A method and system for intelligent campus resource allocation management based on big data

Abstract

The present invention discloses a method and system for intelligent campus resource allocation management based on big data, belonging to the technical field of intelligent campus management. The reservation requirements instructions of users are received through a reservation medium and uploaded to a resource data center to form a temporary resource data cluster; the temporary resource data cluster is sorted according to the execution time sequence of the reservation requirements instructions, and an instruction sliding window is set. By means of sliding grouping, the reservation requirements instructions are grouped according to the scale value of the instruction sliding window (i.e., the number of personnel for the user demand item), and the first optimization of resource allocation is carried out; after the first optimization, a secondary object set to be optimized is generated, and the executability of integrating the reservation requirements instructions into the secondary object set to be optimized is evaluated to conduct the executability evaluation of the secondary optimization, so as to complete the optimization process of resource allocation. Furthermore, in order to improve the quality of campus life, the present invention can efficiently and accurately allocate resources according to user requirements, and improve resource utilization rate and user satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of smart campus management, and specifically provides a method and system for smart campus resource allocation management based on big data. Background Art

[0002] With the rapid development of information technology and the continuous advancement of smart campus construction, the effective management and efficient allocation of campus resources have become important issues for improving campus service quality and optimizing resource allocation; traditional manual allocation methods are not only inefficient but also difficult to meet large-scale and diverse user needs. Especially in aspects such as campus activity arrangements, classroom and facility reservations, problems such as resource conflicts and uneven distribution often occur.

[0003] In the context of smart campuses, the introduction of big data technology provides new ideas for solving the above problems; by collecting and analyzing the usage data of various resources on campus, user demand data, etc., it is possible to achieve accurate prediction and dynamic monitoring of resource usage, thereby providing more scientific and reasonable decision-making support for resource allocation; however, most existing smart campus resource allocation systems stay at the level of data collection and display, lacking in-depth analysis and optimization algorithms for specific demand scenarios, resulting in the efficiency and effectiveness of resource allocation still needing to be improved.

[0004] Especially in the management of campus activity reservations, due to the diverse types of activities, large time spans, and varying numbers of participants, how to quickly and accurately allocate campus resources according to users' reservation needs to ensure the smooth progress of activities has become an urgent problem to be solved; existing reservation systems often adopt simple first-come-first-served or fixed-time period allocation strategies, ignoring the time correlation between user needs and the continuity of resource usage, resulting in low resource utilization and poor user experience. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for smart campus resource allocation management based on big data to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] A smart campus resource allocation management system based on big data, the system includes: a reservation medium module, a resource data center module, a deployment optimization module, and a feedback center module;

[0008] The reservation medium module is used to generate a user's reservation demand instruction and upload it to the resource data center, and the reservation demand instruction records user demand items and the time to be executed;

[0009] The resource data center module is used to generate temporary resource data clusters according to user demand items. The temporary resource data clusters are used to record reservation demand instructions, sort the reservation demand instructions recorded in the temporary resource data clusters according to the execution time sequence of the reservation demand instructions, and are also used to set instruction sliding windows for different user demand items.

[0010] The deployment optimization module is used to perform sliding grouping on the reservation demand instructions in the temporary resource data cluster according to the scale value of the instruction sliding window, perform the first optimization of resource deployment based on the result of the sliding grouping, trigger the judgment task of secondary optimization to be performed, and generate a set of secondary optimization objects to be processed.

[0011] The feedback center module performs an executable evaluation of the secondary optimization to be performed based on the set of secondary optimization objects to be processed and the execution time recorded in the reservation demand instructions.

[0012] Furthermore, the reservation medium module supports the custom editing of user demand items, sends the result of the custom editing to the resource data center for validity verification, and after passing the validity verification, the custom edited user demand items are loaded into the resource data center for filing to uniformly configure and provide options for user demand items in the reservation medium.

[0013] Furthermore, the resource data center module includes a temporary resource data cluster generation unit and an instruction sliding window setting unit;

[0014] The temporary resource data cluster generation unit is used to receive the reservation demand instructions of users and generate temporary resource data clusters according to user demand items;

[0015] The instruction sliding window setting unit is used to set the instruction sliding window for user demand items, and the scale value of the instruction sliding window is the number of personnel for user demand items.

[0016] Furthermore, the deployment optimization module includes a sliding grouping unit and a first optimization unit;

[0017] The sliding grouping unit is used to set a grouping number function according to the scale value of the instruction sliding window and perform sliding grouping on the reservation demand instructions in the temporary resource data cluster;

[0018] The first optimization unit performs the first optimization of resource deployment based on the result of the sliding grouping and triggers the judgment task of secondary optimization to be performed to generate a set of secondary optimization objects to be processed.

[0019] Furthermore, the feedback center module includes a secondary optimization object selection unit and a secondary optimization evaluation feedback unit;

[0020] The secondary object to be optimized selection unit is used to select any reservation requirement instruction from the set of secondary objects to be optimized and identify the execution time to be in the reservation requirement instruction;

[0021] The secondary optimization evaluation and feedback unit is used to evaluate the executability of integrating the reservation requirement instruction into the set of secondary objects to be optimized and update the optimization completion progress of the set of secondary objects to be optimized.

[0022] A method for intelligent campus resource allocation and management based on big data, the method includes the following steps:

[0023] Step S1: Generate a reservation requirement instruction of a user through a reservation medium and upload it to the resource data center, and the resource data center generates a temporary resource data cluster according to the user demand item;

[0024] Step S2: Sort the reservation requirement instructions recorded in the temporary resource data cluster according to the execution time to be of the reservation requirement instruction, and set an instruction sliding window for different user demand items through the resource data center;

[0025] Step S3: Perform sliding grouping on the reservation requirement instructions in the temporary resource data cluster according to the scale value of the instruction sliding window, and the constraint condition for sliding grouping is the number of personnel for the user demand item; based on the result of sliding grouping, perform the first optimization of resource allocation, trigger the judgment task of secondary optimization to be performed, and generate a set of secondary objects to be optimized;

[0026] Step S4: Based on the set of secondary objects to be optimized, perform an executable evaluation of the secondary optimization to be performed according to the execution time to be recorded in the reservation requirement instruction.

[0027] Further, the specific implementation process of the step S1 includes:

[0028] The user generates a reservation requirement instruction through the reservation medium, and the reservation requirement instruction records the user demand item and the execution time to be, and the user demand item refers to a campus activity item, and the execution time to be refers to the time when the user expects to participate in the campus activity item;

[0029] The resource data center receives the reservation requirement instruction of the user and generates a temporary resource data cluster according to the user demand item. Among them, one user demand item corresponds to generating one temporary resource data cluster, and the reservation requirement instruction is recorded in the temporary resource data cluster;

[0030] In the above method, the user demand items include table tennis items, basketball items, reading items, playground and off-campus and other activity items.

[0031] Further, the specific implementation process of the step S2 includes:

[0032] Unify the options of user demand items in the reservation medium, and uniformly encode the user demand items. Denote any i-th user demand item as , sort the reservation demand instructions in the order of the execution time from the earliest to the latest, and denote the temporary resource data cluster generated corresponding to the user demand item as ;

[0033] The resource data center sets an instruction sliding window for the user demand item . The scale value of the instruction sliding window is the number of personnel for the user demand item.

[0034] Further, the specific implementation process of step S3 includes:

[0035] Slide and group the reservation demand instructions in the temporary resource data cluster according to the scale value of the instruction sliding window, and set the grouping number function . In the formula, represents the floor function symbol, x represents the independent variable of the grouping number function, represents not greater than the largest integer, represents the number of reservation demand instructions included in the temporary resource data cluster, represents the scale value of the instruction sliding window of the user demand item. Let , substitute it into the grouping number function formula, and obtain the grouping number of the user demand item ;

[0036] Based on the result of the sliding grouping, perform the first optimization of resource allocation, and the optimization method is as follows:

[0037] Send each instruction sliding window in the state of the grouping number to the administrator port;

[0038] If , it means that there are reservation demand instructions in the temporary resource data cluster in the un-slid and grouped state, and screen out all the un-slid and grouped reservation demand instructions to form a subset of the temporary resource data cluster , denoted as the secondary object set to be optimized ;

[0039] In the above method, for the reservation demand instructions in the un-slid and grouped state, recommendations for secondary activity items and subsequent secondary optimization evaluations can be carried out to integrate the un-slid and grouped reservation demand instructions into other activities with insufficient team formation numbers.

[0040] Further, the specific implementation process of step S4 includes:

[0041] Select any reservation requirement instruction from the secondary object set to be optimized and denote it as , where y represents the coding serial number of the reservation requirement instruction, and identify the execution time recorded in the reservation requirement instruction and denote it as ;

[0042] Evaluate the executability of the secondary object set formed by integrating the reservation requirement instruction into the j-th user requirement item . The evaluation method is as follows:

[0043] ;

[0044] In the formula, represents the executability of the reservation requirement instruction integrated into the secondary object set to be optimized , represents the number of reservation requirement instructions included in the secondary object set to be optimized , represents the execution time of the h-th reservation requirement instruction in the secondary object set to be optimized , represents the time interval between the execution time and the execution time ;

[0045] If the executability is greater than or equal to the preset evaluation threshold, then integrate the reservation requirement instruction into the secondary object set to be optimized ;

[0046] When the number of reservation requirement instructions included in the secondary object set to be optimized is equal to the scale value of the instruction sliding window of the user requirement item , the optimization of the secondary object set to be optimized is completed.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In a method and system for intelligent campus resource allocation management based on big data provided by the present invention, in the method part, a reservation requirement instruction of a user is received through a reservation medium and uploaded to a resource data center to form a temporary resource data cluster; the temporary resource data cluster is sorted according to the execution time sequence of the reservation requirement instruction, and an instruction sliding window is set. By means of sliding grouping, the reservation requirement instructions are grouped according to the scale value of the instruction sliding window (i.e., the number of personnel in the user requirement item), and the first optimization of resource allocation is carried out; after the first optimization, a second set of objects to be optimized is generated, and the executability of integrating the reservation requirement instruction into the second set of objects to be optimized is evaluated to perform the executability evaluation of the second optimization, so as to complete the optimization process of resource allocation. Furthermore, in order to improve the quality of campus life, the present invention can efficiently and accurately allocate resources according to user requirements, improving resource utilization rate and user satisfaction.

[0048] In the system part, it includes a reservation medium module, a resource data center module, a deployment optimization module, and a feedback center module; the reservation medium module is used to generate and upload a reservation requirement instruction; the resource data center module is used to generate a temporary resource data cluster, sort, and set an instruction sliding window; the deployment optimization module is used for sliding grouping and the first optimization, and generates a second set of objects to be optimized; the feedback center module is used for the executability evaluation of the second optimization.

[0049] In addition, the system also supports the custom editing of user requirement items, and performs validity verification and filing through the resource data center; the resource data center module, the deployment optimization module, and the feedback center module all include multiple subunits to implement their respective functions. Description of the Drawings

[0050] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0051] Figure 1 It is a schematic diagram of the steps of a method for intelligent campus resource allocation management based on big data of the present invention. Detailed Embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] In the first embodiment: A smart campus resource allocation and management system based on big data is provided. The system includes: a reservation medium module, a resource data center module, a deployment optimization module, and a feedback center module;

[0054] The reservation medium module is used to generate a reservation demand instruction of a user and upload it to the resource data center, and the reservation demand instruction records user demand items and the time to be executed;

[0055] Among them, the reservation medium module also supports custom editing of user demand items, sends the result of the custom editing to the resource data center for validity verification, and after passing the validity verification, the custom-edited user demand items are loaded into the resource data center for filing to uniformly configure and provide options for user demand items in the reservation medium.

[0056] The resource data center module is used to generate a temporary resource data cluster according to user demand items. The temporary resource data cluster is used to record reservation demand instructions, sort the reservation demand instructions recorded in the temporary resource data cluster according to the time to be executed of the reservation demand instructions, and is also used to set an instruction sliding window for different user demand items;

[0057] Among them, the resource data center module includes a temporary resource data cluster generation unit and an instruction sliding window setting unit;

[0058] The temporary resource data cluster generation unit is used to receive a reservation demand instruction of a user and generate a temporary resource data cluster according to user demand items;

[0059] The instruction sliding window setting unit is used to set an instruction sliding window for user demand items, and the scale value of the instruction sliding window is the number of personnel for user demand items.

[0060] The deployment optimization module is used to perform sliding grouping on the reservation demand instructions in the temporary resource data cluster according to the scale value of the instruction sliding window, perform the first optimization of resource deployment based on the result of the sliding grouping, trigger a judgment task for secondary optimization to be performed, and generate a set of secondary optimization objects to be processed;

[0061] Among them, the deployment optimization module includes a sliding grouping unit and a first optimization unit;

[0062] The sliding grouping unit is used to set a grouping number function according to the scale value of the instruction sliding window and perform sliding grouping on the reservation demand instructions in the temporary resource data cluster;

[0063] The first optimization unit performs the first optimization of resource deployment based on the result of the sliding grouping and triggers a judgment task for secondary optimization to be performed to generate a set of secondary optimization objects to be processed.

[0064] The feedback center module performs an executable evaluation of the secondary object to be optimized based on the set of secondary objects to be optimized and through the execution time recorded in the reservation requirement instruction.

[0065] Among them, the feedback center module includes a secondary object selection unit to be optimized and a secondary optimization evaluation and feedback unit.

[0066] The secondary object selection unit to be optimized is used to select any reservation requirement instruction from the set of secondary objects to be optimized and identify the execution time in the reservation requirement instruction.

[0067] The secondary optimization evaluation and feedback unit is used to evaluate the executability of integrating the reservation requirement instruction into the set of secondary objects to be optimized and update the optimization completion progress of the set of secondary objects to be optimized.

[0068] Please refer to Figure 1 , in the second embodiment: A method for allocating and managing smart campus resources based on big data is provided and applied to the first embodiment above. The method includes the following steps:

[0069] Step S1: Generate a reservation requirement instruction of a user through a reservation medium and upload it to the resource data center. The resource data center generates a temporary resource data cluster according to the user demand item.

[0070] Exemplarily, the user generates a reservation requirement instruction through the reservation medium. The reservation requirement instruction records the user demand item and the execution time. The user demand item refers to a campus activity item, and the execution time refers to the time when the user expects to participate in the campus activity item.

[0071] The resource data center receives the reservation requirement instruction of the user and generates a temporary resource data cluster according to the user demand item. Among them, one user demand item corresponds to generating one temporary resource data cluster, and the reservation requirement instruction is recorded in the temporary resource data cluster.

[0072] Step S2: Sort the reservation requirement instructions recorded in the temporary resource data cluster according to the execution time order of the reservation requirement instructions, and set an instruction sliding window for different user demand items through the resource data center.

[0073] Exemplarily, uniformly configure the options of the user demand items in the reservation medium, uniformly encode the user demand items, and record any i-th user demand item as , sort the reservation requirement instructions in the order from the earliest to the latest execution time, and record the temporary resource data cluster generated corresponding to the user demand item as ;

[0074] Resource data center sets user requirement items Instruction sliding window, where the scale value of the instruction sliding window is the number of personnel for the user requirement item.

[0075] Step S3: According to the scale value of the instruction sliding window, perform sliding grouping on the reservation demand instructions in the temporary resource data cluster. The constraint condition for sliding grouping is the number of personnel for the user requirement item; based on the result of the sliding grouping, perform the first optimization of resource allocation and trigger the judgment task for secondary optimization to be performed, generating a set of objects to be secondarily optimized.

[0076] Exemplarily, according to the scale value of the instruction sliding window, perform sliding grouping on the reservation demand instructions in the temporary resource data cluster and set the grouping number function , where represents the floor function symbol, x represents the independent variable of the grouping number function, represents not greater than the largest integer, represents the number of reservation demand instructions included in the temporary resource data cluster, represents the scale value of the instruction sliding window of the user requirement item. Let , substitute it into the grouping number function formula, and obtain the grouping number of the user requirement item ;

[0077] Based on the result of the sliding grouping, perform the first optimization of resource allocation. The optimization method is as follows:

[0078] Send each instruction sliding window in the state of the grouping number to the administrator port;

[0079] If , it means that there are reservation demand instructions in the temporary resource data cluster in the un-sliding grouped state. Screen out all the un-sliding grouped reservation demand instructions to form a subset of the temporary resource data cluster , denoted as the set of objects to be secondarily optimized .

[0080] Step S4: Based on the set of objects to be secondarily optimized, perform an executable evaluation of the secondary optimization through the execution time recorded in the reservation demand instructions.

[0081] Exemplarily, select any reservation demand instruction in the set of objects to be secondarily optimized , denoted as , where y represents the coding serial number of the reservation demand instruction. Identify the execution time recorded in the reservation demand instruction , denoted as ;

[0082] Evaluation Appointment Requirement Instruction Integrate into the j-th user requirement item The formed secondary object set to be optimized The executability is evaluated as follows:

[0083] ;

[0084] In the formula, represents the evaluation appointment requirement instruction Integrated into the secondary object set to be optimized The executability of represents the secondary object set to be optimized The number of evaluation appointment requirement instructions included in represents the secondary object set to be optimized The time to be executed for the h-th evaluation appointment requirement instruction in represents the time to be executed And the time interval between the time to be executed ;

[0085] If the executability is greater than or equal to the preset evaluation threshold, then the evaluation appointment requirement instruction is integrated into the secondary object set to be optimized ;

[0086] When the number of evaluation appointment requirement instructions included in the secondary object set to be optimized is equal to the scale value of the instruction sliding window of the user requirement item , the secondary object set to be optimized Optimization is completed.

[0087] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "containing" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0088] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A smart campus resource allocation management method based on big data, characterized in that: The method comprises the following steps: Generate the user's reservation demand instruction through the reservation medium and upload it to the resource data center, and the resource data center generates a temporary resource data cluster according to the user's demand project; The options of user demand items in the reservation medium are uniformly configured, and the user demand items are uniformly coded, and any i-th user demand item is recorded as D i , sort the reservation demand instructions in the order of the execution time from the earliest to the latest, and put the user demand project D i The corresponding temporary resource data cluster is denoted as AD (D i ); Resource data center sets user demand project D i The instruction sliding window is a scale value of the instruction sliding window, wherein the scale value of the instruction sliding window is the number of personnel in the user's required project; According to the scale value of the instruction sliding window, the temporary resource data cluster AD (D i ) in the reservation demand instruction to slide group and set the number of group functions In the formula, floor[·] represents the symbol of the rounding function, x represents the independent variable of the grouping function, Indicates not greater than NUM[AD(x)] represents the number of reservation demand instructions contained in the temporary resource data cluster, V(x) represents the scale value of the instruction sliding window of the user demand project, let x = D i , substitute into the grouping number function formula, and get the user demand project D i The number of groups floor[AD(D i )]; Based on the results of sliding grouping, the first optimization of resource allocation is performed as follows: The number of groups floor[AD(D i )] Each instruction sliding window in the state is sent to the administrator port; If NUM[AD(x)]>floor[AD(x)]×V(x), it means that the temporary resource data cluster AD(D i ) contains reservation demand instructions in the unsliding group state, and all reservation demand instructions in the unsliding group are screened out to form a temporary resource data cluster AD (D i ), denoted as the secondary optimization object set ad(D i ); In the second optimization object set ad(D i ) and select any reservation demand instruction, denoted as y|D i , where y represents the coding number of the reservation demand instruction, and the reservation demand instruction y|D is identified. i The waiting time recorded in is denoted as t(y|D i ); Evaluate appointment demand instruction y|D i Incorporate the jth user demand item D j The secondary optimization object set ad(D j ) is evaluated as follows: In the formula, E[y|D i →ad(D j )] indicates the reservation request instruction y|D i Incorporate the secondary optimization object set ad(D j )'s executability, NUM[ad(D j )] represents the secondary optimization object set ad(D j ) contains the number of reservation demand instructions, t h [ad(D j )] represents the secondary optimization object set ad(D j ) is the waiting time for the hth reservation demand instruction, |t(y|D i )-t h [ad(D j )]| represents the execution time t(y|D i ) and waiting time t h [ad(D j )]; If the executable degree E[y|D i →ad(D j )] is greater than or equal to the preset evaluation threshold, the reservation demand instruction y|D i Integrate into the secondary optimization object set ad(D j )middle; When the secondary optimization object set ad(D j ) contains the reservation demand instruction equal to the user demand item D j When the scale value of the instruction sliding window is j )Optimization completed.

2. According to a method for managing smart campus resources based on big data as described in claim 1, it is characterized by: The user generates a reservation demand instruction through a reservation medium, wherein the reservation demand instruction records the user's demand item and the time to be executed, wherein the user's demand item refers to a campus activity item, and the time to be executed refers to the time when the user expects to participate in the campus activity item; The resource data center receives the user's reservation demand instruction and generates a temporary resource data cluster according to the user's demand items, wherein one temporary resource data cluster is generated corresponding to one user demand item, and the reservation demand instruction is recorded in the temporary resource data cluster.

3. A smart campus resource allocation management system based on big data, executing the smart campus resource allocation management method based on big data as described in claim 1, characterized in that: The system includes: a reservation medium module, a resource data center module, a deployment optimization module and a feedback center module; The reservation medium module is used to generate a user's reservation demand instruction and upload it to the resource data center, and the reservation demand instruction records the user's demand items and the time to be executed; The resource data center module is used to generate a temporary resource data cluster according to user demand items, the temporary resource data cluster is used to record reservation demand instructions, is used to sort the reservation demand instructions recorded in the temporary resource data cluster according to the order of execution time of the reservation demand instructions, and is also used to set instruction sliding windows for different user demand items; The allocation optimization module is used to perform sliding grouping on the reservation demand instructions in the temporary resource data cluster according to the scale value of the instruction sliding window, perform the first optimization of resource allocation based on the result of the sliding grouping, and trigger the judgment task of the second optimization to generate the second optimization object set; The feedback center module performs an executable evaluation of the secondary to-be-optimized object set based on the to-be-optimized object set and through the to-be-optimized time recorded in the reservation demand instruction.

4. According to the big data-based smart campus resource allocation and management system of claim 3, it is characterized in that: The reservation medium module supports custom editing of user demand items, and sends the results of custom editing to the resource data center for validity verification. After passing the validity verification, the custom edited user demand items are loaded into the resource data center for archiving to unify the configuration and provide options for user demand items in the reservation medium.

5. According to the big data-based smart campus resource allocation and management system of claim 3, it is characterized in that: The resource data center module includes a temporary resource data cluster generating unit and an instruction sliding window setting unit; The temporary resource data cluster generating unit is used to receive the user's reservation demand instruction and generate a temporary resource data cluster according to the user's demand items; The instruction sliding window setting unit is used to set the instruction sliding window of the user demand project, and the scale value of the instruction sliding window is the number of people in the user demand project.

6. According to the big data-based smart campus resource allocation and management system of claim 3, it is characterized in that: The deployment optimization module includes a sliding grouping unit and a first optimization unit; The sliding grouping unit is used to set the grouping number function according to the scale value of the instruction sliding window, and to perform sliding grouping on the reservation demand instructions in the temporary resource data cluster; The first optimization unit performs the first optimization of resource allocation based on the result of sliding grouping, and triggers the judgment task of the second optimization to generate a second set of objects to be optimized.

7. According to the big data-based smart campus resource allocation and management system of claim 3, it is characterized in that: The feedback center module includes a secondary optimization object selection unit and a secondary optimization evaluation feedback unit; The secondary object to be optimized selection unit is used to select any one reservation requirement instruction from the secondary object to be optimized set, and identify the to-be-executed time in the reservation requirement instruction; The secondary optimization evaluation feedback unit is used to evaluate the feasibility of integrating the reservation demand instruction into the secondary optimization object set, and to update the optimization completion progress of the secondary optimization object set.

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