CN113268704B - Fuzzy analytic hierarchy process-based data center address selection method and system - Google Patents
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Abstract
The invention provides a data center site selection method and a system based on a fuzzy analytic hierarchy process, which are characterized in that firstly, related influence factors which need 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 to calculate the priority membership value of different schemes aiming at the same evaluation index, then, the analytic hierarchy process is adopted to calculate the weight of the relative importance degree of the next layer of different evaluation indexes relative to the previous layer of evaluation indexes, then, the priority membership weighting summation is carried out on the schemes to be selected, the previous layer of evaluation indexes are obtained, and the priority membership value of the top layer of evaluation indexes is finally obtained and the 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 key technical research of multi-station integrated substation planning construction based on 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 fusion refers to a novel energy information body construction mode which fully utilizes the existing space, manpower, energy supply, automatic management and other resources of a transformer substation, nearby new energy is consumed, overall construction schemes such as the transformer substation, a data center station, an energy storage station, a 5G base station, a Beidou ground enhancement station and the like are designed and optimized, and overall energy saving and efficiency improvement are realized through actions such as one-station multi-use of the transformer substation, resource integration, business mode innovation and the like.
At present, mature schemes and application experience are provided for site selection of a transformer substation, but most of site selection characteristics of a data center built in combination with the transformer substation stay at 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 the subsequent large-scale popularization and construction of the fusion station, and the later data center application and commercial operation are most likely to be affected due to unreasonable site selection, so that resource waste is caused.
Disclosure of Invention
Considering that mature schemes and application experience are already available for site selection of the transformer substation at present, most of site selection characteristics of a data center built in combination with the transformer substation stay at 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 the subsequent large-scale popularization and construction of the fusion station, and the later data center application and commercial operation are most likely to be affected 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 location method and system based on a fuzzy analytic hierarchy process, which comprises the steps of firstly analyzing relevant influence factors which need to reach a total target, establishing a hierarchical relation among the influence factors, regarding each influence factor as an evaluation index, aiming at the same evaluation index, adopting a fuzzy theory to calculate priority membership values of different schemes, adopting a analytic hierarchy process to calculate the weight of the relative importance degree of the next layer of different evaluation indexes relative to the previous layer of evaluation indexes, then carrying out priority membership weighted summation on the schemes to be selected to obtain the previous layer of evaluation indexes, and so on, finally obtaining the priority membership value of the top layer of evaluation indexes and selecting the scheme preferentially.
The technical scheme is as follows:
a data center site selection method based on a fuzzy analytic hierarchy process is characterized by comprising the following steps:
step S1: analyzing the data center site selection influence factors to obtain influence factors;
step S2: establishing a hierarchical relationship of data center site selection influencing factors;
step S3: sequencing different schemes by adopting a fuzzy method aiming at the single influence factor of the bottommost layer from top to bottom respectively, and obtaining respective priority membership values;
step S4: obtaining the weight proportion of different influence factors of the same layer to the influence factors 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 of the influence factors of the upper layer and the comprehensive priority membership value;
step S6: step S3-step S5 are circularly executed, and comprehensive membership values of the influence indexes are sequentially obtained layer by layer;
step S7: and taking the scheme with the highest priority membership value according to the final top-level comprehensive index as a recommended scheme.
Preferably, in step S1, the influence factors obtained by the analysis include: weather, land price, electricity price, network resources, policy environment and peer development.
Preferably, in step S2, analysis is performed with respect to the influence factors, and a hierarchical relationship between the influence factors is established.
Preferably, according to step S3, the priority membership value of different schemes corresponding to each influence factor of the bottommost layer is obtained;
step S3 specifically comprises the following steps;
step S31: solving a priority relation matrix of the influence factors:
for indexes which cannot be specifically quantified, a qualitative method is adopted to obtain a priority relation matrix:
wherein n is the number of alternatives;
the elements in the matrix are solved by adopting a nine-scale expert scoring method;
for the influence factors of specific data targets which can obtain different schemes, calculating by adopting a quantitative method to obtain a priority relation matrix, and dividing the calculation steps of index value normalization and fuzzy priority membership value:
normalization formula:
fuzzy priority membership value calculation formula:
the following priority relation matrix is obtained according to the above:
step S32: on the basis of obtaining a priority relation matrix, obtaining a fuzzy consistency matrix:
in the matrix described above
Step S33: on the basis of obtaining the fuzzy consistency matrix, the priority membership value of different schemes under a certain influence factor is calculated according to the following formula:
i=1, 2,..n, where Yij is the i-th row and j-th column value in the fuzzy consistent matrix
Preferably, in step S4, the weight of the lowest layer influence factor for the last layer influence factor is obtained by using a hierarchical analysis method, and the solving steps are specifically as follows:
step S41: obtaining a judgment matrix A by adopting a nine-scale method:
wherein m is the number of lower layer influence factors, aij is the importance degree of the influence factor i relative to the influence factor j, and nine scale methods are adopted for estimation;
step S42: solving the maximum eigenvalue lambda max of the matrix A;
step S43: calculating deviation consistency indexes of the judgment matrix:
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, step S46 is entered, if not, the judgment matrix is adjusted until the consistency test is satisfied;
step S46: the weight values of the different influencing factors are calculated as follows:
W＝[W1,W2,...,Wm]
preferably, in step S5, the weights of the lower layer different influence factors for the upper layer influence factors obtained in step S4 are multiplied by different priority membership values of the different schemes for the same influence factor in step S3 to obtain comprehensive priority membership values of the different schemes for the upper layer influence factors.
i=1, 2,..n, where n is the number of alternatives;
k=1, 2,..m, where m is the number of lower layer influencing factors;
ti is the comprehensive priority membership value of the ith scheme to the upper layer index;
si: the priority membership value of the ith scheme to the kth index of the lower layer;
wk: the k index of the lower layer is the weight occupied by the index of the upper layer.
Preferably, in step S6, step S5 is looped until the priority membership value of different schemes under the top-most comprehensive index is obtained.
And an address selecting system according to the above data center address selecting method based on the fuzzy analytic hierarchy process, characterized by comprising, based on a computer system:
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 the scale data obtained by the nine-scale expert scoring method;
the computing module is used for executing the operation corresponding to the preset formulas of each step;
and the display module is used for displaying and outputting the calculation result.
The method and the system are based on a computer technology, can extract corresponding priority information from a plurality of objective factors influencing the site selection of the transformer substation, and finally form a reliable and effective evaluation result through corresponding operation, and have important guiding value for the site selection of the transformer substation, especially the site selection of a multi-station fusion data center, so that the cost reduction and the synergy are realized.
Compared with the prior art, the method has the following advantages:
(1) And establishing an addressing influence factor influencing the multi-station fusion data center module and a hierarchical relation model between the addressing influence factor influencing the multi-station fusion data center module, and improving the comprehensiveness and accuracy of the judgment of the addressing influence factor.
(2) The fuzzy theory is adopted to solve the priority membership value of the lower layer influence factors under different alternative schemes, and the difficulty of multi-scheme comparison is reduced and the judgment error caused by subjectivity is reduced through quantitative comparison.
(3) 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) Qualitative and quantitative combination promotes the scientificity of multi-station fusion data center module site selection.
Drawings
The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a schematic diagram of the general steps of a fuzzy analytic hierarchy process in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hierarchical relationship between influencing factors according to an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present patent more comprehensible, embodiments accompanied with figures are described in detail below:
the general steps of the fuzzy analytic hierarchy process proposed in this embodiment are shown in fig. 1:
(1) And analyzing the specific influence factors of the data center modules in the multi-station fusion station, wherein the specific influence factors mainly comprise weather, ground price, electricity price, network resources, policy environment and peer development conditions.
(2) Analyzing the influence factors, establishing a hierarchical relationship among the influence factors, dividing the hierarchical relationship into four layers from top to bottom as shown in fig. 2, wherein the comprehensive indexes comprise 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 for early-stage examination and approval of urban engineering with policy support of a data center is relatively advantageous; the operation cost is mainly limited by weather and electricity price, and the areas with low weather temperature are favorable for natural heat dissipation and reduce the electricity consumption; the income is limited by the development of network resources and peers, and the areas with rich network resources and better development of peers generally indicate that the leasing market of the data center is required to be vigorous, the leasing rate is high, and the income is relatively good.
(3) And (3) 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 method comprises the following specific steps:
1) Determining a priority relation matrix of influence factors
The method comprises two schemes of qualitative and quantitative:
(1) for the situation that the specific quantitative indexes cannot be obtained in policy environment, network resources, peer development and the like, a qualitative method can be adopted to obtain a priority relation matrix:
(where n is the number of alternatives)
The elements in the matrix are obtained by adopting a 0.1-0.9 nine-scale expert scoring method, and the following table shows:
| scale value | Scale definition |
| Xij＝0.1 | The j scheme is extremely superior to the i scheme |
| Xij＝0.2 | The j scheme is much better than the i scheme |
| Xij＝0.3 | The j scheme is better than the i scheme |
| Xij＝0.4 | The j scheme is better than the i scheme |
| Xij＝0.5 | The i scheme is as good as the j scheme |
| Xij＝0.6 | The i scheme is better than the j scheme |
| Xij＝0.7 | The i scheme is better than the j scheme |
| Xij＝0.8 | The i scheme is much better than the j scheme |
| Xij＝0.9 | The i scheme is extremely superior to the j scheme |
(2) For the influence factors of specific data labels of different schemes such as ground price, weather, electricity price and the like, a priority relation matrix can be calculated by adopting a quantitative method, and the method comprises two steps of index value normalization and fuzzy priority membership value calculation:
normalization formula:
(i=1, 2,., n, where n is the total number of alternatives
Fuzzy priority membership value calculation formula:
the following priority matrix can be obtained according to the above equation:
(where n is the number of alternatives)
2) On the basis of obtaining a priority relation matrix, obtaining a fuzzy consistency matrix:
(where n is the number of alternatives)
In the matrix described above
3) On the basis of obtaining the fuzzy consistency matrix, the priority membership value of different schemes under a certain influence factor is calculated according to the following formula:
(i=1, 2,., n, where Yij is the i-th row, j-th column value in the fuzzy consistent matrix
(4) And (3) obtaining the priority membership value of different schemes corresponding to each influence factor (including land price, policy environment, weather, electricity price, network resource and peer development) at the bottommost layer according to the step (3).
(5) The analytic hierarchy process is adopted to obtain the weight of the lowest layer influence factor to the upper layer influence factor, and the weight specifically comprises the weight of land price and policy environment to construction cost, the weight of weather and electricity price to operation cost, the weight of construction cost and operation cost to cost, and the weight of network resource and peer development to income. The solving steps are as follows:
(1) obtaining a judgment matrix A by adopting a nine-scale method in the steps (3) - (1):
(where m is the number of lower-layer influence factors, aij is the importance of influence factor i relative to influence factor j, and 9 scale is used for estimation)
(2) The maximum eigenvalue λmax of matrix a is found.
(3) Calculating deviation consistency indexes of the judgment matrix:
(4) 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 |
(5) if the consistency is satisfied, the step (6) is entered, and if the consistency is not satisfied, the judgment matrix is adjusted until the consistency test is satisfied.
(6) The weight values of the different influencing factors are calculated as follows:
W＝[W1,W2,...,Wm]
(6) And (3) multiplying the weights of the lower-layer different influence factors to the upper-layer influence factors, which are obtained in the step (5), by the different priority membership values of the different schemes aiming at the same influence factor in the step (4) to obtain the comprehensive priority membership value of the different schemes aiming at the upper-layer influence factor.
i=1, 2,..n, where n is the number of alternatives;
k=1, 2,..m, where m is the number of lower layer influencing factors;
ti is the comprehensive priority membership value of the ith scheme to the upper layer index;
si: the priority membership value of the ith scheme to the kth index of the lower layer;
wk: the k index of the lower layer is the weight occupied by the index of the upper layer.
(7) And (6) circulating until the priority membership value of different schemes under the top-level comprehensive index is obtained, and selecting the scheme with the largest membership value as the recommended site selection scheme.
The embodiment provides a specific implementation scheme of the method based on a computer system, which 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 the scale data obtained by the nine-scale expert scoring method;
the computing module is used for executing the operation corresponding to the preset formulas of each step;
and the display module is used for displaying and outputting the calculation result.
The following will further describe the contents of this embodiment by using a specific embodiment:
assume three different addressing schemes, the basic conditions of which are as follows:
the method according to the patent comprises the following steps:
1. establishing an influence factor model as shown in fig. 2;
2. according to the steps (3) - (2), a priority relation matrix of the ground price influence factors is obtained:
3. calculating a fuzzy consistent matrix of the ground price influence factors according to the steps (3) -2):
4. according to the steps (3) -3), the priority membership value of the ground price influence factors in three schemes: land price: [0.2268 0.3715 0.4017]
5. The same theory can respectively calculate the priority membership values corresponding to the policy environment, the annual average air temperature, the industrial electricity price, the network environment and the peer development under three schemes:
policy environment: [0.2650 0.3448 0.3902]
Annual average air temperature: [0.3963 0.2235 0.3682]
Industrial electricity price: [0.2485 0.3182 0.4213]
Network environment: [0.3808 0.3130 0.3130]
The same line develops: [0.3911 0.2891 0.3231]
6. And (5) obtaining the weight of the land price and the policy environment on the construction cost according to the step (5).
(1) Firstly, acquiring a judgment matrix:
(2) obtaining a characteristic value of λmax=0.8;
(3) calculating deviation consistency indexes of the judgment matrix: ci= -1.2;
(4) judging consistency:satisfying the following requirements;
(5) the weight ratio of the land price and policy environment to the construction cost is [ 0.75.25 ].
7. And (3) solving the priority membership value of different schemes for the construction cost of the influence factors according to the step (6) to be [0.2364 0.3648 0.3988].
8. By analogy, one can calculate:
(1) the weather and electricity prices occupy weight of [ 0.43.0.66 ] for the operation cost, and the priority membership value of different schemes for the operation cost of the influence factor is [0.3344 0.3061 0.4364];
(2) the construction cost and the operation cost have the weight of [ 0.71.35 ] for the total cost, and the priority membership value of different schemes for the cost is [0.2849 0.3662 0.4359];
(3) the weight of the network resource and the peer development for the benefits is [ 0.71.0.35 ], and the priority membership value of different schemes for the benefits is [0.4073 0.3234 0.3354];
(4) the weight of the cost and the benefit to the comprehensive index is [ 0.35.71 ], and the priority membership value of different schemes to the comprehensive index is [0.3889 0.3578 0.3907].
9. According to the final comprehensive index priority membership value, the site C is the optimal scheme, the site A is the suboptimal scheme, and the site B scheme is relatively not recommended.
The present patent is not limited to the above-mentioned best mode, any person can obtain other various data center location methods and systems based on fuzzy analytic hierarchy process under the teaching of the present patent, and all equivalent changes and modifications made according to the scope of the present patent should be covered by the present patent.
Claims (4)
1. A data center site selection method based on a fuzzy analytic hierarchy process is characterized by comprising the following steps:
step S1: analyzing the data center site selection influence factors to obtain influence factors;
step S2: establishing a hierarchical relationship of data center site selection influencing factors;
step S3: sequencing different schemes by adopting a fuzzy method aiming at the single influence factor of the bottommost layer from top to bottom respectively, and obtaining respective priority membership values;
step S4: obtaining the weight proportion of different influence factors of the same layer to the influence factors 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 of the influence factors of the upper layer and the comprehensive priority membership value;
step S6: step S3-step S5 are circularly executed, and comprehensive membership values of the influence indexes are sequentially obtained layer by layer;
step S7: taking the scheme with the highest priority membership value according to the final top-level comprehensive index as a recommended scheme;
in step S1, the influence factors obtained by the analysis include: weather, land price, electricity price, network resources, policy environment and peer development conditions;
in step S2, analyzing the influence factors, and establishing a hierarchical relationship among the influence factors;
according to the step S3, the priority membership value of different schemes corresponding to each influence factor of the bottommost layer is obtained;
step S3 specifically comprises the following steps;
step S31: solving a priority relation matrix of the influence factors:
for indexes which cannot be specifically quantified, a qualitative method is adopted to obtain a priority relation matrix:
wherein n is the number of alternatives;
the elements in the matrix are solved by adopting a nine-scale expert scoring method;
for the influence factors of specific data targets which can obtain different schemes, calculating by adopting a quantitative method to obtain a priority relation matrix, and dividing the calculation steps of index value normalization and fuzzy priority membership value:
normalization formula:
fuzzy priority membership value calculation formula:
the following priority relation matrix is obtained according to the above:
step S32: on the basis of obtaining a priority relation matrix, obtaining a fuzzy consistency matrix:
in the matrix described above
Step S33: on the basis of obtaining the fuzzy consistency matrix, the priority membership value of different schemes under a certain influence factor is calculated according to the following formula:
i=1, 2,..n, where Yij is the i-th row and j-th column value in the fuzzy consistent matrix
In step S4, the weight of the lowest layer influence factor for the last layer influence factor is obtained by using an analytic hierarchy process, and the solving steps are specifically as follows:
step S41: obtaining a judgment matrix A by adopting a nine-scale method:
wherein m is the number of lower layer influence factors, aij is the importance degree of the influence factor i relative to the influence factor j, and nine scale methods are adopted for estimation;
step S42: solving the maximum eigenvalue lambda max of the matrix A;
step S43: calculating deviation consistency indexes 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, step S46 is entered, if not, the judgment matrix is adjusted until the consistency test is satisfied;
step S46: the weight values of the different influencing factors are calculated as follows:
W＝[W1,W2,...,Wm]
2. the fuzzy analytic hierarchy process-based data center addressing method of claim 1, wherein:
in step S5, the weights of the lower layer different influence factors on the upper layer influence factors obtained in step S4 are multiplied by the different priority membership values of the different schemes on the same influence factor in step S3 to obtain the comprehensive priority membership value of the different schemes on the upper layer influence factors:
i=1, 2,..n, where n is the number of alternatives;
k=1, 2,..m, where m is the number of lower layer influencing factors;
ti is the comprehensive priority membership value of the ith scheme to the upper layer index;
si: the priority membership value of the ith scheme to the kth index of the lower layer;
wk: the k index of the lower layer is the weight occupied by the index of the upper layer.
3. The fuzzy analytic hierarchy process-based data center addressing method of claim 2, wherein: in step S6, the step S5 is circulated until the priority membership value of different schemes under the topmost comprehensive index is obtained.
4. The system for addressing a data center based on fuzzy analytic hierarchy process of claim 2, wherein the computer system 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 the scale data obtained by the nine-scale expert scoring method;
the computing module is used for executing the operation corresponding to the preset formulas of each step;
and the display module is used for displaying and outputting the calculation result.
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