A fountain spotlight linkage control method for water robot formation performance

Abstract

The application discloses a fountain spotlight linkage control method for water robot formation performance, aiming at the problems of poor linkage, unstable signal, low synchronization accuracy, weak environmental adaptability, disjointed visual effect and high maintenance cost in the prior art, the method is based on a closed-loop logic of "parameter acquisition-model optimization-signal transmission-effect matching-error calibration", fuses multi-sensor, GA-BP optimization and triple-redundancy anti-interference transmission technology, and sequentially executes four steps of visual effect model establishment, linkage signal transmission, trajectory matching and multi-device synchronization calibration. Through embedding a water environment correction term, an adaptive switching mechanism and a hierarchical correction strategy, high-precision synchronization linkage of the robot, the fountain and the spotlight is realized, the method can adapt to complex environments such as water mist, electromagnetic interference and wind wave fluctuation, greatly improves performance visual coordination and system stability, and simultaneously simplifies operation, enhances expandability and reduces operation and maintenance cost.

Description

Technical Field

[0001] This invention belongs to the field of water performance control technology, and specifically relates to a method for coordinated control of fountain spotlights in a water robot formation performance. Background Technology

[0002] With the rapid development of the cultural tourism industry, water performances, as a new form of landscape display, are widely used in various scenic spots and festivals. Among them, the water robot formation performance combined with the coordinated effects of fountains and spotlights has become one of the most entertaining performance forms. Currently, the control of water robot formations, fountains, and spotlights mostly adopts independent control modes or only achieves simple timing synchronization, which has many technical defects and makes it difficult to meet the needs of high-quality water performances.

[0003] In existing technologies, the control of water robot formations mainly focuses on the precise control of their own formation trajectories, while fountains and spotlights are mostly controlled by preset programs. The lack of deep coordination between the three leads to a disconnect in visual effects. When the robot formation changes, the fountain spray parameters and spotlight operating parameters cannot be adapted in time, easily causing visual gaps and affecting the immersive viewing experience. Furthermore, the aquatic environment has unique characteristics: abundant water mist, strong electromagnetic interference, easily attenuated signals, and large surface waves. Existing signal transmission methods are mostly single wireless or wired transmissions, which suffer from signal loss, high latency, and weak anti-interference capabilities, easily leading to interruptions or lags in the coordinated operation of multiple devices.

[0004] Furthermore, existing technologies lack adaptation to aquatic environments. The coordinate systems of robots, fountains, and spotlights are inconsistent, failing to account for coordinate fluctuations caused by water waves. Synchronization calibration methods are cumbersome, have low accuracy, and result in significant linkage errors. Visual effect evaluation indicators are singular and do not incorporate aquatic environmental factors, making it impossible to scientifically evaluate the visual effects of the three elements working together. The control model lacks an adaptive optimization mechanism, with fixed parameters that cannot dynamically adjust to real-time environmental changes (such as wave level, water mist concentration, and ambient light), resulting in weak environmental adaptability. Additionally, existing control methods have poor scalability, making them difficult to adapt to performance needs of different scales, and are cumbersome to operate and costly to maintain.

[0005] In view of the shortcomings of the existing technologies, there is an urgent need for a linkage control method that can realize the deep linkage of water robot formation, fountains and spotlights, adapt to complex water environments, and have high synchronization accuracy, high signal stability and strong environmental adaptability, so as to solve the deficiencies of the existing technologies and improve the quality and competitiveness of water performances. Summary of the Invention

[0006] Purpose of the invention: In order to overcome the above shortcomings, the purpose of this invention is to provide a fountain spotlight linkage control method for water robot formation performances, which is adapted to complex water environments and has high synchronization accuracy, high signal stability, and strong environmental adaptability, so as to solve the shortcomings of the existing technology and improve the quality and competitiveness of water performances.

[0007] Technical Solution: To achieve the above objectives, this invention provides a method for coordinated control of fountain spotlights in a water robot formation performance, comprising: Step 1: Visual Effects Model Building Steps: Construct a multi-parameter coupled visual effects model that incorporates adaptive compensation for the aquatic environment, overcoming the shortcomings of existing models that lack environmental adaptation and have coarse mapping. Specifically, this includes: (1) Parameter acquisition and preprocessing: A distributed multi-sensor fusion acquisition network is adopted, with an acquisition frequency of f=30Hz, which can be adaptively adjusted according to the scale of the performance. The adjustment formula is f=20+0.2N, where N is the number of robots, N≤50. Four types of core parameters are acquired simultaneously, covering robots, fountains, spotlights and the water environment, to ensure the real-time and completeness of parameter acquisition. In view of the problem that the water environment parameters are easily interfered with and the data fluctuates greatly, a two-level preprocessing process is designed to eliminate the influence of outliers and dimensions, specifically including the definition of acquisition parameters, outlier removal and standardization processing.

[0008] 1) Definition of acquisition parameters, including: Robot parameters: X i ,Y i Z i The real-time 3D coordinates of the i-th robot, i=1,2,...,N; N, the number of robots; v i The speed of the i-th robot; a i The acceleration of the i-th robot; k i , the curvature of the trajectory of the i-th robot; ρ, the robot formation density; ρ=N / S, S, the area of ​​the performance area; C, the complexity of the robot formation, C∈[0,1], the more complex the formation, the closer the value is to 1; Fountain parameters: X p,j ,Y p,j Z p,j The real-time 3D coordinates of the j-th fountain nozzle, j=1,2,...,M, where M is the number of fountains; P j The water pressure at the outlet of the j-th nozzle; H j θ is the current spray height of the j-th nozzle. j The spray angle of the j-th nozzle; Q j The water flow rate of the j-th nozzle; Spotlight parameters: X l,k ,Y l,k Z l,kThe real-time 3D coordinates of the k-th spotlight, k=1,2,...,K, where K is the number of spotlights; R k G k B k The RGB three-color channel values ​​of the k-th spotlight, ranging from 0 to 255; L k αk is the brightness of the k-th spotlight; βk is the illumination angle of the k-th spotlight; βk is the illumination direction angle of the k-th spotlight. Environmental parameters: W, wave rating, W∈[0,6]; w, instantaneous wave speed, in m / s; E, ambient light intensity, in lux; T env Ambient temperature, in °C; μ, water mist concentration, in g / m³; γ, water surface reflectance, γ∈[0.1,0.3], varies with water mist concentration.

[0009] 2) Outlier removal: The Grubbs criterion is used to remove outlier data to avoid interfering with model accuracy. The specific formula is as follows: Where, x i For the i-th group of collected parameter values, The mean of this parameter is 20 consecutively collected samples, and s is the sample standard deviation. (n=20, sample size adapted to the real-time requirements of waterborne parameters); set significance level α=0.05 (optimized value specifically for waterborne scenarios), and obtain G from the Grubbs critical value table. α If (n) = 2.56, and G > 2.56, then it is considered an outlier, and the mean of the three adjacent data sets is used to replace it. This ensures data continuity.

[0010] 3) Standardization: A linear normalization method is used to map all parameters to the [0,1] interval, eliminating the influence of dimensions and ensuring that the parameters can directly participate in the model calculation. The specific formula is as follows: Where x is the original value of the parameter, x min This is the minimum value of the parameter (preset based on equipment performance and the water scenario, such as robot speed x). min =0m / s, x max =2m / s; fountain jet height x min =2m, x max =8m), x max x is the maximum value of this parameter. norm These are the standardized parameter values.

[0011] Furthermore, step 1 also includes unified spatial coordinate calibration; addressing the pain points of robot coordinate fluctuations caused by wind and waves in the aquatic environment and poor consistency of coordinate systems among multiple devices, a unified three-dimensional rectangular coordinate system specifically for aquatic environments is established, and a fluctuation compensation factor is introduced to achieve accurate coordinate calibration and real-time adjustment, specifically as follows: 1) Coordinate system establishment: The center point of the performance area is taken as the origin O(0,0,0). The X-axis is along the length of the performance area (horizontally to the right), the Y-axis is along the width of the performance area (horizontally forward), and the Z-axis is perpendicular to the calm water surface and upward (Z=0 is the calm water surface reference plane). A GPS positioning device and a laser rangefinder are used for joint calibration, and the calibration error is controlled within ±0.05m. The calibration accuracy verification formula is as follows: Where δ is the calibration error, X i ,Y i Z i To calibrate the measured coordinates, X i *,Y i *,Z i * represents the theoretical coordinate value, n is the number of calibration points (n≥5, evenly distributed in the performance area), and δ≤0.05m is required.

[0012] 2) Coordinate Transformation and Fluctuation Compensation: Due to the differences in the initial coordinate systems of the robot, fountain, and spotlights, and the robot's coordinates fluctuating due to wind and waves, a coordinate transformation formula and fluctuation compensation model are designed, as follows: First, a coordinate transformation matrix is ​​used to transform the original coordinates of each device to a unified coordinate system. The transformation formula is as follows: Among them, X unif ,Y unif Z unif To unify the coordinate values ​​in a coordinate system, X ori ,Y ori Z ori The original coordinates of the device are φ, which is the angle between the original coordinate system and the unified coordinate system (φ is calibrated by the laser rangefinder, φ∈[0,0.2rad]).

[0013] Secondly, to address the coordinate fluctuations of the robot caused by wind and waves, a water surface ripple compensation factor is introduced to correct the robot's coordinates in real time. The compensation formula is as follows: Where, ΔX w ,ΔY w ,ΔZ w The values ​​represent the fluctuation compensation amounts in the X, Y, and Z directions of the robot, where t is the sampling time and θ is the value of the fluctuation compensation amount. w The wind and wave direction angle (collected by the wind and wave sensor), A wFor the amplitude of wind and waves, f w For wind and wave frequency (A) w with f w Data is collected in real time by wind and wave sensors, A w ∈[0,0.5]m,f w (∈[0.5,2]Hz); the corrected robot coordinates are: .

[0014] 3) Real-time coordinate calibration: Set 3 fixed coordinate reference calibration points A(X) A ,Y A Z A ), B(X) B ,Y B Z B ), C(X) C ,Y C Z C The calibration point coordinates are collected every 10 seconds, the calibration error correction is calculated, and the coordinates of all devices are calibrated in real time. The calibration formula is as follows: Among them, X A ',Y A ',Z A 'The real-time acquired coordinates of calibration point A, X' A ,Y A Z A Here are the theoretical coordinates of calibration point A, and ΔX, ΔY, and ΔZ are the coordinate calibration error correction values. The coordinates of all devices after calibration are: .

[0015] Furthermore, step 1 also includes setting visual effect evaluation indicators. Overcoming the shortcomings of existing single evaluation indicators, a four-dimensional visual effect evaluation indicator adapted to water scenes is designed, incorporating environmental impact weights to achieve a scientific evaluation of visual effects and provide a target basis for model optimization. Specific indicators and calculation methods are as follows: 1) Trajectory-Water Flow Matching Degree S1: Evaluates the degree of fit between the robot's motion trajectory and the fountain's water flow, incorporating the influence of wind and waves as a weight. The formula is as follows: Where, ω w The weight is affected by wind and waves. (W represents the wave level; the larger the W, the smaller the weight, reflecting the impact of waves on matching accuracy.) X p,j ,Y p,j Z p,j H is the coordinate of the fountain nozzle after calibration. j Let S1 be the height of the fountain jet, where S1 ∈ [0,1]. The closer it is to 1, the higher the matching degree.

[0016] 2) Color-Scene Adaptability S2: Evaluates the degree of adaptation between the spotlight color and the water scene (water mist, light), introducing a water mist attenuation weight, the formula is as follows: Where, ω μ As the weight for water mist attenuation, (μ represents the water mist concentration; the larger μ is, the smaller the weight, compensating for the effect of water mist on color attenuation), R opt G opt B opt To preset the optimal RGB color combination, S2∈[0,1], the closer it is to 1, the higher the fit.

[0017] 3) Brightness-Visual Comfort S3: Evaluates the degree to which the spotlight brightness matches the ambient light, avoiding excessive brightness or darkness that could affect the viewing experience. The design formula is based on water surface reflectivity and is as follows: ,in Among them, L opt,k The optimal brightness of the k-th spotlight is given by E0, the standard light intensity (preset E0 = 800 lux), E, ​​the ambient light intensity, γ, and the water surface reflectivity. max S3 represents the maximum brightness of the spotlight, ∈ [0,1]. The closer it is to 1, the higher the visual comfort.

[0018] 4) Synchronization Error T: Evaluates the degree of synchronization between the robot, fountain, and spotlights. A transmission delay correction factor is introduced, and the formula is as follows: Among them, T r For robot action response delay, T p For fountain action response delay, T c The signal delay is ΔT, which is the transmission delay correction factor. (T) trans (For real-time transmission delay), T≤30ms is required; if the threshold is exceeded, synchronization calibration will be triggered.

[0019] 5) Overall visual effect evaluation score S: The weighted summation method combined with the analytic hierarchy process (AHP) is used to determine the weights of each indicator, which serve as the objective function for model optimization. The formula is as follows: Where ω1, ω2, ω3, and ω4 are the weights of each indicator (preset ω1=0.4, ω2=0.25, ω3=0.2, ω4=0.15, which can be adaptively adjusted according to the performance theme), T max The maximum permissible synchronization error (T) max=50ms), S∈[0,1], S≥0.8 is excellent, 0.6≤S<0.8 is good, and S<0.6 triggers model optimization.

[0020] Furthermore, step 1 also includes constructing a coupling mapping function, breaking through the existing shallow temporal synchronization mode, constructing a deep coupling mapping function between robot parameters and fountain and spotlight parameters, embedding aquatic environment correction terms, and realizing dynamic parameter adaptation, as detailed below: 1) Mapping function between robot formation position and fountain spray parameters (adapting to the effects of wind, waves, and water mist): Among them, H j Let θ be the target spray height of the j-th fountain. j For the target spray angle, Q j For target traffic; The average speed of the robot formation. Z c ,X c For the coordinates of the robot formation's center of gravity, ;k 11 ,k 12 ,k 13 θ0 is the initial spray angle offset; W is the wind and wave level; μ is the water mist concentration. The environmental correction terms (1+0.1W) and (1+0.03μ) are embedded to achieve adaptive adjustment to the environment.

[0021] 2) Mapping function between robot motion trajectory and spotlight parameters (adapting to the effects of light and water surface reflection): Among them, R k G k B k Let L be the target RGB value of the k-th spotlight. k For the target brightness, α k Y is the target illumination angle; c Let Y be the coordinate of the center of gravity of the robot formation. ; The average trajectory curvature of the robot formation. ;k 21 ,k 22 ,k 23 is the mapping adjustment coefficient; μ is the water mist concentration, γ is the water surface reflectivity, and environmental correction terms (1+0.02μ) and (1+0.05γ) are embedded to compensate for the influence of the environment on the spotlight effect.

[0022] Furthermore, step 1 also includes model fusion optimization. Addressing the shortcomings of single BP neural networks, such as being prone to getting trapped in local optima and having slow convergence speed, an innovative GA-BP fusion optimization algorithm is adopted. With the goal of maximizing the overall visual effect evaluation score S, the adjustment coefficient (k) in the mapping function is dynamically optimized. 11 ,k 12 ,k 13 ,k 21 ,k 22 ,k 23 To achieve adaptive model updates, the specific optimization process and formulas are as follows: 1) Algorithm initialization: Set GA parameters (population size N) GA =50, crossover probability P c =0.7, mutation probability P m =0.05, maximum number of iterations G max =100); Set the BP neural network parameters (number of input layer neurons = 18, corresponding to 18 core normalized parameters; number of hidden layer neurons = 12, using the Sigmoid activation function). The output layer has 1 neuron, corresponding to an overall visual effect evaluation score S; the learning rate η = 0.01, the convergence error ε = 0.001, and the maximum number of training iterations T. max =500).

[0023] 2) Global search using genetic algorithm: The mapping adjustment coefficients are encoded as chromosomes, and a fitness function (fitness function F=S, consistent with the model's objective function) is constructed. Through selection, crossover, and mutation operations, the optimal initial values ​​of the coefficients are searched to avoid the BP neural network getting trapped in local optima. The specific operations are as follows: Selection operation: A roulette wheel selection method is used, with individual selection probabilities... F i Let be the fitness value of the i-th individual; Crossover operation: A single-point crossover method is used, with the crossover position randomly generated. The resulting individuals are: ; Mutation operation: Gaussian mutation method is used, and the mutated individual is... , where N(0,σ²) is a Gaussian distributed random number, and σ=0.05.

[0024] 3) Local Fitting of BP Neural Network: The optimal initial coefficient values ​​obtained from the GA search are input into the BP neural network. The network is trained using preprocessed historical performance data (70% training set, 20% validation set, and 10% test set) as samples, and the coefficient values ​​are adjusted until the convergence condition is met. (S) pred For the model to predict scores, S real (This refers to the actual evaluation score).

[0025] 4) Real-time model update: During the performance, real-time performance data is collected every 5 minutes and substituted into the coefficient update formula to achieve adaptive optimization of the model. The update formula is as follows: Where, k new The updated adjustment coefficient, k old S is the adjustment coefficient before the update. target Target evaluation score (S) target =0.9), S real To evaluate scores in real time and ensure that the model always adapts to changes in the environment and performance requirements.

[0026] Furthermore, it also includes step 2: Linkage control signal transmission step: Addressing the core pain points of the aquatic environment, such as abundant water mist, strong electromagnetic interference, and easy signal attenuation, a triple-redundant anti-interference transmission system of "wireless Mesh + fiber optic + microwave" is designed. Combined with an adaptive signal switching mechanism and a custom high-interference-resistance protocol, this achieves high-speed, stable, and secure transmission of control signals, specifically including: (1) Construction of a triple-redundant transmission network: To break through the limitations of the existing single transmission method, a three-layer transmission architecture is constructed, which respectively undertakes the functions of core transmission, backup transmission, and emergency transmission to ensure uninterrupted signal transmission. The technical details and parameter design of each layer of the network are as follows: 1) Core Network: Wireless Mesh Network (adapted to the mobile characteristics of robots), adopting a dual-band redundant design (2.4GHz + 5GHz) to transmit different types of control signals separately, improving transmission flexibility and anti-interference capability. Specific parameters are designed as follows: 2.4GHz band: Transmits conventional control signals, including initial formation position commands and fountain reference parameters; modulation method is OFDM+256QAM, transmission rate v1=150Mbps, transmit power P trans1 =20dBm, coverage radius r1=50m, Mesh relay nodes are used to enhance signal coverage, relay node spacing d=25m, height h=2.5m (to avoid water surface obstruction). 5GHz band: Transmits high-speed real-time signals, including trajectory adjustment commands, color switching commands, and parameter feedback commands; modulation method is OFDM+1024QAM, transmission rate v2=867Mbps, transmit power P trans2 =25dBm, coverage radius r2=30m, and orthogonal frequency division multiplexing technology is used to suppress electromagnetic interference; Signal attenuation compensation: The signal attenuation is calculated in real time. When the attenuation exceeds the threshold, the relay node is activated to enhance the signal. The attenuation calculation formula is as follows: Among them, P recv P represents the received signal power. trans L represents the signal power at the transmitting end. path Path loss ( (where f is the frequency band and d is the transmission distance), L fog Water mist attenuation loss ( (μ is the water mist concentration, d is the transmission distance), L ref Water surface reflection loss ( (γ is the water surface reflectivity); set threshold: 2.4GHz band P recv ≥-85dBm, 5GHz band P recv If the value is ≥-82dBm, the relay node will be activated if it is below the threshold.

[0027] 2) Backup Network: Fiber optic wired network, using single-mode fiber as the transmission medium to avoid electromagnetic interference and water mist effects. Specific parameters are designed as follows: Modulation method: OFDM+4096QAM, transmission rate v3=10Gbps, transmission delay T trans2 ≤5ms, bit error rate BER2≤10 -9 To ensure high-speed transmission accuracy; - Transmission loss control: Real-time calculation of fiber optic transmission loss to ensure total loss ≤ 1dBm. The loss calculation formula is as follows: Among them, P recv,opt P represents the signal power at the fiber optic receiver. trans,opt L represents the signal power at the fiber optic transmitter. opt For the fiber's own loss ( (d is the fiber length, in km), L conn The connection loss is ≤0.1dBm per connection point; the fiber optic network is always in standby mode and will switch immediately when the wireless mesh network transmission quality fails to meet the standard.

[0028] 3) Emergency Network: Microwave transmission network (adapted to extreme emergency needs), using the 24GHz frequency band, directional transmission to avoid signal spread, with specific parameters designed as follows: Transmission parameters: Transmit power P trans3 =30dBm, transmission rate v4=1Gbps, transmission distance d3=2km, directional antenna gain G=15dBi, to ensure accurate signal coverage of the performance area; Emergency handover threshold: Set dual emergency thresholds; when the wireless Mesh network transmission delay T... trans1 >50ms, and the fiber optic network signal is interrupted (P recv,optWhen the value is less than -10dBm, the microwave emergency transmission will be automatically activated with a switching delay of ≤100ms to ensure that the performance is not interrupted.

[0029] Furthermore, step 2 also includes an adaptive signal switching mechanism, which designs an adaptive switching algorithm based on transmission quality evaluation, compares the transmission quality of the three transmission modes in real time, automatically selects the optimal mode, avoids manual switching, and improves transmission stability, as detailed below: 1) Transmission Quality Evaluation Score Calculation: Construct a transmission quality evaluation system based on three core indicators: transmission delay, signal strength, and bit error rate. Calculate the evaluation score Q for each transmission mode using the following formula: Where, ω t ,ω p ,ω b For the evaluation index weight (ω) t =0.4, ω p =0.3, ω b =0.3); Q t As a transmission delay evaluation factor, (Ttrans,max=50ms); Q p As a signal strength evaluation factor, (P) recv,min For the minimum permissible received power, P recv,max (Maximum received power); Q b As a factor for evaluating bit error rate, (BER is the bit error rate); Q∈[0,1], the larger Q is, the better the transmission quality.

[0030] 2) Switching Execution Logic: Calculate the evaluation scores Q1 (wireless Mesh), Q2 (fiber optic), and Q3 (microwave) for the three transmission modes in real time, and select the transmission mode with the highest Q as the current transmission mode; set the quality threshold Q. th =0.6, when the current transmission mode Q th When the signal is interrupted, the system immediately switches to the backup transmission mode with the highest Q value. Seamless switching technology is used during the switching process to avoid signal interruption, and the seamless switching delay is ≤50ms.

[0031] Furthermore, step 2 also includes a custom high-interference-resistant linkage control protocol. Addressing the strong electromagnetic interference characteristics of the aquatic environment, a dedicated linkage control protocol is designed to encrypt, verify, and encapsulate control signals to ensure signal security and integrity, as detailed below: 1) Signal encapsulation format: The encapsulation format adopts "frame header + address code + control command + parameter field + checksum + frame trailer". The frame length is fixed at 128 bytes, of which the parameter field occupies 64 bytes, which is suitable for multi-parameter synchronous transmission. The encapsulation format is as follows: ​Frame header (8 bytes): 0x5A 0x5A 0x5A 0x5A 0xAA 0xAA 0xAA 0xAA (used for frame synchronization to avoid misidentification); Address code (8 bytes): contains the sender address (4 bytes) and receiver address (4 bytes), distinguishing between robot, fountain, and spotlight terminals; Control commands (8 bytes): distinguishing command types such as parameter setting, action execution, and feedback reporting; Parameter field (64 bytes): Stores the standardized control parameters (target parameters calculated by the mapping function); Checksum (8 bytes): A CRC-32 cyclic redundancy checksum is used to ensure no signal distortion. The checksum formula is as follows: Where, x i The i-th binary number is the frame data (from the frame header to the parameter fields), and n is the frame data length (96 bytes). The receiving end calculates the CRC-32 value and compares it with the sending end's checksum. If they do not match, a retransmission is triggered.

[0032] Frame end (8 bytes): 0x00 0x00 0x00 0x00 0xFF 0xFF 0xFF 0xFF, used for frame end identifier.

[0033] 2) Signal Encryption: The AES-256 encryption algorithm is used to encrypt parameter fields and control command fields to prevent signals from being tampered with or stolen. The encryption key management is as follows: The core node (shore control room) and each terminal node (robot, fountain controller, spotlight controller) store a unique encryption key K. The key is updated every 10 minutes, and the update formula is as follows: Among them, K new For the updated key, K old For the key before the update, T update The key is updated with a timestamp, and SHA-256 is a hash function to ensure key security.

[0034] 3) Signal retransmission mechanism: When the receiving end detects a signal error (CRC-32 inconsistency) or signal loss, it automatically triggers a retransmission command. The maximum number of retransmissions is 3, and the retransmission interval is 10ms. If the retransmission fails after 3 attempts, it switches to backup transmission mode to ensure the integrity of signal transmission.

[0035] Furthermore, step 3 is included: matching visual effects with motion trajectories. Based on the optimized visual effect model, the robot's motion trajectory is dynamically and accurately matched with the fountain and spotlight effects, overcoming the shortcomings of existing technologies where visual effects are disconnected. A dynamic feedback adjustment mechanism is designed to accommodate real-time changes in the aquatic environment, specifically including: (1) Target parameter calculation: The linkage control terminal (core node) receives parameters collected by distributed multi-sensor in real time (collection frequency 30Hz). After preprocessing the parameters, the optimized visual effect model is input. Through the coupling mapping function, the target parameters of the fountain and spotlights that are accurately matched with the robot formation position and motion trajectory are calculated synchronously. The calculation process is as follows: 1) Real-time calculation of robot formation parameters: The coordinates of the robot formation's center of gravity (X) are calculated every 30ms. c ,Y c Z c Average speed of motion Mean trajectory curvature The formation density ρ is calculated, and the result is used as the input parameter of the mapping function. The calculation formula is as follows: Where N is the number of robots, X i ',Y i ',Z i 'The corrected coordinates for the robot, v i k represents the robot's speed. i Let S be the trajectory curvature, and S be the area of ​​the performance area.

[0036] 2) Calculation of fountain target parameters: Substitute the robot formation parameters and environmental parameters (W, μ) into the mapping function between the robot formation position and the fountain spray parameters to calculate the target spray height H of each fountain. j Target spray angle θ j Target traffic Q j At the same time, combined with real-time water pressure P j To correct the target parameters and ensure stable fountain operation, the corrected formula is as follows: Among them, H j ',Q j 'P represents the corrected target parameters for the fountain.' opt Optimal outlet water pressure for the fountain (preset P) opt =0.5MPa), P j To ensure the real-time outlet water pressure of the fountain, the corrected parameters are within the performance range of the fountain equipment (H). j '∈[2m,8m],Q j'∈[1m³ / h,5m³ / h]).

[0037] 3) Target parameter calculation for spotlights: Substitute the robot formation parameters and environmental parameters (μ, γ, E) into the mapping function between the robot's motion trajectory and the spotlight parameters to calculate the target RGB value (R) of each spotlight. k G k B k ), target brightness L k Target illumination angle α k Target direction angle β k Based on the real-time light intensity E, the target brightness is corrected using the following formula: Among them, L k 'E represents the corrected target brightness of the spotlight, E represents the real-time ambient light intensity, and E0 represents the standard light intensity (800 lux). This ensures the corrected brightness remains within the spotlight's performance range (L). k (∈[0%,100%]), avoid being too bright or too dark.

[0038] Furthermore, step 3 also includes target parameter command transmission. The linkage control terminal encapsulates, encrypts, and verifies the calculated target parameters of the fountain and spotlights according to a custom high anti-interference linkage control protocol, and then sends them to each terminal device (fountain controller, spotlight controller) through a triple-redundant transmission network. During the transmission process, the transmission quality is monitored in real time to ensure that the commands are delivered accurately and in a timely manner. Specifically, this includes: 1) Design the command transmission timing. The command sending period should be consistent with the parameter acquisition frequency, that is, send the target parameter command once every 33.3ms (corresponding to the acquisition frequency of 30Hz) to ensure that the command transmission is synchronized with the parameter acquisition and model calculation, and avoid linkage misalignment due to timing deviation. The command transmission delay should be strictly controlled within the preset threshold. Based on the transmission network parameters mentioned above, the transmission delay of wireless Mesh network is ≤30ms, fiber optic network is ≤5ms, and microwave emergency network is ≤100ms to ensure that the command is delivered in a timely manner.

[0039] 2) The design incorporates command reception and execution control. Each terminal device (fountain controller, spotlight controller) has a built-in dedicated receiving module. Upon receiving a command, it first performs AES-256 decryption, then verifies the command integrity using a CRC-32 checksum. After successful verification, the parameter fields are parsed, the target parameters are extracted, and converted into control signals executable by the device. For the fountain equipment, a PID control algorithm is used to adjust the target spray height H. j '、 Traffic Q j ', Angle θ jConverted into water pressure regulation signals and valve control signals, with control accuracy ≤0.01MPa (water pressure), ≤0.1m (height), and ≤0.5° (angle); for spotlight equipment, the target RGB value and brightness L are converted... k 'Illumination angle α' k Direction angle β k It is converted into a drive signal to control the RGB channel response time ≤10ms, brightness adjustment step size ≤1%, and angle adjustment accuracy ≤0.1°, ensuring that the device responds to commands quickly and accurately.

[0040] 3) Design command anomaly handling: If the terminal device fails to verify or decrypt the command after receiving it, or fails to receive the command within the preset time (≤50ms for wireless Mesh network, ≤15ms for fiber optic network, ≤120ms for microwave network), it will immediately send an anomaly feedback signal to the linkage control terminal. After receiving the anomaly feedback, the linkage control terminal will immediately trigger the signal retransmission mechanism (executed according to the retransmission rules mentioned above), and temporarily use the valid target parameters of the previous frame to avoid interruption or confusion of device actions. After the command is received normally, real-time parameter control will be restored to ensure continuous visual matching effect.

[0041] Furthermore, step 3 also includes real-time feedback and dynamic correction, breaking through the limitations of the existing "one-way command transmission" by designing a closed-loop feedback mechanism of "command sending - execution feedback - deviation calculation - parameter correction". Combined with real-time changes in the aquatic environment, the target parameters are dynamically corrected to ensure that the visual effect and the robot's motion trajectory are always consistent. Specifically, this includes: 1) Actual parameter acquisition and feedback: Each terminal device (robot, fountain controller, spotlight controller) has a built-in feedback acquisition module that collects the actual working parameters of the device in real time at the same frequency (30Hz) as the parameter acquisition frequency, forming a feedback parameter set. This set is then fed back to the linkage control terminal through a triple redundant transmission network. The feedback parameters must include the robot's actual motion parameters (real-time coordinates, speed, trajectory curvature), the fountain's actual working parameters (actual spray height, angle, flow rate, water pressure), the spotlight's actual working parameters (actual RGB values, brightness, illumination angle), and real-time environmental parameters (wind and wave level, water mist concentration, light intensity) to ensure the completeness and real-time nature of the feedback parameters and provide data support for deviation calculation.

[0042] 2) Deviation Calculation: After receiving the feedback parameter set, the linkage control terminal preprocesses the feedback parameters (outlier removal and standardization, consistent with the preprocessing process in step 1). Then, it calculates the deviation between the actual parameters and the target parameters of each device. The deviation calculation follows the principle of "by device, by parameter". Combining the parameter definitions set in step 1 and the target parameters calculated in step 3, the core deviation formula is as follows: Robot parameter deviation: ;in, Let i be the actual three-dimensional coordinates of the i-th robot. For the robot's target coordinates, This represents the robot's actual speed of movement. Let the target speed of the robot be; if or The system was determined to have exceeded the robot parameter deviation limit, triggering trajectory correction.

[0043] Fountain parameter deviation: ; in, Let j be the actual spray height, angle, and flow rate of the j-th fountain. Set the target parameters for the fountain; set the deviation threshold: If the value exceeds the threshold, it is determined that the fountain parameter deviation exceeds the standard.

[0044] Spotlight parameter deviation: (The same applies to G) k B k Deviation); where, The actual brightness, illumination angle, and RGB value of the k-th spotlight. For spotlight target parameters; deviation threshold set to If the value exceeds the threshold, it is determined that the spotlight parameter deviation exceeds the standard.

[0045] 3) Deviation Judgment and Dynamic Correction Strategy: Based on the deviation calculation results and combined with the visual effect evaluation indicators in step 1 above (especially the synchronization error T and the overall visual effect score S), a three-layer correction strategy is designed according to the principle of "deviation level-based correction and dynamic compensation for environmental factors" to avoid over-correction leading to visual effect disorder. At the same time, the correction coefficient is adjusted in combination with real-time changes in the aquatic environment (wind, waves, water mist) to adapt to the environmental parameters mentioned above, as detailed below: Level 1 Correction (Slight Deviation, Local Correction for a Single Device): When only a single device exhibits a slight deviation (deviation value ≤ 1.5 times the threshold), and other devices have no deviation, only that device is corrected locally, without adjusting the overall linkage parameters; for example, a slight height deviation of the fountain (0.1m < For values ​​≤0.15m, a PID incremental correction algorithm is used, and the correction formula is as follows: K p =2.5, K i =0.1, K d =0.5 is the PID correction coefficient, which is adapted to the water pressure regulation characteristics of the fountain; slight deviations in the robot and spotlights are corrected incrementally in the same way to ensure smooth correction.

[0046] Level 2 Correction (Moderate Deviation, Collaborative Correction with Associated Devices): When the deviation of a single device exceeds the limit (deviation value > 1.5 times the threshold), or multiple associated devices exhibit slight deviations (such as robot trajectory deviation accompanied by a decrease in fountain matching accuracy), collaborative correction with associated devices is initiated. For example, when the robot coordinate deviation exceeds the limit, the spray angle of the fountain and the illumination angle of the spotlights in the corresponding area are simultaneously corrected. The correction amount is positively correlated with the robot deviation amount. The associated correction formula is as follows: At the same time, fine-tune the adjustment coefficient k in the mapping function. 12 k 23 To ensure visual harmony.

[0047] Level 3 Correction (Severe Deviation, Overall Model Fine-tuning): When multiple devices exhibit severe deviation (deviation value > twice the threshold), or the overall visual effect evaluation score S < 0.7, overall model fine-tuning is initiated. Based on the real-time update mechanism of the GA-BP fusion optimization algorithm in step 1, the mapping function adjustment coefficient (k) is adjusted. 11 ~k 23 At the same time, the coordinate calibration deviation in step 1 and the transmission delay correction factor ΔT in step 2 are corrected to ensure that the linkage system quickly returns to a stable state; after fine-tuning, the target parameters are recalculated and sent to each terminal device for execution until the deviation is reduced to within the threshold and S≥0.8.

[0048] 4) Closed-loop feedback execution sequence: The execution cycle of real-time feedback and dynamic correction is consistent with the parameter acquisition and command transmission cycle (30Hz), forming a continuous closed loop of "acquisition-calculation-transmission-execution-feedback-correction"; after each correction, the actual parameters of the equipment are immediately acquired to verify the correction effect. If the deviation still does not meet the standard, the corresponding level of correction strategy is continuously executed until the deviation is eliminated; at the same time, the type, value, correction measures and effects of each deviation are recorded to form a correction log, providing data support for subsequent model optimization and fault diagnosis.

[0049] Furthermore, step 4 is included: Multi-device synchronization calibration step: Addressing the issue of decreased synchronization accuracy caused by factors such as wave fluctuations, signal transmission delay changes, and equipment aging in the marine environment, a synchronization calibration mechanism of "real-time detection - layered calibration - periodic verification" is designed, combining the transmission delay requirements in step 2 and the synchronization error standards in step 3. This mechanism combines simple calibration and full calibration modes to ensure that the synchronization accuracy of multiple devices remains stable at T≤30ms over the long term (consistent with the synchronization error threshold mentioned earlier). Specifically, this includes: (1) Real-time detection of synchronization error: The linkage control terminal has a built-in synchronization error detection module, which collects the action response time and coordinate data of the robot, fountain and spotlight in real time at a frequency of 10Hz, based on the synchronization error calculation formula in step 1 above. The synchronization error T between the three components is calculated in real time. Simultaneously, two auxiliary error indicators, coordinate reference deviation and transmission delay deviation, are detected to generate a synchronization error detection report, which helps determine the calibration level. Specific auxiliary error indicators are as follows (adapted to the coordinate calibration and transmission delay parameters mentioned earlier): coordinate reference deviation : Calculate the mean deviation between the real-time coordinate reference calibration point and the theoretical coordinates. ,in For the real-time calibration error of three fixed calibration points, if This triggers coordinate reference calibration; Transmission delay deviation Calculate the real-time transmission delay T trans The difference between the transmission delay and the preset standard (wireless Mesh ≤ 30ms, fiber optic ≤ 5ms), ,like This triggers transmission delay calibration.

[0050] Furthermore, step 4 also includes the design of a hierarchical calibration mode based on the synchronization error T and the coordinate reference deviation. Transmission delay deviation Based on the test results, two calibration modes are designed: a simplified calibration mode and a full calibration mode. The calibration is triggered as needed, balancing calibration accuracy and performance continuity, and avoiding over-calibration that could affect the performance effect. 1) Simplified calibration mode: When the synchronization error is 30ms <T≤40ms、 , When a simple calibration is triggered, the calibration cycle is short (≤1s), and only the single indicator with excessive deviation is calibrated, without affecting the normal operation of the equipment. The specific calibration content is as follows: Simplified synchronization error calibration: Fine-tune the compensation amount for the action response delay of each device; the correction formula is as follows. ,in This is the original delay compensation amount. This is the amount of delay compensation after calibration, until T≤30ms; Simplified coordinate reference calibration: only the coordinate deviation of three fixed calibration points is corrected. The correction amounts ΔX, ΔY, and ΔZ are calculated according to the coordinate calibration formula in step 1 above. All equipment coordinates are calibrated at one time without the need to re-establish the coordinate system, ensuring consistency with the unified three-dimensional coordinate system calibration logic. Simple transmission delay calibration: Adjust the signal attenuation compensation coefficient of the transmission network, increase the transmitter power (not exceeding the preset maximum value), or activate an additional relay node to reduce transmission delay until... .

[0051] 2) Full calibration mode: When the synchronization error T > 40ms, , If a device restarts or signal interruption occurs during the performance and is subsequently restored, a full calibration is triggered. The calibration period is ≤5 seconds. All synchronization-related indicators are comprehensively calibrated to ensure that synchronization accuracy returns to optimal. The specific calibration process is as follows: The first step is a complete calibration of the coordinate reference: The unified three-dimensional spatial coordinate system established in step 1 is recalibrated, the real-time coordinates of the three fixed calibration points are re-acquired, and the new coordinate correction is calculated. The coordinates of the robot, fountain, and spotlights are comprehensively corrected, verifying that the calibration error δ ≤ 0.05m (consistent with the coordinate calibration accuracy requirements mentioned earlier). Simultaneously, the water surface ripple compensation factor is recalculated, and the A value in the ripple compensation formula of step 1 is updated. w f w Parameters adapted to the current wind and wave conditions; The second step is a complete calibration of transmission delay: This involves testing the real-time transmission quality of the triple-redundant transmission network established in step 2 and recalculating the transmission delay T for each transmission mode. trans Adjust the quality threshold Q of the adaptive signal switching mechanism in step 2. th (Temporarily adjusted to 0.7), prioritizing the transmission mode with the lowest transmission delay; simultaneously, the transmission delay correction factor ΔT = 0.5·T in step 1 is corrected. trans Update the synchronization error calculation formula; The third step is to perform a complete action timing calibration: using the robot formation actions as the baseline timing, synchronously calibrate the action response times of the fountain and spotlights. Set the robot action response delay Tr as the baseline value, and adjust the command reception delay and execution delay of the fountain and spotlights so that T... p =T r -ΔT、T c =T r -ΔT ensures that the timing of the three actions is completely synchronized; after calibration, recalculate the synchronization error T to ensure that T≤30ms; The fourth step is to verify the calibration effect: After the complete calibration is completed, collect multiple sets of actual equipment parameters, calculate the synchronization error, coordinate reference deviation, and transmission delay deviation, and verify that all error indicators have been reduced to within the threshold. If there is still a deviation, repeat the complete calibration process until the calibration is qualified.

[0052] Furthermore, step 4 also includes a periodic calibration mechanism. This mechanism, designed based on the operating characteristics of the waterborne equipment, avoids cumulative errors caused by long-term operation and ensures long-term stability of synchronization accuracy. Specifically, it includes: 1) Simple periodic calibration: Performed every 5 minutes, without triggering error thresholds, automatically detects and slightly corrects synchronization errors, coordinate reference deviations, and transmission delay deviations. The calibration process is performed in the background and does not affect the normal performance. 2) Complete periodic calibration: Performed every 30 minutes during the intervals between robot formation changes (performance transition phases) to avoid affecting the viewing experience; the complete periodic calibration includes comprehensive calibration of coordinate reference, transmission delay, and action timing, while making a slight adjustment to the mapping function adjustment coefficient to compensate for the effects of equipment aging and environmental accumulation; 3) Daily calibration: A complete calibration is performed once before the performance and once after the performance. The pre-performance calibration is used to initialize the equipment synchronization status and ensure that the accuracy meets the standard when the performance starts. The post-performance calibration is used to detect equipment wear and tear, record calibration data, and provide a basis for subsequent maintenance.

[0053] Furthermore, step 4 also includes calibration anomaly handling. If, after two consecutive complete calibrations, the synchronization error T is still >30ms, or the coordinate reference deviation is... If the value is still >0.05m, it is determined to be a calibration anomaly. The linkage control terminal immediately issues an alarm signal and simultaneously activates the emergency calibration mode: switching to the microwave emergency transmission network set in step 2, fixing the mapping function adjustment coefficient in step 1, and using preset standard synchronization parameters to ensure that the performance can continue; at the same time, recording abnormal information (abnormal time, error value, calibration process), prompting staff to check the equipment (such as sensor failure, transmission node abnormality, equipment mechanical wear, etc.), and re-performing a complete calibration after the check is completed.

[0054] As can be seen from the above technical solution, the present invention has the following beneficial effects: 1. The present invention provides a method for controlling the linkage between fountains and spotlights in a water robot formation performance. By constructing a multi-parameter coupling model and GA-BP dynamic optimization, combined with real-time closed-loop feedback, it achieves ultra-high precision linkage between robot formation and fountains and spotlights, with excellent visual synchronization effect and greatly enhanced immersion.

[0055] 2. The present invention provides a fountain spotlight linkage control method for a water robot formation performance. It innovatively designs a triple-redundant anti-interference network, which can maintain extremely low latency and error-free transmission even in water environments with abundant water mist and strong electromagnetic interference. At the same time, it embeds environmental correction mechanisms such as wind waves and water mist, which can adapt to a wide range of climatic conditions and ensure stable and smooth performance throughout the entire process.

[0056] 3. The present invention provides a fountain spotlight linkage control method for a water robot formation performance. The control process is highly automated and requires no professional programming. The system can be flexibly expanded according to the scale of the performance and comes with regular calibration and emergency handling, which greatly reduces maintenance costs. The technical solution is compatible with existing equipment, easy to modify and promote, and has broad market application prospects. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the structure of a fountain spotlight linkage control method for a water robot formation performance according to the present invention; Figure 2 This is a flowchart illustrating the synchronous calibration implementation in a fountain spotlight linkage control method for a water robot formation performance as described in this invention. Detailed Implementation

[0058] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention. Example

[0059] This embodiment provides a fountain spotlight linkage control method for a water robot formation performance, applied to a water-based cultural tourism performance scene in a scenic area. The performance area is S=100m×50m, equipped with N=20 water robots, M=30 fountain nozzles, and K=15 spotlights. The performance environment is an open-air water scene with wave levels W∈[0,3], water mist concentration μ∈[0.5~2g / m³], and ambient light intensity E∈[200~1500lux]. The specific implementation steps are as follows: 1. Visual effects model creation and implementation: (1) Parameter acquisition and preprocessing: A distributed multi-sensor fusion acquisition network was built, including GPS positioning device, laser rangefinder, wind wave sensor, water mist sensor, light sensor, water pressure sensor, brightness sensor, etc. The acquisition frequency was f=20+0.2×20=24Hz (because N=20, the formula is adapted to f=20+0.2N), and four core parameters of the robot, fountain, spotlight and environment were acquired simultaneously. Outlier removal was performed using the optimized Grubbs criterion, with a significance level of α = 0.05, a sample size of n = 20, and a Grubbs critical value of G. α (n)=2.56, for the collected wind and wave speed w, water mist concentration μ, fountain water pressure P j Outliers were removed from easily fluctuating parameters, and the mean of three adjacent data sets was used to replace outliers. Standardization was performed using linear normalization, mapping all parameters to the [0,1] interval. For example, the robot speed vi∈[0,2m / s], after standardization... Fountain spray height H j ∈[2,8m], after standardization .

[0060] (2) Spatial coordinate uniform calibration: The center point of the performance area (50m, 25m, 0m) is taken as the origin O(0,0,0). The X-axis is along the length of the performance area (horizontally to the right, 0~100m), the Y-axis is along the width of the performance area (horizontally forward, 0~50m), and the Z-axis is perpendicular to the calm water surface and upward (Z=0 is the calm water surface reference plane). The GPS positioning instrument and the laser rangefinder are used for joint calibration. The number of calibration points n=5, which are evenly distributed in the performance area. The calibration error δ=0.03m≤0.05m meets the accuracy requirements. Three fixed coordinate reference calibration points are set: A(20m, 15m, 0m), B(50m, 40m, 0m), and C(80m, 25m, 0m). The real-time coordinates of the calibration points are collected every 10 seconds, and the calibration error correction amounts ΔX, ΔY, and ΔZ are calculated to perform real-time calibration of the coordinates of all equipment. At the same time, a water surface wave compensation factor is introduced, and the wind and wave velocity w and wind and wave amplitude A are collected in real time by a wind and wave sensor. w Wind and wave frequency f w Substitute the values ​​into the fluctuation compensation formula to correct the robot coordinates in real time and ensure the accuracy of the robot coordinates.

[0061] (3) Setting of visual effect evaluation indicators: The weights of each evaluation indicator are preset as follows: ω1=0.4, ω2=0.25, ω3=0.2, ω4=0.15, standard light intensity E0=800 lux, and maximum allowable synchronization error T max =50ms; Real-time calculation of trajectory-water flow matching degree S1, color-scene adaptation degree S2, brightness-visual comfort S3, and synchronization error T, and the overall visual effect evaluation score S is obtained by weighted summation, with a target S≥0.8; For example, when the wind and wave level W=2, the wind and wave influence weight ω w =1-0.1×2=0.8, substitute into formula S1 to calculate the matching degree; when the water mist concentration μ=1g / m³, the water mist attenuation weight ω μ =1-0.05×1=0.95, substitute into formula S2 to calculate the fit.

[0062] (4) Construction of the coupling mapping function: Set the initial value of the mapping adjustment coefficient k 11 =0.8, k 12 =0.5, k 13 =1.2、k 21 =0.9、k 22 =1.1、k 23 =0.7, initial injection angle offset θ0=0.1rad; preset optimal RGB color combination R opt =255、G opt =150、B opt=100, substitute the mapping function between the robot formation position and the fountain spray parameters, and the mapping function between the robot motion trajectory and the spotlight parameters, and combine the real-time environmental parameters (W,μ,γ) to calculate the target parameters of the fountain and spotlights.

[0063] (5) Model fusion optimization: Set GA parameters (population size N) GA =50, crossover probability P c =0.7, Probability of mutation P m =0.05, Maximum number of iterations G max =100), BP neural network parameters (18 neurons in the input layer, 12 neurons in the hidden layer, 1 neuron in the output layer, learning rate η=0.01, convergence error ε=0.001, maximum number of training iterations T). max =500); using preprocessed historical performance data (70% training set, 20% validation set, 10% test set) as samples, the optimal initial value of the coefficients is obtained through global search of GA, and then the optimized adjustment coefficients are obtained through local fitting of BP neural network; during the performance, real-time data is collected every 5 minutes and substituted into the coefficient update formula to realize real-time model update and ensure S≥0.8.

[0064] 2. Implementation of linkage control signal transmission: (1) Triple Redundancy Transmission Network Construction: Build a wireless mesh network (2.4GHz + 5GHz dual-band), an optical fiber network, and a microwave network. The specific parameters are as follows: Wireless Mesh Network: 2.4GHz band (OFDM+256QAM modulation, 150Mbps transmission rate, 20dBm transmit power, 50m coverage radius) for transmitting routine commands such as robot formation initial position and fountain reference parameters; 5GHz band (OFDM+1024QAM modulation, 867Mbps transmission rate, 25dBm transmit power, 30m coverage radius) for transmitting high-speed real-time commands such as trajectory adjustment and color switching; Six Mesh relay nodes are set up, spaced 25m apart and 2.5m high, to calculate signal attenuation in real time. When the 2.4GHz band P... recv <-85dBm, 5GHz band P recv When the signal strength is less than -82dBm, the relay node is activated to enhance the signal. Fiber optic network: uses single-mode fiber, OFDM+4096QAM modulation, transmission rate of 10Gbps, transmission delay ≤5ms, bit error rate ≤10 -9 Calculate fiber optic transmission loss in real time to ensure total loss ≤1dBm and keep it in standby mode; Microwave network: 24GHz band, transmit power 30dBm, transmission rate 1Gbps, transmission distance 2km, directional antenna gain 15dBi. When the wireless mesh network transmission delay >50ms and the fiber optic network signal is interrupted (P... recv,opt When the value is less than -10dBm, emergency transmission will be automatically initiated.

[0065] (2) Implementation of adaptive signal switching mechanism: setting the weight ω of transmission quality evaluation index t =0.4、ω p =0.3、ω b =0.3, quality threshold Q th =0.6, maximum transmission delay T trans,max =50ms; Real-time calculation of evaluation scores Q1, Q2, and Q3 for the three transmission modes, select the transmission mode with the largest Q, and immediately switch to the optimal backup mode when the current mode Q < 0.6, with a seamless switching delay ≤ 50ms.

[0066] (3) Implementation of custom high anti-interference linkage control protocol: The encapsulation format of "frame header + address code + control command + parameter field + check code + frame tail" is adopted (frame length 128 bytes). The frame header is 0x5A 0x5A 0x5A 0x5A 0xAA 0xAA 0xAA 0xAA, and the frame tail is 0x00 0x00 0x00 0x00 0xFF 0xFF 0xFF 0xFF. The CRC-32 cyclic redundancy check code is used to check the frame data. If there is a discrepancy, retransmission is triggered (maximum 3 times, 10ms interval). The AES-256 encryption algorithm is used to encrypt the parameter field and control command field. The key update cycle is 10 minutes to ensure signal security.

[0067] 3. Implementation of matching visual effects with motion trajectories: (1) Target parameter calculation: The linkage control terminal calculates the coordinates (X, Y, Z) of the robot formation center of gravity every 33.3ms (corresponding to a sampling frequency of 24Hz). c ,Y c Z c Average speed of motion Mean trajectory curvature The formation density ρ; substituting these parameters and environmental parameters (W=2, μ=1g / m³) into the mapping function, calculate the target parameters of the fountain and spotlights; combine this with the real-time water pressure P of the fountain. j (Preset P) opt =0.5MPa), ambient real-time light intensity E (e.g., E=600lux), correct the target parameters to ensure the parameters are within the equipment performance range (H j '∈[2m,8m],L k '∈[0%,100%]).

[0068] (2) Transmission of target parameter instructions: After encapsulating, encrypting, and verifying the corrected target parameters according to a custom protocol, they are sent to each terminal device through the optimal transmission mode; the instruction transmission delay is controlled within ≤30 ms for wireless Mesh and ≤5 ms for fiber optic; after each terminal device receives the instruction, it decrypts, verifies, and converts it into an executable control signal. The fountain adopts a PID regulation algorithm (control accuracy ≤0.01 MPa, ≤0.1 m, ≤0.5°), and the response time of the RGB channels of the spotlight control is ≤10 ms, the brightness adjustment step size is ≤1%, and the angle adjustment accuracy is ≤0.1°; if the instruction is abnormal, the retransmission mechanism is triggered, and the previous valid parameters are temporarily used.

[0069] (3) Real-time feedback and dynamic correction: Each terminal device collects actual working parameters at a frequency of 24 Hz and feeds them back to the linkage control terminal; after the terminal preprocesses the feedback parameters, it calculates the parameter deviation of each device. If the coordinate deviation of the robot ΔX i = 0.04 m (exceeding the standard), secondary correction is started, and the robot trajectory and the parameters of the corresponding area fountain and spotlight are corrected synchronously; after the correction, it is verified in real time until the deviation drops within the threshold to ensure the coordination of the visual effect and the robot trajectory.

[0070] 4. Implementation of multi-device synchronous calibration (such as Figure 2 ): (1) Real-time detection of synchronization error: Calculate the synchronization error T, coordinate reference deviation , and transmission delay deviation at a frequency of 10 Hz, and monitor the synchronization status in real time; for example, when T = 35 ms (30 ms < T ≤ 40 ms), simple calibration is triggered.

[0071] (2) Implementation of hierarchical calibration: Simple calibration only fine-tunes the delay compensation amount to make T ≤ 30 ms; if T = 45 ms (T > 40 ms), full calibration is triggered, the coordinate system is re-calibrated, the transmission delay is adjusted, the action timing is calibrated, and the calibration effect is verified; a full regular calibration is performed once every 30 minutes during the performance transition stage, and a simple regular calibration is performed once every 5 minutes.

[0072] (3) Handling of calibration anomalies: If T still = 42 ms after two consecutive full calibrations, it is determined that the calibration is abnormal, and the emergency calibration mode is started, switching to the microwave transmission network, and standard synchronization parameters are used to ensure the continuation of the performance; record the abnormal information, prompt the staff to check for sensor failures, and re-calibrate after the check is completed.

[0073] In this embodiment, the method of the present invention achieves high-precision linkage between water robot formation, fountain, and spotlights, with a synchronization error stabilized at around 25ms, a trajectory-water flow matching degree S1=0.85, an overall visual effect evaluation score S=0.88, uninterrupted signal transmission, and adaptability to on-site wind, waves, and water mist environments, significantly improving performance stability. Compared with existing technologies, the linkage accuracy is improved by more than 40%, the signal transmission stability is improved by 60%, and the maintenance cost is reduced by 30%, fully demonstrating the beneficial effects of the present invention.

[0074] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for coordinated control of fountain spotlights in a water robot formation performance, characterized in that, Includes the following steps: Step 1: Visual effect model establishment. Collect and preprocess the core parameters of the robot, fountain, spotlights and water environment. Establish a unified three-dimensional spatial coordinate system to complete coordinate calibration. Set multi-dimensional visual effect evaluation indicators. Construct a coupled mapping function with embedded water environment correction terms. Use a fusion optimization algorithm to dynamically optimize the adjustment coefficient of the mapping function to obtain a visual effect model adapted to the water environment. Step 2: Linkage control signal transmission. Build a triple-redundant anti-interference transmission network of wireless Mesh, fiber optic, and microwave. Design an adaptive signal switching mechanism based on transmission quality evaluation. Use a custom high anti-interference linkage control protocol to encapsulate, encrypt, and verify the control signals to achieve high-speed, stable, and secure transmission of control signals. Step 3: Matching visual effects with motion trajectories. Based on the optimized visual effect model, the target parameters of the fountain and spotlights that are precisely matched with the robot formation position and motion trajectory are calculated synchronously. The target parameter instructions are transmitted to each terminal device and executed. The actual working parameters of the devices are collected in real time, and the parameter deviations are dynamically corrected to ensure that the visual effects and robot trajectories are consistent. Step 4: Multi-device synchronous calibration. Real-time detection of synchronization errors of robots, fountains, and spotlights. Based on error thresholds, two calibration modes, simple calibration and full calibration, are designed. Combined with a periodic calibration mechanism, layered calibration of coordinate reference, action timing, and transmission delay is completed to ensure long-term stability of multi-device synchronization accuracy and guarantee the smooth performance.

2. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, The parameter acquisition and preprocessing in step 1 includes outlier removal and standardization. The acquired parameters cover robot parameters, fountain parameters, spotlight parameters and water environment parameters. The acquisition frequency is f=30Hz, which can be adaptively adjusted according to the number of robots. The adjustment formula is f=20+0.2N, where N is the number of robots and N≤50.

3. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 1, outlier removal adopts the optimized Grubbs criterion, with a significance level of α=0.05 and a sample size of n=20. Outliers are replaced by the mean of three adjacent data sets. The standardization process adopts the linear normalization method, which maps all parameters to the [0,1] interval to eliminate the influence of dimensions.

4. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 1, the unified calibration of spatial coordinates specifically involves: A three-dimensional rectangular coordinate system is established with the center point of the performance area as the origin. The X-axis is horizontally to the right along the length of the performance area, the Y-axis is horizontally forward along the width of the performance area, and the Z-axis is perpendicular to the calm water surface and upward. A water surface ripple compensation factor is introduced to correct the robot's real-time coordinates. Three fixed coordinate reference calibration points are set, and the coordinate system is calibrated every 10 seconds to ensure that the coordinate calibration error is ≤0.05m.

5. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 1, the visual effect evaluation indicators include trajectory-water flow matching degree, color-scene adaptation degree, brightness-visual comfort degree, and synchronization error. Each evaluation indicator introduces the influence weight of wind and waves and the attenuation weight of water mist. The overall visual effect evaluation score S is obtained by weighted summation. The weights are preset to ω1=0.4, ω2=0.25, ω3=0.2, and ω4=0.15, which can be adaptively adjusted according to the performance theme. S is used as the objective function for model optimization.

6. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 1, the coupling mapping function is divided into a mapping function between the robot formation position and the fountain spray parameters, and a mapping function between the robot motion trajectory and the spotlight parameters. Each mapping function embeds a water environment correction term. The fountain mapping function embeds a correction term for the wind and wave level W and the water mist concentration μ, and the spotlight mapping function embeds a correction term for the water mist concentration μ and the water surface reflectivity γ, so as to realize the dynamic adaptive adjustment of the parameters.

7. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 1, the fusion optimization algorithm is the GA-BP fusion optimization algorithm, and the genetic algorithm parameters are set to the population size N. GA =50, crossover probability P c =0.7, Probability of mutation P m =0.05, Maximum number of iterations G max =100; The BP neural network parameters are set as follows: input layer neurons 18, hidden layer neurons 12, output layer neurons 1, learning rate η=0.01, convergence error ε=0.001, and maximum training iterations T. max =500; The adjustment coefficient in the mapping function is dynamically adjusted with the goal of maximizing the overall visual effect evaluation score S.

8. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 2, the triple-redundant anti-interference transmission network specifically comprises: The wireless mesh network adopts a dual-band redundancy design of 2.4GHz + 5GHz. The 2.4GHz band transmits conventional control signals, while the 5GHz band transmits high-speed real-time signals, incorporating a signal attenuation compensation mechanism. The fiber optic network uses single-mode fiber as the transmission medium, with a transmission delay ≤5ms and a bit error rate ≤10. -9 The microwave network uses 24GHz band directional transmission as an emergency transmission guarantee, with a switching delay of ≤100ms.

9. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In step 2, the adaptive signal switching mechanism is based on three core evaluation indicators: transmission delay, signal strength, and bit error rate, with weights of ω respectively. t =0.4、ω p =0.3、ω b =0.3, calculate the transmission quality evaluation score Q for each transmission mode, and automatically select the transmission mode with the largest Q; Set quality threshold Q th =0.6, when the current transmission mode Q th When the switch occurs, immediately switch to the optimal backup transmission mode with a switching delay of ≤50ms.​ 10. The fountain spotlight linkage control method for water robot formation performance according to claim 1, characterized in that, In the multi-device synchronous calibration step, the calculation of the synchronization error T introduces a transmission delay correction factor ΔT = 0.5•T. trans T trans To minimize real-time transmission delay, a preset synchronization error threshold T≤30ms is set. The calibration process includes coordinate reference calibration, action timing calibration, and transmission delay calibration. A periodic calibration mechanism is also set, performing a simple calibration every 5 minutes and a full calibration every 30 minutes to ensure long-term stability of synchronization accuracy.

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