Basketball board multifunctional expansion method and system based on LED transparent screen

Abstract

The invention relates to the technical field of sports equipment, and discloses a basketball board multifunctional expansion method and system based on an LED transparent screen, and the method comprises the steps: analyzing a function detuning factor corresponding to a basketball board, so as to determine a multi-dimensional performance optimization target point corresponding to the basketball board; setting an expansion priority corresponding to the function expansion module; somatosensory interaction features are extracted from the interaction user image data, and interaction satisfaction indexes of the basketball board about the user are analyzed; calculating the visual fidelity corresponding to the display picture image, querying the area illumination condition corresponding to the application area, and determining the display fidelity corresponding to the basketball board; performing variable optimization analysis on the display fidelity to obtain a display optimization variable, performing coupling analysis on the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor corresponding to the basketball board, performing expansion optimization processing on the function expansion module, and performing module updating processing on the basketball board to obtain an expanded basketball board. The multifunctional expansion effect of the basketball board with the LED transparent screen can be achieved.

Description

TECHNICAL FIELD

[0001] The application relates to a multifunctional expansion method and system of a basketball board based on an LED transparent screen and belongs to the technical field of sports equipment. BACKGROUND

[0002] In the field of intelligent sports equipment and display technology, the LED transparent screen gradually becomes a core component of the intelligent upgrading of the basketball board due to the characteristics of high transmittance and thinness. By embedding the LED transparent screen in the basketball board, multifunctional expansion such as event data visualization, dynamic advertisement display and training assistance can be realized to meet the diversified scene needs of sports events and daily training.

[0003] The existing multifunctional expansion method of the LED transparent screen basketball board is to realize function expansion through a corresponding control program by hardware stacking, such as sensors (such as ultrasonic waves, cameras), processors and other components. The function expansion is realized through the linkage of the control program, such as integrating ultrasonic sensor detection to play the ball-in replay. However, this method has the following defects: 1. Lack of adaptive analysis of application scenarios (such as indoor and outdoor light differences and usage frequency changes). In strong light environment, the display picture is easy to appear reflection blur. If the brightness is too high in dim light, it will cause light pollution problem. 2. The user interaction experience and display performance are not fully considered for collaborative optimization, which leads to not obvious overall function optimization effect, thereby leading to low user satisfaction. SUMMARY

[0004] The application provides a multifunctional expansion method and system of a basketball board based on an LED transparent screen, which mainly aims at the multifunctional expansion effect of the LED transparent screen basketball board.

[0005] To achieve the above-mentioned purpose, the application provides a multifunctional expansion method of a basketball board based on an LED transparent screen, which comprises: Obtaining user feedback data corresponding to an existing LED transparent screen basketball board and its corresponding service scene, based on the user feedback data, analyzing the function mismatch factors corresponding to the basketball board to determine the multi-dimensional performance optimization target point corresponding to the basketball board; Based on the multi-dimensional performance optimization target point, determining the function expansion module in the basketball board, collecting the scene efficiency parameters of the service scene, based on the scene efficiency parameters, setting the expansion priority of the function expansion module; Collecting the interactive user image data of the basketball board in the application area, extracting the somatosensory interaction features from the interactive user image data, based on the somatosensory interaction features, analyzing the interactive satisfaction index of the basketball board about the user; Collecting a display picture image corresponding to the basketball board, calculating a visual fidelity corresponding to the display picture image, querying a regional light condition corresponding to the application region, combining the regional light condition and the visual fidelity to determine a display fidelity corresponding to the basketball board; Performing variable optimization analysis on the display fidelity to obtain a display optimization variable, performing coupling analysis on the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor corresponding to the basketball board, combining the interaction optimization factor and the expansion priority to perform expansion optimization processing on the function expansion module, and performing module update processing on the basketball board to obtain an expanded basketball board.

[0006] Optionally, the function misfit factor corresponding to the basketball board is analyzed based on the user feedback data, including: Performing invalid cleaning processing on the user feedback data to obtain target feedback data; Extracting a feedback keyword in the target feedback data, performing semantic analysis on the feedback keyword to obtain a high-frequency feedback problem point; Performing failure mode analysis on the high-frequency feedback problem point to obtain a potential failure root cause; Performing correlation analysis on the potential failure root cause to obtain the function misfit factor corresponding to the basketball board.

[0007] Optionally, the multi-dimensional performance optimization target point corresponding to the basketball board is determined, further including: Performing clustering processing on the function misfit factor to obtain a clustered misfit factor; Extracting a misfit factor knowledge element in the clustered misfit factor, and screening out a representation knowledge element in the misfit factor knowledge element; Based on the representation knowledge element, performing dimension mapping processing on the clustered misfit factor to obtain a misfit correlation performance dimension; Performing target point identification on the misfit correlation performance dimension to obtain an initial multi-dimensional performance target point; Performing optimization feasibility analysis on the initial multi-dimensional performance target point to obtain the multi-dimensional performance optimization target point corresponding to the basketball board.

[0008] Optionally, the function expansion module in the basketball board is determined based on the multi-dimensional performance optimization target point, including: Performing function demand analysis on the multi-dimensional performance optimization target point to obtain a target point associated function; Performing path decomposition processing on the target point associated function to obtain a function execution path; Querying a device module cluster in the basketball board, and extracting module attribute information corresponding to the device module cluster; performing semantic matching processing on the function execution path and the module attribute information to obtain a function module matching matrix; performing conflict resolution processing on the function module matching matrix to obtain a target function module matrix; determining a function expansion module in the basketball board from the device module cluster based on the target function module matrix.

[0009] Optionally, the setting of the expansion priority corresponding to the function expansion module based on the scene performance parameter comprises: performing spatiotemporal slicing processing on the scene performance parameter to obtain a performance spatiotemporal feature tensor; performing quantum coding processing on the performance spatiotemporal feature tensor to obtain a quantum feature map; performing dimension reduction mapping processing on the quantum feature map to obtain a scene key feature; calculating a similarity coefficient between the scene key feature and a hardware parameter of the function expansion module; setting the expansion priority corresponding to the function expansion module based on the similarity coefficient.

[0010] Optionally, the extracting of the somatosensory interaction feature from the interactive user image data comprises: performing denoising processing on the interactive user image data to obtain a target user image; extracting a user skeleton point in the target user image to obtain a skeleton point cloud sequence; performing motion intention decoding on the skeleton point cloud sequence to obtain a motion intention feature; analyzing a biomechanics parameter of the motion intention feature to obtain a biomechanics feature; performing motion pattern classification on the biomechanics feature to obtain a motion pattern cluster; performing feature extraction on the motion pattern cluster to obtain a somatosensory interaction feature.

[0011] Optionally, the analysis of the interactive satisfaction index of the basketball board with respect to the user based on the somatosensory interaction feature comprises: performing calibration processing on the somatosensory interaction feature to obtain a synchronous somatosensory feature, and matching an interactive parameter set corresponding to the synchronous somatosensory feature of the basketball board; performing weight allocation processing on the synchronous somatosensory feature to obtain a weighted somatosensory feature; performing emotion state analysis on the weighted somatosensory feature to obtain a somatosensory emotion state; performing coding processing on the weighted somatosensory feature to obtain a feature coding value; combining the feature coding value, calculating an interactive satisfaction index corresponding to the somatosensory emotion state. Based on the interaction satisfaction index, an interaction satisfaction indicator of the basketball board with respect to the user is determined from the interaction parameter set.

[0012] Optionally, the calculation of the visual fidelity corresponding to the display image comprises: An image resolution and an image physical size corresponding to the display image are obtained, and the image pixel density corresponding to the display image is calculated by combining the image resolution and the image physical size according to the following formula: ; Wherein, A represents the image pixel density corresponding to the display image, B represents the width pixel in the image resolution, D represents the height pixel in the image resolution, E represents the image width in the image physical size, and F represents the image height in the image physical size; The maximum luminance and the minimum luminance corresponding to the display image are read, and the luminance contrast corresponding to the display image is calculated by combining the maximum luminance and the minimum luminance. The visual fidelity corresponding to the display image is calculated by combining the image pixel density and the luminance contrast.

[0013] Optionally, the calculation of the visual fidelity corresponding to the display image by combining the image pixel density and the luminance contrast comprises: The visual fidelity corresponding to the display image can be calculated according to the following formula: ; Wherein, The visual fidelity corresponding to the display image is represented by V, The pixel density weight is represented by G, and the image pixel density is represented by G, The luminance saturation threshold is represented by S, The luminance weight is represented by H, and the luminance contrast is represented by H, The luminance reference value is represented by R, The synergistic attenuation coefficient is represented by K, The pixel density sensitivity coefficient is represented by P, The luminance sensitivity coefficient is represented by L.

[0014] In order to solve the above problems, the application further provides a multifunctional expansion system of a basketball board based on an LED transparent screen, which comprises: An optimization target analysis module is used to obtain user feedback data corresponding to an existing LED transparent screen basketball board and its corresponding service scene, based on the user feedback data, analyze the function misfit factors corresponding to the basketball board, and determine the multi-dimensional performance optimization target point corresponding to the basketball board. A priority setting module is configured to determine a function expansion module in the basketball board based on the multi-dimensional performance optimization target, collect a scene efficiency parameter of the service scene, and set an expansion priority of the function expansion module based on the scene efficiency parameter; An interaction satisfaction index analysis module is configured to collect interaction user image data of the basketball board in an application area, extract somatosensory interaction features from the interaction user image data, and analyze an interaction satisfaction index of the basketball board with respect to a user based on the somatosensory interaction features; A display fidelity determination module is configured to collect a display screen image corresponding to the basketball board, calculate a visual fidelity corresponding to the display screen image, query a regional lighting condition corresponding to the application area, and determine a display fidelity corresponding to the basketball board in combination with the regional lighting condition and the visual fidelity. An expansion optimization module is configured to perform variable optimization analysis on the display fidelity to obtain a display optimization variable, perform coupling analysis on the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor corresponding to the basketball board, perform expansion optimization processing on the function expansion module in combination with the interaction optimization factor and the expansion priority, and perform module update processing on the basketball board to obtain an expanded basketball board.

[0015] Compared with the problems described in the background art, the application analyzes the function mismatch factors corresponding to the basketball board based on the user feedback data, and then obtains the key mismatch dimensions affecting the performance of the basketball board, providing data support for subsequent processing of determining the multi-dimensional performance optimization target point corresponding to the basketball board. Based on the multi-dimensional performance optimization target point, the application determines the function expansion module in the basketball board, which can accurately match the performance optimization demand and the module technical capability, avoid redundant function development, realize efficient coupling of hardware resources and user demand, significantly improve the pertinence and practicality of the basketball board function expansion, and further, the application extracts somatosensory interaction features from the interactive user image data, which can accurately capture the body action, force habit and motion trajectory of the user in the basketball game, provide data support for analyzing the interactive satisfaction index of the basketball board about the user. The application calculates the visual fidelity corresponding to the display picture image, which can quantify the clarity of the display picture image, and then provide basis for subsequent determination of display fidelity. Finally, the application obtains display optimization variables by variable optimization analysis of the display fidelity, which can dynamically adapt the pixel density and brightness contrast parameters in different lighting environments, eliminate visual distortion problems such as picture blur and whitening, and perform coupling analysis on the interactive satisfaction index and the display optimization variables to obtain the interactive optimization factor corresponding to the basketball board. Based on the synergistic relationship between user operation habit and display parameter, the interactive performance such as shooting trajectory tracking accuracy and data feedback response speed can be intelligently optimized, so that the training feedback and visual experience reach dynamic balance. Therefore, the basketball board multifunctional expansion method and system based on LED transparent screen provided by the embodiment of the application can realize the multifunctional expansion of the basketball board based on LED transparent screen. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the basketball board multifunctional expansion method based on LED transparent screen provided by an embodiment of the application is shown. Figure 2 The module diagram of the basketball board multifunctional expansion system based on LED transparent screen provided by an embodiment of the application is shown.

[0017] The purpose implementation, functional characteristics and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

[0019] The embodiment of the present application provides a basketball board multifunctional expansion method based on an LED transparent screen. The execution subject of the basketball board multifunctional expansion method based on the LED transparent screen includes but is not limited to at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the present application. In other words, the basketball board multifunctional expansion method based on the LED transparent screen can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster and the like.

[0020] Embodiment 1 Referring to Figure 1 FIG. 1 is a flowchart of a basketball board multifunctional expansion method based on an LED transparent screen provided by an embodiment of the present application. In the embodiment, the basketball board multifunctional expansion method based on the LED transparent screen includes: S1, obtaining user feedback data corresponding to an existing LED transparent screen basketball board and a service scene corresponding thereto, analyzing a function mismatch factor corresponding to the basketball board based on the user feedback data, to determine a multi-dimensional performance optimization target point corresponding to the basketball board.

[0021] The present application obtains the key mismatch dimension affecting the performance of the basketball board by analyzing the function mismatch factor corresponding to the basketball board based on the user feedback data, thereby providing data support for subsequent processing of determining the multi-dimensional performance optimization target point corresponding to the basketball board. The user feedback data is multi-source data such as user operation logs, questionnaire survey scores, after-sales maintenance records and the like corresponding to the existing LED transparent screen basketball board. The service scene is an installation environment (such as indoor and outdoor light intensity, use frequency, temperature and humidity and the like) corresponding to the existing LED transparent screen basketball board. The function mismatch factor is a matching deviation (such as display blur under strong light, interactive response delay and the like) between the user demand and the actual performance corresponding to the basketball board. Further, the user feedback data corresponding to the existing LED transparent screen basketball board and the service scene corresponding thereto can be obtained through a user feedback collection platform, such as an APP questionnaire.

[0022] As an embodiment of the present application, the analysis of the function mismatch factor corresponding to the basketball board based on the user feedback data includes: invalid cleaning processing is performed on the user feedback data to obtain target feedback data; feedback keywords in the target feedback data are extracted, and semantic analysis is performed on the feedback keywords to obtain high-frequency feedback problem points; failure mode analysis is performed on the high-frequency feedback problem points to obtain potential failure root causes; correlation analysis is performed on the potential failure root causes to obtain the function mismatch factor corresponding to the basketball board.

[0023] The target feedback data is a structured, valid and complete data set obtained after the user feedback data is subjected to invalid cleaning processing such as format unification, noise filtering and missing value filling; the feedback keywords are high-frequency words in the target feedback data that can represent the core problem of user feedback, which are extracted by a natural language processing technology; the high-frequency feedback problem point is a main problem and a form of expression of the basketball board function that is explicitly extracted after the feedback keywords are subjected to semantic analysis; and the potential failure root cause is a possible root cause of the problem that is mined in combination with the function module architecture of the basketball board after the high-frequency feedback problem point is subjected to failure mode analysis.

[0024] Further, the user feedback data can be subjected to invalid cleaning processing by combining a data cleaning algorithm (including an outlier detection algorithm, a repeated value elimination algorithm and a missing value interpolation algorithm) with manual checking to obtain target feedback data; the feedback keywords in the target feedback data can be extracted by a TextRank algorithm; the feedback keywords can be subjected to semantic analysis by a BERT-based natural language processing model and domain dictionary matching to obtain high-frequency feedback problem points; the high-frequency feedback problem points can be subjected to failure mode analysis by a fault tree analysis method to obtain potential failure root causes; the potential failure root causes are subjected to correlation analysis to obtain the functional misalignment factors corresponding to the basketball board. In detail, the correlation analysis of the potential failure root causes can be quantified by using an association rule mining algorithm (such as Apriori) to calculate the support and confidence of the root cause combination (such as the probability of the simultaneous occurrence of “insufficient heat dissipation of the driving chip” and “high-temperature environment” reaching 75%); a root cause cluster (such as the conduction path of “insufficient heat dissipation→chip overheating→display driving abnormality”) with strong correlation is identified by constructing a root cause causal relationship diagram by using a Bayesian network; and the high-correlation root cause cluster is integrated into a functional misalignment factor, for example, “insufficient heat dissipation of the LED driving chip under a high-temperature environment leads to the decline of the stability of the display module”.

[0025] The application determines the multi-dimensional performance optimization target point corresponding to the basketball board, and then accurately locates the optimization direction from multiple dimensions such as display performance, interactive experience and environmental adaptability, wherein the multi-dimensional performance optimization target point is a specific target point that needs to be improved in multiple performance dimensions such as display accuracy, touch response speed, environmental tolerance and energy efficiency.

[0026] As an embodiment of the application, the determination of the multi-dimensional performance optimization target point corresponding to the basketball board further includes: The functional misalignment factors are subjected to clustering processing to obtain clustered misalignment factors; Misalignment factor knowledge elements in the clustered misalignment factors are extracted, and representation knowledge elements in the misalignment factor knowledge elements are screened out. mapping processing on the clustering malfunction factors based on the representation knowledge element, to obtain a malfunction correlation performance dimension; target point recognition on the malfunction correlation performance dimension, to obtain an initial multi-dimensional performance target point; optimization feasibility analysis on the initial multi-dimensional performance target point, to obtain a multi-dimensional performance optimization target point corresponding to the basketball board.

[0027] The clustering malfunction factors are a malfunction factor grouping set with similar characteristics or correlation relationships obtained after clustering processing of the functional malfunction factors; the malfunction factor knowledge element is a basic knowledge unit (such as “LED driving chip”, “insufficient heat dissipation”, “causing display abnormalities”) in the clustering malfunction factors extracted by a knowledge extraction technology; the representation knowledge element is a key knowledge unit (such as the “interaction response speed” representation word extracted from “touch delay” and “response lag”) in the malfunction factor knowledge element that can represent the core characteristics of the corresponding clustering malfunction factor; the malfunction correlation performance dimension is a performance influence dimension (such as mapping “strong light display blur” to the “display brightness self-adaptive adjustment” dimension) corresponding to display performance, interaction experience, environmental adaptability, etc. established after dimension mapping processing of the clustering malfunction factors based on the representation knowledge element; and the initial multi-dimensional performance target point is a specific target (such as “the brightness adjustment range reaches 500-2000 nits in a strong light environment”) that can be quantified and optimized on each performance dimension determined after target point recognition of the malfunction correlation performance dimension.

[0028] Further, the functional malfunction factors can be clustered by a density peak clustering algorithm (such as DPC) combined with a malfunction factor similarity matrix constructed by domain expert knowledge, to obtain the clustering malfunction factors; the malfunction factor knowledge element in the clustering malfunction factors can be extracted by a knowledge extraction model based on a graph attention network (GAT); the representation knowledge element in the malfunction factor knowledge element can be screened by a keyword weight algorithm fusing TF-IDF and TextRank; the clustering malfunction factors can be subjected to dimension mapping processing based on the representation knowledge element, through cross-domain semantic mapping by a pre-constructed intelligent analysis ontology model of historical buildings, to obtain the malfunction correlation performance dimension; the initial multi-dimensional performance target point can be obtained by target point recognition on the malfunction correlation performance dimension by a multi-objective optimization algorithm (such as NSGA-III) combined with a performance index threshold library of the basketball board; and the multi-dimensional performance optimization target point corresponding to the basketball board can be obtained by optimization feasibility analysis on the initial multi-dimensional performance target point by a virtual verification platform based on digital twinning combined with a cost-benefit analysis model.

[0029] S2, based on the multi-dimensional performance optimization target, determine the function expansion module in the basketball board, collect the scene efficiency parameters of the service scene, based on the scene efficiency parameters, set the expansion priority of the function expansion module.

[0030] The application can accurately match performance optimization demand and module technical ability, avoid redundant function development, realize efficient coupling of hardware resources and user demand, and significantly improve the pertinence and practicality of basketball board function expansion by determining the function expansion module in the basketball board based on the multi-dimensional performance optimization target.

[0031] As an embodiment of the application, the determination of the function expansion module in the basketball board based on the multi-dimensional performance optimization target comprises: Performing function demand analysis on the multi-dimensional performance optimization target to obtain target point associated functions; Performing path decomposition processing on the target point associated functions to obtain function execution paths; Querying the device module cluster in the basketball board and extracting the module attribute information corresponding to the device module cluster; Performing semantic matching processing on the function execution paths and the module attribute information to obtain a function module matching matrix; Performing conflict resolution processing on the function module matching matrix to obtain a target function module matrix; Based on the target function module matrix, determine the function expansion module in the basketball board from the device module cluster.

[0032] Wherein, the target point associated function is a specific function requirement set obtained after the multi-dimensional performance optimization target point is subjected to function requirement analysis (such as converting "insufficient impact resistance" into "cushion structure design requirement"); the function execution path is a function implementation logic chain obtained after the target point associated function is subjected to path decomposition processing (such as the execution steps of "light ray perception→signal processing→backlight control"); the device module cluster is a set of existing or reusable function modules in the basketball board (such as display module, touch module, heat dissipation module, etc. hardware unit); the module attribute information is the technical parameters and performance indicators corresponding to the device module cluster (such as hardware interface protocol, energy consumption threshold, historical failure rate, etc.); the function module matching matrix is a demand-resource matching quantization matrix generated after the function execution path and the module attribute information are subjected to semantic matching processing (such as the matching degree score of "light ray perception" and "light sensitive sensor module"); the target function module matrix is a conflict-free module combination scheme obtained after the function module matching matrix is subjected to conflict resolution processing (such as the optimal combination after excluding interface incompatible modules).

[0033] Further, the multi-dimensional performance optimization target point can be subjected to function requirement analysis by a domain ontology-based semantic analysis engine (such as a BERT pre-training model combined with a geophysical exploration knowledge graph), to obtain a target point associated function; the target point associated function can be subjected to path decomposition processing by a hierarchical task network (HTN) planning algorithm combined with a geological profile topological structure constraint, to obtain a function execution path; the device module cluster in the basketball board can be queried by a knowledge graph-based module retrieval engine (using the graph database Cypher query language); the module attribute information corresponding to the device module cluster can be extracted by an automatic feature extractor (integrating regular expressions and machine learning information extraction models); the function execution path and the module attribute information can be subjected to semantic matching processing by a cross-modal attention matching network (fusing geological profile image features and module attribute vectors), to obtain a function module matching matrix; the function module matching matrix can be subjected to conflict resolution processing by a multi-objective conflict resolver based on quantum annealing (setting constraints such as geological structure compatibility and exploration cost), to obtain a target function module matrix; based on the target function module matrix, a function expansion module in the basketball board is determined from the device module cluster by a digital twin driven reinforcement learning decision engine (simulating module synergistic effects in a virtual geological environment).

[0034] The application realizes dynamic coupling of module expansion strategy and real-time service scene by setting the expansion priority of the function expansion module based on the scene performance parameter, avoids resource mismatch caused by fixed priority, and significantly improves the precision and scene adaptability of basketball board function upgrade, wherein the scene performance parameter is a real-time dynamic characteristic parameter set of the service scene (such as key indicators influencing module performance, such as light intensity, environmental temperature, use frequency, and multi-person concurrent operation frequency); the expansion priority represents the implementation order quantitative weight of the function expansion module based on scene adaptation degree and cost benefit evaluation (reflecting the function value and resource input priority of the module in the current scene); further, the scene performance parameter of the service scene can be collected by an intelligent monitoring terminal integrating a multi-sensor array (including a light sensor, a temperature sensor, a pressure sensor, and an Internet of Things communication module).

[0035] As an embodiment of the application, setting the expansion priority of the function expansion module based on the scene performance parameter comprises: spatiotemporal slicing the scene performance parameter to obtain an efficiency spatiotemporal characteristic tensor; quantum encoding the efficiency spatiotemporal characteristic tensor to obtain a quantum characteristic map; dimensionality reduction mapping the quantum characteristic map to obtain a scene key feature; calculating a similarity coefficient between the scene key feature and the hardware parameters of the function expansion module; setting the expansion priority of the function expansion module based on the similarity coefficient.

[0036] The efficiency spatiotemporal characteristic tensor is a structured data set generated by spatiotemporal slicing of the scene performance parameter, which has time series and spatial distribution dimensions; the quantum characteristic map is a high-dimensional feature correlation map formed by quantum state mapping and entanglement operation through quantum encoding of the efficiency spatiotemporal characteristic tensor; the scene key feature is a core feature set extracted by Gray code mapping and Hamming distance screening through dimensionality reduction mapping of the quantum characteristic map; and the similarity coefficient represents a parameter matching degree quantitative value calculated by an algorithm such as weighted Euclidean distance between the scene key feature and the hardware parameters of the function expansion module.

[0037] Further, the scene performance parameters can be processed by spatiotemporal slicing based on a micro-electro-mechanical system (MEMS) sensor array and a spatiotemporal grid division algorithm to obtain performance spatiotemporal feature tensors, such as dividing the basketball court environment temperature into a three-dimensional grid according to the time axis (every 15 minutes) and the backboard space region (9 blocks such as left upper / middle / right lower), and generating a time-stamped temperature field spatiotemporal matrix; the performance spatiotemporal feature tensors can be processed by quantum encoding of a quantum state mapping system constructed by a quantum dot array to obtain a quantum feature map, such as mapping the impact frequency (value in the spatiotemporal matrix) of different regions of the backboard to the spin state of the quantum dot, and constructing a non-local correlation map of the impact frequency and the material fatigue degree by using the quantum entanglement effect; the quantum feature map can be processed by a Gray code mapping and Hamming distance calculation unit implemented by a field programmable gate array (FPGA) to obtain scene key features, such as converting high-dimensional quantum features into a 4-bit Gray code sequence, and screening out the combination feature of "high temperature period + high frequency impact in the middle of the backboard" as the key scene identifier by Hamming distance; the similarity coefficient between the scene key features and the hardware parameters of the functional expansion module can be calculated by a parallel distance calculation engine constructed by a memristor cross array; based on the similarity coefficient, the expansion priority of the functional expansion module is set by a multi-objective game decision system driven by digital twinning, such as simulating the "summer league high temperature + professional player high frequency dunk" scene in the digital twin, and calculating the dynamic expansion order of the heat dissipation module (priority 0.85) superior to the impact resistance module (priority 0.62) by a game algorithm.

[0038] S3, collect the interactive user image data of the basketball board in the application area, extract the somatosensory interaction features from the interactive user image data, and analyze the interactive satisfaction index of the basketball board with the user based on the somatosensory interaction features.

[0039] The application can accurately capture the body movements, force habits and movement trajectories of the user in the basketball game by extracting the somatosensory interaction features from the interactive user image data, and provides data support for analyzing the interactive satisfaction index of the basketball board with the user, wherein the interactive user image data is image and video data collected by devices such as multi-angle camera arrays and depth sensors in the application area of the basketball board, containing user body movements, position changes and interaction processes with the basketball board, the somatosensory interaction features are dynamic feature parameters reflecting human-computer interaction behaviors extracted from the interactive user image data, such as user body joint movement trajectories, action amplitudes, force directions, shooting / dribbling postures, and further, the interactive user image data of the basketball board in the application area can be collected by 4K ultra-high-definition infrared cameras around the basketball court.

[0040] As an embodiment of the present application, the extracting somatosensory interaction features from the interactive user image data comprises: The interactive user image data is denoised to obtain a target user image; User skeleton points in the target user image are extracted to obtain a skeleton point cloud sequence; The skeleton point cloud sequence is decoded for motion intention to obtain motion intention features; Biomechanical parameters of the motion intention features are analyzed to obtain biomechanical features; The biomechanical features are classified for motion patterns to obtain motion pattern clusters; The motion pattern clusters are extracted for features to obtain somatosensory interaction features.

[0041] The target user image is a clear image obtained by removing noise interference from the interactive user image data; the skeleton point cloud sequence is a point cloud sequence formed by extracting user skeleton key points from the target user image and arranging them in time dimension; the motion intention features are feature vectors representing action purposes, such as walking, grabbing, etc., obtained by decoding the skeleton point cloud sequence for motion intention; the biomechanical features are biomechanical parameters extracted from the motion intention features, such as joint torque, limb acceleration, muscle activation mode, etc.; and the motion pattern clusters are a set of action categories with similar motion features formed by clustering the biomechanical features through a motion pattern classification algorithm.

[0042] Further, the interactive user image data can be denoised by a median filter algorithm to obtain a target user image; user skeleton points in the target user image can be extracted by a multi-modal OpenPose skeleton detection system to obtain a skeleton point cloud sequence; the skeleton point cloud sequence can be decoded for motion intention by a fusion of a bidirectional long short-term memory network (Bi-LSTM) and a skeleton graph attention network (Skeleton-GAT) to obtain motion intention features; biomechanical parameters of the motion intention features can be analyzed by an AnyBody biomechanical simulation engine based on human dynamics inverse problem solving to obtain biomechanical features, such as knee joint torque peak (187 N·m), elbow joint angular velocity (3.2 rad / s), quadriceps activation (78%), center of gravity displacement trajectory (X-axis ± 0.15 m, Y-axis ± 0.22 m), ground reaction force peak (2.3 times body weight), etc. 64-dimensional kinematics and dynamics parameters; the biomechanical features can be classified for motion patterns by an unsupervised action pattern classifier combining density peak clustering (DPC) and autoencoder dimension reduction to obtain motion pattern clusters; and the motion pattern clusters can be extracted for features by a quantum dot neural network to obtain somatosensory interaction features.

[0043] The application analyzes the interactive satisfaction index of the basketball board about the user based on the somatosensory interaction feature, thereby understanding the satisfaction type of the user about the basketball board, and providing data support for subsequent analysis and processing.

[0044] As an embodiment of the application, the analysis of the interactive satisfaction index of the basketball board about the user based on the somatosensory interaction feature comprises: Calibration processing is performed on the somatosensory interaction feature to obtain a synchronous somatosensory feature, and an interactive parameter set corresponding to the synchronous somatosensory feature is matched for the basketball board; Weight distribution processing is performed on the synchronous somatosensory feature to obtain a weighted somatosensory feature; Emotional state analysis is performed on the weighted somatosensory feature to obtain a somatosensory emotional state; Encoding processing is performed on the weighted somatosensory feature to obtain a feature encoding value; The interactive satisfaction index corresponding to the somatosensory emotional state is calculated in combination with the feature encoding value; Based on the interactive satisfaction index, the interactive satisfaction index of the basketball board about the user is determined from the interactive parameter set.

[0045] The synchronous somatosensory feature is a precise motion feature sequence obtained by calibration processing of the somatosensory interaction feature to eliminate time delay, spatial offset and other noises; the interactive parameter set is a dynamic matching set containing display interaction parameters such as brightness (500 cd / m 2 ), refresh rate (120 Hz), data visualization form, etc., corresponding to the synchronous somatosensory feature of the basketball board; the weighted somatosensory feature is a vector obtained by assigning different dimension feature weights to the synchronous somatosensory feature according to the importance of the motion (such as the length of time the gaze focus stays, the synchronization rate of group motion, etc.) after weight distribution processing; the somatosensory emotional state is a "smooth-happy" "stuck-depressed" emotional label and confidence obtained by emotional state analysis of the weighted somatosensory feature in combination with heart rate variability and facial micro-expression recognition; the feature encoding value is a feature vector obtained by encoding processing of the weighted somatosensory feature, which normalizes heterogeneous data such as joint torque and gesture speed to dimensionless values in the interval 0-1; the interactive satisfaction index represents a 0-100 quantitative satisfaction index of the fusion physiological feature encoding and emotional weight (such as smoothness encoding 0.8 x happiness emotional weight 0.6) corresponding to the somatosensory emotional state.

[0046] Further, the somatosensory interaction feature can be calibrated by a Kalman filter-based multi-sensor timestamp alignment FPGA hardware acceleration module to obtain a synchronous somatosensory feature; the basketball board can be matched with the interaction parameter set corresponding to the synchronous somatosensory feature by a dynamic mapping engine, that is, the interaction parameters of the basketball board at the time point corresponding to the synchronous somatosensory feature are obtained; the synchronous somatosensory feature can be subjected to weight distribution processing by combining an audience cheering decibel value (85 dB+) and an attention weight generation network of a gaze heat map; the weighted somatosensory feature can be subjected to emotional state analysis by integrating facial micro-expression recognition (AU unit analysis); the weighted somatosensory feature can be subjected to encoding processing by a quantum dot self-encoder-driven cross-modal feature normalization system to obtain a feature encoding value; the feature encoding value is combined to calculate an interaction satisfaction index corresponding to the emotional state of the body by a three-dimensional evaluation fusion algorithm (physiological feature x emotional weight x scene parameter) based on fuzzy logic rules; based on the interaction satisfaction index, the interaction satisfaction index of the basketball board with respect to the user is determined from the interaction parameter set, for example, when the interaction satisfaction index is greater than a preset index, the corresponding parameter (such as brightness) in the interaction parameter set is taken as the interaction satisfaction index, and the preset index can be set to 0.8 or can be set according to the actual application scenario.

[0047] S4, collect the display picture image corresponding to the basketball board, calculate the visual fidelity corresponding to the display picture image, query the regional lighting condition corresponding to the application area, and determine the display fidelity corresponding to the basketball board in combination with the regional lighting condition and the visual fidelity.

[0048] The application can quantify the clarity of the display picture image by calculating the visual fidelity corresponding to the display picture image, thereby providing a basis for subsequent display fidelity determination processing, wherein the display picture image represents the content of the playing visual interface corresponding to the basketball board, and the visual fidelity is a quantification of the clarity of the display picture image. Further, the corresponding display picture image can be collected by a spectral vision sensor inside the basketball board.

[0049] As an embodiment of the application, the calculation of the visual fidelity corresponding to the display picture image comprises: The image resolution and image physical size corresponding to the display picture image are obtained, and the image pixel density corresponding to the display picture image is calculated by the following formula in combination with the image resolution and the image physical size: ; Wherein, A represents the image pixel density corresponding to the display picture image, B represents the wide pixel in the image resolution, D represents the high pixel in the image resolution, E represents the image width in the image physical size, and F represents the image height in the image physical size. Read the maximum value and the minimum value of the brightness corresponding to the display picture image, and combine the maximum value and the minimum value to calculate the brightness contrast corresponding to the display picture image. Combine the image pixel density and the brightness contrast to calculate the visual fidelity corresponding to the display picture image.

[0050] Wherein, the image pixel density represents the density of pixel distribution per inch of physical size (such as 90PPI, that is, 90 pixels per inch), which represents the basic fineness and detail restoration ability of the display; the brightness contrast represents the ratio of the maximum brightness to the black brightness (such as 1000:1, that is, the bright part is 1000 times darker), which represents the distinction between bright and dark areas and the visual sharpness.

[0051] Further, the image resolution and the image physical size corresponding to the display picture image can be obtained through the display EDID protocol analysis tool (such as DisplaySpecifications software, hardware I 2 C bus reading module); the maximum value and the minimum value of the brightness corresponding to the display picture image can be read through the spectral radiometer; and the ratio between the minimum value and the maximum value of the brightness is calculated to obtain the brightness contrast corresponding to the display picture image.

[0052] Further, as an optional embodiment of the present application, the combination of the image pixel density and the brightness contrast to calculate the visual fidelity corresponding to the display picture image comprises: The visual fidelity corresponding to the display picture image can be calculated by the following formula: ; Wherein, represents the visual fidelity corresponding to the display picture image, represents the pixel density weight, G represents the image pixel density, represents the brightness saturation threshold, represents the brightness weight, H represents the brightness contrast, represents the brightness reference value, represents the synergistic attenuation coefficient, represents the pixel density sensitivity coefficient, represents the brightness sensitivity coefficient.

[0053] The pixel density weight is used to measure the weighting coefficient of the influence degree of pixel density on visual fidelity, reflects the sensitive weight of the human eye to the change of pixel density, the luminance saturation threshold is a threshold parameter that the improvement effect of luminance contrast on visual fidelity tends to be saturated after reaching the critical value, the luminance weight is used to adjust the weighting coefficient of the importance of luminance contrast in visual fidelity calculation, reflects the influence proportion of the luminance factor, the luminance reference value is used as a reference standard value for luminance contrast normalization calculation, for comparing the relative level of the current luminance contrast, the synergistic attenuation coefficient represents the attenuation parameter of the synergistic effect between pixel density and luminance contrast, the greater the relative proportion difference between the two, the more significant the attenuation effect on visual fidelity, the pixel density sensitive coefficient reflects the reference parameter of the sensitivity of the human eye to the pixel density, which is usually a critical reference value of the pixel density, used to measure the sensitive effect of the current pixel density, the luminance sensitive coefficient measures the reference parameter of the sensitivity of the human eye to the luminance contrast, as a reference threshold for luminance contrast sensitivity effect calculation.

[0054] Further, the pixel density weight and the luminance weight are obtained by visual perception experiment data fitting or machine learning algorithm optimization (such as designing a contrast experiment based on the Fechner law, letting the subjects score different PPI / CR combinations, and determining the weight coefficient through regression analysis); the luminance saturation threshold and the luminance reference value are obtained according to the research results of human eye visual characteristics or industry standard specifications (such as taking 3000:1 as the luminance saturation threshold, corresponding to the saturation inflection point of human eye perception of high contrast, and taking the ITU-R BT.709 standard as the reference value); the synergistic attenuation coefficient is fitted by quantifying the proportion imbalance degree of pixel density and luminance contrast (such as calculating the absolute value of |PPI / CR|, and mapping the attenuation coefficient γ through an exponential function); the pixel density sensitive coefficient is calibrated based on the experimental data of human eye resolution threshold; the luminance sensitive coefficient is determined according to the perception experimental results of the Weber law on luminance difference.

[0055] ​The application determines the display fidelity corresponding to the basketball board by combining the regional lighting condition and the visual fidelity, can intelligently adjust the display parameters of the basketball board according to the light intensity and the display picture fineness and contrast performance, effectively avoids the problems of picture whitening under strong light and detail blurring under dim light, always presents the user with a clear, comfortable and close-to-real visual perception training feedback picture, and significantly improves the interaction quality and use experience under different lighting scenes, wherein the regional lighting condition is a comprehensive representation of the lighting parameters such as the environmental light intensity, color temperature and distribution characteristics of the application area (such as the superimposed intensity of natural light and artificial lighting in the training venue, the local highlight area caused by the local high light material, etc.), the display fidelity is the matching degree of the visual parameters such as the picture pixel density, brightness contrast and color restoration degree of the basketball board to the actual scene (reflecting the fidelity level of the display picture in detail clarity, light and shade level and color authenticity), further, the regional lighting condition corresponding to the application area can be queried through the environmental light sensor array (including an illuminometer, a color temperature instrument and a polarized light detector) integrated in the basketball board frame; the display fidelity corresponding to the basketball board is determined by combining the regional lighting condition and the visual fidelity, for example, when it is detected that the environmental light intensity in the venue is high, the display brightness is automatically increased and the contrast is optimized, and the sharpening algorithm is dynamically adjusted according to the picture pixel density to ensure that the details are clear under strong light; if the environmental light is dim, the dark field detail restoration ability is enhanced to avoid information loss in the backboard shadow area.

[0056] S5, variable optimization analysis is performed on the display fidelity to obtain a display optimization variable, coupling analysis is performed on the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor corresponding to the basketball board, expansion optimization processing is performed on the function expansion module in combination with the interaction optimization factor and the expansion priority, and module update processing is performed on the basketball board to obtain an expanded basketball board.

[0057] The application can dynamically adapt the pixel density and brightness contrast parameters under different lighting environments by variable optimization analysis of the display fidelity to obtain display optimization variables, eliminate visual distortion problems such as picture blur and whitening, and couple the interactive satisfaction index and the display optimization variables to obtain the interactive optimization factor corresponding to the basketball board, intelligently optimize the interactive performance such as shot trajectory tracking accuracy and data feedback response speed based on the synergistic relationship between user operation habits and display parameters, and dynamically balance training feedback and visual experience, wherein the display optimization variable is a quantifiable adjustment parameter (such as brightness, contrast, pixel density, color restoration, etc.) corresponding to the display fidelity, which realizes the adaptation of display effect and environmental lighting by dynamically optimizing these parameters, the interactive optimization factor is a comprehensive adjustment element obtained by coupling analysis of the interactive satisfaction index and the display optimization variable, further, the display fidelity can be analyzed by a self-adaptive parameter adjustment model (integrating lighting response curve and visual perception function, dynamically searching the optimal solution space of parameters such as pixel density and contrast) based on particle swarm optimization to obtain the display optimization variable, the interactive satisfaction index and the display optimization variable can be coupled by a coupling saliency analysis system (using LSTM network to learn the time sequence correlation characteristics of interactive indicators and display parameters, generating a multi-dimensional coupling weight matrix) based on neural network to obtain the interactive optimization factor corresponding to the basketball board.

[0058] The application combines the interactive optimization factor and the expansion priority to perform expansion optimization processing on the function expansion module and module update processing on the basketball board to obtain an expanded basketball board, thereby improving the expansion effect of the basketball board, further, a multi-objective priority scheduling algorithm based on Pareto optimality (using the interactive optimization factor as a function value evaluation coefficient to form a weighted decision matrix with the expansion priority, and dynamically generating a module integration sequence) is used to perform expansion optimization processing on the function expansion module and module update processing on the basketball board to obtain an expanded basketball board.

[0059] Compared with the problems described in the background art, the application analyzes the function mismatch factors corresponding to the basketball board based on the user feedback data, and then obtains the key mismatch dimensions affecting the performance of the basketball board, thereby providing data support for subsequent processing of determining the multi-dimensional performance optimization target point corresponding to the basketball board. Based on the multi-dimensional performance optimization target point, the function expansion module in the basketball board can be determined, which can accurately match the performance optimization demand and the module technical capability, avoid redundant function development, realize efficient coupling of hardware resources and user demand, significantly improve the pertinence and practicality of the basketball board function expansion, and further, the somatosensory interaction features can be extracted from the interactive user image data, which can accurately capture the body action, force habit and motion trajectory of the user in the basketball movement, thereby providing data support for analyzing the interactive satisfaction index of the basketball board with respect to the user. By calculating the visual fidelity corresponding to the display picture image, the clarity of the display picture image can be quantified, and then the basis for subsequent display fidelity determination processing is provided. Finally, by performing variable optimization analysis on the display fidelity, the display optimization variable is obtained, which can dynamically adapt the pixel density and brightness contrast parameters in different lighting environments, eliminate visual distortion problems such as picture blur and whitening, and perform coupling analysis on the interactive satisfaction index and the display optimization variable to obtain the interactive optimization factor corresponding to the basketball board. Based on the synergistic relationship between the user operation habit and the display parameter, the interactive performance such as the shooting trajectory tracking accuracy and the data feedback response speed can be intelligently optimized, so that the training feedback and the visual experience reach a dynamic balance. Therefore, the basketball board multifunctional expansion method and system based on the LED transparent screen provided in the embodiment of the application can realize the multifunctional expansion of the basketball board based on the LED transparent screen.

[0060] Embodiment 2: As Figure 2 shown, it is a functional module diagram of a basketball board multifunctional expansion system based on an LED transparent screen.

[0061] The basketball board multifunctional expansion system based on the LED transparent screen 200 can be installed in an electronic device. According to the functions to be realized, the basketball board multifunctional expansion system based on the LED transparent screen can include an optimization target point analysis module 201, a priority setting module 202, an interactive satisfaction index analysis module 203, a display fidelity determination module 204 and an expansion optimization module 205. The modules of the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0062] In the embodiment of the application, the functions of each module / unit are as follows: The optimization target analysis module 201 is used to acquire user feedback data corresponding to an existing LED transparent screen basketball board and a service scene corresponding thereto, analyze a functional mismatch factor corresponding to the basketball board based on the user feedback data, and determine a multi-dimensional performance optimization target point of the basketball board. The priority setting module 202 is used to determine a functional expansion module in the basketball board based on the multi-dimensional performance optimization target point, collect a scene efficiency parameter of the service scene, set an expansion priority of the functional expansion module based on the scene efficiency parameter. The interaction satisfaction index analysis module 203 is used to collect interactive user image data of the basketball board in an application area, extract a somatosensory interaction feature from the interactive user image data, and analyze an interaction satisfaction index of the basketball board with respect to a user based on the somatosensory interaction feature. The display fidelity determination module 204 is used to collect a display picture image corresponding to the basketball board, calculate a visual fidelity of the display picture image, query a regional lighting condition corresponding to the application area, and determine a display fidelity of the basketball board in combination with the regional lighting condition and the visual fidelity. The expansion optimization module 205 is used to perform variable optimization analysis on the display fidelity to obtain a display optimization variable, perform coupling analysis on the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor of the basketball board, perform expansion optimization processing on the functional expansion module in combination with the interaction optimization factor and the expansion priority, and perform module update processing on the basketball board to obtain an expanded basketball board.

[0063] In detail, the modules in the basketball board multi-functional expansion system 200 based on an LED transparent screen in the embodiments of the present application use the same technical means as the basketball board multi-functional expansion method based on an LED transparent screen in the above Figure 1 , and can produce the same technical effects, which will not be described here again.

[0064] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application.

Claims

1. A multi-functional expansion method of a basketball board based on an LED transparent screen, characterized in that, The method comprises: acquiring user feedback data corresponding to an existing LED transparent screen basketball board and a service scene corresponding thereto, analyzing a function dissonance factor corresponding to the basketball board based on the user feedback data to determine a multi-dimensional performance optimization target point corresponding to the basketball board; based on the multi-dimensional performance optimization target point, determining a function expansion module in the basketball board, collecting a scene efficiency parameter of the service scene, setting an expansion priority corresponding to the function expansion module based on the scene efficiency parameter; collecting interactive user image data of the basketball board in an application area, extracting somatosensory interaction features from the interactive user image data, analyzing an interaction satisfaction index of the basketball board with respect to a user based on the somatosensory interaction features; collecting a display screen image corresponding to the basketball board, calculating a visual fidelity corresponding to the display screen image, querying a regional lighting condition corresponding to the application area, and determining a display fidelity corresponding to the basketball board in combination with the regional lighting condition and the visual fidelity; performing variable optimization analysis on the display fidelity to obtain a display optimization variable, coupling the interaction satisfaction index and the display optimization variable to obtain an interaction optimization factor corresponding to the basketball board, and performing expansion optimization processing on the function expansion module in combination with the interaction optimization factor and the expansion priority, and performing module update processing on the basketball board to obtain an expanded basketball board.

2. The LED transparent screen-based basketball board multifunctional expansion method of claim 1, wherein, The analysis of the function dissonance factor corresponding to the basketball board based on the user feedback data comprises: performing invalid cleaning processing on the user feedback data to obtain target feedback data; extracting feedback keywords from the target feedback data, and performing semantic analysis on the feedback keywords to obtain high-frequency feedback problem points; performing failure mode analysis on the high-frequency feedback problem points to obtain potential failure root causes; performing correlation analysis on the potential failure root causes to obtain the function dissonance factor corresponding to the basketball board.

3. The LED transparent screen based multi-functional expansion method for basketball board according to claim 1, characterized in that, The determination of the multi-dimensional performance optimization target point corresponding to the basketball board further comprises: performing clustering processing on the function dissonance factor to obtain clustered dissonance factors; extracting dissonance factor knowledge elements from the clustered dissonance factors, and screening out representation knowledge elements from the dissonance factor knowledge elements; performing dimension mapping processing on the clustered dissonance factors based on the representation knowledge elements to obtain dissonance correlation performance dimensions; performing target point identification on the dissonance correlation performance dimensions to obtain an initial multi-dimensional performance target point; performing optimization feasibility analysis on the initial multi-dimensional performance target point to obtain the multi-dimensional performance optimization target point corresponding to the basketball board.

4. The LED transparent screen based multi-functional expansion method for basketball boards according to claim 1, characterized in that, The determination of the function expansion module in the basketball board based on the multi-dimensional performance optimization target point comprises: performing function demand analysis on the multi-dimensional performance optimization target point to obtain target point associated functions; performing path decomposition processing on the target point associated functions to obtain function execution paths; querying a device module cluster in the basketball board, and extracting module attribute information corresponding to the device module cluster; performing semantic matching processing on the function execution paths and the module attribute information to obtain a function module matching matrix; The function module matching matrix is subjected to conflict resolution processing to obtain a target function module matrix; Based on the target function module matrix, a function expansion module in the basketball board is determined from the device module cluster.

5. The LED transparent screen based basketball board multi-functional expansion method according to claim 1, wherein, The function expansion module is set with an expansion priority based on the scene performance parameter, which includes: The scene performance parameter is subjected to spatiotemporal slicing processing to obtain a performance spatiotemporal feature tensor; The performance spatiotemporal feature tensor is subjected to quantum coding processing to obtain a quantum feature map; The quantum feature map is subjected to dimension reduction mapping processing to obtain a scene key feature; A similarity coefficient between the scene key feature and a hardware parameter of the function expansion module is calculated; The function expansion module is set with an expansion priority based on the similarity coefficient.

6. The LED transparent screen based basketball board multi-function expansion method of claim 1, wherein, The somatosensory interaction feature is extracted from the interactive user image data, which includes: The interactive user image data is subjected to denoising processing to obtain a target user image; User skeleton points in the target user image are extracted to obtain a skeleton point cloud sequence; The skeleton point cloud sequence is subjected to motion intention decoding to obtain a motion intention feature; Biomechanical parameters of the motion intention feature are analyzed to obtain a biomechanical feature; The biomechanical feature is subjected to motion pattern classification to obtain a motion pattern cluster; The motion pattern cluster is subjected to feature extraction to obtain a somatosensory interaction feature.

7. The LED transparent screen based basketball board multi-function expansion method of claim 1, wherein, The somatosensory interaction feature is used to analyze an interaction satisfaction index of the basketball board with respect to a user, which includes: The somatosensory interaction feature is subjected to calibration processing to obtain a synchronous somatosensory feature, and an interaction parameter set corresponding to the synchronous somatosensory feature of the basketball board is matched; The synchronous somatosensory feature is subjected to weight distribution processing to obtain a weighted somatosensory feature; The weighted somatosensory feature is subjected to emotional state analysis to obtain a somatosensory emotional state; The weighted somatosensory feature is subjected to coding processing to obtain a feature coding value; The somatosensory emotional state is combined with the feature coding value to calculate an interaction satisfaction index; The interaction satisfaction index is used to determine an interaction satisfaction index of the basketball board with respect to a user from the interaction parameter set.

8. The LED transparent screen based basketball board multi-function expansion method of claim 1, wherein, The visual fidelity corresponding to the display picture image is calculated, which includes: An image resolution and an image physical size corresponding to the display picture image are obtained, and the image pixel density corresponding to the display picture image is calculated by the following formula based on the image resolution and the image physical size: ； Wherein, A represents the image pixel density corresponding to the display picture image, B represents the width pixel in the image resolution, D represents the height pixel in the image resolution, E represents the image width in the image physical size, and F represents the image height in the image physical size; The maximum value and the minimum value of the brightness corresponding to the display picture image are read, and the brightness contrast corresponding to the display picture image is calculated based on the maximum value and the minimum value of the brightness; The visual fidelity corresponding to the display picture image is calculated based on the image pixel density and the brightness contrast.

9. The LED transparent screen based multi-functional expansion method for basketball boards according to claim 8, characterized in that, The visual fidelity corresponding to the display picture image is calculated based on the image pixel density and the brightness contrast, which includes: The visual fidelity corresponding to the display picture image can be calculated by the following formula: ； wherein, represents a visual fidelity corresponding to a display screen image, represents a pixel density weight, G represents an image pixel density, represents a luminance saturation threshold value, represents a luminance weight, H represents a luminance contrast, represents a luminance reference value, represents a cooperative attenuation coefficient, represents a pixel density sensitivity coefficient, represents a luminance sensitivity coefficient.

10. A LED transparent screen based multi-functional expansion system for basketball boards, characterized in that, The system comprises: An optimization target analysis module is configured to acquire user feedback data corresponding to an existing LED transparent screen basketball board and a service scene corresponding thereto, analyze a functional mismatch factor corresponding to the basketball board based on the user feedback data, and determine a multi-dimensional performance optimization target point corresponding to the basketball board. A priority setting module is configured to determine a functional expansion module in the basketball board based on the multi-dimensional performance optimization target point, collect a scene efficiency parameter of the service scene, set an expansion priority corresponding to the functional expansion module based on the scene efficiency parameter. An interactive satisfaction index analysis module is configured to collect interactive user image data of the basketball board in an application area, extract a somatosensory interactive feature from the interactive user image data, and analyze an interactive satisfaction index of the basketball board with respect to a user based on the somatosensory interactive feature. A display fidelity determination module is configured to collect a display picture image corresponding to the basketball board, calculate a visual fidelity corresponding to the display picture image, query a regional lighting condition corresponding to the application area, and determine a display fidelity corresponding to the basketball board in combination with the regional lighting condition and the visual fidelity. An expansion optimization module is configured to perform variable optimization analysis on the display fidelity to obtain a display optimization variable, perform coupling analysis on the interactive satisfaction index and the display optimization variable to obtain an interactive optimization factor corresponding to the basketball board, perform expansion optimization processing on the functional expansion module in combination with the interactive optimization factor and the expansion priority, and perform module update processing on the basketball board to obtain an expanded basketball board.

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