Big data center site selection method and system based on multi-factor comprehensive evaluation method

Abstract

The invention discloses a big data center site selection method and system based on a multi-factor comprehensive evaluation method, and relates to the technical field of comprehensive evaluation and information, and the method comprises the steps: preliminarily determining the site selection range of a big data center; constructing a big data center site selection evaluation system; performing weight distribution on each index, and establishing a big data center site selection model; analyzing the sensitivity of multiple factors, and constructing a decision support system; and selecting the area with the highest score, and determining the big data center site selection. According to the big data center site selection method based on the multi-factor comprehensive evaluation method provided by the invention, the multi-factor comprehensive evaluation method is utilized, an evaluation method is provided for big data center site selection under the background of'dual carbon 'and global promotion clean energy replacing fossil energy, time can be saved for selecting the geographic position of a big data center in the future, and the selection efficiency of the big data center is improved. Utility of the big data center is improved, and construction cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive evaluation and information technology, and in particular to a big data center site selection method and system based on a multi-factor comprehensive evaluation method. Background Art

[0002] With the rapid development of the digital economy and AI industry, big data centers have received widespread attention as technical support for data storage, processing and analysis. Their role is to support enterprises and organizations to extract valuable information from massive data, thereby achieving accurate decision-making, business optimization and innovative applications. Big data centers can process data from different sources, including sensors, social media and transaction records, to provide support for data-driven decision-making. In addition, big data centers can improve the speed and efficiency of data processing through efficient computing and storage resources to meet the needs of real-time analysis. Therefore, the necessity of building a big data center lies in its ability to enhance the competitiveness of organizations, improve operational efficiency, and promote the application of new technologies such as artificial intelligence and machine learning, thereby providing a solid data foundation for business development.

[0003] In addition, the site selection of big data centers needs to take into account a variety of factors in order to maximize their effectiveness. Studying the site selection of big data centers is of great significance because it directly affects the efficiency, cost and reliability of data processing. Reasonable site selection can optimize energy consumption, reduce operating costs, and improve the speed and stability of data transmission. In addition, the choice of geographical location also involves the availability of network infrastructure, the management of disaster risks, and the support of the local policy environment. By comprehensively considering these factors, the efficient operation of the data center can be ensured, and a good foundation can be provided for future expansion and technological upgrades, thereby enhancing overall competitiveness and sustainable development capabilities.

[0004] From the above content, it can be seen that the construction and development of big data centers is necessary, but multiple factors need to be considered comprehensively during the construction process. Therefore, in order to ensure the feasibility of the construction of big data centers, a multi-factor comprehensive evaluation method needs to be applied in the site selection process.

[0005] There are still several issues that need further study:

[0006] (1) It is necessary to select appropriate evaluation factors and set weights for them during the site selection process. Different construction purposes have inconsistent assessments of the importance of each factor, resulting in subjectivity and inaccuracy in the evaluation results;

[0007] (2) In comprehensive evaluation, the assessment of potential risks is often relatively simple, failing to fully consider complexity and uncertainty, which may lead to the fragility of site selection decisions;

[0008] (3) The site selection of a big data center involves multiple stakeholders (such as government, enterprises, and communities), and each party may have different needs and concerns about the site selection, which complicates the comprehensive evaluation process. Summary of the invention

[0009] In view of the above-mentioned problems, the present invention is proposed.

[0010] Therefore, the technical problem solved by the present invention is: using a multi-factor comprehensive evaluation method that combines the analytic hierarchy process with the superior and inferior solution distance method to divide the regional temperature, clean energy supply, land cost, talent resources, natural disaster conditions and other elements of the big data center site selection problem into levels such as goals, criteria, and plans. This method can not only determine the weight of the evaluation target, but also standardize and arrange the index data for analysis. This method reduces the limitations of a single method and ensures that the selected address is more in line with actual conditions and objective needs. While achieving the rationality of site selection, the optimal solution for the site selection of the big data center is obtained to ensure the maximum utilization of regional resources.

[0011] In order to solve the above technical problems, the present invention provides the following technical solutions: a big data center site selection method based on a multi-factor comprehensive evaluation method, comprising:

[0012] Determine the site selection scope of the big data center and divide the area into candidate areas according to demand;

[0013] Construct a big data center site selection evaluation system based on factors and data such as energy supply, natural environment, and economic development in each candidate area;

[0014] Calculate the weight distribution for the evaluation system of each indicator, determine the proportion of each evaluation indicator in the overall goal, and establish a big data center site selection model, that is, calculate the comprehensive score of each candidate area based on the quantitative evaluation system and weight distribution;

[0015] Analyze the sensitivity of multiple factors, determine the feasibility of quantified evaluation indicators, and avoid unreasonable evaluation indicators;

[0016] Select the region with the highest score to determine the location of the big data center.

[0017] As a preferred solution of the big data center site selection method based on the multi-factor comprehensive evaluation method described in the present invention, wherein: the site selection range is determined based on the construction requirements of the big data center.

[0018] As a preferred scheme of the big data center site selection method based on multi-factor comprehensive evaluation method described in the present invention, the big data center site selection evaluation system includes regional temperature, power supply, clean energy supply, water resources, natural disaster risks, land costs, human resources, and regional development distribution.

[0019] As a preferred solution of the big data center site selection method based on the multi-factor comprehensive evaluation method described in the present invention, wherein: the construction of the big data center site selection evaluation system also includes using Min-Max normalization to transform all evaluation indicators;

[0020] The Min-Max normalization is expressed as,

[0021]

[0022] Among them, m represents all evaluation indicators, is the normalized evaluation value of the mth indicator in the nth region.

[0023] As a preferred solution of the big data center site selection method based on the multi-factor comprehensive evaluation method described in the present invention, wherein: the establishment of the big data center site selection model includes dividing the hierarchical structure based on the site selection requirements;

[0024] Score each pair of criteria or options to establish an overall judgment moment;

[0025] Each column of the overall judgment moment is summed, and then each element is divided by the sum of its column to obtain a normalized comparison matrix;

[0026] Calculate the average value of each row of the normalized matrix to obtain the weight αm of each standard;

[0027] Complete the weight calculation and summarize the total site selection score for the nth region.

[0028] As a preferred solution of the big data center site selection method based on the multi-factor comprehensive evaluation method described in the present invention, the total site selection score is expressed as:

[0029]

[0030] As a preferred solution of the big data center site selection method based on multi-factor comprehensive evaluation method described in the present invention, wherein: the construction of the decision support system includes adopting a local sensitivity analysis method to check the impact on the final decision result and avoid unreasonable evaluation indicators.

[0031] Another object of the present invention is to provide a big data center site selection system based on a multi-factor comprehensive evaluation method, which can ensure that the selected address is more in line with actual conditions and objective needs by constructing a big data center site selection system based on a multi-factor comprehensive evaluation method.

[0032] To solve the above technical problems, the present invention provides the following technical solutions: a big data center site selection system based on a multi-factor comprehensive evaluation method, comprising: a scope determination unit, an evaluation system unit, a weight allocation unit, a sensitivity analysis unit and a site selection decision unit; the scope determination unit is used to preliminarily determine the site selection scope of the big data center; the evaluation system unit is used to build a big data center site selection evaluation system; the weight allocation unit is used to implement weight allocation for various indicators and establish a big data center site selection model; the sensitivity analysis unit is used to analyze the sensitivity of multiple factors and build a decision support system; the site selection decision unit is used to select the area with the highest score to determine the site selection of the big data center.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the big data center site selection method based on the multi-factor comprehensive evaluation method are implemented as described above.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the big data center site selection method based on a multi-factor comprehensive evaluation method as described above.

[0035] Beneficial effects of the invention: The big data center site selection method based on a multi-factor comprehensive evaluation method provided by the invention utilizes a multi-factor comprehensive evaluation method to provide an evaluation method for the site selection of a big data center under the background of "dual carbon" and the global promotion of clean energy to replace fossil energy. It can save time for the future selection of the geographical location of the big data center, improve the effectiveness of the big data center, and reduce construction costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 An overall flow chart of a big data center site selection method based on a multi-factor comprehensive evaluation method is provided for one embodiment of the present invention.

[0038] Figure 2 The present invention is a site selection flow chart of a big data center site selection method based on a multi-factor comprehensive evaluation method.

[0039] Figure 3 This is an overall judgment matrix of a big data center site selection method based on a multi-factor comprehensive evaluation method of the present invention.

[0040] Figure 4The present invention discloses a logic diagram of a big data center site selection method based on a multi-factor comprehensive evaluation method.

[0041] Figure 5 This is an overall structural diagram of a big data center site selection system based on a multi-factor comprehensive evaluation method according to the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Example 1

[0045] Reference Figure 1-Figure 4 , which is an embodiment of the present invention, provides a big data center site selection method based on a multi-factor comprehensive evaluation method, including:

[0046] Reference Figure 2 , Step 1: Preliminary determination of the location scope of the big data center

[0047] At the beginning of site selection, we first investigate the relevant policies of the corresponding region and clarify the construction requirements of the big data center, including storage capacity, processing capacity, service level and scalability. According to the management scope of the local government and construction needs, we determine the site selection range of big data and set the number of each site selection area within the range (1, 2, ..., n, ..., N).

[0048] Step 2: Build a big data center site selection evaluation system: determine key indicators, real-time data collection and quantitative analysis

[0049] (1) Determine the evaluation indicators for big data center site selection

[0050] Determination of the site selection indicators for a big data center is the basis for site selection. To ensure the reliability, security and cost-effectiveness of the data center, this patent proposes to consider the following factors as evaluation indicators, including: regional temperature, power supply, clean energy supply, water resources, natural disaster risks, land costs, talent resources, and regional development distribution.

[0051] The difference between the process of the present invention and other site selection methods is mainly reflected in the limitation of evaluation indicators and the evaluation effect. Specifically, the present invention selects sites through specific evaluation indicators, which include multiple dimensions such as energy supply, cost, safety, and benefit impact. The selection of these evaluation indicators makes the present invention more comprehensive and accurate in the evaluation process and can better meet the needs of specific scenarios. Evaluation indicators proposed by the present invention:

[0052] ① Regional temperature: The operation of a big data center generates a lot of heat, which requires an efficient cooling system. The regional temperature directly affects the efficiency and energy consumption of the cooling system. Lower temperatures help reduce cooling costs and improve energy efficiency. Therefore, choosing a region with a suitable temperature is crucial to the long-term operating costs and sustainability of a big data center.

[0053] ② Power supply: Big data centers require a stable and sufficient power supply. When selecting a site, the local power supply capacity and cost should be considered to reduce operating costs.

[0054] ③ Clean energy supply: When selecting a site, consider whether there are renewable energy options to promote the green operation of the big data center.

[0055] ④ Distribution of water resources: The heat dissipation systems of big data centers mostly use water cooling systems, which requires consideration of water supply conditions, including water quantity and quality.

[0056] ⑤ Natural disaster risk: Big data centers store a large amount of critical data and have extremely high requirements for security and stability. Natural disasters such as earthquakes and floods may cause devastating blows to data centers, resulting in data loss and service interruption. Therefore, assessing natural disaster risks is an indispensable part of big data center site selection to ensure the safety and reliability of the data center.

[0057] ⑥ Land cost: Big data centers need to occupy a large amount of land for the construction of computer rooms and supporting facilities. Land cost directly affects the construction investment of big data centers. Choosing areas with relatively low land costs and in line with planning requirements will help reduce construction costs and improve the economic benefits of the project.

[0058] ⑦ Talent resources: The construction and operation of a big data center requires the support of professional technical personnel. Selecting a region with rich talent resources and strong technical strength will help attract and retain outstanding technical talents and provide continuous technical support and innovation power for the big data center. At the same time, a good talent environment will also help enhance the competitiveness and market position of the big data center.

[0059] ⑧Regional development: The construction of a big data center must comply with local regional development plans, have policy support and incentives, and require the purchase of related parts and equipment. If there are related industries at the corresponding location, costs can be further reduced while activating related industries.

[0060] (2) Real-time data collection

[0061] Collect real-time data from local governments, power grid companies, ecological environment departments, telecommunications companies, publications, and the Internet.

[0062] (3) Quantify the relevant indicator data and normalize them to unify the order of magnitude

[0063] Since the various types of data collected have different units and dimensions, directly using these data for analysis or modeling may be misleading. Through normalization, data of different dimensions can be converted into pure values ​​without units, ensuring data availability and analysis accuracy.

[0064] ① Regional temperature: The place with the lowest winter temperature in my country is the Northeast, where the temperature can drop below -30°C. As for the highest temperature, the Turpan Basin in northwest China often experiences extreme high temperatures in summer, with the highest temperature recorded exceeding 47°C. Based on the above data, the temperature can be directly converted into data. The specific quantitative formula is as follows:

[0065]

[0066] in, Temp is the temperature quantitative index data of the nth area. n is the average temperature of the nth region.

[0067] ② Power supply: The quantification process of power supply indicators mainly considers power installed capacity, power generation and power consumption. The specific quantification formula is as follows:

[0068]

[0069] in, The quantitative indicator data of power supply in the nth region, IC n , G n , C n is the installed capacity, power generation and power consumption of each n-th region. Power consumption C n Define the buy volume as a negative value and the sell volume as a positive value.

[0070] ③Clean energy supply: Clean energy usually refers to non-carbon energy that does not produce carbon dioxide when generating electricity. Its quantification method is mainly calculated by recording the ratio of the production of various clean energy in the corresponding region over a certain period of time to the total power generation in the region. The specific quantification formula is as follows:

[0071]

[0072] in, Quantitative indicator data for clean energy supply in the nth region, is the production of various clean energy in each nth region.

[0073] ④ Distribution of water resources: To quantify the water sources in the corresponding regions, it is necessary to collect data on precipitation, river runoff, lake water volume, and groundwater volume in each region, and evaluate the availability of water resources. The specific quantitative formula is as follows:

[0074]

[0075] in, is the quantitative indicator data of water resources distribution in the nth region, are the pollution equivalent values ​​of precipitation, rivers, lakes, and groundwater in the nth region, respectively, P n , R n , L n , W n They are respectively the precipitation, river runoff, lake water volume and groundwater volume in the nth region.

[0076] ⑤ Natural disaster risk: Natural disaster risk assessment is a systematic process that involves analyzing potential natural disasters and assessing their possible impact on big data centers. The specific quantitative formula is as follows:

[0077]

[0078] in, is the quantitative indicator data of natural disaster risk in the nth region, are the expected loss consequences of the vth type of specific natural disaster in the nth region, is the probability of occurrence of the vth type of specific natural disaster in the nth region.

[0079] ⑥ Land cost: The construction of a big data center requires a large area of ​​land, and the land cost greatly affects the construction cost of the big data center. The quantification of land cost can directly convert the local land price. The specific quantification formula is as follows:

[0080]

[0081] in, is the quantitative indicator data of land cost in the nth region, LV n are the land prices in the nth area respectively.

[0082] ⑦ Human resources: Quantitative evaluation of a region’s human resources involves multiple dimensions. This invention considers the education index, skill index, and experience index. The specific quantitative formula is as follows:

[0083]

[0084] in, SC is the quantitative indicator data of talent resources in the nth region. n ,UC n , MC n 、DC n They are the number of associate students, undergraduate students, master students, and doctoral students in the nth region respectively.

[0085] ⑧Regional development: Quantitative evaluation of regional development of a region is achieved by constructing a system of economic indicators. The specific quantitative formula is as follows:

[0086]

[0087] in, is the quantitative indicator data of regional development of the nth region, GDP n are the GDP of the nth region respectively.

[0088] After the above evaluation indicators are quantified, they are not in the same order of magnitude. In the subsequent evaluation, they need to be converted into dimensionless values ​​of the same order of magnitude. The present invention uses Min-Max normalization to convert all evaluation indicators. The specific formula is as follows:

[0089]

[0090] Among them, m represents all evaluation indicators, including: regional temperature Temp, electricity supply Ele, clean energy supply Clean, water resources Water, natural disaster risk ND, land cost LV, human resources TQI, regional development RDI, is the normalized evaluation value of the mth indicator in the nth region.

[0091] Step 3: Assign weights to each indicator and establish a big data center site selection model

[0092] Based on the corresponding indicators in step 2, the weight distribution of each indicator is further calculated to lay the foundation for calculating the total score of site selection.

[0093] 3.1 Determine goals and hierarchy

[0094] The objectives, standards, sub-standards and plans of the big data center site selection model are divided into the highest level, middle level, second-middle level and lowest level according to their interrelationships. The specific levels are shown in Table 1.

[0095] Table 1 Hierarchical structure table

[0096]

[0097] 3.2 Comparison of indicators

[0098] The present invention uses a scale of 1 to 9 for evaluation, and the specific settings are shown in Table 2.

[0099] Table 2 Index table importance comparison quantification table

[0100]

[0101] Score each pair of criteria or options based on judgment and establish an overall judgment matrix A, such as Figure 3 shown.

[0102] 3.3 Calculating weights

[0103] First, each column of the comparison matrix is ​​summed, and then each element is divided by the sum of its column to obtain a normalized comparison matrix. Finally, the average value of each row of the normalized matrix is ​​calculated to obtain the weight αm of each standard.

[0104] After completing the weight calculation, the total site selection score of the nth region is summarized as follows:

[0105]

[0106] Step 4: Analyze the sensitivity of multiple factors and build a decision support system

[0107] In big data center site selection or other multi-factor decision-making problems, sensitivity analysis is used to understand the impact of decision variables on the final decision results. Through sensitivity analysis, we can identify which factors have the greatest impact on the decision results and the tolerance of changes in these factors. This paper adopts local sensitivity analysis, that is, by changing the value of a certain evaluation index, we can see the impact on the final decision result. Finally, we summarize all the above contents to build a decision support system. The specific logic is as follows: Figure 4 shown.

[0108] Step 5: Select the region with the highest score and determine the location of the big data center.

[0109] The present invention aims at measuring the data homogeneity and scarcity of site selection methods for big data centers, rationalizes the layout of big data centers, improves their effectiveness, and proposes a solution to the site selection problem of big data centers.

[0110] Example 2

[0111] Reference Figure 2 , which is an embodiment of the present invention, provides a big data center site selection system based on a multi-factor comprehensive evaluation method, including:

[0112] Range determination unit 100, evaluation system unit 200, weight allocation unit 300, sensitivity analysis unit 400 and site selection decision unit 500;

[0113] The range determination unit 100 is used to preliminarily determine the site selection range of the big data center;

[0114] The evaluation system unit 200 is used to construct a big data center site selection evaluation system;

[0115] The weight allocation unit 300 is used to allocate weights to various indicators and establish a big data center site selection model;

[0116] The sensitivity analysis unit 400 is used to analyze the sensitivity of multiple factors and build a decision support system;

[0117] The site selection decision unit 500 is used to select the area with the highest score and determine the site selection of the big data center.

[0118] Example 3

[0119] An embodiment of the present invention is different from the first two embodiments in that:

[0120] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0122] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0123] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A big data center site selection method based on a multi-factor comprehensive evaluation method, characterized in that: include: Determine the site selection scope of the big data center and divide the area into candidate areas according to demand; Construct a big data center site selection evaluation system based on factors and data such as energy supply, natural environment, and economic development in each candidate area; Calculate the weight distribution for the evaluation system of each indicator, determine the proportion of each evaluation indicator in the overall goal, and establish a big data center site selection model, that is, calculate the comprehensive score of each candidate area based on the quantitative evaluation system and weight distribution; Analyze the sensitivity of multiple factors, determine the feasibility of quantified evaluation indicators, and avoid unreasonable evaluation indicators; Select the region with the highest score to determine the location of the big data center.

2. The big data center site selection method based on multi-factor comprehensive evaluation method according to claim 1 is characterized by: The site selection range is determined based on the construction requirements of the big data center.

3. The big data center site selection method based on multi-factor comprehensive evaluation method as claimed in claim 2 is characterized by: The big data center site selection evaluation system includes regional temperature, power supply, clean energy supply, water resources, natural disaster risks, land costs, human resources, and regional development distribution.

4. The big data center site selection method based on multi-factor comprehensive evaluation method according to claim 3 is characterized by: The construction of the big data center site selection evaluation system also includes converting all evaluation indicators using Min-Max normalization; The Min-Max normalization is expressed as, Among them, m represents all evaluation indicators, is the normalized evaluation value of the mth indicator in the nth region.

5. The big data center site selection method based on multi-factor comprehensive evaluation method according to claim 4 is characterized by: The establishment of the big data center site selection model includes dividing the hierarchy based on site selection requirements; Score each pair of criteria or options to establish an overall judgment moment; Each column of the overall judgment moment is summed, and then each element is divided by the sum of its column to obtain a normalized comparison matrix; Calculate the average value of each row of the normalized matrix to obtain the weight αm of each standard; Complete the weight calculation and summarize the total site selection score for the nth region.

6. The big data center site selection method based on multi-factor comprehensive evaluation method according to claim 5 is characterized by: The total site selection score is expressed as, 7. The big data center site selection method based on multi-factor comprehensive evaluation method according to claim 6 is characterized by: The construction of the decision support system includes adopting a local sensitivity analysis method to check the impact on the final decision result and avoid unreasonable evaluation indicators.

8. A system using the big data center site selection method based on a multi-factor comprehensive evaluation method as described in any one of claims 1 to 7, characterized in that: include: A scope determination unit (100), an evaluation system unit (200), a weight allocation unit (300), a sensitivity analysis unit (400), and a site selection decision unit (500); The range determination unit (100) is used to preliminarily determine the site selection range of the big data center; The evaluation system unit (200) is used to construct a big data center site selection evaluation system; The weight allocation unit (300) is used to allocate weights to various indicators and establish a big data center site selection model; The sensitivity analysis unit (400) is used to analyze the sensitivity of multiple factors and build a decision support system; The site selection decision unit (500) is used to select the area with the highest score and determine the site selection of the big data center.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the big data center site selection method based on a multi-factor comprehensive evaluation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the big data center site selection method based on a multi-factor comprehensive evaluation method described in any one of claims 1 to 7 are implemented.

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