A recognition configuration method and system of a beverage robot

Abstract

This invention relates to the field of module recognition, and more particularly to a method and system for recognizing and configuring a beverage robot. The method includes the following steps: when a new physical functional module contact signal is detected, configuration protocol information is read, and the robot enters a module confirmation state; the functional execution code of the physical functional module is downloaded, abstracted and encapsulated, and a functional semantic data package is constructed; the functional semantic data package is sent to the activated robot to enter a pre-configured operating state; transient response data of the robot when the functional module is inserted is collected, and a current ecological impact assessment is performed to generate an impact feature set; configuration parameters are adjusted and matched based on the impact feature set, and the configuration state is executed to complete the module configuration operation. This invention enables the rapid, safe, and efficient insertion, removal, and operation of new functional modules in a beverage robot.

Description

Technical Field

[0001] This invention relates to the field of module recognition, and more particularly to a recognition configuration method and system for a beverage robot. Background Technology

[0002] With the continuous development of intelligent manufacturing and service robot technologies, beverage robots, as an important carrier of intelligent catering and automated services, have been widely used in coffee shops, fast food restaurants, offices, and homes. To meet diverse beverage preparation needs, modern beverage robots increasingly adopt modular designs, expanding functionality and allowing for personalized customization by inserting different physical functional modules (such as grinding, stirring, heating, and flavoring modules). This modular design not only enhances the flexibility and scalability of beverage robots but also significantly improves equipment maintenance efficiency and service adaptability. During the operation of beverage robots, the safe and reliable access and correct functional identification of modules have become critical issues. Because functional modules from different manufacturers or batches may differ in interface specifications, communication protocols, instruction sets, and status reporting rules, failure to accurately identify and configure new modules can lead to instruction conflicts, abnormal power supply, or functional failures, and in severe cases, even equipment damage or service interruptions. Furthermore, modules may be affected by contact jitter, electrical interference, or abnormal data transmission during insertion and removal, increasing the complexity of identification and configuration. Traditional module management methods rely heavily on manual configuration or fixed module identification logic, lacking intelligent and real-time identification capabilities. They are unable to cope with the rapid insertion and removal of various types and specifications of modules and the complex interaction requirements, resulting in low efficiency and safety hazards when expanding the functions of beverage robots. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method and system for identifying and configuring a beverage robot, thereby resolving at least one of the aforementioned technical issues.

[0004] To achieve the above objectives, the present invention provides a method for identifying and configuring a beverage robot, comprising the following steps: When a new physical functional module contact signal is detected, the configuration protocol information is read and the module confirmation state is entered. Download the function execution code of the physical function module, abstract and encapsulate it, and construct a functional semantic data package; The functional semantic data packet is sent to activate the robot to enter the pre-configured running state; Collect transient response data of the robot when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set; The configuration parameters are adjusted and matched based on the influence feature set, and the configuration status is executed to complete the module configuration job.

[0005] This specification provides a beverage robot identification and configuration system for executing the beverage robot identification and configuration method described above, including: The protocol analysis unit is used to read the configuration protocol information and enter the module confirmation state when a new physical functional module contact signal is detected. The functional encapsulation unit is used to download the functional execution code of the physical functional module, perform abstract encapsulation, and construct a functional semantic data package; The pre-configuration unit is used to send the functional semantic data packet to the robot to activate it and enter the pre-configuration running state; The evaluation unit is used to collect the robot's transient response data when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set. The adjustment unit is used to adjust and match configuration parameters based on the influence feature set, execute configuration status, and complete the module configuration job.

[0006] The beneficial effects of this invention are as follows: By detecting new physical functional module contact signals and reading configuration protocol information, the module type, communication format, and interaction instruction set can be automatically identified the moment the module connects, allowing the robot to confirm the module status without human intervention. This process can quickly establish the communication foundation between the module and the control unit, ensuring compatibility between the module and existing functional nodes, while reducing initialization delays and potential conflict risks. Downloading and abstracting the functional execution code to form a functional semantic data package standardizes the module's complex action logic, triggering conditions, and functional dependencies into structured data. This operation not only facilitates unified scheduling and invocation by the control unit but also allows for early identification of internal functional coupling relationships within the module, avoiding action conflicts or resource competition, and improving operational safety and reliability. Sending the functional semantic data package to the control unit and activating the robot to enter a pre-configured operating state allows the module to complete action scheduling, triggering condition initialization, and resource allocation before formal task execution. The module can enter a controllable state in a short time, ensuring reasonable action node sequence, trigger delay, and resource usage, and avoiding conflicts in multi-module collaboration.

[0007] By collecting transient response data during module insertion and generating an impact feature set, the system can comprehensively quantify the effects of module integration on robot power fluctuations, current peaks, communication rhythm, mechanical micro-vibrations, and control cycle offsets. Comparative analysis of transient and regular operating data identifies the potential impacts of module insertion on system energy consumption distribution, power scheduling, power utilization, and overall stability, providing a scientific basis for adjusting configuration parameters. Configuration parameters are adjusted and matched based on the impact feature set, and the configuration state is executed, enabling seamless integration of the module with the robot's existing functional nodes. Optimization of power channel allocation, current limiting, communication baud rate, data frame priority, and I / O port binding ensures reasonable action sequence, triggering conditions, and resource utilization. After executing the configuration state, the module can immediately participate in task operations, achieving a complete closed loop from insertion recognition, function analysis, resource scheduling to action execution. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the steps of the identification and configuration method for a beverage robot according to the present invention; Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0009] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0010] This application provides a method and system for identifying and configuring a beverage robot. The execution entities of the method and system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0011] Please see Figures 1 to 3 The present invention provides a method comprising the following steps: When a new physical functional module contact signal is detected, the configuration protocol information is read and the module confirmation state is entered. Download the function execution code of the physical function module, abstract and encapsulate it, and construct a functional semantic data package; The functional semantic data packet is sent to activate the robot to enter the pre-configured running state; Collect transient response data of the robot when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set; The configuration parameters are adjusted and matched based on the influence feature set, and the configuration status is executed to complete the module configuration job.

[0012] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a beverage robot identification and configuration method according to the present invention. In this example, the steps of the beverage robot identification and configuration method include: When a new physical functional module contact signal is detected, the configuration protocol information is read and the module confirmation state is entered. In this embodiment, when the physical functional module contacts the robot module slot, the high-sensitivity contact sensor inside the slot immediately detects a signal change, triggering the module identification process. The sensor type can be capacitive or photoelectric, with a sampling period of 1 to 5 milliseconds. When a valid contact signal is detected for more than 2 consecutive milliseconds, it is determined that the module has been connected. The configuration protocol information stored in the module is read through the module interface, including the communication format, interaction command set, and status reporting rules. The communication format field defines the data frame length, verification method, and frame interval. The interaction command set includes the function call interface and input / output parameter constraints. The status reporting rules define the event trigger frequency, data fields, and trigger conditions. The reading process uses segmented data acquisition, with each segment ranging from 128 to 512 bytes in length, and the reading interval is controlled between 1 and 2 milliseconds.

[0013] Download the function execution code of the physical function module, abstract and encapsulate it, and construct a functional semantic data package; In this embodiment, under module confirmation status, the function execution code is downloaded to the robot control unit for parsing via an interface. During the download process, the communication baud rate is set to 500 kbps to 1 Mbps, data is read sequentially in blocks of 128 to 512 bytes, and integrity is checked for each data block. After downloading, the function execution code is parsed to identify the function type, action triggering conditions, and behavior response mode. The action delay ranges from 10 milliseconds to 50 milliseconds, and the action duration is from 20 milliseconds to 200 milliseconds. Function call relationships are analyzed to construct an internal functional dependency graph of the module. Nodes represent functional units, edges represent call or event triggering dependencies, and execution order and delay constraints are marked. The functional semantic features and dependency graph are abstracted and encapsulated to generate a functional semantic data package. The encapsulated content includes functional node identifiers, triggering condition tables, behavior response mode sets, dependency relationships, and resource constraints. The data package length is 1KB to 5KB.

[0014] The functional semantic data packet is sent to activate the robot to enter the pre-configured running state; In this embodiment, after generating the functional semantic data packet, it is sent to the robot control unit via a high-speed interface to activate the module into a pre-configured operating state. The interface communication rate is set between 500 kbps and 1 Mbps, and the data packet segment length is between 128 and 512 bytes. Each segment is verified after transmission to ensure data integrity and correct sequence. After loading, the control unit arranges the module action nodes into the scheduling queue in an optimized order according to the functional nodes, trigger conditions, behavior response modes, and dependencies contained in the functional semantic data packet. The action delay range is set to 10 to 50 milliseconds, and the action duration is set to 20 to 200 milliseconds. Interrupt priority and resource usage are loaded according to the data packet definition, and the power channel output provides stable power according to the specified levels.

[0015] Collect transient response data of the robot when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set; In this embodiment, the robot's response at the moment of insertion is collected under the pre-configured operating state of the module. The collected data includes power supply transient waveform changes (sampling frequency 1 kHz to 5 kHz, recording time 50 to 200 milliseconds), bus communication rhythm disturbance characteristics (abnormal frame intervals, frame drops, sampling accuracy 1 to 2 milliseconds), mechanical micro-vibration response (accelerometer sensitivity 0.01 to 0.05 g, sampling frequency 500 Hz to 1 kHz), and control cycle timing offset (offset range 0.5 to 5 milliseconds). The collected data is compared with normal operating data to evaluate the impact of module insertion on energy consumption, power scheduling, power occupancy, and stability. During the analysis, the peak transient voltage and current of each power channel, communication frame priority conflict rate, micro-vibration peak value and duration, and control cycle offset distribution are calculated to generate a comprehensive feature set.

[0016] The configuration parameters are adjusted and matched based on the influence feature set, and the configuration status is executed to complete the module configuration job.

[0017] In this embodiment, configuration parameters are adjusted and matched based on the generated impact feature set to generate the final configuration parameter combination. Adjustments include power channel voltage level (±5% fluctuation), maximum current limit (increased buffer by 10% to 20%), communication baud rate and data frame priority rearrangement, I / O port binding optimization, and interrupt priority fine-tuning to ensure high-priority event latency is less than 5 milliseconds. After the configuration parameter combination is loaded into the control unit, module configuration state actions are executed, including scheduling action nodes in an optimized order, setting the action latency range to 10 to 50 milliseconds, setting the action duration to 20 to 200 milliseconds, ensuring stable power output, and sending communication frames according to priority. The loading process lasts approximately 100 to 300 milliseconds. After completion, the module actions can be executed stably, resource usage is reasonable, and the robot enters an operable state.

[0018] In this embodiment, see Figure 2 The specific steps for reading configuration protocol information and entering the module confirmation state when a new physical functional module contact signal is detected are as follows: When the module slot sensor of the beverage robot detects a new physical functional module contact signal, it triggers the module information scanning process. The configuration protocol information of the physical function module is read based on the module interface; the configuration protocol information includes the communication format, interaction command set and status reporting rules. The configuration protocol information is structured and parsed to obtain the interaction logic characteristics; Based on the interaction logic features and the internal protocol library, a multi-dimensional matching is performed to calculate the protocol version similarity, interface specification consistency and functional conflict probability, and the matching results are obtained. When the matching result is successful, an access permission identifier for the physical functional module is generated; Update the beverage robot entry module confirmation status according to the access permission identifier.

[0019] In this embodiment, the module slot is equipped with a highly sensitive contact sensor for real-time monitoring of the insertion status of functional modules. The sensor can be capacitive, photoelectric, or mechanical micro-motion type, with a sampling period set between 1 and 5 milliseconds to ensure immediate detection of signal changes when the module contacts the slot. When the module contact signal exceeds a set threshold, such as a capacitance change exceeding ±5% or a mechanical switch remaining closed for more than 2 milliseconds, it is determined that a new physical functional module has been connected. After triggering the module information scanning process, the slot enters a scan-ready state and prepares to initiate interface communication initialization. After the module information scan is initiated, a communication connection is established with the functional module through the module interface, and its configuration protocol information is read. The read content includes the communication format (such as data frame length and verification method), the interaction instruction set (function call interface and parameter definition), and the status reporting rules (event reporting frequency, trigger conditions, and data field format). During interface communication, the interface electrical characteristics are initialized, a handshake command is sent to confirm the module's communicable state, and then various protocol information is gradually acquired to ensure the integrity of the communication frame and the accuracy of the data. The read configuration information, after caching and preliminary verification, forms a complete data set, providing a foundation for the extraction of interactive logic features.

[0020] After reading the complete configuration protocol information, it undergoes structured parsing to transform the raw data into computable interaction logic features. The communication format fields are parsed to extract the frame header, frame length, verification method, and data field structure. Then, the interaction instruction set is parsed, transforming the function call interface, input / output parameters, and call constraints into a standardized data structure. Next, the status reporting rules are parsed to extract event triggering conditions, data reporting frequency, and data field types. Through structured processing, complete interaction logic features are obtained, which can describe the module's functional interfaces, communication constraints, and event reporting behavior. After the interaction logic features are extracted, a multi-dimensional matching process is performed with the internal protocol library to evaluate the compatibility and security of module access. The matching process includes three aspects: protocol version similarity, interface specification consistency, and probability of functional conflicts. Protocol version similarity is calculated by comparing the module protocol version number and the internal library version number, and combining the key instruction set difference score to obtain a similarity value between 0 and 1; interface specification consistency is calculated by comparing the data frame structure, parameter type and calling order, and the standard is judged to be compliant when the threshold is reached; functional conflict probability is assessed by analyzing the overlap of module function instructions with the function interfaces of connected modules and the conflict of resource usage, and assessing the potential impact on the overall operation.

[0021] Upon successful matching, an access permission identifier is generated for the physical functional module to indicate that it has passed protocol compatibility and functional safety verification. The generation method logically combines the module's physical slot number, unique device ID, and matching result information to form a globally unique identifier, which is stored in the module management table. Based on the access permission identifier, the module status is updated to "Module Confirmed," enabling the beverage robot to securely identify the module during task scheduling and function invocation.

[0022] In this embodiment, see Figure 3 The specific steps for downloading the function execution code of the physical function module, abstracting and encapsulating it, and constructing a functional semantic data package are as follows: Based on the confirmed status of the module, download the function execution code of the physical function module; The function execution code is parsed to identify the module's function type, action triggering conditions, and behavior response mode, and to generate functional semantic features; Perform function call relationship analysis on the function execution code to obtain a function dependency graph; The functional semantic features and functional dependency graph are abstracted and encapsulated to construct a functional semantic data package.

[0023] In this embodiment, after the module enters the confirmation state, its function execution code is downloaded to the control unit for processing via the module interface. The download process initializes interface communication parameters, such as setting the communication rate to 500 kbps to 1 Mbps and the data packet length to 128 bytes to 512 bytes, ensuring stable and complete transmissions each time. The control unit reads the function execution code segment by segment and performs verification after each data segment is completed to confirm that the number of received bytes and the checksum are consistent, avoiding packet loss or misalignment. During the download process, the control unit records the starting address and length of each code segment and sets the data transmission interval to 1 millisecond to 2 milliseconds to prevent excessive interface load. The downloaded function execution code is parsed by the control unit to extract the function type, action triggering conditions, and behavior response mode. The parsing process identifies the action interfaces and parameter ranges contained in the module, such as a response delay of 10 milliseconds to 50 milliseconds for each action, an input signal voltage range of 0.5 volts to 3.3 volts, and an action duration of 20 milliseconds to 200 milliseconds. Next, the triggering conditions are parsed, mapping external sensor signals, level states, or status flags to triggering conditions, and analyzing the action execution sequence under different condition combinations. Behavioral response patterns include action sequence, parallel and serial execution constraints, and event triggering priorities.

[0024] After generating functional semantic features, the function execution code is analyzed for function call relationships, forming a functional dependency graph within the module. Nodes in the dependency graph represent individual functional units; for example, the average execution time of each functional unit is 15 to 100 milliseconds. Edges between nodes represent call or event triggering relationships, and dependency types and execution priorities are labeled. Analysis methods include identifying function call chains, parameter passing relationships, and conditional dependencies to determine the order of actions and concurrency constraints. The dependency graph is represented visually or through data structures, showing the call hierarchy and triggering order of functional units. For example, some functions may have a 5 to 20 millisecond delay in triggering to ensure a safe order. After obtaining the functional semantic features and dependency graph, they are abstracted and encapsulated to generate a unified format functional semantic data package. During encapsulation, function types, triggering conditions, behavior response patterns, and dependencies are standardized and encoded. For example, each action node includes action type, trigger delay range (10 to 50 milliseconds), execution duration (20 to 200 milliseconds), priority, and resource usage information. Dependency edges between nodes include call type, delay constraints, and order priority. The encapsulated functional semantic data packet can be 1KB to 5KB in length and is structured and stored in the module management table, enabling fast reading and retrieval during multi-module coordinated operations. Through the functional semantic data packet, module functional information is abstracted into standardized data that is manageable, schedulable, and shareable, achieving unified functional description, cross-module reuse, and action control scheduling, while also providing a data foundation for subsequent security policies and task allocation.

[0025] In this embodiment, the specific steps for sending the functional semantic data packet to activate the robot to enter the pre-configured running state are as follows: The functional semantic data packet is sent to the configuration management unit; Based on the configuration management unit, configuration parameters are adjusted to generate configuration update parameters; the configuration update parameters include functional structure, control strategy, and resource scheduling rules. Based on the updated configuration parameters, the robot is activated and enters the pre-configured running state.

[0026] In this embodiment, after the functional semantic data packet is generated, it is sent to the configuration management unit through the module interface. During the sending process, the communication channel is initialized, the interface rate is set to 500 kbps to 1 Mbps, and the data packet segment length is 128 bytes to 512 bytes. Each segment is verified after transmission to ensure data integrity and correct sequence. The data packet contains information such as functional node identifiers, action triggering conditions, behavior response modes, and inter-node dependencies. A data segment interval of 1 to 2 milliseconds is set during transmission to prevent excessive interface load and ensure successful transmission of each data segment. After receiving the data packet, the configuration management unit parses each functional node and its dependencies, stores them in the internal module management table, and marks the module status as "pending configuration," providing complete and parsable functional information for subsequent configuration parameter adjustments.

[0027] After receiving the functional semantic data packet, the configuration management unit performs comprehensive analysis and parameter adjustment on the module functions to generate configuration update parameters. Based on the dependencies between functional nodes, the execution order of module functions is rearranged to optimize the functional structure, ensuring that the action triggering delay is within the range of 10 to 50 milliseconds and that the execution order meets parallel and serial constraints. Secondly, based on the functional response mode and triggering conditions, a control strategy is generated, including action priority, triggering condition mapping, response time optimization, and exception handling rules. For example, when actions conflict, the trigger is delayed by 5 to 20 milliseconds to ensure safety. Combining module resource usage and action call frequency, resource scheduling rules are generated, including power allocation, sensor access order, and execution thread allocation. The overall length of the configuration update parameters is approximately 2KB to 6KB. After structured encapsulation, it can be directly used for loading into the control unit to achieve functional structure optimization. After the configuration update parameters are generated, the control unit activates the robot into a pre-configured running state based on these parameters. The activation process includes loading the module functional nodes into the execution scheduling queue in the optimized order, setting the action triggering priority and triggering delay range (10 to 50 milliseconds) according to the control strategy, and allocating power, sensor access, and thread usage according to the resource scheduling rules. The module's motion state is initialized, actuators and sensors are placed in standby mode, and the monitoring interface confirms that each node can receive commands. The entire loading and initialization process is completed within 50 to 200 milliseconds, ensuring that the module can enter the operating state in a short time. After activation, the robot is in a pre-configured operating state and can immediately respond to function call commands. Module actions are executed according to preset control strategies and resource allocation sequences, achieving safe operation and collaborative operation of functional modules.

[0028] In this embodiment, the specific steps for collecting the robot's transient response data when the functional module is inserted, conducting a current ecological impact assessment, and generating an impact feature set are as follows: Based on the pre-configured operating state, the robot transient response data is collected when the functional module is inserted; the robot transient response data includes power transient waveform changes, bus communication rhythm disturbance characteristics, micro-vibration response generated by the mechanical structure, and control cycle timing offset changes; Identify the routine operational data of the beverage robot; Based on the aforementioned routine operational data, an assessment of the current ecological impact of the robot's transient response data is performed, generating an impact feature set. The set of influencing features includes energy consumption distribution, power dispatch, power occupancy, and system stability.

[0029] In this embodiment, the robot's response at the moment of module insertion is collected through an interface and sensing units. The collected data includes power transient waveform changes, bus communication rhythm disturbance characteristics, mechanical structure micro-vibration response, and control cycle timing offset. Power transient waveform changes are acquired through voltage and current sampling circuits, with a sampling frequency set between 1 kHz and 5 kHz and a waveform recording duration between 50 milliseconds and 200 milliseconds to capture potential voltage dips, current peaks, and recovery processes at the moment of insertion. Bus communication disturbance characteristics are captured by a bus listener to detect changes in data frame intervals, frame loss, and signal jitter, with a sampling accuracy of 1 to 2 frames per millisecond. Mechanical micro-vibration response is collected using accelerometers installed on key structural parts, with a sensitivity between 0.01 g and 0.05 g and a sampling frequency between 500 Hz and 1 kHz, recording the impact of module insertion on the robot's micro-vibration. Control cycle timing offset is collected by reading the control cycle timer and scheduling event records; the offset range can vary from 0.5 milliseconds to 5 milliseconds.

[0030] Before acquiring transient response data, a baseline of routine robot operation data needs to be established. Routine operation data includes power supply voltage and current fluctuation ranges, bus communication timing, mechanical structure vibration levels, and control cycle stability. Power supply data is referenced based on a steady-state voltage fluctuation range of ±1% to ±2% and a current variation of 0.1 to 0.5 A; bus communication rhythm maintains a uniform frame interval with a deviation not exceeding 1 to 2 milliseconds; mechanical structure vibration levels are between 0.01 g and 0.03 g; and control cycle timing offsets are kept within 1 millisecond. By collecting and recording this routine operation data, an operational benchmark for each robot module under conditions free from external interference can be formed, which can be used for comparative analysis with inserted transient response data.

[0031] After establishing a baseline of routine operational data, it is compared and analyzed with transient response data collected during module insertion to assess the impact of module insertion on the robot's current ecosystem. During the assessment, deviation analysis is performed on the power supply transient waveform changes and routine voltage and current to identify transient peaks and recovery delays occurring at the moment of insertion; the amplitude and duration of abnormal frame intervals, frame drops, or signal jitter are analyzed for bus communication disturbance characteristics; the amplitude peak, frequency distribution, and vibration duration are calculated for mechanical micro-vibration response; and the offset amplitude and frequency distribution are statistically analyzed for control cycle timing offset. Through comprehensive analysis of these parameters, the impact of module insertion on energy consumption, power scheduling, power utilization, and overall stability can be quantified, generating a complete set of impact features. Based on transient response analysis, the generated impact feature set includes energy consumption distribution, power scheduling, power utilization, and stability indicators. Energy consumption distribution is generated by recording the transient and average power consumption of each functional node during module insertion, forming an energy consumption distribution map of each module and subsystem; power dispatch is identified by analyzing the current distribution and load sequence when power peaks occur, to determine whether module insertion leads to energy resource contention; power occupancy is quantified by statistically analyzing the module current fluctuation amplitude, duration, and instantaneous load, to quantify the impact of the module on the overall power supply; system stability is generated by analyzing control cycle offset, communication timing disturbances, and mechanical micro-vibration response, to produce a stability score or level.

[0032] In this embodiment, the specific steps for adjusting and matching configuration parameters based on the influence feature set and executing the configuration state to complete the module configuration job are as follows: Based on the configuration update parameters, electrical operation requirements are calculated to obtain a requirement sequence; the requirement sequence includes power channel number, power supply voltage level, maximum current limit value, communication baud rate, data frame priority, interrupt response level, memory address mapping range, and I / O port binding relationship. Dynamic buffer expansion analysis is performed on the demand sequence to obtain the demand buffer range parameters; Based on the influence feature set and the aforementioned demand buffer range parameters, a load capacity assessment is performed to obtain the load capacity assessment coefficient. Based on the load-bearing evaluation coefficient, the configuration parameters are adjusted and matched to output the configuration parameter combination; The beverage robot is configured based on a combination of configuration parameters to complete the module configuration task.

[0033] In this embodiment, after the configuration update parameters are generated, the electrical operation requirements of each functional node of the beverage robot are calculated based on the module functional structure, control strategy, and resource scheduling rules, forming a complete requirement sequence. The requirement sequence includes power channel number, power supply voltage level, maximum current limit, communication baud rate, data frame priority, interrupt response level, memory address mapping range, and I / O port binding relationships. The power channel number identifies the power output channels required by different functional modules. The power supply voltage level is set between 3.3V and 24V, and the maximum current limit is set between 0.1A and 10A to accommodate the load requirements of different modules. The communication baud rate is set between 100 kbps and 1 Mbps depending on the module interface type. The data frame priority ensures priority transmission of critical control commands, and the interrupt response level controls the trigger delay of high-priority events to between 1ms and 5ms. The memory address mapping range and I / O port binding relationships define the module's access permissions and port constraints to shared resources. After obtaining the requirement sequence, dynamic buffer expansion analysis is performed to ensure that electrical resources can meet actual operational requirements under module load fluctuations or transient interference. During the analysis, margin calculations were performed for each power channel, current limit, communication frame priority, and I / O port load. For example, the power channel voltage could fluctuate within ±5% of the rated range, and the current buffer was set to 10% to 20% of the maximum load. The communication baud rate and data frame priority were dynamically adjusted, reserving buffers for high-priority frames to prevent control delays caused by short-term bus congestion. A safety margin of 1 to 3 milliseconds was reserved for interrupt response levels to ensure timely processing of high-priority events. Redundant allocation was also performed for memory address mapping ranges and I / O port binding relationships to ensure that the same resource would not conflict when accessed by multiple modules.

[0034] After generating the demand buffer range parameters, they are comprehensively analyzed together with the transient response impact feature set to evaluate the load-bearing capacity of the beverage robot under the access of new functional modules. The load-bearing assessment process includes power supply load analysis, communication bandwidth assessment, I / O port utilization, and control cycle load analysis. Power supply load is assessed by comparing transient voltage and current fluctuation amplitudes with buffer range parameters to determine the impact of transient shocks on power supply capacity; communication bandwidth assessment is conducted by analyzing data frame priority, bus disturbance characteristics, and buffer margin to calculate potential congestion risks; I / O port utilization is assessed by statistically analyzing port access frequency, conflict probability, and buffer resource occupancy to determine interface load capacity; control cycle load is assessed by analyzing timing offset, action trigger delay, and buffer safety interval to determine whether control tasks can be completed on time. Based on the load-bearing assessment coefficients, configuration update parameters are optimized and matched to form the final configuration parameter combination. Specifically, when the load assessment coefficient is low, the control unit adjusts the power channel allocation, raising or lowering the voltage level by ±5% to optimize power distribution; dynamically adjusts the maximum current limit value, retaining a margin of 10% to 20%; reallocates the communication baud rate and data frame priority to ensure smooth transmission of high-priority control commands; fine-tunes the interrupt response level according to event priority to ensure that the response latency of critical actions is less than 5 milliseconds; and rearranges the memory mapping and I / O port binding relationship according to load requirements to avoid access conflicts.

[0035] After the configuration parameter combination is generated, the beverage robot executes the module configuration operation according to the combined parameters. During execution, the power supply channel outputs according to the optimized voltage level and current limit value to ensure that each functional module is safely powered within its rated power range; bus communication transmits instructions according to data frame priority and baud rate requirements to ensure timely execution of control instructions; interrupt response triggers critical events according to the level settings; I / O port binding relationships and memory mapping rules are loaded into the control unit to ensure that the module accesses resources and allocation strategies are consistent. The entire configuration loading and execution process is usually completed within 100 to 300 milliseconds. After completion, the robot enters the operable state, and each functional module works collaboratively according to the preset strategy to achieve safe and stable integrated operation of the new functional module.

[0036] In this embodiment, the specific steps for configuring the beverage robot based on the combination of configuration parameters to complete the module configuration operation are as follows: The beverage robot is configured and executed based on the combination of configuration parameters to complete the module configuration task. The physical functional modules were functionally usable, and the verification results were obtained. When the verification result indicates that the functional module is operating normally, the configuration recognition process is complete. When the verification result indicates that the functional module is malfunctioning, an error signal is generated and uploaded to the cloud system.

[0037] In this embodiment, after generating a complete set of configuration parameters, the control unit loads these parameters into the robot execution environment to complete the module configuration. The loading process includes synchronous settings for power supply channels, current limiting, communication baud rate, data frame priority, interrupt response level, memory mapping, and I / O port binding. The power supply channel outputs regulated current according to the voltage level of the configuration combination (e.g., 3.3V, 5V, or 24V), with the current limit set between 0.1A and 10A to ensure the module operates within a safe power range. Communication frames send and receive control commands according to data frame priority and baud rate (100 kbps to 1 Mbps), ensuring that the response latency for critical functions is within 1 to 5 milliseconds. The interrupt response level is set for event trigger management, with high-priority event trigger latency kept within 5 milliseconds. Memory address mapping and I / O port binding relationships are loaded into the control unit, ensuring that the resource access and execution order of each module conform to the configuration strategy. After module configuration, functional availability verification is performed on the physical functional modules to confirm their normal operation in the configured state. The verification process includes real-time checks on the module's action interface, triggering conditions, behavior response modes, and resource usage. The action interface is triggered by sending control commands, and the delay and duration of the module's actions are observed to ensure they conform to the configured parameters. The delay range is generally controlled between 10 and 50 milliseconds, and the action duration is between 20 and 200 milliseconds. The response to triggering conditions is verified through multiple consecutive signal simulation tests, confirming that the module can correctly trigger actions under different combinations of conditions. The behavior response mode is verified by collecting data on the action sequence, concurrent execution, and event priorities to ensure they conform to the preset control strategy. Synchronous monitoring of power channel current and voltage fluctuations, the correctness of communication frame transmission, and I / O port occupancy confirms that the module will not cause abnormal power consumption or communication congestion in the configured state.

[0038] When the verification results show that the functional module can correctly execute various actions, trigger conditions, and response modes according to the configuration parameter combination, and that power supply, current, communication, and I / O usage are all within safe ranges, the functional module is considered to be operating normally. The control unit updates the module status to "Configuration identification complete," marking that the module has been successfully integrated into the beverage robot's operating environment. After completing the configuration identification process, the module can immediately participate in beverage making tasks or other automatic operations, and its action sequence, trigger conditions, and resource usage are all running safely according to the preset strategy. If the functional availability verification shows that the module's actions are not triggered according to the preset, the response delay is too long, communication frames are lost, or I / O port conflicts occur, the module is considered to be operating abnormally. The control unit will generate an abnormal signal, including the module's unique identifier, abnormality type, action delay range, abnormal power usage, and communication status information. The abnormal signal is uploaded to the cloud through a secure communication channel for remote monitoring, analysis, and abnormal handling. During the upload process, the data packet size is controlled to be between 1KB and 5KB, and the sending rate is adjusted to 500 kbps to 1 Mbps to ensure complete and timely information. The robot can trigger local protection mechanisms based on the type of anomaly, such as restricting module actions, isolating abnormal modules, adjusting power output, or suspending the execution of related tasks.

[0039] In this embodiment, a beverage robot identification and configuration system is provided, used to execute the beverage robot identification and configuration method described above, including: The protocol analysis unit is used to read the configuration protocol information and enter the module confirmation state when a new physical functional module contact signal is detected. The functional encapsulation unit is used to download the functional execution code of the physical functional module, perform abstract encapsulation, and construct a functional semantic data package; The pre-configuration unit is used to send the functional semantic data packet to the robot to activate it and enter the pre-configuration running state; The evaluation unit is used to collect the robot's transient response data when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set. The adjustment unit is used to adjust and match configuration parameters based on the influence feature set, execute configuration status, and complete the module configuration job.

[0040] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0041] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for recognizing and configuring a beverage robot, characterized in that, Includes the following steps: When a new physical functional module contact signal is detected, the configuration protocol information is read and the module confirmation state is entered. Download the function execution code of the physical function module, abstract and encapsulate it, and construct a functional semantic data package; The functional semantic data packet is sent to activate the robot to enter the pre-configured running state; Collect transient response data of the robot when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set; The configuration parameters are adjusted and matched based on the influence feature set, and the configuration status is executed to complete the module configuration job.

2. The identification and configuration method for the beverage robot according to claim 1, characterized in that, The specific steps for reading configuration protocol information and entering the module confirmation state when a new physical functional module contact signal is detected are as follows: When the module slot sensor of the beverage robot detects a new physical functional module contact signal, it triggers the module information scanning process. The configuration protocol information of the physical function module is read based on the module interface; the configuration protocol information includes the communication format, interaction command set and status reporting rules. The configuration protocol information is structured and parsed to obtain the interaction logic characteristics; Based on the interaction logic features and the internal protocol library, a multi-dimensional matching is performed to calculate the protocol version similarity, interface specification consistency and functional conflict probability, and the matching results are obtained. When the matching result is successful, an access permission identifier for the physical functional module is generated; Update the beverage robot entry module confirmation status according to the access permission identifier.

3. The identification and configuration method for the beverage robot according to claim 1, characterized in that, The specific steps for downloading the function execution code of the physical function module, abstracting and encapsulating it, and constructing a functional semantic data package are as follows: Based on the confirmed status of the module, download the function execution code of the physical function module; The function execution code is parsed to identify the module's function type, action triggering conditions, and behavior response mode, and to generate functional semantic features; Perform function call relationship analysis on the function execution code to obtain a function dependency graph; The functional semantic features and functional dependency graph are abstracted and encapsulated to construct a functional semantic data package.

4. The identification and configuration method for the beverage robot according to claim 1, characterized in that, The specific steps for sending the functional semantic data packet to activate the robot to enter the pre-configured running state are as follows: The functional semantic data packet is sent to the configuration management unit; Based on the configuration management unit, configuration parameters are adjusted to generate configuration update parameters; the configuration update parameters include functional structure, control strategy, and resource scheduling rules. Based on the updated configuration parameters, the robot is activated and enters the pre-configured running state.

5. The method according to claim 1, characterized in that, The specific steps for collecting robot transient response data when the functional module is inserted, conducting current ecological impact assessment, and generating an impact feature set are as follows: Based on the pre-configured operating state, the robot transient response data is collected when the functional module is inserted; the robot transient response data includes power transient waveform changes, bus communication rhythm disturbance characteristics, micro-vibration response generated by the mechanical structure, and control cycle timing offset changes; Identify the routine operational data of the beverage robot; Based on the aforementioned routine operational data, an assessment of the current ecological impact of the robot's transient response data is performed, generating an impact feature set. The set of influencing features includes energy consumption distribution, power dispatch, power occupancy, and system stability.

6. The identification and configuration method for the beverage robot according to claim 1, characterized in that, The specific steps for adjusting and matching configuration parameters based on the influence feature set and executing configuration status to complete the module configuration job are as follows: Based on the configuration update parameters, electrical operation requirements are calculated to obtain a requirement sequence; Dynamic buffer expansion analysis is performed on the demand sequence to obtain the demand buffer range parameters; Based on the influence feature set and the aforementioned demand buffer range parameters, a load capacity assessment is performed to obtain the load capacity assessment coefficient. Based on the load-bearing evaluation coefficient, the configuration parameters are adjusted and matched to output the configuration parameter combination; The beverage robot is configured based on a combination of configuration parameters to complete the module configuration task.

7. The identification and configuration method for a beverage robot according to claim 1, characterized in that, The demand sequence includes power channel number, power supply voltage level, maximum current limit, communication baud rate, data frame priority, interrupt response level, memory address mapping range, and I / O port binding relationship.

8. The identification and configuration method for the beverage robot according to claim 1, characterized in that, The specific steps for configuring the beverage robot based on configuration parameter combinations to complete the module configuration task are as follows: The beverage robot is configured and executed based on the combination of configuration parameters to complete the module configuration task. The physical functional modules were functionally usable, and the verification results were obtained. When the verification result indicates that the functional module is operating normally, the configuration recognition process is complete. When the verification result indicates that the functional module is malfunctioning, an error signal is generated and uploaded to the cloud system.

9. A recognition and configuration system for a beverage robot, characterized in that, The method for performing the identification and configuration of the beverage robot as described in claim 1 includes: The protocol analysis unit is used to read the configuration protocol information and enter the module confirmation state when a new physical functional module contact signal is detected. The functional encapsulation unit is used to download the functional execution code of the physical functional module, perform abstract encapsulation, and construct a functional semantic data package; The pre-configuration unit is used to send the functional semantic data packet to the robot to activate it and enter the pre-configuration running state; The evaluation unit is used to collect the robot's transient response data when the functional module is inserted, conduct a current ecological impact assessment, and generate an impact feature set. The adjustment unit is used to adjust and match configuration parameters based on the influence feature set, execute configuration status, and complete the module configuration job.

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