaining a judgment matrix A by adopting a nine-scale method:
in the formula, m is the number of the lower-layer influence factors, aij is the importance degree of the influence factor i relative to the influence factor j, and a nine-scale method is adopted for estimation;
step S42: solving the maximum eigenvalue lambda max of the matrix A;
step S43: calculating the deviation consistency index of the judgment matrix:
step S44: judging whether the consistency meets the following formula:
wherein the RI values are shown in the following table:
step S45: if the consistency is satisfied, the step S46 is entered, if not, the judgment matrix is adjusted until the consistency check is satisfied;
step S46: the weight values of different influence factors are calculated according to the following formula:
W＝[W1,W2,...,Wm]
6. the fuzzy analytic hierarchy process-based data center location method of claim 5, wherein:
in step S5, the weights of the different lower-layer impact factors to the upper-layer impact factors obtained in step S4 are multiplied by the different priority membership values of the different schemes for the same impact factor in step S3 to obtain the comprehensive priority membership values of the different schemes for the upper-layer impact factors:
1, 2., n, where n is the number of candidate options;
k 1, 2.., m, where m is the number of lower layer impact factors;
ti is the comprehensive priority membership value of the ith scheme to the upper-layer index;
si: the preferential membership value of the ith scheme to the kth index of the lower layer;
wk: the weight of the kth index of the lower layer relative to the index of the upper layer.
7. The method of claim 6, wherein the method comprises: in step S6, the process loops through step S5 until the priority membership values of the different schemes under the top-most comprehensive index are obtained.
8. The system of claim 6, wherein the computer-based system comprises:
the input module is used for inputting the original data corresponding to each influence factor;
the memory is used for storing the original data corresponding to each influence factor and the calculation result data of each step;
the nine-scale method input module is used for inputting scale data obtained by a nine-scale expert scoring method;
the calculation module is used for executing the operation corresponding to the preset formula in each step;
and the display module is used for displaying and outputting the calculation result.
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