Smart campus resource allocation management method and system based on big data

Abstract

The invention discloses a smart campus resource allocation management method and system based on big data, and belongs to the technical field of smart campus management. Receiving a reservation demand instruction of a user through a reservation medium, and uploading the reservation demand instruction to a resource data center to form a temporary resource data cluster; sorting the temporary resource data clusters according to a to-be-executed time sequence of the reservation demand instructions, setting an instruction sliding window, grouping the reservation demand instructions according to a scale value (namely the number of personnel of a user demand project) of the instruction sliding window in a sliding grouping manner, and performing first optimization of resource allocation; and after the first optimization, generating a secondary to-be-optimized object set, and performing secondary to-be-optimized executable evaluation by evaluating the executable degree of the reservation demand instruction fused into the secondary to-be-optimized object set, so as to complete the optimization process of resource allocation. In order to improve the quality of campus life, the resource allocation can be efficiently and accurately carried out according to the user demand, and the resource utilization rate and the user satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart campus management, and in particular to a smart campus resource allocation management method and system 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. The traditional manual allocation method is not only inefficient, but also difficult to cope with large-scale and diversified user needs, especially in campus activity arrangements, classroom and facility reservations, etc., where resource conflicts and uneven distribution often occur.

[0003] In the context of smart campuses, the introduction of big data technology provides a new approach to solving the above problems. By collecting and analyzing usage data of various resources on campus, user demand data, etc., accurate prediction and dynamic monitoring of resource usage can be achieved, thereby providing more scientific and reasonable decision-making support for resource allocation. However, most of the existing smart campus resource allocation systems remain at the data collection and display level, lacking in-depth analysis and optimization algorithms for specific demand scenarios, resulting in the efficiency and effectiveness of resource allocation still need to be improved.

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

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

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A smart campus resource allocation and management system based on big data, the system includes: a reservation medium module, a resource data center module, an allocation 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.

[0007] Furthermore, the reservation media 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 media.

[0008] Further, 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.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] A method for allocating and managing smart campus resources based on big data, the method comprising the following steps: Step S1: Generate a user's reservation demand instruction through a reservation medium and upload it to a resource data center, which generates a temporary resource data cluster according to the user's demand items; Step S2: sorting the reservation demand instructions recorded in the temporary resource data cluster according to the order of execution time of the reservation demand instructions, and setting the instruction sliding window of different user demand items through the resource data center; Step S3: According to the scale value of the instruction sliding window, the reservation demand instructions in the temporary resource data cluster are slidingly grouped, and the constraint condition of the sliding grouping is the number of personnel in the user demand project; based on the result of the sliding grouping, the first optimization of resource allocation is performed, and the judgment task of the second optimization is triggered to generate a second optimization object set; Step S4: Based on the set of objects to be optimized for the second time, an executable evaluation of the objects to be optimized for the second time is performed by using the execution time recorded in the reservation demand instruction.

[0012] Furthermore, the specific implementation process of step S1 includes: 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; In the above method, user demand items include table tennis items, basketball items, reading items, and playground and extracurricular activities.

[0013] Furthermore, the specific implementation process of step S2 includes: 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 , sort the reservation demand instructions in the order of the execution time from the earliest to the latest, and sort the user demand items The corresponding temporary resource data cluster is recorded as ; Resource data center sets up user demand projects An instruction sliding window is provided, wherein the scale value of the instruction sliding window is the number of personnel of the user's required project.

[0014] Furthermore, the specific implementation process of step S3 includes: According to the scale value of the instruction sliding window, the temporary resource data cluster The reservation demand instruction in the sliding grouping and setting the grouping number function , where represents the symbol of the rounding function, x represents the independent variable of the grouping function, Indicates not greater than The largest integer, Indicates the number of reservation demand instructions contained in the temporary resource data cluster. Represents the scale value of the instruction sliding window of the user demand project, let , substitute into the grouping number function formula, and get the user demand items Number of groups ; Based on the results of sliding grouping, the first optimization of resource allocation is performed as follows: The number of groups Each instruction sliding window in the state is sent to the administrator port; like , it indicates a temporary resource data cluster There are reservation demand instructions in the unslid group state, and all reservation demand instructions in the unslid group are filtered out to form a temporary resource data cluster A subset of , recorded as the secondary optimization object set ; In the above method, for the reservation demand instructions in the unslid grouping state, secondary activity projects can be recommended, and subsequent secondary optimization evaluation can be performed to integrate the reservation demand instructions in the unslid grouping state into other activities that still have insufficient team members.

[0015] Furthermore, the specific implementation process of step S4 includes: In the second set of objects to be optimized Select any reservation demand instruction from , where y represents the coding number of the reservation request instruction, identifying the reservation request instruction The waiting time recorded in is recorded as ; Evaluate appointment requirements instructions Incorporate the jth user demand project The set of secondary objects to be optimized The feasibility of the evaluation is as follows: ; In the formula, Indicates reservation request instruction Incorporating the secondary set of objects to be optimized The executability of Represents the set of objects to be optimized The number of reservation request instructions contained in Represents the set of objects to be optimized The waiting time for the hth reservation demand instruction in Indicates the execution time and execution time the time interval between If the executable If the value is greater than or equal to the preset evaluation threshold, the reservation demand instruction will be Integrate into the secondary optimization object set middle; When the secondary optimization object set The reservation requirement instruction contained in is equal to the user requirement item When the scale value of the instruction sliding window is Optimization completed.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a smart campus resource allocation management method and system based on big data provided by the present invention, in the method part, the user's reservation demand instruction is received through the reservation medium, and uploaded to the 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 demand instruction, and an instruction sliding window is set, and the reservation demand instruction is grouped according to the scale value of the instruction sliding window (i.e. the number of personnel of the user demand project) by sliding grouping, and the first optimization of resource allocation is performed; after the first optimization, a secondary set of objects to be optimized is generated, and the executable degree of the reservation demand instruction integrated into the secondary set of objects to be optimized is evaluated, and the executable evaluation of the secondary set to be optimized is performed to complete the optimization process of resource allocation. In order to improve the quality of campus life, the present invention can efficiently and accurately allocate resources according to user needs, improve resource utilization and user satisfaction.

[0017] The system part 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 reservation demand instructions; the resource data center module is used to generate temporary resource data clusters, sort and set instruction sliding windows; the deployment optimization module is used for sliding grouping and first optimization, and generates a second set of objects to be optimized; the feedback center module is used for executable evaluation of the second optimization.

[0018] In addition, the system also supports customized editing of user demand projects, and performs validity verification and archiving through the resource data center; the resource data center module, deployment optimization module and feedback center module all contain multiple sub-units to realize their respective functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0020] Figure 1 It is a schematic diagram of the steps of a smart campus resource allocation and management method based on big data of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] In the first embodiment of the present invention, a smart campus resource allocation and management system based on big data is provided, the system comprising: a reservation medium module, a resource data center module, an allocation 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; Among them, the reservation media module also 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 media.

[0023] 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; Wherein, 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.

[0024] 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; Wherein, 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.

[0025] The feedback center module performs an executable evaluation of the secondary optimization based on the secondary optimization object set and the execution time recorded in the reservation demand instruction; Wherein, 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.

[0026] See also Figure 1 In the second embodiment, a method for allocating and managing smart campus resources based on big data is provided to be applied to the first embodiment, and the method includes the following steps: Step S1: Generate a user's reservation demand instruction through a reservation medium and upload it to a resource data center, which generates a temporary resource data cluster according to the user's demand items; Exemplarily, 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.

[0027] Step S2: sorting the reservation demand instructions recorded in the temporary resource data cluster according to the order of execution time of the reservation demand instructions, and setting the instruction sliding window of different user demand items through the resource data center; For example, 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 , sort the reservation demand instructions in the order of the execution time from the earliest to the latest, and sort the user demand items The corresponding temporary resource data cluster is recorded as ; Resource data center sets up user demand projects An instruction sliding window is provided, wherein the scale value of the instruction sliding window is the number of personnel of the user's required project.

[0028] Step S3: According to the scale value of the instruction sliding window, the reservation demand instructions in the temporary resource data cluster are slidingly grouped, and the constraint condition of the sliding grouping is the number of personnel in the user demand project; based on the result of the sliding grouping, the first optimization of resource allocation is performed, and the judgment task of the second optimization is triggered to generate a second optimization object set; Exemplarily, according to the scale value of the instruction sliding window, the temporary resource data cluster The reservation demand instruction in the sliding grouping and setting the grouping number function , where represents the symbol of the rounding function, x represents the independent variable of the grouping function, Indicates not greater than The largest integer, Indicates the number of reservation demand instructions contained in the temporary resource data cluster. Represents the scale value of the instruction sliding window of the user demand project, let , substitute into the grouping number function formula, and get the user demand items Number of groups ; Based on the results of sliding grouping, the first optimization of resource allocation is performed as follows: The number of groups Each instruction sliding window in the state is sent to the administrator port; like , it indicates a temporary resource data cluster There are reservation demand instructions in the unslid group state, and all reservation demand instructions in the unslid group are filtered out to form a temporary resource data cluster A subset of , recorded as the secondary optimization object set .

[0029] Step S4: Based on the set of objects to be optimized for the second time, and by using the execution time recorded in the reservation demand instruction, an executable evaluation is performed for the objects to be optimized for the second time; For example, in the second set of objects to be optimized Select any reservation demand instruction from , where y represents the coding number of the reservation request instruction, identifying the reservation request instruction The waiting time recorded in is recorded as ; Evaluate appointment requirements instructions Incorporate the jth user demand project The set of secondary objects to be optimized The feasibility of the evaluation is as follows: ; In the formula, Indicates reservation request instruction Incorporating the secondary set of objects to be optimized The executability of Represents the set of objects to be optimized The number of reservation request instructions contained in Represents the set of objects to be optimized The waiting time for the hth reservation demand instruction in Indicates the execution time and execution time the time interval between If the executable If the value is greater than or equal to the preset evaluation threshold, the reservation demand instruction will be Integrate into the secondary optimization object set middle; When the secondary optimization object set The reservation requirement instruction contained in is equal to the user requirement item When the scale value of the instruction sliding window is Optimization completed.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0031] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in 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: Step S1: Generate a user's reservation demand instruction through a reservation medium and upload it to a resource data center, which generates a temporary resource data cluster according to the user's demand items; Step S2: sorting the reservation demand instructions recorded in the temporary resource data cluster according to the order of execution time of the reservation demand instructions, and setting the instruction sliding window of different user demand items through the resource data center; Step S3: According to the scale value of the instruction sliding window, the reservation demand instructions in the temporary resource data cluster are slidingly grouped, and the constraint condition of the sliding grouping is the number of personnel in the user demand project; based on the result of the sliding grouping, the first optimization of resource allocation is performed, and the judgment task of the second optimization is triggered to generate a second optimization object set; Step S4: Based on the set of objects to be optimized for the second time, an executable evaluation of the objects to be optimized for the second time is performed by using the execution time recorded in the reservation demand instruction.

2. According to a method for managing smart campus resources based on big data according to claim 1, it is characterized in that: The specific implementation process of step S1 includes: 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. According to a method for managing smart campus resources based on big data as described in claim 1, it is characterized in that: The specific implementation process of step S2 includes: 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 , sort the reservation demand instructions in the order of the execution time from the earliest to the latest, and sort the user demand items The corresponding temporary resource data cluster is recorded as ; Resource data center sets up user demand projects An instruction sliding window is provided, wherein the scale value of the instruction sliding window is the number of personnel of the user's required project.

4. According to a method for managing smart campus resources based on big data according to claim 3, it is characterized in that: The specific implementation process of step S3 includes: According to the scale value of the instruction sliding window, the temporary resource data cluster The reservation demand instruction in the sliding grouping and setting the grouping number function , where represents the symbol of the rounding function, x represents the independent variable of the grouping function, Indicates not greater than The largest integer, Indicates the number of reservation demand instructions contained in the temporary resource data cluster. Represents the scale value of the instruction sliding window of the user demand project, let , substitute into the grouping number function formula, and get the user demand items Number of groups ; Based on the results of sliding grouping, the first optimization of resource allocation is performed as follows: The number of groups Each instruction sliding window in the state is sent to the administrator port; like , it indicates a temporary resource data cluster There are reservation demand instructions in the unslid group state, and all reservation demand instructions in the unslid group are filtered out to form a temporary resource data cluster A subset of , recorded as the secondary optimization object set .

5. According to the big data-based smart campus resource allocation management method of claim 4, it is characterized in that: The specific implementation process of step S4 includes: In the second set of objects to be optimized Select any reservation demand instruction from , where y represents the coding number of the reservation request instruction, identifying the reservation request instruction The waiting time recorded in is recorded as ; Evaluate appointment requirements instructions Incorporate the jth user demand project The set of secondary objects to be optimized The feasibility of the evaluation is as follows: ; In the formula, Indicates reservation request instruction Incorporating the secondary optimization object set The executability of Represents the set of objects to be optimized The number of reservation request instructions contained in Represents the set of objects to be optimized The waiting time for the hth reservation demand instruction in Indicates the execution time and execution time the time interval between If the executable If the value is greater than or equal to the preset evaluation threshold, the reservation demand instruction will be Integrate into the secondary optimization object set middle; When the secondary optimization object set The reservation requirement instruction contained in is equal to the user requirement item When the scale value of the instruction sliding window is Optimization completed.

6. A smart campus resource allocation management system based on big data, executing a smart campus resource allocation management method based on big data as described in any one of claims 1-5, 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.

7. According to the big data-based smart campus resource allocation and management system of claim 6, 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.

8. According to the big data-based smart campus resource allocation and management system of claim 6, 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.

9. According to the big data-based smart campus resource allocation and management system of claim 6, 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.

10. The big data-based smart campus resource allocation and management system according to claim 6, 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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