Intelligent interaction control method and system for electronic toy

Abstract

The invention relates to the technical field of electronic toys, and discloses an intelligent interaction control method and system for electronic toys, and the method comprises the steps: obtaining the real-time motion state and environment information of each toy in a toy cluster, and carrying out the signal analysis, and obtaining an interaction data flow; performing compressed encoding on the interaction data stream to obtain an interaction instruction packet, and transmitting the interaction instruction packet to each toy; receiving an interaction instruction packet in response to a first toy in the toy cluster, and controlling the first toy to analyze the interaction instruction packet to obtain interaction logic to be executed by the first toy and real-time action state and environment information of a second toy in the toy cluster; according to the real-time action state and the environment information of the second toy, the interaction logic of the first toy is adjusted, a self-adaptive behavior instruction for the first toy is obtained, and the self-adaptive behavior instruction is stored in a behavior instruction cache; the self-adaptive behavior instruction in the behavior instruction cache is obtained through the low-power-consumption kernel, and an action driving module of the first toy is driven to complete the instruction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic toys, and in particular to a smart interaction control method and system for electronic toys. BACKGROUND

[0002] With the development of the Internet of Things and intelligent technology, electronic toys, as an important carrier for children's education and entertainment, have gradually evolved from single function to diversified and interconnected. Modern electronic toys not only need to provide rich interactive content, but also need to have the ability to quickly respond to environmental changes and user needs.

[0003] However, the current interaction mode of most electronic toys relies on pre-set programs or cloud instructions, which results in their lack of ability to dynamically adapt to complex scenarios. For example, in multi-player games or collaborative tasks, the toys cannot real-time perceive the status of other toys around them and make corresponding adjustments, limiting the richness and collaboration of interaction. SUMMARY

[0004] The present application provides a smart interaction control method and system for electronic toys to improve the real-time and adaptability of multi-toy collaborative interaction and optimize user experience.

[0005] In a first aspect, to solve the above technical problems, the present application provides a smart interaction control method for electronic toys, comprising: Obtaining the real-time action state and environmental information of each toy in a toy cluster and performing signal analysis to obtain an interaction data stream; Compressing and encoding the interaction data stream to obtain an interaction instruction package defining the real-time action state, the environmental information, and the interaction logic to be executed by each toy, and transmitting the interaction instruction package to each toy; In response to a first toy in the toy cluster receiving the interaction instruction package, controlling the first toy to analyze the interaction instruction package to obtain the interaction logic to be executed by the first toy and the real-time action state and environmental information of a second toy in the toy cluster; wherein the second toy is each toy in the toy cluster except the first toy; Adjusting the interaction logic of the first toy according to the real-time action state and environmental information of the second toy to obtain adaptive behavior instructions for the first toy, and storing the adaptive behavior instructions in a behavior instruction cache; Obtaining adaptive behavior instructions in the behavior instruction cache through a low-power core, and driving the action driving module of the first toy to complete the instructions.

[0006] In a second aspect, the present application provides a smart interaction control system for electronic toys, comprising: The data acquisition module is configured to acquire real-time action states and environmental information of each toy in the toy cluster and perform signal analysis to obtain an interaction data stream. The compression and encoding module is configured to compress and encode the interaction data stream to obtain an interaction instruction package defining the real-time action states, the environmental information and interaction logic to be executed by each toy, and transmit the interaction instruction package to each toy in the toy cluster. The instruction analysis module is configured to, in response to a first toy in the toy cluster receiving the interaction instruction package, control the first toy to analyze the interaction instruction package to obtain interaction logic to be executed by the first toy and real-time action states and environmental information of a second toy in the toy cluster, wherein the second toy is each toy other than the first toy in the toy cluster. The behavior adjustment module is configured to adjust the interaction logic of the first toy according to the real-time action states and environmental information of the second toy to obtain adaptive behavior instructions for the first toy and store the adaptive behavior instructions in a behavior instruction cache. The instruction execution module is configured to acquire adaptive behavior instructions in the behavior instruction cache through a low-power kernel and drive an action driving module of the first toy to complete instructions.

[0007] In a third aspect, the present application further provides an electronic device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the intelligent interaction control method for electronic toys according to any one of the above.

[0008] In a fourth aspect, the present application further provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the intelligent interaction control method for electronic toys according to any one of the above when the computer program runs.

[0009] Compared with the prior art, the present application has the following beneficial effects: (1) The present application can timely understand the current behavior of each toy and the environmental conditions by acquiring real-time action states and environmental information of each toy in the toy cluster and performing signal analysis to generate an interaction data stream, and convert the original signals into a data form for subsequent processing and analysis, so that the system can identify and understand the behavior states and environmental information of the toys, thereby achieving dynamic perception and real-time response to the behavior of the toys.

[0010] (2) The application compresses and encodes the interaction data stream to generate an interaction instruction package containing real-time action state, environmental information and interaction logic to be executed by each toy, and through compression and encoding, the data transmission amount can be reduced and the transmission efficiency can be improved under the premise of ensuring data integrity and accuracy.

[0011] (3) The application improves the flexibility and environmental adaptability of multi-toy cooperation through adaptive adjustment logic, so that the toy can flexibly execute interaction logic according to different situations, solves the problem of static interaction of traditional toys caused by preset programs, and prolongs the endurance time of the toy through low-power design. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is a flow diagram of a smart interaction control method for an electronic toy provided by the first embodiment of the application; Figure 2 is a structural diagram of a smart interaction control system for an electronic toy provided by the second embodiment of the application. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0014] Referring to Figure 1 , the first embodiment of the application provides a smart interaction control method for an electronic toy, including the following steps: S11, obtaining real-time action state and environmental information of each toy in a toy cluster and performing signal analysis to obtain interaction data stream; S12, compressing and encoding the interaction data stream to obtain an interaction instruction package defining real-time action state, environmental information and interaction logic to be executed by each toy, and transmitting the interaction instruction package to each toy; S13, in response to the first toy in the toy cluster receiving the interaction instruction package, controlling the first toy to analyze the interaction instruction package to obtain interaction logic to be executed by the first toy and real-time action state and environmental information of the second toy in the toy cluster; wherein the second toy is each toy in the toy cluster except the first toy; S14, adjusting the interaction logic of the first toy according to the real-time action state and environmental information of the second toy to obtain adaptive behavior instructions for the first toy, and storing the adaptive behavior instructions in the behavior instruction cache; S15, acquiring adaptive behavior instructions in the behavior instruction cache through the low-power kernel, and driving the action driving module of the first toy to complete the instructions; The application can realize dynamic perception and real-time response to toy behavior by acquiring real-time action states and environmental information of each toy in the toy cluster and performing signal analysis to generate an interactive data stream. The interactive instruction package is transmitted to each toy in combination with compression encoding technology, and the interactive logic of the first toy is adjusted through analysis, and finally the instructions are executed through the low-power kernel driving. Not only does it solve the problem of static interaction caused by pre-set programs in traditional toys, but also improves the flexibility and environmental adaptability of multi-toy collaboration through adaptive adjustment logic, and the low-power design prolongs the endurance time of the toy.

[0015] In step S11, real-time action states and environmental information of each toy in the toy cluster are acquired and signal analysis is performed to obtain an interactive data stream, including: acquiring real-time action states and environmental information from sensors of each toy in the toy cluster. The real-time action states and environmental information are denoised and formatted to obtain a first data set. If the timestamps of the first data set are aligned, the real-time action states and environmental information are time axis calibrated through a data synchronization mechanism to obtain a second data set. The second data set is feature extracted and pattern matched to generate a standardized data stream, and the standardized data stream is converted into an interactive data stream.

[0016] In some embodiments, the implementation process of step S11 includes the following specific steps: first, real-time action states and environmental information are acquired from sensors of each toy in the toy cluster. The real-time action states include the motion state of the toy, such as the inclination angle, the running speed, etc., and the environmental information covers the temperature, the light intensity, etc. of the surrounding environment. For example, in the application scenario of an intelligent interactive toy car, the toy car can be equipped with an accelerometer and a gyroscope to capture real-time action states, and through temperature sensors and light sensors to collect environmental information. These sensors collect data at a frequency of 100 times per second to generate an original data stream containing acceleration, angular velocity, temperature and light intensity.

[0017] As a feasible embodiment, when denoising and formatting the real-time action states and environmental information, a moving average filtering algorithm can be used to remove high-frequency noise in the acceleration signal. For example, if there is random jitter in the original acceleration data, a smooth first data set can be generated after filtering, with data points reduced to 50 per second, and the numerical units are unified into a standardized format, such as converting light intensity into lux units. This preprocessing step not only improves the readability of the data, but also provides a unified data basis for subsequent analysis.

[0018] As a specific example, if the timestamps of the first data set are not completely aligned, for example, the timestamp of the real-time action state is 10:00:00.123, and the timestamp of the environmental information is 10:00:00.125, the time axis is calibrated through the data synchronization mechanism. The calibration method can use linear interpolation to adjust the timestamp deviation to a unified time point to generate the second data set. For example, through interpolation calculation, the timestamp of the environmental information is calibrated to 10:00:00.123, ensuring the consistency of the action and the environmental information in the time dimension. This calibration can effectively avoid analysis errors caused by time deviation, thereby improving the real-time performance of the interactive response.

[0019] For example, when performing feature extraction and pattern matching on the second data set, the motion trajectory features of the toy car, such as turning radius or acceleration pattern, can be extracted and matched with the pre-set "sharp turn" or "smooth driving" pattern. For example, when the acceleration peak is 3.0 m / s² and the angular velocity is 0.5 rad / s, it is determined as a "sharp turn" pattern, and a standardized data stream is generated. This feature extraction method enhances the understanding of the toy to the user's operation, making the interaction more intelligent.

[0020] In some embodiments, the standardized data stream is further converted into an interactive data stream. For example, the "sharp turn" pattern is converted into an instruction to trigger the toy car lights to flash, and the data stream contains a command with a light flashing frequency of 2Hz. Subsequently, the system will determine whether the interactive data stream meets the pre-set threshold, for example, the light flashing frequency needs to be between 1-3Hz. If it meets the requirement, the final interactive data stream is generated to drive the toy car to perform the light flashing effect. This conversion and judgment mechanism ensures the reliability and safety of the interactive instruction, while improving the immersion of the user experience.

[0021] It can be understood that the above process forms a complete logic chain through multi-level data processing from collection to final interaction. The optimization of each step, such as denoising, time axis calibration and feature extraction, significantly improves the response speed and interaction accuracy of the toy. For example, time axis calibration reduces data processing delay, and pattern matching improves the accuracy of action recognition, so that the toy car can adjust its behavior in real time according to user operation and environmental changes, bringing a smoother and personalized interactive experience. The rigor and multi-faceted support of this technical solution ensure the stability and intelligent performance of the toy in complex environments.

[0022] In some embodiments, the sensor data collection and processing capabilities of this method have important application value in the field of meteorological service and related scenarios of topographic mapping. For example, in a topographic mapping task, the toy cluster can perceive the terrain changes (such as slope, temperature difference, etc.) in real time through environmental sensors, and generate collaborative mapping instructions through interactive data streams. For example, when a toy detects an abnormally high temperature on a slope, it can trigger other toys to adjust the mapping path to avoid the high-temperature area. This dynamic adaptation capability significantly improves the collaborative efficiency and task reliability of multiple toys in complex terrain.

[0023] In step S12, the interactive data stream is compressed and encoded to obtain an interactive instruction package defining the real-time action state, environmental information, and the interactive logic to be executed by each toy, including: obtaining the real-time action state and environmental information of each toy from the interactive data stream. The real-time action state and environmental information are compressed by Huffman coding algorithm to generate a first data packet. Determine whether the compression ratio of the first data packet reaches the compression ratio threshold, if not, adjust the encoding parameters of the first data packet for re-compression until a second data packet with a compression ratio reaching the compression ratio threshold is obtained. For the second data packet, the real-time action state and environmental information are encapsulated using a pre-established instruction package template, and data integrity verification is performed after encapsulation until a third data packet that meets the data integrity verification requirements is obtained. The third data packet is sent to the receiving end, and the third data packet is decoded at the receiving end, and the real-time action state and environmental information are extracted to determine data consistency until a fourth data packet with data consistency meeting the requirements is output. For the real-time action state and environmental information in the fourth data packet, an execution instruction is generated, and it is determined whether the execution instruction meets the real-time requirement, if yes, the execution logic, real-time action state and environmental information matching the execution instruction are encoded to generate an interactive instruction package.

[0024] In some embodiments, the implementation process of step S12 includes the following specific steps: obtaining the real-time action state and environmental information of each toy from the interactive data stream. The real-time action state refers to the motion trajectory characteristics of the toy, such as shaking, rotating, inclination angle, etc., while the environmental information includes external parameters such as temperature, humidity, and light intensity. For example, when the toy is a smart building block, its built-in sensor can detect that the building block is being shaken rapidly, outputting acceleration data of 2.5 m / s² and an environmental temperature of 25°C. These data constitute the initial interactive data stream, providing a basis for subsequent compression and encoding. The collection process needs to ensure data accuracy and avoid noise interference to ensure the reliability of subsequent processing.

[0025] As a feasible embodiment, Huffman coding algorithm is used for lightweight compression of real-time action state and environmental information. Huffman coding assigns short codes to high-frequency data and long codes to low-frequency data based on data frequency, thereby reducing data volume. For example, in the real-time action state data of the building block, the shaking action has a high frequency and occupies 2 bits after coding, while the temperature data changes less and occupies 4 bits after coding. After compression, a first data packet is generated. Assuming that the original data is 100 KB, the compressed data is 60 KB, and the compression ratio is 60%. If the preset compression ratio threshold is 50%, the coding parameters need to be adjusted, such as increasing the symbol table precision or optimizing the coding strategy, to generate a second data packet after re-compression, until the compression ratio is optimized to 45%. This process significantly reduces the bandwidth requirement of data transmission and improves the overall interaction efficiency.

[0026] As a specific embodiment, for the second data packet, a pre-established instruction packet template is used for packaging. The instruction packet template can define the distribution rules of the data fields, such as the real-time action state occupying the first 8 bytes and the environmental information occupying the last 4 bytes. Taking the intelligent building block as an example, its shaking action can be coded as “01”, and the temperature 25°C can be coded as “00110001”, and packaged into a third data packet. After packaging, data integrity verification is required, such as using the CRC32 check algorithm to verify the integrity of the data packet structure. If the verification result matches, it means that the data packet has not been lost or corrupted during transmission, thereby ensuring the reliability of the instruction packet in complex environments.

[0027] For example, when decoding the third data packet at the receiving end, an efficient transmission protocol such as MQTT protocol can be used for data analysis. For example, the building block data packet is transmitted to the receiving end through Wi-Fi, and after decoding, it is restored to the real-time action state “01” and the temperature “00110001”, generating a fourth data packet. The system further judges whether the analysis result is consistent with the original data, such as by comparing the numerical difference before and after decoding to ensure data consistency. If the consistency meets the requirements, it enters the subsequent processing steps.

[0028] In some embodiments, for the real-time action state and environmental information in the fourth data packet, an execution instruction is generated. For example, the real-time action state “01” can be mapped to the execution instruction “turn on the LED of the building block”, and the temperature data can trigger the coordinated response of other toys. The system judges whether the execution instruction meets the real-time requirement, such as the instruction generation time being less than 50 ms. If it meets the requirements, the execution logic, real-time action state and environmental information that match the execution instruction are encoded to generate the final interaction instruction packet. For example, if the building block needs to adjust the light brightness according to the temperature change, the interaction instruction packet can contain the brightness adjustment parameter and the execution priority.

[0029] It can be understood that the above scheme forms a complete closed loop from data acquisition to instruction generation, the core scheme focuses on efficient data processing, and the extended scheme improves reliability through optimization compression and verification. Each link supports each other to ensure smooth data flow and accurate instructions, providing stable interaction capability for intelligent toys.

[0030] In some embodiments, the compression and packaging mechanism of the method has important application value in the field of meteorological service and related scenarios of topographic mapping. For example, in the task of topographic mapping, the toy cluster can perceive the terrain changes (such as slope, temperature difference, etc.) in real time through environmental sensors, and generate collaborative mapping instructions through interactive instruction packages. For example, when a toy detects an abnormal temperature rise on a slope, it can trigger other toys to adjust the mapping path to avoid the high-temperature area. In addition, in meteorological services, the toy cluster can transmit environmental data (such as wind speed, air pressure) through compression to quickly generate warning instructions. For example, when strong winds are detected, the toy cluster is triggered to avoid obstacles. This dynamic adaptation capability significantly improves the collaborative efficiency and task reliability of multiple toys in complex terrain or extreme weather conditions.

[0031] In step S14, the interaction logic of the first toy is adjusted according to the real-time action state and environmental information of the second toy, and an adaptive behavior instruction for the first toy is obtained, including: generating a data structure matching the interaction logic between toys in the toy cluster according to the real-time action state and environmental information of the second toy. According to the data structure, an instruction distribution sequence is generated, and the interaction logic of the first toy is adjusted according to the instruction distribution sequence to obtain an adaptive behavior instruction for the first toy.

[0032] In some embodiments, the implementation process of step S14 includes the following specific steps: generating a data structure matching the interaction logic between toys in the toy cluster according to the real-time action state and environmental information of the second toy. The real-time action state refers to the motion trajectory characteristics (such as position, speed, attitude) of the toy, and the environmental information includes external parameters such as temperature, humidity, light intensity, and obstacle distance. For example, in a multi-toy collaborative scenario, toy B detects that it is in a "moving forward" state, and the environmental information shows that the ground is flat and there are no obstacles. The system will integrate these information into a data structure containing moving direction, speed threshold and path planning parameters. This data structure provides a unified interaction logic basis for subsequent instruction distribution.

[0033] As a feasible embodiment, when the information fusion module generates a data structure through multi-source data integration, it combines the sensor data of the toy itself with the state information of other toys in the cluster. For example, after toy B receives the "move 10 cm" instruction sent by toy A, the fusion module synchronously analyzes the real-time action state (such as current speed) and environmental information (such as the distance to the front obstacle) of toy B, and generates a data structure containing "moving distance = 10 cm", "obstacle distance = 20 cm", and "ground friction coefficient = 0.8". If the preset threshold requires the obstacle distance to be greater than 15 cm and the ground friction coefficient to be higher than 0.7, the system determines that the data structure is valid and proceeds to the next step of processing.

[0034] As a specific embodiment, when the logic control unit generates an instruction distribution sequence based on the data structure, it will decompose the interaction logic into executable control steps. For example, for the above data structure, the logic control unit generates a distribution sequence: "start the motor to move forward at a speed of 5 cm per second → continue moving for 2 seconds → stop". If the sensor detects that the distance to the front obstacle shortens to 10 cm, the distribution sequence will be dynamically adjusted to "slow down to 3 cm per second → move for 3 seconds → stop", thereby avoiding the risk of collision. This hierarchical processing mode ensures the flexible response capability of the toy in complex environments.

[0035] For example, in the field of meteorological service technology, the toy cluster can collect wind speed, air pressure and other parameters in real time through the environmental perception unit, generating a data structure containing "wind speed = 10 m / s" and "air pressure = 1013 hPa". The logic control unit generates an instruction distribution sequence accordingly, such as "trigger wind avoidance instruction → adjust toy posture to reduce wind resistance". In the topographic mapping scenario, the toy cluster can perceive changes in slope, temperature difference, etc., generating a data structure of "slope = 15°" and "temperature difference = 5°C", and generating an instruction distribution sequence such as "adjust the mapping path to avoid high temperature areas → reduce the moving speed to adapt to the slope". This cross-domain application significantly improves the adaptability of the toy cluster in dynamic environments.

[0036] For example, the generation of instruction distribution sequence also takes into account the needs of multi-toy collaborative interaction. Suppose toy A and toy B form a mapping team, and toy A detects a terrain mutation in front, generating a data structure containing "obstacle height = 30 cm" and "ground inclination angle = 20°", the logic control unit will generate a collaborative instruction distribution sequence: "toy A slows down to 2 cm per second → toy B adjusts the light intensity to enhance visibility → the two toys move with a distance of 5 cm", this multi-instruction synchronous execution mechanism enhances the coordination and efficiency of the cluster task.

[0037] It can be understood that the generation of data structure and instruction distribution sequence depends on the iterative calculation of dynamic programming. For example, in the interactive scenario of toy C, the system dynamically plans the optimal path of "turning left by 30 degrees first and then moving forward by 15 centimeters" by analyzing the real-time action state (such as the current position) and environmental information (such as the target point coordinates), and generates the corresponding instruction distribution sequence. If the actual moving distance deviation exceeds the preset threshold (such as 0.5 centimeters) during execution, the system will recalculate the path and update the distribution sequence, forming a closed-loop optimization mechanism. This dynamic adjustment capability solves the interaction delay problem caused by the rigidity of the preset program of traditional toys.

[0038] In some embodiments, the interactive logic adjustment mechanism of the method has important application value for extreme scenarios in meteorological services and topographic mapping. For example, in strong wind weather, the toy cluster can monitor the wind speed change in real time through the environmental perception unit, generate data structure and trigger instruction distribution sequence, such as "turn off unnecessary functions to reduce energy consumption → lock the toy posture to prevent tipping". In complex terrain, the toy cluster can perceive the degree of ground slipperiness and slope, generate data structure and adjust the instruction distribution sequence, such as "reduce motor speed to increase traction → segment the moving task to reduce energy consumption", which significantly improves the stability and task completion rate of the toy cluster in harsh environments.

[0039] In some embodiments, the implementation process of step S15 includes the following specific steps: obtaining adaptive behavior instructions from the behavior instruction cache through the low-power kernel, and driving the action driving module of the first toy to complete the instructions. The adaptive behavior instruction is a dynamically generated and adjustable control instruction according to the real-time action state and environmental information of other toys in the toy cluster, which is stored in the behavior instruction cache for subsequent execution. The low-power kernel runs in the embedded system of the toy, and needs to efficiently process instructions under limited resources to ensure the real-time performance of instruction acquisition and energy consumption control.

[0040] As a feasible embodiment, the low-power kernel adopts a hierarchical storage strategy to optimize data reading efficiency. For example, the kernel extracts high-priority instructions from the cache through a priority queue mechanism. Assuming that there are 10 instructions stored in the behavior instruction cache, 3 of which involve moving actions (such as "move forward 10 centimeters"), 5 of which involve sound feedback (such as "play a prompt tone"), and 2 of which involve light display (such as "turn on the LED"), the kernel prioritizes the moving action instructions according to the power state of the current toy, reducing the calling of high-energy light instructions. This resource allocation method matches the efficiency of instruction acquisition with the low-power requirement through dynamic priority adjustment.

[0041] As a specific example, when the instruction storage mechanism parses the adaptive behavior instruction, it can decompose the instruction into action type, execution duration, and parameter value through the instruction decoder. For example, an instruction is "move forward 10 cm at a speed of 5 cm / s", the decoder splits it into action type "move", parameters "distance 10 cm, speed 5 cm / s". After parsing, the system verifies whether the parameters are within the execution range of the toy hardware (such as the maximum speed of the motor is 6 cm / s). If it meets the conditions, the instruction is marked as executable, and this parsing method ensures the accuracy and executability of the instruction by decomposing the instruction content in detail.

[0042] For example, when the action driving module converts the instruction execution sequence and parameters into control signals, it can drive the motor of the toy through pulse width modulation (PWM) technology. For the "move forward 10 cm" instruction, the module generates a PWM signal corresponding to the motor to control the motor to rotate at a fixed frequency, ensuring the accuracy of the moving distance. Assuming that the toy is executing on a smooth ground, after the signal is output, the motor runs for 2 seconds to complete the movement. If the ground friction increases (such as a wet and slippery terrain), the module can adjust the signal strength in real time (such as reducing the duty cycle to reduce the speed), improving the accuracy of action execution. This dynamic signal adjustment mechanism is particularly important in terrain mapping scenarios, for example, in terrain with varying slopes, the toy needs to adjust the PWM signal according to the slope angle to maintain stable movement.

[0043] In some embodiments, the low-power execution mechanism of this method has important application value in the field of meteorological services and related scenarios of terrain mapping. For example, in meteorological services, the toy cluster can monitor the wind speed changes in real time through environmental sensors and generate adaptive behavior instructions to adjust the toy posture (such as lowering the center of gravity to enhance wind resistance). In terrain mapping tasks, the toy needs to dynamically adjust the PWM signal parameters according to the ground slope and friction, such as reducing speed on steep slopes to prevent slipping. In addition, in complex terrain, the toy cluster can optimize the instruction execution parameters through coordinated response data, for example, when detecting that the terrain in front is a muddy area, the adaptive behavior instruction can be adjusted to "reduce the moving speed to 3 cm / s" to extend the endurance time and ensure the task completion rate.

[0044] For example, in a multi-toy collaborative scenario, the low-power core needs to dynamically adjust the instruction execution strategy in combination with the communication stability verification result. Assuming that toy A detects that the communication channel has a high packet loss rate due to environmental interference, the core can preferentially extract low-energy instructions (such as "pause movement") from the cache, and quickly turn off the motor through the PWM signal to reduce energy consumption. This real-time response mechanism significantly improves the stability and task reliability of the toy in harsh environments (such as strong wind weather or complex terrain).

[0045] It can be understood that step S15 realizes efficient analysis and accurate execution of adaptive behavior instructions through the cooperation of the low-power kernel and the action driving module. The core is to optimize resource allocation and adapt to complex environmental requirements through hierarchical storage, priority scheduling and dynamic signal adjustment. For example, in weather services, toys can adjust PWM signals according to real-time wind speed to maintain a stable posture; in topographic mapping, toys can dynamically adjust movement parameters according to ground friction. This technical solution not only solves the problem of static interaction caused by pre-set programs in traditional toys, but also prolongs the endurance time through low-power design, significantly improving the flexibility and environmental adaptability of multi-toy cooperation.

[0046] In some further embodiments of the application, the method further comprises: S16, in the process of driving the action driving module, the communication stability of the first toy is verified, and the communication stability verification result is obtained.

[0047] S17, if the communication stability verification result meets the preset result, based on the real-time action state and the environmental information, the cooperative response data for cooperating each toy is generated.

[0048] S18, based on the cooperative response data, the execution parameters of the adaptive behavior instructions of the action driving module are optimized and adjusted.

[0049] The application introduces a communication stability verification mechanism in the process of driving the action module, which effectively deals with the common signal interference and interruption problems in wireless communication by monitoring the communication state in real time and dynamically switching channels or retransmitting instruction packets. At the same time, based on the real-time action state and environmental information, the cooperative response data is generated, and the execution parameters are optimized, so that the toy can quickly adjust the behavior according to the environmental change, which significantly improves the stability and adaptability of the system in complex environment.

[0050] In step S17, based on the real-time action state and the environmental information, the cooperative response data for cooperating each toy is generated, including: based on the weighting coefficients defined in the preset weight table, the real-time action state and the environmental information are weighted and integrated to obtain the cooperative input data. According to the cooperative input data, each toy is sorted based on the timestamp and priority to obtain the scheduling execution sequence. Based on the scheduling execution sequence and using the response generation algorithm based on the linear regression model, the cooperative response data is calculated.

[0051] In some embodiments, the implementation of step S17 includes the following specific steps: during the process of driving the action driving module, the first toy is subjected to communication stability verification, and a communication stability verification result is obtained. The communication stability verification result refers to a technical index for judging whether the current channel performance meets the preset threshold value by monitoring the data transmission delay and packet loss rate of the communication channel. For example, in the meteorological service scenario, if the delay threshold of the communication channel is set to 50 milliseconds and the packet loss rate threshold is set to 2%, when the actual delay is 30 milliseconds and the packet loss rate is 1%, the system determines that the channel performance meets the requirements. At this time, real-time action state and environmental information can be obtained from the sensor interface through a predefined communication protocol (such as MQTT or Modbus), providing a basis for subsequent collaborative response data generation.

[0052] As a feasible embodiment, if the communication stability verification result meets the preset result, collaborative response data for coordinating each toy is generated based on real-time action state and environmental information. The real-time action state includes the motion trajectory characteristics (such as position, speed, and attitude) of the toy, and the environmental information covers external parameters such as temperature, humidity, illumination intensity, and obstacle distance. For example, in the terrain mapping task, a toy detects that it is in the "moving forward" state, and the environmental information shows that the "ground slope is 15°". The system will integrate these data based on a preset weight table. The weighting coefficients defined in the preset weight table can be dynamically adjusted according to the task priority, for example, in a steep slope environment, the weight of real-time action state may be increased to 0.8, and the weight of environmental information may be reduced to 0.2, in order to prioritize the safety of the moving path.

[0053] As a specific embodiment, each toy is sorted based on timestamp and priority according to the collaborative input data, and a scheduling execution sequence is generated. The timestamp is used to identify the real-time nature of data collection, and the priority reflects the importance of the task. For example, in a multi-player game scenario, assuming that the timestamp of toy A is 10:00:00 and the priority is high (such as "avoid obstacles immediately"), the timestamp of toy B is 10:00:01 and the priority is medium (such as "adjust light brightness"), and the timestamp of toy C is 10:00:02 and the priority is low (such as "play prompt sound"), the execution sequence "A-B-C" is generated according to the priority, which ensures that high-priority tasks are executed first, avoiding delays in critical operations due to interference from low-priority tasks.

[0054] For example, based on the scheduling execution sequence and using a response generation algorithm based on a linear regression model, collaborative response data is calculated. The linear regression model predicts the optimal response parameters by analyzing the relationship between historical data and current input. For example, in a meteorological service scenario, if the real-time action state shows that the toy needs to "reduce the moving speed to adapt to strong wind", and the environmental information shows that "the wind speed is 10 m / s", the linear regression model can predict the best speed adjustment parameter (such as reducing the speed from 5 cm / s to 3 cm / s) combined with historical data. This prediction mechanism improves the accuracy of response data through mathematical modeling, ensuring the consistency of the toy's behavior in complex environments.

[0055] In some embodiments, the collaborative response data generation mechanism of the method has important application value in the field of meteorological service and related scenarios of topographic mapping. For example, in meteorological monitoring, the toy cluster can generate collaborative response data by weightedly integrating real-time action state (such as toy posture) and environmental information (such as wind speed, air pressure) to adjust the toy behavior (such as locking the posture to prevent falling). In topographic mapping, the toy can prioritize the "avoid obstacles" task by scheduling the execution sequence, and optimize the moving path (such as planning the shortest detour route) based on the linear regression model. This dynamic adjustment capability significantly improves the collaborative efficiency and task reliability of multiple toys in extreme weather or complex terrain.

[0056] For example, under the premise that the communication stability verification result meets the preset condition, the system will continuously monitor environmental changes and dynamically update the collaborative response data. Assuming that toy A detects a terrain mutation (such as a 20 cm high obstacle) in front of it while executing the "move forward" task, the system will re-weight the real-time action state and environmental information, generate new collaborative input data, and adjust the execution sequence (such as inserting a "slow down" instruction). The linear regression model then calculates the optimized response parameters (such as adjusting the moving speed to 2 cm / s), ensuring that toy A safely completes the task in complex terrain. This closed-loop mechanism solves the collaborative failure problem caused by task conflicts or priority confusion in traditional methods through real-time feedback and prediction optimization.

[0057] In step S18, based on the collaborative response data, the execution parameters of the adaptive behavior instruction of the action driving module are optimized and adjusted, including: based on the collaborative response data, using time series analysis to judge the environmental change rate. Based on the environmental change rate, the interactive instructions that the first toy needs to complete within a specified time are predicted, and the execution parameters are adjusted based on the prediction results.

[0058] In some embodiments, the implementation of step S18 includes the following specific steps: based on the cooperative response data, using time series analysis to judge the rate of environmental change, and adjusting the execution parameters of the action driving module based on the prediction results. Time series analysis is a technology that analyzes trend changes through consecutive data points, and its core is to calculate the change amount of environmental parameters per unit time. For example, in the intelligent toy interaction scene, if the real-time action state shows that the toy is in the "forward movement" mode, and the environmental information shows that the current light intensity gradually changes from 500 lux to 800 lux, then the system calculates the light change rate as 30 lux / s through time series analysis. This rate judgment provides a dynamic basis for subsequent execution parameter optimization.

[0059] As a feasible embodiment, the calculation of the rate of environmental change needs to ensure that the timestamp accuracy of the sensor data reaches the millisecond level. For example, the system records the sampling values of light intensity in the past 10 seconds (such as 500, 530, 560…800 lux), fits the change trend through a linear regression model, and obtains an average change rate of 30 lux / s. If the change rate exceeds the preset threshold (such as 20 lux / s), the system determines that the environment is in a rapid change state and needs to actively adjust the execution parameters to adapt to the new environment. For example, in the meteorological service scene, if the wind speed change rate is detected to be 5 m / s², the system can predict that the wind speed will increase to 12 m / s in the next 1 second, so as to reduce the toy movement speed in advance to enhance the wind resistance stability.

[0060] As a specific embodiment, when adjusting the execution parameters based on the prediction results of the rate of environmental change, a mapping function needs to be generated in combination with the cooperative response data. For example, in the terrain mapping task, if the system predicts that the terrain slope in front will increase from 5° to 15° in 3 seconds, the weight table in the cooperative response data will dynamically adjust the weight of the real-time action state (such as movement speed) to 0.9 and the weight of the environmental information (such as slope) to 0.1. The mapping function then converts the prediction results into specific parameters: reduce the motor drive speed from 5 cm / s to 3 cm / s, and extend the drive time to ensure stable movement of the toy on the steep slope. This parameter optimization is realized through pulse width modulation (PWM) technology, for example, adjusting the duty cycle of the PWM signal from 60% to 40% to reduce the motor speed and extend the torque output time.

[0061] For example, in a weather service scenario, the system detects a rapid rise in ambient temperature from 25°C to 35°C at a rate of 10°C / min through time series analysis. Based on this prediction, the collaborative response data triggers the execution of parameter adjustments, such as turning off unnecessary functions (e.g., light displays) to reduce energy consumption, while reducing the frequency of toy movements to extend the battery life. In terrain mapping, if the system predicts that the ground friction in front will decrease by 30% due to rain, the execution parameters will dynamically adjust the motor speed to a more conservative 2 cm / s and increase the pulse width through PWM signals to increase traction. This proactive adjustment significantly reduces the delay or failure of interaction caused by environmental changes.

[0062] In some embodiments, the parameter optimization mechanism of this method has important application value for weather service and terrain mapping related scenarios. For example, in strong wind weather, the toy cluster can predict the wind speed trend through time series analysis and adjust the attitude control parameters in advance (such as lowering the center of gravity). In complex terrain, the system can dynamically adjust the moving path based on the slope change rate, such as triggering a "detour" instruction and optimizing motor drive parameters when detecting that the ground inclination angle will exceed the safety threshold. This dynamic adaptation ensures the stability and task completion rate of the toy in extreme environments.

[0063] For example, in a user interaction scenario, the system determines that the user touch frequency has increased from 3 times / sec to 6 times / sec at a rate of 0.3 times / sec² through time series analysis. Based on this prediction, the collaborative response data generates a mapping function to adjust the duty cycle of the PWM signal from 50% to 70% to drive the toy to play a higher frequency of prompt sound and accelerate the animation playback. This real-time response mechanism improves the immersion of user interaction and the liveliness of toy behavior.

[0064] It can be understood that the closed-loop design of time series analysis and execution parameter optimization enables the toy to dynamically adapt to the environment and user behavior. For example, when the light intensity changes rapidly, the system not only adjusts the movement speed, but also optimizes the light brightness through PWM technology, so that the toy can exhibit more vivid visual effects in bright environments. This multi-dimensional collaborative mechanism solves the interaction delay problem caused by the rigidity of traditional toys due to pre-set programs, significantly enhancing the self-adaptation ability of toys in complex dynamic scenarios.

[0065] Referring to Figure 2 The second embodiment of the present application provides an intelligent interaction control system for an electronic toy, comprising: A data acquisition module for acquiring real-time action state and environmental information of each toy in the toy cluster and performing signal analysis to obtain interaction data stream; The compression encoding module is configured to compress and encode the interactive data stream to obtain an interactive instruction package defining real-time action states, environment information and interactive logics to be executed by each toy, and transmit the interactive instruction package to each toy in the toy cluster. The instruction analysis module is configured to, in response to the first toy in the toy cluster receiving the interactive instruction package, control the first toy to analyze the interactive instruction package to obtain the interactive logics to be executed by the first toy and the real-time action states and environment information of a second toy in the toy cluster, wherein the second toy is each toy other than the first toy in the toy cluster. The behavior adjustment module is configured to, according to the real-time action states and environment information of the second toy, adjust the interactive logics of the first toy to obtain adaptive behavior instructions for the first toy, and store the adaptive behavior instructions in a behavior instruction cache. The instruction execution module is configured to acquire the adaptive behavior instructions in the behavior instruction cache through a low-power kernel, and drive the action driving module of the first toy to complete the instructions.

[0066] It should be noted that the intelligent interactive control system for electronic toys provided by the embodiments of the present application is used to execute all process steps of the intelligent interactive control method for electronic toys provided by the above embodiments, and the working principles and beneficial effects of the two are one-to-one correspondence, thus not being repeated.

[0067] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, for example, an intelligent interactive control program for electronic toys. The processor implements the steps in each of the above-mentioned intelligent interactive control method embodiments for electronic toys when executing the computer program, for example Figure 1 the step S11 shown. Alternatively, the processor implements the functions of each module / unit in the above-mentioned system embodiments when executing the computer program, for example, an intelligent interactive control module for electronic toys.

[0068] Illustratively, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0069] The electronic device can be a computing device such as a desktop computer, a notebook computer, a palm computer, a smart tablet, etc. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0070] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor is the control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines.

[0071] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0072] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or system, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electric carrier signals and telecommunication signals.

[0073] It should be noted that the above-described system embodiments are only illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0074] The above-described specific embodiments further detail the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the scope of protection of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A smart interactive control method for electronic toys, characterized in that, include: The system acquires the real-time motion status and environmental information of each toy in the toy cluster and performs signal parsing to obtain the interactive data stream. The interactive data stream is compressed and encoded to obtain an interactive instruction package that defines the real-time action state, the environmental information, and the interactive logic to be executed by each toy. The interactive instruction package is then transmitted to each toy. In response to the first toy in the toy cluster receiving the interaction instruction packet, the system controls the first toy to parse the interaction instruction packet to obtain the interaction logic to be executed by the first toy, as well as the real-time action status and environmental information of the second toy in the toy cluster; wherein, the second toy is all the other toys in the toy cluster except for the first toy; Based on the real-time action status and environmental information of the second toy, the interaction logic of the first toy is adjusted to obtain adaptive behavior instructions for the first toy, and the adaptive behavior instructions are stored in the behavior instruction cache. The adaptive behavior instructions in the behavior instruction cache are obtained through a low-power kernel, and the motion driving module of the first toy is driven to complete the instructions.

2. The method according to claim 1, characterized in that, The process of acquiring the real-time motion status and environmental information of each toy in the toy cluster and performing signal parsing to obtain an interactive data stream includes: Real-time motion status and environmental information are obtained from the sensors of each toy in the toy cluster; The real-time action state and the environmental information are denoised and formatted to obtain a first data set; If the timestamps of the first data set are aligned, the real-time action status and the environmental information are time-axis calibrated through a data synchronization mechanism to obtain the second data set; Feature extraction and pattern matching are performed on the second dataset to generate a standardized data stream, and the standardized data stream is then converted into the interactive data stream.

3. The method according to claim 1 or 2, characterized in that, The process of compressing and encoding the interactive data stream yields an interactive instruction package that defines the real-time action state, the environmental information, and the interactive logic to be executed by each toy, including: The real-time motion status and environmental information of each toy are obtained from the interactive data stream; The real-time action state and environmental information are lightweightly compressed using the Huffman coding algorithm to generate the first data packet; Determine whether the compression ratio of the first data packet reaches the compression ratio threshold. If not, adjust the encoding parameters of the first data packet and re-compress it until a second data packet with a compression ratio reaching the compression ratio threshold is obtained. For the second data packet, the real-time action status and environmental information are encapsulated using a pre-established instruction packet template, and data integrity verification is performed after encapsulation until a third data packet that meets the data integrity verification requirements is obtained. The third data packet is sent to the receiving end, where the third data packet is decoded and the real-time action status and environmental information are extracted to determine data consistency, until a fourth data packet with satisfactory data consistency is output. For the real-time action status and environment information in the fourth data packet, an execution instruction is generated. It is determined whether the execution instruction meets the real-time requirements. If so, the execution logic matching the execution instruction, the real-time action status and the environment information are encoded to generate an interactive instruction packet.

4. The method according to claim 1, characterized in that, The step of adjusting the interaction logic of the first toy based on the real-time action state and environmental information of the second toy to obtain adaptive behavior instructions for the first toy includes: Based on the real-time motion status and environmental information of the second toy, a data structure matching the interaction logic between each toy in the toy cluster is generated; An instruction distribution sequence is generated based on the data structure, and the interaction logic of the first toy is adjusted according to the instruction distribution sequence to obtain adaptive behavior instructions for the first toy.

5. The method according to claim 1, characterized in that, The method further includes: During the process of driving the motion driving module, the communication stability of the first toy is verified, and the communication stability verification result is obtained. If the communication stability verification result meets the preset result, then based on the real-time action status and environmental information, collaborative response data for coordinating the various toys is generated; Based on the collaborative response data, the execution parameters of the action-driven module for executing the adaptive behavior command are optimized and adjusted.

6. The method according to claim 5, characterized in that, The step of generating coordinated response data for coordinating the various toys based on the real-time action status and environmental information includes: Based on the weighting coefficients defined in the preset weighting table, the real-time action state and environmental information are weighted and integrated to obtain collaborative input data; Based on the collaborative input data, the toys are sorted according to timestamps and priorities to obtain a scheduling execution sequence; The collaborative response data is calculated based on the scheduling execution sequence and using a response generation algorithm based on a linear regression model.

7. The method according to claim 5 or 6, characterized in that, The optimization and adjustment of the execution parameters for the action-driven module to execute the adaptive behavior instruction based on the collaborative response data includes: Based on the aforementioned collaborative response data, the rate of environmental change is determined using time series analysis. Based on the rate of environmental change, the interactive instructions that the first toy needs to complete within a specified time are predicted, and the execution parameters are adjusted based on the prediction results.

8. An intelligent interactive control system for electronic toys, characterized in that, include: The data acquisition module is used to acquire the real-time motion status and environmental information of each toy in the toy cluster, and to perform signal parsing to obtain the interactive data stream; The compression encoding module is used to compress and encode the interactive data stream to obtain an interactive instruction package that defines the real-time action state, the environmental information, and the interactive logic to be executed by each toy, and transmits the interactive instruction package to each toy in the toy cluster. The instruction parsing module is used to respond to the first toy in the toy cluster receiving the interaction instruction packet, control the first toy to parse the interaction instruction packet, obtain the interaction logic to be executed by the first toy, and the real-time action status and environmental information of the second toy in the toy cluster; wherein, the second toy is all the other toys in the toy cluster except the first toy; The behavior adjustment module is used to adjust the interaction logic of the first toy based on the real-time action status and environmental information of the second toy, to obtain adaptive behavior instructions for the first toy, and to store the adaptive behavior instructions in the behavior instruction cache. The instruction execution module is used to obtain adaptive behavior instructions from the behavior instruction cache through a low-power kernel and drive the motion driving module of the first toy to complete the instructions.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements an intelligent interactive control method for an electronic toy as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform an intelligent interactive control method for electronic toys as described in any one of claims 1 to 7.

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