Football field intelligent operation management method based on mobile application

Abstract

The invention discloses a football field intelligent operation management method based on a mobile application, and relates to the technical field of football field operation, and the method comprises the following steps: constructing a unified field resource data model; a schedule availability quantification and conflict determinable mechanism based on a resource model; dynamic scheduling driving quantity construction and load balancing triggering oriented to real-time requirements are carried out; a cross-site collaborative scheduling optimization and intelligent migration decision-making mechanism; an equipment operation load coupling prediction and maintenance scheduling linkage mechanism; and a multi-dimensional operation performance evaluation and self-adaptive strategy decision-making mechanism. According to the method, the unified site resource data model is constructed, the site schedule, the facility operation state, the maintenance period and the use record are subjected to collaborative modeling, and dynamic linkage management of appointment scheduling, resource configuration and equipment operation and maintenance is realized, so that the site utilization rate can be effectively improved, and the equipment operation and maintenance risk can be reduced; the refinement level and the user experience of site operation management are remarkably improved, and the method has relatively high practical value and popularization and application prospects.

Description

Technical Field

[0001] This invention relates to the field of stadium operation technology, specifically to a mobile application-based intelligent operation and management method for football fields. Background Technology

[0002] With the continuous advancement of digital development in the sports industry, the operation and management model of football fields, as important sports infrastructure, is gradually transforming from traditional manual management to information-based and intelligent management. Currently, some football stadiums have introduced mobile application reservation systems, enabling users to book online, make payments, and manage basic schedules, which has improved venue operation efficiency to some extent. However, most existing technologies only focus on single-level reservation management, lacking the ability to coordinate the overall operation of venue resources. On the one hand, existing systems typically manage venue schedules, facility operating status, and maintenance plans in a fragmented manner, making it difficult to achieve unified modeling and dynamic linkage of multi-dimensional operational data. This leads to reservation conflicts or uneven resource allocation during peak periods, while utilization rates are low during off-peak periods, hindering further improvement in overall operational efficiency. On the other hand, existing technologies generally lack the ability to couple and analyze operational schedules with equipment operating status. Venue scheduling is often independent of maintenance tasks such as lighting equipment, turf maintenance, and security inspections, easily causing equipment overload or a mismatch between maintenance cycles and actual usage needs, thereby increasing maintenance costs and affecting the quality of venue services. Therefore, how to build an intelligent operation and management method that can realize the coordinated scheduling of venue resources, the joint management of equipment operation and maintenance, and the dynamic optimization of operation strategies has become an important technical problem that urgently needs to be solved in the field of digital management of football fields. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a mobile application-based intelligent operation and management method for football fields, thereby resolving the problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for intelligent operation and management of football fields based on mobile applications, comprising the following steps:

[0006] S1. Construct a site resource data model, and perform structured modeling of the type information, time slot information, facility operation status information, maintenance cycle information and usage record information of football fields, and form a set of semantic constraints for site resources to characterize the operational status and availability of the site.

[0007] S2. Based on the site resource data model, the total operable time, reservation occupancy time, maintenance lock time and downgraded saleable conversion time of each site within the preset statistical window are quantitatively calculated to obtain the availability index of the venue's availability, and the reservation requests are conflict-determined based on the availability index.

[0008] S3. Based on the availability index, combined with the actual reservation demand duration generated by the mobile application, calculate the effective schedulable capacity and demand intensity of the venue, and construct a scheduling pressure index. Determine whether to trigger the load balancing scheduling process based on the comparison result between the scheduling pressure index and the preset scheduling trigger threshold.

[0009] S4. When the load balancing scheduling process is triggered, candidate sites and candidate time slots are screened based on the scheduling pressure index and the effective schedulable capacity of the site. The migration adaptation index is used to calculate the rationality of the migration of the reservation request between different candidate sites or candidate time slots, and cross-site collaborative scheduling optimization results are generated.

[0010] S5. Based on the results of collaborative scheduling optimization, the usage intensity of each site within the forecast period is quantitatively analyzed, and a comprehensive equipment load index is constructed by combining historical equipment operation data and inspection data. Based on the comprehensive equipment load index, suggestions for equipment maintenance priority and maintenance time window are generated.

[0011] S6. Construct a comprehensive operational performance index based on the availability index, the scheduling pressure index, and the comprehensive equipment load index, and adaptively adjust the site operation strategy according to the comprehensive operational performance index.

[0012] To further optimize this technical solution, in step S1, the dimensions of the football field type information include at least indoor / outdoor, standard 11-a-side / 7-a-side / 5-a-side, natural / artificial / mixed turf type, and nighttime availability level;

[0013] Each dimension corresponds to calculable operational constraints, including "Nighttime Availability Level," which determines the controllable range of the lighting system and energy consumption billing strategy, and "Lawn Type," which determines the maintenance cycle and tolerable load threshold.

[0014] To further optimize this technical solution, the availability index for booking periods in step S2 is constructed as follows:

[0015]

[0016] in,

[0017] This refers to the availability index of available slots;

[0018] This refers to the reservation occupancy time within the statistics window;

[0019] Maintenance lock duration within the statistics window;

[0020] The downgraded saleable duration within the statistical window;

[0021] This refers to the total available operating time within the statistical window;

[0022] Using the constructed availability index Conflicts in appointment requests can be identified and resolved.

[0023] Further optimization of this technical solution includes conflict determination and handling, including:

[0024] If a particular time slot has a high-priority locking reason, that time slot is directly deemed unsaleable; if it is downgraded to be saleable, different prices and service terms are generated based on a conversion ratio; if it is saleable but... If the timeframe is below the operational threshold, the scheduling will not be changed.

[0025] To further optimize this technical solution, the method for calculating the effective schedulable capacity of the site in step S3 is as follows:

[0026]

[0027] in, This refers to the effective schedulable capacity of the site within the statistical window;

[0028] Based on the effective schedulable capacity of the site The scheduling pressure index is constructed as follows:

[0029]

[0030] in, The scheduling pressure index; For demand intensity, This includes the duration of paid reservations, strong intent requests that appear repeatedly on the confirmation page but not paid, and the intended duration of group / event booking applications.

[0031] To further optimize this technical solution, the scheduling pressure index... The comparison result with the preset scheduling trigger threshold determines whether to trigger the load balancing scheduling process, including:

[0032] Preset a scheduling trigger threshold The parameters are set by the operator based on the venue's positioning and service commitments;

[0033] when At that time, the output should trigger the load balancing operation and output the set of driving variables { } as scheduling input;

[0034] when At this time, the output trigger signal does not require forced load balancing.

[0035] To further optimize this technical solution, in step S4, when step S3 determines... When the system enters the scheduling optimization state, the system filters the candidate site set. The candidate sites consist of similar sites, adjacent sites in the same area, and downgraded sites that can be sold with alternative functions.

[0036] Introducing the migration adaptation index This model is used to evaluate the overall rationality of a reservation request being moved from a target site to a candidate site. The model is constructed as follows:

[0037]

[0038] in,

[0039] This indicates the user's original reservation time, obtained through the reservation request time from the mobile application.

[0040] This indicates the alternative availability periods available at the candidate venue, obtained by converting the timestamp of the candidate availability period unit.

[0041] The maximum acceptable migration time deviation threshold set for the operator;

[0042] Rate the service matching degree;

[0043] The system calculates for all candidate sites. Preset a migration acceptance threshold and By comparing the results, the final candidate site schedule is determined, and cross-site collaborative scheduling optimization results are generated.

[0044] To further optimize this technical solution, the calculation model for the comprehensive load index of the equipment in step S5 is as follows:

[0045]

[0046] in,

[0047] The overall load index of the equipment;

[0048] Use intensity weighting coefficients per unit;

[0049] This refers to the equipment degradation risk coefficient.

[0050] when When the preset equipment safety threshold is exceeded, the system automatically triggers maintenance scheduling strategies, including arranging lawn maintenance windows in advance, reducing the nighttime lighting operation level, or automatically restricting the scheduling of high-intensity events.

[0051] To further optimize this technical solution, in step S6, the calculation model for the comprehensive operational performance index is as follows:

[0052]

[0053] in, It is a comprehensive operational performance index used to uniformly measure the overall quality of site operation.

[0054] To further optimize this technical solution, the comprehensive operational performance index... Adaptive adjustments to venue operation strategies, including:

[0055] The system calculates in each statistical period And automatically adjust operational strategies based on their changing trends; when When the value continues to rise, it indicates that both operational efficiency and user experience are in good condition, and the system gradually increases the site utilization threshold to improve revenue; when During a descent, the system automatically matches an optimization strategy based on the cause of the descent.

[0056] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a mobile application-based intelligent operation and management method for football fields as described in the first aspect of the present invention.

[0057] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a mobile application-based intelligent operation and management method for football fields as described in the first aspect of the present invention.

[0058] Compared with existing technologies, this invention provides a mobile application-based intelligent operation and management method for football fields, which has the following beneficial effects:

[0059] This mobile application-based intelligent operation and management method for football fields constructs a unified field resource data model, collaboratively modeling field availability, facility operating status, maintenance cycles, and usage records. Based on this, it introduces availability indices, scheduling pressure indices, and comprehensive equipment load indices to achieve dynamic and interconnected management of scheduling, resource allocation, and equipment operation and maintenance. This effectively improves field utilization and reduces equipment operation and maintenance risks. Simultaneously, it adaptively optimizes operational strategies through a comprehensive operational performance index, enabling the system to automatically adjust scheduling and operation and maintenance strategies based on real-time demand changes. This significantly enhances the precision of field operation and management and improves user service experience, demonstrating high practical value and promising prospects for widespread application. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating a mobile application-based intelligent operation and management method for football fields proposed in this invention. Detailed Implementation

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0065] Example 1:

[0066] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for intelligent operation and management of football fields based on mobile applications, including the following steps:

[0067] S1. Construct a site resource data model, and perform structured modeling of the type information, time slot information, facility operation status information, maintenance cycle information and usage record information of football fields, and form a set of semantic constraints for site resources to characterize the operational status and availability of the site.

[0068] Specifically, firstly, a resource primary key is established with "site resources" as the core entity. This primary key is used to achieve cross-system mapping of the same site across mobile applications, access control / lighting controllers, and inspection work order systems, preventing the same site from becoming different objects in different modules and causing scheduling conflicts that cannot be determined. Secondly, the site is structurally classified according to operationally visible dimensions: the site type dimension includes at least indoor / outdoor, standard 11-a-side / 7-a-side / 5-a-side, lawn type (natural / artificial / hybrid), and nighttime availability level. Each dimension must correspond to subsequently calculable operational constraints. For example, the "nighttime availability level" will determine the controllable range of the lighting system and energy consumption billing strategy, while the "lawn type" will determine the maintenance cycle and the tolerable load threshold. Furthermore, to support the linkage between "time slots" and "facility status" within the same resource view, time is organized into operable "slot units." Each slot unit is the smallest decision-making granularity for subsequent reservations and scheduling optimization (e.g., 30 minutes or 60 minutes), and each slot unit needs to be bound to the venue's availability status, the reason for locking, and the source of responsibility for locking (e.g., maintenance lock, event lock, government event lock, emergency security lockdown, etc.). Simultaneously, a unified definition of "operable status" is established for facility status and maintenance cycles: each sub-facility, such as lighting, sprinklers, lawns / ground, fencing and entrances / exits, electronic access control, and timekeeping / billing devices, has its own status, but these must ultimately converge into a venue-level operable status to determine whether a slot is available for sale and whether downgraded operation is necessary (e.g., allowing only daytime use or only training without events). In addition, this step also needs to define structured fields for usage records, including at least the reservation source, actual entry time, actual exit time, number of users, whether an overtime occurred, whether a facility malfunction occurred, whether a complaint occurred, whether manual intervention was triggered, and the maintenance work order number associated with that usage. Finally, when adding new facilities or new operating rules, version numbers and compatibility strategies are used to ensure that old data can still be interpreted by the new model, avoiding the problem that historical data cannot be used for scheduling optimization after mobile device upgrades.

[0069] S2. Based on the site resource data model, the total operable time, reservation occupancy time, maintenance lock time, and downgraded saleable conversion time of each site within the preset statistical window (e.g., the next 7 days or the next 30 days, the window length is configured by the operator) are quantitatively calculated to obtain the availability index of the site's availability, and the reservation requests are conflict-determined based on the availability index.

[0070] The availability index for each venue is constructed as follows:

[0071]

[0072] in,

[0073] This is the availability index for available slots.

[0074] The reservation occupancy time within the statistics window is obtained by summing the schedule units of the reservation orders, and is derived from the mobile application reservation records and order status.

[0075] The maintenance lock duration within the statistics window is generated from the maintenance plan and lock records, and originates from the maintenance cycle rules and lock entries generated by the maintenance work order system in step S1.

[0076] The term "downgraded saleable duration" within the statistical window means that when a venue can only be opened to the public in a downgraded service mode during certain periods (e.g., lighting failure makes it unusable at night, or damaged fencing makes it only suitable for training and not for competition), these periods are not completely unusable, but their saleable capacity should be included according to the downgraded ratio. The downgraded ratio is configured by the operator based on the service level and billing strategy, and is automatically triggered by the facility status (from lighting controllers, access control, and inspection results).

[0077] The total available operating time within the statistical window is derived from the schedule unit configuration and operating rules in step S1.

[0078] Using the constructed availability index Conflicts in appointment requests can be identified and resolved.

[0079] On the mobile app, when users select a venue and date range, instead of directly displaying a bold "empty / full" indicator, a different approach is taken to avoid revealing sensitive operational details. Generate user-friendly, bookable prompts (e.g., "Available / Moderate / Limited availability") based on core calculation results, and simultaneously display them on the operations side. The "conflict-determinable" rule is applied as follows: If a particular time slot is locked for a high-priority reason (e.g., security lockdown, forced maintenance), the time slot is directly determined to be unsaleable; if it is downgraded to saleable, different prices and service terms are generated based on a conversion ratio; if it is saleable but overall... If the number of reservations falls below the operational threshold (the threshold is configured by the venue strategy and originates from operational strategy parameters), the schedule will not be changed (schedule optimization is a subsequent step). Instead, "risk warnings and order rule constraints before reservations" will be implemented in this step, such as limiting the length of consecutive reservations for a single user or restricting the use of certain types of activities.

[0080] S3. Based on the availability index, and combined with the actual reservation demand duration generated by the mobile application, calculate the effective schedulable capacity and demand intensity of the venue, and construct a scheduling pressure index. Determine whether to trigger the load balancing scheduling process based on the comparison result between the scheduling pressure index and the preset scheduling trigger threshold.

[0081] The effective schedulable capacity of a site is calculated as follows:

[0082]

[0083] in, This refers to the effective schedulable capacity of the site within the statistical window. This is not equivalent to "idle time," but rather unifies the "schedulable effective capacity" after accounting for occupancy, locking, and degradation into a single input, facilitating subsequent cross-site comparisons. Next, to introduce real-time demand intensity, the demand intensity within the window is defined. The acquisition method is not a general "data collection", but strictly limited to the aggregation of "effective demand" generated within the window of the mobile application: including the schedule duration of paid reservation orders, strong intention requests that repeatedly appear in a short period of time but have not been paid for, and the intended occupancy time of group / event booking applications; these demand items are defined as traceable fields through order state machine and behavioral events to avoid mistaking casual browsing for real demand.

[0084] Based on the effective schedulable capacity of the site The scheduling pressure index is constructed as follows:

[0085]

[0086] in, The scheduling pressure index; Demand intensity.

[0087] when When the value approaches 1, demand and effective capacity are nearly in equilibrium; when... When the value is greater than 1, the demand exceeds the available capacity, and the system needs to implement load balancing strategies such as "cross-site traffic distribution, cross-time period migration, and price and service level linkage" in subsequent steps; when When the value is much less than 1, it indicates that the supply is sufficient. The subsequent strategy should focus on increasing the fill rate and activating the off-peak period, rather than forcibly diverting the flow.

[0088] The scheduling pressure index The comparison result with the preset scheduling trigger threshold determines whether to trigger the load balancing scheduling process, including:

[0089] Preset a scheduling trigger threshold The parameters are set by the operator based on the venue's positioning and service commitments. For example, for sports venues, the parameters can be set as follows: Set it to a lower value to ensure availability redundancy, while training venues can be set to a higher value to maximize utilization.

[0090] when At that time, the output should trigger the load balancing operation and output the set of driving variables { } as scheduling input.

[0091] when At that time, the output trigger signal does not require forced load balancing, but will still As input for subsequent service strategies (such as promotions, release of membership benefits, and launch of training packages).

[0092] S4. When the load balancing scheduling process is triggered, candidate sites and candidate time slots are screened based on the scheduling pressure index and the effective schedulable capacity of the site. The migration adaptation index is used to calculate the rationality of the migration of the reservation request between different candidate sites or candidate time slots, and cross-site collaborative scheduling optimization results are generated.

[0093] Unlike the traditional approach of simply recommending other available venues, the core of this step lies in establishing a unified migration evaluation mechanism that can comprehensively measure the stability of users' booking intentions, the degree of matching between venue services and operational revenue, thereby ensuring that scheduling adjustments will neither damage the user experience nor cause resource allocation imbalances.

[0094] When step S3 determines When the system enters the scheduling optimization state, the system filters the candidate site set, which consists of similar sites, adjacent sites in the same area, and downgraded sites available for sale with alternative functions.

[0095] Introducing the migration adaptation index This model is used to evaluate the overall rationality of a reservation request being moved from a target site to a candidate site. The model is constructed as follows:

[0096]

[0097] in,

[0098] This indicates the user's original reservation time, obtained through the reservation request time from the mobile application.

[0099] This indicates the alternative availability periods available at the candidate venue, obtained by converting the timestamp of the candidate availability period unit.

[0100] The maximum acceptable migration time deviation threshold set for the operator is configured from the operational strategy parameters.

[0101] The service matching score is formed by weighting dimensions such as venue type matching, lawn grade matching, and ancillary facilities matching. The score data comes from the model labels and user reservation type information in step S1.

[0102] The system calculates for all candidate sites. Preset a migration acceptance threshold and Compare and determine the final candidate site schedule. If the number of migrations exceeds the threshold, the system provides intelligent migration suggestions to the user on the mobile application, such as recommending alternative locations or time periods, along with compensation strategies (such as price discounts or extended usage time) to improve the migration success rate and generate cross-location collaborative scheduling optimization results. Simultaneously, this step also introduces a migration stability constraint mechanism to prevent frequent migrations from causing operational chaos; that is, the system only allows a single order to migrate no more than a preset threshold within the statistical window, the threshold being derived from user behavior model analysis.

[0103] S5. Based on the results of collaborative scheduling optimization, the usage intensity of each site within the forecast period is quantitatively analyzed, and a comprehensive equipment load index is constructed by combining historical equipment operation data and inspection data. Based on the comprehensive equipment load index, suggestions for equipment maintenance priority and maintenance time window are generated.

[0104] Traditional operation and maintenance (O&M) systems are typically independent of the operational system, triggering maintenance only after equipment failure. This step, however, achieves an "operation-driven O&M" collaborative mechanism by predicting equipment load trends. First, based on the scheduling results determined in step S4, the expected usage intensity of each site within the statistical window is quantified and converted into equipment load prediction input parameters.

[0105] The calculation model for the overall equipment load index is shown below:

[0106]

[0107] in,

[0108] It is a comprehensive load index for equipment, used to assess the operating pressure of lighting systems, sprinkler systems, lawn support systems, and access control systems during the forecast period.

[0109] The intensity weighting coefficient is used as the unit, which is calculated from a combination of factors such as venue type, user density, and event level. The data comes from reservation records and event type tags.

[0110] The equipment degradation risk coefficient is calculated from data such as the equipment's cumulative operating time, historical failure frequency, and inspection anomaly records, and is derived from the equipment monitoring system and maintenance record database.

[0111] when When the preset equipment safety threshold is exceeded, the system automatically triggers maintenance scheduling strategies, including scheduling lawn maintenance windows in advance, reducing the operating level of nighttime lighting, or automatically restricting the scheduling of high-intensity events. At the same time, the system will also write back the equipment load prediction results to the model in step S1 to update the facility's operational status and provide realistic feedback for subsequent operational analysis.

[0112] S6. Construct a comprehensive operational performance index based on the availability index, the scheduling pressure index, and the comprehensive equipment load index, and adaptively adjust the site operation strategy according to the comprehensive operational performance index.

[0113] Unlike traditional single utilization rate statistics, this method emphasizes a four-dimensional collaborative evaluation of "resource efficiency, user experience, equipment health, and revenue level" to form a sustainable operation closed loop.

[0114] The calculation model for the comprehensive operational performance index is as follows:

[0115]

[0116] in, It is a comprehensive operational performance index used to uniformly measure the overall quality of site operation.

[0117] The comprehensive operational performance index Adaptive adjustments to venue operation strategies, including:

[0118] The system calculates in each statistical period And automatically adjust operational strategies based on their changing trends; when When the value continues to rise, it indicates that both operational efficiency and user experience are in good condition, and the system gradually increases the site utilization threshold to improve revenue; when During a descent, the system automatically matches an optimization strategy based on the cause of the descent. For example, if the descent is mainly caused by... If the increase is due to an increase, the system will prioritize implementing equipment maintenance and reinforcement strategies; if the decrease is due to an increase... If the price is too high, the system will prioritize implementing a scheduling and dynamic price adjustment strategy. Furthermore, this step will also... The metrics are written into the historical operation database, and machine learning is used to predict future operational trends, providing decision support for managers. Through this step, the system ultimately forms a closed-loop intelligent operation system covering resource allocation, equipment maintenance, service scheduling, and adaptive adjustment of operational strategies, thereby maximizing the utilization of football field resources, minimizing maintenance costs, and continuously optimizing the user experience.

[0119] Example 2:

[0120] Here is a practical application of the intelligent operation and management method for football fields based on mobile applications described in Embodiment 1:

[0121] This application solution uses a cluster of football fields in an urban comprehensive sports center as its application platform. It leverages a mobile application platform, intelligent IoT devices for the fields, and an operation management system to build an integrated operation system suitable for centralized management of multiple fields, commercial event operation, and public fitness services. By constructing a digital field operation platform, the solution unifies and integrates football field resource management, user service management, equipment operation management, and operational decision analysis, achieving intelligent operation of the football field throughout its entire lifecycle.

[0122] In the implementation process, a unified venue resource management system was first deployed within the sports center to digitally model the standard 11-a-side football pitches, 7-a-side training pitches, and 5-a-side recreational pitches within the park. The system assigns a unique resource identifier to each pitch and records information such as pitch dimensions, turf type, lighting level, available opening hours, and historical maintenance cycles. Simultaneously, by connecting with intelligent access control equipment, lighting control systems, lawn irrigation systems, and video surveillance equipment, the system obtains real-time operational status data for the venue facilities and synchronizes this information to the operations platform, creating a venue-level operational status monitoring interface.

[0123] Secondly, a mobile application system is deployed on the user server side, providing reservation services to social users, training institutions, and event organizers. After users select their target venue, usage time, and activity type through the mobile application, the system automatically calculates the availability of venue slots based on a venue resource model and dynamically displays recommended available time slots on the reservation interface. When users select high-demand time slots, the system will automatically recommend nearby venues or similar time slots, and combine this with a differentiated pricing strategy to guide users to allocate their time reasonably, thereby achieving a dynamic balance of reservation demand. After a successful reservation, the mobile application automatically generates an electronic admission pass, which is linked to the venue's access control system. Upon arrival, users complete entry verification by scanning a QR code or facial recognition.

[0124] Furthermore, in the operation scheduling management phase, the system automatically generates venue usage load forecasts based on daily reservation data and simultaneously analyzes equipment operating pressure. When the system predicts that continuous high-intensity use of a particular venue may lead to turf wear or lighting equipment overload, the operations platform will automatically adjust subsequent schedules, such as limiting consecutive events or inserting maintenance buffer time. Simultaneously, the system automatically generates maintenance work orders and pushes them to the mobile terminals of maintenance personnel, guiding them to carry out maintenance tasks such as turf maintenance, lighting inspection, and perimeter security checks. After completing maintenance, maintenance personnel provide feedback on the execution results via their mobile terminals, and the system automatically updates the venue facility status and restores the availability for reservation.

[0125] In event and training operation scenarios, the system supports batch scheduling and priority resource guarantee functions. When large-scale events or youth training institutions request centralized use of venues, the system automatically generates the optimal scheduling combination based on historical operational data and venue utilization trends, and makes reasonable adjustments to the reservations of ordinary users to ensure the smooth operation of events. At the same time, the system supports a membership service system, automatically providing membership benefit recommendations based on user usage frequency, evaluation feedback, and event participation, such as priority reservation rights, discount packages, or exclusive training time slots, to enhance user stickiness.

[0126] At the operational management decision-making level, the system generates operational performance evaluation reports by periodically analyzing data such as site utilization, equipment health status, user satisfaction, and revenue structure. Managers can view usage trends for different time periods, site types, and user groups through the management backend and adjust operating hour strategies, pricing strategies, and maintenance cycle strategies accordingly. For example, when the system analysis detects a sustained increase in nighttime usage demand, it can automatically suggest increasing investment in nighttime lighting resources; when it detects consistently low utilization rates for a certain type of site, it can suggest launching specialized training activities or promotional activities to improve utilization.

[0127] Through the above application solutions, sports centers can achieve intelligent management of the entire process of football field reservation services, equipment maintenance and operation decision-making, effectively improving the efficiency of field resource utilization, reducing manual management costs, extending equipment lifespan, and significantly improving user reservation experience and service quality. It is applicable to various operation scenarios such as urban sports complexes, school stadiums and commercial football training bases.

[0128] Example 3:

[0129] This embodiment also provides a computer device applicable to a mobile application-based intelligent operation and management method for football fields, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the mobile application-based intelligent operation and management method for football fields as proposed in the above embodiment.

[0130] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a mobile application-based intelligent operation and management method for football fields as proposed in the above embodiments.

[0131] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0132] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0134] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0135] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent operation and management of football fields based on mobile applications, characterized in that, Includes the following steps: S1. Construct a site resource data model, and perform structured modeling of the type information, time slot information, facility operation status information, maintenance cycle information and usage record information of football fields, and form a set of semantic constraints for site resources to characterize the operational status and availability of the site. S2. Based on the site resource data model, the total operable time, reservation occupancy time, maintenance lock time and downgraded saleable conversion time of each site within the preset statistical window are quantitatively calculated to obtain the availability index of the venue's availability, and the reservation requests are conflict-determined based on the availability index. S3. Based on the availability index, combined with the actual reservation demand duration generated by the mobile application, calculate the effective schedulable capacity and demand intensity of the venue, and construct a scheduling pressure index. Determine whether to trigger the load balancing scheduling process based on the comparison result between the scheduling pressure index and the preset scheduling trigger threshold. S4. When the load balancing scheduling process is triggered, candidate sites and candidate time slots are screened based on the scheduling pressure index and the effective schedulable capacity of the site. The migration adaptation index is used to calculate the rationality of the migration of the reservation request between different candidate sites or candidate time slots, and cross-site collaborative scheduling optimization results are generated. S5. Based on the results of collaborative scheduling optimization, the usage intensity of each site within the forecast period is quantitatively analyzed, and a comprehensive equipment load index is constructed by combining historical equipment operation data and inspection data. Based on the comprehensive equipment load index, suggestions for equipment maintenance priority and maintenance time window are generated. S6. Construct a comprehensive operational performance index based on the availability index, the scheduling pressure index, and the comprehensive equipment load index, and adaptively adjust the site operation strategy according to the comprehensive operational performance index.

2. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S1, the dimensions of the football field type information include at least indoor / outdoor, standard 11-a-side / 7-a-side / 5-a-side, natural / artificial / hybrid turf type, and nighttime availability level; Each dimension corresponds to calculable operational constraints, including "Nighttime Availability Level," which determines the controllable range of the lighting system and energy consumption billing strategy, and "Lawn Type," which determines the maintenance cycle and tolerable load threshold.

3. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S2, the availability index is constructed as follows: in, This refers to the availability index of available slots; This refers to the reservation occupancy time within the statistics window; Maintenance lock duration within the statistics window; The downgraded saleable duration within the statistical window; This refers to the total available operating time within the statistical window; Using the constructed availability index Conflicts in appointment requests can be identified and resolved.

4. The intelligent operation and management method for football fields based on mobile applications according to claim 3, characterized in that, The conflict can be determined and handled, including: If a particular time slot has a high-priority locking reason, that time slot is directly deemed unsaleable; if it is downgraded to be saleable, different prices and service terms are generated based on a conversion ratio; if it is saleable but... If the schedule is below the operational threshold, the schedule will not be changed.

5. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S3, the effective schedulable capacity of the site is calculated as follows: in, This refers to the effective schedulable capacity of the site within the statistical window; Based on the effective schedulable capacity of the site The scheduling pressure index is constructed as follows: in, The scheduling pressure index; For demand intensity, This includes the duration of paid reservations, strong intent requests that appear repeatedly on the confirmation page but not paid, and the intended duration of group / event booking applications.

6. The intelligent operation and management method for football fields based on mobile applications according to claim 5, characterized in that, The scheduling pressure index The comparison result with the preset scheduling trigger threshold determines whether to trigger the load balancing scheduling process, including: Preset a scheduling trigger threshold The parameters are set by the operator based on the venue's positioning and service commitments; when At that time, the output should trigger the load balancing operation and output the set of driving variables { } as scheduling input; when At this time, the output trigger signal does not require forced load balancing.

7. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S4, when step S3 determines... When the system enters the scheduling optimization state, the system filters the candidate site set. The candidate sites consist of similar sites, adjacent sites in the same area, and downgraded sites that can be sold with alternative functions. Introducing the migration adaptation index This model is used to evaluate the overall rationality of a reservation request being moved from a target site to a candidate site. The model is constructed as follows: in, This indicates the user's original reservation time, obtained through the reservation request time from the mobile application. This indicates the alternative availability periods available at the candidate venue, obtained by converting the timestamp of the candidate availability period unit. The maximum acceptable migration time deviation threshold set for the operator; Rate the service matching degree; The system calculates for all candidate sites. Preset a migration acceptance threshold and By comparing the results, the final candidate site schedule is determined, and cross-site collaborative scheduling optimization results are generated.

8. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S5, the calculation model for the comprehensive load index of the equipment is as follows: in, The overall load index of the equipment; Use intensity weighting coefficients per unit; This refers to the equipment degradation risk coefficient. when When the preset equipment safety threshold is exceeded, the system automatically triggers maintenance scheduling strategies, including arranging lawn maintenance windows in advance, reducing the nighttime lighting operation level, or automatically restricting the scheduling of high-intensity events.

9. The intelligent operation and management method for football fields based on mobile applications according to claim 1, characterized in that, In step S6, the calculation model for the comprehensive operational performance index is as follows: in, It is a comprehensive operational performance index used to uniformly measure the overall quality of site operation.

10. A method for intelligent operation and management of football fields based on mobile applications according to claim 9, characterized in that, The comprehensive operational performance index Adaptive adjustments to venue operation strategies, including: The system calculates in each statistical period And automatically adjust operational strategies based on their changing trends; when When the value continues to rise, it indicates that both operational efficiency and user experience are in good condition, and the system gradually increases the site utilization threshold to improve revenue; when During a descent, the system automatically matches an optimization strategy based on the cause of the descent.

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