Cabinet leasing management method and system based on big data analysis

Abstract

The invention discloses a cabinet lease management method and system based on big data analysis, and relates to the technical field of cabinet lease management, and the method specifically comprises the steps: 1, determining a cabinet meeting or approaching a lease requirement based on the lease requirement of a user, and comparing the lease requirement and the cabinet function and type data, screening to obtain the cabinets meeting the leasing requirements, and setting a requirement satisfaction degree function to obtain the cabinets close to the leasing requirements; 2, collecting the historical rental data of the cabinets meeting or approaching the rental requirements, establishing a matching degree function, calculating the matching degrees of all the cabinets meeting or approaching the rental requirements, and determining the types and models of the cabinets with high matching degrees and the remaining number of the cabinets; 3, integrating the matching degree function of the cabinets and the number of the cabinets to obtain a leasing strategy meeting leasing requirements, and optimizing the leasing strategy based on user budget; according to the invention, the overall benefit and competitiveness of the cabinet leasing business can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cabinet rental, and specifically relates to a cabinet rental management method and system based on big data analysis. Background Art

[0002] With the rapid development of the Internet and information technology, data centers, as core facilities for data storage and management, are becoming increasingly important. As one of the infrastructures of data centers, the management efficiency of cabinets directly affects the performance and cost of data centers. Traditional cabinet management methods have many shortcomings, including unreasonable resource allocation, untimely status monitoring, and chaotic lease management.

[0003] In order to solve these problems, big data analysis technology has been introduced into cabinet rental management. Big data analysis technology can collect and integrate information from multiple data sources, including the physical status of the cabinet, usage records, lease information, etc. Through data integration, a comprehensive cabinet management view can be formed to provide strong support for decision-making. Using big data analysis technology, the performance of the cabinet can be monitored and analyzed in real time. By analyzing historical data, the future resource demand and failure trend of the cabinet can be predicted, so as to allocate resources and repair faults in advance. Based on the results of big data analysis, dynamic allocation and optimization of cabinet resources can be achieved. According to changes in business needs, the resource configuration of the cabinet can be automatically adjusted to improve resource utilization and reduce energy consumption. Summary of the invention

[0004] The purpose of the present invention is to provide a cabinet rental management method and system based on big data analysis, which is used to solve the technical problem of unreasonable cabinet rental management resource allocation in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A cabinet rental management method based on big data analysis, comprising:

[0007] Step 1: Determine the user's rental requirements, and simultaneously determine the type and model of the cabinet. By comparing the rental requirements, cabinet functions and type data, select cabinets that meet the rental requirements and cabinets that are close to the rental requirements;

[0008] Step 2: Collect historical rental data of cabinets that meet or are close to the rental requirements, establish a matching function, calculate the matching degree of all cabinets that meet or are close to the rental requirements, and determine the types and models of cabinets with high matching degrees and the remaining quantity of the cabinets;

[0009] Step 3: Comprehensively consider the matching function of the cabinet and the number of the cabinet to obtain a leasing strategy that meets the leasing requirements, and optimize the leasing strategy based on the user budget.

[0010] Furthermore, a specific method for dividing cabinet types and models according to cabinet functions and type data is as follows: cabinets are divided into different types according to their functions, and the types of cabinets corresponding to different functions are randomly sorted. The symbol i is used to represent the cabinet type serial number, i∈{1,...,N}, i is an integer variable in the range of 1 to N, and the rental platform contains only N cabinets with different functions. Cabinets of the same type are divided into different models due to different type data. The type data includes: size data, geographic location data and performance data. Cabinets with the same type data are recorded as cabinets of the same model. The models included in the cabinet type are represented by j, which is an integer variable in the range of 1 to M. (i, j) is used to represent the cabinet of the i-th type and j-th model.

[0011] Furthermore, by comparing the leasing requirements, cabinet functions and type data, cabinets that meet the leasing requirements and cabinets that are close to the leasing requirements are screened. The specific method is as follows:

[0012] By comparing the rental requirements, the cabinet function and type data, when the cabinet function and type data all meet the user's restrictions in the rental requirements, the cabinet is determined to be a cabinet that meets the rental requirements;

[0013] For cabinets that do not meet all the user's rental requirements, the requirement satisfaction function is set based on the rental requirements, cabinet functions and type data, and the requirement satisfaction Y of different cabinets and user requirements is calculated. u (i, j), set the required threshold F, when Y u When (i, j) is greater than or equal to F, it is determined that the i-th type j-model cabinet can match the user's rental requirements, and the cabinet is recorded as a cabinet close to the rental requirements.

[0014] Furthermore, the specific method of setting the requirement satisfaction function by integrating the leasing requirements, cabinet functions and type data is as follows:

[0015] Using the formula represents the degree of satisfaction of the requirements, where u represents the u-th rental requirement, r(u) represents the number of business requirements that the user needs the cabinet to complete in the rental requirement u, and r u (i, j) represents the number of business requirements in the rental requirement u that can be met by the i-th type j-model cabinet, d u (i, j) represents the distance between the location of the cabinet and the location requirement in the rental requirement u, k represents the type of cabinet side length, v k (u) represents the required length of the kth side dimension in the rental requirement u, represents the length of the kth side length of the i-th type j-model cabinet that does not meet the rental requirement u, q(u) represents the number of cabinet performance requirements in the rental requirement u, q u(i, j) represents the number of cabinets of type i and model j that can meet the performance requirements of the rental requirement u, k r Represents the weight coefficient of business requirements, k d Represents the weight coefficient for geographical location requirements, k v represents the weight coefficient regarding the size requirement, k q Represents the weight coefficient regarding performance requirements.

[0016] Furthermore, the specific method of establishing the matching function is:

[0017] Using the formula represents the matching function, where i represents the cabinet type serial number, and j represents the models included in the cabinet type;

[0018] t(i, j) represents the rental time of the i-th type and j-th model cabinet in the time period T;

[0019] g(i, j) represents the average rental price of the i-th type and j-th model cabinet during the rental period;

[0020] h(i, j) represents the cabinet accident frequency of the i-th type and j-th cabinet during the rental period, which is expressed by the ratio of the number of accidents of the i-th type and j-th cabinet during the rental period to the number of rentals;

[0021] f(i, j) represents the favorable comment rate of the i-th type and j-th cabinet in the time period T, which is expressed by the ratio of the number of favorable comments of the i-th type and j-th cabinet to the number of rentals of the cabinet by users in the time period T;

[0022] Furthermore, the matching function of the cabinet and the number of the cabinet are combined to obtain a leasing strategy that meets the leasing requirements. The specific method is as follows:

[0023] First, sort the cabinets from high to low by matching degree, determine the remaining number of the cabinet with the highest matching degree, and record the cabinet with the highest matching degree as A. According to the user's quantity requirement for the rental cabinet, determine whether the remaining number of cabinet A meets the quantity requirement. If so, the leasing strategy is to rent A that meets the user's quantity requirement. If not, determine cabinet B whose matching degree is only less than A, determine the remaining number of B, and determine whether the remaining numbers of A and B meet the quantity requirement. If so, the leasing strategy is to rent A and B that meet the user's quantity requirement. If not, determine cabinet C whose matching degree is only less than B. Repeat the above steps until the cabinet that meets the user's quantity requirement is determined, thereby determining the leasing strategy.

[0024] Furthermore, the optimization method for the rental strategy based on the user budget is:

[0025] S2. The total price of the leasing strategy exceeds the user's budget. The cabinet with the highest average leasing price in the current leasing strategy is selected as the cabinet to be replaced. The cabinet set that does not appear in the leasing strategy is recorded as the remaining cabinets. Among the remaining cabinets, a cabinet set with an average leasing price lower than the cabinet to be replaced is searched and recorded as the low-priced cabinet. Among the low-priced cabinets, the cabinet with the closest matching degree to the cabinet to be replaced is determined as the replacement cabinet.

[0026] S3, compare the remaining number of replacement cabinets with the required rental number of cabinets to be replaced. When the remaining number is less than the rental number, replace all the replacement cabinets with the cabinets to be replaced, and then determine a new replacement cabinet through step S2. When the remaining number is greater than or equal to the rental number, use the replacement cabinet to replace all the cabinets to be replaced to obtain a new rental strategy.

[0027] Compare the rental price of the new rental strategy with the user's budget requirements. If the rental price is less than or equal to the user's budget requirements, keep the rental strategy unchanged. If the rental price is greater than the user's budget requirements, replace the cabinets in the new rental strategy by repeating S2 and S3 until the rental price of the replaced rental strategy is less than or equal to the user's budget requirements, or the rental price of the replaced rental strategy reaches the minimum value, and output the rental strategy. Based on the user's budget requirements, the output rental strategy is the cabinet rental plan that best meets the user's requirements.

[0028] The present invention also provides a cabinet rental management system based on big data analysis, comprising:

[0029] The rental screening module screens cabinets that meet the rental requirements based on user rental requirements by comparing cabinet function and type data, sets a requirement satisfaction function by integrating rental requirements, cabinet function and type data, evaluates cabinets that do not meet the rental requirements, and screens cabinets that are close to the rental requirements based on the requirement threshold;

[0030] The rental matching module collects the historical rental data of cabinets that meet or are close to the rental requirements, establishes a matching function by extracting the rental feature data in the historical rental data, calculates the matching degree of all cabinets that meet or are close to the rental requirements, and sorts the cabinet types and models according to the matching degree;

[0031] The leasing allocation module determines the leasing strategy that meets the user quantity requirements by comprehensively sorting the cabinets according to the degree of matching, the remaining number of cabinets and the number of users required, optimizes the leasing strategy according to the user's budget requirements, and finally outputs the cabinet leasing plan that best meets the user's requirements.

[0032] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0033] 1. The present invention divides cabinets according to function and type data, thereby screening out cabinets that meet or are close to user requirements, which helps to clarify the functional positioning of different cabinets and improve the pertinence of cabinet rental management. Classification according to cabinet function and type data can ensure the effective use of cabinet rental resources and reduce resource waste caused by fuzzy cabinet function positioning;

[0034] 2. The present invention establishes a matching function by extracting leasing feature data from historical leasing data, providing a scientific basis for cabinet leasing decisions. Through data-based decision analysis, the risk of cabinet leasing decisions can be reduced. By understanding the customer's leasing needs and preferences, the present invention recommends accurate and appropriate cabinet types and models.

[0035] 3. The present invention obtains a leasing strategy that meets the leasing requirements by integrating the matching function of the cabinets and the number of cabinets, and optimizes the leasing strategy based on the user's budget, which can more effectively utilize cabinet resources and avoid idle or wasted resources. At the same time, leasing according to the actual needs of users also helps to improve the rental rate and utilization rate of cabinets. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0037] Figure 1 A step diagram of a cabinet rental management method based on big data analysis is shown;

[0038] Figure 2 A step diagram of a method for optimizing a rental strategy based on a user budget is shown;

[0039] Figure 3 A flowchart of a cabinet rental management system based on big data analysis is shown. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1, as Figure 1 , Figure 2A cabinet rental management method based on big data analysis is shown, which specifically includes the following steps:

[0042] Step 1: Determine the user's rental requirements, and simultaneously determine the cabinet type and model. By comparing the rental requirements, cabinet functions and type data, select cabinets that meet the rental requirements and cabinets that are close to the rental requirements. The rental requirements refer to the user's restrictions on the cabinet functions and type data of the desired rental cabinets. The rental requirements include: the user's budget requirements, quantity requirements, business requirements, size requirements, geographical location requirements and performance requirements for the rental cabinets. Cabinets are divided into different types according to their functions, and the cabinet types corresponding to different functions are randomly sorted. The symbol i is used to represent the cabinet type serial number, i∈{1,...,N}, i is an integer variable in the range of 1 to N, i=1 represents the cabinet type with a serial number of 1, and so on, i=N represents the cabinet type with a serial number of N, and the rental platform contains only N different types of cabinets. Cabinets with the same function and the same type are divided into different models due to different type data. The type data includes: size data, geographical location data and performance data. Cabinets with the same type data are recorded as cabinets of the same model. The models included in the cabinet type are represented by j, and j is an integer variable in the range of 1 to M. Assuming that the number of models included in cabinet type a is the largest in {1,...,N}, then M represents the number of models contained in cabinet type a, where j=1 represents the first model, and so on, j=M represents the Mth model. (i, j) is used to represent the cabinet of the jth model of the i-th type. For cabinets of different types and models, by comparing the rental requirements, cabinet functions and type data, the cabinets that meet the rental requirements are screened, that is, the function and type data of the cabinet include the user's restrictions on each part of the cabinet in the rental requirements.

[0043] For cabinets that do not meet all the user's rental requirements, weight coefficients are set for the business requirements, size requirements, geographical location requirements, and performance requirements in the rental requirements. Based on these weight coefficients, a requirement satisfaction function is set. The specific formula is as follows:

[0044]

[0045] Among them, Y u (i, j) represents the satisfaction degree between the i-th type and j-th model of the cabinet and the rental requirement u, where u represents the u-th rental requirement, and r(u) represents the number of business requirements that the user needs the cabinet to complete in the rental requirement u. u (i, j) represents the number of business requirements in the rental requirement u that can be met by the i-th type j-model cabinet, d u(i, j) represents the distance between the location of the cabinet and the location requirement in the rental requirement u, k represents the type of cabinet side length, k=1 represents the cabinet length, k=2 represents the cabinet width, k=3 represents the cabinet height, v k (u) represents the required length of the kth side dimension in the rental requirement u, represents the length of the kth side length of the i-th type j-model cabinet that does not meet the rental requirement u, q(u) represents the number of cabinet performance requirements in the rental requirement u, q u (i, j) represents the number of cabinets of type i and model j that can meet the performance requirements of the rental requirement u, k r Represents the weight coefficient of business requirements, k d Represents the weight coefficient for geographical location requirements, k v represents the weight coefficient regarding the size requirement, k q represents the weight coefficient of the performance requirement. The above weight coefficients can be determined according to the specific situation and needs, and are usually jointly formulated and confirmed by professionals or relevant stakeholders. In this embodiment, k is set r =0.6, k d =0.8, k v =0.7, k q =0.7.

[0046] The higher the cabinet meets the user's rental requirements, the closer the cabinet is to the user's rental requirements. Set the requirement threshold F. When Y u When (i, j) is greater than or equal to F, it is determined that the i-th type j-model cabinet can match the user's rental requirements, and the cabinet is recorded as a cabinet close to the rental requirements.

[0047] Step 2: Collect historical rental data of cabinets that meet or are close to the rental requirements, establish a matching function, calculate the matching degree of all cabinets that meet or are close to the rental requirements, determine the types and models of cabinets with high matching degrees and the remaining number of the cabinets, set a time period T, and for the types and models (i, j) of cabinets that meet or are close to the rental requirements, collect their historical rental data, and extract rental feature data from the historical rental data. The rental feature data includes: cabinet rental time, cabinet rental price, cabinet accident frequency and cabinet praise rate. Maximize the cabinet praise rate and minimize the cabinet rental price and cabinet accident frequency as the objective function. Take the rental requirements, cabinet functions and type data as constraints to determine the matching function for cabinet rental. The specific expression is as follows:

[0048]

[0049] Among them, i represents the serial number of the cabinet type, j represents the model included in the cabinet type, and the satisfaction function of the cabinet that meets the rental requirements is equal to Y u (i, j) = k r (k d +k v +k q );

[0050] t(i, j) represents the rental time of the i-th type and j-th model cabinet in the time period T;

[0051] g(i, j) represents the average rental price of the i-th type and j-th model cabinet during the rental period;

[0052] h(i, j) represents the cabinet accident frequency of the i-th type and j-th cabinet during the rental period, which is expressed by the ratio of the number of accidents of the i-th type and j-th cabinet during the rental period to the number of rentals;

[0053] f(i, j) represents the favorable comment rate of the i-th type and j-th cabinet in the time period T, which is expressed by the ratio of the number of favorable comments of the i-th type and j-th cabinet to the number of rentals of the cabinet by users in the time period T;

[0054] Calculate the matching degree of all cabinets that meet or are close to the rental requirements, and sort the cabinet types and models according to the matching degree.

[0055] Step 3: Combine the matching function of the cabinet and the number of cabinets to obtain a leasing strategy that meets the leasing requirements, and optimize the leasing strategy based on the user budget; first, sort the cabinets from high to low by matching degree, determine the remaining number of cabinets with the highest matching degree, and record the cabinet with the highest matching degree as A. According to the user's quantity requirement for the leased cabinets, determine whether the remaining number of cabinet A meets the quantity requirement. If so, the leasing strategy is to lease A that meets the user's quantity requirement. If not, determine cabinet B whose matching degree is only less than A, determine the remaining number of B, and determine whether the remaining numbers of A and B meet the quantity requirement. If so, the leasing strategy is to lease A and B that meet the user's quantity requirement. If not, determine cabinet C whose matching degree is only less than B. Repeat the above steps until the cabinet that meets the user's quantity requirement is determined, thereby determining the leasing strategy;

[0056] The optimization method of the rental strategy based on the user budget is as follows:

[0057] S1. According to the determined leasing strategy, obtain the specific type and model of the leasing cabinet, determine the rental quantity and price of each leasing cabinet, calculate the leasing price that the user needs to pay according to the leasing strategy, compare the leasing price of the leasing strategy with the user's budget requirement, and if the leasing price is less than or equal to the user's budget requirement, keep the leasing strategy unchanged and output the leasing strategy;

[0058] S2. If the rental price is greater than the user's budget requirement, determine the cabinet D with the highest average rental price in the rental strategy and the matching degree of the cabinet, determine the cabinet E whose matching degree is only less than D, and judge whether the rental price of the cabinet is less than D. When the rental price of E is greater than or equal to D, determine the cabinet E1 whose matching degree is only less than E, and judge whether the rental price of E1 is less than D, until the cabinet Ea whose rental price is less than D and whose matching degree is closest to D is found;

[0059] S3. Determine whether the remaining quantity of Ea is greater than or equal to the rental quantity of D in the leasing strategy. When the remaining quantity of Ea is less than the rental quantity of D, find the cabinet Eb whose rental price is less than D and whose matching degree is closest to Ea through operation S2, and determine whether the sum of the remaining quantities of Ea and Eb is greater than or equal to the rental quantity of D. When the sum of the remaining quantities of Ea and Eb is greater than or equal to the rental quantity of D, use Ea and Eb to replace D in the leasing strategy. In this leasing strategy, the rental quantity of Ea is equal to the remaining quantity of Ea before the leasing strategy is replaced, and the rental quantity of Eb is equal to the rental quantity of D before the leasing strategy is replaced minus the remaining quantity of Ea. When the remaining quantity of Ea and Eb is less than the rental quantity of D, perform operation 2 until the sum of the remaining quantities of all cabinets found is greater than or equal to the rental quantity of D, and use these cabinets to replace cabinet D in the leasing strategy, and record the replaced leasing strategy as the new leasing strategy;

[0060] Operation S1 is used to determine whether the rental price of the new rental strategy meets the user's budget requirement. If the rental price is less than or equal to the user's budget requirement, the rental strategy is kept unchanged. If the rental price is greater than the user's budget requirement, the cabinets in the new rental strategy are replaced by repeating S2 and S3 until the rental price of the replaced rental strategy is less than or equal to the user's budget requirement, or the rental price of the replaced rental strategy reaches the minimum value. The rental strategy is then output. Based on the user's budget requirement, the output rental strategy is the cabinet rental plan that best meets the user's requirements.

[0061] Based on the output leasing strategy, a leasing cabinet that meets the user's requirements is allocated, thereby realizing the leasing management of the cabinet.

[0062] Embodiment 2, as Figure 3 A cabinet rental management system based on big data analysis is shown, which specifically includes:

[0063] Rental screening module: determine the user's rental requirements, and simultaneously determine the cabinet type and model. By comparing the rental requirements, cabinet functions and type data, screen out cabinets that meet the rental requirements and cabinets that are close to the rental requirements. The rental requirements refer to the user's restrictions on the cabinet functions and type data of the desired rental cabinets. The rental requirements include: the user's budget requirements, quantity requirements, business requirements, size requirements, geographical location requirements and performance requirements for the rental cabinets. Cabinets are divided into different types according to their functions, and the cabinet types corresponding to different functions are randomly sorted. The symbol i is used to represent the cabinet type serial number, i∈{1,...,N}, i is an integer variable in the range of 1 to N, i=1 represents the cabinet type with serial number 1, and so on, i=N represents the cabinet type with serial number N, and there are only N types in the rental platform. Cabinets with different functions. Cabinets of the same type are divided into different models due to different type data. The type data includes: size data, geographical location data and performance data. Cabinets with the same type data are recorded as cabinets of the same model. The models included in the cabinet type are represented by j, and j is an integer variable in the range of 1 to M. Assuming that the number of models included in cabinet type a is the largest in {1,...,N}, then M represents the number of models contained in cabinet type a, where j=1 represents the first model, and so on, j=M represents the Mth model. (i, j) is used to represent the cabinet of the i-th type and j-th model. For cabinets of different types and models, by comparing the rental requirements, cabinet functions and type data, the cabinets that meet the rental requirements are screened, that is, the function and type data of the cabinet contain the user's restrictions on the various parts of the cabinet in the rental requirements.

[0064] For cabinets that do not meet all the user's rental requirements, weight coefficients are set for the business requirements, size requirements, geographical location requirements, and performance requirements in the rental requirements. Based on these weight coefficients, a requirement satisfaction function is set. The specific formula is as follows:

[0065]

[0066] Among them, Y u (i, j) represents the satisfaction degree between the i-th type and j-th model of the cabinet and the rental requirement u, where u represents the u-th rental requirement, and r(u) represents the number of business requirements that the user needs the cabinet to complete in the rental requirement u. u (i, j) represents the number of business requirements in the rental requirement u that can be met by the i-th type j-model cabinet, d u (i, j) represents the distance between the location of the cabinet and the location requirement in the rental requirement u, k represents the type of cabinet side length, k=1 represents the cabinet length, k=2 represents the cabinet width, k=3 represents the cabinet height, v k (u) represents the required length of the kth side dimension in the rental requirement u, represents the length of the kth side length of the i-th type j-model cabinet that does not meet the rental requirement u, q(u) represents the number of cabinet performance requirements in the rental requirement u, q u (i, j) represents the number of cabinets of type i and model j that can meet the performance requirements of the rental requirement u, k r Represents the weight coefficient of business requirements, k d Represents the weight coefficient for geographical location requirements, k v represents the weight coefficient regarding the size requirement, k q Indicates the weight coefficients of performance requirements. These weight coefficients can be determined according to specific circumstances and needs, and are usually jointly formulated and confirmed by professionals or relevant stakeholders;

[0067] The higher the cabinet meets the user's rental requirements, the closer the cabinet is to the user's rental requirements. Set the requirement threshold F. When Y u When (i, j) is greater than or equal to F, it is determined that the i-th type j-model cabinet can match the user's rental requirements, and the cabinet is recorded as a cabinet close to the rental requirements.

[0068] Lease matching module: collect historical lease data of cabinets that meet or are close to lease requirements, establish a matching function, calculate the matching degree of all cabinets that meet or are close to lease requirements, determine the types and models of cabinets with high matching degrees and the remaining number of the cabinets, set a time period T, and for the types and models (i, j) of cabinets that meet or are close to lease requirements, collect their historical lease data, and extract lease feature data from the historical lease data. The lease feature data includes: cabinet lease time, cabinet lease price, cabinet accident frequency, and cabinet favorable rating. Maximizing the cabinet favorable rating and minimizing the cabinet lease price and cabinet accident frequency are used as the objective function. Taking the lease requirements, cabinet functions, and type data as constraints, the matching function for cabinet lease is determined. The specific expression is as follows:

[0069]

[0070] Among them, i represents the serial number of the cabinet type, j represents the model included in the cabinet type, and the satisfaction function of the cabinet that meets the rental requirements is equal to Y u (i, j) = k r (k d +k v +k q );

[0071] t(i, j) represents the rental time of the i-th type and j-th model cabinet in the time period T;

[0072] g(i, j) represents the average rental price of the i-th type and j-th model cabinet during the rental period;

[0073] h(i, j) represents the cabinet accident frequency of the i-th type and j-th cabinet during the rental period, which is expressed by the ratio of the number of accidents of the i-th type and j-th cabinet during the rental period to the number of rentals;

[0074] f(i, j) represents the favorable comment rate of the i-th type and j-th cabinet in the time period T, which is expressed by the ratio of the number of favorable comments of the i-th type and j-th cabinet to the number of rentals of the cabinet by users in the time period T;

[0075] Calculate the matching degree of all cabinets that meet or are close to the rental requirements, and sort the cabinet types and models according to the matching degree.

[0076] Lease allocation module: The matching function of the integrated cabinet and the number of the cabinet are used to obtain a leasing strategy that meets the leasing requirements, and the leasing strategy is optimized based on the user budget; first, the cabinets are sorted from high to low by matching degree, and the remaining number of the cabinet with the highest matching degree is determined. The cabinet with the highest matching pair is recorded as A. According to the user's number requirements for the leased cabinets, it is determined whether the remaining number of cabinet A meets the quantity requirements. If so, the leasing strategy is to lease A that meets the user's quantity requirements. If not, cabinet B with a matching degree only less than A is determined, and the remaining number of B is determined. It is determined whether the remaining numbers of A and B meet the quantity requirements. If so, the leasing strategy is to lease A and B that meet the user's quantity requirements. If not, cabinet C with a matching degree only less than B is determined. The above steps are repeated until the cabinet that meets the user's quantity requirements is determined, thereby determining the leasing strategy;

[0077] The optimization method of the rental strategy based on the user budget is as follows:

[0078] S2. The total price of the leasing strategy exceeds the user's budget. The cabinet with the highest average leasing price in the current leasing strategy is selected as the cabinet to be replaced. The cabinet set that does not appear in the leasing strategy is recorded as the remaining cabinets. Among the remaining cabinets, a cabinet set with an average leasing price lower than the cabinet to be replaced is searched and recorded as the low-priced cabinet. Among the low-priced cabinets, the cabinet with the closest matching degree to the cabinet to be replaced is determined as the replacement cabinet.

[0079] S3, compare the remaining number of replacement cabinets with the required rental number of cabinets to be replaced. When the remaining number is less than the rental number, replace all the replacement cabinets with the cabinets to be replaced, and then determine a new replacement cabinet through step S2. When the remaining number is greater than or equal to the rental number, use the replacement cabinet to replace all the cabinets to be replaced to obtain a new rental strategy.

[0080] Compare the rental price of the new rental strategy with the user's budget requirements. If the rental price is less than or equal to the user's budget requirements, keep the rental strategy unchanged. If the rental price is greater than the user's budget requirements, replace the cabinets in the new rental strategy by repeating S2 and S3 until the rental price of the replaced rental strategy is less than or equal to the user's budget requirements, or the rental price of the replaced rental strategy reaches the minimum value, and output the rental strategy. Based on the user's budget requirements, the output rental strategy is the cabinet rental plan that best meets the user's requirements.

[0081] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0082] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cabinet rental management method based on big data analysis, characterized in that: include: Step 1: Determine the user's rental requirements, and simultaneously determine the type and model of the cabinet. By comparing the rental requirements, cabinet functions and type data, select cabinets that meet the rental requirements and cabinets that are close to the rental requirements; Step 2: Collect historical rental data of cabinets that meet or are close to the rental requirements, establish a matching function, calculate the matching degree of all cabinets that meet or are close to the rental requirements, and determine the types and models of cabinets with high matching degrees and the remaining quantity of the cabinets; Step 3: Comprehensively consider the matching function of the cabinet and the number of the cabinet to obtain a leasing strategy that meets the leasing requirements, and optimize the leasing strategy based on the user budget.

2. According to the method for cabinet rental management based on big data analysis of claim 1, it is characterized in that: The cabinet type and model are determined by: Cabinets are divided into different types according to their functions, and the types of cabinets corresponding to different functions are randomly sorted. The symbol i is used to represent the cabinet type serial number, i∈{1,...,N}, i is an integer variable in the range of 1 to N, and the rental platform contains only N cabinets with different functions. Cabinets of the same type are divided into different models due to different type data. The type data includes: size data, geographic location data and performance data. Cabinets with the same type data are recorded as cabinets of the same model. The models included in the cabinet type are represented by j, j is an integer variable in the range of 1 to M, and (i, j) is used to represent the cabinet of the i-th type and j-th model.

3. The cabinet rental management method based on big data analysis according to claim 1 is characterized in that: The cabinets that meet the rental requirements and cabinets that are close to the rental requirements are screened. The specific method is as follows: By comparing the rental requirements, the cabinet function and type data, when the cabinet function and type data all meet the user's restrictions in the rental requirements, the cabinet is determined to be a cabinet that meets the rental requirements; For cabinets that do not meet all the user's rental requirements, the requirement satisfaction function is set based on the rental requirements, cabinet functions and type data, and the requirement satisfaction Y of different cabinets and user requirements is calculated. u (i, j), set the required threshold F, when Y u When (i, j) is greater than or equal to F, it is determined that the i-th type j-model cabinet can match the user's rental requirements, and the cabinet is recorded as a cabinet close to the rental requirements.

4. The cabinet rental management method based on big data analysis according to claim 1 is characterized in that: Comprehensive leasing requirements, cabinet functions and type data are used to set the requirement satisfaction function. The specific method is as follows: Using the formula represents the degree of satisfaction of the requirements, where u represents the u-th rental requirement, r(u) represents the number of business requirements that the user needs the cabinet to complete in the rental requirement u, and r u (i, j) represents the number of business requirements in the rental requirement u that can be met by the i-th type j-model cabinet, d u (i, j) represents the distance between the location of the cabinet and the location requirement in the rental requirement u, k represents the type of cabinet side length, v k (u) represents the required length of the kth side dimension in the rental requirement u, represents the length of the kth side length of the i-th type j-model cabinet that does not meet the rental requirement u, q(u) represents the number of cabinet performance requirements in the rental requirement u, q u (i, j) represents the number of cabinets of type i and model j that can meet the performance requirements of the rental requirement u, k r Represents the weight coefficient of business requirements, k d Represents the weight coefficient for geographical location requirements, k v represents the weight coefficient regarding the size requirement, k q Represents the weight coefficient regarding performance requirements.

5. The cabinet rental management method based on big data analysis according to claim 1 is characterized in that: Establish a matching function. The specific method is: Using the formula represents the matching function, where i represents the cabinet type serial number, and j represents the models included in the cabinet type; t(i, j) represents the rental time of the i-th type and j-th model cabinet in the time period T; g(i, j) represents the average rental price of the i-th type and j-th model cabinet during the rental period; h(i, j) represents the cabinet accident frequency of the i-th type and j-th model cabinet during the rental period; f(i, j) represents the rate of favorable comments on the i-th type and j-th model cabinet within the time period T.

6. The cabinet rental management method based on big data analysis according to claim 1 is characterized in that: The matching function of the integrated cabinet and the number of cabinets are used to obtain a leasing strategy that meets the leasing requirements. The specific method is as follows: First, the cabinets are sorted from high to low according to the matching degree, and the remaining rentable quantity of each cabinet is clearly recorded. According to the user's demand for the number of rental cabinets, if the remaining quantity of the cabinet at the top of the ranking can meet or exceed the user's quantity requirement, the leasing strategy is to directly lease the cabinet to the user, and the quantity just meets the user's demand. If the remaining quantity of the cabinet at the top of the ranking is not enough to meet the user's demand, it will be further screened. The goal is to find a group of cabinet combinations that are as high as possible in the ranking, with as few cabinet types as possible, and at the same time their total remaining quantity can meet the user's rental needs. The leasing strategy is obtained by determining the specific allocation of each cabinet in the combination.

7. The cabinet rental management method based on big data analysis according to claim 1 is characterized in that: The optimization method of the rental strategy based on the user budget includes: S1. According to the determined leasing strategy, determine the rental quantity and price of each rental cabinet, compare the rental price of the leasing strategy with the user's budget requirement, and if the rental price is less than or equal to the user's budget requirement, keep the leasing strategy unchanged and output the leasing strategy; S2. The total price of the leasing strategy exceeds the user's budget. The cabinet with the highest average leasing price in the current leasing strategy is selected as the cabinet to be replaced. The cabinet set that does not appear in the leasing strategy is recorded as the remaining cabinets. Among the remaining cabinets, a cabinet set with an average leasing price lower than the cabinet to be replaced is searched and recorded as the low-priced cabinet. Among the low-priced cabinets, the cabinet with the closest matching degree to the cabinet to be replaced is determined as the replacement cabinet. S3, compare the remaining number of replacement cabinets with the required rental number of cabinets to be replaced. When the remaining number is less than the rental number, replace all the replacement cabinets with the cabinets to be replaced, and then determine a new replacement cabinet through step S2. When the remaining number is greater than or equal to the rental number, use the replacement cabinet to replace all the cabinets to be replaced to obtain a new rental strategy. Compare the rental price of the new rental strategy with the user's budget requirements. If the rental price is less than or equal to the user's budget requirements, keep the rental strategy unchanged. If the rental price is greater than the user's budget requirements, replace the cabinets in the new rental strategy by repeating S2 and S3 until the rental price of the replaced rental strategy is less than or equal to the user's budget requirements, or the rental price of the replaced rental strategy reaches the minimum value, and output the rental strategy. Based on the user's budget requirements, the output rental strategy is the cabinet rental plan that best meets the user's requirements.

8. A cabinet rental management system based on big data analysis, applied to a cabinet rental management method based on big data analysis as claimed in any one of claims 1 to 7, characterized in that: include: The rental screening module screens cabinets that meet the rental requirements based on user rental requirements by comparing cabinet function and type data, sets a requirement satisfaction function by integrating rental requirements, cabinet function and type data, evaluates cabinets that do not meet the rental requirements, and screens cabinets that are close to the rental requirements based on the requirement threshold; The rental matching module collects the historical rental data of cabinets that meet or are close to the rental requirements, establishes a matching function by extracting the rental feature data in the historical rental data, calculates the matching degree of all cabinets that meet or are close to the rental requirements, and sorts the cabinet types and models according to the matching degree; The leasing allocation module determines the leasing strategy that meets the user quantity requirements by comprehensively sorting the cabinets according to the degree of matching, the remaining number of cabinets and the number of users required, optimizes the leasing strategy according to the user's budget requirements, and finally outputs the cabinet leasing plan that best meets the user's requirements.

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