Table tennis practice equipment cleaning demand prediction system based on big data analysis

Abstract

The invention relates to a table tennis practice equipment cleaning demand prediction system based on big data analysis, and belongs to the technical field of data analysis. According to the system, equipment use data and environment data are collected in real time through a data collection module, a pollution index is dynamically calculated through a pollution index calculation module, a future cleaning demand time point is predicted based on a long-short-term memory network, and a decision execution module outputs a cleaning signal according to a prediction result. The problem of resource waste caused by fixed-period cleaning is solved, precise quantification of the equipment pollution state and intelligent prediction of the cleaning requirement are achieved, the maintenance efficiency is remarkably improved, and resource consumption is reduced.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, specifically relating to a system for predicting the cleaning needs of table tennis practice equipment based on big data analysis. Background Technology

[0002] In the field of intelligent management of sports equipment, improving the efficiency and lifespan of sports equipment through data collection and analysis technology has become an important research direction. Among them, the maintenance and management of table tennis training equipment is a key link in ensuring training quality and user experience.

[0003] The cleaning and maintenance of table tennis racket rubber and ball directly affects the friction of the shot, the spin effect, and the lifespan of the equipment. Scientifically determining when to clean aims to balance maintenance costs and performance, avoiding resource waste or performance degradation.

[0004] Current technologies primarily rely on fixed-cycle cleaning methods, lacking adaptability to actual user frequency, environmental conditions, and individual habits. This impersonal management approach cannot accurately respond to the actual state of equipment contamination accumulation, leading to premature cleaning resulting in resource waste, or delayed cleaning causing decreased hitting performance and shortened equipment lifespan. Due to the lack of dynamic prediction capabilities based on real-time usage data, traditional methods struggle to achieve an optimal balance between resource conservation and performance maintenance. Therefore, there is an urgent need to develop an intelligent management system capable of scientifically predicting cleaning needs. Summary of the Invention

[0005] The present invention aims to provide a table tennis practice equipment cleaning demand prediction system based on big data analysis, in order to solve the problems of resource waste and performance maintenance imbalance caused by the existing technology's reliance on fixed-cycle cleaning methods, which cannot accurately adapt to the actual state of pollution accumulation.

[0006] The technical solution of this invention is that the system includes a data acquisition module, a pollution index calculation module, a cleaning demand prediction module, and a decision execution module. The data acquisition module is used to collect multi-dimensional usage data of the table tennis practice equipment and environmental data in real time. The pollution index calculation module dynamically calculates the current pollution index based on the output of the data acquisition module using a weighted fusion algorithm. The cleaning demand prediction module receives the output of the pollution index calculation module and, combined with historical cleaning records and equipment material parameters, generates future cleaning demand time points through a time-series prediction model. The decision execution module outputs a cleaning execution signal or a delayed maintenance signal according to the instructions of the cleaning demand prediction module.

[0007] Furthermore, the data acquisition module specifically includes a usage frequency monitoring unit, an environmental sensor unit, and a user behavior recording unit. The usage frequency monitoring unit collects data on the number of shots and swing intensity per unit time through inertial measurement units installed on the racket handle and the edge of the table. The environmental sensor unit integrates temperature and humidity sensors and particulate matter concentration sensors to monitor the temperature, humidity, and concentration of suspended particulate matter in the training area in real time. The user behavior recording unit records the equipment storage method and storage time after each training session via a user terminal application.

[0008] Furthermore, the pollution index calculation module performs the following processing flow: First, the multi-source data output by the data acquisition module is preprocessed for normalization to eliminate dimensional differences. Then, a weighted function is constructed with usage frequency, ambient humidity, particulate matter concentration, and volatile intensity as input variables. The weight coefficients of the weighted function are determined through historical data regression analysis, with usage frequency weighted at 0.4, ambient humidity weighted at 0.3, particulate matter concentration weighted at 0.2, and volatile intensity weighted at 0.1. Finally, the weighted summation result is mapped to a pollution index range of 0 to 100, where 0 represents a clean state and 100 represents a severely polluted state.

[0009] Furthermore, the cleaning demand prediction module employs a Long Short-Term Memory (LSTM) network as its core prediction engine. The network's input features include a 30-day contamination index sequence, equipment material type codes, and historical cleaning interval data. After training with 100,000 training samples, the LTM network can output daily contamination index predictions for the next 7 days. When the predicted contamination index for any day exceeds a preset threshold of 75, the module immediately generates a cleaning demand instruction and triggers the decision execution module.

[0010] Furthermore, the decision execution module includes a signal comparison unit and an instruction distribution unit. The signal comparison unit compares the cleaning demand instructions output by the cleaning demand prediction module with the current system maintenance status in real time. If the system is in standby mode and there are no higher priority tasks, a cleaning execution signal is sent to the instruction distribution unit. If the system is in another maintenance cycle, a delayed maintenance signal is generated and the cleaning task is inserted into the execution queue. The instruction distribution unit transmits the cleaning execution signal to the designated cleaning equipment controller via a wireless communication protocol, or sends a maintenance reminder notification to the user via a user terminal application.

[0011] Furthermore, the system also integrates an adaptive learning module. This module continuously collects data on the performance recovery of equipment after actual cleaning and compares it with the output of the predictive model. When the prediction error exceeds 15% for five consecutive times, the adaptive learning module automatically initiates the model parameter optimization process, using the gradient descent algorithm to update the weight parameters of the long short-term memory network, thereby enabling the predictive model to gradually adapt to the user's individual usage habits and local environmental characteristics.

[0012] Furthermore, the pollution index calculation module incorporates a dynamic weight adjustment mechanism. This mechanism automatically adjusts the weight allocation in the weighting function based on the material type of the equipment. For porous rubber materials, the weight of ambient humidity is increased to 0.35 while the weight of usage frequency is decreased to 0.35. For smooth rubber materials, the weight of particulate matter concentration is increased to 0.25 while the weight of ambient humidity is decreased to 0.25. This dynamic adjustment ensures that the pollution index calculation more closely reflects the actual pollution characteristics of different materials.

[0013] Furthermore, the cleaning demand forecasting module is also equipped with an abnormal usage detection submodule. This submodule monitors sudden changes in usage frequency data in real time. When it detects that the daily usage frequency exceeds three times the historical average, it immediately initiates an emergency forecasting process. The emergency forecasting process uses a simplified calculation model to quickly assess the current pollution index and generate immediate cleaning suggestions within 5 minutes, effectively responding to sudden high-intensity usage scenarios.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Through multi-dimensional data collection and dynamic pollution index calculation, the pollution status of equipment can be accurately quantified; by using long short-term memory networks for time-series prediction, the pollution accumulation trend can be effectively captured and the cleaning needs can be warned in advance. 2. The adaptive learning mechanism ensures that the system continuously optimizes prediction accuracy and gradually adapts to individual usage characteristics; dynamic weight adjustment and anomaly detection functions further enhance the system's adaptability to different materials and usage scenarios.

[0015] 3. The entire system changes the extensive management model of fixed-cycle cleaning, significantly reducing maintenance resource consumption while ensuring the best performance of equipment, and realizing intelligent and precise equipment maintenance management. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall technical solution architecture of the table tennis practice equipment cleaning demand prediction system based on big data analysis proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of the pollution index calculation module in this invention; Figure 3 This is a logical flow diagram of the cleaning demand prediction module in this invention; Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow of the decision execution module in this invention; Figure 5 This is a schematic diagram of the multi-level interaction relationship and data flow between the adaptive learning module and the dynamic weight adjustment mechanism in this invention. Detailed Implementation

[0017] Please refer to the attached document. Figures 1 to 5 This embodiment details the technical implementation of a table tennis practice equipment cleaning demand prediction system based on big data analysis. The system aims to achieve accurate prediction and automated management of table tennis practice equipment cleaning needs through multi-dimensional data perception, dynamic pollution assessment, intelligent demand prediction, and precise decision execution. The core components of the system include a data acquisition module, a pollution index calculation module, a cleaning demand prediction module, a decision execution module, and an adaptive learning module. These modules interact and transmit commands through standard data interfaces and communication protocols, forming a closed-loop intelligent maintenance ecosystem.

[0018] The data acquisition module is responsible for collecting multi-dimensional data generated during the use of the table tennis practice equipment in real time. This data forms the basis for subsequent analysis and prediction by the system. The data acquisition module specifically includes three functional units: a usage frequency monitoring unit, an environmental sensor unit, and a user behavior recording unit. The usage frequency monitoring unit is implemented through inertial measurement units installed inside the handle of the table tennis racket and at specific locations on the edge of the table tennis table. This inertial measurement unit typically integrates a three-axis accelerometer and a three-axis gyroscope, capable of continuously recording the motion state of the equipment at a sampling frequency of 100 Hz.

[0019] For a table tennis racket, the inertial measurement unit (IMU) primarily captures the peak acceleration and duration at the moment of each shot, and uses a specific algorithm to analyze and determine the number of shots and average swing intensity per unit time. For the table tennis table, the IMU monitors the vibration signal generated by the ball impacting the table surface, and combines this with the assistance of a sound sensor to determine the number of shots per unit time. All this data, after initial filtering and feature extraction, is encapsulated into data packets of a specific format and transmitted to the system's central processing unit via Bluetooth Low Energy.

[0020] The environmental sensor unit, another important component of the data acquisition module, is an integrated sensor node deployed in key locations within the table tennis training area, such as the side of the table, inside equipment storage cabinets, and near ventilation openings. The core of this sensor node includes a high-precision temperature and humidity sensor and a laser-based particulate matter concentration sensor.

[0021] The temperature and humidity sensors collect ambient temperature and relative humidity values ​​every 30 seconds, with a measurement range of 0°C to 50°C and humidity range of 10% to 95%, with accuracies of ±0.5°C and ±3%, respectively. The particulate matter concentration sensor monitors the concentration of PM2.5 and PM10 suspended particulate matter in the air in real time, with a range of 0 to 500 micrograms per cubic meter, and data updates once per minute. All environmental data is timestamped and includes a device identifier, and is transmitted to a central database via a wireless LAN.

[0022] The user behavior recording unit is implemented through a user terminal application installed on the user's smartphone or tablet. After each table tennis training session, the user manually records the equipment storage method through the application interface or automatically triggers the recording via near-field communication technology. The storage methods mainly include several typical modes such as direct exposure to the air, placing in a sealed equipment bag, and hanging in a ventilated place.

[0023] Simultaneously, the application utilizes the terminal's location services and timing functions to automatically record the storage time of the equipment from the end of training until its next use. The storage environment is matched against a pre-set site database based on GPS location information, categorizing it into indoor constant-temperature environments, indoor non-constant-temperature environments, outdoor shaded environments, and outdoor direct sunlight environments. This behavioral data is uploaded to a cloud server via an application programming interface (API), where it is aggregated with other collected data.

[0024] The pollution index calculation module receives all multi-source data from the data acquisition module and executes a series of calculations to output a comprehensive, quantitative current pollution index. Please refer to the appendix. Figure 2 The module's internal processing begins with data normalization preprocessing. Because data from different units have different units and numerical ranges—for example, usage frequency is measured in times per minute, humidity in percentages, and particulate matter concentration in micrograms per cubic meter—direct fusion would lead to an imbalance in numerical dominance. Therefore, the module first performs min-max normalization on each input variable, linearly transforming it to the range of 0 to 1. Specifically, for any input variable... Its normalized value Through formula The calculation shows that, among which and These are the minimum and maximum expected values ​​of the variable, determined based on historical statistical data.

[0025] After normalization preprocessing, the pollution index calculation module calls its core weighted fusion algorithm. This algorithm constructs a weighted function based on four key input variables: frequency factor, ambient humidity factor, particulate matter concentration factor, and volatile matter intensity factor. The specific form of the weighted function is a weighted summation. The weight coefficients of each factor are not fixed but are determined through multiple linear regression analysis of a large amount of historical pollution data.

[0026] In the typical initial configuration of this system, the weighting coefficients for the frequency factor are set to 0.4, the environmental humidity factor to 0.3, the particulate matter concentration factor to 0.2, and the swing intensity factor to 0.1. These weighting coefficients reflect the relative influence of each factor on the accumulation of pollution on table tennis equipment.

[0027] The pollution index calculation module also integrates a key feature: a dynamic weight adjustment mechanism. This mechanism automatically adjusts the weight allocation of each factor in the weighting function based on the different types of rubber materials used in table tennis rackets. The system maintains a material database, which stores common rubber material types and their corresponding optimal weight configurations. When the system learns the rubber material of the current equipment through user input or automatic recognition, it queries this database and applies the corresponding weights. For example, for porous rubber materials, which are more prone to absorbing moisture and sweat, the system increases the weight of the ambient humidity factor from the baseline of 0.3 to 0.35, while correspondingly decreasing the weight of the usage frequency factor from 0.4 to 0.35. For smooth rubber materials, which are more sensitive to airborne particulate matter, the system increases the weight of the particulate matter concentration factor from 0.2 to 0.25, while decreasing the weight of the ambient humidity factor from 0.3 to 0.25. This dynamic adjustment ensures that the pollution index calculation more closely reflects the actual pollution sensitivity characteristics of different materials.

[0028] After the weighted summation is completed, an intermediate value between 0 and 1 is obtained. The pollution index calculation module then transforms this intermediate value into a pollution index range of 0 to 100 using a linear mapping function. The mapping function is as follows: ,in The pollution index represents the final output. This represents the median value obtained from the weighted summation. Therefore, when the median value of the weighted summation is 0, the contamination index is 0, indicating that the equipment is in an ideal clean state; when the median value of the weighted summation is 1, the contamination index is 100, indicating that the equipment is in a severely contaminated state and urgently needs cleaning. The calculated contamination index, along with the timestamp and equipment identifier, is stored in the historical database and pushed to the cleaning demand prediction module in real time.

[0029] The cleaning demand prediction module is the intelligent core of the system, responsible for predicting the equipment's cleaning needs for a future period based on historical and real-time contamination data. Please refer to the appendix. Figure 3 This module employs a Long Short-Term Memory (LSTM) network as its core prediction engine. LTM is a special type of recurrent neural network structure, particularly adept at processing and predicting time-series data. The LTM network model deployed in this system is designed to receive a 30-day contamination index sequence as its input layer. This sequence contains 30 feature values ​​for each day's data points. In addition to the contamination index sequence, the input features include the equipment material type code and the number of days since the most recent historical cleaning interval. The material type code is a single-bit code indicating the rubber material category. The historical cleaning interval provides information on the impact of the previous cleaning operation on the cumulative contamination baseline.

[0030] The Long Short-Term Memory (LSTM) network model underwent training and optimization in two phases: offline and online. In the offline training phase, over 100,000 sets of historical equipment usage data and their corresponding actual pollution records were used as training samples. These samples covered different users, different environmental conditions, different equipment materials, and different usage intensities. The training objective was to minimize the mean squared error between the network's predicted future pollution index and the actual observed values. The trained model possesses the ability to capture complex patterns of pollution index changes over time, including periodic fluctuations, upward trends, and sudden changes.

[0031] During the deployment and operation phase, the cleaning demand forecasting module initiates its forecasting process daily at set intervals. It extracts the pollution index sequence for the specified equipment over the past 30 days from the historical database, combines this with the current equipment material code and cleaning interval data, and inputs this information into a pre-trained Long Short-Term Memory (LSTM) network model. After the model runs, it outputs the predicted pollution index values ​​for each of the next 7 days. These predictions reflect the expected evolution trajectory of the pollution index under current usage patterns and environmental conditions.

[0032] The cleaning demand forecasting module has a preset cleaning trigger threshold, typically set to 75. The module compares the predicted pollution index for each of the next seven days with this threshold. When the predicted pollution index for any future day exceeds 75, the module immediately generates a cleaning demand instruction. This instruction contains key information such as the date the forecast was triggered, the specific date the index exceeded the threshold, the predicted pollution index value, and the associated equipment identifier.

[0033] In addition, the cleaning demand forecasting module is equipped with an abnormal usage detection submodule. This submodule is independent of the main Long Short-Term Memory (LSTM) network forecasting process and focuses on real-time monitoring of usage frequency data streams. It maintains a daily average usage frequency calculated based on data from the past 90 days. When the real-time usage frequency data indicates that the current day's usage frequency exceeds three times the historical average, the abnormal usage detection submodule is immediately activated. It initiates an emergency forecasting process that uses a pre-trained simplified computational model.

[0034] The simplified model sacrifices some long-term accuracy for speed, enabling it to quickly integrate currently known data within 5 minutes, reassess the current contamination index, and determine whether it is approaching or exceeding the cleaning threshold. If the assessment determines that immediate cleaning is required, an immediate cleaning recommendation instruction is generated, which takes higher priority than regular prediction instructions.

[0035] The decision execution module receives instructions from the cleaning demand forecasting module and is responsible for translating them into specific control actions or user notifications. Please refer to the appendix. Figure 4 The decision execution module comprises two core units: a signal comparison unit and an instruction distribution unit. The signal comparison unit acts as the system's logical arbitration center. It continuously monitors the instructions output by the cleaning demand prediction module, including regular cleaning demand instructions and abnormal, immediate cleaning suggestion instructions. Upon receiving an instruction, the signal comparison unit first queries the system's current maintenance status. System maintenance status includes standby status, cleaning in progress status, and status of other maintenance tasks in progress.

[0036] If the system is currently in standby mode and no higher-priority tasks are queued, the signal comparison unit immediately sends a cleaning execution signal to the instruction distribution unit. The cleaning execution signal contains all the parameter information required to perform the cleaning operation, such as the target equipment identifier and cleaning intensity level. If the system is currently in another maintenance cycle, such as another piece of equipment being cleaned, or the system self-test program is running, the signal comparison unit will not immediately trigger cleaning but will generate a delayed maintenance signal. This delayed maintenance signal will insert the cleaning task into the task queue according to priority rules and may adjust the order of tasks in the queue.

[0037] The instruction distribution unit is the output interface of the decision execution module. Upon receiving a cleaning execution signal from the signal comparison unit, it executes the specific instruction distribution action based on the system configuration and hardware connectivity. A common distribution method is to transmit the cleaning execution signal to the connected automated cleaning equipment controller via wireless communication protocols such as ZigBee or WiFi. For example, the system can control a robotic arm equipped with an ultraviolet disinfection and microfiber wiping device to automatically clean a ping-pong paddle. Another distribution method is to send a maintenance reminder notification to the user's smartphone by calling the background service interface of the user terminal application. The notification may include suggested cleaning time, predicted contamination index, and cleaning method suggestions. For delayed maintenance signals, the instruction distribution unit will send a task queue notification to the user terminal, informing them of the estimated execution time.

[0038] The adaptive learning module is a key component for achieving continuous performance optimization in the system. Please refer to the appendix. Figure 5 This module monitors the system's predictive performance and adjusts model parameters as needed. The adaptive learning module continuously collects equipment performance recovery data after actual cleaning operations. This data can be obtained in two ways: first, through sensors connected to the cleaning equipment to monitor the amount of contaminants removed during cleaning; and second, through user feedback, where users rate the condition of the cleaned equipment on the application.

[0039] The adaptive learning module compares and analyzes the actual cleaning results with the predictions made by the cleaning demand prediction module. It calculates prediction error metrics, such as absolute percentage error. This module maintains an error log window, continuously tracking the error values ​​of the most recent predictions. When the system detects that the absolute percentage error of five consecutive predictions exceeds 15%, the adaptive learning module determines that the performance of the current prediction model is insufficient to meet the accuracy requirements and automatically initiates the model parameter optimization process.

[0040] The parameter optimization process employs gradient descent as the core optimizer. This process first extracts a large amount of recent training data from the historical database. Then, using the current weights of the Long Short-Term Memory (LSTM) network as the initial point, it calculates the gradient of the loss function with respect to each weight and updates the weights in the reverse direction of the gradient. Hyperparameters such as the learning rate are carefully set to ensure stable and effective training. Through multiple iterations, the model parameters are gradually updated, making the predicted output closer to the actual observations. This process allows the prediction model to gradually adapt to specific user habits, unique characteristics of the local environment, and changes in equipment performance over time, thereby achieving personalized and accurate predictions.

[0041] The above five modules work together to form a complete and adaptive system for predicting the cleaning needs of table tennis practice equipment. The data flow begins with the data acquisition module, undergoes quantification by the contamination index calculation module, is intelligently analyzed by the cleaning needs prediction module, is translated into action by the decision execution module, and finally receives feedback and optimization through the adaptive learning module. The entire system achieves accurate perception, trend prediction, and intelligent decision-making regarding the contamination status of the equipment, significantly improving maintenance efficiency and extending equipment lifespan.

Claims

1. A system for predicting the cleaning needs of table tennis practice equipment based on big data analysis, characterized in that, include: The data acquisition module is used to collect multi-dimensional usage data and environmental data of table tennis practice equipment in real time; The pollution index calculation module is used to dynamically calculate the current pollution index based on multi-source data output by the data acquisition module, using a weighted fusion algorithm. The cleaning demand forecasting module receives the output of the pollution index calculation module and, in conjunction with historical cleaning records and equipment material parameters, generates future cleaning demand time points through a time-series forecasting model. The decision execution module is used to output cleaning execution signals or delayed maintenance signals according to the instructions of the cleaning demand prediction module. The adaptive learning module is used to continuously collect data on the performance recovery of equipment after actual cleaning and compare it with the output of the prediction model. When the prediction error exceeds 15% for 5 consecutive times, the model parameter optimization process is automatically started.

2. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 1, characterized in that, The data acquisition module includes a usage frequency monitoring unit, an environmental sensor unit, and a user behavior recording unit. The frequency monitoring unit collects data on the number of hits and swing intensity per unit time through an inertial measurement unit installed on the racket handle and the edge of the table. The environmental sensor unit integrates a temperature and humidity sensor and a particulate matter concentration sensor to monitor the temperature, humidity, and concentration of suspended particulate matter in the air in real time at the training site. The user behavior recording unit records the equipment storage method and storage environment duration after each training session through the user terminal application.

3. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 1, characterized in that, The pollution index calculation module performs the following processing flow: First, the multi-source data output by the data acquisition module is preprocessed by normalization to eliminate dimensional differences. Then, a weighted function is constructed with usage frequency, ambient humidity, particulate matter concentration, and swirling intensity as input variables. The weight coefficients of the weighted function are determined through regression analysis of historical data, with usage frequency weighted at 0.4, ambient humidity weighted at 0.3, particulate matter concentration weighted at 0.2, and swirling intensity weighted at 0.

1. Finally, the weighted summation result is mapped to a pollution index range of 0 to 100.

4. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 3, characterized in that, The pollution index calculation module also introduces a dynamic weight adjustment mechanism; this mechanism automatically adjusts the weight allocation in the weighting function according to the material type of the equipment; for porous rubber materials, the environmental humidity weight is increased to 0.35 and the usage frequency weight is decreased to 0.35; for smooth rubber materials, the particulate matter concentration weight is increased to 0.25 and the environmental humidity weight is decreased to 0.

25.

5. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 1, characterized in that, The cleaning demand prediction module uses a long short-term memory network as its core prediction engine. The input features of this network include a 30-day pollution index sequence, equipment material type codes, and historical cleaning interval data. After being trained with 100,000 training samples, the long short-term memory network can output the predicted pollution index values ​​for each day for the next 7 days. When the predicted pollution index for any day exceeds the preset threshold of 75, the module immediately generates a cleaning demand instruction.

6. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 5, characterized in that, The cleaning demand prediction module is also equipped with an abnormal usage detection submodule. This submodule monitors sudden changes in usage frequency data in real time. When it detects that the daily usage frequency exceeds three times the historical average, it immediately initiates an emergency prediction process. The emergency prediction process uses a simplified calculation model to quickly assess the current pollution index and generate immediate cleaning suggestions within 5 minutes.

7. The table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 1, characterized in that, The decision execution module includes a signal comparison unit and an instruction distribution unit. The signal comparison unit compares the cleaning demand instruction output by the cleaning demand prediction module with the current system maintenance status in real time. If the system is in standby mode and there are no higher priority tasks, a cleaning execution signal is sent to the instruction distribution unit. If the system is in another maintenance operation cycle, a delayed maintenance signal is generated and the cleaning task is inserted into the execution queue.

8. A table tennis practice equipment cleaning demand prediction system based on big data analysis according to claim 7, characterized in that, The instruction distribution unit transmits cleaning execution signals to the designated cleaning equipment controller via a wireless communication protocol, or sends maintenance reminders to the user via a user terminal application.

9. A system for predicting the cleaning needs of table tennis practice equipment based on big data analysis according to claim 1, characterized in that, The adaptive learning module uses the gradient descent algorithm to update the weight parameters of the long short-term memory network, thereby enabling the prediction model to gradually adapt to individual user habits and local environmental characteristics.

10. A system for predicting the cleaning needs of table tennis practice equipment based on big data analysis according to claim 1, characterized in that, The system operates within a multi-timescale hierarchical framework, comprising a data acquisition layer, a computational analysis layer, and a decision execution layer. The data acquisition layer is responsible for real-time data acquisition and transmission; the calculation and analysis layer is responsible for pollution index calculation and cleaning demand forecasting; and the decision execution layer is responsible for instruction comparison and execution control.

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