Stadium reservation management method and system based on big data analysis

Abstract

The invention relates to the technical field of big data analysis and intelligent decision, in particular to a stadium reservation management method and system based on big data analysis. The method comprises the following steps: S1, acquiring an initial reservation demand of a stadium, and distributing an initial reservation supply to the initial reservation demand based on the initial reservation demand; s2, extracting initial reservation change information and initial reservation cancel information based on the initial reservation supply; and based on the initial reservation change information and the initial reservation cancel information, extracting stadium maintenance information after initial reservation supply and stadium maintenance information after the initial reservation change information and the initial reservation cancel information.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis and intelligent decision-making, and in particular to a sports venue reservation management method and system based on big data analysis. Background Art

[0002] In recent years, efficient reservation management of sports venues has directly affected the public's sports experience and resource utilization. A high-quality reservation system needs to have dynamic response capabilities to cope with sudden changes, cancellation requests and sports venue maintenance needs. However, the traditional static allocation model often leads to idle resources or insufficient supply, especially during peak hours or emergency maintenance scenarios, which significantly reduces user satisfaction.

[0003] Existing management systems mostly rely on fixed rules or manual intervention, and are difficult to adapt to frequent appointment changes and dynamic maintenance needs in real time:

[0004] Resource rigidity: Static allocation strategies cannot be flexibly adjusted, resulting in periodic resource waste;

[0005] Maintenance delay: Manual scheduling of maintenance tasks is inefficient and prone to conflicts with user appointments;

[0006] Lack of resilience: There is a lack of quantitative assessment of the stadium’s ability to resist change, making it difficult to optimize long-term resource planning. Summary of the invention

[0007] In order to overcome the disadvantage of imbalance in dynamic resource supply and demand, the present invention provides a sports venue reservation management method and system based on big data analysis.

[0008] The technical solution of this scheme is: a sports venue reservation management method based on big data analysis, comprising the following steps:

[0009] S1: Obtaining initial reservation demand of sports venues, and allocating initial reservation supply to the initial reservation demand based on the initial reservation demand;

[0010] S2: extracting the initial reservation change information and the initial reservation cancellation information based on the initial reservation supply; extracting the sports venue maintenance and upkeep information after the initial reservation supply and the sports venue maintenance and upkeep information after the initial reservation change information and the initial reservation cancellation information based on the initial reservation change information and the initial reservation cancellation information;

[0011] S3: Based on the initial reservation change information and the initial reservation cancellation information, the elasticity index of the sports venue resources is calculated using the elasticity index formula; based on the elasticity index formula, the reservation success coefficient of the sports venue is calculated using the reservation management formula;

[0012] S4: Based on the sports venue reservation success coefficient, reallocate the demand for sports venue reservation management.

[0013] Preferably, the obtaining of the initial reservation requirements of the stadium and the allocation of the initial reservation supply based on the initial reservation requirements include:

[0014] Based on the initial reservation requirements of the stadium, extract the initial allocation time, the net change impact time, the cancellation release time, and the total available supply time of the stadium; allocate the initial reservation supply resources based on the dynamic supply coefficient.

[0015] Preferably, the allocation of the initial reservation supply resources based on the dynamic supply coefficient includes: The formula for the dynamic supply coefficient is as follows,

[0016]

[0017] where γ is the dynamic supply coefficient, t alloc is the initial allocation time, t adjust is the net change impact time, t cancel is the cancellation release time, T total is the total available supply time, α is the change impact attenuation factor, β is the cancellation time amplification coefficient, is the average change time offset.

[0018] Preferably, based on the initial reservation supply, extract the initial reservation change information and the initial reservation cancellation information; based on the initial reservation change information and the initial reservation cancellation information, extract the stadium maintenance and maintenance information after the initial reservation supply and the stadium maintenance and maintenance information after the initial reservation change information and the initial reservation cancellation information, including:

[0019] Based on the initial reservation supply information, extract the available time period information of the stadium and the resource occupancy status;

[0020] Based on the initial reservation change and the initial reservation cancellation information, construct a double-ended priority queue, and trigger the stadium maintenance decision through the time offset sorting.

[0021] Preferably, the construction of the double-ended priority queue based on the initial reservation change and the initial reservation cancellation information and the triggering of the stadium maintenance decision through the time offset sorting include:

[0022] Construct a double-ended priority queue, where: the initial supply information is stored at the head of the queue, the initial cancellation information is stored at the tail of the queue, and the initial reservation change information is inserted into the head / tail of the queue according to the positive / negative of the time offset;

[0023] Record the cancellation times in the double-ended priority queue, as well as the corresponding stadiums and times for each cancellation. Specifically, for initial reservation changes, calculate the difference between the initial reservation change time and the initial reservation supply time. If it is positive, the initial reservation change time is postponed, i.e., moved closer to the end of the queue; if it is negative, the initial reservation change time advances the change time, i.e., moves closer to the front of the queue.

[0024] If it is an initial reservation cancellation, record the cancellation times, as well as the corresponding stadiums and times for each cancellation. When the cumulative cancellation duration exceeds the threshold, trigger equipment maintenance.

[0025] Based on the changes in the double-ended priority queues of each stadium, automatically generate maintenance work orders based on the proportion of urgent tasks in the queue.

[0026] Preferably, the step of automatically generating a maintenance work order based on the changes in the double-ended priority queues of each stadium and based on the proportion of urgent tasks in the queue includes:

[0027] Set a threshold based on the changes in the double-ended priority queue based on the initial change time information.

[0028] Set a time offset threshold k1:

[0029] When the change time offset Δt > k1, trigger early maintenance.

[0030] When Δt < k2, delay maintenance.

[0031] Preferably, the step of calculating the stadium resource elasticity index using the elasticity index formula based on the initial reservation change information and the initial reservation cancellation information includes: The elasticity index formula is as follows,

[0032]

[0033] where E is the elasticity coefficient, A i is the supply surplus in the i-th time period, C i is the initial change in the i-th time period, D i is the initial cancellation in the i-th time period, M k is the planned maintenance time at the k-th maintenance, N k is the actual maintenance time at the k-th maintenance, w is the weight factor of the maintenance deviation, n is the number of time periods, x is the number of maintenance times, and m is the minimum value.

[0034] Preferably, the step of calculating the stadium reservation success coefficient using the reservation management formula based on the elasticity index formula includes: The reservation success coefficient is as follows,

[0035]

[0036] where Si is the reservation success coefficient, E i is the i-th elasticity coefficient, γ i is the i-th dynamic supply coefficient, γ 0 The benchmark dynamic supply coefficient, λ is a hyperparameter.

[0037] Preferably, based on the reservation success coefficient of the stadium, reallocation is performed for the needs of stadium reservation management, including: The reallocation formula is as follows,

[0038]

[0039] where Q is the total duration of the reservable time slots finally allocated to the i-th stadium, S i is the reservation success coefficient of the i-th stadium, and m is the total number of stadiums.

[0040] Preferably, the stadium reservation management system based on big data analysis includes:

[0041] Dynamic supply allocation module: Based on the initial reservation demand, extract the initial allocation time, change net impact time, and cancellation release time; allocate resources through the dynamic supply coefficient formula;

[0042] Maintenance decision trigger module: Construct a double-ended priority queue, with the initial supply stored at the head of the queue and cancellation events stored at the tail; insert into the queue according to the positive or negative of the change time offset; trigger maintenance when the cumulative cancellation duration exceeds the threshold;

[0043] Elasticity evaluation module: Calculate the elasticity coefficient; generate the reservation success coefficient by combining the dynamic supply coefficient;

[0044] Resource reallocation module: Calculate the new allocation weight; preferentially allocate high-elasticity and high-supply stadiums, and iteratively update the queue and coefficients in real time.

[0045] Beneficial effects: By constructing a dynamic supply allocation model and a double-ended queue maintenance decision mechanism, the present invention first solves the problem of rigid resource allocation caused by reservation changes and cancellations in traditional stadium management, ensuring the efficient matching of initial supply and dynamic demand. On this basis, by quantifying the resource elasticity index and reservation success coefficient, the carrying capacity of the stadium and the maintenance deviation degree are accurately evaluated, strengthening the adaptive ability of resource scheduling. Then, based on the time offset threshold and queue state analysis, the equipment maintenance window period is intelligently identified and maintenance work orders are automatically generated, realizing the collaborative optimization of preventive maintenance and resource release. Finally, through the closed-loop iterative resource reallocation algorithm, the supply-demand relationship among multiple stadiums is dynamically balanced, systematically improving the overall resource utilization rate and reservation management efficiency, and providing full-link decision support for the operation of smart stadiums. Description of the Drawings

[0046] Figure 1 This is a flowchart of a sports venue reservation management method based on big data analysis according to the present invention;

[0047] Figure 2 This is a schematic structural diagram of a sports venue reservation management system based on big data analysis according to the present invention. Specific embodiments

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 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.

[0049] Embodiment 1: A sports venue reservation management method based on big data analysis, as Figure 1 shown, includes the following steps:

[0050] S1: Obtain the initial reservation requirements of the sports venue, and based on the initial reservation requirements, allocate initial reservation supplies for the initial reservation requirements;

[0051] S2: Based on the initial reservation supplies, extract the initial reservation change information and the initial reservation cancellation information; based on the initial reservation change information and the initial reservation cancellation information, extract the sports venue maintenance and maintenance information after the initial reservation supplies and the sports venue maintenance and maintenance information after the initial reservation change information and the initial reservation cancellation information;

[0052] S3: Based on the initial reservation change information and the initial reservation cancellation information, calculate the sports venue resource elasticity index using the elasticity index formula; based on the elasticity index formula, calculate the sports venue reservation success coefficient using the reservation management formula;

[0053] S4: Based on the sports venue reservation success coefficient, re-allocate the requirements for sports venue reservation management.

[0054] Obtaining the initial reservation requirements of the sports venue, and based on the initial reservation requirements, allocating initial reservation supplies for the initial reservation requirements includes:

[0055] Based on the initial reservation requirements of the sports venue, extract the initial allocation time, the change net impact time, the cancellation release time, and the total available supply time of the sports venue; allocate the initial reservation supply resources based on the dynamic supply coefficient.

[0056] For further illustration, dynamic resource allocation is achieved by quantifying key time parameters. First, four core elements are extracted from the initial reservation requirements: the initial allocation time (the time slot reserved for the user in the plan), the net change impact time (the net change in the time slot caused by the user's adjustment of the reservation), the cancellation release time (the available reservation time slot released again after the user cancels), and the total available supply time (the overall available opening time of the stadium). Subsequently, based on the dynamic supply coefficient model, the above parameters are combined with the change impact attenuation factor and the cancellation time amplification coefficient weight to calculate the resource supply capacity in real time and dynamically adjust the reservation quota for each time slot. By mathematically modeling the superimposed impact of changes and cancellations on the resource pool, an adaptive matching of supply and demand fluctuations is achieved.

[0057] Based on the dynamic supply coefficient, the initial reservation supply resources are allocated, including: The formula for the dynamic supply coefficient is as follows.

[0058]

[0059] where γ is the dynamic supply coefficient, t alloc is the initial allocation time, t adjust is the net change impact time, t cancel is the cancellation release time, T total is the total available supply time, α is the change impact attenuation factor, β is the cancellation time amplification coefficient, and is the average change time offset.

[0060] For further illustration, the calculation formula of the dynamic supply coefficient γ is divided into two parts: the main fractional term reflects the dynamic adjustment ability of resources, and the standard deviation term measures the change stability. In the main fractional term, the numerator subtracts α times (α is the attenuation factor, weakening the interference of frequent changes) of the net change impact time (the net change in the time slot caused by the user's adjustment) from the initial allocation time (the planned reserved time slot), superimposes β times (β is the amplification coefficient, strengthening the availability of the cancelled and released resources) of the cancellation release time, and then divides by the total available supply time (the total opening duration of the stadium) to form the basic supply ratio. The additional square root term calculates the standard deviation of the deviation of each change time from the average deviation, reflecting the degree of fluctuation of the change time sequence: the greater the fluctuation, the higher the elastic demand. The formula dynamically adjusts the supply strategy by quantifying the superimposed impact of changes and cancellations on resources and combining the evaluation of time sequence stability, so as to achieve an accurate matching of resource allocation and demand fluctuations.

[0061] Based on the initial reservation supply, the initial reservation change information and the initial reservation cancellation information are extracted; based on the initial reservation change information and the initial reservation cancellation information, the stadium maintenance and preservation information after the initial reservation supply and the stadium maintenance and preservation information after the initial reservation change information and the initial reservation cancellation information are extracted, including:

[0062] Extract the available time slots information and resource occupancy status of the stadium based on the initial reservation supply information;

[0063] Based on the initial reservation change and initial reservation cancellation information, construct a double-ended priority queue, and trigger the stadium maintenance decision through time offset sorting.

[0064] For further explanation, realize the intelligence of maintenance decision-making through the double-ended queue mechanism. First, extract the available time slots and resource occupancy status to clarify the reservable capacity and real-time occupancy of each time slot in the stadium. Subsequently, construct a double-ended priority queue: the head of the queue stores the initial reservation supply information (such as the confirmed reservation time slot), and the tail of the queue stores the time slots released by cancellation events; insert into the queue according to the positive or negative of the change time offset Δt - if Δt>0 (the change time is postponed), insert near the tail; if Δt<0 (the change time is advanced), insert near the head. Dynamically reflect the maintenance window priority through queue sorting: tasks with a large negative offset at the head of the queue trigger preventive maintenance (such as equipment inspection), and emergency repair is triggered when the cumulative cancellation duration at the tail of the queue exceeds the threshold. Convert the time series fluctuations into the basis for generating maintenance work orders to achieve the coordinated optimization of resource release and maintenance scheduling.

[0065] Based on the initial reservation change and initial reservation cancellation information, construct a double-ended priority queue, and trigger the stadium maintenance decision through time offset sorting, including:

[0066] Construct a double-ended priority queue, where: the head of the queue stores the initial supply information, the tail of the queue stores the initial cancellation information, and insert the initial reservation change information into the head / tail of the queue respectively according to the positive or negative of the time offset;

[0067] Record the cancellation times in the double-ended priority queue and the corresponding stadium and time for each cancellation. Specifically, if it is an initial reservation change, calculate the difference between the initial reservation change time and the initial reservation supply time. If it is positive, the initial reservation change time is postponed, that is, closer to the tail of the queue; if it is negative, the initial reservation change time advances the change time, that is, closer to the head of the queue;

[0068] If it is an initial reservation cancellation, record the cancellation times and the corresponding stadium and time for each cancellation, and trigger equipment repair when the cumulative cancellation duration exceeds the threshold;

[0069] Based on the change situation of the double-ended priority queue of each stadium, automatically generate maintenance work orders based on the proportion of emergency tasks in the queue.

[0070] For further explanation, the construction and maintenance decision logic of the double-ended priority queue is as follows: The queue is divided into the head (storing the confirmed initial reservation time slots) and the tail (storing the cancelled and released time slots). Reservation change events are dynamically inserted into the queue according to the time offset Δt (the difference between the change time and the original reservation time): If Δt is positive (e.g., changing from 10:00 to 11:00), it is regarded as a time delay and inserted near the tail to avoid affecting recent resource allocation; if Δt is negative (e.g., changing from 11:00 to 9:00), it is regarded as a time advance and inserted near the head to prioritize the handling of resource conflicts in the nearby time slots. Cancellation events record the stadium, time, and cumulative duration. When the total cancellation duration of a certain stadium exceeds a preset threshold (e.g., 2 hours / week), an equipment maintenance work order is automatically triggered. The queue analyzes the proportion of emergency tasks in real time (the number of tasks with negative offset at the head of the queue / the total number of tasks). When the proportion exceeds the limit, preventive maintenance instructions (such as cleaning, safety inspection) are generated to achieve a closed-loop response from resource dynamic adjustment to maintenance strategy.

[0071] Based on the changes in the double-ended priority queues of each stadium, maintenance work orders are automatically generated based on the proportion of emergency tasks in the queue, including:

[0072] Set a threshold based on the changes in the double-ended priority queue based on the initial change time information;

[0073] Set the time offset threshold k1:

[0074] When the change time offset Δt > k1, trigger early maintenance;

[0075] When Δt < k2, delay maintenance.

[0076] For further explanation, the maintenance threshold is set based on the change time offset rule in the queue: The positive offset threshold k1 (e.g., Δt > 3 hours) indicates that the time slot is released later, triggering early maintenance (using the idle period for maintenance); the negative offset threshold k2 (e.g., Δt < -2 hours) reflects that the time slot is occupied overly early. To avoid resource conflicts, maintenance is delayed until the low peak period. The threshold dynamically calibrates the maintenance urgency in the queue to ensure priority supply during high-load periods and efficient execution of maintenance during idle periods.

[0077] Based on the initial reservation change information and the initial reservation cancellation information, calculate the resource elasticity index of the stadium using the elasticity index formula, including: The elasticity index formula is as follows,

[0078]

[0079] where E is the elasticity coefficient, A i is the supply margin in the i-th time period, C i is the initial change in the i-th time period, D i is the initial cancellation in the i-th time period, Mk For the k-th maintenance during the scheduled maintenance time, N k For the actual maintenance time during the k-th maintenance, w is the weight factor of the maintenance deviation, n is the number of time periods, x is the number of maintenance times, and m is the minimum value.

[0080] For further explanation, the elasticity coefficient E measures the comprehensive ability of the stadium to handle reservation fluctuations and maintenance stability. The formula is divided into two parts:

[0081] Resource adjustment ability term: The numerator calculates the cumulative value of the absolute sum of the supply surplus A i Exceeding the change C i And the cancellation D i (Negative values are taken as 0), reflecting the absorption ability of resource redundancy to fluctuations; the denominator is the total fluctuation volume, and the larger the ratio, the stronger the elasticity.

[0082] Maintenance stability term: The denominator is the maintenance plan M k And the standard deviation of the deviation from the actual execution time N k Multiplied by the weight w. The smaller the standard deviation (higher maintenance punctuality), the larger the elasticity value.

[0083] Quantify the anti-interference ability (resource surplus buffer) and operation and maintenance reliability (maintenance punctuality) of the stadium under dynamic reservations, and guide the priority allocation of high-elasticity stadiums to improve the overall scheduling efficiency.

[0084] Based on the elasticity index formula, use the reservation management formula to calculate the stadium reservation success coefficient, including: The reservation success coefficient is as follows,

[0085]

[0086] Among them, S i Is the reservation success coefficient, E i Is the i-th elasticity coefficient, γ i Is the i-th dynamic supply coefficient, γ 0 The benchmark dynamic supply coefficient, and λ is a hyperparameter.

[0087] For further explanation, the formula is used to comprehensively evaluate the reservation management efficiency of the stadium and includes two parts:

[0088] Supply capacity benchmark ratio γ i Is the dynamic supply coefficient of the i-th stadium (reflecting the efficiency of resource dynamic allocation);

[0089] γ 0 Is the benchmark value (such as the historical average or the ideal supply level), and the ratio measures the strength of the current supply capacity relative to the benchmark.

[0090] Elasticity gain term E i is the elastic coefficient (anti-fluctuation and maintenance of stability), and the larger the value, the stronger the elasticity;

[0091] The exponential function maps elasticity to the interval of 0-1, and λ controls the gain speed (for example, when λ = 0.5, the elastic influence is gentle, and when λ = 2, it quickly approaches the upper limit).

[0092] When the supply capacity γ i is higher than the benchmark and the elasticity E i is relatively high, S i will increase significantly;

[0093] Even if the supply of stadiums with poor elasticity is sufficient, the success coefficient will be inhibited (the exponential term approaches 0). Quantify the synergistic effect of stadium resource supply and elasticity, and preferentially allocate stadiums with high supply and high elasticity to improve the overall reservation success rate and resource utilization rate.

[0094] Based on the stadium reservation success coefficient, reallocate for the needs of stadium reservation management, including: The reallocation formula is as follows,

[0095]

[0096] where Q is the total duration of available reservation slots finally allocated to the i-th stadium, S i is the reservation success coefficient of the i-th stadium, and m is the total number of stadiums.

[0097] For further explanation, the formula is used to calculate the finally allocated available reservation duration of each stadium. The numerator S i : The reservation success coefficient of the i-th stadium (the result of the comprehensive dynamic supply capacity γ i and the elastic coefficient E i ), which reflects the resource utilization efficiency and anti-interference ability of the stadium.

[0098] The denominator is the sum of the reservation success coefficients of all stadiums, which is used for normalization to ensure the fairness of the allocation ratio.

[0099] T total : The total available supply time of the stadium (such as the total opening hours in a week).

[0100] Allocate the total resource T total dynamically according to the proportion of the reservation success coefficient of each stadium. The higher the reservation success coefficient (sufficient supply and strong elasticity), the more allocated duration, and vice versa. For example, if a stadium S i accounts for 30%, then allocate 30% of the total duration to it.

[0101] Allocate resources by quantifying performance (S i ) to incentivize stadiums to optimize supply and maintenance strategies while maximizing overall resource efficiency.

[0102] Embodiment 2: Based on Embodiment 1, a reservation management system for stadiums based on big data analysis includes:

[0103] Dynamic supply allocation module: Based on the initial reservation demand, extract the initial allocation time, change net impact time, and cancellation release time; allocate resources through the dynamic supply coefficient formula;

[0104] Maintenance decision trigger module: Construct a double-ended priority queue, with the initial supply stored at the head of the queue and cancellation events stored at the tail; insert into the queue according to the positive or negative of the change time offset; trigger maintenance when the cumulative cancellation duration exceeds the threshold;

[0105] Elasticity evaluation module: Calculate the elasticity coefficient; generate a reservation success coefficient in combination with the dynamic supply coefficient;

[0106] Resource reallocation module: Calculate the new allocation weight; preferentially allocate stadiums with high elasticity and high supply, and iteratively update the queue and coefficients in real time.

[0107] The above has introduced the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation method of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A sports venue reservation management method based on big data analysis, characterized in that: The following steps are involved: S1: Obtaining initial reservation demand of sports venues, and allocating initial reservation supply to the initial reservation demand based on the initial reservation demand; S2: extracting initial reservation change information and initial reservation cancellation information based on the initial reservation supply; Based on the initial reservation change information and the initial reservation cancellation information, extract the sports venue maintenance and upkeep information after the initial reservation is provided and the sports venue maintenance and upkeep information after the initial reservation change information and the initial reservation cancellation information; S3: Based on the initial reservation change information and the initial reservation cancellation information, the elasticity index of the sports venue resources is calculated using the elasticity index formula; based on the elasticity index formula, the reservation success coefficient of the sports venue is calculated using the reservation management formula; S4: Based on the sports venue reservation success coefficient, reallocate the demand for sports venue reservation management.

2. A sports venue reservation management method based on big data analysis according to claim 1, characterized in that: The obtaining of the initial reservation demand of the sports venue and allocating the initial reservation supply to the initial reservation demand based on the initial reservation demand includes: Based on the initial reservation demand of the sports venue, the initial allocation time, the net impact time of the change, the cancellation release time and the total available supply time of the sports venue are extracted; and the initial reservation supply resources are allocated based on the dynamic supply coefficient.

3. A sports venue reservation management method based on big data analysis according to claim 2, characterized in that: The allocation of initial reservation supply resources based on the dynamic supply coefficient includes: the dynamic supply coefficient formula is as follows: Among them, γ is the dynamic supply coefficient, t alloc is the initial allocation time, t adjust is the net impact time of the change, t cancel To cancel the release time, T total is the total available supply time, α is the change impact attenuation factor, β is the cancellation time magnification factor, is the average change time offset.

4. A sports venue reservation management method based on big data analysis according to claim 1, characterized in that: extracting initial reservation change information and initial reservation cancellation information based on the initial reservation provision; Based on the initial reservation change information and the initial reservation cancellation information, extracting the sports venue maintenance and upkeep information after the initial reservation is provided and the sports venue maintenance and upkeep information after the initial reservation change information and the initial reservation cancellation information, including: Based on the initial reservation information, extract the available time period information and resource occupancy status of the sports venue; Based on the initial reservation change and initial reservation cancellation information, a double-ended priority queue is constructed, and the stadium maintenance decision is triggered by time offset sorting.

5. A sports venue reservation management method based on big data analysis according to claim 4, characterized in that: The method of constructing a double-ended priority queue based on the initial reservation change and the initial reservation cancellation information, and triggering a stadium maintenance decision by sorting by time offset, includes: Construct a double-ended priority queue, where the head of the queue stores the initial supply information, the tail of the queue stores the initial cancellation information, and the initial reservation change information is inserted into the head / tail of the queue according to the positive or negative time offset; Record the number of cancellations in the dual-ended priority queue and the stadium and time corresponding to each cancellation. Specifically, if it is an initial appointment change, calculate the difference between the initial appointment change time and the initial appointment supply time. If it is positive, the initial appointment change time will be postponed, that is, closer to the end of the queue; if it is negative, the initial appointment change time will advance the change time, that is, closer to the head of the queue; If it is an initial reservation cancellation, the number of cancellations and the stadium and time corresponding to each cancellation will be recorded. When the cumulative cancellation time exceeds the threshold, equipment maintenance will be triggered; Based on the changes in the dual-end priority queues of each stadium, maintenance work orders are automatically generated based on the proportion of urgent tasks in the queue.

6. A sports venue reservation management method based on big data analysis according to claim 5, characterized in that: The maintenance work order is automatically generated based on the changes of the dual-end priority queues of each stadium and the proportion of urgent tasks in the queue, including: Set a threshold based on the change situation of the initial change time information in the double-ended priority queue; Set the time offset threshold k1: When the change time offset Δt > k1, trigger early maintenance; When Δt < k2, delay maintenance.

7. The method for sports venue reservation management based on big data analysis according to claim 1 is characterized in that: Based on the initial reservation change information and the initial reservation cancellation information, calculate the sports venue resource elasticity index using the elasticity index formula, including: The elasticity index formula is as follows, Where E is the elastic modulus, A i is the supply margin in the i-th time period, C i is the initial change in the i-th time period, D i is the initial cancellation in the i-th time period, M k is the planned maintenance time at the kth maintenance, N k is the actual maintenance time at the kth maintenance, w is the weight factor of the maintenance deviation, n is the number of time periods, x is the number of maintenance times, and m is the minimum value.

8. The method for sports venue reservation management based on big data analysis according to claim 1 is characterized in that: Based on the elasticity index formula, calculate the sports venue reservation success coefficient using the reservation management formula, including: The reservation success coefficient is as follows, Among them, S i is the reservation success coefficient, E i is the i-th elastic coefficient, γ i is the ith dynamic supply coefficient, γ0 is the benchmark dynamic supply coefficient, and λ is a hyperparameter.

9. The method for sports venue reservation management based on big data analysis according to claim 1 is characterized in that: Based on the sports venue reservation success coefficient, reallocate for the needs of sports venue reservation management, including: The reallocation formula is as follows, Among them, Q is the total duration of the available time slots finally allocated to the i-th sports venue, S i is the reservation success coefficient of the i-th sports stadium, and m is the total number of sports stadiums.

10. A sports venue reservation management system based on big data analysis, used in the sports venue reservation management method based on big data analysis as claimed in any one of claims 1 to 9, It is characterized by including: Dynamic supply allocation module: Based on the initial reservation demand, extract the initial allocation time, change net impact time, and cancellation release time; Allocate resources through the dynamic supply coefficient formula; Maintenance decision trigger module: Construct a double-ended priority queue, with the initial supply stored at the head of the queue and the cancellation event stored at the tail of the queue; Insert into the queue according to the positive or negative of the change time offset; Trigger maintenance when the cumulative cancellation duration exceeds the threshold; Elasticity evaluation module: Calculate the elasticity coefficient; Generate the reservation success coefficient in combination with the dynamic supply coefficient; Resource reallocation module: Calculate the new allocation weight; Prioritize the allocation of sports venues with high elasticity and high supply, and iteratively update the queue and coefficients in real time.

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